Student Poster Book of Abstracts
2016 IEEE Power and Energy Society General Meeting
Boston,Massachusetts, United States
July 17-21 2016

IEEE PES GM 2016 Student Poster List

Smart Cities
S.No.
16STUGM001
16STUGM002

Poster Title
Impacts of Imperfect Information on Customer-sited
Distributed Energy Storage Investment Decisions
Robust Reserve Capacity Provision and Peak Load
Reduction from Buildings in Smart Grids

Smart Sensors, Communication and Control in Energy Systems
S.No.
Poster Title
A Hybrid Robust Power System State Estimation Method
16STUGM003
with Non-Gaussian Measurement Noise
16STUGM004 Management Scheme for Parallel Connected Hybrid Energy
Storage in Electric Vehicles
Multiple Communication Architectures with Optimization
16STUGM005
for Smart Transimission Grid
Abnormal
Overheating
Detectability Analysis Based on
16STUGM006
Cross Gramian for a Supercapacitors String
Graphical User Control Interface for an Advanced Battery
16STUGM007
Management System
Distribution System Properties Based Event Driven
16STUGM008
Communication Network Modeling

Smart Grid Technology
S.No.
Poster Title
Challenges in Applying Decomposition Based Hybrid
16STUGM009
Forecasting Algorithm on Practical Cases
Towards Optimal Coordinated Operation of Distributed
16STUGM010
Internet Data Center Microgrids
A Game Theoretic Approach to Analyze the Dynamic
16STUGM011
Interactions of Multiple Residential Prosumers Considering
Power Flow Constraints
Privacy Preserving Energy Trading Mechanism among
16STUGM012
Competing Agents in a Microgrid
Advanced
Battery
Management & Diagnostic System for
16STUGM013
Smart Grid Infrastructure
16STUGM014 Provision of Power System Flexibility Through AggregatedResidential Demand Response
16STUGM015 A Novel Approach for sizing Electric Vehicles Parking Lot
located at any bus on a network
Optimal Power Flow of Multiple Energy Carriers with
16STUGM016
Multiple Kinds of Energy Storage
Smart Distribution Systems
16STUGM017
Large-scale PEVs Behaving as A Grid-scale Storage
16STUGM018
System: An Aggregate Model Approach
Autoregressive Model for Individual Consumption Data 16STUGM019
Sparsity Recovery and Significance Test
DEx.py - A Data Exchange Platform Between Power
16STUGM020
System Simulators
1

Student Name
Jingkun Liu
Sarmad Hanif

Student Name
Junbo Zhao
Christopher R.
Lashway
Fan YeVita Lystianingrum
Nicole Bugay
Thanatheepan
BalachandranStudent Name
Yamin Wang
Wenbo Qi
Tao Chen

M.Nazif Faqiry
Ahmed T. Elsayed
Qinwei Duan
Claude Ziad El-Bayeh
Linna Ni
Yazhou Jiang
Hongcai Zhang
Pan Li
Rahul Kadavil

IEEE PES GM 2016 Student Poster List

16STUGM021
16STUGM022
16STUGM023
16STUGM024

16STUGM025
16STUGM026
16STUGM027
16STUGM028
16STUGM029
16STUGM030
16STUGM031
16STUGM032
16STUGM033

16STUGM034
16STUGM035
16STUGM036
16STUGM037

A two-layer model for microgrid real-time dispatch basedon ESS charging/discharging hidden costs
Critical Load Serving Capability by Optimizing Microgrid
Operation
Online Bad Data Detection for Synchrophasor
Measurements: A Data-driven Approach
New Control Structure for Load Frequency Control of aHybrid AC/DC Microgrid
Effects of Aggregate Load Control on the PhysicalComponents of Distribution Networks
Demand Response via Large Scale Charging of Electric
VehiclesNeuro-Fuzzy Based UPQC Controller for Power Quality
Improvement in Micro Grid System
A Three-Way Win Demand-Response Management Strategy
A Framework for Reliability Evaluation of Electric Vehicle
Charging Stations
Dynamic Electric Vehicle Charging Considering System
and Customer Constraints
Role of Active Consumers and Pricing on Distribution
Systems
Weather Station Selection for North Carolina Electric
Membership Cooperation
Load Regulation of a Smart Household with PV-Storage andElectric Vehicle by Dynamic Programming Successive
Algorithm Technique
Generalized Steady-state Model for Energy Router with
Applications in Power Flow Calculation
Optimal Usage of Battery Energy Storage in Smart
Microgrids
Impact of Controlling Zero Sequence Current in a ThreePhase Four-Wire LV Network with PV Units
Benefits and Pitfalls of Using Percent Error as a Metric for
Day-Ahead Forecasting

Cyber and Physical Security of the Smart Grid
S.No.
Poster Title
Locally Coordinated Cyber-Physical Attacks Targeting
16STUGM038
Undetectable Line Outages
False Data Injection Attacks on Power System State
16STUGM039
Estimation with Limited Information
Effects of Cyber-Attacks on the Performance of Neural
16STUGM040
Network Controlled BFCL Against Transient Stability
16STUGM041 Data Integrity Attack Detection Through Spatio- Temporal
Correlation in Distributed Generation
Synchrophasor Measurements Based Detection and
16STUGM042
Identification of Cyber Attacks Target on Transmission
Systems Substations
2

Zhong Zhang
Pavan Penkey
Meng Wu
Mohammad Reza
Khalghani
Stephanie Crocker
Zhe YuUday KishanRenduchintala
Pramod Herath
Mohsen Ghavami
MaighaKumarsinh Jhala
Jingrui XIE
Faeza Hafiz

Jianqiang Miao
Chirath D.
Pathiravasam
F. H. M. Rafi
S. SuffianStudent Name
Zhiyi Li
Jiazi Zhang
Mohammad Ashraf
Hossain Sadi
Hasan Ul Banna
Bo Cui

IEEE PES GM 2016 Student Poster List

16STUGM043
16STUGM044
16STUGM045
16STUGM046
16STUGM047

Likelihood of Cyber Data Injection Attack to Power
Systems
Cyber-Enabled Power Distribution Network Reliability
Assessment
Estimating the Impact Severity of False Data Attacks on
Power Systems
Data Mining Algorithms for Synchrophasor Bad Data
Detection
Ambient Data Analysis of Oscillatory Modes in Power
Systems

Yingshuai Hao
Mohammad HeidariKapourchali
Sagnik Basumallik
M. Zhou
P. J. Lau

Advanced Computational Methods for Power System Planning, Operation, and Control
S.No.
Poster Title
Student Name
A Game-Theoretic Analysis of Wind Power Generator
Yu Xia16STUGM048
Bidding Strategies in Electricity Markets
Marcos Netto
16STUGM049 A robust extended Kalman filter for power system dynamic
state estimation using PMU measurements.
Mayank Panwar
16STUGM050 Performance Metrics for Dynamic Reserve Management inElectric Microgrids
Claudia M. C. Arvizu
16STUGM051 Extraction of Spatiotemporal Patterns from Measured Data
via Nonlinear Laplacian Spectral Analysis
Risk-Based Dynamic Security Assessment for High
Sohom Datta
16STUGM052
Penetration of Renewable Generation
Transmission Contingency-Constrained Unit Commitment
Yaowen Yu
16STUGM053
with High Penetration of Renewables via Interval
Optimization
The Holomorphic Embedding Method Applied to the
Shruti Rao
16STUGM054
Power-Flow Problem
Yuan Hu
16STUGM055 Integrated Generation and Transmission Expansion Planningwith Carbon Capture Operating Constraints
Directed Graph Based Carbon Flow Tracing for Demand
Tao Sun
16STUGM056
Side Carbon Obligation Allocation
Multi-Time Step Service Restoration for Reconfigurable
Bo Chen
16STUGM057
Advanced Distribution Systems and Microgrids
Novel Synchrophasor based Real-Time Monitoring andAmarsagar Reddy
16STUGM058
Characterization of Delayed Voltage Recovery
RamapuramMatavalamShort-Term Distribution-Level Net Load Forecasting with
Xiaorong Sun
16STUGM059
High Solar Penetration
Chao Yan
16STUGM060 A Multi-level Adaptive GP-VM Algorithm for Composite
System Reliability Evaluation Considering Rare Events
Yuta Aihara
16STUGM061 System Voltage and Reactive Power Control using Conceptof Fuzzy Programming
A Novel approach on Upgrade and Renewal of
Yasukuni Nagakawa
16STUGM062
Transformers using Optimal Portfolio Selection
Fast Transient Stability Screening Method using Principal
Yui Koyanagi
16STUGM063
Component Analysis for Multi-swing Step-out
Ryu Tanabe
16STUGM064 Load Frequency Control with Storage Battery and Generator
using a Recurrent Neural Network
3

IEEE PES GM 2016 Student Poster List

16STUGM065
16STUGM066

Multi-swing Study using Time-Domain Equal-Area Criteria
Optimal Scheduling of Integrated AC-DC Systems Using
Convex Semidefinite Programming

Ako Sato
Seyedmahdi
Moghadasi

16STUGM067

A Security-Enhanced Unit Commitment Model for Systems
with Stochastic Resources
Fast Prediction of Cascading Overload on Transmission
Section Based on DC Sensitivity and Compensation Method
Fast Decomposition-Based State Estimation Using
Automatic Graph Partitioning and ADMM
Electricity Load Forecasting in Micro-grids
Distributed Computing Platform for a Decentralized Voltage
Stability
Continuous-time Generation Scheduling for Ramping
Scarcity Healing
Multivariable Uncertainty Analysis for More Reliable
Dynamic Simulations of Power Systems based on Combined
Trajectory Sensitivity
Comparison of Probabilistic and Fuzzy Methods for
Estimating Dynamic Thermal Line Rating
Implementation of an Architecture for Steady State Model
Synthesis of Active Distribution Networks using
Syncrophasors
Microgrid Controller Hardware-in-the-Loop Testing with
Model Development of Common Equipment

Nikita G. Singhal

16STUGM068
16STUGM069
16STUGM070
16STUGM071
16STUGM072
16STUGM073

16STUGM074
16STUGM075

16STUGM076

Engineering Software Needs for the Restructured Grid
S.No.
Poster Title
Evaluation of Power Distribution Systems Operational
16STUGM077
Resilience

System-Wide Events and Analysis Methods
S.No.
Poster Title
Mitigate
Overestimationof Voltage Stability Margin by
16STUGM078
Coupled Single-Port Circuit Models
Insulation Coordination Analysis for Substation Surge
16STUGM079
Arrester Applications
Real-Time Detection of Angle Instability using
16STUGM080
Synchrophasors
16STUGM081 Real-time detection and localization events in power systemwith subspace tracking and clustering method

Intelligent Monitoring and Outage Management
S.No.
Poster Title
Risk Assessment for Vegetation Impact on Outages in
16STUGM082
Electric Distribution Systems
Condition Monitoring of a Large-scale PV Power Plant in
16STUGM083
4

Ying Mu
Leilei Xiong
Hamed Chitsaz
Hyojong Lee
Roohallah Khatami
Jae-Kyeong Kim

Soheila Karimi
Farhan Mahmood

M. Backes

Student Name
Manish Mohanpurkar

Student Name
Haoyu Yuan
Hardik Parikh
Yuan Zhi
Wenting Li

Student Name
Tatjana Dokic
Amit Dhoke

IEEE PES GM 2016 Student Poster List

16STUGM084
16STUGM085

Australia
An Adaptive Restorative Method for Resilient PowerDistribution Networks
Predicting Impact of Weather Caused Blackouts on
Electricity Customers Based on Risk Assessment

Integrating Renewable Energy into the Grid
S.No.
Poster Title
16STUGM086 A 24 Hours Ahead Forecast of Solar Irradiance for Remote
Microgrids using Markov Switching Model
Using
Supplementary
Adaptive Dynamic Programming to
16STUGM087
Improve Control Performance of Distributed Generation
Systems
16STUGM088 Assessing the Dispatch Flexibility of Coordinated Solar andHydro Generation
Modified
OptimalPower Flow on Storage Devices and
16STUGM089
Wind Power Integrated System
Fault Ride Through Support to VSC-HVDC Connected
16STUGM090
DFIG Based Offshore Wind Farm by Variable Braking
Resistor
16STUGM091 Disturbance Investigation of High Penetration Level Hybrid
Power Systems
Risk-Aware Congestion Management Framework with
16STUGM092
Wind Power Uncertainty-Induced Dynamic Line Transfer
Margins
Property of Wind Power Forecast Errors and the Impact to
16STUGM093
the Scheduling of Reserves and Storage Devices
Spatial-Temporal Solar Power Forecast through Use of
16STUGM094
Gaussian Conditional Random Fields
Conditional
probabilistic modeling of wind power forecast
16STUGM095
error
Impact of Converter Interfaced Generation (CIG) on Grid
16STUGM096
Performance
16STUGM097 Network Reconfiguration with Relaxed Radiality Constraint
for Increased Hosting Capacity of Distribution Systems
16STUGM098 Investigation of Non-Synchronous Penetration Level and Its
Impact on Frequency Response in a Wind Dominated Power
System
16STUGM099 Investigation of Voltage Performance of an LV Distribution
Network for Improving Rooftop Photovoltaic Uptake inAustralia
Hierarchical Voltage Control to Improve Security Margin
16STUGM100
for Systems with High Wind Penetration
Constraintsof Wind Power Ramp Event in Robust Unit
16STUGM101
Commitment
Chance-Constrained Two-Stage Stochastic Unit
16STUGM102
Commitment under Uncertain Wind Power Output Using
Benders Decomposition
Reserve Margin for Wind Generation and Market
16STUGM103
5

Ranadhir Sarkar
Qin YanStudent Name
Ayush Shakya
Ujjwol Tamrakar

Jingwei Yang
Wenlei Bai
Gilmanur Rashid

Guanyu Tian
Sajjad Abedi

Yuchen Tang
Bei Zhang
Yida Ye
DeepakRamasubramanian
Masoud Davoudi
Nahid Al Masood

Shohana Rahman
Deeba
Yidan Lu
Jiaming Li
Yao Zhang

Mojgan Hedayati

IEEE PES GM 2016 Student Poster List

Implications of Stochastic Reserve
Estimating the Impact of Proposed Wind Farms on
Electricity Prices

Payam ZamaniDehkordi

16STUGM105
16STUGM106
16STUGM107

Scenario Reduction with Submodular Set Optimization
EMS and Sizing of a Fuel Cell–Based Auxiliary Power Unit
Data Driven Economic Dispatch in Systems with Variable
Generation

Yishen Wang
Saleh Ziaeinejad
Mohammad Sadegh
Modarresi

16STUGM108

Managing Energy Storage and Water Heaters Loads for
Solar-powered Residential Homes
Design LLC Resonant Converter Battery Charger Using
1kHz Sinusoidal Ripple Current
Enabling Flexible Consumers to participate in Forward
Markets by Wind Power Forecast Updates
Remedial Action Scheme With Wind Power Integration
Towards a Power System with High Penetration of
Renewables
Impact of DC Voltage Droop Control Structures on DC
Oscillations in a Multi-Terminal HVDC Grid
Distribution Planning Criteria and Tools for Future
Distributed Energy Resource Penetration Scenarios
Employing ARIMA to Improve Wind Power Forecasting in
ERCOT
Highly Clustered Residential PV: Use of distributed battery
storage for local voltage management

Gonzague Henri

16STUGM104

16STUGM109
16STUGM110
16STUGM111
16STUGM112
16STUGM113
16STUGM114
16STUGM115
16STUGM116

Substation and Distribution Automation
S.No.
Poster Title
Optimal Resilience Enhancement Strategy for Distribution
16STUGM117
Systems under Extreme Weather Events
Outlier Issues in Harmonic Source Location Based on
16STUGM118
Parameter Identification Method
Convergence Study for Distribution Load Capability
16STUGM119
Estimators

Dynamic Performance and Control of Power Systems
S.No.
Poster Title
16STUGM120 Robust Improvement of Network Stability with Wide Area
Control of Multiple HVDC Links
Exploring Adaptive Interpolation to Mitigate Non-Linear
16STUGM121
Impact on Estimating Dynamic States
Predictive Set Point Modulation to Mitigate Transients in
16STUGM122
Power Systems
Verification of Cascading Failures based on Generator
16STUGM123
Power Dispatch
6

Yin Li
Lars HerreRen Liu
Ming Wei
Florian Thams
David Mulcahy
Fathalla A. Eldali
K. Murphy

Student Name
Shanshan Ma
Kexuan Tang
Nicholas S. Coleman

Student Name
P. Agnihotri
Shahrokh Akhlaghi
Hooman Ghaffarzadeh
Zhe XuIEEE PES GM 2016 Student Poster List

16STUGM124
16STUGM125
16STUGM126

16STUGM127
16STUGM128
16STUGM129
16STUGM130
16STUGM131
16STUGM132
16STUGM133
16STUGM134
16STUGM135
16STUGM136
16STUGM137
16STUGM138

Modeling and Analysis of Electrical Heating System Basedon Entransy Dissipation-Based Thermal Resistance Theory
MILP-based Splitting Strategy Searching Considering Island
Connectivity and Voltage Stability Margin
Modeling Protection Systems in Time-Domain Simulations:
An Analytical Method to Detect Mis-operating Relays for
Unstable Power Swings
Performance Evaluation of the Generalized Control Tuning
Procedure for MMC-VSC Systems
Fast Parallel Stochastic Subspace Algorithms for LargeScale Oscillation Monitoring
Study on real-time clustering method for power systemtransient stability assessment
An Analytical Model of a Single Phase DQ-controlled
Inverter for Power System Short Circuit Calculations
PMU-based Reduced-order Modeling of Power System
Dynamics via Selective Modal Analysis
PHEVs Modeling for Autonomous Operation of Microgrid
Resonance with Inter-area Mode in Kundur System from
Forced Oscillation
Validating Inertia Emulation of Wind Turbine Generators
Considering Deadband and Safety Limits
Virtual Actuators for Damping of Inter-area Oscillations
Wide Area Measurement Based Real Time Voltage Stability
Monitoring
Event Analysis Offline Engine Based on PMU Data inPower System
A Novel Control Strategy for Stand-alone Photovoltaic
System Based on Virtual Synchronous Generator

Market Interactions in Power Systems
S.No.
Poster Title
16STUGM139 Financial Assessment for Aggregated Residential Resourcesin the Demand Response Programs in ERCOTRevenue Adequacy of Wholesale Electricity Markets with
16STUGM140
Demand Response Providers
An Agent-Based Framework For Modeling Retail Energy
16STUGM141
Markets with Demand Response
16STUGM142 A Decentralized Framework of the Future Retail Electricity
Markets
16STUGM143 Dual Pricing Algorithm for Cost Allocation in Non-Convex
Electricity Markets
16STUGM144 Bi-level Network Planning Model Considering Generationmarket Equilibria Subject to Inefficient Network Pricing
16STUGM145 Integrating Consumers’ Differentiated Supply Valuation inDistribution Network Planning
Multi-Proxy Interchange Scheduling under Uncertainty
16STUGM146
Advantages of a Three-Stage Stochastic Market-Clearing
16STUGM147
7

Dai Yuanhang
Yifan Zhou
Mojdeh AbdiKhorsand
S. Arunprasanth
Tianying Wu
Fu Su
Javier Alvidrez
Benjamin P. Wiseman
Vishal Verma
Hongda Ren
Yichen Zhang
M. Ehsan Raoufat
Ruoxi Zhu
Yang Zheng
Yan Guo

Student NameLong Zhao
Hao Ming
Kaveh Dehghanpour
Josue Campos do
Prado
Robin Broder
Hytowitz
Pengcheng Ding
Hadi Karimi Khouzani
Yuting Ji
FarzanehIEEE PES GM 2016 Student Poster List

Model vs. a Two-Stage One
Bidding Strategy for an Energy Storage in Joint Energy andReserve Markets
Economic Viability of Price Arbitrage Operation of Gid
Scale Batteries in Alberta’s Energy only market
Market Design with Renewable Capacity
Revenue Analysis of Battery Aggregation for Frequency
Regulation in Wholesale Markets

Abbaspourtorbati
Ehsan Nasrolahpour

16STUGM152

Cooperation among Energy Storages

16STUGM153

Real-Time Implementation of Multiagent-Based Game
Theory Reverse Auction Model for Microgrid Market
Operation

Jesus E. ContrerasOcana
Mehmet H. Cintuglu

16STUGM148
16STUGM149
16STUGM150
16STUGM151

Asset Management
S.No.
Poster Title
Daily Battery Energy Allotment for a Data Center Virtual
16STUGM154
Power Plant Participating in Demand Response
Improving Grid Resilience by Cooperative Electricity and
16STUGM155
Gas Planning
A Weather Condition Incorporated Data-driven
16STUGM156
Maintenance Scheme for Utility Poles
Multi-Source Information Fusion for Power Transformer
16STUGM157
Condition Assessment

Flexible AC Transmission System
S.No.
Poster Title
Applications and Modeling of the Hybrid Power Flow
16STUGM158
Controller
16STUGM159 Integrating TCSC to Enhance Transmission Capability andSecurity: Feasibility Studies for Korean Electric Power
SystemPower Electronics
S.No.
Poster Title
16STUGM160 Three-Phase Isolated Soft-Switching DC-DC Converter forHigh Power Applications
Sequence Domain Transfer Matrix Model of Three Phase
16STUGM161
Voltage Source Converters
16STUGM162 Influence of point-on-wave of high frequency distortion on
luminosity variations with LED lamps
16STUGM163 General Control Strategy to Limit Peak Currents of Virtual
Synchronous Generator Under Voltage Sags
New Active Start-up Scheme for MMC HVDC System to
16STUGM164
Interconnect Wind Farm
Optimization of Proportional-Resonant Controller
16STUGM165
8

A.I. Adebayo
Cynthia Bothwell
Alberto CasteloBecerra

Student NameLabi Bajracharya;
Chengcheng Shao
Jun Zhong
Yi CuiStudent Name
Behnam Tamimi
Hyun-Jae Lee

Student Name
Tao Li
Shahil Shah
Gaurav Singh
Tianyi Chen
Jae-Hyuk Kim
Jeehoon Lee

IEEE PES GM 2016 Student Poster List

16STUGM166
16STUGM167

Parameters in Active Filter
New Configuration of Hybrid DC Circuit Breaker for
Voltage Source Converter HVDC System
Design of a Hybrid MOV-TVS Diode Surge Protection
DeviceElectrical Machine and Drives
S.No.
Poster Title
Analysis of Neural Network Vector Control for IPM
16STUGM168
Machine in Electric Vehicles

Power System Modeling and Simulation
S.No.
Poster Title
Secondary Circuit Model Generation Using Limited PV
16STUGM169
Measurements and Parameter Estimation
Online Tracking of Transmission Line Parameters Using
16STUGM170
SCADA Data
A Hardware-in-the-Loop Simulation of AGC for Large
16STUGM171
Scale Wind-Gas Coordinating Power Generation
Interfacing
SFA- and GAM-Type Dynamic Phasors for
16STUGM172
Modeling of Integrated AC-DC Power Systems
High Speed Digital Distance Relaying Scheme for Extra
16STUGM173
High Voltage Transmission Lines
A Test Cases Library for Methods Locating the Sources of
16STUGM174
Sustained Oscillations
A Hybrid Simulation Tool for the Study of PV Impacts in
16STUGM175
Distribution Networks
Stochastic Generation and Transmission Planning
16STUGM176
Considering Future Renewable and Gas Opportunities inQueensland
An Analytical Method for Probabilistic Modeling of the
16STUGM177
Steady-State Behavior of Secondary Residential System
Modeling of Multi-terminal VSC-based HVDC System
16STUGM178
Building Realistic Transmission Line Topologies for
16STUGM179
Synthetic Power System Test Cases
Equivalent Electromagnetic Model of Self-blocking MMC
16STUGM180
with DC fault isolation capability
A
Practical
Real-Time
Hybrid Simulator for Modern Large
16STUGM181
HVACDC Power Systems Interfacing RTDS and External
Transient Program
16STUGM182 Enhancing Stochastic Unit Commitment with Data Mining
16STUGM183 Bidding and Offering Strategy for a Merchant Compressed
Air Energy Storage Plant
Enhanced Reserve Policies for Power Systems in the
16STUGM184
Presence of Stochastic Resources
16STUGM185 A Hydro-Reservoir Generic Storage Model for Short-TermHydro-Thermal Coordination
16STUGM186 Using Synchronized Phasor Data for Online Determination
9

Yoon-Seok Lee
D. T. Khanmiri

Student Name
Hoyun Won

Student Name
Jouni Peppanen
Yang Wang
Chen Fang
Yingwei Huang
Xingxing Jin
Slava Maslennikov
Ali Hariri
Juliana Barbosa Nunes

Tianyu Ding
Mohammed Alharbi
Adam B. Birchfield
Wang Xiang
Shuqing Zhang

Chao Li
Soroush Shafiee
Nan LiManuel Alvarez
Moosa Moghimi Haji

IEEE PES GM 2016 Student Poster List

16STUGM187
16STUGM188
16STUGM189
16STUGM190
16STUGM191
16STUGM192
16STUGM193
16STUGM194
16STUGM195
16STUGM196
16STUGM197
16STUGM198
16STUGM199
16STUGM200

of External System Equivalent
Optimal Placement of a Continuously Variable Series
Reactor Considering Contingencies
Impacts of Series FACTS on Generation Cost in Korea
Power System by Using Optimal Power Flow
Risk-based constraint relaxation for look-ahead dispatch
Transmission Congestion Management Considering Parking
Lots and Demand Response Programs
Urban Distribution Grid Topology Reconstruction via Lasso
Sequential Power Flow Simulation of Integrated Dynamic
Wireless Power Transfer Systems
Distributed Economic Dispatch on a Detailed Real-timePower System Simulator
A classification-based methodology for finding initialsolutions in Transmission Expansion Planning Problems
Integrated Electricity Generation, CHPs, and Boilers
Expansion Planning: Alberta Case Study
Situation Awareness of Available Transfer Capacity Using
PMU Data
Optimal PI Controllers for Switch Model Power Electronic
Interfaces
Enhancing AGC for Multi-Area Power Systems in the
Presence of Variable Generation
Speed Deviation Signals of Synchronous Generators
Derived from PMU Data
Setting a Carbon Tax for the Electricity Sector

10

Xiaohu Zhang
Young Joon Lee
Xian Guo
Amin Mohsenzadeh
Yizheng Liao
Haipeng Xie
Zhenhuan Ding
Daniel Marin-Diego
Mejia
J. Li
Y. WeiA.Arzani
I. Jayawardene
P. Arunagirinathan
D. J. OlsenImpacts of Imperfect Information on Customer-sited
Distributed Energy Storage Investment Decisions
Jingkun Liu1, Ning Zhang1, Daniel Kirschen2, and Chongqing Kang1
1 Department of electrical engineering
Tsinghua University
Beijing, 100084 China

2 Department of electrical engineering
University of Washington
Seattle, WA 98195 USA

Abstract—Residential and small commercial users ofelectricity are increasingly thinking about using distributed
energy storage to reduce their electricity bills, integrate PV, andparticipate in demand response. When choosing the power andenergy capacity of the energy storage devices that they will install,they should consider both investment and operation costs. Ideally,
these decisions should be based on perfect information about
future needs. In practice, they may have perfect information
about the past but can only imperfectly forecast actual real-time
operation. This papers models and compares decisions based on
perfect and imperfect information and thus estimate the value of
perfect information. Numerical results are obtained based on
actual data from Ireland. The results quantify the overestimation of the benefit that arises from the assumption that theavailable information perfectly reflects actual future operation.

In order to quantify the gap between ideal and realistic
benefits, we model investment decisions based on perfect
information as well as the operation considering imperfect
information.

I.

INTRODUCTION

If the cost of energy storage devices continues to decline,
customer-sited distributed energy storage (CDES) devices will
become economically justifiable for residential consumers.
These CDES devices can be used to store energy when prices
are low and discharge this energy when prices are high. They
could also be used to maximize the benefits from theinstallation of residential PV, and to participate in demand
response, or other schemes that would reduce their electricity
bills or achieve other benefits.

II. KEY METHOD
For simulating the ideal decision of each user, an
optimization model is set up considering time series of loads
and electricity prices. The objective function is to minimize thetotal annual cost, including the cost of investment in energy
storage by the customer and its electricity bill. Formally:(1)

min CiCDES  IiCDES  OiUser   cP, CDES Pi Cap  cE, CDES EiCap + t  Pi ,Ct  Pi ,Dt  di ,t   t  Pi ,Ct  Pi ,Dt  di ,t   ttT

Since users are not able to implement complex real-time
control strategies, in order to simulate the realistic operation,
we assume that users can forecast the average electricity priceof the next day. When the real-time price is lower than thisaverage price minus a certain buffer value, customers charge
their CDESs. On the other hand, when the real-time price ishigher than the average price plus a certain buffer value, they
discharge.
III. KEY RESULTS
Table 1 shows partial results for 200 residential users
having access to real-time electricity prices.
TABLE I. RESULTS FOR VARIOUS ENERGY STORAGE INVESTMENT COSTS

Fig. 1 The structure of CDES

Each consumer considering the installation of a CDES
device must decide the power and energy capacity that would
be optimal. This optimization is typically based on past energy
consumption and price data. However, the value of a CDES
calculated using this data is likely to be optimistic because this
this information is imperfect and uncertain factors (such as load
fluctuations, price variations, and control methods), will
influence actual operation and reduce the benefits.

Energystorage
cost
($/kW
&$/kW
h)
100/300
80/24060/18040/12020/60

Total costwith no
CDES ($)

357275.90
357275.90
357275.90
357275.90
357275.90

Ideal total
cost with
CDES ($)

354383.99351255.02
345555.49333497.41
306782.47

Ideal
cost
saving
rate

0.81%
1.69%
3.28%
6.66%
14.13%

Realistic
total costwith CDES($)

Realisticcost
savingrate362529.77
362748.49
359949.35
353932.25
339114.93-1.47%-1.53%-0.75%
0.94%
5.08%

We can see that realistic saving are significantly lower thanthe corresponding ideal ones. This should be fully consideredin CDES investment decision. When energy storage prices are
relatively high the ideal cost saving rate may be positive.
However, the realistic rate may be negative, meaning thatinvesting in CDES is not economically justifiable. As the unit
price of energy storage devices decreases, the cost saving rates
of both ideal and realistic situation improves.

Robust Reserve Capacity Provision and Peak Load Reduction
from Buildings in Smart Grids
Sarmad Hanif1,2 ∗ , Dante Fernando Recalde Melo1 † , Mehdi Maasoumy4 ‡ , Tobias Massier1 k , Thomas Hamacher3 § , Thomas Reindl2 ¶1
TUM CREATE Limited, Singapore 138602
2
Solar Energy Research Institute of Singapore (SERIS), National University of Singapore (NUS), Singapore 117574
3
Technical University of Munich (TUM), Garching 85748, Germany
4
Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA 94720, USA∗ sarmad.hanif@tum-create.edu.sg, † e120025@e.ntu.edu.sg, ‡ maasoumy@eecs.berkeley.edu,
k tobias.massier@tum-create.edu.sg, § thomas.hamacher@tum.de, ¶ thomas.reindl@nus.edu.sg

Abstract—This paper proposes a robust demand-side control algorithm
in a smart grid environment for heating, ventilation and air conditioning(HVAC) systems. A robust model predictive control (RMPC) scheme
in a receding horizon fashion is deployed, which optimizes electricity
cost and capacity market participation of the HVAC system, whilesatisfying comfort and operational constraints of the building and utility,
respectively. Thermal load uncertainties experienced by the HVAC system
are included to perform a realistic assessment of the developed controller.
The National Electricity Market of Singapore (NEMS) is used as a casestudy and the developed RMPC scheme is tested for various price signals
and scenarios. Numerical simulation results show the effectiveness of thedeveloped framework to be readily adopted by utilities – interested inrealizing a grid-friendly and economicaly eficient demand response (DR)strategy.
Index Terms—Demand Response, Energy Market, Heating Ventilation
Air-Conditioning (HVAC), Robust Model Predictive Control (RMPC).

I. I NTRODUCTION

II. C ONTROLLER D ESIGN

min K (uk ) + K (−rk ) + ρk + βk

(1a)subject to 
φ0k Pf,uk Pc,uk Ph,uk ≤ 1 1 1 βk

(1b)

u∗
,−r∗
k
k

(1c)

C
max xCk+1 = Axk + Buk + E(d̂k + wk )

(1d)

C
NC
max xN+ Buk + E(d̂k + wk ) + Br rk
k+1 = Axk

(1e)kwk k≤σk
kwk k≤σkC+x−k − k ≤ xk ≤ xk + k

(1f)

NC
≤ x+
x−
k + kk − k ≤ xk−
uk − rk ≤ uk ≤ u+k − rk(1h)

rk , uk − rk , k ≥ 0

(1i)

(1g)

III. R ESULTS
(a)
(b)
(c)

Nominal MPC scheme (NMPC)RMPC scheme without peak-power-penalty (PPP)RMPC scheme with PPP of 1.5 (SGD/kW)

(a)
24
deg C

Buildings are considered as one of the major contributors ofgreenhouse gas emissions and electricity consumption. Within abuilding, the HVAC system consumes the largest portion of the energy. Space cooling/heating along with the thermal inertia of buildings
provides an inherent flexibility in the consumption of electricity.
Usage of space cooling’s flexibility can provide: (1) reduced building
operational cost, (2) ancillary service provision to the grid and, (3)grid secuirity.

Utility PeakPower
Signal𝐝𝑘 = [𝑑𝑘|𝑘 , 𝑑𝑘|𝑘+1 … , 𝑑𝑘|𝑘+𝑁 ]𝚽𝑘 = [𝛷𝑘|𝑘 , 𝛷𝑘|𝑘+1 … , 𝛷𝑘|𝑘+𝑁 ]𝞂𝑘 = [𝜎𝑘|𝑘 , 𝜎𝑘|𝑘+1 … , 𝜎𝑘|𝑘+𝑁 ]2220
23−01−14

XC
R

XNC
R

24−01−14

Tolerance
25−01−14(b)Disturbance∗∗∗𝐮𝑘∗ = [𝑢𝑘|𝑘, 𝑢𝑘|𝑘+1… , 𝑢𝑘|𝑘+𝑁]∗∗∗
𝐫𝑘∗ = [𝑟𝑘|𝑘, 𝑟𝑘|𝑘+1… , 𝑟𝑘|𝑘+𝑁]

Building
EnergyManagement
System𝑘Time instant𝑁
Prediction Horizon
𝑥0
Initial State
𝐀, 𝐁, 𝐄, 𝐁𝐫 System Matrices
𝐝𝑘Disturbance Vector
𝞂𝑘Disturbance Bound Vector𝚽𝑘Utility Peak Power Signal
Smart Grid
𝒒𝒓𝒂𝒅 𝒌Irradiation Prediction Vector
Interface𝐛𝑘
Reserve Capacity Price Vector
𝐜𝑘
Energy Price Vector∗𝐮𝑘
Optimal Consumption Schedule𝐫𝑘∗
Optimal Reserve Capacity Schedule𝑥0 ,𝐀, 𝐁, 𝐄, 𝐁𝐫

22

20
23−01−14
∗
𝑢𝑘|𝑘

XC
R

XNC
R

24−01−14

Tolerance
25−01−14

(c)
24
deg C

RobustModel
Predictive
Control
Schemedeg C

24

𝐛𝑘 = [𝑏𝑘|𝑘 , 𝑏𝑘|𝑘+1 … , 𝑏𝑘|𝑘+𝑁 ]𝐜𝑘 = [𝑐𝑘|𝑘 , 𝑐𝑘|𝑘+1 … , 𝑐𝑘|𝑘+𝑁 ]

22
XC
R

Market∗𝑟𝑘|𝑘Fig. 1. Experimental set-up of the RMPC control scheme.

20
23−01−14

PPP

24−01−14

XNC
R

Tolerance

PPP

25−01−14

1

A Hybrid Robust Power System State Estimation Method with
Non-Gaussian Measurement Noise
Junbo Zhao, Student Member, IEEE, Gexiang Zhang, Member, IEEE, Massimo La Scala, Fellow, IEEE

Abstract—In practical applications like power systems, the distributionof the measurement noise is unknown or frequently deviates fromthe assumed Gaussian model, often being characterized by heavy tails
and sometimes generating impulse noise, named outliers. Under these
conditions, the performances of the conventional state estimation (SE)methods will be greatly degraded. To solve these problems, this paper
proposes a hybrid robust power system SE method. In the first stage,a generalized maximum (GM)-estimator that uses conventional SCADA
measurements is proposed with a novel robust scale and Huber-type Mestimator. Its result is further combined with the PMU measurement toachieve a linear robust estimation in the second stage. The proposed GMestimator bounds the influences of bad data and the estimation residuals,
yielding good statistical efficiency for Gaussian noise or even other nonGaussian heavy tailed distributions, like Laplace, Gaussian mixture, etc.
Numerical tests on the IEEE-30 bus system under various cases verify
the effectiveness and robustness of the proposed method.

(a)I. K EY E QUATIONS
For an n-buses power system, the relationship between the measurement vector z and state vector x (i.e., voltage magnitudes andangles) can be described as follows:z = h(x) + e,(1)

where h(·) is a nonlinear measurement vector function; e is theerror vector that accounts for the uncertainty in the measurements
and is not necessary following Gaussian distribution. In this paper,
the finite sum of known Gaussian distributions is used to approximate
the empirical non-Gaussian measurement noise, i.e., ϵ-contaminatedmodel
G(e) = (1 − ϵ)Φ(e) + ϵK(e),(2)
where Φ(e) is the majority distribution of the measurement noise,
which is usually modeled as Gaussian distribution; K(e) is theunknown distribution, which is taken to be heavy-tailed density, such
as Laplacian density or Gaussian density with large variance. To
handle the non-Gaussian measurement noise with good estimation
statistical efficiency while maintaining robustness to bad data, therobust estimator is proposed that minimizes the following objectivefunctionm∑J=
ϖi2 ρ (rSi ),(3)
i=1

where ϖi is the weight used to bound the influence of leverage point
and is calculated by using the projection statistics estimator; ρ(·) is anonlinear convex function and is usually chosen as Huber-ρ function
{ 1 2
r ,
for |rSi | < c
2 Si
ρ (rSi ) =,(4)
c |rSi | − c2 /2, elesewhere
where rSi = ri /sϖi is the standardized residual; ri = zi − hi (x) is
the residual associated with the ith measurement; c is the breakdown
point; s is the proposed robust scale estimation for estimating the
unknown non-Gaussian noise, i.e.,s = 1.1926 · fm · lomed lomed |ri − rj | ,i=1,...,m

j̸=i

(5)

School of Electrical Engineering, Southwest Jiaotong University, Chengdu
610031, China, and the Bradley Department of Electrical Computer Engineering, Virginia Polytechnic Institute and State University, Northern Virginia
Center, Falls Church, VA 22043 USA.

(b)

Fig. 1. The absolute estimation error of the system state with non-Gaussian
measurement noise (a) Laplace measurement noise; (b)Gaussian mixture
model based measurement noise

where fm is a correction factor; the lomed operator is a median
operator. Using the iteratively re-weighted least squares (IRLS)
algorithm and the first-order Taylor series approximation, the stateat the k iteration is updated as()
H T Qk R̂−1 H ∆xk = H T Qk R̂−1 (z − h (xk )) ,(6)
where ∆xk = xk+1 − xk ; H is the Jacobian matrix; Q =diag(q(rSi )); q(rSi ) = ψ (rSi )/rS ; ψ (rSi ) = ∂ρ (rSi ) /∂rSi ;iR̂ = diag(s2i ). The estimated states are further augmented with
the PMU measurement to yield a linear state estimation. Since baddata may occur in PMU measurements, we propose to use the robust
estimation by using the similar objective function in (3) with robust
Huber M-estimator and the novel robust scale.
II. K EY R ESULTS
The absolute estimation error representing the difference between
the estimated state and the true state, is used to assess the performanceof the estimators. Fig. 1 shows the comparison results obtained onIEEE 30-bus test system. It can be observed from this figure that theestimation biases for the traditional two-stage weighted least squares(WLS) estimator with both Gaussian mixture model based noise andLaplace noise are much higher than the proposed robust estimator.
This verifies that the proposed robust estimator can effectively boundthe influences of the long thick-tailed distributions while maintaining
good estimation statistic efficiency.

Management Scheme for Parallel Connected
Hybrid Energy Storage in Electric Vehicles
Christopher R. Lashway, Ahmed Elsayed, Student Members, IEEE , Osama Mohammed, Fellow, IEEE
Department of Electrical and Computer Engineering, Florida International University,
Miami, FL 33174, USA
Email: clash006@fiu.edu, mohammed@fiu.edu

Fig. 1. Hybrid Pulse Power Characterization (HPPC) Test Profile.

+
Electric
Motor
A

M

Current
Measure.
Li‐IonBattery‐

Voltage
V Measure.

SB4

SB2

+

Index Terms—lithium ion batteries, supercapacitors, electricvehicles, energy management

I. KEY FIGURES
There are 2 main objectives of the proposed management
scheme: 1) Reduce the cycling and stresses on the lithium ion
battery, thus increasing its lifetime and 2) reduce the energy
gone to waste which cannot be absorbed by the battery at a high
SoC. The HESS management scheme is broken into 3 control states:
1) State 1: The lithium ion battery is fully charged and SC is
fully discharged. Discharge pulses are handled by the battery
while regenerative breaking pulses are handled by the SC. This
only exposes the battery to 1 cycle until the SC is fully charged.
2) State 2: The SC reaches a full charge. In order to continue
harvesting regenerative breaking energy, the SC and lithium ion
battery are connected in parallel to absorb the discharging andcharging pulses. The SC helps reduce battery current as it
trickle charges near a full charge but in order to utilize thecharging energy, the battery must be exposed to cycling.
3) State 3: The lithium ion battery has discharged considerably
where the SC is still near 100% SoC. The charging pulse isapplied only to the battery and discharge pulse is handled by theSC, however, it is limited in duration as a result of reduced
energy density. At this point, the system returns to State 1 andprogresses through each state until the lithium ion battery bankhas reached the full discharge cut-off voltage.

SB3

SB1

DC/DC Converter

Abstract — As the electric vehicle (EV) gains traction in themodern automotive market, a focus has been placed on thedevelopment of efficient and long-lasting methods to store energy.
In order to exploit the advantages of a hybrid energy storage
system (HESS), new management schemes are needed which
understand the mechanics of each energy storage device. Many
EVs have introduced regenerative braking as a method torecharge the battery bank while in operation. Unfortunately, thecombination of long duration discharge drive currents and short,high powered charging currents from regenerative breaking placebatteries under enormous stress resulting in shorter lifetimes.
Moreover, when the battery bank is at a high state of charge,
electrochemical constraints will limit current injection during
regenerative braking thus some energy is left to waste. In thiswork, a parallel-connected lithium ion battery bank andsupercapacitor HESS demonstrates a new multi-state EV control
and management scheme. Unnecessary cycling of the lithium ion
battery array is reduced while the efficiency of a regenerative
braking pulse is increased. The scheme is demonstrated
experimentally using the DOE Hybrid Pulsed PowerCharacterization test profile procedure.

‐+

Regenerative
Braking
System

‐

SC3

SC1
A

+

Current
Measure.
Voltage
V Measure.
Super
Cap.

DC BusSC2

SC4‐Fig. 2. Schematic diagram for the HESS – HPPC Testing Platform.

Fig. 3. Experimental Results (a) ES Voltages (b) ES Currents (c) DC Bus
and HESS Terminal Voltages.

1Multiple Communication Architectures with
Optimization for Smart Transimission Grid
Fan Ye, Student Member, IEEE, and Anjan Bose, Fellow, IEEE
Abstract—Phasor measurement units (PMU) are widespread
installations recently. Many PMU-based applications are underdeveloped by not only scholars but also the people from theindustry. To support generous information generated from PMUs
and meet the strict latency requirements of these applications,appropriate network infrastructures are supposed to utilize. This
paper presents four different communication architectures
supporting PMU packets exchanging between the control center(CC) and substations (SS) in smart transmission grid. It also
provides the location optimization of CC these communicationarchitecture, and of power system control boundary issues since
they both have effects on the communication performances. The
IEEE-118 bus test system is a base power system in which PMU
messages are communicating. Transient stability within wide area
monitoring and control (WAMC) system is considered in order tomake sure the validity of communication results. One conclusion
for this paper is that the optimal decentralized architecture has
data delays in an acceptable level thus might be a better solution
for PMU-based applications.

II. KEY COMMUNICATION ARCHITECTURES
Fig. 1 is taking three areas as an example, each area with
one sub control center that could help PMU packets to be
forwarded. Since we don’t know the best location where thecontrol center resides, so Fig. 2 only shows substation
unknown (X).
CC

SubCC2SubCC1

SubCC3

I. INTRODUCTION
Phasor measurement unit (PMU) is receiving much attention
and has widespread installation within substation (SS) in thepast decade. Because of PMUs’ availability across power
grids, many PMU-based applications are under developed,
even some of them are utilized in the real life. Althoughapplications based on PMU measurements have become
available, and could maintain or improve the system overall
stability, the communication networks that support PMU
messages exchanging between CC and SSs is not supposed tobe negligible. These applications share one common thing thatthey all have strict latency requirements like transient stability
within 100ms, small signal stability within 1s, state estimation
within 1s, and so on. To support the PMU packets deliveringand minimize the packets’ delays, four different
communication architectures, star and mesh networks bothincluded, are introduced and studied, followed by the powersystems transient stability demonstrating the communication
results. Moreover, for mesh networks, we propose an
optimization algorithm to locate the best location for CC.
More specially, this algorithm can help researchers or system
operates decide the number of subareas and each area’s control
boundary for decentralized communication architecture. The
NS3 software is used to simulate the communication networks.
And the IEEE-118 bus test system is a case study in this paper.

Area1

Area2

Area3

Fig. 1. Decentralized communication architecture, this figure is taking threeareas as an example.
CC

PMU Data
Control Signal

Sub X

Fig. 2. The architecture is made up of communication lines that are along thesame right-of-way as the power transmission lines.

Abnormal Overheating Detectability Analysis
Based on Cross Gramian for a Supercapacitors String
Vita Lystianingrum1,2, Branislav Hredzak1, Vassilios G. Agelidis1
Australian Energy Research Institute and School of Electrical Engineering and Telecommunications
The University of New South Wales, Sydney, NSW, 2052, Australia
2
Department of Electrical Engineering
Institut Teknologi Sepuluh Nopember, Surabaya, 60111, Indonesia
email: vita.lystianingrum@student.unsw.edu.au

1

Abstract—Model-based temperature estimation has been
widely studied and applied in many applications, including for
power/energy storage applications such as batteries orsupercapacitors (SCs). In this work, sensors placement for atemperature monitoring and diagnostic system is studied, withrespect not only to the state estimation during normal condition
but also abnormal overheating detection. The abnormal
overheating is modelled as an unknown additional disturbance.
Several measures based on the cross Gramian matrix are used as
the basis for the optimal sensors placement for the overheating
detectability. The placement selection is evaluated from different
overheating simulation scenarios. A “naïve” sensor placement
which intuitively places the sensors in an evenly distributedmanner along the string is included to confirm the usefulness ofthe cross Gramian measure-based sensors placement.
Keywords—temperature;
estimation;
thermalobservability; controllability; supercapacitors;

sensors;

I. METHODOLOGY DESCRIPTION

8

7

6

5

4

3

2

1

air flow
direction

Sensors Combinations
based on measure:
Determinant“Naïve” (Not basedon any measure)

Considering a thermal model in state-space form, twomatrices Bp and p related to the additional abnormal heatsource are added to the state space model as shown in (2).
(2)
x  Ax  Bu  B p p, y  Cx
The cross Gramian matrix Wco matrix is used to analyse
different sensors placement (C matrices) for the different
possibilities (represented by different B̃ matrices). It is definedas

0

TABLE I.

Trace

Abnormal overheating event is modelled as an additional
abnormal heat source Qab_chem in the normal heat balance as anaddition to the generated heat QgendT
(1)
Cc c  Qab_chem  Qgen  Qreleasedt

II. RESULTS AND CONCLUSION

Condition number

Fig. 1. Forced-cooled SC string, N = 8 [1].

Wco  e At BCe At dt

Sensors placement is evaluated using three measures: theWco matrix’s determinant, condition number and trace. The
mark “✚” is defined as a performance indicator, in which thefirst “✚” means that the overheating is detected in less than thespecified time. The second “✚” means that the overheating
location detected is correct. The table column in black colour
indicates a SC cell having a sensor, for which case any
overheating in those cells is detected almost immediately.

(3)

DETECTION PERFORMANCE COMPARISON
Overheating Location
Cell 1 Cell 2 Cell 3 Cell 4 Cell 5 Cell 6 Cell 7 Cell 8

2,5,8

✚✚

3,5,8

✚✚

✚✚

✚✚

3,6,8

✚✚

✚✚

4,6,8

✚

5,6,8

✚

6,7,8

✚

✚✚
✚

✚✚
✚✚

✚✚

✚

✚✚

✚✚

✚✚
✚✚
✚✚

1,7,8
✚✚

1,5,8
2,4,7

✚✚

✚

✚✚

✚

✚✚
✚

Based on the specified detection performance indicator, thedeterminant measure and the condition number measuredelivered the most satisfying results compared to the
combinations suggested by the trace measure. A “naïve” sensor
placement which intuitively placed the sensors in an evenly
distributed manner along the string was also included in theevaluation matrix. This placement gave less satisfying result, as
did the placement suggested by the trace measure. Theseobservations have confirmed the usefulness of analysing the
input-to-state-to-output relationship using the cross Gramian
matrix and evaluating suitable measures for sensors placement.
REFERENCES[1] V. Lystianingrum, B. Hredzak, V. G. Agelidis, and V. S. Djanali, "On
Estimating Instantaneous Temperature of a Supercapacitor String Using
an Observer Based on Experimentally Validated Lumped Thermal
Model," IEEE Trans. Energy Conv., vol. PP, pp. 1-11, 2015.

Graphical User Control Interface for an
Advanced Battery Management System
Nicole Bugay, Student Member, IEEE, Osama Mohammed, Fellow, IEEE
Department of Electrical and Computer Engineering, Florida International University
Miami, FL 33174, USA
Email: nbuga001@fiu.edu, mohammed@fiu.edu
Abstract - Energy storage systems can be used tocreate a power grid that is more efficient, reliable,
and economical while also assisting in efficiently
integrating renewable energy. As implementation
increases, a battery management system isnecessary to monitor and maintain safe, optimal
operation of each battery stack in the energy
storage system. The purpose of this research is tocreate and implement an advanced graphical userinterface for a unique battery management system(BMS). The BMS allows each battery in the stack
to be individually monitored and managed whileallowing different charging profiles to be applied.
The graphical user interface was created on theLinux QT platform and has been displayed on a
BeagleBone microcontroller board. This embedded
board was then implemented into the physicalcircuitry of the battery management system. To
manage measurements and control commands, acustom communication scheme has been
implemented to pass query, control, andconfiguration messages over a serial port.

Figure 2: Hierarchy of the Battery Management
System (BMS)

I. KEY FEATURES(a)

Figure 1: Communication of the Battery
Management System(b)Figure 3: BMS Graphical User Interface (a) Battery
Information Window (b) Load Information Window

Distribution System Properties Based Event Driven
Communication Network ModelingThanatheepan Balachandran, and Visvakumar Aravinthan
Department of Electrical Engineering and Computer Science
Wichita State University
Wichita, Kansas, 67260
txbalachandran@wichita.edu
Abstract—Smart distribution system initiative requires
increased usage of distribution feeder-level communication
infrastructure to improve automation. Reliable communication
requires information received within the allowable delay andminimum processing at the decision maker (control center) level.
A communication protocol for distribution feeder is presented
with few applications.

I. INTRODUCTION
As a part of smart grid initiative, the future distribution
system is expected to utilize automation to improve its
performance. This requires improved communication
especially at distribution level. The new technologies are
expected to decrease the downtime and increase the real-time
controllability [1]. Recent literature shows the increased
interest to develop dedicated communication protocols [2].
Based on operating needs, the communication systemshould provide quick decision–making tools for distribution
system operations. This becomes critical for dynamic
management of distribution system during normal andabnormal conditions. And requires distributed sensor
placement along with the communication infrastructure.
II. DISTRIBTED SNESOR BASED POWER DISTRIBUTION
The future distribution system with communication
enabled sensor network is shown in Fig. 1.

(a) Data inflow to single node(b) Data outflow in a super frame.
Fig. 2: Proposed data flow model

B. Time Sensitive Data Communication
Upon observing an event the communication time slot will
be determined based on the severity of the event. For example
a fault will be detected by all the upstream nodes at the sametime. To limit them from colliding with each other, a protocol
based on the fault current is proposed in this work. The timeslot to send the data for the jth node can be determined by:( N  j) F T j   f (i fault , ishortcircuit )Nk where N is the total number of sensors, F is the slots forcommunication, and k is a heuristic parameter. f is a functionof fault current and short circuit current for the jth node. This
work uses exponential and linear functions.
III. ANALYSIS AND RESULTSIEEE 37 node feeder is used to evaluate the effectiveness ofthe proposed work. Faults with different impedance were
injected at different nodes. The average time to communicate
for 100 different fault scenarios is plotted in Fig. 3.

(a) Electric Network(b) Communication-Sensor Network
Fig. 1Future Cyber Enabled Distribution System

IEEE Utility Communication Architecture (UCA) [5] is atechnical specification proposed by EPRI. According to thestandard time sensitive information requires a separate
protocol. This work proposes a single architecture for
distribution feeder level using the three-layer OSI model.
A. Unified Communication Protocol
Each node could have multiple data communication
requirement (e.g. current, voltage, maximum power, sudden
variation in current etc.). Some of these would be time
sensitive. All these would use single channel to reduce the costof communication infrastructure as shown in Fig. 2.a. The
time sensitive information will have high priority and will
utilize a contention based slot in a frame as shown in Fig, 2.b.
non-critical information will use a predefined slot in the frame
and a queue will be formed if the information cannot be sentin one slot.

Fig. 3 Time to send the time critical message

IV. CONCLUSION
This work proposes a unified communication frameworkfor distribution feeders. For the time critical information a
protocol is developed to reduce the time to communication.
REFERENCES
[1]

[2]

[3]

Mingyue, Z.: “Measurements and Channel Characteristics of LV PowerLine Communications Networks in China,” in Proc. IEEE Conference inPower Line Communications and Its Applications, 2006.
Garlapati S., Kuruganti T., Buehrer M., and Reed J. “SMAC: A Soft
MAC to Reduce Control Overhead and Latency in CDMA-Based AMI
Networks,” IEEE/ACM Trans. Networking, available as an early accessthrough www.ieeexplore.org
IEEE-SA TR 1550-1999, “Utility Communication Arcitecture (UCA)
Verstion 2.0”

1Challenges in Applying Decomposition based
Hybrid Forecasting Algorithm on Practical
Cases
Yamin Wang, Lei Wu, Senior Member, IEEE, and Shouxiang Wang, Senior Member, IEEE

environmental factors.
II. KEY FIGURES
IMF4

5
0

IMF6

IMF5

-5
0
5

150

200

250

300

350 364

50

100

150

200

250

300

350 364

50

100

150

200

250

300

350 364

50

100

150

200

250

300

350 364

50

100

150

200

250

300

350 364

IMF7

0

-1
0
20

r

100

0

-5
0
1

10
0

0

Fig. 1. Sub-series decomposed by 274 (red) and 364 (blue) wind speed data
IMF4

20
15

0

0

IMF5
100

200

300

364

5

0

300

364

100

200

300

364

100

200

300

364

100

200

300

364

100

200

300

364

IMF7

r

IMF4

314

334

354 364

294

314

334

354 364

294

314

334

354 364

294

314

334

354 364

294

314

334

354 364

IMF7

IMF6

IMF5

294

r

IMF2
IMF3

Wind Speed

IMF1
IMF2

200

0

-5
-5
0
100
200
300 3640This paper gives an in-depth analysis on the EMD based
5
1
forecasting algorithm and possible alternatives to illustrate the00challenges via practical applications on wind speed and solar
-5
-1
0
100
200
300
364
0
2
20
irradiation forecasts. The first issue is that sub-series of the0
10
original TS with newly obtained data may be significantly
-2
0
0
100
200
300
364
0
different from the one used in training the forecasting model,
Fig.
2.
Subseries
obtainedbyEMD
and in turn the established models may not suit for newly
125
decomposed sub-series and have to be trained more frequently.
100
The second challenge is that strong correlations between key
-5
8
274
10
6
environmental factors and the original TS are not sustained in
04
sub-series and in turn fail to provide useful information for
-10
2
274
294
314
334
354 364274
improving forecasts of individual sub-series.
50
The forecasting accuracy of EMD based models is
0
-5
-10
-5
compared with the non-decomposition based forecasting
274
294
314
334
354 364274
1
5models including ARMA and persistence method. Furthermore,00
the correlation between environmental factors and individual
-1
-5
274
294
314
334
354 364274
512sub-series is also analyzed. In our case studies, environmental
100factors are added into the training of each sub-series and its
8
-5
274
294
314
334
354 364274
forecasting accuracy is compared with the model without

IMF3

100

0

-5

IMF6

IMF1

5
0
0
5

5

-5
0
5

10

Index Terms—EMD, time series forecasting.

I. INTRODUCTION

50

0
-5
0
5

Wind Speed

Abstract—The basic idea of the EMD based forecasting
method is to decompose the complicated original TS into acollection of sub-series and build specific forecasting models forindividual sub-series. Final forecasting results of the original TS
are obtained by adding up forecasting results of individual subseries. However, the traditional EMD based forecasting algorithm
presents two challenges, which have not been thoroughly
discussed in literature and could impede its effective application
on practical cases: (1) Decomposed sub-series are very sensitive tothe original TS. That is, sub-series with newly obtained TS data
may be significantly different from the one used in training theforecasting model. In turn, the established model may not suit for
newly decomposed sub-series and has to be trained more
frequently; and (2) Key environmental factors usually play a keyrole in improving wind/solar forecasting results via nondecomposition based methods. However, it is difficult toincorporate key environmental factors into forecasting models of
individual sub-series as they do not present strong correlations ascompared to the original TS data. It is expected that this research
could bring more attentions to improved algorithms for solving
the challenge of the EMD based forecasting method and make it
more suitable for practical cases.

Fig.3 Subsiers obtained by EMD in practical cases

Towards Optimal Coordinated Operation ofDistributed Internet Data Center Microgrids
Wenbo Qi

Jie Li

Department of Electrical and Computer Engineering
Clarkson University
Potsdam, USA
qiw@clarkson.edu

Department of Electrical and Computer Engineering
Clarkson University
Potsdam, USAjieli@clarkson.edu

Abstract—This paper presents an optimal coordinatedoperation framework for minimizing the overall electricity costof distributed internet data center (IDC) microgrids, which
consists of cost of purchasing power from main grid, theoperation cost of onsite generations, and the cost of carbon
emissions. With forecasted day-ahead and real-time locational
marginal prices (LMPs), the proposed model could help IDC
microgrids operator actively participate in the wholesale andreal-time electricity markets and achieve economic operation.
Numerical simulations demonstrate the effectiveness of the
proposed optimal coordinated scheduling framework.

Case 2: Optimal coordinated operation of distributed IDC
microgrids with both day-ahead/real-time market participations.

Index Terms—Internet data center, Microgrid, Demand
response, Coordinated operation

Fig. 2. Forecasted day-ahead LMPs ($/MWh)

I. INTRODUCTION
The rapid deployment of large-scale distributed internet
data centers (IDCs) requires extensive electricity consumption.
II. PROPOSED MODEL AND KEY FORMULATIONSIDC Microgrids Operator

Fig. 3. Schedule of IDC Workloads in Case 1 (CPU)Microgrid 1

Microgrid 2

IDC 1

IDC 2

Microgrid N

····

IDC N

IDC Microgrids
Main Grid
ISO/RTO 1

ISO/RTO 2

Power flow

ISO/RTO N
Bilateral communication

Workload

Fig. 1. Operation model of distributed IDC microgrids

A. Objective Formulation

Fig. 4. Forecasted real-time LMPs ($/MWh)   LMP DA  P g   LMP RT , f  P g  P g  

t ,d
t ,d
t ,d
t ,d
t ,d 
 t d 

Min 
 (1)
     f d ,i ( Pt ,d ,i )  SUt ,d ,i  SDt ,d ,i )     CosttE,d  t d it dIII. KEY RESULTS
The following cases are studied:
Case 0: Base case when each IDC site operates independently.
Case 1: Optimal coordinated pre-schedule of distributed IDC
microgrids based on forecasted day-ahead LMPs.
This work was supported in part by the U.S. National Science Foundation
grant and PFI:BIC-1534035.

Fig. 5. Schedule of the Change of Real-Time IDC Workloads Compared to
the forecasted ones in Case 2 (CPU)A Game Theoretic Approach to Analyze theDynamic Interactions of Multiple Residential
Prosumers Considering Power Flow Constraints
Tao Chen, Student Member, IEEE
Hajir Pourbabak, Student Member, IEEE
Department of Electrical and Computer Engineering
University of Michigan-Dearborn

Abstract—This paper proposes a game-theoretic approach to
study the dynamic interactions between different residential
customers with the capability to provide self-generated power ina small-scale residential distribution system, using the DistFlow
algorithm to calculate the AC flow. In line with the high penetration of distributed renewable energy, some electric customers canplay a more active role in the reconfigurable distribution network.
They are not only electricity consumers, but also producers
in the future electricity retail market. This paper presents themechanism for how they can interact with each other under thegame-theoretic framework and considers some line constraint
conditions with fast effective power flow calculation. It alsoutilizes a economic directed method to take into account theoverload line constraints to improve the economic operation.

Wencong Su, Member, IEEE
Department of Electrical and Computer Engineering
University of Michigan-Dearborn
Dearborn, MI, USA

II. K EY EQUATIONS
M in Ji = Ci

(1)

M ax Ji = (Ri − Ci )(2)

C(PiG ) = γi PiG(3)pi (qi ) = αi − βi qi
XX
M ax Ji =pj (qj )skj −C(PiG )PiG

(4)

j

(5)

i

Z(x) = argmax Ψ(x, y) x, Z(x) ∈ X

(6)

y∈X

I. I NTRODUCTION
Electricity, as a flexible energy source, is still an
increasingly critical infrastructure for the post-industrial,
information-based world economy, even in this new century.
Recently, with the high penetration of renewable energy anddistributed energy resources (DERs), some electric customers
with power generation capabilities can play a more active rolein the reconfigurable distribution network. Smart houses orresidential green buildings tend to have rooftop photovoltaic
panels, wind turbines and Vehicle-to-Grid (V2G) electricvehicles to provide self-generated power. They can then sell
their surplus power to others. They are not only electricity
consumers, but also producers in the future electricity retailmarket. Thus they are called prosumers. Meanwhile, theirinteractions and energy utilization become increasingly
complicated and should be well studied using some novel
analytical tools, like game theory. And the Nikaido-Isoda
function and the relaxation algorithm (NIRA) can be utilized
to find the Nash Equilibrium (NE) point in the electricity
market while taking price-demand elasticity into account. In
this paper, a kind of economic directed method is proposed
here to relieve the line pressure during the peak loading timerather than just taking into account the power loss. It also
considers the line constraints in a fast effective way, such as
DistFlow algorithm.

xk+1 = (1 − θk )xk + θk Z(xk ),0 < θk < 1max Ψ(xk , y) < ε(xk ,y)∈X(7)(8)

III. K EY FIGURES
Formulate the op-miza-on
model of diﬀerent prosumers
Combine with the power ﬂow
constraint by DistFlow algorithm
Transform the op-miza-on
mode to the game theore-cmodel with payoﬀ func-ons
Solve the model with Nikaido-Isoda
func-on and relaxa-on algorithm
Find equilibrium point of theen-re system
Fig. 1. The flowchart of determining the energy transaction strategies in the
distribution system with multiple residential prosumers

1

Privacy Preserving Energy Trading Mechanism
among Competing Agents in a Microgrid
M.Nazif Faqiry, IEEE Student Member, Sanjoy Das, Anil Pahwa, Fellow IEEE
Abstract— Energy trading is a key feature of the emerging

I. SYSTEM MODEL

III. SIMULATION RESULTS
12

Volume in kWh

108

SWP solution
Auctioneer Solution

6
4
2
0
-2
0

50

100

150

200

Volume in kWh

25

15
10
5
0

1

2

3

4

5

86
84
82
80
Using Social Welfare Solution
Using Auctioneer Solution
50

100
150
200
Number of iterations

Grid

SellerMaximize:
PrivatePrivateUtility
Generation

UtilityAmount in cents;

Social Welfare

300

0.25
0.2
0.15
0.1
0.05
50

100

150

200

Number of iterations
Maximize:

250

Market price
Sellers bidding

0.3

Buyer

6

Index of sellers

76
0

Iterative Double Auction

300

Generation
Availability
Allocation
SWP solution

20

78

II. PROBLEM FORMULATION

250

Number of iterations

Social welfare

smart grid system. In this research, we investigate a privacy
preserving energy allocation mechanism for energy trading
among competing buyers and sellers, i.e. agents, in a microgrid(𝜇𝐺). The proposed mechanism is modeled for rational agentswith hidden private information, aiming to optimize self-utility
and profit. We achieve this goal through the presence of acentral auctioneer whose aim is to accomplish an efficient,incentive compatible, and weakly budget balanced trading
among agents. We propose a novel iterative mechanism forenergy trading with the following main contributions. 1) We
introduce a generic model in which both buyers and sellers ofenergy can take strategic decisions so as to maximize theirutility and profit. Contrary to the previous studies that consider
a negative cost function for sellers in the social welfare function
maximization problem to provide them with incentives forparticipation, we consider the sellers saturating energy usage
utility as the incentive for their participation. Buyers aim to
maximize their energy usage utility less their payment amountwhereas sellers maximize their own energy usage utility plus
the payment they receive for selling a smartly selectedamounted of their production. 2) It is an efficient mechanism,
i.e. social welfare is maximized. 3) We preserve the privacy ofagents by iteratively solving the auction in the auctioneer levelby considering surrogate functions for the utility functions ofbuyers and sellers. 4) Agents are better off attending theauction, i.e. the auction is incentive compatible. 5) Our
proposed mechanism is inherently modeled to be strongly
budget balanced.

250

300

Advanced Battery Management & Diagnostic
System for Smart Grid Infrastructure
Ahmed T. Elsayed, Student Member, IEEE, Christopher R. Lashway, Student Member, IEEE, andOsama A. Mohammed, Fellow, IEEE

I. INTRODUCTION
The proposed topology offers a variety of features andcapabilities which can be summarized in the following:• It is capable of controlling each individual unit within aseries/parallel array, i.e. each single unit can be treated,
controlled and monitored separately from the others.
• The capability to charge some units within a battery array
while other units continue to serve the load. By applying apulsed charging profile at different frequencies and duty
cycles, the BMS can regulate the charging energy to each
unit. Hence, SoC balancing can be accomplished without theneed for power electronic converters.
• It is able to electrically disconnect a unit and allow theoperator to perform the required maintenance orreplacement without affecting the performance of the wholearray.
• Considering the appropriate design, selection of relays andother components, the system can be expanded forcontrolling other types of energy storage.
• The system can be designed and implemented at low cost.
II. KEY FIGURES
Fig. 1 depicts a schematic diagram for the Proposed BMS.
The developed laboratory prototype is shown in Fig. 2. Fig. 3
shows four BMS units connected to a hybrid energy storagearray composed of (2 Lithium-ion batteries, 1 Lead-Acidbattery and 1 Super capacitor).
III. KEY RESULTS
The developed unit was put in operation for a continuous
40-hour test. During this test, the system performed the+Charging
RelaysPosi tive
Bus Rel ay
Current
Measure.

A

BypassRelay

Voltage
V Measure.

14.7 V
Charging
circuitDC

Negative
Bus Rel ay

Fig. 1. Proposed Individual Battery Management Unit Schematic.

Fig. 2. Implemented unit of Battery Management System.

Figure 3. Utilization of the developed BMS Utilizing Various Energy Storage
Types.
BMS Voltage
15

Voltage (V)an advanced Battery Management System (BMS). The basic
concept is to divide each series battery array into sub-arrays
where each battery is individually monitored and managed. The
proposed BMS continuously monitors the voltage, current, andenergy of each battery. Based on these measurements, the BMS
can calculate individual State of Charge (SoC) levels and C-rates.
Furthermore, the system has the capability to isolate eachindividual battery to apply different charging profiles andadvanced diagnostics to detect and correct problems. Pulsed
charging is deployed using different duty cycles for SoC
balancing. The isolated battery is bypassed to maintain
uninterrupted supply to the load despite reduced series arrayvoltage. A unidirectional DC-DC boost converter maintains a
constant output voltage level to the load regardless of the numberof batteries connected until the problem is corrected. A hardware
implementation of the proposed BMS is explained in detail. The
performance of the system is tested experimentally under
different loading conditions including heavy pulsed loads.
Index Terms— Battery management system, energy storage,
lead acid battery, microgrids, pulsed load, smart grid.

different functions (supplying a load, pulsed charging, thensupplying a load again and constant charging) without
recording any issues or failures.

10
5
0
0

5

10

15

20

25

30

35

40

25

30

35

40

Time (h)(a)

BMS Current
10

Current (A)Abstract— This paper presents the design and implementation of

0
-10
0

5

10

15

20
Time (h)(b)

Fig. 4. Endurance and robustness test (a) BMS voltage (b) BMS current

Provision of Power System Flexibility ThroughAggregated Residential Demand Response
Qinwei Duan, Student Member, IEEE
Department of Electrical and Electronic Engineering
The University of Hong Kong, Hong Kong SAR
qwduan@eee.hku.hk
Abstract—With the smart appliances being widely adopted inresidential households in the near future, it became possible foraggregators to schedule these controllable appliances to provide
flexibility in power system operation. In this paper, an optimal
appliance scheduling model is proposed for the demand response
aggregator who manages the flexible demand of the households
in an area. According to their different ways in provision offlexibility, the classification of the appliances is presented.
Aggregator flexibility usage level is proposed to evaluate thedegree of flexible resource utilization at the demand side.
Simulations are carried out and the results show that the
proposed model can provide flexibility to system operation whilst
satisfying the maximum appliance utilization of customers.

I
flexible appliance 𝑘 in household 𝑖 at time 𝑡, respectively. 𝑃𝑖,𝑗,𝑡II
and 𝑃𝑖,𝑘,𝑡 represents the power demand of Class I and Class II
O represents
appliances in household 𝑖 at time 𝑡, respectively. 𝑃𝑖,𝑡
the total inflexible power demand in household 𝑖. 𝑃𝑖𝑇𝐺 is the
IImin and 𝑃IImax are
targeted power demand of household 𝑖. 𝑃𝑖,𝑘𝑖,𝑘the minimum and maximum power specified to Class IIappliances, respectively.

Index Terms—Aggregator, demand response, power system
flexibility, smart appliance.

where 𝑃𝐷 is the scheduled demand for the aggregator.

𝜂𝑡𝐴 =

III.

CLASSIFICATION OF FLEXIBLE APPLIANCESII.

KEY MODEL

Residential household demand response scheduling:
𝑇

𝑁

𝑀

Maximizing: ∑(∑ 𝑢𝑖,𝑗,𝑡

∙ 𝑃I

II
𝑖,𝑗,𝑡 + ∑ 𝑃𝑖,𝑘,𝑡 )𝑡=1 𝑗=1

(1)

𝐿
𝑖,𝑡

𝑗=1

𝑀
𝑖,𝑗,𝑡

∙ 𝑃I

II
𝑇𝐺
𝑖,𝑗,𝑡 + ∑ 𝑃𝑖,𝑘,𝑡 ≤ 𝑃𝑖,𝑡

(2)

𝑘=1

IImin ≤ 𝑃II ≤ 𝑣
IImax𝑣𝑖,𝑘,𝑡 ∙ 𝑃𝑖,𝑘𝑖,𝑘,𝑡 ∙ 𝑃𝑖,𝑘𝑖,𝑘,𝑡O + ∑𝑀 𝑃IImin )𝑃𝐷 − (𝑃𝑖,𝑡𝑘=1 𝑖,𝑘

7500
7000
6500
6000
5500
5000
4500
4000
3500
3000
2500
2000
1500
1000
500
0

(4)

KEY RESULTS AND DISCUSSION
Committed Flexible Demand

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

Fig.1. Aggregated demand and committed flexible demand of the aggregator.
1.2
1.1
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

Fig.2. Aggregator flexibility usage level with committed flexible appliances.

𝑘=1

Subject to
𝑃O + ∑ 𝑢

I + ∑𝑀 𝑃II∑𝐿𝑗=1 𝑢𝑖,𝑗,𝑡 ∙ 𝑃𝑖,𝑗,𝑡
𝑘=1 𝑖,𝑘,𝑡

Aggregated DemandPower Demand (kW)

Typically, the flexible appliances in the households can beclassified into two types: 1) Class I flexible appliances: Thetasks of these appliances can be paused at one time and resumed
later, such as washing machine, dryer and refrigerator. The
power demand of these appliances may remain constant overtime. 2) Class II flexible appliances: This type of applianceprovides flexibility through varied power demand over time,such as the charging of EV/Battery at variable charging rate, airconditioning operating at different power levels whentemperatures changes. In addition, there is another type of
appliances which are critical to the basic living needs of ahousehold thus remain inflexible, such as lighting and cooking.
Together, the three types of appliances are formulated in thescheduling model.

Aggregator Flexibility Usage Level

I.

Aggregator flexibility usage level:(3)

where 𝑢𝑖,𝑗,𝑡 and 𝑣𝑖,𝑘,𝑡 are two binary variables representing the
ON/OFF status of Class I flexible appliance 𝑗 and Class II

The power demand of the committed flexible appliances
incurs very small fluctuations across the time horizon, as shownin Fig.1. Hence the scheduling of flexible appliances actively
distributes the power demand in a more “flat” manner. From
Fig.2, it can be seen that most of the time, the aggregator’s
usage of flexibility resources is within expectation (below 1).
However, there is a spike that appears in the early morning. This
results from the contradiction between the expectations of lowlevel power demand and the actual higher committed flexible
demand. In the future work, the flexible appliances can bescheduled according to variations in the aggregated demand.

A Novel Approach for sizing Electric Vehicles Parking Lot located at any
bus on a network
Claude Ziad El-Bayeh, Imad Mougharbel, Maarouf Saad, Ambrish Chandra, Serge Lefebvre, Dalal Asber, Laurent Lenoir
Abstract—Many studies have concentrated on how to optimize the size and placement of
Parking Lots (PLs) on a distribution network. Within this context, this study consists ofpresenting a systematic approach for sizing the PL in terms of number of simultaneously
plugged-in vehicles. This approach is original since it takes into account the loads’ profile on
the bus where the PL is connected (it is valid for any load profile on the bus), the PL
manager benefits and the vehicles’ owners requirements. Multiple technical constraints arerespected, those imposed by the PL infrastructure and those related to the distribution
network. A mathematical model and an algorithm are developed for this purpose using
MATLAB.
Keywords-Parking Lot; Electric Vehicles; Charging Discharging Modes; Size of a Parking
Lot.

I.

INTRODUCTION

Many papers studied the optimal size and location of an Electric Vehicle Parking Lot
on the distribution network using optimization techniques, but these studies are notaccurate enough due to the fact that load fluctuations are not considered. For instance,
the optimal sizing and location of a PL on a network consider that the loads on buses areconstant, and the PL is also considered constant. While in a real power grid, both PL andthe loads on the buses are fluctuating. Due to this a new approach of finding theappropriate involved energy at the PL level at any time according to any load fluctuation
is proposed. Moreover, the charging/discharging modes are controlled in this study in a
way to respect the constraints imposed by the utility, the substation, and the PL. From
this study, one can conclude that the PL could be located at any bus (having any load
profile) on the power network if the technical constraints are respected. Other
contributions in this paper are, (i) a mathematical model that determines the appropriate
number of PEVs in a PL, (ii) a new algorithm is used to determine the appropriate
number of PEVs in a PL, (iii) a ratio between the available energy in the PL and theenergy measured on the bus is used for sizing purposes, (iv) satisfaction and benefit
factors are introduced, (v) and finally the size of the PL in terms of the number ofchargers is found based on measurements performed on the bus during some previous
years.
I.

KEY EQUATIONS

The appropriate number of charging/discharging PEVs at instant “t” isdetermined in the following equation:

𝑁𝐶ℎ𝑎𝑟𝑔𝑒𝑟𝑠 =
I.

𝑌∙𝐷
∑𝑑=1
𝑁𝑎𝑝𝑝,𝑑𝑀∙𝑓
𝑌∙𝐷

KEY FIGURESIn the following example, the size of a Parking Lot is calculated and it is equal to440 PEVs. Figure 1 (a) presents a Parking Lot with 100 PEVs which is below thereference size, therefore, the PEVs charge at zone 1 until they are fully charged, in zone
2 the PEVs are not charged because they are already fully charged, and the energy inzone 2 is not used by the Parking Lot, it is considered as a wasted energy in which theParking Lot did not benefit from it and it didn’t maximize its revenue, in zone 3 somePEVs are discharging to supply energy to the load when the demand exceeds the limit of
the transformer. In figure 1 (b) the number of PEVs in the Parking Lot is equal to 440,
which is the reference limit, therefore the Parking Lot is maximizing its benefit, it
shown that the zone 1 is largely extended, which means that the Parking Lot isbenefiting from all the energy presented on the grid.

(a)

(b)

Fig. 1: Charging and discharging modes are applied to the Base Load Power 𝑃𝐺𝑟𝑖𝑑 (𝑡) for a PEVs’ number
equal to 100.

𝐽

𝑁𝑎𝑝𝑝 (𝑡) = � �𝑁𝐶ℎ𝑎𝑟𝑔𝑒𝑑,𝑗 (𝑡)�𝑗=1

The appropriate number of charging/discharging PEVs during a period of timein a day is presented in the following equation:
𝑁𝑎𝑝𝑝 =𝑡𝛼 ∫𝑡 1 �𝐴(𝑡) ∙ �𝑅𝐿𝑃(𝑡) − 𝑃𝐺𝑟𝑖𝑑 (𝑡)�� 𝑑𝑡
0

𝐸𝑃𝐸𝑉 (1 − 𝑆𝑂𝐶𝑃𝐿 )the appropriate number of chargers in a Parking Lot (Actual Size of the PL):

Fig. 2: Efficiency of the Parking Lot.

Fig. 3: Benefit of the Parking Lot.

Optimal Power Flow of Multiple Energy Carriers
with Multiple Kinds of Energy Storage
Linna Ni, Changsen Feng
College of Electrical Engineering
Zhejiang University
Hangzhou 310027
China
nilinna918@163.com

Fushuan Wen, Md. Abdus Salam
Electrical and Electronic Engineering
Institut Teknologi Brunei
Bandar Seri Begawan BE1410
Brunei{fushuan.wen, abdus.salam}@itb.edu.bn

Abstract—As the organization mode and technology ofworldwide energy production and consumption are undergoingsignificant changes, the study on Energy Internet has attracted
extensive attentions due to its broad application prospects. The
power to gas (P2G) technology can not only enhance thecapability of accommodating fluctuating renewable energy
generation output, but also offers more choices for energy storage
and delivery. In this context, the coordinated multi-energy
storage including electricity, heat and gas is first formulated andincorporated into the modeling framework of the well-establishedenergy hub, and then the optimal dispatch of multiple energy
power flows with multi-energy storage coordination presented.
Finally, a 4-bus multi-energy system is served for demonstrating
the proposed method.

II. KEY EQUATIONSP
L − (C S ) ⋅   =0E
min f (Pi , Fα , N)
Li − Ci Pi =0 or Li − C′i Pi′ = 0, ∀i ∈ Hub
Pi ≤ Pi ≤ Pi , ∀i ∈ Hub
Pci ≤ N i Pi ≤ Pci , ∀i ∈ Hub
0 ≤ N i ≤ 1, ∀i ∈ HubGα (Pi ) = 0, ∀α ∈ {electricy, gas, heat ,}Fα ≤ Fα ≤ Fα , ∀α ∈ {electricy, gas, heat ,}Keywords—Energy Internet; multiple energy system;
coordinated storage of multiple kinds of energy; energy hub; power
to gas; optimal power flow

(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)

III. KEY FIGURES

I. INTRODUCTION

This work is jointly supported by National High Technology Researchand Development Program of China (863 Program) (No. 2015AA050202),
and National Natural Science Foundation of China (No. 51361130152, No.
51477151)

3
2.5

electricity storage

energy storage/p.u.

2
1.5

natural gas
storage1heat storage

0.5

actural wind
power output

0
-0.5
-1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

estimatedmaximum wind
power output

time/hFigure 1. The day-ahead schedule of multi-energy storage operation
54
power output/p.u.

In the past decade, the organization mode and technology
of energy production and consumption has been undergoingsignificant changes around the globe. First, the renewableenergy technology has been developed quickly for mitigating
theenergy and environmental crisis.
Secondly,thedevelopment of plugged-in electric vehicles introduces thecoupling between power systems and transportation systems.
Thirdly, benefited from “shale-gas revolution”, distributed gas
turbines and cogeneration units are more popular, and theinteractions between power systems and gas systems are
becoming an issue of extensive concerns. Finally, theestablishment of the Advanced Metering Infrastructure (AMI)
and rapid development of information technologies, such as
Internet of Things, cloud computing, big data and energy
router have laid the technical foundation for cyber physicalenergy system, which makes the collaborative optimization ofmultiple energy carriers possible. The emerging power to gas(P2G) technology utilizes electricity to generate synthetic
natural gas (SNG) through electrochemical reaction so as toimplement the two-way conversion of electricity and naturalgas. Based on existing research reports, coordinated storage in
multi-energy carriers including electricity, heat and gas isformulated and incorporated into the modeling framework ofan energy hub, and on this basis the optimal dispatch of multicarrier power flow considering multiple energy storage
devices is presented.

PG1 (With)

3

PG1 (Without)
PG2 (With)

2PG2 (Without)1Wind Power (With)Wind Power (Without)

0
-1
-2

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Energy Hub (With)
Energy Hub (Without)time/tFigure 2. The day-ahead schedules of generators and energy hubs

Smart Distribution Systems
Yazhou Jiang and Chen-Ching Liu
Washington State University, Pullman, WA, USA
Email:{yjiang1, liu }@eecs.wsu.edu
Abstract—The increasing emphasis on system reliability andresilience is changing the way distribution systems are plannedand operated. As of March 2013, around 8,000 remote control
switches (RCSs) have been deployed on 6,500 distribution feeders
in the U.S. through the Smart Grid Investment Grant (SGIG)
program; an estimated total of 65 million smart meters have been
installed across the country by 2015. The higher level of
distribution is transforming traditional distribution systems into
smart distribution systems (SDSs).The availability of smart meterdata and remote control capability from RCSs gives distribution
operators an opportunity to monitor distribution systems in nearreal time and take control actions promptly. In this study, thedevelopment of SDSs and resulting benefits of enhanced systemcapabilities are discussed. A new method, called Temporal Causal
Diagram (TCD), is used to incorporate outage reports from smart
meters for enhanced outage management. To fully utilize the fast
operation of RCSs, the spanning tree search algorithm is used todevelop service restoration strategies. Optimal placement of
RCSs and the resulting enhancement of system reliability are
introduced. Distribution system resilience with respect to extreme
events is presented. Test cases are used to demonstrate the benefitof SDSs. Future research in a smart distribution environment is
proposed.
Keywords—Feeder restoration; outage management; remotecontrol capability; smart distribution system; smart meter

B. Service Restoration

F-aAuto. R1

F3

F2

L1

L2

SM8
SM10

F: fuseSM: smart meter
L: lateral

Brk [9,14,0.64]

R1 [9,14,0.64][T,T+ΔT]
R2[9,11,0.82]

Fuse 1 is represented by F1
T: 9:10:13:000am
T:30 seconds

1
4

0 :3 smart meters do not report the outage
30
1

F3 [5,6,0.83]

t2[T,T+ΔT]1
2

t3

F4 [3,4,0.75]
1
3

0
1

1 :4 smart meters report the outage
4

Fig.2. TCD for enhanced outage management.

Z23

Z6

Z7

Z19

Z14

Z2

Z33

Z12
Z16

Z3

Z13

Z10

Z9

Z32

Z36

Z26

Z27

Z11
Microgrid 2

Z64

Z70

Z48

Z58

M

Z65

Z57

Z75

Z44

Z62

Z55

Z68

Z69

Z45

Z63

Z60

Z79

Z46

Z47

Z78
Z59

Z54

Z42

Z61

Z80

Z73

Z52

Z43

Z50

Z49

Z56

T3

Z72

Z76

Z66

Z51

Z67

Z53

T7
Z95

Z108

Z77

T6

Microgrid 3

Z113

Z105

Z109

S

M

SubTransmission
Node

Z111

Z116

Z115

Z97

Z88

F-c

Z81

Z104

Z102

Z110

Z84

Z98

Z85

Z103

Z100

Z119

Z86

Z87

Z83 Z93

Z118

Z99

Z94

Z82

Z96

Z90

Z89

Z106 Z107

FB-c
Z92
Z114

Z112

Z101

Z91
Z120

Z117

F-d

Z121

Z144

Z142

Z135

Z148

T4

Z149

Z145

Z151

Z156

Z137

Z140

Z159

Z124 Z150

Z128 Z138

Z125 Z143

Z126

Z127 Z139

Z141

Z155

Z152

T5

Z153

Z136 Z129

Z130

Z123

Z158

Z122

Z134

Z146 Z147

FB-d

Z154

Microgrid 4

M

Z132 Z131 Z133

Z160

Z157

F-a Feeder Id

Voltage Regulator

Load Zone

Feeder Breaker

Sectionalizing Switch

Tie/Microgrid Switch

M Microgrid

Fig.3. One-line diagram of the 4-feeder 1069-node test system.
Table I. SERVICE RESTORATION PLAN
Switching operations withoutswitching operations withmicrogrids
microgrids
Open: Z46-Z47, Z96-Z89
Close: Z136-Z120, Z53-Z96, Z45- Open: Z50-Z43, Z90-Z92
Z90
Close: Z45-Z90, Z73Partial Restoration, 315.04 kVA
Microgrid 2, Z136-Z120load should be shed at F-b

Fault
location

WSU Microgrid
Including (2)1 zoneIncluding
2 zones(3) Including (4) Including
5 zones

4 zones

Transformer

City Hall

Hospital

Fig.4. Restoration path from WSU generators to critical loads at Pullman.
Step

Table II. VOLTAGES AT THE CRITICAL LOADS*
1
2
3
4

Critical Load

Volt.
(kV)

Volt.
(p.u.)

Volt.
(kV)

Volt.
(p.u.)

Volt.
(kV)

Volt.
(p.u.)

Volt.
(kV)

Volt.
(p.u.)

City Hall

0

0

0

0

7.914

0.993

7.889

0.99

Hospital

0

0

0

0

0

0

7.873

0.988

* The nominal voltage is 7.967 kV.

[T,T+ΔT]

F2 [4,5,0.8]

t1

Z41

Z74

[T,T+ΔT][T,T+ΔT]

Z40

FB-b

Generator

Fig.1. One-line diagram of a simple distribution system.

0
3

Z5

Z38

SM13 SM14

SM2

F1 [0,3,0]

Z21

Z71

F-b

(1)

SM9

[T,T+ΔT]

Z18

Z39

T2

G

SM7 L3

SM6

Z8

T1

L4

SM3

Z30

Z37

F4

SM5

Z4

Z22

Z34

SM11 SM12

SM4
SM1

Z24

Z20

C. Distribution System Resilience with Respect to Extreme
EventsAuto. R2
F1

Feeder

Z1

Z17

Z35

FB-a

Z139

Brk

Microgrid 1

Z25

M
Z31

I. KEY RESULTS
A. TCD for Enhanced Outage Management

Z15

Z28
Z29

1

Large-scale PEVs Behaving as A Grid-scale Storage
System: An Aggregate Model Approach
Hongcai Zhang, Student Member, IEEE, Zechun Hu, Member, IEEE, Zhiwei Xu, Student Member, IEEE,and Yonghua Song, Fellow, IEEE

Abstract—Large-scale plug-in electric vehicles (PEVs) utilizing
vehicle-to-grid (V2G) technology have the potential to collectively
behave as a storage system under the control of an aggregator,e.g., providing ancillary services to the grid and promoting integration of renewable energy. However, due to PEVs’ stochastic
behavior and large population, it is hard to forecast PEVs’
charging demands accurately, schedule and control their charge
and discharge power efficiently. This paper proposes a storagelike aggregate model (SLAM) for large-scale PEVs. Utilizing
SLAM, a fleet of PEVs is modeled as simply as a single grid-scalestorage system so that the difficulty for forecasting PEV charging
demands are reduced. And the charge and discharge schedulingproblem based on SLAM has low computational burden due tothe reduction of problem scale. A heuristic charge and discharge
power dispatch strategy is designed to efficiently assign the powerschedules to individual PEVs during real-time operation which
can also guarantee the accuracy of SLAM.
Index Terms—Plug-in electric vehicle, vehicle-to-grid, aggregate model.

I. I NTRODUCTION
This paper aims to propose a storage-like aggregate model
for a fleet of PEVs with V2G technologies (a V2G feet) sothat the V2G fleet can behave as a grid-scale storage system
to benefit the future power systems.
II. S TORAGE - LIKE AGGREGATE M ODEL
We utilize the summation of energy and power constraintsof all individual PEVs to represent the V2G fleet’s aggregate
energy and power constraints, including: 1) uncontrollable
PEV charging power of the V2G fleet, i.e., P u ; 2) controllable
upper and lower energy boundaries, i.e., E +/(−) , in which theupper boundary E + corresponds to the fastest trajectory forthe controllable part of the V2G feet to consume energy, whileE − corresponds to the slowest trajectory; 3) controllable upperand lower power boundaries, i.e., P +/(−) , which corresponds
to the aggregated maximum charging and discharging power.
The set {P u , E +/(−) , P +/(−) } sufficiently describes the aggregated charging demands of the whole V2G fleet and its
scale is independent of the PEV population size (see Fig. 1).
Since the set {P u , E +/(−) , P +/(−) } for large-scale PEVs
is more stable and smooth than stochastic individual PEV
charging demands, it is more easier to forecast.
P

0

Pu

tamin

tamax

(a) P u

t
td
max

E

E+

E max
E need
0

E−

P

P+

P−

tamax

t
td
max

P max
0

tamin
E min

tamax

tdmaxtP min(b) E +/(−)

Fig. 1. Storage-like Aggregate Model of a V2G fleet.

tamin(c) P +/(−)

L
Lmax

Discharging
priority decreases
Group1
Group2

Charging
priority decreases

...
Lmin

GroupM

SoCmaxSoC

SoCminFig. 2. Charge/discharge Logic of Laxity-SoC Based Strategy.

III. C HARGE AND D ISCHARGE P OWER S CHEDULINGThe charge and discharge power scheduling strategy basedon SLAM is formulated as a mixed-integer model as follows:
min f (Ptd , Ptc )(1)

st: Dt Pt− ≤ Ptd ≤ 0, ∀t(2)c(t) ≤ (1 − Dt )Pt+ , ∀t

0≤P
Dt ∈ {0, 1}, ∀t

(3)
(4)

Ptev = Ptd + Ptc , ∀t

(5)

Et− ≤

t
X

Pτev ∆t ≤ Et+ , ∀t

(6)

= (Ptu + Ptc ) /η c + Ptd η d , ∀t

(7)

τ =t0
grid

Pt

where, objective (1) is a function of controllable charging
power Ptc and discharging power Ptd ; equations (2)–(6) are themathematical formulation of SLAM; the V2G power scheduleat the grid side Ptgrid is calculated by (7). Because the proposed
strategy only schedules the aggregate V2G power of the fleet,the scale of the problem is very small and irrelevant with the
population of the V2G fleet, it can be efficiently solved.
IV. R EAL - TIME P OWER D ISPATCH S TRATEGY
To guarantee the modeling accuracy of SLAM and the effectiveness of the power scheduling strategy, we use a heuristic
strategy to dispatch the V2G power schedules to each PEV,
which uses charging demand laxity and SoC to determine
charging and discharging priories for the PEVs. The charging
demand laxity of PEV i, i.e., Li , is the difference between theremaining parking duration and the required charging time.
We divide PEVs into M groups in the L-SoC plane (seeFig. 2). PEVs in groups with lower indices can discharge tothe grid, while those in groups with higher indices tend to
get recharged from the grid. The laxity-SoC based dispatch
strategy utilizes the logic shown in Fig. 2 to assign V2G power
to each PEV, which requires little computational resources andcan be efficiently implemented during real-time operation.
Numerical experiments on providing reserves and promoting renewable energy integration are conducted to verify theeffectiveness of the proposed strategy.

Autoregressive Model for Individual Consumption
Data - Sparsity Recovery and Significance Test
Pan Li and Baosen Zhang

Yang Weng and Ram Rajagopal

Electrical Engineering
University of Washington
Seattle, WA 98195
Email: {pli69, zhangbao}@uw.edu

Civil and Environmental Engineering
Stanford University
Stanford, USA 94035
Email: {yangweng, ramr}@stanford.edu

Abstract—Understanding consumer flexibility and behavior
patterns is becoming increasingly vital to the design of robustand efficient energy saving programs. Accurate prediction of
consumption is a key part to this understanding. Existing
prediction methods usually have high relative errors that canbe larger than 30%. In this paper, we explore sparsity in users’
past data and relationship between different users to increase
prediction accuracy. We show that using LASSO and Granger
Causality techniques, prediction accuracy can be significantly
improved in comparison to existing algorithms. We use mean
absolute percentage error (MAPE) as the criteria.

I. I NTRODUCTION
With recent technological advances, smart meters capableof bidirectional communication and remote control have beenbrought into households. These devices allow a number ofinnovative services such as remote metering, dynamic pricing
and new ways to perform load forecasting. There exists anextensive literature on short term load forecasting. Due to thenon-obvious selections of variables and difficult parametrization, AI models is not considered. More specifically in ourwork, besides the simple averaging technique, we recover
the sparsity for univariate time series and multivariate timeseries under the framework of autoregressive models. We
argue that our methods are simpler and more intuitive that
AI methods, thus are more adaptable and interpretable forpractical purposes. We also compare the proposed methods
with AR(1) model and simple averaging method which uses
previous similar days to predict consumption data.
II. T HE P ROPOSED M ETHODS
A. Sparse Univariate Autoregressive Model
Suppose we regard hourly consumption data as a stochastic time series, the autoregressive time-series model can be
expressed as:I

yt = β0 + ∑ βi yt−i + εt

(1)

i=1

where {εt } is a white noise process, {yt } is the consumption
time series for each individual user, βi is the coefficient fororder (lag order) i in the autoregressive model, and t is thetime tag in the scale of hours.
Besides performing an AR(1) model, We recover a sparse
autoregressive model with a much higher order by LASSO.

B. Pairing Users in Vector Autoregressive Model
One way to leverage the fact that we have many user’s datais to perform vector autoregression(VAR). In this model, kusers are included into the model. The regression model canbe extended as:
I

Vt = A0 + ∑ Ai Vt−i + φ t

(2)

i=1

where Vt = [yt,1 yt,2 ...yt,k ]T is a vector composed of kusers’ consumption data at time t, I is the number of orders to
be considered, A i ’s are the coefficient matrix for orders, and
φ t is the k dimensional white noise.
In our work we fix k = 2. The optimal pair of users to be
included into the model is selected by Granger Causality Test.
III. S IMULATION R ESULTS
We use the data from Pacific Gas and Electric Company.
The results for the four methods are shown in Table I, where
CauT stands for test based on granger causality.
TABLE I: Results for the four methods considered in thispaper.
Averaging

AR(1)

LASSO

CauT

mean (MAPE)

0.359

0.339

0.225

0.218

sd (MAPE)

0.237

0.151

0.094

0.090

Above all, multivariate autoregression with granger causality test and LASSO all the best performance among the four
methods.
IV. C ONCLUSION AND FUTURE WORKIn this paper, we pose and analyze consumption prediction
methods based on autoregressive models. Besides simple averaging and AR(1) model, we adopt LASSO to recover thesparsity in lag orders. Furthermore, we extend the idea fromindividual users to possible relations between users and use
granger causality to recover the relevance between users. The
simulation results show that LASSO selection outperforms
both simple averaging method and autoregressive model withlag order one for individual consumption prediction. In addition, adding the lag order one historical data from anotherrelevant user will enhance the prediction accuracy. In the
future, we will use the results in this paper to investigate thedemand response effect for users.

1

DEx.py - A Data Exchange Platform Between
Power System Simulators
Rahul Kadavil, Graduate Student Member, IEEE, and Siddharth Suryanarayanan, Senior Member, IEEE
Abstract—This work provides an overview of a power systemmodel creation platform developed in the Python environment.
The tool creates detailed distribution topologies linked to atransmission system for use in power system co-simulation.
Detailed results using a standard test system will be presented
in the poster.
Index Terms—co-simulation, distribution, GridLAB-D, load
modeling, MATPOWER, PowerWorld.

I. I NTRODUCTION
The advent of the Smart Grid Initiative has led to an increased penetration of distributed energy resources and electricvehicles at the distribution level [1]. These resources need tobe modeled in great detail to study their effects on the electricgrid. Existing power system simulators such as PowerWorld,
MATPOWER, and GridLAB-D cater specifically to the transmission and the distribution system respectively. This leads
to the challenge of creating a unified power system model
populated with detailed distribution assets that aggregate to arealistic load profile at the transmission level. Data exchange
dot py (DEx.py) aims to overcome this limitation by linking
the transmission and distribution simulators. DEx.py createsa combined customizable distribution and transmission powersystem model for performing co-simulation. This allows theimplementation of co-simulation studies thereby resulting in aholistic power system model.

primary feeder, the load points, distribution transformers, andcables per load point for each transmission bus. Fig.1 andFig.2 present an overview of the detailed customization options
available to the user for creating a distribution topology.
DEx.py currently populates the distribution system with residential load class. Future work includes extending the loadclass to include industrial and commercial categories alongwith the ability to co-simulate GridLAB-D and MATPOWER
for each time-step of the simulation.

Fig. 1. Line parameter configuration for distribution modeling in DEx.py.

II. T HE DE X . PY INTERFACE
DEx.py provides a graphical user interface, developed in the
Python environment for rapid creation of distribution network
parameters linked from a transmission system. The formatof the transmission and distribution network models createdby DEx.py are limited to MATPOWER and GridLAB-D
simulators due to their open-source nature. In the initial phase,
DEx.py performs the following two functions: (i) distribution
network creation, and (ii) populating the network with end-use
loads. The distribution network thus created, forms an input
to GridLAB-D, and is linked to a transmission bus present
in a transmission system modeled in MATPOWER. As an
example case scenario, each load bus in the Roy Billinton
Test System (RBTS) is modeled by DEx.py as a distribution
system in GridLAB-D [2]. The end-use load profiles for each
distribution system aggregate to the peak load capacity at eachtransmission bus in the RBTS.
The distribution topology is created by setting the upperbounds for the number of primary feeders, sub-feeders per
R. Kadavil, and S. Suryanarayanan are with the ECE Dept.,
Colorado State University, Fort Collins, CO 80523, USA, e-mail:{rahul.kadavil,sid}@colostate.edu
This work is sponsored by a seed grant from the Energy Institute atColorado State University.

Fig. 2. End-use load parameter customization for distribution modeling in
DEx.py.

III. ACKNOWLEDGMENT
The authors would like to thank Dr. Tim Hansen of South
Dakota State University for his advice.
R EFERENCES[1] DNV GL Energy. (2014, Sep.) A review of distributed energy
resources. (Date last accessed on Mar. 10, 2016). [Online]. Available:
http://goo.gl/44DoWC[2] R. Billinton and S. Jonnavithula, “A test system for teaching overall powersystem reliability assessment,” IEEE Transactions on Power Systems,
vol. 11, no. 4, pp. 1670–1676, Nov. 1996.

A two-layer model for microgrid real-time dispatch
based on ESS charging/discharging hidden costs
Zhong Zhang, Student Member, IEEE
Jianxue Wang, Member, IEEE
School of Electrical Engineering, Xi’an Jiaotong University Xi’an, Shaanxi, 710049, China

Keywords—Discharging opportunity cost, marginal charging
cost, energy storage system, microgrids, real-time dispatch

I. INTRODUCTION
ESS is a critical device in MG, which contributes to thestable operation of MG and plays an important role in load
shifting. To manage ESS effectively during MG real-time
operation, two key issues need to be addressed: evaluation of
its real-time regulating cost and its coordinative dispatch overmultiple intervals. Since the single-step real-time dispatch
method cannot deal with the economic efficiency of ESS
among multiple periods, a two-layer scheduling model,
consisting of the day-ahead scheduling layer and real-time
dispatch layer has been proposed in literatures. Day-aheadscheduling drafts economic generation schemes based on
forecasting data, whose forecasting error is handled by realtime dispatch. The real-time unbalanced power is mainly
matched by ESS and controllable generators. However,effective ways to evaluate the battery real-time operation cost
and to trade off charging/discharging profits across multiple
periods have not been addressed yet. So this paper is focused todeal with these problems in order to improve the real-time
management of ESS by adopting two hidden costs for ESS:
DOC and MCC.
II. METHOD AND KEY EQUATIONS
A. Framework of the proposed two-layer model
A two-layer dispatch model is proposed for MG as shownin Fig. 1. This model is composed of a look-ahead dispatch
layer and real-time dispatch layer.

0Start Forecasting PV, WT
and load1...

Formulation of lookahead dispatch problem

UpdateSolving look-ahead dispatch
problemLook-ahead
dispatch layer

YES

Modeling of MCC and DOC for ESS

Mod(t, m)=0
Real time data of
PV, WT and loadtProposal of real-time
dispatch strategy

t+1

NO

Real-time
dispatch results

Real-time
dispatch layer

t=t+1

Fig. 1. A two-layer model for MG real-time dispatch

B. Optimality conditions of the ED problem and modeling of
MCC/DOC
Optimality conditions of the ED problem are derived fromKKT conditions (1)-(3) of its Lagrange formulation. Eq. (1)points out that the system marginal cost is equal to MCC
during the charging stage. Eq. (2) indicates that system
marginal cost is equal to DOC during the discharging stage.
The ESS is suggested to be neither charged nor discharged
when system marginal cost is between the MCC and DOC as
certified by Eq. (3).
λ* (t )  μ*ηch
λ* (t )  ν* / ηdis

（ESS in chargingstage）(1)（ESS in discharging stage）(2)

μ ηch  λ (t )  ν / ηdis（ ESS in idle stage）
*

*

*

(3)

Where, λ (t) represents the MG marginal cost, μ ƞch, v /ƞdis are
the MCC and DOC of ESS, respectively.
***

C. Practical and economical real-time dispatch strategy for
MG based on MCC and DOC of ESS
The dispatch strategy offer assurance to cost minimization
conditions after real-time dispatch. In details, ESS is charged
and discharge when λ*(t) is equal to MCC and DOC,
respectively. It keeps idle when λ*(t) is between MCC and
DOC. The strategy is summarized in Fig. 2.
Type of periods defined by
day-ahead scheduling

Abstract—For real-time dispatch in microgrid (MG), it isdifficult to evaluate the real-time charging and discharging costsof energy storage system (ESS). In this paper, two hidden costs,
discharging opportunity cost (DOC) and marginal charging cost
(MCC), are put forward for ESS, which are conductive tocoordinate ESS and controllable generators. A two-layer model ispresented for MG real-time operation. The upper layer lookahead dispatch produces power settings for generators. Then,
two hidden costs of ESS are analyzed and modelled throughLagrangian formulation of the look-ahead dispatch problem. On
the basis of the two hidden costs, an efficient and time-saving
corrective strategy is proposed for the lower layer real-time
dispatch. Simulation results validate the correctness of the two
hidden costs for ESS. Compared with existing real-time dispatch
methods, the proposed method is proved with excellent
performance.

Discharging
periodESS idle
periodCharging
periodESS
Decrease Pdis

ESS
Increase Pdis

Ⅴ

Ⅵ
G

Decrease ∑Pi

Increase ∑Pi

Ⅲ

Ⅳ

G

ESS
Increase Pch
Ⅰ

ESS
Decrease Pch
Ⅱ

ΔPerr<0
ΔPerr>0
Real-time condition

Fig. 2. The “Six zone diagram” of real-time dispatch strategy for MG

Critical Load Serving Capability by Optimizing
Microgrid Operation
Pavan Penkey
Student Member, IEEE
penk1970@vandals.uidaho.edu
University of Idaho
ECE Department
Moscow, ID, 83844, USA

Brian K. Johnson
Senior Member, IEEEbjohnson@uidaho.edu
University of Idaho
ECE Department
Moscow, ID, 83844, USA

In order to improve reliability of power supply, utilities areinterested in forming microgrids in abnormal situations. In
some cases the microgrid is used to minimize the adverse
impacts on the most critical loads, such as hospitals,
government offices, and police stations. The costs associatedwith failure to supply critical loads are significant. The ability
to create a microgrid largely using existing generation assets
with only the addition of control devices can result insignificant savings during major events. Apart from these there
are many other advantages of forming a microgrid like
reduction in power losses, reliability, and enabling thedevelopment of sustainable and green electricity.
A significant portion of the power supply to the City of
Spokane comes from Montana through a single 500kV line. If
the line goes down, under some circumstances Avista’s system
could go down. The main objective of this project is to performa study of establishing a microgrid to supply high priority loads
in downtown Spokane area using hydroelectric facilities on the
Spokane River. Implementation of the microgrid will require
disconnection from the main grid and reconfiguration to pickup critical loads.
The information for modeling the microgrid is taken fromdifferent simulation packages (i.e.) the generators andtransmission lines which are modeled in Powerworld as part ofthe larger WECC transmission model. Spokane downtown
distribution network is implemented in a separate, disconnected
Powerworld model with different MVA bases. The remainderof the distribution network that supplies few critical loads aremodeled in SynerGEE. The modeling and simulation of
microgrid is done in Powerworld simulation software as asingle system by porting the distribution data from the different
models. There are two hydro facilities available in downtown
Spokane with a few small solar installations available. The idea
here is to make use of these hydro facilities and more solar
installations to form a microgrid to supply critical loads.
Different scenarios are considered like seasonal variation ofload and seasonal variation of hydro generation with watersupply and in available PV output to determine various ways ofsupplying the critical loads, by identifying different feeder
configurations.
In this case the microgrid is brought on line by first
disconnecting from the grid, and then energizing transmission
lines in the microgrid with hydro generation under no loadconditions and then adding the critical loads in a sequence by
maintaining the stability and almost constant frequency. A

Herbert L. Hess
Senior Member, IEEE
hhess@uidaho.edu
University of Idaho
ECE Department
Moscow, ID, 83844, USA

microgrid master controller will be developed to take care ofload shedding or generation shedding in case of imbalance inthis system. An automatic generation control system will
manage the (upgraded) governors to bring frequency to nominal
value. Additional analysis studied the impact additional
renewable energy sources along with a battery. Stability
analysis of the entire system put together by imposing few
constraints. The results of the study demonstrate that microgrid
development for downtown Spokane can potentially be appliedin other areas of the Avista system or for other utilities.

Fig.1.Avista’s transmission line model within the scope of microgrid

Fig.2.Microgrid overview with critical loads and hydro generation

The fig-1 shows us the Avista’s transmission model in ourarea of interest. Fig-2 shows the block diagram showing thehydro generation and existing distribution network within
microgrid.

Online Bad Data Detection for Synchrophasor
Measurements: A Data-driven Approach
Meng Wu and Le Xie
Department of Electrical and Computer Engineering
Texas A&M University
College Station, TX 77843, USA{marie126, le.xie}@tamu.edu

Abstract—Synchrophasor measurement has become an increasingly important source of information for power systemmonitoring, control, and protection. Although synchrophasor
data contains rich information on system dynamics, its data
quality problem may affect the accuracy of analysis. In order to obtain a clean data set prior to further analysis, thispaper proposes an online data-driven algorithm to detect bad
synchrophasor measurements caused by either physical instrumentation errors or intentional malicious attacks. The algorithm
applies density-based local outlier factor (LOF) analysis anddetect bad synchrophasor measurements whose spatial-temporalcorrelations follow a specific pattern. The advantages of the
proposed algorithm include: 1) it is purely data-driven, without
involving any knowledge on network parameters or topology,
which avoids the impact of wrong parameter/topology information on the detection results; 2) it encounters no convergenceissue and has fast computation performance, which is desirable
for online application; 3) it is suitable for detecting bad data inall kinds of PMU outputs (voltage, current, real/reactive power)
under both normal and fault-on operating conditions.

I. P ROPOSED A LGORITHM
The proposed bad data detection algorithm is shown in Fig.
1.

1) for one particular synchrophasor measurement, its normal
data segment has week temporal correlation with its bad data
segment or eventful data segment.
2) for one particular synchrophasor measurement, its baddata segment has week spatial correlation with the corresponding data segments of its neighborhoods.
With proper definition of ’distance’ between two synchrophasor data segments, local outlier factor (LOF) analysis
is able to identify density-based outliers which follow thespatial-temporal pattern of bad data, without responding tosystem physical events.
II. K EY R ESULTS
Practical synchrophasor voltage magnitude measurements
are used to test the algorithm. Small constant attacks are added
to PMUs No. 13, 25, 33, 36, 39. The attacking time lasts from200th snapshot to 800th snapshot. There is a line-tripping fault
recorded by the data.
Table I shows the detection results. All the five attacked
PMUs can be correctly detected. The detection errors for
detecting starting and ending time of the attacks lie within
one snapshot. Computation time for each snapshot lies around
20ms. There is no false alarm introduced by the line-tripping
fault.
TABLE I.

BAD DATA I DENTIFIED BY THE A LGORITHM

PMU No.

Attack Starting Snapshot

Attack Ending Snapshot

13

200 (LOF = 24.2255)

801 (LOF = 22.7153)

25

200 (LOF = 29.9320)

801 (LOF = 24.7428)

33

200 (LOF = 29.5266)

801 (LOF = 26.4662)

36

200 (LOF = 28.4775)

801 (LOF = 25.1248)39

200 (LOF = 22.1083)

801 (LOF = 21.4784)

III. S UMMARY

Figure 1. Flowchart of the proposed algorithm.

In the algorithm, the following spatial-temporal features ofsynchrophasor data are used to differentiate normal data, baddata and system physical events:

A data-driven approach using LOF analysis is proposed todetect physical instrumentation errors and intentional malicious attacks in synchrophasor measurements. The algorithm
requires no knowledge on system parameters/topology, and has
fast computation performance. The effectiveness of the algorithm is verified by case studies using practical synchrophasor
measurements.

New Control Structure for Load Frequency Control of aHybrid AC/DC Microgrid
Mohammad Reza Khalghani, Graduate Student Member, IEEE, Sarika Khushalani Solanki, Senior
Member, IEEE, Jignesh Solanki, Senior Member, IEEE
Lane Department of Computer Science and Electrical Engineering, West Virginia University Morgantown, WV 26505, USA
mk0005@mix.wvu.edu, Sarika.Khushalani-Solanki@mail.wvu.edu, and Jignesh.Solanki@mail.wvu.edu

Abstract- Microgrids have many energy resourcesthat have stochastic nature; therefore, keeping thebalance between power products and demands andconsequently frequency is a critical issue. In thisabstract, one control framework and one control
methodology are suggested to enhance Loadfrequency control (LFC) of hybrid AC/DC
microgrids in islanded operation mode. DC part of
microgrid can participate in LFC of the AC part ofthis system using this paper's proposed method.
Already, a paper applied the common LFC
methodology in transmission systems on an AC
microgrid. In this method, the microgrid is divided
into two virtual areas, which are connected to eachother via an artificial tie-line, and then each area is
responsible for keeping its own frequency. In thiswork, we tend to modify this method to be used forhybrid AC/DC microgrids. In this modified method,for each of AC and DC parts of the microgrids, a
special control area is defined. These two virtualareas have a connection through a bi-directional
AC/DC converter, like a tie-line in transmission
systems. For each of these AC and DC areas, a PI
controller must be applied to control the virtual Area
Control Error (VACE). In order to properly control
these areas, a self-tuning PI controller is suggested
that is based on human brain emotional learning. For
the sake of stochastic inputs, including PV, PHEV
and wind turbines, in microgrids, a controller that
can handle these uncertainties is proposed. Emotional
controller can control dynamic systems without
having the system models as a self-tuning way.

In order to represent better results, emotional
controller is applied for controlling VACE. In thefollowing the equations of emotional controller are
mentioned. The related equations for emotional
controller can be considered in [2]. In this section, someof equations that can be applied for this system areillustrated. The computational model output MO (theresponse of Amygdala-orbitofrontal emotional learning
system to input stimulant SI, and emotional) for each
VACE is equal to:
MO=(Ga-Goc).SI=G(SI, EC, ...).SI(3)

where, Ga and Goc are the equivalent gains of
Amygdala and orbitofrontal units respectively. The
Amygdala–orbitofrontal emotional learning system
output is equal to a varying gain G multiplied by input
stimulant SI. A simple and appropriate suggestion toformulate stimulant input signal of VACE is a template
similar to PID.
t.

SI  K P .VACE  K I . VACE.dt  K D .VA C E(4)0

The emotional signal EC generally must show thedesirability of the LFC controller and closed-loop system
performance. Therefore, EC signal can be obtained inequation (5).
EC=aec1.VACE+aec2.MO(5)

II. KEY FIGURESIn Figure 1, the proposed control framework isdemonstrated.

I. KEY EQUATIONSThere are two equations for modeling VACE ofeach of AC and DC part of the hybrid microgrid. This
proposed method was inspired from [1]. At first, VACE
for AC part is mentioned below:
VACE=(α ∆f + ∆Ptie)*F(storage)*G(PV)(1)

Where, α is frequency bias factor, ∆f is frequency
deviation, ∆Ptie is the power that transfer between DC and AC
parts of the hybrid microgrid, and F(storage) and G(PV) are
functions for modeling storage and PV effects on this VACE.

Figure 1. Model of a hybrid AC/DC microgrid using the new

Other technologies, like wind turbines and PHEV, fuelframework
cell, can be modeled in this definition too. For DC part, References:
VACE can be defined as the following:[1] F. D. Mohammadi, M. J. Ghorbani, A. Feliachi and M. A.
VACE=( ∆Ptie)*F(storage)*G(PV)"Novel Load Frequency Control approach based on(2) Choudhry,
Virtual Area Error in a microgrid including PV and battery,"In equation (2), since DC microgrid does not havefrequency concept, the related term is removed. In thisway, we can control the power of DC microgrid toparticipate in LFC of AC part of a hybrid microgrid.

PES General Meeting Conference & Exposition, pp. 1-5, 2014.
[2] M.A. Shamsi-nejad, M.R. Khalghani, “DVR control using
adaptive PI controller based on human brain learning,”
Proceedings of 17th Conference on Electric Power Distribution
Conference (EPDC), Tehran, Iran, 2012.

Effects of Aggregate Load Control on the Physical Components ofDistribution Networks
Stephanie J. Crocker and Johanna L. Mathieu
I. I NTRODUCTION

II. M ETHOD
We utilize time series simulations to determine the steadystate effects of load control on distribution networks overthe course of a typical year. We investigate three types ofgrid constraints: voltage levels, line ratings, and transformer
ratings. For each type of constraint we monitor for violationsof both emergency and continuous ratings.
Because our findings depend heavily on the specific parameter values of network components, and on the assumed
capacity for controllable loads, we use Pacific Northwest
National Lab’s prototypical feeders. This set of feeders is
composed of real utility data; the feeders are “prototypical”
because they are representative of other feeders in the
S.J. Crocker and J.L. Mathieu are with the Department of ElectricalEngineering and Computer Science, University of Michigan, Ann Arbor,
MI 48104 USA {sjcrock,jlmath}@umich.edu

Number of Lines/Occurences

Load control schemes have been designed for different
types of grid services, from reserves to regulation. A significant body of research has been devoted to load control
for real-time energy balancing (e.g. regulation, 5 minuteenergy market, energy imbalance market, etc.). For suchapplications, non-disruptive load control is desirable and canbe achieved with loads that naturally cycle on and off such as
refrigerators and air conditioners. If thousands of such loads
are coordinated together, then slight changes in the individual
behavior of each load adds up across the population andresults in substantial controllable capacity.
The challenge for demand response of large aggregationsof loads is performing automated control while minimizing
the cost of implementation. A few researchers have addressed
reducing the cost of the control systems sensing and communication infrastructure. But to date, most load control
strategies have focused on the control problem without
addressing the potential costs to the distribution network towhich the aggregation of loads is connected.
Under the right circumstances, a synchronized response by
an aggregation of loads could potentially cause power flows
above the ratings of network components, or cause voltage
profiles to deviate from the regulated range. If such issues
occurred frequently, maintenance costs for the distribution
network would increase as marginally rated network components aged faster than expected. Voltage profile issues might
require that additional hardware be added to the grid. In thiswork we investigate the nature and severity of distribution
network issues that could be induced by load control.

600
500
400
300
200
100
0
0%

10%

20%

30%

40%

50%

60%

Current of Each Line as a Percentage of its Continuous Line Rating

Fig. 1. Number of lines with current flow at varying fractions of theircontinuous rating, during the single minute of the simulated year withgreatest power flow at the substation transformer. Note that all lines stayunder 60% of their continuous ratings.

same geographic area. We use an accompanying script that
populates the feeders with the appropriately sized loads.
For simulation software, we use GridLAB-D, which has athree-phase unbalanced power flow solver, detailed network
component models, controllable load models, and interfaces
readily with the prototypical feeder data.
In order to determine whether load control could ever be
detrimental to a network, we run each feeder through a worst
case scenario. This scenario consists of commanding allcontrollable loads to turn on at the same instant, holding themon for 1 minute, and then letting the population relax back tonormal operation for 3 hours. We repeat these pulses over thecourse of a full simulation-year, and measure the frequency
and magnitude of any constraint violations that occur. (It is
important to look at a full simulation-year because GridLABD incorporates hourly weather patterns into its simulations,which in turn affects space conditioning loads.)
III. P RELIMINARY R ESULTS AND C ONCLUSION
So far we have run this analysis on one feeder locatedin the southeastern U.S. The results for this feeder indicatethat network components are not overloaded when subject toworst-case load control. Over the course of the simulationyear, we found that 1 of 320 pole-top transformers experienced constraint violations. Primary lines experienced noissues, as shown in Fig. 1. The voltage profile was also
maintained within its standard range.
For the final results, we plan to add a model of in-rushcurrent when motors start up to ensure the analysis captures
peak power flows. Additionally, we plan to run our analysis
on a variety of feeders to determine if performing load
control on feeders with different configurations and weather
patterns will lead to more severe issues. We aim to conduct
a survey of ten or more feeders in order to identify the typesof networks that are most likely to be strained by aggregateload control.

Demand Response via Large Scale Charging ofElectric Vehicles
Zhe Yu† and Lang Tong†

Scheduler

ISO

Electricity price Regulation Signal
Fig. 1.

Architecture of a charging station

Regulation signal
Trajectory tracking
Whittle index w. LLLP

58

Power usage

56
54
52
50
48
46
44
42
5

Fig. 2.

10

15

20

25
Time

30

35

40

45

Regulation signal tracking: M = 50, 10% regulation capacity.

0.03
Trajectory tracking
Whittle index w/o LLLPWhittle Index w. LLLP

0.02
Error

I. I NTRODUCTION
With the substantially growth of Electric Vehicles (EVs)and EV charging services, the potential of participating in
demand response programs by EV charging service providers
has attracted considerable interest. To a EV charging service
provider who has the capacity of serving a large number ofEVs, the economic benefit of providing demand response canbe substantial.
In this paper, we consider the problem of providing a formof ancillary service by a large scale EV charging service
provider with fast charging capabilities and the capacity ofserving hundreds of vehicles. An example of such a service
provider can be one that operates at large public or private
parking facilities. An essential characteristic of such charging
services is that it can shift substantial demand without seriously jeopardizing the quality of service because, among thelarge number of EVs in the facility, there is a substantial laxity
in fulfilling the charging demand.
We introduce a stochastic dynamic programming model forlarge scale EV charging which captures randomness in arrivals, demand, deadlines, charging costs, and demand response
requirement. To handle scalable solution, we first establish
indexability of the problem and seek an index policies withcomputable indices. Numerical simulations demonstrate thatthe proposed policy makes significant charging profit whileproviding perfect ancillary service under various uncertainties.

Ancillary Service Bids

0.01

0
0.02

0.04

0.06

0.08

0.1
0.12
0.14
Regulation capacity

0.16

0.18

0.2

0.22

4500

RewardAbstract—The problem of centralized scheduling of large
scale charging of electric vehicles (EVs) with demand response
options is considered. A stochastic dynamic programming modelis introduced in which the EV charging service provider faces
stochastic demand, convex non-completion penalties, and random
demand response requirements. Formulated as a restless multiarmed bandit problem, the EV charging problem is shown
to be indexable, thus low complexity index policies exist. An
enhancement of the Whittle’s index policy based on spatial
interchange according to the less laxity and longer processing
time (LLLP) principle is presented. Numerical results illustrate
the performance improvement and the capability of handlingvarious operation uncertainties of the proposed index policy.
Index Terms—Charging of electric vehicles; Deadline scheduling; Demand response; Multi-armed bandit problem; Whittle’s
index.

Trajectory tracking
Whittle index w/o LLLPWhittle Index w. LLLP

4000

II. K EY EQUATIONS
3500
0.02

supπ
subject to

nP
∞ PN

Eπ
PN

t=0

t
i=1 β Rai [t] (S̃i [t]) | S̃i [0]i=1 ai [t] = M [t],∀t.

III. K EY F IGURE

o

(1)

0.04

0.06

0.08

0.1
0.12
0.14
Regulation capacity

0.16

0.18

0.2

0.22

Fig. 3.
Regulation signal tracking accuracy and charging reward Vs.
regulation capacity.

Neuro-Fuzzy Based UPQC Controller forPower Quality Improvement in Micro Grid System*Uday Kishan Renduchintala, *Chengzong Pang
*Department of Electrical Engineering and Computer Science, *Wichita State University, Wichita, USA
uxrenduchintala@wichita.edu
Abstract— The Power Quality problem is addressed by
Unified Power Quality Conditioner (UPQC) which provides acoordinated control on voltage harmonics and unbalances in a
micro grid. These micro grids with the proposed converter are
applied to general renewable energy sources which will lead tomaximum utilization of power at environmental free conditions
with low losses improving the efficiency of the system. The
proposed system will strive to preserve Power Quality at the
distribution end during switching from grid connected mode toisolated mode. This paper also proposes the concept of neurofuzzy controller for frequency tracking and extraction ofharmonics in grid voltage and load currents. This system isverified using Matlab/Simulink with the results being
satisfactory.

The photovoltaic generating plant is considered as the
distribution generation system in this project. And along withthis proposed potential conditioner, it is necessary to have an
efficient controller for frequency tracking and extraction ofharmonics in grid voltage and load currents. For this purpose
the Neuro-Fuzzy controller which is one of the vital controller
in adaptive techniques has been designed and preferred overthe conventional controllers such as PI controller. This neuralnetwork controller consists of two inputs that are Δe and Δdeand it has one output that is f ∈ {Δe, Δde}.

Keywords— Microgrid System, Unified Power Quality
Conditioner, Fuzzy Controller, Neuro Controller, Power Quality

I. PROPOSED SYSTEM
A new converter has been designed consisting of powerelectronic devices, having simple structure without any powertransformer and DC link capacitor to compensate the reactive
power. One of the compensating device form the FACTS
family called as Unified Power Quality Conditioner, has thecompetence to improve the Power Quality, when it is installedon the distribution side. Therefore it is expected that this device
will turn out as one of the most powerful solutions when
dealing with power quality problems pertaining to micro girds.
The UPQC is a combination of series and shunt controller witha distribution generation system.

Fig 1: THD of load voltage without Controller

Proposed microgrid
configuration
Load 1Microsource

CB 12

CB 13

Grid bus Vg
PCC
22KV
infeed

CB 2
load
CB 9

CB 1

Fig. 2: THD of load voltage with Neuro Fuzzy Controller

CB 3
load
CB 6

Transformer
22/0.4KV

CB 5

Ll

CB 4

Lo
ad
2

Lt
Rl

ig

CB 7

CB 8
Cse
CB 11
Lse

CB 15

CB 16

Vdc/2

Vdc/
2

Load 3

Ig(k)
Vg(k)

ControldiagramVmg(k)

Fig. 1 Proposed Micro grid System

MicrosourceII.

CONCLUSION

Generally, reducing power quality problems occurred in thesystem is compensated by microgrid alone. But UPQC with
DG is implemented for compensating all types of voltage
fluctuations. The system is practically implemented with twocontrollers PI and Neuro-Fuzzy and the results areexperimentally verified. Simulation results show theeffectiveness of UPQC to reduce the harmonics. The results in
Fig. 1 and Fig. 2 shows that the system has better control andlow total harmonics distortions are obtained with Neuro-Fuzzy
controller than the conventional PI controller.

A Three-Way Win Demand-Response
Management Strategy
Pramod Herath, Student Member, IEEE, Ganesh K. Venayagamoorthy, Senior Member, IEEE
Real-Time Power and Intelligent Systems (RTPIS) Laboratory
The Holcombe Dept. of Electrical and Computer Engineering
Clemson University, SC 29634, USA
Emails: Pramod.U.Herath@ieee.org, gkumar@ieee.org
Abstract – A new demand response management
strategy is proposed by introducing a service provider
to be a middleman between the customers and a utility.
The task of the service provider is to constantly find an
optimum solution where the customers’ cost of energy
consumption is minimal while maximizing comfort andconcurrently the utility is served with an optimal
demand distribution. The service provider earns apercentage of the customers’ and utility’s net income.
The aim of the service provider is to achieve an optimal‘win-win-win’ generation, operation and consumption.
To achieve this task particle swarm optimization
algorithm is utilized. The cost savings for customer sideand utility side, as well as the dissatisfaction of thecustomer are mathematically modeled. The algorithm
developed is applied to a physical laboratory smart
neighborhood that has twelve different representative
customers.
Index Terms—Demand
management, smart grid,

I.

Response,

Demand-side

on them on rescheduling dishwasher. In the demand
response scheme proposed in this paper these effects
are also considered by allowing customer evaluation
on each of the appliance. To solve the optimization
problem, constrained particle swarm optimization isapplied.
II.

KEY FIGURES

Figure 1: Clemson University RTPIS Lab Smart
Neighborhood Research Platform

INTRODUCTION

Demand response has been practiced for sometime now. Different models of demand-side
management have been introduced. However, it
could be noticed that most of the demand side
management is carried out by the utility itself. This
raises concerns such as whether the consumer getsthe fair payback for the comfort that they give up inparticipating in demand response. As a solution forthis concern, we introduce a third character, a neutral
party called the 'service provider' who sits between
the consumer and the utility. The service provider
comes to an agreement with the utility to aggregate
some demand response savings and also comes to anagreement with the consumer for a saving in theirelectricity bill. The service provider then proceeds tocollect the constraints from the consumer and theutility to generate a plausible demand responseschedule for the consumers.
In addition, it can be noticed that almost all thedemand response schemes concentrate only on powerusage. Even most of the research work ignore theeffects on the comfort of the consumer. For instance,
the effect on the consumer on rescheduling thewashing machine might not be the same as the effectFigure 2: The role of the service provider

REFERENCES
P. Faria, Z. A. Vale, J. Soares and J. Ferreira,“Particle swarm optimization applied to integrated
demandresponseresources
scheduling,”
Computational Intelligence Applications In Smart
Grid (CIASG), 2011 IEEE Symposium on, Paris,
2011, pp. 1-8.

A Framework for Reliability Evaluation of Electric
Vehicle Charging Stations
Mohsen Ghavami

Somasundaram Essakiappan

Chanan Singh

Department of Electrical & Computer
Engineering
Texas A&M University
College Station, USA
mohsen@tamu.edu

Energy Production & Infrastructure Center
University of North Carolina – CharlotteCharlotte, USAsomasundaram@uncc.edu

Department of Electrical & Computer
Engineering
Texas A&M University
College Station, USA
singh@ece.tamu.edu

electric (HEV) vehicles. This will also inevitably result in alarge number of EV chargers in the distribution grid. The powerelectronic systems in EV chargers have opened up a wide rangeof research topics. The performance of a system can beevaluated by efficiency, reliability, power density and cost. In
order to calculate the charging stations reliability and expected
lifetime; the failure rate needs to be calculated as accurately as
possible. MIL-HDBK-217 is used in this paper to calculate thefailure rates of power electronic components used in electricvehicle charging stations.

Abstract— The focus of this paper is to develop a framework
to evaluate and analyze the reliability of the power electronic
circuits in electric vehicle chargers, which are expected to be amajor component of the utility load in the future. A general
infrastructure of electric vehicle charging systems is describedand different charging levels are discussed. To illustrate anumerical reliability analysis, two high power dc-dc topologies
are selected and the mean time between failures of theseconverters that are widely used in electric vehicle charging
stations are calculated and compared. The impact of eachcomponent in the power electronic circuit on overall system
reliability is discussed and in particular the sensitivity totemperature, safety factor and capacitor type are studied. These
factors have the highest impact on the system reliability. The
framework developed in this paper has the potential to serve as atemplate for reliability evaluation and comparison of future onboard and off-board electric vehicle chargers.

II. RELIABILITY ANALYSIS FOR SELECT TOPOLOGIESTo poweroutletQ1
Bidirectional
ac-dc
converter

Keywords— component reliability; dc-dc converters; inverters;
electric vehicles; charging stations

I. INTRODUCTION

Q2

Energy security, air pollution and greenhouse gas emissions are
the main reasons for the governments to set ambitious targets
for the integration of electric vehicles into the power system
grid. Transportation applications command a large proportionof fossil fuel consumption and therefore there has been an
increased interest in introducing electric (EV) and hybrid-

Co

Lo
S1

S3

Lr

np

ns

S5

S2

Co
HighS8frequencytransformer

Off-boardEVbattery
pack

Ibattery

S7

Cr
S4

IbatteryFig.1. Topology 1 analyzed in this paper: a non-isolated topology for
low power charging

To poweroutletBidirectional
ac-dc
converter

L2

L1

EV
batterypack

S6

On-board

Fig.2. Topology 2 analyzed in this paper: this is a high-frequency isolated inductive charging topology for use in higher-power rating chargers
which are usually employed in commercial charging stations

Dynamic Electric Vehicle Charging Considering
System and Customer Constraints
Maigha, Student Member, IEEE

Dr. Mariesa L. Crow, Fellow, IEEE

Department of Electrical and Computer Engineering
Missouri University of Science and Technology
Rolla, Missouri, USA
mmck6@mst.edu

Department of Electrical and Computer Engineering
Missouri University of Science and Technology
Rolla, Missouri, USA
crow@mst.edu

Abstract— Transportation electrification coupled withrenewable energy generation can lead to energy independence
through environmentally sustainable and resilient infrastructures.
Uncoordinated charging of electric vehicles at higher market
penetrations can lead to problems on the power system. A welldesigned charging scheme needs to consider system impacts as wellas customer convenience. In this study, we discuss a dynamic
charging algorithm which constraints electric vehicle charging
during peak hours, results in valley filling and choses an
economical schedule for the vehicle owner with consideration tobattery degradation in vehicle-to-grid applications.
Keywords—electric vheicles, vehicle-to-grid, dynamic charging,battery degradationI.

II. FORMULAT ION AND RESULT S
Minimizing the charging cost was designed as a mixed integer linear program. The battery degradation and valley filling objectives were designed as quadratic functions.
The results show that the optimization algorithm resulted in
shifting the electric vehicle load to the valley period withoutcausing an increase in system peak demand.
Furthermore, vehicle-to-grid implementationshows
promising demand-response capabilities. Using this, theaggregated electric vehicle fleet could act as a future player in a
dynamic market framework.

INT RODUCT ION

Coordinated charging/discharging of electric vehicles can
reduce their loading impacts, defer infrastructural
reinforcement requirements and improve power quality andreliability, thus leading to economic power system operations.
Since customer is the central entity in the process, herconvenience and incurred cost is of crucial importance. Battery
degradation costs are an integral part of this cost model due totheir high costs. Therefore, electric vehicle charge schedulingpresents itself as a multi-dimensional optimization problem.
In this research, a dynamic scheduling algorithm is designed
to achieve valley filling and peak shaving (in case of vehicleto-grid operation) at least charging cost to the customer. The
cost component includes time-of-use electricity pricing andbattery degradation costs. Intuitively, increasing the revenues
through vehicle-to-grid operation conflicts with a decrease inbattery degradation costs and thus leads to a multi-objective
approach. The preliminary results of a centralized control
paradigm addressing these issues will be presented in thisposter.

Fig. 1. Coordinated load profile with valley-filling

In addition to technological advancements and market
adaptation, customer acceptability is of crucial importance tothe success of a scheduling strategy. Thus, any new paradigm
needs to facilitate gradual customer behavioral change
supported by conducive policies and regulations. This aspect
will also be addressed briefly.
Fig. 2. Load profiles under vehicle-to-grid and grid-to-vehicle modes

Role of Active Consumers and Pricing onDistribution Systems
Kumarsinh Jhala, Bala Natarajan, Anil Pahwa

Larry EricksonElectrical and Computer Engineering
Kansas State University
Manhattan, KS
kumarsinh@ksu.edu

Chemical Engineering
Kansas State University
Manhattan, KS

Abstract— The future of the grid is to empower prosumers.
Prosumers are expected to be engaged in managing energy use,
production, storage and providing other ancillary services in a
dynamic and interactive manner. Analyzing the impact of large
scale penetration of active consumers on distribution grid will be
crucial. This research illustrates, how real-time pricing ofelectricity can be used to best maintain grid stability and reliability
while maintaining fairness and optimizing payoffs (or) satisfactionof all stakeholders. Objective of this research is to help grid
operators prepare for the “Future Modernized Grid” through basic
research and analysis, leading to new insights on the impact ofactive consumers on grid operations and cost.
This work models behavior of active consumers who strategize
their electricity consumption to minimize the total cost of electricity.
A temporal and spatially varying real-time pricing scheme isdeveloped that result in desired load profile by influencing behaviorof active consumers. This work develops the pricing scheme fromthe perspective of the electric utility, in which the electric utilities
decide optimal real-time electricity prices that minimize peak-toaverage load ratio by influencing charging behavior of active
consumers. Active consumers decide optimal charging strategy
based on real-time prices in order to minimizes their cost. Real-timeelectricity prices are decided by electric utility, which depends on
behavior of active consumers. A model predictive control (MPC)
based optimal pricing scheme for electric utility and optimal
charging strategy for active consumers is proposed. Effects ofproposed approach on load profile is varied using simulation. A
scenario is considered where N feeders are connected to atransformer and each feeder is connected to multiple homes (seeFigure 1). In this case we assume certain percent penetration ofactive consumers, who manages their EV charging load. Figure 2
shows optimal real-time pricing for this case and effective load
profile. From result it can be seen that using proposed optimal
pricing scheme can help utilities to achieve favorable load profile
using real-time pricing as a tool to influence behavior of active
consumers.
Second part of this research analyzes effect of multiple ofactive consumers on distribution system voltage. Current scenario
based analysis cannot be used to examine effect of random spatial
penetration of active consumers and spatio-temporal variation ofgeneration across the network. Results from such methods ofanalysis are limited to considered scenario, and testing all possible
scenarios is computationally intractable. This work proposes astochastic method of grid analysis that provides probabilistic viewof the system and provides likelihood of system parameters

exceeding operational bounds. This work develops an analyticalmethod to compute upper bound of sensitivity matrix, which iscomputationally efficient. This analytical method is used to analyze
aggregated effect of random penetration of active consumers on
feeder voltage and find out probability of voltage change being
higher than allowed values.

Fig 1. System Structure

Fig 2. Real-time electricity pricing and load

1

Weather Station Selection for North Carolina
Electric Membership Cooperation
Jingrui Xie
Abstract-- Electric load forecasting is a fundamental businessproblem in the utility industry with its broad applications in
power system operations and planning, financial planning, risk
management, demand side management, and so forth. Weather
variables, especially temperature variables are the driving factors
for residential electric demand. Thus, utilities have been looking
for effective weather station selection techniques in order toacquire high quality weather data for load forecasting.
In this poster, a recent invented weather station selection
framework is applied to North Carolina Electric Membership
Cooperation (NCEMC), a co-op that serves 93 of 100 counties
with diversified weather conditions in the state of North Carolina.
This framework utilizes a widely adopted multiple linear
regression model as the forecasting engine. Both of single weather
station and the simple average combination of weather stations
within the service territory of NCEMC are evaluated regarding
the accuracy of the temperature variable for the load forecasting.
The framework can also be applied to evaluate other weatherrelated variables such as humidity, while only the temperature
variable is considered to preserve the comprehensibility of thisstudy.
The results from the experiments show this implemented
weather station selection framework outperforms otherbenchmarks. Furthermore, the selected weather station
combination strategy is reliable and robust for the purpose ofload forecasting. The implementation of this framework at
NCEMC demonstrates its practical value to the utility industry.

Framework [1] is applied to North Carolina Electric
Membership Cooperation (NCEMC). NCEMC serves 93 of
100 counties with diversified weather conditions in the state ofNorth Carolina as shown in Figure 1. A widely adopted
multiple linear regression model [2] will serve as theforecasting engine of the weather station selection framework.
The framework will first evaluate the performance of each of
the candidate weather stations within the service territory of
NCEMC, and then evaluate the effectiveness of taking simple
average combination of weather stations. The evaluation isconducted with regard to the accuracy of the temperature
variable for load forecasting. But, the same framework can beapplied to evaluate other weather-related variables such as
humidity, while only the temperature variable is considered topreserve the comprehensibility of this study
The results from the experiments show this implemented
weather station selection framework outperforms otherbenchmarks. Furthermore, the selected weather station
combination strategy is reliable and robust for the purpose ofload forecasting. The implementation of this framework at
NCEMC demonstrates its practical value to the utility industry.

Index Terms—Load forecasting, weather station selection,
multiple linear regression model.

I. INTRODUCTION

E

lectric load forecasting has been widely used by different
departments of the utilities for various purposes such assystem operations and planning, customer side management,
financial planning, risk management, and so forth. Weather
variables, especially temperature variables play a vital role indriving residential electric demand. Weather data withpremium quality and can best represent the weather conditionsof utilities’ service territories significantly contribute to obtainload forecasts with high accuracy. Utilities have been looking
for an effective and practical weather station selection
techniques over years.
In this poster, a recent invented weather station selection
Jingrui Xie is with the Energy Production and Infrastructure Center,
University of North Carolina at Charlotte, Charlotte, NC, USA (e-mail:jingrui.rain.xie@gmail.com)

Figure 1. Service territory of NCEMC
II. REFERENCES[1]

T. Hong, P. Wang, and L. White, “Weather station selection for electricload forecasting,” Int. J. Forecast., vol. 31, no. 2, 2015.

[2]

T. Hong, P. Pinson, and S. Fan, “Global energy forecasting competition
2012,” Int. J. Forecast., vol. 30, no. 2, pp. 357–363, Aug. 2014.

Load Regulation of a Smart Household with PV-Storage andElectric Vehicle by Dynamic Programming Successive
Algorithm Technique
Faeza Hafiz, Poria Fajri and Iqbal Husain
Electrical and Computer Engineering Department
North Carolina State University
Raleigh, NC, USAfhafiz@ncsu.edu

Extended Abstract —The objective of this project is to regulate householdload by means of coordinated control between solar PV
storage and electric vehicle battery storage. In a smart
household system, it may not be possible to use the solar
energy when it is produced. So when the demand is lower
especially during daytime working hours, solar power can
be stored in a storage, which can be used when demand
increases especially at night time when the household
members come back from work or other activities.
Similarly, when the homeowner plugs in his PEV or
PHEV to the grid at night, it will also increase the power
demand of the household. If a PEV or PHEV is capableof delivering power to the household, then it may
discharge in times of higher demand and charge up
whenever the demand is low. So, to charge and dischargeof both storage components require a coordinated control
strategy capable of balancing the household systems’power demand.
Dynamic programming (DP) technique is considered
as an effective method for solving the optimized strategy
for charging and discharging of storages. But it is quite
complicated to handle multi-dimensional problem
through DP because it requires a lot of time and memory.
To manage the computational complexity problem,
dynamic programming successive algorithm (DPSA) is
used to obtain the optimal charging strategies byminimizing the overall load variance of daily householdload demand.
The household system including the solar storage and
PHEV storage is presented in Fig. 1. Fig.2 shows the
improvement of load variance with the control strategy
which helps to reduce the peak load of the system. Fig. 3
represents corresponding PHEV storage and PV storage
charging and discharging strategies. This method is going
to be expanded to minimize the load variance in a
distribution level and study the effect of coordinatedcontrol among distributed energy sources using DPSA.

PV StringBattery
Storage

Grid
PHEVHouseholdFig. 1: Smart Household System

Fig. 2: Regulated profile after control

Fig. 3: (a) PHEV and (b) PV battery storage charging anddischarging profile

Generalized Steady-state Model for Energy Router with
Applications in Power Flow Calculation
Jianqiang Miao, Ning Zhang, Chongqing KangState Key Lab. of Power Systems, Department of Electrical Engineering, Tsinghua University, Beijing, 100084, China

Abstract—Energy router is an emerging conceptual device
based on advanced power electronic technique. It is able torealize the flexible and dynamic electric power distribution in
power systems as information router in the Internet. It is ofgreat interest to investigate how the energy router can beused to improve the power system operation. This paper
proposes a generalized configuration of the energy router.
The steady-state model for the energy router is derived tobuild its power flow equations. An iterating algorithm is
proposed for system power flow calculations considering
energy router. Case studies are carried out on a modified
IEEE RTS-79 system with energy router under different
control strategies. Results show that the energy router is able
to promote the operation of power system in a more flexible
manner in terms of adjusting branch power flow, improving
bus voltage and reducing active power losses of the grid.
Index Terms—energy router, energy internet, steady-statemodel, flexibility, power flow.

I.

The real and imaginary parts of the power injection at
port i of the energy router can be expressed as:
Psi 

Qsi 

U s1 s1

DCΦ,1

DCΨ

PsK , QsK ACΦ,K
U sK  sK

Figure 1.

DCΦ

DCΦ,K

ACΨ,M U sK  M  sK  M

Configuration of the generalized energy router2i

U si2 (2)X ci

Ri  U ci2  U siU ci cos i  +X iU siU ci sin i
Ri2  X i2

(3)
(4)

The relation of the voltage and current on each side canbe expressed by active energy balancing equations:U d  I d   U d  I d   Pstatic  Pdynamic

II.

(5)

CASE STUDY

A modified IEEE RTS-79 system is used todemonstrate the proposed steady model of the energy
router.
Case
No

2
PsK  M , QsK  M

R X
2
i

U ci  mU
i di

PsK 1 , QsK 1DCΨ,1ACΨ,1 U sK 1 sK 1

DCΨ,M

X i U si2  U siU ci cos i   RU
i siU ci sin i

Pci  U di I di 1
Ps1 , Qs1 ACΦ,1(1)

Ri2  X i2

The power exchanged between the DC side and the ACside at port i is expressed as:

KEY EQUATIONS AND DESCRIPTIONS

The configuration of the generalized energy router isshown in Fig. 1. It consists of two sides (voltage levels):
the primary side (with subscript Φ) and the secondary side(with subscript Ψ). Each side can be placed with several
ports, e.g. K ports for the primary side and M for thesecondary side. Each port is made of a flexible AC/DC
convector that release or extract power from the DC bus.
The two DC buses are linked using a DC/DC transformer.
The proposed configuration can be easily expanded tomultiple voltage levels.

Ri U si2  U siU ci cos i   X iU siU ci sin i

Po
rt
No
1
2
3
4
1
2
3
4

m

θ/°

0.509
0.540
0.489
0.492
0.559
0.535
0.466
0.496

-6.93
-7.54
5.90
10.6
-4.30
-6.42
5.68
5.65

PdΦ/M
W

PdΨ/
MW

Power
losses
/MW-764.8

770.
3

69.25

-552.7

557.
5

49.01

Table. I shows the results of each case and compares
them with base case operating state of the original system,
where there is no energy router.

Optimal Usage of Battery Energy Storage in
Smart Microgrids
Chirath D. Pathiravasam, Student Member, IEEE, Ganesh K. Venayagamoorthy, Senior Member, IEEE
Real-Time Power and Intelligent Systems (RTPIS) Laboratory
The Holcombe Dept. of Electrical and Computer Engineering
Clemson University, SC 29634, USA
Emails: Chirath.D.Pathiravasam@ieee.org, gkumar@ieee.org
Abstract – Optimum utilization of variable anduncertain renewal energy sources is a key challenge inthe modern power system. To overcome this challenge
an Adaptive Critic Design (ACD) approach: Intelligent
Dynamic Energy Management System (I-DEMS) is
proposed to control the dispatch signals of a microgrid
with the implementation of optimal or near-optimal
solution. The actor optimization of I-DEMS is
implemented with neural networks and is trained withthe outcomes of a decision tree based on the predefinedpriorities. I-DEMS optimizes the usage of Battery
Energy Storage (BES) and presents a higher reliability
with a lower battery capacity. The performance of the IDEMS based BES will be compared against a decision
tree (D-DEMS) based BES.

II.

KEY FIGURES

Fig 1. Microgrid system with Dynamic Energy Management
SystemIndex Terms— Adaptive critic designs; battery energy
storage system; BESS sizing; BESS state of charge;
decision tree; dynamic energy management system;
micro-grids; renewable energy;

I.

INTRODUCTION

The intermittency of renewable energy sources
causes them to be non-dispatchable and can effectunhealthy conditions of the power system if they are
not intelligently utilized.
In this project, a microgrid (fig. 1) consists of windand solar pv power generation, battery energy
storage, backup diesel generator, critical loads andcontrollable loads is considered.
I-DEMS (fig. 2) consists of a controller which
produces control signals for the microgrid and a critic
network (Forward Looking Network (FLN)) which
provides the signal for the controller to adapt in orderto optimize for future states of the microgrid. The
cost-to-go function, J(t), is approximated by FLN andutility function (fig. 3) guides FLN in evaluating
controller performance.
On the other hand, in D-DEMS, dispatch decisions
are made by considering a set of pre-definedpriorities and prevailing system states.
BES performance will be evaluated by the numberof charging-discharging cycles recorded in two
systems in the considered time duration (1 day). A
cost-benefit analysis would be done to showcase theimprovements and trade-offs of using I-DEMS in asmart microgrid to utilize BES optimally.

Fig 2. ADHDP based I-DEMS implementation.

Fig 3. Utility function of the microgrid.

REFERENCES[1] G. K. Venayagamoorthy; R. K. Sharma; P. K. Gautam; A.
Ahmadi, "Dynamic Energy Management System for a Smart
Microgrid," IEEE Transactions on Neural Networks andLearning Systems , vol.PP, no.99, pp.1-14

Impact of controlling zero sequence current in a threephase four-wire LV network with PV units
Fida Hasan Md Rafi, Md. Shamiur Rahman, Student member, IEEE, M. J. Hossain, J. Lu, Senior Member, IEEE
Queensland Micro and Nano Technology Center, Griffith School of Engineering, Griffith University, Gold coast, Australia.
Fida.rafi@griffithuni.edu.au, mdshamiur.rahman@griffithuni.edu.au, j.hossain@griffith.edu.au, j.lu@griffith.edu.au
Abstract—The dynamic impacts of controlling zero sequencecurrent in a three-phase four-wire low voltage (LV) distribution
network with a four-leg voltage source inverter (VSI) and PV
installations are investigated in this paper. The PV VSI isdesigned to control active power at unity power factor (p.f) withadditional neutral and zero sequence current controls.
Inherently, most of the three-phase four-wire LV distribution
networks exhibit voltage unbalance characteristics due to theconnection of single- and three-phase nonlinear loads as well as
occurrence of asymmetrical faults in the networks. The control
over zero sequence current can improve the inherent voltage
unbalance in the LV networks. The transient characteristics of
the unbalanced network are investigated utilizing dynamic
software PSCAD/EMTDC for the Energex 11 kV/420 V real LV
distribution network in Brisbane, Australia. The performance ofthe designed four-leg VSI is compared with the traditional
three-leg VSI experiencing different asymmetrical faults. The
results show that, with the additional degree of freedom fromthe four-leg VSI, faster fault recovery, lower neutral voltage and
MVA requirement are achieved for unbalanced LV networks.

I.

S1

S3

DT- 750 kVA
SP4548L
ring 2

S5

1 day customer load data
at P6141

Figure 3. Neutral current from unbalanced single phase loading.

3Leg- 1.10s
4 Leg-1.05s
3Leg- 2.12 s
4 Leg-2.06 s

Figure 4. Reactive power requirement compensation.

AC
Loads

L2a,b,cVan

1L-G fault
PCC

Fault point

DC sources

Vcf
Vbn
Vcn

Vn

KEY SIMULATION RESULTS

420 m

Ipcc

IVSI
L1a,b,c,N

Cdc

(3)

KEY FIGURES

Figure 1. Considered LV network diagram from Energex, Australia.
S7

III.

11 kV/420 V
Total 150 customers
Load: Max 2 kVA and min
0.5 kVA with P.f 0.95 lag
PV unit-P6141

Total 1.8 km
urban arearing 1+cos(t    )cos(t    )0X  
  V   sin( t    )   V   sin( t   )   V 0 
0




X




 




0
0
0
cos(t   )
 X  





Vp-p=60 V

EnergexLV Network
P6141-H

Vp-p=200 V

Neutral path
S4

S8

S6

I_invd±I_invq±
Vsd±
Vsq±

I_invN

S2

Rd

S8

S7 S1

S4 S3

S6 S2

Saturation block (+1 to -1)

SN SA SB SC

INref =0S5

Cfa,b,c

abc
dq0

PI

I0

abc

ӨN -1
ω * Lf * I_invq(± )Id+ref PI*
1,2- Id-ref =0+
1/Vdc Vsd (±) I_invd (±)

Vsq (±)
Iq+Ref =0
Sq=Sq +Sq
+ PI
*
3,4
±
Iq ref =0
S0 Sd Sq
ω * Lf *I_invd(± ) I_invq (±)+

I0ref =0±

Sd=Sd++Sd-PI
I_inv N

dq0

-

ӨP

Ipcc
Vpcc

140 V (p-p) reduction

3leg

4leg

PLL

Vdc_ref
Ppcc Ppcc_ref
Qpcc
Qpcc_refFigure 5. Zero sequence voltage (V0) compensation.

Table 1. Performance comparison

Figure 2. Designed four-leg VSI connection and controls diagram.

Fault

II. KEY EQUATIONS
Positive, negative, and zero sequence components and neutral
current can be controlled following the equations (1)-(3);I n   (I a  I b  I c )(1)
I0  (Ia  Ib  Ic) / 3  In / 3
X  
 d   cos   sin  0 X  X q    sin  cos  0  X    
01  X  
X 0   0
 

2L-G fault

(2)

QGrid
compensation
3 Leg

4 Leg

kVar

kVar

3L-G

300

220

1L-G

N/A

N/A

2L-G

160

50

kVar
(less)

27%

69%

V0
compensation

V0

Settlingp-ptime(s)(less)

4 Leg

3 Leg

4 Leg

Vp-p

Vp-p

(faster)

N/A

N/A

50%

200

60

70%

50%

190

50

70%

same

Benefits and pitfalls of using percent error as a metricfor day-ahead forecasting
Stephen SuffianDiego Ponce de Leon Barido

Electrical and Computer Engineering
Villanova University

Energy and Resources Group
University of California - Berkeley

Abstract- Day-ahead demand forecasting is used by grid
operators to schedule generation units or set market prices forlarge power grids. These forecasts, once focused solely on load
patterns, must increasingly take into account changes in nondispatchable renewable resources such as wind and run-of-theriver hydroelectricity. Day-ahead forecasts are used to schedulegeneration units through processes called unit commitment andeconomic dispatch, which solve an objective function to meetdemand while minimizing system cost. Error leads to suboptimal commitment of units leading to more expensive
electricity generation. Much of the literature has focused on
improved day-ahead prediction algorithms using modern
machine learning approaches in order to mitigate the additional
uncertainty of non-dispatchable generators. Percent error is acommon metric used to compare different forecasting
approaches and assess the added value of each algorithm on grid
operations. While percent error can be easily calculated and used
to compare results across datasets, there are two issues with its
use as a demand forecasting metric. First, error is not uniformly
distributed across time or across generation mixes. Secondly, theeffect of forecast error on increasing the overall cost ofgeneration is indirect and variable. The relationship between
error and increased system cost depends upon which units were
initially committed using the day-ahead forecast and which units
were used as operating reserves to correct for the error. This
research is the first step in exploring the dynamic relationshipbetween percent error and system cost. Nicaragua is used as acase study due to its available hourly demand and generation
dataset (July 2015-Present) and relatively high (~50%)
penetration of renewable generation. Results show that percent
error does not have a clear relationship to system cost increases,
leading to a need for future research that that focuses on
reducing costs and not solely percent error when developing
forecasting algorithms.

Dr. Pritpal Singh
Electrical and Computer Engineering
Villanova University

Fig. 2. Day-ahead error in MW and percent error by type (While demand has
a narrow and normally distributed error,the error from predicted run-of-theriver hydro and wind production lead to a less smooth net demand error
distribution)

Keywords—renewable energy; forecasting error; system cost

Fig. 1. Day-ahead forecast percent error by hour of day and month of year

Fig. 3. Comparison of day-ahead forecast error and increased per-unit systemcost (A weak relationship (r2=.37) between error and increased system costs
indicate the importance of exploring alternative forecating metrics that are
better proxies for increased system cost, or using system cost directly if
possible)Locally Coordinated Cyber-Physical Attacks
Targeting Undetectable Line Outages
Zhiyi Li, Graduate Student Member, IEEE, and Mohammad Shahidehpour, Fellow, IEEE
Robert W. Galvin Center for Electricity Innovations, Illinois Institute of Technology
Email : zhiyi.li@hawk.iit.edu; ms@iit.edu
Abstract—This paper models a method for coordinating cyberattacks with physical attacks to cause undetectable line
outages in a local power network. First, the lines’ reactance inthis local area is estimated and the rest area is equivalently
represented. Next, mathematical model of the locallycoordinated cyber-physical attack which could cause
undetectable line outages while maximizing the risk of power
flow redistribution, is formulated and then transformed into amixed-integer linear programming problem. To solve thisproblem faster, a novel heuristic method especially suitable forparallel processing is developed, which is expected to provide
optimal or appropriate near-optimal solutions. Case studies
are conducted to check the correctness of the model.
Numerical results also illustrates the effectiveness andefficiency of the proposed heuristic method.

system, as illustrated by Fig. 2. Since the system
information is limited, initial physical attacks are mounted
on certain lines to determine the network equivalence,
followed by physical attacks for deteriorating the system
conditions. Besides, cyber attacks are decomposed into two
steps for the convenience of analysis; topology preserving
attack (TPA) could be seen as the first-step cyber attack, if
it is not feasible, then the second-step load redistribution
attack (LRA) is considered.
First-Stage:

Topology Preserving Attack

Determine network equivalence

Mask Outages

Second-Stage:

BudgetConstrained

Deteriorate system conditions

I. KEY IDEAS

Physical Attacks

Since attackers could only observe and take control ofthe measurements in a local area of the interconnected
power systems, the system under consideration is divided
into two separate parts, namely, observable andunobservable part, as illustrated in Fig. 1(a); the local area
where all measurements are reachable and alterable isdefined as observable part while the remaining system isdefined as unobservable part. In theory, the observablesystem states remain unchanged after equivalently
representing the coupling of unobservable part. In otherwords, the unobservable part can be replaced by equivalentlines added between boundary buses and fictitious
injections at boundary buses, as illustrated in Fig. 1(b).

Unobservable
Part

Inner
SystemEquivalent
Lines

II. KEY RESULTS
We have conducted case studies based on a six-bus
system, where the observable part is assumed to include
buses 1,2,4 and lines 1-2, 1-4, 2-4, and the remaining
network is equivalently modeled by a single line between
buses 2 and 4. To mask the outage of line 1-2 resulting
from the physical attacks as shown in Fig. 3 (a), TPA alters
two bus load and one line flow measurements as shown in
Fig. 3(b). Since the injections are too large to bypass thedetection, LRA is performed to adjust the measurements to
be within allowable ranges. Fig. 3(c) illustrates that LRA
needs to modify two bus load measurements and all threeline flow measurements. The resulting measurements may
misguide operators regarding the local outages in this area
and thus operators could hardly make appropriate decisions
until more severe conditions occur.
87.1 MW
1

BoundaryBuses(a) Original Network

Cyber Attacks(False Data Injection)

Fig. 2 Illustration of the locally coordinated cyber-physical attacks

...

...

Observable Part

Inner
SystemLoad Redistribution Attack2
outagedBoundaryBuses1

87.1 MW2

1

87.1 MW
16.5 MW37.1 MW37.1 MW2
53 MW

16.5 MW25.1 MW(b) Equivalent Network
70 MW

Fig. 1 Original network and its equivalence
4

Coordinating cyber attacks with physical attacks arehelpful to mask line outages in the targeted local area andimpose more severe consequences on the whole power

70 MW
EquivalentLine(a) After Physical Attack

454.1 MW
EquivalentLine(b) After TPA

4

EquivalentLine(c) After LRA

Fig. 3 Locally coordinated cyber-physical attacks in a six-bus system

False Data Injection Attacks on Power System State
Estimation with Limited Information
Jiazi Zhang, Zhigang Chu, Lalitha Sankar, and Oliver Kosut
Dept. of Electrical, Computer, and Energy Engineering
Arizona State University
Tempe, AZ, 85281

cause a line overflow in the IEEE 24-bus RTS systemeven given incomplete or inaccurate information.
II. K EY F IGURES
21

22

23

~

17

16

19

20

~

~

14

15
24

13

11

12

~

3

9

10

8

5
1

This work is supported in part by the National Science Foundation under
grant CNS-1449080.

~

~

2

~

1) Introduce a bi-level optimization problem to find unobservable line-overflow FDI attacks on DC SE when theattacker’s knowledge is mostly limited to a given subnetwork. In particular, the attacker has access to the following information: (i) inside the sub-network, perfectsystem information including topology, load data, andgenerator data, (ii) outside the sub-network, estimated(i.e. possibly incorrect) generator data for the marginal
generators, and (iii) estimated power transfer distribution
factor (PTDF) data for the entire network.
2) Using this bi-level optimization problem for limited
information attacks, we demonstrate that an attacker can

6

4

7

Fig. 1. IEEE 24-bus RTS System decomposed into attack internal network
and attack external network.

M a x im a l
P F (% )

1 1 0
1 0 5
M a x im a l P F , L o c a l

M a x im a l P F , G lo b a l

1 0 0
0 .0 0l1 -n o r m o f A tta c k
O p tim iz a tio n S o lu tio n l0 -n o r m o f& A C P F (% )V e c to r (r a d )A tta c k V e c to r

This paper studies false data injection (FDI) attacks, inwhich the attacker replaces a subset of measurements withcounterfeits before they are processed by the cyber layer. The
most effective FDI attacks are those which are unobservableto the state estimation (SE). In this paper, we build from thisprior work [1] to study the bi-level attack optimization problem
where the first level problem models the attacker’s goal and thesecond level problem models the system response, but restrict
the attacker’s knowledge of the system, i.e., focus on a classof limited information FDI attacks.
The contributions of this paper are as follows:

EMG~

I. I NTRODUCTION

~

~

~

18

~

Abstract—This paper studies physical consequences of worstcase unobservable false data injection (FDI) attacks on theelectric power system. In particular the focus is on FDI attacks
wherein the attacker can only change measurements in an attack
sub-network of the entire network and has limited knowledge of
network parameters outside of this sub-network. The goal of thislimited resource attack is to cause a physical line overflow for achosen target line that will not be observable via measurements.
To determine the worst possible consequences of such a classof FDI attacks, a bi-level optimization problem is introduced
wherein the first level focuses on maximizing the target line
flow subject to limited attack resources constraints, while thesecond level formulates system response to such attacks via DC
optimal power flow (OPF). The attack model with limited systemknowledge is reflected in the DC OPF formulation that only takes
into account the system information for the attack sub-network.
The vulnerability of this attack model is illustrated for the IEEE
24-bus RTS system.

0 .0 20 .0 4l1 -n o rm , L o c a l

0 .1

(a )

0 .0 60 .0 80 .1 00 .0 80 .1 00 .0 80 .1 0l1 -n o rm , G lo b a l

0 .0
0 .0 00 .0 20 .0 40 .0 6(b )1 6

l0 -n o rm , L o c a l

1 2

l0 -n o rm , G lo b a l

8
40
0 .0 01 1 2

0 .0 2
P 28 fro m

1 0 8

0 .0 4(c )o p tim iz a tio n p ro b le m

C y b e r S 28 fro m

0 .0 6

P h y s ic a l S 28 a fte r A C O P FA C O P F

1 0 4
1 0 0
0 .0 00 .0 20 .0 40 .0 60 .0 80 .1 0(d )l1-n o r m

c o n s tr a in ts (r a d )Fig. 2. The maximum power flow on the target line, the l1 -norm and l0 -normof solved attack vector c v.s. the l1 -norm constraint (N1 ) when load shift (τ )is limited by 10%; target branch 28 (bus 16 - bus 17).

R EFERENCES[1] J. Liang, O. Kosut, and L. Sankar, “Consequences and vulnerability
analysis of false data injection attack on power system state estimator,”
IEEE Trans. Power Systems, under review, 2015.

1

Effects of Cyber-Attacks on the Performance of
Neural Network Controlled BFCL Against
Transient Stability
Mohammad Ashraf Hossain Sadi, Student Member, IEEE and Mohd. Hasan Ali, Senior Member, IEEE
Transient stability is the ability of a power system to regain
its stability in case of sudden and severe faults in the system[1]. There are several methods and auxiliary devices, such as
fault current limiters [2] for enhancing the transient stability of
power systems. The bridge type fault current limiter (BFCL) is
currently a very much popular auxiliary stability improving
device to the power engineers and researchers throughout theworld [2]. This paper proposes a neural network predictive
controlled bridge type fault current limiter (FCL) to enhance
the transient stability of multi-machine power system. The
effect of cyber-attack on the controller performance isinvestigated. The transient stability performance of the neuralnetwork predictive controlled bridge type FCL is comparedwith that of a fuzzy logic controlled bridge type FCL.
In power systems, different field devices, such as Remote
Terminal Units (RTUs), Phasor Measurement Units (PMUs),
etc., are connected with each other and managed by theSupervisory Control and Data Acquisition (SCADA) system
for master control. In this paper, the Denial of Service (DoS)
and tampering the communication signal cyber-attacks havebeen considered in the event of a global control signal transferfrom the SCADA to the fuzzy logic controller of the bridge
type FCL. Further, the effect of these two types of cyberattacks on the performance of the bridge type FCL is alsopresented. To the best of the author’s knowledge, there is nowork available on the effect of cyber-attack in controller
signal accumulation process for fault current limiters, which isanother novelty of this work.
Another salient feature of this work is that, the transientstability performance of the neural network predictive
controlled bridge type FCL is compared with that of fuzzy
logic controlled bridge type FCL.
For the simulation of the transient stability, the IEEE 39
bus power system shown in Fig. 1 is used. Fig. 1 also
represents a SCADA system based on the NASPInetarchitecture [3]. The field devices like Intelligent Electronic
Devices (IEDs) or RTUs are used to collect state responses
from the generators situated in diverse locations of the IEEE
39 bus power system. Then the state responses from thegenerators are transferred to the SCADA system through IP
based communication networks for further processing.
Simulations are performed by using the Matlab/Simulink
software. Both balanced and unbalanced permanent andtemporary faults at different points of the IEEE 39 bus powersystem have been considered. Fig. 2 represent the generators
total kinetic energy responses for three-phase-to-ground (3LG)
permanent fault at point B with the presence of neural network
predictive controlled bridge type FCL and fuzzy logic
controlled bridge type FCL considering the DDoS cyberattack. Simulation results indicate that the cyber-attack has

profound effect on the controller performance and the system
becomes fully unstable even with the presence of the neuralnetwork predictive controlled bridge type FCL. Furthermore,
the performance of the proposed neural network predictive
controller is better than that of the fuzzy logic controller.

Fig. 1. IEEE 39 bus system with BFCL and connected to SCADA.

Fig. 2. Total kinetic energy response considering DDoS cyber-attack and 3LG
permanent fault at position B.

REFERENCES
[1]
[2]

[3]

P. Kundur, Power System Stability and Control. New York, USA:
McGrwa-Hill, 1994.
M. Jafari, S. Naderi, M. Hagh, M. Abapour, and S. Hosseini,"Voltage sag compensation of point of common coupling (PCC)
using fault current limiter," Power Delivery, IEEE Transactions
on, vol. 26, pp. 2638-2646, 2011.
R. B. Bobba, J. Dagle, E. Heine, H. Khurana, W. H. Sanders, P.
Sauer, et al., "Enhancing grid measurements: Wide area
measurement systems, NASPInet, and security," Power andEnergy Magazine, IEEE, vol. 10, pp. 67-73, 2012.

Data Integrity Attack Detection Through SpatioTemporal Correlation in Distributed Generation
Hasan Ul Banna, Sarika Khushalani Solanki, Jignesh Solanki
Lane Dept. Of Computer Science and Electrical EngineeringWest Virginia University
Morgantown, USA
hu0001@mix.wvu.edu, {sarika.khushalani-solanki, jignesh.solanki}@mail.wvu.edu
Abstract— Real time algorithm without or with minimum

prior knowledge and topology of the system, for accurately
detecting data integrity attacks, is of great concern.
Spatial-temporal correlation is one of such techniques todetect data integrity attacks. A density based outlier
detection algorithm is incorporated with spatial-temporalcorrelation computation to predict a bad data attack inreal time. In decentralized market, a malicious generator
can earn more profit by compromising its bus’ distributed
controller and compromising state estimator and injecting
the false data in the network. This shows that besides
increasing the focus on avoiding attacks in real timescenario the focus should also be given to avoid attacks at
the planning stage or during Optimal dispatch calculationin distributed market.
Keywords—Distributed optimal power dispatch, k-distance,
local outlier factor, reachability density

I.

INTRODUCTION

In centralized electricity market ISO is responsible to perform
DC optimal power flow calculation to determine the optimal
power dispatch point. However, in distributed market each bus
has to communicate with its neighbors in an iterative method
to reach an optimal dispatch point. This gives chances to thepotential attackers to inject false data to gain more economic
benefit. Another factor that makes the attacker’s work easy isthat most distributed algorithm assume that the information
shared among neighbors are correct and they do not consider
the credibility of the injected data which may not be true inreal world situation.
By detecting the outliers in density based spatio-temporalcorrelations in measurements [1], data integrity attacks can bedetected according to their correlation strength as described intable 1.
II.

TABLE I.

CORRELATION WITH AND WITHOUT ATTACK

For measurements without attack, their normalized
Correlation

Without Attack
NormalEventful
Mode
Mode

SpatialCorrelation
Temporal
Correlation

StrongWith Attack
Normal Eventful
Mode
Mode

StrongWeak
Weak

standard deviations are similar representing strong
spatial correlation. On the other hand, for bad ortempered measurements, their normalized standard
deviations are different, as compare to previous case,representing weak temporal correlation.
b) Calculationofk-distanceandk-distanceneighborhoodc) Computation of reachability distance and reachability
densityd) Calculation of Local Outlier Factor (LOF) using local
reachability density.
By calculating LOFs of all measuring units an attack may be
detected if LOF of any of the unit cross a predefined set point.
This LOF’s set point is system dependent and can be obtained
through offline training and history of the measurements.
III.

KEY FIGURE

PROPOSED METHODOLOGY

Rank of a matrix is a measure of how strongly the columns orrows are correlated. In a matrix where columns or rows arelinearly independent the rank is high as compare to a matrix
where columns or rows may be produced easily with a linear
combination of one or the other. For a system in normal
operating mode, the measurements at different nodes of thesystem are strongly correlated with each other. On the other
hand, the rank is high when the system is exposed to an event(during disturbance). Complete flow of this attack

detection methodology has been explained below andshown in fig.1.
a) Calculation of the distance between measurements ofdifferent PMUs based on their normalized standard deviation.

Fig. 1. Flow of the Proposed Attack Detection Algorithm

IV.

REFERENCES

Markus M. B., Hans P. K., Raymond T. N., Jörg S.,“LOF: Identifying Density Based Local Outliers”,
International Conference on Management of Data, Dallas, TX
2000
[1]

IEEE PES GM 2016

1

Synchrophasor Measurements Based Detection and
Identification of Cyber Attacks Target on
Transmission Systems Substations
B. Cui, Student Member, IEEE, A. Srivastava, Senior Member, IEEE

Abstract—A novel technique utilizing Phasor Measurement
Units (PMUs) for cyber attacks detection and identification arepresented in this work. The proposed set of methods collect transmission system operating data from PMUs at each substation incertain geographic area and employ the data for cyber attacks
diagnostic purpose in power grid transmission systems.
Index Terms—cyber attacks, Phasor Measurement Units,
Transmission Network

program will query the network log from the substation which
send abnormal synchrophasor data and when the substation
log has been collected. The program will used the Bayesian
Network to check if there is signs of cyber intrusion happenedat this substation. Combined with diagnostic result from measurements data, the program can assert if there is a cyber attack
happens in the system and identify the cyber attack at one of
the four catagories.

I. I NTRODUCTIONT

HE current power grid transmission system is vulnerable
to malicious cyber attacks with greater complexity andmore and more communication devices. However, the increasing number installation of Phasor Measurement Units (PMUs)
improves power grid resilience and reliability by reducing thenumber and curation of outages. Our research work discovers
that the malicious cyber attacks can be identified and detectedwith synchrophasor data from PMUs. So the cyber attacks
target on power system transmission system substations has
been addressed via PMUs measurement data combined with
substations computer network log files.
II. M ETHODOLOGY
In this work, a set of methodologies which can be used
to detect and identify cyber attacks is introduced focused on
attacks against substations in transmission network of power
grid. During our work, we found out that different types ofcyber attacks on substations level could leads to different characteristics of synchrophasor data types. So we analyze impacts
of different types of cyber attacks as well as the different
PMU data patterns caused by cyber attacks. In this work,
the cyber attacks target on substations equipped with PMUs
are classified into 4 categories: PMU ID changing/swappingor PMU data manipulation detection, Database Command Injection/DELETE, GPS spoofing, Open Circuit Breaker. Whenone of these four-type cyber attacks happens in the system,
an power system event will be caused in the system. The
diagnostic program which is designed to operate at operation
center will be launched to run detection and identification
program based on collected PMUs measurements data. For
each type of different 4 possible cyber attacks, the program
will use four different algorithm to identify it. Then theThis research was funded by National Science Foundation (NSF).
B. Cui, A. Srivastava are with the School of Electrical Engineering andComputer Science, Washington State University, Pullman WA 99164 USA,e-mail: asrivast@eecs.wsu.edu

Fig. 1. Detection and Identification of Cyber Attacks Procedure Chart

III. S UMMARY
In this poster, our research works focus on how to develop
a set of methodologies for cyber attacks detection and identification for future advanced smart grid. The diagnostic program
has been proposed to complete this work with synchrophasor
measurements and transmission substation computer network
log files. With different characteristics of measurements data
and activity signs from substation log files, the malicious
cyber attacks against transmission system can be detected andidentified.

Likelihood of Cyber Data Injection Attack
to Power Systems
Yingshuai Hao, Meng Wang, Joe Chow
Rensselaer Polytechnic Institute

ABSTRACT
1. From the perspective of intruders, we study when and how to conduct malicious cyber data attack on the dynamic power system with the goal tomaximize the cumulative expected financial benefits.
2. Unlike existing papers focused on the effect of cyber data attack on power system at one time instant, for the first time we consider its influenceon the system across time and study how this affects the decision-making to launch attacks.
3. We propose to use Markov decision process to model an intruder’s strategy, and model the dynamics of system loads with Markov Chains. Linear
programming are employed to determine the optimal attack policy. The intrusion probability of each measuring device and the attack likelihood ofeach line is analyzed.

UNOBSERVABLE CYBER DATA ATTACKS
In normal operation,

With injected errors on the measurementsof currents of line 13 and 23:

Bus 3Line 23

I13
V1

Z13
PMU 1

eV
I13Z13V3Line 13

I23

Z23

Bus 1

Z13

eV
I23Z23V2

Z23

1. Protection of Intruded Measuring Devices If The Attack Is Detected
Some recently proposed methods can detect the cyber data attacks. If thelaunched attack is detected, we assume the intruded measuring devices
will be protected in the next time step.
No Attack1

Under Attack
1-PD(s,a)State 1:
Open to attack

1

Bus 1

Bus 2

V3'

PMU 2

PMU 1

V1

V2

PMU 2

Bus 3

INFLUENCE OF CYBER DATA ATTACKS

Bus 2

1

PT

PD(s,a)State 0:Protected

0

0

1-PT
Fig. The state transition graph of measuring devices

2. Financial Benefits From Undetected Attacks

Now the equation still holds with the injected errors on themeasurements and existing bad data detectors cannot detectsuch data attacks.

If the undetected attack results in a change of the system congestion
pattern, the intruder can gain financial benefits from the attack.

EVENT SEQUENCE OF CYBER DATA ATTACKS
st

1 measurement

Device State
UpdateData
Transmission
DeviceSampling

Data Arrived at Control
Centernd

2 measurement

Data
Transmission

Data Arrived at
Control Center...
kth measurement
Data
Transmission

DeviceSampling

Bad Data
Processing,
Cyber Attack Device
State
Detection, Update
State
Estimation

The time step is the duration between two consecutive
instants of state estimation.
The intruder’s current attack action affects the state ofmeasuring state in the next time step, and thus the futureavailable actions as well as the resulting rewards.

t+1

Time step t

MARKOV DECISION PROCESSCASE STUDY
Generation Center

Formulate the intruder’s decision-making process as an MDP todetermine the following issues:
1. When to launch an attack;2. Which buses to attack;3. How much error to inject.

E

③

②

⑤

$14
40MW
$15
170MW

Action at+1

④

①
A

$30
520MW

B

C
300

300

Load Unit: MW

The evolution of loads inthe system are modeled
as Markov Chains.
Each load is assume tohave 2 states. One is thebase case shown in the
figure. Another is half ofthe base load.

Fig. PJM 5-bus test system

The system state is determined from the economic dispatch.
Time step t+1

Fig. Markov decision process

The probability that the system transits from state st to st+1 with action
at is P(st+1 | st, at).
The intruder’s aim is to find the optimal attack policy that yield
the maximal expected net reward.

Intrusion Probability of Measuring Devices

Attack Probability of Part of Lines
1

Attack probability

Time step t

State
st+1

$35
200MW
400

Measuring Device

Action at

D

⑥

$10
600MW

Reward R(st+1|st,at)
Cost G(st,at)
Cost G(st+1,at+1)
State
st

Load Center

0.8
0.6

Line 1
Line 2
Line 40.4
0.2
00 0.5 1 1.5 2 2.5 3 3.5 4
C: Tuning Parameter of Attack Detection Probability

Intrusion probability

DeviceSampling

Attacks can happen during either device sampling or datatransmission to the control center.

1
0.8
0.6

Device on bus A
Device on bus C
Device on bus D

0.4
0.2
0

0 0.5 1 1.5 2 2.5 3 3.5 4
C: Tuning Parameter of Attack Detection Probability

ACKNOWLEDGMENT
This work was supported in part by the ERC Program of the National Science Foundation and DOE under the Data Analytics and Data Architecture
Supplement to NSF Award Number EEC-1041877 and the CURENT Industry Partnership Program, and in part by NYSERDA Grants #36653 and#28815.

Cyber-Enabled Power Distribution Network
Relaibility Assessment
Mohammad Heidari-Kapourchali, and Visvakumar Aravinthan
Department of Electrical Engineering and Computer Science
Wichita State University
Wichita, Kansas, 67260
mxheidarikapourchali@wichita.edu
Abstract—The smart distribution system initiative requires
higher usage of a distribution feeder-level communication
infrastructure to improve system automation. This puts higher
requirements on developing a communication protocol for high –priority events and situational awareness. The interaction of
cyber and power components has the potential to affect system
reliability. This paper evaluates the impact of communication
failures on the distribution network reliability indices.
Keywords— communication protocol; reliability; smart devices;I. INTRODUCTION
The 2007 U.S. Energy Independent Act has necessitatedmodernization of the power grid, and two out of ten points
focus on improving system reliability through communication
infrastructure [1]:1. Increase the use of digital information to improvereliability, security, and efficiency.
2. Deploy real-time, automated, interactive technologies formetering, communications concerning grid operations andstatus, and distribution automation.
Based on operating needs, the communication systemshould provide quick decision-making tools for distribution
system operations. This becomes critical during an abnormal
event and requires distributed sensor placement along with
communication infrastructure [2], [3].
II. CYBER-ENABLED DISTRIBUTION NETWORKThe future cyber-power infrastructure at distribution level
and the modeling of cyber system impacts on outage
management are shown in the subsections.
A. Sensors and Actuators









  Pf5 Pf4 Pf3 Pf2  1  Pf5 PS5 PS4 PS3 PS2  Pf5 1  Pf4 PS4 PS3 PS2









 Pf5 Pf4 1  Pf3 PS3 PS2  Pf5 Pf4 Pf3 1  Pf2 PS2  1

And restoration time is
r15  rsw  (1  )  rsg  1 rsw  0  rsg  rsw

Fig. 2: Sample network containing manual and remote-controlled switches

III. ANALYSIS AND RESULTS
A 27 node test feeder is used to analyze the effect of cyber
unavailability on outage management by optimally placing thefault detectors and remote controlled switches. Fig. 3 shows thecandidate places for the switches and fault detectors in red.

Fig. 3: Typical 27-node practical distribution feeder

Fig. 4 shows the effect of communication availability anddevice cost on placing smart devices.

Fig. 4: Sensitivity analysis on total investment cost

Fig. 1: Future distribution feeder communication infrastructure

B. Reliability Model
Availability of communication medium can be affected inreal time. The probability of unsuccessful transmission ofRayleigh fading wireless link is modeled as [4] dk 
Pcyber  outage  d , snr   1  exp   snr where d is the distance between two communicating nodes.
A simple network cyber infastructrue is shown in Fig. 2.
The fault is assumed on section 5 and its effect on load 1 is ofinterest in this example. The probability of unsuccessful smart
operation is given by:

IV. CONCLUSION
The proposed work shows the importance of incorporating
communication availability in power system planning. This
work can be extended to time constrained applications such as
solar ramping effects.
REFERENCES[1] Mingyue, Z.: “Measurements and Channel Characteristics of LV PowerLine Communications Networks in China,” in Proc. IEEE Conference inPower Line Communications and Its Applications, 2006.
[2] “Optically Coupled Current Sensors for Outdoor Applications,” Technical
Datasheet,
OptiSense
NetworkInc.
Available:
http://www.optisense.net/Documents/Optisense%20Current%20Sensor%
202008.pdf.
[3] Kim K., Kim H., Jung J., and Kim H., “AFAR: A Robust and DelayConstrained Communication Framework for Smart Grid Applications,”
Elsevier Computer Networks Journal Vol. 91. Aug. 2015[4] E. Khandani, J. Abounadi, E. Modiano, and L. Zheng, "Reliability androute diversity in wireless networks," IEEE Trans. WirelessCommunications, vol. 7, pp. 4772-4776, 2008

Estimating the Impact Severity of False Data Attacks
on Power Systems
Sagnik Basumallik, Student Member, IEEE

Sara Eftekharnejad, Member, IEEE

Department of Electrical Engineering
Syracuse University
sbasumal@syr.edu

Department of Electrical Engineering
Syracuse University
setfekha@syr.edu

Abstract— Cascading blackouts in power systems are themost severe type of failures. They occur due to various reasons:component failure, operator action, hidden failures, etc. In thiswork, cascading failures due to false data injection attacks inpower system are studied. A Phasor Measurement Unit (PMU)
only state estimation system is formulated and measurements arecompromised to change the state estimation solution and theeffect on power network is observed. Similar tests are carried out
on a hybrid state estimator comprising of both PMU andtraditional measurements and the impact is analyzed.
Keywords—cascading
Estimation

failure,

false

data,

PMUs,

n

J ( x)   ( zi  hi ) / Rii

The state estimate can be obtained by following iterative
method.
xk 1  xk  [[H (xk )]T [ R]1[ H ( xk )]]1[[ H ( xk )]T [ R]1[z h(xk )]] (2)An all PMU based State Estimator solution is calculated
according to the following equation:x*  [[ B ( x)]T [W ]1[ B ( x )]]1[[ B ( x)]T [W ]1[ M ]]State

I. INTRODUCTION
Cascading blackout is a chain of events which results inoutage of many components in power system due to an initial
disturbance. They result in widespread customer dissatisfaction
and huge loss to utility companies. Cascading failures occur
due to various reasons: voltage collapse, hidden failures,component failures, operator wrong actions [1]. With highchances of cyber-attacks on power system, it is necessary to
study the correlation between false data injection attacks andcascading blackouts.
Nowadays, more and more PMUs are deployed across thepower network, replacing conventional meters. For example,
many relay manufacturers have equipped digital relays with
PMUs these days. Incorporating PMUs make the system morerobust, however, there are still chances of false data attacks
being carried out on PMU measurements [2]. These false data
injections cannot be detected by the bad data detection
programs, and the State Estimation (SE) still converges, givinga wrong estimate of state variables, which can result in trippingof lines or wrong operator actions.
In this work, we study the effects of such attacks on PMUs
and their subsequent impacts, especially cascading failures. We
begin with a state estimation which involves only PMU
measurements. These measurements are compromised byinjecting false data and their impact on power system isstudied. Next, a hybrid state estimator, incorporating both
PMU and traditional meter measurements, is developed and asimilar impact analysis due to false data injection is carried out.
II. KEY EQUATIONS
The traditional state estimation can be formulated as aminimization of the following objective function.

(1)

i 1

(3)

III. METHODOLOGY
Traditional State Estimation involves meter measurementsof active and reactive power flows and current injections. Thisis a non-linear and iterative process. PMU technology toestimate the state of the system replaces all traditional
measurements with bus voltage phasors and line current
phasors. When all traditional meters are replaced with PMUs,
the state estimation becomes linear and does not require
iterations [3]. Such a linear state estimation system with only
PMU measurements is first formulated. The impact of false
data injection is observed on a power system test-bed. In
particular, cases that cause cascading blackouts are identified.
These blackouts may be a result of critical operator actions due
to wrong State Estimation solution results, or system
violations. A similar study is carried out using a hybrid-state
estimator involving both PMU and traditional meters
measurements. Critical measurements in IEEE 14 bus system
are identified. Outage of a single branch that results incascading failure is investigated. Simulations are carried out in
MATLAB and DSATools Power System Analysis Toolbox(PSAT).
IV. REFERENCES
[1]

[2]

[3]

M. Vaiman, K. Bell, Y. Chen, and B. Chowdhury, "Risk assessment ofcascading outages: Part I—Overview of methodologies," in proc. IEEE
Power and Energy Society General Meeting, SanDiego, CA, July 2011.
S. Pal, B. Sikdar “A mechanism for detecting data manipulation attacks
on PMU data,” in proc. IEEE International Conference on
Communication Systems (ICCS), Macau, November 2014.
A. Gomez-Exposito, A. Abur, P. Rousseaux, A. de la Villa Jaen, and C.
Gomez-Quiles "On the use of PMUs in power system state estimation,"in proc. 17th Power Systems Computation Conference, Vol. 22,
Stockholm Sweden,August 2011.

Data Mining Algorithms for Synchrophasor
Bad Data Detection
Mengze Zhou

Anurag K Srivastava

Yinghui Wu

School of Electronic Engineering andComputer Science
Washington State University
Pullman, WA, USA
mengze.zhou@wsu.edu

School of Electronic Engineering andComputer Science
Washington State University
Pullman, WA, USA
asrivast@eecs.wsu.edu

School of Electronic Engineering andComputer Science
Washington State University
Pullman, WA, USA
yinghui@eecs.wsu.edu

Abstract—With the advent of Phasor Measurement Units(PMUs), high resolution synchronized phasor measurements
enables real time system monitoring and control. PMUs provide
the real time phasor information for critical power systemapplications like remedial action schemes. PMUs generally feeds
data to local Phasor Data Concentrator (PDC) and controllers
located at substations before data delivery to control centers.
Data mining and machine learning techniques can be applied toclean the data in PDC or other digital devices before any criticalcontrol action is taken. In this paper, a voting based PMU baddata detection algorithm is proposed using data mining
techniques. With the integration of four different outlier
detection methods, which are Chebyshev, Linear Regression, Kmeans, and Density-based spatial clustering of applications withnoise (DBSCAN), we build a robust technology for PMU data
cleaning. As a result, we will end up with a more reliable andtrustworthy PMU based applications.
Keywords—Power system real time operation; PMUs;
Chebyshev; k-means; Linear regression; DBSCAN; SCADA.

I. INTRODUCTION
Identifying outliers or anomalies in PMU data is critical fortaking right control actions required for reliable power system
operation. Many data mining techniques are available [1-2],
which can be applied for the PMU bad data detection. This
research is still in fancy and there is no single method that isable to locate all types of outliers with high accuracy.

Recall =𝐷𝑒𝑡𝑒𝑐𝑡𝑒𝑑 𝐵𝑎𝑑 𝐷𝑎𝑡𝑎 ∩ 𝐴𝑐𝑡𝑢𝑎𝑙 𝐵𝑎𝑑 𝐷𝑎𝑡𝑎

Precision=

𝐴𝑐𝑡𝑢𝑎𝑙 𝐵𝑎𝑑 𝐷𝑎𝑡𝑎
𝐷𝑒𝑡𝑒𝑐𝑡𝑒𝑑 𝐵𝑎𝑑 𝐷𝑎𝑡𝑎 ∩ 𝐴𝑐𝑡𝑢𝑎𝑙 𝐵𝑎𝑑 𝐷𝑎𝑡𝑎

False Positive = 1-

𝐷𝑒𝑡𝑒𝑐𝑡𝑒𝑑 𝐵𝑎𝑑 𝐷𝑎𝑡𝑎
𝐷𝑒𝑡𝑒𝑐𝑡𝑒𝑑 𝐵𝑎𝑑 𝐷𝑎𝑡𝑎 ∩ 𝐴𝑐𝑡𝑢𝑎𝑙 𝐵𝑎𝑑 𝐷𝑎𝑡𝑎
𝐷𝑒𝑡𝑒𝑐𝑡𝑒𝑑 𝐵𝑎𝑑 𝐷𝑎𝑡𝑎

III. SIMULATION REASULTS
PMU data is generated using real hardware PMU andReal Time Digital Simulator (RTDS) to test the accuracy ofdeveloped techniques. Several different scenarios are modeled
to generate know bad data. The four bad data detection
methods developed in this work is tested separately in terms
of precision, recall and false positive. The results show thatthe four methods have different sensitivity to different
possible bad data. For example, linear regression and k-meansare very sensitive to outliers, but they are not good atclustering the data points. DBSCAN is very reliable forclustering the data points without having to give
predetermined cluster number, which is the case of k-means
methods. The proposed voting based robust anomaly detection
takes advantage of all these four detection techniques andtherefore gives a more reliable result. Table 1 is an example ofresults of Chebyshev method using RTDS and PMU data.
Table 1. Result of Chebyshev method using RTDS PMU data
Chebyshev
Recall0.5416
Precision
0.9285
False Positive
0.0714

II. DATA MINING APPROACH AND VALIDATION METRICS

IV. SUMMARY AND FUTURE WORKIn this paper, data mining techniques like Chebyshev,
DBSCAN, K-means and Linear Regression have been applied
for PMU bad data detection. These methods are tuned using
test data and also using real PMU data from the field.
Validation is based on metrics like recall, precision and false
positive. Recall is also known as sensitivity or true positive
rate. It’s the ratio of correctly predicted bad data compared toactual bad data. Recall is the ability of a method to find all
outliers. Precision evaluates the ability of a method in
detecting all the outliers without false detection. False positive
evaluates possibility of false bad data detection. Generally, wewant false positive as small as possible. All the equations forthese metrics to compute the accuracy of proposed data
mining algorithms are shown next.

The voting based method proposed in the paper utilizing
four different data mining approaches is much more reliable
than any single method existing in literature. Simulation results
using synthetic data and known bad data shows superiority ofthe proposed method. There is a tradeoff between accuracy andfaster bad data detection. Due to the large computation timerequired, some of the approach can only be used in off line
detection. More work is needed to develop high accuracy andfast algorithms at the same time.
V. REFERENCES[1][2]

B. G. Amidan; T. A. Ferryman; S.K.Cooley “Data Outlier Detection
using Chebyshev Theorem,” IEEE Aerospace Conference, 2005.
T. A. Ferryman; B. G. Amidan, “Statistical Analysis of Abnormal
Electric Power Grid Behavior”, 43rd Hawaii International Conference
on System Sciences (HICSS), 2010.

Ambient Data Analysis of Oscillatory Modes inPower Systems
Pikkin Jim Lau, Student Member, IEEE and Vaithianathan Venkatasubramanian, Fellow, IEEE
School of Electrical Engineering andComputer Science
Washington State University
Pullman, Washington 99164
Email: plau@eecs.wsu.edu

Abstract—Highly non-linear modern power systems impose
great challenge on security monitoring. Real-time modal monitor is core from which damping ratio and mode frequency
are extracted. In practice, effective modal monitor based onphasor-measurement-units (PMUs) plays significant role in mode
detection. Power systems with inadequate damping would bevulnerable to disturbance and may lead to potential instability.
This poster provides discussions on electro-mechanical oscillations in Kunder two-area system. Investigation is established fromobserving natural oscillation close to modal frequency powersystems under various damping conditions. Power systems modes
are pre-estimated by small-signal analysis. Ambient data from
PMUs is applied to validate theoretically estimated modes inreal-time.

I. I NTRODUCTION
In general, measurement-based modal analysis methods
can be categorized according to data input: 1)ringdown; and2)ambient methods. Methods for analyzing ringdown data
are suitable for decaying data after disturbances such astripping of transmission lines and generators. On the contrary,
methods for ambient data analysis deal with system responses
to small load fluctuation. Common time-domain method forambient data is subspace identification, for instance [1]. As
alternative, frequency domain decomposition (FDD) is theanalytic method under study for ambient data analysis [2].
The Kunder two-area system is shown in Fig. 1 [3]. To test
reliability, 1% white noise further injected into loads at Buses
7 and 9.
II. C ONCLUSION
Analytic results of FDD method for 2% to 10% damping
ambient data are consistent with pre-estimation of small-signal

Fig. 1. One-line diagram of modified Kundur two-area power systemanalysis. Contingency cases will be further prepared for future
research on ringdown analysis.
R EFERENCES[1] H. Khalilinia and V. M. Venkatasubramanian, ”Modal Analysis of Ambient PMU Measurements Using Orthogonal Wavelet Bases,” in IEEE
Transactions on Smart Grid, vol. 6, no. 6, pp. 2954-2963, Nov. 2015.
[2] J. Ning, S. A. N. Sarmadi and V. Venkatasubramanian, ”Two-Level Ambient Oscillation Modal Estimation From Synchrophasor Measurements,”
in IEEE Transactions on Power Systems, vol. 30, no. 6, pp. 2913-2922,
Nov. 2015.
[3] P. Kunder, Power System Stability and Control, P. 813, McGraw-Hill,
1994.

1

A Game-Theoretic Analysis of Wind Power
Generator Bidding Strategies in Electricity Markets
Yu Xia, Student Member, IEEE, Muhammad K. Marwali, Senior Member, IEEE, and Joe H. Chow, Fellow, IEEE

Abstract—– In this poster, we investigate optimal wind power
generator (WPG) bidding strategies in electricity markets. Assuming the cost of wind power generation is zero, WPG would
maximize its profit by determining the optimal amount of wind
power to bid in the day-ahead market (DAM). Significant windpenetration is considered in our problems such that WPG’s
actions will affect energy prices. In addition, the existence of
WPG’s optimal bidding strategies in both cooperative gamesand non-cooperative games is shown. The proposed stochastic
optimization models consist of the DA energy market clearing,
the real-time (RT) energy market clearing, and WPG’s profit
maximization. The models also take into account the uncertainty
of wind power production. The models have been implemented
using M ATLAB and tested on a system with 10 conventional
generators and 2 wind power generators. The effect of wind
power forecast errors is also discussed.

subject to
X

X

Pit +

(8)
(9)
(10)
(11)
(12)

C. Objective Function
πjtotal = πjDA + πjRT
where
πjDA =

X(13)

λDA,t αtj Pjf,t

(14)

t

πjRT =XXtqs (λRT,t (Pjw,s,t − αtj Pjf,t ))

(15)

s

II. K EY R ESULTS
0.8
0.75
0.7

α2: 0.5
α : 0.74

N.E.

1

0.65
α1

From the Independent System Operator (ISO) point of view,wind participation in DAM with reliable DA wind forecast will
lower the DA energy prices and reduce the need of ramping of
the conventional generators in real-time (RT) operation. WPGs
can also benefit from participating in DAM with increased
overall revenues by being paid for a position in DAM. The
investigation in this poster provides a systematic analysis on
how wind should bid into DAM.

∀s ∈ S

j
i
min
t
Pi
≤ Pi ≤ Pimax , ∀i ∈ Ns
Pit − Pit−1 ≤ Riup , ∀i ∈ Ns
Pit−1 − Pit ≤ Ridown , ∀i ∈ Ns
Pjw,s,t = min(Pjw,s,t , PjC ), ∀j ∈ Nw

Index Terms—Electricity markets, Nash equilibrium, optimal
bidding strategies, stochastic optimization, wind power.

I. I NTRODUCTION

Pjw,s,t = Ptload : λRTs ,α2: 0.66
α1: 0.66

0.6
0.55
0.5
0.45
0.4
0.4

min

Pit ,UitNs
T X
X

F (Pit , Uit ) =

Ns
T X
X

optimal α2
0.5

0.6
α2

0.7

0.8

2

Uit (ai Pit +bi Pit +ci ) (1)t=1 i=1t=1 i=1

α2: 0.74
α1: 0.5

optimal α1

A. Day-ahead Market Unit Commitment

Fig. 1. Optimal α1 and α2 in a non-cooperative game with continuous RT
pricing and perfect forecast (N.E. denotes the Nash equilibrium)

subject to
1.25

Pit +

X

t
αtj Pjf = Ptload

: λDA
t ,

∀t ∈ Tji
min t
Pi Ui ≤ Pit ≤ Pimax Uit , ∀i ∈ Ns , t ∈ T
Pit − Pit−1 ≤ Riup , ∀i ∈ Ns , t ∈ T
Pit−1 − Pit ≤ Ridown , ∀i ∈ Ns , t ∈ T
Uit ∈ binary, ∀i ∈ Ns , t ∈ T

optimal α

(2)

1.15

(5)
(6)

min
t
Pi

Ns
X
i=1

F (Pit ) =X
i

Region 2
α :1
2
α :1

1.05

1

1

N.E.
Segment 2

0.95
0.9

Region 1

0.85
0.8
0.8

B. Real-time Market Dispatch.

optimal α2Segment 1

1.1

(3)
(4)

1

1.2

α1

X

0.9

1

α2

1.1

1.2

1.3

Fig. 2. Optimal α1 and α2 in a non-cooperative game with discontinuous
pricing and perfect forecast2(ai Pit + bi Pit + ci )

(7)

1

A robust extended Kalman filter for power system
dynamic state estimation using PMU measurements
Marcos Netto1,2 , Student Member, IEEE, Junbo Zhao1,3 , Student Member, IEEE, and Lamine Mili1 , Fellow, IEEE

Abstract—This paper develops a robust EKF to estimate rotor
angles and speeds of generators. Using a batch-mode regression
form, the filter processes together predicted state vector and
PMU measurements to track the system dynamics faster than
the standard EKF. Our filter is based on a robust GM-estimatorthat bounds the influence of vertical outliers and bad leverage
points, which are identified by means of the projection statistics.
Good statistical efficiency under Gaussianity of the process andthe observation noise is achieved thanks to the use of the Huber
cost function, which is minimized via the IRLS algorithm. The
asymptotic covariance matrix is derived via the covariance matrixof the total influence function of the GM-estimator.

I.

Init.: x̂0k|k , Σ0k|k , W k−1 and Rk

x̂k|k−1 , Σk|k−1 , g(x̂k|k−1 ), and H k(step 1) z̃ k and H̃ k(step 2) P S, $ and Q$(step 3) R̃, S k , y k and Ak

K EY E QUATIONS

A. Prediction step:(j)

Init. IRLS: x̂k|k , j = 0x̂k|k−1 = f (x̂k−1|k−1 ),(1)Σk|k−1 = F k−1 Σk−1|k−1 F Tk−1 + W k−1 .

(2)

(j+1)

(step 4) r S , Q and x̂k|k(step 5) Σk|k

B. Outlier identification and Robust prewhitening:z k − g(x̂k|k−1 ) + H k x̂k|k−1x̂k|k−1




=

Hk
I




xk +

vk
−δ k|k−1


,
(3)



E ẽk ẽTk = R̃k =



Rk
0

0
Σk|k−1

= S k S Tk .

−1
−1
S −1
k z̃ k = S k H̃ k xk + S k ẽk ,(j+1)

x̂k|k ← x̂k|k

Y

(j+1)

x̂k|k

(j)

− x̂k|k< tol.

Nj =j+1(4)(5)
Fig. 1.

GM-EKF algorithm flowchart.

C. Robust filtering, covariance matrix and updating:
20

J(x) =

mX$i2 ρ(rSi ),

15

(6)

i=1

ρ(rSi ) =0.5 rS2 i ,c rSi − 0.5 c2 ,rSi < cotherwise,m∂J(x) X $i ai=−
ψ(rSi ) = 0,
∂x
s
i=1

−1
(j+1)
x̂k|k = ATk Q(j) Ak
ATk Q(j) y k .

(7)
(8)

2 CAPES Foundation, Ministry of Education of Brazil.
3 Southwest Jiaotong University, China.

50−5true value
EKF
GM−EKF
UKF

−10

(9)



EΦ ψ 2 (rSi )
T
T
T
−1
−1
Σk|k =  
 2 (Ak Ak ) (Ak Q$ Ak )(Ak Ak ) .
0EΦ ψ (rSi )(10)
1 Virginia Polytechnic Institute and State University, U.S.

10

δ5−1 [degrees]

−15

Fig. 2.

0

1

2

3

4
5
time [seconds]

6

7

Rotor angle of Generator 5 with loss of PMU #5 from 4 to 6s.

8Performance Metrics for Dynamic Reserve
Management in Electric Microgrids
Mayank Panwar, Student Member, IEEE, Siddharth Suryanarayanan, Senior Member, IEEE, andRob Hovsapian, Member, IEEE
Abstract—This work presents some new performance metrics
for dynamic reserve management in microgrid operation. The
metrics consider multiple performance measures of an asset
present in the microgrid. The metric for each asset quantifies its
reliability-based value in the current state of operation. A systemlevel metric is obtained through the weighted combination of theindividual performance metrics, dispatched capacity, and net
capacity available as reserve. This metric is used for managementof reserves during real-time dispatch of the microgrid.
Simulations are performed on a notional microgrid obtained bymodifying the IEEE 13-node test feeder by integration of adispatchable and a non-dispatchable Distributed Energy
Resource (DER). Results will be shown in the poster.

Dispatch can also include multiple criteria such as peak load
reduction, emissions, reliability, and other operator-defined
criterion. Under such cases, reserve management can be evenmore challenging. Therefore, a systematic approach is
required to capture the performance capability of the
microgrid to mitigate the risk posed by the variability of nondispatchable DERs.

Index Terms—Microgrids, performance metrics, reliability,
reserve management.
Fig. 1. Diagram for microgrid operation with microgrid capacity reserve

I. INTRODUCTIONT

HIS work presents new performance metrics for dynamic
reserve management during real-time dispatch in anelectric microgrid. Dynamic reserve management becomes
non-trivial by virtue of various levels of dispatchability ofeach asset in a heterogeneous mix of assets in a microgrid. We
propose a method to calculate and quantify the reliabilitybased value of capacity reserve by considering various
performance measures. Some NERC reliability metrics andother previously developed metrics are used for quantifying
this value [1-3]. The reliability is also quantified for the
dispatched power and is used to optimize the reserve based on
proposed performance metrics. Two metrics, namely,
reliability value of unused reserve (rvur) and reliability costof unused reserve (rcur), are shown in (1) and (2).
p w +pμ wμrvur = ∆μ [ e epe +pμrcur = ∆μ [] = prr[pe we + pμ wμ ] kW

(pe −∆μ )we +pμ wμ +∆μ w∆μpe +pμFig. 2. Diagram for microgrid operation without any microgrid reserve

The reliability values computed in cases shown in Fig.1 andFig. 2 establish a bound on change in net value of reliability
that can occur in the current state by utilizing all the microgrid
assets, and is given in (3) as difference of (1) and (2).
R = prr[∆μ we − ∆μ w∆μ ] kW]

= prr[(pe − ∆μ )we + pμ wμ + ∆μ w∆μ ] kW
where prr =(1)

The proposed performance metrics capture multi-facetedmeasures of performance that can be used for dynamic reserve
management in real-time microgrid dispatch. Fig. 2 shows acase where all the microgrid capacity is used with no capacity
remaining as reserve, thereby minimizing import from EPS.

(2)∆μpe +pμFig. 1 describes the relationship of the metrics for a typicalgrid-connected mode of microgrid operation. Here, both theelectric power system (EPS) and microgrid assets serve thesystem load. Such an energy management decision will occur
in a typical economic dispatch considering operational costs.
This work is supported and funded through joint collaborative research
initiative between Colorado State University, Fort Collins, CO, USA, andIdaho National Laboratory, Idaho Falls, ID, USA (contract number 5-300722).

(3)

Let cμ = pμ + ∆μ and L = pμ + pe , substituting in (3) and2

rearranging terms, R = (cμ − pμ ) [we − w∆μ ]/L. Applicationof the proposed performance metrics and simulations resultsfor dynamic reserve management in real-time microgrid
dispatch will be presented in the poster.
REFERENCES
[1]
[2]
[3]

NERC Generating Unit Statistical Brochure 2007, October 2008.
Essential Reliability Services Task Force Measures Framework - NERC
Report, December 2015.
M. Panwar, “Reliability quantification and visualization for electric
microgrids,” M.S. thesis, Dept. Elect. and Comp. Eng., Colorado State
Univ., Fort Collins, CO, 2012.

Extraction of Spatiotemporal Patterns from Measured
Data via Nonlinear Laplacian Spectral Analysis
Claudia M. C. Arvizu, Arturo R. Messina
Graduate Studies Program in Electrical Engineering
Cinvestav, IPN
Guadalajara, Mexico
cmcastro{aroman}@gdl.cinvestav.mx
Abstract— A technique for extracting the dominantspatiotemporal patterns of variability from high-dimensional
simulation (measurement) trajectories is presented. The
proposed framework integrates concepts from nonlinear
dimensionality reduction methods with nonlinear Laplacian
spectral analysis, to extract physically meaningful information
about the temporal evolution of low-frequency electromechanicalmodes. Due to its multiscale and multitemporal nature, thegeneral techniques developed here are also useful for estimating
mode shapes, as well as to identify coherent behavior andmonitoring power system health. The use and interpretation of
the proposed framework is demonstrated using transient stability
data.
Keywords— Diffusion maps, Laplacian spectral analysis,
Nonlinear dimensionality reduction.

areas associated with the exchange of energy among
generators

Fig. 1.

TABLE I.

PRONY ANALYSIS RESULTS (PRS) FOR THE TEMPORAL
COEFFICIENTS.

PRS for 16 speed
deviations

I. INTRODUCTION
Measurement-based methods of mode estimation havegained considerable attention for power system healthmonitoring and fault diagnosis. Nonlinear dimensionality
reduction techniques provide a powerful tool to extract theessential dynamic coordinates or modes of high-dimensional
simulation trajectories and can be used for prediction, fault
diagnosis, process monitoring, and coherency analysis.
Dimension reduction via nonparametric spectral methods, inparticular, offers the added advantage of preserving both local
and global information

Temporal coefficients in (10) obtained using NLSA for the first
eigenfunction l=1.

PRS for ao

PRS for xo

MODE

Freq.
(HZ)

Damp.
(%)

Freq.
(HZ)

Damp.
(%)

Freq.
(HZ)

Damp.
(%)

1

0.677

7.54

0.674

6.79

0.671

6.83

2

1.106

-0.181

1.107-0.1191.107-0.357

II. NONLINEAR LAPLACIAN SPECTRAL ANALYSIS
Nonlinear Laplacian spectral analysis is a dimension
reduction method used for the extraction of spatiotemporal
patterns from high-dimensional data generated by nonlinear
dynamical systems. Laplacian spectral analysis decomposes a
signal xi(tj), into a set of l spatiotemporal patterns,lx ( t )   xk ( t ) .
k 1III. APPLICATIONThe clustering technique presented above is tested on the 68buses, 16-machine, 5-areas system. Eigenvalue analyses show
two well damped modes at about 0.51 Hz and 0.79 Hz, and an
unstable mode at about 1.10 Hz involving the interaction ofmachines in Areas 1 and 2; the system exhibits five coherent
The authors are grateful to the National Council of Science andTechnology of Mexico (CONACYT) on Grant #300956

Fig. 2.

Cluster of speed deviations of system generators.

IV. CONCLUSIONSIn this paper, a technique for extracting the dominantspatiotemporal patterns of variability from wide-areameasurements is presented. Simulations carried out on transientstability data show that the proposed methods are robust andcan be used to characterize mode shapes and coherent
behavior. Emphasis is placed on the use of unsupervisedadvanced machine learning techniques based on kernel
methods. The proposed methodology integrates dynamic
features at all scales and can therefore be used to selectively
analyze specific dynamic phenomena of concern.

Risk-Based Dynamic Security Assessment for High
Penetration of Renewable Generation
Sohom Datta, Student Member, IEEE and Vijay Vittal, Fellow, IEEE
School of Electrical, Computer and Energy Engineering,
Arizona State University, Tempe, AZ, USA
Email: sohom.datta@asu.edu, vijay.vittal@asu.edu
Abstract— The reliability requirements for operation andplanning of the North-American Bulk Electric System are definedby the North American Electric Reliability Corporation (NERC)
transmission reliability standards. Typically, these reliability
requirements are based on deterministic criteria which provide
safe but conservative operating limits. Additionally, withincreased penetration of converter-based renewable energy into
the electricity grid, the overall dynamic performance of the grid isaltered and the uncertainty of the variable generation needs to beincorporated, necessitating a probabilistic approach tocharacterize reliability standards. A new operational risk metricfor transient stability security assessment is defined to explore theefficacy of applying risk-based security assessment (RBSA) for
electricity grids with high penetration of converter-interfaced
generation. The major challenge behind transient stability RBSA
is the accurate estimation of the impact/consequence of transientstability events. A robust technique to quantify the impact of thetransient stability event is proposed by modeling the major
protection systems and estimating the amount of generator
tripping and load shedding required to stabilize the systemfrequency to settle to a new operating point. A parameter
independent risk metric is proposed to quantify risk which uses
expected unserved MW load as a measure of impact. The effect of
renewable generation penetration levels on risk estimation havebeen examined in this work. A stochastic model for wind
generation is also included in the risk estimation. The operationalrisk metric is also used in estimating safe operating limits bysetting an acceptable risk threshold. The simulation results showthat RBSA not only provides significantly relaxed security limits,
but also helps in identifying critical aspects of system reliability
that are not possible using conventional deterministic methods.

II. KEY FIGURES

Fig. 1. System-wide equi-risk contours in terms of risk metric ρ

Fig. 2. Risk metric ρ estimation using stochastic wind model

I. KEY EQUATIONS
Risk is defined as the product of the probability of an
occurrence of an event and the consequence of that particular
event [1]. The operational risk metric ρ is estimated as the
product of the corrective load shedding required formaintaining system stability in MW and the total probability ofoccurrence of the event. The probability of wind generation
output can be evaluated from a fitted cumulative distributionfunction.
Risk ( K | X )  Pr ( K | X ) Im ( K | X )Nc  Pr ( K  Ei | X ) Im ( K  Ei | X )Fig. 3. Comparison of deterministic and RBSA security limits

i 1



Pr ( K  Ei ) 
L

4

100

 Pr Eik Pr  A  n  | Ei Pr PW TG Pr K | Ei   A  n 

k 1 n 1 w  0This work was supported by the National Science Foundation under the
Grant EEC-9908690 at the Power System Engineering Research Center.

[1]

REFERENCES
J. D. McCalley, V. Vittal, Y. Dai, W. Fu, A. Irizarry-Rivera, V. Van
Acker, H. Wan and S. Zhao, “Risk based security assessment,” EPRI,
Final Rep. TR-113276, July 1999.

Transmission Contingency-Constrained Unit
Commitment with High Penetration of
Renewables via Interval Optimization
Yaowen Yu, Student Member, IEEE, Peter B. Luh, Fellow, IEEE, Eugene Litvinov, Fellow, IEEE, Tongxin Zheng,
Senior Member, IEEE, Jinye Zhao, Member, IEEE, Feng Zhao, Member, IEEE, and Dane A. Schiro, Member, IEEE
Abstract--Reliability is an overriding concern for powersystems that involve different types of uncertainties including
contingencies and intermittent renewables (wind and solar). A
large number of “N – 1” transmission contingencies make unit
commitment extremely complex. Existing approaches consider
these contingencies one at a time and are thus time-consuming.
This complexity is now compounded by the drastic increase inrenewables. To overcome the above difficulties, this poster
innovatively models loss-of-one-line contingencies by continuousintervals of corresponding generation shift factors (GSFs).
Multiple contingencies are captured in one constraint, so that the
problem size is significantly reduced. Renewable generation is
also modeled by intervals in a consistent framework. The
resulting model is a mixed-integer linear programming (MILP)
problem. Conservativeness of the overall model is reduced
through improved interval computation and shrinking theintervals of GSFs. To efficiently solve large-scale problems, ouradvanced algorithm, Surrogate Lagrangian Relaxation, is used.
Numerical results demonstrate that the new approach is effective
in terms of computational efficiency, solution robustness, andsimulation costs.

consistent frameworko Boundary conditions considering worst-case renewable
and worst-case contingency together may be conservativeo Improve the interval computation to reduce theconservativenesso The overall model is an MILP problem
II. SOLUTION METHODOLOGY

I. KEY MODELING IDEAS
 Novel interval model of transmission contingencies:o Transmission constraints: Power flow ≤ fmax

Fig. 2. The flowchart of the solution methodology.

III. NUMERICAL RESULTS

Fig. 1. Illustration of the novel interval contingency model.

 Interval inequality can be applied: max(LHS) ≤min(RHS) Major advantage: Capture multiple contingencies in
one constraint to significantly reduce the problem size
 Uncertain renewable generation modeled by intervals in a

Yaowen Yu and Peter B. Luh are with the Dept. of Electrical & Computer
Engineering, University of Connecticut, Storrs, CT 06269-4157 USA (e-mail:yaowen.yu@engr.uconn.edu).
Eugene Litvinov, Tongxin Zheng, Jinye Zhao, Feng Zhao, and Dane A.
Schiro are with Business Architecture and Technology, ISO New England,
Holyoke, MA 01040-2841 USA.

Three problems are tested to demonstrate properties of ourinterval approach. In Example 1, a simple six-bus problem istested to demonstrate solution robustness of the interval model
against transmission contingencies and examine its
conservativeness. In Example 2, a modified IEEE Reliability
Test System with six wind farms is tested to compare the new
approach with a deterministic approach. The benefits ofredundant constraint identification and removal are also
exhibited. In Example 3, a modified IEEE 118-bus systemwith ten wind farms is tested to demonstrate the computational
efficiency of Surrogate Lagrangian Relaxation.
IV. CONCLUSION
This poster develops a novel interval optimization approachto manage both “N – 1” transmission contingencies anduncertain renewables in unit commitment. Our approach has
the broader impacts on managing uncertainties in otherproblems, including “N – 2” transmission contingencies, andrenewables in microgrids and smart buildings.

The Holomorphic Embedding Method Applied
to the Power-Flow Problem
Shruti Rao, Student Member, IEEE, Yang Feng, Member, IEEE, Daniel Tylavsky, Senior Member, IEEE
Muthu Kumar Subramanian, Member, IEEE
Email: srao12@asu.edu; yang-feng@siemens.com; tylavsky@asu.edu; muthukumar.subramanian@alstom.com
1Abstract—The Holomorphic Embedding Load-Flow Method(HELM) solves the power-flow problem to obtain the bus
voltages as rational approximants, that is, a ratio of complexvalued polynomials of the embedding parameter. The proof of its
claims (namely that (a) it is guaranteed to find a solution if itexists, (b) it is guaranteed to find only an operable solution, and(c) that it unequivocally signals if no solution exists) are rooted
in complex analysis and the theory developed by Antonio Triasand Herbert Stahl. HELM is one variant of the holomorphic
embedding method (HEM) for solving nonlinear equations, thedetails of which may differ from those available in its publishedpatents. An understanding of HEM is provided and one possible
PV bus model compatible with the HEM is introduced. Resultsof the implementation on the IEEE 118-bus, 300-bus and
ERCOT systems are discussed.

I. KEY EQUATIONS
A. Holomorphic Functions
The system of equations needed to solve for bus voltages
from the power-flow problem can be holomorphicallyembedded as given by (1) - (4).
NS *Yik transVk ( )  * i *  Yi shuntVi ( ) , i m(1)Vi ( )k 0Vi ( )  1  (Vi sp  1) , i  slack
N

Y
k 0

V ( ) 

ik trans kPi  jQi ( ) Yi shuntVi ( ) , i  p*
Vi ( * )2*
Vi ( )  Vi ( * )  1    Vi sp  1, i  pV * ( * )  V *[0]  V *[1]    V *[n] n

II. KEY RESULTS
The algorithm, which included bus type switching and tapchanging, was tested successfully on the 6057-bus ERCOT
system. The convergence guarantee is evidenced by the HEM
finding a solution from a flat-start for the ERCOT system
while NR failed to converge. This section presents the results
for some numerical tests that were performed on the IEEE
118- and 300-bus systems in order to compare theperformance of the described HEM with the NR method.

(2)
(3)
(4)
Fig. 1 Highest absolute PBE mismatch vs. iteration
numberwherei  k
Y  YYik trans   ii i shunt

Y
i

k
ik


n
V ( )  V [0]  V [1]    V [n]  

there are no shunts and the generator bus voltages aremaintained at 1.0. The Padé approximants which can be used
to approximate a truncated infinite series outside of its ROC
but within the function’s domain are then used to obtain thevoltages from the Vi(α). Provided certain conditions apply tothe function, Stahl’s theory, which originated from the workof Nuttal, proves that the sequence of the near-diagonal Padé
approximants is the maximal analytic continuation for a given
power series.

(5)
(6)

Vi(α) and Vi*(α*) given by (6) are substituted into (1) - (4)and the coefficients of respective powers of α on both sides
are equated to obtain the recurrence relations for calculating
the power series coefficients, Vi[n], for n>0. At α=1, theoriginal power balance equations (PBE’s) are recovered fromthe embedded system of equations and the high voltage (HV)
solution can be retrieved. At α=0, (1) - (4) yields the so-called
germ of the power series which is the physical solution for the
no-load, no-generation case for a general PF problem when

Fig. 2 Number of iterations vs. loading level of thesystemIntegrated Generation and Transmission Expansion
Planning with Carbon Capture Operating Constraints
Yuan Hu, Student Member, IEEE, Tao Ding, Member, IEEE, Zhaohong Bie, Senior Member, IEEE, Haoran Lian
State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University
Xi’an, Shaanxi, 710049, China
huyuan23@stu.xjtu.edu.cn, tding15@mail.xjtu.edu.cn, zhbie@mail.xjtu.edu.cn
Abstract—To reduce carbon emission from the power generation
sectors, retrofitting the power plants with carbon capturesystem or building new carbon capture power plant (CCPP) is apromising low-carbon power generation option. Under this newplanning background, this paper formulates an integrated
generation and transmission network expansion planning model,
in which the operating mechanisms of CCPP is taken into
account. Thus, a multi-period mixed integer linear programing
model is set up to determine the installation time, capacity andposition of candidate transmission lines and generating units.
The effectiveness of the proposed model is validated on a six-bus
system and IEEE 118-bus system.
Index Terms-- Carbon capture power plant, generation
expansion planning, transmission network expansion planning.

system to analyze the effect of carbon emission on the
integrated generation and transmission expansion planningmodel, three cases are designed: Case 1, The carbon emission
cost is not considered in the proposed model; Case 2, Bothcandidate and existing generating units are non-capture units.
Case 3, Existing generating units are non-capture units, while
the candidate generating units are carbon capture units.
TABLE I and TABLE II shows the comparisons of different
objcetives among Case 1, Case 2 and Case 3 on two test
system. It can be concluded that although the investment costand generation cost of Case 3 is the highest comparing toother two cases, the total cost is the lowest thanks to thecontribution of its lowest carbon emission cost.
TABLE I. OBJECTIVE FUNCTIONS FOR THREE CASES OF SIX-BUS SYSTEMI.

KEY MODELThis paper established a multi-period integrated
framework of generation and transmission network expansion
planning, in which the operating mechanisms of CCPP aretaken into account. The proposed co-planning model can be
expressed as follows:
Objective function: minimize the total present value ofthe investment cost and operation costs.t. (1) Equipment investment constraints(2) Power flow constraints(3) Generation constraints(4) Active power balance constraints(5) Operating constraints of units of CCPPs
In the objective function, the investment cost includes theinvestment cost of new generating units and transmission
lines, and the operation cost consists of power generation costand carbon emission cost over the planning horizons.
II.

KEY RESULTS

The proposed multi-period expansion planning model isconducted on a six-bus system and the IEEE 118-bus tested

This work was supported by China Postdoctoral Science Foundation(2015M580847), National Natural Science Foundation of China (51577147),
and the Independence research project of State Key Laboratory of ElectricalInsulation and Power Equipment in Xi’an Jiaotong University (EIPE14106).

Objective function
Investment Cost (M$)
Generation Cost (M$)
Carbon Emission Cost (M$)
Total Cost (M$)

Case 122.230295.037
168.417485.684

Case 2
17.574305.042156.072478.688

Case 334.114318.786
114.898467.798

TABLE II. OBJECTIVE FUNCTIONS FOR THREE CASES OF 118-BUS SYSTEM
Objective function
Investment Cost (M$)
Generation Cost (M$)
Carbon Emission Cost (M$)
Total Cost (M$)

III.

Case 1144.5194363.1193611.416
8119.053

Case 2185.133
4372.571
3479.128
8036.832

Case 3245.9704527.207
3180.058
7953.234

CONCLUSION

A multi-period integrated framework of generation andtransmission network expansion planning is established withthe consideration of operating mechanisms of CCPP.
Furthermore, the study on two test systems suggests that the
expansion planning result is very different from thetraditional planning. Although the investment cost of the
proposed model is relatively higher than the traditional one,the total cost including investment cost, generation cost andcarbon emission cost will be reduced in the long run, thanks
to lowest carbon emission cost of CCPP.

Directed Graph Based Carbon Flow Tracing for
Demand Side Carbon Obligation Allocation
Tao Sun, Donghan Feng, Teng Ding & Lixia Chen

Shi You

Key Laboratory of Control of Power Transmission andConversion, Ministry of Education, Department. ofElectrical Engineering
Shanghai Jiao Tong University
Shanghai 200240, China
donghan.feng.dr@ieee.org

Centre for Electric Power and Energy, Department ofElectrical Engineering
Technical University of Denmark
Kgs. Lyngby 2800, Denmark

Abstract—In order to achieve carbon emission abatement,
some researchers and policy makers have cast their focus on demand side carbon abatement potentials. This work addresses the
problem of carbon flow calculation in power systems and carbon
obligation allocation at demand side. A directed graph based
method for tracing carbon flow is proposed. In a lossy network,
matrices such as carbon losses, net carbon intensity (NCI) andfootprint carbon intensity (FCI) are obtained with the proposed
method and used to allocate carbon obligation at demand side.
Case studies based on realistic distribution and transmission
systems are provided to demonstrate the effectiveness of the proposed method.

Ct = ∑ NCI k × PLk + ∑ ∑ CLij

N

N

k =1

i =1 j∈Γ+ ( i )(4)III. KEY RESULTS
A transmission system of 2224 buses with more than 600
substations is studied here to apply the directed graph basedcarbon flow tracing method. The FCI distribution of the system is shown in Fig.1, where the hollow circles mean substations without loads.

I. INTRODUCTION
The carbon obligation is transferred through complicatedmarketing processes to the consumers in terms of electricity
bills, which may result in price volatility and speculations [1].
Therefore, it is worthwhile to investigate the possibility of directly allocating carbon obligation at demand side. In [2], theconcept of "carbon emission flow in networks" introduced in [3]is calculated with a linear equation-based method to address
the carbon obligation allocation issue from a consumptionbased perspective which is time consuming especially for alarge scale power system. In this work, a directed graph based
method is proposed to calculate the carbon flow and address
carbon obligation allocation at demand side in lossy powersystems.
II. KEY EQUATIONSThe element of contribution factor matrix D is defined as
N

CFNgross= ∑ Dik PGk σ ki(i = 1, 2,

N ).

(1)

k =1

where Dik can be calculated during the tracing process asfollow (take FCI tracing process for example):⎧(k = i )⎪1⎪
Dik = ⎨ ∑ (| Pmi | / PNm ⋅ Dmk ) (k ≠ i, ∃m ∈ Γ − (i), Dmk > 0) (2)⎪m∈Γ− (i )⎪0(otherwise)⎩
If the circulating part contains the sequence of nodes(i1 , i2 ,..., im , i1 ) , a circulating coefficient is defined for the
proportion of power remaining after a circulation as:
Pi i m −1(3)
Ti = m 1 ∏ ( Pik ik +1 / PNik )PNim k =1
When allocating carbon obligation at demand side, it is optional whether to include carbon losses. If only the loadcaused emission at each node is accounted

Fig. 1 FCI of the large scale power system
Table I Comparison of solving carbon flow for different systems
Number of nodes
14
300
2224
3120
Directed graph based
0.001s 0.011s 0.163s 0.236s
Linear equation based 0.002s 0.194s 0.822s 2.402s
REFERENCES

9241
1.517s49.726s[1] Q. P. Zheng, S. Rebennack, P. M. Pardalos, M. V. F. Pereira,and N. A. Iliadis, Handbook of CO2 in Power Systems. Berlin,
Heidelberg: Springer Berlin Heidelberg, 2012.
[2] B. Li, Y. Song, and Z. Hu, "Carbon Flow Tracing Method forAssessment of Demand Side Carbon Emissions Obligation,"
Sustainable Energy, IEEE Transactions on, vol. 4, pp. 11001107, 2013.
[3] C. Kang, T. Zhou, Q. Chen, Q. Xu, Q. Xia, and Z. Ji, "Carbon
emission flow in networks," Scientific reports, vol. 2, 2012.

Multi-Time Step Service Restoration for
Reconfigurable Advanced Distribution Systems andMicrogrids
Bo Chen, Karen L. Butler-Purry
ECE Department
Texas A&M University
College Station TX, USA{bchen, klbutler}@tamu.edu

Chen Chen, Jianhui Wang
Energy Systems Division
Argonne National Laboratory
Lemont IL, USA{morningchen, jianhui.wang}@anl.gov

Abstract—An innovative multi-time step distribution service
restoration (DSR) methodology is proposed. The DSR problem ismodeled as a Mixed Integer Linear Programming (MILP) model,
which aims to restore as much load as possible by assigning theswitching actions and distributed generation (DG) dispatchingactions to each time step. Furthermore, the proposed DSR
algorithm could be applied for both distribution systems andmicrogrids affected by the power outages, and allows to restore thesystem from either a general blackout state, or a partial blackout
state. Case studies show the effectiveness of the proposed DSR
methodology.

II. KEY RESULTS
633

Step 2:

Step 1:
650

650

632

632

671

DG1

DG1

645

633

633

692

634

632

650

680

671

DG1

Keywords—Service Restoration, Distribution Systems,
Distributed Generation, Mixed Integer Linear Programming.

675

Step 4:

Step 3:
650

692

634

DG2

645

646

684

632

680

671

DG1

DG2

645

646

684

652

611

I.

KEY FIGURES
Fig. 2. Step-by-Step restoration for a modified IEEE 13 node test feeder

Start

Data of Network,
Load and DG

DMSDatabase

Run the DSR
Algorithm

SCADA Database
CIS
OMS
IVR
GIS
Weather Forecast
CLPU Estimation

Set k=1
Execute the kth Stepof the DSR PlanYES

New Events
Reported?

Fig. 3. Nodal voltage at each step during the restoration

NO

III. KEY CONTRIBUTIONS

K=k+1

K<=n?

1.

YES

NO

All Loads Restored?

NO

YES

ManualTermination?
YES

NO

2.
3.

Stop

4.
Fig. 1. Flowchart of the proposed DSR methodology

Model the service restoration problem as a multi-time step
problem, which ensure the feasibility of each control
actions.
DGs in the system can be flexibly dispatched to maximize
the restored customers.
The tie switches are fully utilized to form feasible
restoration paths that allow to restore as many customers
as possible.
Objective functions and constraints are linearized, and the
problem is formulated as an MILP problem, which can be
efficiently solved by commercial solvers.

Novel Synchrophasor based Real-Time Monitoring
and Characterization of Delayed Voltage Recovery
Amarsagar Reddy Ramapuram Matavalam
Department of Electrical & Computer Engineering
Iowa State University, Ames, IA,amar@iastate.edu

Venkataramana Ajjarapu
Department of Electrical & Computer Engineering
Iowa State University, Ames, IA,vajjarapu@iastate.edu

Abstract — Voltage instability due to large disturbances
plays a major role in many blackout incidents. Online
tools that can assess stability of the system have been
recommended in order to reduce the occurrences of large
scale blackouts. This paper proposes a model freealgorithm that can assess the short term voltage stability
due to Delayed Voltage Recovery in real time based on
voltage measurements from PMU’s. A new method to
identify the problem of delayed voltage recovery at a bus
based on comparing probability distributions of thevoltage response to a reference probability distribution is
proposed and is tested on the IEEE 162 bus system. It is
successful in detecting and quantizing the delayed
recovery in voltage signals with added noise. The
implementation of the methodology in a real time cyberphysical test bed is described using the Phasor models in
Opal-RT and the usefulness of implementing the methodin the test bed is described.

Therefore, developing real time tools to address
aforementioned issues to the bulk power system and improve
the reliability has been an important issue for power systemanalysis and control. It is necessary to take advantage of thestate-of-the-art techniques for online voltage stability
monitoring and control to develop a systematic approach toaccurately and flexibly assess voltage security under
uncertainty and steer the system away from undesirable
operating points.

Keywords — Delayed Voltage Recovery, Phasor Measurement
Units, FIDVR, Voltage Stability, Real Time Test Bed, Opal-RT

I.

INTRODUCTION

Voltage stability in general plays a major role in many
blackout incidents, including the one in North America in
2003. A post blackout analysis report provided a list ofrecommended actions and procedures in order to enhance thereliability of the system. Evaluation and adoption of better realtime tools for operators and reliability coordinators as well as
strengthening reactive power and voltage control practices in
NERC regions are among the principal recommendations [1,2]. The economic impacts of such large blackout events arecatastrophic. The estimated economic cost of the August 2003
blackout was around $7 - $10 billion [3].
In order to support the development of new tools andreduce the occurrence of large scale blackouts, the energy
independence and security act of 2007 (EISA) has included as
one of its main provisions the development of the smart grid
initiative, which can be defined as follows: “the policy of theUnited States to support the modernization of the Nation’s
electricity transmission and distribution system to maintain areliable and secure electricity infrastructure that can meet
future demand growth” [4]. It encompasses the deployment andintegration of renewable resources and supports increased useof digital information and technology to improve reliability,
security, and efficiency of the grid.

A description and analysis of short term voltage stability
incidents is given in [5]. Most of the research work in shortterm voltage stability is focused on the dynamic modeling andaggregation of fast acting load components like induction
motor loads as these are the main contributors to short term
voltage stability. Transient stability analysis using Lyapunov
exponent for a finite time span is proposed in [6]. The first stepis to estimate the model parameters using least square
optimization from the available PMU data over a time window.
These parameters are then used for calculation of the Lyapunov
exponents. This method is computationally expensive andstability can be ascertained only after a certain time window.
The proposed methodology is a real-time algorithm and isbased only on measurements from the PMU and does notdepend on the model. It is described in the next section.
II.

TYPES OF SHORT TERM VOLTAGE INSTABILITY

Short term large disturbance voltage stability is anincreasing concern for industry because of the increasing
penetration of induction motor and electronically controlledloads. The short term voltage instability is mainly caused bystalling of induction motor loads, which draw six times
reactive power than that of the nominal one, following acontingency within a time frame of few seconds. Short term
voltage instability can manifest in the form of fast voltage
collapse or delayed voltage recovery. Fast voltage collapse isan extreme event when the voltage at a bus rapidly decays to a
low voltage quickly. This occurs when the amount of inductive
load is very high, generally, with a large number of small
induction motors.
A moderate amount of inductive load (~30%), with a large
number of small induction motors, will lead to delayed voltage
recovery after a disturbance (fault, etc.). Delayed voltage
recovery is the phenomena where the recovery of the voltage
after a disturbance is delayed and results in sustained low
voltages for several seconds. The sustained low voltages in thesystem can lead to cascading events towards a blackout. A

1

Short-Term Distribution-Level Net Load
Forecasting with High Solar Penetration
Xiaorong Sun, Student Member, IEEE, Peter B. Luh, Fellow, IEEE, Kowk. W. Cheung, Fellow, IEEE,
and Wei Guan, Member, IEEE
Abstract--Net load at the distribution level represents the net
demand (load minus solar generation) of a substation, a feeder, atransformer or a customer. Reliable net load forecasts provide
valuable information for utilities’ energy production, distribution
and trading operations. In this paper, the characteristics of solar
generation and the net load are first presented. Impacts of solar
penetration on distribution-level load at different levels areanalyzed based on the synthesized data of feeder total load andscaled solar generation. A wavelet neural network-based net load
forecaster is developed with selected inputs capturing loadfeatures and performing good predictions.
Index Terms—High solar penetration, net load forecasting,wavelet neural network.

Aerosol Optical
Depth (AOD)
AzimuthDew Point
Wind-Chill
Temperature
Precipitation

0.52

0.50

0.49

0.47

0.44

0.41

0.38

0.36

0.48
0.36

0.49
0.36

0.49
0.36

0.49
0.36

0.48
0.36

0.48
0.35

0.46
0.35

0.45
0.33

0.47

0.45

0.44

0.42

0.39

0.36

0.33

0.30

0.40

0.41

0.42

0.42

0.42

0.42

0.42

0.41

III. WAVELET NEURAL NETWORK-BASED NET LOADFORECASTING

I. INTRODUCTION
Motivated by the environmental concerns and a desire to
reduce electricity bills, the number of residential andcommercial customers with solar photovoltaics panels (PV)
has increased significantly in recent years. Generally, theinstallation of solar PV systems is “Behind The Meter” andutilities can only see the net consumption of each customer.
For some special cases, both the solar generation andelectricity demand can be measured. Net load at the
distribution level thus represents the net demand (load minus
solar generation) of a substation, a feeder, a transformer or acustomer. Reliable net load forecasts provide valuable
decision support information for utilities’ energy production,
distribution and trading operations.
II. CHARACTERISTICS OF THE NET LOAD

Fig. 2. The WNN-based net load forecaster.
Table II Forecasting performance (Mean Absolute Percentage Errors) withdifferent combination of input factors.
WNN
Penetration level

Day-1

Day-7

MLR

5.93

6.95

9.26

6.08

5.94

5.95

7.24

9.53

7.06

6.96

6.40

6.07

7.58

10.28

7.21

15%

6.98

6.55

6.26

8.46

11.24

7.53

20%

7.50

7.14

6.79

9.41

12.27

7.96

25%

8.36

7.73

7.25

10.40

13.33

8.51

30%

9.21

8.37

7.73

11.42

14.42

8.79

35%

9.76

8.47

7.95

12.51

15.21

9.25

I1

I2

I3

0% (No solar)

5.63

5.82

5%

6.94

10%

I1: Original: similar day load + today’s load + Temp. + RH + day index
I2: Today’s load + Temp. + direct normal irradiance (DNI) + day index +season index
I3: Today’s load + Temp.+ DNI + precipitation + day index + season index

IV. CONCLUSION

Fig. 1. Weekly net feeder load profile
Table I Correlations between weather factors and the net load
Weather factor
Direct Normal
Irradiance

Correlation coefficient under different solar penetration levels
0%
5%
10%
15%
20%
25%
30%
35%
0.02

-0.05

-0.13

-0.20

-0.27

-0.33

-0.39

-0.45

This work was partially supported by a grant from GE. The views
expressed in this paper are solely those of the authors and do not necessarily
represent those of GE.

A framework of short-term distribution-level net load
forecasting with high solar penetration is presented. It
provides day-ahead hourly forecasts for all net loads. Impactsof solar generation on net feeder loads are investigated to help
identify solar relevant factors. The developed WNN has
potential applications in forecasting peak load and time,
substation/feeder with batteries, and distribution-level net loadwith wind or other dispatchable renewable resources.

A Multi-level Adaptive GP-VM Algorithm for
Composite System Reliability Evaluation
Considering Rare Events
Chao Yan, Student Member, IEEE, Tao Ding, Member, IEEE, Zhaohong Bie, Senior Member, IEEE,
Giambattista Luca, Lucarelli, Student Member, IEEE, Xifan Wang, Fellow, IEEE

Abstract—Variance Minimization (VM) technique is one of themost popular methods for importance sampling (IS), but it has
never been successfully applied to composite (generation andtransmission) system reliability evaluation due to the difficulty ofsolving. In this paper, Geometric Programming (GP) is firstly
introduced to repeatedly solve the VM optimization model inmulti-levels to adaptively obtain the optimal IS parameters used
in a IS-Monte Carlo Simulation (MCS) based composite system
reliability evaluation considering rare events. Then, the IEEE
Reliability Test System and its modified version are used to test
the proposed methodology, and the proposed method iscompared with another important technique for IS of CrossEntropy (CE) in estimation accuracy and convergence
performance.
Index Terms—Variance Minimization (VM), Geometric
Programming (GP), rare events, importance sampling (IS),reliability evaluation.

I. INTRODUCTION
To pick up enough rare events whose probabilities are
extremely small, MCS will involve extensive computation.
However, it is worth stressing that the accurate calculation of
rare-events in highly reliable system is necessary to compare
several planning alternatives.
To address this issue, recently, importance sampling (IS)approach has gained great popularity. The key point of IS
techniques is to choose a biased distribution, namely, optimal
importance sampling parameters (ISPs), which “encourages”
the important values. A good choice of ISPs will minimize theconvergence variance and maximize the evaluation efficiency.
Generally, there are two methods to solve the optimal ISPs:
Cross-Entropy (CE) and Variance Minimization (VM). VM isa model that directly minimizes the variance of the estimation
through importance sampling. Admittedly, VM also ispromising in power system reliability if it can be efficiently
solved. Here, we propose a new method employing Geometric
Programming (GP) to solve the VM model, which we call as“GP-VM”.
II. METHODSNThis work was supported by China Postdoctoral Science Foundation(2015M580847) and National Natural Science Foundation of China(51577147).

B. Variance Minimization

𝒗𝐯𝐦 =𝐚𝐫𝐠𝐦𝐢𝐧𝐯 varl̂𝐦𝐢𝐧𝒗 ∑Ni=1 H 2 (Xi ) (X i 





1

f (X ;u)

N

f



i ;v)

C. Geometric Programming1* 
X Gij N L
X Lij  
1 X Lij 
1 N   NG 1 X Gij min  ci   vG j
1  vG j
1  vL j  vL jv Ni 1   j 1
j 1
 

(4-a)
(4-b)
s.t.
0  vG j  1 , vG j  t j  1 , j=1,2,….,NG









0  vL j  1 , vL j  s j  1 , j=1,2,….,NL(4-c)

III. RESULTSTABLE I.
Methods

THE ESTIMATION PARAMETERS OF LOLP (A)Estimation Parameters (LOLP)
ARE

MRE

ACOV

MCOV

CE-IS

1.43%

4.53%

0.01460.0149GP-VM-IS

0.99%

2.02%

0.01350.0138TABLE II.
Methods

A. Importance Sampling̃v [H]= 1 ∑N
E
i=1 H(Xi )W(Xi ; u, v)

Fig. 1. A comparison between normal sampling and importance sampling

THE ESTIMATION PARAMETERS OF EENS (A)Estimation Parameters (EENS)
ARE

MRE

ACOV

MCOV

CT(s)

CE-IS

1.59%

5.10%

0.01090.011

3329.8GP-VM-IS

0.88%

2.41%

0.01050.01093331.5

System Voltage and Reactive Power Control using Concept of Fuzzy Programming
Yuta Aihara, Takayuki Ito, and Shinichi Iwamoto
Power Systems Laboratory, Department of Electrical Engineering and Bioscience
Waseda University, Tokyo, 169-8555, Japan.
Email:iwamoto.lab.pwrs@gmail.com

Abstract — Large scale photovoltaic power (PV) penetration
causes several problems such as system voltage increases,
fluctuations, and deviations in the case of PV output forecast
failure. In order to solve the problem, this paper applies theconcept of fuzzy programming to system voltage and reactive
power control, and provides flexibility to accommodate thesystem’s soft constraints and short term capacities.

Ⅰ. Proposed method
One of the major methods used in overall system voltage
and reactive power control is the local Voltage and Reactive
Power Control (VQC).

Fig4. Modified Ward-Hale model
Ⅱ. Reuslts
a.

The transitions of the load bus voltages

Load Voltage (pu)1.1

fig1. Fundamental function of the local VQC

Voltages are controlled in the range, but that constraints are soft.
Fuzzy programming is a mathematical method in which these soft
constrains can be considered. In fuzzy programming, we define
membership functions mG  f i  x  0  mG  f i  x   1.0  for each of the
i

1.09
1.08
1.07
1.06
1.05
1.04
1.03
1.02
1.01
1
0.99
0.98
0.97
0.96
0.95
0.94
0.93
0.92
0.91
0.9

i

objective functions f i  x  . If membership function is close to 1.0, it

4

8

12
time(h)

16

20

24

Fig4. The transitions of the load bus voltages

means f i  x  is close to the desirable value. On the other hand, if it isclose to 0.0, f i  x  is not the desirable value.

0b.

The transitions of the load bus voltage with fuzzy programming

Load Voltage (pu)1.1

Fig2. An example of Membership function

In this paper the concept of the fuzzy programming tosystem voltage and reactive power control, and the validity ofthe proposed method is shown using a 6 bus modified WardHale model.

1.09
1.08
1.07
1.06
1.05
1.04
1.03
1.02
1.01
1
0.99
0.98
0.97
0.96
0.95
0.94
0.93
0.92
0.91
0.9

0

4

8

12
time(h)

16

20

24

Fig5. The transitions of the load bus voltage with fuzzy programming

REFERENCES[1] T.Ito, and S.Iwamoto, " System Voltage and Reactive Power
Control using Concept of Fuzzy Programmingfor Large-Scale Photovoltaic Power Penetration" IEEJ PEPSE 2015.
(in Japanase)

A Novel approach on Upgrade and Renewal ofTransformers using Optimal Portfolio Selection
Yasukuni Nagakawa, Yuta Mori, Shinichi Iwamoto
Power Systems Laboratory, Department of Electrical Engineering and Bioscience

Waseda University, 169-8555, Tokyo Japan
Email: iwamoto.lab.pwrs@gmail.com

Abstract— A large number of electrical equipment have been
installed during the rapid economic growth period in the world.
Today, the time for upgrading electrical equipment has come,because most installed equipment has been used for more than 30
years. It should be noted that developed countries expect a low
growth rate in electricity demand in which the most is a 1.2%
transition rate. The present study applies the optimal portfolio
selection method, which is one of the most typical models ofmathematical programming for financial engineering, toelectrical equipment planning programs. Many methods areavailable that provide plans with considerable returns byconsidering risks. In this paper, a safety-first model is applied,which is especially aimed at minimizing risk. The method
determines the optimal upgrading program using multiple
evaluations considering risks and returns based on reliability andeconomics. In this paper, the IEEJ East 10-machine 47-bus
system is used for a 20-year simulation.

0.14

probability of fault [%]

0.12

0.04

1

11

21
31
aging [year]

41

Fig. 3 Probability of Transformer Faults.

III. KEY RESULTS
Table 1 Evaluation Values of Each Method   capacity _ evaluation

Line No.
(SubstationNo.)

no use ofmethods

Comparison
methodProposedmethod   operating _ evaluation

50

654.6

562.3

167.8

51

213.6

278.7

213.6

52

103.1

103.1

103.1

53

133.4

278.6

133.4

54

253.7

278.8

253.7

55

706.8

181.6

170.7

56

655.4

269.5

167.5

57

706.2

171.4

169.4

58

566.7

167.7

163.4

59

54.6

138.6

54.6

60

38.9

103.6

38.9

61

695.0

169.9

164.7

sum

4782.12703.6

1800.8

II. KEY FIGURES
(20)

(37)

<49>

(36)

<35>

G10

<34>

<61>

<60>
<48>

<37>

G8
G4

<41>

(11)

(18)
<47>

(9)

(2)

G2

<40>

(12)

(5) G5

(13)
<9>
<6>

<16>
<44>

<8>

(25)

<18>

<11>

(23)

<15>

(44)

(24)

<4>

<50>

<51>
(39)

(38)

(28)

<14>

<13>

(27)
<52>
(40)

(45)

<23>

(16)

<31>

<32>

<38>

<39>

<19>
<20>

<24> (31)
(29)

<57>
<12>

<45>

<17>

(26)
<56>

<5>

<46>
(17)

(15)

<10>

G6 (6)

(7)

G7

(14)

(3) G3(21) <3>

(8)

(4)

<43>

<42 <7>
>

<2>

<33>

<36>

G9
(1)

<1>

<59>

(19)

(47)

(22)

0.06

0

F    fault _ evaluation

G1

0.08

0.02

I. KEY EQUATIONS

(10)

0.1

<26> (32)

<28>

(35)

<58>
<21>

(46)

<22>

(30)
<53>
(41)

<27>

(33)

<25>

<29>

<30>

<54>
(42)

(34)
<55>
(43)

Fig. 1 IEEJ EAST10 47 Bus System
REFERENCES

existence probability of translators
[%]

6

[1]

5
4
3

2
1
0
1

4

7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58
aging [years]

Fig. 2 Probability of Existence of Transformers

Y. Mori ・S. Iwamoto: “Study on Upgrade and Renewal ofTransformers using Safety-First Model of the Optimal
Portfolio Selection”, IEEJ PEPSE, (2015) (in Japanese)Fast Transient Stability Screening Method
using Principal Component Analysis
for Multi-swing Step-out Using
Yui Koyanagi, Junnosuke Kobayashi, and Shinichi Iwamoto
Power Systems Laboratory, Department of Electrical Engineering and Bioscience,
Waseda University, Tokyo, 169-8555, JAPAN

Email: iwamoto.lab.pwrs@gmail.com
Abstract—Recently, online stability monitoring systems
have become more important owing to the increasing
complexity of power systems. Moreover, there has been aconcern regarding multi-swing step-out due to theJapanese longitudinal power system. In this paper, a fast
screening method is proposed considering multi-swing
step-out using principal component analysis (PCA). In the
proposed method, computers learn patterns of PCA intransient stability data as a form of library. In order toreduce the number of data in the library, k-means method,
one of the partitioning-optimization clustering method, isapplied to extract features in the data. In addition,
Gaussian mixture model is also applied to extract thefeatures from different perspective. Simulations for the
proposed method are performed using the IEEJ 10
machine 47 bus system to confirm the validity of thescreening method.

START

Clustering of Library Datak-means Method

Estimation of Gaussian
Mixture Model

Library Reduction

Library Reduction

STOP

Fig. 2. Flow Chart of Proposed Clustering and Library Reduction

Ⅲ. KEY RESULTSTABLE 1. Result of Fault Screening
No. of Library Cases
409
No. of Test Sample

1065
30

Ⅰ. KEY EQUATIONS
The proposed method for PCA is based on two basic
equations shown below.

PCL    w…(1)

PCA  PCLT  SL…(2)

Safe-side judgments

0Dangerous-side judgments0Misrecognitions

30

No. of Misjudgments

Matching Time16.2 [s]Fault Recognition Rate

97.1%

The proposed method for clustering is based on Eq. (3) to (5).

xkt 1 

1

x

Ckt  xi Ckt 

…(3)

i

K

g Ck      xk  xi 

2

…(4)

k 1 xi Ckpx     k N x k ,  k 
K

…(5)

k 1

Fig. 3 Reference Counts of Each Library Data Number

Ⅱ. KEY FIGURESTABLE 2. Result of Fault Screening (Clustering of Library Data)
No. of Library Cases
177
No. of Test Sample

1065
29
Safe-side judgments

0Dangerous-side judgments0Misrecognitions

29

No. of Misjudgments

Matching Time

8.7 [s]Fault Recognition Rate

97.3%

REFERENCES[1]
Fig. 1. Flow Chart of Proposed Fault Screening Method

J. Kobayashi・S. Iwamoto: “Fast Screening Method forTransient Stability Considering Multi-swing Step-out Using
Principal Component Analysis”,IEEJ PEPSE, (2015) (inJapanese)Load Frequency Control with Storage Battery andGenerator using a Recurrent Neural Network
Ryu Tanabe, Takato Otani, and Shinichi Iwamoto
Power Systems Laboratory, Department of Electrical Engineering and Bioscience
Waseda University, Tokyo, 169-8555, Japan
Email:iwamoto.lab.pwrs@gmail.com

Ⅰ. KEY EQUATIONS, ............................................ (1)Hidden and Output Layer, .................................. (2), ............................................ (3)Input Layer, ............................................. (4)Ⅱ.

KEY FIGURESⅢ.

KEY RESULTS

0.03
Frequency Deviation [Hz]Index Terms-- Load Frequency Control, Battery, Renewable
Energy, Machine Learning, State of Charge, Power System

Fig.3 Load Frequency Control Model

0.02
0.01
0
-0.01
-0.02
Comaprison Method
Proposed Method

-0.03
-0.04
0

500

1000

1500
Time [s]

2000

2500

3000

2500

3000

2500

3000

Fig.4 Load Frequency
0.005
0SOC [p.u.・h]Abstract—A frequency issue is one of the concerns caused byrenewable energies. There is a possibility of losing a balance ofthe demand and the supply followed by a frequency disturbancewhen large amounts of the renewable energies are introduced. Asa countermeasure against the problem, storage battery systems
for the load frequency control are beginning to be introduced inJapan. Batteries have faster response, because it is a powerelectronics equipment and does not have mechanical components.
However, a state of the charge of the battery needs to be
maintained in order to make efficient use of the battery. In thispaper, in order to use the introduced storage battery effectively, acontrol by a recurrent neural network is proposed. Recently,
neural networks are attracting attention as an innovative control
method. By training the neural network with particle swarm
optimization, the frequency of the system and the state of thecharge of the storage battery are maintained simultaneously.

-0.005-0.01-0.015
Comaprison Method
Proposed Method-0.02-0.025
0

500

1000

1500
Time [s]2000

Fig.5 State of Charge

Fig.1 Neuron Model

GEnerator Output[p.u.]

0.1
Comaprison Method
Proposed Method

0.08
0.06
0.04
0.02
0
-0.02
0

[1]
Fig. 2 Fully Recurrent Network

500

1000

1500
Time [s]2000

Fig.6 Generator Output
REFERENCES
T.Otani, S.Iwamoto: “A Study on Load Frequency Control
Dispatch by Machine Learning Method in Transmission System”,
IEEJ PESPE, (2015) (in Japanese)Multi-swing Stability Study using Time-Domain
Equal-Area Criteria
Ako Sato, Masashi Hitosugi, Shinichi Iwamoto
Power Systems Laboratory, Department of Electrical Engineering and Bioscience
Waseda University, Tokyo, 169-8555, Japan
Email:iwamoto.lab.pwrs@gmail.com

Abstract-- For operating a power system, an analysis of
the transient stability is necessary. In particular, in alongitudinal power system such as the Japanese powersystem, the occurrence of multi-swing stability is an issue ofconcern. Further, the prediction of its occurrence isbecoming an important issue. In principle, an equal-area
criterion (EAC) method cannot be applied to a
multi-machine system. However, a time-domain equal-area
criteria (TDEAC) method, which is developed by extending
the concept of EAC, can be applied to the multi-machine
system by considering the horizontal axis as the time.
However, the conventional TDEAC is used only for the
first-swing stability stepping-out. In this paper, a new
analysis method of multi-swing stability is proposed after
observing the second swing in the TDEAC. The validity ofthe proposed method is shown with the simulation results
for a three-machine nine-bus system.

P  ~pei

A2

P  ~mi0

t0 t sE1E0
sin  …………………………..(2)Xδ: rotor angle [rad]，ω: angular velocity [rad/s], M: inertia
constant [s]，Pm: mechanical input [p.u.], Pe: electrical output[p.u.]
E1: generator internal voltage [p.u.], E0: infinite-bus voltage[p.u.], X: reactance [p.u.]

Ⅱ. KEY FIGURES

7

P=1.63
Q=0.06793

𝑡𝑐 [s]

True judge

Acc1

Dcc1

Acc2

Dcc2

4-5

0.29

stable0.91880.97401.2942

1.29560.30

N swings

0.98441.04281.43371.42690.31

first swing

1.05231.0493

5.09691.44660.19

stable0.77300.81830.88070.88390.20

N swings

0.85690.91171.03951.0366

0.21

first swing

0.94490.82705.39301.23780.23

stable0.85610.90571.03771.04020.24

N swings

0.92870.98411.1607

1.1426

0.25

first swing

1.00380.99355.46781.13360.25

stable1.00381.0457

1.1661

1.17150.26

N swings

1.0814

1.1472

1.3132

1.3026

0.27

first swing

1.1615

1.11625.8222

1.5855

7-5

9-6

9-8

Fig. 3 Simulation results

fault case𝑡𝑐 [s]

first swing

second swing

4-5

0.29

Acc1<Dcc1

Acc2<Dcc2

stable0.30

Acc1<Dcc1

Acc2>Dcc2

N swings

0.31

Acc1>Dcc1

Acc2>Dcc2

first swing

0.19

Acc1<Dcc1

Acc2<Dcc2

stable0.20

Acc1<Dcc1

Acc2>Dcc2

N swings

0.21

Acc1>Dcc1

Acc2>Dcc2

first swing

0.23

Acc1<Dcc1

Acc2<Dcc2

stable0.24

Acc1<Dcc1

Acc2>Dcc2

N swings

0.25

Acc1>Dcc1

Acc2>Dcc2

first swing

0.25

Acc1<Dcc1

Acc2<Dcc2

stable0.26

Acc1<Dcc1

Acc2>Dcc2

N swings

0.27

Acc1>Dcc1

Acc2>Dcc2

first swing

7-5

9-6

5

6

M [s]

Xd’ [p.u.]

1

100

0.324

2

18

0.232

3

12

0.174

4
1

tu

tc

fault case

8

GeneratoriFig. 2 Concept of TDEAC for the multi-machine systems

P=0.85
Q=-0.1092
P=1.25
Q=0.500fei

Ⅲ. KEY RESULTS

d



dt
 ………………….......(1)dM Pm  Pe  dt

P=1.00Q=0.350i

P ~A1

Ⅰ. KEY EQUATIONSPe   i

P=0.716
Q=0.270

P=0.90Q=0.300
9-8

proposed method

Fig. 4 Determination results of the proposed method
Fig. 1 3 machine 9 bus system

REFERENCE[1] M.Hitosugi・S.Iwamoto: “A Study of Multi Swing Stability using
Time Domain Equal-Area Criteria”, IEEJ PEPSE, (2013) (inJapanese)1

Optimal Scheduling of Integrated AC-DC Systems
Using Convex Semidefinite Programming
Seyedmahdi Moghadasi, Student Member, IEEE, Sukumar Kamalasadan, Member, IEEE,

HE congestion on power transmission systems induced
by demand growth results in higher electricity price andpushes the system closer to its stability limit. Voltage source
converter (VSC) based DC transmission systems can alleviate
this burden by providing DC corridors for active power andmanaging reactive power at converter terminals. Non-convex
OPF methdos and separate scheduling of AC and DC systems
result in local optimum solutions. This poster presents a
unified convex AC-DC OPF formulation which includes both
AC and DC side equations together.

20800
20800

II. K EY E QUATIONS
The convex relaxation is derived by imposing convex supersets over the joint AC-DC feasible sets of OPF problem andminimizing cost function over these sets as shown in (1)-(5).
X
M in
Ci (x, xDC , W, WDC , λ)(1)
(2)

H(xDC , WDC , λ) = 0
G (x, W, λ) ≥ 0(3)

G (xDC , WDC , λ) ≥ 0
F (W, WDC , γc ) = 0

(4)

T

(5)

W =VV

T
WDC = VDC VDC

W  0, WDC  0
The convex AC-DC OPF formulations consists of four major sections. The function C(.) in (1) defines the objectivefunction which is the operating cost. The function H(.) in(2) models the AC and DC power flow constraints which areconvexified using SDP. The constraint G(.) in (3) models AC
The authors are with the Power, Energy and Intelligent Systems Laboratory
and Energy Production Infrastructure Center (EPIC) and ECE Department,
University of North Carolina at Charlotte, Charlotte, NC 28223 USA (e-mail:smoghada@uncc.edu, skamalas@uncc.edu).

Qcmax=0.66
Qcmax=0.66
Qcmax=1
Qcmax=1

8800

8800

4800

4800

0.90

1.20

1.20

1.50

1.50

2.40

2.40

(a)

i

H(x, W, λ) = 0

Qcmax=0.33
Qcmax=0.33

12800
12800

1.80
2.10
1.80
2.10
LOADING FACTORLOADING FACTOR(a)OPerating Cost [k$]s.t.

Qcmax=0
Qcmax=0

16800
16800

0.90

OPerating Cost [k$](W,WDC )OPERATING COST [$]T

The impact of converter reactive power capacity Qcmax on
the optimal operating point of AC-DC system is examinedusing proposed convex AC-DC OPF. Fig. 1a demonstrates
operating cost at four levels of Qcmax . The HVDC transmission line is considered between bus 5 and bus 14 in 14-bus
test system. It can be seen that increment of reactive power
capacity provide the system a larger feasible area so that the
OPF can converge at higher loading factors λ. The operating
cost of optimal point is reduced as the Qcmax is increased.
Fig. 1b depicts four cross sections of Fig. 1a at loading levelsof λ = 1.4, 1.5, 1.6, 1.7 respectively. The cost saving will
be saturated beyond some points of Qcmax as identified byvertical lines in Fig. 1b.
OPERATING COST [$]I. P ROBLEM S TATEMENT

III. KEY SIMULATION RESULTS

OPerating Cost [k$]Index Terms—AC-DC, OPF, Convex Relaxation, Semidefinite
Programming (SDP), Voltage Source Converter (VSC), HVDC.

and DC system operational constraints using SDP variables.
The final part (4) models the connection between AC andDC
through converter efficiency
 sides (γc ). The vectors V =Re{v}T Im{v}T and VDC = Re{vDC }T demonstrate
system voltages, and λ represents the system loading factor.

9.2
λ1=1.407.8

λ1=1.40

7.8
7.7
7.7

2.70

2.70

9.2

7.8

7.8

OPerating Cost [k$]Abstract—In this poster, a new convex optimal power flow(OPF) formulation for optimal scheduling of integrated ACDC transmission systems is developed using semi-definite programming (SDP). This method calculates the optimum operating
points for AC and DC system together. The main advantage ofthe proposed method is that it finds the optimal schedule whencompared to other non-convex methods or separate schedulingof AC-DC systems. The effectiveness of proposed methods areevaluated using IEEE 14-bus and 57-bus incorporating HVDC
transmission system. The results show the capability of convex
formulations to find the optimal operating schedule at various
loading factors for integrated AC-DC systems.

7.7

8.8

λ1=1.58.8

λ1=1.5

8.4

8.4

7.7

8.0
0.0 0.1 0.2 0.3 0.7 1.0
0.0 0.1 0.2 0.3 0.7 1.0
8.0
Qcmax [p.u.]
Qcmax [p.u.]
10.70.0 0.1 0.2 0.3 0.7 1.0 11.4 0.0 0.1 0.2 0.3 0.7 1.0
Qcmax [p.u.]
Qcmax [p.u.]
10.2
10.9

10.7
10.2

9.7
9.2
8.7
8.2

11.4

9.7

10.4

10.9

9.2
8.7
8.2

9.9

10.4
λ1=1.60λ1=1.7

9.4

9.9

8.9

0.0 λ1=1.60
0.1 0.2 0.3 0.7 1.0 9.4
Qcmax [p.u.]
8.9
0.0 0.1 0.2 0.3 0.7 1.0 (b)λ1=1.7

0.0 0.1 0.2 0.3 0.7 1.0
Qcmax [p.u.]

0.0 0.1 0.2 0.3 0.7 1.0

Qcmax[p.u.] of increasFig. 1: 14-busQcmax
test [p.u.]system; (a): The
Impact(b)
ing Qcmax on optimal operating cost; (b): Four crossSections of Fig. 1a at λ = 1.4, 1.5, 1.6, 1.7 p.u.

IV. C ONCLUSION
The results demonstrate the capability of proposed convex
method to find the optimal scheduling for AC-DC systems.

A Security-Enhanced Unit Commitment
Model for Systems with Stochastic Resources
Nikita G. Singhal, Nan Li, Student Member, IEEE, and Kory W. Hedman, Member, IEEE
School of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, AZ 85287, USA
Email: nsinghal@asu.edu; nanli4@asu.edu; kory.hedman@asu.edu

Abstract— This paper presents a security-enhanced unit
commitment formulation that facilitates the integration ofstochastic resources and partly addresses the reserve
deliverability issue. In this formulation, the securityconstrained unit commitment model (SCUC) is enhanced
to incorporate dynamic reserve policies (post-contingency
nodal reserve deployment transmission constraints) that
aim to model the predicted effects of nodal reserve
deployment on critical transmission lines and improve thedeliverability of reserves post-contingency. Further, thedynamic reserve policies are developed using data-mining
techniques, like support vector regression, and are utilizedbased on the realized system operating condition; thereby,
assisting in providing flexibility to satisfy physical network
constraints when the system state deviates from theforecast. The results show that the proposed approachconsistently outperforms the traditional approaches.

generator contingencies (𝐺 𝑁𝐶 ) whereas (4) restricts thepost-contingency flow (due to generator loss and reserve
deployment) on inter-zonal links (𝐿𝐶 represents criticallines) for critical generator contingencies (𝐺 𝐶 ).
II. KEY FIGURES
Zone 1: Export Zone

Renewable
Uncertainty

DemandM Uncertainty

SupplyM

Fig. 1. Underlying reserve deliverability issue. The key
contribution of this research is to estimate the reserve deployment
factors such that constraint (4) is respected.

I. KEY EQUATIONS

III. METHODOLOGY

(1)
(2)
(3)

Contingency Analysis
Solve DASCUC

Contingency
Analysis

Construct Data
Mining Model..
.

Load scenario 1

Solve DA SCUC

Contingency Analysis

Multiple load
scenarios

Load scenario n

Model Testing (out-of-sample) or
Evaluation Phase

Model Building and Training Phase

Fig. 2. Simulation setup for model building and testing.

IV. KEY RESULTS
4.395

DA SCUC Cost (M$)

Contemporary approaches to model reserve
requirements may not be satisfactory in the near future
because reserves are scheduled in accordance with adeterministic reserve criterion whose robustness, in theevent of a power disturbance, is not guaranteed.
Furthermore, such an approach does not take into
account the effects of stochastic resources and nodal
reserve deployment on critical transmission lines postdisturbance resulting in increased thermal violations andreserve deliverability issues. A mathematicalrepresentation of the reserve model used in this work,
which is an extension of the models presented in [1] and[2], is given below:𝑐∑𝑘∈𝑍 𝑟̃𝑘𝑡≥ 𝑝𝑐𝑡 + 𝑟𝑐𝑡∀𝑐 ∈ 𝐺, 𝑡 ∈ 𝑇𝑐𝑟̃𝑘𝑡 ≤ ∑𝑔∈𝐺(𝑘) 𝑟𝑔𝑡∀𝑐 ∈ 𝐺, 𝑘 ∈ 𝑍, 𝑡 ∈ 𝑇𝑐𝑟̃𝑘𝑡≤ 𝛼(𝐹̅𝑙𝑡 ± 𝐹𝑙𝑡 )∀𝑐 ∈ 𝐺 𝑁𝐶 , 𝑘 ∈ 𝑍, 𝑡 ∈ 𝑇𝑐−𝐹̅𝑙𝑡 ≤ 𝐹𝑙𝑡 − 𝑝𝑐𝑡 𝑃𝑇𝐷𝐹𝑛(𝑐),𝑙 + ∑𝑔∈𝐺̅ 𝑟𝑔𝑡 𝛽𝑔,𝑙,𝑡≤ 𝐹̅𝑙𝑡𝐶𝐶∀𝑐 ∈ 𝐺 , 𝑙 ∈ 𝐿 , 𝑡 ∈ 𝑇

Zone 2: Import Zone

Reserves

4.385
4.375
4.365
4.355
0

𝑐
Here, 𝑟̃𝑘𝑡
represents reserve in zone 𝑘 that is classified
as deliverable for contingency 𝑐 in time 𝑡, 𝐹̅𝑙𝑡 represents
the emergency line capacity, 𝐹𝑙𝑡 represents the precontingency flow on the inter-zonal link (connecting
𝑐zones 𝑘 and contingency zone 𝑐), and 𝛽𝑔,𝑙,𝑡
represents
the reserve deployment factor for generator 𝑔, line 𝑙,time 𝑡 and contingency 𝑐, which is estimated offline
from data-mining. Equation (1) requires there to be
sufficient reserve to cover the loss of any generator, (2)models reserve held within the zones, (3) limits theamount of reserve that can be shared (𝛼 signifies thereserve sharing policy) between zones for non-critical

50

100

150

200

Expected Security Violations (MWh)(4)

Base, 𝛼=0.8

Base, 𝛼=0.7

Base, 𝛼=0.6

Base, 𝛼=0.5

Base, 𝛼=0.4

Proposed

Fig. 3. Preliminary results on a modified IEEE 73-bus test casewith 7% demand uncertainty. The marker size is indicative of thenumber of violations in each case.

REFERENCES
[1]

[2]

J. D. Lyon, M. Zhang, and K. W. Hedman, “Locational reserve
disqualification for distinct scenarios,” IEEE Trans. Power Syst.,
vol. 30, no. 1, pp. 357-364, Jan. 2015.
Y. Chen, P. Gribik, and J. Gardner, “Incorporating post zonal
reserve deployment transmission constraints into energy andancillary service co-optimization,” IEEE Trans. Power Syst.,
vol. 29, no. 2, pp. 537-549, Mar. 2014.

Fast Prediction of Cascading Overload on
Transmission Section Based on DC Sensitivity andCompensation Method
Ying Mu, Baohui Zhang, Senior Member, IEEE

Zhiqian Bo, Senior Member, IEEE

School of Electrical Engineering
Xi’an Jiaotong University
Xi’an, 710049, China
Abstract—If the overload branch is tripped, the power flow willtransfer to other branches in corresponding transmission section.
Therefore, fast prediction of cascading overload is of greatsignificance for security protection. This paper proposes analgorithm combined with DC sensitivity and compensation
method after the traditional study of branch outage. Based on
DC power flow model, branch active power transferring
sensitivity is introduced to describe the impact of overload
branch outage on other branches. The compensation method isapplied with an equation of active power into nodes simulating
the branch outage. The original sensitivity matrix is effectively
used regardless the change of network and complicated power
calculation is avoided. RCTL (relative cluster of the transmissionline) will be analyzed for cascading overload prediction. And
results will provide the basis for whether to take urgent control
measures to eliminate the overload. The rapidity and validity of
the algorithm is verified by calculation results of CEPRI-36
system.

XJ Group of State Grid
Xuchang, 461000, China
avoid the change of sensitivity matrix caused by the change of
network, compensation method is applied with an equation ofactive power increments into the two end nodes, simulating thebranch outage. RCTL (relative cluster of the transmission line)
is defined as branches which easily become overloaded causedby power flow transferring, and it’s composed of transmissionsection in which the overload branch lies and alert branches.
Fast prediction of cascading overload after the trip of overload
branch provides the basis for whether to take urgent control
measures to eliminate the overload.
II. KEY EQUATIONS[Pl ]  ([ H ][ B]1 )[P]  [ D P ][P] 





Sl i  Pl / Pi Pl  Sl i  Pi  Sl  j  (Pi )Index Terms—Transmission section, cascading overload
prediction, DC sensitivity, compensation method, transferring
sensitivity.



I. INTRODUCTIONPi  Pj  Pij    Pij 





Pl    Pl    l     Pm   Pl .max The lack of cooperation between traditional overload
protection and auto-mechanisms may lead to cascading
overload trip and destruction on the network integrality and its
transmission capacity. The transmission section is an importantpart of the system so that it's crucial to carry out security
protection of transmission section. In order to avoid cascading
trip, the action of individual protections and automatic need tobe harmonized from the perspective of the whole systemsecurity. If the trip of overload branch may cause cascading
overload in corresponding section, emergency control should
be executed to eliminate the overload; if not, the overload
branch should be tripped by relays. In this paper, whether otherbranches will become overloaded caused by power flowtransferring after branch outage is studied. And it’s of greatsignificance for security protection of transmission section andprevention of widespread blackouts.
Combined with DC sensitivity and compensation method,
this paper proposes a fast algorithm for cascading overload
prediction. The trip of overload branch will cause the change ofactive power of other branches in corresponding section andthe impact is described as branch active power transferring
sensitivity without complicated power calculation. In order to ( Sl i  Sl  j )  Pi

m

0

m



0

III. KEY FIGURESSTARTStore sensitivity matrix [DP] in advanceN

Whether a branch isoverloaded?

Y
Search for sensitivity of the end nodes ij
Calculate transferring sensitivity  l m 
and compensation factor μPl   Pl   l     Pm   Pl.max ?
m

0

m

0

Y

l C

N
Trip the overload
branchStart emergency
control

END

Fig. 1. Fig. 1. Flow chart of cascading overload predictionFast Decomposition-Based State Estimation Using
Automatic Graph Partitioning and ADMM
Leilei Xiong and Santiago Grijalva
School of Electrical and Computer Engineering
Georgia Institute of Technology, Atlanta, GA USA
leileix@gatech.edu, sgrijalva@ece.gatech.edu
Abstract—State estimation is a critical function in the energy
management system that processes measurements from the fieldand determines the most likely state of the power system. As
more sensing is added and wider areas need to be estimated inreal time, the computational speed of state estimation becomesincreasingly more important. This paper proposes a scalable
method that uses automatic graph partitioning to decompose a
global state estimation problem into a series of smaller subproblems and forces these sub-problems to reach a consensus
using the alternating direction method of multipliers (ADMM).
The proposed method is demonstrated on the IEEE 14 bus, 57
bus, 118 bus, and 300 bus systems. Simulation results suggestthat partitioning the global state estimation problem into
smaller state estimation problems greatly improves thecomputational performance, even for a serial implementation.

I.
INTRODUCTION
There have been numerous research efforts to speed up SE.
The motivation for increased performance is two-fold. First, as
more measurements are added into the system, more speed isneeded to process them in real time. Secondly, fasterprocessing would enable real-time wide-area SE. Because thespeed of SE depends on the size of the problem (the number ofmeasurements and the number of states to be estimated), theability to decompose the global SE problem into smaller
problems is essential. This paper explores two questions thathave been largely undiscussed in the literature: 1) how the
global SE problem could be automatically decomposed into
smaller problems, and 2) the impact of the number of subproblems on the computational speed.
II.
METHODOLOGY
In this paper, we first introduce automatic graphpartitioning as a systematic method for virtually dividing a
power system into an arbitrary number of sub-areas. Then we
propose a fast decomposition-based SE approach, anenhancement to traditional WLS SE, which uses thepartitioning results to automatically decompose the global SE
problem into smaller sub-problems and solve them in unison
using ADMM. Lastly we explore empirically the impact of thenumber of sub-problems on the computational speed of the
global problem for a serial implementation and extrapolate thespeedup seen for the considered test cases to larger systems.

Figure 1. Fast decomposition-based SE algorithm flowchartIII.

KEY RESULTS

Figure 2. Timing results for 4 standard IEEE test systems

Figure 3. Potential speedup for larger systems

1Electricity Load Forecasting in Micro-grids
Hamed Chitsaz, Student Member, IEEE and Hamidreza Zareipour, Senior Member, IEEE

Abstract—Electricity load forecasting plays a key role in operation of power systems and micro-grids. A precise ShortTerm Load Forecast (STLF) of the micro-grid can enhance themanagement of its renewable and conventional resources andimprove the economics of energy trade with electricity markets.
As a consequence of the highly non-smooth and volatile behaviorof the load time series in a micro-grid, its STLF is even a more
complex process than that of a power system. For this purpose,a new prediction method based on Self-Recurrent Wavelet Neural
Network (SRWNN) is proposed as the forecasting engine. The
efficiency of the proposed method is examined on real-world hourly
data of an educational building within British Columbia Instituteof Technology (BCIT) campus micro-grid.

Fig. 2. Architecture of the SRWNN.

I. I NTRODUCTION

1

1

0.9

0.8

0.8

Normalized Load

Normalized Load

Micro-grids are integrated energy systems composed of Distributed Energy Resources (DERs) and multiple electrical loads[1]. A micro-grid is considered as a small-scale version of the
traditional power grid, which can be operated either in parallel
to or islanded from the existing power grid. Micro-grids canbring several benefits to the power industry, e.g., supplying
loads in remote areas, which reduces the total system expansion
planning costs [2]. Moreover, microgrid load forecasting is an
essential tool for optimal scheduling of microgrid resources,
and minimization of the operation cost and the environmental
impacts. However, as a consequence of the highly non-smoothand volatile behavior of the load time series in microgrids,
their STLF is a more complex process than that for powersystems. Therefore, a forecasting model capable of capturing
such volatilites is highly needed. Fig. 1 illustrates more severe
load fluctuations in a building in BCIT microgrid compared to
BC power system over one-year hourly load data.
0.9

0.7

0.6

0.5

0.2

0.7

0.6

0.5

0.3

0.3

Mar.

Apr.

May

Jun.

Jul.

Aug.

Sep.

Oct.

Nov.

Dec.

Jan.

Feb.

(a) BC’s power system electricity
load

0.2

III. N UMERICAL R ESULTS
To show the efficiency of the proposed method for day-aheadload forecast, 10 test months of hourly load data from a buildingin BCIT have been considered. Two forecast error measures aredefined to evaluate forecasts: normalized Root Mean Square
Error (nRMSE) and normalized Mean Absolute Error (nMAE):ψ ( x) = e.cos(5 x)v
u N
u 1 X LACT(t) − LFOR(t)tnRMSE =(
)2 × 100(1)−0.5 x 2

(1)

N

t=1

LPeak

N

nMAE =

1 X LACT(t) − LFOR(t)|| × 100
N

t=1

LPeak

(2)

Table I shows that SRWNN results in lower forecasting errors
in electricity load prediction of the micro-grid compared with
MLP and WNN models.
TABLE IF ORECASTING ERRORS , IN %, OF SRWNN, WNN AND MLP.

0.4

0.4

Fig. 1) Architecture of the SRWNN
The architecture of a SRWNN is shown in Fig. 1, which is a feed forward network with four layers. As seen in Fig.
1, X=[x1, …, xM] is the input vector of the forecast engine and y is the target variable. The inputs x1, …, xM of theforecast engine can be from the past values of the target variable and past and forecast values of the related
exogenous variables. For instance, past values of electricity load along with the past and forecast values oftemperature can be considered for electricity load prediction, provided that their data is available. A feature selection
technique can be used to refine these candidate features and select the most effective inputs for the forecast process.
In this research work, we use the feature selection method of [x8]. This method is based on the information theoretic
criterion of mutual information and selects the most informative inputs for the forecast process by filtering out theirrelevant and redundant candidate features in two stages. However, since this method is not the focus of this paper,
it is not further discussed here, and the interested reader can refer to [x8] for details of this feature selection method.
For the sake of simplicity, no exogenous variables have been considered in this paper. In other words, the target
variable is the electricity load of the next time interval that the forecasting engine presents a prediction for it using
the past values of electricity load itself. Moreover, Multi-period forecast, e.g. load prediction for the next 24 hours,
is reached via recursion, i.e. by feeding input variables with the forecaster’s outputs. For instance, forecasted load
for the first hour is used as y(t-1) for load prediction of the second hour provided that y(t-1) is among the selectedcandidate inputs of the feature selection technique.
The input layer of the forecast engine transmits M input variables, which are selected by the feature selection
technique, to the next layer without any changes. The second layer, which is called the mother-wavelet layer,
consists of N×M neurons that each has a self-feedback loop. In this paper, Morlet wavelet function has been
considered as the activation function of neurons in the mother-wavelet layer, which is defined as follows:

Mar.

Apr.

May

Jun.

Jul.

Aug.

Sep.

Oct.

Nov.

Dec.

Jan.

Feb.

(b) Building electricity load in BCIT

Fig. 1. One-year hourly load data of BC’s power system and BCIT

II. M ETHODOLOGY
To cope with highly non-smooth behavior of electricity loadin micro-grids, a neural network based forecasting model, called
Self-recurrent Wavelet Neural Network (SRWNN), has been
proposed in this work. The architecture of the SRWNN is shownin Figure 2. THe inputs of the model are historical data ofelectricity load and calendar information, and the output is theload prediction for the next hour.
H. Chitsaz and H. Zareipour are with the Department of Electrical andComputer Engineering, and D. Wood is with the department of Mechanicaland Manufacturing Engineering, Schulich School of Engineering, Universityof Calgary, Calgary, AB, Canada T2N 1N4 (e-mail: hchitsaz@ucalgary.ca;
h.zareipour@ucalgary.ca; dhwood@ucalgary.ca).

Month
May
Jun.
Jul.
Aug.
Sep.
Oct.
Nov.
Dec.
Jan.
Feb.
Average

MLP
nRMSE
nMAE
8.44
6.22
9.92
7.55
10.41
7.92
10.40
8.14
11.88
8.40
10.45
7.68
6.34
4.89
6.21
4.58
6.93
4.74
6.85
5.29
8.78%
6.54%

WNN
nRMSE
nMAE
5.96
4.05
5.44
4.27
7.04
5.26
6.57
4.95
7.83
6.01
4.81
3.83
4.62
3.56
4.54
3.35
4.86
3.40
5.06
3.94
5.67%
4.26%

SRWNN
nRMSE
nMAE
5.23
3.80
4.86
3.80
5.43
4.01
6.46
4.80
6.28
4.82
4.24
3.28
4.30
3.21
4.22
3.05
4.25
3.11
4.58
3.50
4.98%
3.74%

IV. C ONCLUSION
In this study, an efficient load forecasting model is proposed
to deal with highly non-smooth behavior of micro-grid loads.
The numerical results demonstrate that the forecasting modelleads to more accurate load predictions in comparison to two
existing models. Accurate microgrid load forecasts are vital for
optimal scheduling of available resources in microgrids.
R EFERENCES[1] A. Khodaei, “Resiliency-oriented microgrid optimal scheduling,” IEEE
Transactions on Smart Grid, vol. 5, no. 4, July 2014.
[2] M. Shahidehpour and M. Khodayar, “Cutting campus energy costs withhierarchical control,” IEEE Electrification Magazine, vol. 1, no. 1, pp. 40–
56, September 2013.

Distributed Computing Platform for a Decentralized
Voltage Stability
H. Lee, Student Member, IEEE, A. Srivastava, Senior Member, IEEE, and D. Bakken, Senior Member, IEEE,
The School of Electrical Engineering and Computer Science, Washington State University
Email: hyojong.lee@wsu.edu, asrivast@eecs.wsu.edu, bakken@wsu.edu
Abstract— With ongoing smart grid implementation, thecomplexity of power system and number of distributed
components have been growing over the years. To
manage the distributed power system components,
centralized applications may not work due to highcomputational and data management requirements.
Furthermore, the high resolution and time synchronizeddata provided by Phasor Measurement Unit (PMU)
requires faster computation and data management for
critical application. Distributed computing provides
means for distributed, fault-tolerant and fast
management of data to support synchrophasors based
critical applications. In this work, the distributed
computation platform called DCBlocks and decentralized
voltage stability (DVS) using proposed platform is
discussed.
Keywords—voltage
stability;
computing, smart grid

I.

PMUs;

distributed

Middle of tie line

Vi / Ii
PMUi
Bus i

Vj / Ij
G

PMUj
Virtual Bus i

add virtual BusesPower Flow

Vi / Ii

Vj / Iji
Bus PMU
i

PMUj
Bus j

Fig. 2. Initialization method

The line flow is determined as given below:
Sij = 𝑉𝑖 𝐼𝑖∗ ± 𝑉𝑗 𝐼𝑗∗
The virtual generator and load are defined by direction of
power flow in a given line. The Voltage stability method
used in this work is based on Thevenin parameter tocompute voltage stability index as given by following
equations:𝑉

INTRODUCTION OF DCBLOCKSThe DCBlocks is a middleware framework that
contains all packages of distributed computation
algorithms. Following features are utilized in
DCBlocks:
 Agreement: the members communicate anagreement among the group members using ascalar value.
 Leader Election: The members elect leader
among the group for decision making for control
or communication action.
 Mutual Exclusion: Only one member can accessthe shared information at the time.
 Voting: The each member vote for selectedvalue from proposed values by other members.

Bus j

Virtual Bus j

VSIs = 1 −∗(𝑉𝑡ℎ,𝑗 × 𝑡ℎ,𝑖) − 𝑆𝐿𝑒𝑎𝑐ℎ𝑙𝑜𝑎𝑑
𝐼𝑡ℎ,𝑗

𝑉

∗

(𝑉𝑡ℎ,𝑗 × 𝑡ℎ,𝑖)𝐼𝑡ℎ,𝑗

𝑉𝑆𝐼 = max⁡(𝑉𝑆𝐼𝑠 , 𝑟𝑒𝑎𝑙(𝑉𝑆𝐼𝑠 ), 𝑖𝑚𝑎𝑔(𝑉𝑆𝐼𝑠 ))III.

SIMULATION RESULTS AND SUMMARY

The voltage instability is observed at but 7, 24, and 30 for
simulated IEEE 30 bus case. The DVS monitoring tool
triggers the DVSc tool for control action to mitigate voltage
instability problem. In this test case, the voltage instability
problem is resolved by using the reactive resource within
the group.

Power system application(as Decentralized Voltage Stability)

DCBlocks
Group
Manageme
nt Block

LeaderElection
Block

ABCAST
Block

Consensus/
Agreement
Block

MutualExclusion
Block

Fig. 1. Flamework of DCBlocks with power applicationII.

DCBLOCKS FOR VOLTAGE STABILITY

The distributed voltage stability (DVS) monitoringalgorithm requires generating network topology ofseveral small power systems clusters given large powersystem network. For this step, initialization method is
performed as shown in fig. 2.
Fig. 3. Voltage stability problem and control within the group

Continuous-time Generation Scheduling for
Ramping Scarcity Healing
Roohallah Khatami, Student Member, IEEE, and Masood Parvania, Member, IEEE
Department of Electrical and Computer Engineering
University of Utah, Salt Lake City, UT 84112
Emails: {roohallah.khatami,masood.parvania}@utah.edu

Abstract—Power generation of generating units is acontinuous-time process in real world. However, the current
discrete-time (e.g., hourly) generation scheduling practice approximates the power generation of units using zero-order piecewise constant trajectories, where the ramping of units is modeled
and constrained as the finite difference of two consecutive power
generation schedules. We argue that this zero-order model does
not efficiently utilize the ramping capability of the generating units and is increasingly contributing to ramping scarcity
events reported by multiple system operators. In this poster,
we first show that current discrete-time generation schedulingpractice amounts to approximating the generation trajectories
with linear splines. We then present a highly accurate modelfor the continuous-time generation trajectory of generating units
that utilizes the spline function space of Bernstein polynomials.
We define the continuous-time ramping trajectory of generating
units as the time derivative of the continuous-time generation
trajectory. We then propose a novel unit commitment (UC) modelthat incorporates the continuous-time generation and ramping
trajectories of the units in the day-ahead generation schedulingand commitment problem. The proposed day-ahead UC model isformulated as an instance of mixed-integer linear programming(MILP), with the same number of binary variables as the traditional hourly UC formulation. Numerical simulation over realload data from the California Independent System Operator(CAISO) demonstrate that the proposed UC model reduces thetotal day-ahead and real-time operation cost, and the number of
events of ramping scarcity in the real-time operations.

characteristic properties that allow us to more easily impose
continuity and smoothness conditions on generation trajectories, which paves the way to define ramping trajectories as thetime derivatives of the generation trajectories. The proposed
method more accurately discerns the ramping requirements of
the load trajectory, and gives the ultimate flexibility to thegenerating units to continuously match the load variations.

II. R ESULTS H IGHLIGHTS
We use the generating units data of the IEEE reliability test
system and load data from CAISO. The generation trajectories
associated with the hourly UC and the proposed continuoustime UC are shown in Fig. 1, where the units are subsumed
under 9 groups. As it is manifest, the continuous-time schedule
for generating units efficiently utilizes their ramping capability
to follow the continuous-time variations of the net-load, whileleaving less energy to schedule in the RT operation. The results
show that Thisin continuous-time
UC,forcompared
the hourly
article has been accepted
inclusion intoa futureissue ofUC,
this journal. Content
additional units are committed during peak load hours, which
3$59$1,$ $1' 6&$*/,21( 81,7 &200,70(17 :,7+ &217,182867,0( *(1(5$7,21 $1
would
considerably avoid ramping scarcity events.

I. I NTRODUCTION AND M ETHODOLOGY
In a market-based framework for trading electricity, several
forward and real-time markets have been designed to economically supply the load, each taking care of certain finite setof commitment decisions and operation schedules. The actual
real-time load of power system can be divided to a part that isscheduled for in day-ahead market, and the deviation between
the day-ahead load profile and the real-time load that needs
to be supplied by the available resources at the subsequent
stages of operation. The real-time load deviation is attributed
to the forecast errors and the approximation errors. While theformer has been studied extensively in the literature, the latterhas not been given enough attention, and the system operators
have continued to schedule the generation in a discrete-timebasis, which is increasingly contributing to ramping scarcity
events. In this work, we have proposed a continuous-timemodel for generation and ramping trajectories of generating
units. The proposed models deploys the spline function spaceof Bernstein polynomials. The Bernstein polynomials possess)LJ  6LPXE QXPEHU R)LJ  '$ *HQHUDWLRQ 7UDMHFWRU\ LQ D &DVH  E &DVH 

Fig. 1. Generation trajectories: a) hourly UC, b) continuous-time UC.

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1Multivariable Uncertainty Analysis for More
Reliable Dynamic Simulations of Power Systems
based on Combined Trajectory Sensitivity
Jae-Kyeong Kim, Student Member, IEEE, and Kyeon Hur, Senior Member, IEEE
School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea
Email: JK.Kim@yonsei.ac.kr, khur@yonsei.ac.kr

Abstract—This paper proposes an efficient multivariable uncertainty technique for more reliable dynamic analysis of thepower system based on the suggested combined trajectory sensitivity. Although some elaborate advanced methods can provide
the accuracy of the particular model and its parameters for the
power system analysis, it does not perfectly avoid the naturally
existing uncertainty, i.e. parameters of the load model derived
from the measurements, which means that the uncertainty should
be reflected for more reliable analysis instead of discovering
flawless variables of structure. Motivated by this situation, thisresearch allows for effectively identifying the tendency of sensitive
parameters so that relatively sensitive parameters are consideredin the multivariable analysis while mathematically correlatedparameters are also determined for more efficient analysis. Thisapproach consequently facilitates the efficient calculation of thenumerical sensitivity for simultaneous multivariable uncertainty
analysis by referring the result of analytical trajectory sensitivity,
which can be adopted in many areas of power system analysis
such as supporting proper decision making for control schemeor operations. Case studies for the Korean power system demonstrate the verification of the proposed method incorporating
the suggested new index, modified trajectory sensitivity index(MTSI), and correlation analysis as well as the feasibility ofthe proposed approach for reflecting simultaneous multivariable
uncertainty with reference to the various results obtained fromthe actual analysis of power system dynamics.

Fig. 1. Multivariable uncertainty boundary derived by using trajectory
sensitivity for case I.

Index Terms—Correlation analysis, dynamic load modeling,
multivariate uncertainty, power system stability, trajectory sensitivityẋ = F (x, y, λ)(1)

0 = G(x, y, λ)(2)where x, y, and λ are vectors of state variables, network
variables, and changeable system parameters, respectively.
For the analysis of evaluating the parametric changes on
the operational security boundary, the mathematical derivation
based on (1) and (2) is induced below:ẋλi = Fx xλi + Fy yλi + Fλi(3)

0 = Gx xλi + Gy yλi + Gλ0(4)

where λi is a specific one of variable system parameters.
II. K EY R ESULTS

Fig. 2. Multivariable uncertainty boundary derived by using trajectory
sensitivity for case II.

1.05

VoriginVsimulation
Vestimation1

Voltage (p.u.)

I. K EY EQUATIONSIn the time domain transient analysis, general power system
can be represented by the following set of dynamic algebraic
equations as below:

0.95

Time: 3
Voltage: 0.9249

0.9
Time: 3
Voltage: 0.8985

0.85

0.8

1

2

3

4

5
6
Time (second)

7

8

9

Fig. 3. Worst case analysis for case II: initial, simulated, and estimated result.

1Comparison of Probabilistic and Fuzzy Methods for
Estimating Dynamic Thermal Line Rating
Soheila Karimi, Student Member, IEEE, Petr Musilek, Senior Member, IEEE, and Andrew M Knight, Senior
Member, IEEE

T1
T2
T3
T4
T5
T6
T7
T8
T9

0.025

Probability

0.02
0.015
0.01
0.005
0
1600

Fig. 1.

1800

2000
2200
Allowable current [A]2400

Estimated probabilistic ampacity at individual towers

1
T1
T2
T3
T4
T5
T6
T7
T8
T9

0.9
0.8

Degree of Membership

0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
1600

Fig. 2.

1800

2000
2200
Allowable current [A]2400

Estimated fuzzy ampacity of all towers
4

3.5

x 10

1

3
0.8
2.5
0.6

2
1.5

0.4

1
0.2
0.5
0
1600

1700

1800

1900

Membership function of ampacity

I. M ETHODOLOGY
In a probabilistic approach to estimate line thermal capacity,
weather variables at each specific line span are modeled as
multivariate Gaussian random numbers to account for the data
uncertainty. The Monte Carlo Simulation (MCS) technique isemployed to generate different scenarios for relevant weather
data as it varies along the length of the line. Then, based on
the conductor heat balance equation, a probability distributionof line ampacity is estimated at different line spans. The rating
for the entire line is then calculated as the minimum of theline ampacities estimated at each span. In the fuzzy DTLR
approach to estimate line thermal capacity, fuzzy numbers
are used to describe the uncertainty of the input weather
data. The approach proposed in [1] is used to calculate thefuzzy capacities at each span at each specific time interval.
The ampacity for each line segment is computed using fuzzy
calculations of conductor thermal model at each line span.
Applying a fuzzy minimum operator, the ampacity of the entire
line is calculated as the minimum of the ampacities over all
line spans.
II. N UMERICAL R ESULTS
The fuzzy and probabilistic DTLR approaches presented inthis study are modeled on a transmission line in the Provinceof Alberta, Canada. Probability distributions of estimatedcapacities at different line sections are plotted in Fig. 1.
Estimated fuzzy ampacity at different line spans is depicted inFig. 2. Figure 3 plots the histogram of line ampacity estimatedusing the probabilistic approach of estimating transmission
line ampacity and compares it with the fuzzy estimation ofline ampacity. As it can be observed in these figures, the
DTLR estimation is similar for both fuzzy and probabilistic
approaches.
III. C ONCLUSION
Results confirmed that the simulation results for the fuzzy
DTLR approach are consistent with that performed by probabilistic DTLR estimation approach. However, the applicationof the fuzzy DTLR approach considerably reduces the computational cost of line rating estimation as the fuzzy DTLR model
does not need multiple simulations to estimate the distributionsof transmission line ampacity.

0.03

Histogram of ampacity−MCS

Abstract—Dynamic Thermal Line Rating (DTLR) incorporates
data on prevailing weather conditions to calculate actual currentcarrying capacity of transmission lines. These weather variables
include ambient temperature, wind speed, wind direction, andsolar intensity. In this paper, probability and fuzzy techniques are
adopted to effectively model the existing uncertainties in weather
variables and their performances are compared.

0
2000

Fig. 3. Comparison of estimated fuzzy ampacity and histogram of probabilistic ampacity of the entire line

R EFERENCES[1] H. Shaker, M. Fotuhi-Firuzabad, and F. Aminifar, “Fuzzy dynamic
thermal rating of transmission lines,” Power Delivery, IEEE Transactions
on, vol. 27, no. 4, pp. 1885–1892, 2012.

Implementation of an Architecture for SteadyState Model Synthesis of Active Distribution
Networks using Syncrophasors
Farhan Mahmood1, Hossein Hooshyar1, and Luigi Vanfretti1,2Electric Power & Energy Systems Department, KTH Royal Institute of Technology, Stockholm, Sweden
2
Research & Development Division, Statnett SF, Oslo, Norway

1

farhanm@.kth.se, hosseinh@kth.se, luigiv@kth.se, luigi.vanfretti@statnett.no,
Opal-RT Simulator

Grid model issimulated in real-time

SSMS
Algorithm

Reconfigurable data buffer
SSMS LabVIEW Application

Estimated Equivalent
Model

Filtered PMU data

Node 1

Node 2

KF Algorithm
I1

V1

Phase Unwrapping
data
bufferKalman Filter LabVIEW Application
Raw PMU Data

Current V2
NI-cRIO PMU1 Amplifier

PDC Stream

S3DK real-timedata mediator

I2

SEL-PDC 5073
NI-cRIO PMU2

Current
AmplifierFig. 2. Hardware in the loop (HIL) Laboratory Test Setup

II. KEY RESULTS
1.1
1
0.9
0.8

Vmag (p.u.)

Vmag (p.u.)

Abstract-- With the increase of renewable generation sourcesin distribution networks, it is becoming necessary to increase
observability between Transmission and Distribution (T&D)
grids for operational security and real-time security assessment.
This is challenging with today’s approach, for example, as
currently most Transmission System Operators (TSOs) have
limiteddistribution
networkobservability(toofew
measurements at the distribution level), insufficient networkmodeling information, challenges with model information
management, and computational issues when handling larger
and larger models in network management functions (at both
T&D). There has been a recent interest in North America andEurope to explore the potential of utilizing Phasor Measurement
Units (PMUs) at distribution networks. Real-time syncrophasor
data can be used to extract key information to be used in
distribution network management system functions.
This poster demonstrates an architecture in which the PMU
data are acquired, curated and analyzed to be used for an
application which preforms steady state model synthesis (SSMS)
of active distribution networks. The Synthesized models can besent to TSOs to be used in their energy management functions.
The work has been conducted within the European project
IDE4L (Ideal Grid for All) at KTH SmarTS Lab where thearchitecture has been developed, implemented and tested using
real-time hardware-in-the-loop simulation setup.

0.7
Raw data
Estimated

0.6
0.5
0.4
0

20

40

60

80

100

120

140

160

0

20

40

60

80
Time (sec)

100

120

140

160

0.8
0.6
0.4

Vang
(rad)
Vang (rad)I. KEY FIGURES

0.2
0
-0.2
-0.4
-0.6

Fig. 3. PMU Data Processing: Kalman Filter extracts the steady statecomponent, filters the noise and compensates for missing data samples from
PMU voltage magnitude & voltage angle signal.
Event1R (p.u.)Ea ( p.u.)

Event2

Event1
Event2a

X (p.u.)Fig. 1. Conceptual Architecture: PMU data polluted by noise and outliers are
fed to Kalman Filter (KF) data processing application via a real-time data
mediator. KF processes the data and extracts the steady state component of
input signal to feed it to the SSMS application. Steady state equivalent modelof active distribution network is synthesized by SSMS application and sent toTSOs.

δ ( rad)Event1

Event2
0

80

160

0

Event1

Event280

160

Number of estimatesFig. 4. Real-time estimation of synthesized model parameters of an active
distribution network by SSMS application.

Microgrid Controller Hardware-in-the-Loop Testingwith Model Development of Common Equipment
Matthew Backes and Kendall Nowocin
MIT Lincoln Laboratory
Lexington, MA{mbackes, knowocin}@mit.edu

Abstract—As electric power customers look for reductionsin the cost of energy, increases in the level of service reliability,
and reductions in greenhouse gas emissions a common solution
is a microgrid. These microgrids are smaller power systems
where distributed energy resources are used to power local
electric load(s). As more functions and capabilities are
required of microgrids, there is a need to be able to havetesting capabilities for microgrid controllers. Controller
hardware-in-the-loop is one method to validate microgrid
controller performance before equipment is installed which
will reduce costs and ensure increased reliability. Models ofcommon power systems equipment were developed to achieve
realistic interactions with the microgrid controller under test.
We describe the current controller hardware-in-the-loop
testbed that was built at MIT Lincoln Laboratory, and the
openly available equipment models developed. This testbed
was able to test microgrid controllers under a variety of
scenarios, including islanding, short-circuit analysis, and cyberattacks.
Keywords—microgrid; power system control; controller
hardware-in-the-loop; generator; excitation system; load profile .

I. INTRODUCTION
A controller hardware-in-the-loop (C-HIL) testbed allows
for the interface of software power system simulations andactual analog and digital signals to be sent to powerelectronics hardware. The benefit of such a testbed is theability to simulate varied and complex power systemscenarios in software, yet be able to determine how actual
hardware devices would respond as if they were actuallydeployed in the field. This leads to a decrease in capital
investment for laboratories, and increase in the speed ofdevelopment of testbed scenarios. The expensive constraintsof having to build a full-power system to test are removed.
As microgrid continue to be developed, there is sure to be
increasing capabilities and functions that microgrids will
provide. To do so, microgrids will need to have a master
controller to make sure the microgrid is reliable and efficient.
However, to date, there has not been a standard model for
microgrid controller developers to build with in mind, and no
testing standards have been officially adopted. With this inmind, the Energy Systems group at MIT Lincoln Laboratory
surveyed the different capabilities of HIL vendors and has
setup a Controller HIL testbed for microgrid controllers to betested using an OPAL-RT real-time digital simulator.
II. MODELED POWER SYSTEM AND EQUIPMENT
The power system used was an anonymized physicalradial feeder with 10 MVA nominal power, 10 buses and 25
lines, 10 load profiles, and a 1 and 4 MVA generator. The 19
relay/monitoring points and 2 other DERs (3.5 MW solar,
and 4 MW 0.5MWh battery) were simulated. Data sheets on
the physical generators were collected and sub-minutemonitoring of the physical load’s power were installed. This
information was used to develop the Matlab SimPower
Systems models of the power system, load profiles, andgenerators that was released by MIT Lincoln Laboratory.
13.8 kV, 1.0 Vpu
I3ɸ: 15730 A @ X/R = 7.9
I1ɸ: 15240 A @ X/R = 2.6

R1500 kcmil
350 ft

1/0 AWG,
1000 ft

1/0 AWG,
50 ft

R4

R3

500 kcmil
51 ft
R2

4/0 AWG,
20 ft

4/0 AWG,
700 ft500 kcmil
1800 ft
500 kcmil
50 ft
R122000 kVA13.8/0.46 kV

1/0 AWG,
50 ft

G1

R11

2000 kVA13.8/0.46 kV

3750 kVA13.8/4.16 kV
R7

M
C
B001

250 hp
460 V

4/0 AWG,
51 ft

I
B004

R17

R10

500 kVA
13.8/0.46 kV

R15

R16
4/0 AWG,
51 ft

R14
4/0 AWG,
51 ft

4/0 AWG,
51 ft
2000 kVA
4.16/0.208 kV

1000 kVA
4.16/0.208 kV

500 kcmil
25 ft

4/0 AWG,
100 ft500 kcmil
25 ft
PV
3.5 MW

P
B003

I
B014

I
B013

3750 kVA13.8/0.46 kV

500 kcmil
50 ft

500 kcmil
125 ft
C
B011

I
B010

P
B022

M

250 hp460 V

4000 kVA
4.16/2.4 kV
4/0 AWG,
80 ft

500 kcmil
25 ft
P
B015

Relay Protection Functions

Battery
4 MVA

Legend
P
I
C
Bxxx

R5
500 kcmil
251 ft

4 MVA

2000 kVA13.8/0.46 kV

I
B009

2000 kVA
4.16/0.208 kV

500 kcmil
25 ft

2/0 AWG,
1010 ft

750 kVA
13.8/0.46 kV

500 kcmil
50 ft

4/0
AWG, 51ft

1000 kVA
4.16/0.46 kV

R8

G2
R9

R13

4000 kVA
4.16/2.4 kV

500 kcmil
100 ft4/0, 1400ft

4/0 AWG,
25 ft

1/0 AWG,
51 ft

4/0 AWG,
20 ft

4/0 AWG,
30 ft

1 MVA

500 kcmil
20 ft

R6
1/0 AWG,
1000 ft

500 kcmil
1851 ftProtection, Relay and Monitoring (PRM)
Priority load
Interruptible load
Critical load
Load I.D.

50

52

50

51

51

27

27

59

59

25

25

MODBUS

52

1547
Table 1-2
Gen.
Synch

MODBUS

III. MODEL UNIT TEST SCENARIOSThe 1 and 4 MVA synchronous generators had modeled
power system connections, machine parameters, andexcitation system (governor and automatic voltage regulator)that were tuned for the respective generator. These models
were connected to a simulated and physical secondary
controller. The unit test of the model consisted of emergency
startup, synchronization of the generator or mains circuit
breaker, changes in real and reactive load, and shutdown.
The load models used within the microgrid model werebased off an actual load profile from a radial feeder. A fullyear of historical load power consumption data was madeavailable to us, and the dynamics of the realistic varying load
were incorporated into the simulation model. From thehistorical data, we were able to distinguish between the
different reactive and resistive loads. The load model
developed used both reactive power loads, typical ofindustrial customers that have induction motors, and resistive
loads, typical of lighting loads. The load model complexity
ensures that the microgrid controller is tested on its ability todispatch enough generation for the dynamic active andreactive power loads.
IV. CONCLUSION
In this work, we have expanded the models in the Matlab
SimPower Systems library with developing better models ofcommon power system equipment for microgrid controller
testing, and reproduced an accurate model of a feeder level
microgrid. We held a symposium in November 2015 todemonstrate our HIL testbed with multiple industry vendors
testing their microgrid controllers. We are now working on
developing the expanded library of models and validatingwith physical equipment. This will result in anothersymposium with additional microgrid controller vendors.

Evaluation of Power Distribution Systems Operational
Resilience
Manish Mohanpurkar, Rob Hovsapian

Sayonsom Chanda, Anurag K. Srivastava

Idaho National Laboratory,
Idaho Falls, Idaho 83415, USA{ manish.mohanpurkar, rob.hovsapian}@inl.gov

School of Electrical Engineering and Computer Science
Washington State University
Pullman, Washington 99163, USA{schanda,asrivast}@eecs.wsu.edu

Abstract— It is essential to improve the resiliency of electric
power distribution systems (PDS) with the increase in extreme
weather events, number of malicious threats and consumers’
need for higher reliability. This work provides a formal
approach to define and evaluate the operational resiliency of
PDS, using a combination of steady-state and real-time
simulation tools. Real time simulation tools helps with analyzing
impact of short term events which might affect operational
resiliency in long term. The proposed theoretical approach isvalidated using Consortium for Electric Reliability Technology
Solutions (CERTS) microgrid and simulation results
demonstrate the ability to quantify the resiliency.
Index Terms—Distribution Systems, Real-Time Simulations,Electric vehicles, Microgrids, Reliability, Resiliency

I. INTRODUCTION
Resilience of Power Distribution System (PDS) has gained
significant traction after impact of Superstorm Sandy (2012)
on the power grid reliability. However, inadequate theoreticalfoundations in the definition and metrics relevant to PDS
resilience challenge the practical implementation of resiliencein electric utilities. PDS resilience is the ability of the
network to resist discontinuity of power supply to criticalloads during stressful operating conditions, and recover fromany damages during the event [1], [2]. PDS resilience metrics
are important: (i) to justify investments in infrastructural
upgrades for higher resilience, and (ii) evaluate the suitabilityof a particular approach to be taken by an operator toadequately enhance the resilience during a contingency or
attack.
There are four main discussion topics in this poster – (i)time dependent definition of resilience (ii) a genericframework for evaluating resilience (iii) using plug-in electricvehicles (PEV) as an example of distributed energy resources
to illustrate the the framework to evaluate PDS resilience, and(iv) using a combination of steady-state and dynamic
simulation tools to determine operational resilience of a PDS(or a microgrid capable of islanding from the PDS).

II. TIME-DEPENDENT DEFINITION OF RESILIENCE
There are several working definitions of PDS resilience.
Some authors have defined resilience as a function of theprobability of attack and the consequence of that attack.
Resilience of PDS has also been defined based on the natureof attack on the system. A framework to determine resiliencebased on evaluation of infrastructure, and time taken to
restore to service has been proposed. However, metrics basedon inventory assumes all resilience-enabling infrastructures
will operate during or after contingency, and cannot be usedin real-time. Cost metrics of damages incurred due to an
attack has been commonly used to report a lack of resilienceof the PDS [2]. The fundamental drawback of each of thesedefinitions of resilience is that these definitions do notconsider the durations of unfavorable events affecting the
PDS. Consequently, the resilience metric derived using thesedefinitions are meaningful only in the context of a specificattack, network topology, or data availability.
For resilience and reliability analysis related to continuityof power supply, steady state analysis is sufficient andnecessary. In order to evaluate PDS resilience to transient
threats, digital real-time simulation (DRTS) is necessary but
not sufficient and must be reported in combination withsteady-state metrics. The proposed resiliency metric in thiswork can be used as a foundation for several further
investigations.
REFERENCES
H. H. Willis and K. Loa, “Measuring the Resilience ofEnergy Distribution Systems,” 2015.
[2]
S. Chanda and A. K. Srivastava, “Quantifying resiliency ofsmart power distribution systems with distributed energy
resources,” in Industrial Electronics (ISIE), 2015 IEEE
24th International Symposium on, 2015, pp. 766–771.
[3]
M. J. Sullivan, “Interruption Costs, Customer Satisfaction
and Expectations for Service Reliability,” IEEE Trans.
Power Syst., vol. 11, no. 2, pp. 989–995, 1996.
[4]
S. Arianos, E. Bompard, a. Carbone, and F. Xue, “Power
grids vulnerability: a complex network approach,” arXiv.org, pp. 1–
16, 2009.
[1]

Mitigate Overestimation of Voltage Stability Margin
by Coupled Single-Port Circuit Models
Haoyu Yuan, Xue Li, Fangxing Li, Xin Fang, and Hantao Cui

Qinran Hu

Department of Electrical Engineering and Computer Science
The University of Tennessee, Knoxville
Knoxville, TN 37996, USA{hyuan2, fli6, xli44, xfang2, and hcui7}@vols.utk.edu

School of Engineering and Applied Science
Harvard University
Cambridge, MA 02138, USA
qhu@ieee.org

I.

OVERESTIMATIONS OF VOLTAGE STABILITY MARGINSThe overestimation is first observed in IEEE 118-bus
system when all the loads and generation are increasing
proportionally. The loading margins of selected critical buses
using CSPC method are shown in Fig. 1. According to Fig. 1,
the most critical bus is Bus 44 with the smallest LM of 3.81
which means the system can support 381% of the base load.
However, the LM of the system calculated throughcontinuation power flow is 2.19 and is represented by the redline in Fig. 1. It is clear that CSPC method overestimates thevoltage stability margin of the IEEE 118-bus system in thiscase. The mismatch in terms of LM is around 1.62 or 162%.

II.

METHODS TO MITIGATE THE OVERESTIMATIONS

A. Modified Coupled Single-Port Model (MD)This approach was originally proposed by Liu and Chu infor compensating underestimation of voltage stability marginusing coupled single-port circuit under non-proportional
increased load.
B. Negative Load Model(NL)
One straightforward solution is to change type of the weakgenerator buses to load buses and treat them as negative loads.
This way, the isolation of the critical bus or buses is broken
and the coupling of outside loads can be modeled into the TE
impedance of the critical.
III.

6
Zload 445

Zth 44 MD
Zth 44 NL

4
3
2
1
0

0

0.5

10

1

1.5

2

Loading factor

Figure 2. Impedances of equivalent circuit of Bus 44 in 118-bus system
8

4
CPF
NL
MD

6

Loading margin

Loading margin (p.u.)

SIMULATION RESULTS

Both methods are then applied to the IEEE 118-bus
system. When applying NL method, generator buses 34, 36,
41, and 42 are changed to load buses. These 4 generator buses
are all generator buses with 0 real power output near criticalload buses.

Impedance (p.u.)

Abstract—Wide-area measurement-based voltage stability
assessment (VSA) by coupled single-port circuit models has been
widely discussed recently. This method models the coupling
effects of load buses within a meshed network into extra
impedance of a single-port model for each load bus. In
simulation studies, overestimations of voltage stability marginusing this approach have been observed when critical load bus
or buses are decoupled from other load buses. In this paper, theoverestimations are reported for the first time through examples
and are further analyzed in details. Moreover, to mitigate suchoverestimations, two methods are proposed: one method uses a
mitigation factor based on actual system reactive power
response; the other method changes the types of certain weakgeneration buses when forming the coupled impedance. Bothapproaches are applied to a sample 4-bus system as well as theIEEE 118-bus system and successfully mitigate theoverestimations.

4
20

13 20 21 22 43 44 45 51 52 53 82 83 94 95 96 97 117
Bus NoFigure 1. Loading margin of selected buses of IEEE 118-bus system.

3

2

1

0

0

0.5

1

1.5

2

Loading factor

Figure 3. Loading margin of Bus 44 in 118-bus system

1

Insulation Coordination Analysis for Substation
Surge Arrester Applications
Hardik Parikh 1, Student Member, IEEE and Hugo Mena 2, Member, IEEE

Abstract— Previous studies have shown that a substationequipments can be damaged by high-frequency waveforms.
Surge arrestors are generally used to divert the additional energyof these waveforms to the ground. The process of selecting anappropriate surge arrestor for a substation equipment is known
as Insulation Coordination. This poster gives an overview of theInsulation coordination process. We focus primarily on the
different calculation methods involved and an analysis to identify
the surge arrestor adequacy.
The technique discussed here provides the efficient fast andreliable method for Insulation coordination analysis. It is mainly
based on the calculation guidelines as prescribed in “IEEE Guide
for the application of Metal-Oxide Surge Arresters for
Alternating-Current Systems” standard C62.22-2009.
Index Terms— Basic Lightning Impulse Insulation Levels(BILs); Basic Switching Impulse Insulation Levels (BSLs);
Chopped Wave Withstand (CWW); Front-of-wave protective
level (FOW); Lightning Impulse Protective Level (LPL);
Switching Impulse Protective Level (SPL); Protective Ratio (PR);Protective Margin (PM); Electro Magnetic Transient Program(EMTP)

I. INTRODUCTION

WITH

the development of protection techniques to avoid
damage due to the high frequency waveforms,
Insulation coordination analysis has attracted a lot attention.
Substation design requires significant attention to Insulation
coordination due to exposure to high frequency surge
waveforms. These all are short duration transient waveforms
which may damage
Insulation strength of the operating
devices. In general Insulation coordination is coordinating theinsulation strength of the device to be protected withprotective device. The main motivation to perform thisanalysis is to design a robust and reliable system that canwithstand the lightning and switching surges. The Insulation
strength of operating equipment is not designed to withstandstress caused by such high frequency waveforms. Proper
design and coordination allows selection of surge arrestors
that can successfully withstand the event and in terms avoid
damage to operating device.

Basic terminology is selecting higher insulation strength of
the surge arrestors than that of equipment to be protected.
However the main criteria for the study are the parameters toconsider for selection, determining the optimum surgearrestors rating and compliance with industrial standards.
II. INSULATION COORDINATION TECHNIQUE AND SURGEARRESTOR SELECTION

In this section analogy for the study are discussed in detail.
The analysis here considers the analytical method forinsulation coordination. The degree of coordination ismeasured by the protective ratio (PR). It can be calculated as
CWW
BIL
BSL
, PRL2=, PRs=. A ratio of 1.2 is generally
PRL1=
FOW
LPL
SPL
considered acceptable for non-self-restoring insulation for
PRL1 and PRL2. Where as a ratio of 1.15 is generally
considered acceptable for non-self-restoring insulation forPRs. Here PRL1 and PRL2 correspond to lighting waveforms
and PRs corresponds to switching waveforms. If the PR
obtained by this method doesn’t satisfy IEEE C62.22-2009
criteria, then the surge arrestors are not suitable for protection.
In such scenario surge arrestors with higher insulation strength
are considered. To demonstrate the method of coordination
Hubbel Power system station class surge arrestor
EVP0024003001 is considered for calculating Table I.
MCOV, BIL, FOW, LPL and SPL ratings are obtained fromthe product catalogue. Whereas CWW, LPL, SPL arecalculated based on the imposed waveforms.
TABLE I INSULATION COORDINATION SUMMARYMCOV BIL FOW LPL SPL
PRL1 PRL2 PRs
(kV)
(kV)
(kV)
(kV) (kV)
24.4

200

81.2

72.5

57.5

2.25

2.29

2.40

It is important that the technique here considers standard
Lightning and switching waveforms. This method doesnot
apply for prolonged duration as it requires consideration ofenergy dissipation calculations and for such analysis EMTP
type models are developed for better accuracy and results.
REFERENCES
IEEE STANDARD

[1]
[2]
1

2

The University Of Texas at Arlington, Arlington, TX 76013,USA
Email: hardik.parikh@mavs.uta.edu
Electric Power Engineers Inc., Austin, TX 78738, USA
Email: hmena@epeconsulting.com[3]

IEEE Std C62.22-2009, “IEEE Guide for the Application of MetalOxide Surge Arresters for Alternating-Current Systems.”
Hileman, Andrew R., “Insulation coordination for power systems”. CRC
Press, 1999. (pp. 557-625).
Agrawal, K. C., “Industrial power engineering handbook”. Newnes,
2001.(pp. 18/587- 18/624).

1Real-Time Detection of Angle Instability using
Synchrophasors
Yuan Zhi, Student Member, IEEE, and Vaithianathan“Mani” Venkatasubramanian, Fellow, IEEE

II. KEY FORMULATIONS

Abstract—The algorithms and the controller proposed in thisposter detect the fast separation of phase angles among thecritical areas automatically by using the synchrophasors, andproceed to mitigate the emerging angle instability by triggering
suitable control action. Briefly, the algorithms initiate tripping of
critical generators in the accelerating part of the system whennecessary, and also initiate load shedding in the decelerating partof the system whenever necessary. The algorithms is in the factthat all the decisions are made in real-time purely based on the
wide-area synchrophasor measurements without any knowledgeof the details of relay actions that may have resulted in the angle
instability phenomenon.

I. INTRODUCTIONS

ynchrophasors enable real-time wide-area monitoring of
power system conditions that serve as an attractive
platform for monitoring real-time operational reliability
status. Even in an ideal future system, where the voltage
phasors may be available at every substation in real-time,
monitoring the dynamic state is only the first step inunderstanding dynamic stability properties of the complex
power system. For instance, in the context of angle stability,
we need to know whether or not a generator or group ofgenerators can potentially become unstable based oninteractions with other components in the system duringcontingency events.
This paper focuses on three algorithms for online angle
instability detection using measurements of the power system
by synchrophasors. The first algorithm is phase angle
algorithm that estimates how much phase angles drift awayfrom the center of inertia angle reference to judge angle
stability properties in real-time. The second algorithm is anenergy function based algorithm that estimates whether a realtime estimate of system energy stays below pre-specified
thresholds. The third algorithm applies system effort functionthat uses action principle from physics to quantify when thesystem effort function becomes unusually large duringcontingencies. This paper extends these three algorithms
towards a comprehensive real-time angle stability detection
and mitigation methodology for large-scale power systems
The three algorithms were tested on Kundur two-area test
system. Also it is tested on a realistic large-scale model of thewestern American power grid.

.
III. KEY RESULTS
Instability threshold for two-area system
Fault Bus1
2
3
4

CCT(s)
0.36
0.38
0.37
0.5

COA(deg)
55.2
60.6
-93.6
-98

TE(pu)
45.9
27.7
40.1
31.8

EFF(pu)
-1.1
-1.2
-1.2
-1.9

Instabilty detection result in two area system (fault during 5s – 5.1s)
Methods
Center of Angle
Total Energy
EffortTrigger Time
for Area1(sec)
6.1333
7.4
6.8666

Trigger Time forArea2(sec)
6.8
7.26667.3666

Mitigating control for Kundur case
MethodGen Reduced (G1)

Load Shed(bus 9)

COA
EnergyEffort

350 MW
500 MW
610 MW

590 MW
767 MW
806 MW

Generation tripping and Load Shedding amount in WECC system
MethodCOA
EnergyEffortGen Reduced
2000 MW
2300 MW
1500 MW

Load Shed
1800 MW
2500 MW
1300 MWReal-time detection and localization events in power system with subspacetracking and clustering method
Wenting Li
Rensselaer Polytechnic Institute
Email: liw14@rpi.edu

Abstract
Based on the real-time synchrophasor measurements, an event detection and localization algorithm
is proposed. It can detect abnormal events by tracking
a low-rank subspace rather than the full data matrix.
Moreoever, noticing the different space distribution of
the event and nonevent parts of the subspace, thispaper proposed the idea of localizing the event area bysolving a subspace cluster issue with robust subspacecluster (RSC). The detection and localization algorithm
was validated using a 68-bus power system model andthe results proved the feasibility and reliability.

1. Introduction
Phasor measurement units (PMUs)in the powersytem are benefical to power system state estimation[1]. Missing data during the PMUs communication canbe solved by some matrix completion algorithm[2],Cyber data attack issues in power network havebeen dealt with through some convex optimization
method[3]. Recently many literatures begin to identify
different types of power system states by analyzing
PMU data[4] [7]. In this paper, it proposed an algorithm to detect various types of abnormal eventsand localize the most related area. Considering thegeographical sparsity [9], our method takes advantagesof the low-rank property of PMU data and does not
require formulating the complex power system models.
It detect events in power system online by tracking a
low-rank subspace. The other main contribution is thatbased on the subspace we proposed one idea of treating
localizing the events as a subspace clustering issue by
noticing the variation of the event and nonevent partsof the subspace, and then seperate the event-relatedarea by the robust subspace clustering methods, whichis accurate even in noise conditions.

Meng Wang
Rensselaer Polytechnic Institute
Email: wangm7@rpi.edu

2. Main algorithm
On normal condition, power system operates at theequilibrium point , thus the PMU data shows a lowrank property , but after event occurs the rank will
increase. Thus a subspace tracking algorithm ,Stream
Pattern dIscoveRy in multIple Time-series (SPIRIT)[10] , being able to automatically adjust the numberof subspace bases was adopted here. Based on it, an
efficient way to detect event is achieved: Project the
new column to the current basis ; estimate reconstruction errors; ;update bases and finally check whether
the variation of reconstruction error is beyond thepermitted range, once this threshold is exceeded, it will
notice the occurence of event.
Noticing the existence of coherent area in powersystem [11][12], the localization of the abnormal
event can be solved by finding the abnormal area
in power system, which is more reliable and can be
implemented through RSC. Oberserving that the eventand nonevent parts of the subspace go to different
dimensional space after event occurs, this difference
can be noticed by the subspace clustering algorithm,
RSC,even in noise conditions, which actually is an
optimization problem below of finding the sparse
coefficients β :min1β∈RN 2kyi −Y βk2l2 +λkβkl1

subject to βi = 0. (1)These coefficients are then used by spectral clustering
method to figure out the abnormal measurements.

3. Conclusion
Online detecting and localizing events based on asubspace is proposed. The simulation showed that theevent can be detected and the related area can be
identified in a short time and this method is effective
for different types of events.

1

Risk Assessment for Vegetation Impact on
Outages in Electric Distribution Systems
Tatjana Dokic and Mladen Kezunovic
Department of Electrical and Computer Engineering
Texas A&M University
College Station, Texas, 77840, USA
tatjana.djokic@tamu.edu, pchen01@tamu.edu, kezunov@ece.tamu.edu
Abstract— The objective of this research is development ofpredictive means for determining optimal tree trimming schedulethat would minimize the risk of vegetation-related outages in
distribution systems. By analyzing impact of vegetation
characteristics to power system outages, it is possible to predict
where and when vegetation may become a risk.

I. INTRODUCTION

Combination of severe weather conditions and vegetation
impact is the most dominant cause of outages in electric
power systems as presented in Fig. 1. Impact of high speed
wind and heavy precipitation may cause trees to come incontact with power lines due to the following reasons: a)branches break off and fly into lines; b) complete trees topple
and are moved by wind, or c) tree branches grow into thelines. The main protection management scheme today istrimming of trees around the power lines.
II. TREE TRIMMING SCHEDULE
Distribution lines don’t have defined right of way andoften they would be placed in a vicinity of the surrounding
vegetation. Due to the high expenses of trimming large areas
populated by many distribution feeders, it is not economical tohave all trees securely trimmed at all times. Often thetrimming in certain areas of the network would be performed
after several outages have already happened.
The process of tree trimming can be substantially improvedby using weather data and tree growth data to automaticallyassess the risk. There is no developed automatic tool for
vegetation management at this time that would be able toprocess data in real time and provide the location of hazardous
vegetation and its time of impact. Thus, this study is of great
importance for automated power system monitoring andoutage management.

Automated analysis of data provides means for developing
dynamic scheduler that takes into account current state of the
network and surrounding vegetation, as well as changing
weather conditions and its impact on vegetation. Thus, thesystem is able to adapt to changing weather and vegetation
conditions. The risk analysis provides information about areasof network with highest risk to vegetation-related outages thatwill be set as most important areas to perform the tree
trimming.
IV. CASE STUDY
The study analyzes a portion of the distribution network
located in Harris County, Texas as presented in Fig. 2. Types
and characteristics of vegetation in the area are used as a basedata. The historical weather data is correlated with historicaloutage data in time and space. Then, vegetation characteristics
for each event are extracted. All data is used as an input to thepredictive model based on linear regression that will
determine the probability of a future outages based onvegetation configuration and weather forecast.

Fig. 1. Typical causes of outages in power systems for Texas 2014.

III. PREDICTIVE TREE TRIMMING SCHEDULER
Following data sources are essential for development of theautomated vegetation management scheduler: vegetation data
(tree canopy height, spread, growth rate, and distance to powerlines), weather (historical and forecast data for wind speed anddirection, precipitation, humidity, and temperature), historical
power system outage data, power system assets data, andhistorical tree trimming data.
Fig. 2. Vegetation types and the location of interest.

Condition Monitoring of a Large-scale PV Power Plant
in Australia
Amit Dhoke, Student Member, IEEE, Rahul Sharma, Member, IEEE and Tapan K. Saha, Senior Member, IEEE
The University of Queensland
Brisbane, Australia
a.dhoke@uq.edu.au, rahul.sharma@uq.edu.au, saha@itee.uq.edu.au

I.

KEY EQUATIONS

PV output prediction model parameters representing shortcircuit current, open circuit voltage, fill factor and power
output are calculated using (1)-(4) respectively. Block
diagram of the condition monitoring system is presented inFig. 1.

I sc  N s .POAI .I scp .[1   (Tm  25)] / 1000

(1)

Voc  N p .Vocp .[1   (Tm  25)][1   .ln( POAI / 1000)](2)

FF  a  (b.POAI  c) / ln( POAI ) 
Tm ( d  e.POAI  f / POAI )B : 27/03/2014
3000

Power (W)

1500
1000
500
0

8

11

14

Fig. 2 depicts the variation in PV power output during
clear sky and cloudy day for measured and simulation results.
Figs. 3 and 4 are showing string current and voltage for faulty
string and bypassed module conditions respectively.
18
String 1
String 2

15
12
9
6
3
0

(4)

5

8

11

14

17

20

Time (Hour)

Figure 3. String current comparison for string fault
500

Condition
Monitoring

Ta

String 2

Fault
Detection

String n

String n

400
300
200
100

Figure 1. Block diagram of condition monitoring system

II.

0String 1
String 2
5

8

KEY RESULTS

11
14Time (Hour)

17

20

Figure 4. String voltage comparison for module bypassed

A : 19/08/2014
12000

Table 1 presents the mean and standard deviation values
for string currents in faulty string condition.

PMes

10000

Power (W)20

String 1

String 1
String 2

PSim

8000

Table 1. Average values and standard deviation for string current

6000
4000
2000
0

17

Figure 2. Power comparisons for clear sky (A) and cloudy (B) day

Voltage (V)Measuredparameters

Temperature

5

Time (Hour)

Health status
Irradiance

PSim

2000

(3)

Pout  I sc .Voc .FF
GHI

PMes

2500

Current (A)Abstract— This paper considers the problem of condition
monitoring and fault detection in an existing solar photovoltaic(PV) plant in Australia. A PV prediction model is proposed toaccurately model the PV plant output. This model is then usedwith three condition monitoring and fault detection methods.
The considered methods involve comparison of measured andmodeled voltage and current ratios with appropriate thresholds
and adjacent strings values. The procedure to calculate
thresholds is described. The proposed PV model and thecondition monitoring approaches are collectively used with real
PV data. As a result, the string disconnection and bypassed
module faults are detected. It is also found that the string levelmonitoring is ideally suited for reliable condition monitoring
and fault detection especially for large PV plants.

5

8

11

14
Time (Hour)

17

20

StringMean Value

Std. Deviation (σ)

String 1
String 2

8.31
4.21

5.81
2.96

An Adaptive Restorative Method for Resilient
Power Distribution Networks
Ranadhir Sarkar, Azwirman Gusrialdi, and Zhihua Qu
Department of Electrical and Computer Engineering
University of Central Florida
Orlando, USA
Email: ranadhir.sarkar@knights.ucf.edu, azwirman.gusrialdi@ucf.edu and qu@ucf.edu

Abstract—In this paper, we propose a restorative method tominimize the unserved demand in a power distribution network
due to line outage. The restoration is achieved by tie-line
switching and by solving a mixed-integer non-linear problem. By
exploiting the information on pre-contingency solution, topology
together with post-contingency line congestion requirements andin combination with an improved greedy search algorithm, amethod is proposed which reduces the search space of the
combinatorial problem.

I. I NTRODUCTION
Modern critical infrastructures like electric power network
are vulnerable to damage due to natural calamities such as
earthquake, hurricane, and flood. In this paper, we consider arestorative problem for power distribution networks with highlevel of renewable penetration which utilizes switching of tielines and sectionalizing switches to minimize the unserved
demand due to line outage. The problem is formulated as amixed-integer nonlinear programming problem which is hard
to solve due to its combinatorial issue. We propose a novel
method which utilizes the information on the pre-contingency
power flow solution, network topology, and post-contingency
line congestion requirements together with the fault location
for restoration.
For a balanced distribution network represented by a set ofnodes V and edges E, let the set of lines outaged is given byE r . We aim to minimize the unserved load due to the line
outage by closing some of the tie-lines E τ and opening someof the sectionalizing switches E s which is formulated asargmin
post

Pij

,xij

subject to

|V|
X
i=1

wi Pi −

X

post
Pij

III. C ASE S TUDY
In this section, case studies for one and multiple line outage
of IEEE 33-bus system are presented. The results are shownin Table I.
TABLE IT IE - LINES , S ECTIONALIZING SWITCHES USED AND % OF LOAD SHEDDINGFOR ONE AND MULTIPLE LINE OUTAGE

Er

j∈Ni

post
max
|Pij
| ≤ xij Pij
,

(1)

post
Pimin ≤ Pi − Pij≤ Pimax ,X

II. M ETHODS F OR T IE -L INE S WITCHING
To reduce the complexity of (1), we present methods oftie-line switching for one and multiple line outage in orderto minimize load shedding. For one line outage, first we
identify the path which connects the nodes which becomes
isolated from the sub-station bus and calculate the available
slot for power flow based on the line capacity and pre-outage
power flow. Next, we compute the power that can be re-routed
through the path after the tie-lines are connected. Search space
is further reduced by chosing a set of tie-lines which are able to
re-route more power than its neighboring tie-lines. In the next
step, we utilize a performance function based on the locationof the outaged nodes to get an intutive idea on how muchdemand can be met to decide the optimal tie-line switching.
After connecting the optimal tie-line, if the unserved demandis greater than zero, we connect additional tie-line from thereduced search space to supply the unserved demand. For
multiple line outage, we propose a method based on one line
outage and in combination with an improved greedy searchalgorithm which reduces the search space of the combinatorial
problem in order to minimize load shedding.

(28, 29)(6, 7)(28, 29)

Switches
xij = 1, (i, j) ∈ E τ xij = 0, (i, j) ∈ E s(25, 29) (18, 33)(31, 32)(8, 21)(31, 32)(25, 29)(18, 33)% Loadshedding
1.35
1.35

τ

xij ≤ |V| − 1, ∀(i, j) ∈ {E ∪ E },xij ∈ {0, 1},
xij = 1, ∀(i, j) ∈ E\{E r ∪ E s },where Pi is the bus injection at node i, Pijpost represent thepower flow in the lines after the outage, Pijmax represent theline capacity of the power lines, xij signifies the switching of
the lines and wi > 0 denotes the weights assigned on bus i.

IV. C ONCLUSION
In this paper, a restorative method of power distribution
networks is presented. The restoration for one and multiple
line outage is achieved by utilizing an improved greedy searchalgorithm which reduces the search space of the proposed
mixed-integer optimization problem.

Predicting Impact of Weather Caused Blackouts on
Electricity Customers Based on Risk Assessment
Qin Yan, Student Member, IEEE, Tatjana Dokic, Student Member, IEEE, Mladen Kezunovic, Fellow, IEEE
Department of Electrical and Computer Engineering
Texas A&M University
College Station, TX, U.S.A
Abstract—This study evaluates the outage probability andelectricity customer cost under the potential weather caused
power blackouts. Risk assessment of the weather impact on
customers is implemented and visualized in ArcGIS map. The
methodology correlates the historical large power outage eventswith the corresponding weather condition of the time, uses
weather forecast data to assess the risk for customers, andcompares the results in different predicted weather conditions.

Fig. 1: Risk analysis for customer impact

In Fig. 2 processing steps for risk analysis are shown.

I. KEY EQUATIONS
The risk assessment framework is formulated and defined
as follows:
R = P[T] ·P[C|T] ·u(C)(1)

where R is the State of Risk of the customer impact from theupcoming weather conditions, Hazard P[T] is the probability ofa Threat intensity T (i.e. a certain weather scenario that may
cause power outage), Vulnerability P[C|T] is the probability of
blackout in case hazardous weather conditions have occurred,and Worth of Loss u(C) is an estimate of customer interruption
losses in case of the power outage.
To estimate the customer loss from a weather-related power
failure, the improved formula is as follows.
𝐿𝑜𝑠𝑠

𝑡,𝐻𝐴

= 𝐶𝐷𝐹

𝑡,𝐻𝐴 (𝐸𝐹,𝐸𝐿𝑡,𝐻𝐴 (𝑑𝑟,𝑠, 𝑑, 𝑤, 𝑐𝑓) +𝑐𝑓, 𝑑𝑟 ) + 𝐻𝐿𝑡,𝐻𝐴 (𝐸𝐹, 𝑐𝑓)(2)where the estimated Loss at target time t for each hazard area
HA includes three monetary terms: existing CDF, additional
economic loss EL caused by unusual environmental features
EF, and health loss HL under outage. EF includes all theweather elements that may influence the customer cost,
including temperature tp, humidity hu, storm type st, windspeed ws, precipitation pr, and others ot. Once t is targeted,season s, time of a day d, day of a week w are given. HA isdetermined by the forecasted weather. The interruption
duration dr can be statistically estimated based on the historical
blackout event data and the forecasted weather condition.
Customer features cf comprise customer type, number ofpeople, time schedule at t, presence of interruption-sensitiveequipment and back-up equipment or generators, etc.

Fig. 2: Spatial integration – processing steps

The severity of the risk is indicated by using successive
colors from green to red in Fig. 3 where a) shows the results
under scenario 1 and b) under scenario 2.

a)

II. KEY FIGURES
Simplified description of Risk assessment is presented asb)
Fig 3: Risk maps for: a) normal weather conditions, b) severe weather
conditions

A 24 Hours Ahead Forecast of Solar Irradiance for
Remote Microgrids using Markov Switching Model
Ayush Shakya, Semhar Michael, and Reinaldo Tonkoski
Department of Electrical Engineering and Computer Science, South Dakota State University, Brookings, SD
Email: ayush.shakya@sdstate.edu
Locational
information such as
longitude andlatitude

Abstract—The integration of photovoltaic (PV) system is
rapidly being utilized to reduce consumption of fuel in remote
microgrids. However, due to the low correlation of load with
the PV power availability, the full potential of PV cannot be
achieved. Similarly, the challenges of PV system such as variation
and uncertainty are the main barriers to the increment of
its penetration. If these challenges can be addressed properly,
the renewable energy source can provide a large portion of
the required electricity. To compensate fluctuation and improvereliability, a large reserve is required, which causes an incrementin the operational cost. Solar irradiance forecast and hence
PV power forecast would reduce reserve requirement and improve utilization of PV energy. Moreover, PV power forecast
in energy management system of remote microgrids can further
manage dispatchable energy resources, thereby improving energy
efficiency. A novel 24 hours ahead solar irradiance forecasting
method for remote microgrids based on Markov Switching Model(MSM) is presented in this paper. This method utilizes locallyavailable past solar irradiance data. The MSM considers Clear
Sky Irradiance (CSI) and Fourier basis functions for three
regimes: low, medium and high regimes for a day corresponding
to extremely cloudy, mildly cloudy and sunny days respectively.
A case study for validation of the algorithm for Brookings, SD,
resulted Root Mean Square Error (RMSE) and Mean Absolute
Percentage Error (MAPE) of 15.2 W/m2 and 11.4%, respectively
for May 10, 2012.

Past solar irradiance data
(www.solaranywhere.com)

Fourier basis
expansion fordaily variation ofirradiance

Computation of Clear
Sky Irradiance (CSI)

Fourier basis
expansion for
yearly variation ofirradiance

Markov Switching Model fitting using Expectation Maximization (EM) Algorithm

Model Selection
Daily Irradiance Forecasts
Error Assessment

Fig. 2. Flowchart for solar irradiance forecasting using Markov Switching
Model.

1000

High Energy State

Medium Energy State

Low Energy State

Actual Irradiance

Forecasted Irradiance

16 17

20

900

I. K EY E QUATIONS

800

x̂(t) =nX

ck φk (t).

Irradiance (W/m2)

700

Pp
yt (St = k) = βk,1 CSI(t) + i=1 βk,1i φ1i (t)Pq+ j=1 βk,2j φ2j (t) + k (t).

(1)
(2)

600
500
400
300

k=1
200

II. K EY F IGURES

0
1

600

Actual Irradiance
Clear Sky Irradiance

2

3

4

5

6

7

8

9

10

11 12 13 14Time (hour)

15

18 19

21 22

200

400

Fig. 3. Variation of solar irradiance for May 10, 2012.

0

Irradiance (W m2)

800

100

Jan 6

Jan 7

Jan 8

Jan 9

Jan 10Day of year

Fig. 1. CSI and actual solar irradiance from January 6 through January 10,
2001 for Brookings, SD.

1

23

24

Using Supplementary Adaptive Dynamic
Programming to Improve Control Performanceof Distributed Generation Systems
Ujjwol Tamrakar, Dipesh Shrestha, Reinaldo Tonkoski, Zhen Ni
Department of Electrical Engineering and Computer Science
South Dakota State University (SDSU)
South Dakota, USAII.

KEY FIGURES AND TABLES

Current Controlled
Voltage Source
Inverter (CC-VSI)

L
C

dq

+
+

vabc

abc
Delay

Supplementary
ADP
-+
+

PI
dq-axis

++

Delay

Delay

Error Currents

-

+

idq

dq
abc

ω

idq*

iabc
vabc
PLL

vabc

Figure 1. Control structure of synchronously rotating dq control frame with
supplementary ADP controller added to PI controller.

INTRODUCTION

Distributed generation systems usually consists of a gridside converter connected to the grid through a LC filter. The
general configuration of the grid side converter with a current
controller is shown in Fig. 1. The control structure usually has
two loops: an outer loop to generate the reference current andan inner loop to track the reference current. The speed responseof the inner current control loop limits the speed of the outer
control loop. Typically, a proportional-integral (PI) controller
is used in the inner loop to track the reference current. However,
the traditional PI controller has limited dynamic response
capability. This has a negative impact on the transient stabilityof the system. A possible solution to increase the dynamic
response would be to increase the switching frequency and thus
the bandwidth of the controller. But this results in higher
switching losses and lower system efficiency. Instead, in thispresentation a supplementary Adaptive Dynamic Programming(ADP) based control is proposed to increase the speed of theinner current control loop. Using the supplementary controller,
the overshoot in the current response was reduced from 27.5%
to 10.0% as shown in Fig. 2. Also, the peak time reduced to
0.39 s from 0.90 s which shows an improvement in speed
response of the controller.

R

vinv

vdq

5.5
Id without ADP

5

Id with ADP
Id reference

4.5

Id Current (A)I.

LC
FilterVdc

ωLiqd

Index Terms—Adaptive dynamic programming, distributed
generation, harmonic analysis, current control

Current
SensorCB

Grid

Abstract—Renewable based distributed generation will play avital role in shaping the power system of the future. Smartadaptive control systems are essential to maintain the quality andreliability of these distributed generation sources. This
presentation discusses the improvement in the transient andsteady performance of current controllers using supplementary
adaptive dynamic programming to existing traditional
Proportional-Integral
controllers.
MATLAB\Simulink
simulations are performed to analyze the performance of thesystem with and without the supplementary control. Simulation
results show with the supplementary control, the overshootduring the step change in current decreased significantly by
17.5%. The peak of the current response also decreased by
0.51ms. Moreover, the supplementary controller showed good
performance during system fault as well by decreasing thecurrent overshoot by 25.7% and 37.5% during a single line toground fault and fault clearance respectively.

4
3.5
3
2.5
2

0.053

0.054

0.055

0.056

0.057

0.058

0.059

0.06

Time (s)Figure 2. Dynamics of the system during the step change in reference current
from 2 A to 4 A with and without supplementary ADP controller.
TABLE I. PERFORMANCE RESULTS WITH STEP CHANGE IN REFERENCECURRENT WITH PI CONTROLLER AND WITH SUPPLEMENTARY ADPCONTROLLER
Maximum Overshoot %Peak Time (ms)

PI controller

27.5

0.903

PI controller with
supplementray
ADP

10.0

0.393

Parameter

Assessing the Dispatch Flexibility of CoordinatedSolar and Hydro Generation
Jingwei Yang∗ , Ning Zhang∗ , Chongqing Kang∗ , Qing Xia∗ , Miao Miao† , Xu Tian†∗ Department of Electrical Engineering, Tsinghua University, Beijing, China† Economic Research Institute, State Grid Qinghai Electric Power Company, Xining, China

I. K EY C ONCEPT
A. Metric of Dispatch Flexibility
In this paper, the generation flexibility of solar-hydro coordinated generation is quantified by the expected proportion thatthe coordinated generation can serve the load. The coordinatedsolar-hydro generation will be considered ideally flexible if theenergy of solar and water inflow is fully utilized, which means
there is no dump of either generation.
) T
( T
∑
∑
s
Lt /
Dt
(1)
λ=1−E
t=1

t=1

B. Evaluation Methodology(1) Acquire system data and generate potential scenarioswith forecasting results of hydraulic inflows and solar
irradiance.
(2) Establish a dispatching model aimed at minimizing load
not served considering the operational constraints of hydrogeneration.
(3) Calculate the generation flexibility λ using the optimization results of all of the scenarios.
II. K EY R ESULTS
The flexibility of solar-hydro coordination depends largelyon the storage capacity of the hydropower system. With alarge active storage capacity, the hydropower plant can reduce
output during the peak hours of PV and saves the water in thereservoir to generate in other time periods, regardless of the
inflow.

Hydro

1400

1400

PV
Dump

1200

Load

1200

1000

800

800

600

600

400

400

200

200

3

1000

Inflow (m /s)

Power (MW)Inflow

0

0
5

10

15

20

5

10

Time (h)1520Time (h)Fig. 1. Influence of different reservoir storage capacity on the dispatch
flexibility of solar-hydro generation

Two interesting phenomena can be observed from the results.
(1) For a certain active storage capacity, the curve between
dispatch flexibility and water inflow is bowed-out in shape(2) For a given amount of water inflows, the larger the active
storage capacity is, the higher dispatch flexibility can beachieved.
Fixed W ater Inflow

Fixed Active Storage Capacity
0.98

1.00

0.96

Dispatch Flexibility

Abstract—Dispatch flexibility denotes the capability of electrical generation that can be dispatched to serve the load. Hydro
power generation with water reservoir has favorable dispatch
flexibility that can be used to mitigate the intermittency anduncertainty of photovoltaic (PV). Hydropower and photovoltaic
can be coordinated by storing water energy in the peak hours ofsolar and shifting it to off-peak periods. In this paper, a metric ofdispatch flexibility is defined to evaluate to what extent the solar
and hydro generation can be coordinated. The methodology formeasuring the dispatch flexibility of solar-hydro coordination is
proposed based on a multi-scenario dispatch model considering
the uncertainty of water inflow and solar irradiation. Results
are drawn with a study on the first and largest solar-hydrocoordination project in China, the Longyang Gorge (LYG)
project. The study quantifies the generation dispatch flexibilityof LYG project and illustrates the potential impact of different
factors on solar-hydro coordinated generation.

0.98

0.96
0.94

dry season

wet season
0.94

0.92
0.92

0.90

0.90
200

400

600

800

1000

0

3

W ater Inflow (m /s)

2

4

6

8

10

12

3

Acitve Storage Capacity (hm )Fig. 2. Dispatch flexibility under different water inflow with fixed storage
capacity of 4 hm3 (left) and under different storage capacity with fixed water
inflow of 600 m3 /s (right)

III. C ONCLUSION
Through the study, three conclusions are drawn: 1) Larger
active reservoir capacity of hydro generation usually provides
higher flexibility for coordinated solar-hydro generation. 2)
Too poor or too rich water inflows bring down the dispatch
flexibility. 3) Better correlation between PV and load usuallyleads to higher dispatch flexibility. Further study can be carriedon to reveal the impact of other factors through the proposed
evaluation model.

Modified Optimal Power Flow on Storage Devices andWind Power Integrated System
Kwang Y. Lee, Fellow, IEEE

Abstract—The intermittent wind power will impact system
operation cost, and voltage profile as well. Voltage profile can beenhanced by implementing energy storage system. Thus the
optimal operation of storage devices under wind energy system isof great interest. In this paper, the uncertainty of wind power istackled by scenario-based approach, and then the optimization
problem becomes deterministic under each scenario. Since theproblem is highly non-linear and non-convex, a novel heuristic
method, artificial bee colony (ABC), is utilized to handle theproblem. The objective is to minimize the total fuel cost while
enhancing the voltage profile. Modified IEEE-30 bus is used totest the validity and effectiveness of the method.

I.

Wind Power (MW)Wenlei Bai, Student Member

160
150
140
130
120
110
100
90
80
0 2 4 6 8 1012141618202224
Hr

KEY EQUATIONS

In electric power system, the goal of OPF is to minimize
objective functions by choosing settings of control variables
and meanwhile complying with constraints:
Min f ( x , u )s.t. g ( x , u )  0h( x, u )  0

Fig. 1. Scenarios of wind power output over 24 hr.

III.

(1)
(2)
(3)

II.

KEY FIGURES(4)

Voltage (p.u)

2

KEY RESULTS

W Storage W VI
W Storage W/O VI
W/O Storage W VI
W/O Storage W/O VI

where f is the objective function, g is the equality constraints
and h is the system constraints. The vector u is independent
variables, and the vector x is dependent variables.
f i ( PGi )  ai  bi PGi  ci PGi

S1
S2
S3
S4
S5
S6
S7
S8
S9
S10

PQ busesFig. 5. PQ bus votlage profiles at 8 pm. (blue: with storage with voltage
improvement, oringe: with storage without voltage improvement, gray:without storage with voltage improvement, yellow: without storage
without voltage improvement)

Fig. 3. Modified IEEE-30 bus system.

Fault Ride Through Support to VSC-HVDC
Connected DFIG Based Offshore Wind Farm by
Variable Braking Resistor
Gilmanur Rashid1 and Mohd Hasan Ali2
Department of Electrical and Computer Engineering
University of Memphis, Memphis, TN, USA
1. grashid@memphis.edu, 2. mhali@memphis.edu
Offshore wind farms (OWF) have turned out to be the next
solution to facilitate the growth of wind power production. For
successful and feasible integration of OWFs into existing grid,the voltage source converter (VSC) based high voltage DC(HVDC) transmission is being widely adopted and drawing
much attention. However, control of a VSC is very challenging
ensuring the system capability subjected to variations andtransients in the AC system or the grid. Special attention isneeded to handle the fault conditions as the grid codes required
continuous operation of the OWF during and after the fault.
So the grid codes necessitates the fault ride through (FRT)
capability of the VSC-HVDC based OWFs. Various solutions
are found in the literature [1] to enhance the fault ride throughcapability of VSC-HVDC based OWFs. Application of a fixedresistor [2] at the HVDC transmission line is a robust way
to enhance FRT but offering a fixed resistance irrespective of
the fault nature and severity may result in over compensationand degraded system performance. In this work a variablebreaking resistor is proposed to provide fault ride throughsupport for the offshore wind farms having voltage source
converter (VSC) based high voltage DC (HVDC) transmission.
Application of a braking resistor at the DC link of the HVDC
transmission line is a proved solution and used by many
researchers. They were mostly designed to tackle most severe
three line to ground (3LG) faults. In those design approaches,the full value of the braking resistance was offered in caseof any kind of fault. Neither the fault type or the depthof voltage sag at the grid is taken into considerations inthis sort of approach. To resolve this problem, a nonlinear
control based variable braking resistor is proposed that would
dynamically insert a variable resistor considering the voltage
type and voltage dip severity. Performance of the nonlinear
controlled braking resistor is planned to be compared withthat of the traditional hysteresis control based fixed braking

resistor. It is expected that nonlinear control based variablebraking resistor is supposed to outperform the conventional
fixed braking resistor.
The conventional approach to design a fixed resistor for thesystem is that the resistor should be able to consume the power
produced by the OWF. Hence the equation comes as
RBR =

2
Vdc

POW F

(1)

Where RBR , Vdc and POW F are fixed resistance of braking
resistor, HVDC transmission line voltage and the OWF power,
respectively. A nonlinear control is designed to generate a duty
cycle d depending on the fault nature and voltage sag depth′
at the AC side. The variable resistance RBR then becomes′

RBR = dRBR

(2)′

The variable resistance RBR will only consume the power thatis produce in excess of the demand and provide appropriate
FRT.
A nonlinear control based variable resistance is proposed inthis work which will offer a variable braking resistor taking
into account the fault severity and nature. It is expected toprovide a better result compared to that of the conventional
fixed resistor based FRT soultion.
R EFERENCES[1] C. Feltes, H. Wrede, F. W. Koch, and I. Erlich, “Enhanced fault ridethrough method for wind farms connected to the grid through vsc-based
hvdc transmission,” Power Systems, IEEE Transactions on, vol. 24, no. 3,
pp. 1537–1546, 2009.
[2] S. K. Chaudhary, R. Teodorescu, P. Rodriguez, and P. Kjar, “Chopper
controlled resistors in vsc-hvdc transmission for wpp with full-scaleconverters,” in Sustainable Alternative Energy (SAE), 2009 IEEE PES/IAS
Conference on, pp. 1–8, IEEE, 2009.

Fig. 1: Topology of the DFIG based offshore wind farm connected through the VSC-HVDC based transmission line

1Disturbance Investigation of High Penetration Level
Hybrid Power Systems
Guanyu Tian and Joe H. Chow
Rensselaer Polytechnic InstituteTroy, NY, USA
tiang@rpi.edu, chowj@rpi.edu

Abstract—This paper presents simulation results and related
analysis of fault transients in a high renwable penetration hybrid
power system. Also, the effect of voltage regulation and frequency
regulation from battery energy storage system (BESS) is shown.
The hybrid power system used here is an extended system
based on a 68-bus, 16-machine system. Extension includes theintegration of 24 solar PV panels and 3 BESSs. Fault transients
studied here are transmission line outage (3-phase fault, lineto-ground fault, and line-to-line fault) and loss of conventional
generation. The simulation tool is PST/EPTool, a Matlab based
prototype simulation program.
Index Terms—Solar PV, BESS, Hybrid Power System, Fault
Transient, Simulation

I. I NTRODUCTIONW

ITH the maturity of power electronics technology, development of maximum power point tracking (MPPT)
algorithms and power control strategies, a hybrid power system
as an environmental friendly and economic efficient solution
to ever increasingly high power demand is being consideredaround the world.
However, high penetration levels of solar PV may bringabout potential operating issues, including system stability.
Thus transient studies of high penetration level hybrid powersystems are important. Through simulation of different transient scenarios, we can obtain insights on the response of solar
PV and battery systems to contingencies, and design proper
controls.

Fig. 1. Hybrid Power System Topology

II. S AMPLE S IMULATION R ESULTS
Using the solar PV and energy storage models given in [1]and [2], the PST/EPTool was used to simulate the system withsolar PV system, as shown in Fig. 1. Figs. 2 and 3 contain thesystem response to two disturbances, illustrating the benefit of
the solar PV system and energy storage system for improving
the system performance.

Fig. 2. Simulation of 3-phase Fault on Line 8-9

R EFERENCES[1] Meng Yue, Dynamic Modeling of a Hybrid Solar/Battery Storage Energy
System and Grid Integration Study, Brookhaven National Laboratory.
[2] M. Ceraolo, ”New Dynamical Models of Lead-acid Batteries”, IEEE
Transactions on Power Systems, vol. 15, pp. 1184-1190, 2000.

Fig. 3. Simulation of Fault on Bus 57 (Loss of G5)

Risk-Aware Congestion Management Frameworkwith Wind Power Uncertainty-Induced Dynamic
Line Transfer Margins
Sajjad Abedi, Miao He
Electrical and Computer Engineering Department, Texas Tech University, Lubbock, Texas 79409
Emails: {s.abedi;miao.he}@ttu.edu

I. K EY E QUATIONS
ŵnt = Fŵ−1t (1 − Pex )(1)n

The probability of congestion occurrence for each line canbe expressed by:π(pl ≥ Plmax ) = π(pl − E[pl ] ≥ Plmax − E[pl ])
pl =

N
X

(2)

N
X

ζn,l .(pn − dn + ŵn )(3)

ζn,l .(pn − dn + E[ŵn ])

(4)

n=1

σp2l = ζ l σ w Rw σ w ζ Tl

(5)

According to the Cantelli’s inequality and using (2)-(5):π(pl ≥ Plmax ) ≤ πcmax
where
πcmax =

(6)

σp2l
2

σp2l + [Plmax − E[pl ]]pl ≤ Plmax +NXp

α2 − 1

ζn,l .(ŵn − E[ŵn ]) − σpl(9)pα2 − 1(7)

The parameter α is called as ”Conservativeness Factor”.
1≥ πcmax
(8)
α2

(10)

n=1

Using the above Eqs., the congestion constraint with LTM is:
N
X

∆

p
α2 − 1(11)

ptl = Bl .∆θl , l ∈ L−Plmax + Γtl ≤ ptl ≤ Plmax − Γtl , t ∈ T , l ∈ L(12)Γtl = −

ζn,l .(ŵnt − E[ŵnt ]) + σpt l

n=1

II. K EY R ESULTS
5

8.5

×10

Conventional Method
Proposed Method, Alpha=1.5
Proposed Method, Alpha=1.75
Proposed Method, Alpha=2

8

7.5

7

6.5
0

100

200

300

400

500

600

700

Wind Power Curtailment (MW)n=1

E[pl ] =E[pl ] ≤ Plmax − σpl

Total Operation Cost ($)

Abstract—Widespread and huge penetration of wind resources
into power systems necessitates more conservative provision ofsystem reliability and security by ensuring adequate committed
reserve and observance of congestion constraints. In addition,wind power curtailment due to the technical limitations of system
operation, such as transmission congestion, should be efficiently
mitigated. To this aim, a risk-aware unit commitment (UC)formulation in a two-settlement market environment is presented.
A spatial probabilistic model for nodal wind resources across thesystem is adopted to integrate the impact of multi-correlated
uncertainty of wind power forecast into the risk constraints
associated with the transmission congestion, called the Dynamic
Line Transfer Margins (LTM). The LTMs are adopted into
the transmission capacity constraint of the UC formulation to
mitigate the likelihood of congestion. Analysis of the proposed
approach and comparative results on the IEEE 24-bus RTS
for various wind power and load scenarios demonstrate thatusing the proposed UC framework with LTM can effectively
result in less likelihood of transmission congestion and improved
mitigation of wind power curtailment in the real-time economic
dispatch decisions.

Fig. 1. Total operation cost versus the wind power curtailment

Property of Wind Power Forecast Errors and the Impact
to the Scheduling of Reserves and Storage Devices
Yuchen Tang, Student Member, IEEE and Jin Zhong, Senior Member, IEEE
Abstract—The penetration of renewable energy sources (RESs)
is keeping increasing in power systems. The uncertainties fromthe RESs lead to more challenges in power system operation andreserve scheduling. In this study, the day-ahead forecast error ofwind power is analyzed. Based on the forecasted and actual
wind output data over two years, the properties of the forecast
error are identified considering the output level of wind
generation and different time scales. Suggestions are proposedto schedule system reserves and storage devices accordingly.

I.

METHODOLOGY

The forecast error of wind generation is an important
issue to the operation of power systems. While the generalproperty of wind forecast errors has been studied for a longtime, some other factors may potentially affect the feature of
forecast errors.
In this paper, the historical data from TenneT on the dayahead forecasted and actual wind generation over year 2013
and 2014 are analyzed from two major aspects. Firstly, thefeature of forecast errors associated with the forecasted wind
output level is analyzed. This issue is closely related to the
day-ahead scheduling of system reserves. And secondly, theproperty of integrated wind forecast errors over one day is
studied, which will assist the planning and scheduling ofstorage devices whose energy capacity are limited.
The forecast wind generation is divided into five levels
based on the forecast output and maximum output. And theforecast error is expressed by the ratio of the forecast error
divided by the forecasted wind output.
II.

of the day (E+ and E-), respectively. These parameters
determine how much upward and downward energy capacity
is needed to compensate the errors over this day.

KEY RESULTS AND DISCUSSIONS

The distribution of forecast errors for single time points
considering different wind output level is shown by Fig. 1:

Figure 1. Distribution of forecast errors for single time points

On the other hand, the concept of integrated forecast error
over one-day period is illustrated by Fig. 2. The maximum
and minimum points of the integrated forecast error in one
day are defined as the positive and negative integrated errors

Figure 2. Daily integrated forecast error

The positive and negative integrated errors for each single
day from 2013 to 2014 are calculated, and the distributions ofthese parameters are shown by Fig. 3.

Figure 3. Distribution of positive and negative daily integrated errors

The results by Fig. 1 and Fig. 3 show the following
features:- The distribution of forecast errors differs significantly
under different wind output level;- The forecast error is usually higher in percentage
when the forecasted wind output is low;
- The forecast output tend to be higher than the actual
output, which may relate to the method of forecast.
Furthermore, these features lead to the following concernson the scheduling of reserves and storage devices:- Although the forecast error in percentage is smallerwhen the wind output is high, the actual value of theerror is still larger, and the influence to power systems
would be heavier. This will require careful design toschedule reserve and storage devices;- The features of integrated forecast errors over one-day
can help to build scheduling strategies for energy
storage devices.

1

Spatial-Temporal Solar Power Forecast throughUse of Gaussian Conditional Random Fields
Bei Zhang, Student Member, IEEE, Payman Dehghanian, Student Member, IEEE,
Mladen Kezunovic, Fellow, IEEE
Department of Electrical and Computer Engineering, Texas A&M University
College Station, TX USA

Abstract—This poster introduces an application of theGaussian Conditional Random Fields (GCRF) model forforecasting the solar power in electricity grids. The introduced
forecasting technique is capable of modeling both the spatial andtemporal correlations of various solar generation stations. It will
be demonstrated in this paper how the suggested solution cansignificantly improve the forecast accuracy compared to theconventional forecasting models such as the persistent (PSS)
model and the autoregressive with exogenous input (ARX) model.
Besides, the GCRF model outperforms the other two models
under the scenarios with unavailable or missing data. The
suggested probabilistic model of the GCRF can also help better
managing the existing uncertainties of the solar generations. The
numerical experiments are conducted through which theeffectiveness of the proposed approach is validated.
Index Terms—Forecast; solar
Gaussian conditional random fields.

power;gl  yi , y j , x   eijl  Sijl   x   yi  y j 1leij   02

 i, j   Gl
otherwise

similarity between outputs yi and yj. And Rk(x) is a single
prediction of yi based on the input variables x. Under this
scenario, the conditional probability distribution P(y|x)
becomes multivariate Gaussian distribution.
II. KEY FIGURES

spatial-temporal;

The GCRF model takes into account both the spatial andtemporal correlations. In this model, entire effort is to obtainthe conditional distribution P(y|x) through a large volume ofhistorical data, and then forecast the output variable based on
the obtained distribution. The conditional distribution P(y|x)
modeled in conditional random fields (CRF) is expressed in
(1):
 N

1
exp   A(α, yi , x)  I (β, yi , y j , x) Z (x, α, β)j i
 i 1(1)Figure 1. Spatial and temporal correlations in the GCRF model.

where A(α,yi,x) is the association potential which associates
the output yi with input vector x; I(β,yi,yj,x) is the interaction
potential to relate two output variables yi and yj. In CRF
applications, A and I could be approximated by linear
combinations of pre-determined feature functions withcorresponding parameters α and β, as denoted in (2) and (3).
KiA(α, yi , x)    k f k ( yi , x)

(2)

k 1
Li

I (β, yi , y j , x)   l gl  yi , y j , x 

(3)

l 1

If the feature functions fk and gl are defined in quadratic
forms, as shown in (4) and (5), the association potential A andinteraction potential I would also be quadratic functions of y.

f k  yi , x     yi  Rk  x   ,2

k  1,..., K(5.b)

where Gl is the graph which imposes the relation between the
output variables yi and yj. Sijl   x  is the function representing

I. KEY EQUATIONS

P ( y x) 

(5.a)

(4)
Figure 2.

MAEs corresponding to various forecasting models

1

Impact of Converter Interfaced Generation (CIG) on
Grid Performance
Deepak Ramasubramanian, Student Member, IEEE and Vijay Vittal, Fellow, IEEE
School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ 85281, USA
Email:dramasu1@asu.edu; vijay.vittal@asu.edu

Abstract—The positive sequence modeling of converter basedsources in commercial transient stability analysis software has
been addressed. A simple and computationally economical modelof the converter has been developed while ensuring a reliable
representation of the detailed converter behavior. The behavior of
the proposed model in positive sequence has the same form as the
behavior obtained from detailed point on wave simulation. Tests
have been carried out on a three generator nine bus equivalentsystem and the 18205 bus Western Electricity Coordinating
Council (WECC) system to assess the impact and performanceof the converters.

Fig. 2. Boundary current converter representation for positive sequence
simulation

Index Terms—Converter-based source, droop control, PLECS,
positive sequence, PSLF, WECC system.

III. K EY R ESULTS

A positive sequence converter model is developed for the
purpose of coupling CIG into the standard production version
of a large scale grid simulation. The model described here isinterfaced with the network by a Thévenin voltage source. Thisapproach has given good convergence of the network solutions
in our simulations in contrast to the alternative boundary
current injection model. Simulations conducted with this
proposed controlled voltage source representation are shownin comparison with point-on-wave simulations to provide acalibration. Applications of this proposed model in simulationsof the WECC system at the 18000 bus level, to illustrate its
performance, are shown.

The validation of the model performance is shown in Fig.
3 for a small 9 bus system. The performance of an all CIG
18205 bus WECC system is shown in Fig. 4.
78

76
78

75

76

74

74

73

72
72
71
0

II. K EY F IGURESThe proposed converter model is as shown in Fig. 1. The
alternative boundary current injection model is shown in Fig.
2.

PLECS
Positive sequence voltage source
Positive sequence boundary current

77

Power (MW)I. I NTRODUCTION

70
4.9
2

4

6

4.95
8

5

10
Time (s)

12

5.05
14

16

5.1
18

20

Fig. 3. Comparison of the active power output of converter at bus 1 between
PLECS and the two positive sequence models

60

Frequency (Hz)

59.98
59.96
60

59.94
59.92

59.95

Arizona

59.9

59.9

Central Northern California

Fig. 1. Voltage source converter representation for positive sequence simulation

59.88

Ed = Vd0 + id Rf − iq Xf
Eq = Vq0 + iq Rf + id Xf(1)This work was supported by the National Science Foundation under the
Grant EEC-9908690 at the Power System Engineering Research Center.

Northwest

59.86

Canada59.86

ArizonaSouthern California
Central Northern California
Northwest
CanadaSouthern California

5

10

15

15
20

16
25

17
30

18
35

40

Time (s)Fig. 4. Frequency (rate of change of voltage phase angle) in five generation
areas for the trip of 2755 MW

Network Reconfiguration with Relaxed Radiality
Constraint for Increased Hosting Capacity ofDistribution Systems
Masoud Davoudi and Valentina Cecchi

Julio Romero AgüeroElectrical and Computer Engineering Department
University of North Carolina at Charlotte

Quanta Technology, LLC
Raleigh, NC 27607

Abstract—In this work, in order to allow for higher hosting
capacity of the distribution system, a network reconfiguration
scheme is presented relaxes the radiality constraint of the system.
The scheme selects the best topology to maximize allowable DG
penetration while satisfying steady state voltage and current
limits using a modified genetic algorithm.
Fig. 1. Modified 69 bus system

I. I NTRODUCTION
Electrical distribution systems have been experiencing proliferation of distributed generation (DG), with benefits such as
improving power delivery efficiency, reducing power losses,
improving grid reliability and resiliency, etc. On the other
hand, high penetration levels of DG may cause significant
negative impacts on the distribution system, partly due to thefact that distribution systems have traditionally been operated
in a radial manner. This work presents a distribution system
network reconfiguration approach with relaxed radiality constraint to maximize the hosting capacity of distribution system
while maintaining the bus voltages and line currents within
their operating limits.
II. P ROPOSED N ETWORK R ECONFIGURATIONIn this work, the maximum allowable DG injection is
calculated which does not violate steady-state bus voltages
and line current limits. To do so, the following optimization
problem is formulated.
n
DGX
max G =
PDGk
(1)
S

V

min

trend can b seen in other buses. As seen in this table, NR
and specifically meshed configuration has helped increase thepresence of DG in the system. As another benefit of meshed
TABLE IC OMPARISON OF PkM ax IN O PTIMUM NR WITH T HE BASE C ASE
Optimum Solution
S1

S2

S3

S4

S5

S6

S7

S8

PDG
(MW)

∆P %

27

0

1

1

0

1

1

1

1

2.79

191.6

46

0

1

0

1

1

1

1

1

4.000

187.6

52

0

0

0

0

0

1

1

1

3.93

0.00

65

1

1

1

0

0

1

0

1

2.90

37.6

67

0

1

1

1

0

1

1

1

3.71

27.00

69

0

1

1

0

0

1

1

1

2.48

23.8

POI

k=1

M ax
< Vbusi < V M ax , Ilinej < Ilinejwhere S is the vector of states of switches in the system,
PDGk and nDG are the active power output of kth DG unit
and the total number of DG units in the system, respectively.
Also, i and j denote the ith bus and jth line in the system,
and Nbus and Nline are the total number of buses and lines
in the system. Genetic Algorithm (GA) is used in this work
to solve the presented optimization problem.
Fig. 2. Comparison of active losses in three scenarios

III. C ASE S TUDY
The test case used in this section is a 69 node, 12.66
kV test system, as shown in Fig. 1. The results of optimum
PkM ax values in the best configuration are compared with the
maximum allowable generation in the base case in Table I.
In this table, only the end-lateral buses are selected, but same

configuration, active losses are decreased in comparison withthe base case scenario. This can be verified by the resultspresented in Fig. III, where active losses in base case, thereconfigured case without DG, and the NR results with DG
are compared together.

Investigation of Non-Synchronous Penetration Leveland Its Impact on Frequency Response in a Wind
Dominated Power System
Nahid-Al-Masood1, Nilesh Modi2, 1 and Tapan Kumar
Saha1

Ruifeng Yan
The Global Change Institute
The University of Queensland
Brisbane, Australia
ruifeng@itee.uq.edu.au

1

School of Information Technology and ElectricalEngineering, The University of Queensland
2
Australian Energy Market Operator Limited (AEMO)*
Brisbane, Australian.masood@uq.edu.au, nilesh.modi@aemo.com.au,
saha@itee.uq.edu.au

I.

October 2015). Effects of NSPL on load shedding and
ROCOF are depicted in Figs. 2 and 3 respectively.
(a) Duration curve

NSPL (%)

50

Oct-13 to Oct-14
Oct-14 to Oct-15

40
30
20
10
0
0

10

20

30

40

50
60
70
Time (%)(b) Probability of occurrence

80

90

100

5

10

15

20

40

45

50

25
Probability (%)

Abstract— South Australia is a southern state of Australia, where
penetration of wind power has continuously increased over thelast few years. In recent times, most of the wind power plants
have been constructed based on type III and IV machines. Thesemachines do not intrinsically offer any support to control
frequency excursion following a major disturbance. These‘asynchronous’ machines together with a high voltage DCinterconnection to the adjacent state Victoria often contribute toa higher level of non-synchronous generation in South Australia.
During substantial presence of non-synchronous generation andhigh import from Victoria, South Australian system may rely ona few synchronous machines for frequency control following a
disturbance. Under such operating scenarios, a big contingency
such as an interconnection trip could result in unacceptablefrequency deviation and rate of change of frequency.
Consequently, the network may experience under frequency load
shedding, which could affect the security of the system. To
explore the above-mentioned issues, this paper evaluates nonsynchronous penetration level in South Australia and investigates
its effect on network frequency response. It also estimates the
maximum allowable non-synchronous penetration level after aninterconnection trip to minimise load shedding and keep rate ofchange of frequency within acceptable range.

20
15
10
5
0
0

25
30
NSPL (%)

35

Figure 1. Duration and distribution of NSPL in South Australia

KEY EQUATIONS

Instantaneous non-synchronous penetration level (NSPL),
equivalent system frequency and rate of change of frequency
after a contingency are expressed using (1)-(3) respectively.
(1)
NSPL = ( Pnon− sync ) /( Psync + Pnon− sync )
i=n

i =n

i =1

i =1

f = [ ∑ ( H i × S i × f i )] /[ ∑ ( H i × S i )](2)

ROCOF = (∆P × f 0 ) /(2 × IR)

(3)

II.

KEY RESULTS

Figure 2. Effect of NSPL on load shedding

Table I shows the estimated maximum NSPL values forvarious interconnection flows.
TABLE I.
MAXIMUM NON-SYNCHRONOUS PENETRATION LEVEL
Trip size (MW) NSPLmax
Comment
250
30%
Load shed=0 MW and ROCOF≤1 Hz/s
350
7%
Load shed =0 MW and ROCOF≤1 Hz/s
4504%Load shed =0 MW and ROCOF≤1 Hz/s
550
3%
Load shed ≠0 MW and ROCOF≤1 Hz/s
650
None
Load shed ≠0 MW and ROCOF>1 Hz/s

Fig. 1 shows the duration and distribution of NSPL in
South Australia for a two-year duration (October 2013 to
Figure 3. Implication of NSPL on ROCOF

Investigation of Voltage Performance of an LV
Distribution Network for Improving Rooftop
Photovoltaic Uptake in Australia
Shohana Rahman Deeba, Rahul Sharma and Tapan
Kumar Saha

Andrew Thomas
Ergon Energy
Maryborough, Australia
andrew.thomas@ergon.com.au

School of Information Technology and Electrical Engineering
The University of Queensland
Brisbane, Australias.deeba@uq.edu.au, rahul.sharma@uq.edu.au,
saha@itee.uq.edu.au

I.
KEY EQUATIONThe expected power output (Pbatcalc) of a battery is
calculated from the measured injected power (Pinj,k) and the
maximum power export limit (Pinjmax) at kth PCC as shown in(1).
Pbatcalc  (Pinj,k  Pinjmax ) /  ;   charging effeciency (1)II.

KEY RESULTS

Active power flow at node 17

2
PV power export limit
1

0

-1
06:00

Date
08:00

10:00

12:00
12.Nov

14:00

16:00

18:00

N17: Active Power (L1) [kW]N17: Active Power (L2) [kW]N17: Active Power (L3) [kW]N17: Reactive Power (L1) [kvar]
N17: Reactive Power (L2) [kvar]
N17: Reactive Power (L3) [kvar]

Figure 2. Real power export from node N17 to N16 for the proposed 5 kW
PV connection
2.5
Calculated Battery Power for Cloudy Sky Day
Calculated Battery Power for Clear Sky Day
2Power (kW)

Abstract— Recently, Queensland State of Australia has seen arapid growth in rooftop solar photovoltaic (PV) installations.
However, the growth can be throttled due to voltage violation
constraints at the distribution feeder level. Consequently,
distribution utilities in Queensland have set a guideline for new
PV inverter connections. During light load condition and underhigh availability of PV power, voltage rise may exceed its
acceptable range in some buses even after the PV inverters areoperated at specified lagging power factor given by the
aforementioned guideline. To address the above-mentioned
issue, this paper investigates the impact of upcoming PV units
on voltage performance of a Queensland distribution system.
This study also determines an allowable power export limit of a
new PV inverter by satisfying standard voltage margins. This
limit is utilised to develop a battery control strategy. This
strategy is then applied to explore the implication of PV-battery
on voltage enactment of the network.

1.5

1

0.5

0
6:59

10:19

13:38

16:38

Time

Figure 3. Battery output power using the proposed strategy
Voltage at N15, N16 and N17
106

Upper Voltage Limit 106%

104

102

100

98

Figure 1. Proposed control scheme for a battery

Fig. 1 shows the proposed control strategy for a battery.
The Real power export by a PV unit at the studied PCC ispresented in Fig. 2. The output power of a battery and its
connecting PCC voltage are illustrated in the Figs. 3 and 4
respectively.

96
06:00

Date
08:00

10:00

N17: Voltage (L1) [%]N16: Voltage (L1) [%]N15: Voltage (L1) [%]12:00
12.Nov

14:00

N17: Voltage (L2) [%]N16: Voltage (L2) [%]N15: Voltage (L2) [%]

16:00

18:00

N17: Voltage (L3) [%]N16: Voltage (L3) [%]N15: Voltage (L3) [%]Figure 4. Voltage profiles of the candidate buses with PV and battery

Hierarchical Voltage Control to Improve Security
Margin for Systems with High Wind Penetration
Yidan Lu and Kevin Tomsovic, Fellow IEEE
Abstract-Large scale integration of wind generation
has raised challenges in system security margin
management. Coordinated Voltage Control (CVC)
needs to be updated to adapt to systems withsignificant input uncertainty due to high windpenetration.
With implementation of wind farm
Automatic Voltage Controller realizing local
coordination
betweenwind
turbinesandconventional units, a wide area Hierarchical Voltage
Control (HVC) considering long term wind
uncertainties under dynamic constraints is proposed
to improve security margins. An equivalent WECC
model is used for HVC performance evaluation.
Keywords: Hierarchical Voltage Control (HVC);
Security Margin; Wind generation;

will respond to the regional commands and act toadjust unit terminal voltage and reactive output.
Here the OPF is formulated by linearization ofrelationships among selected bus voltages, COI
transfer limit and system boundaries in detected intransient analysis with N-1 contingencies. The
performance of this HVC is illustrated in Fig. 1.
It Fig. 1, it can be seen that HVC
performance weakens as loading level approaches
the lower or upper bound. To further investigate
the impact of wind penetration on the proposed
HVC effectiveness under extreme loading
conditions, the feasibility region of the control
scheme is searched and plotted in Fig. 2.

Ⅰ.Introduction
Wind generation has been growing rapidly and willcontribute to 20% of power supply by 2020
according to Department of Energy (DOE) [1].
However, due to reactive output limitation andunpredictability, wind units can undermine system
controllability and bring challenges to reactive
reserve management. The proposed HVC isdesigned to cope with characteristics of wind
generation and to optimize security margins.

Fig. 1.

HVC Performance on WECC Scenarios

Ⅱ. Equivalent WECC System
The reduced WECC system preserves major
500kV buses of the WECC planning model. The
equivalence of this system has been validated bybenchmarking of frequency responses with fielddata and COI transfer limit with CAISO study
result. Yearly scenarios with variation on windpenetration and loading level are generated with
WECC Hourly Loading Profile [2]. For eachscenario, security margin is defined as difference
between COI base flow and the limiting flow.
TPL-001-WECC-RBP-2 was applied for transient
study with application of N-1 contingencies.

Ⅲ.Hierarchical Voltage Control Scheme
A three-layer control strategy would run OPF
hourly in the control center to assign voltage andreactive references. Regional agents will operate
every 10 minutes to minimize the difference
between actual value and reference on piloting bus
voltages and generator reactive outputs based on
estimation of COI transfer limit. Lastly, local AVR

Fig. 2.

Feasibility Region of Proposed HVC

Ⅳ.Conclusion
The proposed HVC is shown to increase security
margin for most of yearly scenarios with a large
feasibility region. However, high wind penetration
could dampen its effectiveness if applied under
extreme loading conditions. This issue can be
resolved by building stronger transmission systems
or applying new reactive compensation devices.

Ⅴ.Reference[1] E. Efficiency, "20% Wind Energy by 2030: Increasing
Wind Energy’s Contribution to US Electricity Supply,"
DOE/GO-102008-2567, US Department of Energy, May.
http://www. nrel. gov/docs/fy08osti/41869. pdf2008.
[2] J. E. Price and J. Goodin, "Reduced network modeling of
WECC as a market design prototype," in Power and Energy
Society General Meeting, 2011 IEEE, 2011, pp. 1-6.

Constraints of Wind Power Ramp Event inRobust Unit Commitment

Abstract—The stochastic nature of wind power brings about
large ramp events and poor prediction accuracy, whichchallenges the secure operation of power systems. Therefore,
this paper elaborately discusses the necessities to account forwind power rampings in unit commitment and proposescorresponding constraints of them. And possible decomposition
implementation on the modified problem is discussed forcomputational efficiency in large systems.

Wind Power (p.u.)

Jiaming Li, Student Member, IEEE Xiaomeng Ai, Jinyu Wen, Member, IEEE
State Key Lab of Advanced Electromagnetic Engineering and Technology
Huazhong University of Science and Technology
Wuhan, China
Jiaming_Li@hust.edu.cn

INTRODUCTION

direction
slope

magnitude

Wind Power (MW)Wind power generation is known to possess two stochastic
features including output uncertainty and volatility. The
former one refers to the difficulty in accurate prediction, while
the latter one usually brings about the ramp events during
which the wind power increases or decreases significantly due
to fast-moving weather phenomena (such as the cold frontal
passage, thunderstorm outflow, etc.). In the recent years,
researches on strong volatility mainly focus on different
prediction methods and statistical analysis of wind power
ramp events (Fig. 1). However, whether it’s necessary toconsider additional information about ramp events when
there’s already probabilistic forecast for future wind powerand how to utilize this information in UC are two importantquestions still not finely answered.

duration

Time (min)Fig. 1. Illustration of wind power ramp event

II.

NECESSITY OF RAMP EVENT CONSTRAINT IN RUC

Current Robust UC models require the total ramp rate of
the thermal generators in a period is larger than the predicted“maximum” change rates determined by adjacent output limitsof the wind farms. (e.g. The considered upward ramps areshown in Fig. 2.) However, wind power ramp rate exceeding
the slopes of the lines in Fig. 2 exists in real-time operation
especially in extreme weather conditions.

Max Ramp-up

0.2
0.1
0
0

50

100
150
Time (min)200

Fig. 2. Maximum Wind Power Ramp-up Considered in Normal RUC

Index Terms-- wind power generation, ramp event, robust
optimization, unit commitment

I.

0.3

III.

NECESSITY OF RAMP EVENT CONSTRAINT

This paper proposed additional constraints (1)~(3) toassure that the ramp rate requirement against the ramp-up
event is met and properly distributed among the online
generators. M (j,w,k) is the adjustment coefficient of generator
j against the output deviation in wind farm w as explained inconstraint (4). The constraints for ramp-down event can bederived in the same manner.

 dr ( j, k ) ≥ ∑ w [ M ( j, w, k ) ⋅ wu( w, k )] dr ( j, k ) ≥ ∑ w [ M ( j, w, k + 1) ⋅ wu( w, k )]dr ( j, k ) ≤ [ p( j, k ) − p( j, k )] max w {tu( w, k )}(2)dr ( j, k ) ≤ v( j, k ) ⋅ RD j tstep

(3)

(1)

p( j , k ) =p ( j, k ) − ∑ w M ( j, w, k )[ p( w, k ) − p ( w, k )] (4)
f

f

IV.

NUMERICAL RESULTSTABLE I. COMPARISON BETWEEN NORMAL RUC AND PROPOSED MODEL

Thermal Output (MW)
Available/Required
Ramp Cap. (MW/min)
Curtailed wind (%)
Cost ($)

Result at the Period with Ramp-up Event
Normal RUC
Proposed Model
2831
2856.324/80

65.6/64.9

WF2:0%/WF3:0%
10226298.18

WF2:37.7%/WF3:0%
10251700.34* Wind power ramp-up at the rate of 40MW/min, at both wind farm 2 and 3
TABLE II. CALCULATION TIME ON SYSTEMS IN DIFFERENT SIZES

Calc.
Time

Direct Solution
Benders Decomp

52 Gens 118Buses
45.6s
6.14s

69Gens 300buses
566.42s
14.2s

It can be seen that the proposed model is more reliableagainst ramp events and the solution efficiency can still beassured if proper solution algorithm is implemented.

1Chance-Constrained Two-Stage Stochastic Unit Commitment under Uncertain Wind Power Output Using Bilinear Benders Decomposition
Yao Zhang, Student Member, IEEE, Jianxue Wang, Member, IEEE, Bo Zeng, Member, IEEE, andZechun Hu, Member, IEEE
Abstract— In this paper, we study a unit commitment (UC)
problem with uncertain wind power output. This problem is formulated as chance-constrained two-stage stochastic programmingwith non-recourse cost. The chance constraint is used to limit theprobability of load imbalance at each operating hour. In this paper, we present the bilinear mixed integer formulation of chanceconstraint, and then derive its linear counterpart using McCormick linearization method. This formulation is stronger andtighter than the widely used big-M linear reformulation. To solve
this challenging problem efficiently, we develop a bilinear variantof Benders decomposition method, which is an easy-to-implement
algorithm. Finally, numerical results show that the proposed
Benders decomposition algorithm is generally an order of magnitude faster than a commercial solver CPLEX on solving chanceconstrained unit commitment problem, and our experiments also
validate that this algorithm can deal with large-scale UC problems
with reasonable time.

SUmin � �(𝐶𝐶𝑔𝑔NL 𝑢𝑢𝑔𝑔𝑔𝑔 + � 𝐶𝐶𝑔𝑔𝑔𝑔
𝛿𝛿𝑔𝑔𝑔𝑔𝑔𝑔 + 𝐶𝐶𝑔𝑔SD 𝑦𝑦𝑔𝑔𝑔𝑔 + 𝐹𝐹𝑔𝑔 (𝑃𝑃𝑔𝑔𝑔𝑔 )) (1)𝑠𝑠∈𝒮𝒮𝑔𝑔

𝛿𝛿𝑔𝑔𝑔𝑔𝑔𝑔 ≤s. t.

𝑆𝑆𝑆𝑆 −1
𝑇𝑇𝑔𝑔,𝑠𝑠+1

� 𝑦𝑦𝑔𝑔,𝑡𝑡−𝑖𝑖

𝑆𝑆𝑆𝑆
𝑖𝑖=𝑇𝑇𝑔𝑔𝑔𝑔∀𝑔𝑔, 𝑡𝑡(3)

�

𝑣𝑣𝑔𝑔𝑔𝑔 ≤ 𝑢𝑢𝑔𝑔𝑔𝑔∀𝑔𝑔, 𝑡𝑡(4)

�𝑦𝑦𝑔𝑔𝑔𝑔 ≤ 1 − 𝑢𝑢𝑔𝑔𝑔𝑔∀𝑔𝑔, 𝑡𝑡(5)𝑖𝑖=𝑡𝑡−𝑇𝑇𝑇𝑇𝑔𝑔 +1
𝑡𝑡
𝑖𝑖=𝑡𝑡−𝑇𝑇𝑇𝑇𝑔𝑔 +1

𝑢𝑢𝑔𝑔𝑔𝑔 − 𝑢𝑢𝑔𝑔,𝑡𝑡−1 = 𝑣𝑣𝑔𝑔𝑔𝑔 − 𝑦𝑦𝑔𝑔𝑔𝑔

� 𝑃𝑃𝑔𝑔𝑔𝑔 + � 𝑊𝑊𝑞𝑞𝑞𝑞 = � 𝐿𝐿𝑏𝑏𝑏𝑏

𝑔𝑔∈𝒢𝒢∀𝑔𝑔, 𝑡𝑡, 𝑠𝑠 (2)� 𝛿𝛿𝑔𝑔𝑔𝑔𝑔𝑔 = 𝑣𝑣𝑔𝑔𝑔𝑔

𝑠𝑠∈𝒮𝒮𝑔𝑔
𝑡𝑡

𝑞𝑞∈𝒬𝒬

𝑏𝑏∈ℬ

∀𝑔𝑔, 𝑡𝑡
∀𝑡𝑡

(6)

(7)

−𝐶𝐶𝑙𝑙 ≤ � 𝐾𝐾𝑙𝑙𝑙𝑙 � � 𝑃𝑃𝑔𝑔𝑔𝑔 + � 𝑊𝑊𝑞𝑞𝑞𝑞 − 𝐿𝐿𝑏𝑏𝑏𝑏 � ≤ 𝐶𝐶𝑙𝑙 ∀𝑙𝑙, 𝑡𝑡 (8)𝑏𝑏∈ℬ𝑔𝑔∈𝐆𝐆(𝑏𝑏)𝑞𝑞∈𝐖𝐖(𝑏𝑏)

𝑃𝑃𝑔𝑔𝑔𝑔 + 𝑟𝑟𝑔𝑔𝑔𝑔 − 𝑃𝑃𝑔𝑔,𝑡𝑡−1 ≤ 𝑅𝑅𝑅𝑅𝑔𝑔−𝑃𝑃𝑔𝑔𝑔𝑔 + 𝑃𝑃𝑔𝑔,𝑡𝑡−1 ≤ 𝑅𝑅𝑅𝑅𝑔𝑔
𝑃𝑃𝑔𝑔𝑔𝑔 ≥ 𝑃𝑃𝑔𝑔min 𝑢𝑢𝑔𝑔𝑔𝑔

𝑃𝑃𝑔𝑔𝑔𝑔 + 𝑟𝑟𝑔𝑔𝑔𝑔 ≤ 𝑃𝑃𝑔𝑔max 𝑢𝑢𝑔𝑔𝑔𝑔 − �𝑃𝑃𝑔𝑔max − 𝑆𝑆𝑆𝑆𝑔𝑔 �𝑣𝑣𝑔𝑔𝑔𝑔∀𝑔𝑔, 𝑡𝑡(9)∀𝑔𝑔, 𝑡𝑡 (10)∀𝑔𝑔, 𝑡𝑡 (11)∀𝑔𝑔, 𝑡𝑡 (12)𝑃𝑃𝑔𝑔𝑔𝑔 + 𝑟𝑟𝑔𝑔𝑔𝑔 ≤ 𝑃𝑃𝑔𝑔max 𝑢𝑢𝑔𝑔𝑔𝑔 − �𝑃𝑃𝑔𝑔max − 𝑆𝑆𝑆𝑆𝑔𝑔 �𝑦𝑦𝑔𝑔,𝑡𝑡+1 ∀𝑔𝑔, 𝑡𝑡 (13)� 𝑟𝑟𝑔𝑔𝑔𝑔 ≥ 𝑅𝑅𝑡𝑡

𝑔𝑔∈𝒢𝒢

𝑔𝑔∈𝒢𝒢

𝑞𝑞∈𝒬𝒬

𝑏𝑏∈ℬ

𝑔𝑔∈𝐆𝐆(𝑏𝑏)𝑞𝑞∈𝐖𝐖(𝑏𝑏)−𝐶𝐶𝑙𝑙 ≤ � 𝐾𝐾𝑙𝑙𝑙𝑙 � � 𝑃𝑃𝑔𝑔𝑔𝑔 (𝜉𝜉) + � 𝑊𝑊𝑞𝑞𝑞𝑞 (𝜉𝜉) − 𝐿𝐿𝑏𝑏𝑏𝑏 � ≤ 𝐶𝐶𝑙𝑙𝑏𝑏∈ℬ∀𝑙𝑙, 𝑡𝑡 (16)𝑃𝑃𝑔𝑔𝑔𝑔 (𝜉𝜉) + 𝑟𝑟𝑔𝑔𝑔𝑔 (𝜉𝜉) − 𝑃𝑃𝑔𝑔,𝑡𝑡−1 (𝜉𝜉) ≤ 𝑅𝑅𝑅𝑅𝑔𝑔∀𝑔𝑔, 𝑡𝑡 (17)−𝑃𝑃𝑔𝑔𝑔𝑔 (𝜉𝜉) + 𝑃𝑃𝑔𝑔,𝑡𝑡−1 (𝜉𝜉) ≤ 𝑅𝑅𝑅𝑅𝑔𝑔∀𝑔𝑔, 𝑡𝑡 (18)

𝑃𝑃𝑔𝑔𝑔𝑔 (𝜉𝜉) ≥ 𝑃𝑃𝑔𝑔min 𝑢𝑢𝑔𝑔𝑔𝑔

𝑃𝑃𝑔𝑔𝑔𝑔 (𝜉𝜉) + 𝑟𝑟𝑔𝑔𝑔𝑔 (𝜉𝜉) ≤ 𝑃𝑃𝑔𝑔max 𝑢𝑢𝑔𝑔𝑔𝑔 − �𝑃𝑃𝑔𝑔max − 𝑆𝑆𝑆𝑆𝑔𝑔 �𝑣𝑣𝑔𝑔𝑔𝑔

∀𝑡𝑡

(14)

∀𝑔𝑔, 𝑡𝑡 (19)∀𝑔𝑔, 𝑡𝑡 (20)

𝑃𝑃𝑔𝑔𝑔𝑔 (𝜉𝜉) + 𝑟𝑟𝑔𝑔𝑔𝑔 (𝜉𝜉) ≤ 𝑃𝑃𝑔𝑔max 𝑢𝑢𝑔𝑔𝑔𝑔 − �𝑃𝑃𝑔𝑔max − 𝑆𝑆𝑆𝑆𝑔𝑔 �𝑦𝑦𝑔𝑔,𝑡𝑡+1 ∀𝑔𝑔, 𝑡𝑡 (21)� 𝑟𝑟𝑔𝑔𝑔𝑔 (𝜉𝜉) ≥ 𝑅𝑅𝑡𝑡

I. MATHEMATICAL FORMULATION

𝑡𝑡∈𝒯𝒯 𝑔𝑔∈𝒢𝒢

𝑃𝑃𝑃𝑃 �� 𝑃𝑃𝑔𝑔𝑔𝑔 (𝜉𝜉) + � 𝑊𝑊𝑞𝑞𝑞𝑞 (𝜉𝜉) = � 𝐿𝐿𝑏𝑏𝑏𝑏 , ∀𝑡𝑡� ≥ 1 − 𝜖𝜖 (15)

∀𝑡𝑡

𝑔𝑔∈𝒢𝒢

(22)

II. SOLUTION METHODOLOGY
Step 1. Initialization:
Initialize the iteration counter, 𝜏𝜏 = 0. Set the lower bound
𝐿𝐿𝐿𝐿 = −∞ and the upper bound 𝑈𝑈𝑈𝑈 = +∞. Set the tolerance 𝜀𝜀.
Step 2. Iteration:
Step 2.a: Update the iteration 𝜏𝜏 ← 𝜏𝜏 + 1. Solve the (linearized) bilinear master problem BMP.
(i) If it is infeasible, then terminate the algorithm.

(ii) Otherwise, obtain the optimal objective 𝑉𝑉𝑙𝑙

(𝜏𝜏)

, the

(𝜏𝜏)
(𝜏𝜏)
(𝜏𝜏)
optimal commitment solution 𝑢𝑢�𝑔𝑔𝑔𝑔 , 𝑣𝑣�𝑔𝑔𝑔𝑔 , 𝑦𝑦�𝑔𝑔𝑔𝑔 and(𝜏𝜏)the scenario indicator 𝑧𝑧𝑛𝑛 .
(𝜏𝜏)(iii) Update the lower bound 𝐿𝐿𝐿𝐿 = 𝑉𝑉𝑙𝑙 .
(𝜏𝜏)

Step 2.b: For all wind power scenarios whose 𝑧𝑧𝑛𝑛 = 0,(i) Solve the feasibility check subproblem FSP n and(𝜏𝜏)obtain the optimal objective Ψ𝑛𝑛 .
(𝜏𝜏)(ii) If Ψ𝑛𝑛 is larger than zero, then supply the feasibility cut (31) to the master problem BMP.
Step 2.c: Construct a MILP problem for deriving the upper bound 𝑈𝑈𝑈𝑈. This problem is composed of constraints(1)-(14), (16)-(22), (24) and (26)-(29) with fixed values
for binary variables 𝑢𝑢𝑔𝑔𝑔𝑔 , 𝑣𝑣𝑔𝑔𝑔𝑔 , 𝑦𝑦𝑔𝑔𝑔𝑔 and 𝛿𝛿𝑔𝑔𝑔𝑔𝑔𝑔 (obtained from
Step 2.a). Then, solve this problem and obtain its optimal
(𝜏𝜏)
(𝜏𝜏)
objective 𝑉𝑉𝑢𝑢 . If this problem is infeasible, 𝑉𝑉𝑢𝑢 is set to(𝜏𝜏)+∞. Finally, update 𝑈𝑈𝑈𝑈 = min�𝑈𝑈𝑈𝑈, 𝑉𝑉𝑢𝑢 �.
Step 3. Stopping Condition:
If (𝑈𝑈𝑈𝑈 − 𝐿𝐿𝐿𝐿)/𝐿𝐿𝐿𝐿 ≤ 𝜀𝜀, then terminate with the commitment
solution associated with the latest upper bound 𝑈𝑈𝑈𝑈. Otherwise,
return to Step 2.

Reserve Margin for Wind Generation and Market
Implications of Stochastic Reserve
Mojgan Hedayati, Student Member, IEEE, Kory W. Hedman, Member, IEEE, and Junshan Zhang, Fellow, IEEE
School of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, AZ85287, USA
Email: mhedayat@asu.edu, khedman@asu.edu, Junshan.Zhang@asu.edu
Abstract— The rapid influx of renewable resources has givenrise to challenges in power system operations. One aspect ismaintaining system reliability at minimal cost in presence of
uncertain renewable resources. The uncertainty and variability of
renewable generation will affect the operating reserve
procurement. Additional operating reserves are needed tomaintain system reliability. Existing optimization solutions forhandling uncertainty, such as scenario-based stochastic
programming and robust programming, are computationallyexpensive, especially when there are multiple wind farms. Suchapproaches are less practical for large-scale systems during realtime operations. This paper investigates using data-mining
techniques to exploit offline stochastic calculations in order togenerate deterministic policies. Such deterministic policies arethen added to real-time operation models. This paper focuses onderiving such policies for wind energy and reserve. One approachfor handling the uncertainty of wind generation is to leave areserve margin when scheduling the wind power. New control
technologies are being developed that enable renewable resources
to transition to assets that are semi-dispatchable. It is anticipatedthat renewables will be expected to provide reserve margins andparticipate in balancing tasks in near future. In order to provide
reserve margin, wind generators hold some expected output forreserves. The extra expected available wind will then be accessible
for providing operational reserve. Prior research works havefocused on modeling uncertainty to determine appropriate wind
reserve margin levels. This paper proposes an offline policy
generation technique to provide a stochastic reserve margin tohedge against the real-time uncertainty of (multiple) wind farm
generation. The proposed policy generation structure uses aforecast-based framework that accounts for wind generation andsystem loading conditions. This paper then focuses on the market
implications of having such policies for providing stochastic
reserve. A testing scheme is proposed to assess the outcome of amarket structure embodying the derived policies. Such impacts
are analyzed in terms of market metrics including renewablegenerator revenue, conventional generator revenue, load payment,
congestion rents and potential uplift payments that such astructure would entail. Furthermore, metrics are developed toquantify the quality of service provided by such semi-dispatchable
resources. Such metrics would allow for differentiating the
products using the quality of service (QoS) provided by semidispatchable resources versus conventional generators. This isestablished by developing risk-based methodologies that accountfor the probability as to whether the required reserve is provided
when needed and also the corresponding consequences in regards
to grid operations. The proposed approach is tested on the
Reliability Test System 1996 (RTS96) with consideration of wind
uncertainties as well as generation contingencies. The results arecompared to those obtained using the existing structure without
imposing the proposed reserve policies. Wind generation data isused to analyze the effectiveness of the presented model inhandling uncertainty and achieving a more secure system
operation.

I. PROBLEM FORMULATIONThe objective function is the total cost shown below, in which thefirst line is the cost of energy production and scheduling spinningreserve from conventional generators as well as the wind generators.
The next term is the expected cost of providing security in scenarios.
This include the cost of deploying spinning reserve, supplied either
through the scheduled reserve or by the excess generation from thewind farm, the penalty cost associated with the wind generator not
being able to provide the scheduled energy, and the cost of
involuntary load curtailment.

II. KEY EQUATIONSMin ∑𝑔,𝑡(𝑐𝑔𝑒 𝑃𝑔𝑡 + 𝑐𝑔𝑟𝑐 𝑟𝑔𝑡 ) + ∑𝑤,𝑡(𝑐𝑤𝑒 𝑃𝑤𝑡 + 𝑐𝑤𝑟𝑐 𝑟𝑤𝑡 )𝑝𝑒𝑛𝑎𝑙𝑡𝑦+−+ ∑ 𝜋𝑠 { ∑ 𝑐𝑐𝑛 (𝐿𝑆𝑛𝑡+ 𝐿𝑆𝑛𝑡) + ∑ (𝑐𝑝 + 𝑐𝑤𝑟𝑒 )𝑃𝑤𝑠𝑡𝑠,𝑡𝑛𝑤+ ∑ (𝑐𝑔𝑟𝑒 𝑟𝑔𝑠𝑡 ) + ∑ (𝑐𝑤𝑟𝑒 𝑟𝑤𝑠𝑡 ) + ∑ (𝑐𝑤𝑟𝑐 ∆𝑤𝑠𝑡 )}𝑔𝑤

𝑤

Subject to:Base case constraints:∑𝑔∈𝑔(𝑛) 𝑃𝑔,𝑡 + ∑𝑘∈𝛿+ (𝑛) 𝑃𝑘,𝑡 − ∑𝑘∈𝛿 −(𝑛) 𝑃𝑘,𝑡 + ∑𝑤∈𝑤(𝑛) 𝑃𝑤,𝑡 =𝑑𝑛,𝑡 , ∀𝑛, 𝑡(1)
𝑃𝑔𝑚𝑖𝑛 𝑢𝑔,𝑡 ≤ 𝑃𝑔𝑡 , 𝑃𝑔𝑡 + 𝑟𝑔𝑡 ≤ 𝑃𝑔𝑚𝑎𝑥 𝑢𝑔,𝑡 ∀𝑔, 𝑡(2)∑𝑞∈𝐺 𝑟𝑞𝑡 ≥ 𝑃𝑔𝑡 + 𝑟𝑔𝑡 + ∑𝑤 𝜉𝑤𝑡 , ∀𝑔, 𝑡(3)𝑓

𝜉𝑤𝑡 ≥ 𝑃𝑤𝑡 − (𝑃𝑤𝑡 − 𝛼𝜎𝑤𝑡 ), ∀𝑤, 𝑡(4)

𝑓
𝑃𝑤𝑡 + 𝑟𝑤𝑡 ≤ 𝛼𝑃𝑤𝑡 , ∀𝑤, 𝑡(5)

𝑃𝑘𝑡 − 𝑏𝑘 (𝜃𝑛𝑡 − 𝜃𝑚𝑡 ) = 0, ∀𝑘, 𝑡(6)−𝑃𝑘𝑚𝑎𝑥 ≤ 𝑃𝑘𝑡 ≤ 𝑃𝑘𝑚𝑎𝑥 , ∀𝑘, 𝑡(7)Second Stage constraints (for scenarios):∑𝑔∈𝑔(𝑛)(𝑃𝑔,𝑡 + 𝑟𝑔𝑠𝑡 ) + ∑𝑘∈𝛿+ (𝑛) 𝑃𝑘,𝑠,𝑡 − ∑𝑘∈𝛿−(𝑛) 𝑃𝑘,𝑠,𝑡 ++−∑𝑤∈𝑤(𝑛)(𝑃𝑤,𝑡 + 𝑟𝑤,𝑠,𝑡 ) + 𝐿𝑆𝑛𝑡
− 𝐿𝑆𝑛𝑡= 𝑑𝑛,𝑡 , ∀𝑛, 𝑡(8)−𝑟𝑔𝑡 ≤ 𝑟𝑔,𝑠,𝑡 ≤ 𝑟𝑔𝑡 , ∀𝑔, 𝑠, 𝑡

(9)𝑃𝑔𝑚𝑖𝑛 ≤ 𝑃𝑔,𝑡 + 𝑟𝑔𝑠𝑡 ≤ 𝑃𝑔𝑚𝑎𝑥 , ∀𝑔, 𝑠, 𝑡

(10)

𝑝𝑒𝑛𝑎𝑙𝑡𝑦−𝑟𝑤,𝑠,𝑡 ≤ 𝑟𝑤𝑡 + 𝑃𝑤𝑡𝑠

, ∀𝑔, 𝑠, 𝑡

(11)III. RISK ANALYSIS METHOD
Risk analysis is performed for wind realization scenarios aswell as generator contingency scenarios. Key equations follow:
𝑝𝑒𝑛𝑎𝑙𝑡𝑦+−
Min ∑𝑠,𝑡{ ∑𝑛 𝑐𝑐𝑛 (𝐿𝑆𝑛𝑡+ 𝐿𝑆𝑛𝑡) + ∑𝑤(𝑐𝑝 + 𝑐𝑤𝑟𝑒 )𝑃𝑤𝑠𝑡+ ∑ (𝑐𝑔𝑟𝑒 𝑟𝑔𝑠𝑡 ) + ∑ (𝑐𝑤𝑟𝑒 𝑟𝑤𝑠𝑡 ) + ∑ (𝑐𝑤𝑟𝑐 ∆𝑤𝑠𝑡 )}𝑔𝑤

𝑤

Subject to:∑𝑔∈𝑔(𝑛)(𝑃𝑔,𝑡 + 𝑟𝑔𝑠𝑡 ) + ∑𝑘∈𝛿+ (𝑛) 𝑃𝑘,𝑠,𝑡 − ∑𝑘∈𝛿−(𝑛) 𝑃𝑘,𝑠,𝑡 ++−∑𝑤∈𝑤(𝑛)(𝑃𝑤,𝑡 + 𝑟𝑤,𝑠,𝑡 ) + 𝐿𝑆𝑛𝑡
− 𝐿𝑆𝑛𝑡= 𝑑𝑛,𝑡 , ∀𝑛, 𝑡(12)−𝑟𝑔𝑡 ≤ 𝑟𝑔,𝑠,𝑡 ≤ 𝑟𝑔𝑡 , ∀𝑔, 𝑠, 𝑡

(13)

𝑃𝑤𝑡 + 𝑟𝑤𝑠𝑡 + 𝑅𝑅𝑤𝑠𝑡 ≤ 𝑊𝑤𝑠𝑡 , ∀𝑤, 𝑠, 𝑡

(14)

References[1] M. He, L. Yang, J. Zhang, V. Vittal, “A Spatio-Temporal Analysis
Approach for Short-Term Forecast of Wind Farm Generation,” IEEE Trans.
Power Syst., vol. 29, no. 4, July 2014.
[2] M. Hedayati-Mehdiabadi, J. Zhang and K. W. Hedman, “Wind power
dispatch margin for flexible energy and reserve scheduling with increased
wind generation,” IEEE Trans. Sustain. Energy, vol. 6, no. 4, pp. 1543–1552,
Aug. 2015.

1

Estimating the Impact of Proposed Wind Farms on
Electricity Prices

Abstract—Estimating the impact of individual wind farms
on electricity prices is a matter of interest to different sectorsof electricity market. A data-driven approach is proposed foranalyzing the effect on wholesale electricity prices of new windfarms integrated to the grid. Numerical results based on Alberta
electricity system are provided.

Price Impact ($)

Payam Zamani-Dehkordi, Student Member, IEEE, Logan Rakai, Member, IEEE, andHamidreza Zareipour, Senior Member, IEEE

I. D ETERMINING THE O BSERVED P RICE I MPACT OF W IND
FARMS

60
40
20
0
1

The observed price impact of this wind farm for hour t canbe determined by removing its capacity from the supply stackand recalculating the hourly price as indicated in Fig. 1.

300

0.8

200

Market Tightness

100

0.6

Wind Generation (MW)

π̆t
πt

1

λt

0.8

Probability

Electricity Price ($/MWh)

Fig. 2. Developed RLWR model to relate the price impact of wind farms,
λ($/MWh), to market tightness, MT and wind generation, ω

ωt

Γt

Γ̆t

Real Impact
Predicted Impact

0.6
0.4
0.2

Net Demand (MW)0Fig. 1.

Impact of an individual wind farm on electricity priceII. M ODEL B UILDING
We apply Robust Locally Weighted Regression (RLWR)[1] as one of the most widely used non-parametric regression
methods. Market tightness in the present work is defined as
the ratio of Effective Market Demand to available conventional
generation. The achieved model is presented in Fig. 2
III. I MPACT OF P ROPOSED W IND FARMS ON E LECTRICITYP RICESIn this work, we rely on generating future scenarios of
explanatory variables based on Monte Carlo Simulation (MCS)[2]. Figure. 3 shows the CDF for both the real and simulated
time series of wind power generation of OWF1. The averageobserved price impact for this farm was $1.15/MWh where as
the estimated value was $1.19/MWh- a 3.5% error, which isreasonably low.
The authors are with Department of Electrical and Computer Engineering,
University of Calgary, Calgary, AB, Canada (e-mail: pzamanid@ucalgary.ca;
lmrakai@ucalgary.ca; h.zareipour@ucalgary.ca).

0

2

4
6
Price Impact ($)

810

Fig. 3. Cumulative probability distribution of real and estimated price impact
of Oldman wind farm in Alberta’s market

IV. C ONCLUSION
We verified the proposed methodology on a single windfarm in Albert’s electricity system. The numerical results
showed that the proposed approach can estimate the priceimpact of proposed wind farms for future development with areasonable accuracy. Estimating future price impact is a key
factor in conducting economic feasibility analysis not only forwind farm developers but also for other generation investors.
R EFERENCES[1] W. Hardle, Applied nonparametric regression. Cambridge Univ Press,
1990, vol. 27.
[2] D. Le, G. Gross, and A. Berizzi, “Probabilistic modeling of multisite windfarm production for scenario-based applications,” Sustainable Energy,IEEE Transactions on, vol. 6, no. 3, pp. 748–758, July 2015.

Scenario Reduction with Submodular Set
Optimization
Yishen Wang, Yuzong Liu, Yury Dvorkin, and Daniel. S. Kirschen
Department of Electrical Engineering
University of Washington Seattle, USA.
E-mail: ywang11@uw.edu


Abstract— Stochastic programming methods have been
proven to effectively deal with the uncertainty and variability of
renewable generation resources. However, their cost andreliability performance metrics depend on the accuracy and thenumber of scenarios modeled. Since a large number of scenarios
causes computational complexity, scenario reduction techniques
are used to select representative scenarios. The common
drawback of existing scenario reduction techniques is that the
number of representative scenarios is a user-defined externality.
We propose a scenario reduction algorithm based on
submodular function optimization to endogenously optimize thenumber of scenarios within a given SRT. This algorithm iscompared, both qualitatively and quantitatively, with the stateof-the-art fast forward selection (FFS) algorithm

TABLE II. SOLUTION TIME COMPARISON
Initial Scenario Number

N=1000Time (s)N=10000

FFS

SSR

FFS

SSR

K=20

0.10

0.15

9.77

8.02

K=100

6.46

0.33

408

16.0

Reduced Scenario Number

SSR Optimal Reduced
Scenario Number

K*

19

136

Time (s)

0.20

15.7

I. METHOD
To alleviate computational complexity of scenario-based
Stochastic Programming (SP) problems, a large initialscenario set is usually reduced to a smaller subset ofrepresentative scenarios. As discussed in [1], the quality of
the SP solution decreases if the reduced set is too small, andthe SP solution time increases if the reduced set is too large[1]. These observations illustrate the need to optimize thenumber of scenarios used in the reduced set to trade offsolution quality and computing time. Among a number ofdifferent reduction algorithms, the Fast Forward Selection(FFS) technique [2] has been proven to be most efficient.
However, in this technique the number of selected scenarios
has to be pre-defined and scalability is not examined in thecontext of larger scenario sets.
We present a new Submodular Scenario Reduction (SSR)
technique based on the submodular function optimization,
which determines the optimal number of scenarios andselects them accordingly. We also provide numerical resultsthat demonstrate that the proposed SRT is mathematicallyconnected to the FFS technique and yields noticeable
computational advantages.
TABLE I.

SCENARIOS SELECTED BY BOTH FFS AND SSR
Initial Scenario N=5000
K

Control Parameter

10

50

100

500

K*

Λ=0.5

8

42

90

433

240

Λ=10

9

50

100

498

22

Figure 1. Submodular Scenario Reduction Results
II. CONCLUSION
The proposed SSR optimizes the number of reduced scenarios
number and outperforms the state-of-the-art fast forward
selection algorithm in terms of computational speed.
REFERENCES
[1]

[2]

Y. Dvorkin, Y. Wang, H. Pandzic, D. S. Kirschen, “Comparison of
Scenario Reduction Techniques for the Stochastic Unit Commitment,”in Proc. of 2014 IEEE Power and Energy Society General Meeting,Washington DC, 2014.
N. Growe-Kuska, H. Heitsch, and W. Romisch, “Scenario reduction
and scenario tree construction for power management problems,” inPowerTech Conference Proceedings, 2003 IEEE Bologna, vol. 3.
IEEE, 2003, pp. 7–pp.

1

EMS and Sizing of a Fuel Cell–Based Auxiliary
Power Unit
Saleh Ziaeinejad, Graduate Student Member, IEEE, and Ali Mehrizi-Sani, Senior Member, IEEE
School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, 99164
Emails: saleh.ziaeinejad@wsu.edu and mehrizi@eecs.wsu.edu
Measure VFC, VDC, and Vbatt

Abstract—Fuel cells have increasingly become an attractive
option for providing clean electricity at a relatively high efficiency. This work considers key features of a proton exchange
membrane (PEM) fuel cell for a fuel cell–based auxiliary power
unit (FC-APU) to supply a portion of electric loads in a vehicle
and proposes 1) an energy management system (EMS) to manage
the power transfer between the load and energy sources withemphasis on low stress on the fuel cell and 2) an iterative method
to size the fuel cell and battery. Experimental results validate
the proposed EMS and sizing algorithms.

VFC<VFC,min?
Vbatt>Vbatt,max?

II. P ROPOSED EMS AND SIZING ALGORITHMS
Fig. 1 shows the flowchart of the proposed EMS. It reduces
the fuel cell stress by preventing fast and frequent changes
in the fuel cell current IF C . It also prevents overcharge andoverdischarge of the battery (energy storage). The EMS isequipped with current and voltage controllers that regulate
IF C and the DC link voltage VDC (voltage of the load point).
The minimum size of the fuel cell is achieved by using it toprovide the average load demand. The battery is then sized forproviding the difference between the fuel cell power and theload demand. Fig. 2 shows a generic load profile. By using
the proposed iterative algorithm, the average load power iscalculated and considered as the basis for finding the fuelcell power. The proposed algorithm then sizes the battery.

Y

N
N* =IFC,sch
IFC* <IFC,sch?
IFC

N

* =0
IFC

Excessgenerated power(IBI < 0)?

YN

Vbatt>Vbatt,min?
Y

N
Disconnect the load
Go toward charge
mode (decrease DBi)

Fig. 1.

* >0 ?
IFC

Y*
Decrease IFC

Y*
Increase IFC

I. I NTRODUCTIONY*
VDC > VDCN

Go toward discharge
mode (increase DBi)

Proposed algorithm for the energy management system (EMS).
Discharge

PL (kW)

Fuel cells are becoming an attractive option for vehicularapplications because of their cleanliness, low noise, reliability,and efficiency. However, fuel cells suffer from the slow
dynamics. A solution to this shortcoming is to use a hybrid
system that employs a fuel cell and an energy storage device
to benefit from their complementary features (high energy andfast dynamics, respectively). In a fuel cell–based auxiliary
power unit (FC-APU), the size of energy devices and thealgorithm of the EMS are two important issues. The systemsize should be minimized without imposing destructive stresson the system. The EMS should maintain the system operating
point within the prescribed limits by controlling the power
flow between different segments of the system.

Y

N

AD1
TD1

ChargeDischarge
PL,maxChargeDischarge

AD2

PL,min

A D3

AC1
TC1

AC2
TD2

Time (min)Fig. 2.

TC2

TD3

T

Generic load profile to demonstrate the sizing algorithm.

battery voltage Vbatt reaches its maximum allowed threshold,IF C is gradually decreased to avoid overcharge of the battery.
Finally, a load is applied and the system behavior is studied.
As shown in Fig. 3(c) for t ∈ [77, 80], when Vbatt decreases,
IF C is increased until it reaches its scheduled value.

III. K EY EXPERIMENTAL RESULTS
An FC-APU is implemented in the laboratory and theeffectiveness of the proposed sizing and EMS algorithms isevaluated by a long-term test. In the first 30 minutes of thetest, an assumed load is used. As shown in Fig. 3(a), IF C is
kept constant at its scheduled value and VDC is regulated.
To evaluate the proposed EMS, the test continues to charge
the battery. Fig. 3(b) for t ∈ [30, 77) shows that when theFig. 3. Experimental results of a long-term test to evaluate the EMS andsizing algorithms

1

Data Driven Economic Dispatch in Systems with
Variable Generation
Mohammad Sadegh Modarresi, Member, IEEE, Le Xie, Member, IEEE,

Abstract—Renewable energy penetration growth in powersystems is not going to stop in near future, and it will cause
constant growth in the uncertainty in the different operation
timeliness of power systems, especially the day-ahead economic
dispatch problem. Numerous approaches has been proposed inrecent years to tackle this problem. In this poster, we will clearly
describe an overview of three main approaches to solve dayahead economic dispatch problem and then we will show thedata driven approach which we are working on. At the end, we
will test the results of using these approaches using real-life data.

I. I NTRODUCTIONZ

ERO emission energy resources are being increasingly
developed in power systems. However, limited forecast
accuracy of these resources shapes a challenge for powersystem operation planning. This poster will show the resultsof a new data driven economic dispatch approach. Also wewill compare the results of our proposed approach with otherthree main economic dispatch formulations. We will show the
drawbacks of other formulations in 21st century energy system
structure. This structure is shown in Fig 1. Unpredictable
renewable resources, fewer and fewer emission allowance andcontrollable demand are main specifics of these systems.

In this poster, we describe our ongoing research on using thehistorical data of the system to perform economic dispatch inpower systems, at the day ahead stage. In other words, insteadof involving various probability estimation to find expectedlevel of intermittent resources availability, we use historicalsystems data. However, since using all of historical data will
give a conservative result, we neglect specific data points andtherefore, we can get a more economically efficient result. Ourdeflection policy is backed by rigorous mathematical structure.
To compare the results of our method to other economic
dispatch models, we used three main economic dispatch
approaches in our study, deterministic, stochastic and robust
economic dispatch. To compare them, we tried to as fairas possible in the formulation as well as simulation. After
formulating the problem, we selected five years worth of data.
We used part of the data for training and the other part for
testing. Our numerical results showed some fascinating facts
about using real-world data instead of probabilistic estimation,
which will be presented in the poster.
A hint of the results has been presented in Fig. 2. As it
can be seen, a great saving in the economic dispatch cost will
occur after removing first few binding constrains during thetraining period. Also, it will be shown that we can confidently
guarantee the level of violation probability after removing eachof these constraints.

Unpredictable

Cost
Emission

PVs

Distribution System

Large
Customers

Load Control

Fig. 1. 21st century structure of energy systems.

Fig. 2. Four hours study of removing 1-150 constraints from data and its
impact on the economic dispatch results.

Managing Energy Storage and Water Heaters Loads
for Solar-powered Residential Homes
Gonzague Henri, Student Member, IEEE

Ning Lu, Senior Member, IEEE

Department of Electrical and Computer Engineering
North Carolina State University
Raleigh, USA
gahenri@ncsu.edu

Department of Electrical and Computer Engineering
North Carolina State University
Raleigh, USA

Abstract—The cost of purchasing and maintaining roof-top
photovoltaics (PVs) and energy storage systems decreased
significantly over the past five years. Beside grid power,
residential consumers have more means to optimize theirelectricity consumptions. The development of communication
technology via the Internet of things make is possible for remotely
control and monitor end use devices by consumers or energy
service providers. This paper discuss optimal control algorithms
for consumers to reduce their utility bills and improve their
comfort. The house is powered by PV and supported by an energy
storage systems. An optimal control strategy that can optimize theoverall system performance with least cost for different electricity
tariffs are proposed.

used for producing the load consumption curves. The
optimization of an ON/OFF problem for the water heater will
formulated as a mixed integer linear programming problem.
There are two goals when switching water heater loads on andoff: reduce the wear-and-tear of the energy storage devices andminimize the size of energy storage. To increase the capabilityof the water heater for regulating the electricity use, we relax thelow and high temperature limit of the water heater to examine
the influence on energy use and user comfort when relaxing theconstraints on performance improvement.

Keywords—demand response; energy storage; home energy
management system; optimization; pricing schemes.

𝑀𝑖𝑛 ∑ 𝑃𝑔𝑟𝑖𝑑,𝑖 ∗ 𝐶𝑖 + 𝑃𝑒𝑥𝑝𝑜𝑟𝑡,𝑖 ∗ 𝑉𝑖

I. INTRODUCTION

where for each block of time 𝑖 in the optimization problem (24hours, or intra hour) 𝑃𝑔𝑟𝑖𝑑,𝑖 is the amount of electricity imported
from the grid at the price 𝐶𝑖 , and 𝑃𝑒𝑥𝑝𝑜𝑟𝑡,𝑖 is the energy back fed
to the grid at the price 𝑉𝑖 . The objective function in (1) will be
subject to the following constraints: 1) device operational
constraints 2) PV production 3) Battery status 4) Battery
minimum and maximum state of charge 5) Battery maximum
power 6) House load.

The high penetration of behind-meter, intermittent solar
resources poses new challenges to grid operation. The duck
curve from California indicates that integrating solar generation
into the grid without considering energy storage or demand side
energy management, require extremely high ramp capability
from traditional generators at mid-afternoon valley to evening
peak periods. Energy storage allows solar power be stored forlater use. Demand management allows solar power be consumed
at the desired time periods. Thus, the coordination between theenergy storage and demand management algorithms will be a
key technology for developing future solar-powered
communities.
II. PROPOSED METHODOLOGY
In this poster, we will present a two-level control method [1].
The first level will be to optimize the energy levels of energy
storage on a day-ahead basis for every hour for the next 24hours. The second tier will be to control the intra-hour
operations of the battery considering the coordination with thewater heater load. The first tier will provide the second tier withthe upper and lower bounds of the State of Charge (SoC) for
each hour during the day. The proposed water heater control
method will control the ON and OFF status of a water heater as
introduced in [2]. The water heater status forecast will be basedon the known data. A random disturbance will be added tomodel the inaccuracy. Data collected at Pecan Street [3] will be

The goal of the optimization will be to minimize the cost,based on the following objective function:
(1)

𝑖∈𝑇

PV valorization mechanisms such as NEM, FIT or RealTime Prices based on day ahead market will be considered. The
authors believe Real Time Pricing is more likely to occur for thevalorization of services of the energy sold to the grid than forretail electricity tariffs.
The main contribution is to consider different grid injection
valorization models in the optimization problem and compare
how the energy storage and the water heater affect each other interms of cost reduction.
REFERENCES
[1]

[2]

[3]

Y. Wang, X. Lin, and M. Pedram, “Adaptive Control for Energy Storage
Systems in Households with Photovoltaic Modules,” IEEE Trans. on
Smart Grid, vol. 5, pp. 992–1001, March 2014.
N. Lu, D. P. Chassin, “A State-Queuing Model of ThemostaticallyControlled Appliances”, IEEE Trans. On Power Systems, vol. 19,
pp.1666–1673, August 2008
Pecan Street [online] http://www.pecanstreet.org/

Design LLC Resonant Converter Battery Charger
Using 1kHz Sinusoidal Ripple Current
Yin Li, Student Member, IEEE

Lingling Fan, Senior Member, IEEE

Department of Electrical Engineering
University of South Florida
Tampa, United States
yin@mai.usf.edu

Department of Electrical Engineering
University of South Florida
Tampa, United States
linglingfan@usf.edu

Abstract— This project proposes the design of a resonantbattery charger charging the Lithium ion battery with thesinusoidal ripple current (SRC). SRC charging is a new charging
technology and 1kHz SRC can enhance charging time, charging
efficiency, charging temperature, and battery life [1]. The
highlight is that the switching frequency of LLC resonant
converter is only 500Hz rather than conventional LLC resonant
frequency, hundred kilo hertz. The battery charger is also
applied to the three-phase grid voltage source using a threephase ac/dc converter with VDC/Q control. For the specificfrequency of SRC and the characteristics of the battery, thedesign of the controller and the LLC resonant converter will be
introduced and validated by the simulation results. The testbeds
are MATLAB/Simpowersystems and real-time simulator, OPALRT.

Using resonant frequency equation to calculate Lr and Lm. The
rule for l is that the lower boost gain is required, its range isnormally from 6-10. In this paper, both of VDC and Vout
designed around 400V causes a smaller gain required, so 8 isselected for l.
II.
KEY RESULTS

Keywords—LLC resonant converter, sinusoidal ripple current,
VDC/Q control, OPAL-RT.

I.

SYSTEM CONFIGURATION

Fig2. 1kHz sinusoidal ripple charging current from MATLAB

A. Three-phase ac/dc converter
To use the three-phase grid voltage source to charge thebattery directly, the VDC/Q control loop is designed in the
charger. It can control the DC voltage for the specific battery.
B. LLC resonant converter
Compared with conventional LLC resonant converter, thisnovel LLC resonant converter has a relative low switchingfrequency to reduce the converter power loss and has a filter
only including a capacitor at the end because it does not
require an inductor to achieve the dc current.
1) Full-bridge dc/ac converter: Its function is to excite the
LLC resonant tank network by converting the dc voltage to asquare waveform. The open-loop control is used to control
IGBTs with 500Hz switching frequnecy and 50% duty cycle.
2) LLC resonant tank network: To obtain a boost gain, theresonant frequnecy should be larger than 500Hz, so 550Hz is
selected for fr. Based on the frequency, 30μF is selected for Cr.

Fig3. 1kHz sinusoidal ripple charging current from OPAL-RT(Scaled down by 10, 2V/div and 1ms/div)

III.

REFERENCE[1] L.-R. Chen, S.-L. Wu, D.-T. Shieh, and T.-R. Chen, “Sinusoidal- ripplecurrent charging strategy and optimal charging frequency study for liion batteries,” Industrial Electronics, IEEE Transactions on, vol. 60, no.
1, pp. 88–97, Jan 2013.

Fig1: Topology of LLC resonant battery charger. Bold elements mean three-phase

POSTER ABSTRACT, IEEE PES GENERAL MEETING, BOSTON (MA), JULY 2016

1

Enabling Flexible Consumers to participate in
Forward Markets by Wind Power Forecast Updates
Lars Herre, Student Member, IEEE, and Lennart Söder, Senior Member, IEEE

Index Terms—smart grids, power system simulation, power
generation planning, power system economics, wind energy
integration.

I. K EY I SSUESLIMITED energy resources in the form of fossil fuels at atime when a paradigm shift in energy policy is occurring
will characterise this century by intensified competition ofrenewable energy sources. Renewable energy from wind power
is seen as one of the most promising and mature large scale
renewable alternatives in a future energy system. Due to theintermittent nature of wind energy resources the power output
is inherently volatile. Additionally, the limited predictabilityof wind speeds results in uncertain wind power forecasts
for power system planning and operation. In a future smart
grid, flexible electricity consumers are seen as one option ofmaintaining the power system in balance.
Notice Time: Here, we will define the time span between
the notification, i.e. information exchange with flexible consumers and the target time of the actual consumption as noticetime. To the authors’ best knowledge, none of the previous
studies have investigated the impact of different notice timesof flexible consumers. Most studies are based on demand
response programs and models which are bound to a fixed timeframe to notify consumers. For enhanced integration of volatilerenewable energy sources such as wind power however, thenotice time is an imperative parameter, since forecasts tend tostochastically improve the closer the target hour is approached.
An illustration of wind power forecast root mean squareerror (RMSE) over the forecast horizon is shown in Fig. 1.
A non linear decrease of the forecast error is obvious, whichsignificantly improves for short term forecasts. The second
graph of Fig. 1 illustrates the development of the probability
L. Herre and L. Söder are with the Department of Electrical Power &Energy Systems, KTH Royal Institute of Technology, Stockholm, Sweden(e-mail: see http://www.kth.se/profile/lherre).

RMSE [% inst. capacity]

distribution function of forecast errors corresponding to theforecast horizon. As the forecast horizon decreases and the10

Probability [%]Abstract—Power systems with a large share of inherently
intermittent energy sources require new approaches to system
operation. Demand side management is seen as one potential
resource for maintaining power balance in a future energy systemwith large amounts of volatile renewable energy generation. Most
demand response programs are characterised by a set time frame
for notification of flexible electricity consumers. For a moredetailed analysis of consumer flexibility, not only price but also
notice time are imperative parameters. Former has been studiedin numerous references whereas the impact of the latter has not
yet been examined in depth. We present consumers’ flexibility
with respect to notice time and present a model to increase
participation of flexible consumers in forward electricity markets
with continuous forecast updates.

German Control Areas

5

0

8
6
4
2
0

-40

-30
-20
-10
Forecast Horizon tn [hours]

0tn = 36htn = 30h
tn = 24htn = 18h
tn = 12htn = 6htn = 1h

-0.2

-0.1
0
0.1
Forecast error [% of inst. capacity]

0.2

Fig. 1. Wind power forecast root mean square error (RMSE) and probability
density function for selected forecast horizons, i.e. notice times

target hour is approached, the standard deviation significantly
decreases. Therefore increased participation can be achievedby updating consumers during consecutive notice times upon
wind power forecast updates.
The framework to analyse the influencing factors of flexible
consumers is strongly bound to the electricity market set-up;
the type of demand response program, market price formulation and success indicators. It is logical that the flexibility
generally decreases as the notice time becomes shorter in mostcases. On the contrary, forecasts of wind power production
become more accurate the closer they approach the target hour.
An issue is thus, how to efficiently balance the task that longer
notice time might imply more flexible consumers on the one
hand, but also more uncertain forecasts on the other hand.
II. K EY F INDINGSAn economic dispatch model with continuous wind power
forecast updates is proposed that allows for the analysis ofnotice time using update intervals. We conclude that updated
flexible consumers can contribute to minimizing power imbalance and thus decrease cost. Both the quantity wind power
forecasts error and notice time dependent elasticity of flexible
consumers impact the electricity price in our study.

2016 IEEE PES GENERAL MEETING

Remedial Action Scheme With Wind Power
Integration
Ren Liu, Student Member, IEEE, Anurag K. Srivastava, Senior Member, IEEE

Abstract—This poster presents a Remedial Action Scheme(RAS) in presence of high wind penetration. RAS Scheme isdeveloped to allow the integration of wind power and protecting
the safety and reliability of the power grid with minimum wind
power generation shedding. A new Distributed Linear State
Estimation (DLSE) is also developed to provide the fast andaccurate data to RAS. The proposed approach is simulated
for small test case in real-time. Test results show the superior
performance of the RAS algorithm for minimum wind power
generation shedding.

speed of RAS is often in the second-level. Thus, traditional

Index Terms—Remedial Action Scheme, Wind Power Generation, Distributed State Estimation, Real-Time Simulation

I. I NTRODUCTION
ince the installation of wind farm is increasing rapidly,
wind power has become an important source of power
generation in smart grids [1]. However, the intermittent anduncertain nature of power generation from wind farm maycause standard ampacity ratings of the small load transmission
lines to be exceeded frequently. Due to the exceeding thetransmission line ampacity ratings, plenty of wind farms haveto be shed, which decrease the usage of the wind farm. In
order to minimize the wind power curtailment and protect thetransmission lines from overload, a new RAS is developed andpresented in this poster. A DLSE is also developed to provide
fast and accurate in put data to RAS.

S

II. P ROPOSED RAS AND S IMULATION M ETHOD
In order to minimize the wind power curtailment, DC power
flow and linear programming technology are integrated into
this RAS. The three different functions of RAS are shown as
following:1) Detection: RAS detects the whether there are overload
conditions happening based on the phasor measurements
on each transmission line.
2) Optimal Switching: RAS utilizes the optimal switchingalgorithm to eliminate overloads condition by controlling
the relay on/off.
3) Wind Power Curtailment: If the optimal switching
cannot solve the overload problem, the third function will
be executed. Based on the DC power flow and linear
programming, RAS finds the optimal solutions to cut off
the minimum wind power.
As the above discussion, RAS can control the relay on/off.
Thus, the input data of RAS must be filtered with noiseand possible malicious attack data. Traditional centralizedstate estimation runs every 2-15 mins [2]. However, operation

Fig. 1. Real-Time Simulation Architecture

centralized state estimation cannot meet the input requirementof RAS. A DLSE is developed to meet the input requirementof RAS. At first, since the whole power system is decomposed
into lots of small areas, the computation task of DLSE ismuch less than the traditional centralized state estimation.
Meanwhile, PMU data is utilized by DLSE to provide the moreaccurate estimated results. In addition, non-iterative method isused for the DLSE, which also increases the operation speedof the DLSE.
In order to evaluate the performance of RAS for minimum
wind power generation shedding, a small test case is simulated
in Real-Time Digital Simulator (RTDS). Real industry PMU
is used to achieve the phasor data from RTDS and send toReal-Time Automation Controller (RTAC), which is the RAS
controller. Pre-determined weather information is also sent to
RTAC. If the optimal switching or wind power curtailment isneeded, control action will be sent from RTAC to relay andrelay will trip the simulated circuit breaker in the RTDS. The
architecture of this real-time simulation with hardware-in-theloop is shown in the Fig. 1.
III. S UMMARY
In this poster, a RAS is developed to minimize the windfarm curtailment and protect the transmission lines fromoverload. In order to provide the fast and accurate input data, a
DLSE is also developed. The performance of RAS is tested in
the real-time simulation with hardware-in-the-loop. Simulation
results show that proposed RAS can keep a higher usage ofwind power than traditional protection method.
R EFERENCES[1] Total electric power industry summary statistics, 2015 and 2014.
[Online]. Available: http://www.eia.gov/electricity/monthly/epm tablegrapher.cfm?t=epmt es1a

2016 IEEE PES GENERAL MEETING[2] T. Berry. Use case 25: State estimation. Electric Power Research Institute.
[Online]. Available: https://www.smartgrid.gov/files/State Estimation.pdf

Towards a Power System with High
Penetration of Renewables
Ming Wei, Student Member, IEEE
Dept. of Electrical and Electronic Engineering
The University of Hong Kong
Hong Kong, China
weiming@eee.hku.hk
Abstract—Many utilities, research centers and companies
have performed renewable energy penetration studies to copewith greenhouse effect and energy crisis. But for many countries
towards a power system with high percentage renewables, themost practical strategy is still the combination of hydro power
and wind power. In order to investigate the feasibility of highpenetration of renewable energy in a power system, this paper
discusses the mutual influence between power system and highrenewable energy penetration. Simulation and case study shows
how much wind energy is needed for a power system expecting
high percentage renewable energy and presents the tradeoff
between wind power expansion and electricity import.
Index Terms—Hydro power, high penetration of renewableenergy, reservoir storage, wind power.

its sufficient wind resource are suitable for simulation in thisstudy. A single year (year 2014) and 10-year (year 2005-2014)
simulations are considered. For year 2014, if the tested systemwant to reach an almost 100% renewable energy system,
yearly wind power output required is the difference between
load and hydro power. We generate wind power output
scenario of 2014 by scaling up the real wind data through awind power scale-up factor (WS). For the 10-year period, we
use data of 2014 as base to evaluate the electricity selfsufficiency issue forward to 2005. Additionally, if the system
maintains original condition, it will cost €5.3*109 to import
deficit electricity (generation cost of conventional units is notconsidered). If the system aims at zero-import, €5.1*109 to €11.4*109 will be invested to build wind farms.

I. INTRODUCTION
In order to reduce greenhouse emissions to a point of nearzero by between 2040 and 2070 (IPCC, 2007), greenhouse gas
mitigation strategies are generally considered by a great
numbers of countries. For example, Sweden, owning sufficienthydro and wind resources, will invest 4.5 billion kronor in 2016as a step towards the target of being a fossil-fuel-free nation by
2050. In practice, due to the limitation of renewable resources
like PV and biological energy, hydro power and wind power are
the most potential combination for many regions in the world.
Correspondingly, reservoir is regarded as a good solution to theseasonality of hydro inflow.
II. PROBLEM FORMULATION
Based on existing energy structure of a power system, thispaper investigates how much wind powers should be expanded
to balance yearly electricity energy for that system expecting ahigh penetration of hydro power and wind power. Threeindicators are employed, electricity import, reservoir level andwind curtailment, to reflect mutual influence between powersystem and renewable energy penetration.
III. SIMULATION AND CASE STUDY
In this study, we assume wind is not storable, and if the
combination of hydro inflow (including reservoir) and wind
power cannot meet demand during some periods, the system
will import electricity from neighborhood power systems.
Considering the inflexibility of building hydro plants andreservoir, we investigate wind power expansion only. The
yearly electricity energy characteristic of Sweden (48% ofelectricity from hydro power and 12% from wind, 2014) and

Fig. 1. Reservoir level, reservoir draw-off of 2014 and wind curtailment with 100%
and 0% of initial reservoir level respectively

Fig. 2. Electricity import for 10-year period with WS=1.292 and 1.6

IV. CONCLUSIONS
Simulation shows that for a single year, electricity importis heavily associated with how much water is reserved in the
beginning of the year and wind curtailment is determined byhourly wind output and demand in a high percentage
renewable energy system. For a longer term, much more wind
power is needed for a system aiming at almost self-sufficient.
From economic view, whether expanding wind power greatly
depends on factors like electricity prices and device andinstallation prices.

Impact of DC Voltage Droop Control Structures on
DC Oscillations in a Multi-Terminal HVDC Grid
Florian Thams

Eduardo Prieto-Araujo

Robert Eriksson

Center for Electric Power and Energy (CEE)Technical University of Denmark

CITCEA
Universitat Politècnica de Catalunya

Markets and System Development
Swedish National Grid

Abstract—This work is focused on analyzing the disturbance
response of eight different dc voltage droop controllers. The
approach is based on multi-variable frequency response analysis
where both ac and dc grid dynamics are incorporated. The corresponding eigenvalues related to resonance peaks are analysedby a participation factor analysis detecting the cause of poorly
damped frequency ranges.

I. I NTRODUCTION
The control of multi-terminal HVDC systems is an emerging research topic due to the very little experience with only afew VSC-HVDC multi-terminal systems been built worldwide.
Due to significant impact of any malfunction it is preferable
to have a distributed control architecture. Then, multiple units
actively participate in the control of the dc grid voltage insteadof having it controlled by a single converter. One of thepreferred control structures enabling power sharing between
several converter is dc voltage droop control. However, intechnical literature several alternative droop control schemes
have been proposed. They can be categorized by two different
types of generalized structures, depending on whether the dc
voltage is used within the first or the second loop as shownin Fig. 1. In each of this two types the signal y can either
resemble idc , iac , Pdc or Pac which leads to a total of 8
different droop control structures. To the knowledge of theauthors no previous work is done on analysing and comparing
the performance of the different control structures (CSs) interms of multi-variable frequency response.
II. M ETHODOLOGY
A three terminal grid is used as study case with two
equivalent grid side converters (GSVSCs) with LC filter andconnected ac grid (Thévenin equivalent representation) and
one windfarm, the control structure of the whole system is
illustrated in Fig. 2. The same CS is used for both GSVSCs

in order to compare the CSs.
The performance of the control structures is analysed by meansof the singular value representation of the system transfer
function. The maximum singular value σ(G(jω)) indicatesthe maximum amplification of the corresponding inputs by
the system seen from a specific output. The analysed inputs
and outputs are the wind power input (∆w in Fig. 2) andthe dc voltage errors (∆z) at the grid side converter. Further,
the eigenvalues, λi , corresponding to the resonance peaks areanalysed with a participation factor analysis in order to detect
the cause of the peaks.

Fig. 2. Control Structure of multi-terminal HVDC gridIII. K EY FIGURESFor the eight different CSs significant different singularvalue (SV) representations can be observed, two examples areshown in Fig. 3. It is obvious that the choice of the droop
gain impacts the voltage error in both cases. Further, the SVsof both CSs differ by up to 40 dB in steady state while thedeviation during the first peak sinks to approx. 20 dB. This
indicates that with CS3 a significant lower voltage error can beachieved than with CS1. Hence, the choice of the CS impacts
the dc oscillations induced by transmitted wind power.

Fig. 3. Singular value representation of G(jω) (dc Voltage error - wind
power input) for a variation of the droop gains for CS1 (left) and CS3 (right)
Fig. 1. Generalized Droop Control Structures

Distribution Planning Criteria and Tools for Future
Distributed Energy Resource Penetration Scenarios
David Mulcahy, David Lubkeman, Ning Lu
Dept. of Electrical and Computer Engineering
North Carolina State University, Raleigh, NC, USA
djmulcah@ncsu.edu
Abstract— Rapid increase of distributed renewable energy
resources on distribution circuits are changing the load
consumption patterns. The variability and uncertainty of thesolar generation will not only affect the feeder voltage profiles
but also potentially cause shifts in load peaks, overloading
distribution transformer and lines, and changes of relay settings.
To address those impacts on distribution feeders, utilities require
new criteria and tools for planning their distribution circuits.
This research proposes a framework and tools to compare theeffects of circuit improvements on increasing the capacity ofphotovoltaic (PV). The methodology creates future PV
deployment scenarios and models the increase in penetration
with the application of mitigation strategies.
Keywords—distributed
generation;
distribution
planning;
photovoltaics; hosting capacity; solar integration; cost benefit analysis

I. INTRODUCTION
This research develops a framework for utilities to makeplanning decisions on feeders with high penetration of solar.
Different grid upgrade options are compared for different solar
penetration scenarios. Network parameters and historicaloperation data of a few typical feeders are obtained from Duke
Energy.
By creating probabilistic photovoltaic (PV)deployment scenarios, we compare improvements on PV
hosting capacity for targeting circuit upgrade options. There
are three key steps in the process: PV deployment scenario
creation, modeling operational scenarios and mitigation
strategies, and decision and cost benefit analysis.
Step 1. PV Deployment Scenario Creation
In this step, probabilistic PV deployment scenarios are
generated based on the anticipated penetration range of PV
capacity (calculated as percentage of the feeder peak load).
The capacity of the PV system ranges from multi-MW solar
farms down to commercial 200 kW systems or aggregatedresidential systems. Probabilities are assigned to each type andpenetration pair based on the types of consumers, the location,and demand of the circuit to be used in the cost benefit analysisof mitigation strategies.
Step 2. Modeling Operational Scenarios and Mitigation
Strategies
In this step, a set of mitigation plans, such as increasing the
conductor wire sizes, altering the placement of voltage
regulators, and changing the settings of voltage regulation
devices, is modeled on a distribution circuit in OpenDSS. Peakand 30% of peak load conditions are used to determine which
PV scenarios will result in violations of the system voltage andcurrent limits. In our future work, the set of violation criteria
can also be expanded to include 24-hour loading scenarios.
Step3. Decision and Cost Benefit Analysis
To make decisions based on the results obtained from Step
1 and 2, we conducted cost benefit analysis and developed avisualization tool for comparing different circuit mitigation
strategies across the deployment scenarios.
The figures below show different methods of comparing
hosting capacity within a circuit (Fig. 1), hosting capacitychanges with mitigation strategies (Fig 2.), and hosting
capacity across deployment scenario (Fig. 3). These
comparisons and the associated costs are used to determine
which mitigation strategies are most resilient to future PV
deployment and criteria to better plan circuit improvements.

Fig. 1. Hosting capacity of single PV installation across distribution feeder

Fig. 1. Hosting capacity with upgrades to 3 phase wire size and voltage
regulator setpoint changes (maximum, 75%, median, 25%, and minimum ateach 3 phase bus)

Fig. 3. Comparison of PV hosting capacity at light load based on placement of
deployment configuration PV installation at top, middle, and end of feeders.
Green shows no violations and red shows a violation.

Employing ARIMA to Improve Wind Power
Forecasting in ERCOT
Fathalla A. Eldali, Graduate Student Member IEEE, Siddharth Suryanarayanan, Senior Member IEEE,and Timothy M. Hansen, Member IEEE
Abstract – Wind power is growing significantly due to its
favorable characteristics such as cost-effectiveness andenvironment-friendly quality. The improvement in wind
power forecast will improve the dispatchability and unitcommitment of wind generation units in the day-aheadmarket (DAM). Wind power forecast improvement
techniques include aerodynamic atmospheric models ortime series based models. The latter is the focus of thiswork that aims to improve wind power forecast using
autoregressive moving average (ARIMA) model. We
employ available historical data of hourly wind power
forecast and actual from the Electric Reliability Councilof Texas (ERCOT) to improve the day-ahead wind power
dispatch. The improvement in the wind power forecast isquantified using vector norms.
I. WIND POWER FORECASTING

Fig. 1. Wind Power Forecasting Process Timeline
II. INTIAL KEY RESULTS
The improvement in wind power forecasts using the ARIMA
technique is expressed by the vector norms (Lp) of the relative error.
𝐿1 , 𝐿2 , and 𝐿∞ depict the reduction of the sum of the absolute RE, theroot mean square RE, and the maximum absolute RE respectively.
Fig.2 shows the improvement achieved in summer 2014 (May 1st –July 31st). The performance of the method on data corresponding tothe other seasons also show improvement.

We established an interface between R® and Python toautomate the process of forecasting wind power, in MW, fora one year period. The following items explain the workflow
and Fig. 1 depicts the timeline of the process.
1- Data: Obtaining the wind power data (actual and forecast)from ERCOT.
2- Error: Computing the difference between the actual andthe forecasted wind power (error), and then selecting themonthly data from the resultant series for the training
period.
3- Tuning: Identifying the optimum ARIMA model (bychoosing p, d, and q values) based on minimum Akaike
information criterion (AIC) [1]. Note, p, d, and q are theautoregressive model order, the number of differencing,
and the moving average model order respectively.
4- Data fitting: Fitting the data points of the error to theselected optimum ARIMA model.
5- Forecasting: Using the fitted model to forecast 24 hoursof future values. The future values represent the predicted
errors.
6- Updating the original forecast: Adding the 24 hour error
values to the available forecast data for the same day. This
represents the new (or improved) forecast of wind power.
7- Performance evaluation: Using relative error (RE) toevaluate the prediction accuracy of the new forecast incomparison to the original forecast.

Fig. 2. Norms of relative error in the summer
ACKNOWLEDGEMENT
The authors would like to acknowledge ERCOT for providing thedata used in this work. F. A. Eldali would like to thank Rahul
Kadavil for his help in developing the interface between R® andPython. Eldali also acknowledges the Ministry of Higher Educationand Scientific Research in Libya for the financial support.
REFERENCES[1] D. Ruppert, Statistics and Data Analysis for Financial Engineering.
NewYork: Springer, 2011, ch. 9.

F. A. Eldali, and S. Suryanarayanan are with the ECE Dept.,
Colorado State University, Fort Collins, CO 80523, USA, e-mail:{fathalla.eldali,sid}@colostate.edu
T. M. Hansen is with the EECS Department, South Dakota State
University, Brookings, SD 57007-2222, USA, email:{timothy.hansen@sdstate.edu}

Highly Clustered Residential PV: Use of distributed
battery storage for local voltage management
Katherine Murphy, IEEE Graduate Student Member, Nirmal-Kumar C. Nair, IEEE SeniorMember
University of Auckland, New Zealand
Abstract—This investigation assesses methods of managing
voltage in a residential low voltage (LV) distribution networkwith clustered solar photovoltaics (PV) and battery storage.
Distribution network operators (DNOs) are becoming
increasingly aware that large amounts of PV connected to theirdistribution network can give rise to power quality issues, namely
over-voltage, posing significant operational risk if not managed.
In order to understand the operational risks and technicalbenefits of grid integrated PV, pilot projects are being initiatedby DNOs. The basis of this work was to provide a model, using
open source software, of the PV-Battery storage project currently
under development with Vector (a New Zealand DNO). A
number of tests were carried out on the developed model todemonstrate the capabilities of the battery storage at managing
network voltage with PV. This network model will be used as a
future tool for future operational analysis of the system inquestion as part of New Zealand’s GREEN Grid1 project.
Keywords— Low voltage network, photovoltaic, PV cluster, overvoltage, battery storage, open source, network modellingI.
INTRODUCTION
A study has been carried out on the future uptake of residential
PV in New Zealand [1]. The results of this study show thatdomestic solar PV installations are projected to grow within
the bounds of a 30-750 MW increase by 2018. With this inmind, distribution utilities are actively looking for methods ofpreparing their networks for this projected future PV uptake.
There are technical issues that can arise with large amounts ofgrid-connected PV systems in a network. In particular, overvoltage issues can arise in distribution networks that havesignificant PV. The use of battery storage can significantly
improve the overall performance of PV. It will be shown inthis study that battery can effectively contribute to mitigating
local over voltage caused by PV, allowing excess generation
to be stored and shifted to be used at peak time [2].
II. CASE NETWORK MODEL
The details of the proposed case network was provided by
Vector. The case network, a first-of-its-kind which is currently
under development in Auckland, New Zealand, consists of fourgrid-connected PV clusters, coupled with battery storage,
which will feed 27 homes. The battery system selected is theTesla Powerwall. The solar clusters comprise of 4 x 12 kW perphase of multi-crystalline panels with a conversion efficiencyof 15.9%. Below in Fig.1 shows the basic arrangement of theresidential LV network.

1

This work supported by New Zealand’s Ministry of Business, Innovation and
Employment (MBIE) GREEN Grid project funding (2012-18).

Fig. 1: Topology of case network

A model of the network was developed using GridLAB-D,
and simulated over an entire day in steps of 1 minute, using
real historical home load and weather data. The battery charge
control was based on power quality measurements at thetransformer and control was set to prevent reverse power flow.
III. SUMMARY OF RESULTS

Fig. 2: Example of results showing voltage seen at N114 throughout the day.
PV without battery charging (left), PV with battery (right).

In summary, without battery, over voltage is seen over the day
simulated. This is shown to the left in Fig.2 where two of thephases breach the upper voltage limit. When battery is
introduced, the over voltage is mitigated, and the minimum
voltage seen at this node is also improved.
IV. REFERENCES[1] K. Murphy and N.-K. C. Nair, " Voltage Control in Distribution
Networks with Penetration of Solar PV: Estimated Voltages as aControl Input,"(Accepted) IEEE Power and Energy Society
General Meeting, 2016.
[2] M. Kabir, Y. Mishra, G. Ledwich, G. Xu and R. Bansal,"Improving voltage profile of residential distributed systems
using rooftop PVs and Battery Energy Storage systems," Applied
Energy, no. 134, pp. 290-300.

1

Optimal Resilience Enhancement Strategy forDistribution Systems under Extreme Weather Events
Shanshan Ma, Student Member, IEEE, Zhaoyu Wang, Member, IEEE

Abstract—This paper proposes an optimal hardening strategy
to enhance the resilience of power distribution networks toprotect against extreme weather events. Different grid hardening
techniques are considered, such as upgrading poles and vegetation management. The problem is formulated as a tri-level
optimization problem to minimize grid hardening investment
and load shedding in extreme weather events. Case studies
demonstrate the effectiveness of the proposed method undermultiple severe weather events and different simulation settings.
Index Terms—Distribution systems, failure probability, extreme weather events, power system resilienceFig. 2. The modified test system

I. I NTRODUCTION
RID hardening is one of the most effective methods toprotect systems against extreme weather events. There
are various grid hardening strategies such as overhead structure
reinforcement, vegetation management, undergrounding, andintegrating black-start resources.
A distribution system is considered to be resilient if it isable to maintain service to critical load following an extreme
weather such as a severe hurricane [1]. This paper proposes
an optimal hardening strategy to enhance the resilience ofdistribution networks. The objective is to minimize load shedding during extreme weathers with minimal hardening cost.
A tri-level optimization model that accounts for the failure
probability of hardened components is formulated.

G

Fig. 3. Category-4 hurricane path tracking

II. K EY F IGURES

Fig. 4. The distribution network before and after hardening

R EFERENCES[1] C. Liu, “Distribution Systems: Reliable But Not Resilient? [In My
View],” Power and Energy Magazine, IEEE, vol.13, no.3, pp.93-96, MayJune 2015.
Fig. 1. Tri-level programming model of distribution hardening problem
S. Ma and Z. Wang are with the Department of Electrical andComputer Engineering, Iowa State University, Ames, IA, 50014.
(Email:sma@iastate.edu,wzy@iastate.edu) .

Outlier Issues in Harmonic Source Location Based on
Parameter Identification Method
Kexuan Tang, Chen Shen

Shuo Liang, Xiuqiong Huang

State Key Lab of Power Systems
Department of Electrical Engineering
Tsinghua University
Beijing, China
14z32@163.com

Guangxi Power Grid Corporation
Nanning, China

Abstract—In this paper, the outlier issues in harmonic source
location using parameter identification method are introduced
and discussed. The theoretical models and outlier issues inparameter identification method are firstly introduced. Zerocrossing criteria of denominators in parameter solving processes
are then obtained through theoretical derivations. Finally, theapplicability of equivalent impedance models is discussed on thebasis of theoretical and simulation results.

I. INTRODUCTION

IV. CASE STUDYThis paper aims at investigating the causes and occurrence
conditions of outliers in harmonic source location usingparameter identification method. Main contributions of thispaper are as follows.
 The major cause of the outlier phenomenon in harmonicsource location based on parameter identification
method is investigated and confirmed.
 The criteria of preventing outliers from occurring fordifferent
impedance models 信号：iL
are derived
and the信号：iL负荷的时域阻抗变化情况
信号：iT负荷的时域阻抗变化情况负荷的时域阻抗变化情况500
1000500accordingly discussed.
applicability of the models
is
RR
R

Results of 5th, 7th and 11th harmonic components inharmonic source location using parameter identification
method are shown in Table I.

L
L

500

L

II. OUTLIER PHENOMENON

00

0

Outliers
in equivalent impedance waveforms appear as-500
isolated burrs with extremely large absolute values, as shown
-500
-1000
-500
0.5 in Fig.
0.25
0.3
0.35
0.4
0.45
0.5
1(a).
0.25
0.3
0.35
0.4
0.45
0.5
0.25
0.3
0.35
0.4
0.45
0.5

0.45

t/s
t/s

0.45

0.5

t/s

负荷的时域阻抗变化情况

RR
L
L

2000
11
1000
10
0
9
-1000
-20008-30007
0.25

0.3
0.3

0.350.32 0.4
Timet/s(s)(a)
Fig. 1.

0.34
0.45

0.36
0.5

Equivalent Impedance (p.u.)

3000
12

信号：iB

阻抗参数/p.u.

Equivalent Impedance (p.u.)

阻抗变化情况
R
L

A. Parallel Impedance Model
The non-zero-crossing condition of denominator forparallel impedance model can be expressed as Uh/U1<1/h.
B. Series Impedance Model
The non-zero-crossing condition of denominator for series
impedance model can be expressed as Ih/I1<1/h2.

阻抗参数/p.u.
阻抗参数/p.u.

R
L

III. ZERO-CROSSING CRITERIA OF DENOMINATORS

Keywords—harmonic source location; non-linearity factor;
outlier; parameter identification; power quality

阻抗参数/p.u.

阻抗变化情况

should be carefully investigated.

3000
R
L

2000
1000

TABLE I.
Non-Linearity
Factor Type
NLFi
NLFi
NLFi
NLFu
NLFu
NLFu

RESULTS OF HARMONIC SOURCE LOCATION

Microgrid
No.
#1
#2
#3
#1
#2
#3

5th
Harmonic
13.2×10-4
0.519×10-4
0.487×10-4
65.7×10-4
1.77×10-4
1.41×10-4

7thHarmonic
0.209×10-4
23.2×10-4
0.347×10-4
1.39×10-4
32.6×10-4
1.85×10-4

11th
Harmonic
3.81×10-4
3.93×10-4
3.22×10-4
25.9×10-4
31.0×10-4
26.3×10-4

As shown in the results, series impedance model fails in
11th harmonic source location by wrongly providing high NonLinearity Factor results, while the results of parallel impedancemodel are still correct. In this case, 11th harmonic components
in microgrid output currents are induced by backgroundharmonic voltage from the external gird. The non-zero-crossing
condition for series impedance model is not satisfied andtherefore the outliers result in wrong results.

0

V. CONCLUSION

-1000
-2000
-3000

0.355 0.3552 0.3554 0.3556 0.3558 0.356
Time (s)(b)

Outliers in equivalent impedance waveforms (a) and a detail (b).

As shown in Fig. 1(b), equivalent impedance waveform
details near outliers are quite similar to a waveform in which
denominator crosses zero. Accordingly, the denominators inparameter solving processes of harmonic source location
This work was supported by the Foundation for Innovative ResearchGroups of the National Natural Science Foundation of China (Grant No.
51321005).

Through theoretical derivations based on equivalence
between differential and difference equations, it is confirmedthat outliers in equivalent impedance waveforms are caused byzero-crossing phenomena of denominators in parameter solving
processes. Furthermore, the zero-crossing criteria ofdenominators for both equivalent load models are derived.
Theoretical and simulation results demonstrate that the nonzero-crossing conditions for parallel impedance model areeasier to satisfy, and hence parallel impedance model isconsidered more applicable for parameter identification methodin harmonic source location.

Convergence Study for Distribution Load
Capability Estimators
Nicholas S. Coleman, Student Member, IEEE, Karen N. Miu, Member, IEEE
II. KEY EQUATIONS
Abstract—Load capability studies estimate the maximum
allowable load variation in any given direction, subject to systemconstraints. Smart grid technologies make it possible to assessload capability over shorter timeframes, and to take corrective
actions in real time, but load capability assessments arecomputationally costly. This work presents a convergence
analysis of three distribution load capability estimators. Series
acceleration methods are shown to decrease the number of
iterations required to obtain high-quality load capability
solutions. Considered metrics include traditional current- andvoltage-based load capability estimators, and a power factor
based estimator, which is presented here.
Index Terms—load capability, numerical methods, power
distribution, power factor

I. INTRODUCTIONDISTRIBUTION load capability (DLC) problems havebeen traditionally associated with long-term planningproblems, such as adding new customers to existing
distribution feeders [1]. In smart distribution systems, realtime measurement, short-term forecasting, and automatic
control make is possible to assess and act upon DLC metrics
in shorter timeframes. This is important in systems withstochastic injections – specifically, distribution systems with
distributed energy resources (DERs).
Computational efficiency is critical when computing DLC
over shorter timeframes. DLC problems require a non-trivial
number of power flow solutions, due to the inherent
nonlinearity of power distribution systems. This work presents
a convergence analysis of various DLC estimators. The
considered DLC metrics are computed with respect to: Branch current ratings [1] Bus voltage magnitude limits, [1] Power factor constraints (introduced here)
The power factor (PF) based estimator is a timely
contribution for use in distribution circuits with high DER
penetration. DERs, such as photovoltaic arrays, provide unity
PF injections through grid-tie inverters, reducing net real
power demand, which may result in significant substation
power factor swings. PF constraints may limit load capability
more strictly than other constraints.
Convergence behavior of the estimators is characterized andAitken’s delta-squared method [2] is applied to decrease thenumber of necessary iterations.

The inputs to the distribution LC problem are the initial net
injections: Pinit , Qinit  n 3 , and net injection variationˆ
forecasts: P̂ , Qn 3. Injections vary according to: P()   Pinit  Pˆ 
Q( )   ˆ Qinit  Q(1)where   is a unitless variation factor. The LC problem is
to solve the following nonlinear program:
(2)
max 
subject to(3)
F (x())  0(4)
G(x())  0
where (3) is a vector equality of electrical constraints, and (4)is a vector inequality of voltage, current and PF constraints.
The DLC estimators studied here are in Table I. Nodal 
values are solved iteratively. Notation is described in Table II.
Table I. Distribution Load Capability Estimators.
Estimator
Equation
Bus Voltage Magnitude

Vi 

Branch Current Magnitude

iI 

Vi ,2M  2 Vi Vi , M

Ii,M



iPF 

Power Factor

2

Vˆi

jCi

( Sˆ j / V j )*Qi  iPF Pi
PF Pˆ  QˆiiiIII. PRELIMINARY RESULTA convergence study has been performed for the currentbased estimator on a 2556-node distribution circuit including
unbalanced injections, with a Newton-Raphson based power
flow solver. Linear convergence has been observed; that is:
iI [k ]  iI *
lim I
c
(5)
k 
 [k  1]   I *iiwhere k is the iteration number, iI [k ] is the solution at
iteration k ,  iI * is the solution, and c is a constant.
REFERENCES
[1]

[2]

K. N. Miu and H.-D. Chiang, "Electric distribution system load
capability: problem formulation, solution algorithm, and numericalresults," Power Delivery, IEEE Trans. on, vol. 15, no. 1, pp. 436-422,
Jan. 2000.
A. Ralston, P. Rabinowitz, “The Solution of Nonlinear Equations,” in A
First Course in Numerical Analysis, 2nd ed., Dover Publications,Mineola, NY: 2001, ch. 8, sec. 7, pp. 358.

Robust Improvement of Network Stability with
Wide Area Control of Multiple HVDC Links
P.Agnihotri

A.M.Kulkarni

A.M.Gole

Department Electical and Computer Engineering
University of Manitoba
Winnipeg, Canada
uamgniho@myumanitoba.ca, aniruddha.gole@umanitoba.ca

Abstract—This paper proposes a Wide Area Measurement
System (WAMS) based control strategy for multiple dc links(multi-terminal or multi-infeed) embedded in ac system which
is robust to the loss of communication, changes in the network
topology, and is inherently able to provide wideband damping.
The strategy uses only a limited set of non-local signals. Thesesignals may be directly communicated to the dc links without having to be centrally collated with other system-wide measurements,
thereby reducing communication delays. The strategy can easily
be adapted during power system operation to achieve targeted
damping of the critical swing modes.
Index Terms—Dynamic Stability, Power Swing Damping Controllers, Wide-Area Measurement Systems, HVDC systems

I. I NTRODUCTION
Wide area measurement signals based controllers for HVDC
and FACTS devices has attracted the attention of many researchers [1] to improve angular stability of the system. However, if several wide area signals are used, it becomes difficult
to intuitively understand the effect of various parameters in
the controller. Furthermore, it becomes a challenge to ensure
the robustness of the control strategy with the change in the
network topology and loss of communication. The objective of
this paper is to investigate a control strategy for multi-infeed
and multi-terminal dc links embedded in the ac system which
is robust to the loss of communication, changes in the network
topology, always improves damping for all the swing modes ofthe system, can be easily adapted to achieve targeted dampingof the critical swing modes, and uses a limited set of local
and non-local signals to ensure the intuitive understanding of
the parameters on the controller.
II. M ETHODOLOGY AND R ESULTS
For dc link damping controllers, we use the phase angulardifference (or frequency difference) signal, as it can resultin enhanced damping for all controllable swing modes evenwith a simple controller structure. This favorable property is
analytically verified using simple power system models in [2].
This paper investigates extension of such a strategy to multiple
embedded dc links with non-local frequency signals. A coordinated strategy is based on composite frequency difference

Department of Electrical Engineering
Indian Institute of Technology Bombay
Mumbai, India
anil@ee.iitb.ac.insignals as obtained below (shown for two dc links but can beextended to a larger number of links)

 


∆y1
κ1 κp
∆f1
=
(1)
∆y2
κp κ2
∆f2
∆fi is the measured frequency difference on the ac terminalsof the dc links. The modulation signal ∆yi modulates dc
power of the ith link. The signals may be used with a simple
controller structure consisting of a filter and washout block.
Some phase lag compensation may be provided to give somesynchronizing effect along with the damping effect. The key
aspect of the proposed strategy is the use of a symmetric positive definite (spd) gain matrix in (1) to derive the composite
signals. This results in enhanced damping for all controllable
swing modes. Furthermore, loss of communication from andto a particular dc link (equivalent to kp = 0), does not destroy
the symmetric positive definiteness and the gain elements canbe tuned to selectively enhance damping of poorly damped
modes. Eigenvalue analysis on a 16 machine system with two
HVDC links are presented to demonstrate the key aspects andthe effectiveness of this strategy.
TABLE I.
Case
NC
I
II
III

E IGENVALUES : L OW F REQUENCY S WING M ODES

Swing Mode I-0.009 ± j 4.692-0.229 ± j 4.586-0.039 ± j 4.691-0.131 ± j 4.665

Swing Mode II-0.113 ± j 4.248-0.244 ± j 4.361-0.141 ± j 4.254-0.191 ± j 4.287

Swing Mode III-0.002 ± j 3.365-0.051± j 3.368-0.343 ± j 3.381-0.195 ± j 3.324

Swing Mode IV-0.080 ± j 2.497-0.338 ± j 2.521-0.127 ± j 2.498-0.231 ± j 2.502

Table I shows the base case with no control (NC) and theeffect on various modes by changing the elements of the gain
matrix in (1). The gains in Case I preferentially increase thedamping of modes I, II, and IV, whereas the gains in Case
II preferentially increase the damping of Mode III. Since inCase III κm = 0, it is a local strategy. In all cases, there is an
improvement in the damping of all modes although to different
extents.
R EFERENCES[1] B. Chaudhuri and B. C. Pal, Robust damping of multiple sw ing modes
employing global stabilizing signals with a TCSC, IEEE Transactions on
Power Systems , Vol. 19, No. 1, pp. 499-506, 2004
[2] T. Smed and G. Andersson, Utilizing HVDC to Damp Power Oscillations,
IEEE T-PWRD , Vol. 9, No. 4, 1994, 2046-2054

Exploring Adaptive Interpolation to Mitigate Non-Linear
Impact on Estimating Dynamic States
Shahrokh Akhlaghi, Student Member, IEEE, Ning Zhou, Senior Member, IEEE, Zhenyu Huang, Senior Member, IEEEn(Φ)  εΦT Qk1εΦAbstract—This paper proposes an adaptive interpolation
approach to improve the performance of the Extended Kalman
Filter (EKF) for estimating dynamic states of a synchronousmachine. This approach consists of two major steps. First, the nonlinearity of state transition function and measurement function isquantified. Second, when the non-linearity is severe, pseudomeasurements are added through interpolation to mitigate thenegative impact of non-linearity on the estimation accuracy. Usingthe 2-area model, it is shown that the proposed approach can makegood tradeoff between estimation accuracy and computation timein estimating the dynamic states of a synchronous machine.

I.

0. Initialization

I. Prediction

Interpolation
through the
Measurement

II. Correction

Yes

No

INTRODUCTION

III. QuasiLinearity?

Timely and accurate information of dynamic states isimportant for monitoring and controlling a power system in realtime. Increasing demand for sustainable energy pushes further
the investigation of new techniques to improve the accuracy ofthe dynamic state estimation (DSE) for reliable operation of apower system [1].
To mitigate the negative impact of non-linearity on
estimation accuracy, [2] and [3] propose to increase the effective
sampling rate of measurements through interpolation. Theinterpolation factor ‘r’ is an important parameter which is thetotal number of pseudo-measurements to be added between two
consecutive measurements. On one hand, a larger ‘r’ is better in
reducing the negative influence of non-linearity and improving
estimation accuracy. On the other hand, a larger ‘r’ demands
additional estimation steps and incurs heavier computation
burden. To achieve a good trade-off between estimation
accuracy and computation time, ‘r’ shall be large enough tomitigate the negative impact of the non-linearity on estimation
accuracy and small enough to avoid adding unnecessary
computational burden. To avoid such drawbacks, ‘r’ shall be
adaptively adjusted according to the level of non-linearity. In
other words, when a system is operated under a highly non-linear
condition, ‘r’ shall be large to improve estimation accuracy.
When the system is working under a linear condition, ‘r’ shallbe small to reduce the computation time. In view of this, thispaper proposes an adaptive interpolation approach to quantify
the non-linearity level and adaptively adjust ‘r’. The objective is
to make a well-informed trade-off between the estimation
accuracy and computation time.
II.

(1.c)

Fig. 1. Modified EKF using the adaptive interpolation method based on
the quasi-linearity test

Fig. 2. The (a)computation time and (b) normalized MSEs of the EKF
estimates for different interpolation factors (r)Fig. 3. The (a) computation time and (b)-(e) MSEs for the no-interpolation(r = 0), adaptive interpolation (r = 0 or r = 3) and blind interpolation(r = 3) methods.

QUASI-LINEARITY TESTTo quantify the non-linearity of the state-transition function
REFERENCES
and Measurement function we are using Quasi-linearity test.
[1] P. Kundur, Power System Stability and Control. New York: McGrawHill, 1994.
Non-linearity index n() for state transition function:[2]

 x  xk  xk 1

(1.a)


   ( xk   x)  ( xk ) ( x) x
x xk 

[3]

(1.b)

Z. Huang, P. Du, D. Kosterev, B. Yang, “Application of extended Kalmanfilter techniques for dynamic model parameter calibration,” IEEE PowerEngineering Society General Meeting, Calgary, Canada, pp. 1-8, July2009.
Z. Huang, P. Du, D. Kosterev, and S. Yang, “Generator dynamic Modelparameter calibration using phasor measurements at the point ofconnection”, IEEE Transactions on Power Systems, vol. 28, no. 2, pp.
1939 - 1949, 2013.

Predictive Set Point Modulation to Mitigate
Transients in Power Systems
Hooman Ghaffarzadeh, Graduate Student Member, IEEE and Ali Mehrizi-Sani, Senior Member, IEEE
School of Electrical Engineering and Computer Sience, Washington State University, Pullman, WA, USA
Email: hooman.ghaffarzadeh@wsu.edu; mehrizi@eecs.wsu.edu

The power system today often needs to operate close to
its operational limits to meet the increasing load demand andachieve higher asset utilization. Transients caused by faults,
large set point changes, or load variations can push the system
past its operational limits, which, depending on its severity,
can cause instability, tripping of protective devices, or violationof the ratings of apparatuses. This situation is exacerbated inlightly damped systems, where disturbances can result in larger
transients and cause more severe violation of the limits. The
infinite bus assumption of the main grid requires most of thesechanges to be negligible compared to the rest of the system andbe easily compensated by the inertia of the system. However,this is not the case for a small-scale power system, such as a
microgrid in islanded mode, where significantly less generation
capacity is available. Thus, there is a need for a strategy to
mitigate transients in such a system under different operating
scenarios.
II. T HE P ROPOSED M ETHOD

1.0

i+d (pu)

0.8
0.6

0.4
0.2

0.0
0.0

0.1

0.2

0.3

0.4

0.3

0.4

(a)
1.0

i+d (pu)

0.8

0.6
0.4

0.2
0.0
0.0

0.1

0.2

(b)
Time (s)Fig. 1. i+
response to a single-phase to ground fault at t = 0.5 s and cleareddafter 0.05 s; (a) without SPAACE; (b) with SPAACE with linear prediction.
1.6

i'+q,sp
i+ q
i q

1.2

id (t), iq (t) (pu)

I. P ROBLEM S TATEMENT

when compared to linear prediction. Moreover, it is shown thatsimilar to a balanced system, SPAACE significantly improves
the response of an unbalanced system.

i+ d
i d

0.8
0.4
0.0
-0.4

0.00

0.10

0.05

0.15

0.20

0.15

0.20

(a)
1.6
1.2

id (t), iq (t) (pu)

Abstract—Power system controllers are designed and tuned
to achieve the desired dynamic performance under prespecified
system conditions. However, changes in the system, e.g., load
variation, generation changes, and faults, can cause departure of
the system from previously assumed parameters. This in turn canchange and reduce damping, which may enable large transients
to violate the operational limits of a controlled apparatus. Set
point automatic adjustment with correction enabled (SPAACE)
is a strategy that modulates the set point of an apparatus tomitigate such transients. Several case studies are presented todemonstrate the superior ability of this strategy for mitigatingtransients in different scenarios compared to conventional PIbased control strategies.

0.8

0.4
0.0
-0.4

0.00

0.10

0.05

(b)
Time (s)+Fig. 2. Response to a step change in id of DER2 from 0.5 pu to 1.0 pu; (a)Set point automatic adjustment with correction enabled without
SPAACE; (b) with SPAACE with linear prediction.
(SPAACE) is an autonomous control strategy that operatesv'
between the primary (typically a PI controller) and secondaryv
controllers of a unit and adjusts the set point sent by thesecondary controller to mitigate transients that may violate
operational limits. SPAACE requires very limited a priori
knowledge of the system and depends only on local variables.
(a)This paper proposes supplemental strategies for SPAACE toenhance its performance and extend its application to moregeneric electric systems.
1.1

+

+

v+d (pu)

0.9

d,sp

d

0.7

0.5

-0.1

0.0

0.1

0.0

0.1

0.2

0.3

0.4

0.2

0.3

0.4

1.1

III. K EY E XPERIMENTAL R ESULTS
The proposed strategies are implemented in different scenarios with voltage and current control modes on a 14bus benchmark test system with balanced and unbalanced
configurations. The case studies show that the proposed
prediction method is able to reduce peaks in the transient
response and shorten (sometimes eliminate) violation times

v+d (pu)

0.9

0.7

0.5

-0.1

(b)
Time (s)Fig. 3. The impact of the intermittency on the performance of SPAACE; (a)without SPAACE; (b) with SPAACE with linear prediction.

1

Verification of Cascading Failures based on
Generator Power Dispatch
Zhe Xu Student Member, IEEE, Agung Julius Member, IEEE, Joe Chow Fellow, IEEE
Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, USA
E-mail: {xuz8, juliua2, chowj}@rpi.edu.

Abstract—A formal verification method for power system cascading failures is proposed. The approach is based on simulating
trajectories and finding robust neighbourhood bounds aroundthe simulated trajectories (or reachtube). The uncertainties suchas initial states and disturbances are considered in calculating
the reachtubes. We model power system as a hybrid systemwith locations representing different swing dynamics and relay dynamics. We present implementation of the verification
approach in a three-machine system which is modified fromthe 2003 Italian Blackout simulation model. We pose and solve
two safety verification problems associated with averting thecascading failures: 1) verification of safety for various generator
mechanical power dispatch schedules; 2) verification of safety forpost-fault remedial actions based on pumped storage.

fault occurred(I1>ITAP)

rx11>1
Relay 1 startsrx12>1
CB11(12) opens

I1<=ITAP
Relay 1 stops
rx22>1
CB21(22) opens

I2>ITAPrx21>1
Relay 2 starts
I2<=ITAP
Relay 2 stops

I3>ITAPrx31>1
Relay 3 starts
I3<=ITAP
Relay 3 stops

rx32>1
CB31(32) opens

Fig. 3: Discrete modes of the hybrid system model.
I.

K EY F IGURESξ(τ, x̃0)ẋ = f (x)0.1257 s
0.1613 sRt

00.0885 s

0

B(R , x̃ )πFig. 1: Robust neighbourhood around the simulated trajectoryξ(τ, x̃0 ) (reachtube) for nonlinear dynamics ẋ = f (x) (anytrajectory starting from the robust neighbourhood B(R0 , x̃0 )will evolve in the reachtube and avoid unsafe set π).

10km
1 CB11 2
CB12

4
G1
9

50km
5
6 CB21 7
40km
CB22
50km
10
11 CB31 12

G2

17

18

50km 19

3
L1
8

15

0.0553 s

0.0885 sFig. 4: Simulation of reachtubes of line 6-7 current withdifferent storage pumping rates: (a) no storage, with initial
radius 0.002 (p.u.); (b) storage pumping rate 0.1 (p.u.), withinitial radius 0.002 (p.u.); (c) storage pumping rate 0.1 (p.u.),with initial radius 0.00063 (p.u.); (d) storage pumping rate 0.2(p.u.), with initial radius 0.002 (p.u.).

L2
13
L3

CB32
50km
14

50km

0.0977 s

16
L4

G3

Fig. 2: Three-machine power system model modified from the2003 Italian Blackout simulation model.

ACKNOWLEDGMENT
This research was partially supported by the National
Science Foundation through grants CNS-1218109 and CNS1550029.

Modeling and Analysis of Electrical Heating System
Based on Entransy Dissipation-Based Thermal
Resistance TheoryDAI Yuanhang, CHEN Lei, MIN Yong, CHEN Qun, HAO Junhong, HU Kang
Abstract—This paper presents the results of a study on themodeling of the electrical heating system. Based on the entransy
dissipation-based thermal resistance theory, heat source, heatexchanger, pipeline and heat load in the system are modeledfrom the perspective of thermo-electricity analogy method andtwo different pipeline models are given. The electrical heating
system is a controllable load enhancing the flexibility of thepower system and the model can effectively describe its dynamic
response characteristics.

I.

MAIN IDEAThis paper studies the modeling and analysis of theelectrical heating system based on the entransy dissipationbased thermal resistance theory from the perspective of
thermo-electricity analogy method. We propose an electricalheating model that has similar form to an electric circuit, andwe can learn the dynamic characteristics of the electricalheating system by using it, which is a beneficial tool for using
electrical heating as a flexible load in power system for windaccommodation.
Thermal storage device can be modeled as an element
which has the same form of differential equations as acapacitor, we use heat capacity Cst to describe it, as in (1).The
heat exchanger can be modeled as an entransy dissipationbased thermal resistance Ren,st as in (2)Electrical Heating& Heat Storage
SystemTenv Rst
H0

Tst

Cst

Primary Pipe Network

Heat
Exchanger 1

Ren,st

Ts,2

Ts,1

Te,o

H 0  Cst

Ren, st 

dTst Tst  Tenv

 H1
dt
Rst

Tst ,o  Te,i
H1

a



(2)

Te,i

H1
Figure 1.

The Equivalent Thermal Circuit for a Heat Exchanger

We give two different methods for pipeline modeling, thenodal model and the time delay model. The complete modelwith a nodal pipeline model is shown in figure 2 and the timedelay pipeline model is shown in figure3. We can learn fromfigure 2 that the complete model proposed in this paper has arather similar form as a simple electrical circuit, which will
provide us with convenience to study it.
Ts,n

Heat
Exchanger 2

Secondary
Pipe
Network

Ren,p

Th,o

Tc,i

Tc,o
Tr,1

Heat Consumer

Ren,h

Th

Rh
Tenv

H2

H1

Tr,2



Gst G p east  e p

Ren,st

Tst,o

Ts,j

Tr,j

G p east  Gst e paSo the heat transfer process of the heat exchanger can be
expressed by the temperature difference divided by theentransy dissipation-based thermal resistance, as shown in
Figure 1, which has the same form as an electric circuit.

Te,i

Tr,n

(1)

H3

Ch

H4

Th,i

Figure 2. The Model of an Electrical Heating System (with Nodal Pipeline Model)
Heat Exchanger

Tst

Ren,stPrimary Pipe Network

Te,iHeat Exchanger

Tc,i

H1

Ren,p

H2

Th,o
EnergyConservation
Time Delay

Te,o

Tc,o

Figure 3. The Pipeline Part of the Electrical Heating System with Time
Delay ModelII.

CONCLUSION

The detailed model of the electrical heating system takes
into account of both the heat transfer process and the heatstorage ability of water in the pipeline. It can be used to study
the overall dynamic response of the electrical heating systemwith thermal storage. Actually in the electrical heating system,
for there are several elements which can be used to store heat
energy, the system can respond totally different when given
different electrical power input without dissatisfying the needsof the heat consumers, which provides great potential forusing electrical heating as a controllable load to enhance theflexibility in power system. Furthermore, this proposed model
can be used for power dispatch to include the state in heat
system, and it can also be used for emergency control in
power system, both of which need further study.

MILP-based Splitting Strategy Searching Considering
Island Connectivity and Voltage Stability Margin
Yifan Zhou, Student Member, IEEE

Qiangming Zhou, Miao Li
State Grid Hubei Electric Power Company,
No. 175, Xudong Road, Wuchang District,
Wuhan, P.R. China
smiling_105@163.com

Wei Hu, Yong Min

State Key Lab of Power Systems
Dept. of Electrical Engineering, Tsinghua University
Beijing, Chinazhou-yf14@mails.tsinghua.edu.cn
Abstract—Splitting control is the last resort of security control.
By splitting the whole system into several isolated islands,
splitting control can ensure system stability and power supply
under extremely emergency situation. In this paper, a mixed
integer linear programming (MILP) based splitting strategy
searching model is proposed. Connectivity constraints andvoltage stability margin in each island are considered to help
obtain more practical splitting strategy. The constraints are
established in a linear form so that the model can be easily
solved. Additionally, a strategy space reduction method based onflow tracing is presented to enhance computation efficiency.
Case studies on four IEEE test systems shows the validity andcomputation performance of the proposed model. Also, thenecessity of considering connectivity and stability is analyzed insimulation.

Vf

Vt   ye3  V f

V 1
L

0



Vt   Vf

Vt   ye3

(14)



0 ˆ
 YLL
VL   MLk Vˆk f  MLk Vˆk f  YLG VG0
kV L

(15)

ˆ . V0L  1 VLLˆV L ,f(16)

ˆ 1  ye3   Vˆ    1  ye V3ˆ   ye  V
ˆf
V
L,t 
3
 L

ˆ t   VˆVL
 L ,f

f
L

II.

t
L

(17)

ˆ   ye (18)
V
L,t 
3

KEY RESULTS

Index Terms—Splitting strategy; mixed integer linear
programming (MILP); connectivity; voltage stability margin.

I.

KEY MODELmin 1e1T  Dmax  D  2eTL L

xe2  1s.t.

1,..., P

ST T  Cg P  Cd D

(6)  θ  (7)

θREF  0

Tmax  y  Tl  Tmax  y

(4)

(8)

 1  y   T  bSθ   1  y (9)Pmin  P  Pmax

Dmin  Di  Dmax

(10)





(d) without voltage stability

(3)
(5)

I  Y V   M kVk  M kVk 1(c) without connectivity(1)y  ze2z  xf , k , z  xt , k , z  xf , k  xt , k  1 k  1,..., PK(b) splitting strategy(2)

x Ik , k  1 k  Pre0(a) original topology

f

t
k

V 
 1  ye3   V f Vt    1  ye3 

Figure 1. Splitting strategy analysis for IEEE 30-bus


TABLIE I Voltage stability margin comparison
Lowest node voltage margins
Test systems

Model in this paper

Model in without
connectivity and stability
1st
2nd
3rd

1st

2nd

3rd

30-bus system

0.18100.18030.1757







(11)

68-bus system

0.14120.13500.13420.26400.2636

0.2585(12)

118-bus system

0.15320.15060.14470.35090.34400.3404(13)

300-bus system

0.14660.13600.11510.36980.36720.3647

Modeling Protection Systems in Time-Domain
Simulations: An Analytical Method to Detect
Mis-operating Relays for Unstable Power Swings
Mojdeh Abdi-Khorsand, Student Member, IEEE, and Vijay Vittal, Fellow, IEEE
School of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, AZ 85287, USA
Email: mojdeh.khorsand@asu.edu and Vijay.Vittal@asu.edu
Abstract—Data from many blackouts in North America
confirm that distance relay mis-operation is one key
factor that may initiate a series of outages, which cancause a blackout. A power swing, triggered by initiating
events, can cause protection system mis-operation. During
an unstable power swing, the voltage magnitude at theelectrical center will be depressed, resulting in protective
relays detecting what appears to be a fault and, thus, theprotective relays will trip additional transmission lines[1]. The relay mis-operations during an inter-area powerswing could result in the creation of uncontrolled islandsin the system, which can lead to a cascading outage.
During unstable power swings, controlled islanding
should be initiated with the goal of minimizing the loss ofload and ensuring synchronism within each generatorgroup. Controlled islanding can be achieved by
implementing an appropriate out-of-step (OOS)
protection scheme. The first step in implementing an
OOS protection scheme is OOS detection. The rate ofchange of the swing impedance is usually implemented
via blinder schemes as a metric to detect power swings.
Blocking and tripping are two main functions of OOS
protection schemes. OOS tripping protection schemes areusually initiated after receiving transfer trip signals. The
transmission lines, along the electrical center, may need tobe blocked from tripping during an unstable power swing
using an OOS blocking function. These transmission lines
should be determined during the planning study anddistance relays should be designed for these lines andequipped with OOS blocking functions.
This paper proposes a method (referred to as theminimum voltage evaluation method in the remainder ofthis paper) that provides a screening tool for OOS andelectrical center detection during transient stability
planning studies, which can be considered an extension of
the proposed voltage dip screening method of [2]. In [2],the system protection and control subcommittee of NERC
suggests a voltage dip screening method in order to
identify power swings and locate the system electricalcenter of a power swing. Empirical evidence shows thatvoltage magnitudes at buses, particularly at those busesconnected to lines along the electrical center, drop to atleast the range of 0.5 and 0.6 pu [2]. While the proposed
method of [2] relies on a voltage drop only at buses, theminimum voltage evaluation method can evaluate voltage
magnitudes anywhere along all transmission assets. The
proposed extension is critical since the electrical centercan occur at a bus or along a transmission line and a
voltage dip screening approach relying only on bus
voltage magnitudes can be highly inaccurate.
The minimum voltage evaluation method calculates
and evaluates the minimum voltage magnitude along thetransmission lines using the results of a transient stability

study. A low minimum voltage (without an actual fault)
can be considered as a flag for an OOS condition as well
as a potential reason for relay mis-operation of therelated transmission line. These transmission lines shouldbe equipped with OOS blocking protective relays.
This paper provides realistic results and confirmationof the proposed method and its achievements by studying
the proposed method on actual data of the WesternElectricity Coordinating Council (WECC) system. In
addition, the proposed method of [3] is implemented andcompared with the minimum voltage evaluation method.

I. RESULTS
The WECC system data, representing the 2009
summer peak load case, is used to perform the analysis.
The outage of the three California-Oregon interties isstudied, which causes an unstable power swing. The OOS
tripping is designed based on the well-known(northeast/southeast) NE/SE separation scheme for the
WECC. Two different OOS blocking schemes are
performed based on the minimum voltage evaluation
method and the proposed method of [3].

(a)(b)
Fig. 1. Bus voltage magnitudes while designing OOS blocking basedon the: (a) method of [3], (b) minimum voltage evaluation method.

(a)(b)
Fig. 2. Bus frequency while designing OOS blocking based on the:(a) method of [3], (b) minimum voltage evaluation method.

References
[1]
[2]

[3]

P. Pourbeik, P. S. Kundur, and C. W. Taylor, “The anatomy of apower grid blackout,” IEEE PES Magazine, Sep. 2006.
NERC, Protection system response to power swings, System
Protection and Control Subcommittee, NERC, Aug. 2013.
Available: http://www.nerc.com.
S. A. Soman, T. B. Nguyen, M. A. Pai, and R. Vaidyanathan,“Analysis of angle stability problems: a transmission protection
systems perspective,” IEEE Trans. Power Syst., vol. 19, no. 3,
pp. 1024-1033, Jul. 2004.

Performance Evaluation of the Generalized Control
Tuning Procedure for MMC-VSC Systems
S. Arunprasanth and U. D. Annakkage

C. Karawita

R. Kuffel

University of Manitoba
Winnipeg, MB, R3T 5V6, Canada
arunprasanth@ieee.org and udaya@ieee.org

TransGrid Solutions Inc.
Winnipeg, MB, R3T 2E1, Canada
ckarawita@tgs.biz

RTDS Technologies Inc.
Winnipeg, MB, R3T 2E1, Canada
rk@rtds.com

Abstract—The proper selection of PI-controller parametersof MMC-VSC systems is a challenging task. At present, theindustry practice is to use the trial and error approach that
consumes a great amount of time. This paper analyses theperformance of the generalized control tuning technique thatcan be used to simultaneously tune PI-controllers of practical
MMC-VSC systems. The performance evaluation is done in-termsof the average time taken to determine suitable PI-controller
parameters that satisfy the predefined performance requirements.
It is showed that the proposed method is simple and fast.

TABLE IAVERAGE C OMPUTATIONAL T IMEShort circuit ratio (SCR)

Average time taken to tune controllers

5.0

less than a minute

3.0∼2 minutes

2.0∼5 minutes

1.5∼30 minutes

I. I NTRODUCTION

III. R ESULTS AND D ISCUSSION

Stability and control of MMC-VSC systems is a popularresearch topic at present. Tuning of VSC control loops is achallenging task as it consists of cascaded PI-control loops.
The control tuning techniques based on simplified transfer
functions are not suitable as the control system is not completely decoupled and also the converters interact through thedc system [1], [2]. Optimization enabled EMT simulation
technique is used to tune VSC controllers in [1] and [3].
However to reduce the computational burden, the time-domain
objective functions consider only the outer-loop controllers. In
addition, the author of [1] has indicated that the simultaneoustuning of converters in a dc grid considering multiple operating
conditions is not practical. Even with a use of a 60 core
specially designed computer, the parallel optimization method
proposed in [3] takes 20 hours to tune the converters of a pointto-point VSC system. Due to these reasons, the practical VSC
systems are tuned by means of the trial and error approach.

The performance of the control tuning algorithm is evaluated by calculating the average time taken to simultaneously
tune all PI-controllers in the point-to-point MMC-VSC systemdescribed in [2]. Different ac system strengths ranging fromvery-weak to strong systems are considered and the performance requirements are set as the same. For each ac system,
the average time taken to tune controllers on a general purpose
computer (4 core) are given in Table I. It can be observedthat the proposed control tuning technique takes only a few
minutes to simultaneously tune all PI-controllers of the MMCVSC system connected to ac systems with SCR values higher
than 2.0. From the results provided in in Table I, it is clearthat the control tuning method proposed by the authors ismore suitable to simultaneously tune converters in a dc gridcompared to the time-domain simulation based optimization
techniques proposed in [1] and [3].

II. G ENERALIZED C ONTROL T UNING T ECHNIQUE

The authors would like to thank the financial supports
received from the Mitacs Accelerate Program, the RTDS
Technologies Inc., the Faculty of Graduate Studies, Universityof Manitoba, and the Government of Manitoba.

A generalized frequency-domain technique to simultaneously tune converters in a practical MMC-VSC system is
proposed by the authors in [2]. The gain tuning problem isformulated as an optimization problem that seeks for required
damping of all oscillatory modes and required negative values
for all decaying modes. The frequency-domain stability attributes are calculated using a validated small-signal stability
model that includes the dynamics of ac system, dc system,
MMCs (arm inductor and average converter losses), andcontrol system (control-loops, PLLs, and measurement filters).
In addition, the tuning method considers multiple operating
conditions to cover the practical operating regions of the
MMC-VSC system.

ACKNOWLEDGMENTR EFERENCES[1] Z. J. Zhou, “Co-ordination of Converter Controls and An Analysis ofConverter Operating Limits in VSC-HVDC Grids,” Ph.D. dissertation,
Dept. Elect. & Comput. Eng., Univ. Manitoba, Canada, 2013.
[2] S. Arunprasanth, U. D. Annakkage, C. Karawita, and R. Kuffel, “Generalized Frequency-Domain Control Tuning Procedure for MMC-VSC
Systems,” IEEE Trans. Power Del., in press.
[3] A. Y. Goharrizi, R. Singh, A. M. Gole, S. Filizadeh, J. C. Muller, and
R. P. Jayasinghe, “A Parallel Multimodal Optimization Algorithm for
Simulation-Based Design of Power Systems,” IEEE Trans. Power Del.,
vol. 30, no. 5, pp. 2128–2137, 2015.

Fast Parallel Stochastic Subspace Algorithms forLarge-Scale Oscillation Monitoring
Tianying Wu and Vaithianathan “Mani” Venkatasubramanian
The School of Electrical Engineering and Computer Science
Washington State University
Pullman, WA 99163, USA
Email: tianying.wu@wsu.edu and mani@eecs.wsu.edu
B  B  B 
p1. 2) Compute the SVD of Bk   j u j  v j  . 3) Checkk

Tk

A. Randomized SVD Method [3]The aim of this method is to compute a rank-k
approximation of the given matrix H, with k  m, n
H  U V T ,(1)
The main steps to achieve the approximate factorization are
as follows: 1) Generate an n×k Gaussian random matrix Ω. 2)
Form the m×k matrix Y = (HHT)q HΩ. 3) Form an m×k
orthogonal matrix Q via QR decomposition such that Y = QR,
where R is an upper triangular matrix. 4) Form the k×n
approximation matrix B = QTH. 5) Compute the SVD of theB 
T B 
corresponding singular vectors satisfy rk ek u j   1 . 4)k

Pt 1  v1

vt

augmenting

vector

0.26

rt 2  , 0.25where t is the number ofrtRandomized
Lanczos
Full SVD

0.24

0.21

II.

4
5
KEY RESULTS

0.26

0.24
0.23
0.22

4

5

Fig. 1.

16

7
Time (min)

8

9

10

6

7
Time (min)

8

9

10

Randomized
Lanczos
Full SVD

14
12
10
8

4

5

6

7
Time (min)

8

9

10

Plots of the estimation results for different SSI algorithms.
Randomized
Lanczos
Full SVDTABLE ISUMMARY RESULTS OF DIFFERENT SSI ALGORITHMS

14
12

Full
SVD

Mode Results

10
8

6

16

Randomized
Lanczos
Full SVD

0.25

0.21

4

f (Hz)

ζ (%)

5

Mean
6
7

0.23179

8

10

Randomized SVD
k = 20, q = 1

Lanczos SVD
k = 160.23160.2317

Time (min)STD

0.00630.00630.0063Mean

11.8046

11.8088

11.8046

STD

1.31611.3104

1.3161REFERENCES

B. Augmented Lanczos Bidiagonalization Method [4]By applying k steps of partial Lanczos bidiagonalization to[1]

the given matrix H mn , with unit initial vector p1 n ,[2]

we get the decomposition,
H T Qk  Pk BkT  rk ekT ,v2klargest singular values needed. 5)0.23
Compute the partial Lanczos
bidiagonalization with initial vectors
Pt+1. 6) Go to Step 2.
0.22

matrix B  Uˆ V T . 6) Form the matrix U  QUˆ .

HPk  Qk Bk ,theFrequency (Hz)ComputeDamping Ratio (%)

KEY APPROACHES

k

j 1

Frequency (Hz)I.

kconvergence. Exit if all desired singular values andDamping Ratio (%)

Abstract—With the installation of synchrophasors widely acrossthe power grid, measurement-based oscillation monitoringalgorithms are becoming increasingly useful in identifying thereal-time oscillatory modal properties in power systems. Whenthe number of PMU channels grows, the computational time ofmany PMU data based algorithms is dominated by thecomputational burden in processing large-scale dense matrices.
In order to overcome this limitation, this paper presents new
formulations and computational strategies for speeding up
Phasor Measurement Unit (PMU) based on-line applications.
Experimental results from archived western American powersystem data demonstrate that the new approaches can provide
significant speedups while retaining modal estimation accuracy.
With proposed fast parallel algorithms, the real-time oscillation
monitoring of the large-scale system using hundreds of PMU
measurements becomes feasible.

(2)

The main steps of the method are as follows: 1) Compute apartial Lanczos bidiagonalization with a random initial vector

[3]

[4]

T. Wu, S. A. Nezam Sarmadi, V. Venkatasubramanian, A. Pothen, and
A. Kalyanaraman, “Fast SVD computations for synchrophasor
algorithms,” IEEE Trans. Power Syst., to appear.
S. A. Nezam Sarmadi, and V. Venkatasubramanian, “Electromechanicalmode estimation using recursive adaptive Stochastic Subspace
Identification”, IEEE Trans. Power Syst., vol.29, no.1, pp. 349–358, Jan.
2014.
N. Halko, P. G. Martinsson, and J. A. Tropp, “Finding structure withrandomness: Probabilistic algorithms for constructing approximate
matrix decompositions”, SIAM Rev., vol. 53, no. 2, pp. 217–288, 2011.
J. Baglama and L. Reichel, “Augmented implicitly restarted Lanczos
bidiagonalization methods,” SIAM J. Sci. Comput., pp. 19–42, 2005.

Study on Real-time Clustering Method for Power
System Transient Stability Assessment
Fu Su, Baohui Zhang, Senior Member,IEEE, Songhao Yang, Huaiyuan Wang
School of Electrical Engineering
Xi’an Jiaotong University
Xi’an, China
susufufu@stu.xjtu.edu.cn

Abstract—This paper addresses the issue in critical machines
identification for real-time assessment and control of powersystem transient stability. To satisfy both accuracy and rapidity,on the basis of clustering study on a 3-unit 9-bus system, a novel
real-time critical machines identification method is proposed.
First, three candidate decomposition patterns are selected by the
top three largest predicted angle variation distance; And then an
index is proposed to choose the pattern whose equivalent OMIB
system operating state goes farthest from the equilibrium point
as the best decomposition pattern. The scheme only needs realtime machine dynamic information, and has the advantage oflittle computation, rapid identification speed and excellent
accuracy. Simulations on IEEE 39-bus 10-unit system validate
the efficiency of proposed scheme.

6) Calculate the deviated degree of the candidate one
machine infinite bus (OMIB) equivalents;
7) Choose the decomposition pattern which has the largest
deviated degree index as the optimal pattern;8) Transient stability analysis based on the equal area
criterion (EAC), transient energy function (TEF), or the
method based on phase-plane trajectory convexity andconcavity;
9) Once the system is detected to be unstable, emergency
control need to be implemented. Otherwise, refresh thedecomposition pattern.
III. SIMULATION RESULT

Index Terms—Real-time, clustering method, critical machines,
transient stability

3.5

5

G1

G2

G3

G4

G5

G6

G7

G8

G9

G1

G10

/rad

/rad

1.5

COI



G4

G5

G6

G7

G8

1

G10

3
2.5
2

0.5

1.5

0

1

-0.5

0.5

-1
0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0
0.2

1

0.3

0.4

0.5

t/s

0.6

0.7

0.8

0.9

1

t/s

Fig1. Swing curves of 10-unit system (left), Predicted angle variation of each machine(right)

TABLE I. DECOMPOSITION PATTERNS (DP1, DP2, DP3)
Decomposition pattern
DP1
DP2
DP3

Critical machines (CMs)G4, 5,6,7
G2, 3, 4 ,5 ,6, 7, 9, 10
G1, 2, 3, 4 ,5 ,6, 7, 9, 10

1.4
DP1

II. REAL-TIME CLUSTERING SCHEME

DP2

DP3

1.2
1

/p.u.

1) Obtained the real-time trajectory (  , , Pm , Pe ) fromWAMS;
2) Calculate predicted angle variation  of each machine;3) Sort the predicted angle variation of all machines andcalculate the distance between the ranked adjacent predicted
angle variation  ;4) Select three decomposition patterns (DP1, DP2, DP3)
based on the top three large predicted angle variation distance;5) Calculate candidate one machine infinite bus (OMIB)
equivalents corresponding to the three decomposition patterns;

G9

3.5

2

Power system transient stability is a challenging issue bothmathematically and physically [1]. Single Machine Equivalent(SIME) methodology has shown excellent performance forassessing the post contingency transient stability status [10].
With the development and installation of wide area
measurement system (WAMS), real-time transient stability
analysis can be realized. The first step is to find the correct
decomposition pattern which divides the system machines into
two groups: critical machines (CMs) and non-critical machines(NMs) [10]. For the purpose of real-time transient stability
analysis, the key of critical cluster identification is to take into
both accuracy and rapidity at the same time.

G3

4

2.5

I. INTRODUCTION

G2

4.5

3

0.8
0.6
0.4
0.2
0
0.2

0.25

0.3

0.35

t/s

Fig2. Deviated degree  of DP1, DP2, DP3

0.4

An Analytical Model of a Single Phase
DQ-Controlled Inverter for Power System Short
Circuit Calculations
Javier Hernandez-Alvidrez

Satish Ranade

Klipsch School of Electrical andComputer Engineering
New Mexico State University
Las Cruces, New Mexico 88003
Email: jaalvidr@nmsu.edu

Klipsch School of Electrical andComputer Engineering
New Mexico State University
Las Cruces, New Mexico 88003
Email: sranade@nmsu.com

Abstract—Based on reasonable approximations of the controller response we derive a simple yet accurate analytical modelfor the fault contribution of a single phase dq-controlled inverter.
The derived model is compatible with typical fault calculation
programs.

I. I NTRODUCTION
The increasing penetration of inverter-interfaced resourcesin transmission and distribution systems underscores the needfor inverter models for short circuit studies used in the designof protection schemes [1]. The fault contribution is determinedby the control architecture, generally limited to 110%-150%
of rated current and is generally not sustained past a cycle or
two. While report [2] provides useful test data, [3] proposes a
Norton model with a current limit where the Norton impedanceis derived from a transfer function of the inverter control
scheme. Studies reported in [4] concluded that the inverter
can be represented as a constant current source equal to thepre-fault inverter current. This paper derives a simple model
based on the approximate response of the current controller
during a fault. The model can be used to calculate fault current
contribution prior to the inverter going into low-voltage ride
through.
A. Inverter Model
We consider a single phase, dq-controlled inverter as shownin Fig. 1 which consists of: the DC bus formed by VDC and
CDC ; the H-bridge and its filter inductance Li ; the dq-control
scheme formed by an in-quadrature phase locked loop and
two PI controllers; the sinusoidal PWM generator; and theinfinite bus with its respective transmission line impedance Lg .
Inductor Lf represents a feeder. Lower case symbols such ase represent instantaneous quantities, while uppercase such as
E are the corresponding phasors. Subscripts d and q represent
components in the synchronous reference frame. The dynamics
of the maximum power point tracker are neglected due to thefact that its time constant is much larger than the dynamics ofthe dq-current control scheme.

Fig. 1. Grid-tied single phase d-q current controlled inverter

II. C ONCLUSION
A simplified analytical model of a single phase, dqcontrolled inverter, that bridges the models in [3] and [4] is
proposed. For inverters with ride-through control this model
is applicable to the 2-5 cycles prior to this control becoming
effective. The model clearly shows why the fault contribution
is often comparable to the pre-fault current. Also, the model
clearly displays the relation between fault contribution and inverter parameters along with pre-fault conditions. Furthermore,
the model can be easily incorporated in a short circuit analysis
program. The model does require knowledge of the parameters
Li and KP . We acknowledge that detailed data on inverters
is generally not available, but we suggest that manufacturers
could provide a value for the effective short-circuit impedance
derived in this study.
R EFERENCES[1] IEEE Power and Energy Society Working Group C17, “Fault current
contributions from wind plants,” http://www.pes-psrc.org/Reports, online.
[2] J. Keller and B. Kroposki, “Understanding fault characteristics of inverterbased distributed energy resources,” National Renewable Energy Laboratory, Tech. Rep., January 2010.
[3] C. A. Plet, M. Brucoli, J. D. F. McDonald, and T. C. Green, “Fault models
of inverter-interfaced distributed generators: Experimental verification andapplication to fault analysis,” in Power and Energy Society General
Meeting, vol. 1. IEEE, July 2011, pp. 1 – 8.
[4] M. Chaudhary, S. Brahma, and S. Ranade, “Interpreting the short circuit
behavior of type 4 wind turbine generator,” in Transmission and Distribution Conference, vol. 1. IEEE, April 2014, pp. 1 – 5.

PMU-based Reduced-order Modeling of Power
System Dynamics via Selective Modal Analysis
Benjamin P. Wiseman, Yang Chen, Student Member, IEEE, Le Xie, Member, IEEE and P. R. Kumar, Fellow, IEEE
Department of Electrical and Computer Engineering
Texas A&M University
College Station, USA

Abstract—This poster investigates how to perform online
system identification employing synchrophasor data. Two
approaches to identifying a reduced-order model are presented:
a purely data-driven approach, and an approach that integrates
online data-driven dynamic system identification with firstprinciple offline selective modal analysis. With prior knowledgeof the frequency range interesting to power system operators, it
is shown that the second approach recovers the key modes of theoriginal system and produces a much reduced-order model ofgrid-level dynamics. Even with the presence of uncertainty about
the actual modes of interest, an automatic tuning scheme isdevised to adaptively adjust the frequency range to improvesystem identification. Numerical examples with synthetic
synchrophasor data demonstrate the efficacy of the proposed
identification approach.
Keywords—Phasor measurement unit (PMU), data-driven
modeling, system identification, selective modal analysis (SMA).

modes [2]. This matrix can be used to determine the lG relevant
states that contribute most to the h modes. SMA is then applied
to obtain a low-order system G that retains the desired hmodes.
If the dynamic model for G can be achieved accuratelyfrom first-principle offline modeling, then online data-driven
identification targeting a specific frequency range can bedirectly applied to recover a mode of interest. This process
assumes that the input/output data for the identification is
selected from the relevant states of the mode of interest.
Since changing operating conditions in real power systems
prevent offline studies from obtaining an accurate dynamic
model, we have also proposed an automatic tuning scheme toadaptively adjust the frequency range of online data-driven
identification to account for small errors in the offline model.

I. INTRODUCTION
This work is motivated by the increasing complexity of
power system dynamics and the need for reducing suchcomplexity in online monitoring and control. On one hand, thegrid structure is undergoing significant changes as more andmore alternative resources are integrated into the system. On
the other hand, new monitoring devices, such assynchrophasors, provide improved capability for online
adaptive modeling and control of power system dynamics.
Combined with continued advances in computing, theavailability of large quantities of synchrophasor data could
enhance monitoring and control for large power systems.

Fig. 1. Topology of simulated system [2].

This integrated approach was applied to synthetic
synchrophasor data from a 4-generator, 28th-order system [2]modeled in MATLAB. A reduced-order model was obtainedusing SMA, and a 2nd-order system was estimated from thedata. This model accurately contained the mode of interest.

II. NOVEL INTERGRATED APPROACH
Our key contribution is the development of an integrated
approach combining online data-driven dynamic system
identification with first-principle-based offline selective modal
analysis, described in [1].
Let a linear state-space model of a dynamic system beobtained from first-principle offline modeling:

III. CONCLUSIONSThis work demonstrates the significant potential to use
synchrophasor data for online identification of power system
dynamics. Future work will validate this approach by using
real-world measurements, as well as investigating piecewise
system dynamic modeling for frequency ranges of interest.
REFERENCES

ẋ = Ax + Bu.

(1)

[1]

From the eigenvalues of A, h modes can be selectedaccording to either a frequency range or a damping ratio ofinterest. Then, the participation matrix P can be calculated,measuring the association between the state variables and the[2]This material is based upon work partially supported by NSF underContract No. CPS-1239116, ECCS-1150944, DGE-1303378, and NSF
Science & Technology Center Grant CCF-0939370.

I. J. Pérez-Arriaga, G. C. Verghese, and F. C. Schweppe, “Selective
modal analysis with applications to electric power systems, part I:
Heuristic introduction,” Power Apparatus and Systems, IEEE
Transactions on, no. 9, pp. 3117–3125, 1982.
P. Kundur, N. J. Balu, and M. G. Lauby, Power system stability andcontrol. McGraw-hill New York, 1994, vol. 7.

1

PHEVs Modeling for Autonomous Operation of
Microgrid
Vishal Verma, Student Member, IEEE, Sarika Khushalani Solanki, Senior Member, IEEE,
and Jignesh M. Solanki, Senior Member, IEEE
Lane Department of Computer Science and Electrical EngineeringWest Virginia University, Morgantown, WV
Email: vv0001@mix.wvu.edu

Abstract—Apart from conventional loads there are many new
kind of loads present in a microgrid such as Plug-in Hybrid
Vehicles(PHEVs). PHEVs are different from other loads in a waythat they are stochastic in nature . Hence it becomes crucial &vital to model PHEVs in a different way then conventional loads.
In this text authors try to model microgrid including PHEVs as
stochastic loads.
Keywords– Microgrid, PHEVs, Markov Chain(MC).

I. I NTRODUCTION
There is continuous increase in demand of PHEVs after theirintroduction to the market and is expected to increase in the
coming years. These PHEVs can be considered as load to themicrogrid and modeling of such load in autonomous operationof microgrid becomes important because of the stochasticity
in their occurrence. PHEV in an islanded microgrid can beconsidered as a secondary load means it can be curtailed i.e.
suppose there is a need to charge ‘n’ PHEVs but grid can only
take care of ‘n-1’ vehicles then last vehicle can be curtailed.
The process of charging PHEVs can be modeled as a Markov
chain(MC) process which means whether next PHEV will becharged or not will only depend upon the present state ofsystem.
Hence two states can be defined for system: State ‘0’ when
PHEV is not connected to the bus and state ‘1’ when PHEV
is connected to the bus.
(0,
if PHEV will not be connectedX(t + s) =(1)
1,
if PHEV will be connected

A microgrid state space equation for autonomous operation
can be written as [1]:




∆xIN
˙V
∆xIN V˙  = Amg  ∆ilineDQ  ∆ilineDQ(2)˙∆iloadDQ∆iloadDQ
When a PHEV is connected the bus voltage and output
current will also depend upon the SOC(state of charge) of
PHEV. Hence the microgrid state variables can be modified toinclude ∆SOC(new state variable) [2] as given in eq.3




∆xIN
˙V
∆xIN V˙  ∆ilineDQ ∆ilineDQ 
0




(3)
˙  = Amg ∆iloadDQ ∆iloadDQ
˙
∆SOC
∆SOC
II. F LOW C HART
Fig.2 shows flow chart for the problem stated in previous
section assuming that current state of system is ‘0’.

Transition diagram for the above equation is shown in Fig.1.

Fig. 2. Flow chart for problem

R EFERENCES
Fig. 1. State transition diagram for charging of PHEV

Once transition diagram is made the probability of system
to be in one of the state given the present system state can be
determined. After determining the next state of system, gain
parameters can be calculated for control purpose.

[1] N. Pogaku, M. Prodanovi, and T. C. Green,“Modeling,
analysis and testing of autonomous operation of an inverter-based
microgrid.” Power Electronics, IEEE Transactions on 22.2 (2007):
613-625. 2[2] S. J. Moura, H. K. Fathy, D. S. Callaway, and J. L. Stein. “A stochastic
optimal control approach for power management in plug-in hybrid electricvehicles.” Control Systems Technology, IEEE Transactions on 19, no. 3(2011): 545-555.

1Resonance with Inter-area Mode in Kundur
System from Forced Oscillation
Hongda Ren, Student Member, IEEE, and Vaithianathan Venkatasubramanian, Fellow, IEEEInterface active power flow (MW)700

Abstract— Low frequency electromechanical oscillation canlead to blackouts in power system. A forced oscillation is observedin western American power system. It could be serious condition
that forced oscillation shows in poorly damped power system.
This poster introduces observation of resonance phenomenon in
Kundur system. To find the serious condition, 3 different
damping ratio cases will be tested to observe resonance whensystem is excited with forced oscillation source. Meanwhile
another 3 possible influence factors will be analyzed in detail in
Kundur system to find their relationship to severity ofresonance.1) forced oscillation frequency difference from interarea electromechanical mode frequency, 2) location of forced
oscillation, 3) forced oscillation magnitude. Simulation resultsshows resonance phenomenon will be observed when 1) thenatural mode of system is poorly damped, and 2) forced
oscillation frequency is close to that mode frequency, 3) forced
oscillation shows in critical location, 4) oscillation magnitude
increases with increasing forced oscillation magnitude. Simulation
result shows small oscillation magnitude of forced oscillation
source will raise multi-times of oscillation magnitude on tie-lines
in Kundur test system.

I. INTRODUCTION

6

7

8

9

10

11

G1

3

G4
L9

Area 1

C7

C9

L7

12
4

2

G2

FO

510
80

90

100

Time (sec)Fig. 2. Oscillation (692~519MW) on tie lines (Bus8-9) in Kundur systemwith poorly damped interarea mode.
Line active power flow (MW)
25

10

-5

80

II. KEY FIGURES OF SIMULATION RESULT
5

573

-20

Many researchers are focusing on detecting oscillation in
power system, which is important for power system stability.
Operators in power system have high alert to poorly damped
mode in system. Oscillation online monitoring withmeasurement based method is studied and applied in powersystem. Normally, natural modes of power system are excitedby random load changes, while forced oscillation comes fromcyclic load variance or mechanical input control issues.
Difference between natural oscillation and forced oscillation isthe damping ratio. The natural mode damping ratio isgenerally greater than zero, while the damping ratio of forced
oscillation is close to zero. This poster chooses 11-bus Kundursystem as test system to study 4 possible effects on resonance

1

637

G3

Fig. 1. Forced Oscillation source shows in Kundur system

Area 2

90

100

Time (sec)Fig. 3. Forced oscillation active power injection on bus 8 in Kundur system

III. CONCLUSION
Figure 2 shows oscillation on tie-lines reach 173MW when 20
MW magnitude forced oscillation is connected in Kundursystem. Based on simulation results, the resonance becomes
more serious when frequency of forced oscillation source iscloser to natural inter-area mode frequency. Resonance withinter-area mode has significant effect on poorly damped
system compared with well damped one. For different
locations in Kundur system, the most serious resonance shows
when forced oscillation is located in the receiving end on Bus
3. Oscillation magnitude of active power on tie lines increases
nonlinearly with forced oscillation magnitude increasing.
Key Reference[1] S. Arash Nezam Sarmadi, and Vaithianathan
Venkatasubramanian, Inter-Area Resonance in Power Systems
From Forced Oscillations. IEEE ” IEEE Trans. Power Syst.,
vol. 31, no. 1, Jan 2016

Validating Inertia Emulation of Wind Turbine Generators Considering Deadband and Safety Limits
Yichen Zhang, Student Member, IEEE, Kevin Tomsovic, Fellow, IEEE, Seddik Djouadi, Senior Member, IEEE

Keywords—Safety verification, hybrid system, wind turbine generator, deadband, inertia emulation, sum-of-square programming.

I.

INTRODUCTION

Among many studies on inertia emulation of wind turbine
generators (WTGs), few focus on quantifying deadband in
inertia emulation of WTGs to ensure that during certain events,
the system frequency can stay within limits of continuous
operation to avoid triggering protections like under-frequency
load shedding (UFLS). Obviously a large deadband canguarantee more power extraction from wind energy and put
less mechanical stress on gearbox, on the other hand, too large
can limits the opportunity for the WTG to provide inertia during a disturbance.
The aforementioned issues have being faced by TSOs like
Hydro-Quebec's TransEnergie's (HQT’s) [1]. Till now, few
methods are proposed to solve these problem except forextensive simulation. In this work, we propose a systematictheoretical analysis method to find a precise answer to theabove question. Our key idea is to model the deadband andother threshold signals as hybrid systems. With the help ofselected modal analysis (SMA), a computationally feasible
reduced-order model can be obtained and finally the above
questions become formulable and solvable based on recent
barrier certificate approaches in hybrid system verification.
II.

PROBLEM FORMULATION AND PRELIMINARY RESULTS

The frequency control of WTGs is shown in Fig. 1. The
deadband part then is modeled as a hybrid system illustrated inFig. 2. A reduced-order model of WTGs with different inertia
emulation gains is then derived based on SMA, coupled with a
one-area load frequency control model through real power. Let
the safety limits of system frequency be represented by apolynomial gU(x), then the safety property of the system can beformulated as
Max Vol( I )s.t.
 B( x )  0 x   I B( x )  0 x  {x : gU ( x )  0}
B( x ) / x  f ( x )  0 x  where I is the safe initial condition. Iterative approaches canbe employed to deal with non-convexity of I. The zero
sublevel of barrier certificate B(x) separates the safe region
from the unsafe region such that no trajectory of the systemstarting from the initial set can cross this level set to reach theunsafe region [2]. The safe region of a maximum power point
tracking (MPPT) under normal operating condition is shown in
Fig. 3.
Different barrier certificates with respect to different modes
can be calculated respectively. Then critical switching instants
between different modes can be validated through the zerocrossing points of barrier certificate value with respect tocorresponding trajectories.
Pgen
Pref

r

Pref

r
(Optimal Tracking)f gridddt

K IE

Fig. 1 Frequency controller of wind turbine generator| f sys | f1| f sys | f 2
Inertia
Emulation

MPPT

MPPT

Fig. 2 Modelling deadband as hybrid systems
Region of Safety2

1.5

Mechanical Power Deivation (PU)

Abstract— In this paper, a hybrid system safety verification
method is employed to determine the largest deadband in inertia
emulation control of wind turbine generators under a disturbance for frequency safety preservation. This information can beused as guidance for trade-off between economic operation ofwind farm and limits of power grid frequency.

1

0.5

0

-0.5

-1

-1.5

-2
-1

-0.5

0
System Frequency (Hz)0.5

1Fig. 3 Safe region of MPPT mode under no disturbance

REFERENCES[1] J. Brisebois and N. Aubut, “Wind farm inertia emulation to fulfill hydroqubec’s specific need,” in Proc. 2011 IEEE Power and Energy Soc. General Meeting, 2011, pp. 1–7.
[2] S. Prajna, A. Jadbabaie, and G. J. Pappas, “A framework for worst-caseand stochastic safety verification using barrier certificates,” IEEE Trans.
Autom. Control, vol. 52, no. 8, pp. 1415–1428, 2007.

1

Virtual Actuators for Damping of Inter-area Oscillations
M. Ehsan Raoufat, Student Member, IEEE, Kevin Tomsovic, Fellow, IEEE, and Seddik M. Djouadi, Member, IEEE

Abstract—Use of actuator redundancy to achieve higher reliability has always been an accepted engineering design techniqueand is used in this study to help ensure power system security.
In our proposed method when an actuator fails or is unavailable(e.g., due to loss of communication), the supervisory controller
redistributes the control signals to the remaining actuators. The
WADC is initially designed to provide satisfactory damping. In
the next step, virtual actuators (VAs) are designed to manage
actuator failures without the need to redesign the nominal
WADC. By inserting this reconfiguration block between thenominal WADC and the new actuator, there is no need to retune
the WADC and the performance of the fault-free system can berecovered. Our proposed block is independent of the nominal
WADC and does not need any information beyond that an
actuator is unavailable. The approach is applied to the 39-bus
New England system. Numerical results show the effectiveness ofthe proposed method subjected to different failures.

related to the VA internal dynamics. Both matrix gains Mr
and Nr should be also chosen for the above virtual actuator
dynamics. In case that fault occurs in the nominal actuator,
the corresponding control input changes accordingly.
II. C ASE S TUDY: N EW E NGLAND 39-B US S YSTEM

Index Terms—Fault-tolerant control, power system oscillations,wide-area control, redundant actuators, supplementary control.

I. FAULT-TOLERANT WIDE-AREA DAMPINGCONTROLLER DESIGN
After linearizion of the system round the operating point,
the linearized MIMO system can be represented as follows:ẋ(t) = Ax(t) + Bu(t) + Bd d(t)(1)

z∞ (t) = C∞ x(t) + D∞2 u(t) + D∞1 d(t)(2)

z2 (t) = C2 x(t) + D22 u(t) + D21 d(t)(3)y(t) = Cx(t) + Dy d(t)(4)

Fig. 1. New England 39-bus system with WADC and virtual actuators.

Our goal is to design a feedback controller to maintain H2
and H∞ norms below some predefined values of ν0 and γ0 ,while minimizing the trade of criterion αkT∞ k2∞ +βkT2 k22
along with satisfying pole placement requirements.
The actuator faults considered arise either when the communication between central WADC and actuator is lost due
to a significant delay, failure due to a cyber-attacks or simply
when the actuator is off-line due to maintenance or component
faults. In these situations, the actuator is unable to receive thecontrol signal from WADC. The reconfiguration block is avirtual actuator and it can be described by following equations:ẋ∆ (t) = A∆ x∆ (t) + B∆ yk (t)(5)

ur (t) = Mr x∆ (t) + Nr yk (t)(6)

yr (t) = Cx∆ (t)(7)

A∆ : = A − B r M r(8)

B ∆ : = B 1 − B r Nr
(B1 , 01×n , 1) :(B , M , N ) :2
2
2(Br , Mr , Nr ) =

(B
,
M
,
N
3
3
3) :



(B4 , M4 , N4 ) :(9)Nominal Actuator
EXC VA(10)
SVC VA
HVDC VA

where output yr goes to the input of the nominal controller,
ur goes to the redundant actuator and state variable x∆ is

Fig. 2. Dynamic response of New England system to three-phase fault at bus
1 when VA redirected WADC signal to G6 , SVC and HVDC respectively.

> REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) <1

Wide Area Measurement Based Real Time
Voltage Stability Monitoring
Ruoxi Zhu, Student Member, IEEE, Chen-Ching Liu, Fellow, IEEE
Abstract—This paper proposes a new wide area real time
voltage stability monitoring algorithm that can not only fit well in
the present day power system infrastructure, but can also fit
equally well in the future smart grid infrastructure with advanced
monitoring and communication facilities. This method includes
receiving data representing a set of system parameters of a powersystem having one or more power buses under a load condition.
The method also includes estimating one or more sets of the systemparameters under one or more additional load conditions based on
the received data and topology information of the power system.
Finally, the Thevenin`s equivalent impedance can be estimatedbased on the actual system parameters and the derived systemparameters. An index called 'Voltage Stability Assessment Index(VSAI)' is computed at every load bus.

II. KEY EQUATIONS
A. Diagrammatic Representation of Inputs and Outputs of the
RT-VSM Algorithm

B. Equations

Index Terms—Voltage stability index, PMU, state estimation,system parameters

III. KEY FIGURES

I. INTRODUCTION

V

Voltage Magnitude of All Buses

1

Vm(in p.u.)

0.8

0.6

0.4

0.2

0
0

5

10

15

20

25

30

20

25

30

Bus Number
VSAI

0.8
0.7
0.6
0.5
VASI

oltage stability is a major concern for power transmission
in electrical power systems. The lack of voltage stability is
believed to be a result of the power system attempting torestore power consumption beyond the capacity of the
combined transmission and generation facilities. If notpromptly corrected, the lack of voltage stability may lead to
unacceptably low voltages (commonly referred to as a voltage
collapse) in at least a part of the power system. As numerous
voltage instability incidents have occurred around the globe inrecent years, more emphasis is being given to perform voltage
stability studies in a better way. With the availability of timesynchronized voltage and current phasors, improved algorithms
can now be developed to efficiently monitor the voltage
stability of a power system. The existing centralized model
based approaches often use multiple power flows to computethe voltage stability margin of a system, but arecomputationally burdensome and are not very suitable for realtime applications. The proposed algorithm in this work is ahybrid approach that uses the PMU data and the network
information to calculate voltage stability indices at all loadbuses in real time. It computes the distance to the point ofvoltage collapse (PoC) in a non-iterative manner by making useof wide area voltage phasor measurements and system
topological information.

0.4
0.3
0.2
0.1
0
0

5

10

15

Bus Number

Figure 1: Key metrics of the IEEE-30 Bus test system during
voltage collapse
REFERENCES[1] S.S. Biswas, A. K. Srivastava, “Voltage Stability Monitoring inPower Systems” US 2014/0244065 A1, Aug. 28, 2014

1

Event Analysis Offline Engine Based on PMU
Data in Power System
Yang Zheng and Vaithianathan “Mani” Venkatasubramanian
Abstract—With the growing implementation of phasor
measurement units (PMU) across the power grid, many PMUbased applications are developed. This paper presents an Event
Analysis Offline (EAO) engine using PMU data to extract
dominant oscillatory modes in power system. Four modal
analysis methods from signal processing theory are used, namely,
Prony's method, matrix pencil method, Hankel total least squares(HTLS) method, and eigensystem realization algorithm (ERA)
method. Results from these engines are processed using a custom
developed set of rules for handling the complexities of modal
analysis from PMU measurements. The results are illustrated on
several actual PMU recordings from power systems in North
America and shows the accuracy of extracting multiple
oscillatory modes and their mode shapes from systemmeasurements.
Index Terms—Event analysis; oscillations; mode shape.

Fig. 2 shows the main window of EAO. From the left partof the window, users can see the imported PMU signals andchange analysis settings. The first plot in the middle shows thesignal selected by users, where possible events are highlighted.
At the bottom, “Frequency Estimates” and “Damping Ratio
Estimates” show the value for frequency and damping of thedominant mode for each method if there exists such a mode.
The upper right part of main window shows the mode shape ofa particular mode for the selected method. The detailed modeshapes window is shown in Fig. 3.

I. INTRODUCTION
The simplified flowchart of EAO is shown in Fig. 1. After
importing PMU data, EAO will do preprocessing and event
detection. When the analysis starts, a sliding window moves
through the data and search for possible events. Once a
possible event detected, EAO will run the estimation methods
to estimate the excited modes in the event.
Fig. 2. EAO's main window.

Fig. 3. The detailed mode shapes window.

REFERENCES[1]
Fig. 1. Simplified flowchart of EAO.

G. Liu, J. Quintero, and V. Venkatasubramanian, “Oscillation
Monitoring System based on wide area synchrophasors in powersystems,” in Proc. IREP symposium 2007. Bulk Power System Dynamics
and Control - VII, Charleston, SC, 2007, pp. 1-13.

A Novel Control Strategy for Stand-alone Photovoltaic
System Based on Virtual Synchronous Generator
Yan Guo1, Laijun Chen1, Ke Li2, Tianwen Zheng1, Shengwei Mei1
1

State Key Lab of Power System, Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
2
Shaanxi Regional Electric Power Group Co., Ltd., Xi’an, China
Email: guoy3.12@sem.tsinghua.edu.cn

Abstract—This paper proposes the concept of PV_VSG, whichmainly means combining Photovoltaic (PV) and Virtual
Synchronous Generator (VSG) together. To control PV_VSG
operating well in stand-alone mode, a novel control strategy is
proposed to match the PV output and the load, which can
prevent the PV output voltage from collapsing. First, thetopology and the operation scenarios are investigated. Then, theprinciples of DC voltage collapse and the operation stability
regions are clearly analyzed. And the detailed control strategy is
deeply presented. Simulation results are also demonstrated toverify the effectiveness of the proposed control strategy.
Index Terms—Photovoltaic, Virtual synchronous generator,
island mode, DC voltage collapse

I.

INTRODUCTION
With the increasing penetration of photovoltaic (PV)generation system, demands for the reliability and theflexibility of PV system increases dramatically. However, PV
inverters are mainly power electronic devices with little inertia
or damping, and researches on VSG now mainly focus on thesituation where the DC input of VSG is stable DC source.
Naturally, an innovative idea combing PV and VSG together ispresented and we call this type of system PV_VSG. In thispaper, the stand-alone mode of PV_VSG is mainly focused.

(a) Control A(b) Control B

Figure 3. simulation result when load increases suddenly

II. PROBLEM FORMULATION
The topology of PV_VSG is shown in Fig.1and Fig. 2
shows the whole control block diagram of the strategy
proposed in this paper.

PV

U dc C uoa

uob

3-phaseload

Lg , Rg

LS , RSebec

ia , ib , ic

0
PI

e*

Pe Qe
VSG
algorithm

P* Q

Power
calculator*

Reference

Figure 1. Topology of VSG

LCL

C0

Load
or
Grid

Dp

P*

+
+
_

Part ii

SVPWM
signal generator

udc

ON/OFF

_
1/ 

Pe

+
Qe
_

Q* +

+
vrms* +

1
Js

1
s


E 

1
ks

E

e*

PWM
Drive

C0
Rd

PV

PPV

udc

ea

uoc

Characteristic input

MPPT（selection）-V

PV _ min

LS , RS(b) Control B

+
_ v

Dq

Part irms

Figure 2. Control block diagram

In this paper, operations with adequate and inadequate
generation capability are considered and stability region
analysis is made.
III.

(a) Control A

Figure 5. simulation results when input drops suddenly

SIMULATION RESULTS

The control strategy proposed above is verified on the
Matlab/Simulink platform.

This work was supported by Foundation for Innovative ResearchGroups of the National Natural Science Foundation of China (51321005),
Tsinghua University Initiative Scientific Research Program (20151080416)
and Program of National Natural Science Foundation of China (E070602).

IV. CONCLUSIONSThis paper firstly proposes an original concept of PV_VSG
and mainly focuses on PV_VSG stand-alone mode. Then the
problems of matching PV output and load and DC voltage
collapse are formulated. Control strategy is also proposed tosolve these two critical problems. Contrasted simulations
verify the effectiveness of the control strategy. Moreover, thiscontrol strategy is easily-extensible and it may be promisingly
applied to grid-connected mode in PV systems.

Financial Assessment for Aggregated Residential
Resources in the Demand Response Programs in
ERCOTLong Zhao, Student Member, IEEE and Wei-Jen Lee, Fellow, IEEE
Department of Electrical Engineering
University of Texas at Arlington
Arlington, TX, USA
long.zhao@mavs.uta.edu
Abstract— In 2011, Senate Bill1125 of Texas allows residential
and commercial electricity customers to participate DR programin ERCOT market as long as reliability is maintained. However,current residential customers are not able to participate in thewholesale market as day-ahead market bidders in ERCOT market,
but residential DR bidding program in day-ahead market has
relatively high possibility to be applied in the near future with thecurrent trend of DR. Demand Response Auction Mechanism(DRAM) Pilot program has been proposed in 2014 by California
Public Utilities Commission (CPUC). DRAM program is a pay-asbid program which allows residential customers with adequate
resources to participate CAISO day-ahead (DA) market during
the availability assessment hours. Besides the curtailment byapplying DR, PG&E is initializing a new program which is called
Excess Supply Pilot (XSP). In XSP program, DR resources are
rewarded by consuming excess solar and wind energy. As one of
distributed energy resources (DERs), residential energy storage(RES) is allowed in DRAM program and could be used as DR
capacity. Thus, residential batteries will play a vital role in the
future power system markets with all new DR programs. In this
proposed work, aggregated residential resources with the DR
programs are assessed under ERCOT real-time (RT) market andday-ahead (DA) market. This work aims to evaluate the financial
benefits of energy storage and the DR programs at residential levelfor both peak curtailment and excess energy consumption.

I. INTRODUCTION
With many advantages of Demand Response (DR), DR
application has been widely adopted by the utilities in the
United States. Currently, there are many different DR programs
which were designed based on different standards and aims.
Also, rapid development and application of renewable energy
resource need a large amount of energy storages to mitigate theintermittence of the renewable generation. So, the energy
storage is becoming a critical factor for the power systems.
Also, several States in the U.S. have approved polices toallowed residential customers to participate DR programs, itmeans DR programs at residential level would have more
flexibility and importance in the future electricity market.
Therefore, it is necessary to develop and assess the applicationof DR programs with residential energy storage (RES) undercurrent electricity markets.
In this proposed work, we will assess the residential energy
storage and the demand response programs for aggregatedhouseholds. we assume the aggregator has 1000 bundledhouses with 10kWh residential energy storage in eachhousehold. Direct Load Control (DLC) with smart appliances
is implemented to each contracted house by the aggregator with
DR programs. Curtailment program and excess energy
consumption program are analysed in ERCOT real-time (RA)market and day-ahead (DA) market.
II. PROPOSED FRAMEWORK
Under ERCOT RT market and DA market, 2 different
applications are proposed. Curtailment and excess energy
consumption programs are applied to each house. As 3 different
DR resources shown in Fig.1., DR programs, RES andcombination of DR with RES are implemented to 4 different
cases that includes curtailment in RT market, excess energy
consumption in RT market, curtailment in DA market andexcess energy consumption in DA market.

ERCOT RT Market
and DA Market

Curtailment

Excess energy
Consumption

1. RES

1. RES

2. DR

2. DR

3. DR+RES

3. RES+DR

Fig. 1. Frameworks
III. KEY RESULT
Simulation results will be presented in the conference.

Revenue Adequacy of Wholesale Electricity
Markets with Demand Response Providers
Hao Ming, Le Xie
Department of Electrical and Computer Engineering, Texas A&M University
College Station, TX 77843-3128, USA
Email: mingming511@tamu.edu, Le.Xie@tamu.edu

Abstract—This paper investigates the fundamental issue ofrevenue adequacy in wholesale day-ahead electricity markets
with the presence of standalone demand response providers(DRPs). In recent years, with the increasing need for flexibility
to provide sustainable electricity services, standalone DRPs haveemerged as a new form of market participants. Such providers
serve as intermediate between ISOs and end-users, and areawarded for the aggregated net demand change committed toISO. Compared with conventional load serving entities, theDRPs are not responsible for providing the electricity services
to consumers. This paper formulates the energy market clearing
process with the presence of such DRPs. It is shown from theformulation that ISO will inevitably face revenue inadequacy withthe participation of DRPs in energy markets. Such inadequacy is
independent of the choice of demand response baseline. Several
possible solutions to this problem are proposed. This finding could
shed some lights on future policy design of demand response
markets with massive level of participation.

Fig. 2.

Benefit of DRP and Visualized Revenue Inadequacy

II. P RICE - ADDER AS A S OLUTION TO R EVENUEI NADEQUACY
PDR
PL
α
∆π =
πc
1 − α DAα :=I. P ROBLEM F ORMULATION

(4)
(5)

A. Problem Formulation: How Revenue Inadequacy Occurs
TABLE IR EVENUE IN T HREE C ASES *
Case
WG
WLSE
WDRP
N et Revenue

LSE-based DR
c P
πDA
GC
c P
−πDA
LC
N/A
0

DRP (original)
c P
πDA
GC
c P
−πDA
LC
c P
πDA
DR
c
πDA PDRDRP (price-adder)
c P
πDA
GC
c
−(πDA
+ ∆π)PLC
c P
πDA
DR
0

III. B ENEFIT A NALYSIS OF M ARKET PARTICIPANTS
Fig. 1.

Three-Layer Financial Structure of DAM with DRP

This seemingly perfect design of market mechanism has afundamental flaw of revenue inadequacy. Example:

min

0.2PG2 + 10PG + 5

(1)

s.t.

PG + PDR = PL

(2)

The solution of this optimization problem is PG = 8, PL =c
10, PDR = 2 and clearing price πDA= 13.2.
WG +WLSE +WDRP = 8∗13.2−8∗13.2+2∗13.2 = 26.4 6= 0
(3)

Fig. 3. Benefits under different market policies(a) GenCo Profit (b) LSE Profit (c) DRP Profit (d) Net Revenue for DAM

An Agent-Based Framework For Modeling Retail
Energy Markets with Demand Response
Kaveh Dehghanpour, Student Member, IEEE
Electrical and Computer Engineering Department
Montana State University
Bozeman, U.S.
Advisor: Hashem Nehrir, Fellow, IEEE
Abstract— Agent-based modeling has an important role inunderstanding the behavior of complex engineering systems. In
this poster presentation, we propose a hierarchical multiagent
system to study the behavior of a retail electrical energy market
with a price-based Demand Response (DR) program. Air
Conditioning (AC) loads, which are controlled by intelligent
control agents, are used for DR in this study (i.e., they change
their power consumption pattern in response to varying retailprices). A profit-oriented retailer agent buys energy on thewholesale market and sells it to the consumers, consisting of both
flexible and inflexible loads. Hence, we have a bi-level decision
problem: at the higher level the retailer agent solves a profit
maximization problem to set the optimal retail prices, while atthe lower level, the AC agents search for optimal consumption
patterns
throughdetermining
temperature
set-points,
considering both the cost of electrical power and consumers’
comfort preferences.

initial conditions. The uncertainty of the system is incorporated
in the immediate reward function of the MDP.

Keywords— multiagent systems; reinforcement learning;
quadratic programming; Markov decision process;

B. Retailer Agent
The retailer develops a linear model of the aggregate
behavior of AC loads. The model learning is performed using
multiple linear regression and is based on the interaction of theretailer agent with the AC agents. This interaction process is in
the form of sending price signals and receiving feedback fromthe AC agents about their expected power consumption. As
the learning process evolves, a drop in estimation error isobserved (Fig. 2), which implies improved performance.
Using the learned model, the retailer is able to solve the profit
maximization problem, which is formulated as a Quadratic
Programming problem. As can be seen in Fig. 3, the profit of
the retailer reaches its maximum after the learning has reached
its final state. The price profile is shown in Fig. 4, with andwithout the DR program.

I. BASIC APPROACH

II. KEY RESULT

We need to model the behavior of agents at the two levelsof the hierarchical multiagent system: the AC agents and theretailer agent. The overall architecture of the system is shownin Fig. 1.
A. AC Agents
To model the behavior of AC agents, Reinforcement
Learning is employed. Hence, the problem of optimal
temperature set-point determination is formulated as a Markov
Decision Process (MDP) and solved using Q-learning. Eachagent has its private MDP. Since the initial house temperatureand the ON/OFF state of the thermostat are unknown, a priori,
the learning process is performed in episodes with random

Fig. 2. Prediction Mean Absolute Error (MAE).

Fig. 3. Profit maximization through learning.

Fig. 1. Architecture of the agent-based market.
Fig. 4. Optimal retail price for different cases.

A Decentralized Framework of the Future RetailElectricity Markets
Josue Campos do Prado
Department of Electrical & Computer Engineering

Wei Qiao
Department of Electrical & Computer Engineering

University of Nebraska-Lincoln
Lincoln, NE 68588-0511 USA
prado.josue@gmail.com

University of Nebraska-Lincoln
Lincoln, NE 68588-0511 USA
wqiao@engr.unl.edu

Abstract—As distributed generation (DG) and smart gridtechnologies (SGT) become more efficient and spread in the
distribution grid, the number of agents taking advantage of such
advances will continue to increase exponentially. These agents
can no longer be considered passive in the future electricity
market framework since they can provide several services which
will benefit the local and main electric systems to which they areconnected. This work aims to propose a decentralized market
framework for the future retail electricity markets considering
DG and SGT as providers of energy and ancillary services.

I.

PROPOSED FRAMEWORKThe proposed retailed electricity market framework is
illustrated in Fig. 1. The market agents are described asfollows:
Retail Market Operator (RMO): Nonprofit organizations
responsible for the retail market operations. They interact
with the smart grid environment by allowing the participationof DG and SGT in local and global services.
DISCOs and Retailers: For-profit organizations that resell
energy to their customers in the retail market. They canprocure electricity from a wholesale market or the distributed
retail market and sell procured electricity to their customers.
Smart Grid Agents: Agents that own smart-grid facilities
and resources such as smart meters for demand response (DR)programs, smart controllable loads, and V2G (vehicle-to-grid)facilities or fleets. Furthermore, microgrids with small DG
units (average capacity up to tens of kW) are also consideredsmart grid agents in this framework. Their surplus energy isused through Net Metering (NM).

Fig. 1. Proposed future retail electricity market framework

II.

In the proposed framework, agents can participate in fivemarkets to provide energy delivery (ED) and other important
services, as described in Tables I and II. All the markets arecoordinated by the RMO.
TABLE I.

TABLE II.
Market

DG Producers: Self-sufficient producers that own larger
DG units than smart grid agents. Their average capacity is up
to hundreds of kW and their load is considered negligible.

N-hour-ahead

Small Consumers: Those who do not participate actively
in the retail electricity market due to lack of interest or
appropriate equipment. For this reason, they are consideredjust consumers, having no direct participation in the proposed
framework.

Emergency

This work was supported in part by the U.S. National Science
Foundation under CAREER Award ECCS-0954938. Any opinions, findings,
and conclusions or recommendations expressed in this publication are thoseof the authors and do not necessarily reflect the view of the NSF.

DISTRIBUTED MARKETS

BalancingAncillary
Services

INTERACTIONS THROUGH BILATERAL CONTRACTS

INTERACTIONS THROUGH OTHER MARKETSParticipants
DICO’s/Retailers
DG producers
DG producers
Smart grid agents
DICO’s/Retailers
DG producers
Smart grid agents

Local and Global Services

DICO’s/Retailers
DG producers
Smart grid agents

Global frequency regulation
Local voltage regulation
Local and global reserve
Local and global Balancing

Local energy delivery
Local balancing
Global balancing
Local backup

Dual Pricing Algorithm for Cost Allocation in
Non-Convex Electricity Markets
Robin Broder Hytowitz1,2, Student Member, IEEE, Richard O’Neill2, Member, IEEE,
Anya Castillo1,2, Student Member, IEEE, and Brent Eldridge1,2
1

Department of Geography and Environmental Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
2
Office of Energy Policy and Innovation, Federal Energy Regulatory Commission, Washington, DC, 20426

Abstract— The challenge to create efficient market clearing

prices in centralized day-ahead electricity markets arises
from inherent non-convexities in unit commitment
problems. When this aspect is ignored, marginal prices
may result in economic losses to market participants whoare part of the welfare maximizing solution. In this essay,
we present an axiomatic approach to efficient prices andcost allocation for a revenue neutral and non-confiscatory
day-ahead market. Current cost allocation practices do
not adequately attribute costs based on transparent costcausation criteria. Instead we propose an ex post multipart pricing scheme, which we refer to as the Dual Pricing
Algorithm. Our approach can be incorporated intocurrent day-ahead markets without altering the market
equilibrium.
I.

ECONOMIC PRINCIPLES FOR COST
ALLOCATION

Under the Federal Power Act (FPA), prices must be justand reasonable, and not unduly discriminatory. This implies
prices can be discriminatory to achieve economic efficiency.
Discriminatory pricing can benefit market efficiency bysignaling the need for economic investments. Uniform, nondiscriminatory pricing may require that costs be distributed
too broadly to provide a meaningful signal in the short-term
for efficient operations as well as long-term investments.
At the economic equilibrium, supply and demand arebalanced and are supported by market-clearing prices. Then,cost allocation ex post results in a multi-part pricing scheme.
Our axiomatic approach requires non-confiscation, i.e., theproducers are paid at least their total production cost andconsumers pay no more than their willingness to pay.
Additionally, the pricing scheme must be revenue neutral, i.e.,
total revenues paid by the consumers equals the total
payments to producers. Revenue neutrality is easily satisfied
when we assume infinitely valued consumption, an
assumption that leads to false conclusions about revenue
adequacy; as such we model price-responsive demand in our
analysis.
II.

KEY EQUATIONS

associated uplift payments can be derived through solving thedual pricing problem below.
𝑝𝑝(1)
min ∑ 𝑑 ∗ 𝑢 + ∑ 𝑝∗ 𝑢
𝑖∈𝐷

𝑖

𝑖

𝑖∈𝐺

𝑖

𝑖

Subject to∑𝑖∈𝐷 𝑑𝑖∗ (𝑢𝑖𝑝 − 𝑢𝑖𝑐 ) + ∑𝑖∈𝐺 𝑝𝑖∗ (𝑢𝑖𝑝 − 𝑢𝑖𝑐 ) = 0(2)𝑝∀𝑖 ∈ 𝐷(3)
Ψ𝑖 = 𝑑𝑖∗ (𝑏𝑖 − 𝜆𝐷𝑃𝐴 + 𝑢𝑖 − 𝑢𝑖𝑐 )𝑝∀𝑖 ∈ 𝐺(4)
Π𝑖 = 𝑝𝑖∗ (𝜆𝐷𝑃𝐴 − 𝑐𝑖 + 𝑢𝑖 − 𝑢𝑖𝑐 ) − 𝑧𝑖∗ 𝑐𝑖𝑆𝑈
𝜆𝐷𝑃𝐴 ≥ 𝑏𝑖∀𝑖 ∈ 𝐷0(5)
Ψ𝑖 , Π𝑖 ≥ 0∀𝑖 ∈ 𝐷+ U𝐺 + (6)𝑝∀𝑖 ∈ 𝐷U𝐺
𝑢𝑖 , 𝑢𝑖𝑐 ≥ 0(7)𝑝𝑐where 𝑢𝑖 and 𝑢𝑖 are uplift payments and charges, respectively,
Ψ𝑖 and Π𝑖 are the demand value and generator profit functions,
respectively, and 𝜆𝐷𝑃𝐴 is the new LMP.

Summing (3c) over D and (3d) over G, we maintain market
surplus. Uplift payments and charges are balanced throughthe revenue neutrality condition in (3b), and non-confiscationin (3f). The optimal solution to the DPA is revenue neutral,i.e., all revenues are distributed to cover all incurred costs,
and therefore revenue adequate. As formulated above, DPA
may have multiple optimal solutions. Additional constraints
can be added so that the resulting prices match stakeholders’sensibilities, but we leave that topic for future discussion.
III.

EXAMPLE PROBLEM

Considering a one period example with the data provided
below, the market-clearing price that maximizes market
surplus is $60/MWh, resulting in generator B operating at a$500 loss. The DPA determines the optimal LMP to be 𝜆𝐷𝑃𝐴=$65.56/MWh, which recovers generator B’s startup costs.
Gen
A
B

TABLE 1. GENERATOR COSTS
Marginal cost ($/MWh) Startup cost ($) Pmin (MW)
40
500
0
60
500
10
Buyer
1
2

Pmax (MW)40
200

TABLE 2. DEMAND FUNCTION
Value ($)
Max demand (MW)
100
100
63
30

REFERENCES[1]

We formulate the single-period, single-bus DPA model, forwhich we define the sets 𝐺𝑈𝑃 = {𝑖|𝑖 ∈ 𝐺, 𝛿∗ < 0}, 𝐺 + = {𝑖|𝑖 ∈ [2]𝐺, 𝑝𝑖 > 0}, 𝐷+ = {𝑖|𝑖 ∈ 𝐷, 𝑑𝑖 > 0}, and 𝐷0 = {𝑖|𝑖 ∈ 𝐷, 𝑑𝑖 = 0}.
[3]
If 𝐺𝑈𝑃 = ∅, then the outcome is non-confiscatory and no ex
post cost allocation is required. However, if 𝐺𝑈𝑃 ≠ ∅, then
we introduce a dual modification to the LMP (𝜆𝐷𝑃𝐴 ) and

R.P. O’Neill, P.M. Sotkiewicz, B.F. Hobbs, M.H. Rothkopf, and W. R.
Stewart Jr. “Efficient market-clearing prices in markets with
nonconvexities,” Eur. J. Oper. Res., vol. 164, pp. 26 9-285, 2005.
C. Ruiz, A. J. Conejo, S. A. Gabriel. “Pricing non-convexities in anelectricity pool,” IEEE Trans. Power Syst., vol. 28, no. 3, 2012.
P.R. Gribik, W.W. Hogan, and S L. Pope, “Market-Clearing Electricity
Prices and Energy Uplift,” Working Paper, John F. Kennedy School ofGovernment, Harvard Univ., Dec. 2007. [Online]. Available:
hks.harvard.edu/hepg/Gribik_Hogan_Pope_Price_Uplift_123107.pdf.

Bi-level Network Planning Model Considering Generationmarket Equilibria Subject to Inefficient Network Pricing
Pengcheng Ding and Benjamin F. Hobbs, Fellow IEEE
Dept. of Geography & Environmental Engineering, The Johns Hopkins University
Abstract—We consider proactive planning of transmission
subjected to the response of a generation market with imperfecttransmission pricing. Previous work has emphasized short term
pricing – either LMP or zonal pricing systems. Here, we consider,for the first time in an equilibrium modeling framework, inefficient long-term pricing and the incentives it provides for generation mix and siting. In particular, we model interconnection andannual transmission charges based on a marginal MW-Milecharging system similar to that of the UK and other countries. We
investigate its impact on generation siting and grid planning by
including these costs in a bi-level network planning model.

I.

NETWORK PLANNING MODEL

In the proactive or anticipative transmission planning paradigm, decisions are made sequentially [1]-[2]. At the top level,
the network owner or ISO makes decisions concerning which
new lines to build and which existing lines to upgrade, anticipating the response from the generation market. Then, given howtransmission is priced in both the short-run (e.g., LMP) andlong-run (e.g., interconnection charges), generators make investment and dispatch decisions in a market framework. Multilevel models can be used to implement this paradigm [3]. Here
we extend the paradigm to the case of inefficient long-run transmission pricing, with the goal of investigating whether network
planning can counter the distorted siting incentives arising from
MW-mile based pricing.

used to calculate the flow at system peak assuming that all generation produces at capacity and sinks a designated hub. The
sum of flows on each line multiplied by its “cost-adjusted”
length is treated as the base MW-Miles. The charge is obtainedby calculating the marginal MW-Miles required to accommodate an extra MW of generation at each node. To obtain a predetermined ratio of revenues collected from demand and generation, a correction factor is applied.
To generalize, we could write the final cost measured in𝜕MW−Miles£/MW/Yr as 𝑇𝐶 = 𝐶1 + 𝐶2
. We formulate𝜕𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦_𝐼𝑛𝑐𝑟𝑒𝑎𝑠𝑒
this cost using shadow prices from the following optimization:s.t.

MIN 𝑇𝑀𝑀 = ∑𝑙 (𝑓𝑙+ + 𝑓𝑙− )𝐶𝐿𝑙∑𝑙∈𝐿_𝐾𝐶𝐿(𝑛) 𝐴𝑛𝑙 (𝑓𝑙+ − 𝑓𝑙− ) = 𝐾𝑛 (dual 𝜆𝑛 ) ∀𝑛∑𝑙∈𝐿_𝐾𝑉𝐿(𝑣) 𝑅𝑣𝑙 (𝑓𝑙+ − 𝑓𝑙− ) = 0 ∀𝑣 ∈ 𝑉
𝑓𝑙+ , 𝑓𝑙− ≥ 0 ∀𝑙

where (𝑓𝑙+ − 𝑓𝑙− ) is the flow on line l, TMM is the total MWMiles, 𝐾𝑛 is the generation capacity at node n, the first two constraints represent KCL and KVL in the DC optimal power flow,
and 𝐶𝐿𝑙 is the cost adjusted length for line l. KCL(n), KVL(v),and V are appropriately defined sets of indices for Kirchhoff’s
laws. Finally, ℎ is the index of the hub node.
Given a solution to the above, we define:𝜕MW−Miles
𝜕𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦_𝐼𝑛𝑐𝑟𝑒𝑎𝑠𝑒_𝑛𝑜𝑑𝑒_𝑛

A. Bi-level Model Structure
1. Bottom Level: Competitive Generation Equilibrium
Problem. Each generation firm decides its dispatch and capacity investments given energy and transmission prices. Its objective is to maximize profit (revenue minus the expense of operations, capacity investments, and interconnection and transmission charges). With this formulation, we consider investment incentives introduced by transmission charges.
Energy prices and LMPs arise from the short-run market clearing constraints, while long-run (annual) transmission charges are
based on a MW-mile model that results from a separate optimization in the equilibrium model (Section I.B). The combination of themarket clearing and the first-order conditions of generators and thelong-run transmission pricing model constitutes an equilibrium
model formulated as a complementarity problem[4].
2. Top Level: Transmission Planning. The ISO makes planning decisions in this model subject to the reaction of the generation market, as captured by the equilibrium model.
B. Interconnection & Annual Transmission Charges Model
We model a marginal MW-mile based charging system, likethe one employed in UK’s Transmission Network Use of System Charges [5]-[6]. A DC optimal power flow model is first
This work is sponsored by NSF Award ECCS-1408401.

=

𝜕𝑇𝑀𝑀
𝜕𝐾𝑛

= 𝜆𝑛 − 𝜆ℎ .

Finally, 𝐶1 and 𝐶2 are found by setting the sum of collectedcharges equal to the annual revenue required from generation.
II.

PRELIMINARY RESULTS AND DISCUSSION

Our preliminary numerical analysis shows that MW-Milebased transmission charges can distort siting decisions of both
thermal and renewable plants relative to the social optimum. We
attribute these effects to the fact that, first, all generation capacity is treated the same in the MW-Mile model, whether peaking,baseload, or variable renewable and, second, the MW-Milemethod does not take into account congestion and, thus, theneeded network reinforcements.
[1] E. Sauma and S. Oren, “Proactive planning & valuation of transmission investments in restructured electricity markets,” J. Regul. Econ.,30(3), 2006.
[2] F. Munoz, B. Hobbs, J. Ho, S. Kasina, “An Engineering-Economic Approach to Transmission Planning Under Market and Regulatory Uncertainties: WECC Case Study,” IEEE Trans.
Power Syst., 29(1), 2014.
[3] D. Pozo, E. Sauma, J. Contreras, “A Three-Level Static MILP Model for Generation andTransmission Expansion Planning,” IEEE Trans. Power Syst., 28(1), 2013.
[4] S. Gabriel, A. Conejo, J. Fuller, B. Hobbs, C. Ruiz, Complementarity Modeling in Energy
Markets, vol. 180. Springer New York, 2013.
[5] “The Statement of the Use of System Charging Methodology,” Natl. Grid Rep., vol. 22, 2007.
[6] G. Strbac, C. Konstantinidis, R. Moreno, I. Konstantelos, D. Papadaskalopoulos, “It’s All
About Grids: The Importance of Transmission Pricing and Investment Coordination in Integrating Renewables,” IEEE Power Energy Mag.,13(4) 2015.

Integrating Consumers’ Differentiated Supply Valuation inDistribution Network Planning
H. Karimi, Student Member, IEEE, D. Papadaskalopoulos, Member, IEEE, and G. Strbac, Member, IEEE
Department of Electrical and Electronics Engineering, Imperial College London
Abstract— The current distribution network planning
framework does not take into account consumers’ actual
valuation of electricity supply due to the lack of observability andcontrollability. The traditional use of a single value of lost load
(VoLL) implies the oversimplified assumption that the valuationof electricity supply is identical for every consumer in the
network, irrespectively of their individual activities, preferences
and perceptions, and for every unit of energy supplied,irrespectively of the specific appliance it is consumed by and thespecific service it provides to the consumer. The envisaged rollout of smart metering provides a unique opportunity for
consumers to communicate their individual supply valuation to
the distribution network operators (DNO). Building on theadvanced metering and control capabilities of the emerging
smart grid, this study explores the impact of integrating thedifferentiated valuation of electricity supply for different
customers and different levels of supplied energy in distribution
network planning. Customer interruption costs are reduced,
since supply interruption of customers with low supply valuationand the non-critical part of their demand is prioritized during
network failures. As a result, the need for capital intensive
network reinforcement is limited and the total network
expenditure is reduced. Furthermore, this study proposes a costreflective network charging scheme based on the principles oflocational marginal pricing, which enables an equitable
treatment of customers with differentiated supply valuation.

function illustrated in Fig. 1c. An illustrative example of a casewhere both the differentiation of supply valuation per
customer and per unit of supplied energy is taken into accountis given in Fig. 1d, where the supply valuation of each of fivedifferent customers of the same sector is captured by adifferent linear decreasing function. The average supply
valuation across all customers and their demand range remains
equal to the benchmark value a in all four models.
Marginal benefit(£/kW)

Marginal benefit(£/kW)

Demand(kW)(b)

Marginal benefit(£/kW)

Marginal benefit(£/kW)

2a

Demand(kW)(d)

Figure 1. Alternative representations of customers’ supply valuation

network charges, distribution network planning, locational
marginal pricing, sequential Monte Carlo simulation.

KEY CONCEPT (CONSUMERS’ DIFFERENTIATED SUPPLYVALUATION)

The valuation of electricity supply by the consumers is
widely expressed in the relevant literature in the form of a“price-demand” function or equivalently “consumers’
willingness to pay” function [1]. Under the traditional network
planning paradigm, this function is assumed constant andequal to a VoLL for all consumers in the network (Fig. 1a). In
reality however, the supply valuation does not vary only
among different customer sectors, but also among different
individual customers, due to their special requirements,
preferences and perceptions regarding energy use. An
illustrative example of this differentiation is given in Fig. 1b;
the VoLL of five different customers of the same sector varies
between a-2x and a+2x. Furthermore, according tomicroeconomic principles, the marginal benefit is not actuallyconstant, but is reduced as the level of served demand
increases. This concept expresses the fact that customers do
not value identically every unit of supplied energy. The first
units of energy, serving critical loads such as lighting andheating, are valued more than additional units of energy
serving non-critical loads, the operation of which can bepostponed or avoided such as washing machines anddishwashers. In the simplest form, this decreasing nature of
supply valuation can be expressed through a linear decreasing

Demand(kW)(c)

Index Terms— Customer interruption cost, distribution

I.

Demand(kW)

(a)

II.

TEST SYSTEM

The examined case studies demonstrate the implications of
the proposed distribution network planning and charging
framework, employing a typical UK HV distribution network,
composed of a primary substation with two 33/11kV
transformers, and two 11kV cable feeders (Fig. 2). Under
normal operation conditions, the network operates with a radialconfiguration. If an outage occurs at one of the feeders, thenormally open switch (NOS) is closed instantly to provide
alternative infeed. The substation can be reinforced by adding
a third transformer of the same size with the existing ones.
Feeders can be reinforced by replacing simultaneously all
sections with cables of double size.
Primary(33kV/11kV)
T1

Feeder 1

T2

Feeder 2

F1 L1

F1 L2

F1 L3

F1 L4

F1 L5

F1 DT1F1 DT2F1 DT3F1 DT4F1 DT5F2 DT1F2 DT2F2 DT3F2 DT4F2 DT5

F2 L1

F2 L2

F2 L3

F2 L4

F2 L5

Figure 2. Test distribution network

REFERENCES[1]

S. Burns and G. Gross, “Value of service reliability,” IEEE Trans. Power
Syst., vol. 5, no. 3, pp. 825–834, Aug. 1990.

Multi-Proxy Interchange Schedulingunder Uncertainty
Yuting Ji and Lang Tong
Abstract—The problem of inter-regional interchange scheduling using a multiple proxy bus representation is considered. A
new scheduling technique is proposed for the multi-proxy bus
system based on a stochastic optimization that captures uncertainty in renewable generation and stochastic load. In particular,
the proposed algorithm iteratively optimizes the interchange
across multiple proxy buses using a vectorized notion of demandand supply functions. The proposed technique leverages theoperator’s capability of forecasting locational marginal prices(LMPs) and obtains the optimal interchange schedule directly
without iterations between operators.

1
Region 1

A. Multiple Proxy Bus Mechanism
A proxy bus is used by system operators to represent the
network in the adjacent control region in modeling the impactof import or export on their internal transmission lines. In
this paper, we focus on the multiple proxy bus system inwhich there are multiple scheduling interfaces between two
interconnected regions with each interface represented by aproxy bus, as illustrated in Figure 1.
B. Stochastic Interchange Scheduling
The stochastic interchange scheduling is to determine theoptimal interchange schedule q such that the overall systemcost is minimized under load and variable generation uncertainty. The main idea of the proposed technique is to reduce themulti-proxy interchange scheduling problem to a single proxy
problem by optimizing one interface flow at a time with fixed
quantities over the others.
The authors are with the School of Electrical and Computer Engineering,
Cornell University, Ithaca, NY 14853, USA (e-mail: yj246@cornell.edu;
ltong@ece.cornell.edu).

q1
q2

Interface 2Interface 1
2

q1

c
d

4

Region 2

6Interface 2

I. I NTRODUCTION

II. M ETHODOLOGY

a
b

3
5

q2

In this paper, we consider the interchange scheduling problem in which there are multiple scheduling interfaces between
two interconnected control regions. The main idea behind the
proposed approach is to reduce the multi-proxy interchangescheduling to a single proxy problem. The technique to obtain
the optimal interchange vector is based on the person-byperson optimization strategy in the decentralized team decision
theory: optimize one interface flow at a time until convergence.
The main advantage of this technique is: there is no need toiterate between operators as most decentralized jointly optimal
dispatch techniques require; a single exchange of demand or
supply curve suffices.

Interface 1

Figure 1: A multiple proxy bus representation.
Mathematically, given the flow vector q̄−j over all interfaces
except j, the optimization problem for interface j is given by(P1 ) minqj ≤Qj2X

Edi [ci (gi∗ (qj , q̄−j , di ))]

i=1

where ci (·), di , and gi∗ (·) are the cost function, the randomload or variable generation, and the optimal regional dispatch
function for area i.
In general, directly solving (P1 ) is intractable, especiallywhen the randomness follows some continuous distribution. To
avoid such a direct solution approach, we propose a schedulingtechnique that uses price functions and solves (P1 ) via anequivalent social welfare maximization problem as follows.
Z qj(P2 ) max
Ed2 [π2j (x, q̄−j , d2 )]−Ed1 [π1j (x, q̄−j , d1 )] dx
qj ≤Qj0where πij is the locational marginal price (LMP) function forarea i at the interface j.
Theorem 1. Problem (P1 ) and (P2 ) have the same optimizer.
C. Proxy-by-Proxy Scheduling Algorithm
The proxy-by-proxy scheduling algorithm, which is outlined
as follows.
1) Initialize q (0) = 0.
(k) (k−1)
and solve2) For iteration k, update q̄−j = q<j , q>j(k)qj sequentially from (P4 ) for j = 1, · · · , m, where mis the number of interfaces.
3) If kq (k) − q (k−1) k2 ≤ δ, terminate; otherwise, go to 2)
for the next iteration k + 1.
Theorem 2. The proxy-by-proxy scheduling algorithm generates a sequence {q (k) }∞k=0 that converges to the global optimal
solution to (P1 ).

1Advantages of a Three-Stage Stochastic
Market-Clearing Model vs. a Two-Stage One
Farzaneh Abbaspourtorbati, Student Member, IEEE, Antonio J. Conejo, Fellow, IEEE, Jianhui Wang, Senior
Member, IEEE, Rachid Cherkaoui Senior Member, IEEE

model represents both the intra-day market and the operation
stage. Therefore, we investigate the effects of representing theintra-day market on day-ahead results.
The novel contribution of this paper is twofold:
1) To propose a non-convex stochastic programming model
to clear the day-ahead market with a prognosis of theintra-day market and the real-time operator.
2) To comprehensively compare such a model with a twostage one, which is more common but less realistic.
II. K EY F IGURES
20
Day−ahead Prices [$/MWh]

Abstract—This paper proposes a non-convex three-stage
stochastic model to clear the day-ahead market under wind
uncertainty: the first stage represents the day-ahead market,
the second stage the intra-day market, and the third stage
the real-time operation. The objective is just to clear the dayahead market, but with a prognosis of the future: the intra-day
market and the real-time operation. A number of consistent windproduction scenarios modeling the uncertainty associated withwind production at the intra-day and operation stages, and nonconvexities due to start-up costs of generators and their minimum
power output are taken into account. To assess the impacts of
the intra-day market on the day-ahead outcomes, we compare
the results obtained from the proposed model with those of asimpler but more common two-stage model, which represents
the day-ahead market and the real-time operation.

I. I NTRODUCTION

price from two−stage model
price from three−stage model

16
14
12
10
8
2

4

6

8

10

12
14
period

16

18

20

22

24

Fig. 1. Day-ahead clearig prices from the three-stage model (blue) and thetwo-stage model (red) over all periods.

200

Expected cost
3−stage2−stage3−stage2−stage3−stage2−stage3−stage 2−stage

160
load profile1

K$

The increase in variable generation in power systems acrossthe world has motivated the development of two-stage stochastic market-clearing models, where the first stage represents
the day-ahead market and the second stage describes thesystem operation. However, an increasing number of electricity
markets with large renewable production include intra-day
markets. Such intra-day markets give stochastic units theopportunity of offering closer to power delivery and thus with
reduced uncertainty.
In the above context, this paper proposes a non-convex
three-stage stochastic model to clear the day-ahead market,
where uncertainty stems from stochastic wind generation. The
first stage represents the day-ahead market, the second stage
describes the intra-day market, and the third stage models thereal-time operation. Therefore, the day-ahead schedules are
decided with a detailed prognosis of the future, which includes
the intra-day market and real-time operation. Non-convexities
due to start-up costs of generators and their minimum power
output are modeled using Mixed-Integer Linear Programming(MILP). Clearing prices are obtained using a relaxed versionof the MILP problem.
It should be emphasised that the proposed model derives
day-ahead schedules and day-ahead prices with an informed
prognosis of the future. This results in a day-ahead outcome“adapted” to the uncertainty involved. On the contrary, a deterministic model generally results in either under-commitment,
which is risky, or over-commitment, which is expensive.
The results obtained using the proposed model are comparedto those from a two-stage model that does not incorporate aprognosis of the intra-day market. In other words, while a twostage model represents only the operation stage, a three-stage

18

120

load profile2
load profile3
load profile4

80

load profile5

40
0case 1case 2case 3case 4

Fig. 2. Expected costs for different load profiles and different limited cases

III. C ONCLUSION
The simulation outcomes show that the three-stage model
results in a lower expected cost, a smaller expected producer
profit, a smaller consumer payment, which results from theoverall lower day-ahead prices obtained from the three-stage
model, as shown in Fig. 1. Potential limits imposed on unit
adjustments at the intra-day stage, and the flexibility of units
for reserve provision do not alter this conclusion. As an
example, Fig. 2 shows the expected costs from both models
for different limited cases as well as different load profiles.
The better outcomes achieved using the three-stage model are
the result of more informed decisions at the day-ahead market.

1

Bidding Strategy for an Energy Storage in Joint
Energy and Reserve Markets
Ehsan Nasrolahpour, Student Member, IEEE, Hamidreza Zareipour, Senior Member, IEEE, andWilliam D. Rosehart, Senior Member, IEEE
6000
5000

Storage Facility's Profit ($)

Abstract—In order to provide multiple uses for merchant storage
facilities and bring them more value, this paper proposes anapproach to make strategic operation decisions for merchant pricemaker storage facilities in real-time and reserve markets as wellas energy market. Through this approach the decisions in energy
market are more accurate in terms of incorporating impacts ofreserve market and uncertainty in real-time market on storage
operation decisions. The merchant storage facility seeks to maximize
its profit, while the market operator aims at maximizing the social
welfare in the day-ahead market and minimizing the imbalancecost regarding the uncertainty in the real-time market. Different
case studies are presented and discussed using an illustrative test
system and the one area IEEE Reliability Test System to evaluate
the performance of the proposed approach.

4000
3000
2000
1000

Index Terms—Energy storage, bidding strategy, Mathematical
Program with Equilibrium Constraints (MPEC), equilibrium models

01Fig. 1.

I. I NTRODUCTION

T

HE interest in integrating large-scale energy storage facilities
into power system has grown considerably over the past few
years. However, storage facilities that only count on arbitrage
revenues are unlikely to support their investment in most market
locations [1]. Thus, in some other jurisdictions such as Midwest
Independent System Operator (MISO), as an encouraging policy
storage facilities are allowed to bid in real-time and reserve
markets as well as energy market to maximize their profit [2].
The existing literature on large-scale storage facility’s operation
mostly focuses on energy market [3].
The main purpose of this paper is to determine the bidding
and offering strategies of storage facility to buy and sell energy
in energy, reserve and real-time markets and analyze the impacts
strategic decisions of storage facilities in both markets on eachother.
II. M ETHODOLOGY
A three-level optimization problem is used to model the storage
facilities maximization profit as the upper level and the market
clearing process in the day-ahead and real-time markets as thefirst and second lower levels, respectively. The objective of
upper-level has three terms and maximize the merchant facility’s
profit through its strategic discharging and charging decisions inthree markets, i.e. energy, reserve, and real-time markets. Notethat energy market price (λEi,t ), upward and downward reserve
P DN
(λU
,λ
)
and
real-time
marketprice (λRTttt,k ) are endogenously
obtained from the lower level problem.
In the lower level problems, the ISO clears the market basedon submitted bids and offers such that the social welfare is
maximized. The primal-dual and Karush-Kuhn-Tucker (KKT)
conditions are applied to transform the bi-level problem into an
MPEC, that can be recast as a Mixed Integer Linear Programming

Real-time market
Reserve market
Energy market

2
Case number

3

Comparing the storage facility’s profit for three cases(MILP).
Maximize
Nt X
Ns 
X


ch,DA
dis,DA
ch
dis
− (λE
+ (λE
t + M Cs ).ps,tt − M Cs ).ps,t

t=1 s=1
 P ch,U P
dis,U P
ch,DN
dis,DN + λU+ rs,t) + λDN.(rs,t+ rs,t)t .(rs,tt
NkX
ch,U Pch,DNch+
Φk − (λRTt,k + M Cs ).(qs,t,k − qs,t,k )k=1

dis,U P
dis,DN dis+ (λRT
−
M
C
).(q
−
q
)
t,k
s
s,t,k
s,t,k

(1)

III. K EY R ESULTS
Three cases are considered including only energy market, jointenergy and reserve markets, and joint energy and reserve market
considering real-time market. The storage facility’s profit for threecases are illustrated in Fig. 1. The figure shows storage participating multiple markets in Case 3 makes most profit comparedto the first two cases.
IV. C ONCLUSION
This paper formulates the bidding strategy of a price-makerindependent storage facility in energy, reserve, and real-time markets as a bi-level problem. The primal-dual and KKT conditions
are applied to transform this problem into an MPEC, that can berecast as a MILP. The numerical results show that the storage
facility makes more profit by participating in multiple markets.
R EFERENCES[1] E. Drury, P. Denholm, and R. Sioshansi, “The value of compressed air energy
storage in energy and reserve markets,” Energy, vol. 36, no. 8, pp. 49594973, Apr. 2011.
[2] Midcontinent Independent System Operator (MISO), “MISO energy storage
study phase 1 report,” Technical Results, Nov. 2011.
[3] H. Mohsenian-Rad, “Coordinated price-maker operation of large energy storage units in nodal energy markets,” IEEE Transactions on Power Systems,
vol. 31, no. 1, pp. 786-797, Jan. 2016.

Economic Viability of Price Arbitrage Operation of
Gid Scale Batteries in Alberta’s Energy only market
A.I. Adebayo, H. Zareipour, A.M. Knight
University of Calgary, Canada. abiola.adebayo@ucalgary.ca, andy.knight@ucalgary.ca

Abstract— with the rising need for energy storage
facilities in power systems of various jurisdictions, it isimperative to investigate the profitability of operatingenergy storage facilities. This paper assesses the economic
viability of a transmission-connected Batteries (Lithium
Ion, Sodium Sulphur and Flow batteries). The systems
under consideration have power rating that is large
enough that the impacts of its operation on the pool pricein the Alberta electricity market should be considered.
Using 5 year historical price data, we run a self-schedulingmixed integer linear programming (MILP) optimization
model to estimate the maximum possible operational profit
operating in Alberta’s “Energy Only” market. The rate of
return on the investment considering 2011 capital costestimate and forecasted 2020 cost estimate are calculated,based on the simulated cash flow. The predicted ROI isshown to vary significantly depending on the windowchosen as “typical” cash flow.

market. Technical constraints such as round-trip efficiency
and storage efficiency are considered and varied to investigate
the impact that they may have on operational profitability.
Due to the significant power capacity of the battery, two
scenarios are considered: treating the BESS as a price taker;
and as a price maker. A 5 year (2010-2014) historical data ofpool price of electricity is obtained from the Alberta Electric
System Operator (AESO) database and is used in the casestudy the price taker assumption. For the price maker, at eachhour of 5 year period under consideration, the actual supply isadjusted to show the impact of charging (additional load) anddischarging (additional generation) on the electricity pool
price. Results obtained for the two scenarios are compared anddiscussed.
III PRELIMINARY RESULTS
The figures below show some of the simulation results in our
analysis.

Keywords—tariff; arbitrage; energy storage; electricty market

I.

INTRODUCTION

Current progress recorded in renewable energy integration into
the grid has stimulated efforts to introduce additional electricalenergy storage to grid systems, in order to buffer theintermittent nature of renewable sources of energy[1]. Windand solar energy have no constant level of availability; output
depends on climatic conditions, which can vary significantly.
This variation in renewable source leads to electrical powerfluctuation, which in turn may lead to voltage and frequency
fluctuation, thus impacting the stability of the grid. It istherefore very important to have a mechanism in place thatwould help stabilize the unstable nature of renewable energy.
Energy storage offers the potential for smooth integration ofrenewable energy into the grid of electric system in various
countries. Even though the global storage capacity is still quite
small, recent trends in energy storage technology show that thegradual reduction in the levelised cost of energy from storage
would eventually lead to more storage facilities coming on
board.
II. METHODOLOGY
In this paper, an optimization problem is formulated to give an
optimal self-schedule of a grid scale Battery Energy Storage
System (BESS) performing arbitrage in Alberta electricity

IV

References[1]

R. H. Byrne and a Silva-monroy, “Potential Revenue
from Electrical Energy Storage in the Electricity
Reliability Council of Texas ( ERCOT ),” 2014. In
Proc. IEEE PES General Meeting, National Harbour,
MD, July, 2014

Market Design with Renewable Capacity
Cynthia Bothwell and Benjamin Hobbs, Fellow IEEE
Department of Geography and Environmental EngineeringJohns Hopkins University, Baltimore, Maryland, USA
Abstract—As many nations move to cleaner energy sources, theimpact of intermittent wind and solar energy resources changes
the way generation investment is made and the way that electricity markets operate. We model how various wind and solar
capacity definitions affect capacity (or “resource adequacy”)
markets, and which market designs best support optimal investment decisions.
Index Terms— adequacy, capacity markets, investment, windI.
INTRODUCTION
As competitive markets become more prevalent for wholesale electricity transactions, there is increasing need to develop
appropriate mechanisms to incent new investment. Market
failures with regard to motivating investment in new generation or motivating investment in clean generation show thatalternative pricing mechanisms may be required to send thecorrect market messages for capacity development, where“correct” is defined as the least-cost mix of investment andoperations that meets load and environmental constraints.
In designing electricity markets and incentives to motivate
generation investment to ensure adequacy, clear objectives
should be defined and tradeoffs examined. A market simulation approach is used here to analyze these tradeoffs.
A particular issue addressed is the optimal amount of capacity credit to give to different categories of renewable generation, and the distortions (increased cost of serving load)that result from deviating from the optimum. We compare theoptimum to methods used in several jurisdictions in which
credit is based on average (rather than marginal) contribution
to system adequacy, or is based on average capacity factors
either over the year or during some crucial period.
II. MODEL
To investigate these issues, a model is built to analyze theinteractions between pricing and capacity mechanisms in two
distinct markets. Hourly load, wind and solar data have beencollected for the summer peaking ERCOT and winter peaking
Germany. Data for each are clustered in aggregated groups to
examine tradeoffs between renewable energy and capacity.
A linear programming model that minimizes total annual
costs (both fixed and variable) is used to simultaneously solvefor both the optimal mix of capacity and the hourly dispatch ofeach generation technology allowing for wind curtailments.
Included in the variable cost is an allowance for lost load witha defined value of lost load (VOLL). In addition to typicalconstraints that balance load and generation, limit dispatch to
the generator capacity, and enforce a reserve margin, conWork supported in part by NSF grants OISE 1243482 (WINDINSPIRE)
and ECCS 1230788

straints also represent Renewable Portfolio Standards, spinning reserves, and forced outages. The model is used to infer
the optimal capacity credit to give to various generation types.
The model is also modified to simulate market equilibria
for capacity markets in which pre-determined (and suboptimal) capacity credits are assigned to different types of capacity. These markets are simulated by adjusting the amount of
installed reserves required so that the same level of expected
unserved energy (EUE) is achieved as the optimal solution,and then noting the resulting increase in annual costs.
III. METHODOLOGYOur analysis involves both defining the optimal mix andthen testing the mix for revenue sufficiency under varying
capacity mechanisms including common industry capacity
value definitions. We also simulate the generation mixes thatarise when suboptimal definitions of capacity credits are used.
The model is applied to each of the two systems under
several scenarios representing combinations of reserve margin, VOLL, renewable requirements, and fuel prices. The
equilibrium mix for each market design and system consists of
installed capacity by technology type, hourly dispatch, windcurtailments and expected unserved energy.
After defining the optimal investment mix for the scenarios, expected revenues are calculated from market clearing
prices. Five alternative capacity mechanisms based on suboptimal capacity credits are analyzed to determine revenue sufficiency across technology types and between renewable facilities with different characteristics. The effects of those mechanisms on equilibrium capacity mixes are then simulated.
IV. RESULTS
Several factors can lead to distortions in capacity mechanisms including age of facility, subsidies, capacity rating versus actual performance, and interactions among facilities. We
describe the changes in resource mix resulting from inefficient
capacity contribution definitions and quantify the inefficiency
both in terms of resource costs and reliability deterioration.
Results show that the least distortion occurs when intermittent
renewable capacity is defined in terms of the marginal contribution to the system (the residual load differential); however,this contribution can be difficult to quantify in a forward planning process since contributions vary significantly from yearto-year. Further, marginal contribution declines with renewable development. Declining returns and facility interactions
imply that a systems perspective is needed for market design.

Revenue Analysis of Battery Aggregation for
Frequency Regulation in Wholesale Markets
Alberto Castelo-Becerra, Student Member, IEEE, and Mo-Yuen Chow, Fellow, IEEE
Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC 27695, USA
Email: acastel2, chow@ncsu.edu
Abstract— Recently, more households and commercial buildings
have been incorporating batteries behind-the-meter to reduce
their electricity bill. Some of the main applications include peakshaving when the battery is coupled with PV, and/or energy
arbitrage under Time-of-Use (TOU) tariffs. However, batteries
operating under these scenarios are underused. In this work, theauthors explore how battery aggregation could be used to accessthe wholesale electricity market, with CAISO data as an example,
providing an additional revenue source for owners. Case I
investigates the additional revenue of providing Frequency
Regulation (FR) when the battery was bought for PV peakshaving, and Net Metering does not apply. Finally, Case II
analyzes the additional revenue of providing FR when the battery
was bought for energy arbitrage under TOU tariffs.

I.

p(t): Price ($/ΔMW) for performance at hour t.
P(t): Aggregated Power (MW) at hour t.
RMM(t): Resource-specific Mileage Multiplier at time t.
A(t): Accuracy of the Aggregated Battery system at time t.
3) Net Energy Revenue:
𝑟𝑛𝑒𝑡𝑒 (𝑡) ≅ 0(3)

AGGREGATION AND REVENUE MODEL

A. Aggregation Model
Virtual Power
Optimal
Plant
Independent
SystemOperator

Battery 1

Dispatch
Battery 1

OptimalBid

Figure 2. ACE signal during one day in 2015.

Household 1
SoC,
Power
Rates

C. Case I: FR as additional application of PV peak shaving.
FR regulation from 00:00 am to 10:00 am.
TABLE II

Aggregator

REVENUE FOR FR UNDER CASE I
Household nAward

OptimalDispatch
Battery nBattery n

SoC,
Power
Rates

Regulation Regulation
Up
Down$5,211.40 $5,284.60

MileageUp$268.19

Mileage
Down$1,527.10

Total$12,291.29

Figure 1. Battery Aggregation Functional Blocks.

TABLE IAGGREGATED BATTERY SYSTEM PARAMETERS

D. Case II: FR as extra application of energy arbitrage
under TOU tariffs. From 6:00 pm to 6:00 am.

Individual
Individual Number of Aggregated AggregatedCapacity (kW) Storage (kWh) Batteries Capacity (kW) Storage (kWh)
2,00
6,40
250
500
1600

B. Revenue Model under CAISO regulation
1) Capacity Revenue:
𝑟𝑐𝑎𝑝 (𝑡) = 𝑝(𝑡) ∗ 𝑃(𝑡)p(t): Price ($/MW) for holding capacity at hour t.
P(t): Aggregated Power (MW) at hour t.
2) Performance Revenue:
𝑟𝑝𝑒𝑟𝑓 (𝑡) = 𝑝(𝑡) ∗ 𝑃(𝑡) ∗ 𝑅𝑀𝑀(𝑡) ∗ 𝐴(𝑡)Figure 3. Energy prices under TOU tariff.

(1)

TABLE IIIREVENUE FOR FR UNDER CASE II

Regulation Regulation Mileage
Up
Down
Up
$10,312.00 $5,352.40 $437.64(2)

MileageDown$1,621.70

Total$17,723.74

Cooperation among Energy Storages
Jesus E. Contreras OcañaMiguel A. Ortega-Vazquez

Baosen Zhang

Department of Electrical Engineering
University of Washington
Seattle, WA 98195
Email: jcontrer@uw.edu

Department of Electrical Engineering
University of Washington
Seattle, WA 98195
Email: maov@uw.edu

Department of Electrical Engineering
University of Washington
Seattle, WA 98195
Email: zhangbao@uw.edu

I. I NTRODUCTION

L

ARGE scale introduction energy storage to the grid has

the potential to increase the efficiency of the powersystem from several dimensions: by shifting load from low tohigh price hours, providing reserves, improving power quality,and even deferring capital investments [1]. Energy storageis of particular importance to the successful integration ofrenewable sources into the power system as it can be used
to mitigate their well-known stochasticity and intermittency.
Storage devices range from large pumped hydro plants tohousehold-level storage units. Although the amount of energy
storage in the grid is currently limited, it has recently undergone an unprecedented growth and is expected to continue
doing so as costs are driven down [2]. From a storage ownerperspective, these devices are capable of arbitraging energy intime, by buying energy at low prices and selling it back laterat higher prices.
This work studies the arbitrage problem in a market setting,where multiple distributed storage units compete in a priceanticipatory manner. Due to competition, the profit obtained in
the non-cooperative Nash equilibrium (NE) is lower than the
profit they would obtain if they had cooperated. The contributions of this work are two mechanisms, one distributed and one
centralized, that can be used to incentivize cooperation amonga group of storages. These mechanisms that do not require
any individual (potentially private) information to exchanged
among participants. They are: i) adding an artificial term to thecost function of each storage unit; ii) longterm cooperation via
an aggregating entity. For each of these mechanisms, we showthat self-interested storage units can obtain the profit obtainedby a coalition of cooperating units.

Problem statement: As the number of storages increases,
the aggregate profit given by the grand coalition (GC) solution
increases and approaches a finite positive number while theaggregate profit under the NE solution approaches zero [3].
Figure 1 shows the aggregate profit for both the GC and the
NE as a function of number of storages.
GC aggregate profit

Aggregate profit

Abstract—We use game theory to study competition andcooperation among a group of storage units. As the number ofenergy storages increases, the profit of storages approaches zerounder competition. We propose two ways in which storages canachieve the maximum possible profit. The first is a decentralized
approach in which storages incur artificial costs that act as incentives for them to behave as a coalition. No private informationneeds to be exchanged between the storages to calculate theartificial cost function. The second is a centralized approach inwhich an aggregator coordinates and splits profits with storages
in order to achieve maximum profit. We do not assume the natureof the storage-aggregator relationship and derive the necessary
conditions for longterm cooperation. We use Nash’s axiomaticbargaining problem to model and predict the profit split between
aggregator and storages.

14γ

NE aggregate profit

0.25
0.2
0.15
0.1
2

4

6

8

10

n

12

14

16

18

20

Figure 1. Aggregate profits for identical storages as a function of number of1storages(n) under the CG and NE solutions. The number 4γ
is the maximum
theoretical profit where γ is a market parameter [3].
II. C OOPERATION VIA A RTIFICIAL C OST F UNCTIONSIn this approach use of artificial cost functions (ACFs)
to drive the NE solution to the GC solution. Having a NEthat equals the GC solution is desirable because: 1) the GC
aggregate profit is larger than the NE aggregate profit, 2) it isstrategically stable, and 3) it is self-enforcing [4].
III. C OOPERATION VIA AGGREGATORIn this approach we explore the possibility of the storages
reaching the GC solution by cooperating with an aggregator.
In this setting, the storages do not have access to the wholesalemarket but instead buy/sell energy from/to an aggregator.
It is shown that the NE of the aggregator-storage game isinefficient. We derive the necessary profit splits that foster
longterm aggregator-storage cooperation to achieve the GC
solution [3]. Finally, we use Nash’s axiomatic approach tothe bargaining problem to predict that risk-neutral aggregatorand storage will equally split the available profits.
R EFERENCES[1] P. Denholm, E. Ela, B. Kirby, and M. Milliga, “The role of energy storage
with renewable electricity generation,” National Renewable National
Laboratory, Tech. Rep., 2010.
[2] “Battery storage for renewables: Market status and technology outlook,”
International Renewable Energy Agency, Tech. Rep., 2015.
[3] J. E. Contreras-Ocaña, M. A. Ortega-Vazquez, and B. Zhang. (2015)“Cooperation and competition among energy storages”. [Online].
Available: http://arxiv.org/abs/1511.02201[4] R. Gibbons, Game Theory for Applied Economists. Princeton University
Press, 1992.

1

Real-Time Implementation of Multi-Agent
Based Game Theory Reverse Auction Model for
Microgrid Market Operation
Mehmet H. Cintuglu, Harold Martin, Student Members, IEEE, Osama A. Mohammed, Fellow, IEEE

Abstract—This paper presents a real-time implementation of amulti-agent based game theory reverse auction model for microgrid market operations featuring conventional and renewabledistributed energy resources (DER). Studies were conducted on agrid connected microgrid consisting of a synchronous generator,
two inverter-based generation units and a combined load. The
multi-agent based platform was applied to monitor, control andperform the reverse auction process of DERs. A competitivegame-theory reverse auction model was investigated to schedule
the DER unit commitment with an hour-ahead market approachfor the twenty-four hours of the day. The proposed methodology
was realistically implemented and operated at Florida International University, Smart Grid Test system. The investigationshows that the proposed algorithm and the industrial hardware
based infrastructure can be implemented in the existing electricutility grid as new assets of the system are added.
Index Terms—Microgrid, game-theory, multi-agent systems,
phasor measurement units, reverse auction

I. INTRODUCTION
Microgrids are small scale electricity networks comprisedof diverse distributed generation units which can operate in
grid-connected or islanded mode. Microgrid economics arehighly dependent on local conditions such as availability offuel, gas, wind, and solar irradiation. In an open market condition, utilities are no longer monopolized hence DER stakeholders are the private entities that can compete with regional
utilities. The electricity trade can be handled locally in a region by several DER investors or cross-border trading by utility wholesale markets and neighboring regional systems.
A vast number of DERs should be able to operate incorporating a large number of complicated operational functions.
The new generation grids require fast and intelligent decision
making algorithms with an efficient communication infrastructure since power system operators are inefficient in dealing with highly active and constantly varying operations infuture grids. Multi-agent based decision making algorithms
incorporating real-time communicative devices offer a reliable
solution for unit commitment in competitive market conditions. Agent based marketing models have applications on avariety of scales from large bulk generation units to small
home based entities. Intelligent agents and other programs
make use of shared ontology to facilitate interoperation.

UTILITY

INVERTER

PMUs & GPS Clock
BUSES & LINES

GENERATORAGENT PLATFORM & PDC

PROGRAMMABLE
LOAD

Fig. 1. Smart grid test bed at Florida International University
ALGORITHM I
AGENT NEGOTIATIONS

Begin
Initialize: All available generators register to yellow page;
Loop
Aggregator agent: inform utility base price, power
demand for next hour;
Aggregator agent: request bids;
if (DER base price < utility base price) thenBidder agents: inform submit bids;
else
Bidder agent: drop not participating;end
until (demand => total bid submitted supply) do
Aggregator agent: inform new base price;
Aggregator agent: request new bids;
Bidder agents: inform submit bids;
end until
Aggregator agent: inform market clearing price;
if first qualified bidder covers all demand thenAggregator agent: agree send power reference toqualified bidder;else
Aggregator agent: agree send power references tofirst and the other qualified bidders;endDaily Battery Energy Allotment for a
Data Center Virtual Power Plant
Participating in Demand Response
Labi Bajracharya† , Santosh Chalise, Reinaldo Tonkoski and Timothy M. Hansen
Department of Electrical Engineering and Computer Science, South Dakota State University, Brookings, SD
Email:† Labi.Bajracharya@sdstate.edu
TX

Abstract—Data centers consume a significant amount of energy
from the grid, and the number of data centers are increasing at ahigh rate. As the amount of demand on the transmission systemincreases, network congestion reduces the economic efficiency of
the grid and begins to risk failure. Data centers have underutilized energy resources, such as backup generators and battery
storage, which can be used for demand response (DR) to benefit
both the electric power system and the data center. Therefore,
data center energy resources, including renewable energy, areaggregated and controlled using an energy management system(EMS) to operate as a virtual power plant (VPP). The data
center as a VPP participates in a day-ahead DR program torelieve network congestion and improve market efficiency. Data
centers mostly use lead-acid batteries for energy reserve in
Uninterruptible Power Supply (UPS) systems that rides throughthe power fluctuations and short term power outage. These
batteries are sized according to the power requirement of thedata center and the backup power duration required for reliable
operation of the data center. Most of the time, these batteries
remain on float charge, with seldom charging and discharging
cycles. Batteries have a limited float life, where at the end of thefloat life, the battery is assumed dead, and require replacement.
Therefore, the unused energy of the battery can be utilized byallocating a daily energy budget limit without affecting the overall
float life of the battery used in data center for the purpose of DR.
This is incorporated as a soft constraint in the EMS model, andthe extra use of battery energy over the daily budget limit willaccount for the wear cost of the battery. A case study is conductedin which the data center is connected to bus 8 of the modified
IEEE 30-bus test system to evaluate the potential economic
savings by participating in the DR program, coordinated by the
Independent System Operator (ISO). We show that the savingsof the data center operating as a VPP and participating in the
DR program far outweighs the expense due to operating its own
generators and battery, combined.

−PbatExtra ≤ 0(5)

PbatM axDischarge ≤ Pbat ≤ PbatM axCharge(6)

SOCmin ≤ SOC(t) ≤ SOCmax(7)

SOC(t = T ) ≥ 0.85

(8)Fig. 1. Datacenter VPP consists of server and computer air room conditioning(CRAC) units as data center load; on-site energy sources such as battery
storage for UPS, PV array, and backup generators; VPP EMS communicateswith the ISO for coordinating its load and energy resources to take part inDR.

Day-ahead
forecasted Solarpower

The objective function and constraints of the VPP EMS
day-ahead optimization model are given below.

Day-ahead data
center load

Day-ahead grid
set-point

Optimization funtion

Ng
T X
X
min
[ [Ci (PGi ) · xi + SU P + M C]

Unit commitment decision ofgenerators, dispatchable
generator power, battery power

t=1 i=1+Cgrid (t) · PdcN etload + CbatW ear · PbatExtra ]s.t. : Psc (t) − PG (t) − PP V (t) + Pbat ≤ Pdc,max (t)(1)

Psc (t) − PG (t) − PP V (t) + Pbat ≥ Pdc,min (t)(2)

xi · PGi,min ≤ PGi ≤ xi · PGi,max

(3)

(4)

II. KEY F IGURES

I. KEY EQUATIONSPGi ,xi

PbatDischarge − PbatExtra ≤ PdailyBudget

t=1

Real time dispatch

Fig. 2. VPP optimization module1

> REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) <1

Improving Grid Resilience by Cooperative
Electricity and Gas Planning
Chengcheng Shao1, Mohammad Shahidehpour2 and Xifan Wang1
1. Department of Electrical Engineering, Xi’an Jiaotong University
2. Robert Galvin Centre for Electricity Innovation at Illinois Institute of Technology
Equation Chapter 1 Section 1
Abstract—The power system is faced up with many extreme
events such as natural disasters and deliberate attacks. Comparedwith it, the underground gas system is less vulnerable. For thegrid resilience improvement, a cooperative electricity and gas
transportation planning problem is proposed to coordinate theexpansion of electric power and natural gas systems considering
the grid damage constraint. The problem is formulated as a
two-stage robust programming problem. The corresponding
algorithm is also developed. The potential of electricity and gas
co-planning in the resilience improvement is revealed andanalyzed via case studies. The validity of the proposed model andmethod have also been verified. The coordination of electric
power and gas system is an important and effective way to
improving the resilience of power system.
Index Terms—Electricity and gas co-planning, resilience,robust programming, power system planning

II. KEY EQUATIONSGiven the high loss and low probability quality of so-called
the grid resilience is evaluated by the maximal load curtailment
after the occurrence of certain events:
RM  max min  pdi ,b,t
(1)
i ,b ,t

The influence of extreme events is described by the number of
power lines forced into outage by them,Z  {z pl ,b,t |  z pl ,b,t  k}.
(2)
pl

The resilience-constrained expansion planning system isformulated as the following two-stage robust problem. The
expansion cost including operation cost and investment isminimized in the objective function while the maximal loadcurtailment is as the inner constraint.
min F 0 ( u,  , p 0 , g 0 )I. KEY IDEAS

s.t. A0 p 0 + B 0 g 0  C 0 u + D 0Compared with the overhead power lines exposed in the air,the underground gas system is much more invulnerable against
the natural disasters and deliberate attacks in areas free fromsevere seismic activities. The scheme shown in Fig.1 (b) is withgreat potential in resilience improvement, via the cooperationof electricity and gas transportation.

max min F ( p)  RM max(3)

Ap + Bg  Cu + Dzz  Z(u,  )III. KEY RESULTS(a) Integrated electricity and gas planning

Fig.2 The Sample System
TABLE I EXPANSION COST COMPARISON OF 4-BUS CASES (103$/YEAR)CASE(b) Resilience improvement via gas transmission
Fig.1 Planning of integrated electricity and gas system

I-1
I-2
(I-3)
I-4

Operation

19980
19980
19980

Electricity Investment
Generator

Lines

Gas pipes
Investment

Total Cost

14505.55
25384.72
7252.78

infeasible
3223.463223.463223.46——4029.32

37709.01
48588.1734485.55

A Weather Condition Incorporated Data-driven
Maintenance Scheme for Utility Poles
Jun Zhong1, Student Member, IEEE, Wenyuan Li1, Fellow, IEEE, Caisheng Wang2, Senior Member, IEEE, Feng Lin2, Fellow, IEEE
State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing,
China. 2Department of Electrical and Computer Engineering, Wayne State University, Detroit, USAzhongjuncqu@gmail.com, wenyuan.li@ieee.org, cwang@wayne.edu, flin@indigo.eng.wayne.edu

1

Abstract—This paper presents a data-driven maintenance scheme
for utility poles with weather data incorporated. An autoregressive
moving average (ARMA) model for wind forecast and a polemechanical strength probabilistic model are developed for
implementing the data-driven maintenance strategy for utility poles.
The reliability and economic benefits of adopting the maintenancescheme is analyzed in a case study. The results show that the
proposed method improves the pole’s reliability, resulting in an
improved reliability and economic performance of the overall
distribution system.

I.

III.
CB3

p

q

j 1

j 1

KEY FIGURE

D7

L10

2MW(250 Customers)

MainSource CB1

M2

M1

L11

57(B3)

Ld9(2)
1(S1)

D8

M10

54(A2)

59(C3)
Ld11(4)

Ld10(1)
1MW(50 Customers)
D1

4MW(300 Customers)
D2

M3

F1

F2

L1

L2

5(A)

CB2

X t   a j X t  j   b j t  j   t

M9L9

KEY EQUATIONSIn this study a data-driven maintenance scheme for utility
poles with weather data incorporated is presented. The predictionof wind speed, pole failure probability caused by wind and thereliability are calculated using (1-5)

M8

M4

D3

Ld3(2)
2MW(200
Customers)

F5
L518(A1)
Ld4(4)

21(B1)
Ld5(2)F8L835(C2)

33(B2)
Ld7(2)
2MW(200
Customers)

Ld8(2)
2MW(200
Customers)

D4

M6

F4
L4

D6

M7

L7

10(C)

8(B)
Ld2(2.50)
2.5MW(100Customers)

3.5MW(280 Customers)

F7

L3

Ld1(3)
3MW(250
Customers)
M5

D5

F3

F6
L6
23(C1)
Ld6(2)

1.5MW(120 Customers)

2MW(50 Customers)

Figure 1. The network of the distribution system.

(1)

IV.

KEY RESULTWind Speed (m/s)
35.00

V  t   t X t

(2)

25.00

V 2 D0  DP
M S   dFW h   C
hdh
0
0
1.6
2
hp

hp

P  P[( M R  Ms0 )  0]  



Ms0

Pfp  1  Prp  

1

Ms

0

2  p

e

(3)

20.00
15.00

(4)

f R ( r )dr

1  M p  p  
2   p 

30.00

10.00
5.00

2

0.00
1hour

(5)

dM p

2hour

3hour

4hour

average value

5hour

6hour

maximum value

7hour

8hour

9hour 10hour

minimum value

Figure 2. The prediction of the wind speed.

ARMA (3, 2) is used to predict wind speed:
X t  0.18 X t 1  1.0115 X t 2  1.8051X t 3  t  0.2958t 1  0.9492t 2

  N (0, 0.3718 )
2

(6)

SAIFI
(interruptions/customer/hr)

0.04

without preventative
maintenance0.035with guy wire installed

0.03

II.

KEY TABLE

0.025

TABLE I. THE RELIABILITY PARAMETERS OF OVERHEAD LINE
Failure rate
(times/yr/km)

0.02

Repairtime

Maintenancefrequency

Recovery
time

0.015

(hrs)

(times/yr)

(hrs)

0.005
0

Overhead line(L)

0.25

5.89

0.5

3.482

Overhead line(M)

0.139

6.99

1

3.482

0.01

Hours

1

2

3

4

5

6

7

8

9

10

Figure 3. SAIFI profiles under the scenarios with and without guy wires installed.

Multi-Source Information Fusion for Power Transformer
Condition Assessment
Yi Cui, Hui Ma, Tapan Saha
School of Information Technology and Electrical Engineering
The University of Queensland, Brisbane, QLD, 4072, Australia
Email: y.cui3@uq.edu.au, huima@itee.uq.edu.au, saha@itee.uq.edu.au
III.

Abstract—This paper presents a multi-source data andinformation fusion framework for power transformer
condition assessment. The proposed method adopts Bayesian
Network (BN), which can integrate every piece of data andinformation obtained from different transformer diagnostic
measurements. Within the Bayesian Network, Monte Carloand Bootstrap methods are employed to extract the mostinformative characteristics regarding transformer condition
from different diagnostic measurements. Reliability metrics
are computed to evaluate the effectiveness of combinations ofdifferent type diagnostic measurements and subsequently
facilitate determining optimal diagnostic strategies involved intransformer condition assessment. Theories, implementations,and results of the proposed method are presented using casestudies in this paper.

I.

KEY RESULTS

KEY EQUATIONSIn Bayesian Network, the joint probability distribution
can be calculated as (1).
P  X 1 , X 2 , X 3 , X 4   P  X 1  P  X 2  P  X 3  P  X 4 | X 1 , X 2 , X 3  (1)The elements number for a particular child node can becalculated as (2).
nCPT   sc  1  i p1 s pin(2)

In Noisy-OR model, the full conditional probabilities of
Y can be calculated as (3).
P Y | X 1 , X 2 ,, X m   1  1  P0   1  Pi  
1 i  m(3)

For Monte Carlo simulation, consider uniform
distribution, a vector containing ten random numbers
within range [0, 1] is converted to binary states by (4).
Pr esent 0.5  xi  1
f  xi    Absent 0  xi  0.5(4)

Fig. 2. Flow chart of optimal diagnosis strategies determination
TABLE I. DIAGNOSIS STRATEGIES UNDER INVESTIGATION
Strategies index
12
3
4

TABLE II.

Fault
symptoms

F2

F3

F4

F5

F6

0.4
0.4

F7

F8

F9

F10

Reliability Metric

F1

Prob
0.18
0.17
…
0.0001

0.45

(5)

j 1

Fault
diagnosis
layerMember 3
SSV1
SSV1
…
SSV2

0.39

0.35

k

RM DS   RM j  Probability j

KEY FIGURES

Measurement configuration
S1, S4, S5
S1, S4
S4, S5
S1, S5SYMPTOM STATE EVIDENCE SETS AND
PROBABILITY IN CONFIGURATION 1

Member 1
Member 2
SSV5
SSV5
SSES1
SSV5
SSV1
SSES2
…
…
…
SSV3
SSV3
SSES80*RM: Reliability Metric value

Then the reliability metric for each diagnosis strategy
can be calculated as (5).

II.

Strategies typeNon-optimized strategy
Optimized strategy
Optimized strategy
Optimized strategy

0.31

0.3
0.25
0.2
0.15

0.1
0.1
S1

S2

S3

S4

S5

S6

S7

S8

S9

Fault
evidence
layer

0.05
Operation
conditions

C1

C2

C3

Selected measurement
in diagnosis strategy

Fig. 1. Multiple source Bayesian network for power transformer conditionassessment

0

1

2
3
Diagnosis strategies index

4Fig. 3. Comparison of reliability metrics for different strategies

RM*
0.48
0.27
…
0.98

One-page extended summary submitted to IEEE PES GM 2016.

1

Applications and Modeling of the Hybrid Power
Flow Controller
Behnam Tamimi, Student Member, IEEE, and Claudio A. Cañizares, Fellow, IEEE

II. RESULTS
I. OVERVIEW
Several flexible ac transmission systems (FACTS)
devices have been proposed in the literature [1]. Amongthese controllers, the unified power flow controller (UPFC)
is a versatile device that can control various systemvariables independently; however, the capital intensive
nature of this controller is a major obstacle for the wide
application of this technology in power systems. Thus, acost effective FACTS controller, hybrid power flow
controller (HPFC), is introduced in [2], which yields
operating characteristics similar to those of the UPFC atlower costs. The core of the device is a variable shuntsusceptance, such as a capacitor bank, accompanied by two
converters and their transformers. Figure 1 shows the circuit
model for the HPFC in an equivalent two-area system. A
variable shunt susceptance with two series voltage sources
represent the main components of the device.

Loadability studies are performed on a two-areabenchmark test system and the voltage profile at the sinkbus is shown in Fig. 2, with and without the HPFC; observethat the HPFC improves the maximum loadability andvoltage profile. Similar studies are conducted on a full-size
model of the Ontario grid and the results for the base andcontingency cases are presented. The feasibility of the
HPFC installation and its impact on the system performanceis examined while the controller operating limits are handled
adequately and efficiently. The corresponding ratings of thecontroller are obtained based on an optimal power flow
scheme minimizing the costs and satisfying relevant
operating constraints. Overall, the outcome of this research
work will provide essential tools and models for evaluating
the impact of integrating an HPFC into practical powersystems.

Fig. 1. HPFC circuit model.

The power flow and optimal power flow models of theHybrid Power Flow Controller (HPFC) for steady-state andcontingency analyses are presented in this poster,
considering the device’s multiple control modes. A strategy
for control limit handling in power flow calculations is
proposed, while considering a discrete passive shunt
capacitor bank in the device. The proposed models aredemonstrated and tested through several studies on a twoarea benchmark test system and a real model of the Ontario
grid (6000+ buses). The analyses demonstrate theapplication of the model for planning and operation studies,
evaluating the performance and the effectiveness of thedevice based on realistic analyses and scenarios.

This work has been supported by NSERC and Hydro One Inc. through a
Collaborative Research and Development (CRD) grant.
B. Tamimi and C. A. Cañizares are with the Department of Electricaland Computer Engineering, University of Waterloo, Waterloo, ON, Canada(e-mail: ccanizares@uwaterloo.ca).

Fig. 2. Loadability study results for the two-area system.

III. REFERENCES
[1]

[2]

I. Axente, R. K. Varma, and W. Litzenberger, “Bibliography of
FACTS: IEEE working group report,” in Proc. IEEE PES General
Meeting, pp. 1–6, 2011.
J. Bebic, P. W. Lehn, and M. R. Iravani, “The hybrid power flow
controller - a new concept for flexible ac transmission,” in Proc. IEEEPES General Meeting, Montreal, Quebec, Canada, pp. 1–8, 2006.

Integrating TCSC to Enhance Transmission
Capability and Security: Feasibility Studies for
Korean Electric Power System
Hyun-Jae Lee, Se-Hyun Kim, Kyeon Hur

Jin-San Choi, Hyeon-Jin Oh

Department of Electrical andElectronic Engineering
Yonsei University
Seoul, South Korea

Korea Electric Power Corporation
Naju, South Korea

Abstract—This paper investigates operational benefits of integrating thyristor controlled series capacitor (TCSC) in Korean
electric power system. Critical challenges of balancing the electricity demand rise and deferred transmission construction due tothe public opposition and environmental impact as well as uniquetopological issues of the power grid, endanger reliability of thesystem. Installation of flexible ac transmission systems (FACTS)
is envisioned to be a low-environmetal impact technology that
effectively resolve these issues. In particular, this research exploits
flow control capability of TCSC and examines the benefits ofimproving transmission capability and security.

I. I NTRODUCTION
Korea electric power corporation (KEPCO) system owns
unique characteristic, as 40% of its total loads are concentrated
in capital region and most of the power generations are locatedin remote are. Large power plants are located far from thecapital region, and the eastern sea power generation area
(EPGA) is particularly planned for expansion from 7 GW to10 GW for 2018 [1]. EPGA depends heavily on two 765 kVtransmission lines (5.7 GW), and the dependency grows as
the generation area expands, which increases risk of losing
security for the loss of transmission lines. KEPCO is considering various solutions to increase both power transmission
capability and reliability of eastern sea power system, but thedeferred transmission system construction, because of serious
opposition by local residents around eastern sea area, obstructs
strengthening power system stability.
Introduction of series FACTS devices including TCSC can
be a viable option to enhance dynamic stability of KEPCO
system as TCSC can provide useful functionalities, such as
power oscillation damping, power flow control, and powertransmission capability enhancement. Numerous series FACTS
devices are designed to provide additional damping against
oscillation between generators and the series compensator,
such as sub-synchronous resonance (SSR) [2]. The series
compensation for KEPCO system is focused on enhancing dynamic stability by reducing generator acceleration by utilizing
additional power transfer capability.
This paper presents a process of the preliminary study toapply TCSC for KEPCO system to enhance dynamic stability

to withstand 765 kV double line trip contingency without
loss of synchronism, excessive voltage dip, and load shedding
with minimum compensation size. It is focused on selecting
location and size of the TCSC for KEPCO system.
II. S TUDY P ROCESS

Fig. 1. Study Process Diagram

The application TCSC is envisioned to help KEPCO systemwithstand critical 765 kV double trip contingency, and thelocation and the size of the series FACTS is decided inaccordance with objectives. First, location scenarios are set
up based on sensitivity analysis. The most effective location
scenario is decided by conducting dynamic stability study.
After location is selected, size of both series FACTS are
decided to minimize compensation and balance power flow
among transmission lines.
R EFERENCES[1] Sixth Basic Power Development Plan, KEPCO, 2013[2] Stig Nilsson, ”Performance evaluation and applications review of existing thyristor control series capacitor devices - TCSC,” CIGRE.,Paris,
Rep.554,WG B4.49, Oct. 2013.

Three-Phase Isolated Soft-Switching DC-DC Converterfor High Power Applications
Tao Li, Student Member, Leila Parsa, Senior Member, IEEE
Department of Electrical, Computer and Systems Engineering
Rensselaer Polytechnic InstituteTroy, NY 12180-3590, USA
E-mail: lit4@rpi.edu, parsa@ecse.rpi.edu
Abstract— A novel three-phase isolated soft-switching DC-DC
converter is proposed in this paper. The converter consists of athree-phase full bridge, a high frequency transformer and athree-phase active rectifier. The converter can achieve zerovoltage switching (ZVS) for all primary switches and zero current
switching (ZCS) for all secondary switches. Switch andtransformer parasitics are utilized to achieve soft switching. The
converter is controlled by secondary phase shift PWM.
Comparison with single-phase counterpart is presented.
Simulation in PLECS demonstrates the operation of proposed
converter.

of oft switching, output voltage ripple and closed-loop step
response are shown in Figure 2.

Index Terms-- DC-DC power converters, Zero current switching,Zero voltage switchingI.

KEY FIGURESThe proposed isolated three-phase DC/DC converter
topology is shown in Fig. 1. The converter consists of a threephase full bridge converter at the input side and a three-phase
active rectifier at output side. All primary side switches areturned off with zero voltage and all secondary switches areturned on with zero current. The phase shift between primaryswitches and secondary switches is used to control powertransfer. The output current is regulated that soft switching canbe achieved over entire load range.
Three-phase converter has following advantages comparedto single-phase converter: tripled harmonic frequencies of input
current and output voltage which leads to a reduction in filter
size, lower RMS current through switches and diodes, andreduction in transformer size [1]. Moreover, the required energy
during soft switching is provided by input/output filter rather
than resonance component as suggested in [2], thus simplifying
the design and manufacturing of transformer. Simulation results

Figure 1. Schematic view of proposed converter
This work was supported in part by the Engineering Research Center Programof the National Science Foundation and the Department of Energy under NSF
Award Number EEC-1041877 and the CURENT Industry Partnership
Program.

Figure 2. Simulation results
REFERENCES
[1]

[2]

E. Agostini and I. Barbi, “Three-Phase Three-Level PWM DC – DC
Converter,” IEEE Trans. Power Electron., vol. 26, no. 7, pp. 1847–1856,
2011.
T. Li and L. Parsa, “Medium frequency soft switching DC/DC converterfor HVDC transmission system,” in Industrial Electronics Society 40th
Annu. Conf. of the IEEE, Dallas, TX, 2014, pp. 1599–1605.

Sequence Domain Transfer Matrix Model of ThreePhase Voltage Source Converters
Shahil Shah and Leila Parsa
Department of Electrical, Computer and Systems Engineering
Rensselaer Polytechnic Institute, Troy, NY 12180-3590, USA
Telephone: (518) 545-9024; E-mail: shahs12@rpi.edu
AbstractInteractions between three-phase grid-connectedvoltage source converter (VSC) and ac network have been
analyzed using the VSC ac-side impedance models in the dqdomain, and more recently, in the sequence domain. Both dq andsequence domain approaches ignore the effects of a network at the
VSC dc-side terminals. Interactions between VSC and dc network
have been similarly analyzed by ignoring the effects of the ac
network. This paper presents a sequence domain transfer matrix
model of a grid-connected VSC, which accurately captures
coupling between the ac and dc sides of the VSC and also between
the positive and negative sequence components of the three-phase
VSC variables. The proposed model is used to analyze stability ofa VSC-based HVDC terminal in an offshore wind farm. It isshown that the analysis of resonance and stability problems, at
low frequencies on the dc-side and/or around the fundamental
frequency on the ac-side, has to consider the complete matrix
model of the VSC and the coupling terms cannot be neglected.
The transfer matrix model and stability analysis predictions arevalidated using simulations of an offshore wind farm with the
HVDC transmission to grid.
Index TermsStability, HVDC transmission, voltage source
converters, Resonance.

PCC

Zp(s)
Zn(s)

OnshoreZdcn(s) Zdc(s) Terminal

Zacn(s)Fig. 1. Offshore wind farm with VSC-HVDC transmission.

|Yss(s)|

0
- 10
- 20
- 30
- 40
- 50

0.1

1 Hz 10 Hz 100 Hz 1 kHz|Yps(s)|

0
- 10
- 20

I. INTRODUCTION- 30

Several researchers have used VSC ac-side dq-domain
impedance models to evaluate interaction between a VSC and ac
power system [2, 3]. However, the dq-domain approach requires
impedance measurement in a synchronous rotating frame [3, 4].
Ref. [5] developed VSC ac impedance models in the sequence
domain to enable the stability analysis using direct measurements.
However, it inaccurately claimed that the sequence domain
impedance models are uncoupled. Moreover, relationshipbetween the dq and sequence domain approaches is not clear.
Both approaches ignore the effects of the dc network on the interaction between the VSC and ac network. Interaction between a
VSC and dc network has been similarly analyzed by ignoring theeffects of the ac-side network [6-8].
This paper presents a sequence domain three-by-three transfer
matrix model of a grid-connected VSC that accurately captures
the VSC linear dynamics. The developed matrix model provides
linear relationship between the dc and three-phase ac terminal
voltages and currents. The transfer matrix can be interpreted as asmall-signal sequence domain three-port model of the VSC.
Stability analyses using the existing impedance models and the
proposed matrix model are compared for a VSC-HVDC terminal
in an offshore wind farm.

- 40
- 50

0.1

1 Hz 10 Hz 100 Hz 1 kHz

- 20
- 25
- 30
- 35
- 40
- 45
- 50

0.1

- 10
- 15
- 20
- 25
- 30
- 35
- 40

0.1

|Yps(s)|1 Hz 10 Hz 100 Hz 1 kHz|Ypp(s)|1 Hz 10 Hz 100 Hz 1 kHz

Fig. 2. Magnitude response of sequence domain transfer matrix elements. Solidlines: analytical model, dots: point-by-point simulation.

vdc (kV)

32.5
30.0

ia, ib, ic (kA)

27.5
2.0
0

2.0
2.5

2.6

2.7
t (s)

2.8

2.9

Fig. 3. Loss of stability when the onshore ac-network strength reduces after lossof a feeder at t = 2.5 s.

1Influence of point-on-wave of high frequency
distortion on luminosity variations with LED lamps
Gaurav Singh, Student Member, IEEE, E. O. Anders Larsson, Member, IEEE,
Sarah K. Rönnberg, Member, IEEE, Math H.J. Bollen, Fellow, IEEE
and E. Randolph Collins, Jr., Senior Member, IEEE

Abstract— Electronic circuits employing switched-mode power
supplies and power line communication cause voltage distortionin the range of 2 to 150 kHz. Thirty two commercially available
LED lamps were subjected to such distortion. It was found thatin most cases distortion in the range of 2 to 150 kHz causes thelight output to drop. This paper especially studies the dependenceof the light output of the lamp, on the point on the fundamental
frequency wave at which the high frequency distortion is added.
It was observed that this point-on-wave has a direct bearing
on both the average light output and the 100 Hz ripple in light
output. Through the use of the cross correlation operator, thisrelationship has been quantified and presented in this paper.

that high frequency distortion similar to PLC causes the light
output from LED lamps to change. It was discovered that theposition on the fundamental wave where the distortion signal
appears, has an impact on these light fluctuations. Using themathematical tool of cross correlation, this ‘point-on-wave’ ofhigh frequency distortion, was defined. The impact of pointon-wave of high frequency distortion on the DC component
and ripple component of light output has been shown in Fig.
1.

I. I NTRODUCTION
S new types of power electronic devices are being
developed the switching frequencies they use have increased. Newer power conditioning circuits employ switchedmode power supplies that cause distortion in the multiple
kilohertz range. A large number of measurements done oncommon household equipment over the last decade show thatmany commercially available devices inject emission in thefrequency range 2 kHz to 150 kHz and cause high frequency
distortion. LED lamps have become popular as a source of
illumination in the past few years. Their high efficiency andcomparatively cheap price are the reasons for this popularity.
This study tries to assess the impact that high frequency
distortion has on the light output of these lamps.

A

II. E XPERIMENTAL RESULTS

Fig. 1. Variation of the ripple component (top) and DC component (bottom)
of light output, as a function of the point-on-wave of high frequency distortion,
for an 11 W LED lamp. The red line indicates the average light output, withbackground distortion only.

In order to understand the effects of high frequency distortion (2 to 150 kHz) on LED lamps, various voltage andcurrent waveforms were captured at different locations in
the Swedish power grid. Out of these, two representative
signals were chosen and the low frequency components inthe voltage were removed using a high pass filter with a cut
off frequency of 500 Hz. These waveforms were reproduced
in a laboratory environment, amplified to match the limits forPower Line Communication (PLC) in this frequency range,and superimposed onto the grid voltage. The resulting voltage
signals were applied to commercially available LED lamps;
voltage, current and illumination were measured with hightime resolution.
One of the prominent sources of high frequency distortionis PLC. The PLC signal appears in the form of packets
of information at a higher frequency (9 kHz to 95 kHz),
superimposed onto the fundamental voltage wave. It was foundIII. C ONCLUSIONS
Based upon the observations presented in this paper, thefollowing conclusions can be drawn:• The presence of high frequency distortion in supply
voltage causes the DC component and ripple componentof light to change.
• The point-on-wave at which the high frequency distortion
appears on the fundamental frequency wave has an impact
on the way the light output from the LED lamp isaffected. For certain point-on-wave conditions, the impact
may be significantly less than some other conditions. Thisis important since the penetration of ‘smart’ devices that
communicate with other devices in the distribution system
will only cause high frequency distortion to increase.

General Control Strategy to Limit Peak Currents of Virtual
Synchronous Generator Under Voltage Sags
Tianyi Chen1, Laijun Chen1, Tianwen Zheng1, Xiping Chen2, Shengwei Mei1
1. State Key Lab of Power System, Department of Electrical Engineering, Tsinghua University, Beijing, China
2. Shaanxi Regional Electric Power Group Co. Ltd., Xi’an, China
Email: chwangch@sina.com

Index Terms—Virtual synchronous generator, general control
strategy, peak current, voltage sags

-50

LS , RSuob

LS , RS

Lg , Rgea

Power
Referenceeb Generator
ec

uoc
C0
Rd

ia , ib , iceaeb notchec filter

Pe

0.5

1

1.5
t(s)

2

2.5

P,Q(kW,kVar)

P,Q(kW,kVar)

0
-5

0.5

1

1.5
t(s)

2

2.5

P

Q*

VSG Core
Algorithmu*u2*mode 2: CRP

SVPWM

0.5

1

1.5
t(s)

2

2.5

3
P
Q

10
5
0
-5

0

0

15

5

3

0

0.5

(a) traditional control

1

1.5
t(s)

2

2.5

3

(b) proposed control

Figure 3. Current and power waveform under CAP mode
40

40

20
i(A)

20
0

-40

-40
0

0.5

1

1.5
t(s)

2

2.5

3
P
Q

P,Q(kW,kVar)

P,Q(kW,kVar)

5
0

0

0.5

1

1.5
t(s)

2

2.5

0

0.5

1

1.5
t(s)

2

2.5

10
5
0
-5

3

3
P
Q

15

10

-5

0
-20

-20

0

0.5

(a) traditional control

1

1.5
t(s)

2

2.5

3

(b) proposed control

Figure 4. Current and power waveform under CRP mode
40
40

20
20
i(A)

0

0

-20
-20

-40

0

0.5

1

1.5
t(s)

2

2.5

3

P,Q(kW,kVar)

5
0

0

0.5

1

1.5
t(s)

2

2.5

0

0.5

1

1.5
t(s)

2

2.5

3

3
P
Q

15

10

-5

-40

P
Q

15

(a) traditional control

mode 1: CAP

-40

3
P
Q

10

Qe

*

10
5
0
-5

0

0.5

1

1.5
t(s)

2

2.5

3

(b) proposed control

s1  s6

Figure 5. Current and power waveform under BC mode

mode 3: BCie ia notch i be  ic filter i sequence extractor

Mode Selector

e

i

power
calculatorPe
Qe

Left: Figure 1. Topology of VSG grid-connected system
Right: Figure 2. Control Scheme of VSG under voltage sags

III.

0

15

e e i  i 
e e

0
-20

i(A)

INTRODUCTION

The VSG topology is shown in Fig.1 and the control
scheme of VSG under voltage sags is shown in Fig.2..
uoa

i(A)

i(A)

20

15

With the increasing penetration of distributed generations,the power grid will gradually develop into an inverterdominant network. To overcome the difficulties of traditional
inverter with little inertia or damping, control strategies basedon Virtual Synchronous Generator (VSG) technology areproposed to improve the power system stability. This paper
proposes a general control strategy with simple expression tolimit peak current of VSG while providing voltage support at
the same time.
II. PROBLEM FORMULATION

U dc

40

0

P,Q(kW,kVar)

I.

50

i(A)

Abstract—Virtual Synchronous Generator (VSG) can facilitate
friendly integration of distributed generations. However, during
voltage sags, traditional VSG control cannot provide reactive
power support, or limit the peak current. To solve these
problems, a general control strategy of VSG under voltage sags
is proposed. First, the topology of VSG and its control scheme
are described to formulate the key problem. Then, three typicalcontrol modes are analyzed individually and the methods ofgenerating power references are designed correspondingly. For
practical application, the calculation of power reference ismodified into a simple and unified form that is applicable to thethree modes. Simulation results are reported to verify thevalidity and effectiveness of the proposed control strategy.

POWER REFERENCE GENERATOR

Constant Active Power (CAP), Constant Reactive Power(CRP) and Balanced Current (BC) are discussed and ageneralized control strategy including the power reference
calculation in the three scenarios can be designed.
IV. SIMULATION RESULTS
The proposed control strategy is verified on the
Matlab/Simulink platform.
This work was supported by Foundation for Innovative ResearchGroups of the National Natural Science Foundation of China (51321005),
Tsinghua University Initiative Scientific Research Program (20151080416)
and Program of National Natural Science Foundation of China (E070602).

V.

CONCLUSIONSIn this paper, a general control strategy to limit peakcurrent of VSG is presented. The proposed control strategy
can effectively limit the peak current to the maximum allowed
value and provide voltage support to the power grid. The
strategy can be applied to three typical control modes that are
widely adopted during grid faults. Besides, the calculation canbe reduced into a simple and compact form which is fast toprocess and easy to realize in practice. The simulation results
show promising effectiveness, indicating that this approach
can be a good alternative in LVRT control of VSG.

New Active Start-up Scheme for MMC HVDC System toInterconnect Wind FarmJae-Hyuk Kim
Dept. of Electrical Engineering
Myongji University
Yongin, Korea
pwm0125@naver.com

Byung-Moon Han
Dept. of Electrical Engineering
Myongji University
Yongin, Korea
erichan@mju.ac.kr

Abstract - This paper proposes a new active start-up scheme for the
MMC HVDC system, which slowly charges the SM capacitors from
Vdc/2N up to Vdc/N. The operation of proposed start-up scheme wasverified through computer simulations for a typical back-to-back
MMC HVDC system.
Index Terms – Modular multilevel converter (MMC), High-voltage
DC (HVDC) system, Sub-module (SM), Active start-up scheme

I. INTRODUCTION
Active start-up schemes for the MMC HVDC system have beendeveloped to remove auxiliary circuits required in the passive start-up
scheme. One scheme applies an open duty for raising the SM capacitor
voltage. Other scheme applies an average capacitor voltage controller
which includes a circulating current controller inside. This paper
proposes a new active start-up scheme using an average capacitor
voltage controller and a feed-forward control loop.

paper both controls were applied to improve the performanceof charging the SM capacitors of the wind farm MMC.
III. OPERATOION VERICFICATION
The proposed start-up scheme was verified by computer simulations
with PSCAD/EMTDC. Each MMC has 12 SMs per arm, 10 SMs fornormal operation and 2 SMs for redundancy operation. The power
rating is 20MW. The DC link voltage is 24kV and the SM capacitor
voltage is 2.4kV. It is assumed that the grid side MMC charges the SM
capacitors until 2.5s and the DC link voltage increases up to 24kV andmaintains by voltage control. The wind farm side MMC starts tocharge the SM capacitors at 4s.
Fig. 2 shows simulation results to verify the proposed start-up
scheme. Fig. 2(b) shows the variation of DC link voltage during startup process. Fig. 2(a) shows the phase current in the wind farm side
MMC. Fig 2(c), (d) shows the variation of SM capacitor voltage of grid
& wind farm side MMC.

II. MMC HVDC SYSTEM

0.50

Phase Current [kA]

Fig. 1 shows a system configuration of MMC HVDC system tointerconnect the wind farm including a proposed active start-up scheme.
The SM capacitors of the grid side MMC are slowly charged up to thenominal voltage of Vdc/N and the DC link voltage reaches Vdc.
However, the SM capacitors of the wind farm side MMC are charged
to Vdc/2N. The voltage difference between the DC link voltage
and the leg voltage brings about high inrush current. Therefore,
the SM capacitors of the wind farm MMC have to be properly
charged from Vdc/2N up to Vdc/N using a specific scheme.
MMC

(a)

(b)

(c)

(d)

MMC
Iabc1

Wind Farm Side
PSC PWM

Vdc/N
Vdc/2Nvc* Voltage

ic* Current

Cotroller

Cotroller∑vcref /2N
Vdc/2N
dc
N

N/D

vc

* D

D
d'c

2

1

Grid Side
Vabc1*

1

Vdq1*

Q 1*

SM Cap Voltage of Grid Side MMC [kV]SM Cap Voltage of Wind Farm Side MMC [kV]dq/abc Idq1

Vd D
N N/D

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

Fig. 2 Operation Analysis Results

Vabc1

PSC PWM

M
2

DC link Voltage [kV]

0.0

Vdc

Wind Farm

-0.50
30.0
25.0
20.0
15.0
10.0
5.0
0.0
3.00
2.50
2.00
1.50
1.00
0.50
0.00
3.00
2.50
2.00
1.50
1.00
0.50
0.00

IV. CONCLUSION
abc/dq

θ1

PLL

Vabc1

*
*
Current Id1 Voltage Vdc

Cotroller

Cotroller

Iq1*

Vdc

Fig. 1 Circuit diagram of VSC HVDC system

The proposed start-up scheme consists of two control
blocks that are composed of average capacitor voltage control
and capacitor voltage feed-forward control. According tocontrol analysis, if only the average capacitor voltage control
was applied, the charging voltage cannot follow the reference
ramp signal. Also, similar result was obtained if only thecapacitor voltage feed-forward control was applied. So, in thisThis paper proposes a new start-up scheme of the MMC HVDC
system to interconnect the wind farm to the utility grid. The proposed
start-up scheme was verified through computer simulations with
PSCAD/EMTDC. The proposed start-up scheme can offer a stable
operation of the MMC HVDC without any auxiliary circuit for precharging.
REFERENCES
[1]

[2]

[3]

Xiaojie Shi, Zhiqiang Wang and Leon M. Tolbert, “Modeling, Control
Design, and Analysis of a Startup Scheme for Modular Multilevel
Converters,” IEEE Trans. Ind. Electron., vol. 62, no. 11, Nov. 2015.
M. Hagiwara and H. Akagi, “Control and Experiment of PulsewidthModulated Modular Multilevel Converters,” IEEE Trans. PowerElectron., vol. 24, no. 7, pp. 1737–1746, Jul. 2009.
K. Shi et al., “A Novel Start-up Scheme for Modular Multilevel
Converter,” IEEE ECCE 2012, pp. 4180-4187, Sept. 2012.

Optimization of Proportional-Resonant Controller
Parameters in Active Filter
Jeehoon Lee, Member, IEEE and Kyeon Hur, Senior Member, IEEE
School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea
Email: jeehun87@gmail.com, khur@yonsei.ac.kr

Abstract—This paper presents an automated design procedure
to optimize a proportional-resonant current controller for the
harmonic compensation in an active power filter. As the powerelectronics becomes more advanced, active filters are becoming
increasingly important and powerful tools for the compensation ofcurrent harmonics. A proportional-resonant controller provides
gains at a certain frequency and eliminates steady state errors.
Therefore, the proportional-resonant controller can be
successfully applied to control system in active filter. However, due
to the complexity of controller structure, it is difficult to obtainsuitable controller parameters. This paper proposes an approachto optimize the controller gains to effectively compensate current
harmonics. The proposed optimization procedure of controller
parameters is based on a genetic algorithm which is suitable evenfor complicated problems or with very high dimensions. To
evaluate our approach, simulation will be carried out by using
PSCAD EMTDC.

II. KEY FIGURE

Optimize controller
parameters
Genetic algorithm

Ish,ref

+

-

HPR

Delay

Plant

HPF

PR controller

Fig. 1. Block diagram of the main current control with optimization procedure

Index Terms—active filter, harmonics, proportional-resonant
control, optimization, genetic algorithm,

Initialization
I. KEY EQUATION
The transfer function of PR controller for active filter takes theform
𝐻PR (𝑠) = 𝐾𝑃 + 𝐾𝐼

𝜔PR ∙ 𝑠2
𝑠 + 2𝜔PR ∙ 𝑠 + 𝜔1 2.

(1)

To compensate various harmonics simultaneously, several PR
controllers are added in a cascade as:𝜔PR ∙ 𝑠𝐻PR (𝑠) = 𝐾𝑃 + ∑ 𝐾𝐼ℎ 2. (2)𝑠 + 2𝜔PR ∙ 𝑠 + (ℎ ∙ 𝜔1 )2

Initial population(Randomly)
Fitness Function
Evaluation

Fittest solution or
Termination?

YESℎ=5,7…

OutputA fitness function of genetic algorithm routine is defined as theintegral of error between reference and measured current. It
takes the form𝑡𝑏

FITNESS = ∫ | 𝐼𝑠ℎ,𝑟𝑒𝑓 (𝑡) − 𝐼𝑠ℎ (𝑡)| 𝑑𝑡 .
𝑡𝑎

(3)

Ish

END
Fig. 2. The flowchart of genetic algorithm

Reproduction
Crossover
MutationNO

New Configuration of Hybrid DC Circuit Breaker for
Voltage Source Converter HVDC System
Yoon-Seok Lee
Dept. of Electrical Engineering
Myongji University
Yongin, Korea
lys1909@hanmail.net

Byung-Moon Han
Dept. of Electrical Engineering
Myongji University
Yongin, Korea
erichan@mju.ac.kr

Abstract—This paper proposes a new configuration of hybrid
DC circuit breaker for the voltage-source converter HVDC
system. The operation of proposed hybrid DC circuit breaker
was verified through computer simulations for the HVDC systemwith 200kV/400MW.

the fault current rises up to 10kA, the current sensor detects it
and turns on T1. When the current through T1 reaches the fault
current of 10kA, CB is opened at this instance. Fig. 2 shows
simulation results to verify the operation of the proposed
hybrid DC circuit breaker. Fig. 2(a) shows the voltage across
CB which is zero in normal operation and 200kV after fault iscleared. Fig. 2(b) shows the current through CB before andafter the fault. Fig 2(c) shows an expanded CB current thatreaches 10kA at 0.3028s and becomes equal to the fault current
at 0.3035s. Fig. 2(d) shows an expanded CB voltage that rises
up to the open circuit voltage at 0.3038s. Therefore, the fault
clearing time of the proposed hybrid DC circuit breaker isabout 3.8ms that is shorter than the nominal value of 4.0ms.

Keywords—Hybrid circuit breaker (HCB), Thyristor auxiliary
circuit, LC resonant circuit, Voltage Source Converter (VSC)

I. INTRODUCTION
A hybrid DC circuit breaker with mechanical breaker andparallel-connected IGBT (insulated gate bipolar transistor)
switch is under development, which still brings about costeffectiveness issue [1]. In order to solve the cost effectivenessissue, a hybrid circuit breaker with mechanical breaker andthyristor auxiliary circuit was proposed in [2,3]. This paper
proposes a hybrid circuit breaker so as to provide enoughbreaking power, breaking speed and conduction loss.

V_CB

225k
200k
175k
150k
125k
100k

12k

I_CB

10k
8k
6k
4k

75k
50k
25k
0
-25k
0.00

2k
0
0.10

0.20

0.30

0.40

0.50

-2k
0.00

(a)

II. PROPOSED CIRCUIT BREAKER

120k

Fig. 1 shows a circuit diagram of HVDC system with theproposed hybrid circuit breaker. Normal transmission current
flows from the sending-end DC source to the receiving-end DC
source through the sending-end hybrid circuit breaker, thetransmission line, and the receiving-end hybrid circuit breaker.
When a fault occurs in the transmission line, each hybrid
circuit breaker removes the fault current at each
D2

D1

60k
40k
20k
0
-20k
0.2990 0.3000 0.3010 0.3020 0.3030 0.3040 0.3050 0.3060 0.3070 0.3080

T2
L

ZnO2

C
T2
L

Sending-end HCB

V_CB
225k
200k
175k
150k
125k
100k
75k
50k
25k
0
-25k
0.2990 0.3000 0.3010 0.3020 0.3030 0.3040 0.3050 0.3060 0.3070 0.3080

(c)

(d)

IV. CONCLUSION
Vdr

ZnO2

0.50

Fig. 2 Operation Analysis Results: (a) CB voltage, (b) CB current,(c) Expanded T1 current, (d) Expanded CB voltage

T1

ZnO1

ZnO1
C

0.40

I_T1

80k

CB
D1

T1

0.30

Rr

CB

Vds

0.20

(b)

100k

D2
Transmission Line

Rs

0.10

Receiving-end HCB

Fig. 1 Circuit diagram of VSC HVDC system

This paper proposes a new circuit configuration of thehybrid DC circuit breaker for the VSC HVDC system. The
operation of proposed hybrid DC circuit breaker was verified
through computer simulations with PSCAD/EMTDC. The
proposed hybrid DC circuit breaker could provide much lower
conduction losses in normal operation and fast breaking
performance of less than 4ms.
REFERENCES

III. OPERATION VERIFICATION
The transmission line with 200km length is divided into
four sections for analyzing typical faults. A fault switch islocated at each section of transmission line to the ground. The
circuit parameters for the VSC HVDC system was assumed
that PDC=200MW, Vds=200kV, Rs=8Ω, Rline=10Ω, Lline=10mH.
Simulation scenario is as follows. T1 is turned on at 0.1s andT2 is turned on at 0.2s. A fault switch is closed at 0.3s. When

[1]

[2]

[3]

C. Meyer, M. Kowal, and R. De Donker, “Circuit Breaker Concept for
Future High-Power DC Applications,” IEEE IAS 2005 Annual Meeting,Proc. vol. 2, pp. 860-866, Oct. 2005.
K. Sano and M. Takasaki, “A Surge-less Solid-State DC Circuit Breaker
for Voltage Source Converter Based HVDC Transmission System,”IEEE ECCE 2012, Proc., pp. 4426-4431, Oct. 2012.
J. Haefner and B. Jacobson, “Proactive Hybrid HVDC Breakers-A key
innovation for reliable HVDC grids,” CIGRE Bologna Symposium, no.
264, pp. 1–8, Sept. 2011.

Design of a Hybrid MOV-TVS Diode Surge
Protection Device
Dawood Talebi Khanmiri, Brad Lehman

Roy Ball

Department of Electrical and Computer Engineering
Northeastern University
Boston, MA, USA
talebikhanmiri.d@husky.neu.edu

MERSEN USA Newburyport-MA, LLC
Newburyport, MA, USA

Abstract— Hybrid Surge Protection Devices (SPDs) use two
or more technologies in a coordinated way to maximize benefits
and overcome shortcomings of the individual technologies. Metal
Oxide Varistor (MOV) and transient voltage suppression (TVS)
diode are two well-known and widely used technologies. MOVTVS diode hybrid SPD is one of promising hybrid SPDs that uses
a coordinated MOV and TVS diode in order to extend thelifetime and provide a better protection, lower clamping voltage
and higher current handling capability. In this paper advantages
and limitations of both technologies are discussed and thecoordination methods of the MOV and TVS diode areinvestigated. Use of resistor, inductance and positive temperature
coefficient components as the coordinating element is studied
through simulations and experimental tests. Advantages anddisadvantages of each method explained.
Keywords— Metal Oxide Varistor (MOV); transient voltage
suppression (TVS) diode; Hybrid Surge Protection Device (SPD);reliability; Surge Protection.

I. INTRODUCTION (HEADING 1)
The continued trend of miniaturization in electronics andlower energy capacities of components have made electronic
circuits and devices more susceptible to overvoltage transientsand surges. Metal Oxide Varistor (MOV) is a sintered ceramic
disk with a highly non-linear voltage current characteristic that
protects sensitive loads from surges and transient overvoltages(TOVs). Since the surge energy is distributed over whole thevaristor block, MOVs have high current handling capability.
However, they are known to degrade over time, when theyexperience high surge currents and long overvoltages.
Transient Voltage Suppression (TVS) diode is an avalanche
junction semiconductor SPD that works similar to two back-toback Zener diodes. TVS diodes provide protection with low
clamping voltage. Their performance does not deteriorate bysurges within their specifications over time. However, typicallythey have smaller current handling and energy absorption
capabilities. Coordinated MOV-TVS diode SPD can provide
better surge protection with high current handling capability
and extended lifetime. In this SPD, abundant small surges are
directed through non-degrading TVS diode, saving the energy
handling capability of the MOV for large surges. A practicalmethod to calculate the coordinating resistance and inductanceis described in this paper. A hybrid MOV-TVS diode SPD isshown in Fig. 1. Figures 2-4 show principles and simulation
results of current sharing between MOV and TVS diode.
The author gratefully acknowledges the support through grant byMersen USA.

II. KEY FIGURES

Figure 1. Hybrid MOV-TVS diode SPD

TVS+R
MOV

TVS
Figure 2. V-I characteristics of MOV, TVS diode, and TVS+R

Surge

MOV

TVS
Figure 3. PSpice simulation of hybrid SPD

Figure 4. Current sharing between MOV and TVS didoe

Analysis of Neural Network Vector Control for
IPM Machine in Electric Vehicles
Hoyun Won and Shuhui Li
Department of Electrical & Computer Engineering
The University of Alabama
Tuscaloosa, AL 35487, USA
Emails: hwon@crimson.ua.edu, sli@eng.ua.edu
Abstract — This paper investigates a neural network vector
control for an interior permanent magnet (IPM) machine in anelectric vehicle (EV). At the present, most electric vehicles areembedded with IPM machines because of the high torquecapability and power density of the machine. Many literatures
conducted research on how to control the machine most
optimally. This paper proposes a neural network vector control
method that implements the optimal control based on adaptive
dynamic programming. As for the training, LevenbergMarquardt and forward accumulation through time algorithm
is used. The paper also develops an EV simulation system byutilizing off-the-shelf EV mechanical and electrical components
and MATLAB SimDriveline and SimPowerSystems in order toevaluate the performance of the neural network controller inclose to practical EV operating conditions. The design of the EV
simulation system considers various electrical subsystems,
vehicle dynamics, shaft and differential, and simple regenerativeand hydraulic braking. The proposed neural network control iscompared with traditional PI-based control. The result showsthat the proposed controller can be a potential replacement of
the existing control scheme, such as PID, fuzzy logic, or etc, andprovides adequate traction control in EV application.

II. KEY FIGURES

Fig. 1. Overview of NN control scheme

Rear ↔ Front
A123 Battery

Index Terms – Electric vehicle, neural network vector control,interior mounted PM machine, Levenberg-Marquardt, Forward
accumulation through time, EV modeling and simulation

Hyd+Reg
Brake

Parker
GVM210‐150P

Differential
FDR: 8.2

HydraulicBrake
ABC Current

I. INTRODUCTION
Currently, both HEVs and EVs utilize an electric motor
called an interior mounted permanent magnet (IPM) machine,
due to high efficiency, compactness, ease of control andcooling, low maintenance, high power density, and hightorque capability. This paper proposes artificial neuralnetworks (ANN) based vector control for the IPM machine.
In EV application, recent ANN researches are focusing
mostly on battery management or estimation system andsupervisory control system. Only few researches wereconducted for the PM machine traction purposes, but nopaper was published regarding using an ANN vector
controller for the IPM machine. Further, the proposed control
will be integrated with full EV simulation model using
MATLAB tools to demonstrate its effectiveness.

Supervisory
Controller

Control Rinehart Inverter
Motor
PM100DX
Control
Hyd: Hydraulic
Unit
Reg: Regenerative
HV: High Voltage
T*: Torque Demand
FDR: Final Drive RatioT*

Hyd+Reg
Brake

Fig. 2. Overview of EV schematic with the name of manufacturer

80
Ref erence
70

ANN
PI

60
Speed (mph)The unique features and contributions of this paper
include: 1) an ANN-based vector controller, 2) a mechanism
to train the ANN controller by using a Levenberg-Marquardt+ forward accumulation through time algorithm, and 3) a fullEV simulation model with ANN control using MATLAB
SimDriveline and SimPowerSystems.

Hyd+RegBrake

HV‐ HV+

50
40
30
20
10
0
0

This work was supported in part by the U.S. National Science Foundation
under Grants 1414379.

50

100

Time (s)150

Fig. 3. Speed traction of EV

200

250

Secondary Circuit Model Generation Using Limited
PV Measurements and Parameter Estimation
Jouni Peppanen, Santiago Grijalva

Matthew J. Reno, Robert J. Broderick

School of Electrical and Computer Engineering
Georgia Institute of Technology
Atlanta, GA, USA

Sandia National Laboratories
Albuquerque, NM, USA

Abstract— This poster and the related paper present anapproach for generating simplified secondary circuit models withlimited SCADA and PV micro-inverter measurement data. The
proposed method is computationally efficient and can be utilizedwith typically available measurement data. The method is applied
to models of three real U.S. utility feeders with PV micro-inverter
measurements. The proposed simplified secondary circuit
modeling approach decreases the PV voltage simulation errors inall the three feeders compared to using generic secondary circuit
models. This paper also presents approaches for improving thefeeder voltage regulating device model set points by utilizing thePV voltage measurements.

parameters are estimated with an average (maximum) errors lessthan 0.35% (3.57%). Compared to the base case of utilizing
generic secondary circuit models, using the simplified
secondary circuit models with estimated parameters reduced theabsolute average PV voltage simulation errors in the three feeder
models on average by 0.57 Volts (19.3% reduction), 1.64 Volts(71.5% reduction), and 0.40 Volts (22.5% reduction) as shownin Figure 3.

I. SECONDARY CIRCUIT PARAMETER ESTIMATIONThis paper assumes that no (or very limited) AMI
measurements but historical PV system measurements areavailable in the secondary circuit. Given this data, the simplified
secondary circuit parameter estimation algorithm (SDSPE)
creates simplified secondary circuit models shown in Figure 1
and estimates their parameters. The objective is to improve thePV (or other sensor) voltage simulation accuracy.
Service Service Lines
Transformer
R1,X1

Transformer
MV BusReferred to LV V0

R0,X0

V12

R2,X2
...

RN,XN

Figure 2. Feeder DC2 capacitor state impact on the PV voltages
QL1

Load with PV
P1,Q1, PPV,QPVDC1The Rest of theSecondary System:
Load(s) but no PV
PO,QO

DC2

VPV

Figure 1. Simplified secondary circuit model with a PV system: available
measurements are in blue, values that can be roughly estimated are in green,
and unknown values and parameters are in red

II. UTILITY FEEDER MODELINGThe SDSPE algorithm was applied to models of three real
California utility feeders (details shown in the paper). Since thefeeder historical LTC set points and capacitor states were notavailable, they had to be estimated from substation SCADA
measurements and PV micro-inverter voltage measurementswith the approaches shown in the paper. For example, the PV
voltage measurements and the simulated capacitor switching
impact on the voltages (Figure 2) indicated that no capacitor
state changes occurred over the measurement time period.
III. PARAMETER ESTIMATION RESULTS
If accurate load power and service transformer medium
voltages are available, the simplified secondary circuit

Figure 3. Feeder QL1 (top), DC1 (middle), and DC2 (bottom) absolute
average voltage error for each PV system simulated with the original (in blue)and estimated (in yellow) parameters

The simplified secondary circuit parameter estimation withlimited data had two major challenges. First, it turned out to bechallenging to accurately model the medium-voltage circuit. In
particular, it was very challenging to accurately identify
historical feeder voltage regulating device operation. Second
and the leading problem was modeling loads with substationload allocation. Load allocation results in smooth load profiles
that may be highly uncorrelated with true individual loads at thesecondary circuit level. AMI data seems to be mandatory to
perform accurate distribution system secondary circuit
parameter estimation.

Online Tracking of Transmission Line
Parameters Using SCADA Data
Yang Wang, Student Member, IEEE, and Wilsun Xu, Fellow, IEEE
Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada
Email: yang5@ualberta.ca, wxu@ualberta.ca

Abstract—Series impedance and shunt admittance parametersof transmission lines are fundamental data for various online
and offline power system studies. This poster shows that theactive power, reactive power and voltage magnitude (P, Q, V)
data measured at the two ends of a transmission line aresufficient to determine the positive sequence line parameters.
The phase angle information is not essential. Since the P, Q
and V data are readily available from the supervisory control
and data acquisition (SCADA) systems, the proposed method
can be easily implemented in existing control centers. Field
test results show that the proposed technique can be a usefuladdition to the energy management systems (EMS) of utility
companies.

II.

sending end
receiving end

winter
MW

-50
-100
-150
0

2000

4000

6000

8000

10000

12000

14000

16000

18000

2000

4000

6000

8000

10000

12000

14000

16000

18000

2000

4000

6000

8000

10000

12000

14000

16000

18000

MVar

60
40
20
0
260
kV

I.

KEY FIGURESKEY EQUATIONS

255
250
0

SCADA

Fig.2 SCADA data obtained from the utility
winter15|Vs|, Ps, Qs

R-est
X-est
R-ref
X-ref

|Vr|, Pr, Qr

IL
X

R

10

B/2

G/2

B/2

p.u.

G/2

5

Fig.1 The nominal pi model for transmission lines

Three equations can be derived from Fig.1 as followings:
IL ( Ps  | Vs | G / 2)  (Qs  | Vs | B / 2)22

2

| Vs |2


0
0

0.1

0.2

0.3

0.4
I

2

rms

0.5

0.6

0.7

(kA)

Fig.3 Estimated line impedance samples

(1)

III.

( Pr  | Vr |2 G / 2)2  (Qr  | Vr |2 B / 2) 2

KEY RESULTS| Vr |2RX( I L )2 R  ( Ps -|Vs |2 G / 2)  ( Pr |Vr |2 G / 2)(2)

Given value

1.68

9.70

( I L )2 X  (Qs  | Vs |2 B / 2) + (Qr  | Vr |2 B / 2)

(3)

Summer peak

2.14 ±0.27

9.61 ±0.38

Winter peak

1.74 ±0.149.78 ±0.31

1.63 ±0.179.57 ±0.30

1.70

9.68

The above three equations are not sufficient for solving four
unknowns (R, X, B and G). One more equation is needed. The
idea here is to use wave propagation speed equation.
1
2 fl
v

(4)
LC
XB

Fault
day

ProposedmethodRelay datausing phasor
based method

0.8

A Hardware-in-the-Loop Simulation of AGC for Large
Scale Wind-Gas Coordinating Power Generation
Chen Fang, Member, IEEE, Songhan Jiang, Student Member, IEEE,Peichao Zhang, Member, IEEE
Chongming
IslandSCADAServerI.

TCP/IP communication (GTNET)INTRODUCTION

The proposed architecture is demonstrated in Fig. 1. The
architecture of the simulation platform consists of three main
subsystems: the primary power system simulated in RTDS,
the AGC system simulated in a PC-based controller, and thecommunication system to close the HIL simulation andreceive data from field.

PQ-controlled
load

CCPP Governor
modelWind Farms

Substations

CCPP

Qianwei II
Wind Far m
Qianwei I
Wind Far m

Chang
jiang

BeiyanWind Far m

Beibaogang
Wind Far m

Hongxing
Shanghai
Grid

Zhong
shuanggang

Changxing

Chenjiazhen
BaobeiJibangNanmenCCP P
Chongming I sland WGPPIEC 60870-5-104, Slave Station (GTNET)
Hardware-in-the-loop via Ethernet
IEC 60870-5-104, Master Station
Day-ahead scheduleof tie-line powe r

Very short-term
forecast of load+_Σ

Economic
Dispatc h

Look-ahead+
dispatch signal +Loa d/spee d
set-point

Look-ahead
Schedule(Update every
15 minutes)

Very short-term
forecast of wind
power

Raw
ACE

CCP P unit
/GovernorΣ

LPF a nd
PI controller

Control
ACE+
Regulation
Signal(Exe cute every
6 seconds)+

Σ

Ope ration
point

Output and ram p
rate limit

AGC

AGC realized in PLC

PROPOSED ARCHITECTURE

PQ-controlled
sourceCommunication

ith no fuel cost and zero greenhouse gas emissions ofwind power, the penetration level of wind power isincreasing rapidly around the world. The target for the
penetration level of wind energy set by the U.S. Departmentof Energy is 20% by 2030, and the European Union’s target
is 26%-34% by 2030. The target of China is 15% by 2030.
Nonetheless, the increasing penetration of wind power willbring technical challenges to power system operation due tothe variability and intermittency of wind power. Moreover,
it’s challenging to balance power system generation and load
for the large scale wind integration. With rapid regulation andless emissions, gas turbine generation is important for the
integration of wind power. Meanwhile, the integration ofwind power with gas turbine still needs to be verified indifferent time scales, and it’s difficult to predict the system
performance and generation commitment for theindeterminacy of wind. Hence advanced automatic generation
control (AGC) strategy should be employed to satisfy theneed for dispatch and system security. The research of the
new technologies demands new simulation tools, however,it’s hard for the power system simulation software now
available to meet the demands of system scale, real time, andonline data individually.
This paper presents a hardware-in-the-loop (HIL)
simulation platform to verify the AGC strategies for large
scale wind-gas coordinating power generation. The proposed
simulation platform integrates the Real Time Digital
Simulator (RTDS), PC-based Programmable Logic (PLC)
Controller, and Supervisory Control and Data Acquisition(SCADA) system for co-simulation.

Primary System Simulated in RTDS

W

II.

Communication

Internet

Index Terms—Wind integration, HIL, AGC, RTDS, CCPP.

Field Data from SCADA

Abstract—With more wind power integrated into grid, wind-gas
coordinating power generation attracts extensive attention, andit’s no longer appropriate to validate automatic generation
control (AGC) algorithm for large scale wind-gas generation
with a single tool. First, this paper proposes an AGC simulationarchitecture that combines Real Time Digital Simulator (RTDS)
and other off-the-shelf devices to implement the hardware-inthe-loop (HIL) simulation. Then the concept of wind-gas
coordinating equivalent power plant (WGPP) is adopted toparticipate in power system dispatching, and several AGC
strategies are proposed accordingly. Case study validates thefeasibility of the AGC simulation platform, and demonstrates
the WGPP concept can improve the tie-line control.

Fig. 1. Block diagram of online simulation architecture.

III.

CONTRIBUTIONS

The contributions of the paper are twofold. First, an AGC
simulation architecture considering large-scale windintegration using the HIL technology is presented. Second,the concept of wind-gas coordinating equivalent power plant(WGPP) is proposed to participate in power system dispatch,and the associated AGC strategies are verified using the HIL
platform.

Interfacing SFA- and GAM-Type Dynamic Phasors
for Modeling of Integrated AC-DC Power Systems
Yingwei Huang, Mehrdad Chapariha, Seyyedmilad Ebrahimi, Navid Amiri, and Juri Jatskevich
Department of Electrical and Computer Engineering
The University of British Columbia
Vancouver, BC, Canada{ywhuang, mehrdadc, ebrahimi, navida, jurij}@ece.ubc.ca
Abstract—This paper focuses on the modeling of integrated acdc backup and generation power systems, which has received
increasing attention due to the emergence of microgrids anddistributed generations. The conventional shifted-frequency
analysis (SFA) type dynamic phasors (DPs) assume a bandpass
spectrum of power system signals, and thus are only suitable tomodel system components wherein the 60Hz fundamental
frequency is dominant. To account for high-order harmonics ofinterest in ac-dc systems, this paper considers another type ofDPs based on the generalized averaging method (GAM), andproposes a possible interface between the SFA- and GAM-type
DP models. Computer studies demonstrate that the proposed DP
interface allows for accurate simulations of both fundamental
frequency components and high-order harmonics in integrated
ac-dc power systems, while providing significant numericaladvantages over the conventional time-domain detailed models.

i abcs k = idc ⋅ s iabc k = idc 0 s iabc k , k ∈ K ,(4)

C. DC Subsystem Modeling
For k = 0, <u>0(k) degrades to dynamic averaging in timedomain: the dc subsystems can be then modeled directly intime-domain.
III.

KEY FIGURES

Index Terms—Dynamic phasor, ac-dc system, synchronousmachine, rectifier system, harmonics.

I.

INTRODUCTION

Fig. 1. Circuit diagram of the ac-dc backup generation system.

This paper focuses on the modeling of integrated future acdc power systems and microgrids similar to Fig. 1, wherein
synchronous machines (SMs) are connected directly to linecommutated rectifiers (LCRs). A possible interface between
the SFA- and GAM-type DPs is presented. Therein, the SM ismodeled as a SFA-type DPs source using a voltage-behindreactance (VBR) formation [1], and the LCR is modeled in
GAM-type DPs for inclusion of desired harmonics [2].
Simulation results are shown with significant improvement in
the numerical efficiency.
II.

KEY EQUATIONS

Fig. 2. Block diagram of the proposed DP interface.
TABLE IISIMULATION EFFICIENCY OF THE SUBJECT MODELS

A. SFA-Type DP Modeling of SMs (SFA-SM)

Vabcs = L"D ⋅d
I abcs + [R D + jω s L"D ]I abcs + E''abcs ,dt'
E 'abcs= K Uu ,,qdabc [eq'' ed'' ]T ,

(1)

Model

=

∑ 2 ⋅ Re[k∈K*v as k s va k +*vbs k s vb k +]*v cs k s vc k .

42198

1553.9DP (1 , 5 , 7 )

153

85.7

DP (1st)

144

43.8

st

[1]

(3)
[2]

This research was supported by the Natural Science and Engineering ResearchCouncil (NSERC) of Canada.

CPU Time, ms

Detailed(2)

B. GAM-Type DP Modeling of LCRs (GAM-LCR)
v dc 0 = v as ⋅ s va 0 + vbs ⋅ s vb 0 + v cs ⋅ s vc 0

No. of Time Stepsthth

Y. Huang, M. Chapariha, F. Therrien, J. Jatskevich, and J. Marti, “A
constant-parameter voltage-behind-reactance synchronous machinemodel based on shifted-frequency analysis,” IEEE Trans. Energy
Convers., vol. 30, no.2, pp. 761-771, Jun. 2015.
C. Liu, A. Bose, and P. Tian, “Modeling and analysis of HVDC
converter by three-phase dynamic phasor,” IEEE Trans. Power Del.,
vol. 29, no.1, pp. 3-12, Feb. 2014.

High Speed Digital Distance Relaying Scheme forExtra High Voltage Transmission Lines
Xingxing Jin, Ramakrishna Gokaraju
University of Saskatchewan, Saskatoon, CanadaI.

The processing time for one sampled packet is 4.04 μs which
is much less than the sampling interval of 208 μs. This highspeed design can be attributed to the massive hardware
parallelism and deep pipelining.

SV

FPGA

IEC 61850 Communication Interface
Ia,b,c,n , Va,b,c,n

Trip

Mimic Filter

I(N), I(N+1)...
V(N), V(N+1)...

3 Phases to Symmetrical Components

Disturbance
Detector

Adaptive Phasor Estimation

Impedance Calculation

Adaptive Mho Characteristics

Distance Element

Figure 2. Overall architecture of the proposed distance relaying scheme.

ALGORITHM

Phaselets are partial integrals of products of the waveform
samples and the sinusoidal coefficients or cosine coefficients
specified by a data window. They are combined into phasors
over a set of linearly increasing window sizes [1].

GOOSE

RTDS

State Machine

Abstract— Full-cycle Fourier and cosine phasor filtering systems
are typical implementations of numerical distance relays with a
response time of close to one cycle. However, a fast sub-cycle
numerical distance element would be useful, specifically for
EHV/UHV transmission systems (400 kV and above). In this work,a phaselet-based distance relaying algorithm is proposed toaccurately find the voltage and current phasors in about 0.675
cycles; this is faster than some alternative state-of-the-art methods
discussed in the literature. The phasor estimation errors areanalyzed using a transient monitoring index to verify theapproach. An adaptive Mho characteristic based on the estimation
errors is used to achieve a fast and secure trip decision. The
scheme is implemented on a Xilinx ML-605 field programmablegate arrays (FPGAs) board, which can achieve speeds similar tosolid-state relays due to its powerful parallel processing units. The
proposed relay is tested using hardware-in-the-loop simulations
and a real time digital simulator (RTDS). The scheme is also tested
in closed-loop fashion using Ethernet-based protocols (IEC 61850
Sampled Value and Generic Object Oriented Substation Events
protocols) to verify its performance in digital substation
environments.

III.

KEY RESULTS

Figure 3 shows the impedance trajectories for three different
methods: the proposed method, HCDFT [2], and FCDFT. Theirtripping times are 0.675 cycles, 0.775 cycles, and 0.825 cycles
correspondingly.

Figure 1. Standard deviation and adaptive Mho characteristics.

A transient monitoring index is proposed to check the qualityof the estimated phasors. The standard deviation curve of thereconstructed impedance for different phaselet filter is shown as
the blue line in Figure 1(a). An adaptive Mho characteristics is
proposed based on the standard deviation of the impedance
error, as shown in Figure 1(b).
II.

HARDWARE IMPLEMENTATIONThe overall architecture of the proposed digital distance
relay is shown in Figure 2. It consists of three modules: an IEC
61850 communication interface, a distance element, and a statemachine.

Figure 3. Impedance trajectory for phaselet, FCDFT, and HCDFT.

Considering both tripping time and security, the proposed
method provides the best performance.
REFERENCES
[1]

[2]

M. Adamiak and W. Premerlani, “A new approach to current differential
protection for transmission lines,” in Electrical Council of New EnglandProtective Relaying Committee Meeting, October 1998.
E. Udren and M. Sackin, “Relaying features of an integrated
microprocessor-based substation control and protection system,”
Development in Power System Protection, pp. 97–101, 1980.

A Test Cases Library for Methods Locating the Sourcesof Sustained Oscillations
Slava Maslennikov1, Senior Member, IEEE, Bin Wang2, Student Member, IEEE, Qiang “Frankie” Zhang1,
Member, IEEE, Feng Ma1, Member, IEEE, Xiaochuan Luo1, Senior Member, IEEE, Kai Sun2, Senior Member,IEEE and Eugene Litvinov1, Fellow, IEEE

Abstract—A test cases library is created to provide a common
foundation to test methods for locating the source of a poorly
damped or forced oscillation by using wide-area measurements.
The library is built on a simplified WECC 179-bus system andincludes both poorly damped and forced oscillation cases. Threesource location methods were fully tested using the library, andtwo selected cases for each method are presented in this paper todemonstrate the advantages and the disadvantages of eachmethod. This library would help validate, evaluate and improvemethods locating the oscillation sources in power systems.

I.

PD_7

D45=-0.5
1.41
D159=-0.5

D45= -2.5
1.27
PD_8 D159=1
1.41
D36= -1
0.46
PD_9 D11= -10 0.69
1.63

-0.40%

159

2 sources
2 unstable local modes-0.86%-1.81%-0.40%

79

1 source
3 unstable modes

TEST CASES LIBRARY

http://web.eecs.utk.edu/~kaisun/Oscillation/

TABLE ICase#

KEY TABLE AND FIGURE

SUMMARY OF POORLY DAMPED OSCILLATION CASES

Freq Damping Source Fault
Description
(Hz)
ratio
bus
bus
D45= -2
1 source
PD_1
1.41
0.01% 45
159
D159=1
1 local mode
D35=0.5
1 source
PD_2
0.37
0.02% 65
79
D65= -1.5
1 inter-area mode
1 source,
0.46
2.22%
D6=2
1 unstable local mode and
PD_3
0.70
1.15% 1130
D11= -6
2 poorly damped inter1.63-0.54%
area modes
1 source
0.46
0.68%
1 unstable inter-area
D6=5
PD_4
0.70
-0.58% 11
6 mode and 2 poorly
D11= -9
1.63
0.54%
damped local and interarea modes
1 source
0.46
0.69%
D6=3
2 unstable local and interPD_5
0.70-0.19% 1130
D11= -8
area modes and 1 poorly
1.63-0.48%
damped inter-area mode
2 sources with
D45=-245&
comparable contribution
PD_6
1.41
-0.93%
159
D159=-0.5
159
into a single unstablelocal mode

2 sources with different
159 contributions into a single
unstable local mode

-1.06% 45&-0.22% 36

Detailed case descriptions, simulated PMU measurements
and the system models are open for the public for free at:

II.

45&
159

D

Fig. 1. WECC 179-bus system

11

A Hybrid Simulation Tool for the Study of PV
Impacts in Distribution Networks
Ali Hariri and M. Omar Faruque
Department of Electrical and Computer Engineering and the Center for Advanced Power Systems
Florida State University, Tallahassee, FL
ahh13@my.fsu.edu, faruque@caps.fsu.edu
Abstract—This work presents a hybrid simulation tool thatcan perform PV impact studies in distribution networks withphotovoltaic (PV) generation systems. The hybrid tool incorporates electromagnetic transient (EMT) and phasor-mode solvers
to combine both of their advantages, accuracy and speed.

I. P RIMARY O BJECTIVE
Power system simulation offers important information regarding the behavior of the system under different conditions.
As PV penetration increases in the distribution side, it causes
new challenges to appear in the network due to the variability
and unpredictability of the PV generation. This brings forth theneed for more advanced and dynamic simulation techniques
in order to better understand the behavior of future systems.
The tool presented in this work is developed in an effort tofacilitate certain PV impact studies that require monitoring ofhigh frequencies and fast transients without having to use afull EMT model of the entire network.
Modern EMT simulation tools offer high accuracy with a
lot of constraints regarding the size of the modeled system andthe total simulation time. Modern electric networks integrate
power electronic-based generation sources which require verysmall time-steps for accurate results. This makes EMT simulation computationally expensive and requires large processing
power and long simulation time. In addition, different types ofstudy require different types of simulations. For this reason,
it is not always necessary to simulate the entire system in
EMT; only the part that requires detailed study would require
EMT simulation while other parts can be simulated using
quasi-static time-series (QSTS) simulation. The developed tool
incorporates an EMT model of the grid-tied PV system witha distribution network modeled in OpenDSS. This allows
the user to perform EMT and QSTS analysis of distribution
systems with interconnected PV.
II. D ESIGN AND M ETHODOLOGY
A. Methods and Procedures
Hybrid simulation involves the simulation of different partsof a system using different simulation approaches. The developed tool consists of three main parts as shown in Fig.
1: (1) the detailed EMT model of the grid-tied PV system,(2) the distribution network modeled in OpenDSS, (3) andthe interface that links the two models for data exchange.
The grid-tied PV system is model using discrete time-domain

Fig. 1. Hybrid simulation tool interface diagram

equations that are derived to describe the behavior of thesystem and are coded in Matlab scripts. The PV system iscomposed of a PV array with maximum power point tracking(MPPT) control, a capacitor at the DC link, a three-phase pulse
width modulation (PWM) voltage source inverter (VSI) withdecoupled control scheme, an LCL filter and the grid. The
distribution system is modeled using the phasor tool OpenDSS
which can model networks with thousands of nodes. OpenDSS
is operated through the COM interface to solve each power
flow of the QSTS analysis with a time step ∆t in the orderof few milliseconds to seconds. While the EMT equation aresolved with a time step δt in the order of a few microseconds.
The two models are interfaced to exchange necessary data at
the end of the larger time step governed by specifically coded
I/O protocols using a COM server.
B. Outcomes and Results
A test case is presented where a distribution network with
PV interconnection is modeled using the hybrid tool anda full EMT tool, SimPowerSystems. The results from both
simulations are compared for validation and accuracy. The tool
was tested under different condition to show the various applications. An islanding detection algorithm based on harmonic
detection was implemented and tested in both simulation tools.
A fault study as well as a steady-state harmonic analysis were
performed. The results presented the time-domain voltage andcurrent signals at the PCC, frequency spectrum and THD
levels for all performed studies. Theses results can not be
produced usign only OpenDSS or other phasor-mode tools.
The results showed a great level of accuracy of the hybrid
tool and its ability to track fast transients over a wide band of
frequencies.

Stochastic Generation and Transmission Planning
Considering Future Renewable and Gas Opportunities
in Queensland, Australia
Juliana Barbosa Nunes, Nadali Mahmoudi, Tapan Kumar Saha, Debabrata Chattopadhyay
School of Information Technology and Electrical Engineering
The University of Queensland
Brisbane, Australia
j.barbosanunes@uq.edu.au, n.mahmoudi@uq.edu.au, saha@itee.uq.edu.au, dchattopadhyay6@gmail.com

TABLE I. NEW GENERATORS PARAMETERS

Wind
Solar PV
OCGT
Hydro

Bus

The proposed problem is solved using the CPLEX solver under General Algebraic Modelling System (GAMS).
The investment model with stochastic generation andtransmission is formulated in (1)-(7).

1
2
3

I
Minimize Cost = ∑𝑊
𝑤=1 𝜋(𝑤) [ ∑i=1(CInvi,w . pci,t,w ) +T
II∑t=1(dt . ∑i=1(Covi . pei,t,w ) + ∑i=1(Cofi . pci,t,w ) +∑Tt=1(dt . ∑i∈ IUTEgas(CfuelIUTEgas . peIUTEgas ) +∑Tt=1(dt . ∑Ii=1(CCO2 . (Emi . pei,t,w ) ] +∑k ∈ Knew(CInvk . xfeKnew )
(1)

𝑙∈𝑛

𝑝𝑐𝑖,𝑡,𝑤 ≤ 𝑝𝑒𝑖𝑚𝑎𝑥 ; ∀ 𝑖 , ∀ 𝑤

𝑝𝑒𝑖𝑚𝑖𝑛 ≤ 𝑝𝑒𝑖,𝑡,𝑤 ≤ 𝑝𝑐𝑖,𝑡,𝑤 . 𝐶𝐹𝑖,𝑤 ; ∀𝑡, ∀ 𝑖 , ∀ 𝑤



𝑚𝑎𝑥
−𝑓𝑒𝑘,𝑡,𝑤

𝑚𝑎𝑥
𝑓𝑒𝑘,𝑡,𝑤; ∀ 𝑘, ∀ 𝑡, ∀ 𝑤

𝑓𝑒𝑘,𝑡,𝑤 = 𝐵𝑘 . (𝜃𝑛,𝑤 − 𝜃𝑛𝑝,𝑤 ); ∀ 𝑡, ∀ 𝑘 , ∀ 𝑤≤ 𝑓𝑒𝑘,𝑡,𝑤 ≤∑𝑖∈ 𝐼 𝑅𝐸

𝑝𝑒𝐼𝑅𝐸,𝑡,𝑤 ≥ 𝑝𝑒𝐼𝑚𝑖𝑛𝑅𝐸
𝑅𝐸 ,𝑤

; ∀𝑤



($/MW)
41,64739,322
4,165
5,000

Emission
Factor(tCO2/MWh)
0
0
0.52
0

Generators

NumberWind
Solar PV
Wind
Hydro
OCGT
Wind
Wind
Solar PV
OCGT
Wind
OCGT

2
1
1
1
1
2
1
1
1
2
3

Max Capacity
(MW)
700
125
400
500
360
700
400
300
1000
800
1550

III. KEY FIGURESCapacity Built (MW )

𝑘∈𝑛

4
5
8
10
12

7000
6000
5000
4000
3000
2000
1000
0

3500
3000
2500
2000
1500
1000
500
0
Case 1Case 2
SolarPV
Hydro
Expected Cost

Case 3

Case 4
OCGT
Wind

Figure 1. Expected power capacity and cost for each case
Expected Power
Generation (TWh)

𝑖∈𝑛



Variable
Cost($/MWh)
12.5
0
10.4
5.0

Fixed Cost

TABLE II. LOCATION AND CAPACITY OF NEW GENERATORSKEY EQUATIONS∑ 𝑝𝑒𝑖,𝑡,𝑤 − ∑ 𝑓𝑒𝑘,𝑡,𝑤 = ∑ 𝑃𝐷𝑁𝐿 ,𝑡,𝑤 ; ∀ 𝑛, ∀ 𝑡

Total
Capacity
(MW)
3000
325
2910
500

Technology

Expected Cost (M$)

I.

II. KEY TABLES

70
60
50
40
30
20
10
0
Case 1
PV
Coal
Wind

52
50
48
46
44
42
40
38
36
Case 2

Case 3

Expected Emission(Mton CO2)

Abstract— This paper assesses long term electricity planningunder uncertain load growth, gas price, renewableproduction and new technologies’ investment costs for thestate of Queensland, Australia in 2030. A stochastic
programming approach is mathematically formulated todecide which technology to be built, while determining its
capacity and location. Additionally, new investments intransmission lines are examined. The problem is modeled as amixed-integer linear programing approach and is solved
using four distinct cases to assess the impact of carbon costs,
the renewable energy target and retirement of coal-based
generation.

Case 4
Gas
Hydro
Expected emission

Figure 2. Expected power generation and expected emission

An Analytical Method for Probabilistic Modelingof the Steady-State Behavior of Secondary
Residential System
Tianyu Ding, Student Member, IEEE, Hao Liang, Member, IEEE, and Wilsun Xu, Fellow, IEEE
Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada
Email: tding@ualberta.ca, hao2@ualberta.ca, wxu@ualberta.ca

I.

PROBLEM DEFINIATIONAs shown in Fig. 1, a secondary residential system consists of aservice transformer and secondary conductors connecting
various households. Each household has several home
appliances. These appliances can be categorized into two types:
linear appliances and nonlinear appliances. All appliances canbe switched on randomly and draw power from the service
transformer. However, when nonlinear home appliances are
switched on, they also inject harmonic currents which
gradually aggregate at the service transformer. The problem tobe addressed can be formally stated as: to develop an
equivalent steady-state model of a secondary residential
system, which can characterize both total power consumption
and total harmonic injection of all the residential households
inside the system.
Service TransformerII.

KEY FIGURESFor each household
For each applianceRandom usage
pattern model

Electricalmodel

Derive the state probability
distribution in each time slot

Derive the means and covariance matricesof the electrical parameters in each time slot

Summation of means and covariance matrices of theelectrical parameters of all appliances in all households toestablish aggregated secondary feeder model in each time slot
Servicetransformer
model

Aggregated secondary
system modelin each time slot

Unscented
Transform

Fig. 2. Flowchart of the proposed analytical method.
PP
S S [ n]

YPN ,h [n]N

I PN ,h [n]

N
YG

YG

(a) Fundamental frequency(b) Harmonic frequency

Fig. 3. Simplified equivalent aggregated secondary system model .

III.
60

kW/kVar

Abstract— Effective modeling of secondary residential systemis critical for numerous demand side innovations in smart grid,such as distributed generation and energy storage integration,
residential micro-grid operation, and demand response
programs. Traditionally, Monte Carlo simulation (MCS)
method is used to establish the model, by capturing therandom usage patterns of home appliances. Yet, thecomputational effort is tremendous, especially for a large
group of residential loads. To address this issue, an analyticalmethod is proposed in this paper. The result is a time varying
probabilistic steady-state equivalent circuit model whoseparameters are characterized by means and covariancematrices. The effectiveness and efficiency of the proposed
method are evaluated based on extensive numerical results andby comparing the results with that obtained by MCS method.

KEY RESULTS

Mean Active Power
95% Confidence Active Power Variation
Mean Reactive Power
95% Confidence Reactive Power Variation

40
20
0
2

4

(a)
Residential House

60
kW/kVar

L houses

40

6

8

10

12
14
Time (h)

16

18

20

22

24

All residents are full-time working

Mean Active Power
95% Confidence Active Power Variation
Mean Reactive Power
95% Confidence Reactive Power Variation

20
0Home Appliance

Fig. 1. Structure of secondary residential system.

2

4

6

8

10

12
14
Time (h)

16

18

20

22

24

(b) 50% residents are part-time working
Fig. 4. Fundamental power variations on a weekday for a secondary systemwith 20 households.

1Modeling of Multi-terminal VSC-based HVDC System
M. M. Alharbi

M. L. Crow

Missouri University of Science and Technology
Rolla, USA
Emial: mmayc3@mst.edu

Missouri University of Science and Technology
Rolla, USA
Email: crow@mst.edu

Abstract—Improving the efficiency and operation of powertransmission is important due to the continual increase in demandfor electric power. HVDC links provide a good solution fortransmitting power over long distances. A model of a threeterminal VSC-HVDC system is presented in this paper. One of theconverters is used to regulate the DC voltage while the others
converters control the active power independently and bidirectionally. The vector control strategy and pulse widthmodulation (PWM) technique are described and implemented in
PSCAD/EMTDC. In addition, the region of controllability as afunction of power flow has been analyzed.

I. CONTROL STRATEGY
Fig. 4. Three-terminal HVDC system

Fig. 5. DC Voltage Response

Fig. 1. Simplified Circuit Diagram of VSC-HVDC
Controllers

Fig. 6. Active Power for the Three-terminal HVDC

Fig. 2. Inner Current Controllers
Qi*

Iq*

Fig. 7. Region of Controllability at VDC=100 kV and 50 kV

Qi
(a)

(b)

Fig. 3. a) Active Power and b) Reactive Power Controller
II. SYSTEM STUDY AND SIMULATION RESULTS
The control of this model is organized as follows: VSC1 isused to control the DC voltage and reactive power, whereas
VSC2 and VSC3 are used to control the active and reactive
power in either direction independently.

Fig. 8. Efficiency at VDC=100 kV and 50 kV

Building Realistic Transmission Line Topologies for
Synthetic Power System Test Cases
Adam B. Birchfield, Student Member, IEEE, Kathleen M. Gegner, Student Member, IEEE,Ti Xu, Student Member, IEEE, Komal S. Shetye, Member, IEEE, Thomas J. Overbye, Fellow, IEEE
Department of Electrical and Computer Engineering
University of Illinois at Urbana-Champaign
Urbana, IL, USA
Abstract—Power systems research can greatly benefit frompublic, non-confidential power system models that match the sizeand complexity of real grids and behave in a statistically similar
way. This poster introduces a line placement methodology toconnect realistic synthetic substations with a network of fictionaltransmission lines across real geographic footprints in North
America. The approach uses the geometric Delaunay triangulation
as a basis for generating the network topology; lines are selected
through an iterative dc power flow process, with additional
constraints to match other properties of real power systems.
Keywords—synthetic networks, power system modeling

I. REALISTIC LOAD AND GENERATION PROFILES
In creating fictitious realistic power system cases, reference[1] presents an approach to geographic placement of load andgeneration substations from entirely public data. Depending on
the desired size and geographic footprint of a case, a clustering
process combines some substations, and a set of nominal voltage
levels is selected (e.g. 115/345 kV). Large generation
substations (>250 MW) and about 15% of load substations are
given a higher-voltage bus and transformer. These substations
are then connected with transmission lines as described in thenext section.
II. TRANSMISSION LINE PLACEMENT
A statistical analysis of the Eastern Interconnect indicatesthat the average node degree (neighbor count) for a substation at
a nominal voltage level is 2.43. To match this statistic, we place
1.22n transmission lines at each nominal voltage level, for n
substations. Once a spanning tree ensures graph connectivity
with n-1 lines, an iterative dc power flow solution is used to add
additional 0.22n transmission lines.

Fig. 2. One-line diagram of 1500-substation test caseAt each dc power flow iteration, selections are made fromthe 3n segments of the Delaunay triangulation, a geometry foundto correlate well with actual transmission lines. Lines are picked
based on voltage phase angle gradient, indicating likely power
flow, as shown in Fig. 1. Additional constraints include avoiding
radial substations, forbidding abnormally long lines, andencouraging parallel circuits to overloaded lines. Lineparameters come from standard EPRI and ACSR guides.
This process is demonstrated in a 1500-substation test caseon the Texas geographic footprint with 2100 buses, 282
generators, and 2857 branches, as Fig. 2 shows. The case is built
in 70 seconds and has no overloaded lines. This case has no
relation to the actual grid in this location, and is entirely
fictitious. However, in several important ways, it is shown tomatch real grid properties.
REFERENCES
[1]

[2]

Fig. 1. Voltage phase angle contour from dc power flow iteration.
The sharp gradient indicates this might be a good location foradding a transmission line.

K. M. Gegner, A. B. Birchfield, T. Xu, K. S. Shetye, and T. J. Overbye,“A Methodology for the Creation of Geographically Realistic Synthetic
Power Flow Models,” in Proc. 2016 IEEE Power and Energy Conf. at
Illinois, Champaign, IL, Feb. 2016.
Z. Wang, A. Scaglione, and R. J. Thomas, “Generating statisticallycorrect random topologies for testing smart grid communication andcontrol networks,” IEEE Trans. Smart Grid,, vol. 1, no. 1, pp. 28–39,
2010

1

Equivalent Electromagnetic Model of Self-blocking
MMC with DC fault isolation capability
Wang Xiang, Weixing Lin, Jinyu Wen

Liangzhong Yao, Zhibing Wang

State Key Laboratory of Advanced Electromagnetic
Engineering and Technology; Huazhong University of
Science and Technology; Wuhan, China
weixinglin@foxmail.com

Renewable Energy Department

I. INTRODUCTION
Modular multilevel converter (MMC) based on half bridge
sub-modules (HB MMC) is vulnerable to DC faults which
promotes the intensive research on DC fault tolerant MMC
topologies.
A variety of SM topologies and new breed of hybrid MMC
with DC fault blocking capability have been proposed. Among
the DC fault tolerant MMC topologies, the self-blocking
MMC (SB MMC) shown in Fig. 1 is an effective andeconomical topology.
Operating principle and DC fault isolating mechanism of
SB MMC has been well studied, but the modelling of SB
MMC has not been fully addressed. Since MMC used in powerindustry typically has hundreds of voltage levels, fast
equivalent modelling is of great importance for simulation of
SB MMC. Several approaches have been introduced toaccelerate the simulation speed of HB MMC, such as theaveraged model, the Thevenin equivalent circuits and thecontrolled voltage source equivalent circuits.
Dynamics of HB MMC in normal operation, during DC
fault and during start-up can be well represented in these
existing fast simulation models. Operating principle of SB
MMC is similar as HB MMC in normal condition but
significantly different during isolating of DC fault. The
existing methods are not sufficient for the fast modelling of SB
MMC. This paper further proposes an equivalent model of SB
MMC that is able to simulate dynamics of SB MMC underboth normal and DC fault isolating scenarios.
II. KEY FIGURES+

T1
SBSM SBSM SBSM
+

vpA

-

HBSM HBSM HBSM

D3

T2

ics
D1
+
vc
D2 D4

D3
+

D4

T3
T1

-

T3

T2

D1 vC
+
D2

Type2 SBSM

Type1 SBSMAC
SBSM SBSM SBSM

+ T1

D1 +
C

- T2

D2

vnA

-

HBSM HBSM HBSM

HBSM

Fig. 1. Topology of a SB MMC

OR

Ffree
ics
Voltage
Balancing
Algorithm

iarm
Tr1

Tf

Fnormalics

T1 (1:N)Varms

T2 (1:N)

VoltageV
Calculation armh

T3 (1:N/2)

Tr2
+
-

ich

vc (1:N)

+
-

SB
arm

+
-

Dh1

Th1

ich

D
+ h

HB
arm

-

L0

Fig. 2. Equivalent electro-magnetic model of an arm of SB MMC

2

Iup(kA)

Abstract—Self-blocking MMC (SB MMC) is able to isolate DC
fault current and is promising for use in overhead HVDC
transmission. Each converter arm of the SB MMC is consisted ofhalf the self-blocking sub-module (SBSM) and half the half
bridge (HB) sub-module. This hybrid design achieves the DC
fault isolation capability without significantly increase of the costand power loss. The detailed electro-magnetic simulation of SB
MMC with hundreds of voltage levels is time consuming. Since a
SBSM can be negatively inserted, the dynamics of SB MMC are
different from HB MMC under DC fault condition. The
conventional equivalent models of HB MMC are not sufficientfor SB MMC. To solve the above challenges, an equivalent
electromagnetic model for SB MMC is proposed and verified inthis paper.

China Electric Power Research Institute, Beijing, China

Iup_det
Iup_equ

1
0

-1
-2
1.94

1.96

1.98

2
2.02 2.04 2.06
Time (s)Fig. 3. Arm current of EEM versus the detailed switching model

III. CONCLUSION
An equivalent electromagnetic model is proposed for SB
MMC. The EEM uses equivalent controlled voltage sources
and related power electronic devices to model the dynamics ofa SB MMC under different operating scenarios. The proposed
EEM can be used for converter level study such as optimal
design of the arm inductors and DC fault current limiting
inductors, DC fault isolating strategy based on SB MMC andall the converter level studies as reported in literatures
regarding HB MMC. Simulation results of two-terminal
9-level SB MMC HVDC system show that the EEM is 7 times
faster than detailed model.

A Practical Real-Time Hybrid Simulator for
Modern Large HVAC/DC Power Systems
Interfacing RTDS and External Transient ProgramShuqing Zhang, Yanan Zhu, Kaijian Ou, Qi Guo, Yun Hu, Wei Li

Abstract—It becomes necessary to utilize electromagnetic/
electromechanical hybrid simulator to modern large HVAC andDC power system, where multiple HVDC links feed into
concentrated load areas. In this paper, a practical real-time
hybrid simulator is proposed. The simulator makes RTDS andexternal transient program running on a computer link together
effectively and smoothly. The entire interface schemes forhybrid simulation are discussed on both algorithm andimplementation layers. Aiming at the problem on unexpected
low frequency non-characteristic harmonics, an impedance
angle preserved circuit is employed, with fast parameter
calculation method. Based on a large case system of China
Southern Grid, the hybrid simulator is compared to theelectromagnetic simulation to validate the proposed scheme.

Figure 2. Interface circuits (a) original one, (b) improved one

Index Terms—hybrid simulation， impedance angle， interface
algorithm and implementation, RTDS and transient program.

I.

INTRODUCTION

To satisfy the demand for electricity in the districts awayfrom energy-generating centers, multiple HVDC links are fed
into modern HVAC power systems with concentrated loads toefficiently deliver required power energy. Moreover, plenty of
power electronic devices of large capacity and high voltage
are put into use in modern power grids. Among benefits ofthese technologies is the fact that we have limited simulation
tools which can describe the tightly coupled electromagnetic
and electromechanical processes happening in the systems
both accurately and efficiently, and finely handle the diversity
and complexity of the operation mode as well.
II.

KEY FIGURESI if ,120
uif ,abc (t )Figure 1. Interface schemes for EMT/TS hybrid simulation on algorithm andimplementation layers

Figure 3. DC voltages of the positive and negative, DC currents of thepositive and negative, DC power

III.

CONCLUSION

By virtual of dependability of RTDS and flexibility oftransient program on computer, the hybrid simulator becomes
one of the most practical and reasonable solutions to simulate
on modern large HVAC and DC power systems accurately.
The interface algorithm mainly includes AC / DC grid
partitioning, three-sequence equivalent modeling and threesequence quantity exchanging, and parallel interaction
protocol. Moreover, impedance angle preserved interface
circuit is suggested to solve the problem that unexpected
abnormal low frequency harmonics emerge at interface buses.
By transferring a group of numbers in float-point andinteger types through GTPPGA card, RTDS and computer canbe combined synchronously to implement the simulation.

Enhancing Stochastic Unit Commitment
with Data Mining
Chao Li, Nan Li, Gregory Thompson, and Kory Hedman
School of Electrical, Computer and Energy Engineering
Arizona State University
Tempe, AZ, U.S.A.
Chao.Li.cidse@asu.edu
Abstract— Stochastic unit commitment is usually formulated as
two-stage stochastic programming or robust optimization wherecommitment decisions are determined in the first stage anddispatch decisions are determined in the second stage after theuncertain data are realized. The two-stage stochastic unit
commitment is difficult to solve with current computational
capability and limited scheduling due to the introduction ofmany scenario-based variables and constraints, however. In thispaper a framework is proposed to shift the computationalburden from the scheduling periods to an offline simulation andstudy with data mining algorithms. As a result, the stochastic
unit commitment is replaced by a deterministic mixed integer
programming with a set of necessary critical constraints
identified from data mining.

I.

In Fig. 2, index 𝑟 represents the identity of the constraint;
index 𝑠 represents the scenario to include the constraint; index
𝑡 represents the time period to include the constraint. The
number of potential constraints is exponential. Offline
simulation and study is carried out to identify the criticalconstraints. Then data mining algorithms are applied to
identify the necessary critical constraints given a new set ofday-ahead forecast data, as described in Fig. 3.

METHODOLOGY

Two-stage stochastic unit commitment (SUC) can betransformed to a deterministic mixed integer program (MIP)
with the inclusion of extra constraints, as shown in Fig. 1.
Figure 3. Data mining to identify necessary critial constraints

II.

CASE STUDY

The unit commitment to withstand any single-generatorfailure (G-1) contingency is studied. Security constraints(index 𝑟) are induced by selection of transmission lines andbuses [1]. The offline study is described in Fig. 4.

Figure 1. Stochastic unit commitment formulation

Benders’ decomposition algorithm finds the constraints(feasibility cuts and optimality cuts) iteratively. The drawbackof Benders’ decomposition is that the master program issolved repeatedly and takes longer time to solve in each
iteration as the algorithm proceeds. If all necessary criticalconstraints, as described in Fig. 2, can be included all at once
solution time can be reduced significantly.
Figure 4. Offline simulation

REFERENCES[1]

Figure 2. Critical constraints

C. Li, M. Zhang, and K. W. Hedman, “Extreme ray feasibility cuts forunit commitment with uncertainty,” submitted to European Journal ofOperational Research.

1

Bidding and Offering Strategy for a MerchantCompressed Air Energy Storage Plant
Soroush Shafiee, Hamidreza Zareipour, Senior Member, IEEE, Andrew M. Knight, Senior Member, IEEE,
Abstract—Compressed air energy storage (CAES) is one of
the few storage technologies that could be used for storing large
amount of energy. We proposes a bidding/offering strategy for amerchant CAES plant based on information gap decision theory(IGDT) to incorporate price forecasting errors when participating
in day ahead market. Short and long term simulations are
conducted to show the performance of the proposed strategy.

I. I NTRODUCTION
HERE is currently significant interest in the use of bulk
electrical energy storage systems such as compressed air
energy storage (CAES). For example, the total installed electricity storage capacity in the US is forecasted to grow from
22 GW in 2014 to 103-152 GW by 2050 [1]. Such systems
are able to provide load-shifting and potentially provide thenecessary flexibility to deal with uncertainties associated withthe growing penetration of renewable resources.
Because of inevitable price forecasting error, in order tomaximize its operating profit, the CAES facility needs not
only an appropriate offering strategy for selling the electricity,
but also a proper bidding strategy for purchasing energy fromthe market. In this study, the information gap decision theory[2] is applied to model the price uncertainty and develop
a risk-constrained bidding/offering strategy for a merchant
CAES participates in the day-ahead energy markets. The IGDT
method applies to decision making problems in an uncertain environment. The method enables the decision maker toformulate optimistic and pessimistic self-scheduling problems
without any assumption on the probability distribution functionof the uncertain parameter and with low computational load.

T

II. M ETHODOLOGYCAES is composed of a compressor, expander, and air
reservoir; The operation cost and efficiency parameters canbe found in [3]. The risk-neutral self-scheduling problem of amerchant CAES plant is formulated as an optimization problem aiming to maximize its profit. Then, the IGDT is applied
to the CAES self-scheduling problem to incorporate priceuncertainty and develop robust and opportunistic schedulingframework. Then, the proposed IGDT-based scheduling problems are applied to construct hourly offering and bidding
curves to submit to the market for each hour, in order to takedifferent levels of risk of price prediction into account.
III. K EY R ESULTS
Numerical simulations are performed for a CAES facility
with 150 MW discharging, 100 MW charging, and 20 hoursof full discharging capability as the storage capacity. Solidline in Fig. 1 depicts the forecast prices for a typical 24
S. Shafiee, H. Zareipour, and AM. Knight are with the Department of Electrical and Computer Engineering, Schulich School of Engineering, Universityof Calgary, Calgary, AB, Canada T2N 1N4 (e-mail: sshafiee@ucalgary.ca;
h.zareipour@ucalgary.ca; aknigh@ucalgary.ca).

hours period. Solving the risk-neutral self-scheduling leads
to $31, 905.0 profit. The CAES self-scheduling problem issolved from both risk-avers and risk-seeking perspectives byapplying the robust and opportunistic IGDT-based schedulingmethods, respectively. 25% robustness and 25% opportunity
level are considered. The robust case finds the maximum
unfavourable deviation in price that limits the reduction in
operating profit to within 25% of the the risk neutral case.
The opportunistic case finds the minimum favourable deviationin price required to return a 25% increase in profit. Figure
1 depicts the price forecast, the worst case and the bestcase of price fluctuation for those two pre-determined robust
and opportunistic risk levels. Figure 2 also illustrates thecorresponding hourly scheduling plans of the CAES plant forthe cases.

Fig. 1: Forecasted price, the worst case of price for robust case andthe best case of price for opportunistic caseFig. 2: Scheduling of storage for risk-neutral, robust and opportunisticcases

The results, obtained from the robust as well as opportunistic self-scheduling problem are employed to construct the stepsof the bidding and offering curves. Thereafter, the after-thefact analysis are conducted with simulated as well as actual
market prices to investigate the performance of the proposedbidding and offering strategy in a competitive market.
IV. C ONCLUSION
This study develops an IGDT-based risk-constrained bidding/offering strategy for a merchant CAES facility taking
price uncertainty into account. The demonstration shows thestrategy avoids uneconomic actions. At the same time, it
benefits from desirable price deviation and thus, higher profits
are achieved.
R EFERENCES[1] “Technology roadmap energy storage,” International Energy Agency,
2014.
[2] Y. Ben-Haim, “Information gap decision theory, designs under severe
uncertainty,” 2nd ed. San Diego, CA: Academic, Feb. 2006.
[3] H. Safaei and D. W. Keith, “Compressed air energy storage with waste
heat export: An alberta case study,” Energy Conversion and Management,
vol. 78, no. 0, pp. 114 – 124, 2014.

Enhanced Reserve Policies for Power Systems
in the Presence of Stochastic Resources
Nan Li, Nikita G. Singhal, Student Member, IEEE, and Kory W. Hedman, Member, IEEE
School of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, AZ 85287, USA
Email: nanli4@asu.edu; nsinghal@asu.edu; kory.hedman@asu.edu

Abstract—With the integration of higher levels ofintermittent stochastic resources, the location, i.e.,deliverability, of reserves has become increasingly criticalin addition to the quantity of reserves. Higher levels of
renewable resources not only increase the reserve levelbut also increase the difficulty to predict the network
flows and the resulting bottlenecks. As existing reserve
rules inadequately reflect changing operating conditions
and fail to locate reserves at prime locations, improved
reserve policies are needed to improve the deliverabilityof reserves and address the uncertainty introduced bystochastic resources. In this work, enhanced reserve
policies that take into account the anticipated operational
state of the network are developed. The proposed reserve
policies are designed to identify critical locations forcontingency reserve and ramping reserves for renewable
uncertainty. The proposed reserve polices are derived
during an offline analysis, which is aimed at keeping theadded computational difficulty to the existing securityconstrained unit commitment (SCUC) model atminimum.

I. MODEL ACCURACY VS. COMPUTATIONALCOMPLEXITY
In recent years, two-stage programming models havebeen widely used to address renewable uncertainties
and unexpected contingency events in the system.
However, because of the high complexity of powersystem and the short time window required to solve theproblem, stochastic programming models have yet
become computationally tractable for actual-size powersystems. In this work, reserve policies that take into
account the anticipated operational state of the network
are proposed to provide enhancement to deterministic
SCUC models. The objective is to develop an effective
approach to address the uncertainties in the system
while keeping the computational complexity scalable
for large-scale power systems.
II. METHEDOLOGY
The methodology of the proposed approach is shown in
Fig. 1. Prior to solving the day-ahead SCUC, an offline
analysis is conducted to derive the enhanced reserve
policies. Monte-Carlo simulation and contingency
analysis are performed to analyze the deliverability ofreserves at each location. By conducting the offline
analysis, transmission bottlenecks and prime reserve
locations are determined. After the offline analysis isperformed, the obtained information is incorporated in
the day-ahead SCUC model to construct enhanced

reserve policies for the deterministic SCUC model. The
simulation procedure is shown in Fig. 2.
Prepare DA inputs

Derive reserve policies
and determine prime
reserve locations

Renewable
Uncertainty

Contingency
Analysis
Offline Analysis

Run SCUC withenhanced reserve
policies

Adjustment phase

DA Approval: Final DayAhead Market Solution

Fig. 1. Methodology of the proposed approach.
Contingency
Analysis
Solve DASCUC

Contingency
Analysis
Offline Analysis Stage

Derive Reserve
PolicyTest UC solution
against contingencies
and renewable
uncertainties

Testing Stage

Fig. 2. Simulation setup.

III. KEY EQUATIONS
The reserve model presented in (1)-(5) is utilized in theoffline analysis stage to evaluate the deliverability ofreserves in each scenario and determine the prime
locations of reserves.
∑𝑔 𝑟𝑔𝑡 ≥ 𝑝𝑔𝑡 + 𝑟𝑔𝑡 , ∀𝑔 ∈ 𝐺, 𝑡 ∈ 𝑇𝑐−𝐹̅𝑙𝑡 ≤ 𝐹𝑙𝑡 − 𝑝𝑐𝑡 𝑃𝑇𝐷𝐹𝑛(𝑐),𝑙 + ∑𝑔 𝑟𝑔𝑡≤ 𝐹̅𝑙𝑡 , ∀𝑐 ∈(1)

𝐺 𝐶 , 𝑙 ∈ 𝐿𝐶 , 𝑡 ∈ 𝑇
𝑐∑𝑔 𝑟𝑔𝑡≥ 𝑝𝑐𝑡 + 𝑟𝑐𝑡 , ∀𝑐 ∈ 𝐺, 𝑡 ∈ 𝑇

(2)
(3)
(4)
(5)

𝑐
𝑐
𝑟𝑔𝑡
≤ Γ𝑔𝑡
𝑟𝑔𝑡 , ∀𝑐 ∈ 𝐺, 𝑔 ∈ 𝐺, 𝑡 ∈ 𝑇𝑐Γ𝑔𝑡∈ {0,1}, , ∀𝑐 ∈ 𝐺, 𝑔 ∈ 𝐺, 𝑡 ∈ 𝑇

REFERENCES
[1]

[2]

Y. Chen, P. Gribik, and J. Gardner, “Incorporating post zonal
reserve deployment transmission constraints into energy andancillary service co-optimization,” IEEE Trans. Power Syst.,
vol. 29, no. 2, pp. 537-549, Mar. 2014.
J. D. Lyon, M. Zhang, and K. W. Hedman, “Locational reserve
disqualification for distinct scenarios,” IEEE Trans. Power
Syst., vol. 30, no. 1, pp. 357-364, Jan. 2015.

1

A Hydro-Reservoir Generic Storage Model for
Short-Term Hydro-Thermal Coordination
Manuel Alvarez, Student Member, IEEE, Sarah Rönnberg, Member, IEEE, Juan Bermúdez, Senior Member, IEEE,
Math Bollen, Fellow, IEEE and Jin Zhong, Senior Member, IEEE,
Abstract—This work presents a new model for the shortterm hydro-thermal coordination (STHC) problem. Given endpoint conditions to conform long-term water releases, and actual
reservoir conditions, a segment of a pre-built piecewise future
cost function will be chosen. The linear characteristic of the
future cost function segment will allow a linear modelling of thehydro-power plant, similar to a generic storage that behaves as athermal machine with an equivalent marginal opportunity cost.

C. STHC Proposed ModelXX
M in Φ =Cmgj · PTj,k · ∆tk + · · ·I. K EY I DEAS
We pursue to link short and long-term hydro-thermal coordination problems [1], through a piecewise future cost function[2] [3], to obtain an equivalent linear production cost modelfor the hydropower plant, and from it to find a generic storage
model that mimics a thermal machine behaviour. The main
contributions are the generic storage model applicable to thehydro-reservoir and the STHCP formulation.

PTmin< PTj,k < PTmax
jj$

II. K EY F IGURES

slope=Kf*

Vfmin

Vf*

Vfmax

Vf

j

+

i

A. Hydro-Reservoir ModelPh· ∆t − S · ∆t
Kh
CF U T (Vf ) = K0 − Kf · Vf

Vf = V0 + a · ∆t −Kf
· (Ph + Ps ) · ∆t
Kh
B. General Energy Storage Model
CPeq = Keq +Qin = Kin · ηin · Pin
Pout = Kout · ηout · Qout

(1)
(2)
(3)

k

Khi

#

(8)

· Poi,k · ∆tk

ST
Phmin
< Phi,k < Phmax
i
i

(9)

Khi · smin
< Psi,k < Khi · smax
i
i
max
Pomin
<
P
<
P
o
oi,k
i,k
i,k
Poi,k = Psi,k + Phi,k
(10)
"

k 
Nu X
X
Pou,r
Khi
min,max· V0i + ai +
· ∆tr
Poi,k
=
∆tk
Khu
u=1 r=1Po(i,k−1)
min,max· ∆tk−1 − Vfi
−
Khi
(11)
IV. K EY S TUDY C ASE R ESULTS
This study case is composed by three hydro-stations, two inparallel hydraulically connected to a third one, located downstream. The scenario 2 was computed after swapping the two
blocks of load shown in the next table.
System Load [MW]∆ta = 8h
2000
∆tb = 16h
1000

Fig. 1. Piecewise future cost function. Thermal cost vs. final volume in thereservoir

III. K EY E QUATIONSIn the following A and B models, substitutions of (1) in (2)and (5) in (6), will lead us to the equivalent production costs
for the hydro-reservoir and the generic storage respectively.

k

X X  Kfi − Kfd 

VariablePTa [MW]PTb [MW]P h1 [MW]P h2 [MW]P h3 [MW]PS1 [MW]PS2 [MW]PS3 [MW]
V f1 hm3 3V f2 hm 
V f3 hm3
λ∆t [$/MWh]Φ [$]Scenario 1∆ta∆tb
800.0000NONO
162.5000
613.7013
368.1494
195.4787
114.7606
390.8200
354.5900
0.00000.00000.00000.00000.00000.0000334.5195
200.0000
323.6170
300.0000
389.2075
300.0000
15.0000
15.0000
3.8700e+05

Scenario 2∆ta∆tb
777.0494
NONO
702.9612
100.0000
609.5388
50.0000
187.5000
72.9506
500.0000
0.00000.00000.00000.00000.00000.0000540.0000
212.3690
440.0000
300.0000
321.6395
309.2706
10.0000
15.0000
5.0900e+05

R EFERENCES(4)

Ef = E0 + Qin · ∆t − Qout · ∆t − D · ∆t

(5)

CF U T (Ef ) = K0 − Kf · Ef

(6)CPeq = Keq + Cmgeq · (Pout + PD − Pf ut ) · ∆t

(7)[1] E. Gil, J. Bustos, and H. Rudnick, “Short-term hydrothermal generation
scheduling model using a genetic algorithm,” Power Systems, IEEE
Transactions on, vol. 18, no. 4, pp. 1256–1264, Nov 2003.
[2] M. V. F. Pereira and L. M. V. G. Pinto, “Multi-stage
stochastic optimization applied to energy planning,” Mathematical
Programming, vol. 52, no. 1, pp. 359–375, 1989. [Online]. Available:
http://dx.doi.org/10.1007/BF01582895[3] M. Pereira, N. Campodónico, and R. Kelman, “Application of stochastic
dual dp and extensions to hydrothermal scheduling,” PSRI, Technical
Report, 1999.

Using Synchronized Phasor Data for Online
Determination of External System Equivalent
Moosa Moghimi Haji, Student Member, IEEE, Wilsun Xu, Fellow, IEEE
Department of Electrical and Computer Engineering
University of Alberta
Edmonton, Canada

Abstract—This paper proposes an online measurement-based
method for determining multi-port Thevenin equivalent of an
external power system. The equivalent circuit parameters are
estimated using synchronized voltage and current phasors
measured by phasor measurement units at the boundary nodes.
The method is intended to work continuously leading to real-time
equivalent models of the external system. Extra measurements
are utilized to deal with the measurement errors and noise. A
method to determine the origin of the disturbances is proposed todetect system changes that can be used for the parameter
identification process. The performance of the proposed method
has been demonstrated by using the IEEE 118 bus system.
Keywords—network equivalence, phasor measurement unit
(PMU), Thevenin circuit, synchronized phasor measurement

I. PROPOSED METHOD I11 I21 I31 0 0 0 0 0 0 1 0 0 
 0 0 0 I11 I21 I31 0 0 0 0 1 0 
 0 0 0 0 0 0 I1 I1 I1 0 0 1 
123
 2 2 2

I
I
I
0
0
0
0
0
0
1 0 0
1
2
3

2
2
2
A   0 0 0 I1 I2 I3 02 02 02 0 1 0  
 0 0 0 0 0 0 I1 I2 I3 0 0 1  I 4 I 4 I 4 0 0 0 0 0 0 1 0 0
 01 02 03 I 4 I 4 I 4 0 0 0 0 1 0 
1
2
3


0
0
0
0
0
0
I14 I24 I34 0 0 1 




T

B  V11 V21 V31 V12 V22 V32  V14 V24 V34  II. PRACTICAL ISSUES
Thevenin equivalent is represented by[V ]  [ Ze ][ I ]  [ E ] where, Ze and E are the equivalent model parameters. V and I
are boundary voltage and current phasors, respectively.

A. Disturbance Side Detection
The sign of the diagonal elements of Ze could be used as acriterion to detect the origin of the disturbance. When the
disturbance is from the external system, negative values appear
in the diagonal elements.
B. Measurement Error
To improve the accuracy of the model, additional data
points could be utilized to have more equations. In this case,least-squares method is applied to find the unknown
parameters.

 Z e,11 Z e,21
 Z e,31

Z e ,12Z e,22Z e,32

C. Fluctuation Level
A minimum fluctuation in boundary power transfer isrequired to be able to deal with measurement errors and noise.

Z e ,13   E1 Z e,23  ,  E2 Z e,33   E3 

Fluci  (Pi , new  Pi ,old
Pi ,oldQi ,new  Qi ,old
Qi ,old)  100% III. SAMPLE RESULTS
Fig. 1. Proposed method and measurement scheme

Ze and E are found by having Nb+1 boundary measurementsby (2)-(4), where, Nb is the number of boundary nodes.
T Z e ,11 Z e,12  Z e,33 E1 E2 E3   A1 B 

TABLE I
ACCURACY OF THE CONTINGENCY ANALYSIS RESULTS

Bus Voltages
MVA Flows

Largest Error (%)

Average Error (%)

0.11
7.22

0.00
0.04



1

Optimal Placement of a Continuously Variable
Series Reactor Considering Contingencies
Xiaohu Zhang, Student Member, IEEE, Kevin Tomsovic, Fellow, IEEE, Eric Heredia, Member, IEEE,and Aleksandar Dimitrovski, Senior Member, IEEE

Abstract—The Continuously Variable Series Reactor (CVSR)
has recently been proposed to control power flow. It has theability to vary transmission line reactance. The cost of the CVSR
is far less than the cost of a similarly rated FACTS device
so numerous CVSRs can be installed in a system to improve
operating margins and reduce congestion. This paper addresses
the optimal placement of CVSRs in a transmission network
considering multi-scenario framework of contingencies. Startingfrom a mixed integer nonlinear programming (MINLP) model,
a reformulation technique is proposed to transform the MINLP
model into a mixed integer linear programming (MILP) modelso that it is computationally tractable for large scale powersystems. Detailed numerical simulations on IEEE sample systems
demonstrate the proposed technique and the capability of thedevice.
Index Terms—Continuously variable series reactor, power flow
control, contingencies, MILP, loadability.

I. S TATIC M ODEL OF CVSR IN DC POWER FLOW
The static model of CVSR in DC power flow can be
represented by a variable inductive reactance with the parasitic
resistance ignored as given in Fig. 1.
CVSR

Vi

Vj

i

j

jxij

jxCVSR
Fig. 1.

(6)

max
min
θij − (1 − δij )θij
≤ zij ≤ θij − (1 − δij )θij
− Mij yij + zij bminij,CV SR ≤ wij≤ zij bmax+
Mij yij
ij,CV SR
− Mij (1 − yij ) + zij bmaxij,CV SR ≤ wij
min≤ zij bij,CV SR + Mij (1 − yij )(7)SR
Pij = (bij0 + δij bCV) · (θi − θj )
ij
min
CV SR
max
bij,CV SR ≤ bij≤ bij,CV SR(1)δij ∈ {0, 1}

(3)

(2)

II. R EFORMULATION T ECHNIQUE
It can be seen that the only nonlinearity lies in the trilinearSR
term δij bCV
θij from (1). To eliminate the nonlinearity, we
ij
introduce a new variable wij which is defined as:(4)

The constraint (1) is the modified to:
(5)

(8)
(9)

III. C ASE S TUDIESThe proposed planning/operation tool is applied to the IEEE
30-bus, 118-bus power systems. The data for the IEEE sample
systems are from the MATPOWER software [1]. The computerused for all the simulations has an Inter Core(TM) i5-2400M
CPU @ 2.30 GHz with 4.00 GB of RAM. The YALMIP
toolbox in MATLAB is selected to implement the MILP
problem, and the CPLEX solver is used to solve.
TABLE IM AXIMUM L OADABILITY AND CVSR L OCATIONS OF THE
M ULTI - SCENARIO C ASE FOR THE IEEE 30-B US S YSTEM

0
1
2
3
4

The active power flow on the line installed with CVSR canbe expressed as:

Pij = bij0 (θi − θj ) + wijminmaxδij θij≤ zij ≤ δij θij

NCV SR

Static representation of CVSR in DC power flow.

SRwij = δij bCVθijij

By introducing two auxiliary variables yij and zij , wij canbe bounded as

Located Branch(i − j)N/A(12-15)(1-2),(12-15)(1-2),(2-4),(12-15)(1-2),(2-4),(2-6),(12-15)

Objective
1.3222
1.34391.35471.36061.3662Time(s)0.1887
41.66
41.98
73.59
91.83

TABLE II
M AXIMUM L OADABILITY AND CVSR L OCATIONS OF THE
M ULTI - SCENARIO C ASE FOR THE IEEE 118-B US S YSTEMNCV SR0
1
2
3
4
5

Located Branch(i − j)N/A(16-17)(14-15),(16-17)(14-15),(16-17),(45-46)(14-15),(16-17)(25-27),(45-46)(14-15),(16-17)(25-27),(33-37),(45-46)

1.1928
1.19831.20351.2063Time(min)
0.0076
1.63
2.32
3.50

1.20855.42

1.2105

5.65

Objective

R EFERENCES[1] R. D. Zimmerman, C. E. Murillo-Sanchez, and R. J. Thomas, “MATPOWER: Steady-state operations, planning and analysis tools for powersystem research and education,” IEEE Trans. Power Syst., vol. 26, no. 1,
pp. 12–19, Feb. 2011.

1

Impacts of Series FACTS on Generation Cost in
Korea Power System by Using Optimal Power Flow
YoungJoon Lee, Student Member, IEEE, Kyeon Hur, Senior Member, IEEE
School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea
Email: ddwlee4125@yonsei.ac.kr, khur@yonsei.ac.kr

Abstract—As the power demand increasing is anticipated in the
future, facilities for stable power dispatch have been required.
Particularly, Flexible AC Transmission Systems (FACTS) aretaking spotlights for alternatives of environmental and social
issues. So KEPCO (Korea Electric Power Corporation) has
planned the installation of parallel FACTS, static compensator(STATCOM), and series FACTS, thyristor controlled series
compensator (TCSC) and fixed series capacitor (FSC), in 2018
Korea power system. In this paper, generation cost, i.e. system
marginal price (SMP), of Korea power system installed FACTS
is investigated with changing compensate rates by using optimal
power flow (OPF). Also in an aspect of the generation cost, affectsof N-2 contingency of main 765kV transmission line are checked.
In addition to main transmission line, ten outage scenarios of
nearby major transmission lines are examined as related factors
to influence the cost.
Index Terms—Flexible AC Transmission System (FACTS),
Thyristor Controlled Series Compensator (TCSC), Optimal
Power Flow (OPF), generation cost.
Fig. 1. Eastern Coast of Korea Power System Diagram

I. KEY EQUATIONS
Cost Function is typical method presenting the generation
cost, it is expressed a quadratic equation.
Ci (Pi ) = A + B · Pi + C · Pi2(1)

where Ci and Pi are cost and power amount of ith generator.
Coeffiencients, A, B and C, are determined by fuel type,generator type, etc.
Optimal Power Flow (OPF) Simulation reflects complex
of power system in addition to the Economic Dispatch (ED)method.

minimizeNX

Ci (Pi )i=1

subject toPi,min ≤ Pi ≤ Pi,max(2)

Qi,min ≤ Qi ≤ Qi,max
X
X
X
X
PG =
PL ,
QG =
QL
0 ≤ PLi ≤ PLi ,maxFig. 2. Series FACTS Impact on Generation Cost Assessment Process

II. KEY FIGURESRisk-based constraint relaxation forlook-ahead dispatch
Xian Guo, James McCalley
Dept. of Electrical and Computer Engineering
Iowa State University
Ames, IA, USA
xianguo@iastate.edu
Abstract— Due to insufficient control capability, constraint
violations of transmission line constraints cause infeasibility insecurity constrained economic dispatch (SCED). Constraint
relaxation is utilized to relieve this infeasibility. However, theallowable relaxation level is related to initial condition and has thetime variant effect. Thus, it would be beneficial if constraint
relaxation is integrated with the framework of look-ahead dispatch,which make use of inter-temporal effects. In addition, we propose risk
metric to constrain that the effects on system security by constraint
relaxation is under control. Shifting from traditional power flow
management to conductor temperature, allows existence of precontingency overflows.
Index Terms—constraint relaxation, risk, look-ahead dispatch

I. MOTIVATION
The previous study we do for risk-based constraint relaxation is
focused on single time interval, which neglects the fact that theallowable relaxation level is closely determined by initialcondition and has time accumulated effects. The optimized
relaxation for single time interval may result in continuous
infeasibility in future operation intervals. Thus, it is meaningful toextend our methods to multiple time interval. In addition, theactual limitation for flows allowed through circuits is conductor
temperature. Focusing on conductor temperature provides us
potential to handle pre-contingency violations, as well as theobtained relaxation will be more precise. In this poster, we
describe and formulate the utilization of risk-based constraint
relaxation for look-ahead dispatch.

 Pre-contingency risk 𝑅𝑖𝑠𝑘𝑝𝑟𝑒 : It is developed to evaluate
system security level under pre-contingency situation, i.e.,
normal condition
 Post-contingency risk 𝑅𝑖𝑠𝑘𝑝𝑜𝑠𝑡 : It is developed to evaluate
system security level under post-contingency situation, i.e.,“n-1” contingency
C. Look-ahead dispatch optimization model
The objective function is to minimize production costs within
the pre-defined multiple time interval. The constraints include
power balance, generation output, ramping rate, regulation
reserve requirement and risk constraints. Considering that nearterm load forecasting and updated system status will yield more
precise and cost-effective dispatch decision, the solution obtained
from the model is utilized in this way-implement for the next timeinterval, and advisory for later time intervals.
III. RESULTS ILLUSTRATION

II. METHODOLOGY
A. Dynamic heat balance equation (HBE)
HBE balances that the energy enters to the conductor equals tothe energy reserved plus the energy leaves, that is to say, heat gainfrom solar radiation 𝑄𝑠 equals to heat loss along conductor 𝐼 2 𝑅
and convection cooling effects from natural heat radiation 𝑄𝑟 andambient surface wind 𝑄𝑐 .
B. Risk constraints
Risk constraints are proposed to guarantee that the effects on
system security imposed by allowing overloading is under control.

Figure 1. Change of load and conductor temperature

A constraint relaxation is allowable if the conductor temperaturedoes not hit limits with violating flow.
IV. CONCLUSION
The risk-based constraint relaxation for look-ahead dispatch can
re-allocate the highly-loading flows in a more economic andefficient way, with controlled stress on system security.
Furthermore, our model can handle both pre-contingency and postcontingency overflows.

Transmission Congestion Management Considering
Parking Lots and Demand Response ProgramsAmin Mohsenzadeh, Chengzong Pang
Department of Electrical and Computer Engineering
Wichita State University, Wichita, USA
Axmohenzadeh@wichita.edu
Abstract—In this paper, a novel approach to determine theoptimal capacity of Emergency demand response programs(EDRP) and PHEV parking lots in order to relieve transmission
congestion is proposed. The behavior of EDRP participants andavailable capacity of parking lot are mathematically modeled.
Results demonstrate optimal contribution of parking lots and
EDRP has a significant effect on mitigating congestion anddecreasing the cost of system.

InEV ,i Congestion ,i  P t

EV ,i

t
EDRP ,iP(4)t PEV ,iIndex Terms—Congestion management, EDRP, parking lot,
Shift factor, Transmission system

I.

PROBLEM FORMULATIONIn this paper, utilities have two options to manage theconsumption and local generation in their own area: (1)reducing loads by implementing EDRP, and (2) increasing
the local generation by injecting the stored power of PHEV tosystem. The main purpose of this paper is to minimize theobjective function considering constrains of system.
T n
 T nbustMin (Pnet,ik )  PEVt ,i InEV ,i  PEDRP,i InEDRP,i  (1)t 1 i1
 t1 k 1
Figure. 1 Schematic of test system

Figure. 2 shows the optimal capacity of EDRP and PHEVs’
parking lot in each bus in order to relieve the transmission
congestion and minimize the total costs of utility. Blue andred charts represent PHEV parking lot and EDRP capacity,respectively.

A.

Emergency demand response programs
In (2) EDRP is economically modeled in response toelectricity price and incentive of ISO in emergency cases.
d (i )   (i )   0 (i )  A ( j )  24d ( i )   d 0 ( i )   j 1 E ( i , i ) 0 0 (i ) 24  ( j )   0 ( j )  A ( j )  

E
(
i
,
j
)
(2)


0 ( j)j 1
 j  iA (j) in $/MWh is the incentive which is paid in j-th hour.

B. Modeling the PHEVs parking lot
Equation (3) shows the capacity of parking lot at each hour.
t
EV ,max

P

SOCn BCn  n 1
CL tdisN(3)

Equation (3) determines how much incentive should be paid
to parking lots’ owners to inject power to the system.

Figure. 2. Optimal capacity of EDRP and parking lot

II.

CONCLUSION

The results proved total cost of system has been decreased
and transmission congestion totally relieved by implementing
the proposed approach.

Urban Distribution Grid Topology Reconstruction
via Lasso
Yizheng Liao

Yang Weng

Ram Rajagopal

Dept. of Civil and Environmental Eng., Stanford University, Stanford, CA 94305
Email: {yzliao,yangweng,ramr}@stanford.edu
Require: |vst | for s = 2, . . . , p, t = 1, . . . , N
1: Normalize and standardize |Vs | such that it has zero mean and unity
variance.
2: for s = 2, . . . , p do
3:
Solve the Lasso optimization problem
(N
)
X
t
t
T s 2
s
s
c
β = argmin(vs − (vS\{s} ) β ) + λkβ k1
β s (1)=0

t=1

cs .
for bus s and find the parameter vector estimate β
4: end for
cs , s ∈ S} via the AND-OR rule to
5: Combine the parameter vectors {β\ .
form an edge estimate EAND-OR

where Φ̃ = 1 − Φ, Φ is the cumulative distribution function of
N (0, 1), and σ̂s2 is the empirical variance of Vs
III. K EY R ESULTS
E RROR RATE FOR IEEE 8- & 123- BUS SYSTEMS , α = 0.05

TABLE I.
Rule

8-Bus

AND
OR
AND-OR0%
14.29%
0%8-Bus
with Loops
10%
0%
0%

123-Bus
3.28%
1.64%
0%

123-Buswith Loops
3.21%
1.61%
0%

123-BusAverage ER (%)

I. S YSTEM M ODELThe problem of distribution grid topology reconstruction isdefined as follows:• Problem: data-driven topology reconstruction based onbus voltage magnitudes.
• Given: (1) a distribution grid represented by the graphical model G = (S, E), as shown in Fig. 1, where thevertex set S = {1, 2, . . . , p} represents all buses andthe edge set E = {eij , i, j ∈ S} represents the setof branches, which are unknown or partially known;(2) a sequence of historical voltage measurements
vst , s ∈ S, t = 1, . . . , N• Find: the grid topology E in the dashed box in Fig. 1

Algorithm 1 Distribution Grid Topology Reconstruction via Lasso

100

Correlation-based alg
Info theory-based alg
AND-OR rule

50
0
0

20

40

60

80

100

120

123-Bus with Loop
Average ER (%)

Abstract—The growing integration of distributed energy resources (DERs) in urban areas raises various reliability issues.
To ensure robust distribution grid operation, grid monitoring
tools are needed, where the topology reconstruction serves as
the first step. However, the topology reconstruction is hard in
distribution grid. This is because 1) the branches are difficultand expensive to monitor since most of them are undergroundin urban areas; and 2) the assumption of radial topology inmany studies is inappropriate for meshed urban grids. To addressthese drawbacks, we propose a new data-driven approach toreconstruct distribution grid topology by utilizing the newly
available smart meter data. Specifically, a graphical model is built
to model the probabilistic relationships among different voltage
measurements. With proof, the topology reconstruction problem
is formulated as a regularized linear regression problem (Lasso)
to deal with meshed network structures. Simulation results show
highly accurate estimation in IEEE standard distribution test
systems with and without loops using hourly smart meter readingsof 110, 000 PG&E residential customers over one year and NREL
solar panel generation data.

100

Correlation-based alg
AND-OR rule

50
0
0

20

40
60
80
100
Number of Unknown Edges (E)120

Fig. 2. Error rate comparison of the proposed algorithm and other existing
methods in IEEE 123-bus system, with and without loop.

II. T OPOLOGY R ECONSTRUCTION WITH L ASSO
Theorem 1. In a distribution grid, if the current injection
at each bus is approximately independent, the voltage ofbus s and the voltages of all other buses, which are notconnected with bus s, are conditionally independent, given
the voltages of the neighbors of bus s, i.e. Vs ⊥ {Vu , u ∈S\{N (s), s}}|VN (s) , where the neighbor set N (s) contains
the vertices of the neighbors of Vs
Lemma 1. Given the probability of false alarm 0 ≤ α ≤ 1,
the regularization parameter λ for bus s isα2σ̂sλs (α) = √ Φ̃−1 ( 2 ),
(1)
2p
N

Time (sec)30

Fig. 1. A graphical model of IEEE 8-bus system with loops. The dashededges are additional branches to form loops. The branches within the reddashed box are assumed to be unknown.

Correlation-based alg
AND-OR rule

20
10
0

0

20

40
60
80
100
Number of Unknown Edges (E)

120

140

Fig. 3. Comparison of computational time of the proposed algorithm andother existing methods in IEEE 123-bus system with loops.
TABLE II.

E RROR RATE FOR IEEE 8- & 123- BUS SYSTEMS WITH
DER, α = 0.05

Rule

8-Bus

AND
OR
AND-OR

14.29%
14.29%
0%8-Bus
with Loops
0%
0%
0%

123-Bus
1.64%
0%
0%

123-Buswith Loops
1.59%
0%
0%

Sequential Power Flow Simulation of Integrated
Dynamic Wireless Power Transfer Systems
Haipeng Xie, Tao Ding, Shiyu Liu and Zhaohong Bie
Xi’an Jiaotong University, China

Abstract—Quasi-continuous and time-varying DWPT charging
loads bring large challenges to the power flow analysis ofintegrated DWPT systems. In this paper, a sequential load modelis proposed to describe the temporal dependency of DWPT
charging demands. Furthermore, a new power flow analysis
method is addressed to deal with quasi-continuous and timevarying DWPT charging loads. Then a daylong sequential power
flow simulation is conducted on a test system. Simulation results
show that the fluctuation of branch flows varies significantly.
Adequate power and energy managements are necessary tointegrated DWPT systems.

can be calculated with back/forward sweep method. Finally,
the continuous power flow can be obtained fromRx
r0 (xa − xk ) xka L(x, t)dx
U (xa ) = α
+ Uk
(3)
Uk
Z xa
z 2 (xa − xk )2
P (xa ) = Pk −
(U
−
U
(x
))
−
L(x, t)dxka2r0 (1 + z 2 )xk(4)III. S OME S IMULATION R ESULTS

I. I NTRODUCTION

II. S EQUENTIAL P OWER F LOW A NALYSIS M ETHOD
To set up the load model of transmit units, a two-level daily
EV flow model is built. In the first level, the traffic flow issampled as follows.

fr (k) = ρk f˜r (k) + k , k = 1, 2, ..., 96(1)
In the second level, the load of a DWPT transmit system isobtained. The main equation is as follows.
"
!
#

nl X
ml
X
t − T l (i) V l (i) − x
L(t, x) =rect
Pcl (i)D
l=1 i=1(2)
Then an equivalent lumped model is proposed to deal with
distributed DWPT loads. After the equivalent, the power flow

1.015
Branch Flow/MW

Branch Flow/MW

1.002
1
0.998
0.996
0.994

0

0.5

1
Distance/km

1.5

1.01
1.005
1

2

0

0.5

1
Distance/km

1.5

2

0.5

1
Distance/km

1.5

2

0
Branch Flow/MW

Branch Flow/MW

0.6
0.4
0.2
0
−0.2
−0.4
0

0.5

1
Distance/km

1.5

−0.5
−1
−1.5
−2
−2.5
0

2

(a) 6:00-6:15(b) 12:00-12:15

4

1.05
1.04

2

1.03

0
1.02
Voltage/p.u.

Power/MWThe capacity of battery and the time of charging restrict
the penetration of EVs. Therein the dynamic wireless powertransfer (DWPT) is an alternative to deal with the problem.
DWPT, also known as in-motion/on-road wireless charging
or on-line electric vehicle (OLEV) technique, enables EVs tobe recharged while they run on the road, which is able toextend the endurance and convenience of EVs substantially. A
power distribution network containing DWPT units, distributed
generators (DGs) and other loads is called an integrated DWPT
system. However, the conventional power flow calculation
method is only computed under each snapshot without taking
temporal dependence into account. To address this problem, asequential power flow simulation of integrated DWPT systems
is investigated: 1) a paradigm of integrated DWPT systems
containing DGs, various loads and potential energy storage
systems was presented; 2) a sequential load model was built
for DWPT charging systems; 3) a sequential power flow
analysis method for integrated DWPT systems was proposed.

−2

1.01
1

−4

0.99

−6
0.98

−8
0

5

10
15
Time/h20(c) Power fed into the feeder

0.97

0

5

10
15
Time/h20(d) Voltage at the feeder’s end

Fig. 1. Some results for the simulation.

TABLE IP OWER AND ENERGY RESULTS
Items

Energy/MWh

Max Power/MW

Substation support-10.75-6.523

Wind generation

30.14

4.838

Solar generation

21.18

4.75

Household consumption

6.961

0.45

EV Charging consumption

33.17

3.055

Net loss

0.43950.1978

Distributed Economic Dispatch on a Detailed Realtime Power System Simulator
Zhenhuan Ding

Ziang Zhang

Department of electrical and computer engineering
Binghamton University
NY, USAzding1@binghamton.edu

Department of electrical and computer engineering
Binghamton University
NY, USAzhangzia@binghamton.edu

Abstract— Distributed algorithms have shown great potential
in solving economic dispatch problem (EDP) of large-scale
systems, especially for overcoming the heavy tasks of data analysis
in modern power system. This study extended the leader-follower
Incremental Cost Consensus (ICC) algorithm to a leaderlessversion. The proposed Leaderless ICC (LICC) algorithm is able tocontinuously track power mismatch of the system and dispatch
generators without a leader. A detailed model based on IEEE 14bus system with five synchronous generators with frequency
control and voltage regulation has been implemented in
Matlab/Simulink and RT-LAB. The effectiveness of proposed
LICC algorithm has been demonstrated on the real-time powersystem simulator.
Keywords— Distributed control, economic dispatch, consensus
algorithm

I. INTRODUCTION

Fig. 1.

Distribute Controlled Cyber-Physical Power System

Large number of distributed generators challenges the
traditional methods for EDP due to the limited communication
ability and calculation capability of the central controller in thesystem. Recent methods proposed in this area including using
distributed algorithm as the control strategy which replaces thecentralized approach.
In this poster, we proposed a LICC algorithm to seek the
optimal operation point of a power system. A detailed IEEE-14
bus model is built to verify the correctness and feasibility of the
proposed algorithm.
II. MODEL OUTLINE AND RESULTS
The proposed LICC algorithm consists of two levels ofconsensus, which are distributed mismatch estimation consensus
and incremental cost consensus. The proposed algorithm isimplemented in the embedded distributed controllers as
communication and control layer. A detailed IEEE-14 bus test
system model is built as the physical layer to verify thecorrection of the method, which is shown in fig 1.
Fig 2 depicts the simulation results. The total demand
increases 100 MW at the time t=1000s. The consequences fromthe real-time simulator indicate that the incremental costs areconverged to the reference, as fig 2(a), which is the optimalvalue calculated by the centralized method. Meanwhile, fig 2(b),(c) and (d) demonstrate the power quality such as frequency andterminal voltage would maintain in an acceptable range during
the dynamics.

Figure 2. Real-time simulation result: (a) incremental cost, (b) power mismatch,(c) frequency, (d) voltage

A Classification-based Methodology for Finding
Initial Solutions in Transmission Expansion
Planning Problems
Daniel Ricardo Marin-Londono

Diego Mejı́a-Giraldo

Operations Department
ISA-Intercolombia
Medellin, Colombia
Email: dmarin@intercolombia.com

Dept. of Electrical Engineering
University of Antioquia
Medellin, Colombia
Email: diego.mejia@udea.edu.co

Abstract—The transmission expansion planning (TEP) problems consists of finding the optimal set of candidate circuit
addition for a power transmission network for attending theload increase requirements at minimum cost. The mathematicalmodel is a large-scale mixed integer program that requires highcomputational resources. It has been known that solving the“pipes and bubles” (P&P) model provides an integer solutionthat is not close to the optimal solution determined by thewell-known disjunctive model. Sometimes, this solution is noteven feasible given that the P&P model does not consider the
network parameters (reactance). To overcome this issue, thisworks proposes a characterization of the electrical power systemusing sensitivity factors that establish a mapping between power
flow and different configurations of addition of candidate circuits.
Results have demonstrated that our initial solution guess arecloser to the optimal solution provided by the disjunctive model.

I. I NITIALIZATION A PPROACH
Our objective is to obtain a better informed initial solution
guess in the TEP instead of using the P&P solution as acommon initialization strategy. Thus, we try to capture theinfluence of new transmission lines in the network power
flows. To do so, we propose the use of a classification
technique that effectively maps number of circuits added andactive power flows. For simplicity, we illustrate our approach
the least-squares (LS) methodology in order to obtain a set of
linear equations in our initialization model as follows:X
fl = βl,0 +
βl,m nmm ∈L+where βl,0 and βl,m are the coefficients obtained from LS
methodology. Once these are computed, the model (1)–(4)needs to be solved in order to obtain the initial solution guess
for the TEP.
II. L INEARIZED I NITIALIZATION M ODEL

minimizen∈Z+X

cl (nl − nex
l )

t

(1)

subject toXβl,0 +
βl,m nm ≤ nl flmax , ∀l ∈ L+

(2)

m∈L+

βl0 +

X

max
βm nm ≤ nex, ∀l ∈ L−
l fl

(3)

m∈L+

X

Ai,l nl flmax ≥PiG − PiD−

X

Ai,l nex flmax , ∀i ∈ B

l∈L−

l∈L+

(4)
where L is the candidate right-of-way set to expand and L−
is set of right-of-ways that are not allowed to expand. Notethat L+ ∪ L− = set of all system corridors. The objectivefunction (1) represents the total cost of investing in new lines.
Thus, cl is the investment cost of a transmission line built on
corridor l, and nex
l is the number of existing transmission lines
on corridor l. Constraints (2) and (3) represent the maximum
power flow on each corridor. And constraints (4) guaranteethat installed transmission capacity can transport the net power
injection in each bus. A is the node-branch incidence matrix.
Finally, nl represent initial solution guess of the number ofcircuits to add to each corridor.
+

III. R ESULTS
The proposed approach was tested using the six-bus Garver
network with a modified set of reactances to make the problem
more challenging. We obtain initial solutions in order to solve
the TEP disjunctive model. The initial solution provided by
the P&P solution is not even feasible. Therefore, the CPLEX
solver takes 1349 branch & bound iterations to achieve ahigh-quality integer solution. However, when we initialize thedisjunctive model using the initial solution provided by ourapproach, the CPLEX solver takes 1144 iterations, i.e., 15%less computational time.
ACKNOWLEDGMENT
The authors want to thank all the researchers involved in
the project “Planeación Integrada Generación Transmisión en
Colombia”, which is funded by UPME and Colciencias.

Situation Awareness of Available Transfer
Capacity Using PMU Data
Yawei Wei, Student Member, IEEE, and G. Kumar Venayagamoorthy, Senior Member, IEEE
Real-time Power and Intelligent Systems Laboratory, Clemson University, Clemson, SC, 29634 USA.
E-mail: yaweiw@clemson.edu, gkumar@ieee.org.
Abstract— The amount of available power transfer capability
available (ATC) at any given time is a function of system-wide
pattern of loads, generations and transmission network availability.
The Independent System Operator (ISO) relies on this criticalindicator to make grid-wise generator control. Similarly, ATC, as a
key index for the power market system, has continuously drawn
significant attention for wholesale power companies, retailers anddistributors. To accurately and quickly compute the power systemtransmission line capabilities, the available transfer capability
needs to be computed fast and accurately. This paper discusses theavailable transfer capability and incremental transfer capability
calculation process using phasor measurement (PMU) data. The
ATC computing results are visually presented in real time. This
paper presents available transfer capability and incremental
transfer capability results for a power system under different
conditions, including solar energy integration.

Figure 1. two-area four-machine system with solar integration

I. Key Figure
In Figs. 1 and 2, two area four machine power system and IEEE 16
machine 68 bus power system model have been presented for real-time
ATC computing. The real-time ATC computing is demonstrated using
real-time digital simulators and PMUs, presented in Fig.3

II. Key Equations
For a general definition, ATC is known as two utilities’ maximum
value of additional active power transfer. ATC is defined in this paperas:ATC =TTC − CBM − TRM(1)

Where the TTC is the total transfer capability; the CBM is the capacity
benefit margin and the TRM is the transmission reliability margin.

Figure 2. IEEE 16-machine 68-bus power system

Figure 3. Real-time power and intelligent systems laboratory

Optimal PI Controllers for Switch Model
Power Electronic Interfaces
A. Arzani, P. Arunagirinathan, I. Jayawardene, and G.K. Venayagamoorthy
Real-Time Power and Intelligent Systems Laboratory
Holcombe Department of Electrical and Computer Engineering, Clemson University, Clemson, SC 29634, USAaarzani@g.clemson.edu, parunag@g.clemson.edu, rjayawa@g.clemson.edu, and gkumar@ieee.org
Abstract— Switch-model power electronic inverters areheavily deployed as the main technology in providing
controllable voltage magnitude and frequency at their outputs,
enabling the flow of variable DC energy resources (DERs) to theAC-side customer and/or AC-grid. Various Distributed Energy
System (DES) architectures have been designed and proposed
depending on many attributes including size, and application of
the installed system. However, what becomes important in allthese architectures is the faultless/seamless operation andreliability of the Power Electronic Interfaces (PEIs) i.e. inverters.
Recent studies demonstrate that enhanced performance of a PEI
can be highly achieved through optimal tuning of its controller
parameters. While Conventional tuning methods such as CohenCoon and Ziegler-Nichols are based on trial-and-error, theiroptimum performance can be achieved for mainly first-ordersystems. For systems with higher order transfer functions and/or
more complexity, application of these tuning methods iscumbersome, costly, and ineffectual. Thus, heuristic approaches
for optimum self-tuning of PEI controllers have been developed,
enabling the move towards harnessing full DESs performance.
Keywords— Optimal PI-Controllers, powerinterfaces, rooftop PV systems, utility-scale solar parks

for PEIs through harnessing the capabilities of particle swarm
optimization (PSO), a heuristic optimization method. The PSO
algorithm developed in MATLAB, and integrated to a realtime digital simulator (RTDS), searching for optimal solutions
for both KW-scale rooftop PV systems with VSI control
structure in dq-reference frame [1] and a multi-megawatt
utility-scale PV system with its PV-VSIs voltage mode
controller in abc-reference frame [2]. Improved steady-stateand dynamic operation of PEIs concludes the effectuality andbenefits of exploiting computational intelligence (CI)techniques for PEIs; enabling smoother renewable energy
integration, reliable delivery of energy at a lower cost, andfurther reduced carbon footprints worldwide.
II.
DC-Link Voltage Control

0
kp1

VDC

-

Reactive Power Control

kp2

Qinjref-

This work is supported by US National Science Foundation (NSF) under
grants IIP #1312260, IIP #1308192, and ECCS #1408141. Any opinions,findings, and conclusions or recommendations expressed in this material arethose of the author(s) and do not necessarily reflect the views of NSF.

DC-Link Voltage Control

Decoupled Current Control-

Vtdref

md

kp1

VDC

-

Lf
Lf
kp4

itqref-eq

Reactive P ower Control

kp2

Qinj refVtqref

uq

-

mq

Ki4/s

Ki2/s

Qinj

vsq

itq

Ki1/s

VDC ref

Vdc
2

ω

1.2

0.8

Controller 1
Controller 2

1.1

Manual PI
PSO-PI

1

0.6

0.9

P (pu)

0.8
P (pu)

0.7
0.6
0.5

0.4
0.2

0.4
0.3
0.2
0

0
0.5

1

1.5

2
2.5
Time (sec)

3

0.02

3.5

4

Controller 1
Controller 2

0.01

0

5

10

15
Time (sec)

20

25

30

0.06

0

Manual PI
PSO-PI

0.04

-0.01

0.02

-0.02

Q (pu)In the last decade, the role of DERs in search for a lowcarbon society through their grid-integration has been
drastically emphasized. This emphasis has been addressed andreplied to by both academia and industries around the globe inmany aspects, among them at component (device) level by
improving the performance of power electronic semiconductor
switches, enabling to manufacture and utilized devices withhigher power handling capabilities and faster switching rates;
at control level through designing real-time computer
controllers, which enable implementation of advanced control
algorithms; and at the system level by carrying out powersystem static and dynamic studies in presence of DESs. Thus,
it can be realized that the power electronic inverters in DESs
take the most important responsibility, since they operate as
the main PEI and should bring the capability of fixating thevoltage/current and frequency at PCC to the grid. Therefore,
their sophisticated and proper functioning is integral to theseamless operation of both the DESs and the AC-grid. For thispurpose a controller self-tuning technology has been proposedud

Fig 1. (a) Rooftop PV-VSI controllers in dq-reference frame, (b) Solar Park PV-VSIs controllers in abcreference frame

Q (pu)INTRODUCTION

kp3
ed

Ki3/s

Ki2/s

Qinj

I.

Vsd

-

itdref

Ki1/s

VDCrefelectronic

KEY FIGURES & RESULTS
itd

-0.03
-0.04

0
-0.02

-0.05
-0.06

-0.04

-0.07
-0.08
0

0.5

1

1.5

2
2.5
Time (sec)

3

3.5

4

-0.06

0

5

10

15
Time (sec)

20

25

30

Fig 2. PV-VSI manual and PSO-PI tuned controller responses with respect to irradiance step change for(a) 53kW Rooftop PV-system, (b) 210MW Solar PV Park

REFERENCES
[1]

[2]

A. Arzani, P. Arunagirinathan, and G.K. Venayagamoorthy,“Development of Optimal PI Controllers for a Grid-Tied Photovoltaic
Inverter”, in Proc. IEEE Symposium on Computational Intelligence
Applications in Smart Grid (CIASG2015), December 2015.
A. Arzani, P. Arunagirinathan, I. Jayawardene, and G.K.
Venaygamoorthy, “Dynamic Performance Enhancement of Utility-Scale
Solar PV Parks in a Multi-Area Power System”, IEEE PES Power
Africa Conference (PowerAfrica), June 2016.

Enhancing AGC for Multi-Area Power
Systems in the Presence of Variable
Generation
Iroshani Jayawardene, Student Member, IEEE, Ganesh K. Venayagamoorthy, Senior Member, IEEE
Real-Time Power and Intelligent Systems (RTPIS) Laboratory
The Holcombe Dept. of Electrical and Computer Engineering
Clemson University, SC 29634, USA
Emails: iroshani.jayawardene.2014@ieee.org, gkumar@ieee.org
Abstract – Increasing penetration of renewable energy
sources into the electric power grid introduces power andfrequency fluctuations. Real-time power system
operations need to be improved to overcome thesefluctuations. In this study, a large photovoltaic (PV)plant integrated two-area four machine power system isstudied. Area 1 provides power interchange to Area 2,
while utilizing the PV power generation in Area 2. An
automatic generation control (AGC) is implemented tominimize power fluctuations and maintain the desired
system frequency while concurrently maximizing thepenetration of PV power into Area 2. AGC performanceis enhanced by predicting PV power generation and area
frequencies. Typical results are studied for the steadystate, variable PV generation and large tie-line
disturbance conditions.

power generation. Each bus on the power system isconsidered as a CCN cell. Fig. 3 shows the prediction
results obtained for PV power and frequency at bus 1.
II.

KEY FIGURES

Fig 1. 200 MW PV plant integrated into a two-area powersystem.

Index Terms—AGC, PV power, predictions, tie-line
bias control, tie-line power flow and frequency control

I.

INTRODUCTION

The variability and uncertainty of renewable energy
introduces challenges to the operations of bulk powersystem. This causes system frequency and tie-line
power fluctuations in power systems. Thesefluctuations can be controlled in multi-area powersystems using automatic generation controls (AGC).
In this study, a two‐area four‐machine power systemwith a large PV plant is considered (fig. 1). Each Area
has two generators and a 210 MW PV plant is
integrated into Area 2. Two AGCs are implemented tocontrol the dynamic‐tie line power flow andfrequencies of the power system.
AGC in Area 1 (fig. 2), uses PV power and Area 1
frequency in deciding the Area 1 generation andregulating frequency limits of the power system. AGC
performance can be further enhanced by predicting PV
power generation and frequencies, where theprediction time step should be ‘synchronized’ with thetime constants in AGCs and latencies incommunication and computing.
A cellular computational network (CCN) is
implemented to provide situational intelligence onnear future values of the generator frequencies and PV

Fig 2. Area 1 AGC functional diagram for the power system
shown in Fig 1.

Fig 3. PV power and frequency prediction results

REFERENCES[1] Jayawardene I, Venayagamoorthy GK, “Reservoir Based
Learning Network for Control of Multi-Area Power Systemwith Variable Renewable Generation”, Neurocomputing –Vol. 170, December 2015, pp. 428-438.

Speed Deviation Signals of Synchronous Generators
Derived from PMU Data
1

Paranietharan Arunagirinathan, Student Member, IEEE, 1,2Ganesh K. Venayagamoorthy, Senior Member, IEEE
1

Real-Time Power and Intelligent Systems Laboratory,
Holcombe Department of Electrical and Computer Engineering, Clemson University, Clemson, SC 29634, USA
2
Eskom Centre of Excellence in HVDC Engineering, University of KwaZulu-Natal, Durban, South Africa
p.a.1984@ieee.org and gkumar@ieee.org
Abstract—Power system control center operation is always
Phasor measurement units (PMUs) are smart sensors
challenging with dynamic changes happening in the system. A
installed in the geographically spread electric power gridchange in operating condition or a system disturbance will cause
network. The advancements in communication infrastructure
the synchronous generators’ rotor to oscillate from its nominal
promises timestamped wide area power system measurements
speed. This results low frequency rotor oscillations in an
received at control centers at much faster rate compared tointerconnected system with multiple generators. Historical, it isexisting measurement techniques used in supervisory control
known that poorly damped low frequency rotor oscillations haveand data acquisition (SCADA) systems. Remote measurements
caused blackouts. Hence, it is very important that the monitoringfrom PMUs enable tuning power system controller parametersof low frequency rotor oscillation modes following a contingency.
remotely. PSSs are tuned concurrently by a heuristic algorithm
A modal analysis tool could use generator speed deviation signals
for multiple operating conditions of the system using
to calculate these low frequency rotor oscillations and its
synchrophasor data [2]. A modal analysis visualization could
corresponding damping ratios. Rotor speed deviation or
display synchronous generators’ oscillation modes andfrequency deviation are used as input signals for power system
damping ratios at control center to give deep insight oncontrollers. The rapid deployment of phasor measurement units
different generators’ PSS performance. This provides(PMUs) on electric power grid network enables wide area
situational awareness to system operators to take quick
synchronized system measurements available at system control
decisions on real-time operations.
centers for control and monitoring purposes. In this study, adetailed analysis on PMU based estimated generator speed
deviation response is carried out and compared to those obtained
from direct measurements of speed deviations at the respective
generating stations. A two-area four machine benchmark system
has been simulated on a real-time digital simulator platform for
purposes of this study.
Index Terms—Modal analysis, phasor measurement unit, powersystem, speed deviations, synchronous generators.

This study investigates the suitably and accuracy of speed
deviation signals of synchronous generators derived using
remote PMUs. A comparison is performed with direct
measurements taken at the generating stations. A two-area fourmachine benchmark system [1] has been simulated on a realtime digital simulator. All four generators are equipped with
PMUs and power system controllers including turbine
govenors, PSSs and AVRs.
REFERENCES

I. INTRODUCTION
Synchronous generators’ rotor speed deviation will respond
immediately following a power demand change in the system.
Hence, power system controllers such as power systemstabilizers (PSSs), governors and wide area PSSs use speed
deviations of synchronous generators as feedback signals for
damping oscillations, and regulating power and speed [1] . The
output signals of PSSs and wide area PSSs are used as an
auxiliary signal in generators’ automatic voltage regulators(AVRs) to damp the low frequency rotor oscillations quickly inorder to keep the system stable [2]- [3]. Wide area control
signals are generated by identifying the coherent groups ofsynchronous generators. Another primary controller ofsynchronous generators is turbine governors, which also uses
rotor speed deviation as the input. The tuning governor
parameters give improved generator frequency responses [4].
This work is supported in part by US Department of Energy (DOE) under grant DE0E000060, the US National Science Foundation (NSF) under grants #1312260, #1308192 and #1232070 and the Duke Energy Distinguished Professor Endowment
Fund. Any opinions, findings and conclusions or recommendations expressed in thismaterial are those of the author(s) and do not necessarily reflect the views ofDepartment of Energy, National Science Foundation and Duke Energy.

[1] P. Kundur, Power System Stability and Control, New York: McGrawHill, Inc, 1994.

[2] P. Arunagirinathan and G. K. Venayagamoorthy, "Multiple Power

System Stabilizers Tuning Using Mean-variance optimization," inIntelligent System Applications to Power Systems, Porto, Portugal,
Septermber 11-16, 2015.

[3] K. Tang and G. K. Venayagamoorthy, "Online coherency analysis ofsynchronous generators in a power system," in Innovative Smart Grid
Technologies Conference (ISGT), Washington DC, USA, 2014.

[4] S. Rosen, P. Arunagirinathan, I. Jayawardene and G. K.

Venayagamoorthy, "Optimal Tuning of Governors on Synchronous
Generators in a Multi-Area Power System with a Large Photovoltaic
Plant," in IEEE PES Power Africa Conference, Zambia, June 28-29,
2016.

Setting a Carbon Tax for the Electricity Sector
Daniel J. Olsen

Miguel A. Ortega-Vazquez

Department of Electrical Engineering
University of Washington
Seattle, WA 98195
Email: djolsen@uw.edu

Department of Electrical Engineering
University of Washington
Seattle, WA 98195
Email: maov@uw.edu

Abstract—Reducing greenhouse gas emissions from electricity
generation is critical to mitigating manmade climate change, andemissions taxes are one of the simplest methods to achieve these
reductions. If a regulator can estimate the impact that a given
tax rate has on the electricity market, they can tune the tax
rate in order to achieve a certain level of short-term emissions
reductions with minimal tax burden. This poster presents a multiagent system consisting of a regulator and a system operator, amethod for converging to the optimal tax rate, and analysis of
the results when implemented in a case study.

I. A PPROACH
The problem is formulated as a multi-agent system: aregulatory agency seeks to reduce emissions beneath a certain
target using a tax on CO2 e emitted, and a system operator
minimizes their cost of supplying electricity given a certain
tax rate. The regulator’s perspective is modeled in Equations(1)-(2) and the system operator’s in (3)-(4).
min P

(1)

subject toEmissions(P ) ≤ Emissionstarget
min F uelCost + P · Emissions

(2)
(3)

subject topower flow, generator, and network constraints(∼203,500 tons), the optimum tax rate (within 1¢) is $5.70/ton,with a resulting daily emissions of ∼202,000 tons.
If the tax rate were $5.69/ton instead, the emissions jump
to ∼204,000 tons. This large jump is due to the non-convexityof the underlying unit commitment problem. In day 5, thehigher tax rate leads to the shutdown of 5 of the 9 runningcoal generators, and the startup of 3 additional gas generators.
The magnitude of these “overshoots” will decrease given ahigher number of representative days, as the impact of eachindividual day’s commitment diminishes.
For this system, the maximum achievable reduction is12.3%, as shown in Table I. There is not much effect oftax rates less than $1/ton, but the impact quickly accelerates
from there. A reduction of 5% (40% of the maximum) can beachieved with a tax rate of $4.65/ton.
TABLE IA NNUAL EMISSIONS TARGETS , TAX RATES , AND RESULTING DISPATCHES
Emissions target(million tons)
79.2 (baseline)
78.4 (-1%)
75.2 (-5%)
71.3 (-10%)
69.4 (-12.3%, min.)Required tax
rate ($/ton)
0
1.63
4.65
14.30
N/A

Fuel Cost(billion $)2.256
2.258 (+0.1%)
2.269 (+0.5%)
2.298 (+1.5%)
2.379 (+5.4%)

Total Cost
(billion $)2.256
2.385 (+5.7%)
2.618 (+16%)
3.318 (+47%)
N/A

(4)

If the regulator’s target is feasible for the system operator toachieve, then the minimum tax rate which achieves this target
can be determined reliably using the Bisection Method [1].
II. C ASE S TUDY
The electrical system for this case study is a simplified
version of the ISO New England System [2]. This network
includes 8 zones, 12 transmission lines, and 76 generating
units: a mix of nuclear, coal, gas, and oil. Five representativedays were generated from historical data.
III. R ESULTS
The minimum-cost dispatch is found using a tax rate of$0/ton CO2 e, and the minimum-emission dispatch is foundas the tax rate approaches infinity. As the tax rate increases,
generation is shifted away from coal and towards gas.
When the desired emissions are halfway between theminimum-cost value and the minimum-emissions value

IV. C ONCLUSION
A method is shown for reliably determining the minimum
carbon tax rate required in order to achieve a certain levelof emissions over a one-year horizon. Further research could
include:• a more complex system with additional actors representing companies who may want to invest in new infrastructure to take advantage of opportunities created by the
impact of carbon taxes• regulator objectives which also consider the total tax
burden• the elasticity of customer demand with respect to total
electricity priceR EFERENCES[1] A. Quarteroni, R. Sacco, and F. Saleri, Numerical Mathematics, 2007.
[2] D. Krishnamurthy, W. Li, and L. Tesfatsion, “An 8-zone test system
based on ISO New England data: Development and application,” IEEE
Transactions on Power Systems, vol. 31, no. 1, pp. 234–246, Jan 2016.

IEEE PES Student Activities Subcommittee
Chair
Dr. Jignesh M. Solanki
Assistant Research Professor
Lane Department of Computer Science and Electrical EngineeringWest Virginia University
395 Evansdale Drive,
Morgantown, WV 26506-6109, USAjmsolanki@csee.wvu.edu

Vice-Chair
Dr. Aaron St. Leger
Assistant Professor
Department Electrical Engineering and of Computer Science
United States Military Academy
646 Swift RoadWest Point, NY 10996-1905, USA
Aaron.StLeger@usma.edu

Secretary
Dr. Valentina Cecchi
Assistant Professor
Department of Electrical and Computer Engineering
The University of North Carolina at Charlotte
9201 University City Blvd.
Charlotte, NC 28223, USA
vcecchi@uncc.edu

WebmasterDr. Anthony S. Desse
Associate Professor
Department of Electrical and Computer Engineering
The College of New Jersey
deesea@tcnj.edu