Student Poster Session
2018 IEEE PES General Meeting
Portland, OR USA
August 5-9, 2018
Poster Categories:
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Advanced Computational Methods for Power System Planning, Operation, and Control
Asset Management
Cyber and Physical Security of the Smart Grid
Dynamic Performance and Control of Power Systems
Electric Machines and Drives
Emerging Software Needs for the Restructured Grid
Flexible AC Transmission Systems
Integrating Renewable Energy into the GridIntelligent Monitoring and Outage Management
Market Interactions in Power Systems
Power System Modeling and Simulation
Smart Cities
Smart Grid Technology
Smart Sensors, Communication and Control in Energy Systems
Substation and Distribution Automation
System-Wide Events and Analysis Methods

IEEE PES Student Activities Subcommittee
Dr. Aaron St. Leger, Dr. Valentina Cecchi, Dr. Sridhar Chouhan, Dr. Anthony Deese
1Advanced Computational Methods for Power System Planning, Operation, and Control
No.
18STUGM001

Poster Title
Identifying Long Term Voltage Stability Caused by Distribution Systems vs Transmission Systems

Student Name (Last First)
Amarsagar Reddy

UG/GradGRAD

18STUGM002

Probabilistic Power Flow Analysis based on theAdaptive Polynomial Chaos-ANOVA Method

Yijun XuGRAD

18STUGM003

Exploring Power System Problems with the Technique behind AI AlphaGo

Du YanGRAD

18STUGM004

Economic Dispatch Considering Risk Using Zig-zag Search Method With Adaptive Step-sizezhang qiwei

GRAD

18STUGM005

Improvement to the Prediction of Fuel Cost Distributions Using ARIMA Model

Zhao Zhongyang

GRAD

18STUGM006

CIM/E Oriented Graph Database Model Architecture and Parallel Network Topology Processing

Zhou Zhangxin

GRAD

18STUGM007

Chance Constrained Optimal Reactive Power Dispatch

Geng Xinbo

GRAD

18STUGM008

Mehrtash Mahdi

GRAD

18STUGM009

Security-Constrained Transmission Expansion Planning with Risk Index of N-1 Security Obtained from PMU
Data
An Integrated Distributed Economic Dispatch-Droop Control Architecture for Distribution System Operation

Wang Shengyi

GRAD

18STUGM010

Flexible Long Term Planning Cooptimization Method Comparison and Validation

Maloney Patrick

GRAD

18STUGM011

Voltage and Thermal Concerns during Geomagnetic Disturbance Corrective Measures

Klauber Cecilia

GRAD

18STUGM012

DC Catenary Line Modeling of Subway Systems

Sezgin Mustafa Erdem

GRAD

18STUGM013

Volt-Var Optimization Algorithm Utilizing Custormer- and Utility-Owned Distributed ResourcesDavid J Mulcahy

GRAD

18STUGM014

Cloud Computing Based Real-Time Energy Management System with RNN-LSTM Wind Forecasting

Azimian Behrouz

GRAD

18STUGM015

Progressive Hedging Algorithm in two stage stochastic optimization of look-ahead unit commitment

Saleh Mehdi

GRAD

18STUGM016

Yipu Zhang

GRAD

18STUGM017

Robust Unit Commitment Considering the Temporal and Spatial Correlations of Wind Farms Using a DataAdaptive Approach
SDP Based ACOPF and Network Reconfiguraiton for Three-phase Distribution System

Li Yikui

GRAD

18STUGM018

Finite-Difference Relaxation for Parallel Compution of Ionized Field of HVDC Lines

Liu Peng

GRAD

18STUGM019

PV Extreme Capacity Factor Analysis

Tang Zefan

GRAD

18STUGM020

Robust Koopman Operator-based Kalman Filter for Power Systems Dynamic State Estimation

Netto Marcos

GRAD

18STUGM021

Information Transfer Based Modal Analysis in Power System

Sharma Pranav

GRAD

18STUGM022

Horizontal Decomposition-Coordination Strategy for Power System Scheduling with Overlapping Intervals

Farnaz Safdarian

GRAD

Asset Management
No.
18STUGM023

Flexibility Scheduling for Large Customers

Poster Title

Student Name (Last First)
Angizeh Farhad

UG/GradGRAD

18STUGM024

Analysis of Vibration Signal for Power Transformer On-Load Tap Changer (OLTC) Condition Monitoring

Seo Junhyuck

GRAD

2

Cyber and Physical Security of the Smart Grid

18STUGM025

On False Data Injection in Wide Area Protection Schemes

Khalaf Mohsen

UG/Grad
GRAD

18STUGM026

Zhu Wentao

GRAD

18STUGM027

Reliability and Vulnerability Assessment of Interconnected ICT and Power Networks Using Complex Network
TheoryLoad Redistribution Attack Detection using Machine Learning: A Data-Driven Approach

Pinceti Andrea

GRAD

18STUGM028

A Coordinated Cyber Attack Detection System (CCADS) for Multiple Substations

Sun Chih-Che

GRAD

18STUGM029

Distribution System Hardening against Natural Disasters

Tan Yushi

GRAD

18STUGM030

Embedding Cyber-Physical Resiliency in Synchrophasor-based Remedial Action Schemes

Nie Zhijie

GRAD

18STUGM031

Pengyuan Wang

GRAD

18STUGM032

Decentralized Power System Integrity Protection Schemes based on MAS: Anomaly Detection and Self-Adaptive
Load Shedding
Data Analytics for Cyber-Physical Security of Transmission Protection System

Foroutan Sayedeh Armina

GRAD

18STUGM033

Impact of Communication Failures on Solar Generation Forecasting

GRAD

18STUGM034

Real-time Cyber Physical Test-bed for validation of Automated Failure Diagnosis in Transmission Network

Heidari Kapourchali
MohammadGholami Amir

18STUGM035

Content Development of IoT for Grid Modernization - a Two Class Series for the Future Power Engineer

Crystal R Eppinger

GRAD

18STUGM036

Singh Vivek Kumar

GRAD

18STUGM037

Federation based Testbed Implementation of Anomaly Detection for Wide Area Protection Scheme in Power
SystemFalse Data Injection Attacks in Electric Energy Markets

Kaviani Ramin

UG

18STUGM038

CP-SAM Cyber-Physical Security Assessment Metric

Venkataramanan Venkatesh

GRAD

Student Name (Last First)
Qianying Mou

UG/GradGRAD

18STUGM040

Poster Title
Calculation of Critical Oscillation Modes for Large Delayed Cyber-Physical Power System Using PseudoSpectral Discretization of Solution Operator
VSC-HVDC for enhancing power system stability in the load restoration stage of black startFeng Wei

GRAD

18STUGM041

Optimal Damping Actuator Selection Through Oscillation Energy Sensitivity Analysis

Silva-Saravia Horacio

GRAD

18STUGM042

Practical Modeling of Flywheel Energy Storage for Primary Frequency Control in Power Grids

Dario Peralta

GRAD

18STUGM043

Liu Jiale

GRAD

18STUGM044

Location and Migration Laws of Out-of-Step Center under Multi-Frequency Oscillation Based on Voltage
Phase Angle Trajectory
Development and Frequency Application of an European Low Voltage Microgrid Network

Ayaz Melike Selcen

GRAD

18STUGM045

Provision for Guaranteed Inertial Response in Diesel-Wind Systems via Model Reference Control

Zhang Yichen

GRAD

18STUGM046

Tube-Based Model Predictive Control of Energy Storage Units for Improving Transient Stability of Power
Systems
Probabilistic Transient Stability Assessment of Renewable Generation Integrated Power System

Kiaei Iman

GRAD

Wang Yingying

GRAD

No.

Poster Title

Student Name (Last First)

GRAD

Dynamic Performance and Control of Power Systems
No.
18STUGM039

18STUGM047

3

18STUGM048

Amelian Sayed Mohammad

18STUGM049

New Optimization-Based Algorithms for a Substation Voltage Controller Using Local Synchrophasor
Measurements
Optimal Design of Virtual Inertia and Damping Coefficients for Virtual Synchronous Machines

Ademola-Idowu Atinuke

GRAD

18STUGM050

A N4SID based estimator for identifying the parameters and sources of forced oscillations

GRAD

18STUGM051
18STUGM052

Towards the New Low-Order System Frequency Response Model of Power Systems with High Penetration of
Variable-Speed Wind Turbine Generators
Oscillation Monitoring and Control of the RTE Power System Using Synchrophasors

Mansouri Habibabadi
MohammadKrpan Matej

UG

18STUGM053

ADAPTIVE ADJUSTMENT OF NOISE COVARIANCE

Farrokhifard
Mohammadreza
Akhlaghi Shahrokh

18STUGM054

Probabilistic Transient Stability Assessment of Renewable Generation Integrated Power System

Wang Yingying

GRAD

18STUGM055

Decreasing the Ice Storm Risk on Power Conductors by DSRs and Dispatch Adjustment

Lashkarbolooki Sahar

GRAD

18STUGM056

Fractional Order PI Control for Microgrid Application

abdulkader rasheed

GRAD

18STUGM057

Parameter Space and Rotor Angle Stability Control of Virtual Synchronous Machine

Qi Chen

GRAD

GRAD

GRAD

GRAD

Electric Machine and Drives
No.
18STUGM058

Poster Title
Design of a Supercapacitor Energy Storage System for Marine Turbines Utilizing Advanced Control

Student Name (Last First)
Aaronson Hannah

UG/GradGRAD

18STUGM059

Fault Ride Through Analysis of Grid Connected Doubly Fed Induction Generator Based Wind System

Vishwanath Gururaj

GRAD

18STUGM060

Design and Performance Analysis of Low Cost Acoustic Chamber for Electric Machines

Pindoriya Rajesh

GRAD

18STUGM061

Demagnetization Fault Detection and Analysis for a Permanent Magnet Synchronous Machine

Usman Adil

GRAD

18STUGM062

Behavioral Device-Level Modeling of Modular Multi-Level Converters in Real-Time for Variable Speed
Drive Applications
Harmonic Mitigation and a Practical Study of Torque Harmonics in Induction Motor Startup

Lin Ning

GRAD

Khaledian Parviz

GRAD

Student Name (Last First)
Andres Arguello

UG/GradGRAD

18STUGM063

Emerging Software Needs for the Restructured Grid
No.
18STUGM064

Poster Title
Dynamic Modeling in OpenDSS: An Implementation Sequence for Object Pascal

Flexible AC Transmission Systems
No.
18STUGM065

Poster Title
Analyzing Mutual Influences of Conventional and Distributed FACTS via Stochastic Co-optimization

Student Name (Last First)
Yuanrui Sang

UG/GradGRAD

18STUGM066

Analysis of Transient Stability According to Initial Operating Condition of STATCOM

Hyeokjin Noh

GRAD

18STUGM067

Rotor Angle Stability of MMC Based HVDC Transmission Under DC Short-Circuit Fault

Wang Peng

GRAD
4Integrating Renewable Energy into the Grid
No.
18STUGM068

Poster Title
Calculation of Fault Level in Power Systems with High Penetration of Renewable Energy Resources

Student Name (Last First)
Aljarrah Rafat

UG/GradGRAD

18STUGM069

Stability Analysis of Microgrids under Disturbances via Reachable Sets

Li YanGRAD

18STUGM070

An in-depth analysis of grid-connected solar plant feasibility and technology selection

Stephens GregGRAD

18STUGM071

An LMI Based Stability Margin Analysis for Active PV Power Control of Distribution Networks with
Time-Invariant Delays
A Framework to Utilize DERs’ VAR Resources to Support the Grid in an Integrated T-D System

Fu Chang

GRAD

Singhal Ankit

GRAD

Quantitative Control Approach for Wind Turbine Generators to Provide Fast Frequency Response With
Guaranteed Rotor Security
Optimal Corrective Control for Bipolar Multi-terminal HVDC Grid

Wang Siqi

GRAD

Jiahong Li

GRAD

Cui Hantao

GRAD

18STUGM076

Bi-Level Arbitrage Potential Evaluation for Grid-Scale Energy Storage Considering Wind Power and LMP
Smoothing EffectState Space Model of Electric Vehicle Aggregator for Frequency Control

Wang Mingshen

GRAD

18STUGM077

Stochastic market clearing for integrated energy systems considering CHP and power to gas technology

Wang Haibing

GRAD

18STUGM078

Optimal Placement of Grid-connected Solar Generation using Geographical Information System

Ospina Ana

GRAD

18STUGM079

Frequency Support of DFIG by Using Improved Inertial Control

Gucheng Xiao

GRAD

18STUGM080

Xu Yeyan

GRAD

18STUGM081

Combined Heat and Power Dispatch Based on Integrated Demand Response and Heat Transmission forWind Power Accommodation
VSG Control for DFIG-based Islanded Wind Farm with LCC-HVDC Integration

Xiuqiang He

GRAD

18STUGM082

Voltage Sensitivity Matrix-Based Volt-Var Control for Unbalanced Distribution Systems using DERs

McEntee CatieGRAD

18STUGM083

Minimizing Curtailments due to Voltage Stability Using Security Constrained OPF

Nycander ElisGRAD

18STUGM084

The Value of Reactive Power for Voltage Control in Lossy Networks

Deakin Matthew

GRAD

18STUGM085

Study and Comparison of Wind Power Correlation Using Two Types of Dataset

Yannan Luo

GRAD

18STUGM086

Application of Solid State Transformers in Hybrid Residential Energy Delivery

Maharjan Manisha

GRAD

18STUGM087

Comparing Capacity Value Metrics of Energy-Limited Resources

Awara SarahGRAD

18STUGM088

Energy Storage Integration in an Islanded Wave Energy System

Wu Eric

GRAD

18STUGM089

Life Cycle Testing of Vanadium Redox Flow Battery and Lithium Ion Energy Storage Systems

Rashid Muhammad

GRAD

18STUGM090

Kang Jaesik

GRAD

18STUGM091

modeling and control of an integrated wind power generation and modular multilevel converter withenergy storage system
Indirect Mechanism Design for Efficient and Stable Renewable Energy Aggregation

Khazaei Hossein

GRAD

18STUGM092

Power-frequency Response Model Identification for Adaptive Virtual Inertia Algorithms

Tamrakar Ujjwol

GRAD

18STUGM093

Enhancement of Grid Connected PV Arrays Fault Ride Through and Post Fault Recovery Performance

Mojallal Aslan

GRAD

18STUGM094

Real-Time Modeling and Battery-in-the-Loop Characterization of an Energy Storage System

Arzani Ali

GRAD

18STUGM095

Robust Microgrid Economic Dispatch Considering Renewable Uncertainty using Interval Optimization

Rooks Cody

GRAD

18STUGM096

Benefits of Short-term PV Forecasting in a Remote Area Standalone Off-grid Power Supply System

Jamal Taskin

GRAD

18STUGM072
18STUGM073
18STUGM074
18STUGM075

5

18STUGM097

A Comparison Study of Dispatching Various Battery Technologies in a Hybrid PV and Wind Power Plant

Yang Yuqing

GRAD

Intelligent Monitoring and Outage Management
No.
18STUGM098

Poster Title
Monitoring the Voltage Stability Margin Using Norton Current Distribution RelationshipStudent Name (Last First)
Wang Qi

UG/GradGRAD

18STUGM099

Fault Classification and Location Identification in a Smart Distribution Network Using ANN

Usman Muhammad Usama

GRAD

18STUGM100

Sub-Synchronous Resonance Detection in Series Compensated DFIG-Based Windfarms

Bo GaoGRAD

18STUGM101

An Distributed Event Diagnosis Solution for Protection Operation in IEC 61850

Touhiduzzaman Md

GRAD

18STUGM102

Toward a Synthetic Model for Distribution System Restoration and Crew Dispatch

Zhigang Ye

GRAD

18STUGM103

Enabling Resiliency Through Outage Management and Real Time Data-Driven Aggregated DERs

Menazzi Matteo

GRAD

Market Interactions in Power Systems
No.
18STUGM104

Poster Title
Building Energy Scheduling with Transactive Approach Having Autonomous Temperature Setting

Student Name (Last First)
Basnet Ashim

UG/GradGRAD

18STUGM105

Cooperative Game Theory for Allocating Cost Savings from Coalitional Energy Management

Han Liyang

GRAD

18STUGM106

Mahmoudian Esfahani
MohammadKiran Deep

GRAD

18STUGM108

Game-theory-based Real-Time Inter-Microgrid Market Design Using Hierarchical Optimization
Algorithm
Reference Bus Independent Components of LMP through a Non-Marginal Choice of its Energy
Component
Who has an incentive to improve the renewable day-ahead forecast?

Spyrou Evangelia

GRAD

18STUGM109

A Bi-Level Optimization Formulation of Priority Service Pricing

Mou Yuting

GRAD

18STUGM110

Transactive Energy Based Multi-microgrids System Operation

Pathiravasam Chirath

GRAD

18STUGM111

Generator contingency modelling in ISOs: Pricing Implications

Saikumar Karthik

GRAD

18STUGM112

Optimal Energy Management for the Integrated Power and Gas Systems via Real-time Pricing

Shu Kangan

GRAD

18STUGM104

Building Energy Scheduling with Transactive Approach Having Autonomous Temperature Setting

Basnet Ashim

GRAD

18STUGM105

Cooperative Game Theory for Allocating Cost Savings from Coalitional Energy Management

Han Liyang

GRAD

18STUGM106

Mahmoudian Esfahani
MohammadKiran Deep

GRAD

18STUGM108

Game-theory-based Real-Time Inter-Microgrid Market Design Using Hierarchical Optimization
Algorithm
Reference Bus Independent Components of LMP through a Non-Marginal Choice of its Energy
Component
Who has an incentive to improve the renewable day-ahead forecast?

Spyrou Evangelia

GRAD

18STUGM109

A Bi-Level Optimization Formulation of Priority Service Pricing

Mou Yuting

GRAD

18STUGM110

Transactive Energy Based Multi-microgrids System Operation

Pathiravasam Chirath

GRAD

18STUGM111

Generator contingency modelling in ISOs: Pricing Implications

Saikumar Karthik

GRAD

18STUGM112

Optimal Energy Management for the Integrated Power and Gas Systems via Real-time Pricing

Shu Kangan

GRAD

18STUGM107

18STUGM107

GRAD

GRAD

6

Power System Modeling and Simulation
No.
18STUGM113

Poster Title
Cost Benefit Analysis of Technology Options for Enabling High PV Penetration

18STUGM114

Transmission Constrained Economic Dispatch via Interval Optimization Considering Wind Uncertainty

Kou Xiao

GRAD

18STUGM115

Shi Qingxin

GRAD

Soleimani Milad

GRAD

18STUGM117

Analytical method to aggregate multi-machine SFR model with applications in power system dynamic
studies
Matlab Simulation Framework for Integration of Battery Storage and Solar Generation in Distribution
Systems
Global Sensitivity Analysis of Islanded Microgrid Power Flow

He KunUG

18STUGM118

Simultaneous Global Identification of Dynamic and Network Parameters in Transient Stability Studies

Francis Benjamin

GRAD

18STUGM119

Zicong Zhang

GRAD

18STUGM120

Comparative Analysis of Hybrid Bipolar HVDC and FACTS Performance for Improving Commutation
Failure Immunity
Impact of Dynamic Load Model on Short-Term Voltage Stability of Korea Power System

Jaemin Moon

GRAD

18STUGM121

Impact of Load Step Change on Thermal and Voltage Stability Limits of Overhead Transmission Lines

Rahman Mahbubur

GRAD

18STUGM122

A Dynamic Inverse-Time Overcurrent Relay Model for Overload Analysis with Varying Overcurrents

Liu Jianan

GRAD

18STUGM123

A Novel MILP-Based Method of Optimal Energy Flow in Gas-electricity System

Liu Tianhao

GRAD

18STUGM124

Modeling of Geomagnetically Induced Currents in the Power Grid Using Time-Domain Simulation

Vemprala Hemanth Kumar

GRAD

18STUGM125

Optimization of Electric Vehicle Aggregation in Energy and Balancing Markets in the Nordics

Herre Lars

GRAD

18STUGM126

Building Very Large Synthetic Power Grids

Birchfield Adam

GRAD

18STUGM127

Initial Work on Formalizing the Multiple-Time Scale Structure of Power System Models

Hin Matthew

GRAD

18STUGM128

Convex Relaxations and Approximations of Chance-Constrained AC-OPF Problems

Lejla Halilbasic

GRAD

18STUGM129

Analysis of Transformers Thermal Response to Geomagnetic Disturbances

Dehghanian Pooria

GRAD

18STUGM130

A Hybrid Simulator for the Study of EP Project Multi-Terminal DC in South Korea Gird

Lee Jeehoon

GRAD

18STUGM131

Design of Scalable and Flexible Distributed Islanded Microgrid for Rural Electrification

Khan Rabia

GRAD

18STUGM132

Synthetic Residential Load Models for Analysis of Energy Management Impact on Smart Cities

Bereta dos Reis Fernando

GRAD

18STUGM133

Finite Element Analysis of Transformers in Subterranean Vaults

Kolln Jaime

UG

18STUGM134

An Object Oriented Matlab Toolbox for Power Distribution System Analysis

Dharmawardena Hasala

GRAD

18STUGM135

Big Data: Application to Transmission and Distribution Co-Simulation for the Future Grid

Velaga Yaswanth Nag

GRAD

18STUGM136

Interdependent Electric and Water Infrastructure Modeling

Zuloaga

GRAD

18STUGM137

Online Identification of Generator Participation Factors using Cellular Computational Networks

Abu Umar

GRAD

18STUGM138

A Real-Time Hardware-in-the-Loop Simulation of Sampled Value based Differential Protection

GRAD

18STUGM113

Cost Benefit Analysis of Technology Options for Enabling High PV Penetration

Yellajosula Jaya Raghavendra
Arun Kumar
Sun Lisha

18STUGM114

Transmission Constrained Economic Dispatch via Interval Optimization Considering Wind Uncertainty

Kou Xiao

GRAD

18STUGM115

Analytical method to aggregate multi-machine SFR model with applications in power system dynamic
studies

Shi Qingxin

GRAD

18STUGM116

Student Name (Last First)
Sun Lisha

UG/Grad
GRAD

GRAD

7

18STUGM116
18STUGM117

Matlab Simulation Framework for Integration of Battery Storage and Solar Generation in Distribution
Systems
Global Sensitivity Analysis of Islanded Microgrid Power Flow

18STUGM118

Simultaneous Global Identification of Dynamic and Network Parameters in Transient Stability Studies

Francis Benjamin

GRAD

18STUGM119

Zicong Zhang

GRAD

18STUGM120

Comparative Analysis of Hybrid Bipolar HVDC and FACTS Performance for Improving Commutation
Failure Immunity
Impact of Dynamic Load Model on Short-Term Voltage Stability of Korea Power System

Jaemin Moon

GRAD

18STUGM121

Impact of Load Step Change on Thermal and Voltage Stability Limits of Overhead Transmission Lines

Rahman Mahbubur

GRAD

18STUGM122

A Dynamic Inverse-Time Overcurrent Relay Model for Overload Analysis with Varying Overcurrents

Liu Jianan

GRAD

18STUGM123

A Novel MILP-Based Method of Optimal Energy Flow in Gas-electricity System

Liu Tianhao

GRAD

18STUGM124

Modeling of Geomagnetically Induced Currents in the Power Grid Using Time-Domain Simulation

Vemprala Hemanth Kumar

GRAD

18STUGM125

Optimization of Electric Vehicle Aggregation in Energy and Balancing Markets in the Nordics

Herre Lars

GRAD

18STUGM126

Building Very Large Synthetic Power Grids

Birchfield Adam

GRAD

18STUGM127

Initial Work on Formalizing the Multiple-Time Scale Structure of Power System Models

Hin Matthew

GRAD

18STUGM128

Convex Relaxations and Approximations of Chance-Constrained AC-OPF Problems

Lejla Halilbasic

GRAD

18STUGM129

Analysis of Transformers Thermal Response to Geomagnetic Disturbances

Dehghanian Pooria

GRAD

18STUGM130

A Hybrid Simulator for the Study of EP Project Multi-Terminal DC in South Korea Gird

Lee Jeehoon

GRAD

18STUGM131

Design of Scalable and Flexible Distributed Islanded Microgrid for Rural Electrification

Khan Rabia

GRAD

18STUGM132

Synthetic Residential Load Models for Analysis of Energy Management Impact on Smart Cities

Bereta dos Reis Fernando

GRAD

18STUGM133

Finite Element Analysis of Transformers in Subterranean Vaults

Kolln Jaime

UG

18STUGM134

An Object Oriented Matlab Toolbox for Power Distribution System Analysis

Dharmawardena Hasala

GRAD

18STUGM135

Big Data: Application to Transmission and Distribution Co-Simulation for the Future Grid

Velaga Yaswanth Nag

GRAD

18STUGM136

Interdependent Electric and Water Infrastructure Modeling

Zuloaga

GRAD

18STUGM137

Online Identification of Generator Participation Factors using Cellular Computational Networks

Abu Umar

GRAD

18STUGM138

A Real-Time Hardware-in-the-Loop Simulation of Sampled Value based Differential Protection

Yellajosula Jaya Raghavendra
Arun Kumar

GRAD

No.
18STUGM139

Poster Title
Optimal Routing and Charging of an Electric Vehicle Fleet for High-Efficiency Dynamic Transit Systems

Student Name (Last First)
Chen TaoUG/GradGRAD

18STUGM140

Hui Hongxun

GRAD

18STUGM141

Analysis of “8•15” Blackout in Taiwan and the Improvement Method of Contingency Reserve Capacity
Through Direct Load Control
A Hierarchical Control Structure for Residential Community Energy Optimization

Paudyal Priti

GRAD

18STUGM142

Home Energy Management System in Co-Simulation Framework

Munankarmi Prateek

GRAD

18STUGM143

Distributed Locational Marginal Pricing based of Smart Home Management

Mohsenzadeh Amin

GRAD

Soleimani MiladGRAD

He KunUG

Smart Cities

8Smart Grid Technology
No.
18STUGM144

Poster Title
Impacts of Smart Inverters on Microgrids&#39; Controls and Protections

Student Name (Last First)
Samkari Husam

UG/GradGRAD

18STUGM145

Multi-parameter Intelligent Energy Forecasting: From Insight to Impact

Sakib Nazmus

GRAD

18STUGM146

Adaptive Phasor Estimation Algorithm Using Improved KFs under Steady-State/Dynamic Conditions

Wang Yinfeng

GRAD

18STUGM147

Robust Unit Commitment and Dispatch Considering with Atmospheric Pollutant Concentration Constraints

Wang Yongcan

GRAD

18STUGM148

Coordinated Power Allocation and Robust DC Voltage Control of UPQC with Energy Storage Unit During
Source Voltage Sag
Distributed Voltage Control for Demand Response

Liu Ziwen

GRAD

Guo Jinrui

GRAD

Shen Yunwei

GRAD

18STUGM151

Progressive Time-differentiated Peak Pricing (PTPP) for Aggregated Air-conditioning Load in Demand
Response Programs
A Multilevel Dual Converter fed Open end Transformer Configuration for Hybrid AC-DC Microgrid

Bandla Krishna

GRAD

18STUGM152

Reactive Power Support from EVs for Distribution Grid Voltage Management

Wang Jingyuan

GRAD

18STUGM153

Joint Planning of BESS and DR for industrial consumers participating in peak-shaving

Hu XiaoGRAD

18STUGM154

Stochastic Optimal Transmission Switching Considering N-1 Security Constraints

Zhang Heng

GRAD

18STUGM155

Peak Energy Management using Renewable Integrated DC Microgrid

Pannala Sanjeev

GRAD

18STUGM156

Wang Jiyu

GRAD

18STUGM157

A Two-Step Load Disaggregation Algorithm for Quasi-static Time-series Analysis on Actual Distribution
Feeders
Optimal Water-Power Flow Problem: Formulation and Distributed Optimal Solution

Zamzam Ahmed

GRAD

18STUGM158

Real Time Tool to Characterize Power System Communication Delays

Lackner Christoph

GRAD

18STUGM159

Diagonal Quadratic Approximation for Decentralized Collaborative TSO DSO Optimal Power Flow

Mohammadi Ali

GRAD

18STUGM160

A Data-driven Voltage Control Framework for Power Distribution Systems

Xu Hanchen

GRAD

18STUGM161

A Machine Learning Approach for Residential Load Extraction using Low-resolution Smart Meter Data

Meng Yao

GRAD

18STUGM162

Nonparametric User Behavior Prediction for Distributed EV Charging Scheduling

Khaki Behnam

GRAD

18STUGM163

Detection of PMU Time Error Sources Using Phase Angle Measurement Data

Idehen Ikponmwosa

GRAD

18STUGM164

Lou Guannan

GRAD

Pourbabak Hajir

GRAD

18STUGM166

Stability Robustness for Secondary Voltage Control in Autonomous Microgrids With Consideration ofCommunication Delays
A Novel Consensus-based Distributed Algorithm for Economic Dispatch Based on Local Estimation of Power
Mismatch
Automated Distribution System Restoration with the Multiagent Q-Learning Based Algorithm

Alvarez Fernandez Inalvis

UG

18STUGM167

Modelling and evaluation of flexible multi-energy systems for low carbon environment

Holjevac Ninoslav

GRAD

18STUGM168

Adoption of IoT framework for DER aggregation and control

Slay Tylor

GRAD

18STUGM169

A Distributed Energy Resource Aggregated System for Providing Ancillary Services

Marnell KevinGRAD

18STUGM170

Online Identification of Power System Electromechanical Modes

Arunagirinatthan
Paranietharan

GRAD

18STUGM149
18STUGM150

18STUGM165

9

Smart Sensors, Communication and Control in Energy Systems
No.
18STUGM171

Poster Title
A Power Waveform Classification Method for Adaptive Synchrophasor Estimation

Student Name (Last First)
QIAN Cheng

UG/GradGRAD

18STUGM172

Non-Invasive System Impedance Estimation Based on Data Selection

Zhang Ori

GRAD

18STUGM173

An advanced maximum power point capturing technique with supercapacitor for PV system

Zhang Lijun

GRAD

18STUGM174

Comparative Analysis of Demand Response and Load Management Algorithms

Vedullapalli DivyaGRAD

18STUGM175

A Non-Contact Method for Real-Time Monitoring of Conductor Sag

Mukherjee Monish

GRAD

Student Name (Last First)
Singh Shailendra

UG/GradGRAD

Student Name (Last First)
Li YanUG/GradGRAD

18STUGM178

Poster Title
D2NDZ: Evaluating Unintentional Islanding Risks in Distribution Grids with Deep Integration ofDistributed Energy Resources
Real-time Operation Pattern Detection using Measurements from a Network of PMUs

Liang Ming

GRAD

18STUGM179

Nonlinear Dynamics Based On-line Detection of Oscillation Using Power System Measurement Data

Cho Hwanhee

GRAD

18STUGM180

Frequency Response Estimation Following Large Disturbances using Synchrophasors

Fernandes LucasGRAD

18STUGM181

A Hybrid Approach towards Event Detection in Multi-machine Power Systems

Banerjee Abhishek

GRAD

18STUGM182

Security versus Computation Time in IV-ACOPF with SOCP Initialization

Sadat Sayed Abdullah

GRAD

18STUGM183

Advanced Situational Awareness: Identification of Localized and Widespread Events

Chatterjee Paroma

GRAD

18STUGM184

Measurement-Based Voltage Stability Indicator for Voltage Dependent and Induction Motor Loads

Kamel Mariana

GRAD

18STUGM185

Advanced Situational Awareness: Identification of Localized and Widespread Events

Chatterjee Paroma

GRAD

Substation and Distribution Automation
No.
18STUGM176

Poster Title
Smart Grid-enabled CVR: An advanced application for Distribution Management System

System-Wide Events and Analysis Methods
No.
18STUGM177

10

Identifying Long Term Voltage Stability Caused byDistribution Systems vs Transmission Systems
Amarsagar Reddy Ramapuram M.
Department of Electrical and Computer
Engineering
Iowa State University, Ames, IA,amar@iastate.edu

Ankit Singhal
Department of Electrical and Computer
Engineering
Iowa State University, Ames, IA,
ankit@iastate.edu

Abstract — Monitoring the long term voltage stabilityof the power grid is necessary to ensure its secure
operation. This paper presents a new phasor based
methodology that distinguishes between long term voltage
stability caused by distribution systems versus
transmission systems. From a conceptual understandingof a simplified system, a Transmission-Distribution
Distinguishing Index (TDDI) is proposed to distinguishbetween the two scenarios. A methodology to calculate the
TDDI for multi-bus systems using quasi-steady statephasor measurements is described and validating results
are presented for the IEEE 9 Bus system with a load
replaced by various distribution feeders. The results
verify that the TDDI can indeed be used to distinguishbetween transmission limited and distribution limited
systems. This information can be utilized by the operator
to effectively choose controls in distribution andtransmission systems to improve the system margin.
Keywords — Long Term Voltage Stability, Transmission
vs Distribution, Thevenin Index, Phasor Measurement
Units.
I.
INTRODUCTION
There is increasing pressure on power system operatorsand on electric utilities to utilize the existing grid
infrastructure to the maximum extent possible and this modeof operation can lead to long term voltage stability problems.
To handle this, operators are adopting real-time tools using
Wide-Area measurements (WAMS) and Phasor Measurement
Units (PMUs) that are providing them with better situationalawareness. The increasing number of PMUs in the grid have
led to various online Voltage Stability Indices (VSI) being
proposed in recent times to monitor the grid in real time [1].
Traditionally, the VSI’s were calculated at a bus byestimating the Thevenin Equivalent using local PMU Voltage
and Current measurements at a Bus [2-6]. However, all thesemethods assume an aggregated load at the transmission leveland do not consider the sub-transmission system or the
distribution system where the loads are actually present.
Ignoring the distribution feeder network and the distributionof loads in the sub-transmission/ distribution network will leadto an error in the voltage stability assessment.
Furthermore, as the distribution systems are often operated
close to their limits (for economic reasons), considering their
topology and loads into the voltage stability assessment mightprovide insights to operators and planners on how to improve

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

the system behavior. In fact, voltage collapse in distribution
feeders has been identified as a critical issue for some time [7]and a major blackout in 1997 in the S/SE Brazilian system is
attributed to a voltage instability problem in one of the
distribution feeders that spread to the transmission grid [8].
Recently, techniques incorporating the distribution systemin the transmission system analysis have been proposed [9]and have been utilized to verify how the increase inDistributed Generation (DG) can improve both the overallsystem margin [10] and the distribution system margin [11].
However, as far as the authors know, none of the existing
methods distinguish between the voltage stability caused by
distribution system versus transmission systems. Our previous
paper [12] describes a method to determine if the voltage
stability limit is due to the distribution system or thetransmission system. This method performs a continuation
power flow [13] and compares the resultant nose point with apredetermined hypersurface based on the distribution
topology. The methodology requires a full-fledged CPF
routine along with the calculation of the hyper surfaces,
making it time consuming for power system operations and soan online methodology would be preferred.
In this paper we address this issue by presenting atechnique based on phasor measurements to estimate thevoltage stability and to also determine if the limit is due to thetransmission or distribution systems. This information will be
useful for operators, especially as the control of DG devices
in distribution limited systems can lead to a larger percentage
increase in the margin [12] and a load shedding action on the
distribution limited systems will lead to a larger improvement
in system margin [12]. Thus, determination of the limiting
system can be used to improve voltage stability with minimum
control. This paper starts by describing a conceptual
understanding of the methodology on a simple system(Section II), presents a technique to estimate the parameters of
the equivalent circuit for multi-bus systems (Section III),
describes how the method works on a test-system andcompares it to existing results (Section IV) and finallyconcludes in Section V.
II. CONCEPTUAL UNDERSTANDING OF METHOD
A block diagram of the conventional power system isshown in Fig. 1, with the various generation, transmission anddistribution circuits. The loads are in the distribution feeders
and vary based on time of day, etc.

1

Probabilistic Power Flow Analysis based on the
Adaptive Polynomial Chaos-ANOVA Method
Yijun Xu, Student Member, IEEE, Lamine Mili, Life Fellow, IEEE

Abstract—While the conventional generalized polynomial chaosmethod exhibits excellent computational efficiency and accuracy
in the probabilistic power flow calculations applied to mediumsize power systems, it suffers from the curse of dimensionality
when applied to large-scale systems and is limited to standard
probability distributions assumed for the input variables. In
this paper, we propose to adaptively reduce the dimension andthereby, to significantly speed up the calculations of the generalized polynomial chaos method via the ANOVA decomposition. We
also propose to make use of the Steltijes’ procedure so that any
probability distribution may be assumed for the input randomvariables.
Keywords—Probabilistic Power Flow, Generalized Polynomial
Chaos (gPC), Analysis-of-Variance (ANOVA).

)!
where NP = (NN+P!P ! − 1. From the polynomial chaos coefficients, the sample mean, µ, and the sample variance, σ 2 , ofthe output z can be determined as follows:µ = a0 ,
σ2 =

NP
X

(2)

a2i E[φ2i ],

(3)

i=1

B. The ANOVA Decomposition
The ANOVA expansion represents a function f with N
random variable in the form ofX
f (ξ1 , . . . , ξN ) = f0 +
fj1 (ξj1 )1≤j1 ≤NI.

I NTRODUCTION+

URRENTLY, two traditional techniques have been utilized to account for power system model uncertainties,
namely, the Monte Carlo (MC) method and the analytical
Method. It turns out that the former is too time-consuming
for large-scale power systems while the latter method canbring some inaccuracy in the results. To overcome theseweaknesses, the generalized Polynomial Chaos (gPC) methodthat was initiated by Wiener and further developed by Xiu
and Karniadakis has been recently used for probabilistic power
flow calculations. While the gPC method shows great accuracy
and efficiency when the number of the input random variables
are not large, it suffers from the curse of dimensionality. It
turns out that the ANOVA decomposition can help polynomialchaos based method to identify the main coupling effects while
ignoring the others. This property will allow us to greatly
alleviate the curse of dimensionality in the gPC method.

C

T HEORETICAL BACKGROUND

A. The Generalized Polynomial Chaos
In the gPC method, the stochastic outputs are represented asa weighted sum of a set of orthogonal polynomial chaos basis
functions constructed from the probability distribution of the
input random variables.
z=

NP
X

ai φi (ξ),

(1)

i=0

Y. Xu, and L. Mili are with the Bradley Department of Electricaland Computer Engineering, Virginia Polytechnic Institute and State University, Northern Virginia Center, Falls Church, VA 22043, USA, (email:yijunxu@vt.edu;lmili@vt.edu;).

(4)

fj1 ,j2 (ξj1 , ξj2 ) + . . . + f1,2,...,N .

1≤j1 <j2 ≤N

Probability density

1000
gPC-1st800

gPC-2nd600

gPC-ANOVA

400

MC

200
0

0.965 0.9655 0.966 0.9665 0.967 0.9675 0.968 0.9685 0.969

Bus Voltage Magnitude (p.u)
(a)

1000

Probability density

II.

X

gPC-1st800

gPC-2nd600

gPC-ANOVA

400

MC

200
0

0.965 0.9655 0.966 0.9665 0.967 0.9675 0.968 0.9685 0.969

Bus Voltage Magnitude (p.u)(b)

Fig. 1.

Plots for probability distribution function of voltage magnitude

TABLE I.

MethodCPU time

AVERAGE CPU T IMES OF THE G PC 2ND - ORDER ,G PC-ANOVA, AND MC M ETHODSgPC(P=2)
4.4955 sgPC-A(0.96)
1.6772 sgPC-A(0.98)
1.8228 sMC2028.5 s

1Exploring Power System Problems with the Techniquebehind AI AlphaGo
Yan DuFangxing Li

Dept. of EECS, UTK
Knoxville, TN, USA

1channels

5

2

3

4

Input data:

7

28
8

6
9

v

heightAbstract—In this work, a data-driven method for power flow
calculation is proposed as opposed to the traditional model-based
method with computation burden. The main idea is to apply thetechnique behind the heated AI-led computer program AlphaGo,
the deep convolutional neural network (CNN), to establish themathematical formulation between bus power injection andvoltage, which is a regression process. The neural network is
constructed via both training stage and testing stage based on alarge data set of existing power flow results. The obtained deep
CNN is further implemented on a series of standard IEEE systems
to verify its accuracy. The proposed study may inspire some new
directions for applying deep learning related techniques in future
power system researches.

Dept. of EECS, UTK
Knoxville, TN, USA

v

16

12

13

11

10
17

18

14
15

width

20

27
22

21

24

25

19
23

29

30

26

 g11 g12
g g
 21 22gn1 gn2

g1n 
g2 n gnn nnb11 b12
b b
 21 22bn1 bn 2

b1 n 
b2 n 
bnn nnPinj ,1 0
 0 P
inj ,2



0
 0

0 
0 


Pinj ,n nnQinj,1 0 0 Q
inj ,2



0
 0

0 
0 


Qinj,n nnv n nn

1  2 n nn

Output data:

v1

v2

(a) Image parameter (b) Power system topology (c) Matrix representation ofposer system
Fig. 1. Mapping power system data to CNN training dataFC

Index Terms—AlphaGo, convolutional neural network, datadriven, deep learning, power flow

Conv1

Conv2

12

n×n×24n×n

The main reason of applying deep CNN to power flow
calculation is that power system has a grid-like structure similar
to image data, and can be described by square matrix, i.e. nodal
admittance matrix. We can treat the topology of power system
as two n×n matrices, i.e. the conductance matrix and thesusceptance matrix, where n is the number of buses. For the
output, two 1×n vectors can be built for bus voltage magnitudeand voltage angle. The advantage of applying the above matrix
representation is that they can be adapted to power system withdifferent scales by simply increasing the dimension of thematrix. Fig. 1. demonstrates how to map power system data to
CNN training data with matrix structure. The CNN structure fortraining is shown in Fig. 2.

1×1

n×n

24

I. INTRODUCTION

II. DEEP CNN-BASED POWER FLOW CALCULATION

Output:[θ1,θ2, ,θn]Loss function:
MSE

n×n
2

The computer program AlphaGo marks the beginning of thedevelopment of modern AI technologies. Nowadays, the AI
technology has made tremendous progresses in a wide variety
of domains including image recognition, speech identification,
natural language processing. However, there’s still little
application of AI in the field of power system. One basic
problem in the power system is the calculation of power flow.
In this work, we propose a data-driven method to solve thepower flow problems. Deep convolutional neural network(CNN), which is the main magic behind AlphaGo, is adopted
as a tool to identify the mathematical formulation between
system decision variables and state variables, and dense
computation is avoided. The performance and accuracy of deep
CNN is also validated on standard IEEE test cases.

Conv3

n×n×24

Fig. 2. CNN structure for voltage training

III. CASE STUDY
The data set for training the above CNN is generated via a
Monte Carlo simulation, where active and reactive bus power
injection varies within a normally distributed range [-0.01,
0.01]. The simulation results are demonstrated in TABLE I. The
errors of  and v are in per unit mean absolute value. As can beobserved from the table, the simulation results from CNN
possess considerable accuracy.
TABLE I POWER FLOW RESULTS OF CNN SIMULATION

Case
5
9
14
30
57
118
145
181



Errors4.7e-41.7e-38.4e-45.3e-41.9e-32.0e-34.7e-22.0e-2v
8.4e-52.6e-41.3e-48.6e-52.4e-49.6e-42.5e-41.2e-3

Time(s)
v
8.4
11.4
8.8
11.5
9.4
12.8
22.3
33.1
77.1
127.9
333.9 203.3
508.9 499.3
906.9 1401.0



IV. CONCLUSIONSThis work proposes a CNN-based approach to solve power
flow problem. Simulation results on standard IEEE test cases
verifies the computation performance of the proposed datadriven method, which indicates the promising potentials of
applying CNN and related deep learning techniques to powersystem studies.

1

Economic Dispatch Considering Risk Using
Zig-zag Search Method With Adaptive Step-size
Qiwei Zhang, Student Member, IEEE, Fangxing Li, Fellow, IEEE

Abstract—Major advantages brought by high wind power
penetration are low operation cost and significant reduction of
detrimental gases. However, it also brings uncertainty andrandomness into power system. Therefore, how to evaluate its
influence in the economic dispatch has been a huge challenge for
future integration of large amount of wind power. Conditional
value at risk (CVaR) is a concept has been widely used in risk
management field and recently has been introduced to powersystem. In this paper, a multi-objective economic dispatch
problem considering conditional value at risk (CVaR) with highwind power penetration is proposed and a modified version ofclassic zig-zag search method (zig-zag AS) is proposed to solve it.
Index Terms—Weibull distribution, conditional value at risk,
economic dispatch, multi-objective optimization, zig-zag searchalgorithm.

I. PROBLEM FORMULATIONA.
Objective Functions
Minimization of fuel costsNF   Fi ( PGi )

(8)

i 1

II. MAIN METHODOLOGYA.

Classic Approach
The classic zig-zag search method consists of two parts: Find
the First Pareto optimal (FFPO) search and a loop of zig-zag
search.
(15)
g1  g 2 ( x)  g1 ( x), g 2 ( x)  g1 ( x)g 2  g1 ( x)  g1 ( x), g2 ( x)  g2 ( x)where <, > is the vector dot production, g1(x) is the gradient of
f1, and g2(x) is the gradient of f2.
B. Zig-zag Search With Optimal Step Size
The step-size for classic zig-zag search method is firstly
defined by user. Here, zig-zag search with adaptive step size is
proposed to determine the step size automatically.
(19) s  f1 (X n 1 )  f1 (X n )   u

min f ( x k   d k )(20)
k2
Replacing d with g , solving equation (21) (22) will generate
the step-size. Then, the step size configuration is completed.
min f 2 ( x k   g k )(21)

Typically, Fi (PGi) is represented by quadratic functions:Fi ( PGi )=ci  bi PGi  ai PGi 2(9) s  f1 ( x k   g k )  f1 ( x k )   uwhere ai, bi , and ci are fuel cost coefficients of the generator i.
 Minimization of CVaR
Under wind power uncertainty, the evaluation of CVaR canbe formulated in equation (10) (11).
q
1F ( PG ,  )   [ f ( PG , PW )   ](10)
q(1   ) k 1

III. CASE STUDY  1 （PW , i k -PW , i s） PW , i k -PW ,i s  0 

2
s
k
s
k
  （PW , i  PW , i ）PW , i -PW , i  0 

f ( PG , PW )  

B.


N

NW

i 1

Gi

i 1

s

Wi

Fig.6. Pareto front with different confidence level

NL

 P + P = P
(11)Technical Constraints
Power balance
i 1

Li

(12)

Generation limits

PGi Min  PGi  PGi Max ,i  N(13)P and P are the minimum and maximum value for power
output respectively.
Min
Gi



Max
Gi

Wind power forecast constraint

PW ,i min  P sW ,i  PW ,i max , i  NW

(16)

(14)
Fig.7. Pareto front comparison(22)Improvement to the Prediction of Fuel Cost
Distributions Using ARIMA Model
Zhongyang Zhao, Student Member, IEEE, Chang Fu, Student Member, IEEE, Caisheng Wang, Senior Member, IEEE
Department of Electrical and Computer Engineering, Wayne State University, Detroit, USA

Carol J. Miller
Department of Civil and Environmental Engineering, Wayne State University, Detroit, USA
Abstract—This paper uses an autoregressive integrated
moving average (ARIMA) model with historical fuel cost data indevelopment of a three-step-ahead fuel cost distribution
prediction. First, the data features of Form EIA-923 are explored
and the natural gas fuel costs of Texas generating facilities areused to develop and validate the forecasting algorithm for the
Texas example. Furthermore, the spot price associated with thenatural gas hub in Texas is utilized to enhance the fuel cost
prediction. The forecasted data is fit to a normal distribution and
the Kullback-Leibler divergence is employed to evaluate thedifference between the real fuel cost distributions and theestimated distributions. The comparative evaluation suggests the
proposed forecasting algorithm is effective in general and isworth pursuing further.

III. FORECASTING PROCESS

Fig. 1.

Fuel cost forecasting process diagram.

IV. PREDICTION PERFORMANCE

Keywords—ARIMA model; fuel cost; natural gas; priceprediction; distribution estimation;

I. INTRODUCTION
The fuel cost distributions can be used to generate large andrealistic power system network models without compromising
the confidentiality of the utilities. However, the EIA-923 form
updates the fuel cost data with a three-month delay. Due to thethree months update lagging of the form, the most current data
is not available for the study. Hence, the motivation of thispaper is to develop a forecasting algorithm to provide moreaccurate FC characterization and distribution estimation
instead of relying on the data with a three-month delay.
II. KEY EQUATIONSIn this paper, the public fuel cost data extracted from Form
EIA-923 are analyzed and utilized for achieving more accurate
probability density estimation using ARIMA model
forecasting.
A. Natural Gas Fuel Cost Processing
∆

′

′

=

B. ARIMA Model
( )∇

=

−

,

= 1,2, …

(1)

+

( ) ,

= 1,2, …

(2)

Fig. 2.
Fitted Normal Distributions for three-month delay fuel costs,
forecasting fuel costs and real fuel costs.

C. Normal Distribution Fitting
( )=

1

(

V. CONCLUSION

)

(3)

√2
D. Kullback-Leibler Divergence of Normal Distributions
1+( − )( || ) =
+
−
(4)
2
2

The results show the proposed forecasting algorithm has asuperior performance over the method that uses the threemonth delayed data. The results also indicate the good
capability of the method in handling the prediction after theturning point. The proposed algorithm is able to be extended
and implemented in other states when there are sufficient data.

CIM/E Oriented Graph Database Model Architecture
and Parallel Network Topology Processing
Zhangxin Zhoua, b, Chen Yuana, Ziyan Yaoa, Jiangpeng Daia, Guangyi Liua,
Renchang Daia, Zhiwei Wanga, and Garng M. Huangbab

GEIRI North America, San Jose, CA, USA
Texas A&M University, College Station, TX, USA

Abstract—CIM/E is an easy and efficient electric power modelexchange standard between different Energy Management
System vendors. With the rapid growth of data size and systemcomplexity, the traditional relational database is not the best
option to store and process the data. In contrast, the graphdatabase and graph computation show their potential advantages
to handle the power system data and perform real-time data
analytics and computation. The graph concept fits power grid
data naturally because of the fundamental structure similarity.
Vertex and edge in the graph database can act as both a parallelstorage unit and a computation unit. In this paper, the CIM/E
data is modeled into the graph database. Based on this model, theparallel network topology processing algorithm is establishedand conducted by applying graph computation. The modelingand parallel network topology processing have been
demonstrated in the modified IEEE test cases and practicalSichuan power network. The processing efficiency is greatly
improved using the proposed method.

Fig. 1. Substation Modeling in CIMGDB

Keywords—CIM/E, Graph Database, Graph Computation,
Parallel Network Topology Processing

I. INTRODUCTION
To design next-generation Energy Management System
software with graph computation technique, the mostfundamental step is to model the power grid data into the graphdatabase. In power grid control center, two basic
configurations are used to describe the power system models:node breaker model and bus branch model. Through network
topology processing, a node breaker model is mapped to a bus
branch model.
The CIM/E is an efficient electric grid model exchange
standard format developed by State Grid Corporation of China(SGCC). The CIM/E model simplifies CIM/XML model byignoring terminal without compromising information toprocess network topology. This paper firstly models the CIM/E
standard data into graph database and implements parallel
topology processing algorithm using graph computation, which
greatly reduces the network topology processing time.
II. CIM/E MODEL AND PARALLEL PROCESSING

This work was supported by State Grid Corporation of China technology
project SGSHXT00JFJSI700138.

Fig. 2 Parallel Network Topology Processing

III. CASE STUDY AND RESULTSTABLE I.

NTP QUERY RESULTS FOR SICHUAN NETWORK

Sichuan Network

Total
Vertices in
Graph
Database

Total Edges
in Graph
Database

NTP
Execution
Time (ms)Modelling objects using
vertices

78411

75277

150

Modelling objects using
vertices and edges

48118

44984

100

1

Chance Constrained Optimal Reactive Power
Dispatch
Xinbo Geng, Student Member, IEEE, Le Xie, Senior Member, IEEE, and Diran Obadina, Member, IEEE

Abstract—The uncertainties from deepening penetration ofrenewable energy resources have already shown to impact not
only the market operations, but also the physical operations
in large power systems. It is demonstrated that deterministic
modeling of wind would lead to voltage insecurity in the reality
where wind fluctuates. This could render deterministic control ofreactive power ineffective. As an alternative, we propose a chanceconstrained formulation of optimal reactive power dispatch
which considers the uncertainties from both renewables andcontingencies. This formulation of a chance constrained optimal
reactive power dispatch (cc-ORPD) offers system operators aneffective tool to schedule voltage support devices such thatthe system voltage security can be ensured with quantifiablelevel of risk. The cc-ORPD problem is a Mixed-Integer NonLinear Programming (MINLP) problem with a joint chanceconstraint and is extremely challenging to solve. Using the BigM approach and linearized power flow equations, the original
cc-ORPD problem is approximated as a Mixed-Integer Linear
Programming (MILP) problem, which is efficiently solvable. Casestudies are conducted on a modified IEEE 24-bus system toinvestigate the optimal operating schedule under uncertainties
and the out-of-sample violation probability.

framework is built upon chance-constrained programming,
which is a natural and efficient tool for decision making inan uncertain environment.
A. Problem Formulation
min hB (QB ) + hC (QC ) + λEC×Ξ PL (c, ξ)s.t. P = AG (c)PG − AG (c)η(c)Pδc − AD PD+ AW diag(PW )(1 + ξ)
Q = AG (c)QG + AC QC + AB QB − AD QD+ AW diag(QW )(1 + ξ)nbX|Vic ||Vjc ||Yij | cos(θic − θjc − φij ), ∀c, i
Pic =The high variability and limited predictability of renewables
impose new challenges on the secure and reliable operationof power systems. There has been a substantial amount ofliteratures showing that deep penetration of renewables could
jeopardize the security and reliability of power systems. For
example, the rapid increase and stochastic nature of renewables
might lead to voltage issues, which could be severe whena stressed system is lack of reactive support. An Optimal
Reactive Power Dispatch (ORPD) problem is often formulated
for better voltage profiles. The ORPD problem aims at finding
optimal settings of current installed Reactive Power Support
Devices (RPSDs) such as SVCs and Capacitor Banks to ensure
system voltage constraints. Although numerous papers havestudied the ORPD problem, most of them adopt a deterministic
formulation and uncertainties from wind are ignored.
II. C HANCE - CONSTRAINED O PTIMAL R EACTIVE P OWER
D ISPATCH ( CC -ORPD)
In this paper, we propose a framework for optimal reactive
power dispatch considering joint uncertainties from wind
uncertainties ξ ∈ Ξ and contingencies c ∈ C. The proposed
Xinbo Geng and Le Xie are with the Department of Electrical andComputer Engineering, Texas A&M University, College Station, TX, 77840.
Email: xbgeng@tamu.edu, le.xie@tamu.edu. Diran Obadina is with Electric
Reliability Council of Texas (ERCOT). Email: Diran.Obadina@ercot.com.
This work is supported in part by the ERCOT, and in part by NSF ECCS1150944 and ECCS-1546682.

(1b)
(1c)
(1d)

j=1
nb

X

Qci =

|Vic ||Vjc ||Yij | sin(θic − θjc − φij ), ∀c, i

(1e)

j=1
nl

X

PLc =


gl |Vi |2 + |Vj |2 − 2|Vi ||Vj | cos(θi − θj ) , ∀c

l=1,l:i∼j(1f)
I. I NTRODUCTION

(1a)

−

+

PC×Ξ |V (c)| ≤ |V (c, ξ)| ≤ |V (c)| for PQ buses+and Q−G ≤ QG (c, ξ) ≤ QG ≥ 1 − −|V (c)| ≤ |V | ≤ |V (c)|

+

(1g)

for PV buses(1h)+QB ∈ {0, Q+
Q−
B },C ≤ QC ≤ QC
i, j = 1, 2, · · · , nb , c = 0, 1, 2, · · · , nc

(1i)

The cc-ORPD problem is a two-stage chance-constrained
programming problem. The first-stage variables are the operating states of RPSDs (QB and QC ) and the voltage set
points of generators (i.e. voltage magnitudes of PV buses). The
second-stage variables include the nodal injection (P and Q),
power imbalance Pδ , total line losses PL , reactive generation
QG , as well as the voltage magnitudes and angles of PQ buses(|V | and θ). Since the parameters AcG and η c depend on thecontingency c, we change the notation to AG (c) and η(c) for
better understanding.
B. Solving cc-ORPD
The cc-ORPD problem is very challenging to solve forthe following three reasons: (1) some decision variables arebinary, thus the feasible region of cc-ORPD is naturally nonconvex; (2) the power flow equations are non-linear equations,which further increase the difficulty of solving cc-ORPD; and(3) the chance constraint Eqn. (1g) induces computationallyintractable issues. Difficulty (3) is solved via sample averageapproximation technique; difficulty (2) is handled by linearizing the AC power flow equation using Jacobian matrix. The
cc-ORPD problem is converted to a Mixed Integer Linear
Program (MILP) and solved using GUROBI.

1

Distributed Transmission Expansion Planning with
Analytical Target Cascading
Mahdi Mehrtash, Student Member, IEEE, Amin Kargarian, Member, IEEE, Ali Mohammadi, Student Member, IEEE
Abstract—In this research a distributed collaborativetransmission expansion planning (TEP) algorithm forinterconnected multi-regional power systems has been studied. A
local TEP is formulated for each region with respect to the region’s
local characteristic and interactions (i.e., tie-line flows) with its
neighbors. Nodal power balances at border buses are modified tomodel the interactions. Realistic planning constraints andobjectives, such as budget constraints, operational costs, and N-1
security criterion, are modeled in the local TEPs. The information
privacy is respected as each local planner needs to share only
limited information related to cross-border tie-lines with otherplanners. To coordinate the local planners, a two-level distributed
algorithm is proposed based on the concept of analytical target
cascading (ATC) for multidisciplinary optimization. While theupper-level solves the local TEPs in parallel, the lower-level seeks
to coordinate neighboring regions. The lower-level problem isfurther replaced in the upper-level optimization by KKT
conditions to relax the need for any form of central coordinator.
This makes the proposed ATC-based TEP a fully parallelized
distributed algorithm that is potentially less vulnerable to cyberattacks and communication failures than the distributed methods
utilizing a coordinator. An initialization strategy is suggested toenhance the performance of the distributed TEP.
Index Terms—Collaborative transmission expansion planning,
analytical target cascading, distributed optimization, information
privacy, N-1 security criterion.

I

I. INTRODUCTION

N electric power industries, transmission networks must haveadequate capacity and appropriate design for reliable, secure,and fair power transmission. TEP is a more challenging
problem in interconnected multi-regional power systems
including several independent networks. Each power system
could have its own local transmission planner. If the planners
separately solve their TEP problem, the grid topology might not
be optimal from the perspective of the whole grid. This separate
planning imposes unnecessary investment costs and reduces thesocial advantage welfare. This study contributes to the literatureby presenting an ATC-based distributed transmission
expansion planning (DTEP) algorithm for interconnected
multi-regional power systems in a collaborative framework
taking into account N-1 security criterion for each region. Eachregion has its independent planning entity. Interactions (i.e.,
power exchange) between the regions through tie-lines ismodeled by a set of pseudo generations and a set of pseudoloads, and the power balance equation at border buses are
modified accordingly.
The authors are with the Department of Electrical and Computer
Engineering, Louisiana State University, Baton Rouge, LA 70803 USA(e-mail: mmehrt3@lsu.edu, kargarian@lsu.edu, amoha39@lsu.edu.)

fji
j

i

Networkm

Network
n

fi’j’
i'

j'

Fig. 1. Two interconnected networks.

fji

fji

j

i

Networkmi'

Network
n

fi’j’

fi’j’

j'

Fig. 2. Modeling power exchange between interconnected networks m and n.

Network n

Network mrUpper-level(level 1)

cm

Lower-level(level 2)

r

f mc

cn

f nc

CoordinatorFig. 3. Bi-level ATC structure for networks m and n.
Algorithm 1. The proposed parallel ATC-based coordination strategy withno coordinator for decentralized TEP implementation
1: Initialize coupling variables, 𝛼, 𝛽, and set 𝜆, 𝑘 = 0
2: While {not converged} do𝑘 =𝑘+1
3:
Solve (12) for all regions in parallel and determine the optimalvalues of coupling variables
4:
Exchange the values of coupling variables between neighboring
regions5:
Update 𝛼 𝑘 = 𝛼 𝑘−1 + 2(𝛽 𝑘−1 )2 (𝑟 𝑘−1 − 𝑓 𝑘−1 )6:Update 𝛽 𝑘 = 𝜆. 𝛽 𝑘−1|𝐹𝑚,𝑘 (Γ)−𝐹𝑚,𝑘−1(Γ)|
7:
If { |𝑓 𝑘 − 𝑓 𝑘 | ≤ 𝜖 and≤ 𝜖 } then
𝑚𝑛

8:
9:
10:

𝑛𝑚

1

Declare convergence
End if
End while𝐹𝑚,𝑘 (Γ)

2

Fig. 4. The 𝑟𝑒𝑙 index (relative error) obtained by DTEP for 24-bus system.

An Integrated Distributed Economic Dispatch-Droop Control
Architecture for Distribution System Operation
Shengyi Wang, Jie Li, and Lei Wu
Department of Electrical and Computer Engineering
Clarkson University
Potsdam, NY, USA
Abstract—With a proliferation of distributed energy resources
integrated into electric distribution systems, secure and economic
operation of distribution systems becomes more challenges, whileconventional distribution system control architecture is expected toexperience a transformation. In this paper, a bi-layer control
architecture is proposed, in which the upper layer executes a
distributed economic dispatch (ED) and the lower layer conducts
individual DERs’ droop control. Specifically, minimum generation
cost is pursued through incremental cost (IC) consensus among all
DERs, and an efficient update law is explored to guarantee
convergence of the consensus algorithm. Furthermore, optimal ED
decisions are dynamically incorporated into individual DERs’ droop
controllers by setting primary frequency control references. A bilayer ED-droop co-simulation framework is developed to verify the
proposed control scheme. A 4-bus medium-voltage primary
distribution system is built in PSCAD, and the IC consensus based
economic dispatch model is implemented in MATLAB.
Furthermore, sensitivity analysis is conducted to explore optimal
coordination between the two layers for the best tradeoff between
power output tracking and total system generation cost.

Substituting (2) into (1), we can derive the following equation:∗𝑤" = 𝑤$"+ 𝛿𝑤$" − 𝑚'( 𝑃"(4)
III ALGORITHM DESIGN FOR IC CA BASED ED
The detailed pseudocode of proposed IC CA based ED isshown in Table I.
TABLE I IC CA AT THE 𝑖 78 AGENT

Initialization:PGi [0] = 0,α i , βi , λi [0] = βi ,dij ,K, ε ,k = 0, DRi ,URi .
Input:PDi [0],PGi0
while k < K[1]Transmit λi [k] and receive λ j [k] from j ∈N i .
[2]Compute λi [k +1] using (27).
[3]Compute PGi [k +1] using (28).
if PGi [k +1] < Max{PGi,min ,PGi0 − DRi }PGi [k +1] = Max{PGi,min ,PGi0 − DRi };end ifIndex Terms—Distributed control, economic dispatch, incremental

if PGi [k +1] > Min{PGi,max ,PGi0 +URi }cost consensus algorithm

PGi [k +1] = Min{PGi,max ,PGi0 +URi };I INTRODUCTION
In this paper, a bi-layer control architecture is proposed, inwhich the upper layer executes a distributed ED and the lower
layer conducts individual DERs’ droop control. Generation costis minimized through IC consensus among all DERs. The major
contributions of this paper are summarized as follows:
1. The proposed ED based droop control obviates therequirement for a central controller and thus the reliability
and flexibility of the control are strengthened significantly.
2. ED decisions are dynamically incorporated into DERs’
droop controllers by setting primary frequency control
references for better tracking optimal power outputs.
3. A bi-layer ED-droop co-simulation framework isdeveloped to mimic coordination between economic
operation and droop frequency control to verify theeffectiveness of the proposed control scheme.

end if[4]Transmit PDi [k],PGi [k] and PGi [k +1] and receive PDj [k],PGj [k] and PGj [k +1] from j ∈N i .
[5]Compute PDi [k +1] using (29).
[6]Update k = k +1.
end while
Output:PGi [K]IV CASE STUDY
The effectiveness of the proposed distributed ED-droop
control architecture is verified via a bi-layer co-simulation
framework as shown in Fig. 1.

II INTEGRATED ED-DROOP CONTROLThe relationship between real power and frequency can be
expressed as:
𝜔" = 𝜔$" − 𝑚'( 𝑃"(1)Mathematical formulation of an integrated ED-droop control
can be expressed as follows:∗+ 𝛿𝑤$"(2)
𝑤$" = 𝑤$"
δ𝑤$" = 𝐾0 𝑃1" − 𝑃" + 𝐾" (𝑃1" − 𝑃" ) 𝑑𝑡
(3)

Fig. 1. A bi-layer ED-droop co-simulation frameworkFlexible Long Term Planning Cooptimization
Method Comparison and Validation
Patrick Maloney, Ping Liu, James McCalley

Abstract—This work compares and evaluates two different
methods for long term planning under uncertainty on a 312 bus
representation of the Western Interconnection (WI). The first,
called stochastic programming (SP), is widely used throughout
industry and specializes at finding flexible investments for thepresent. The second, called adaptation, takes an alternative
approach and attempts to find a flexible trajectory through all
time. This work provides a overview of the differences between
the two methods and compares their performance within a Monte
Carlo based validation methodology

III. A DAPTIVE P ROGRAMMING F ORMULATIONmin

Cap,4Cap

X

It,c 4 Capt,c +

t

X
t>d,s

Non-anticipative constraints (Implicit)
Capt,c = Capt−d,c + 4Capt−d,c ∀ t
0

I. M ODEL AND S IMULATION S ETUP
The long term planning algorithms are run over an ∼20
year planning horizon on a 312 bus representation of the
WI with emphasis on the Bonneville Power Administration’s
operational area (Figure 1).

Fig. 1. WI system Map. Green circles represent BPA buses and pink squares
represent buses outside of BPA area.

Generic SP and adaptation formulations for expansion planning are given by equations (1)-(4)) and (5)-(9). Conceptual
diagrams of the investment space are given in Figure 2.
II. S TOCHASTIC P ROGRAMMING F ORMULATIONmin

Cap,4Cap

I1,c 4 Cap1,c +

X

(5)
(6)
(7)

Capt,s = Capt,c + 4Capt−d,s ∀ t > d, s

(8)Operational consts for each scenario s.

(9)Fig. 2. Red arrows to red dots and clouds indicate “here and now” and “waitand see” investments respectively in the stochastic programming investment
decision space on the left. The same nomenclature applies to the adaptive
program on the right, however, the analogous “here and now” investments (orinvestments common to all scenarios) span multiple time periods forming aflexible trajectory through time.

IV. VALIDATION

Ps (It,s 4 Capt,s + OCt,s )t>d,sNon-anticipative consts. (e.g. 4 Cap1,s = 4Cap1,c )Capt,s = Capt−d,s + 4Capt−d,s ∀ t > d, s
Operational constraints for each scenario s.

0

Ps (It,s β 4 Capt,s + OCt,s )

(1)
(2)
(3)
(4)

Investment plans from each programming methodology arecompared in a Monte Carlo based folding horizon simulation.
The comparison exposes the scenario based trajectories fromSP and the core trajectory from adaptation to out of sample
parameter values developed using Markov Chains. The differences in the two approaches are evaluated for both the initialtime period and for the full planning horizon.

Voltage and Thermal Concerns during Geomagnetic
Disturbance Corrective Measures
Cecilia Klauber, Student Member, IEEE and Thomas Overbye, Fellow, IEEE
Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas 77843
Email: cklauber@tamu.edu, overbye@tamu.edu

Abstract—Geomagnetic disturbances caused by solar activity
can induce quasi-dc geomagnetically induced current (GIC) flows
in the power grid. These currents lead to half cycle saturation ofhigh voltage transformers, resulting in heating, damage, reactive
power losses and ultimately voltage stability issues. Previous work
has sought to mitigate these damages through topology control
algorithms, but these operational actions can lead to unintended
consequences, if not considered. In this work, GIC distribution
factors and reactive power distribution factors are used toquickly generate the potential voltage deviation due to various
corrective actions to inform operators making such decisions.
The computational benefits of these linear methods enables
quick results for even large systems. Furthermore, the resulting
approximate redirected GIC flows are fed to a transformer
heating model to quickly provide insight as to the effects ofthe corrective measures on thermal aspects that can lead totransformer damage.

I. I NTRODUCTION
The operation of the electric power system can beseverely impacted by geomagnetic disturbances (GMD) andthe changes in the earth’s magnetic and electric fields therein.
The currents induced by these fields flow in the earth’s crust
as well as conductors such as the electric transmission system,
leading to half-cycle saturation of transformers and resulting
in reactive power losses, voltage drop, and transformer heating
and damage. GIC mitigation schemes, namely line switching, previously sought to minimize reactive power losses. A
comprehensive scheme will consider the ac effects on thegrid, in addition to the quasi-dc currents induced during a
GMD, as well as the thermal state of the transformers. The
development of GIC line outage distribution factors (GLODFs)
greatly aids this analysis by providing a quick means of finding
the redistribution of GICs during line switching. In conjunction
with reactive power distribution factors or as an input toa transformer heating model, the expected voltage deviation
at a bus or the change in transformer temperature can beevaluated as a function of the current state and impending
change without having to re-solve the full ac power flow, oreven the GIC dc network.
II. T OPOLOGY C ONTROL A LGORITHM AND
R ESULTING C ONSIDERATIONS
The localized nature of GICs and the GLODFs are leveraged
to formulate a mixed-integer programming (MIP) problem,
efficiently solvable by commercial solvers, to select the best
sets of candidate switching lines for certain objectives, suchas minimum reactive power losses. To address voltage andFig. 1. Flow chart of topology control with voltage and thermal considerations

thermal concerns, only those line switching schemes deemed
among the best need to be analyzed. The GLODFs are again
invoked to estimate the resulting redistribution of GICs flowing
in the system. Then the expected voltage deviation can be
estimated as a linear combination of the pre-switching states
and the redistributed GICs. The new flows also serve as
the input to the transformer heating model and produce theexpected transformer temperature across the system. These
calculations can be programmed in parallel, quickly providing
comprehensive considerations for an operator during an event.

Fig. 2. Voltage contour over the UIUC 150-bus system

III. C ONCLUSION
The proposed methodology is applied during simulated disturbances over the UIUC 150-bus case (Fig. 2). The output, alist of potential topology control actions and the consequential
voltage and thermal effects, is efficiently calculated for a given
storm. These results provide system operators with valuable
information in the event of geomagnetic disturbances, which
in tandem with operators’ expertise and intuition can ensure
the reliable operation of the electric power grid. The results
are also scaled to the Texas 2000-bus synthetic case.
ACKNOWLEDGMENT
This work was supported in part by the National Science Foundation undera Graduate Research Fellowship and the Award EAR 15-20864.

DC Catenary Line Modeling of Subway Systems
Mustafa Erdem SEZGİN, Student Member, IEEE, Murat GÖL, Senior Member, IEEE
Department of Electrical and Electronics Engineering
Middle East Technical University
Ankara Turkey
erdems@metu.edu.tr, mgol@metu.edu.tr

Abstract— Today’s technology and trend for clean energy,
made electrified traction systems more and more popular aroundthe world. As number and capacities of those systems increase,
electrical modeling of those systems became a must for power
quality studies and dynamic distribution system analysis.
However, those models have inaccuracies due to electricalparameters and high computational burden. This paper proposesa linearized DC catenary line model for real subway systems,
which has a little computational burden. Moreover, the parameter
approximations employed by the method provides more accurate
results. The proposed method is validated using real measurement
data.

III. SYSTEM MODELING
Rc

Rc

Lc
Cc

DC
Substation

Cc

Cr

Subway
Train

Lr

Rr

Cr

Lr

Rr

Fig. 2. System configuration for distributed parameter model.

𝑑𝑉
𝑑𝐼 𝑑𝑥
= 𝑅𝐼 + 𝐿
𝑑𝑥
𝑑𝑥 𝑑𝑡
𝑑𝐼
𝑑𝑉 𝑑𝑥
=𝐶
𝑑𝑥
𝑑𝑥 𝑑𝑡

Keywords— DC Catenary Line, DC Voltage Variation,
Distributed Parameters, DC System Analysis, Electrical FEM
Analysis.

I. INTRODUCTION
In this paper, a linearized dynamic voltage model isdeveloped for the DC catenary line. Due to the movement of
the train along the rails, DC voltage at the catenary line has atime and position dependent characteristic, which causes bothaccuracy and computational burden problems. The developedmodel is generalized to reflect the real behavior of a subway
system, such that operations of multiple trains are considered.
Validation of the method is realized with real field data.

Lc

By solving (3) and (4);𝑅𝐶𝑣

𝐼(𝑥) = 𝐶1 𝑒 1−𝐿𝐶𝑣2
𝑉(𝑥) = (𝐶1𝑥𝑅𝐶𝑣
1 − 𝐿𝐶𝑣 2𝑥+ 𝐿𝐶1 ) 𝑒 1−𝐿𝐶𝑣2 + 𝑉1
𝐶𝑣

(6)

(7)

𝑃 = [𝑉𝐶 (𝑥 = 0) − 𝑉𝑅 (𝑥 = 0)] × 𝐼𝐶 (𝑥 = 0)

(8)

𝑉𝑅 (𝑥 = 𝑙) = 0(9)𝐼𝐶 (𝑥 = 0) = 𝐼𝑅 (𝑥 = 0)(10)

Fig. 1. Comparison between radius assumptions

ℎ𝑅
2

(5)

𝑉𝐶 (𝑥 = 𝑙) = 𝑉𝑠𝑜𝑢𝑟𝑐𝑒

IV. VALIDATION OF THE METHOD

𝑟𝑒𝑞,𝑝𝑟𝑜𝑝𝑜𝑠𝑒𝑑 =(4)

Boundary conditions can be written as;

II. ELECTRICAL PARAMETERS

𝐴
𝑟𝑒𝑞,𝑐𝑜𝑛𝑣𝑒𝑛𝑡𝑖𝑜𝑛𝑎𝑙 = √
𝜋

(3)

(1)
(2)
Fig. 8: Simulation result and measurement of the voltage

Volt-Var Optimization Algorithm Utilizing
Custormer- and Utility-Owned Distributed ResourcesDavid Mulcahy, Catie McEntee, Jiyu Wang, Ning Lu
Department of Electrical and Computer Engineering
North Carolina State University , Raleigh, NC, USA
djmulcah@ncsu.edu
Keywords— Volt-Var Optimization, Mixed-Integer Nonlinear
Programs, Distributed Energy Resources, OpenDSS, Photovoltaics

I. INT RODUCT ION
Rooftop photovoltaic (PV) systems and utility-scale PV
farms have proliferated rapidly in recent years as utilities andcustomers have moved toward low-cost, low-emission
electricity generation. High penetration of solar generation on
distribution circuits can cause violations of voltage standards,
larger voltage fluctuations, and increased operation of utilityowned device. In these circuits, voltage control is increasingly
difficult while relaying solely on traditional voltage control
devices like capacitors, on-load tap changers (LTC) and line
voltage regulators. There is a significant need for voltage
control methods that can maintain voltage limits andcoordinate all available resources to manage voltage in realtime.
While the integration of PV can worsen these voltage
issues, the PV inverters and the integration of smart controls
for customer loads can provide better voltage control thanconventional resources. When voltages are high, customers
with smart inverters and/or controllable loads can increaseload, curtail PV, or absorb reactive power to reduce voltages.
When voltages on the feeder are low, reducing load, or
injecting reactive power can increase voltages. These resources
can operate in conjunction with traditional methods of discreteswitching of capacitors or changing regulator taps. However,combining all these resources effectively requires newalgorithms for voltage control and distribution circuit
operation.
This research proposes a mixed-integer nonlinear program(MINLP) to schedule the dispatch of distribution resources.
The algorithm incorporates the controls of photovoltaic (PV)smart inverters (reactive power and PV curtailment),
controllable load, voltage regulators, and capacitors to maintainvoltage across the feeder.
II. M ET HODOLOGY
The optimization ut ilizes a voltage sensitivity matrix for all
resources based on [1] using an OpenDSS model of a feeder
and approximates the voltage across the circuit after control
actions (eqn. 2) The objective function (1) min imizes the costof control actions (eqn. 1) which include PV curtailment,
reactive power inject ion/absorption, capacitor switching, andregulator tap changes (Δ𝑃𝑖𝑃𝑉, Δ𝑄𝑖𝑃𝑉 , Δ𝑃𝑖𝐿𝑜𝑎𝑑,Δ𝑆𝑖, 𝑎𝑛𝑑 Δ𝑇 ,respectively). Additionally, (1) min imizes the change involtage between time periods while maintain ing voltage

within limits (eqn. (3)). Individual loads (increase anddecrease) have a quadratic price wh ile PV curtailment has alinear price based on opportunity cost of PV p roduction.
Discrete actions from regulators and capacitor switching h ave
a fixed price per switching operation.

s.t.

(2)
𝑉 min ≤ 𝑉 i ∙≤Vmax(3)

III. CASE ST UDY
A case study for one day (5-min resoltuion load and PV
profiles) was performed on a rural, distribution feeder with
371 load nodes and 75 PV systems (20% penetration). The

voltage results across the feeder are shown in figure 1
Figure 1. Max and Min Votlage across circuit by control method

compared to the system without voltage control, withconventional control, and with our VSM-based MINLP
control. The results show how the diurnal maximu m votlage
and minimu m voltage stay within the ANSI voltage limits
more frequently and with less deviation than the other control
methods.
IV. REFERENCE S[1]Zhu, Xiangqi, Jiyu Wang, David Mulcahy, David L. Lubkeman, Ning Lu,
Nader Samaan, and Renke Huang. "Voltage-Load Sensitivity Matrix Based
Demand Response for Voltage Control in High Solar Penetration Distribution
Feeder." 2017 IEEE Power & Energy Society General Meeting. IEEE, 2017

Cloud Computing Based Real-Time Energy Management Systemwith RNN-LSTM Wind Forecasting
Dan LuInamori School of Engineering
Alfred University
Alfred, USA
lu@alfred.edu

Abstract—With wind and solar resources, power generation
facilities can be close to the loads via distributed networks. It is
possible to isolate local renewable power generations in theextreme weather events, if energy storages are included in theislanding operations. Since wind speeds randomly vary, it iscritical to predict the power generations based on the historical
data and minute-ahead data. With Internet of Things (IoT) and
Real-Time Hardware-in-Loop (RT-HIL), one can monitor andcontrol power flows. Using machine learning and cloudcomputing, one may be able to predict wind power generation 10
minutes ahead of the actual events. In this study, Recurrent
Neural Networks (RNN) with Long Short-Term Memory (LSTM)
method is used for “10-minute-ahead wind power
forecasting.” A real-time simulation is carried out in a distribution
network via OPAL RT-HIL. Results will be presented, along withobservations.
Keywords—recurrent neural networks; wind power; forecasting;hardware-in-loop; real-time simulaiton

I.

INTRODUCTION

By 2030, the renewable energy penetration level in New
York State’s electric power generation will be 50%. New windfarms are being proposed, planned and installed in upstate New
York, including southern tier and western New York, where
electrical loads near the new wind farms are relatively
low. Local consumptions with energy storages such as batteries
can be formed in distribution network; one can envision a
resilient power network to resist power outages due to extreme
weather and other unexpected events. Forming a community
power island may be beneficial. Thus, it is critical to develop a
predictive methodology based on the historical data and existing
data. In this study, our model is based on electrical power gridin Alfred, NY; the wind power generation data are from a systemof 100 kW located on Alfred State College campus. Our
ultimate goal for the project is to design a real-time energy
management system with live data from wind turbines nearby,
harvested via IoT data acquisitions systems. We’ll use an IEEE
33 bus system as an example of distribution network because
this benchmark is similar to Alfred power grid. With OPAL-RT
simulations, we can study the effectiveness of forecasting
schemes.
II.

METHODOLOGY

A. Wind power forecasting
Among different methodologies, RNN with LSTM provides
suitable performance in terms of accuracy and computation
time. The LSTM structure is depicted in Fig. 1 where Xt is thereal time wind power generation acquired from IoT data

Xingwu Wang
Inamori School of Engineering
Alfred University
Alfred, USAfwangx@alfred.edu

acquisition system, ht is the hidden state which can be
represented by the previous state ht-1 and the current input Xt
under a control of a set of weight coefficient (stored as the shortterm memory), and Ct is the cell state which keeps long shortterm memory (seasonal or monthly information of historicaldata) and Ct-1 will be trained along with ht-1 to the updated Ct.
Ct-1

Training

Ct

ht-1

Traininght

RT windXt
power
Fig.1. LSTM structure for 10 minutes ahead wind power forecasting

Figure 2 shows the forecasted wind power and actual data
gathered. After initial “machine learning,” the predication
yields close resemblance of actual data, however, with alatency. Such time delay can be corrected by systematicallyshifting the time. It takes 20 seconds to forecast the next 10
minutes’ wind speed, indicating a promising methodology forreal-time operation.
Wind power [kW]Behrouz Azimian
Inamori School of Engineering
Alfred University
Alfred, USA
mr.behrouz.azimian@ieee.org

Fig. 2. Forecasted and actual wind power generation

B. HIL power system simulation
The predicted output is sent to simulated renewable energy
integrated power system (OPAL-RT), and real time response of
the entire system is monitored to ensure system security
constraint compliances by generating correction control
signals. For example, wind power spillage can be managed tomeet the optimum operation condition of the system. The whole
RT-HIL system is depicted in Fig. 3.
Distribution network with energy storage and wind turbine
Cloud computing(Data acquisition)

RNN-LSTM

Host PC

RT-HIL simulator(OPAL-RT)

Fig. 3. Real-time simulation of distribution network

Control unitProgressive Hedging Algorithm in Two Stage
Stochastic Optimization of Look-Ahead Unit
Commitment (SLAC)
Mehdi Saleh

Kory Hedman

School of Computing, Informatics, Decision Systems
Engineering
Arizona State University
Tempe, AZ, USA
mehdi.saleh@asu.edu

Department of Electrical Engineering
Arizona State University
Tempe, AZ, USA

Abstract— Modeling uncertainties coming from renewablesources and generator outages in the unit commitment
framework add a great deal of complexity to the model. With
today’s computational capabilities for mixed integer
programming problems, solving multiple renewable scenariosand generator contingencies could be time inefficient. Progressive
Hedging (PH) is a scenario-based decomposition method to solve
stochastic model in a time efficient manner. Dealing with large
scale MIP problems could arise some issues that is application
dependent. In the paper, we propose PH heuristics such as
scenario bundling, forcing solutions, and slamming to obtain asolution in a desired timeframe.

invoking PH algorithm, data is preprocessed and the network
topology is built.

Keywords—Unit commitment, Progressive Hedging, mixed
integer programming, stochastic optimization.

I.

INTRODUCTION (HEADING 1)

The industry is in the process of moving towards
technologies like SLAC. This SLAC is a tool that will sit rightin front of the decision-making process where SCED resides,
which is in the real-time operations and market environments.
The SLAC will sit at an hour-ahead time stage. Note that thetechnology that we are developing will also be deployedwithin real-time SCED tools in the future as well as within
day-ahead security-constrained unit commitment (SCUC) andday-ahead residual unit commitment (RUC) tools. Progressive
Hedging (PH) is a scenario-based decomposition method tosolve stochastic model in a time efficient manner. Dealing
with large scale MIP problems could arise some issues that is
application dependent. In this paper, we propose PH heuristics
such as scenario bundling, forcing solutions, and slamming toobtain a solution in a desired timeframe for an hour ahead unit
commitment model.
II. METHODOLOGY
A horizontal decomposition method is applied to theextensive form of the SLAC model (Fig 1) where each
subproblem is consisted of a single renewable scenario and agenerator contingency. Before creating the subproblem andkory.hedman@asu.edu

Figure 1. Horizontal decomposition of extensive form
After processing the data, subproblem is solved and thesolution is passed to deliverability test stage. If there are no
violations is reported the final solution is handed over to PH
server to obtain convergence among different solutions ofsubproblems. Figure 2 summarizes the flowchart of a single
scenario and a single generator contingency subproblem.
Pre-Processing
Initiate PH
sub-problem
UpdatethresholdNo
Flowdeviation< 5% ?
Yes

SLAC PH Sub-problem

Collect input
data
PTDF cutoff
PTDF cutoff
feasibility test
Build topology

Run Stochastic
Look-Ahead
Commitment PH
Sub-problem

Deliverability Test
Perform precontingency power
flow analysis
Perform
contingency
analysis

No
Violation?

PH Server

ReportSolution

Yes

Update monitoredtransmission limit
subsetFigure 2. Subproblem flowchart
III. REFERENCES[1] Watson, Jean-Paul, and David L. Woodruff. "Progressive
hedging innovations for a class of stochastic mixed-integer
resource allocation problems." Computational Management
Science 8.4 (2011): 355-370.
[2] Gade, Dinakar, et al. "Obtaining lower bounds from theprogressive hedging algorithm for stochastic mixed-integer
programs." Mathematical Programming 157.1 (2016): 47-67.

Robust Unit Commitment Considering the
Temporal and Spatial Correlations of Wind Farms
Using a Data-Adaptive Approach
Yipu Zhang, Xiaomeng Ai, Jinyu Wen
Huazhong University of Science and Technology,
Wuhan, China

Jiakun Fang
Aalborg University
Aalborg, Denmark

Abstract—In robust optimization problems, building a proper
uncertainty set for the stochastic variables plays an importantrole. Due to the restricted mathematical formulations of theuncertainty sets, the results derived from conventional two-stage
robust optimization are usually over conservative. In this paper,
a novel data-adaptive robust optimization method for the unit
commitment is proposed for the power system with wind farms
integrated. The extreme scenario extraction and the two-stage
robust optimization are combined in the proposed method. The
data-adaptive set consisting of a few extreme scenarios is derived
to reduce the conservativeness by considering the temporal andspatial correlations of multiple wind farms. Numerical results
demonstrate that the proposed data-adaptive robust optimization
algorithm is less conservative than the current two-stage
optimization approaches while maintains the same level ofrobustness of the solution.
Index Terms—Unit commitment, two-stage robust optimization,
temporal and spatial correlations, data-adaptive uncertainty set,
extreme-scenario

I.
KEY EQUATIONS
A data-adaptive robust optimization (DARO) method is
proposed in this section. The proposed method considers thecorrelation of the wind farms by building a data-adaptive set asshown in (1).
N
  i e1 pie,i n  3    R
(1)

Ne

pi  1, pi  0 i 1The approach is divided into 3 steps in detail. Firstly, theMVEE algorithm is used to determine the approximate regionof the historical scenarios. Then the extreme scenarios and thedata-adaptive set is obtained by the algorithms from linear
algebra. Finally, the two-stage robust optimization is adopted tosolve the robust UC problem.





II.

KEY RESULTS

A. System description
To validate the effectiveness of the proposed algorithm, a 6bus distribution network is used as the test system. The
topology is shown in Fig. 2.

Cheng Luo
Northern Indiana Public Service Company
Merrillville, USA
Load@5

PG@3

PG@1
5

PW@1

W

1

3
PG@2

PW@3 W

2
6
Load@6

Load@4

4

Fig. 2. 6-bus System topology

B. Comparative study on DARO and conventional TRO
TABLE I. THE COMPARISON RESULTS

Method𝛼𝑅𝑂

Traditional robust
optimization
100%

DARO
100%

𝑈𝐶𝑡 (h)41

37

𝑂𝑈𝐶 (MWh)
Operation cost in the worst
scenarios (CNY)
Operation cost in theforecasted scenarios (CNY)
Operation cost in the historicalscenarios SH1(CNY)
Operation cost in the historicalscenarios SH2 (CNY)
Operation cost in the historicalscenarios SH3 (CNY)Calculating time (s)

7150

6380

61910.23

52102.15

52099.94

51314.0751987.97

51206.66

52329.76

51513.51

52098.94

51310.09

104.9928.53

III. CONCLUSION
In this paper, a robust unit commitment model considering
the temporal and spatial correlation of wind farms is proposed.
Combining the advances of scenario-generation algorithms andtwo-stage robust optimization, the proposed data-adaptive
robust method can make full use of the empirical knowledge
obtained from the historical data. Case studies show that the
proposed algorithm improves the utilization rate of the online
unit capability significantly and reduces the operation cost
while guarantees the robustness of the system. This leads to theimproved economy of the decision with uncompromised
security. In addition, the computational efficiency issignificantly improved so that the proposed method isapplicable to large systems.

SDP Based ACOPF and Network Reconfiguration for
Three-phase Distribution System
Yikui Li, Jie Li, and Lei Wu
Department of Electrical and Computer Engineering
Clarkson University
Potsdam, NY, USA
Email:{yikliu, jieli, lwu}@clarkson.edu
Abstract—In emerging distribution systems with aproliferation of distributed energy resources (DER) and flexible
demand assets, operation characters of the unbalanced networkand voltage regulation devices need to be accurately addressed
for ensuring the secure and economic operation. This poster
introduces an AC optimal power flow model and a network
reconfiguration model, both of which are based on semidefinite
programming (SDP). Numerical studies illustrate effects of the
proposed approach.

unique characteristics of unsymmetrical distribution systems.
The ACOPF problem is formulated as a chordal relaxation
based SDP model, and a tighter convexification model of VRs
is proposed to mitigate solution inexactness. Analyticalconditions are presented and proved to determine whether
global optimal solution to the original ACOPF problem can be
retrieved from solutions of the chordal relaxation based SDP
model.

Keywords—Distribution systems; AC Optimal power flow;
Distribution network reconfiguration; Semidefinite programming

III. DISTRIBUTION SYSTEM RECONFIGURATION MODEL [2]

I. INTRODUCTION
Distributed energy resources (DER) present uniqueadvantages in providing clean and cheap energy locally,
reducing active power losses, and enhancing energy resiliency
in distribution systems. Motivated by these incentives, existing
distribution systems are experiencing new changes by
including a deeper penetration of DERs. However, as anotherside of a coin, the proliferation of DERs in distribution
systems also brings new challenges. Specifically, distributionlines have compatible resistance and reactance, which makes
voltage magnitudes also sensitive to active power injections
from DERs. Thus, voltage fluctuation caused by intermittent
power outputs of DERs, such as voltage sag and voltage swell,is one of the prominent problems in emerging distribution
systems. It could degrade power quality and cause damages toelectrical equipment. Particularly, voltage swell issues could
more likely emerge in distribution feeders with excessive
DERs. Furthermore, bidirectional power flows induced byproliferated DERs in distribution systems, by contrast withpast unidirectional power flows, could cause potential risk ofline congestion and require reconfiguration of protection
systems.

Distribution network reconfiguration has long been usedby distribution system operators to achieve certain operation
objectives, such as reducing system losses or regulating bus
voltages. Co-optimizing network topology and DERs’
dispatches could further enhance such operational benefits.
Thus, this section focuses on the optimal network
reconfiguration problem of distribution systems under an
unbalanced ACOPF framework, which rigorously addresses
operation characters of unbalanced network, DERs, and VRs.
Two VR models with continuous and discrete tap ratios arestudied and compared. The proposed co-optimization problem
is formulated as a mixed-integer semidefinite programming(MISDP) model with binary variables indicating lineswitching statuses and tap positions. Several acceleration
strategies by studying the structure of distribution networks
are explored for reducing the number of binary variables andenhancing the computational performance.
IV. CASE STUDIES
Numerical case studies are conducted on modified IEEE
34-bus and 8500-node distribution systems. Numerical studies
illustrate effects of the proposed approaches. Computational
performances of the proposed ACOPF and network
reconfiguration models are discussed.

II. DISTRIBUTION SYSTEM ACOPF MODEL [1]This section focuses on the modeling and solution approachof AC optimal power flow (ACOPF) problems for unbalanced
three-phase distribution systems with DERs and voltage
regulators (VR). ACOPF problem is essentially nonlinear andnonconvex because of the quadratic relationship among phasevoltages and complex power injections. Challenges ofnonlinearity and non-convexity are further deteriorated byThis work was supported in part by the U.S. National Science Foundation
grants PFI:BIC-1534035, CNS-1647135, and ECCS-1653179.

REFERENCES
[1]

[2]

Y. Liu, J. Li, L. Wu and T. Ortmeyer, “Chordal relaxation based ACOPF
for unbalanced three-phase distribution system with DERs and voltage
regulation transformers”, IEEE Trans. Power Syst., vol. 33, no. 1, pp.
970–984, Jan. 2018.
Y. Liu, J. Li, and L. Wu, “Coordinated optimal network reconfigurationand voltage regulator/der control for unbalanced distribution systems,”
IEEE Trans. Smart Grid, (Early Access).

1

Finite-Difference Relaxation for Parallel
Computation of Ionized Field of HVDC Lines
Peng Liu, Dept. of Electrical and Computer Engineering
University of Alberta
Edmonton, Canada
pliu3@ualberta.ca
Venkata Dinavahi, Dept. of Electrical and Computer Engineering
University of Alberta
Edmonton, Canada
dinavahi@ualberta.ca

Abstract
Ionized field calculations for high-voltage direct current (HVDC) transmission line is a computationally demanding problem,
which can benefit from the application of massively parallel high-performance compute architectures. The finite element method(FEM) commonly employed to solve this problem is both memory and execution time intensive. In this paper, a finite-difference
relaxation (FDR) method is proposed to solve a unipolar and a bipolar ionized field problem in an HVDC line. The novel FDR
method has several advantages over FEM. First, the scheme is suitable for massively parallel computation and runs much faster:Compared with the commercial FEM software Comsol Multiphysics, the speed-up is more than 14 times in CPU parallelizationand 35 times in graphics processor parallel implementation, while providing high accuracy. Moreover, the set of equations in FDR
need not be assembled; instead, it is solved by a relaxation scheme and requires much less memory than FEM. Additionally,
differentiated grid size with interpolation techniques is proposed to improve the flexibility of FDR for problem domain containing
irregular geometries or disproportional sizes.
Index Terms
Finite-difference method, graphics processors, HVDC lines, ionized field, Jacobi method, multi-core, many-core, parallel
algorithms, relaxation.

PV Extreme Capacity Factor Analysis
Zefan Tang1 , Peng Zhang1 , Jaemo Yang2 , Kunihiro Muto1 , Marina Astitha2 , Joseph N. Debs3 ,David A. Ferrante3 , Devon Marcaurele3 , Isabelle M. Hazlewood4 , Dale Hedman4

In this paper, k-means clustering is utilized to spatiotemporally divide the utility service territory into k clusters basedon five meteorological variables: solar irradiance, humidity,
temperature, wind speed, and cloud coverage. The objective
is to combine more extreme data from multiple PV systems
with the similar behaviors in terms of ECFs.
EVA is subsequently applied to evaluate the probability
distributions of ECFs. It uses the extreme data selected througha threshold from each PV system in the same cluster. The
effects of different k and thresholds are both evaluated.
Key conclusions: 1) different k and thresholds can lead todifferent return levels with great dissimilarity; 2) the higher
threshold, the higher return levels; and 3) the value of k can
be determined through Silhouette Value.

Latitude

41.8
41.6
41.4
41.2

-73.5 -73 -72.5 -72-73.5 -73 -72.5 -72-73.5 -73 -72.5 -72

Longitude

Longitude

Longitude

1.00

1.00

1.04

0.98

1.02

0.98
Return Level
Upper Bound
Lower Bound

0.96

Return Level
Upper Bound
Lower Bound

1.00
0.98
0.96

0.94

k=4, 99%

0.94
0.92

k=5, 99%

k=3, 99%

0.94
0.92

Return Level
Upper Bound
Lower Bound

0.96

2

5 10 20 50 100 200 0.92

0.90

2

Return Period (Year)
5 10 20 50 100 200 0.88 2

5 10 20 50 100 200

0.90

0.90
0.86
0 1 2 3 4 5 6
0 1 2 3 4 5 6
0 1 2 3 4 5 6Reduced Variate, -log(-log(F)) Reduced Variate, -log(-log(F)) Reduced Variate, -log(-log(F))

Fig. 3. Comparison results of the return levels for different k in February
when the threshold is 99%. The upper and lower bounds are the 95%
confidence intervals.
0.98

0.98
Return Level
Upper Bound
Lower Bound

0.96
0.94

0.90
Return Level
Upper Bound
Lower Bound

0.96

0.88

0.94
k=3, 98%

0.92

k=4, 98%

0.92

0.90
5 10 2050 100200

0.87

k=5, 98%

0.86

0.90
2

Return Level
Upper Bound
Lower Bound

0.89

2

Return Period (Year)
0.85
5 10 20 50 100 200
2

5 10 20 50 100 200

0.88

0.88
0.84
0 1 2 3 4 5 6
0 1 2 3 4 5 6
0 1 2 3 4 5 6Reduced Variate, -log(-log(F)) Reduced Variate, -log(-log(F)) Reduced Variate, -log(-log(F))

1
k=3

k=4

k=5

2
3

2

2

3
0

Sil=0.3157

3
4

0.5

1

Silhouette Value

Sil=0.3334

4

Fig. 4. Comparison results of the return levels for different k in February
when the threshold is 98%. The upper and lower bounds are the 95%
confidence intervals.

Sil=0.1681

5
0

0.5

Silhouette Value

1

0

0.5

1

0.94

Fig. 1. Comparison results of the Silhouette Value for different k in February.

This material is based upon work supported by Eversource Energy Center
under Grant No. 6200980 and 6200990, National Science Foundation US
Ignite under Award No. 1647209, and UConn Academic Plan Level 1 underAward No. 2806600.
1 Dept. of Electrical and Computer Engineering, University of Connecticut,
Storrs, CT 06269
2 Dept. of Civil and Environmental Engineering, University of Connecticut,
Storrs, CT 06269
3 Eversource Energy, Berlin, CT 06037
4 Connecticut Green Bank, Rocky Hill, CT 06067

0.85

0.92

Silhouette Value
Capacity Factor

Cluster

k=5Fig. 2. Comparison results of the clustering for different k in February. Eachdot represents each weather site. Different colors mean different clusters.

1
1

k=4

k=3

41

Capacity Factor

I. M AIN R ESULTS

42

Capacity Factor

Abstract—Spatiotemporally analyzing the extreme photovoltaic(PV) capacity factor is of great importance for system planningand protection. However, due to the scarce number of the extreme
data, the traditional extreme value analysis (EVA) could not
be directly applicable. This paper addresses this challenge by
combining data from multiple PV sites. The k-means clustering
method is developed to spatiotemporally divide the utility service
territory into k clusters, such that PV systems in each cluster will
have similar behaviors in terms of the extreme capacity factor(ECF). The value of k is determined by using the Silhouette Value,
and the extreme data of each PV site are selected by setting athreshold. Specifically, the effects of different k and thresholds areevaluated. The results illustrate that different k and thresholds
can have different return levels with great dissimilarity.

Return Level
Upper Bound
Lower Bound

0.92

k=3, 97%0.9

0.9

Return Level
Upper Bound
Lower Bound

0.84

Return Level
Upper Bound
Lower Bound

k=4, 97%0.83

k=5, 97%

0.88
0.82

0.88
2

0.86
Return Period (Year)
5 10 20 50 100200

2

Return Period (Year)
0.81
5 10 20 50 100200

2

Return Period (Year)
5 10 20 50100 200

0.86

0.8
0.84
0
2
4
6
0
2
4
6
0
2
4
6
Reduced Variate, -log(-log(F)) Reduced Variate, -log(-log(F)) Reduced Variate, -log(-log(F))

Fig. 5. Comparison results of the return levels for different k in February
when the threshold is 97%. The upper and lower bounds are the 95%
confidence intervals. With a low threshold, the return level is almost constantfor different return periods.

1

Robust Koopman Operator-based Kalman Filter forPower Systems Dynamic State Estimation
Marcos Netto, Student Member, IEEE, and Lamine Mili, Life Fellow, IEEE
Virginia Polytechnic Institute and State University

Abstract—We develop a robust Koopman Kalman filter (GMKKF) to estimate the rotor angle of synchronous generators. The
approach is data-driven and model independent. The design phaseis carried out offline and requires estimates of the generators’rotor angle and active/reactive power; in real-time, only measurements of active/reactive power at the terminal of the generators
are assumed. We investigate the probability distribution of thetransformed state variables by means of Q-Q plots and verify
that they follow a Student’s t-distribution with 20 degrees of
freedom when the initial state vector is normally distributed. Ourfilter presents high statistical efficiency under this assumption.
We carry out numerical simulations on the IEEE 39-bus system.
They reveal that the GM-KKF has a faster convergence rate than
the non-robust Koopman Kalman filter. They also show that thecomputing time of the GM-KKF is roughly reduced by one-third
as compared to the one taken by our previously developed robustGM-extended Kalman filter.

x
columns of C x and C h are, respectively, defined as C:,i
= vix
h
h
and C:,i = vi . If the KEFs form a complex conjugate pair,




Ai,i
Ai,i+1cos(arg λ) sin(arg λ)= |λ|,Ai+1,i Ai+1,i+1− sin(arg λ) cos(arg λ)
x
x
h
h
C:,i
= <{vix }, C:,i+1= ={vix }, C:,i
= <{vih }, and C:,i+1=h={vi }. Finally, we haveh(x̄k ) = C h xk ,xk = Axk−1 ,II.
TABLE I.

I. K EY E QUATIONSConsider an autonomous discrete time nonlinear systemȳk = h(x̄k ).

(6)

The GM-KKF is a robust estimator of (6).
N UMERICAL R ESULTSC OMPUTATION TIME PER DISCRETE - TIME STEP.
KKF
10.6395

Time (milliseconds)x̄k = f (x̄k−1 ),x̄k = C x xk .

GM-EKF220.1923

GM-KKF83.3937

5(1)

4

x = X (x̄),

y = Y(ȳ),(2)

Quantiles of data

3

A. Design of Nonlinear Observers
One design approach is to seek a nonlinear transformationsuch that (1) can be converted into a canonical observer form,
xk = Fk−1 xk−1 + α(ȳk ),yk = Hk xk + β(ȳk ).

2
1
0
-1
-2

(3)

-3
-3

B. Generalized maximum-likelihood Koopman Kalman filter
Interestingly, the Koopman canonical transformation does
not yield output injection terms. Therefore, we neglect them.
Instead, we include additive error terms in (3),xk = Fk−1 xk−1 + wk−1 ,pyk = Hk xk−1 + ek .

(4)

30
25

Let F
(KEFs) of (1), with h(x), x ∈ F . Then,h(x̄) =

φi (x̄)vih .

(5)

i=1

h
i>
Define TU (x̄) = φ̂1 (x̄), ..., φ̂p (x̄) , whereφ̂i = φi , if the i-th KEF is real, andφ̂i = 2<(φi ) and φ̂i+1 = −2=(φi ), if i and (i + 1)-th
KEFs form a complex conjugate pair.
The nonlinear change of coordinates is given by the KEFs.
If the i-th KEF is real, the block diagonal Ai,i = λi and the(degrees)i=1

φi (x̄)vix ,

0

1

2

3

4

=20

True value
KKF
GM-KKF

20

2

x̄ =

p
X

-1

Fig. 1. The Q-Q plot of the transformed state variables, TU (x̄), indicatesthat they roughly follow a Student’s t-distribution with 20 degrees of freedom.

= span{φi }pi=1 be a subset of Koopman eigenfunctions
p
p
X

-2

Quantiles of Student's t-distribution,

15
-2
10
-4
5

6

7

8

9

10

0
-5
0

5

10

15

20

25

30

Time (seconds)

Fig. 2. Comparison between the KKF and the proposed GM-KKF. Multiple
outliers placed from t = 7 to t = 8 seconds.

Information Transfer Based Modal Analysis inPower System
Pranav Sharma, Subhrajit Sinha, Umesh Vaidya, Venkatramana Ajjarapu
Department of Electrical and Computer Engineering, Iowa State University, Ames, Iowa, USA
Email: spranav@iastate.edu

Abstract—Information theory has been used for influencecharacterization in a network, having application in biology,
finance and physical infrastructure from a long time. Recent
work on information transfer theory extends the concept tocharacterize influence in a dynamical system. This poster present
a case for information transfer for small signal analysis in powersystem. A comparative study of information transfer based modal
analysis and known technique of participation factor is presented,
demonstrating advantage over the later methodology.

I. K EY C ONCEPTS
Information transfer is based on the notion of uncertainty
propagation which gives a physical meaning to interaction of
dynamic states. The information transfer from a state x to statey (Tx→y ), as the dynamical system z(t + 1) = S(z(t)) + ξ(t)evolves from time step t to time step t + 1 is defined as•

Quantify the contribution of collection of states (subspace) in modes, thereby extracting meaningful information about physical nature of the system[2].
II. R ESULTS

Here IEEE 9 bus system is considered with 4th order generator dynamic model[3]. We compute information transfer fromgenerator subspace to critical mode, thus identifying influential
generator in the system. Further we quanitfy influence ofindividual states on system mode, thus validting with known
phenomena of participation factor
Load 5

T1

T3

G1

Tx→y = H(y(t + 1)|y(t)) − H6x (y(t + 1)|y(t))(1)

Information transfer can be understand analytically for alinear system. Considering a discrete linear model with anadditive Gaussian noise into the system, we can define information transfer for linear system(2).

6

4
9

3

7

Load 9>
where z = x y , H(y(t + 1)|y(t)) is the entropy of y attime t + 1 conditioned on y(t) and H6x (y(t + 1)|y(t)) is theconditional entropy of y(t + 1), conditioned on y(t), when x
is absent from the dynamics.
A. Information transfer in linear systems

G3

5
1

Load 7
8

T2
2

G2

(a)

(b)

Fig. 1. (a) IEEE 9 bus system. (b) Information transfer from generator statecluster to critical mode.

3

3

2.5

Σ(t + 1) = AΣ(t)AT + (σξt )2 I

(2)
(3)

Where (3) is the covariance matrix defined on the system.
This covariance matrix essentially captures the propagation of
uncertainty in system dynamics introduced by the noise. The
information transfer from subspace xi to xj {where xi , xj ∈ x
is given by 4. Here Σsxj is the Schur complement of Σxj inΣt .
2|Axj xi Σsxi A>1
xj xi + σ I|
Txi →xj = log(4)2
σ2
The key contribution of information transfer based modal
analysis are as follow:• Address the issue related to uncertainty in initial condition, where participation factor fails to capture the correct
system behavior[1].

G3 Voltage state cluster

1.5
1

1.5
1
0.5

0.5
0

/3
!3
Eq0 3
Ef0 d32

G3 swing State Cluster

2

Info. transfer

xt+1 = Axt + σξtInfo. Tranfer

2.5

0
-0.5

0

200

400

600

800

1000

0

200

400

600

800

1000

time

time

(a)

(b)

Fig. 2. (a) Information transfer from state cluster in G3 near SNB. (b)Information transfer from individual states of G3 to critical mode

R EFERENCES[1] E. H. Abed, M. A. Hassouneh, and W. A. Hashlamoun, “Modal participation factors revisited: One definition replaced by two,” in American
Control Conference, 2009. ACC’09., pp. 1140–1145, IEEE, 2009.
[2] J. Van Ness and F. Dean, “Interaction between subsystems of a powersystem,” in American Control Conference, 1994, vol. 2, pp. 1553–1557,
IEEE, 1994.
[3] P. W. Sauer and M. Pai, “Power system dynamics and stability,” Urbana,
1998.

1

Horizontal Decomposition-Coordination Strategy forPower System Scheduling with Overlapping Intervals
Farnaz Safdarian, Student Member, IEEE, and Amin Kargarian, Member, IEEE

t2 t3

N_

t2 t3

N_

t2 t3

...

tn

toN→N+

tn

...

tn

N+

toN→N+

...

tn

N
(a)
toN_→N

t1 t2...

toN→N+

tn

toN_→N...

t1 t2

toN_→N

Abstract—This paper presents a horizontal time decompositionand coordination strategy to reduce the computational complexity of
power system multi-interval operation problems with the main focus
on dynamic security-constrained economic dispatch (SCED). The
considered scheduling horizon is decomposed into multiple smaller
sub-horizons. The concept of overlapping time intervals is introduced
to model ramp constraints of generating units for transition from one
sub-horizon to another sub-horizon. A sub-horizon includes 𝒏internal intervals and one or two overlapping time intervals. The
overlapping intervals are modeled with shared variables thatinterconnect the consecutive sub-horizons, and a local rampconstrained SCED subproblem is formulated for each sub-horizon.
To find the optimal solution for the whole operation horizon, a
distributed coordination strategy is developed to coordinate the
SCED solutions obtained in the sub-horizons. The coordination
algorithm is based on auxiliary problem principle (APP).
Furthermore, we present an accelerated APP and an initialization
strategy to enhance the convergence performance of the coordination
strategy. The proposed algorithm is applied to solve a week-ahead
SCED on the IEEE 118-bus system and a 472-bus system, andpromising results are obtained.
Index Terms— Time decomposition, distributed optimization,
security-constrained economic dispatch, accelerated auxiliary
problem principle, overlapping time intervals.

t2 t3

...

tn

N+

N

(b)
Fig. a) Three consecutive sub-horizons with overlapping time intervals and b) subhorizons decomposition with overlapping time intervals.

III. CASE STUDY
The proposed algorithm is applied to IEEE 118-bus system. The
operation horizon is divided into seven sub-horizons, each
including 24 intervals.

I. INTRODUCTION
The larger the system is, the more the number of variables andconstraints is, and consequently the larger the size of the
optimization problem will be. Computational burden and costs
increase with growing the system size. Centralized algorithms may
not be able to handle such large optimization problems within an
acceptable time. Most techniques presented in the literature tosolve ED in a distributed manner implement a geographicaldecomposition to divide the power system into several smaller
subsystems. Since subsystems are coupled physically throughcross-border tie-lines, any decisions made in one subsystem affects
decisions made in other subsystems. The intertemporal constraints
or consistency constraints, which originate from limits of ramping
capabilities of generating units, interconnect decisions made in atime interval to decisions made in other intervals and increase
complexity of the decision-making. Another factor that increases
the size and computational complexity of the ED problem is N-1
security criteria. The ED problem with N-1 security criteria iscalled security-constrained economic dispatch (SCED).
II. THE PROPOSED TIME DECOMPOSITION STRATEGY
To handle coupling intertemporal constraints, we present theconcept of overlapping (or coupling) time intervals between theconsecutive sub-horizons as illustrated in Fig. a. We duplicate thefirst interval of each sub-horizon 𝑁 and consider that as interval𝑛 + 1 of sub-horizon 𝑁− . We separate the sub-horizons andduplicate the overlapping time intervals in each sub-horizon asshown in Fig. b

Fig. Difference between shared variables of unit 25 as an example.

We apply the proposed algorithm on a larger system with 472
buses.
Algorithm
Centralized
Normal distributed
Accelerated distributed

TABLE I
Iteration
2+1
2+1

RESULTS
𝑟𝑒𝑙
1e-08
1e-08

TABLE II
Algorithm
Centralized
Normal distributed
Accelerated distributed

RESULTS WITH MANIPULATED RAMPINGS
Iteration
Time (s)
𝑟𝑒𝑙
1900
9+1
1e-06
532
9+1
1e-07
532

Time (s)
2399
147
147

Afterwards, we manipulate the standard ramping of some units
to make sure that ramping of several generating units are heavily
binded in some hours and some consistency constraints are active.

Even in this case, the distributed method decreases the time.
IV. CONCLUSION
The simulation results showed that the proposed method reduced
the computational time of SCED for the IEEE 118-bus systemfrom 45% to 400% depending on the problem. We then applied the
proposed method to a 472-bus system as a larger system andobserved that as the size of the problem (that depends on the sizeof the system and the number of contingencies) increases, thedistributed algorithms show better performance compared to theconventional centralized SCED.

Flexibility Scheduling for Large Customers
Farhad Angizeh, Student Member, IEEE, Masood Parvania, Member, IEEE,
Mahmud Fotuhi-Firuzabad, Fellow, IEEE, and Abbas Rajabi-Ghahnavieh, Member, IEEE

Abstract—In this poster, we will present a decision-making
tool for enabling large customers to determine how they adjust
their electricity usage from normal consumption patterns in
expectation of gaining profit in response to changes in prices
and incentive payments offered by the system operators. We
first define customers’ flexibility options which includes any
onsite activities making net change in the energy supplied by
the grid. Then, we propose our model which simultaneously
determines the optimal integration of the potential flexibility
options comprising flexible loads rescheduling, utilizing onsite
generation (OG) and energy storage system (ESS), along withenergy procurement from the grid that allows the large customers
to optimize their energy portfolio from different sources including
bilateral contracts and the energy market. The characteristicsof the proposed integrated flexibility scheduling and energy
procurement model and its benefits are investigated throughseveral case studies conducted on a test large industrial load.

I. I NTRODUCTION AND M ETHODOLOGY
Customers play an effective role in today’s competitiveelectricity markets where they would be able to save money bystrategically scheduling their various onsite energy solutions.
Thus, a savvy customer can optimize its energy consumption
profile by intelligently modifying and thence procuring itfrom available resources so that its total electricity cost is
minimized. In this work, we introduce customers’ flexibility option including flexible loads and production processes
rescheduling, utilizing energy storage system (ESS), and operating distributed generation (DG) units. Then, we argue
that although the individual flexibility options would equip
customers to participate in the DR program, the three options
could be integrated to capture the interdependencies andthe relative costs and benefits among the options, delivering
additional flexibility at lower costs. The integrated model
intends to combine the three models into a unified flexibility
scheduling model where the power generation by OG and/or
energy stored in ESS are utilized in order to compensate
some or all of the curtailed flexible loads and/or supplying
inflexible parts of customer’s demand. The proposed integrated
model, formulated as a mixed-integer linear programming
problem, simultaneously optimizes the integrated flexibility of
the customers for hourly load reductions in the system, as
well as energy portfolio procurement from different sources
including bilateral contracts and the market (See Fig. 1).

Flexibility Options
Onsite Generation(OG)

Energy Storage
System (ESS)

Bilateral Contracts& Market Price

Flexible Loads

Estimated Demand

Integrated Flexibility
Scheduling Model

Flexibility Scheduling

Modified Demand

Energy Procurement

Fig. 1. Customer’s Integrated Flexibility Scheduling Framework

Optimal hourly load reductions and scheduled flexibility
options during 4th working day
Fig. 2.

Fig. 3. Estimated and modified demands of the customer

II. R ESULTS
Here we adapt the proposed integrated model to solve theflexibility scheduling and energy procurement for a sample
large industrial manufacturer over a one-week period. The
simulation results reveal that optimal scheduling of the flexibility options, which is illustrated in Fig. 2, helps customers

to reduce the energy procurement costs by being flexible
to modify demand and prevent buying energy at the more
expensive hours. Fig. 3 demonstrates the stemmed modified
demand for a one-week horizon.

Analysis of Vibration Signal for Power Transformer
On-Load Tap Changer (OLTC) Condition Monitoring
Junhyuck Seo, Student Member IEEE, Hui Ma, Member IEEE, Tapan K. Saha, Senior Member IEEE
The University of Queensland, Brisbane, Australia
Abstract—Vibration measurement has been used for powertransformer’s On-Load Tap Changer (OLTC) condition monitoring.
In this paper, a signal processing method based on Savitzky-Golay
filter is developed. The proposed method is applied to process a seriesof vibration signals measured from an OLTC when the OLTC
operates throughout a number of its tap positions and subsequently
evaluate the condition of these tap positions. The Savitzky-Golay
filter extracts the information embedded in the originally measured
signal without latency. As such, it transforms effectively complicate
feature of vibration signal into a simplified form of envelope curve.
After being processed by the Savitzky-Golay filter, the vibration
signals corresponding to different tap positions are compared. The
comparison results help to understand the characteristics of vibration
signals for monitoring the mechanical condition change in OLTC.

Fig. 2 Comparison of the time shifts in outputs of moving average (MA), Low-passfilter (LPF), Hilbert transform (HT), wavelet transform (WT)’s approximation, andSavitzky-Golay (SG) filter.

II. RESULTS & ANALYSIS

Index Terms— Condition assessment, On-Load Tap Changer(OLTC), power transformer, Savitzky-Golay filter, vibration.

I. METHODOLOGY & KEY EQUATIONS

Fig. 1 Graph illustration of Savitzky-Golay filter.

∑(1)

The above polynomial should satisfy the requirement of aminimum mean-squared error
as
∑

∑
∑
∑

2

∑

(2)

∑

0
∑

∑

Fig. 3. The result of correlation coefficients of ten trials at each tap position. Labels
along theta direction indicate the number of trial and labels in radius axis show thecorrelation coefficient.

,(3)0, 1, … ,(4)
(5)

Fig. 4. The result of the similarity coefficients at 4th tap position. The value at circle
is a correlation coefficient before waveform alignment and the value at asteriskshows a coefficient after the adjustment.

Among a number of filters in Fig. 2, the adopted SavitzkyGolay shows superior performance in 1) depicting well the overall
feature of vibro-acoustic signals; 2) causing no time delay in thefiltration process; and 3) achieving high resolution of output.

The proposed method integrates Savitzky-Golay filter, waveformcomparison and waveform alignment methods. The case studies
demonstrated it could be used for effectively comparing vibration
signal from OLTC and identifying a mechanical condition change
of OLTC.

∑∑∑

On False Data Injection in Wide Area Protection
Schemes
Ehab El-Saadany

Ali Hooshyar

Mohsen Khalaf

ECE Department
EECS
ECE Department
University of Waterloo
Lassonde School of Engineering
University of Waterloo
Waterloo, Ontario, Canada
York University, Toronto, Ontario, Canada
Waterloo, Ontario, Canada
On leave with Khalifa University, PI, UAE
Email: hooshyar@cse.yorku.ca
Email: m.khalaf@uwaterloo.ca
Email: ehab@uwaterloo.ca

Abstract—Maintaining power system stability and normal
operation are the most important roles of the protection system.
Recently, wide-area protection (WAP) makes use of the wide system information and sends specific data to specific local locations
to help them make their own decisions. This paper investigates,
for the first time, the problem of false data injection (FDI) in WAP
schemes. The problem is general in all WAP schemes. To perform
this investigation, the underfrequency load shedding (UFLS) is
considered as the case under study throughout this work. To show
the generality of the problem in UFLS schemes, three different
UFLS schemes are simulated using PSCAD and applied to theIEEE 39-bus system. The results show the FDI attacks affectthe operation of the UFLS schemes and hence, affect the gridstability and operation.

I. I NTRODUCTION

II. I MPORTANT E QUATIONS
2Hi dfi= Pmi − Pei = ∆Pi
fn dt
∆Pi =

2

i=1

fc = Pi=1N

i=1 Hi

59.4
59.2

16

17

18

19

20

21

20

21

Time (sec)(1)(a) System frequency with attack
1

i=1 Hi dfc

Hi fi

59.6

59

PN

fn

60
59.8

58.8
15

dt

(2)

where
PN

60.2

(3)

System disturbance (pu)

∆P =

N
X

Fig. 1: High-level schematic of the WAMPAC

System frequency (Hz)In wide area UFLS protection schemes, the values of thefrequency and/or ROCOF are sent from the local SCADA
systems to the control center, and consequently, the control
center sends control actions to different areas in the system.
These control actions include shedding loads in one or morearea from the system. Manipulation of frequency signals
affects the output of these schemes such that they shed a higher
amount of load or disconnect a complete area while no need tothis. At the same time, the attacker can block the real values ofthe system frequency, in the case of real system disturbances,such that everything appears to be normal. This latter action
can lead to blackouts and a system equipment damage.

0.5

0

-0.5

-1
15

16

17

18

19

Time (sec)(b) The amount of disturbance (swing equation output) with attack

Fig. 2: UFLS scheme performance with coordinated attack

Reliability and Vulnerability Assessment ofInterconnected ICT and Power Networks Using
Complex Network Theory
Wentao Zhu, Student Member IEEE, Mathaios Panteli, Member IEEE, and J. V. Milanović, Fellow IEEE
Abstract—The increasing reliance of power systems on
Information and Communication Technologies (ICTs) places
higher requirements on the reliable and secure operation of ICT
networks. This paper proposes an approach to measure theeffect of failure/misoperation of ICT network components on thereliability of power grid. A new vulnerability index, namely thesystem security index (SSI), based on Complex Network Theory(CNT), is derived for this purpose. The vulnerability of each
ICT network component is quantified based on the influence ofits failure/misoperation on the probability of the
failure/misoperation of the power system. The proposed
approach is demonstrated on a real UK’s power station intertrip
scheme and compared with the conventional fault-tree analysis.
Index Terms—Information and Communication Technology(ICT), Complex Network Theory (CNT), reliability,
vulnerability

I.

INTRODUCTION

Interactions between physical power system andInformation and Communication Technology (ICT) network
should not be ignored and new understanding and methods forassessing the dependability and reliability of thisinterconnected system must be developed.
A framework based on Complex Network Theory (CNT)
has been proposed in this paper to study the reliability ofinterconnected ICT and power systems, as shown in Fig. 1.

II.

METHODOLOGY

A System Security Index (SSI) is introduced to assess the
security level of the interconnected system.
SSI 

1


Rij
ne nc iVc Ves , jVet ,i  j(1)where ne and nc are the number of EP system nodes and ICT
network nodes respectively. Rij is the reliability of the mostreliable path between i and j, which is calculated as
Rij   (1  Pc ( SBM ))
cVc

(2)

where Pc(SBM) is the probability of security-based
misoperation of ICT network component c within the mostreliable path between i and j.
The ICT component vulnerability V(C) is therefore
analyzed by comparing the SSI and SSI*, where SSI* is the
System Security Index when the probability of security-based
misoperation of the analyzed ICT element is set to zero.
V (C ) 

SSI  SSI *
SSI

(3)

III. KEY RESULTS AND CONCLUSIONS
The results of vulnerabilities of the most critical ICT
network components are presented in Fig. 2.

Fig. 2. ICT network components vulnerability indexa)b)Fig. 1. a) Operating logic of intertrip scheme b) Complex network model ofintertrip scheme

The results of the analysis using the newly proposed
approach confirm the findings of the past, conventional, faulttree analysis; however the complex network model is found to
be more scalable and easy-to-implement. The proposed
methodology can be easily extended by including more logic
relationships to facilitate analysis of large interconnected
systems.

Load Redistribution Attack Detection using
Machine Learning: A Data-Driven Approach
Andrea Pinceti, Lalitha Sankar, and Oliver Kosut
School of Electrical, Computer and Energy Engineering
Arizona State University
Tempe, AZ, 85287

Abstract—Three detection techniques are presented against awide class of cyber-attacks that maliciously redistribute loads bymodifying measurements. The detectors use different anomaly detection algorithms based on machine learning techniques: nearest
neighbor method, support vector machines, and replicator neuralnetworks. The detectors are tested using a data-driven approachon a realistic dataset comprised of real historical load data in theform of publicly available PJM zonal data mapped to the IEEE
30-bus system. The results show all three detectors to be very
accurate, with the nearest neighbor algorithm being the mostcomputational efficient.
Index Terms—machine learning, neural network, false data
injection (FDI) attack, load redistribution attack, cybersecurity

I. I NTRODUCTION
The state estimators (SE) employed by the majority ofsystem operators as part of their energy management systems(EMS) are vulnerable to false data injection (FDI) attacks.
By intelligently modifying some system measurements, anattacker can create an erroneous representation of the stateof the system. This leads the EMS to redispatch generation insuch a way that it will create line overloads in the physicalsystem. This type of threat belongs to a broad class of attacks
called load redistribution attacks.
In this work, we develop countermeasures against load
redistribution attacks in the form of detectors that analyze themeasured loads, before they are used to perform economic
dispatch, and determine if they represent a realistic state of
the system or if they have been maliciously engineered. Our
approach exploits the fact that system operators have accessto historical load data which can be used to learn patterns
in an offline manner and design machine learning algorithms
that can check load consistency in real time scenarios. We use
nearest neighbor algorithm, support vector machines (SVM)
and replicator neural networks to design three different detectors which are tested on realistic historical load data created forthe IEEE 30-bus system. To design a detector that works forany possible load pattern that might occur, we exploit publicly
available PJM zonal data [1] to create realistic load data for
multiple years for the IEEE 30-bus system.

Fig. 1. Receiver operating characteristic of the three detectors with 10% load
shift attacks.
TABLE IP ERFORMANCE OF THE DETECTORS WITH 15% LS ATTACKS
Detector

MD

Nearest neighbor

SVM

FA

# of false alarms

0.21920001.138 × 10−4

10.00211.138 × 10−4102.277 × 10−4

2

Replicator

0.01250

0

Neural Network01.138 × 10−4

1

modified through a cyber-attack. The measured load configuration to be tested is given as input to the detector,
which generates a scalar value based on a metric specific to
the machine learning technique used: minimum distance fornearest neighbor, confidence score for SVM, and replication
error for replicator neural network. This value is comparedagainst a predetermined threshold to label the loads as normal
or attacked. Figure 1 and Table I show the performance of thethree techniques against attacks of varying magnitude.

II. D ETECTION A LGORITHMS
The three detectors proposed analyze the measured loads
of a power system during any hour of the day and determine
if they are normal loads or if they have been maliciously

R EFERENCES[1] PJM. (11 Nov 2017). PJM Metered Load Data [Online]. Available:
https://www.pjm.com/markets-and-operations/ops-analysis/historicalload-data.aspx.

A Coordinated Cyber Attack Detection System(CCADS) for Multiple Substations
Chih-Che Sun

Chen-Ching Liu

Energy Systems Innovation Center
Washington State University
Pullman WA, USA
chih-che.sun@wsu.edu

Energy Systems Innovation Center
Washington State University
Pullman WA, USA
liu@wsu.edu

Abstract— Since cyber attacks have become a major threat
in smart grids, various detection algorithms have beendeveloped to protect cyber-power systems. However, most of
existing cyber security solutions still lack the capability tohandle coordinated cyber attacks. In this research, a cyberprotection system is proposed to detect coordinated cyberattacks by inspecting relations among abnormal events insubstations. The techniques of Fuzzy Cognitive Map (FCM)
and Time Failure Propagation Graph (TFPG) are used for
detection algorithms to generate attack index numbers,
representing the likelihood of coordinated cyber attacks. This
work has been simulated and validated by the Smart City
Testbed (SCT) at Washington State University (WSU).

include time domain simulation, dynamic analysis, state
estimation, and optimal power flow. With the OPC
communication, PowerFactory can be connected with thecyber module to provide a simulation environment for cybersystem events.
IDSs in each substation

Relation
layer

Keywords—Cyber security, coordinated cyber attacks,
intrusion detection system, smart grid

I. INTRODUCTION
In large scale coordinated cyber attack events, attacker(s)have a well-organized plan to launch an attack. The attack
actions have some logic relations among one another. Thiskind of attacks can make a greater impact on the powersystems than a single or a multiple attack. Currently, IDS isregarded as a important line of defense against cyber
intruder(s), installed on different power system facilities.
Nonetheless, most of the intrusion detection algorithms only
cope with a single or a multiple cyber attack event. In thisresearch, the proposed CCADS [1] is an on-line detection
system, capable of detecting coordinated cyber attacks byidentifying the relations based on detected anomaly eventsand security logs at multiple substations. Based on FCM,
Relation Algorithm (RA) is developed as a detection
algorithm. RA correlates the detection log files from IDSs in[1], and generate a state value for each defined relation basedon the concept of FCM model. The state value is theindicator of the strength of relations between attack actions.
The Correlation System collects the highest state values fromeach relation and generates the state value for identificationof coordinated attacks. When the state value exceeds a user
defined threshold value, the detected event is considered to
be a coordinated cyber attack. Fig. 1 shows the structure of
the proposed CCADS.
SCT is a hardware-in-a-loop cyber-physical system
testbed at WSU. DIgSILENT PowerFactory is used as a
power system simulation tool. The simulation functions
This research is supported by U.S. National Science Foundation under
the award number ECCS-1824577 and ECCS-1711048.

IDS
IDS
IDS
IDS

Detectionlayer

AB relation

CS relation

GD relation

Abnormalbehavior

Criticality of
substations

Geography data

Decision
layer

Correlation system

Fig. 1. Structure of CCADS.

II. TEST CASESIn the test attack scenario, attackers successfullycompromised four critical substations in the IEEE 39 bus
system. Since the relation correlation system reports a highsimilarity index, CCADS triggers an alarm to notify anattack event. Fig. 3 shows the simulation results.

Fig. 2. Coordinated cyber attack event is detected.

REFERENCES[1]

C. C. Sun, J. Hong, and C. C. Liu, “A Coordinated Cyber Attack
Detection System (CCADS) for Multiple Substations,” in 2016
Power Syst. Computation Conf. (PSCC), Genoa, IT, 2016, pp. 1-7.
J. Hong, C.-C. Liu, and M. Govindarasu, “Integrated Anomaly Detection
for Cyber Security of the Substations,” IEEE Trans. Smart Grid, vol. 5, no.
4, pp. 1643–1653, Jul. 2014.

1

Distribution Systems Hardening against Disasters
Yushi Tan, Student Member, IEEE Arindam K. Das, Member, IEEE, Payman Arabshahi, Senior Member, IEEE,and Daniel S. Kirschen, Fellow, IEEE
function of the budget for one repair crew. The MILP and theheuristic approaches produced almost identical results under
the f (E[·]) measure so that their plots almost overlap. The
plots corresponding to the E[f (·)] measure are also very close.

Objectives byalgorithms
f (E[·]) by Heuristicf (E[·]) by MILP
E[f (·)] by Heuristic
E[f (·)] by MILP

17

Aggregate harm

Abstract—Distribution systems are often crippled by catastrophic damage caused by natural disasters. Well-designed hardening can significantly speed-up the post-disaster restoration
process. This performance is quantified by a resilience measureassociated with the operability trajectory. The distribution systemhardening problem can be formulated as a two-stage stochastic
problem, where the inner operational problem addresses theproper scheduling of post-disaster repairs and the outer problem
the judicious selection of components to harden. We propose
a deterministic single crew approximation with two solution
methods, an MILP formulation and a heuristic approach. We
provide computational evidence based on several IEEE test
feeders, which demonstrates that the heuristic approach provides
near-optimal hardening solutions in a computationall efficientmanner.

16

15

0

10

20

30

40

50

BudgetATURAL disasters have caused major damage to electricity distribution networks and deprived homes andbusinesses of electricity for prolonged periods. Kwasinski etal. [1] reported that facilities, which had been hardened in
Christchurch at a cost of $5 million, remained serviceable
immediately after the September 2010 earthquake and saved
approximately $30 to $50 million in subsequent repairs. Hardening minimizes the potential damages caused by disruptions,
thereby facilitating restoration and recovery efforts [2]. However, as indicated in [3], it is difficult to quantify a priori what
the expected performance improvement might be. Hence theneed for a systematic decision process balancing cost versus
potential future benefit.
In civil engineering, resilience is illustrated using the “operability trajectory” Q(t) [4], which shows the increase in
infrastructure functionality over time and provides an effective
visual representation of the ‘goodness of the restoration process’. Letting T be some restoration Rtime horizon, a resilienceTmeasure, R, can be defined as R = 0 Q(t)dt.
To the best of our knowledge, this research is the first to
consider the restoration process in conjunction with hardening.
Our approach is formulated as a two-stage stochastic optimization problem. The first stage selects from the set of hardening
choices and determines the extent of hardening to maximize
the expected resilience measure, while the second stage solves
the operational problem for each scenario by optimizing thescheduling of post-disaster repairs in distribution network withparallel repair crews. Since an ideal formulation of the problem
is hard to solve and also turns out to be impractical, wedeveloped a deterministic single crew approximation withan MILP formulation and a heuristic approach to solve thehardening problem.

N

II. K EY R ESULTS
We varied the hardening budget from 0 to 50 on the IEEE
13 node test feeder. Fig. 1 shows the aggregate harm as a

Fig. 1: Aggregate harm vs. hardening budget for the IEEE 13
node test feeder.
Even if hardening decisions at the planning stage are madebased on a single crew operational schedule, the resilienceof the system would still improve if multiple crews are
deployed at the operational stage. To demonstarte this point,Fig.
2mshows the normalized improvement in harm β(m) :=H m −HA
for different numbers of crews. We can observe thatHm
the normalized improvement in harm due to hardening remains
above 8%.

0.087

Improvement of harm β

I. I NTRODUCTION

0.086

0.085

0

10

20

30

40

50

Number of repair crews m

Fig. 2: Normalized improvement in harm as a function of thenumber of repair crews, m, for the IEEE 8500 node test feeder.

R EFERENCES[1] A. Kwasinski, J. Eidinger, A. Tang, and C. Tudo-Bornarel, “Performanceof electric power systems in the 2010 - 2011 Christchurch, New Zealand,
Earthquake Sequence,” Earthquake Spectra, vol. 30, no. 1, pp. 205–230,
2014.
[2] M. Omer, The Resilience of Networked Infrastructure Systems: Analysis
and Measurement, vol. 3. World Scientific, 2013.
[3] M. Rollins, “The hardening of utilitiy lines-implications for utility pole design and use.” http://woodpoles.org/portals/2/documents/TB_
HardeningUtilityLines.pdf, 2007. [Online; accessed 25-March-2018].
[4] D. A. Reed, K. C. Kapur, and R. D. Christie, “Methodology for Assessing
the Resilience of Networked Infrastructure,” IEEE Systems Journal, vol. 3,
pp. 174–180, June 2009.

Embedding Cyber-Physical Resiliency inSynchrophasor-based Remedial Action Schemes
Zhijie Nie, Jing Xie, Anurag K. Srivastava
School of Electrical Engineering and Computer Science
Washington State University
Pullman, WA, U.S.A.
{zhijie.nie, jing.xie6, anurag.k.srivastava}@wsu.edu

Abstract—Emerging technology in decentralized computing infrastructure provides new opportunities for real-time data-driven
analytics used in smart grid applications. Remedial action scheme(RAS), designed to protect the system under predetermined
operating conditions, relies on the network communication in the
wide-area measurement system (WAMS). In order to improve
the resiliency of network environment and to secure the datatransmission, the Resilient Information Architecture Platform
for Smart Grid (RIAPS) has been developed for the use of
power system analytics. In this poster, two typical remedial action
schemes, wind farm generation curtailment and under-frequency
load shedding are presented. Experiments based on RIAPS are
conducted under a hardware-in-the-loop (HIL) cyber-physicaltestbed using a modified IEEE 14-bus system.
Index Terms—remedial action scheme, cyber-physical system,
phasor measurement unit, decentralized computing

GTNET software PMUs, the measurements from the RTDS arecaptured ans transmitted under C37.118.1-2011 synchrophasor
data protocols.
The implementation of the distributed computational blocks(controllers) is carried out by using BeagleBone Blacks(BBBs), where the RIAPS platform is established. The windcurtailment RAS setup uses three BBBs to solve the optimal
wind curtailment, and to transmit control action commands toeach wind farm. The BBBs are connected to RTDS and the
PMUs using Ethernet cables. Control commands of optimal
solution are sent back to RTDS in text strings over its listening
port-interface using standard TCP sockets.

I. I NTRODUCTION
Due to an increasing number of installations of phasor
measurement units (PMUs), the resiliency and security of
network communication becomes new requirements of themodern power system. In [1], a data transmission platform
called RIAPS (Resilient Information Architecture Platformfor Decentralized Smart Systems) is developed to improve
the resiliency and security for power system analytics. Witha resilient and secure data platform established, many ofreal-time analytics in power systems become realizable, forexample, state estimation. Based on the previous work, theanalytic of distributed linear state estimation (DLSE) has beendeveloped [2] and latter implemented on the RIAPS platform.
And in this paper, the authors utilize the distributive simplex
method to distribute the computation in both state estimation
and DCOPF-formulated optimal wind curtailment problem.
This poster is going to present the analytics development using
RIAPS platform, and to demonstrate the hardware-in-theloop (HIL) cyber-physical testbed for two RAS applications,which are wind curtailment and under-frequency load shedding(UFLS).
II. D ESCRIPTION OF HIL C YBER -P HYSICAL T ESTBED
Fig. 1 demonstrates our proposed hardware-in-the-loop
cyber-physical testbed. Real-Time Digital Simulator (RTDS)
is used to simulate the power system in a real-time manner. A
modified IEEE 14-bus system is considered and divided into
three groups. With one installed SEL hardware PMU and eightFig. 1. HIL cyber-physical testbed for RAS applications.

ACKNOWLEDGMENT
The authors would like to thank Advanced Research
Projects Agency-Energy (ARPA-E) and Power Systems Engineering Research Center (PSERC) for the technical andfinancial supports in this work.
R EFERENCES[1] S. Eisele, I. Mardari, A. Dubey, and G. Karsai, “Riaps: resilient information architecture platform for decentralized smart systems,” in RealTime Distributed Computing (ISORC), 2017 IEEE 20th International
Symposium on. IEEE, 2017, pp. 125–132.
[2] R. Liu, A. K. Srivastava, D. E. Bakken, A. Askerman, and P. Panciatici,“Decentralized state estimation and remedial control action for minimum
wind curtailment using distributed computing platform,” IEEE Transactions on Industry Applications, vol. 53, no. 6, pp. 5915–5926, Nov 2017.

Decentralized Power System Integrity Protection
Schemes based on MAS: Anomaly Detection and
Self-Adaptive Load Shedding
Pengyuan Wang

Manimaran Govindarasu

Department of Electrical and Computer Engineering
Iowa State University
Ames, IA. USA.
pywang@iastate.edu

Department of Electrical and Computer Engineering
Iowa State University
Ames, IA. USA.

Abstract—With the wide application of the communication andinformation technologies in modern power system, malicious
cyber attacks have become great concerns, especially for the wide
area protection and control schemes. Traditional cyber security
technologies can be applied to alleviate the situation, but the
problem will not get solved by merely securing the periphery ofschemes. To make the critical functions per se situation-awareand attack-resilient is a promising path. This paper presents apeer-to-peer multi-agent architecture for power system integrity
protection and control schemes (SIPS), which provides analternative implementation for the non-resilient legacy SIPS andthe centralized ones. Every agent within the architecture isembedded into one substation/power plant, and it collects local
data as well as information from its community. All the agents
possess the similar level of intelligence and interact in a peer-topeer manner. To verify the validity of the proposed SIPS
architecture, coordinated anomaly detection and situation
adaptive optimal load shedding are further investigated as two
sub-problems. Anomaly detection takes place within each
intelligent agent based on the local data but the final detection
conclusion will be achieved by the consensus of interconnectedagents. A data-driven algorithm named as Support Vector
Machine embedded Layered Decision Tree (SVMLDT) is
proposed for the anomaly detection. Global data propagationlays the foundation for the situation adaptive load shedding suchthat a protocol is proposed to facilitate the data propagation.
Dynamic programming is utilized to get the remedial actions foreach agent. A load rejection SIPS adopted in reality is mapped
onto IEEE 39 bus system as a study case. Via evaluation, it showsthat the proposed decentralized SIPS attains better attack
resiliency than the centralized solution under Denial of Service(DoS) attack and when part of the decentralized SIPS getscompromised, the rest of it can still deliver the remedial actions
desired.
Keywords—SIPS decentralization; cyber attack resilience;
Multi-Agent System; anomaly detection; optimal load shedding.

I. AGENTS INTERACTION PROTOCOL

Fig. 1 Agents interaction flow

II. MAIN EVALUATION RESULTS

Fig. 2 On-line Anomaly Detection Resutls

Data Analytics for Cyber-Physical Security ofTransmission Protection System
S. Armina Foroutan, Anurag Srivastava

Sindhu Suresh

School of Electrical Engineering and Computer Science
Washington State University
Pullman, Washingtons.foroutan@wsu.edu

Siemens Corporate Research
Princeton, New Jersey

Abstract—The transmission protection system is critical fornormal operation of power systems and mis-operation of themcan cause cascade failures. Failure diagnosis of these misoperations which can be caused by either physical malfunction ofprotection relay or cyber intrusion to the relay, usually become
very complex. Therefore, a systematic approach seems to benecessary to determine the root cause of the mis-operation as
well as a framework to collect data, test and study the proposed
approach. In this paper we first introduce a framework to model
the mis-operation of protection relays using Real Time Digital
Simulator (RTDS) and then a data analytic tool is proposed using
data provided by Phasor Measurement Unit (PMU)s to detectmalfunctions and identify whether they are cyber or physical.
Simulation results using IEEE 14 bus system are also presented
showing how the framework and tool confirm the actual incident.
Index Terms—Transmission Protection, Distance relay, RTDS,
Data analytic, Failure diagnosis, Phasor measurement unit, Relay
malfunction, Hardware-in-the-loop

I. I NTRODUCTION
In power systems, protection system mis-operations often
lead to cascading effects and system wide failures. Distance
protection relays have been known to incorrectly initiate
tripping signal due to apparent impedances that fall intozone settings of line relays or malicious intrusion to therelays. However, failure diagnosis of protection system usually
become very complex due to high number protection relays,available information and level of expertise of the protectionengineer. This Poster address this issue by introducing aframework to collect relevant cyber data as well as minimizing
the number of PMU measurements need to be taken from thesystem. Then a data analytic tool is proposed to identify cyber/physical anomalies in Transmission system. At last, RTDS
simulation results are presented based on HIL validation.
II. M ETHODOLOGY AND T ESTBED
After the occurrence of a fault in the system, the protection
relays at the end-point of the line should trips and isolate thefault. However, sometimes one of these relays is malfunctioned
and stays close. In this case, the relevant relays on theadjacent lines trip to isolate the fault. In such cases, if thestatus of the breakers on the lines connected to the bus thatmalfunctioned relay resides, is symmetric, then all lines withat least one tripped relay can be candidates of location of theFig. 1. A test case of relay malfunctions

fault. Figure 1 shows a test case of such conditions. In order to
identify the root causes of the failure, first all lines and buses
adjacent to tripped relays are selected as so called ProNet
and corresponding PMU measurements are collected. Then,corresponding scenarios related to the situation are produced
and cyber data corresponding to suspicious relays are alsocollected. Then all these data can be fed to the analytic tool
to pinpoint the location of fault and malfunctioned relays.
III. S IMULATION T ESTBED
We simulate this scenario in RSCAD utilizing a distance
protection relay as HIL with RTDS. RSCAD sends out thesimulated voltage and current signals via GTAO card to thehardware relay. Then the output dry contacts of relay areconnected to digital interface panel of RTDS to communicate
breaker commands. In our simulations all relays simulated by
RTDS except one which is replaced by hardware relay. We also
consider two possible scenario to validate our assumptions;normal operation where all relays react normally to the fault
and ProNet scenario where two relays misoperate; a simulated
relay malfunction due to physical incident and a hardware
relay misoperation due to cyber intrusion to the relay. Cyber
intrusion is also modelled by emailing BlackEnergy3 to theoperator and gaining access to the victim’s PC as well as therelay and finally reconfiguring the relay setting.
IV. ACKNOWLEDGEMENTS
We would like to show our gratitude to Bo Cui, Arman
Ahmed and Mohammad Touhiduzzaman for assistance withdata analytics and cyber intrusion.

1

Impact of Communication Failures on Solar
Generation Forecasting
Mohammad Heidari Kapourchali, Student Member, IEEE, Mojtaba Sepehry, Student Member, IEEE,
and Visvakumar Aravinthan, Senior Member, IEEEjckd



Abstract—The power from solar resources has reached grid
parity and must be predicted based on real-time observations
available up to the current time step to ensure efficient powersystems operation. However, solar data in the form of time series
generated by a network of sensors are not always available.
Communication failure leaves the energy management dataacquisition system with incomplete solar time series, thus leading
to inaccurate forecasts. This poster addresses the impact ofpartially observable measurements on short-term solar power
prediction.
Index Terms— Communication failure, spatio-temporal solar
prediction, missing data

I. PROBLEM DEFINITION
Fig. 1 shows an abstract view of solar PV sites with acommunication infrastructure for real-time measurements ofsolar generation data.

Fig. 3. State-space diagram for two-node connection between typical solar siteand control center
III. RESULTS

The publicly available NREL solar and meteorological
data within the state of Kansas are used in this work.

Fig. 4. Solar PV sites in the state of Kansas
Fig. 1. Communication network for real-time measurements of PV plants

Solar forecasting in a partially observable environment is adata analytics problem that includes both pattern completionand prediction.
Photovoltaic power generation varies with geographicaland meteorological factors.

PPV  [ PPV , STC 

GT
1000

 [1-   (T j - 25)]](1)

 N PVs  N PVp
Fig. 5. Half-normal plot of factor effects

II. PARTIALLY OBSERVABLE SOLAR PV MEASUREMENTS

Fig. 2. Representation of higher dimensional data
This work was supported by the Power Systems Engineering ResearchCenter (www.pserc.org), Project No: T-53.
M. H. Kapourchali is with the Department of Electrical Engineering andComputer Science, Wichita State University, Wichita, KS 67260 USA (email: mxheidarikapourchali@shockers.wichita.edu)

Fig.6. Visual comparison for sustained failure lasting for seven consecutive
days
IV. CONCLUSION

This work is the first step towards more efficiently andsystematically designing renewable forecasting algorithms. It
gives insight on how to design communication infrastructures
for PV fleets, and whether or not to install the measurement
requirements for PV sites.

Real-time Cyber Physical Test-bed for validation ofAutomated Failure Diagnosis in Transmission Network
Amir Gholami, Anurag Srivastava
School of Electrical Engineering and Computer Science, Washington state University

Email: amir.gholami@wsu.edu
Abstract—Protection part of the transmission
system in power grid plays a significant role indetection and isolation of the faulty part of thegrid. Also it might be useful in finding thelocation of the fault. In the case of contingency, it
is very important for the relays to be properly
working and accomplishing the isolation process.
Otherwise, the fault will be spread into the otherparts of the grid. In addition, it is of highimportance to differentiate between a cyber-attack
and physical malfunctioning. In worst casescenario, it is also possible to be attacked by a
Cyber hacker at the same time of a physicalmalfunctioning of the relays. In this poster, twodifferent cases have been studied and validated
using real-time monitoring of the system in
OPAL-RT. Malfunctioning Case Studies havebeen developed and actual and possible scenarios
have been extracted. Then the case studies havebeen analyzed in OPAL-RT real-time simulationand then, using the 5-digit algorithm (proposed by[1]), the system has been validated.

I. INTRODUCTION
Generally speaking, the distance protectionof the transmission lines is a set of current andvoltage transformers, protective relays, circuit
breakers, and batteries. Any type of fault inany of the aforementioned components maylead to the propagation of the fault to the otherparts of the system, as a result of which it may
ends up with a black-out, which is kind ofdisaster as far as the damage to the system andthe restoration effort and money areconcerned.
In order to address this issue, in this work
two standard power transmission systems
have been studied, which are IEEE 68-bus
system and WECC 179-bus system
accordingly.
Firstly, the 68-bus system is to be examinedin terms of the relay malfunctioning situations.
To do so, some case studies of combinations ofrelay malfunctioning is developed and theactual and possible scenarios have beentheoretically investigated. As it can be seenfrom Fig. 1, in case of two relays
malfunctioning (relay r2 and r6) the fault
current will not be disrupted on the fault line
(line 3-4), but rather it will be propagated tothe adjacent lines as well. (lines 4-5 and 4-14
and then lines 5-8 and 5-6)

Fig. 1. Relay malfunctioning case-study

In the next step, in order for the analysis to becompleted in real time, the model of the system has
been built-up in Hypersim (Fig. 2) and thedeveloped case studies for relays malfunctioning
has been implemented in the model. Then using
the OPAL-RT real time simulation, the cases havebeen implemented and the real time results havebeen extracted.
As the final step of this work, the acquired datafrom the phasor measurement units (PMUs) of thesystem have been analyzed using the 5-digit
algorithm and as a result of which, the actual andpossible scenarios, and also physical and cyberattacks have been differentiated.
Parallel to this work, to implement a moresophisticated test cases in a bigger standard powertransmission system, the WECC 179-bus system ischosen and modeled in OPAL-RT. (Fig. 3) Using
the real time simulation, some more complex test
cases have been implemented in this system andthe resiliency of the system have been examined.

Fig. 2. OPAL-RT model of the 68-bus system

Fig. 3. OPAL-RT model of the 179-bus system
ACKNOWLEGMENT

This research is supported by National Science
Foundation (NSF). We also thank Dr. Bo Cui who put
his efforts in this project and provided us withassistance on the way to achieve the goals of thisproject.
REFERENCES[1] B. Cui, A. Srivastava and P. Banerjee, "Automated Failure
Diagnosis in Transmission Network Protection System
Using Synchrophasors," in IEEE Transactions on Power

Content Development of IoT for Grid
Modernization - a Two Class Series for the FuturePower Engineer
1st C. Eppinger

2nd Robert B. Bass

Dept. of Electrical and Computer Engineering
Portland State University
Portland, OR, USA
crystal.eppinger@pdx.edu

Dept. of Electrical and Computer Engineering
Portland State University
Portland, OR, USA
robert.bass@pdx.edu

Index Terms—Internet of Things, Demand Response, Energy
Management Systems, Distributed Management Systems

I. I NTRODUCTION
The grid modernization initiative is transforming our aging
grid infrastructure into a grid that both communicates andmakes fast, intelligent decisions. The sought-after engineer
will be equipped with power systems knowledge and computerscience (CS) expertise. This two class series bridges the gap
between power engineering and CS by introducing students
to the Internet of Things (IoT) as it pertains to the gridmodernization effort. Grid modernization requires interaction
and control of energy used and produced by millions of devices
at the power distribution level. Transactive power along with
communication data model standards are being developed anddeployed. Additionally, connecting all of the smart devices
using internet protocols places significant emphasis on cybersecurity.

This project utilizes active learning techniques, such as”doing” and ”teaching”, in an effort to maximize the students
information retention.
Class II
The second class will place emphasis on a project to explore advanced topics relating to IoT-based grid applications.
This class teaches standard communication data models, andintroduces relevant cyber security techniques. Class lectures
will be divided into two parts, one on course content and theother for the students to present project deliverables.
The second class project will involve a class collaborative
effort to network the distributed energy resources (DERs) of
a fictional building. The students must use an existing energy
management system (EMS) template to control the DERs tomanage building demand while minimizing total cost. The
project encourages time management, interdepartmental collaboration and communication, and the creation of standards
and protocols.

II. K EY C OMPONENTS
Each 1 hour and 50 minute class session will be split
into 2, 25-30 minute mini-lectures followed by 15 minutesof discussion and activity. The mini-lectures will be separated
by a 10 minute break. Every class session will end with at least
10 minutes of time for questions, clarification and assistancewith the assignments and the project.
Class IThe first class will offer an introduction to the elements ofthe modern grid, such as distributed energy resources (DER)
control and transactive power principles. It will lightly touchon communication protocols to familiarize students with theterms required for an in-depth study in the second class of theseries.
The first class series project is intended to familiarize
students with the open-source, IoT framework. The students
will create a free standing manual that offers step-by-step instructions on software installation, bash scripting and interface
development.

Fig. 1. Second Class Series Project Diagram

Federation based Testbed Implementation of
Anomaly Detection for Wide Area Protection
Scheme in Power System
Vivek Kumar Singh, Manimaran Govindarasu
Department of Electrical & Computer Engineering
Iowa State University, Ames, IA, USA
vsingh@iastate.edu, gmani@iastate.edu
Abstract—Today’s smart grid consists of numerous,
geographically distributed systems which are controlled byinterconnected, high-speed communication and dataprocessing devices. Significant advances in synchrophasors
such as phasor measurement units (PMUs) in smart gridare providing real-time monitoring to protect the grid
health at the micro level. However, as PMU technology
evolves, so does the potential for increased vulnerability tocyber physical attacks. Synchrophasor based Wide Area
Protection Schemes (WAPS) rely on PMUs to detect
abnormal conditions, both natural and malicious, and toprovide predetermined corrective actions through the wide
area communication network to maximize grid stability.
The existing multitude of vulnerabilities in the communication and phasor devices make them particularly
susceptible to be compromised through the advanced
persistent threat (APT). This poster presents our current
approach in developing a federation based testbed using
Iowa State University’s Power Cyber and the US Army
Research Laboratory’s resources to detect coordinatedcyber-attacks targeting the centralized WAPS. The
implemented network topology and experimentalarchitecture are based on the modified IEEE 39 bus system
which is simulated concurrently on two, separate OPALRT, real time digital simulator (RTDS). Initially, we will
demonstrate the stealthy, coordinated attacks on thesimulated generator and line while disguising the attacks
as benign anomalies. Machine learning based random
forest classifier algorithm will be adopted to detect themulti-class of attack vectors. Future evaluations will
determine how data transmission latency in real-world
scenarios affect the federation’s anomaly detection
performance during communications delays andmeasurement uncertainty.

Donald Porschet, Morris Berman, Edward Shaffer
US Army Research Laboratory
Adelphi, MD, USAFalse Data Injection Attacts in Electric Energy Markets
Ramin Kaviani

Kory W. Hedman

Arizona State University, Tempe, AZ

Arizona State University, Tempe, AZ

Abstract— Monitoring and controlling an extremely hugesystem like an electric power system is very complicated andbecomes more challenging with the threat of cyber-attacks. Whilethe cyber layer enables many advanced features and provides
benefits to operations,, it may put the physical part under manyrisks and attacks. One way of performing a cyber-attack is tochange the real data with fake data, which is called a false data
injection (FDI) attack. The goal of a FDI attack could be either
financial or physical damage. In this paper the financial side as aresult of FDI attacks in electricity market is investigated.
Without the presence of a detection mechanism there could be
irrecoverable damages in system. Therefore, nowadays, allenergy management systems include bad data detectors in orderto detect errors. In this research, we propose a detection
mechanism using the electric power grid’s natural features, alongwith the use of duality theory from linear optimization theory, todetect FDI attacks in energy markets.
Keywords—cyber attacks; false data injection attack; cyberattack detection

I. INTRODUCTION
By emerging the concept of cyber physical systems many
researches are conducted in this field, among which the paper
by Liu, et al [1] is one of the first researches about the so-called
FDI attack. They addressed the condition which need to besatisfied by attacker to result an undetectable attack. In [2], theauthors investigated physical consequences of an unobservable
FDI attack on AC SE on physical system. In [3] as the first
paper which tried to evaluated financial damages of FDI attack,the authors showed how the attacker is able to gain profit inelectricity markets by bidding into the day-ahead market as a
virtual bidder.
II. DUALITY THEORYIn linear optimization, there is a primal problem, which isreflective of the mathematical program that is being solved.
Each primal problem then has a dual; together, they form a
primal-dual pair. The dual problem describes the relationshipfor all of the dual variables of the corresponding primal
constraints. Those dual variables are the shadow prices on thevarious constraints within the primal, also known as Lagrange
multipliers, which are the settlement prices used for LMPs,
flowgate marginal prices, and other prices used to settle theelectric energy markets. A simplified DCOPF (the primal
formulation) along with its dual is presented below. For thiswork, we will derive the dual formulation for the electricenergy market problem and show how the information in the
dual can be used to improve the ability to detect an attack.

III. CONCLUSION
While the auction formulations for electric energy markets
are reflective of the power grid, the market participants,
security criteria, etc., the mathematical program has acorresponding dual that describes the interaction of prices
across the system. This work demonstrates how duality theory
can be used to help detect FDI attacks by evaluating the impactof the attack on the dual formulation, to be able to predict what
measurements an attacker may modify in order to make ahigher profit.
REFERENCES
[1]

[2]

[3]

Y. Liu, P. Ning, and M. K. Reiter, “False data injection attacks against
state estimation in electric power grids,” in Proceedings of the 16th
ACM Conference on Computer and Communications Security, ser. CCS’09. New York, NY, USA: ACM, 2009, pp. 21–32.
J. Liang, L. Sankar, and O. Kosut, “Vulnerability analysis andconsequences of false data injection attack on power system state
estimation,” IEEE Transactions on Power Systems, vol. 31, no. 5, pp.
3864–3872, 2016.
L. Xie, Y. Mo, and B. Sinopoli, “Integrity data attacks in power market
operations,” IEEE Transactions on Smart Grid, vol. 2, no. 4, pp. 659–
666,
2011.

1

CP-SAM: Cyber-Physical Security Assessment
Metric for Microgrid Resiliency
V. Venkataramanan, Student Member, IEEE, S. Zonouz, Member, IEEE, A. Hahn, Member, IEEE, andA. Srivastava, Senior Member, IEEE

Abstract—Trustworthy and secure operation of the cyberphysical power-grid infrastructures calls for resilience against
malicious and accidental failures. The objective is to recover
the safe (partially degraded) operation of the system to supply
critical loads despite multiple contingencies in the system. To
take timely actions, we need to continuously measure the cyberphysical security of the system. We propose a cyber-physicalsecurity assessment metric (CP-SAM) based on quantitative
factors affecting resiliency and utilizing concepts from graphtheoretic analysis, probabilistic model of availability, Markov decision process, cryptographic solutions, message-authentication,
controllers and algorithmic trust management embedded acrossdifferent layers of the microgrid system. These factors are
combined into a single metric using analytic network process
to compute CP-SAM. The metric will be a valuable asset for i)comparing the power-grid architectures during the offline design
process; and ii) online decision-making to select optimal proactive
countermeasure strategies to drive the power grid towards statesthat can tolerate upcoming adverse events more effectively. We
will implement and demonstrate the application of our proposed
CP-SAM in a real-world power-grid test-bed for off-line decision
process and on-line decision making to enable the resiliency.

TABLE I: Definitions of System Performance Metrics
MetricDefinitions

Reliability

Ability of the system to supply all its connected loads(e.g. SAIFI, SAIDI).
Ability of the system to supply all the connected load under contingencies (e.g. ’N-1’ or ’N-x’) achieved throughcontingencies screening and detail contingencies analysis.
Protection of systems from compromise and disruption
based on Confidentiality, Integrity and Availability (CIA)concepts (e.g. encryption, firewalls).
Ability of the cyber-physical system to supply the loads
under contingencies or to meet the requirements specified
by the system operators (generally without the selfhealing considerations).
Ability of the system to self-adapt and reconfigure its
topology dynamically to tolerate multiple contingencies
and supply critical load. Our proposed metric can indicate
cyber-physical security measures embedded in the system
to enable resiliency.

PhysicalSecurity
Cyber
Security
CyberPhysical
Security
Resiliency

I. I NTRODUCTION
The objective of this paper is to define a cyber-physicalsecurity assessment metric (CP-SAM) that can be used toassess the cyber physical security to enable the microgrid
resiliency. In power system, performance of the system can bemeasured using different metrics. Table I shows the definitions
used in this paper for the terms resiliency, reliability, andsecurity. The objective of a secure cyber physical microgrid
system is to make sure that the critical loads of the microgrids
are supplied even with extreme adverse events. These criticalloads are identified in advance, and CP-SAM is used toasses the ability of the microgrid to supply these loads after
disturbances.
II. C ASE S TUDIESThe microgrid operator can obtain a better understandingof the state of the system by using metrics such as CP-SAM.
Various case studies are considered to better understand theusefulness of the proposed metric. Fault trees are developedfor each these scenarios to identify the key interdependencies
between the various factors for that particular scenario.
III. S UMMARY
This paper proposes a novel methodology of assessing
the cyber-physical security to enable microgrid resiliency. A
V. Venkataramanan, A. Hahn, and A. Srivastava are with Washington State
University, Pullman, WA 99163 USA. (E-mail: asrivast@eecs.wsu.edu)
S. Zonouz is with Rutgers University, New Jersey, 08854 USA (E-mail:saman.zonouz@rutgers.edu).

Fig. 1: CP-SAM for Various Cases

cyber-physical security assessment metric (CP-SAM) is obtained by resiliency oriented modeling of all the cyber-physicallayers in the microgrid and considering the factors affecting
the microgrid resiliency. Various factors across different layers
in the microgrid including physical, communication, security,and device level factors. CP-SAM for the microgrid resiliency
is derived using fuzzy Choquet Integral for easy interpretation.
The various steps in the fuzzy Choquet Integral are also
explained, including the steps to determine the interaction
index, and determining the fuzzy weights using various gametheoretic techniques. Fault trees are also provided that help
decide on the factors that contribute to each scenario. Various
scenarios including loss of node, loss of link, and a Netflix
inspired Chaos Monkey type attack in which a random set ofnodes is removed are studied. CP-SAM can be used by system
operators to quickly judge the security of the microgrid in
extreme contingencies, and decide on control actions.

1

Calculation of Critical Oscillation Modes for Large
Delayed Cyber-Physical Power System Using
Pseudo-Spectral Discretization of Solution Operator
Qianying Mou, Student Member, IEEE, Hua Ye, Member, IEEE, and Yutian Liu, Senior Member, IEEE

I. K EY T ECHNIQUES
By utilizing the properties of the solution operator, efficientstability determination and eigenvalue analysis of DCPPS canbe achieved. 1) Reliable Stability Determination. The stabilityof DCPPS can be directly determined by the largest modulus|µ1 |. If |µ1 | > 1, the system is unstable. If |µ1 | < 1, the system
is asymptotically stable. |µ1 | = 1 denotes that the system iscritical. 2) Critical Eigenvalue Computation. The rightmost
eigenvalues λ of DCPPS, i.e., those with the largest real parts,
can be recovered from µ with maximum moduli, for further
stability analysis and controller design.
Im

λi

0

Im
µʹk

λʹi

λj

Im

Im
λi
λj

λʹj
λk
λʹk
θ
Re

µʹi
λk

Re

θ
0

Re

1 µk
µ
i

0

µʹj
µj 
1

−1

Re

−1

(b) λ′(a) λ(c) µ ,µ′Fig. 2. Spectral mapping of the solution operator with coordinate rotationpreconditioning. (a) λ′ is achieved by clockwise rotating λ about the origin
θ radian, i.e., λ′ = λe−jθ . (b) The effect of eigenvalue rotation is equivalent
to anticlockwise rotate the coordinate about the origin θ radian. (c) The
eigenvalues λ of DCPPS with damping ratios less than ζ (= sin(θ)) aretransformed into those µ′ of the preconditioned solution operator and located1 jθ
outside the unitary circle. λ relates to µ′ as λ = λ′ ejθ = he ln µ′ .
′
−jθ
′
λ
h
λe
h
Conversely, µ = e=e.

II. K EY R ESULTS
The real-life large DCPPS includes 516 buses, 936 transformer branches and transmission lines, 114 generators, 299
loads and two wide-area damping controllers. The dimensionof state variable vector is n = 1128 and the dimension ofalgebra variable vector of the system is l = 4637. The timedelays of two wide-area damping control channels are τ1 =0.09 s and τ2 = 0.10 s. The dimension of T M,N is 34968.
When 100 and 120 eigenvalues are computed by SOD-PS, thecomputational time is 423.79 s and 435.82 s, respectively.
14

14 C

A
Q

↓

12
G

10

j13
12

E

E
H

A

j13
Q←

10

F

F

Im

Index Terms— Cyber-physical power system, delay differential
equation, eigen-analysis, small signal stability, solution operator,
spectral discretization, spectral transformation, time delay, widearea measurement system.

Im

Im

Abstract— In eigen-analysis of large delayed cyber-physicalpower system (DCPPS), power engineers are interested in criticalelectromechanical oscillation modes with damping ratios lessthan a specified threshold. To efficiently compute these modes,
a method based on pseudo-spectral discretization of the solution
operator of DCPPS (SOD-PS) is presented in this paper. First,
the unique spectral mapping properties of solution operator are
analyzed. The largest eigenvalues in moduli of the operator
correspond to the ones of DCPPS with the largest real parts.
Second, a rotation-and-multiplication preconditioning technique
is presented to enhance the dispersion among eigenvalues of thesolution operator’s discretized matrix. Third, critical electromechanical oscillation modes of DCPPS with the least damping
ratios are captured with priority and an accelerated convergence
rate by the implicitly restarted Arnoldi algorithm (IRA). Subsequently, the small signal stability of DCPPS can be readily
and reliably determined. In SOD-PS, the unique property of
Kronecker product and the inherent sparsity in augmented
system matrices are fully exploited to guarantee efficiency andscalability. The accuracy and efficiency of the presented method
are intensively studied and thoroughly validated on the 16generator 68-bus test system and a real-life large transmission
grid.

8

8
j7

Im

6

j7
6

λi
λj

B

1 µk

λk
0

Re

µi
−1

0

4
−4

µj 
1

Re

−1

−2
Re
(a)
SOD − PS

(a) λ

(b) µ

Fig. 1. Spectral mapping of the solution operator. (a) Eigenvalue of DCPPS λ;1(b) Eigenvalue µ of the solution operator T (h). λ relates to µ by λ = hln µ.
The authors are with Key Laboratory of Power System Intelligent Dispatch
and Control of Ministry of Education (Shandong University), 17923 Jingshi
Road, Ji’nan 250061, China (e-mail: muqy@mail.sdu.edu.cn).

B

0

4
−4

D

−2
Re
(b)

0

EIGD

Fig. 3. Eigenvalues computed by SOD-PS with θ = 17.46◦ and by EIGD
around two shift points s = j7 and j13. (a) 100 eigenvalues obtained by SODPS and two sets of 50 eigenvalues obtained by EIGD; (b) 120 eigenvalues
obtained by SOD-PS and two sets of 80 eigenvalues obtained by EIGD.

VSC-HVDC for enhancing power system stability
in the load restoration stage of black start
Wei Feng, Fangxing Li
Department of Electrical Engineering and Computer Science
The University of Tennessee, Knoxville
Knoxville, TN 37996, USA{wfeng4, fli6}@vols.utk.edu

Abstract— In the load restoration stage of power system
black start, it is very important to enhance the restoration
stability. For a good black start system, it is important to avoid
transient voltage and inrush current and frequency variation.
However, tradition methods cannot perform well in this. This
course project presents Voltage Source Converter based HVDC
(VSC-HVDC) used as black start source. The physical model ofAC system and VSC-HVDC is founded and analyzed. Based on
the simulation software PSCAD/EMTDC, a coordinated control
method of VSC-HVDC and generator is presented to improvestability of AC/DC hybrid system. Simulation results show thatthe coordinated control method has excellent dynamic
characteristic such as controlling active quickly, avoid inrush
current and transient over voltage, and improving the frequency
stability of AC system.
Keywords— black start, VSC-HVDC, Load restoration,
Governor, Frequency stability

I. INTRODUCTION
The tradition definition of ‘black start’ emphasize selfrecovery, shows that the process of restoring an electric power
station or a part of an electric grid to operation without relying
on the external transmission network. Normally, there are two
common methods for black start, hydropower units and gas
turbine generator. As for traditional black start, there are a lotof problems to be considered in the restoration of powersystem after black out, such as: generator's self-excitation;
continuing fundamental frequency over voltage, switchingsurge and resonant over voltage while switching on a long noload line; ferromagnetic resonance while charging
transformers; the stability of voltage and frequency and so on.
The development of VSC-HVDC gives a new method for‘black start’. Unlike self-recovery, VSC-HVDC depends on thelarge-scale interconnected of model grid. When a blackout
occurs in one system, the VSC station can firstly be a firewall
to stop the blackout spreading to another system. And then, theneighboring working system can be treated as a power supply
via VSC. The basic reason is that VSC-HVDC is a passive
inverter that can work without supply of inactive power andvoltage from AC system, moreover, within the capacity, it can
supply power back to the system.

II. MODEL AND CONTROL STRATEGY
A. Physical model of VSC-HVDC
Firstly, modeling a VSC-HVDC to connect two AC
system. In this model, power could transfer two-way andoperate in different voltage and capacity.

Fig.1 Physical model of VSC-HVDC system

B. Control strategy
When a blackout happens, measuring devices in station
could detect rapidly and cut off the fault side. Then, switch to‘power’ model to support the blackout area.

Fig.2 Simulink model of test system

III. SIMULATION RESULTS
The physical model and control strategy are applied in amixed system with two AC system and test the ability ofrecovery after blackout. Compared results of hydro-power, gas
turbine and VSC-HVDC are given as below.

Fig.3 Frequency response of different methods

Optimal Damping Actuator Selection ThroughOscillation Energy Sensitivity Analysis
Horacio Silva-Saravia, Yajun Wang, Héctor Pulgar-Painemal, Kevin Tomsovic
Department of Electrical Engineering and Computer Science
University of Tennessee, Knoxville, TN, 37996

I. I NTRODUCTION

86

2

Imaginary (rad/s)

10(c)= 0.6%

4

0
-1

-0.8

-0.6

-0.4

Ek (t) (%)

0
0

1

2

3

4

5

1
0.5

0.5
0.25

0
0

1

2

3

4

5

Time (s)-0.2

II. R ESEARCH IMPACT
The analytical calculation of the total action sensitivity
with respect to different actuators can provide a valuable
tool to enhance power system dynamics, specially when highpenetration of renewable energy pushes several modes to havecritical damping ratios. Applications can be developed forplanning considering dynamic strengthening and also for online control allocation. Figure 3 shows the oscillation energy
and machine speed deviation after applying on-line selectionof actuators in the WECC system.
100
Base case

Ek (%)

decreasing KD(a)= 11.5%50

Fig. 2: Oscillation energy and total action of SMIB system.

Traditional analysis of power system oscillations focuseson critical eigenvalues. However, systems with several criticaleigenvalues lack a general dynamic performance index toanalyze the problem. The method described in this paper
provides a solution to this problem. Consider a SMIB systemwith a damping torque coefficient Kd . As the damping torqueis reduced, the stability is compromised and the dynamic
performance is worsened as shown in Figure 1.

(b)= 5.8%100

S0 (t)

Abstract—This paper proposes a new method to analyze andselect damping control actuators in stressed systems with highpenetration of renewable energy. Traditional single dominantmode analysis fails to provide effective control actions whenseveral modes have similar low damping ratios. This work
addresses this problem by considering all modes in the formulation of the system kinetic oscillation energy. The integralof energy over time defines the total action as a measure of
dynamic performance, and its sensitivity allows comparing theperformance of different actuators in the system to select themost effective actuator to damp the oscillation energy. The
concept is explained in the single-machine infinite bus (SMIB)
system. Time domain simulations are performed in the WesternElectricity Coordinating Council (WECC) system to validate thecontribution of this work.

0

ESS140 out

ESS63 out5000Real (1/s)

2

4

6

8

10

Time (s)Fig. 1: Eigenvalue plot of SMIB system.
139

0.02

The dynamic performance can be assessed by calculating
the area S 0 of the oscillation energy in Figure 2. Thus, thefinal value of S 0 (total action) works as a tool to compare
different actuators.
This material is based on work supported by the National Science Foundation under Grant No. 1509114. This work is also supported by theEngineering Research Center Program of the National Science Foundationand the Department of Energy under NSF Award No. EEC-1041877 and the
CURENT Industry Partnership Program.

0
Base case

ESS140 out

-0.02
0

2

4

6

8

10

Time (s)Fig. 3: Kinetic energy of WECC system for disturbance at
SG29.
The sensitivities point out that ESS140 must be disconnectedto achieve an optimal performance for the post-fault condition.

Practical Modeling of Flywheel Energy Storage
for Primary Frequency Control in Power Grids
Dario Peralta, Student Member, IEEE, Claudio Cañizares, Fellow, IEEE, and Kankar Bhattacharya, Fellow, IEEE
Department of Electrical and Computer Engineering
University of Waterloo
Waterloo, Ontario, Canada{d2peralt, ccanizar, kankar}@uwaterloo.ca

Abstract—This poster presents the development of a dynamic
model for frequency regulation studies, practical and useful forsystem operators, of a Flywheel energy storage (FES) system connected to a power system to provide Primary Frequency Control(PFC), and study its effects on system stability. The proposed
practical model is tested using a commercial simulation package,and compared with respect to a previously reported test systemequipped with a high-capacity wind generator, which createssignificant frequency and voltage fluctuations, demonstrating its
application for solving PFC problems in power networks.
Index Terms—Flywheel energy storage, frequency control,
frequency stability, modeling, power system stability, simulation.

I. I NTRODUCTION
The development and design of a Flywheel energy storage(FES) model to study the provision of primary frequency
control (PFC) is presented in this paper. The FES systemcomprises of an induction generator/motor, a flywheel represented as a High inertia mass constant (H), and two bidirectional voltage source converters (VSCs), and is modeledin DSATools® . The base system used here is extracted from[1], as shown in Fig. 1.

r

FES Speed Limiter

FES

K G

r

ref

Frequency Deviation Control

f network1
1   Ls

Pref

1 .05

 Ds
1   Is

Pbase

1.05

KP

f ref

Fig. 2: FES Speed/Frequency Control.

III. R ESULTS
There is a significant improvement in the frequency response of the FES system as compared to the Base system, as
shown in Fig 3, ensuring frequency stability of the system.
Also, the voltage fluctuations are reduced, and the voltage
level is increased to its nominal value. Finally, the mechanicaltorque of the synchronous generator varies considerably in
the Base system, while its variation is reduced with the FES,
due to the contribution of the FES active power to frequency
control.

20.05 + j0.3
6.6 / 2.2 KV

15 MW

10 MVA

62
60
58
56

1.0

Base system
FES system

13.5 MW3

6.6 / 0.69 KVWind Energy System (WES)
10 MVA; H=1.5

Fig. 1: Test system configuration.

II. FES S PEED /F REQUENCY C ONTROLThis control determines the active power reference Pref ,i.e., in the active power control of the flywheel-side VSC,
to be injected to/from the FES, responding to the frequency
deviations detected at the terminal of the system. To prevent
either the shortage or the surplus of energy stored in the FES,a speed limiter is included, as shown in Fig. 2.
This work has been supported by NSERC Energy Storage Technology(NEST) Network, Canada, http://www.ryerson.ca/nestnet/

Torque (p.u.)

Synchronous
Generator
6.6 KV
30 MVAFESFrequency (Hz)1

Voltage (p.u)

Base system

0.95
1.0
0.8
0.6
0.4
0

50

100

Time (s)Fig. 3: System response without and with FES.

R EFERENCES[1] R. Takahashi and J. Tamura, “Frequency stabilization of small powersystem with wind farm by using flywheel energy storage system,” in Proc.
IEEE International Symposium on Diagnostics for Electric Machines,
Power Electronics and Drives, Sept 2007, pp. 393–398.

Location and Migration Laws of Out-of-Step Center
under Multi-Frequency Oscillation Based on Voltage
Phase Angle Trajectory
Fei Tang1, Jiale Liu1, Qingfen Liao1, Yu Liu2, Gucheng Xiao1 and Chenxu Wang1(1. School of Electrical Engineering, Wuhan University, Wuhan, 430072, Hubei Province, China;
2. School of Foreign Languages, South-Central University for Nationalities, Wuhan, 430074, Hubei Province, China)

liujiale@whu.edu.cn

1

c1 0.50
50

 3  1

-3.14

3.14

0

-3.14

Eq. (2) can be further transformed into the constraint
condition which the voltage angle need satisfy, as is shown In
Fig. 2. The phase angle trajectory, denoted by the gray curve,
starts from the point A to B to C. The point C on the red curve
satisfies the condition of appearance of OSC. Accordingly theoccurrence time and location of OSC are access to bepredicted in advance based on Eq.(2) and Fig.2.
3C2

 3  1

1

I. INTRODUCTION

B

21t

0

-1

II. METHODOLOGY
Based on theoretical derivation of the oscillation center
location in IEEE9-bus system with three generators, themigration rules of the oscillation center in multi-frequency
conditions are proposed as follows:(1)

A case study of line E1—EO is analyzed to illustrate the
distribution position of the oscillation center (OC), which isshown in Fig.1. When δ2=δ3, the value of c1 reaches maximum(c1max), denoting the OC migrates to the point farthest awayfrom the central point. When δ2≠δ3, c1 varies between zeroand c1max. The migration distance is positively correlated to thecoherence between generator2 and generator3. The condition
for existence of OSC are expressed as Eq.(2).

tan(io )  ni / mi  0, cos(io )  mi / mi2  ni2  0 (2)

A

-2
-3
-3

-2

-1

0

 2  1

1

2

3

Fig. 2. Voltage phase angle trajectory for locating out-of-step center

III. APPLICATION FOR SIMULATING POWER SYSTEM
2

31

With the rapid development of UHV AC/DC projects, thepower system is vulnerable to instability of multi-group modes.
As a result, it is urgent to research the migration rule of the outof-step center (OSC) in multi-frequency oscillations.

5 2  1

Fig. 1. Migration laws of oscillation centers in line E1-EO

Keywords—multi-frequency oscillation; out of step; voltage
phase angle trajectory; oscillation center; positioning mi2  ni2  mi
, mi  mi2  ni2
ci  1  2mi  mi2  ni2
mi  mi2  ni2
0,

3.14

31t

Abstract—With the rapid development of ultra-high voltage
AC/DC hybrid power grid projects and the large-scale ofrenewable energy integration, it puts high demand for safety andstability control of the power system. Splitting control (orislanding control) is the last resort to preserve the power systemfrom severe blackouts. In China, the out-of-step center (i.e. thephysical point of an oscillating system where the voltage is nearto zero) is applied to determining the splitting locations. However,
the increasingly complicated power system might oscillate inmulti-frequency after large disturbance, which thus leads todynamic migration of out-of-step centers. To address this issue,
this paper derives the migration function of the out-of-step centerin three equivalent machines system, based on which the
migration laws are analyzed. Accordingly, the voltage angle
trajectories method is employed to determine the time andlocation of splitting control in advance, which could guarantee
the real-time implementation of islanding control. Finally, IEEE
9-bus and IEEE 39-bus test systems in PSS/E verify theeffectiveness of the proposed method.

0
2
2

0

2

21
Fig. 3. Voltage phase angle trajectories of line E1—EO during 1.5s - 4.5s

Focused on the line E1—EO, the simulation study in PSS/E
obtains the voltage phase angle trajectories composed by blue
dots in Fig.3. During 1.5~4.5s, the intersection of the phaseangle trajectory with the red curve illustrates that the OSC
occurs in the line E1—EO. The validity and reliability of thetheory are well tested since the characteristics of phase angle
trajectories confirm to the voltage frequency results. In
addition, prediction of the OSC position provides enough time
for splitting measures.

Development and Frequency Application of an
European Low Voltage Microgrid Network
Melike Selcen Ayaz, R. Azizipanah-Abarghooee, Student Member, IEEE and Vladimir Terzija, Fellow, IEEE, University of Manchester,UK
Abstract A new practical microgrid benchmark system basedon Europeian CIGRE data is developed in this paper to investigate
distribution based power system dynamics. This test system is
modeified to integrate Distributed Energy Resources (DERs). The
dynamic frequency studies are applied on this modified low
voltage CIGRE benchmark model to demonstrate the frequency
nadir improvement in the microgrid system with the increased
renewable energy penetration. Two gas turbines, two batteryenergy storage, two wind turbines and four photovoltaic power
plants are appended into the proposed microgrid system. The PV
units have a total power of 210 kVA, two WTs are Type 4 directdrive permanent magnet synchronous generator (PMSG) (powerof 500 kVA) and the batteries are respectively rated to 35 kVA and
25 kVA with power factor equal to 0.85. The DIgSILENT Power
Factory software is deployed to simulate all the case studies.
Simulation results evaluate the dynamic performance of all
resources as well as the system in terms of the power outputs of
the DERs. Furthermore, the frequency response and Rate of
Change of Frequency (RoCoF) criterion under a load event, anddifferent penetration level of renewable energy sources are
explored. The derived model will enable the researchers to bothworking with a real network model and designing a flexible
microgrid studies and applying the sensitivity analysis for future
distribution network studies.

topologies [1] portrayed in Fig. 1. The proposed network is agood candidate for testing discrepant solutions in the areas of
microgrids in terms of power system frequency, stability,
dynamics and smart grid analysis.

S1

S2

S3

S4

I0

R0

C0

SG

Gas
Turbine1

I1

R1

C1
C2

R2
C11

R12

R4

R13

R14

C12

Gas
Turbine2

C18
C7

C9

Wind
Turbine2

C17
PV4

C8

Load

BESS1
R17

C16

C6

C19

PV1

R9

C15
C5

Wind
Turbine1

WT

R7

R18

C14

R15

R6

R8

PV3

C4
C13

R5
R16

C3

C10

SG

R3

R11

C20

Bus
I2

Neutral earthing

R10
BESS2

External grid

PV2

Fig. 1. Modified benchmark model- LV practical test system with DERs

III. SIMULATION RESULTS

Keywords DERs; DIgSILENT; Microgrids; Penetration ofrenewables; Frequency response

I. INTRODUCTION
The system frequency stability depends on the system inertia,
which is the kinetic energy of rotating masses of synchronousmachines connected to grid. The power electronic interfaces
considered in Distributed energy sources (DERs) decouple theinertia from the system, which can lead to the power systeminstability. Thus, the microgrid systems are under risk of large
frequency oscillations in case of future low and variable inertia
system especially in distribution networks. Integrating thebattery storage systems (BESSs) in order to contribute to theprimary frequency response is a primitive solution to solve theissues mentioned, in realistic dynamic test system including
DERs such as photovoltaic (PV) power plant, wind turbines(WTs). The location of the DERs and BESSs are selectedaccording to the sensitivity analysis to provide system frequency
stability. After the placement procedure, the modeled LV
distribution model based on European CIGRE information isconsidered as the microgrid.
II. PROPOSED EUROPEAN LV MICROGRID MODEL
This benchmark model provides a high variety of testing forintegration of DERs. The network consists of 37 nodes with the
combination of residential, industrial and commercial
subnetworks, comprising of 6 residential, 8 commercial loads
and industrial load connected by underground and overhead line

Fig 2.Comparison Results of Frequency Response Characteristics Under aDisturbanceFig 3. Comparison results of RoCoF Characteristics Under a Disturbance

IV. CONCLUSIONSThe significance of a practical microgrid system is to meet theneeds of the researchers for power system dynamic analysis.
Simulation results show that increase of WTs and PVs affectthe frequency regulation; however, the use of GTs and BESSs
can regulate the frequency without a massive frequency drop.
[1] CIGRE, Benchmark Systems for Network Integration of Renewable andDistributed Energy Resources, April. 2014.

1

Provision for Guaranteed Inertial Response in
Diesel-Wind Systems via Model Reference Control
Yichen Zhang, Student Member, IEEE, Alexander M. Melin,, Member, IEEE, Seddik M. Djouadi, Member, IEEE,
Mohammed M. Olama, Member, IEEE, Kevin Tomsovic, Fellow, IEEE

Abstract—Frequency performance has been a crucial issue forislanded microgrids. On the one hand, most distributed energy
resources (DER) are converter-interfaced and do not inherently
respond to frequency variations. On the other hand, current
inertia emulation approach cannot provide guaranteed response.
In this paper, a model reference control based inertia emulation
strategy is proposed for diesel-wind systems. Desired inertia canbe precisely emulated through the proposed strategy. A typicalfrequency response model with parametric inertia is set to be thereference model. A measurement at a specific location delivers
the information about the disturbance acting on the diesel-windsystem to the reference model. The objective is for the speed of thediesel generator to track the reference so that the desired inertial
response is realized. The controller is implemented in a nonlinear
three-phase diesel-wind system fed microgrid using the Simulink
software platform. The results show that exact synthetic inertia
can be emulated and adequate frequency response is achieved.

Parameterized Reference Model



̂

1
R̂

1
Pˆv
1
Pˆm 
1
 Dˆ  2 Hsˆ
1  ˆd s
1  ˆsm s

1 
ˆ 
Dˆ  2 Hs

ˆ, Pˆm , Pˆv

K
uie

r
Pg

d , Pm , Pv

Ppom,i

Pe

Point of Measure i

Physical Plant

Index Terms—Inertia emulation, low-inertia microgrid, dieselwind system, model reference control, polytopic uncertainty.

Intra-block signal

Inter-block signal

Fig. 1. Realization of MRC on one diesel-wind system.

I. M OTIVATION
Inertia emulation functionality has been widely integrated
within wind turbine generators (WTGs) to improve the frequency response by coupling the kinetic energy stored in
WTGs in proportion to the rate-of-change of frequency (RoCoF). This configuration, however, is difficult to assess andcontrol how much synthetic inertia can be provided since
the emulated inertia is time-varying. In this work, a novel
inertia emulation strategy is proposed for diesel-wind systems,
through which desired inertia can be precisely emulated.

If the speed of DSG can track the speed of reference modelwith the support of WTG, that is, ∆ω = ∆ωd , then thefollowing relation holds∆Pg = 2Ĥs∆ω − 2HD s∆ω = 2Hie s∆ω

Therefore, exact synthetic inertial response 2Hie s∆ω is emulated by the WTG. Finally, the MRC approach is employed torealize the tracking objective. This reference tracking can berealized by means of feedback control. The preliminary resultis shown in Fig. 2.

II. M ODEL R EFERENCE C ONTROL - BASED I NERTIAE MULATION(a)60∆Ppom = 2Ĥs∆ω = 2HD s∆ωd + ∆Pg(1)

Power Variation (kW)

Speed (Hz)

59.7

59.6

600

400

200

59.5

0

59.4

-200
1

1.5

70

(b)

2

2.5

(d)
0.6

Control Input

No IE
Conventional IE K=0.03
Conventional IE K=0.1
MRC-based IE

69

No IE
Conventional IE K=0.03
Conventional IE K=0.1
MRC-based IE

800

59.8

69.5

(c)

1000
Reference 1-1
No IE
Conventional IE K=0.03
Conventional IE K=0.1
MRC-based IE

59.9

Speed (Hz)Fig. 1 illustrates the MRC-based inertia emulation on adiesel-wind system. It consists of a parameterized reference
model and a physical plant. A reference model similar to thefrequency response model will be chosen with desired inertia
Ĥ. The physical plant is the diesel-wind unit.
The idea to achieve near-ideal synthetic inertial response of
WTGs can be recast as a tracking problem. As illustrated inFig. 1, let 2Ĥs∆ω and 2HD s∆ωd be the inertial responseof the reference model and DSG, respectively, where Ĥ isthe desired inertia constant and Ĥ − HD = Hie > 0. Oncesubjected to a same disturbance ∆Ppom , the power balance
condition holds as

(2)

68.5

Conventional IE K=0.03
Conventional IE K=0.1
MRC-based IE

0.4
0.2

68
0
67.5
1

1.5

2

Time (s)

2.5

1

1.2

1.4

1.6

1.8

2

2.2

Time (s)Fig. 2. Performance under conventional and MRC-based inertia emulation
with Config. 1. (a) Speeds of DSG and reference model. (b) WTG speed. (c)WTG active power variation. (d) Control input.

Tube-Based Model Predictive Control of Energy Storage
Units for Improving Transient Stability of Power Systems
Iman Kiaei, Student Member, IEEE, and Saeed Lotfifard, Member, IEEE
School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99164 USA
E-mail: iman.kiaei@wsu.edu; s.lotfifard@wsu.edu
Abstract—In smart grids energy storage units are installed at
different locations of the network. This work proposes a method
based on model predictive control (MPC) for enhancing thetransient stability of power systems by controlling the charging
and discharging state of superconducting magnetic energy
storage (SMES) systems installed throughout power systems. To
enhance the performance of the controller in presence ofexogenous disturbances and modelling uncertainty a controller
based on tube-based MPC is developed. The proposed controller
determines output active and reactive powers of SMES units ateach time instance to improve the selected transient stability
index defined based on the distance of the rotor angles ofgenerators and the center of inertia of the power system.

VLowerLimit ≤ VSMES ≤ VUpperLimit(8)

Pmin ≤ PSMES ≤ Pmax
Qmin ≤ QSMES ≤ Qmax

(10)

(9)

III. SIMULATION RESULTS
The proposed control strategy is applied to a 3 machine
system. A three-phase short circuit fault occurs on the line
between buses 4 and 9 at 0.1 seconds and is cleared at 1.1
seconds by tripping the faulted line. The proposed controller
based on tube-based MPC determines the output active andreactive powers of SMES units.

I. INTRODUCTION
Transient stability is the ability of a power system towithstand large disturbances. When a contingency occurs in
power systems the power balance between generation and load
is disturbed. In this condition, unstable generators should be
disconnected from the grid and the system operator should
execute emergency control actions to balance out thegeneration and load. In this work, to enhance the transientstability of the power system, a coordinated control system for
managing output active and reactive power of SMES units
installed throughout power systems is proposed. This
coordinated control is based on the tube-based model
predictive control which consists of two MPCs, inner and outer
MPCs.

3
2

4

1

10

G

G

5

6
7

11
G
SMES

9
8

SMES

Fig. 1. Schematic of thesimulated power system

Fig. 2. Relative rotor angles of the generators
at buses 10 & 11 for the fault at 0.1 sec.

II. PROPOSED TUBE-BASED MODEL PREDICTIVE CONTROLLER
The objective function of the Tube-based MPC is asfollows:min2(∑ (θ i (t ) − θ COI (t ) ) + ∑ λ j ∆U j (t )

NG

i =1

NS

j =1

)

2

(1)

The device constraints, SMES output power bounds, and thesystem wide constraint, voltage limit are considered as follows:(2)θi (t ) = ωi (t ) − ω sωsω i (t ) =2H i(Pmi − Pei − Di (ω i (t ) − ω s ) )

(a)

(3)

Pei = ∑ Vi V j Yij cos(∠Yij + θ j − θ i )(4)

Qei = −∑ Vi V j Yij sin (∠Yij + θ j − θ i )

(5)

n

j =1

n

j =1

Ks
U Pr ef
1 + Ts × s
Ks
QSMES (output ) =U Qref
1 + Ts × s
PSMES (output ) =

(6)
(7)

(b)
Fig.3. Trajectories of the rotor angle for MPC#1 and MPC#2 of the tubebased MPC (a) generator at bus#10. (b) generator at bus#11.

Probabilistic Transient Stability Assessment ofRenewable Generation Integrated Power System
Yingying Wang, Student Member, IEEE, Mojdeh Khorsand, Member, IEEE, and Vijay Vital, Fellow, IEEE
School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA
ywang935@asu.edu, Mojdeh.Khorsand@asu.edu, and Vijay.Vittal@asu.edu
Abstract—The stochastic characteristic of wind power has
introduced challenges to traditional deterministic dynamic
security assessment of power systems. A probabilistic approachbased on Monte-Carlo simulation (MCS) is proposed in thiswork to integrate wind power uncertainty and variability intotransient stability analysis of power grids. Fault probability
including fault rate, fault type and fault location are taken into
consideration in the transient stability assessment. Systemstability is evaluated in term of loss of load to maintain stability
after the disturbance and its probability. A synthetic 36-bus test
system is utilized to verify the proposed approach. The
simulation results show the effectiveness of the proposed
approach for incorporating wind power uncertainty andvariability into transient stability assessment, as well as
identifying critical contingencies that affect system stability.
Index Terms-- Monte Carlo simulation, wind power uncertainty
and variability, transient stability.

I.

INTRODUCTION

Integration of renewable resources introduces newcomplexities to the stability assessment of power systems. The
main sources of the complexity stem from the uncertain natureof the renewable sources like wind and solar generation, theequipment failure characteristics and the altered inertia in thesystem due to the power electronics interface to the grid. It is
recognized that the traditional deterministic methods toincorporate the increased sources of uncertainty are notadequate and hence comprehensive evaluation methods need
to be developed. Moreover, deterministic approaches may not
be capable of considering factors like the variability of energy
resources and changing equipment characteristics due toincreasing use of power electronics. To integrate new
characteristics brought by connected wind power, probabilistic
stability assessment methods are needed.
II.

Fig. 1 Probabilitstic stability assesement procedure

Based on the stochastic model, wind turbine output scenarios
can be generated as sequential processes. Some initial
simulation results are given in Fig. 2 and Fig.3.

Fig. 2 Stochastic wind power output

METHODOLOGY, MODEL AND INITIAL RESULTS

As a probabilistic approach, Monte-Carlo simulation is widelyused as the method of selecting system states. It is the mostwidely application in power systems is generation adequacy
assessment. Monte-Carlo simulation includes non-sequential
MCS via states sampling and sequential MCS based on states
duration time sampling.
In this work, sequential MCS is used to generate thechronological status of wind power, while non-sequential
MCS method is used for contingency selection. The overall
approach is show in Fig. 1.

Fig. 3 System risk based on time domain simulation results

New Optimization-Based Algorithms for a
Substation Voltage Controller Using Local
Synchrophasor Measurements
Noah Badayos, Farrokh Habibi-Ashrafi,
Armando Salazar, and Backer Abu-Jaradeh
Southern California Edison
Westminster, CA
noah.badayos@sce.com

S. Mohammad Amelian, V. “Mani” Venkatasubramanian
School of Electrical Engineering and Computer Science
Washington State University
Pullman, WA
s.amelian@wsu.edu
Abstract— This poster presents a substation local voltage
controller (SLVC) with available phasor measurement unit
(PMU), via optimal usage of its reactive control resources, i.e.,shunt reactive devices and transformer taps. Two optimization
formulations with different objectives are introduced based on
various operating criteria in electric utilities. The first approachaims to minimize the required reactive power injection such thatit corrects the substation bus voltages between the determined
limits and as close as possible to the optimal values. The second
one minimizes number of switching actions, that correct thevoltages in the same way mentioned above. Genetic algorithm(GA) is used for solving these discrete optimization problems.
Performance of the proposed formulations is tested and analyzed
through simulations for a typical substation in Southern
California transmission network. Finally, comparison of theobtained results from the two approaches are discussed.
Keywords—Volt-VAr control, synchrophasor measurement, ,local voltage control, optimization, genetic algorithm.

I. INTRODUCTION
This poster presents the improved version of an SLVC
introduced in [1], using optimization approaches, towards
finding the best local control actions within a substation. SLVC
uses local PMU measurements and network topology, topredict the local bus voltages after each of the possible control(switching) actions. This prediction is carried out using a Local
Voltage Estimator (LVE) which uses linearized reactive power
flow equations to estimate the post-switching voltages. Next,
two optimization formulations (briefly described above) aresolved to bring a deviated bus voltage back within the predetermined limits and as close as possible to its optimal value.
As a result, reactive power reserves of a substation in terms ofavailable MVAr would be maximized, as one of the main
performance objectives in power system operation. The two
discrete formulations easily fit in as Integer Non-Linear
Programming problems. In order to solve this discrete
optimization problem, genetic algorithm (GA) is selected,
which is capable of handling discrete optimization problems
and is proved to be effective in power systems [2]. Since weare solving a relatively small-scale optimization problem,
typical speed or convergence issues with GA in dealing withlarge-scale problems will not be problematic here.

Fig. 1. Diagram of the substation under study.
TABLE I.

Device/Bus

Simulation results of the proposed formulations (F1 & F2)

Case 1

F1
X500 Cap. 0
X230 Cap. 0
X66 Cap.
-1
X13.8-Reac1 0
X13.8-Reac2 0
Tap_230/66 0
Tap_500/230 0
X500
X230
X66

F2
0
0
0
0
1
0
0

1.060
1.009
1.030

Case 2

Case 3

Case 4F1 F2 F1 F2 F1 F2
0
0
0
1
1
2
0
0
2
0
2
0
-2 -1
0
0
0
0
0
1
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
Resulting Voltages (pu)
1.060
1.050
1.049
1.007
1.002
1.000
1.023
1.023
1.032

Case 5

Case 6

F1
2
2
0
0
0
0
0

F1
-2
0
-1
2
1
0
0

F2
2
2
0
0
0
0
0

1.051
1.001
1.020

F2
-2
0
-1
1
2
0
0

1.060
1.006
1.030

for a typical 3-level substation in Southern California
transmission network, shown in fig. 1, are presented in Table I.
They represent the optimal switching combinations of theavailable reactive control devices in response to voltage
deviations for six study cases, and the resulting voltages.
Comparing the results with the optimal ones from exhaustive
search, it was seen that the proposed formulations overcome
the bus voltage deviations and take the corresponding optimal
control action to modify the bus voltages.
REFERENCES[1][2]

II. KEY RESULTS

Resulting voltages and optimal reactive controls for different
cases: Formulation 1&2
Reactive injections (MVAr) and tap changings

V. Venkatasubramanian, et al. “Hierarchical two-level voltage controller
for large power systems,” IEEE Trans. Power Syst, vol. 31, no. 1, 2016.
M. Amelian, et al. "Novel Optimization-Based Algorithms for a
Substation Voltage Controller Using Local PMU Measurements," In
Proc. of the 51st Hawaii Int'l. Conf. on System Sciences, 2018.

Optimal Design of Virtual Inertia and Damping
Coefficients for Virtual Synchronous Machines
Atinuke Ademola-Idowu, Baosen Zhang
Electrical Engineering Department,
University of Washington, Seattle, WA 98195
Email: {aidowu, zhangbao}@uw.edu

Abstract—Increased penetration of inverter-connected renewableenergy sources (RES) in the power system has resulted ina decrease in available rotational inertia which serves as an
immediate response to frequency deviation due to disturbances.
The concept of virtual inertia has been proposed to combat thisdecrease by enabling the inverters to produce active power in
response to a frequency deviation like a synchronous generator. In
this paper, we present an algorithm to optimally design the inertia
and damping coefficient required for an inverter-based virtual
synchronous machine (VSM) to participate efficiently in theinertia response portion of primary frequency control. We design
the objective function to explicitly trade-off between competing
objectives such as the damping rate the the frequency nadir.
Specifically, we formulate the design problem as a constrained
and regularized H2 norm minimization problem, and develop an
efficient gradient algorithm for this non-convex problem. This
proposed algorithm is applied to a test case to demonstrate its
performance against existing methods.

An augmented H2 norm is chosen as the objective function
for this optimization problem which captures the required
frequency response metrics. The optimization problem is thenformulated as (2) and computed using using (3):22minimize JT (m, d) = G(m, d) 2 + βkmk2
(2a)
m,d

subject to

m≤m≤m

(2b)

d≤d≤d
T

T

A(m, d)P + P A(m, d) + B(m)B(m) = 0
AT (m, d)Q + QA(m, d)T + C(m)T C(m) = 0

P  0;

where G(m, d)

Q0

(2c)
(2d)
(2e)
(2f)

2
= Tr(B(m)T Q(m, d)B(m))
2

αk+1 = ProjC [αk − γ 5 J(αk )](3)

T

I. P ROBLEM S TATEMENTAs the electric power grid transitions from the traditional
state only conventional generators to a mix of conventional
generators and inverter-connected RES, the immediate response capability by the synchronous machine in the eventof a power imbalance to reduce the rate of frequency decline
is reduced. This results in an increased rate of change of
frequency (ROCOF) and consequently, a higher frequency
deviation which results in a low frequency nadir, that is, themaximum frequency deviation. The ROCOF, frequency nadir
and settling time/frequency are important frequency response
metrics in the power systems network.
II. P ROPOSED M ETHOD
To solve this problem, we design an optimal virtual anddamping coefficient for a VSM to emulate the inertial responseof synchronous machine a based on the ROCOF and frequency
deviation. The dynamics of the power system network suitable
for analyzing the response of the generators when subjected to
disturbances can be represented in first order state space form
as in (1):
"
# 

 

0
I
4̇δ
4δ
0
˙ = −M −1 L −M −1 D 4ω + M −1 4P (1)
4ω
|
{z
}
| {z }=A=B

where the M and D are the inertia and damping coefficientmatrix respectively whose parameters are the inertia anddamping values of the individual generators and the virtualinertia and damping parameters of the VSM to be designed.

where α = [m d] and C is the set of feasible m and d values.
The gradient is derived as in (4):
!
!


∂J
∂A
∂(BB T )∂(C T C)= 2Tr
P Q + TrQ + Tr P
∂α
∂α
∂α
∂α
(4)
2
The gradient of βkmk2 is given as 2βm.
III. K EY R ESULTS
The optimization problem in (2) is implemented on a modified
12 bus three-area test case with a disturbance step input
applied to one of the nodes, in this case, node 6.

Fig. 1: Frequency deviation at node 6 due to step input. The
optimal response balances between a fast and slow ROCOF togive smoother response

Fig. 2: Frequency deviation at all nodes due to step input
in node 6. It shows how the optimal coefficients results ina balanced smoother response in all the nodes. LHS: combination of dmax mmin , MID: combination of dopt mopt , [RHS]:
combination of dmax mmax .

A N4SID based estimator for identifying theparameters and sources of forced oscillations
Mohammad Mansouri and Andrew M Knight
Department of Electrical and Computer Engineering, University of Calgary, Calgary, Canada

I. I NTRODUCTION
In power system operation, reliable delivery of electrical power is of paramount concern. Nonetheless, reliability
faces various challenges such as electromechanical modes andForced Oscillations (FOs). Although electromechanical modes
and FOs occur in the same frequency range, they are fundamentally different. Electromechanical modes are properties ofthe power systems dynamics that are typically excited by load
variations, whereas FOs are caused by a rogue inputs driving
the system. FOs limit inter-area electric power capability,
negatively affect the estimation accuracy of natural modes, andseriously threaten the security and stability of power systems.
Hence, conducting studies on FOs is of great importance. In
this paper, a measurement-based method called N4SID andcategorized as a blind identification method, is proposed.
II. P ROPOSED M ETHOD

where

,
1/2

Γi = U1 S1

X̂i+1 = Γ†i−1 o̧i−1

(1)

,

(2)

Γi−1 = Γi3) Solve the set of linear equation for A and C:

  
 
A  
ρw
X̂i+1
=
X̂i +
C
ρv
Yi|i

0.02

0.015

0.01

0.005

0

-0.005-0.01-0.015
0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

Time (sec)Figure 1. The oscillation of the output power on G2 after load change

4) Determine Q, S, and R from: 
1 ρw 
Q S
= [
. ρw
ST R
j ρv


ρv ]

(4)

5) Determine Σs , G, and Λ0 from:Σs = AΣs AT + Q
G = AΣs C T + S
Λ0 = CΣs C T + R

solve f or Σssolve f or G
solve f or Λ0(5)6) Determine P AND K f by solving the Riccati equation:

Voltages, currents, electromechanical modes, and FOs aremeasured by PMUs installed on buses in power system.
Considering these measurements as system’s outputs, we will
have a blind identification problem. Therefore, we can employ
N4SID, which categorized as a blind identification, to identify
the sources and parameters of FOs. We can also recognize FOsfrom Electromechanical oscillation because system dynamic is
identified, too. The N4SID algorithm can be organized in thefollowing steps:
1) The measured data is formed in a Hankel matrix format,
divided in two parts:past data and future data, and the
projection of future data on past data is calculated.
The singular value decomposition of the projection is
extracted as U SV , and non-zero part of U and S are
called U1 and S1 .
2) Determine X̂i and X̂i+1 as:
X̂i = Γ†i o̧i0.025

The oscillation of output power in G 2 (pu)

Abstract—A blind identification method based on Subspace
State Space System IDentification (N4SID) is proposed to identify
the sources and parameters of the Forced Oscillations (FOs) in a
power system. The proposed method has some advantages suchas simplicity, robustness, and being on-line.

(3)

P
Kf

= AP AT + (G − AP C T )∗(Λ0 − CP C T )−1 (G − AP C T )T= (G − AP C T )(Λ0 − CP C T )−1(6)7) Finally, the identified forward innovation model is:
xfk+1
yk

= Axfk + K f efk= Cxfk + efk(7)III. C ASE S TUDY: F OUR -M ACHINE , T WO -A REA S YSTEM
The case study to demonstrate the effectiveness of the
N4SID strategy is based on the well-known four-machine,
two-area system. The dynamic performance of G2 under adisturbance is shown in Fig. 1. Using the proposed method,
the forward innovation model of the system is identified. The
dominant mode, whose frequency and damping are 0.55Hz and
0.08, respectively, can easily be extracted from the identified
system. The analysis of the identified system can also help us
to distinguish between the FOs and electromechanical modes.
IV. C ONCLUSION
This paper presents a measurement-based strategy for identifying the parameters and sources of FOs that have a ability
to distinguish between FOs and electromechanical modes. The
simulation confirms the effectiveness of the proposed method.

Towards the New Low-Order System Frequency
Response Model of Power Systems with High
Penetration of Variable-Speed Wind Turbine
Generators
Matej Krpan

Igor Kuzle

Faculty of Electrical Engineering and Computing
University of Zagreb
Zagreb, Croatia
Email: matej.krpan@fer.hr

Faculty of Electrical Engineering and Computing
University of Zagreb
Zagreb, Croatia
Email: igor.kuzle@fer.hr

Abstract—In the recent years, frequency support fromconverter-connected wind power generation has been a hot topic
in the field of power system dynamics and control. At thesame time, the share of wind generation in the power systems
worldwide has significantly risen. Therefore, it is necessary
to update existing low-order system frequency response (SFR)
models of power systems. In this paper, an approach to low-order
SFR modelling of a power system with significant penetrationof wind power generation is proposed by taking into accountthe different operating regimes of variable-speed wind turbine
generators (VSWTGs). The results are compared to the nonlinear
transient stability dynamic models to show that the low-ordermodel adequately describes the nonlinear model. The proposedmodel can be used (e.g. by researchers, students or power systemengineers) to qualitatively simulate single-area power systemfrequency behaviour for different operating scenarios.

I. I NTRODUCTION
As the share of VSWTGs and other converter-connected
generation in the power systems worldwide is exponentially
increasing, coupled with the introduction of virtual inertia
and power electronics control for frequency support, theparadigm of what is a power system is changing. Low-order
SFR models provide a simple platform for studying powersystem frequency changes by taking into account only themost significant system dynamics in the time scale of interest
which is ≤ 30 s. Thus, it is necessary to step towards new
low-order model of a power system by taking into accountthe impact of converter-connected generation with frequency
support capabilities on the system frequency behaviour. In
this paper, the discussion on different operating regimes of
VSWTGs is presented alongside with how those regimes arereflected to the SFR model of a power system through the
low-order models of VSWTGs. An approach to linearizing
VSWTGs by taking into account different operating regimes
is presented.
II. S UMMARY OF RESULTS•

Zone I: low wind speeds, VSWTGs do not participate infrequency support, system inertia reduction;

Fig. 1. Operating zones of a VSWTG (a); comparison of SFR model tononlinear model for zones II/III (b) and IV (c)Zone II/III: medium wind speeds, VSWTGs can participate in frequency support, rotor speed control only;• Zone IV: high wind speeds, VSWTGs can participate infrequency support, pitch angle control active.
•

A. Key equations
Low order-models for zones II/III (1) and IV (2),(3):N (s)2Hω0 s − (kdel ω02 + a1 ω0 )=.
D(s)2Hω0 s − ω0 (a1 − 2kdel ω0 )N (s) = 2HTs s3 + (2H − a11 Ts −Te,0Ts )s2 −ω0Te,0)s − a13 Ki ,
ω0D(s) = 2HTs s3 + (2H − a11 Ts )s2 −− (a11 + a13 Kp )s − a13 Ki .

(1)

(2)

− (a11 + a13 Kp +(3)

Oscillation Monitoring and Controlof the RTE Power System
Using Synchrophasors
M.farrokhifard, V. “Mani” Venkatasubramanian
School of Electrical Engineering and Computer Science
Washington State University
Pullman, WA
M.maddipourfarrokhi@wsu.edu@wsu.edu
Abstract— This poster will carry out a study of modes’
characteristics by available PMU data in RTE portion ofEuropean power grid. Frequency Domain Decomposition (FDD)
and Stochastic Subspace Identification (SSI) methods are used
for modes identification. Analysis of oscillation phenomena in the
RTE portion of the European power grid by Implementation of
WSU oscillation analysis tools is another aim of this poster. The
effectiveness of the oscillation algorithms by using simulated
PMU data from dynamic models of the RTE system wherein the
expected answers are known from small-signal analysis of the
dynamic models is tested. Furthermore, appropriate control
algorithms will be proposed in collaboration with RTE to address
the oscillation issues in RTE system.

1

5

6

7

8

9

10

11

3

G1

G4
L9

Area 1

L7

4

2

G2

Area 2

G3

Fig. 1. Kundur two area test system

Keywords—
Frequency
DomainDecomposition,
Synchrophasor
Measurement,
Stochastic
Subspace
Identification, Power System Oscillation

I. INTRODUCTION
Recent advances in design of fast oscillation monitoringalgorithms have paved the way for real-time detection andanalysis of electromechanical oscillations from wide-area
synchrophasor measurements in large power interconnections.
The oscillations if left unmitigated can lead to unwanted
tripping of transmission lines and generators that could
cascade into devastating blackouts. Oscillation monitoringalgorithms developed at Washington State University havepreviously been implemented and tested in North American
power grid and in India. In this poster, we will study
oscillation phenomena in the RTE portion of the European
power grid by using available synchrophasor data. Suitabilityof ambient versus ringdown analysis algorithms for analyzing
recent oscillation events in RTE will be investigated.
Oscillation analysis results using transmission level PMUs
will be compared with corresponding results using distribution
level Phasor Measurement Units (PMUs). The effectiveness ofthe oscillation algorithms will be tested and improved byusing simulated PMU data from dynamic models of the RTE
system wherein the expected answers are known from smallsignal analysis of the dynamic models. New oscillation
analysis and control algorithms will be developed incollaboration with RTE to address the oscillation issues in
RTE.

Fig. 2. A view of WSU DMO analyzing simulated PMU data

II. KEY RESULTS
Since RTE PMU data and results cannot be shown now inthis abstract, we show a case study in which PMU simulated
data of Kundur two area test system, shown in Fig. 1, is
imported in WSU Damping Monitor Offline (DMO) tool. Thissystem has an inter area mode with frequency of 0.56 Hz anddamping ratio of 4 percent and a local mode with frequency of
1.02 Hz and damping ratio of 1.5 percent, as shown in Fig. 2.
REFERENCES
[1]

[2]

Guoping Liu, J. Quintero and V. M. Venkatasubramanian, "Oscillation
monitoring system based on wide area synchrophasors in powersystems," 2007 iREP Symposium - Bulk Power System Dynamics andControl - VII. Revitalizing Operational Reliability, Charleston, SC,
2007, pp. 1-13
S. A. Nezam Sarmadi, and V. Venkatasubramanian, “Electromechanicalmode estimation using recursive adaptive Stochastic Subspace
Identification”, IEEE Trans. Power Syst., vol.29, pp. 349–358, Jan.
2014.

Adaptive Adjustment of Noise Covariance in Kalman
Filter for Dynamic State Estimation
Shahrokh Akhlaghi, Student Member, IEEE, Ning Zhou, Senior Member, IEEE, Zhenyu Huang, Senior Member, IEEE
Abstract—It is well known that the covariance matrixes of process
noise (Q) and measurement noise (R) have a significant impact on
the Kalman filter’s performance in estimating dynamic states. To
address this problem, this paper proposes an adaptive filtering
approach to adaptively estimate Q and R based on innovation andresidual to improve the dynamic state estimation accuracy of theextended Kalman filter (EKF). It is shown through the simulationon the two-area model that the proposed estimation method ismore robust against the initial errors in Q and R than theconventional method in estimating the dynamic states of asynchronous machine.

I. INTRODUCTION
Timely and accurately estimating the dynamic states of asynchronous machine (e.g., rotor angle and rotor speed) is
important for monitoring and controlling the transient stabilityof a power system over wide areas [1]. [2] proposed theunscented Kalman filtering to estimate power system dynamic
states. Akhlaghi, Zhou and Huang [3] proposed an adaptive
interpolation approach to mitigate the impact of non-linearity indynamic state estimation (DSE). These studies have laid a solid
ground for estimating the dynamic states of a power system andalso revealed some needs for further studies.

AEKF Approach

II. DYNAMIC STATE ESTIMATION MODEL

A. Adaptive Extended Kalman Filter (AEKF)
1
1T
Sk  E  k  kT   E  k kT   H k  Pk H k 

Rk  E  k  kT   H k  Pk H k 
1

1T

(1.a)
(1.b)
(1.c)
(1.d)

1) Innovation Based Adaptive estimation of Q

Qk   Qk 1  (1   )( Kk dk dkT KkT )Priori Covariance Matrixxˆk  ( xˆk1 , uk 1 )Pk  k  1 Pk1k  1  Qk 1

Innovation

Measurement Covariance Updated k   zk  hk ( xˆk ) 

Rk   Rk 1  (1   )( k  kT  H k  Pk H k  )1

1TCorrection

Kalman Gainxˆk  xˆk  K k d k

K k  Pk H k   H k  Pk H k   Rk 

Residual k   zk  hk ( xˆk ) 

Fig. 1.

1

1T

1

1T

1T

k=k+1

Step II

1

State Covariance UpdatePk  I  K k H k  Pk , Qk   Qk 1  (1   )( K k d k d kT K kT )1

Implementation flowchart of the proposed AEKF

TABLE I. MSES OF THE ESTIMATED POSITION FROM THE CEKF
0.01 Qtrue
0.1 Qtrue
10 Qtrue
100 Qtrue
MSE
Qtrue
0.051
0.083
0.09840.0987
0.09880.01 Rtrue
0.219
0.051
0.083
0.09840.09880.1 Rtrue
3.54
0.219
0.051
0.083
0.098
Rtrue
27.28
3.54
0.219
0.051
0.083
10 Rtrue
41.40
27.28
3.54
0.219
0.051
100 Rtrue
TABLE II. MSE OF THE ESTIMATED POSITION FROM THE AEKF
0.01 Qtrue
0.1 Qtrue
10 Qtrue 100 Qtrue
MSE
Qtrue
0.07140.07870.07880.07880.07890.01 Rtrue
0.09
0.076
0.07830.07860.07870.1 Rtrue
0.12
0.089
0.072
0.073
0.0736
Rtrue
0.13
0.089
0.087
0.076
0.076
10 Rtrue
0.17
0.089
0.081
0.078
0.074
100 Rtrue

Fig. 2. Comparison of AEKF and CEKF when the initial Q is set to berelatively less than the proper value.
[1]
[2]

[4]
[5]

(3)

P0 , Q0 , R0

Step I[3](2)

State Error Covariance Initializationx̂0State Prediction

One important problem that needs to be addressed in using
the KF is how to properly set up the covariance matrixes ofprocess noise (i.e., Q) and measurement noise (i.e., R). Note thatthe performance of the KF is highly affected by Q and R [5].
Improper choice of Q and R may significantly degrade the KF’s
performance and even make the filter diverge. To address thischallenge, this paper proposes an estimation approach toadaptively adjust Q and R at each step of the EKF to improve
DSE accuracy. An innovation-based method is used toadaptively adjust Q. A residual-based method is used toadaptively adjust the R. A simple example is used to evaluate
the impact of Q and R on the performance of EKF. Then,
performance of the proposed approach is evaluated using a twoarea model [1].
 d
 dt  0 r d r  1 T  T  K 
 m e D r
 dt
2
H

 deq
1
 E fd  eq  ( xd  xd )id  dt Td0
 de
1
d


 ed  ( xq  xq )iq  dt Tq0

State Initialization

REFERENCES
P. Kundur, Power System Stability and Control. New York, NY, USA:
McGraw-Hill, 1994.
H. G. Aghamolki, Z. Miao, L. Fan, W. Jiang, D. Manjure, “Identificationof synchronous generator model with frequency control using unscented
Kalman filter,” Electric Power Systems Research, vol. 126, pp. 45-55,
Sep. 2015.
S. Akhlaghi, N. Zhou, Z. Huang, “A Multi-Step Adaptive Interpolation
Approach to Mitigating the Impact of Nonlinearity on Dynamic State
Estimation,” in Proc. IEEE Transaction on Smart Grid.
N. Zhou, D. Meng, Z. Huang, G. Welch, “Dynamic state estimation using
PMU Data: a comparative study,” IEEE Trans. Smart Grid, vol. 6, no. 1,
pp. 450-460, Jan. 2015.
A. H. Mohamed and K. P. Schwarz, “Adaptive Kalman filtering f or
INS/GPS,” J. Geodesy, vol. 73, no. 4, pp. 193-203, 1999.

Probabilistic Transient Stability Assessment ofRenewable Generation Integrated Power System
Yingying Wang, Student Member, IEEE, Mojdeh Khorsand, Member, IEEE, and Vijay Vital, Fellow, IEEE
School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA
ywang935@asu.edu, Mojdeh.Khorsand@asu.edu, and Vijay.Vittal@asu.edu
Abstract—The stochastic characteristic of wind power has
introduced challenges to traditional deterministic dynamic
security assessment of power systems. A probabilistic approachbased on Monte-Carlo simulation (MCS) is proposed in thiswork to integrate wind power uncertainty and variability intotransient stability analysis of power grids. Fault probability
including fault rate, fault type and fault location are taken into
consideration in the transient stability assessment. Systemstability is evaluated in term of loss of load to maintain stability
after the disturbance and its probability. A synthetic 36-bus test
system is utilized to verify the proposed approach. The
simulation results show the effectiveness of the proposed
approach for incorporating wind power uncertainty andvariability into transient stability assessment, as well as
identifying critical contingencies that affect system stability.
Index Terms-- Monte Carlo simulation, wind power uncertainty
and variability, transient stability.

I.

INTRODUCTION

Integration of renewable resources introduces newcomplexities to the stability assessment of power systems. The
main sources of the complexity stem from the uncertain natureof the renewable sources like wind and solar generation, theequipment failure characteristics and the altered inertia in thesystem due to the power electronics interface to the grid. It is
recognized that the traditional deterministic methods toincorporate the increased sources of uncertainty are notadequate and hence comprehensive evaluation methods need
to be developed. Moreover, deterministic approaches may not
be capable of considering factors like the variability of energy
resources and changing equipment characteristics due toincreasing use of power electronics. To integrate new
characteristics brought by connected wind power, probabilistic
stability assessment methods are needed.
II.

Fig. 1 Probabilitstic stability assesement procedure

Based on the stochastic model, wind turbine output scenarios
can be generated as sequential processes. Some initial
simulation results are given in Fig. 2 and Fig.3.

Fig. 2 Stochastic wind power output

METHODOLOGY, MODEL AND INITIAL RESULTS

As a probabilistic approach, Monte-Carlo simulation is widelyused as the method of selecting system states. It is the mostwidely application in power systems is generation adequacy
assessment. Monte-Carlo simulation includes non-sequential
MCS via states sampling and sequential MCS based on states
duration time sampling.
In this work, sequential MCS is used to generate thechronological status of wind power, while non-sequential
MCS method is used for contingency selection. The overall
approach is show in Fig. 1.

Fig. 3 System risk based on time domain simulation resultsDecreasing the Ice Storm Risk on Power Conductors
by DSRs and Dispatch Adjustment
Sahar Lashkarbolooki, Anil Pahwa

Al Tamimi, Ryan Yokley

Department of Electrical and Computer Engineering
Kansas State University
Manhattan, Kansas
lashkarb@ksu.edu, pahwa@ksu.edu

Sunflower Electric Power Corporation
Hays, Kansas
atamimi@sunflower.net, ryokley@sunflower.net

Abstract— This poster presents an approach for leveraging
distributed series reactance (DSR) and generation dispatch
adjustment to prevent ice buildup on power conductors during
ice storm. DSRs are deployed to increase current on at-risk lines
by blocking the power flow on parallel routes. Also, shifting someof the wind turbine generations to non-wind ones during thestorm reduces the risk of huge power flow fluctuation on powerlines which occurs due to wind turbine shutoffs under heavy
winds. These approaches are implemented hourly based on thesensitivity of power flow on the at-risk lines with respect toimpedance changes due to DSRs and generation dispatch
adjustment. The dispatch adjustment could be limited by nonwind generation ramp rates and the wind generations capacity
during storm. In addition, the DSRs can be limited by voltage
stability issues and their technical specifications. Leveraging bothmethods simultaneously is helpful to cover their limitations todetermine a suitable solution.

f mn
 n  1
1   m  2  m   n 
X mn X mn  X mn X mn  X mn

B. Sensitivity calculation with respect to dispatch adjustments
 0  1  0 
 P 
0

0
 m


0
0


 Pm 
 
 



 1  m 
 1
0



 
 
,
 B     P  , P  
1  0 
0      B 1  0 ,   B    P     0 0 Pm   m 
 
 



1


n
 0 
 
 1




0
0


 
 
 0 
 0 
 0  N
 Pm 

f12 

Keywords—DSRs; dispatch; sensitivity; ice-storm;

I. KEY EQUATIONS
A. Sensitivity calculation with respect to impedance changes:
DC power flow:
B1N   1   P1 
 B11 B12B
B2 N    2   P2  21 B22      
BNN   N   PN  BN 1 BN 2
B111  B12 2   B1m m  B1n n   B1N  N  P1

Bk11  Bk 2 2 

 B11
B
 21


 Bm1
 Bn1


B
 N1

 Bkm m  Bkn n 

B12
B22
Bm 2
Bn 2
BN 2

Bmm
Bnn

Bmn
Bnm

(3)

1   2

f12
1 1  2()
Pm X 12 Pm Pm
ff new  f old 
* Pm
Pm

X 12



(4)

(5)
(6)

C. Net effect of DSRs by sensitivity calculation
summ 

f 203f
f
f* xm  220 * xm  227 * xm  229 * xm
xm
xm
xm
xm

(7)

D. Net effect of dispatch adjustments by sensitivity calculation(1)

summ 

f 203f
f
f* Pm  220 * Pm  227 * Pm  229 * Pm
Pm
Pm
Pm
Pm

(8)

 BkN  N  Pk

 1  X 
 mn  0  

B1N   2  
0


X mn



B2 N 



 


1
  1
   m   2  m  2  n 
X mn
BmN 
 X
 X mn   mn

BnN  1
  1
   n   2  n  2  m X mn
  X   X mnmn
 
BNN  


 

0

    N X mn (2)

Fig. 1. Power flow results.The solid lines are the initial power flows and thedashed lines are updated ones in presence of DSRs and dispatch adjustment.

Fractional Order PI Control for Microgrid
Application
Rasheed Abdulkader
Electrical Engineering Dept.
University of Arkansas
Fayetteville, AR USA
rabdulka@uark.edu

Roy McCannElectrical Engineering Dept.
University of Arkansas
Fayetteville, AR USA
rmccann@uark.edu

Abstract— The increasing use of distributed energy resources
has motivated the development of microgrid structures toimprove reliability of electric power distribution systems.
However, as microgrids become more complex, there is anincreased occurrence for instabilities. This research investigates
fractional order proportional--derivative (FOPI) control
technique which is implemented in a decentralized manner tostabilize the voltage levels throughout a microgrid structure;
hence, the algorithm would be synchronized into a grid systemforming a smart grid. A method for analyzing three parallelconnected inverters based upon distributed generation ispresented. To validate the proposed control algorithm, Matlab
simulation is used and FOPI is compared to conventional control
methods such as PI and PID. The proposed control algorithm
demonstrates an improved performance response to regulate thesystem analyzed.

performance of the control loop. The system analyzed and thetransfer function is derived for each source based on [4].

Keywords—Microgrid, FOPID, transient stability, distributed
generation.

I.

INTRODUCTION

In recent years, the deployment of renewable resources atthe distribution level has acquired an increasing attention toutilities around the world. Thus, it can be a promising solution
to the issue of increasing fossil fuel cost. While the formationof microgrids from distributed generation systems plays acritical role in efficient utilization of energy, resources and cancontribute heavily to the stability of the public power grid [1].
This trend has many advantages and numerous distributed
generation poses many challenges that should be eliminated.
Thus, regulation and stability of the system is of a great
concern and it is the objective of this proposal.
While different topologies are being proposed forinterfacing microgrids with the national grid, still the use ofinterconnected power electronic converts can make instabilityof the power flow in a system [2]. Hence integrating
distributed generators into the power grid might introduces abidirectional power flow with the resulting system whichchanges the whole power flow pattern of the existing system
and generates concern for its stability [3].
In this paper, the problem of FOPI controller design
strategy is examined, consequently the proposed algorithm [allows the integration of the fractional order scheme into
existing PI/PID control loop which would result in enhanced

Factional order control has acquired an increasing attention
recently in the control community due to their better control
performance when compared to traditional integer ordercontrollers Fractional order calculus dates to three centuries
back, hence it would provide a novel modeling approach forsystems with extraordinary dynamical properties byintroducing a non- integer fractional order system [5]. While
there is a variety of control regulation methods applied tomicrogrids such PI/PID control for their simplicity, whereas
they lack robust voltage regulation and have limited
capabilities when applied to complex systems [6].
RESULTSTo achieve preliminary results a fractional FOPID
controller is applied using Matlab by evaluating a step
response to the system. Simulation results demonstrates
improved that FOPI leads to improved stability margins andsystem performance as compared to classical PI.

Fig1. Step response PID/FOPIDREFRENCES[1]X. Tang, W. Deng, and Z. Qi, “Investigation of the
Dynamic stability of microgrid,” IEEE Trans. Power Syst.,
vol. 29,no.2, pp. 698−706, Mar. 2014.

[2] M. Reza, D. Sudarmadi, F.A. Viawan, W.L. Kling,and L. Van Der Sluis, "Dynamic Stability of Power
Systems with Power Electronic Interfaced DG," in Power
Systems Conference and Exposition, 2006, Atlanta, 2006,
pp. 1423−1428.
[3] A. B. M. Nasiruzzaman and H. R. Pota, “A new model ofcentrality measure based on bidirectional power flow forsmart and bulk power transmission grid” in EEEIC 2012.,
May. 2012, pp.1−6.
[4 M. Naderi, Y. Khayat, Y. Batmani, H. Bevrani,” Robust
Multivariable Microrid Control Synthesis and Analysis”, In

3rd International conference on power and Energy
Systems Engineering, CPESE 2016, 8-12 September
2016, Kitakyushu, Japan.
[5] Tepljakov, Aleksei . Fractional-order Modeling andControl ofmDynamic Systems. Springer International
Publishing AG 2017.
[6] M. Mahmoud, N. Alyazidi, M. Abouheaf, Adaptive
intelligent techniques for microgrid control systems: A
survey, In Electrical Power and Energy Systems
Volume 90, March 2017, 292-305.

Parameter Space and Rotor Angle Stability Control of
Virtual Synchronous Machine
Chen Qi, Student Member, IEEE, Keyou Wang, Member, IEEE and Guojie Li, Senior Member, IEEE
MOE Key Lab of Power Transmission and Power Conversion and Control
Shanghai Jiao Tong University
Shanghai, China 200240
Emails: ee.qichen@sjtu.edu.cn; wangkeyou@sjtu.edu.cn; liguojie@sjtu.edu.cn

Abstract—The virtual synchronous machine (VSM) technology isable to control the inverter to mimic the behaviors of the traditional
synchronous generator. In this paper, the rotor angle stability of VSM
is thoroughly analyzed under the classic second-order dynamic modelin a single machine (inverter) infinite bus system. Possible situations
covering the global parameter space are presented, analyzed and verified through simulations. Furthermore, the idea of global bifurcation
structure shaping (GBSS) control is proposed, which can eliminate
the attractor that causes the rotor angle instability under emergency
and thus guarantee the stability.

I. S YSTEM M ODEL

8

2
6

(2)

0

SEP solution
-1

-2
5

0

0

20

5HJLRQ



D



0.9

1.2

1.5

1.8

The GBSS control is proposed to actively move the operating
point of the VSM into the region 3 during emergency, which
eliminates the periodic solution that causes the instability andshapes the system trajectories to keep the stability. The control
diagram and the simulation result are shown in Fig. 3, where theunstable scenario in Fig. 2(a) can be controlled to be stable with
the GBSS technique.
2SHUDWLRQVWDWXV
GHWHUPLQH

Vmea


0.6

Fig. 2. System state trajectories (δ, ∆ω) when the VSM operates in (a) region 2(may lose stability) and (b) region 3 (always keeps stability).

5HJLRQ



0.3

(b)



Fig. 1. Parameter space of the normalized VSM dynamic model.

According to Fig. 1, the system stability properties can be
expressed as:1) Region1: when β > 1, there is only one attractor for thesystem in the cylinder coordinate, which is the periodic
solution. All system trajectories are attracted by this periodic
solution. Therefore, the system definitely loses stability.
2) Region2: when β < 1 and α < α∗ (β), there are two
attractors for the system in the cylinder coordinate, which
are the SEP solution and the periodic solution. Systemtrajectories are attracted be either one of the attractors. Whenthe trajectory is attracted by the SEP solution, the system isstable, otherwise the system loses stability.
3) Region3: when β < 1 and α > α∗ (β), there is only
one attractor in the cylinder coordinate, which is the SEP
solution. All system trajectories are attracted by this SEP
solution. Therefore, the system is always stable.

gPWM

Dn

(PHUJHQF\
RSHUDWLRQDe

9ROWDJH
FRQWURO

G

Z

VLQ

3:0

1RUPDORSHUDWLRQ

E

V





15

(a)




10

IV. GBSS C ONTROL

5HJLRQ

SEP solution

1

4

D
'Z

P

JZs

V



E 

Periodic solution(1)

For the studied system, a parameter space is proposed, whichis shown in Fig. 1.



3

10

0

dδ
= z
dτ
dz
= −αz − sin δ + βdτII. PARAMETER S PACE

The proposed parameter space is validated using the VSM
simulations performed in Matlab/Simulink. The dynamic behaviorsof the system in region 2 and 3 are shown in Fig. 2 respectively.

2

The VSM dynamics are analyzed based on its normalized
second-order model, which can be expressed asIII. VSM VALIDATION

Zs

Pset

(a)
10

16
14
12
10
8
6
4
2
0
3

6

4

5

6

7

8

9

10

(b)
Fig. 3. GBSS control diagram and the simulation result.

Design of a Supercapacitor Energy Storage System for Marine Turbines
Utilizing Advanced Control
1.

Hannah Aaronson1, Brian Polagye1, Robert Cavagnaro2
Department of Mechanical Engineering, University of Washington
2. Advanced Physics Laboratory, University of Washington
Seattle, WA, USA
haarons@uw.edu

Abstract— Previous studies optimizing control strategies forcross-flow (i.e., “vertical axis”) marine turbines found thatsinusoidally varying rotor speed within a single rotation can
increase mechanical power output by up to 50%. However,this control strategy, known as intracycle control, also comeswith a cost: power output is estimated to fluctuate between
producing 22 kW and drawing 15 kW twice per two second
rotation of a two-bladed turbine. This power quality is
incompatible with direct use. Here, we describe an energy
storage system that smooths the power output from a peak to
average ratio of 8.8 to 1.4 without introducing energy losses
that offset the efficiency gains from this control strategy.
Keywords— Marine turbines; marine renewable energy; energy
storage system, supercapacitors, simulation

II. SIMULATION RESULTS
The proposed system is modeled in Matlab Simulink, andrepresentative results are shown in Fig. 2. The original power
delivered to the grid is in blue, highlighting the large powerfluctuations over a short time period. After adding ESS Part I,
turbine power draw from the grid is eliminated. When both ESSPart I and ESS Part II are used, the peak to average ratio of thepower delivered to the grid is reduced from 8.8 to 1.4. The ESS
introduces energy losses between 6 to 8%, due to the internal
resistance of the capacitors and switches. The ESS appears tosmooth power to an acceptable degree and is feasible toimplement on a turbine in terms of its complexity, size, andefficiency. The next step of this research project is to validate thesimulation results experimentally, using lab-scale hardware.

I. SYSTEM DESIGN
The design objectives of the proposed energy storage system(ESS) are to eliminate power draw from the grid, minimize thepeak to average ratio of the output power, and minimize energy
losses internal to the ESS. The proposed ESS has two parts, as
highlighted on the DC bus between the turbine and grid in Fig. 1.
Part I, inspired by research on regenerative braking for electricvehicles, uses a supercapacitor to eliminate the need for theturbine to draw from the grid. This power draw normally occurs
when the turbine is commanded to accelerate during some phasesof intracycle control. The capacitor charges when the turbine
produces power (and vice versa), with excess power available to
the grid once the capacitor is fully charged. Part II includes asupercapacitor and a bi-directional DC-DC converter. This uses a
PI controller to maintain constant power delivery to the grid byrequiring the supercapacitor to charge when instantaneous poweris greater than the average power per cycle, and discharge when
it is less than the average power per cycle.

Fig. 2. Simulation results of power delivered to thegrid using the energy storage system.

Fig. 1. Simplified simulation layout of the proposed two-part energy storage system, with Part I highlighted in red and Part II highlighted in blue.
.

Fault Ride Through Analysis of Grid Connected
Doubly Fed Induction Generator Based Wind System
M V Gururaj, Student Member IEEE, Narayana Prasad Padhy, Senior Member IEEE
Department of Electrical Engineering, Indian Institute of Technology Roorkee, India

In this paper, the theoretical analysis of the DFIG based windsystem during the fault is presented. Further, in order to study
the practical behavior of the DFIG system, a 2.2 KW rated
DFIG machine is connected to the programmable voltage
source. The performance of the system is analyzed during
normal and fault operation. During normal operating
conditions the machine is controlled to exchange power withthe grid at UPF with the help of RSC. A three phase to ground
fault is introduced for a duration of 150ms to test therobustness/stability of the controller. The experimental results
are in match with the theoretical analysis during FRT. This
study helps to develop fault ride through improvement
techniques for the DFIG system in future.

Power (kW)Grid Voltages (V)Grid current (A)INTRODUCTION

(c)

(d)

DC link Voltage (V)

I.

(b)

(a)

Grid Voltage (V) &current (A)Index Terms-- DFIG, fault Ride Through, Grid side converter(GSC), Rotor side converter (RSC), Wind energy.

III. KEY HARDWARE RESULTS(100V/div)(e)

Fig 1: Normal operation: (a) Grid voltages (b) power flow to the grid (c) Grid
voltage, current of phase A. (d) Three phase grid currents (e) DC link voltage.
200

StatorVoltages (V)Abstract— The world is slowly moving towards renewable based
power generation to avoid the demerits associated with theconventional generation mainly global warming. Further, theincreased depletion rate of fossil fuels over the years has enhanced
the research investments on the renewables like photovoltaic(PV), Wind etc. Among variable speed wind generators, doubly
fed induction generators (DFIG) based wind plants has gained
more popularity over their counterparts. In this paper, theoperation of DFIG based wind system in the grid connected modeis studied along with fault analysis.

200
0

0
-200

-200

Stator flux (wb)

t1

(a)

t1

2
1.5

1.5
1

1
0.5

0.5
(b)

50

RSC currents (A)When fault occurs, the grid voltages drops. The voltage of the
DFIG stator (𝑣𝑠𝑠 ) before and after fault is given below.
𝑉𝑝𝑟𝑒 𝑒 𝑗𝜔𝑠𝑡 ,𝑡<0(1)
𝑣𝑠𝑠 = {𝑗𝜔𝑠 𝑡
(1 − 𝑝)𝑉𝑝𝑟𝑒 𝑒,𝑡≥0
The expression of the stator flux during fault conditions isgiven below.
𝑝𝑉𝑝𝑟𝑒 −𝑡/𝜏
(1 − 𝑝)𝑉𝑝𝑟𝑒 𝑗𝜔 𝑡
(2)
𝑠 +
𝜓𝑠𝑠 =
𝑒
𝑒 𝑠
𝑗𝜔𝑠
𝑗𝜔𝑠
Further, each component of the flux induces emf in the rotor,
𝑒𝑟𝑟 as shown below.
𝑡
𝐿𝑚
−
𝑒𝑟𝑟 =
𝑉𝑝𝑟𝑒 (𝑠(1 − 𝑝)𝑒 𝑗𝜔𝑟 𝑡 − (1 − 𝑠)𝑝𝑒 −𝑗𝜔𝑚 𝑡 𝑒 𝜏𝑠 ) (3)
𝐿𝑠

t0

t0

2

50
0

0
-50

-50
(c)

870

Speed (rpm)II. FAULT ANALYSIS OF DFIG

870
860

860
850

850
840

840
7.4

7.5

7.6

7.7

7.8

(d)
Time (s)Fig 2: Experimental results during fault (a) Stator voltages (b) stator flux (c)RSC currents. (d) Rotor speed in rpm

Design and Performance Analysis of Low Cost
Acoustic Chamber for Electric Machines
R. M. Pindoriya, Student Member, IEEE, B. S. Rajpurohit, Senior Member, IEEE and R. Kumar
Indian Institute of Technology Mandi, Himachal Pradesh, India
pindoriya_rajesh@students.iitmandi.ac.in
Abstract—This work discusses the design, and performance
analysis of low cost acoustic chamber for measurement of acoustic
noise and vibration of electric machines. The purpose of this paper
is to calculate acoustic transfer function, reverberation time, soundpower level and cut-off frequency. To obtain the transfer functionof the acoustic chamber, a pair of microphones and amplifier as asource of sound have been used. Noise measurements are
conducted in both an ordinary environment (laboratory) andacoustic chamber and validated through Inverse Squared Law.
The results obtained are found to be helpful in understanding thephysical features and characteristics of the acoustic chamber.

has been generated and recorded by two microphones i.e. oneplaced inside the AC and another outside of it.

Microphone-2

Amplifier as anoise source

I. INTRODUCTION
Nowadays consumers demand more smooth and silent
devices, whether it is a computer, vacuum cleaner, electrical
drives or washing machine. The free-field enclosure required totest electrical devices. The free-field enclosure may be anechoicCchamber, semi-anechoic chamber or acoustic chamber.
Organizations
likeInternational
Organization
forStandardization(ISO),
International
ElectrotechnicalCommission (IEC), IEEE etc. provide suitable guidelines
regarding to the construction and testing for the chamber [1].
An Acoustic Chamber (AC) has been designed andassembled at IIT Mandi, as shown in Figs. 1-2, to perform
suitable studies on vibration and acoustic noise of electricmachines/drives. Fig. 1 shows the schematic of AC with sensor
placements and broad dimensions. A wooden ply of thickness of
12 mm is used for making walls of AC with egg shape wedge
acoustic foam (density 32 kg/m3) attached at both sides withsound barrier sheet made up of polymer vinyl sheet (density 1.07
mg/m2).

Sound barrier sheet
Wooden ply thickness of 12 mm
thickness of 3 mm
Fig. 1. Schematic layout of acoustic chamber.
Microphone-2

Microphone-1 andvibration sensor(Inside chamber)

Inverter

DAQ

AmplifierPC

Acoustic chamber

Fig. 2. An experimental setup for characterize of acoustic chamber.

Transfer function plot of forward transfer function (𝐻𝑆 (𝑓)) ona semi-log scale is plotted in Fig. 3. It is found that the cut-off
frequency of AC is 40 Hz. Fig. 4 shows the noise spectrum
record by microphone-1, which is placed inside the AC, withhighest noise of around 1.2*e-3 Pa (approximate 40 dB).

The general expression for the Sound Pressure Level (SPL)
difference as a function of acoustic transfer functions can beobtained as [1]:∫∞𝐷 = 𝐿𝑆 − 𝐿𝑅 = 10𝑙𝑜𝑔 ⌈ −∞∞2

|𝐻𝑆 (𝑓)| 𝑑𝑓∫−∞|𝐻𝑅 (𝑓)|

2

𝑑𝑓

⌉

(1)

where LS and LR is the SPL measured by sensors in the source
room and in the receiving room (cabin) in dB, respectively.
𝐻𝑆 (𝑓) and 𝐻𝑅 (𝑓) are the forward and reverse acoustic transfer
function, respectively, between certain microphone and soundsource position in the source room and the receiving room,
respectively. An experimental setup for characterization andperformance analysis of AC is shown in Fig. 2. An amplified
sound signal of 60 dB and 6000 Hz from an external amplifierFig. 3. Bode plot of forward
transfer function 𝐻𝑆 (𝑓).

Fig. 4. Noise spectrum of acoustic
chamber.

So, based on above results we conclude that this AC is provide
free-field environment to measure acoustic noise and vibrationof electric machines.
REFERENCE[1]

A. C. Binojkumar, B. Saritha, and G. Narayanan, “Acoustic Noise
Characterization of Space-Vector Modulated Induction Motor Drives—
An Experimental Approach,” IEEE Trans. Ind. Electron., vol. 62, no. 6,
pp. 3362–3371, Jun. 2015.

Demagnetization Fault Detection and Analysis fora Permanent Magnet Synchronous Machine
Adil Usman, Student Member, IEEE and Bharat S. Rajpurohit, Senior Member, IEEE
School of Computing and Electrical Engineering
Indian Institute of Technology Mandi
Mandi, Himachal Pradesh, India
adilusman@ieee.org

Abstract— This poster will present the results of a

DEMAGNETIZATION RESULTS

demagnetization effect [1] [2] on the Permanent Magnet
Synchronous Machine (PMSM). A PMSM model for both for
healthy and faulty conditions [3] has been developed using a
Finite Element Modeling Method (FEMM) tool [4]. The
model of a faulty machine can be developed by changing one
or more parameters of the Permanent Magnet (PM) in therotor of a PMSM in order to incorporate a demagnetization
fault in the machine.
This paper compares and focuses on the demagnetization
fault in PMSM by changing two parameters of the Permanent
Magnet. One of which is changing the magnetic coercivity(HC) parameter of a PM while the other is changing the lengthof a PM which signifies a broken magnet, in the rotor of themachine. The change in coercivity parameter and magnet
length is used as the demagnetization factor which changes themachine parameters such as flux linkage and current in thecoil. Since currently our research is focused on observing only
the effect of change in magnetic flux density (BM) (which is
directly proportional to the back emf of the machine) against
the angular displacement of the rotor (ϴ) for a healthy andfaulty PMSM. Therefore any deviation observed in magnetic
flux density (BM) identifies the demagnetization fault in
PMSM. In order to know the cause of demagnetization which
could be either due to change in coercivity or change inmagnet dimensions (length), the simulation results obtained
distinguishes them from each other.
The results clearly detect the demagnetization effect in PMof a PMSM along with identifying the cause of it. Secondly
the change in coercivity (HC) which causes an equivalentchange in magnetic flux density (BM), validates the direct
relation of HC with BM in a liner magnetic circuit. This work is
an elaboration to the existing research work done on modelingdemagnetization faults in Permanent Magnet Direct Current(PMDC) Motors which is accepted for presentation in IEEEPES GM 2018.
Index Terms—Demagnetization Faults, Finite Element
Modeling Method (FEMM), Permanent Magnet DC Motor(PMDC), Permanent Magnet Synchronous Motor(PMSM).
REFERENCES
1.
2.
3.

4.

P. C. Krause, O. Wasynczuk, S. D. Sudhoff, Analysis of Electric
Machinery, New York:IEEE Press, 1994.
J. Faiz and H. Nejadi-Koti, "Demagnetization Fault Indexes in
Permanent Magnet Synchronous Motors—An Overview," in IEEE
Transactions on Magnetics, vol. 52, no. 4, pp. 1-11, April 2016.
J. Hong et al., "Detection and Classification of Rotor
Demagnetization and Eccentricity Faults for PM Synchronous
Motors," in IEEE Transactions on Industry Applications, vol. 48, no.
3, pp. 923-932, May-June 2012.
www.femm.info

Fig.1 Demagnetization effect of a PMSM due to change in coercivity factor.
Number of Poles=8, Pole Angle=2°, Hc healthy= 979000 A/m, k (coercivity
factor) =0.8, Hc faulty= 783200A/m

Fig.2 Demagnetization effect of a PMSM due to change in coercivity factor.
Number of Poles=8, Pole Angle=2°, Hc healthy= 979000 A/m, k (coercivity
factor) =0.5, Hc faulty= 489500A/m.

Fig.3 Demagnetization effect of a PMSM due to the change in magnet lengthof a PM in PMSM. Number of Poles=8, Pole Angle=2°, Hc healthy= 979000
A/m, k (coercivity factor) = 1
CONCLUSION

The flux density plots obtained from the FEMM modeling canbe further utilized in analyzing the performance of themachine.

1

Behavioral Device-Level Modeling of Modular
Multi-level Converters in Real-Time for Variable
Speed Drive Applications
Ning Lin, Dept. of Electrical and Computer Engineering
University of Alberta
Edmonton, Canada
ning3@ualberta.ca
Venkata Dinavahi, Dept. of Electrical and Computer Engineering
University of Alberta
Edmonton, Canada
dinavahi@ualberta.ca

Abstract
This paper presents the real-time hardware-in-the-loop (HIL) emulation of an induction machine (IM) driven by a modular
multi-level converter (MMC) on the field-programmable gate array (FPGA). The insulated gate bipolar transistors (IGBTs) andanti-parallel diodes of the MMC are modeled with nonlinear static and dynamic characteristics to provide not only accurate
system-level performance of the converter but also an insight into the power losses under different operation conditions. Dueto the large network size of the MMC, its solution in conjunction with the induction machine fifth-order model proved to be asignificant computational challenge. Therefore, circuit partitioning based on the transmission line modeling (TLM) is proposed,
which introduced an interface to the electrical network for the IM as well as split the multi-loop MMC into several smaller
sub-circuits in terms of matrix size, and consequently enabled a fully parallel implementation on the FPGA. Control strategies
for the MMC and IM are also emulated in hardware, and due to the large latency difference between sub-circuits and controllers,
the overall system hardware design is divided into several layers, each having an independent time-step ranging from 500 ns to
4 µs so as to attain the goal of real-time execution. Comparison of transient and steady-state results from the HIL emulation andoff-line simulation tools shows high accuracy of the modeling approach as well as the efficacy of proposed multiple time-steps
in achieving real-time.
Index Terms
Field-programmable gate array (FPGA), induction machine, modular multi-level converter (MMC), parallel processing, partitioning algorithms, real-time systems

Harmonic Mitigation and a Practical Study ofTorque Harmonics in Induction Motor Startup
Parviz Khaledian, Brian K Johnson, Saied Hemati
Dept. of Electrical and Computer Engineering,
University of Idaho
Moscow, USA

Abstract—In this paper, the nature of harmonics due to theoperation of nonlinear power system components, the effects ofharmonics and methods to eliminate the harmonics will be
discussed briefly. Therefore, it helps to compare the compensation
methods and choose the best one based on the application.
Harmonics are the major source power quality problems for
power systems. Therefore, harmonic mitigation techniques areinevitable in power systems and sometimes in the power
converters themselves. This paper reviews the common harmonic
mitigation techniques. Further, it includes a case study that
introduces a different approach to practically mitigate theunwanted harmonics in a 12000 hp synchronous motor that failed
to start up with a soft starter.
Index Terms—Active filter, harmonic mitigation, power
converter, power quality, torque harmonics.

I. THE PROPOSED METHOD IN THE CASE STUDY
We take three steps: describe the issue, determine its causes,and present a practical mitigation technique.
1: The problem: A 12000 hp synchronous motor failed to startup with a soft starter.
2: The causes: The SCRs elements of the starter introduce
harmonics to the system. Therefore, the resultant rotating flux
consists of the odd harmonics in addition to the fundamental
frequency. The fundamental wave revolves synchronously at
synchronous speed Ns, whereas 5th harmonics revolve with
Ns/5 backward and the 7th harmonics with Ns/7 in forward
direction. The harmonic torques result in vibrations, and noise.
This phenomenon is known as crawling. There are several
torque dips on the resultant motor torque T (Fig. 1).
If the torque dip is lower than the 𝑇𝑙𝑜𝑎𝑑 , the motor will nothave enough torque to start successfully. That is happeningaround 1/7 of the synchronous speed.

Fig. 2. Five series of startup current measurements for different SCR phases

Fig. 3. Five series of startup voltage measurements for different SCR phases

The procedure is as follows: (1) Initially, SCRs start at 60%
voltage. Then SCRs ramp up to full voltage in one second, where
the starter doesn’t contain a high degree of harmonic current. (2)The bypass contactor would then close approximately threeseconds later, bringing the motor across the line. (3) The SCRs
are off for about one second. (4) SCRs are back on at about 50%
voltage and ramp up. (5) The motor gets synchronized.
As soon as we pass the 1/7th of the synchronous speed theharmonic torques are not an issue for startup, because the torqueis more than the load torque.

Figure 1. Torque characteristics considering space harmonics.

3: Practical mitigation technique: In our case, the remedy is bymodifying the startup procedure.
The torque harmonics generated based on input voltage
harmonics can be distinguished as following:
𝑇6𝑘+1 = 𝐴6𝑘+1 cos⁡(6𝑘𝜔𝑠 𝑡 + 𝜃6𝑘+1 ) =′′𝛹𝑚(6𝑘+1) 𝐼𝑟1
cos⁡(6𝑘𝜔𝑠 𝑡)−𝛹𝑚1 𝐼𝑟(6𝑘+1)
sin⁡(6𝑘𝜔𝑠 𝑡)

To diminish the impact of 𝑇7 (𝑇6𝑘+1 for k=1) on the resultant′
torque T (Eq. 1), we decrease the 𝐼𝑟1. It is done by a new start
up procedure (Fig. 2 and 3). We have different SCR phases
ranging from 70% to 100%.

(1)

II. CONCLUSIONS
Various harmonic sources and several strategies to eliminate
or compensate their effects have been discussed. At the end, acase study with actual measurements and its practical remedy ispresented. By considering several alternative methods tomitigate harmonics, the decision will be based on the objectives
for facility power quality and the budget available.

Dynamic Modeling in OpenDSS:An Implementation Sequence for Object Pascal
Andrés Argüello, Weld L. Cunha, Tiago R. Ricciardi, Ricardo Torquato and Walmir Freitas

II. DYNAMIC S IMULATION IN O PEN DSS, S MALL H YDROG ENERATOR C ODING IN O BJECT PASCAL
The main contribution of this paper consists of a step-bystep procedure in Pascal to understand the use and implementation of user-written dlls in OpenDSS for dynamic analysis,
with examples of a fourth order dq synchronous machine anda non-linear second order hydro turbine based shaft model.
new generator.Gen bus1=b2 kVnom=13.8
˜ kVAnom=5000 UserModel=SyncGen model=6
˜ UserData=(kVnom=13.8 kVAnom=5000
Phases=3 H=3 D=1 Rs=0.001 Xd=1.5 Xld=0.2
Xq=1.5 Xlq=0.2 Tld0=3.0 Tlq0=0.0081
kWsp=0.2 efd=1.0) ShaftModel=HydroTurb
˜ ShaftData=(ref=0.2 Tw=3 T1=1)−1
U̇ = TW(H − H0 )Ġ = T1−1 (gref − g)−1

H = (U/G)2

TW = LUr (ag Hr )g = G (gF L − gN L )Pm = (U − UN L ) H

(1)

0.1

Active power (pu)

0.2
0.15
0.1
0.05

1.6
0.15

Excitation voltage (pu)The integration of more controllable distributed energy
resources such as energy storage and distributed generation
brings together the necessity of specialized studies at MV
and LV level, such as dynamic performance evaluation. While
dynamics study at the transmission level well consolidated in
power engineering, it presents novel challenges in distribution.
OpenDSS is a script-driven simulation engine for distribution networks, widely used for power flow analysis, harmonic
power flows and short circuit studies, yet it is not limited
to these features since EPRI encourages users to contributeto its continuous development. A characteristic of OpenDSS
that remains relatively unexplored is its capability to run
dynamic simulations. Nevertheless, it only includes a classic
synchronous machine model in the generator element to study
its swings during disturbances and an induction machine.

Gate opening reducing
from 0.2 to 0.1 pu
0.15

0.2

1.4

Excitation voltage increasingfrom 1.0 to 1.5 pu

1.2
0.1
1

0.4
0.05

Reactive power (pu)

I. I NTRODUCTION

0.2

0.25
0.25

Terminal voltage (pu)

Abstract—This paper presents a procedure to implement userwritten models in OpenDSS for dynamic simulations using its
predefined generator element and the software’s original sourcecode in Object Pascal. The implications on the coding of a
dynamic model, the internal structure and the solving algorithm
sequence are addressed in order to guide the user through suchprocess. The aforementioned is illustrated by implementing afourth order synchronous machine as generator model and asecond order hydro turbine as shaft model.
Index Terms—Distribution networks, Dynamic modeling, Hydraulic turbine, Object Pascal, OpenDSS, Synchronous machine,
User-written model

Gate opening (pu)Department of Systems and Energy, University of Campinas, Campinas, SP, Brazil
andresag@dsee.fee.unicamp.br

0.2
0
-0.2

1.015
1.01
1.005
1
0.995
10

15

20

25

30

35

40

45

Time (s)Fig. 1. Model response to turbine gate and excitation voltage steps.

ω̇ = (2H)−1 (Tm − Tel − D (ω − 1)) 
0 −1
e˙0d = (Tq0)−eld + Xq − Xq0 iq
0 −1
e˙0q = (Td0)
ef d − e0q − (Xd − Xd0 ) id
Tm = Pm /ωTel = e0d id + e0q iq + Xq0 − Xd0 id iq!
!
!
e0d − vdRs Xq0id
1=(2)e0q − vqiq
Rs 2 + Xd0 Xq0 −Xd0 Rsδ̇ = ω0 (ω − 1)

III. C ONCLUSIONS
The authors are currently working in the implementationof a Python-based platform and a dynamic model library,with storage systems, static converters, protection and control
devices, prime sources (wind turbines and PV panels), andhigher order machines. These models are expected to widen
the analysis capabilities of OpenDSS, as an additional tool for
educational, research and industry purposes.

Analyzing Mutual Influences of Conventional andDistributed FACTS via Stochastic Co-optimization
Yuanrui Sang and Mostafa Sahraei-Ardakani

Department of Electrical and Computer Engineering
University of Utah
Salt Lake City, USA{yuanrui.sang, mostafa.ardakani}@utah.edu
Abstract—Distributed flexible AC transmission systems (DFACTS) is an attractive power flow control technology, featuring
low cost and flexibility for re-deployment. Optimal allocation of
D-FACTS and the mutual influence between existing FACTS
and newly planned D-FACTS are challenging but important
issues that need to be addressed. This paper proposes a cooptimization model of FACTS and D-FACTS based on stochastic
optimization, considering the uncertainties caused by fluctuatingload and renewable energy generation. Using this model, thelocation and set points of FACTS and D-FACTS can be cooptimized; in a system with existing FACTS, the locations of
FACTS can be pre-determined and the locations of D-FACTS
can be optimized. The study shows that existing FACTS affects
the optimal locations of D-FACTS and adding D-FACTS into thesystem affects the optimal set points of existing FACTS. Thus, it
is essential to co-optimize the two technologies to maximize theireconomic benefits.
Keywords—distributed flexible AC transmission systems (DFACTS), flexible AC transmission systems (FACTS), optimal
allocation, power system economics, stochastic optimization

I.

II.

KEY RESULTSTABLE I. EXPECTED DISPATCH AND WIND CURTAILMENT SAVINGS

FACTS
locations

22
22,23
19,22,23

Maximum
invest
-ment
($/h)
10
20
30
10
20
30
10
20
30

D-FACTS not cooptimized with
FACTS
Expected
Expectedwind
dispatchcurtailcost
mentsavingssavings
4.07%
23.17%
4.57%
24.42%
4.73%
24.93%
5.31%
30.21%
5.78%
29.84%
5.97%
31.53%
5.91%
31.00%
6.24%
32.83%
6.55%
33.74%

D-FACTS cooptimized with
FACTS
Expected
Expectedwind
dispatchcurtailcost
mentsavingssavings
4.01%
21.63%
4.57%
24.42%
5.20%
27.92%
5.31%
30.21%
5.78%
29.84%
6.16%
32.07%
5.94%
34.21%
6.26%
35.79%
6.60%
33.79%INTRODUCTION

In recent years, distributed flexible AC transmission
systems (D-FACTS) have become an increasingly popular
power flow control technique, with the successful deploymentof its commercial version, Smart Wires, in many transmission
systems [1]. D-FACTS is a light-weight version of
conventional flexible AC transmission systems (FACTS),
which can be attached to transmission line conductors or
towers in a relatively large quantity to achieve a desired power
flow control capability [2]. An issue that arises from theincreasing popularity of D-FACTS is its co-optimization withother power flow control technologies, such as TCSC. This
paper aims at filling this gap by proposing a computationallyefficient co-optimization model of FACTS and D-FACTS
considering uncertainties in the network and study the mutual
influence of the two technologies when D-FACTS are
allocated in a system with existing FACTS, such as theinfluence of FACTS on the optimal locations of D-FACTS,
and the influence of D-FACTS on optimal FACTS set points.
Simulations were carried out on a modified RTS-96 test
system; uncertainties of renewable generation and load
fluctuation were both considered. Results do not only provethat D-FACTS is a more economic option than FACTS, butalso show the mutual influences between the two technologies
need to be considered in order to maximize the economic
benefits when allocating D-FACTS in a system with existing
FACTS.

Fig. 1. FACTS set point comparison when D-FACTS locations were cooptimized and not co-optimized with existing FACTS in the system

REFERENCES
[1]

[2]

Smart Wires Press Release (2017, November). Smart Wires’
Millennium Milestone. Smart Wires, Union City, CA. [Online].
Available:
https://www.smartwires.com/2017/11/27/smart-wiresmillennium-milestone/
D. Divan and H. Johal, “Distributed FACTS—A New Concept forRealizing Grid Power Flow Control,” IEEE Trans. Power Electron.,
vol. 22, no. 6, pp. 2253-2260, Nov. 2007.

Analysis of Transient Stability According to Initial
Operating Condition of STATCOM
Seungryul Lee

Hyeokjin Noh and Byongjun Lee*

Smart Power Grid Research Center
Korea Electrotechnology Research InstituteUiwang, Republic of Korea
srlee@keri.re.kr

School of Electric Engineering
Korea University
Seoul, Republic of Korea
leeb@korea.ac.kr

Abstract—In spite of the continuous increase of electric power
demand, the Korean power system is difficult to construct
additional transmission facilities due to social acceptability
problem. Therefore, FACTS is considered for ensuring transientand voltage stability due to large disturbance such as double fault
in 765kV transmission line. In addition, as the proportion ofrenewable energy including wind power is expanded, it isnecessary to introduce a FACTS that can flexibly cope withunstable renewable energy output changes. In order to effectively
increase the flexibility of the existing system, there is a need for
efficient configuration and operation of the FACTS in a situation
where the power grid acceptability is limited. In the case of
STATCOM, it is possible to control to output a certain reactive
power initially according to the system condition. In case of alarge-scale assumption failure, it is necessary to provide inductive
output for securing reactive power reserve. When disturbanceoccurs at this time, the reactive power output changes to thevoltage control mode out of the predetermined range. If thereactive power output does not recover within the control range
after a fault, there is also a Slow Reset Control controller which
arbitrarily lowers the scheduled voltage value and brings in thereactive power output value within a given range. Thus various
studies have been conducted to utilize the reactive power control
mode of STATCOM. In this paper, we analyze the transientstability according to the initial reactive power output value of
STATCOM.

Fig. 2. Corresponding Power-Angle Representation of Voltage and Reactive
Power

II. KEY RESULTS

Keywords—STATCOM; Transient Stability; Initial Operating
Point

I. KEY FIGURES

Fig. 3. Transient Stability Results According to Voltage and Reactive Power

III. CONCLUSION
Various control schemes are being studied for efficient
operation of STATCOM. In this paper, the initial operating
point of STATCOM is classified into voltage and reactive
power output magnitude, and its influence is analyzed. In
conclusion, we confirmed that the higher the voltage command
value of the STATCOM and the larger the inductive reactive
power output, the more the transient stability is secured.
REFERENCES[1]

Fig. 1. Steady State Powerflow Boundary Conditions (STATCOM)[2]

M. H. Haque, “Improvement of First Swing Stability Limit by Utilizing
Full Benefit of Shunt FACTS Devices” IEEE Trans. Power Syst., vol. 19,
no. 4, pp. 1894–1902, Nov. 2004.
Narain G. Hingorani, “Understanding FACTS”, IEEE PRESS, pp. 135207, 1999

1

Rotor Angle Stability of MMC Based HVDC
Transmission Under DC Short-Circuit Fault
Peng Wang, Maren Kuschke, and Kai Strunz
Sustainable Electric Networks and Sources of Energy (SENSE) Lab, Technische Universität Berlin , Germany

X1

δG，xdl

B2

Zf2

X2

XL
E1

Infinite bus
E3

δ1

E2

0°

δ2

Fig. 1 Single-machine infinite bus AC-DC power system.

The DC short-circuit fault goes through the following
process. The inserted capacitors begin to discharge through theupper arm, the lower arm, and the DC link short-circuit path at
the fault instant. The IGBTs are blocked for self-protection
when the overcurrent is detected. With the IGBTs blocked, thefault current in the arm flows through the freewheeling diodes.
At the same time, the capacitors are bypassed and stopdischarging. With the capacitors bypassed and the arms
conducting, the AC grid suffers from symmetrical three-phaseshort-circuit fault. At the first zero-crossing point of the arm
current, the arm gets into non-conducting state. The AC gridcurrents then feed into the DC grid. At last, the AC circuit
breaker opens to isolate the DC fault.
Stage I

Prefault

Stage II

Stage III

Stage IV

t0

tb

tzc

tob

Fault
instant

Blocking
IGBT

Zerocrossing

Opening
breakerFig. 2 Four stages of pole-to-pole fault process.

Pac0

Pm

a

A2

A1

Pe0-prefault

c

b

d

Pe1-postfault

Pdc0

0.5Pdc0
δ0

δ1

δ2

δ

Fig. 3 Power-angle characteristics under DC short-circuit fault.

The system is stable under the condition that
(4)

𝐴1 ≤ 𝐴2 .

The critical mechanical power 𝑃m,critical can be obtained from(4). With 𝑃m > 𝑃m,critical , the system is unstable at the DC fault.
III. CASE STUDY
The critical mechanical power for the transmission system inFig. 1 is 𝑃m,critical = 0.815 p. u.. The simulation results for two
different 𝑃m are given in Fig. 4.
1.01
1.005
1
0.995
0.99
0.8

1.2

Ω/ p.u.

E

Zf1

Pe

𝑃AC / p.u.

B1
G1

At the short-circuit fault, the active power 0.5𝑃dc0 of thefaulty pole is interrupted. The disturbed equilibrium between
the mechanical and electrical torques results in the acceleration
or deceleration of the rotor [2]. Based on the equal-area
criterion, the transient stability is analyzed, as shown in Fig. 3.

0.6
0.4
0.2
1
0.5
0
-0.5

𝑃DC / p.u.

The single-machine infinite bus AC-DC transmission system
is shown in Fig. 1. The generator is connected to an infinite bus
through a bipolar MMC-HVDC and an AC corridor. The half
bridge submodule (HBSM) topology is used. The HBSM has
the advantages of low losses and high scalability [1]. A pole-toground fault happens at one pole, while the other healthy polecontinues to transmit power.

ROTOR ANGLE STABILITY ANALYSIS

Ω/ p.u.

I. DC SHORT-CIRCUIT FAULT PROCESSII.

𝑃AC / p.u.

Index Terms—bipolar HVDC, DC short circuit, equal-area
criterion, MMC, transient stability.

Regardless of the fault location, the fault process is separated
into four stages, as shown in Fig. 2. Those are the capacitor
discharging stage, the diode freewheeling stage, the AC current
feeding stage, and the AC circuit open stage. As the impedancesin the arms and the DC link short-circuit path are small, theactive power in the faulty pole can be regarded as zero.

𝑃DC / p.u.

Abstract—The modular multilevel converter (MMC) has
become a prominent topology for high-voltage direct-current(HVDC) applications. The DC short-circuit fault is the most
severe condition, which is a challenge for the HVDC transmission.
Under the DC short-circuit fault, the synchronous generator may
encounter rotor-angle instability. This transient characteristic isanalyzed with equal-area criterion. To quantify the transientstability, the critical mechanical power 𝑷𝐦,𝐜𝐫𝐢𝐭𝐢𝐜𝐚𝐥 is defined. A
single-machine infinite bus AC-DC transmission system is appliedin PSCAD/EMTDC to verify the transient stability characteristics.

40

45

50

55

60

1.1
1
0.9
1
0.5
0
-0.5
-1
1
0.5
0
-0.5
39

40

41

42

43

44

45

Fig. 4 Stable and unstable operation with 𝑃m = 0.75 p. u. and 𝑃m = 0.85 p. u..

References[1] Debnath, Suman, et al. "Operation, control, and applications of the modular
multilevel converter: A review." IEEE Trans. Power Electron., vol. 30, no. 1,
pp. 37-53, Jan. 2015.
[2] Kundur, Prabha, Neal J. Balu, and Mark G. Lauby. Power system stability
and control. Vol. 7. New York: McGraw-hill, 1994.

Calculation of Fault Level in Power Systems
with High Penetration of Renewable Energy
Sources
Rafat Aljarrah, Student Member, IEEE, Hesamoddin Marzooghi, Member, IEEE, and Vladimir
Terzija, Fellow, IEEE
II. CALCULATION METHODS

Abstract— This paper investigates the changing fault level insystems with high penetration of renewable energy sources(RESs). It aims to evaluate the steady state fault calculation
methods which are traditionally used in conventional powersystems. Simulation results include fault level calculation using
the available calculation methods\standards for various
penetrations of RESs are obtained in the 2‐area test system. The
calculation results are validated against the dynamic simulation
results obtained from using DIgSILENT PowerFactory.

TABLE IUTILIZED METHODS FOR STEADY STATE CALCULATIONS

Description

IEC1

IEC standards with no contribution
from the converter
IEC standards using static converterfed drive
Complete method (dynamic voltage
support) with 0 MVA sub-transient
Complete method (dynamic voltage
support) with rating MVA subtransient

IEC2
Complete 1
Complete 2

Index Terms— Dynamic simulation, fault level calculation,
renewable energy sources, steady state calculation.

F

MethodI. INTRODUCTION

AULT level is an important parameter usually used toreflect the system strength at a faulty point. In
conventional power systems, fault level has had relatively highvalues due to very high fault level contribution fromsynchronous generators (SGs) [1]. This contribution can beeasily calculated by modeling the SG as a voltage behind areactance. However, with the increasing penetration ofrenewable energy sources (RESs, fault level calculation might
become more challenging. This is due to the different
characteristics of the short circuit currents might be supplied
from the RESs which mainly utilize fully rated converters(FRCs), so their contribution to the system fault level isdependent on the converter interfaces [2, 3]. This necessitate
the need to examine the steady state calculation standards insuch expected future scenarios in order to assess the mismatch
between the dynamic simulations and the steady statecalculations and to propose solutions if any problem exists.

III. CASE STUDY AND RESULTS

Fig. 2. Adjusted two-area test system with a RES penetration on Bus 2

Fig. 3. Results of dynamic simulation versus steady state calculation
methods at bus 6

REFERENCES[1][2]
Fig. 1. Short circuit characteristics in response to a three-phase bolted
fault: a) SG current, and b) RES utilizing FRC[3]

N. D. Tleis, Power Systems Modelling and Fault Analysis: Theory
and Practice: Newnes, 2008.
R. Teodorescu, M. Liserre, and P. Rodriguez, Grid converters forphotovoltaic and wind power systems: John Wiley & Sons, 2011.
L. Freris, and D. Infield, Renewable energy in power systems: John
Wiley & Sons, 2008.

Stability Analysis of Microgrids under Disturbances
via Reachable Sets
Yan Li∗ , Peng Zhang†
Department of Electrical and Computer Engineering
University of Connecticut, Storrs, Connecticut 06269–4157
Emails: ∗ yan.7.li@uconn.edu, † peng.zhang@uconn.edu

Abstract—A stability assessment approach via reachable set
calculation is presented to efficiently evaluate the dynamics of
microgrids. Through the reachable set-based method, the boundsof all possible trajectories of a microgrid under a series of disturbances can be directly obtained, which makes repeated traditional
time-domain simulations unnecessary. Moreover, a zonotope isused to better quantify these uncertainties and is integrated into
the reachable sets calculation procedure. Extensive testing showsthat reachable set calculations enable an efficient analysis of
disturbances impacts on a microgrids dynamics, as well as offer apotent tool for evaluating how far the system is from its stability
margins and what actions should be taken by system operators.

• The stability margin can be efficiently obtained, which
verifies reachable set is able to identify stability margins.
25% Uncertainty
10% Uncertainty
1% Uncertainty

I. M ETHODOLOGY D ESCRIPTION
Reachable set calculation aims at finding the bounds of all
possible system trajectories under various disturbances. The
calculation process can be presented as follows: First, theoriginal nonlinear DAEs of a dynamic system are abstracted
into linear differential inclusions at each time step, obtaining
a finite-dimensional state matrix A = [aij ] ∈ Rn×n . Itsreachability model can then be expressed as follows:∆ẋ ∈ A∆x ⊕ P,

Fig. 1 3-D reachable set of DER(1)

Second, a reachable set can be obtained at each time step
via a closed-form solution:
Re (tk+1 ) = φ(A, r)Re (tk ) ⊕ Ψ(A, r, p0 ) ⊕ Ipe (p∆ , r),(2)Re (τk ) = C(Re (tk ), φ(A, r)Re (tk ) ⊕ Ψ(A, r, p0 ))⊕Ipe (p∆ , r) ⊕ Iξe ,(3)

Fig. 2 Time domain simulation verification

II. T EST C ASES
A typical microgrid system that is deeply integrated with
distributed renewables and DC power loads is used to test andvalidate the reachable set approach. Fig. 1 shows the reachable
sets of control signals in DER under disturbances. Fig. 2 shows
the corresponding simulation verification. Fig. 3 shows thestability margin obtained via reachable sets. From Fig. 1 toFig. 3, it can be seen that:• The sizes of zonotopes along reachtubes increase as theuncertainty level increases.
• The time domain trajectories are fully enclosed by reachable
sets, which validates the over-approximation capability ofreachable set.
This material is based upon work supported by the National Science
Foundation under Grant No. 1611095, and UConn Academic Plan Level 1
under Award No. 2806600.

Stability Margin

B

Reachable set under
34% uncertainty
A
Reachable set under
32% uncertainty

Reachable set
under 30%
uncertainty
Reachable set
under 20%
uncertainty

Fig. 3 Stability margin of Microgrid

An in-depth analysis of grid-connected solar plantfeasibility and technology selection
Greg Stephens, Chris Dieterle, Eklas Hossain, Senior Member, IEEE
Oregon Tech, and Portland General Electric
Greg.Stephens@oit.edu; Chris.Dieterle@pgn.com; eklas.hossain@oit.edu
Abstract— Energy storages appeared as the equipment ofchoice to overcome the problems arising from the different
time-periods of peak renewable generation and peak demand.
In this work, the possibilities to increase a proposed solar
plant’s peak-shaving efficiency using a MW-scale energy
storage system is discussed; along with an algorithm for storage
system control, and a study into the suitable storage systems
available commercially.
Keywords– Feasibility study, Grid connected energy storage,

Solar plant, Peak shaving.
I. GRID SUPPORT BY ENERGY STORAGE
In this work, a feasibility study of a grid connectedsolar plant is conducted. The site selection factors are reviewed
along with the construction of an intuitive mathematical model.
Based on the modeling, a feasibility analysis, selection ofproper storage, and its control algorithm are demonstrated.

Fig. 3: SOC control algorithm with an altered sequence than the previous one.
SOC status is checked first here.

III. COMPARISON OF AVAILABLE ENERGY STORAGES
Table 1. Comparison of possible storage technologies [2].

Technology

Pumped Hydro(small)CAES(aboveground)
FlywheelFig. 1. Energy storage discharging during peak hours and charging during peakgeneration to increase the efficiency of renewable energy [1].

II. STORAGE SYSTEM CONTROL ALGORITHM

Lead Acid
Batteries(advanced,
demonstration)
Lithium Ion
Batteries
Sodium Sulfur
Batteries
Flow Batteries(vanadium)

Duration(hrs)

Efficiency(%)

Lifetime(cycles)

5>10,000

0.25

Variesby plant
85-87

3.2-4

75-90

4,500

2-4

90-94

4,500

6-8

80-82

>13,000>100,000

Cost($/kWh)4204303904307,8008,800
6251,150

9001,700
7.2
75
4,500
4455554
65-70>10,000
780830Proper site selection and the right technology can go a long way
in improving solar plant performance.
REFERENCES[1] Cleverson Takiguchi “Energy storage for solar PV generation
integration” EE Publishers May 13th, 2015.

Fig. 2. Flow chart of battery storage control algorithm by checking the state of
power difference and state of charge.

[2] D. Rastler., “Electric Energy Storage Technology Options: A
White Paper Primer on Applications, Costs, and Benefits,” EPRI,
Palo Alto, CA, 2010. 10206

An LMI Based Stability Margin Analysis for Active
PV Power Control of Distribution Networks with
Time-Invariant Delays
Chang Fu, Caisheng Wang, Saeed Alyami
Department of Electrical and Computer Engineering, Wayne State University, Detroit, Michigan USA
chang.fu@wayne.edu; cwang@wayne.edu; saeed.alyami2@wayne.edu
Abstract—High penetration of photovoltaic (PV) generators
can lead to voltage issues in distribution networks. Various
approaches including the real power control through PV inverters have been proposed to address voltage issues. However,among different control strategies, communication delays areinevitably involved and they need to be carefully considered in
the control loop. Those delays can significantly deteriorate thesystem performance with undesired voltage quality, and may also
cause system instability. In this paper, according to the inverter
based active power control strategy, a linearized state spacemodel with communication delay is presented. A delay dependent
stability criterion using linear matrix inequality (LMI) approachis used to rigorously obtain the delay margins based on different
system parameters. The method can handle multiple PVs in the
distribution network as well.
Index Terms—active power curtailment, communication delay,
LMI, modeling, photovoltaic generators.

I. I NTRODUCTION
As one of the most important clean renewable sources
for sustainable energy development, PV generation has been
rapidly increased for more than two decades worldwide. The
majority of the PV systems has been and will be installed
in distribution networks. As a result, the PV penetrationlevel will become unprecedentedly high (e.g. well over 50%)and continue to grow around the world. A poor voltage
profile in a distribution network may lead to issues of power
quality, equipment safety, system reliability and stability, andthus can raise system losses and cause equipment damages.
Overvoltage is one of the most significant concerns among
the above mentioned challenges, and limits the capacity of
PV accommodation.
Linear matrix inequality (LMI) based stability methods havebeen studied extensively in recent decades. For distribution
networks with high penetration of PVs, it is necessary tohave a controller that can guarantee the systems’ stability
and performance while the systems are subject to various
disturbances (load and solar irradiance variations) and communication delays. Therefore, in this paper, in addition to control
the voltage in the distributed network via active power, an LMI
based stability criterion will also be studied according to thestate space model considering communication delays.
II. K EY F ORMULAS
Assume that an uncertain time-invariant time delay in [0,τd ], i.e., τ ∈ [0, τd ]. Then if there exists P > 0, Q > 0,

V > 0 and W such that(1, 1)−W T Ad
−AT W−Qd V Ad A V Ad Ad(1, 4)T0

AT ATd V
ATd ATd V−V0
(1, 4)
0 
<0
0 
−V

(1)

where
(1, 1) , (A + Ad )T P + P (A + Ad ) + W T Ad + ATd W + Q(1, 4) , τd [W T + P ]then the system is asymptotically stable.
III. K EY R ESULTS

Fig. 1. Voltage and PV active power output at Node 6.

Fig. 2. Relationships among τd , KP 4 & KI4 .

A Framework to Utilize DERs’ VAR Resources to
Support the Grid in an Integrated T-D System
Ankit Singhal

Venkataramana Ajjarapu

Department of Electrical andComputer Engineering
Iowa State University
Ames, Iowa 50010
Email: ankit@iastate.edu

Department of Electrical andComputer Engineering
Iowa State University
Ames, Iowa 50010
Email: vajjarap@iastate.edu

Abstract—Increasing penetration of inverter-based distributed
energy resources (DERs) opens up interesting opportunities forthe transmission systems. We present a hypothesis that the numerous DERs in var control mode can be seen as geographically
distributed var devices (mini-SVCs) and if controlled properly,
can be exploited to increase system flexibility by providing local
var support to the grid as an ancillary service. Based on thispremise, a var support framework is proposed in this paper. It
utilizes a novel D-OPF formulation for unbalanced three-phasefeeders enabling the estimation of the maximum var support
that can be provided by the DERs to the grid at different
operating points without compromising the distribution network
performance. Further, a co-simulation method is developed toinvestigate the true impact of the proposed DER var support on
the grid in an integrated Transmission-Distribution (T-D) system.
Index Terms—Distributed Energy Resources, Transmission
System, Solar Integration, VAR Support.

I. I NTRODUCTION
The conducive environment for distributed energy resources(DER) growth is pushing its penetration to as high as 100% .
While the DER integration can impact the grid both adversely
and positively, the purpose of this paper is to explore theopportunities offered by the DERs to improve transmission
system performance. In particular, we present a hypothesis
that, from the grid perspective, thousands of DER devices with
volt/var control capability can be seen as the geographically
distributed var resources (‘mini- static voltage compensators)
to improve system performance. It is known that the bulk
transmission system needs to install its own reactive powerdevices such as SVC and capacitors at certain locations toenhance the system flexibility and voltage stability. Usually,
these devices are very costly whereas due to the distributed
nature, DERs can provide more flexible and localized var
support to the bulk system at less economic cost, if controlled
properly. Based on this premise, we propose a DER var
support framework that utilizes the DERs volt/var capability
for the benefits of the grid and verifies its impact in an
integrated transmission-distribution (T-D) network.
Overall, the objective of this work is to investigate the var
support opportunities provided by the DERs for the grid benefits by proposing a DER var support framework. The support
framework has two main novel aspects i.e. distributed optimal

power flow (D-OPF) and the T-D co-simulation which intend
to provide following unique contributions: 1) To estimate the‘day ahead maximum var support curve’ for the grid while
enforcing distribution system operating limits; 2) To verify
the true impact of var support on the gridusing an integrated
T-D co-simulation; 3) To confirm our proposition that the
DERs can be exploited as mini-SVCs to provide flexibility
and ancillary services to the grid; and 4) To provide a generalframework which enables further investigation of the DERs
utilization for various grid support applications in an integrated
T-D environment.
II. P ROPOSED VAR S UPPORT F RAMEWORK
Fig.1 depicts the overall framework of providing var support
to the grid from DERs in an integrated T-D system, proposedin this work. The objective of this framework is to estimate
the maximum var support capacity curve and send it to thetransmission system operator (TSO). This is achieved througha D-OPF module which estimates the maximum available var
support at the feeder substation at different operating points
throughout the day while ensuring the distribution system
is within its operational limits. This capability works as an
potential ancillary service to TSO which can be utilized whenneeded. The results will be presented and discussed in theposter.
Transmission Network

Maximum
available VAR
support info.

D-OPF
Maximize var
support

𝑉0

Inverter Q
set-points

Distribution Feeder 1
Transmission bus

Boundary bus

DER data
Load data
Network data

Distribution Feeder 𝑛Distribution bus

Inverter based DER

Fig. 1. The DER var support framework for an integrated T-D system

Quantitative Control Approach for Wind Turbine
Generators to Provide Fast Frequency Response
With Guaranteed Rotor Security
Siqi Wang

Kevin Tomsovic

Dept. of Electrical Engineering and Computer Science
The University of Tennessee
Knoxville, TN, USA
swang31@utk.edu

Dept. of Electrical Engineering and Computer Science
The University of Tennessee
Knoxville, TN, USA
tomsovic@utk.edu

Abstract— The high penetration of wind generation reduces theinertial response of the system. This can be compensated by enabling the double-fed induction generator (DFIG) to provide fast
frequency response. This paper proposes a quantitative control
approach for DFIG to deliver fast frequency response in the inertial response time scale with guaranteed rotor security based on
estimation of maximum extricable energy. The proposed approach not only provides adequate inertial response but also
ensures the rotor speed is kept within a specified operating
range.

Considering converter overload limit Plim  1.2p.u .
tpi
Pdel
|max (Vw , Tdel )  min( Pdel , P lim  P0 (Vw ))(3)

The sensitivity of active power injection change to frequency nadir incremental can be expressed as:minS  f na
/ P

(4)

Figure 1 shows the flow chat of the proposed approach.

Index Terms— Active power control; DFIG; high wind penetration; inertial response; kinetic energy

I.

INTRODUCTION

With more synchronous generators being replaced by windturbine generators, the system overall inertial response isreduced [1]. DFIG is able to provide fast frequency response
similar as conventional generators. A general approach is tointroduce a support function as a supplementary control loop,
adding to the active power reference of DFIG. This paper proposed a quantitative control approach for DFIG to provide fast
frequency response with guaranteed rotor security to answer
the following two questions: 1) How to quantitatively design
the power surge function to maximize a DFIG’s contribution
without stalling the rotor? 2) How to coordinate multiple
DFIGs under different operating conditions to provide fast andadequate system response?
II.

PROPOSED DFIG FFR APPROACH

The maximum extricable energy for DFIG during inertial
response period can be expressed as follows:Edel  (




12

KVw2

 2C pC p
|t  0  J

r (0)
s2

Tdel )r  (KVw|t 0 Tdel )r2
216
 2
2s2


36

KVw2

C p


|t  0(1)JConsidering a delivery time Tdel , the maximum active
power injection can be calculated as:
Pdel 

Edel
Tdel

(2)

Figure 1. Flow chat of proposed approachIII.

CONCLUSIONS AND FUTURE WORKThis paper presents a quantitative control approach for
DIFG to provide fast frequency response by incorporating a
supplementary power injection to the existing DFIG active
power reference. The active power injection is calculated withconsideration of maximum extricable energy and converter
overload limits. The proposed approach not only improves theoverall frequency response but also ensures the rotor speed is
kept within a safe range.
REFERENCES[1] J. Conto. Grid challenges on high penetration levels of wind power. In
Proc. IEEE Power Energy Soc. Gen. Meeting (PES GM), pages 1–3, July2012.

Optimal Corrective Control for Bipolar Multiterminal HVDC Grid
Jiahong Li, Hua Ye, Yutian Liu

Yanling Du, Haitao Liu,

Key Laboratory of Power System Intelligent Dispatch andControl of Ministry of Education (Shandong University)
17923 Jingshi Road, Ji'nan 250061, China
lijiahongjhh@163.com, {yehua, liuyt}@sdu.edu.cn

State Grid Jibei Electric Power Company
Beijing 100053, China

liuhaitaob@126.com

Abstract—An outage of DC transmission lines or converters
occurs in bipolar multi-terminal HVDC grid (MTDC) may result
in security violation. Corrective control strategies are therefore
needed to ensure the operational security of the system. For
MTDC grid following N-1 contingencies, a power shift-based
optimal corrective control method is presented in this paper.
Certain power are shifted from the faulty pole to the normal poleor among different converter stations within a pole, so that theamount of renewable energy accommodation of the MTDC ismaximized. The method is intensively studied on a modified New
England 10-machine 39-bus test system with inclusion of a fourterminal bipolar DC grid.

The bipolar MTDC grid may violate the security
constraints when an outage of stressed DC transmission lines
or converters occurs. Since the positive and negative poles ofthe MTDC grid are operated independently, it is a promising
way to guarantee the operational security of MTDC grid byshifting certain power from the faulty pole to the normal pole(i.e., pole-to-pole power shift, pink dotted lines 1 and 2 shownin the Fig. 1) or by shifting power among different converter
stations within a pole (i.e. station-to-station power shift, thegreen dotted line 3 in Fig. 1).

Index Terms—Corrective control, multi-terminal HVDC, power
shift, renewable energy integration, security analysis.

The aim of corrective control is to ensure the security of thepower system with bipolar MTDC grid embedded when N-1
contingency of DC grid occurs. The cheapest and the mosteffective control measure is the power shift. In some cases thatthe wind and solar energy is abundant, the least amount of
renewable energy should be curtailed and the smallest numberof generators in the AC grid should be rescheduled to balance
the hybrid grid. To address this, a mixed integer programming
model is established.

I.

INTRODUCTION

Like the AC power grid, after an outage of DC
transmission line in the bipolar MTDC grid, power will
immediately transfer to the parallel paths. In this case, an
intractable problem is the possible overloads on DC
transmission lines caused by outage of stressed DC lines andconverters. To deal with such critical DC N-1 contingencies
and to eliminate the resultant overloads, an optimal corrective
control method is presented in this paper by using the power
shift capability of the bipolar MTDC grid. First, two types ofpower shifts, including pole-to-pole and station-to-station
power shifts are introduced. Then, corrective control strategies
are obtained by intensively analyzing different load level ofconverters and different type of outages. Third, a mixed-integer
non-linear programming model is established to address allthese corrective control strategies.
II. POWER SHIFT IN THE BIPOLE MTDC GRID
Without loss of generality, the symmetric bipolar fourterminal HVDC grid illustrated in Fig. 1 is used to address thecorrective control problem in MTDC grid.
Converter
station #2

Converter
station #1

III.

OPTIMAL CORRECTIVE CONTROL FOR MTDC GRIDmin
 Ptrans1  Ptrans3  Pinc 2   w1  ui  PG i 
u
iSG

1

P

iSG

IV.

N

Line
1-3

G2

Line
2-4
3

TABLE I POWER FLOWS ON THE DC LINES BEFORE AND
AFTER THE CORRECTIVE CONTORL (P.U.)
Case 1

FaultyPole

WF & PVP

Converter
station #3

N

Pdc42-43
Pdc42-44
Pdc43-45
Pdc44-45
Pdc42-43
Pdc42-44
Pdc43-45
Pdc44-45

Pre-control

Post-control

01.1886-1.2118
1.58471.13220.0650-0.0687
0.464601.1751-1.2118
1.50001.13920.0579-0.06160.5425

Case 2Pre-control Post-control

01.1886-1.2118
1.88301.15720.0400-0.0437
0.738900.8935-1.2118
1.50001.43240.0630-0.06140.8117TABLE II POWER TRANSFER OPTIMAL REAULTS IN CASES 1 AND 2 (P.U.)

P
N

NormalPole

AC
Network

Line 3-4
2

KEY RESULTS

Cases 1 and 2 are used to verify the correctness of the
optimal corrective control for the outage of DC line in thepositive pole (faulty pole).

P
N

(1)

+w2  ui  w3  Pcut1  Pcut 3 Line 1-2

WF & PV

2

G4

Converter
station #4

Fig. 1. Schematic of pole-to-pole and station-to-station power shift

Case
1
2

Ptrans1

0
0.3

Ptrans3
0.08520.0500Pinc2
00.2926

Pcut1
0
0

Pcut3
0
0.04

Bi-Level Arbitrage Potential Evaluation for Grid-Scale
Energy Storage Considering Wind Power and LMP
Smoothing EffectHantao Cui, Student Member, IEEE; Fangxing Li, Fellow, IEEE;
Xin Fang, Member, IEEE; Hao Chen, Honggang Wang
Abstract—This paper deals with extended-term energy
storage (ES) arbitrage problems to maximize the annual revenue
in deregulated power systems with high penetration wind power.
The conventional ES arbitrage model takes the locational
marginal prices (LMP) as an input and is unable to account for
the impacts of ES operations on system LMPs. This paper
proposes a bi-level ES arbitrage model, where the upper level
maximizes the ES arbitrage revenue and the lower level simulates
the market clearing process considering wind power and ES. The
bi-level model is formulated as a mathematical program withequilibrium constraints (MPEC) and then recast into a mixedinteger linear programming (MILP) using strong duality theory.
Wind power fluctuations are characterized by the GARCH
forecast model and the forecast error is modeled by forecast-bin
based Beta distributions. Case studies are performed on amodified PJM 5-bus system and an IEEE 118-bus system with aweekly time horizon over an annual term to show the validity ofthe proposed bi-level model. The results from the conventionalmodel and the bi-level model are compared under different ES
power and energy ratings, and also various load and windpenetration levels.

I

price. Fig. 1 (c) and (d) shows the comparison of ES power
output and SOC from the conventional and the bi-level
models. The positive values of the ES power indicate ES
discharging activities, while the negative ones indicate ES ischarging. It is important to note that the power is seen fromthe power grid side so that the round-trip efficiency has been
included. Therefore, the maximum power the ES can supply issmaller than the 50 MW power rating due to self-discharging,and the charging power is greater than 50 MW to compensate
for the motor losses.
In conclusion, the proposed bi-level can reflect the ES
operations and wind power fluctuations on the LMPs; incontrast, the conventional model must rely on historical or
pre-computed LMP data as inputs. Based the system operating
condition, the conventional model would either over-estimate
or underestimate the annual arbitrage revenue using static
price inputs when considering line flow limits.

I. SUMMARYn this paper [1], we focus on the extended-term arbitrage
revenue study, which can be used to evaluate existing ES
systems or incorporated into cost-benefit analysis for planning.
To consider the price changes due to arbitrage activities, a bilevel model is proposed by maximizing the arbitrage potential
in the upper-level model and simulating the market-clearing
process in the lower level. Instead of relying on price inputs or
price forecasts, the upper-level model uses the prices generated
on the lower level and adjusts the ES outputs, which, in turn,affects the price in the lower level. Linear transformation
techniques are applied to reformulate it as a single level
mathematical programming with equilibrium constraints(MPEC) and then a mixed-integer linear programming (MILP).
Comparing with the conventional model, the proposed bilevel ES arbitrage model incorporates the LMP informationfrom the market clearing process in the lower level problem.
By using appropriate information such as wind forecast andestimated generation bids, this method can yield more accurate
LMP forecasts and arbitrage potential evaluations.
Fig. 1 (b) compares the LMP at bus D obtained from thetwo models in a time horizon of 168 hours (7 days), wheresome LMP bottoms are driven up but more LMP peaks are
reduced in the bi-level model. The LMP changes show averification of the LMP smoothing effect as the discharging
operation of ES during higher LMP hours, as well as the
charging of ES during the low LMP hours, affects the clearing

Fig. 1. Comparisons of (a) Bus D load, (b) Wind farm outputs, (c) Bus D LMP,(d) ES Power and (e) ES SOC from the two models[1] H. Cui, F. Li, X. Fang, H. Chen, H. Wang. Bilevel Arbitrage
Potential Evaluation for Grid-Scale Energy Storage Considering
Wind Power and LMP Smoothing Effect. IEEE Transactions on
Sustainable Energy, 2018, 9(2), 707-718.

State Space Model of Electric Vehicle Aggregator for
Frequency Control
Mingshen Wang, Fangxing Li
Department of Electrical Engineering and Computer Science, the University of Tennessee, Knoxville, TN 37996, USA.
mwang38@utk.edu, fli6@utk.edu
Abstract—With the obvious power imbalance caused by therenewable energy in the power system, the electric vehicles (EVs)are suggested to be an alternative for the frequency control. The
existing models that managed a large quantity of EVs forcentralized control achieved high accuracy at the expense of theheavy calculation workload and the high requirement for realtime communication. This paper develops a state space model(SSM) that provides a probability to realize the real-time power
control of EV aggregator (EVA) with the high accuracy andcalculation efficiency but the low requirement for real-timecommunication. The SSM, a reduced model based on the state
space method, accurately describes the EVA with different
connecting states and various state of charge (SOC) states.
Considering the charging characteristics and the travelling
behaviors of EVs, the SSM realizes the state transition prediction
and the regulation capacity estimation with the Markov chain
method, which has much higher calculation efficiency than theexisting models. The SSM is used for the frequency control, andthe SOC adaptive coefficient is used to derive the identicalcontrol signal (ICS) and improve the prediction accuracy. The
SSM lowers the requirement for real-time communication byreplacing some real-time process with offline process. The ICS ismuch suitable for real-time dispatching by transmitting the same
control signal to each EV. The simulation results validate theeffectiveness of the SSM of EVA for the frequency control.

I. MODELING FRAMEWORK
A. State Model of an Individual EV

 Si  k   Pi  k  c,i  T / Qi , Pi  k   0, CSSi  k  1   Si  k  ,Pi  k   0, IS (1) Si  k   Pi  k  / d,i  T / Qi , Pi  k   0, DS
where k is the time step; Si is the SOC of EV i; Pi is the
charging power; CS/IS/DS is abbreviation of Charging
State/Idle State/Discharging State; Si is the battery capacity.
B. State Transition of EVA

Markov estimation method and analytical method can beused to obtain the A (State Transition Matrix).
C. State Space Model of EVA x  k  1  Ax  k   Bu  k   Cv  k  y  k   Dx  k (3)where u/v is the (Ns×1) input vector that indicates responding
modes; y is a (1×1) output vector; B/C/D is constant matrixes.
II. FREQUENCY CONTROL WITH STATE SPACE MODEL

Fig. 3. Frequency regulation with SSM of aggregated EVs.

Fig. 4. Structure of identical control signal.
III. STUDY RESULTS

Fig. 5. Probability density of prediction errors of EVA (Tp=5 min).

Fig. 5. Power profiles of EVA with SOC adaptive coefficient.

Fig. 1. Operation area of an EV.

Fig. 2. State space of aggregated EVs.

The state transition of EVs in one time step:x  k  1  Ax  k (2)where A is a (3Ns×3Ns) matrix and is derived to describe thetransition of the EVs from one state interval to another; x is
(3Ns×1) state vector.

Fig. 6. Power profiles of EVA without SOC adaptive coefficient.

The SSM realize the real-time power control of EVA withthe high accuracy and calculation efficiency with ICS.

Stochastic market clearing for integrated energy
systems considering CHP and power-to-gas
technology
Haibing Wang, Student Member, IEEE,Fangxing Li, Fellow, IEEE
Dept. of Electrical Engineering and Computer Science
University of Tennessee
Knoxville, USA{hwang104, fli6}@utk.edu
Abstract—This paper utilizes natural gas-fired power plant(NGFPP), combined heat and power (CHP) and power-to-gas(P2G) technology as energy conversion approaches to construct
integrated power, natural gas and heat system, aimed atincreasing the energy utilization efficiency. A two-stage stochastic
market clearing model for the integrated energy system is
proposed to analyze the impact of P2G on the wind curtailment
rate and operation cost.
Keywords—integrated energy systems; stochastic programming;
CHP; P2G; market clearing

I. INTRODUCTION
Multi energy integration is beneficial to increase theenergy utilization efficiency. Power system and natural gas
system integration was studied in the previous literature. This
paper contains CHP units to cover the heat load and introduces
the P2G technology to prevent the curtailment of renewableenergy. The integrated power, natural gas and heat system is
described in Fig.1, where NGFPPs and P2G process couple
the natural network and power network, and CHP units couple
the natural gas network, power network and district heat
network. A scenario-based market clearing model is proposed
to analyze the impact of CHP and P2G on the operation of
integrated energy system.

Fig.1. Description of integrated energy systems containing
CHP and P2GThis work was supported by the China Scholarship Council.

Fig.2. Framework of two-stage stochastic market clearing
model of integrated energy systems
II. MODEL FRAMEWORK AND SIMULATION
The model framework is described in Fig.2. Day-aheadmarket and real-time operation are optimized simultaneously.
Day-ahead dispatch decisions are made before the realizationof wind scenarios, while real-time operation depends on windscenarios. The input includes hourly gas/power/heat load,
wind scenarios, units information, gas price and network
topology. The output includes hourly output of units and gas
wells, wind curtailment, power-to-gas conversion andoperation cost. Simulation was made on a six nodes systemand seven nodes natural gas system, depicted in Fig.3, toverify the effectiveness of the proposed model and investigate
the benefits of P2G process.

Fig.3. Case study of six nodes power system and seven nodes
natural gas system

1

Optimal Placement of Grid-connected Solar
Generation using Geographical Information System
Ana M. Ospina, Student Member, IEEE, Milad Soleimani, Student Member, IEEE,
Mladen Kezunovic, Life Fellow, IEEE
Abstract—This work presents a decision making approachfor selecting an optimal placement of the grid-connected solar
generation using Geographical Information System (GIS) as thedecision making tool. Terrain analysis for assessment of solar
radiation, as well as buildings and vegetation spatial data isanalyzed in order to determine the shadow impact that canbe anticipated for medium or large-scale PV installation. In
addition, different historical weather conditions are considered
and integrated into the model to show the impact of this variable
on the solar generation output. Some details of the methodology
and initial results related to selection of potential sites for PV
installation are presented. To illustrate the process and proposed
methodology, an example using large scale synthetic networks is
implemented.
Index Terms—PV Generation, Power System, Geographical
Information System, Decision Making, Optimal Placement.

I. I NTRODUCTION
HE high penetration of PV generation being installed in
the transmission and distribution grid may be causing
severe problems in power systems operation such as line overload, voltage instability, power quality deterioration, among
others. In order to perform a detailed analysis that leads tothe decision about the optimal placement, the realistic modelsof the grid and PV integration options allowing considerationof maximum number of constraint that are crucial for planningand operational purposes is necessary. We propose a methodology to create an input layer using GIS that assists the decisionmaking process for the optimal location of PV generation
based on solar radiation, vegetation location, placement of
supporting structures, historical weather patterns and grid
operating regimes.

T

II. G EOGRAPHICAL I NFORMATION S YSTEM AS A
D ECISION M AKING S UPPORT T OOL
A. Terrain Analysis
The terrain analysis using GIS is an accurate and adequate
approach to determining the potential advantages or disadvantages of integration of solar generation into the distribution
and transmission systems [1]. Using different public sourcesof information, it is possible to determine the optimal location
for PV generation under different conditions as illustrated inFig. 1.
B. Decision Making Process
Multi-criteria decision analysis is frequently used in thedecision making process for power system planning and operation. In this case, using the results of the terrain analysis, it is
A. Ospina (ana.ospina@tamu.edu), M. Soleimani and M. Kezunovic arewith the Department of Electrical and Computer Engineering, Texas A&M
University, College Station, TX, 77840 USA.

Fig. 1. Terrain analysis using Geographical Information System.

possible to evaluate the impacts that PV generation can haveon the current distribution power system [2]. Fig. 2 shows thedecision making process applied to a synthetic network.

Fig. 2. Decision-Making process for optimal location of PV generation.

III. C ONCLUSION
This poster presents a systematic, practical and easily implementable methodology to optimally locate PV generation from
GIS information using public data. These type of models are
crucial to carry out planning and operation studies required
to assess the impacts of the integration of non-conventionalenergy sources, such as solar or wind generation, into the gridin order to improve power systems reliability and resiliency.
R EFERENCES[1] P. Quesada, A. Arguello, J. Quiros-Tortos and G. Valverde, Distribution
Network Model Builder for OpenDSS in Open Source GIS Software, 2016
IEEE T&D PES Latin America. Morelia, Mexico.
[2] Y. Kakumoto, Y. Koyamatsu, A. Shiota, Y. Qudaih and Y. Mitani
Application of Geographic Information System to Power Distribution
System Analysis, 3rd ed CPESE 2016. Kitakyushu, Japan.

Frequency Support of DFIG by Using
Improved Inertial Control
Tang Fei1, Xiao Gucheng1*, Liu Dichen1, Liao Qingfen1, He Zhijuan2, Liu Yu3,
Liu jiale1,Wang Chenxu1
School of Electrical Engineering Wuhan University Wuhan, China

1

Computer school, Wuhan University Wuhan, China

2

School of Foreign language, South-Central University for Nationalities Wuhan, China

3

Email: xiaogucheng@whu.edu.cn

Doubly fed induction generator (DFIG) almost makes
no contribution to the power system inertia for the rotor of
the DFIG is connected to power grid through theconverters. To provide inertial response, temporary
over-production inertial control (TOPC) [1] are applied in
DFIG. TOPC could provide constant additional power
support in a period which significantly help arrest thefrequency nadir (FN). If parameters are not set properly,
over-speed will occur. Moreover, the power reduction
between the deceleration and acceleration phase will cause
a serious second frequency dip (SFD). This paper proposesa modified temporary over-production inertial control(MTOPC) to arrest the FN while causing a small SFD.
II. METHODOLOGY
The proposed MTOPC scheme consists of two parts:
the deceleration phase and the acceleration phase. The
whole inertial response process of the MTOPC issummarized in Fig. 1.
III. KEY RESULTS

The performance of the MTOPC is verified in a
modified WSCC 9-bus system. The results are comparedwith no inertial control and the conventional TOPC.
The parameters of the MTOPC and TOPC are shown in
TABLE I. The simulation results are shown in Fig. 2.

Pmt/s(a) Reference power function

Active power/pu

1
0.8
0.6
0.4
0.2

C

Pe

B

no over-speed
D

Pm

A

E

1

PMMPT

0
0.6

0.7 0.8 0.9
1.1
1
Rotor speed/pu(b) Power-speed trajectory

1.2

Fig. 1 Operational characteristics of the MTOPC scheme
TABLE I.

PARAMETERS OF THE MTOPC AND TOPC

Controllers

ΔPboost (pu)Parameters
ΔPrec (pu)Tboost (s)Trec (s)

MTOPC

0.1

/

/

/

TOPC

0.1

20

0.1

27

Frequency (Hz)I. INTRODUCTION

Acceleration
phase

e

ΔPboost

Pref0

50

49.8

Proposed MTOPC
No inertial control
Conventianal TOPC

49.6
0Active power (pu)

Index Terms—temporary over-production inertial control;
over-speed; second power dip; frequency nadir

Deceleration

1 phaseP

20

40
60
80
Time (s)(a) System Frequency

100

Proposed MTOPC
No inertial control
Conventianal TOPC

Time (s)(b) Active power output

Rotor speed (pu)

Abstract—This paper proposes a modified temporary
over-production inertial control. It consists of two parts: thedeceleration phase and the acceleration phase. In the
deceleration phase, the reference active power maintains
constant first. Then it starts to decrease linearly with thedecreasing of the rotor speed when the DFIG has released
half of the available energy stored in the rotating masses. In
the acceleration phase, the reference active power switches tothe mean value of mechanical power and electromagnetic
power under maximum power point tracking control.
Compared with the temporary over-production inertial
control, the modified temporary over-production inertial
control is simplified with one parameter and there is noover-speed. Moreover, the performance of the second
frequency dip is improved with less power reduction. The
simulation results show that the modified temporary
over-production inertial control has the same ability to arrest
the frequency nadir as temporary over-production inertial
control while improving the performance of the second
frequency dip.

Proposed MTOPC
No inertial control
Conventianal TOPC

Time (s)(c) Rotor speed

Fig. 2 Results of providing frequency support by DFIGREFERENCES[1] Ullah, Nayeem Rahmat, T. Thiringer, and D. Karlsson. "Temporary
Primary Frequency Control Support by Variable Speed Wind
Turbines— Potential and Applications." IEEE Transactions on
Power Systems, vol. 23, no. 2, pp. 601-612, 2008.

Combined Heat and Power Dispatch Based on
Integrated Demand Response and Heat Transmi
ssion for Wind Power Accommodation
Yeyan Xu, Qingfen Liao, Fei Tang*, Dichen Liu, Deping Ke, Sicheng Peng, Zheng Yang
Department of Electrical Engineering Wuhan University Wuhan, China
Email: tangfei@whu.edu.cn

 eei (t )powertranformer

 ehi (t )electric heating
devicet  g … t  N g

…

t 1

t
t

Output Portst  Ngt 1 t  2 t  3 t  4 t  5

Downstream
Node m……

… t  g

Fig.2 Vertical section of a node in the supply network
N gi i (t )   G  (t  k t ) (t  k t )gi

g g k 0

g

g

G

mm

C. Bi-level optimal dispatch for IDR
Price-based IDR is utilized and the end-users scheduleload curves in response to the price signals. The lower
level optimal dispatch is to minimize the total cost of thecustomer side; The upper level is to maximize the profit of
the IEGM operator.
Tn

C1  min   cet Pei (t )   t cet Phi (t ) t
T

J

t 1

j 1

j
t
C2  max[C1   (cgt  FMT, t  c gr Pt ) t ]

II.
III.

SIMULATION RESULTS

Table 3 The set of simulation scenes

Scene
1
2

IDR

annular pipeline network







3
4







i

eh

heatingdemand eci (t ) electric refrigeration
deviceiec

 hci (t ) thermal refrigeration
device

Heat
Load

i

coolingdemand

hc

 (t)

heat
exchanger

i
hh

H

i

energy demandsof end-users

energy convertors of end-users

Fig.1 The energy conversions inside an end-useriPhi (t )   eci

Pei (t )+1[ pci (t ) 
i

i

 ec

pei (t ) TFig.7 Generation curves
in Scene1

Fig.8 Generation curves
in Scene2

Fig.9 Generation curves
in Scene3

Fig.10 Generation curves
in Scene4

i

 hc
H

phi (t )] (1)

 hc
 hc
i
i
B. Quasi-Dynamics model of the pipeline temperaturte in
the annular pipeline network

For the heat balance, the quasi-dynamics model ofthe annular pipeline temperature is established as (2). It
shows that  i (t ) is the mixing temperature of the flow
masses injected by the uptstream nodes before period t.

(3)t 1 i 1

power
demand

T

i

(2)

im

INTRODUCTION

The IEGM is a combined heat and power micro-grid,containing end-users, CHP units and wind turbines.
A. Linear Model of the complementation between different
kinds of load in the end-user
The energy conversions inside an end-user is shown as
Fig.1. The Linear model is shown as (1). The heat load islinearly decreased with the rise of electrical load.

Electrical
Load

Upstream
Node gt  Ng… t  g ……

t 1 t  2

t

…

I.

Node i
Input Ports…

Abstract—The fixed heat-power ratio has limited theflexibility of CHP(combined heat and power) units for wind
power accommodation. Multi-energy end-users possess
electrical load and heat load. The complementation between
the two kinds of load enables the end-users to participate in
IDR(integrated demand response), which makes the CHP
units more flexible. The heat transmission can decouple thereal-time balance between the heat supply and the heat load.
This paper integrates IDR with heat transmission to promote
wind power accommodation in IEGM. Firstly we formulate a
simplified linear model of the complementation between
different kinds of load in the end-user. Then the annular heattransmission network is formulated by the quasi dynamics
model of the pipeline temperature. An IDR bi-level optimal
dispatch model is proposed to gain a “win-win” situation
between the IEGM operator and end users. The simulation
results verify that the proposed dispatch can improve wind
power accommodation and operational economy.
Index Terms—combined power and heat dispatch;
integrated demand response; heat transmission; bi-level
optimal dispatch; integrated energy micro-grid(4)

VSG Control for DFIG-based Islanded Wind Farmwith LCC-HVDC Integration
Xiuqiang He, Hua Geng, Geng Yang

Xin Zou

Department of Automation
Tsinghua University, Beijing, China

State Power Economic Research InstituteBeijing, China

The WF is equivalent to two single-machine DFIG-based
WTs (300 and 500MVA). The simulation results shown in Fig.
2 can verify the effectiveness of the proposed control algorithm. Based on the batteries, the constant-alternating-voltage
control of GSCs generates the initial ac voltage as a reference
for the system startup. Later, the proposed VSG control algorithm of RSCs of islanded DFIGs makes the DFIGs mimic theoperational characteristics of SGs, such as inertia simulation,
excitation and regulate voltage. The proposed frequency control algorithm in the rectifier of HVDC achieves the active
power balance of the SEB and the SEB frequency stability.


r

is
1
1
is v
2
sLv  Rv
s1

Power
Calc.
Ps Q sMPPT 1 vsg
 Js

1 Pr ef
2
s2

Dp

Un
Us

PROPOSED CONTROL ALGORITHM

The proposed system topology is shown in Fig. 1 (rightpart). The battery cannot only help generate the SEB voltage
through grid side converters (GSCs), but also provides energy
to start DFIGs through rotor side converters (RSCs). Systemstartup process is summarized as follows:
Step 1: The back-to-back converter dc-bus capacitor of WT ischarged (close s4 in Fig. 1); The SEB voltage is generated
through the GSC based on the constant-alternating-voltage
control (set s5 ~ s7 to positon 2);
Step 2: The pre-synchronization of DFIG is achieved based on
the VSG pre-sync control (open s0, set s1 ~ s3 to positon 2);
Step 3: Close s0, and the RSC is switched to VSG normal (vs.
pre-sync) control (set s1 ~ s3 to positon 1). Later, the GSC isswitched to the unity power factor control (set s5 ~ s7 to positon 1).
Step 4: Unlock HVDC and the corresponding frequency control (set s8 to positon 1) once active power starts to increase.

Dq

RSC control

udc
1u


utd
1


utq
0

1 Qr ef

PI

dc 

PI

utd 
utq 

2
s3

0

igcq

PI

GSC control

Qs



PI

Rv1 Ir abc

0

1 Enable
2
s8

Ur ef




1 igcd2 igcds5

1 igcq

PI
u gd

e jt

PLL

Rectifier control



f



0

PI
u gq


2
s6 igcqf  1
1

II.



r

SVPWM

Transmitting electricity from islanded WFs (e.g., offshore
WFs) into the main grid through LCC-HVDC (HVDC for
short hereinafter) is a critical issue from the operation principle point of view: 1) conventional vector controls are incompetent); 2) Startup of DFIG requires external power sources tosupply energy for rotor excitation. This paper proposes a battery based system topology to realize its black startup, and a
VSG control algorithm to mimic the operational characteristics of SGs with HVDC connection.

 b  srssin(θr) sin(θr-2π/3)0sin(θr+2π/3)
E
Ur abc

PI

id


1
2
s7

PI

PLL





id

SVPWM

INTRODUCTION

r

RSC
s0
s4

GSC

αr e
id
udi


udi
1

Inverter control



PI

udi

Fig. 1. System topology and proposed control algorithm.

Fig. 2. Simulation results (partial).



dc bus of converter

ut
us

0

I.

RESULTS AND CONCLUSIONS

αiv

SEB

Index Terms—DFIG, LCC-HVDC, startup, VSG, wind farm.

III.



Abstract—Doubly-fed induction generator (DFIG)-based islanded wind farms (WFs) cannot be directly integrated by linecommutated converter (LCC)-HVDC through conventional vector control algorithms, due to the lack of a voltage source support on the sending-end bus (SEB) of HVDC. This paper proposes a novel system topology and a virtual synchronous generator (VSG) based control algorithm for islanded DFIG-based
WFs with LCC-HVDC integration. In the system topology, batteries are installed at the back-to-back converters of wind turbines (WTs) to generate the terminal voltage and then supply
energy for rotor excitation, therefore to provide a prerequisite of
the system startup. After the startup, synchronous generator
characteristics are exhibited by the DFIGs via the VSG control
algorithm. Moreover, a frequency control algorithm is applied
into the rectifier of HVDC to achieve active power balance andfrequency stability of the SEB. Simulations performed on
PSCAD/EMTDC verify the proposed control algorithm.

Voltage Sensitivity Matrix-Based Volt-Var Control
for Unbalanced Distribution Systems using DERs
Catie McEntee, David Mulcahy, Jiyu Wang, Ning Lu
FREEDM Systems CenterNorth Carolina State University
Raleigh, NC, USA
cmmcente@ncsu.edu

Abstract— This study explores a control strategy that
optimizes the use of utility control devices as well as distributed
customer-owned resources to control voltage and power factor on
a distribution feeder. Control actions include voltage regulator
tap changes, shunt capacitor switching, reactive power injection
and absorption by smart inverters, PV curtailment, andcustomer demand response. This strategy uses a Voltage
Sensitivity Matrix (VSM) in a mixed-integer linear program tochoose the least-cost combination of control actions thatsuccessfully brings all voltages within desired voltage limits,
maintains an acceptable power factor at the top of the feeder andminimizes changes in voltage between time periods.

 VLSM  P

Vi  Vi 
1

0

P

Total

i, j

j



j S PV  S Loadj S PV  S Load

VLSM i , j  s jcap

 VLSM  Qcap

jS cap

VLSM i , j  s j
reg

Q

Total

i, j

j

(3)
reg

jS reg

Subject to:Vmin Vi  V
1

max

(4)

III. KEY RESULTS

I. INTRODUCTION
Increased DER penetration can exacerbate voltage issues
on distribution feeders, but also offers opportunities for
distribution system operators to more precisely and efficiently
control voltage. In this control scheme, five types of control
actions are coordinated to find an acceptable voltage solution
at the least cost. Each possible control action is assigned a
cost: voltage regulators have a cost per tap change; capacitors
have a cost per switching operation; customer loads have apiecewise linear per-kWh cost function; and PV plants have a
per-kWh curtailment cost as well as a per-kVAR reactive
power cost (which applies for both injection and absorption).
Each of these control actions is limited by the respective
resource availability. The effect of each control action isestimated using a Voltage Sensitivity Matrix (VSM). The
VSM and control costs are used in a mixed integer linear
program to find the optimal control actions for each 5-minutetime period for a given distribution feeder or substation.
II. OBJECTIVE FUNCTION

Fig. 1. Total reactive power injection, PV curtailment and demand
response for one day simulation

Table 1. Comparison of conventional control and VSM volt-VAR
control actions for one day simulation

IV. CONCLUSION

min

PV

CP

  P   C   Q
PV

PV

j

Q

jS PVj Qj

PV , abs

jS PV   C  P
K

PV , injDR

Load 

kj ,kLoad 

 Ck  Pj , kDR     V  V 
upik 1 jS Load

(1)

down

i

i S vWhereVi

up , t Vidown , t

 Vi  Vi
1, t

1, t 1

This work is funded by the U.S. Department of Energy SunShot Initiative.

(2)

This control strategy allows utilities to take advantage ofcontrollable customer-owned loads and generators to assist or
replace voltage regulators and capacitors for voltage control.
The algorithm effectively keeps voltages within desired limits,smooths the voltage variation throughout the day, and reduces
the number of tap changes and capacitor switching operations.
This reduction can contribute to lower maintenance costs forthe utility especially where PV variability would cause
excessive tap changes under traditional control strategies.

Minimizing Wind Power Curtailments Using OPF
Considering Voltage Stability
Elis Nycander and Lennart SöderRobert Eriksson and Camille Hamon

Department of Electric Power and Energy Systems
KTH Royal Institute of Technology Stockholm, Sweden
Email: {elisn,lsod}@kth.se

Svenska Krafnät, Stockholm, Sweden
Email: {Robert.Eriksson,Camille.Hamon}@svk.se

Abstract—As the amount of wind power in power systems
has increased it has become necessary to curtail wind powerin some high-penetration situations. In order to assess the needfor curtailment arising from voltage stability considerations wedevelop a security constrained optimal power flow for minimizing
the expected curtailment. We find that with a very high windpenetration and wind farms operating at unity power factor
curtailment becomes necessary to satisfy voltage limits. In thiscase the optimal solution is to curtail at a single bus rather
than curtailing by a smaller amount at several buses. However,allowing for reactive power production from wind farms reduces
the need for curtailments.
Index Terms—Wind power, Curtailment, Voltage stability, OPF

Table I: Summary of load flow for original Nordic 32 system
Area
North
CentralSouthwest
External
Total

Generation (MW)
4630
2850
1590
2300
11370

Load (MW)
1180
6070
1390
2300
10940

B. Power factor constraintss|Qsw,j | ≤ κw Pw,j
1 − βjs , j ∈ W(2)s|Qsg,j | ≤ κg Pg,j, j ∈ G̃s
|Qg,j | ≤ κg Pg,j , j ∈ G \ G̃

(3)

We develop an optimal power flow to minimize the expectedcurtailments of wind power, subject to security constraints
under contingencies and different wind power scenarios. The
objective is to investigate under what situations voltage stability may limit transmission from an area with high windpenetration and the curtailments arising as a result.

For high wind power penetration in exporting area (North)
there is insufficient reactive power capability which requires
curtailment (Figure 1) to keep voltage limits. Curtailment
occurs at a single bus (Figure 2). When allowing wind farms
to operate at variable PF there is no need for curtailment.
300

C1, base caseC2, no wind at 1021
C3, wind PF 0.9

II. P ROBLEM FORMULATION
A. Problem
X

Pw,j βj  =

j∈W

X
s∈S

X

ps

s
Pw,j
βjs

(1)

j∈W

Curtailment (MW)250

min E (4)

III. R ESULTS

I. I NTRODUCTION



Losses (MW)
266
150
14
0
430

200
150
100
50

g(x) = 0h(x) ≤ 00% 0.80 0.82
MW 3488 3575

0.84
3662

0.86
3750

0.88
3837

0.90
3924

0.92
4011

0.94
4098

0.96
4186

0.98
4273

Wind power in base scenario

GEN 13 BUS 4041

GEN 19 BUS 4062

GEN 25 BUS 1013

GEN 8 BUS 2032

GEN 14 BUS 4042

GEN 20 BUS 4063

GEN 26 BUS 1014

GEN 3 BUS 1014

GEN 9 BUS 4011

GEN 15 BUS 4047

GEN 21 BUS 4063

GEN 27 BUS 1012

GEN 4 BUS 1021

GEN 10 BUS 4012

GEN 16 BUS 4047

GEN 22 BUS 4071

GEN 28 BUS 1022

GEN 5 BUS 1022

GEN 11 BUS 4021

GEN 17 BUS 4051

GEN 23 BUS 4072

GEN 29 BUS 1021

GEN 6 BUS 1042

GEN 12 BUS 4031

GEN 18 BUS 4051

GEN 24 BUS 1011

GEN 30 BUS 2031

Base scenario

19

20% wind increase

Figure 2: Active power of generators for 99.5% penetration in North for case C1.

29

20
21
22

14
15
16
17
18
13

11
12

6
8
9
10

5
1
2
3
4

10
11
12
13

7
8

0

GEN 31 BUS 203230
31

GEN 7 BUS 1043

GEN 2 BUS 1013

24
25
26
27
28

GEN 1 BUS 1012

7

9

6

500

23

23

Figure 1: Curtailment for increasing wind penetration.

24
25
26
27
28
29
30
31

1000

Variable
Fixed

1
2
3
4
5

P (MW)

1500

North
CentralSouthwest
External

14
15
16
17
18
19
20
21
22

2000

1.00
4360

The Value of Reactive Power
for Voltage Control in Lossy Networks
Matthew Deakin, Thomas Morstyn, Malcolm McCulloch

Dimitra Apostolopoulou

Department of Engineering Science
University of Oxford, Oxford, UK

Department of Electrical & Electronic Engienering
City, University of London, London, UK

Abstract—Reactive power has been proposed as a method ofvoltage control for distribution networks. The value of reactive
power can therefore be measured according to an increase intransferred energy, where the transferred energy is defined as thetotal generated energy, less the total network losses. If network
losses are ignored, an error in the valuation of a given amountof reactive power will be observed. The non-linear closed-form
solution of a two-bus network is studied, and non-trivial upperand lower bounds are determined for this ‘valuation error’. The
properties predicted by this two-bus network are demonstrated
to hold on a three-phase unbalanced distribution test feeder.

Z

Vt
St

Vg
Sg

Sl

S0

PS
DG

Fig. 1. Two bus power flow model.

I. I NTRODUCTION
Reactive power is one of a number of technologies thatcan be used as a method of increasing the amount of distributed generation (DG) connected to distribution networks,
by reducing the voltage at generators that is seen (for a given
real power generation). Sinking reactive power changes the
network losses. Without taking into account the changes inlosses, we cannot fairly assess the value of the reactive power.

Fig. 2. Lower (l) and upper (r) bounds on valuation error E .

III. R ESULTS & C ONCLUSIONS

II. R EACTIVE P OWER VALUATION
We study the closed-form solution of the two bus network
shown in Fig. 1 to study network losses Pl (using notation
S(·) = P(·) + jQ(·) as apparent, real, reactive powers respectively). These losses come about due to the network impedanceZ, representing an equivalent positive sequence impedancefrom generator to source. For a given load S0 and generation
profile PS , we can calculate the generated and transferredenergy as
Z T
Z T
Eg =
Pg (τ, Q̂g ) dτ , Et =Pt (τ, Q̂g ) dτ ,00

for a given time period T , given reactive power limit Q̂g , gridvoltage Vt , and maximum generator voltage Vg . The increasein energy generated and transferred, as ‘approximate’ and‘true’ reactive power valuations (∆Eg , ∆Et respectively, as
Et includes changes to network losses), are thus
∆Eg = Eg (Q̂g ) − Eg (0) ,∆Et = Et (Q̂g ) − Et (0) .

As such, we can define both energy and instantaneous (power)
valuation errors respectively as∆Eg − ∆Et
E (Q̂g ) =,∆Et

The power valuation error P bounds the energy valuation
error E . We therefore calculate P as Q̂g tends to zero, andat a maximum power transfer (MPT) point, to bound E (seeFig. 2). These bounds are irrespective of the generation profile.
Three case studies were run (on the IEEE 34 bus distribution
test feeder) with 2.7, 4.8 and 6.7 MWp distributed generation,using the generation profile of Fig. 3. The two-bus modelis reasonably accurate, slightly over-estimating E . This iscaused by the voltage dependency of reactive power loads.
We conclude that the significance of these losses is modelledaccurately using the two bus model. The model can be used
to show when losses can (and cannot) justifiably be ignored.

125
100
75
50
25
0

1
0.5
0
0

6

12

18

24

OpenDSS
2 bus

0

0.25

0.5

0.75

∆Pg − ∆Pt
P (Q̂g ) =.
∆Pt
Fig. 3. Generation profile (l), and case study results (r).

1Study and Comparison of Wind Power Correlation
Using Two Types of Dataset
Yurong Wang, Member, IEEE, Yannan Luo

Zeyu Dai

School of Electrical Engineering, Southeast University
Nanjing 210096, China
Email: wangyurong@seu.edu.cn

State Grid Taizhou Power Supply Company
Taizhou 225300, China

Abstract—With the integration of large-scale wind farms,
stability and economy of power grid are greatly challenged.
Considering the stochastic characteristics of wind and thecoupling relationship of geographically distributed wind farms,
this paper presents the comparison of analysis methods and results
when using wind speed dataset and wind power output dataset inwind power spatial correlation research based on copula.
Combined with several revised criteria, a novel method is
proposed to select copula type and judge goodness of fit. In thecase study, copula models and typical scenarios using the twodatasets are highlighted and compared. Study results clarified theadvantages and disadvantages of using different datasets.
Index Terms—Copula, evaluation index,
correlation, typical scenario, wind power.

wind

power

III. CASE STUDY
A. Binary Frequency Histograms(a) Result using wind speed dataset

I. INTRODUCTION

B. A Novel Method for the Selection of Copula Function

With the access of multiple large-scale wind farms, power
grid faces serious problems in system operation, especially thegeneration scheduling and unit commitment problems.
Researchers found that there is some type of correlation between
geographically distributed wind farms. Copula theory is used to
study the output correlations of multiple wind farms based onwind speed dataset and wind power output dataset in this paper,and a novel method to select copula model is proposed based on
three revised indices in order to evaluate the accuracy ofmodeling.

TABLE I.
Type
Criterion

COPULA SELECTION USING WIND SPEED DATASET
normalCopula××

Spearman×

AIC

Euclidean

A. Relationship of Wind Speed and Wind Power Output

Kendall0, 0 ≤ 𝑉 ≤ 𝑉𝑐𝑖 𝑜𝑟 𝑉 ≥ 𝑉𝑐𝑜{

𝑉 − 𝑉𝑐𝑖, 𝑉 ≤ 𝑉 ≤ 𝑉𝑟
𝑉𝑟 − 𝑉𝑐𝑖 𝑐𝑖
𝑃𝑊𝑇𝐺,𝑟 , 𝑉𝑟 ≤ 𝑉 ≤ 𝑉𝑐𝑜

ClaytonCopulaKendallType
Criterion

II. KEY EQUATIONS

tCopulaEuclidean

TABLE II.

𝑃𝑊𝑇𝐺 = 𝑃𝑊𝑇𝐺,𝑟(b) Result using wind power dataset

Fig. 1. Binary frequency histogram using two different datasets.

AIC

GumbelCopula
√

√

×

○

×

√

COPULA SELECTION USING WIND POWER DATASET
normalCopula○√

tCopulaClaytonCopula×

Frank
Copula×

Spearman

(1)

Frank
Copula○×

GumbelCopula××○×

IV. CONCLUSIONS

B. Copula Theory
𝐹(𝑥1 , 𝑥2 , ⋯ , 𝑥𝑁 ) = 𝐶(𝐹1 (𝑥1 ), 𝐹2 (𝑥2 ), ⋯ , 𝐹𝑁 (𝑥𝑁 ))(2)

C. Mixed-Copula Theory
𝐶 = 𝜔1 𝐶1 + 𝜔2 𝐶2 + 𝜔3 𝐶3𝐶 = 𝜋𝐶1 + (1 − 𝜋)𝐶2

(3)
(4)

This paper finally come to conclusions as follow:
1) Under the same research method, copula model can
describe the correlation of wind speed better than wind power
output in this case study.
2) The results of clustering using wind power is better for
further research, but incorporating copula method to describe thecorrelation of wind power output produces larger error.

Application of Solid State Transformers in Hybrid
Residential Energy Delivery
Manisha Maharjan and Rajesh Kavasseri
Department of Electrical Engineering and Computer Science
North Dakota State University
Fargo, North Dakota
Email: manisha.maharjan@ndus.edu
Abstract—The integration of renewable energy sources likesolar and wind, has been continuously increasing in the residential distribution system. These require improved controllability
to maintain proper coordination between stochastic renewablesources and load. Solid state transformers (SSTs) are powerelectronic devices which consist of the converter models thatcan reduce the conversion stages in hybrid AC/DC residential
distribution systems. This work presents the preliminary resultsof integrating this simplified model of SST in an example model ofresidential distribution system, comprised of a residential housewith AC, DC loads, solar panel and battery system. This will
create a baseline for using these simplified models to compare theperformance of such hybrid system in the residential distribution
systems. Traditional transformers replaced with these SSTs solve
the purpose of bi-directional power flow and provide integrationof renewable energy sources and DC loads,without add-on powerelectronic components, reducing complexity.

I.

K EY R ESULTThe preliminary results show that the load is being suppliedby solar power and battery primarily. When there is deficiency
in solar power and battery is undercharged or off, the load
power consumption is fulfilled through the utility. SST supports the bi-directional power flow as surplus power is fed
back to the utility, when battery charging is not required.

I NTRODUCTION

Renewable energy sources like residential photovoltaics(PV) in low voltage distribution feeders help in addressing
energy demand without adding large generation and transmission units [1]. Due to the decrease in installation cost of PV,
the residential PV market has been growing rapidly. Increasein the use of modern DC appliances render the prospect ofenergizing these DC devices from renewable energy sources
directly. The key idea of this work is to explore the use of
simplified solid state transformer (SST) [2] in the residential
distribution system, which incorporates DC power sources likePV and battery with residential AC as well as DC loads.
II.

III.

Fig. 2. Power consumed and delivered by load, solar, battery and utility ina 24.hour period.

K EY F IGURE

Fig. 3.

Battery ampere-hour and state of charge for 24 hour period.

R EFERENCES[1]

Solar Energy Industries Associations. (2016) Solar Market Insight
Report 2016 Q4. [Online]. Available: http://www.seia.org/researchresources/solar-market-insight-report-2016-q4[2] M. Maharjan, U. Tamrakar, S. Bajagain, T. M. Hansen, and R. Tonkoski,“A steady-state equivalent model of solid state transformers for voltage
regulation studies,” in 2017 IEEE Power Energy Society General Meeting, July 2017, pp. 1–5.

Fig. 1.

A example model for residential distribution system.

Comparing Capacity Value Metrics of EnergyLimited Resources
Sarah Awara
Department of Electricaland Computer Engineering
University of Calgary
Calgary, Canada
saawara@ucalgary.ca

Hamidreza Zareipour
Department of Electricaland Computer Engineering
University of Calgary
Calgary, Canadahzareipo@ucalgary.ca

Summary— Capacity value, or capacity credit, measures thecontribution of an added generation resource to meet the demandin a power system. Unlike conventional generators, whose
available capacity depends on the mechanical availability, energylimited resources have limiting factors that affect their ability tooperate continuously on a daily basis. Examples of energy-limited
resources are renewable and energy storage resources. With theincrease in energy-limited resources in power systems, it is criticalto accurately determine their capacity value during generation
planning to maintain system reliability. This poster will focus oncomparing the capacity value of wind computed by different
capacity value methods for different locations in Alberta.

I. INTRODUCTION
The reliability of supply is a major concern in electric powersystems. This reliability will increase when more renewables
and energy storage resources are added to the current electric
power system. However, when energy-limited resources replacedispatchable generators, there is a concern about the reliabilityof the system [1]. Higher amounts of reserve capacity are needed
to maintain the system’s adequacy. Therefore, it is important todetermine the capacity value (CV) of generation resources toensure that the reliabitiy of the system is met during power
generation planning.
The CV of a generator resource depends on the risk modelcharacteristics used to calculate the CV. The risk model for solar
is different than the risk model used to calculate the CV ofstorage. Based on the literature, the CV of a renewable facility
can have a wide range between 5% and 95% of the nameplate
capacity and some of the factors that contribute to this range are
related to geography, penetration levels of the technology andthe correlation of generation and demand [2].

methodology calculates the CV based on the capacity factor ofrenewables during a set number of hours.
The probabilistic-based method uses extensive data from all
hours of the day to compute the CV with a higher resolution.
The probabilistic-based methodology is illustrated in Fig. 1. In
this approach, the generation model only considers conventional
generation and is developed by computing the Capacity Outage
Probability Table (COPT). Wind power time series are included
by means of a net load model. For a specified period underinvestigation, the wind time series is treated as negative load andsubtracted from the load time series. The generation model andthe net load model are convolved to compute the Loss of Load
Expectation (LOLE) and Expected Unserved Energy (EUE).
Alberta is currently designing a capacity market. One of the
major topics discussed by the Alberta Electric System Operator(AESO) is capacity value determination. Therefore, this poster
will compare the capacity value of wind using different
probabilistic-based and approximation-based methods using
historical data.

Fig. 1. Capacity Credit Determination Methodology

II. RESEARCH & METHODOLOGYWhen calculating the CV of a specific technology, theliterature includes several different risk models with different
assumptions. Also, when comparing the capacity value studiesof the same system using different methodologies, it is criticalto pay attention to the details of the method [3].
There are two classes for quantifying capacity value:
probabilistic-based and approximation-based methods. Most
system operators use the approximation-based method (capacity
factor methodology) to calculate the CV of renewables. ThisWe acknowledge the support of the Natural Sciences and Engineering ResearchCouncil of Canada (NSERC), CRDPJ 477323 – 14 (Cette recherche a été
financée par le Conseil de recherches en sciences naturelles et en génie duCanada (CRSNG), CRDPJ 477323 – 14). This project was funded in part byAlberta Innovates, which supports and accelerates research, innovation andentrepreneurship. This project is also supported by Rocky Mountain Power.

Andy Knight
Department of Electricaland Computer Engineering
University of Calgary
Calgary, Canada
aknigh@ucalgary.ca

III. REFERENCES[1]

M. Amelin and L. Söder, "Taking Credit: The Impact of Wind Poweron Supply Adequacy- Experience from the Swedish Market," IEEE
Power and Energy Magazine, vol. 8, no. 5, pp. 47-52, 2010.

[2]

D. Gami, R. Sioshansi and P. Denholm, "Data Challenges in Estimating
the Capacity Value of Solar Photovoltaics," IEEE Journal of
Photovoltaics , vol. 7, no. 4, pp. 1065-1073, July 2017.

[3]

P. O. Aguirre, C. J. Dent and G. P. Harrison, "Realistic Calculation ofWind Generation Capacity Credits," in CIGRE, Calgary, 2009.

Energy Storage Integration in an Islanded Wave
Energy System
Eric Wu, Andrew M. Knight
Department of Electrical and Computer Engineering, University of Calgary
Abstract— Energy storage systems are vital for integrating
renewable energy resources into the existing power system in a
reliable and sustainable manner. In particular, an islanded
microgrid should be able to rely on renewable energy systems
when grid faults occur. This research will endeavor to determine
an energy storage control system capable of ensuring the reliable
operation of an islanded microgrid heavily populated byrenewable energy sources.

I. INTRODUCTION
Microgrids combine distributed energy resources, energy
storage and loads together. The ability to operate during gridfaults and support remote communities have made microgrids apopular topic among fellow researchers. Renewable energy
represents one of the distributed energy resources commonly
implemented in microgrids. Similar to microgrids, renewableenergy is experiencing prominent growth as a result of its
promise to supply clean and limitless energy. However, powerintermittency is a well-known issue associated with renewableenergy that can compromise the reliability and resiliency thatmicrogrids promise. Power intermittency refers to the varying
and fluctuant nature of renewable resources. To mitigate powerintermittency, energy storage can be used. The control strategy
for energy storage systems depends on whether the microgrid
is operating in grid-connected or in islanded operation. In thisresearch, the control strategies needed for a microgrid
operating in islanded operation will be explored. Furthermore,
the microgrid of interest will consist of a wave energy system,a battery energy storage system and a constant power load. In
the model, three different wave energy systems will beconsidered. The first wave energy system will consist of asingle Wave Energy Converter (WEC). A second WEC will be
added to form the second wave energy system. Finally, thethird wave energy system will contain a total of three WECs.
For each of these systems, an arbitrary, but reasonably sized,
battery storage system and load will be connected at thesystem’s Point of Common Coupling (PCC). The objective of
exploring wave energy systems with varying sizes is toevaluate the flexibility of the battery storage’s control system.
By analyzing the power waveforms generated at the PCC ofeach system, the effectiveness of the battery storage’s control
system can be assessed.
II. METHODOLOGY AND RESULTS
A. Islanded Wave Energy System
Figure 1 illustrates the generic islanded wave energy
system investigated in this research.

Fig 1. Islanded Wave Energy System with Battery Storage and a ConstantPower LoadAs shown in Fig. 1, each WEC contains a back-to-backconverter for active and reactive power control. By
appropriately controlling an adequately sized battery storage
system, a constant power flow can be guaranteed for theconstant power load, regardless of the power supplied by thewave energy system. Initially, a single WEC with battery
storage is connected at the PCC in Figure 1. A 120Ω resistor isused in place of the constant power load and the system ismodelled in a simulation software. In the simulation, thebattery is controlled as a voltage source by setting its d-axis
voltage equal to one.
B. Power Flows Measured at the PCC
Figure 2 shows the power flowing through each of the threesources considered in Figure 1 for a system with one WEC.

Fig 2. Power Flow at PCC measured out of WEC and into Load and Battery

As observed in Figure 2, the battery storage system is able toaccommodate the power required by the load by supplying the
deficit or absorbing the excess power generated by the WEC.

Life Cycle Testing of Vanadium Redox Flow Battery and Lithium Ion Energy
Storage Systems
Muhammad Rashid, Andy Knight
Department of Electrical and Computer Engineering
University of Calgary
Calgary, AB, Canada
Email: muhammad.rashid2@ucalgary.ca, andy.kinght@ucalgary.ca
I.

INTRODUCTION

The integration of energy storage systems in power grids
has gained vast interest in recent years. Energy storage
systems can take many forms with sizes varying from few
kilowatt-hours to many gigawatt-hours. Such systems canresolve some of the challenges faced by the different
segments of power grids. They can also aid in the integrationof renewable energy generation.
II.

METHODOLOGYThis abstract focuses the performance of Battery Energy
Storage Systems (BESS). Life cycle testing is being
performed on two types of BESS. The first is a vanadium
redox flow battery and the other is a Lithium-Ion backup
power system. The main objective of the test is to measure
the performance of the BESS in terms of round trip
efficiency and how it correlate to operational conditions suchambient temperature. The test also measures the degradationin efficiency caused by the continuous cycling.
A. Vanadium redox flow battery
A Vanadium Redox Flow Battery (VRFB) employs
vanadium electrolytes as the energy storage element. It also
includes all the necessary hardware such as flow pumps andion exchange membrane. The life cycle testing on the VRFB
is carried out by commercially available DC battery testing
equipment that charges and discharges the battery at rates
that are specified by the user, also called C rate.
The maximum allowable continuous C rate as per the
manufacturer’s specifications is used for this testing work.
At the time of writing this abstract, a total of 165 cycles havebeen performed on the VRFB.
An important metric of evaluating the performance offlow batteries is the electrolyte temperature. This metric is
directly correlated to the round trip efficiency of the VRFB.
Figure 1 clearly shows such correlation.
B. Lithium-Ion energy storage system
The second BESS that is also being tested in this work isa fully integrated lithium-ion energy storage system. It is acommercially available backup power system intended foruse in residential dwellings. In contrast to the VRFB, thissystem comes fully integrated with a battery management
system and inverter. The user does not have direct access to
the lithium-ion battery; neither can they specify the settingsneeded for the life cycle testing since the system is designed
for autonomous operation.

Figure 1: VRFB efficiency and electrolyte temperature

In order to perform the intended testing procedures, atesting setup had to be implemented in order to force thesystem to perpetually charge and discharge the battery to full
capacity. The diagram in Figure 2 shows how the testing
setup was implemented.
The setup incorporates measurement and control devices
as well a space heater to serve as a constant power load. The
measurement devices are one potential transformer (PT) two
current transformers (CT-1 & CT-2). The control devices are
two solid-state relays (SW-1 & SW-2). In order to force thesystem to go into charging or discharging modes, thereadings from CT’s are monitored and as per a user defined
control sequence, the relays are actuated accordingly. The
voltage and current measurements are used to calculate thesystem round trip efficiency and capacity degradation.

Figure 2: Li-Ion energy storage system testing setupModeling and Control of an Integrated Wind Power
Generation and Modular Multilevel Converter with
Energy Storage System
1

Jaesik Kang, Member, IEEE, 1Sangmin Kim, Member, IEEE, 1Wooyoung Shin, Member, IEEE, 1Sehwan Lim,
Member, IEEE, 1Jae Woong Shim, Member, IEEE and 1Kyeon Hur, Senior Member, IEEE
1
School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea
Email: jaesikkang84@gmail.com, tkdminn@gmail.com, shinwoo0514@naver.com, bulls92@hanmail.net,
khur@yonsei.ac.kr

Abstract—This paper analyzes the internal dynamics of modular
multilevel converter (MMC) for exploiting sub-modules (SM) asan energy storage system for smoothing the fluctuating output
power of wind power generation. The fluctuating output power ofthe wind generation may cause an oscillatory impact on aninterconnected grid, and it thus aggravates the grid stability. In
conventional method for achieving the stability enhancement,
energy storage system (ESS) is a key role of maintaining thechanges in renewable generation system, which might beconsidered as slightly unnecessary solution in high-voltage direct
current (HVDC) transmission system for the wind power
generation due to the controllability of the converter for HVDC.
In this paper, the MMC system and controller with energy
storage management is modeled and developed by investigating
the internal dynamics of the MMC, and a simple system with asynchronous generator (SG) and wind power plant (WPP) basedon PSCAD/EMTDC is simulated to demonstrate the efficacy ofthe proposed control scheme for establishing a role of the ESS in
the MMC, and it requires that the system be stabilized by the
ESS based MMC system to avoid the oscillatory mode from thefluctuating output power of the wind generation system.

Fig. 1. Internal dynamics of MMC with a decoupled control of ESS for an
integrated wind generation system: (a) behavior for AC current, (b) DC
current and (c) circulating current.

Fig. 2. Two area test system.

Index Terms—Modular multilevel converter (MMC), highvoltage direct current (HVDC), wind power generation,
PSCAD/EMTDC, energy storage system (ESS), synchronous
generator (SG).

II. KEY RESULTS

I. KEY FIGURES

Fig. 3. The fluctuating and mitigated output power of the wind generation
with and without ESS operation in the MMC.

III. REFERENCE
[1]
[2]

[3]

Jae Woong Shim, et. al., “Impact Analysis of Variable Generation on
Small Signal Stability”, in Proc. Australian Universities PowerEngineering Conf., Perth, Austrailia, Oct 2014.
Shenghui Cui and Seung-Ki Sul, “A Comprehensive DC Short-Circuit
Fault Ride Through Strategy of Hybrid Modular Multilevel Converters(MMCs) for Overhead Line Transmission”, IEEE Trans. on PowerElectronics, Vol. 31, No. 11, pp. 7780-7796. Nov. 2016.
Zhenhua Jiang, et. al., “Modeling and Control of an Integrated Wind
Power Generation and Energy Storage System”, in Proc. IEEE Powerand Energy Soc. General Meeting, 2009.

Indirect Mechanism Design for Efficient and Stable
Renewable Energy Aggregation
Hossein Khazaei

Yue ZhaoDept. of Electrical and Computer Engineering
Stony Brook University
Stony Brook, NY, 11794 USA
Email: hossein.khazaei@stonybrook.edu

Dept. of Electrical and Computer Engineering
Stony Brook University
Stony Brook, NY, 11794 USA
Email: yue.zhao.2@stonybrook.edu

Abstract—Mechanism design is studied for aggregating renewable power producers (RPPs) in a two-settlement power
market. Employing an indirect mechanism design framework,a payoff allocation mechanism (PAM) is derived from thecompetitive equilibrium (CE) of a specially formulated market
with transferrable payoff. Given the designed mechanism, thestrategic behaviors of the participating RPPs entail a noncooperative game: It is proven that a unique Nash equilibrium(NE) exists among the RPPs, for which a closed form expression
is found. Moreover, it is proven that the designed mechanism
achieves a number of key desirable properties at the NE: theseinclude efficiency (i.e., an ideal ”Price of Anarchy” of one),stability (i.e., ”in the core” from a coalitional game theoretic
perspective), and no collusion. In addition, it is shown that a
set of desirable ”ex-post” properties are also achieved by thedesigned mechanism. Extensive simulations are conducted andcorroborate the theoretical results.

I. I NTRODUCTION
Renewable energies play a central role in achieving asustainable energy future. However, renewable energies suchas wind and solar power are inherently non-dispatchable, andyet highly uncertain and variable. Aggregation of statisticallydiverse renewable energy sources can decrease the uncertaintyof the renewable power producers (RPPs). A key question inaggregating RPPs is how to allocate the total payoff of anaggregation among its member RPPs. In this paper, we propose
a mechanism to address this question.
II. P ROBLEM F ORMULATION
We consider the case where an aggregator represents a
set of N RPPs (denoted by N ) in the DA-RT market. In
the DA market, aggregator asks the RPPs to submit theirDA∑ commitments {ci } to it. Aggregator then commits cN =i∈N ci in the DA market. In the RT market, the aggregatorcollects all∑the realized generations from the RPPs, denotedby xN = i∈N xi . The realized payoff of the aggregator is
PN = pf cN − pr,b (cN − xN )+ + pr,s (xN − cN )+(1)

We denote the expected payoff of the aggregator in the DA
market by πN = E [PN ].

We use the following payment allocation mechanism (PAM)to allocate the realized payoff of the aggregator in (1) among
the RPPs. The realized payoff of RPP i in the aggregation is

f
r,b

if xN − cN < 0p ci + p (xi − ci )f
Pi = p ci + p∗ (xi − ci )(2)
if xN − cN = 0 ,

 f
r,s
p ci + p (xi − ci )if xN − cN > 0
where p∗ can arbitrary be chosen within the range pr,s ≤ p∗ ≤pr,b . The expected payoff of the RPP i in the aggregation isdenoted by πi = E [Pi ].
III. C ONTRACT G AME
Within the aggregation, each RPP tries to maximize its
expected payoff. This competitive behavior is modeled using
a non-cooperative game termed the contract game, where theplayers are the RPPs forming the aggregation, the strategy ofeach RPP is its DA commitment, i.e. ci , and the payoff ofeach RPP is πi .
It is proved that, under mild assumptions, the contract game
always possesses an unique pure Nash equilibrium (NE) witha closed form formula: The strategy of RPP i at this NE is
[
]
⋆,o
c⋆,ne
=
E
X
X
=
c
,
(3)
i
N
i
N
where Xi∑is the random generation of the RPP i, and theXN =i∈N Xi is the total random generation of theaggregator. c⋆,o
N is the optimal commitment of the aggregator
which maximizes the πN .
Some of the properties of this mechanism are:• Existence of an unique pure Nash equilibrium with closed
form formula to compute it.
• Optimality of the coalition.
• Ex-ante in the core: Being in the core in an ”ex-ante”
sense means that the RPPs’ expected payoffs satisfy thefollowing condition: if any subset T of the RPPs leave
the aggregation, separately form their own aggregation,and then participate in the market and get their highest
possible expected payoff, they will get an expected payoff
no higher than the sum of their expected payoffs originally from the aggregator at the unique pure NE of thecontract game.

Power-frequency Response Model Identification for
Adaptive Virtual Inertia Algorithms
Ujjwol Tamrakar, Student Member, IEEE, Reinaldo Tonkoski, Member, IEEE
Department of Electrical Engineering and Computer Science
South Dakota State University
Brookings, South Dakota 57007
Email: ujjwol.tamrakar@sdstate.edu
Abstract—The rapid growth in renewable energy systems(RESs) has transformed the power system to an inverter dominated system. Virtual inertia systems are being integrated into
the power system to improve the frequency stability either
through energy storage systems and/or RESs. The total inertia
of a power system is thus no longer constant as with theconventional generator dominated system. Hence, the powerfrequency response of a system changes continuously over-time
based on the number of virtual inertia units that are online.
This make it difficult to tune the controllers so that the system
can emulate inertia optimally. So, for optimal tuning of thesevirtual inertia algorithms, an online model of the power-frequency
response is needed. This poster presents some preliminary results
on identifying the power-frequency response of model based onreal-time measurement data.

I.

the frequency measurements to make the measurements more
realistic.

K EY E QUATIONS

The integration of components such as photovoltaics (PV),wind and energy storage systems has resulted in significant
decrease in the power system inertia. Furthermore, the inertia
is time-varying and dependent on the system operating conditions. Virtual inertia systems are proposed in the literature
to counteract this reduction in inertia. To optimally tune acontroller an online modeling technique which can model thetime-varying, dynamic of the relationship between the active
power and frequency is needed. A linear least-square estimation method is presented in this poster which can identify the
dynamic system based on input and output measurements. In
general, an estimated dynamic system can be represented as:ŷ(t|θ) = φT θ

Fig. 1.

Simulation setup used for model identification.

III.

K EY R ESULTS

A transfer function with 2 poles and 1 zero provided agood fit of 77.44% with the testing dataset. The fitted model
along with the original measured data is illustrated in Figure 2.
The dotted-lines around the fitted response represents the 95%
confidence interval. Higher order models were also tested but
it did not provide any significant improvement in the fit of themodel.

(1)

where φ is a vector of of input and output data and θ is thepoles and zeros that represent the system dynamics. For aknown set of measured input-output data, Z N , defined as:
Z N = {u(1), y(1), ..., u(N ), y(N )}(2)

The unknown of parameter of the model, θ, can be estimatedby minimizing:
1 X
2
VN (θ) =ky(t) − ŷ(t|θ)k
(3)
N
II.

K EY F IGURESThe simulation setup used for power-frequency response
identification is shown in Fig. 1. A 13 KVA hydro system ismodeled along with the governor and the excitation systems.
The inertia constant is set to 2 s. Step load changes of 250
W are applied and the corresponding change in frequency andinput power is logged at a sampling rate of 10 Hz. Random
Gaussian noise (mean =0, variance = 0.001) is also added toFig. 2.

Identified power-frequency response model and its verification.

IV.

C ONCLUSIONS AND F UTURE W ORK

A linear least-square estimation technique can effectively
predict the frequency response of a power system. A online
recursive algorithm which can be updated based on systemmeasurements will be developed in the future to account for
the inertia variability.

1

Enhancement of Grid Connected PV Arrays
Fault Ride Through and Post Fault Recovery
Performance
Aslan Mojallal, Student Member, IEEE, and Saeed Lotfifard, Member, IEEE

Abstract—The proposed control scheme is composed of twocontrollers: Finite-Time Sliding Mode Control (FTSMC) andProxy-Based Sliding Mode Control (PBSMC). The FTSMC, whichassures the finite time convergence of the tracking error, controls
the DC/DC converter of GCPA during voltage sags to assure theDC-link capacitor voltage does not exceed its limits. PBSMC
controls the DC/AC inverter. PBSMC provides accurate and fast
tracking feature during normal condition (similar to PID
controller) and is capable of very smooth resuming to the desiredreference value/trajectory after large disturbances that may leadthe control signal to exceed the limits. Therefore, during post-fault
voltage recovery period, PBSMC exits saturation faster whichsignificantly enhances the recovery performance of GCPA.
Index Terms—Low Voltage Ride Through (LVRT), Nonlinear
Control, Photovoltaic (PV), Recovery enhancement.

I. INTRODUCTION
In this research effort, a Finite Time Sliding Mode Control(FTSMC) method is used to assure fast and stable operation of
DC/DC converter during voltage sags. FTSMC guarantees the
tracking error reaches the sliding surface in ﬁnite time. As
nonlinear FTSMC is used in the proposed method, thecontroller can operate effectively in wide range of operation
condition. A (Proxy Based Sliding Mode Control) PBSMCbased controller is also proposed for the DC/AC inverter toenhance the post-fault recovery performance of the Grid
Connected PV Array (GCPA). PBSMC provide fast response
during normal operation of the system while provides smooth
resuming to the desired value after large disturbances.
II. KEY EQUATIONS(b)

Condition for Stability: dG( i pv )i1i
 1 
e1 
 dc    1   2 .3 . e1 3  e1  0
LC
LC LC 
di pv

*
pv

(5)

B. PBSMC Equations:
The following control law is obtained for PBSMC:dd  
ki .  k p .  k 
dt
dt  df  F.sat  .kd
 F  K
 





(6)

III. KEY RESULTS:TABLE ICONTROL PARAMETERS
DC/AC Inverter, current
DC/AC Inverter, DC voltage
regulator
regulator
0.3
20
10-7
2 p.u.

kpki
kd
Current Limit

5000 Hz

α1

kp

ki

kd
Voltage Limit

DC/DC Converter
Switching Frequency

PBSMC
K, F

0.01, 1.25

7
800
10-4
1.25 p.u.

FTSMC
2.05

α2

50

α3

0.85

Case (a): Conventional Control System
Case (b): FTSMC-controller and PID:
Case (c): FTSMC-controller and PID controller with antiwindup scheme
Case (d): Conventional control for DC/DC converter and
PBSMC controller for DC/AC inverter
Case (e): FTSMC and PBSMC-controller

A. FTSMC Equations:(a)Control law:1u  ( 1  sgn( S FTSMC ))2(1)
Fig. 1. DC-Link Voltages of GCPA for 3-phase voltage sag to 0.2p.u.

where:SFTSMC  e1  1e1   2 . e1

3

.sgn( e1 )

v pv
vdc
u
L
L
u .i*pv u .idc
u .e dG( i pv )e1   1 
e1 

LC
di pv
LC
LC
e1 

(2)
(3)
(4)
Fig. 2. Injected reactive power by GCPA for 3-phase voltage sag to 0.2p.u.

Real-Time Modeling and Battery-in-the-Loop
Characterization of an Energy Storage System
Ali Arzani1 and Ganesh K. Venayagamoorthy1,21Real-Time Power and Intelligent System Laboratory, Clemson University, SC, USA
2
School of Engineering, University of KwaZulu–Natal, Durban, South Africa
aarzani@g.clemson.edu and gkumar@ieee.org

I.

hardware in the loop (PHIL) is based on the ideal transformer
method (ITM) interface algorithm. ITM is widely used due toits higher accuracy and low processor burden. The real time
digital simulator (RTDS) simulation environment (RSCAD)
generates reference current for VSI based on measured HuT
feedback voltage. This procedure is not straightforward as thetime-delay introduced in the closed loop can lead to instability
and also deteriorates the accuracy of the experiment.
Therefore, a stability analysis (theoretic or/and dynamic
simulation) is necessary before performing the PHIL
experiment and appropriate protection schemes are applied(software and hardware). The testing of BIL of Fig. 1b provide
means to analyze different operational issues of the battery
such as: charge/discharge cycling operation effect on thebattery cells, modules and the entire system; charge/discharge
controller issues taking into account interaction with battery
cells and VSI.

INTRODUCTION

The quest towards inertia less Distributed Energy Systems(DES) dispatchability at higher penetration levels (>25%) in
the grid demands incorporation of Battery Energy Storage
Systems (BESS). In this study, development of a battery
hardware-in-the-loop (BIL) simulation platform for testing
BESS under different operating conditions. It consists of an
actual battery interfaced with a real-time simulation model ofthe system in RSCAD as seen in Fig. 1b. Battery electricalequivalent circuit models are developed for estimation of Stateof Charge (SOC) and its comparison with hardware battery
SOC measurements for verification of accuracy.
The initial platform consists of testing three BESS SOC in
RSCAD, where: (a) Charge/discharge is through a hardware
amplifier source/sink feature. (b) High power residential load
is the hardware interfaced to RTDS, (c) Li-ion battery is the
HIL with RTDS simulation. The implementation of the power

(a)

(b)

Fig 1. (a) BESS components for vehicle to-grid support, (b) BIL Testing Platform

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 are those of the author(s) and do not necessarily reflect the views of NSF.

Robust Microgrid Economic Dispatch Considering
Renewable Uncertainty using Interval Optimization
Cody Rooks, Student Member, IEEE, Xiao Kou, Student Member, IEEE, and Fangxing Li, Fellow, IEEE
Department of Electrical Engineering and Computer Science
The University of Tennessee
Knoxville, TN 37996{crooks2, fli6}@utk.edu

Abstract—Renewable energy systems create a challenging problem for
microgrid operators to balance supply and demand in an economicallyefficient way. The intermittent nature of renewable generators result in
dispatch cost fluctuations depending on their power output, requiring arobust solution approach. Interval analysis and optimization is used toformulate and solve the economic dispatch problem of a microgrid systemgiven renewable generator uncertainties.
Keywords—microgrids, economic dispatch, interval optimization,
electricity markets

1.

BACKGROUND

Similar to the challenges faced by an Independent System
Operator (ISO), a microgrid operator will attempt to balance a portionor all of its load in an economically optimized way. In other words, itwill attempt to solve the economic dispatch problem in its own
domain. A microgrid operator has several different types of
distributed energy resources (DERs) at its disposal. These DERs willtake the form of both dispatchable generators, e.g. fuel cells andmicroturbines, and non-dispatchable generators, e.g. solar PV andsmall wind turbines. The intermittent nature of renewable generators
creates an optimization problem of a stochastic nature, as the outputof these systems can never be determined exactly. Therefore, robust
methods must be employed that can account for this uncertainty.
Interval analysis and optimization is a promising approach to solve
this problem for a variety of reasons: it does not require probabilistic
models (only upper and lower bounds of uncertain variables), it iscomputationally efficient, and results in a range of solutions that canserve as a meaningful reference for operators.

and lower, pessimistic (𝑍𝑚𝑎𝑥 ) boundary is solved, resulting in aninterval solution.
𝑍𝑚𝑎𝑥 = 𝑚𝑎𝑥 min 𝑐𝑇𝑖 𝑥𝑖

𝑍𝑚𝑖𝑛 = min 𝑐𝑇𝑖 𝑥𝑖
𝑥

s.t.

𝑥

s.t.

𝑏𝑖 ≤ 𝐴𝑖 𝑥𝑖 ≤ 𝑏𝑖

(𝐴𝑥)𝑖 = 𝑏𝑖 𝑜𝑟 𝑏𝑖 : 𝜆𝑖 , 𝛿𝑖

𝐸𝑖 𝑥𝑖 ≤ 𝑑𝑖𝐸𝑖 𝑥𝑖 ≤ 𝑑𝑖 : 𝜈𝑖

𝐿 𝑖 ≤ 𝑥 𝑖 ≤ 𝑈𝑖

𝐿𝑖 ≤ 𝑥𝑖 ≤ 𝑈𝑖 : 𝛾𝐿𝑖 , 𝛾𝑈𝑖

In the algorithm, the combinatorial max-min problem istransformed into a single maximization problem using strong duality
theory as well as the Big-M method [2].
4.

RESULTS

The approach was tested on an IEEE 13-bus distribution system,
modified with solar PV injections, microturbines and a fuel cell.

Figure 2. Modified IEEE 13-bus system
40

Microgrid Dispatch Cost

35

MICROGRID SYSTEM MODELLING

30

25

$/MWh

2.

A microgrid can operate in either islanded or grid-connected
mode. In grid-connected mode, the microgrid can import energy fromthe transmission system at the locational marginal price (LMP) of thepoint of common coupling (PCC). Linearized DistFlow [1] equations
have been commonly used and justified in distribution systemmodelling. The equations developed for this work model real andreactive branch flows and node voltages.

20

15

10

5

0
1

2

3

4

5

6

7

20% Forecast Error Sol. Range

8

9

10

11

12 13
Hour

14

15

15% Forecast Error Sol. Range

16

17

18

19

20

21

22

23

24

10% Forecast Error Sol. Range

Figure 3. Microgrid Dispatch Cost Intervals

The solution range shows the interval of dispatch costs fordifferent forecast levels of solar generator output. The range of costs
increases as the interval of solar output increases.
Figure 1. Linearized DistFlow Equations

3.

INTERVAL PROBLEM FORMULATION & ALGORITHM

In the interval optimization method, an upper, optimistic (𝑍𝑚𝑖𝑛 )This work was supported by the Engineering Research Center
Program of the National Science Foundation and the Dept. ofEnergy of USA under NSF Award Number EEC-1041877
and the CURENT Industry Partnership Program.

REFERENCES[1] Wu, F.F., “Network reconfiguration in a distribution system for loss reduction andload balancing,” IEEE Transactions on Power Delivery, 1989, 4, 1401-1407.
[2] X. Kou, F. Li, W. Feng, “Transmission Constrained Economic Dispatch via Interval
Optimization Considering Wind Uncertainty,” (unpublished).

IEEEBenefits of Short-term PV Forecasting in a RemoteArea Standalone Off-grid Power Supply System
Taskin Jamal, GM Shafiullah, Craig Carter, SM Ferdous, Md. Moktadir Rahman
School of Engineering & Information Technology
Murdoch University
Perth, WA, Australia
Abstract—The application of short-term PV forecasting instandalone off-grid power supply (SOPS) systems in remote
electricity networks is an approach to enable higher levels of PV
penetration without hampering the network stability. In thisstudy, a diesel run remote electricity network has been considered
where high levels of PV penetration has been found technoeconomically feasible. Some certain common scenarios, forexample, maximum PV variability and abrupt load change havebeen considered in analyzing system performance and systemstability. The application of short-term PV forecasting showsfavorable outcomes when accommodating high PV variability andabrupt load fluctuation compared to the same system conditions
when PV forecasting is not considered. The study contributes tothe ongoing research of developing smart and efficient control
mechanisms that enable higher levels of PV penetration in remote
electricity networks worldwide.
Index Terms—Remote Electricity Networks, Short-term PV
Forecasting, Higher PV Penetration, System Stability

I. INTRODUCTION
PV systems have also been introduced into standalone offgrid power supply (SOPS) systems in remote areas all aroundthe world as a sustainable power generation source. Though PV
systems offer an economically viable solution in the long term
to provide electricity in remote networks, the issue of PV output
variability and other technical challenges have been identified as
the primary challenges for integration of higher amounts of PV
power in SOPS systems. PV power variability requires thesystem dispatchable generators to follow not only load changes
but also accommodate PV power variations. Experts believe thatan adequate mechanism is still not available to combat the shortterm power fluctuations on power systems due to PV output
variability. Study shows, higher PV penetration could befacilitated if PV power variability could be forecast, to maintainacceptable performance of the power system.
Prediction of cloud locations greatly increases the accuracyof PV output forecasts, when used in conjunction with regional
meteorological and historical data. Higher forecasting accuracy
ensures lower costs related to control of spinning reserve andflexible generator operation and maintenance. The introductionof PV forecasting can reduce the system operation andmaintenance costs and associated risks due to better planningand management of the whole system.
This study analyses the system performance and stability ofa remote SOPS system while addressing the impact of abrupt
load fluctuations and high PV variability, both before and after

applying the short-term PV power forecasting, facilitated by sky
camera technology. Power system impacts arising from PV
variability are shown in Table I. Table II presents thefundamental differences between three primary forecasting
methods. Table III shows the application areas of PV forecastingfor different entities.
TABLE I: Power system impacts from PV variability
Timescale of Variability
Seconds
Minutes
Minutes to Hours
Hours to Days

Potential Impact on Power System
Power Quality
Regulation Reserves
Load Following
Unit Commitment

TABLE II: DIFFERENT METHODS OF PV FORECASTING
30 SecSatellite
Imagery
15 Min1 Hour10-100 metres

1km

2-50km

3-8km radius

65◦S-65◦NWorldwide

10s to Minutes

5 hours

10 days

Application
Area

Ramps,
Regulation

Load following

Approach

Statistical

Statistical

MethodSky Imagery

Sampling Rate
SpatialResolution
SpatialCoverage
Forecast
Horizon (Max)

NWP

Unit
commitment,
Regional power
estimation
Physical

TABLE III: PV FORECASTING APPLICATION AREA AND ADVANTAGES
EntityConsumer
Third Party
UtilityCompany

o
o
o
o
o
o
o
o
o
o

Role / Usage
Behavioral attitude on using solar PV system
Decision making on system design
Decision making on increasing system size
Managerial issues
Handling load aggregation issues
Decision making on services needed to provide
Predict generator capacity scheduling
Fossil fuel arrangement decision
Network management and planning
Operation and maintenance of supply system

II. CONCLUSION
Introduction of sky camera based short-term PV forecasting
predicts PV output variability well ahead of time, which in thelong run enables higher levels of PV penetration into SOPS
systems in remote areas. This study has successfully identified
the effectiveness of the short-term PV forecasting in a SOPS
system considering several worst case scenarios.

A Comparison Study of Dispatching Various Battery
Technologies in a Hybrid PV and Wind Power Plant
Yuqing Yang
School of Photovoltaic &Renewable Energy Engineering,
UNSW.

Chris Menictas
School of School of Mechanicaland Manufacturing Engineering,
UNSW.

Abstract— This paper presents a comparison study forvarious types of battery technologies using selected BESS
parameters in a hybrid PV and wind power plant (HPP), taking
the aging of the batteries into account. The optimization problem
was reformulated as a Mixed Integer Linear Programming(MILP) problem, with the aim being to minimize the overall
operating cost. IBM CPLEX was used to solve the problem. For
forecasting, two techniques, i.e., the persistence and Elman
Neural Network, were applied for both day-ahead and hourahead forecasting. Moreover, four case studies were performed,
including the scenario without battery, day-ahead dispatch, dayahead dispatch with a rolling horizon and hour-ahead dispatch.
The simulation results showed that hour-ahead dispatch
demonstrated the highest profitability. Furthermore, among
different battery technologies, lithium-ion batteries resulted in
the highest operating profits, whereas lead-acid batteries had theshortest payback period and vanadium redox flow batteries
demonstrated the least degradation from cycling.

Stephen Bremner
School of Photovoltaic &Renewable Energy Engineering,
UNSW.

Merlinde Kay
School of Photovoltaic &Renewable Energy Engineering,
UNSW.

II. KEY RESULTS AND CONCLUSION

Keywords -- Battery Energy Storage System, Battery
Scheduling, Forecasting Errors, Hybrid PV and Wind Power Plant.

I. INTRODUCTION
Battery energy storage systems (BESS) have been playing
increasingly important roles in the modern power network.
There are a wide range of battery technologies that have beenused for demonstrations or in commercial applications. These
include the Lead Acid Battery (LAB) , Lithium-ion Battery(LIB), Sodium Sulfur Battery (NAS) and Vanadium Redox
Flow Battery (VRB). It is noted that many research articles
concentrate on battery energy storage system applications
without clarifying the type of storage system technology byusing a generic model. Therefore, the generic model for batteryenergy storage systems is the most commonly used in a wide
range of studies.
The contributions of this work include: 1) providing acomprehensive comparison study by analyzing theperformances of four different battery technologies, namely
LAB, LIB, NAS and VRB, in the operation of a hybrid PV andWind Power Plant (HPP), using the reported battery parametersfrom previous and technical reports. 2) adopting a genericbattery model for four different technologies and an aging
process linearized by using a degradation rate. 3) implementing
three different dispatch strategies, i.e. day-ahead dispatch, dayahead dispatch with a rolling horizon and hour-ahead dispatch,to evaluate the performance of different battery technologies.

In this work, four different types of batteries were optimallydispatched for a hybrid power plant, aiming at minimizing theoverall operating cost of the hybrid power plant. Moreover,two forecasting techniques and three dispatch strategies were
implemented to evaluate the performances of different battery
technologies. From the simulation results, it is noticeable thathour-ahead dispatch in Case 4 achieves the highest profits.
However, among the four battery technologies, lithium-ion
batteries demonstrated the best performance in terms of theoperating profitability, whereas lead-acid batteries had theshortest payback period when the capital costs of battery
technologies were taken into account. Besides that, from theperspective of battery aging, vanadium redox flow batteries
demonstrated the least degradation from cycling.

Monitoring the Voltage Stability Margin Using
Norton Current Distribution Relationship
Qi Wang1, Yufeng Guo1,2, Jilai Yu1,*, Dongrui Zhang1, Jie Wan1,Jin Zhong2
1. School of Electrical Engineering and automation, Harbin Institute of Technology, Harbin, China
2. Department of Electrical and Electronic Engineering, HKU, Hong Kong, ChinaI.

INTRODUCTION

This paper discuss the corresponding law between Norton
current distribution and the maximum power transfer. Basedon this law, a new voltage stability index is proposed. The
proposed index is calculated with voltage and current
magnitude provided by on-line PMU data. In addition, theindex can be extended to dispersion indexes, each offering
the voltage stability information of specified line.
II.

Where, I L( kl ) n is the current flow into bus n from line l at
the k th point.
k 1)
As can be seen from (18), a higher value of Hl(nindicates a higher ability of maintaining bus voltage stabilityk 1)by line l . Furthermore, a negative value of Hl(n meansline l do not provide any power for maintaining bus voltage
stability.

III. KEY RESULTS
The proposed method is tested on the 10-bus system and39-bus system.

Voltage level (p.u.)

Abstract—The impedance matching condition has been afundamental assumption of many existing studies on voltage
stability assessment. In this paper, it has been proven that the
Norton current distribution law is equivalent to the impedance
matching condition. Based on this law, a new voltage stability
index is proposed, which can be further divided into bus
voltage stability index and line voltage stability index. These
indexes of the focused load bus can be calculated by phasor
measurement units data. Then, the voltage stability margin canbe assessed by computing the difference between the index
value and 1. The effectiveness of the proposed on-line voltage
stability index are verified from simulation results perform in a
10-bus system and 39-bus system. Moreover, practicaloperating constraints such as the generator power limits aretaken into account for practical assessment of the proposed
method. Index Terms-- impedance matching condition; Norton
current distribution law; voltage stability index; phasor
measurement units; voltage stability margin

Fig. 1. Bus voltage and Voltage stability indexes corresponding toloading factor increasing at Bus 7 and 10 (10-bus system)KEY EQUATIONS

A. new voltage stability index

H ( k 1)  1 

I L( k )I L( k 1)

/ 1

UL

(k )

UL

(1)

( k 1)

The index shown in (1) means that:(1) System is within the limits of transmission capacity
when H ( k 1)  1.0 ;(2) System is beyond the limits of transmission capacity
when H ( k 1)  1.0 ;(3) System is in critical state when H ( k 1)  1.0 ;B. extend the index to dispersion indexes
k 1)
H l(
n  1

I L( kl ) n
I L( kl n1)/ 1

U L( kl ) n
U L( kl n1)(l  1, m) (2)

Fig. 2. Voltage stability indexes corresponding to loading factor
increasing at Bus 4, 7 and 10 (39-bus system)

IV. CONCLUSIONSThis paper proposed a new online voltage stability index
based on the Norton current distribution relationship. It ismathematically proven that the Norton current distribution
law is equivalent to the impedance matching condition. With
this new method, both bus voltage stability and the ability oflines to maintain bus voltage stability can be calculated online. Simulations results in 10-bus system and 39-bus systemshow that the voltage stability indexes are effective at
different scenario. Moreover, the proposed new index uses
only voltage and current amplitude information from PMU
measurements, which reduces the communication
complexity.

Fault Classification and Location Identification in a
Smart Distribution Network Using ANN
Muhammad Usama Usman∗ , Juan Ospina, Md. Omar FaruqueElectrical and Computer Engineering and Center for Advanced Power Systems, Florida State University
2000 Levy Avenue, Tallahassee, FL., USA. Email: ∗ musman@fsu.edu

Abstract—This paper presents a novel approach to classify andlocate different types of faults in a smart distribution network(DN). The proposed method is able to classify all types offaults that can occur in a DN and then based on fault type,it can identify the approximate fault location (FL) with a highaccuracy. The method is based on artificial neural networks
pattern recognition which uses data from µPMUs/smart meters
placed at different locations in a DN. The proposed techniqueneeds fault-on voltages of all the nodes connected to the endof line/branches in order to classify and locate different types
of faults. The method is tested on a modified IEEE-37 bus
system with distributed generation along with dynamic loading
conditions and varying fault resistances.

A. Fault Type Classification

I. M ETHODOLOGY

Fig. 3: Confusion Matrix for Fault Classification
B. Fault Location Identification
TABLE I: FL Identification Results for faults on the nodes

Fig. 1: Flowchart of fault location identification procedure
II. C ASE S TUDY AND V ERIFICATION

Fault
Type
ABC-G
ABC
AB-G
BC-G
CA-G
AB
BC
CA
A-G, B-G, C-G

Fault
Placement
All 37 nodes
All 37 nodes
All 37 nodes
All 37 nodes
All 37 nodes
All 37 nodes
All 37 nodes
All 37 nodes
All 37 nodes

Fault
Resistance (ohm)
0-5
0-5
0-5
0-5
0-5
0-5
0-5
0-5
0-5

Accuracy for faults
on the nodes
100%
99.5%
100%
100%
100%
100%
100%
100%
99.7%

TABLE II: FL Identification Results for faults on the lines

Fig. 2: IEEE-37 Bus Test Feeder with PV

Line
Type
702-703738-711734-710720-707744-728733-734714-718

Line
Length1320ft
400ft
520ft
920ft
200ft
560ft
520ft

Fault
Placement
Every 200ft
Every 50ft
Every 50ft
Every 100ft
Every 30ft
Every 50ft
Every 50ft

Fault
Resistance (ohm)
0.5, 1.5, 2.50, 1, 22, 3, 4
0.3, 1.3. 2.6
0.001, 0.1, 0.8
0.05, 2.5, 5
1, 2, 3

Faulted Nodes
702 and 703
738 and 711
734 and 710
720 and 707
744 and 728
733 and 734
714 and 718

>1

1

Sub-Synchronous Resonance Detection in
Series Compensated DFIG-Based Windfarms
Bo GaoDepartment of Electrical and Computer Engineering, University of Alberta
Abstract—Series compensated DFIG-Based wind farms arevulnerable to sub-synchronous resonance (SSR). The SSR
components need to be detected at early stage to provide early
warning. The SSR frequency is uncertain and the components arealways very small at the early stage. Hence, it needs long sampling
period for transitional FFT algorithm. In this paper, voltage andcurrent waveforms are used to exactly compute the SSR frequency
and the magnitude. The results show that the new algorithm is fast
and accurate for the early stage SSR components measurements.
Index Terms—DFIG, Windfarm, SSR, Detection, Waveform,

I. INTRODUCTION
Series compensated DFIG-based windfarms are vulnerable to
SSR. SSR has been widely investigated and lots of paper
presented mathematical analysis and simulation work. For SSR
detection, the traditional method is to conduct the FFT to givethe information of the frequency and the magnitude of SSR
components. However, this may be some issues as thefrequency of SSR components is uncertain, which means the
sampling window need to vary for different SSR frequencies.
This may lead to long time to obtain the SSR frequency andmagnitude. In addition, FFT is steady-state concept, and if theamplitude of SSR components vary, FFT results about themagnitude will have a large error.
Other mathematic based method has also been introduced to get
the frequency and magnitude of the SSR components. Prony,
Recursive Least Square (RLS) algorithms, Kalman Filter
algorithm have been used to compute the SSR and thesemethods can track the SSR components exactly. However,these algorithms require lot of computation and the complexity
make them unsuitable for on-line calculation.
With the Waveform Measurement Unit (WMU) available inpower system, SSR components can be computed by
waveforms with less computation work. The waveform-basedalgorithms use both the voltage and current waveforms. The
results show that the waveform-based algorithm can calculate
the frequency and the magnitude fast even when the magnitudeof SSR components is relative low to the fundamental
frequency components.

II. KEY STEPS
Start

Waveform Measurements

Waveform Analysis and SSR
information extractionNo

SSR Components
Increasing

YES
Early WarningEnd
Fig.1 SSR computation steps

III. SIMULATION RESULTS

(a)

(b)

Fig.2 SSR extraction. (a) current waveform, (b) SSR components

1

An Distributed Event Diagnosis Solution forProtection Operation in IEC 61850
M. Touhiduzzaman, Student Member, IEEE, and A. Hahn, Member, IEEE, and S. Lotfifard, Member, IEEE,

Abstract—This poster presents a novel distributed event-based
diagnosis solution (DDS) for substation automation system (SAS),
based on the backward reachability graph analysis (B-W) of
behavioral Petri-net (BPN). We develop a hierarchical model andpropose a distributed event diagnoser for SAS that focuses on theIEC 61850 communication protocol. This distributed diagnoser
produces a local diagnosis result which is consistent and correct
without the use of centralized diagnosis scheme. A case study
on substation automation system is discussed to verify our
proposed distributed diagnoser scheme. The result highlighted
different scenarios that successfully identified different protection(distance or differential fault), cyber (cyber-attack) and systemmul-function events.

token will be continually analyzed using B-W reachability
graph to identify events.
II. K EY F IGURES AND R ESULT
TVTR

TCTR

TVTRt

TCTRt

MMXU

Measurements

MMXUt

Bordered LN box

Protection IED
INt

x

PDIS

PDIF

PDISt

PDIFt

PTRC_ISyPTRC_IFOUTt
2PTRCt
1PTRCt

E_CB2

PTRC

PTRC

z

PRTCrrc_t
BRBFt
BRBF

I. I NTRODUCTION
Due to the complexity of cyber-physical SAS, It become
more difficult for the substation intelligent electronic device(IED) to detect unknown failure modes and cyberattack.
Also, traditional IED diagnosis solution is centralized which
required a large number of agents to analyze to detect the fault.
In this work, we design an agent based DDS that guarantees
that local diagnosers provide correct diagnosis result during
the identification of events in the SAS.

RREC

p

PRTCb_t
XCBRcoff

XCBRsoff

RRECt

XCBRsonXCBRConXCBRson_t

XCBRsoff_t

Circuit breaker

CBon

CBoff

Physical System

Fig. 1: BPN model for substation IED

A. Stage 1: Modeling substation automation using BPN
In SAS, each physical device is divided into logicalnodes(LN) according to IEC 61850 protocol where each LN
has their own naming convention and communicate eachother through GOOSE messages. In order to construct thesubstation automation accordance with real system, we use thehierarchical BPN approach [1] where the entire SAS model of
one substation is divided into five blocks(Fig. 1).
B. Stage 2: Distributed diagnosis solution (DDS)In the DDS, the centralized system is divided into a set ofn subsystem (single substation IED). Each of the subsysteminteracting each other by passing the token through bordered
places. The BPN distributed diagnosis solution (DSS) for asubsystem i is defined as:

Fig. 2: B-W reachability analysis of simple case study
Scenarios for CB2 open and<p, x, ><PDIS,
PDIF><∅, ∅><∅, ∅>

status signal on<2CBCoff(b),2CBSon(b),stateInconsistency (cyber-attack)

2CBon(w), 2RREC(b)><2CBCoff(w),2CBSon(b),<∅, ∅><∅, ∅><b, w><w, ∅>

System malfunction<w, ∅><b, •>Bus 2 and line fault (z1 forInconsistency(cyber-attack)

DSSi = {Ni , Bi , F ti , (γi+ , γi− ), ζ}

2CBon(w),2RREC(b)><p(b), PDIS(w), PDIF_f(w) 2CBCon(w),x(w), 2CBoff(w),2RREC(b)><p(w), PDIS(b), PDIF(b), 2CBCon(w)2CBoff(w),2RREC(b)>where, N = (P, TN , TOR , F ) is the behavioral petrinet model,
B = (Bin , Bout , Bx ) is the set of bordered places, F t is theset of possible fault initiating from subsystem i, (γi+ , γi− ) is
the initial local observation originated from subsystem i andζ represents the final local observation which only take theinformation from B and F t. In this DDS, ζ will decide what
type of events(e.g. fault, cyber-attack, system mal-function)
occur to the subsystem, and also decide whether those eventsoccur inside the subsystem or outside the subsystem. Once theinitial places have been identified that contain token, then those<p(w), PDIS(b), PDIF_f(w), 2CBCon(w),2CBoff(w),2RREC(b)><p(w), PDIF(b), 2CBCon(w),2CBoff(w),2RREC(b)><p(b), x(b), PDIF(b), 2CBCon(w),2CBoff(w),2RREC(b)>sub2)<w, ∅><b, ∅>

Line fault<w, ∅><∅, •>Bus 2 fault<b, b><∅, ∅>Bus 2 fault and line fault (z2<b, b><∅, ∅>

Line fault (z2 for sub2)<b, ∅><∅, ∅>

System malfunction

for sub2)<p(b), x(b), PDIF_f(w), 2CBCon(w),2CBoff(w),2RREC(b)><p(b), PDIS_f(w), PDIF(b), 2CBCon(w),2CBoff(w),2RREC(b)>Fig. 3: Event identification by analyzing DDS
R EFERENCES[1] L. Portinale, “Behavioral petri nets: A model for diagnostic knowledge
representation and reasoning”, IEEE Trans. Syst., Man Cybern. B, vol.
27, no. 2, pp. 184-195, Apr. 1997.

Toward a Synthetic Model for Distribution System
Restoration and Crew Dispatch
Bo Chen, Member IEEE, Zhigang Ye, Student Member IEEE, Chen Chen, Member IEEE, Jianhui Wang, Senior
Member, IEEE and Kai Wu, Senior Member IEEE
Argonne National Laboratory and Xi’an Jiaotong University

Abstract— In this paper, we introduce a synthetic model that
integrates service restoration model and crew dispatch model
based on a universal routing model. The proposed model can
provide the estimated time of restoration (ETR) for each load,
switching sequence for safely operating remotely/manuallyoperated switches, and dispatch solutions for crew with different
skill sets. The proposed models are formulated as a mixed-integer
linear programming model (MILP), and their effectiveness isevaluated via the IEEE 123 node test feeder.
Keywords— Distribution service restoration, crew dispatch,mixed-integer linear programming, repair, switching sequence
management.

I.
INTRODUCTION
atural disasters are increasingly reported during the recent
years to have caused wide-spread social and economic
effects, including large-scale power outages that normallytake utilities companies several weeks to completely restore theelectricity services [1-2]. In case of extreme weather events
that can cause multiple power outages within a short period oftime and leave a large region of utility service territories
without power, seamless coordination among OMS modules is
the key to maximize the restoration effort. The work proposedin this paper is motivated by the fact that a synthetic model thatcan effectively integrate DSR and crew dispatch models is stilllacking in the literature. This paper introduces a synthetic
model for DSR and crew dispatch model.

N

Fig. 2. Modified IEEE 123 node test feeder partitioned into 15 node cells by
16 switches.

G

EA

1

TABLE I. INTERDEPENDENCE BETWEEN DSR AND CREW DISPATCH
DomainInterdependence Description
Crew forCrew for
DSR
Operation
RepairA crew operates a manually operated√√
switch to energize components.
A switch can be operated only after the√√lock status, if any, is removed.
A faulted component can be energized√√
only after being repaired. The crew canoperate the switch right after the repair.
To repair a faulted component, thecomponent should be isolated by
opening upstream/downstream switches√√√
to ensure crew safety. A switch cannot
be energized when an operation crew isin the process of operating it.

C8

C9

0 1

28

47

13

Crew for Repair

C4

C5
379

C10
407

t/min.

t/min.
B1-7

32

292
Sub150RA

C3

307 308 327 353

B76-77 B87-89

OA

Crew for Operation

KEY TABLES AND FIGURES

C2C6141 142
C15 C7DG

2II.

C1 C11

EA
Substation

21

B13-18150

B13-18B23-25B18-135 B151-300
312

B57-60210 218

338

364

392

t/min.

L49
319

t/min.

Node Cells energized by Substation

Operation Crew Travel Status

Repair Crew Travel Status

Node Cells energized by DG

Operation Crew Work Status

Repair Crew Work Status

Fig. 3. Repair and Restoration Sequence for EA/OA/RAREFERENCES[1][2]

Y. Wang, C. Chen, J. Wang, and R. Baldick, “Research on Resilience ofPower Systems Under Natural Disasters-A Review,” IEEE Transactions
on Power Systems, vol. 31, no. 2, pp. 1604-1613, 2016.
"Hurricanes Nate, Maria, Irma, and Harvey Situation Reports,"
https://www.energy.gov/oe/downloads/hurricanes-nate-maria-irma-andharvey-situation-reports.

Enabling Resiliency Through Outage Management
and Real Time Data-Driven Aggregated DERs
Matteo Menazzi, Student Member, IEEE

Anurag K. Srivastava, Senior Member, IEEE

Washington State University, matteo.menazzi@wsu.edu

Washington State University, asrivast@eecs.wsu.edu

Abstract—This project aims to enhance distribution system resiliency by effectively controlling and utilizing Distributed Energy
Resources (DERs) for outage management and restoration underhazard scenarios, leveraging available measurements from smart
meters and micro-PMUs. To achieve this, the current industry
practice of just using an aggregate statistical model for thewhole secondary network is not sufficient. A novel secondary
distribution network equivalent model is proposed, composedof an Equivalent Controllable DER (ECDER), an EquivalentControllable Load (ECL), and the Residual Power Injection(RPI), comprising, in turn, losses, uncontrollable loads and DERs
power, necessary to ensure total power injection equivalency.
The model features 3 decision variables: ECDER, ECL, anda binary variable which allows to shed specific loads using
respective smart meters. The model is meant to allow an optimal
power restoration algorithm, running at primary distribution
system level, to determine the optimal value of ECDER, ECL,
and the loads to be shed, without having to consider either adetailed secondary network or each controllable DER and load
individually. Uncontrollable loads and DERs power is modeled asa parameter, which is determined feeding available measurements
into Nonlinear Autoregressive Neural Networks (NARnet), whose
output is subsequently adjusted considering cold load pickup phenomena. System losses and, therefore, RPI change infunction of the decision variables in order to maintain total power
injection equivalency, so that, as seen from the primary network,
the same total power is injected into the secondary system.

II. K EY R ESULTS
The secondary test system has been completely modeled in
GridLAB-D and compared with the relative equivalent models, implemented in MATLAB. Total active power injection,
relative percentage error and rms error versus execution time
are shown in Fig. 2, 3, 4 for all the six models.

Fig. 2. Total injected power from complete GridLab-D and equivalent models.

I. K EY FACTS
Fig. 1 depicts the utilized secondary distribution test systemand the proposed equivalent model. ECDER and ECL values
are distributed to single DERs and loads according to theirindividual power limits. Six models, differing in system losses
estimation techniques, have been studied. Models 1-4 arealgorithm based, while Models 5-6 are analytic, meaning RPI
is expressed as an explicit function of the state variables,
allowing deterministic optimization techniques to be used.

Fig. 3. Total injected active power percentage error.

Fig. 4. Active and reactive power rms percentage error versus execution time.

Fig. 1. Secondary distribution test system and proposed equivalent model.

ACKNOWLEDGMENT
This project is supported by the DOE under award number DE-OE0000878.
Project partners include Argonne National Lab, Commonwealth Edison, GE
Grid Solutions, Massachusetts Institute of Technology, Seattle City Light,
Virginia Polytechnic Institute and State University, and NRECA.

Building Energy Scheduling with Transactive
Approach Having Autonomous Temperature Setting
Ashim Basnet
The University of Hong Kong
Student Member, IEEE
Hong Kong, HKSAR
ashim@eee.hku.hk

I. INTRODUCTION
The residential buildings utilizes mainly three energy
resources: heat, natural gas and electricity. Heating loads canprovide significant amount of operational flexibility in responseto the changes in market prices and ambient temperature. As heat
demand can be fulfilled from multiple energy vectors, flexibility
is further enhanced. A building can economically schedule theenergy intensive devices without compromising the user defined
comfort level. In this poster, a building has been envisaged as a
virtual energy market having electricity, gas and heat market asshown in Fig. 1. These energy markets are interconnected by
combined heat and power (CHP) plant, gas boiler heater, powerto-gas converter (P2G) and Electric Heater (EH). In each energy
market, there are sets of suppliers and demands. P, G, H denotes
the electricity, gas and heat quantity respectively in Fig. 1, while
subscripts S and D indicates whether they are supply or demand
quantity. User sets the desired maximum and minimum
temperature, which is then used to interpolate linear relationshipagainst minimum and maximum day-ahead electricity priceobtained from the utility. Based on the interpolation relation,
hourly day-ahead prices and ambient temperature condition, theautonomous temperature setting is obtained and hence the valueof the heating load demand bid in the virtual heat market. The
proposed framework has been modelled as linear program
having minimization of total energy cost with three energybalance equality constraints for electricity, natural gas and heat
energy. From the equality constraints, the dual variables are
generated which act as the virtual transactive energy prices
inside the building. The optimization solves for the supply
quantities within the proposed framework having few fixedenergy loads. This concept of virtual transactive energy prices
can be useful for scheduling of distributed energy resources infuture applications.

Results from the case studies suggest that thescheduling of the heating devices are obtained from the
proposed framework as shown in Fig. 2. CHP has been foundto be turned off during low electricity price period in contrast to
EH which is only turned on in this period. Gas boiler remains
the major source of heating energy for the building. P2G isnever used accounting to high virtual building electricity pricewhen compared to virtual building gas price. During the hours
when the gas grid supply is congested, the building gas pricebecomes higher than grid gas price as shown in Fig. 3. The
virtual heat price is high during the morning period when
ambient temperature is low and virtual energy prices of gas andelectricity are higher, while at afternoon, heat price becomes
lower. The autonomous temperature setting saved the operation
costs for building rather than having a fixed temperature.

Fig 1. Virtual Building transactive Market
BoilerGas (Grid)

400
Power consupmtion in kW

Keywords—Transactive Market, Building Energy
Management

II. KEY RESULTS

CHP (Heat)
Gas(P2G)Electric Heater
Electricity (Grid)2000
1 2 3 4 5 6 7 8 9 10 11 12
13 14 15 16 17 18 19 20 21 22 23 24
Hours

Fig 2. Scheduling of Energy Suppliers
0.15

Price in $/Kwh

Abstract— Residential buildings consume significant share of
the total energy, yet the efficient building energy scheduling ishardly considered. These buildings can operate flexibly inresponse to the change in the energy markets and environment
conditions to gain financial benefits. In this poster, scheduling ofenergy consuming devices in a building has been proposed, where
the building has three virtual transactive energy markets, namely
heat, gas and electricity markets. Each market is supposed to havemultiple suppliers and demands and clears their marginal energy
prices. Autonomous temperature setting of the heating system isbased on the interpolation of extreme day ahead market prices andtemperature settings input by end-user. Case studies demonstratethat the transactive control of heating devices for the building canautonomously operate the heating system in a flexible way
providing the optimal scheduling of heating devices.

Building Heat Price
Building Electricity Price
Day Ahead Gas Price

Building Gas Price
Day Ahead Electricity Price

0.1

0.05

0

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

Fig 3. Transactive Market Prices in the BuildingThis work was supported by the National Key R&D Project ofChina under Grant 2016YFB0901903.

Cooperative Game Theory for Allocating Cost
Savings from Coalitional Energy Management
Liyang Han, Thomas Morstyn, Malcolm McCulloch
Department of Engineering Science
University of Oxford
Oxford, UK{Liyang.Han, Thomas.Morstyn, Malcolm.McCulloch}@eng.ox.ac.uk
B. Cooperative Game Formulation
The value functionPv is defined as the coalitional energy
cost savings: v(S) = i∈S C({i}) − C(S), ∀S ⊆ N .
We use vector x ∈ RN as the payoff allocation whose
entry xi represents
P the payment to member i ∈ N . x is an
imputation if i∈N xi = v(N ), and xi ≥ v({i}), ∀i ∈ N .
We test two important imputations: the Shapley value andthe nucleolus, and show that both imputations guarantee
savings
for all players, but only the nucleolus u is stabilizing:Pi∈S ui ≥ v(S), ∀S ⊂ N .
II. K EY R ESULTS AND D ISCUSSION
10Grand Coalition Load [kW]Abstract—Distributed energy storage (ES) is widely regarded
as a tool to mitigate the supply-demand imbalance caused byintermittent generation and variable loads in a distribution network. Because a retail supplier generally offers a lower electricity
sell price (e.g. feed-in tariff) than its buy price, ES owners aremonetarily incentivized to store excess generation for later usage.
However, because each ES system tends to be operated only
to minimize the energy cost of its direct owner, the joint loadbalancing effect of multiple ES systems becomes insignificant.
A centralized ES control strategy can be used to provide local
energy services such as peak shaving and energy shifting toincrease the utilization of local renewable generation, but how to
encourage ES owners to participate in such a scheme remainsa challenge. This paper proposes the novel use of cooperative
game theory to incentivize optimized ES system control for a
prosumer1 grand coalition. The optimization objective is to gain
the highest joint monetary benefit for the participants throughcollaborative operation of multiple ES systems, and cooperative
game theory is used to allocate this profit to each individual
prosumer in a way that ensures the grand coalition providing
the best economic outcome for every participant.
Index Terms—Cooperative game theory, energy management,
nucleolus, prosumers

50
5
w/o ES
w/ Non-Cooperative ES
w/ Cooperative ES

10

A. Coalitional Energy Cost
For each prosumer i, we define:qi ∈ R1×K : net energy load without ES, [kWAC ∆t]bi ∈ R1×K : ES operation variables, [kWAC ∆t]
The energy cost for any coalition S ⊆ N is defined as
n X
o
X |
C(S) = min pb [(b|i + q|i )]+ + ps [(bi + q|i )]−bi ,∀i∈S

i∈S

i∈S

where [z]+(−) = max(min){z, 0}, and bi s.t. ES constraints.
1 proactive-consumers with distributed energy resources that actively manage their consumption and production of energy

00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00
Time

Fig. 1: 12-prosumer energy coalition example: 24-hour load profiles
3.0
Energy Cost and Imputation [£]I. F RAMEWORKLet a group of N prosumers form a grand coalition N
indexed by i ∈ N := {1, 2, ..., N }. For each prosumer i we
assume a noncontrollable load, and/or a generation source, andno more than one ES system, which operation is constrainedby its energy capacity, charging and discharging power limits,and the requirement to restore its original state of charge (SoC)
at the end of each analysis period.
For each analysis period, we consider K timesteps (t =1, 2, ..., K) with a time interval of ∆t, and we define:pb ∈ R1×K : Electricity buy price, in [$/kWAC ∆t]
ps ∈ R1×K : Electricity sell price, in [$/kWAC ∆t]where pbt > pst , ∀t ∈ [1, K].

Energy Cost w/o Coop
Energy Cost w/ Nucleolus
Nucleolus
Energy Cost w/ Shapley
Shapley Value

2.5
2.0
1.5
1.0
0.5
0.0
0.5
1.0

1

2

3

4

5

6 7
Player8

9 10 11 12

Fig. 2: 12-prosumer coalitional energy costs and cost savings (24 hrs)

We test our model using load and PV data from theCustomer-Led Network Revolution trials. Fig. 1 shows thatthe cooperative ES operation, simply with the objective tominimize the coalitional energy cost, is significantly more
effective in load balancing than the non-cooperative ES operation. Fig. 2 confirms that all players can financially benefit
from participating in the grand coalition. Further analysis
shows that the nucleolus is stabilizing, meaning it guarantees
that no groups of players could achieve higher savings byleaving the grand coalition to form smaller coalitions.

Game-theory-based Real-Time Inter-Microgrid Market
Design Using Hierarchical Optimization Algorithm
Mohammad Mahmoudian Esfahani, Abla Hariri, Student Members IEEE and Osama A. Mohammed, Fellow, IEEE
Florida International University.
Abstract—This paper proposes a Game-theory-based real-timereverse auction model for inter-microgrid market in a multimicrogrid system using a hierarchical optimization algorithm toaddress the optimal operation of microgrids in distribution
systems. This market is organized to optimally assign the net
power mismatch inside an area to microgrids’ market agentsaiming at minimizing both the dependency on the utility and themarket clearing cost. To implement the proposed framework in a
Multi-Agent System (MAS), a fast and reliable communication
middleware
isrequired.
Therefore,adata-centriccommunications middleware based on the real-time publishsubscribe protocol is deployed for this purpose, where each agent
is developed as a domain participant combining a publisher andsubscriber. To validate the effectiveness of the proposed market
framework, it is applied on the modified 37-bus IEEE distribution
test feeder system, and the numerical results are obtained andpresented.

i-th AgentInterface

...

DDE

DDE

DDS
Communication System
Fig. 1: Data exchange between agents through the DDS
1

L6
42
G3 KW
150 KW

4

5

20

16
15

14

L3
85 KW

23

24

27

MG1

11

G2
150
KW

9

DESS 2150 KW

L2
38 KW

22

L3
126 KW

L7
42 KW

MG2

25

26
DESS 1200 KW

28

L8
85 KW

L5
L3
42 KW 126 KW

29

L6 31
85 KW
32

10

L4
85 KW

13

17

19

L1
42 KW

21
G1
100
KW

12

L1
85 KW

G2
150 KW 18

L2
42 KW

L4
42 KW

I. INTRODUCTION

G1

7 200 KW
8

3

6

MG3
DESS 180 KW

DESS 1100KW

L5161 KW

2

Index terms— Multi-agent system; Inter-microgrid market;
Game theory; Hierarchical optimization; Data distribution
service; Demand response.

33

30

L7
42 KW

G2
350 KW35

G1
300 KW

L1
42 KW

37

34

L4
140 KW

36

L2
85 KW

Fig 2: The modified IEEE 37-bus distributed network

II. NUMERICAL RESULTS
Round 4
Round 5
Energy mismatch

Round 1Round 2Round 316

Bid (¢/kWh)In this paper, a game-theory-based inter-microgrid market
algorithm is proposed for the real-time energy management in a
multi-microgrid system, where all market players (DGs, DESSsand Loads’ DR) can participate in this market. Real-time in thiswork is defined as a 5-minutes-ahead market. It means that thismarket is executed in 5 minutes before the actual operationinterval to minimize the forecasted energy mismatch in a multimicrogrid area in order to determine energy transactions
between microgrids. It is anticipated to increase the accuracy
significantly compared with running the real-time market one
hour before the operation interval. Since this algorithm needs tobe solved very fast, a hierarchical optimization algorithm isdeveloped and utilized for this purpose to distribute theoptimization between several agents aiming at decreasing thesolution time compared with solving an overall optimization
problem in the upper layer agent. All agents’ calculations andoptimization process are developed using the programming
language of DIgSILENT PowerFactory 2017 and the Dynamic Data
Exchange (DDE) is deployed to transfer data between agentsthrough the DDS to meet the communication requirements ofthe proposed MAS framework [11]. Fig. 1 shows the structureof each agent and the interface used for communication.

n-th AgentPowerFactory

PowerFactory

15

MG2 MG3

MG2

Utility Price = 1514

MCP = 1413

MCP = 13

MG1

MG3

MG2

MCP = 13.5
MG1
MG1
MG1

12

MG2

MG3

MG3
65
kWh

MG1

MG1

0

20

40

60

Energy (kWh)80

Fig. 3: Results of the game-theory-based reverse auction100Reference Bus Independent Components of LMP through a
Non-Marginal Choice of its Energy Component
Deep Kiran, Student Member, IEEE, A R Abhyankar, Member, IEEE and B K Panigrahi, Senior Member, IEEE
Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, India 110016
Email: dkiran5@gmail.com, abhyankar@ee.iitd.ac.in, bkpanigrahi@ee.iitd.ac.in

Abstract—In the loss compensated direct current optimal
power flow (LCDCOPF) markets, the reference bus plays acrucial role in the decomposition of locational marginal price(LMP) components. For the correct valuation and settlementof financial instruments in these markets, it is important torationally decompose the LMP into its components. Therefore,
this paper proposes a simple non-marginal method for calculatingreference bus independent components of LMP. The efficacy ofthe proposed method is supported by numerical results.
Index Terms—Locational marginal prices, energy component,
loss component, congestion component and reference bus independent.

I. L OSS C OMPENSATED DCOPF M ODELIn LMP markets, surplus (S) is accumulated with theindependent system operator (ISO) in market settlement whichcorresponds to both loss and congestion. A well-definedcomponents of LMP for effective congestion management andloss allocation at each bus location is one of the pre-requisite
for these hedging instruments [1], [2]. The typical LCDCOPF
model is shown below in (1)-(5) [1], [2].
X
fi (Gi )
(1)
Min.
Gi ,L

[λ] :

i

i

i

It is assumed that ΛE
i is same across the network [1], [2],therefore, from (7),X
X⇒
Λi Gi = ΛE
Di
(8)
i
i

P
E
i Λi Gi⇒Λi = P
i Di

i

(9)

Using the fact that dispatch and LMP remains same, to makea non-marginal choice for ΛE
i in (9) leads to its reference bus
independency. Thus, ΛLi can be written asEΛLi = (a) − Λi(10)III. N UMERICAL R ESULTS
The proposed method derive the components of LMP basedon the LCDCOPF model in [1], [2]. The results are shown for
5-bus [2] system.
A. 5-Bus SystemiXEcongestion (ΛCi ) component of LMP. The dependency of Λion reference bus can be eliminated as
X
X
X
ΛE
(7)
Λi Gi =
Λi Di − S =i Di(Gi − Di ) = L

(2)

X

(3)

TABLE IC LEARING RESULT OF 5- BUS SYSTEM WHEN COMPARED WITH [1]i[γ] :L =LFi (Gi − Di ) − ωi[µl , µl ] : − Pl ≤X

SFil (Gi − Di − DFi L) ≤ Pl∀l (4)i[αi , αi ] :Gi ≤ Gi ≤ Gi ∀i(5)

In above model, LF , ω and DF are calculated based on the
optimal solution of DCOPF model (which is a pre-requisite in[1]). On the contrary, in [2], these terms are updated based onan AC power flow solution in an iterative manner.
II. P ROPOSED M ETHOD
LMP (Λi ) is defined by the partial derivative of Lagrangefunction with respect to D as,
X
XX
SFil DFi µl ∀i (6)SFil µl −Λi = γ − LFi γ +| {z }
i
l
l
(a)
{z
}
|
(b)

In (6), the terms in (a) correspond to energy (ΛEi ) and loss
(ΛLi ) component of LMP and the terms in (b) correspond to

Λi
15.85
24.18
27.20
35.00
10.00

Bus
A
B
C
D
E

Proposed method
ΛE
ΛL
ΛC
i
i
i
14.87
13.42
-12.43
14.87
14.02
-4.70
14.87
14.07
-1.73
14.87
13.70
6.44
14.87
13.30
-18.16[1] with Reference @ A
ΛE
ΛL
ΛC
i
i
i
28.28
0.00
-12.43
28.28
0.60
-4.70
28.28
0.65
-1.73
28.28
0.28
6.44
28.28
-0.12
-18.16[1] with Reference @ D
ΛE
ΛL
ΛC
i
i
i
28.57
-0.29
-12.43
28.57
0.32
-4.70
28.57
0.37
-1.73
28.57
0.00
6.43
28.57
-0.41
-18.16TABLE IIC LEARING RESULT OF 5- BUS SYSTEM WHEN COMPARED WITH [2]
Bus
A
B
C
D
E

Λi
15.85
24.09
27.11
35.00
10.00

Proposed method
ΛE
ΛL
ΛC
i
i
i
14.81
7.32
-6.29
14.81
7.82
1.46
14.81
7.86
4.44
14.81
7.55
12.64
14.81
7.23
-12.04[2] with Reference @ A
ΛE
ΛL
ΛC
i
i
i
22.14
0.00
-6.29
22.14
0.49
1.46
22.14
0.53
4.44
22.14
0.23
12.64
22.14
-0.10
-12.04[2] with Reference @ D
ΛE
ΛL
ΛC
i
i
i
22.36
-0.23
-6.29
22.36
0.27
1.46
22.36
0.31
4.44
22.36
0.00
12.64
22.36
-0.32
-12.04

R EFERENCES[1] E. Litvinov, T. Zheng, G. Rosenwald, and P. Shamsollahi, “Marginal loss
modeling in lmp calculation,” IEEE Trans. Power Syst., vol. 19, no. 2,
pp. 880–888, 2004.
[2] Z. Hu, H. Cheng, Z. Yan, and F. Li, “An iterative lmp calculation method
considering loss distributions,” IEEE Trans. Power Syst., vol. 25, no. 3,
pp. 1469–1477, Aug 2010.

1

Who has an incentive to improve the renewable
day-ahead forecast?
Evangelia Spyrou, Student Member, IEEE, Benjamin F. Hobbs, Fellow, IEEE

Abstract—Improving forecasts of renewable generation is one
way to improve the efficiency of grid operation with high renewable penetration ([1],[2]). Ideal market incentives would enable participants to capture the economic benefits of improved forecasts.
But in practice, market failures might discourage market participants from providing better renewable forecasts or methods. We
consider how Residual Unit Commitment (RUC) market processes
might introduce such a failure by enabling wind plants to take advantage of inaccurate/imprecise ISO forecasts.

M

I. INTRODUCTIONULTIPLE agents use renewable forecasts in electricity
markets: generators, traders, load, and independent system operators (ISO). ISOs use the forecasts for many purposes
but here we focus on RUC. ISOs utilize RUC to ensure that
adequate capacity is committed for next day’s demand in casethe financial day-ahead (DA) market commits inadequate capacity, which can occur for several reasons. For instance, net
virtual supply can displace the commitment of physical resources, lower than forecasted demand might clear, or renewable offers might be overly optimistic. RUC procurement and associated costs significantly increased in California in 2016.
Most ISOs use their own DA forecasts of renewable generation in RUC (e.g., CAISO, ERCOT, SPP). We investigate if useof ISO forecasts in RUC provides market participants a disincentive to integrate better forecasts.
II. MOTIVATING EXAMPLE
We assume a simple one-bus, one-time period, energy-onlymarket with identical granularity for DA and real-time markets.
Our system consists of three generators (see Table 1) and has aperfectly forecasted demand of 660 MW. We assume that the
ISO forecasts 250 MW of wind day-ahead, and has a 50%
chance of -20 MW error, and 50% chance of +20MW error.
Table 1. Capacity and costs of generators in the example
Plant
Capacity (probability) / Pmin
Start-up / Variable cost
Gen 1
400 MW (p=1) / 100 MW$0 / $5.47/MWh
Gen 2
60 MW (p=1) / 12 MW$437 /$26.11/MWh
Wind
230 (p=0.5) / 270(p=0.5)$0 / $0/MWh

We compare the average settlement (“avg. settl.”) for windbidders and load under two wind bidding strategies: (a) windbidders offer the perfect forecast in DA market, (b) wind bidders offer the lesser of their perfect forecast and the ISO
forecast. We provide the market clearing results for both windrealizations (i.e., 230 MW/270 MW) under each strategy in Tables 2 and 3. We assume that the DA uplift costs are paid byload and the RUC uplift costs are paid by the party whoseschedule differs from the ISO forecast (here wind). Uplift credits are equal to unit’s market shortfall.

Table 2. Market clearing under (a): wind offers perfect forecast(low / hi wind)
DA
RUC
RT
Gen 1
400MW/390MW -/(-12MW)
Gen 2
30MW/0MW-/commit-/(+12MW)
Wind
230MW/270MW Load
660MW/660MW Price($/MWh) 26.11/5.47
26.11/5.47
Start-up costs$437/$0-/$437-

Avg Settl.
$6,256$985*$3,399*$10,640*-

Table 3. Market clearing under (b): wind bids strategically(low / hi wind)
DA
RUC
RT

Avg Settl.

Gen 1
400MW/398MW -/(-20MW)
Gen 2
30MW/12MW-/Wind230MW/250MW -/+20MW
Load
660MW/660MW Price($/MWh) 26.11/5.47
26.11/5.47
Start-up costs$437/$437* Adjusted for uplift for unrecovered start-up & energy costs$6,256$985*$3,741$10,982*-

The real-time dispatch and commitment is identical for both
strategies but the cost allocation among participants is different.
Wind generators would prefer strategy (b) whereas load would
prefer strategy (a). Thus, if wind bidders had access to a perfect
forecast and were strategic, they would prefer to offer based on
the perfect forecast only when it was lower compared to theISO’s to avoid any costs incurred by RUC.
Under both bidding strategies, the total system cost is $3,113,
which is inefficient. If RUC used the perfect forecast, then Gen
2 would never be committed under high wind conditions, saving in expectation $342—so the quality of forecast used in RUC
might affect the system cost.
III. CONCLUSIONS AND FUTURE WORK
Our example demonstrates a simple case in which RUC
might increase system costs if (i) the quality of ISO forecast issystematically inferior to market participants’ forecasts and (ii)
RUC leads to commitment of additional units. According to our
example, RUC costs might discourage strategic renewable bidders from providing better forecasts to the system. In the poster,we will provide more motivating examples, present simulation
results, and discuss alternative market designs.
IV. REFERENCES[1][2]

B. Kroposki et al., "Achieving a 100% Renewable Grid: Operating Electric Power Systems with Extremely High Levels of Variable Renewable
Energy," IEEE Power and Energy Mag., 15(2), 61-73, Mar.-Apr. 2017.
Q..Wang et al., “The value of improved wind power forecasting: Grid
flexibility quantification, ramp capability analysis, and impacts of electricity market operation timescales,” Appl. Energy, 184, 696-713, 2016.

Acknowledgments. E. Spyrou is an Onassis Foundation Scholar and would
like to thank the Foundation for the support of her research. B. Hobbs is supported by the National Science Foundation.

A Bi-Level Optimization Formulation of Priority
Service Pricing
Yuting Mou

Anthony Papavasiliou

Philippe ChevalierCenter for Operations Researchand Econometrics
Université catholique de Louvain
Email: yuting.mou@uclouvain.beCenter for Operations Researchand Econometrics
Université catholique de Louvain
Email: anthony.papavasiliou@uclouvain.beCenter for Operations Researchand Econometrics
Université catholique de Louvain
Email: philippe.chevalier@uclouvain.be

Abstract—Priority service pricing is a promising approach formobilizing residential demand response, by offering electricity
as a service with various levels of reliability. Higher levelsof reliability correspond to higher prices. The proper pricing
guarantees that consumers self-select a level of reliability that
corresponds to the reliability that the system can offer. However, traditional theory for menu design is based on numerous
stringent assumptions, which may not be respected in practice,such as a well-behaved (convex) cost functions. In addition, theobjective of the menu design is to maximize social welfare,
while the profit requirement of the aggregator is not accountedfor. Moreover, no guidelines are provided regarding how todiscretize the menu into finite options. In this study, we design apriority service menu as the equilibrium solution to a Stackelberg
game where an aggregator moves first with a menu offering,and residential consumers react by selecting menu options andrevealing their valuation. The Stackelberg game is modelled asa bi-level optimization problem involving the aggregator andconsumers, and then reformulated as a mixed-integer problem.
As a consequence of this approach, the menu design problem canbe integrated within a day-ahead unit commitment model, andprofit requirements of the aggregator can be modeled explicitly.
The approach is illustrated on a toy numerical example as well
as a large-scale model of the Belgian power market.

the authors describe contracts whereby power is offered toconsumers with various degrees of reliability. Higher degrees
of reliability correspond to higher prices. The proper pricing of
the contracts guarantees that consumers self-select a level ofreliability that corresponds to the reliability that the system
can actually offer, and indirectly reveal their valuation for
power. However, traditional theory for menu design is basedon numerous stringent assumptions. We aim to relax theseassumptions in this paper.

I. I NTRODUCTION[1] R. Dhulst, W. Labeeuw, B. Beusen, S. Claessens, G. Deconinck, andK. Vanthournout, “Demand response flexibility and flexibility potential ofresidential smart appliances: Experiences from large pilot test in belgium,”
Applied Energy, vol. 155, pp. 79–90, 2015.
[2] Y. Mou, H. Xing, Z. Lin, and M. Fu, “Decentralized optimal demand-side
management for phev charging in a smart grid,” IEEE Transactions on
Smart Grid, vol. 6, no. 2, pp. 726–736, 2015.
[3] H. Xing, Y. Mou, M. Fu, and Z. Lin, “Distributed bisection method for
economic power dispatch in smart grid,” IEEE Transactions on powersystems, vol. 30, no. 6, pp. 3024–3035, 2015.
[4] H. Xing, M. Fu, Z. Lin, and Y. Mou, “Decentralized optimal schedulingfor charging and discharging of plug-in electric vehicles in smart grids,”
IEEE Transactions on Power Systems, vol. 31, no. 5, pp. 4118–4127,
2016.
[5] S. S. Oren, “A historical perspective and business model for load response
aggregation based on priority service,” in System Sciences (HICSS), 2013
46th Hawaii International Conference on. IEEE, 2013, pp. 2206–2214.
[6] H.-p. Chao and R. Wilson, “Priority service: Pricing, investment, andmarket organization,” The American Economic Review, pp. 899–916,
1987.
[7] A. Papalexopoulos, J. Beal, and S. Florek, “Precise mass-market energy
demand management through stochastic distributed computing,” IEEE
Transactions on Smart Grid, vol. 4, no. 4, pp. 2017–2027, 2013.
[8] K. Margellos and S. Oren, “Capacity controlled demand side management: A stochastic pricing analysis,” IEEE Transactions on Power
Systems, vol. 31, no. 1, pp. 706–717, 2016.

The operation of power systems has become increasingly
challenging due to the large-scale integration of renewableenergy sources. Demand-side management is a promising
approach for overcoming this challenge The LINEAR projectin Belgium unveils substantial demand response potential
in the residential sector [1]. In the domain of demand-side
management, researchers have focused on price-based control
and direct load control. The former method is based on pricesignals provided to consumers, whereby consumers react tothe price signal by adjusting their electricity consumption.
However, if the price is too volatile, consumers face substantial
price uncertainty, which is politically objectionable. In direct
load control, household appliances, such as electric vehicles
and air conditioners, would be controlled directly by aggregators. Nevertheless, this method is criticized as being too
intrusive and ideally control is imposed behind the meter.
Another paradigm based on priority service is proposed in[5], with the underlying economic theory dating back to [6]. In
this paradigm, electricity supply is perceived as a service thatcan be offered with various degrees of quality. Specifically,

II. M ETHODOLOGY
In this study, we design a priority service menu as theequilibrium solution to a Stackelberg game where an aggregator moves first with a menu offering, and residential
consumers react by selecting menu options and revealing theirvaluation. The Stackelberg game is modelled as a bi-level
optimization problem involving the aggregator and consumers,
and then reformulated as a mixed-integer problem. Numericalsimulations are also carried out.
R EFERENCES

Transactive Energy Based Multi-microgrids System
Operation†∗ Chirath Pathiravasam, Student Member, IEEE, ∗‡ Ganesh Venayagamoorthy, Senior Member, IEEE
∗ Real-Time Power and Intelligent Systems Laboratory

Holcombe Department of Electrical and Computer Engineering, Clemson University, Clemson, SC, 29634, USA† Department of Electrical Engineering, University of Moratuwa, Katubedda, Sri Lanka‡ School of Engineering, University of KwaZulu-Natal, Durban, South Africa{chirath.d.pathiravasam, gkumar}@ieee.org

Abstract—High proliferation of distributed energy resources(DER) demands controllability in the distribution system. Microgrid is one of the solutions to mitigate variability of the
distribution system. However, complete independent operationof microgrids may not be cost effective. To overcome thischallenge, predictive and prescriptive analytics based transactive
retail energy framework is proposed to optimize the dispatch
in a multi-microgrid system. A transactive distribution system
operator (TDSO) will act as the aggregator of the microgrids
and other prosumers. TDSO will also perform system parameter
predictions to optimize future energy dispatches. Dispatch of the
microgrids are done using decision trees based on the microgrid priorities. The transactive operation enables microgrids toincrease reliability with lower energy storage capacities.
Index Terms—Transactive energy; dynamic energy management system decision tree; predictive analytics; micro-grids;
renewable energy

Fig. 1: Microgrid system with Dynamic Energy Management
System

I. I NTRODUCTION
The increasing penetration of DER and prosumers will
add to requirements of reliability of the system. Prosumers
willingness to participate in energy markets will need tobe satisfied by either the distribution utility or distribution
system operator: TDSO. TDSO will aggregate generation anddemand of the multi-microgrid distribution system. A realtime price based transactions with microgrids and among
microgrids are performed by the transactive energy frameworkof TDSO which in turn participate in the bulk energy market.
Participation in energy markets by TDSO and prosumers suchas microgrids can be viewed as a nested market.
In this project, each microgrid 1 consist of wind and solar
pv power generation, battery energy storage, backup diesel
generator, critical loads and controllable loads is considered.
Individual microgrids are managed by Dynamic Energy Management Systems (DEMS) [1].
The predictive analytics platform of the TDSO utilizes
individual microgrid parameters such as wind speed and solar
irradiance to predict the future variable renewable energy
generation. The predictions are performed in a cellular computational networks framework [2].The forecast renewableenergy will be used by the microgrid DEMS to dispatch
resources and TDSO to transact energy with microgrids andthe main grid (ISOs).

Fig. 2: Transactive energy framework of TDSOR EFERENCES[1] G. K. Venayagamoorthy, R. K. Sharma, P. K. Gautam, and
A. Ahmadi, “Dynamic energy management system for asmart microgrid,” IEEE transactions on neural networks
and learning systems, vol. 27, no. 8, pp. 1643–1656, 2016.
[2] C. Pathiravasam and G. K. Venayagamorthy, “Spatiotemporal characteristics based wind speed predictions,” in2016 IEEE International Conference on Information andAutomation for Sustainability (ICIAfS), Dec 2016, pp. 1–6.

1

Generator Contingency Modelling in ISOs:
Pricing Implications
Karthik Saikumar, Student Member, IEEE, and Kory W. Hedman, Member, IEEE

Abstract—Electric Energy Market clearing models rely on
solving bid-based security constrained energy and ancillary cooptimization problem. Security constraints need to be modelled in
the optimization model to ensure that the market solution issecure, and the system can survive a N-1 event. Typically, market
clearing models have been using a deterministic approach to deal
with generator contingencies by enforcing a quantitative
procurement constraint that would procure a specific amount ofreserves either zonally or system wide to ensure sufficient reserves
are available after a generator contingency event. However,recent industry initiatives suggest there is a movement to explicitly
modelling uncertainty in market models. The California
Independent System Operator (CAISO) has proposed the explicit
modelling of critical generator contingencies to capture the post
contingency flow constraints in the optimization models.
Meanwhile, the Midcontinent ISO has implemented theincorporation of zonal reserve deployment transmission
constraints for post generator contingency scenarios. However, theinclusion of stochastic scenario-based modelling into market
models can have an impact on pricing and settlements. This work
aims at analyzing the pricing impacts such stochastic modelling byleveraging concepts from duality theory.
Keywords – Electric Energy Markets, Generator Contingency,
Duality Theory, Pricing

I. INTRODUCTION
Modelling of various contingencies in market models is
critical in ensuring that the market solution respects the security
constraints that the system operator needs to conform to and toalso ensure that the economic signals from the pricing thatcomes out of the security constrained market model accuratelyreflects the cost of respecting the security constraints. One of
the crucial barriers to applying scenario based stochastic
modelling to market models with respect to generator andtransmission contingencies has been the impact stochastic
modelling can have on pricing.
II. PRICING IMPACTS
The impact of stochastic modelling on pricing is mainly duetthe presence of a second stage recourse decision variable. The
loss of a transmission element does not mandate a change in
generation set points post contingency as only the system
topology has changed and as a result, line flows. This change
can be captured using a Line Outage Distribution Factor(LODF). However, for the loss a generator, there is a need tochange dispatch set points of all or some of the remaining

generation fleet after the loss of a single generator.
The typical constraint for a transmission security modellingis:𝐹𝑘,𝑐 = 𝐹𝑘0 + 𝐿𝑂𝐷𝐹𝑘,𝑐 ∗ 𝐹𝑐0
𝑅𝑎𝑡𝑒 𝐶
𝐹𝑘,𝑐 ≤ 𝑃𝑘𝑚𝑎𝑥
Since the generator has no second stage recourse action, theonly major impact on the pricing is that the LMP will now
capture a new post contingency congestion component.
However, for generator contingencies, there needs to be asecond stage decision variable. One possible way to capture theresponse of the remaining generation fleet to the loss of a single
generating fleet is the Generation Distribution Factor (GDF).
The change in generation and the corresponding impact ontransmission line flows is captured as shown below:

𝑃𝑔,𝑐 = 𝑃𝑔 + 𝐺𝐷𝐹𝑔,𝑐 ∗ 𝑃𝑔=𝑐𝑅
𝑅𝑎𝑡𝑒 𝐶∑ 𝑃𝑇𝐷𝐹𝑘,𝑛∗ (𝑃𝑔,𝑐 − 𝑙𝑛𝑐 ) ≤ 𝑃𝑘𝑚𝑎𝑥𝑛

The link between the second stage dispatch in the post
generator contingency scenario and the first stage decisionvariable of the pre-contingency dispatch is masked by a static
term called the GDF for certain critical generator contingencies.
This linkage has a direct impact on the way the dual for the
given primal problem is formed. By leveraging duality theory,
we can observe the impact on generation rent and locational
marginal pricing.
III. REFERENCES[1] CAISO, “Draft final proposal: Generator contingency andremedial action scheme modeling,” Jul. 2017[Online].
Available:
https://www.caiso.com/Documents/DraftFinalProposalGeneratorContingencyandRemedialActionSchemeModeling_updatedjul252017.pdf[2] Y. Chen, P. Gribik, and J. Gardner, “Incorporating post
zonal reserve deployment transmission constraints into energy
and ancillary service co-optimization,” IEEE Trans. Power
Syst., vol. 29, no. 2, pp. 537–549, Mar. 2014.

Optimal Energy Management for the Integrated
Power and Gas Systems via Real-time Pricing

Abstract— This work proposed a bi-level formulation for energy
management in the integrated power and natural gas system via
real-time price signals. The upper-level problem minimizes theoperational cost, in which dynamic electricity price and dynamic
gas tariff are proposed. The lower level problem is the arbitrage
model of gas-fired plants and P2Gs stations, in which thetransient gas flow is introduced. This bi-level model is relaxed toa mixed-integer quadratic programming problem using the
Karush-Kuhn-Tucker optimality conditions. Results show thatthe dynamic price and tariff can make gas-fired units and P2Gs
plants follow the system operator’s preferences such as wind
power accommodation, mitigation of unsupplied load andrelieving the network congestion.
Index Terms—integrated energy system, bi-level programming,
equilibrium constraints.

I.
METHODOLOGY
The optimal energy management via dynamic pricing in the
integrated power and gas system is formulated using thefollowing bi-level model as shown in Fig. 1.

Electricity price ($/M Wh)

Jiakun Fang, Zhe Chen
Aalborg University
Aalborg, Denmark50

adjusted price for P2G 2

20

4

7

critical action price for P2G 2

10
13
16
Time (hour)2219Fig. 2. The electricity price relevance for P2Gs
290
adjusted price260
230

original pricecritical price

200
1

4

7

10
13
16
Time (hour)1922Fig. 3. The electricity price relevance for gas-fired unit
30

Power (M W)

Upper-level Programming
Electrical Power System
OF: Profit Maximization

Cheng Luo
Northern Indiana Public Service Company
Merrillville, USA
critical action price for P2G 1
adjusted price for P2G 1

80

1

Electricity price ($/M Wh)

KangAn Shu, Xiaomeng Ai, Jinyu Wen
Huazhong University of Science and Technology
Wuhan, China

P2G2

P2G1

25
20
15

GFU

10
5
0
1

Dynamic electricity
price

Arbitrage operation

4

7

10
13
16
Time (hour)

19

22

19

22

Fig. 4. The arbitrage operation of P2Gs.

GFU&P2G Arbitrage Formulation
OF: Profit Maximization
Dynamic gas tariff

gas pressure (bar)56.0
gas pressure limits @N9
54.0
52.0
gas pressure @N9

50.0
48.0

original gas pressure @N9

46.0

Gas System Constraints

1

Lower-level Programming

Fig. 5. The gas pressure at node 9.

Fig. 1. The framework of bi-level energy management

The bi-level problem can be transferred into mix-integer
quadratic programming using the KKT optimality conditions
II. RESULTS
The Graver 6-bus system and the 12-node natural gas
system are used as the test case.

4

7

10
13
16
Time (hour)III. CONCLUSION
The price signals are proposed to guide the operation of the
GFU and P2G stations according to the upper-level system
operator’s preference. Simulation results demonstrate that thereal-time pricing can successfully mobilize the arbitrage
operation of GFU and P2Gs to accommodate wind power,
satisfy load shortage and relieve gas state variables off-limit.

Cost Benefit Analysis of Technology Options for
Enabling High PV Penetration
Lisha Sun, David Lubkeman, Mesut Baran
Dept. of Electrical and Computer Engineering
North Carolina State University
Raleigh, USA

Abstract—This paper provides an economic comparison of threetechnologies that enable high PV penetration on the distribution
system: Medium voltage solid state transformer (SST), grid edge
regulator and smart inverter. In order to compare these threetechnologies, simulations are performed on a representative
utility feeder using OpenDSS. Quasi-static time series analysis
over a year is used to predict the effectiveness of high PV
penetration and more aggressive conservation voltage reduction
enabled by each technology. Comprehensive cost benefit
analyses are conducted based on these simulation results. The
cost benefit analysis results indicate that the medium voltage
solid state transformer (MV SST) is the most cost effective
option with a 3 year payback period for utilities that are trying
to accommodate high PV penetration in the distribution grid.
Index Terms--Cost benefit analysis, Grid edge regulator, HighPV penetration, Smart inverter, Solid state transformer.

I.

HIGH PV PENETRATION OVERVOLTAGE MITIGATIONTECHNOLOGIESMany technologies have been developed to address
overvoltage issues in order to enable higher PV penetration.
The MV SST addresses the overvoltage issue by regulating
the voltage on the customer side and isolating the customer
from seeing overvoltage on the distribution primary. The gridedge regulator can be used as a mitigation method byregulating the voltage on the customer side of the distribution
transformer. The smart inverter is being promoted for use in
new PV interconnections. The smart inverter does not regulate
voltage directly. Instead, it controls the reactive power
injection to impact the local customer voltage.
TABLE I.

TECHNICAL COMPARISON OF MV SST, GRID EDGE
REGULATOR AND SMART INVERTER

Product
Power Rating
Input Voltage

MV SST
0-100 kVA
3.6 kV Vac

Grid Edge
50 kVA
240 Vac

Smart Inverter
12 kW - 30 kW
1000 Vdc

Output Voltage

120Vac/200Vdc

240 Vac480/277 Vac

Volt. Regulation

Any

VAR Compensation

Any

Efficiency
DC Port

97%
Yes

± 10%
10% of rating(lead. Or lag.)
≥ 99%
No

0-1power factor(lead. Or lag.)98.30%
No

Jeffrey Thomas, Joe DeCarolis

Dept. of Civil, Construction, and Environmental Engineering
North Carolina State University
Raleigh, USA

II.

SIMULATION AND RESULTIn order to quantify the benefits of the MV SST, grid edge
regulator and smart inverter, OpenDSS quasi-static time series
simulation are performed based on a representative utility
feeder.
Q

SST LossVR
Model(No Loss)
SST Conceptual Model(a)
Limited Q
Grid Edge Regulator
XFMR(VR with loss)(b)

HV Side(12.47kV)HV Side(12.47kV)

Limited Q
HV Side(12.47kV)

XFMR

PV
Load

InverterPV

Load

Smart Inverter

PV
Load

(c)

Figure 5. (a) FREEDM SST (b) Grid edge regulator (c) Smart Inverter
TABLE II.

SIMULATION RESULTS

MV SST (32 used) Grid Edge (32 Used) Smart Inverter (133 used)
Diff Δ
EnergyEnergyPeak
EnergyPeak
to BasePeak kW
DER
CVR
Total

MWh-yr.

-1,187-534-1,721

-2
-146
-147

III.

MWh-yr.

-1,110-534-1,644

kW

-3
-146
-149

MWh-yr.

kW-1,082-153-1,236

-11
-36
-46

COST BENEFIT ANALYSISThis session describes the cost and benefits estimation andalso discusses the cost benefit analysis results. A
comprehensive cost estimation is performed and the benefits
quantified by the quasi-static circuit simulation discussed in
the previous section are monetized.
TABLE III.

COMPARISON OF FINANCIAL METRIS

Estimated
Estimated
Utility
AnnualInvestment Cost Benefits
MV SST$160k$91k
Grid Edge
$163k
$82k
Smart Inverter
$148k
$53k
Technology

Estimated
Net Present
Value
$665k
$590k
$330k

Discounted
Payback
Period
3.0 years
3.5 years
4.8 years

Transmission Constrained Economic Dispatch via
Interval Optimization Considering Wind Uncertainty
Xiao Kou, Student Member, IEEE, Wei Feng, Student Member, IEEE, and Fangxing Li, Fellow, IEEE

Index terms—economic dispatch, interval optimization,
renewable energy integration, strong duality, wind uncertainty.

I.

INTRODUCTION

Large-scale wind integration can reduce the fuel costs andcarbon emissions. However, the stochastic and intermittency
natures of renewables presents substantial challenges for thegeneration scheduling of committed units. In this context, it isimperative to study the optimization techniques that canmanage uncertainties to guarantee the safe and economic
operations of the bulk power system.
The conventional method to schedule generators’ power
outputs is to formulate a fuel cost optimization problem withdeterministic parameters, subjecting to power balance andother reliability requirements. In this framework, all theparameters are viewed as fixed values. The deterministic
methods are effective tools for analyzing those cases where
the fluctuation is small. However, the wind disturbance may
cause significant difference on the optimization solutions.
Moreover, wind fluctuation can cause generation-demand
imbalance, transmission violations, and frequency deviation.
At that time, the deterministic model would fail to handle thelarge deviations between the forecasted and actual wind power
generations. To solve this problem, growing attention has been
paid to probabilistic methods, such as stochastic programming.
However, the disadvantage is that these approaches heavily
relies on the accuracy of wind power probability distributionfunction. Since the current wind power forecasting technology
is still immature, this may lead to considerable prediction
errors and further affects the optimality of the generation
schedule.

It’s based on the above concerns that in this work wepropose an interval optimization based economic dispatch
model to address the wind power uncertainty. The mostprominent advantage of interval optimization is that it only
focuses on the boundaries of the uncertain variables, i.e. wind
power, which can be derived from historical data fairly
precisely, and does not require their accurate PDF formulation.
As a consequence, interval optimization can provide a morerobust generation schedule range under real-time scenarios by
avoiding the forecast errors.
The main contributions of this work are summarized asfollows: firstly, interval optimization is applied to get thepossible generation cost range; secondly, strong duality theory
is introduced to transform the max-min problem into a single
level maximization problem; and thirdly, big-M is employed
to convert the combinatorial problem into a MILP problem,
which ensures the global optimal solution can be obtained
within a short time.
TEST SYSTEM

II.
1

5 WT1

2

28
7
WT2

3

13

4

8

6

16

12

9

10

11

17
27
14

20
18

22

21
WT3

19

15

23
24

29

25

30

26

Figure 1. Configuration of the modified IEEE 30 bus system.
WT1

10

wind output/(MW)Abstract— High penetration renewable energy integration can
negatively affect the secure operations of power grids. In thiswork, an interval optimization based algorithm is presented tosolve the transmission-constrained economic dispatch problem
considering wind uncertainty. The aim of introducing interval
optimization is to determine the possible generation cost range,
which consists of a lower (optimistic) boundary and an upper(pessimistic) boundary. The optimistic value is obtained bysolving a trivial LP problem. However, the pessimistic model is acombinatorial max-min problem. To solve the pessimistic model,
the strong duality theory is first applied to transfer the max-min
problem into a minimization problem. Then the big-M method is
employed to manage the combinatorial equality constraints for
efficient calculation. The proposed algorithm is solved byinterfacing GAMS with MATLAB. Simulation results on amodified IEEE 30 bus system validate the effectiveness of the
proposed methodology.

WT2

WT3

8
6
4
2
1

3

5

7

9

11

13

time/(h)1517Figure 2. Wind power output.

19

21

23

Analytical Method to Aggregate Multi-Machine
SFR Model with Applications in Power System
Dynamic Studies
Qingxin Shi, Fangxing Li
University of Tennessee, Knoxville

Dept. of Electrical Engineering and Computer Science
Knoxville, TN, United States
Abstract— The system frequency response (SFR) model
describes the average network frequency response after adisturbance and has been applied to a wide variety of dynamic
studies. However, the traditional literature does not provide ageneric, analytical method for obtaining the SFR model
parameters when the system contains multiple generators. In
this project, an analytical method is proposed for aggregating
the multi-machine SFR model into a single-machine model. The
verification study indicates that the proposed aggregated SFR
model can accurately represent the multi-machine SFR model.
The results show the method is promising with broad potential
applications.
Index Terms-- System frequency response (SFR), multi-machine,frequency control, model reduction.

i   i

Pd 

P



Pm

Pm1

Pm 2

Pmi

f

1
2Hs  D

K m1

1  FH 1TR1 s
1  TR1 s

1
1  TC1 s

1
1  TG1 s

1
R1

Generator #1

Km2

1  FH 2TR 2 s
1  TR 2 s

1
1  TC 2 s

1
1  TG 2 s



1
R2

Generator #2K mi

1  FHiTRi s
1  TRi s

1
1  TCi s

1
1  TGi s

1
Ri

Generator #i

...
...

(a) Muti-machine model
Pd 

P



Pm

f

1
2Hs  D1  FH TR s
1  TR s

1
1  TC s1
1  TG s



1
R

N

i

i 1

i 1

(2)

i

N

X   i X i

(3)

i 1

Then, it is proved by Mathematical Induction Method.

ANALYTICAL METHOD TO AGGREGATE SFR MODELThe SFR model is shown in Fig. 1. The droop value Ri is
correlated to Si (after reaching the steady state, ∆Pmi=Si·∆f/Ri).
Also, the multiple turbine governors are summed up with aconstant gain Kmi=Si/Ssys, which represents the portion of
rated power of machine i w.r.t. the whole system. The
equivalent droop value R is given by (1):N ,    1

Since a larger λi value means that generator #i has a larger
rated power and more sensitive frequency droop, it has alarger impact on the equivalent X. Therefore, we guess that
the ASFR model parameters equal to the weighted average of
those of each machine.

II.

SERIFICATION STUDY

The performance of the proposed model is by time-domain
simulation of a six-machine system. The parameters of eachmachine are in a wide range, as listed in TABLE I. The
frequency response of a step disturbance ∆P is shown in Fig.
2. It can be concluded that the ASFR model can accurately
represent the bus frequency response of a small-scale system.
This model can be applied to load-frequency control studies.
TABLE I. PARAMETERS OF 6-MACHINE SFR MODEL.
Km
TG (s)TC (s)TR (s) FH
1/R
(pu)
0.14
0.20
0.37
10.5
0.28
13.33
0.18
0.12
0.24
9
0.17
10
0.19
0.27
0.41
6
0.23
20
0.22
0.30
0.48
14
0.32
16.67
0.14
0.22
0.36
12
0.39
20
0.13
0.19
0.21
8.5
0.24
20
1.00
0.231
0.363
10.0
0.278
16.5

Gen.
No.
1
2
3
4
5
6
Equ.

λ
0.113
0.109
0.230
0.222
0.169
0.157
1.00

0

 f [ Hz ]I.

equivalent parameters X={TG, TC, TR and FH} represent the
combined effect of N turbine governors. We define thenormalized gain λi of each branch in Fig. 1 (a).

-0.1
-0.2
-0.3

ASFR model
MM-SFR model

-0.4
-0.5
0

(b) Aggregated single-machine model

5

10

15

20

25

0.06

[ p.u. ]Fig. 1. Aggregation of SFR model.

0.04

P

m

N
N
K
1
  mi    i(1)
0.02
ASFR modelR i 1 Ri
i 1MM-SFR model
0
0
5
10
15
20
25
where the equivalent gain is defined as κi=Kmi/Ri. In theTime [ s ]aggregated SFR (ASFR) model (shown in Fig. 1 (b)), the four Fig. 2. Frequency & generation response of ASFR model.

Matlab Simulation Framework for Integration of Battery
Storage and Solar Generation in Distribution Systems
Milad Soleimani, Student Member, IEEE, Ana M. Ospina, Student Member, IEEE,
Mladen Kezunovic, Life Fellow, IEEE.

Abstract—This poster presents a microgrid model including
battery storage, photovoltaic (PV) generation, and theconverters which connected them into the grid. The goal of the
proposed model is to provide a realistic simulation framework inwhich an optimum interaction of electric vehicle (EV) chargers
and PV generation is achieved using an energy battery storage.
The model is divided in two parts: power system and powerelectronics model. Different scenarios are considered in a subhourly time frame. The optimum scenario for the next steps iscalculated and the charging/discharging order is given to thebattery. Some details and results of the implemented model aredemonstrated in this poster.
Index Terms—Electric Energy Storage,
Modelling, Simulation Framework.

I.

PV

III.

ALGORITHM FOR SIMULATION INTEGRATION

Providing such a model has a considerably heavy
calculation burden and requires a huge capacity of random
access memory (RAM). In the flowchart shown in Figure 2, analgorithm is proposed to make such a modelling possibleusing ordinary computers.

Generation,

POWER SYSTEM MODEL

The optimum decision for the charging/discharging of thebatteries in the microgrid is being made every five minutes
and the order will be sent to the battery charger to store energy
in the battery or discharge it back to the grid. The schematic of
the model used for the system part is shown in Figure 1.
II.

POWER ELECTRONICS MODEL

When the decision is made by the power system model,
the converters will start to charge or discharge the batteries
based on the received order.

Figure 2. Flowchart of the implemented algorithm.

Figure 1. Matlab implemented simulation framework.

The Work is supported by Qatar National Research Fund Project"Integration of Solar Generation and Electrical Vehicles in the Smart Grid".

Global Sensitivity Analysis ofIslanded Microgrid Power Flow
Kun He, Xiaoyuan Xu, Han Wang, and Zheng Yan
Department of Electrical Engineering
Shanghai Jiao Tong University, Shanghai, China
Abstract—With the increasing penetration of renewable energies
into microgrids (MGs), the uncertainties of MG operation are
more remarkable. Therefore it is necessary to evaluate theinfluence of random input variables on the state of systems. In
this paper, a global sensitivity analysis (GSA) based on the Sobol
method is proposed to evaluate the influences of uncertainties on
islanded MG (IMG) power flow. First, considering the droop
control of distributed generations and uncertainties ofrenewables and loads, an IMG probabilistic power flow model isestablished. Then, the Sobol method based on Monte Carlosimulation (MCS) is used to perform GSA and the LevenbergMarquardt (LM) method is used to solve power flow. Finally,
the proposed method is tested using a 33-bus IMG system, andthe influences of renewable generation on power flow andfrequencies are analyzed.

C. Sobol sensitivity coefficientsk f ( x)dx  f    f dx    f dx dx ...   f
2

2
0

2

i 1

i

i

2
ij

1 i  j  k

D   f ( x )dx  f , Dm...n   f
2

2
0

i

2
m... n2
12 k

j

Si  Di D(6)kSTi  Si   Sij  ...  S1 i kj iIII. CASE STUDIES
WT2

18

A1区

17

35

Index Terms—Droop control, global sensitivity analysis,
Levenberg-Marquardt method, microgrid, Sobol method

16
15

21

A3区
3

4 5

14

DS
37

20
19
2

6 7 8

13

9 10 11 12 34

1

29 30 31 32 33
38

A4区

DG

Fig. 1 Structure of the 33-bus IMG.
1
0.9

Variables in A1
Variables in A2
Variables in A3
Variables in A4

0.8

FSC

0.7
0.6

0.5

0.1
0

(2)

 f  ...  f

1 i  j  k

ij

12 k

(3)

 f m...ndxw =0, m  w  n
0

f j1 ... jl dx  0, (i1 , i2 ,..., is )  ( j1 , j2 ,..., jl )

A1-A3

A1-A4

A2-A3

Fig. 2 FSCs of random variables with respect to power flow between
different zones.
1

Cumulative probabilityF ( x)  0, x  (,U , θ)Case 1Case 2
Case 30.8

0.6
0.48170.4

0.4816
0.48150.2
0.4814

0
-600

-400

-200

0

-124

-123.8-123.6

200

400

600

-123.4800

Active power from A2 to A3 (kW)1

i1 ...is28

0.2

B. High-dimensional Model Representation (HDMR)

f

27

25
36

0.3

(1)

i 1

26

24

0.4

 PLi  P0iU i 1  hpf ,i   3  







Q

Q
U
1

h





0i i 
qf , i3  Li

f ( x)  f0   fi 

23

PV

A. Power flow equation of IMG

k

WT1

A2区

II. KEY EQUATIONS PGi  U1i  U i  miQGi   1    ni

(4)

(5)

dxm ...dxn

22

I. INTRODUCTION
The uncertainty of distributed generation (DG) output isimportant for islanded microgrid (IMG) system operation.
Sobol GSA method considers system nonlinearities andinteractions of variables, and evaluates the influences of input
variables based on the fractions of the output variance. The
influences of renewable-based DG (RDGs) on the IMG
frequency and power flow are analyzed under different droop
control strategies.

dx

Fig. 3 Cumulative distributions of power flow for different cases.

Simultaneous Global Identification of Dynamic and
Network Parameters in Transient Stability Studies
Andrija T. Sarić 1, Aleksandar M. Stanković 2Mark K. Transtrum, Benjamin L. Francis
1

Dept. of Physics and Astronomy
Brigham Young University, UT, USA
mktranstrum@byu.edu

1

2

Abstract—This paper describes a global identification procedurefor dynamic power system models in the form of differential andalgebraic equations. Power system models have a number offeatures that makes their improvement challenging – they are
multi-level, multi-user and multi-physics. Not surprisingly, they
are nonlinear and time varying, both in terms of states (memory
variables) and parameters; and discrete structures, such asgraphs, are strongly blended with continuous dynamics, resulting
in network dynamics. Transient stability models are used as aprototypical example. Our method is based on informationgeometry, and uses advances in computational differential
geometry to characterize high-dimensional manifolds in the spaceof measurements. The results are illustrated on the case of a IEEE
14-bus test system with 58 parameters in our realization.
Index Terms— System Identification, Global Optimization,
Parameter Estimation.

I.

Dept. of Power, Electronics and Com. Engineering

University of Novi Sad, Faculty of Technical Sciences, Serbia

INTRODUCTION

We use an IEEE 14-bus test system as our dynamical model.
The system is initially in steady state and is perturbed at t = 0,
and the subsequent transient is observed with measurementsfrom all generators and buses in the system. We use a global
system identification procedure known as the Manifold
Boundary Approximation Method (MBAM) to simultaneously
reduce the dynamical model and estimate network andcomponent parameters, given these observations. In addition,
we use sensitivity analysis to examine the possibility ofadditional reduction using partial response matching.

Dept. of Electrical and Computer Engineering
2
Tufts University, Medford, MA, USA1asaric@uns.ac.rs; 2 astankov@ece.tufts.edu
I.

KEY FIGURES

Figure 1. Parameter values along a geodesic in step 2. Here, 𝑇𝑇"𝑑𝑑0 →0 in Bus
6 (i.e., its log value goes to negative infinity in red) corresponding
to a singular perturbation that removes the d-axis subtransient. In
order to compensate for this approximation, several other parametervalues change (blue lines), while most other parameter values areconstant (black curves).

TABLE I. REDUCTION STEPS.
Step Number ofparameters
1
57
2
56
3
55
4
54
5
53
6
52
7
51
8
50
9
49
10
48
11
47
12
46
13
45

Reducedparameter𝑥𝑥"𝑑𝑑 →𝑥𝑥′𝑑𝑑
𝑇𝑇"𝑑𝑑0 →0𝐵𝐵2,5 → 0𝑥𝑥"𝑞𝑞 →𝑥𝑥′𝑞𝑞
𝑇𝑇"𝑞𝑞0 →0𝑥𝑥"𝑞𝑞 →𝑥𝑥′𝑞𝑞
𝑇𝑇"𝑞𝑞0 →0𝑥𝑥′𝑑𝑑 →𝑥𝑥𝑑𝑑
𝐵𝐵6,13 → 0
𝑥𝑥"𝑑𝑑 →𝑥𝑥′𝑑𝑑
𝑇𝑇"𝑑𝑑0 →0𝑥𝑥′𝑑𝑑 →𝑥𝑥𝑑𝑑
𝑇𝑇′𝑑𝑑0 →0Reduced element
Bus 6
Bus 6
Line 2-5
Bus 8
Bus 8
Bus 6
Bus 6
Bus 1
Line 6-13
Bus 8
Bus 8
Bus 6
Bus 6

Type

Singular Limit
Network Reduction

Figure 2. Eigenvalue spectra of the FIM, shown for (a) the full model with allobservations, (b) the reduced model with all observations, (c)-(e) the
full model with three types of partial observations. The dashed line
marks the smallest eigenvalue of the reduced model.

Singular Limit

II.
Singular Limit
Network Reduction
Singular Limit
Singular Limit

KEY RESULTS

Reduction from 58 to 45 parameters was achieved for thefull set of system observations. The 13 parameters removed
from the model cannot be identified using these observations.
Conversely, these parameters are unimportant for the observed
system behavior. Fig. 2 shows that additional reduction of themodel could be achieved by observing only part of the system.

Comparative Analysis of Hybrid Bipolar HVDC and
FACTS Performance for Improving Commutation
Failure Immunity
Zicong Zhang, Kisuk Kim, Gilsoo Jang
Department of Electrical Engineering, Korea University, Seoul, Korea
zicongyouyou@korea.ac.kr, kks1213@korea.ac.kr, gjang@korea.ac.kr
Abstract— This article will discuss a new transmission
method that is different from traditional high-voltage direct
current(HVDC) transmission. The new way will use Hybrid
bipolar HVDC to compare with the flexible AC transmission
systems (FACTS) which based on Real Time Digital
simulator(RTDS). An interconnected power system includes
generators, hybrid bipolar HVDC, FACTS and variable loads.
Experiments are needed to compare the commutation failure
immunity index(CFII) of the system under different conditions
and to make a delicate analysis of the commutation failure andthe change of the extinction angle at the time of the faults. Single
phase inductive ground fault will be used respectively on therectifier side and the inverter side. At the same time, its
comparative analysis of the transient states and fault recovery
time of each line for faults. Further analysis of the hybrid bipolar
HVDC and FACTS for evaluating the performance of thetransmission lines.
Keywords— HVDC; Hybrid bipolar HVDC; FACTS;
Commutation failure immunity index(CFII); RTDS

I.

where,
is the lowest inductive fault impedance.
Therefore, the CFII is the critical fault level that does not cause
a commutation failure, expressed as a percentage of the HVDC
rated power. Because of the time step of RTDS is very
shorter(about 2us), they can provide better performance whenanalyzing system transient states.

(a)

(b)

Figure 1. (a)Traditional HVDC System with FACTS (b) Hybrid Bipolar HVDC
System

II. KEY RESULT
A. Traditional HVDC System with FACTS

INTRODUCTION

This paper will compare the hybrid bipolar HVDC withbipolar LCC-HVDC which connect with FACTS. In the past,
the LCC-HVDC transmission mode needs to take into accountthe commutation failure. FACTS and VSC-HVDC will be used
separately to connect with LCC-HVDC which specificcomposition shown in Figure 1. VSC-HVDC will effectively
provide the required commutation voltage for LCC-HVDC
commutation, and when a fault occurs, it can shorten the fault
recovery time by using the black start. FACTS can effectively
reduce the impedance of AC system and improve therobustness of AC system. CFII is a brand new important
indicator for evaluating that LCC-HVDC can avoid
commutation failure during operation. CFII represents thetransient characteristics of robustness of LCC-HVDC
significantly and more directly than calculates the changing ofvoltage drop(ΔV) when the mathematical models of the powersystem become more complex than before. The larger value of
CFII meant the lager level of the fault that can be sustained
without the commutation failure. CFII equation is shown as
(1):
(1)

Figure2. Traditional HVDC with FACTS extinction angle when the fault
occurs at the rectifier side, which the SCR of the rectifier is 1.77

B. Hybrid Bipolar HVDC System

Figure3. Hybrid Bipolar HVDC extinction angle when the fault occurs at therectifier side, which the SCR of the rectifier is 1.77

C. Talbe of Cases
Case

CFII(%)

Recovery Time

Hybrid HVDC

47.51

0.1667s

HVDC with FACTS

38.62

0.3114sImpact of Dynamic Load Model on Short-Term
Voltage Stability of Korea Power System
Jaemin Moon, Student Member, IEEE, Jae-kyeung Kim, Member, IEEE, Kyeon Hur, Senior Member, IEEE

Keywords—dynamic load model, structural impact, regional
impact

1

0.8
0.6
0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

3.5

4

4.5

5

3.5

4

4.5

5

Time(s)(a) Region 1
Voltage(pu)Abstract—This paper study structural and regional impact ofdynamic load model on short-term voltage stability. Analyses
imply the needs of developed models structure changed from ZIP
load model which Korea power system operator conventionallyuse and precise modeling of loads near the accident duringcontingency analysis. For analyzing structural impact of load
model, we apply complex load model and ZIP load model in
Korea power system, and then compare those results. Also, we
analyze regional impact of load model by comparing results
which apply complex load model for different region of Korea
power system in each simulation.

Voltage(pu)School of Electrical and Electronic Engineering
Yonsei University
Seoul, Korea
Email: yonseimoon@gmail.com, JK.Kim@yonsei.ac.kr, khur@yonsei.ac.kr

1
0.8
0.6
0

I. KEY FIGURE

0.5

1

1.5

2

2.5

3

Time(s)1

Voltage(pu)Voltage(pu)(b) Region 2

0.8

0.6

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Time(s)

1

1.5

2

2.5

3

3.5

4

4.5

5

Voltage(pu)Time(s)전압(pu)(b) Large motor: 10%, Small motor: 40%, Exponential: 50%
1

0.8

0.6

0

0.5

1

1.5

2

2.5

3

0.5

1

1.5

2

2.5

3

(c) Region 3
Figure 2. Bus voltage with different region appliedcomplex load model

0.8

0.5

0.6

Time(s)

1

0

0.8

0

(a) ZIP load model

0.6

1

3.5

4

4.5

5

Time(s)(c) Large motor: 20%, Small motor: 80%
Figure 1. Bus voltage with different load model structureImpact of Load Step Change on Thermal and Voltage
Stability Limits of Overhead Transmission Lines
Mahbubur Rahman, Student Member, IEEE; Valentina Cecchi, Member, IEEE
Energy Production & Infrastructure Center
University of North Carolina at Charlotte
Abstract– The power handling capability of a transmission line ismainly constrained by thermal and voltage stability limits. When
there is a change in system load, the line reaches a new steady-state
conductor temperature after a certain period of time unless it
violates the thermal and voltage stability limits. This paper
investigates the impact of a step change in system load on both
limiting factors and proposes an approach to determine thecritical limiting factor and corresponding time during that change.
For higher accuracy, the change in line current and resultant
conductor temperature, as well as the effect that a variation in
conductor temperature has on line resistance are taken into
account. Noticeable differences were found on the line’s powertransfer limiting factor under different levels of load step change.
Moreover, the impact line length on the power transfer limiting
factors with respect to load changes is also studied in this work.

I.
POSTER OVERVIEW
The motivation of the work comes from the fact thatvariation of system load affects both thermal and voltage
stability conditions of a transmission line. Given a change insystem load, the line current changes due to two factors: 1. thechange in load itself, and 2. the change in line impedance dueto the change in conductor temperature (Tc) caused by thechange in load. The step change in line current introduces a
gradual change in line Tc, which in impacts the line impedance.
This variation in line impedance effects the current flowing
through the line to serve the new system load. This continuous
variation in line impedance and current have a direct effect onthermal and voltage stability limits of transmission lines.
Start
ss(Steady state Tc )In this paper, a methodology, as in Fig. 1, is proposed todetermine the critical limiting factor and the corresponding time
to reach it during a load step change. Line stability factor, LQP[1] is used to determine the voltage stability index of different
lines of a system. The iterative process continues until the line
under consideration reaches: i. the maximum allowable Tc, ii) a
new steady state Tc, with LQP < 1.0, iii) the LQP index of 1.0.
The proposed method was applied on a two bus system witha 200 mi long line under specific ambient conditions. When thesystem load was increased by three different percentages fromthe steady state, three different outcomes are observed as in Fig.
2. Fig. 3 shows the impact of the line lengths on the powertransfer limiting factors of the transmission line.

Figure 2: Conductor temperature and voltage stability index (LQP)
over time for different scenerios

Newton-Raphson
Newton-Raphson
PowerPower Flow
Flow
Calculation
Calculation ofof LQP
LQP
LQP
LQP <
< 1.0
1.0
YES
Line
Line Current
Current from
from
Power
Power Flow
Flow

NO

Dynamic
Dynamic HBE
HBE toto obtain
obtainupdatedupdated TTcc
TTcc << TTcmax
cmax
YES

NO

UpdatedUpdated line
line resistanceresistanceEnd(main limiting factor
and critical time)

Figure 3: Critical time over increase in load for two different line
lengths for Hawk conductor

REFERENCES

Figure 1: Flowchart to determine critical factor and critical time[1]
This work was supported in part by the National Science Foundation award#1509681 “A Novel Electric Power Line Modeling Approach: Coupling of
Dynamic Line Ratings with Temperature-Dependent Line Model Structures.”

A. Mohamed and G. B. Jasmon, “A new clustering technique for powersystem voltage stability analysis,” Electric Machines & Power Systems,
vol. 23, no. 4, pp. 389–403, 1995.

A Dynamic Inverse-Time Overcurrent Relay Model forOverload Analysis with Varying Overcurrents
Jianan Liu

Wei Qiao

Department of Electrical & Computer Engineering
University of Nebraska-Lincoln
Lincoln, NE 68588-0511 USA
jianan1991@huskers.unl.edu

Department of Electrical & Computer Engineering
University of Nebraska-Lincoln
Lincoln, NE 68588-0511 USA
wqiao3@unl.edu

Abstract—Most of the past overload cascading failure analyses either
ignored the effect of inverse-time overcurrent relays with the assumptionthat transmission/distribution lines will trip once overloaded, or assumed
static inverse-time characteristics of the inverse-time overcurrent relays
even under varying overcurrent conditions. This work presents a dynamic
inverse-time overcurrent relay model. The inverse-time characteristics
of overcurrent relays corresponding to the current overload states arederived according to the proposed dynamic inverse-time overcurrent relay
model. The proposed dynamic inverse-time overcurrent relay model canbe applied to analyze overload cascading failures in power systems, suchas determining the trip sequence of transmission/distribution lines whenan overload cascading failure occurs.

Inverse-time overcurrent relays have been widely installed in
North America in the form of electromechanical relays and thenmicroprocessor relays. In 1996, an IEEE standard [1] was issued,
which defined the inverse-time characteristics for overcurrent relays
and provided an integral equation for microprocessor overcurrent
relays to emulate their dynamics. However, most of the overloadcascading failure analysis did not take inverse-time overcurrent relays
into consideration, thus assuming that transmission/distribution lines
will trip once overloaded [2]. The static inverse-time overcurrent relay
model, which reveals the reciprocal characteristics between the relay
operating time and input overcurrent, has been adopted in a few
papers [3]. However, the static model is not accurate to calculate
the relay operating time under varying input overcurrent conditions.
In an overload cascading failure procedure, contingencies will occur
continuously, which will result in redistribution of power flows and,thus, variation of the relay input overcurrent. A dynamic inverse-timeovercurrent relay model can be used to predict the relay operatingtime under varying overcurrent conditions. Furthermore, it can beused to determine the trip sequence of transmission/distribution lines,
which reveals how the cascading failure spreads along with time.
II. K EY E QUATIONS AND C ASE S TUDY
A. Static Inverse-time Overcurrent Relay Model
A
+ B)
(1)
Mp − 1
where T D is the time dial setting, A is a constant to determine theinverse type, B is a constant to simulate saturation, M is the ratioof the input current to the pickup current, p is a constant to simulate
specific characteristics, and t is the time delay for relay contacts toclose when M > 1.
t = T D(

B. Dynamic Inverse-time Overcurrent Relay ModelAn+ Bn )
Mp − 1

i=1

󰁜

(1 −

n−1

Bn = B

i=1

(1 −

ti
)
tmax
i

(3)

ti
)
tmax
i

(4)

where An and Bn are the coefficients in the nth overcurrent states
which depend on the previous states, ti is the actual time elapsed
and tn are the time delays for relay contacts
during the ith state, tmaxi
to close during the ith and nth state, respectively.
C. Case Study

I. I NTRODUCTION

tn = T D(󰁜n−1

An = A(2)

Fig. 1. Dynamic inverse time characteristics of an overcurrent relay when M
increases from 1 to 2 with a step of 0.2 every two seconds.

In Fig. 1, the curves with M = 1.2 and M = 1.4 correspondto the characteristics after the input overcurrent has held 1.2 timesof the pickup value for two seconds and 1.4 times for another two
seconds, respectively. The last curve with M = 2.0 corresponds tothe present overcurrent state. By using the M = 2.0 curve, it is easy
to predict the relay’s operating time after five varying overcurrents.
R EFERENCES[1] IEEE Standard Inverse-Time Characteristic Equations for Overcurrent
Relays, IEEE Standard C37.112, 1996.
[2] B.A. Carreras, V.E. Lynch, and M.L. Sachtjen, “Modeling blackout
dynamics in power transmission networks with simple structure,” Proc.
IEEE International Conference on System Sciences, Jan. 2001, pp. 1-9.
[3] Y. Cai, Y. Cao, Y. Li, T. Huang, and B. Zhou, “Cascading failure
analysis considering interaction between power grids and communication
networks,” IEEE Trans. Smart Grid, vol. 7, no. 1, pp. 530-538, Jan. 2016.

A Novel MILP-Based Method of Optimal Energy
Flow in Gas -electricity System
Tianhao Liu 1, Student Member, IEEE

Yufeng Guo 2, Member, IEEE

1. Department of Electrical and Electronic Engineering
The University of Hong Kong, Hong Kong, China

2. School of electrical engineering and automation,
Harbin Institute of Technology, China

Abstract—This poster proposes a novel method to calculate
the optimal energy flow of gas-electricity system. Because ofnonconvex and nonlinear properties of gas transmission lines, theoptimal problem of gas-electricity system is difficult to solve. To
tackle with this problem, one-dimensional piecewise linear
approximation method is proposed to reformulate natural gas
transmission constraints. Accordingly, the optimal model of gas electricity system is reformed as a mixed integer linear
programming (MILP) problem. Compared with the nonlinear
method, the proposed method can be solved by existing
optimization techniques, which can be easily applied in optimal
operation and planning of multiple energy system. The proposed
method is verified by a six-bus power system with a seven-node
gas system and a 118-bus power system with a fourteen-node gas
system. The tests show that the proposed method can reduce
computation time with a relatively precise result.
Keywords—MILP; gas-electricity system; optimalpiecewise linear approximation method

operation;I.
INTRODUCTION
The fast and accurate calculation of gas-electricity modelis essential for optimal operation and planning of gaselectricity system. Previous works provided nonlinear
programming method or mixed integer nonlinear
programming method. These methods don’t have the generaltechnique and the optimum solution can’t be guaranteed,which can only be effective in small scale model. Whenconsidering nonlinear and nonconvex characteristics of largescale gas-electricity system, the method is not useful. The
contribution of this paper is providing a one-dimensional
piecewise linear approximation method, which can compute
fast as well as accurate. The proposed method is effective inlarge-scale system.

b) Gas transmission constraints:

f

g ,t

n ,t

(1)

i ,t

jli

Pij ,t  (i ,t   j ,t ) / X(3)

Pijmax  Pij ,t  Pijmax , Pgmin  Pg ,t  Pgmax ,0  lshi ,t  Li ,t(4)

Pg ,t  Pg ,t 1  RU g , Pg ,t 1  Pg ,t  RDg(5)

Where,  i ,t is the angle of bus i and time t. Li,t is electricity
load at bus i. RUg / RUd is ramp up/down limit of generation g.

(7)

The left side of Eq (7). can be reformulated as:
N 1

2
2
2
f mn
,1, t   ( f mn , k 1, t  f mn , k , t ) mn , k ,t  Cmn ( pm ,t  pn ,t )

(8)

k 1

N 1

f mn,t  f mn,1,t   ( f mn , k 1,t  f mn , k ,t ) mn , k ,t(9)0   k ,t  1, k 1,t  k ,t ,k ,t   k ,t

(10)

k 1

Compressor model: pm,t  Rc pn,t , 0  fc  F(11)

 sgn,t  sg

(12)

max
c

sg

min
n,t

max
n ,t

min
n

,p

 pn,t  pmaxnWhere,  is set of gas fired generation connect to node n.
sgn,t is the gas supply. pn,t is the square of pressure at node n
and time t. Cmn is gas flow transmission parameter.  mn, k ,t isposition in the segment of transmission mn at time t.  k isbinary variable. f c is gas flow through the compressor. Rc is thesquare of ratio of compressor, Fcmax is the upper limitationtransmission of the compressor. By using reformation method
mentioned in Eq (7). The NLP model is changed to MILP
model. A typical six-bus power system with seven-node gastransmission system and a IEEE 118-bus power system with afourteen-node gas system [1] are taken as examples. We
calculate the model by using nonlinear method and onedimensional piecewise linear approximation method. The
proposed method is solved by MILP solver CPLEX and NLP
model is solved by the NLP solvers CONOPT on GAMS,
respectively.
gasn

KEY RESULTS

The segments of MILP model is 20. From the table, it canbe seen that in a), the proposed method use 60% computation
time with the same accuracy. In b), the proposed method cansolve the problem in 3.372s, while NLP can’t solve.
a) A typical six-bus power system with seven-node gastransmission system
Models

Methods

Times (s)TC ($)

NLP

CONOPT

0.325$1369744.54

MILP

CPLEX

0.187

$1369744.54b) A IEEE 118-bus power system with a fourteen-node
gas system
Models

Methods

Times (s)TC ($)

NLP

CONOPT

NA

NA

MILP

CPLEX

3.372

$7472695.2457

Reference[1] http://motor.ece.iit.edu/data/OPF_MCES/

This work was supported by the Key R&D Project of China under grant
number: 2016YFB0901903.

(6)gngasIII.

Where, Pg,t is power generation at bus g and time t. sd n,t is gas
demand at node n and time t. lshi ,t is Load shedding at bus i andtime t. bg and cg are the coefficients of power generation
function. g is contract gas or coal price at bus g. n is contract
gas price at node n. VOLL is the price of load shedding.
a) Power transmission constraints(2)
 Pg ,t  lshi,t  Li,t   Pij ,tg iGsg n,t  sdn,t   (bg Pg ,t  cg )2sgn( pm,t , pn,t ) f mn
,t  Cmn ( pm,t  pn ,t )II. THE PROPOSED METHODSThe objective function of gas-electricity model:
TC   (bg Pg ,t  cg )g   sdn ,t n  VOLL * lshi ,tmn , tmln

Modeling of Geomagnetically Induced Currents in thePower Grid Using Time-Domain Simulation
Hemanth Kumar Vemprala, Student Member, IEEE and Dr. Bruce A. Mork, Fellow, IEEE
Dept of Electrical and Computer Engineering, Michigan Technological University, Houghton, MI.
Email: hvempral@mtu.edu, bamork@mtu.edu

SUB 6
6

7

T6

XFMR

SUB 3

Y

T15

8
Line

T7

XFMR

18

XFMR
A

T3

Y

Y
XFMR

SUB 5
5

A

XFMR
A

T5

20

T8

19
XFMR
Y

T4

11

Y

SUB 8

12

Y

Sw. St a 7

13

XFMR

T10

XFMR

T9

Y

Y

XFMR
Y

14
T11

XFMR

V

SUB 1

Y

4

2

PE

SUB 4
3

T2

1
T1
Y

XFMR
Y

Y

XFMR
Y

Y

XFMR
A

XFMR
A

T12

T13

A

XFMR
A

The time-domain simulation mitigates all these problems andprovides a scope for user-defined modeling depth based on the
study requirements. In this work, the time series of geoelectricfield values are dynamically computed in ATP MODELS using
GMD event data and using piecewise-layered earth model. The
proposed modeling approach of GIC aids in studying the GMD
vulnerability assessment in the time domain. Further, this poster
illustrates the modeling approaches and presents a simulationcase using a benchmark of 1989 GMD event (TPL-007-1) thatcaused the Hydro Quebec blackout. The system impact
assessment, mainly in the increased transformer VAr losses andhalf-cycle saturations, are researched and presented. As a pathforward, this modeling approach shall be extended to a real-time
simulation tool envisioned to monitor the GMD event and effects
on the network.

15

16
17

SUB 2

A

Abstract— A GIC/GMD event could produce a large scaledisturbance in the power network and is categorized as highimpact, low-frequency (HILF) event. With long and crowded HVtransmission corridors and unavoidable solar storms impending,a wide-spread flow of GIC could produce undesirable effects.
GIC has the ability to knock the system out of normal operating
conditions and has occurred in the past. At present the system
impact and equipment impact assessments due to GMD are
analyzed largely by a steady-state phasor analysis which uses asnapshot of geomagnetic storm at a particular event and amodified load-flow engine to emulate GIC at the neutrals. These
ill-effects are not completely captured in phasor domain
simulation due to absence of nonlinearity, frequency dependency,
and partly also due to over-simplified models.

T14

Fig. 2. IEEE 20-bus benchmark case for GIC study modeled in EMTP-ATP

II. KEY SIMULATION RESULTS
The Fig. 3 presents the typical half-cycle saturation effectand a quasi-dc current flow.
1.5

1.5

1.0

1.0

0.5

0.5

0.0

0.0

-0.5

-0.5

-1.0

-1.0
-6

-2

(f ile NonlinearTest.pl4; x-v ar c:SW

2

6-XX0001) t:FLUX

10

14

-2

2

(f ile NonlinearTest.pl4; x-v ar c:SW

(a)

14

6

10

14

18

22

-XX0001) t:FLUX

(b)

22
18

10

14
6

10
2

6
-2

2

-6
0.00

0.02

0.04

0.06

(file NonlinearTest.pl4; x-var t) c:SW -XX0001

I. PROPOSED FRAMEWORK

0.08

0.10

-2
0.00

0.02

(file NonlinearTest.pl4; x-var t) c:SW -XX0001(c)

Core equivalent current flowing through Trf-2
GIC current through neutral-2

450

0.04

0.06

0.08

0.10

(d)
300
(GIC)

(A)

200

350

The overview of the geomagnetic induced current calculation in ATP
is demonstrated in Fig. 1. Fig. 2 presents the 20 bus IEEE benchmark
case modeled in ATP.

100
250
0
150
-100
50

-50
0.0

-200

-300
0.2

0.4

(file GIC_Model_3Wnd_singleTrf_source.pl4; x-var t) c:X0003A-T0002A
c:XX0026-XX0019

0.6
c:X0003B-T0002B

0.8

(s)

1.0

c:X0003C-T0002C(e)Fig. 3. Typical half-cycle saturation on power transformers (a) flux vs I
characteristics for a power transformer with GIC = 33.33 amps/ph. (b)transformer characteristics for GIC current of 67 amps/ph. (c) & (d) half-cycle
saturation currents with GIC of 100 and 200 amps through its neutral
respectively. (e) Line current flows into transformer with GIC of time varying
0.01 Hz, 100 A (Ipeak) through its neutrals[1][2]
Fig. 1. Concept of GIC calculation

REFERENCES
North American Electric Reliability Council (NERC), Application
Guide, “Computing Geomagnetically-Induced Current in the BulkPower system,” Dec. 2013.
R. Horton, D. Boteler, T. J. Overbye, R. Pirjola and R. C. Dugan, "A
Test Case for the Calculation of Geomagnetically Induced Currents," inIEEE Transactions on Power Delivery, vol. 27, no. 4, pp. 2368-2373,
Oct. 2012.

POSTER ABSTRACT, IEEE PES GENERAL MEETING, PORTLAND, OR, AUGUST 2018

1

Optimization of Electric Vehicle Aggregation in
Energy and Balancing Markets in the Nordics
Lars Herre, Student Member, IEEE, Jacob Dalton,

Abstract—Aggregators of flexible residential loads, such as
electric vehicles can enter the electricity market and bid in both
energy and ancillary services markets. This research provides
an optimal bidding strategy for an aggregator of electric vehicles
that places bids in the energy market and in the ancillary services
market. In this analysis we model the bidding strategy of arisk averse aggregator and include uncertainty from prices andvehicle availability. The results show the separated profits fromancillary services and costs from the energy market. We conclude
with an estimation of the value of aggregation and the potential
in the Nordic electricity market.

and Lennart Söder, Member, IEEE

The constraints are summarized in Eq. (2) - (8). The profit (Π)
is comprised of the revenue in the day-ahead (DA) balancing
market (ΠR ), cost from day-ahead energy purchase (ΠE ),cost from real-time (RT) energy consumption (ΠC ) and cost
penalties for deviation of the RT consumption from the DA
purchase (ΠE ) in hour t and scenario ω.
The DA balancing price is λDA,R, the energy prices are
t
DA,E
λt
and λRT,E,
andthepenaltyis
M . The probability oft,ω
scenario ω is given by πω .

Index Terms—electricity market, stochastic optimization, electric vehicle aggregation.

I. K EY I SSUES

T

HE electrification of private transport is a technology of
growing interest that can provide flexibility to the powersystem if adequately utilized. Electric vehicles (EV) are canbe considered as temporary energy storages with availability
constraints. If aggregated in sufficient numbers or combinedwith other assets they can fulfill the minimum bid size of
specific markets.
An aggregator of EVs can enter the electricity market andbid in both energy and balancing markets. This research
provides an optimal bidding strategy for an aggregator ofelectric vehicles that places bids in the energy and balancing
markets in the Nordic countries. In this analysis we consider
uncertainty (ω ∈ Ω) from prices and vehicle utilization andmodel risk averse aggregator that aims to maximize its profits
in Eq. (1).
max . (1 − β) · E [Πω ] + β · CVaRs.t.

Πω =πω
ΠR
t =

XX

(1)

∀t (3)

An illustration the EV aggregator’s bidding strategy fora winter day is shown in Fig. 1. It can be seen that theconsumption is largely shifted towards the hours where thebalancing market prices and the respective bids are high.

∀t (4)

II. K EY F INDINGS

E
C
P
ΠR
t − Πt − Πt,ω − Πt,ω ∀t, ω (2)tX ω DA,R[λt
RtDA ]

Fig. 1. DA energy bid E DA , real-time energy consumption E RT averaged
over all scenarios and DA regulation bid RDA over a 16 hour period. ∆E isthe deviation between DA energy bid and real-time consumption in different
scenarios.

t

ΠE
t =

X DA,E[λt
EtDA ]tΠCt,ω =πωΠPt,ω =πωX X RT,E[λt,ω 4 Et,ω ]ωtXX

M · 4Et,ω∀t, ω (5)∀t, ω (6)ωtRT
4Et,ω =Et,ω
− EtDA∀t, ω (7)RT
EV fleet power/energy constraintss on Et,ω∀t, ω (8)L. Herre, J. Dalton and L. Söder are with the Department of ElectricalPower & Energy Systems, KTH Royal Institute of Technology, Stockholm,
Sweden (e-mail: see http://www.kth.se/profile/lherre).

We present a portfolio optimization model for an aggregatorof electric vehicles, that is bidding in two markets: an energy
market (day-ahead and real-time) and a balancing market(FCR: frequency containment reserves). We apply this model
to a risk averse aggregator of 1000 electric vehicles in theNordic electricity market.
The results demonstrate the applicability of the proposed
optimization problem formulation. We also explore the economic potential for an aggregator in the Nordic electricity
market. Further, we investigate the value of aggregation andthe conditional value at risk CVaR.

Building Very Large Synthetic Power Grids
Adam B. Birchfield, Student Member, IEEE and, Thomas J. Overbye, Fellow, IEEE
Department of Electrical and Computer Engineering
Texas A&M University
College Station, TX, USA
Abstract—Power systems research can benefit from nonconfidential, fictitious power systems datasets that matchcharacteristics and statistics of real grids. This poster introduces
the scaling of the grid synthesis approach to a 70,000 bus system.
The approach extends previous work in placing substations withload and generation, dispatching the system economically, anditeratively building the dc-solvable power system with atransmission planning algorithm. Then the system can beaugmented with reactive power compensation devices, using an
iterative reactive power planning algorithm, to obtain a solvableac power flow solution. The resulting 70,000 bus case is able to be
fully shared.
Keywords—synthetic power grids, power system analysis

I. SCALING THE SYNTHETIC CREATION ALGORITHMS TO VERYLARGE GRIDS

For creating fictitious realistic power system cases, previous
work including [1]-[3] describe a methodology that starts withpublic data on population and generators. A synthetictransmission system is designed using an automated approachby first clustering substations and connecting them internally,
then iteratively adding and removing transmission lines to meeta variety of statistical metrics. Geographic constraints are key,as the algorithms use the Delaunay triangulation, geographic
length, and geographic feature matching to capture realistic
constraints [1]-[2]. In addition, power flow constraints are met
in avoiding overloading lines in base and contingency
conditions.
Once the transmission network is in place, an iterative
reactive power planning algorithm builds on [3]. This process
augments the system with voltage control devices such as shunt
capacitors and reactors to obtain a feasible ac power flow
solution with acceptable voltage characteristics.
II. EXAMPLE 70,000 BUS CASE
The above processes are demonstrated in at 70,000 bus
example case. This case has the geographic footprint of the U.S.
portion of the eastern interconnect, a region covering all or partof 39 states, serving about 600 GW of peak load with over
10,000 generating units. Nominal voltage levels of 69 kV
through 765 kV are modeled in the system, with the approach tohandling the multiple overlapping networks based on [2]. The
single-line diagram of the case is shown in Fig. 1.
A major impact of this work is that publicly available test
cases can be used to spur power systems research andinnovation. As one of the largest realistic power system test
cases published, this 70,000 bus system is particularly useful for

Fig. 1. One-line diagram of the 70,000-bus test case. This system wasbuilt with as synthetic methodology, is fictitious, and does not represent
the actual power grid in this location.

testing scalability of research algorithms and the suitability ofideas for large-scale actual power system models. Because thesystems are geographically-based, they are useful for
visualization research, research on geomagnetically induced
current, and coupling with actual geographically-based data. A
variety of engineering design and practice is found in thissystem, with a diversity of voltage levels, modelingassumptions, generation mixes, and load density patterns acrossa wide geographic footprint. With much actual power systemdata subject to strict confidentiality concerns, synthetic grids
offer researchers and innovators additional platforms for testingand demonstration.
REFERENCES
[1]

[2]

[3]

A. B. Birchfield, T. Xu, K. M. Gegner, K. S. Shetye and T. J. Overbye,“Grid structural characteristics as validation criteria for synthetic
networks,” IEEE Transactions on Power Systems, vol. 32, no. 4, pp. 32583265, July 2017.
A. B. Birchfield, T. Xu, K. S. Shetye, and T. J. Overbye, “Building
synthetic power transmission networks of many voltage levels, spanning
multiple areas,” 2018 51st Hawaii International Conference on System
Sciences, January 2018.
A. B. Birchfield, T. Xu, and T. J. Overbye, “Power flow convergence andreactive power planning in the creation of large synthetic power grids,”
IEEE Transactions on Power Systems, to be published, 2018.

Initial Work on Formalizing the Multiple-Time
Scale Structure of Power System Models
Matthew Hin
Center for Applied Mathematics
Cornell University
Ithaca, NY, USA
mfh72@cornell.edu

Abstract—Power system models are incorporating more andmore quantities that evolve on vastly different time-scales. A
common approach is to perform a hybrid simulation of atransient stability model and an electromagnetic model. The work
here proposes a multiple time-scale formalism for interfacingthese two models and analyzes the framework numerically using
two power systems: a 3-bus test grid and the 2224-bus GreatBritain transmission network.
Index Terms—multiple time scales, power system dynamics,
electromagnetic transients

I. F ORMAL M ODEL
To illustrate the hierarchy of subsystems, we can makeexplicit the time scale separation between mechanical, electromechanical, and electromagnetic states form a 3-time scale
system:ẋ = f (x, y, z),(1)ẏ = g(x, y, z),(2)η ż = h(x, y, z).

(3)

In this full system, we have written the model with respect tothe time scale relevant to electromechanical states and introduce two parameters  and 0 < η < 1. The former represents
the time-scale separation between the slow mechanical states x
and fast electromechanical states y, while the latter represents
the separation between y and the electromagnetic transientstates z. We also derive a corresponding hierarchy of slow, fast,
and transient subsystems. We obtain the slow subsystem byrescaling time to the slowest time-scale τ = t and considering
the limit  → 0:This subsystem serves as an electromechanical correction to
the slow subsystem. We obtain the transient subsystem byrescaling time (τ = ηt) and considering the limit  → 0:x0 = 0,(10)y 0 = 0,(11)0z = h(x, y, z).

Here, all slower states are treated as constant parameters while
the transient states are allowed to evolve under little to no
algebraic constraints.
II. C ASE S TUDIES
We test this formalism using two power systems: a smallscale 3-bus system shown in Fig. 1 and the 2224-bus GreatBritain transmission network [1]. These power systems are
populated by generators with third order synchronous machines regulated by a nonlinear voltage exciter [2] and loads
with constant power demand and first order induction motors.
The electromagnetic states are low-pass time-varying phasors[3].

x0 = f (x, y, z),(4)

0 = g(x, y, z),(5)

0 = h(x, y, z).

(6)

R EFERENCES

This slow subsystem has many similarities to simple swing
equations, but does not suffer from excessive model reductions
as the electromechanical states are still constraining the evolution of the mechanical states. We obtain the fast subsystem
by returning to (??) and considering the limit  → 0:

R EFERENCES

ẋ = 0,(7)ẏ = g(x, y, z),(8)

0 = h(x, y, z).

(9)

(12)

Fig. 1. A simple 3-bus power system.

[1] “Power
Systems
Test
Case
Archive.”
Internet:
http://www.maths.ed.ac.uk/optenergy/NetworkData/index.html, Mar. 31,
2013 [Apr. 20, 2018].
[2] C. Chu, & H.D. Chiang, “Constructing Analytical Energy Functions
for Network-Preserving Power System Models”. Circuits Syst Signal
Process vol. 24, pp.363-383, 2005.
[3] V. Venkatasubramanian, H. Schättler, & J. Zaborszky, “Fast TimeVarying Phasor Analysis in the Balanced Three-Phase Large ElectricPower System”. IEEE Trans. Automat. Contr., vol. 40, pp. 1975-1982,
Nov. 1995.

Convex Relaxations and Approximations ofChance-Constrained AC-OPF Problems
Lejla Halilbašić, Pierre Pinson, and Spyros Chatzivasileiadis
Technical University of Denmark (DTU){lhal, ppin, spchatz}@elektro.dtu.dk

I. I NTRODUCTION
While power system operations are increasingly relying
on the AC Optimal Power Flow (OPF) to identify optimal
decisions, higher shares of intermittent renewable generation
add an additional layer of complexity, calling for modelingapproaches which account for uncertainty. Literature considers
uncertainty either in the form of stochastic formulations, which
optimizes over several possible realizations, or in the form ofrobust formulations, where chance constraints are incorporated
in the optimization problem accounting for a continuous
range of uncertainty. This work focuses on chance-constrained
optimization.
As the AC-OPF is a nonlinear and nonconvex problem, it
is impossible to formulate tractable chance constraints able
to cover the whole continuous uncertainty space. Instead,literature has proposed tractable approximations. Chance constraints define the maximum allowable violation probability
 of inequality constraints and reduce the nonconvex feasible
space of the AC-OPF to a desired confidence region (CR),which is also nonconvex. The main challenge of the chanceconstrained AC-OPF lies in approximating the unknown nonconvex CR. In this work, we propose the first formulation ofa chance-constrained second-order cone optimal power flow
(CC-SOC-OPF), which (a) coupled with an AC feasibility
recovery is able to identify solutions within the CR (i.e.,
solutions feasible to the original chance-constrained AC-OPF),(b) finds more cost-efficient solutions than the alternative
nonconvex approach based on the full power flow equations,(c) leverages the benefits of iterative convex programming
to provide convergence guarantees and a high computational
efficiency, and (d) introduces new parameters which allow toreshape the convex approximation of the CR offering a highzrCC − SOC < zCC − AC



εSmax

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

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β100

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Abstract—This paper deals with the impact of linear approximations for the unknown nonconvex confidence region of chanceconstrained AC optimal power flow problems. Such approximations are required for the formulation of tractable chanceconstraints. In this context, we introduce the first formulation of achance-constrained second-order cone (SOC) OPF. The proposed
formulation provides convergence guarantees due to its convexity,
while it demonstrates high computational efficiency. Combinedwith an AC feasibility recovery, it is able to identify better solutions than chance-constrained nonconvex AC-OPF formulations.
To the best of our knowledge, this paper is the first to performa rigorous analysis of the AC feasibility recovery procedures
for robust SOC-OPF problems. We identify the issues that arise
from the linear approximations, and by using a reformulation of
the quadratic chance constraints, we introduce new parameters
able to reshape the approximation of the confidence region. We
demonstrate our methods on the IEEE 118-bus system.




















→β37



Fig. 1. Maximum violation probability of apparent branch flows max,
S
generation cost of the CC-SOC-OPF (zCC-SOC ) and the recovered CC-SOC→ and β ↔ .
OPF solution (zrCC-SOC ) as functions of β37
100

degree of flexibility w.r.t. the robustness of the solution. We
perform a rigorous investigation of how the approximationof the quadratic chance constraints impacts the CC-SOC-OPF
and its recovered solution.
II. C ASE S TUDY
We evaluate the performance of the proposed CC-SOCOPF on the IEEE 118 bus test system with two wind farms.
The wind power output is assumed to follow a Gaussian
distribution. The constraint violation probabilities of the CCSOC-OPF solutions are evaluated empirically using 10’000
Monte Carlo simulations.
We use the solution of the CC-SOC-OPF as an input toan AC power flow analysis to recover the feasibility of thesolution. We investigate the impact of the approximation of
the quadratic chance constraints through a sensitivity analysisof the input parameters β. Our results depicted in Fig. 1 showthat higher values of β increase the level of conservatism
and thus, reduce the maximum allowable violation probability
increasing the generation cost of both the CC-SOC-OPF
and its recovered solution. We identify 18 operating points,
highlighted in the pink box, which are AC feasible, fulfill
the original chance constraints and are still cheaper than
the nonconvex CC-AC-OPF. This highlights the potential of
convex relaxations to determine the boundaries of the CR andthe true optimal of nonconvex problems. Despite the required
tuning, β provides the flexibility to vary the shape of theconvex approximation and direct the solution from a true lower
bound back into the original feasible space and the CR.

Analysis of Transformers Thermal Response to
Geomagnetic Disturbances
Pooria Dehghanian, Student Member, IEEE, Thomas J. Overbye, Fellow, IEEE, and Katherine R.
Davis, Member, IEEE
Abstract—Solar geomagnetic storm releases a burst of plasma
carrying intense magnetic field to flow into the magnetosphere.
This results in a sudden change in the earth’s magnetic field
which induces currents in long conductors such as powertransmission lines on the earth’s surface. The geomagneticallyinduced currents (GICs) are a potentially catastrophic threat tolarge-scale power system operations especially the bulk
transformers, possibly leading to severe outages. This poster aims
to conduct a hotspot temperature analysis of transformer heating
due to a GICs-caused half-cycle saturation. A sensitivity analysis
is accomplished to realize the effect of GICs on transformer
temperature response. The proposed approach is applied to the
20-bus and 500-bus test systems facing a geomagnetic disturbance(GMD) event and the temperature responses are estimated inboth normal and contingency scenarios.
Keywords—geomagnetic disturbances; hotspot temperature;transformer; geomagnetic induced current

corresponding GIC values as the input to the heating function.
The heating function is closely dependent on transformer
characteristics such as cooling constant.
B. Case study
The proposed heating model will be demonstrated on the
20-bus and 500 bus test cases. The results of the heating
function for the time series input GIC, shown in Fig. 2, areapplied in different scenarios. For instance, Fig. 3 demonstrates
the transformer thermal behavior during the system normal
operating condition for 20-bus test case.
A precise determination of the transformer thermal
behavior is generally difficult due to its sensitivity to manyfactors such as ambient temperature, materials, core type,
cooling constant, etc. However, the proposed approach offers
actionable information to operators to approximately predict
the thermal behavior of transformers facing GMD events.

I. INTRODUCTION
GICs are injected into the grounding points of the electric
power grid. Power transformers are the most common gateway
for GICs resulting in an offset of the alternative current
sinusoidal flux in the transformer cores. As a result,transformers may face asymmetric or half-cycle saturations
imposing additional eddy current in various segments of thecore, winding, and metallic parts. Such induced overcurrent
leads to an intensified temperature of windings and metallic
plates. Motivated by procedure given in [1], Figure 1 illustrates
the suggested architecture for power system analysis impacted
by GICs. The developed GIC model is based on the effective
GIC current characterized by uniform earth conductivity. The
corresponding temperature impacts are studied through asensitivity analysis approach implemented in two different casestudies.

Figure 2. Real-time time series of GIC/temperature for the studied transformer

II. TRANSFORMER THERMAL ASSESSMENT IMPACTED BY GICS
A. Approximate Transformer Heating Model
In order to find the transformer heating response, an
asymptotic thermal behavior is considered using the available
NERC metallic hotspot thermal data to a DC-step injectedcurrent [1]. The impulse response of the transformer heating isfound from the step unit function. The benchmark GMD Efield data from the case in 1989 is utilized to assess the
GMD Event

Earth
Conductivity
Model

REFERENCES[1] “Screening criterion for transformer thermal impact assessment”.
Developed by the NERC project 2013-03.

XFMR Thermal
Model

System DC Model

GIC Model
Data

Figure 3. Temperature heat map of all transformers in the 20-bus test case when
the simulated GMD event hits the grid.

Hot Spot Temperature

System AC Model

GIC
AnalysisGIC

VAR LossAnalysis

VARs

Model/Measurement
Adjustments

Power Flow
Analysis

P,Q,V

Criteria

Fail

Figure 1. Overall architecture suggested for analysis of power grids impacted by GICs.

Pass

Operating
Procedure andDecesion Making

A Hybrid Simulator for the Study of EP Project
Multi-Terminal DC in South Korea Gird
Jeehoon Lee, Student Member, IEEE
School of Electrical andelectronic Enginnering
Yonsei University
Seoul, Korea
Jeehun87@yonsei.ac.kr

Kyeon Hur, Senior Member, IEEE
School of Electrical andelectronic Enginnering
Yonsei University
Seoul, Korea
khur@yonsei.ac.kr

Abstract—This poster introduces a hybrid simulator that isused to study on the impacts of MMC tap to LCC-HVDC(MTDC) operation in South Korea Grid. Main reason for MTDC
study is to demonstrate that the MTDC operation is beneficial interms of better system stability compared to Double Bi-pole
LCC-HVDC in South Korea grid. However, as the increase in
HVDC and FACTS device complicate the grid operation, precise
analysis of grid is required. Conventional simulation has
challenges that focus on either detailed models in small system or
simplified models in large system. Hybrid simulator approachaddresses these challenges by using both EMT and phasordomain simulation methods. Using the Hybrid simulator in thissituation, it is possible to perform the integrated simulationconsidering the reaction and characteristics of the Korea AC
system, away from the limited EMT study. Simulation Studies
were conducted for the stipulated fault scenarios. The objectiveof this study is to prove that MTDC configuration has effect on
reducing the number of east generator to be tripped for stability
in case of 765kV double line contingency.

Figure 2 MTDC hybrid Simulation (RTDS & TSAT)

II. KEY RESULT

Keywords—Hybrid Simulator, MTDC, MMC, LCC-HVDC, Tap,
RTDS, TSAT, South Korea grid

I. KEY FIGURE

REFERENCES[1]

Figure 1 MMC tap to LCC HVDC

[2]

Lin, X., Gole, A. M. and Yu, M. “A Wide-Band Multi-Port System
Equivalent for Real-Time Digital PowerSystem Simulators.” Power
Systems, IEEE Transactionson, 24, 237-249.
Liang, Y., Lin, X., Gole, A. M. and Yu, M. “Improved CoherencyBased Wide-Band Equivalents for Real-Time Digital Simulators.”
Power Systems, IEEE Transactions on, 26, 1410-1417

Design of Scalable and Flexible Distributed
Islanded Microgrid for Rural Electrification
Rabia Khan and Noel N. Schulz
School of Electrical Engineering and Computer Science
Washington State University
Pullman, WA
Abstract—DC microgrids are a viable solution to provide
electricity to off-grid communities in developing countries. The
microgrids designed for rural areas mostly constitutecentralized generation and storage that lack scalability andflexibility. This paper aims to describe a design and model
distributed scalable DC islanded microgrid for rural
electrification. The modified Newton Raphson Power flow issolved for the designed system. The performance of thedesigned system is evaluated by computing distribution
efficiency, line losses, and maximum voltage drop for distributed
microgrid and comparing with the centralized microgrid. The
adaptation of the distributed DC microgrid to scalability andflexibility is validated by adding new power generation sources
and increasing the demand. Additionally the designed model is
optimized in Hybrid Optimization Model for Electric Renewables(HOMER) based on the cost of energy for the top three energy
stricken locations, i.e., Uganda, Ethiopia, and Brazil. The low
cost of energy, low power losses, and high efficiency show thatthe distributed scalable DC microgrid serves as an optimal
design for rural electrification which can be extended in future
based on the increased power demand.

Fig. 1. Proposed design of the distributed microgrid.

I. INTRODUCTION
The fossil fuels used for the generation of electricity havebeen depleting for decades because of their high consumption.
The
Renewable energy resources have played a vital role toprovide electricity since 2000. More than one billion people livearound the world without electricity [2]. The locally available
renewable energy resources can be used to enlighten the livesof millions of people living in countries where electricity is notavailable or is highly intermittent. DC microgrids are a viable
solution to provide electricity to the off-grid communities indeveloping countries. The distributed scalable DC islanded
microgrid is designed and modeled in this paper. The cost ofelectricity is a major factor in energy-deprived countries, so thecost of energy for the designed system is calculated using
HOMER for three different locations that have the highest
energy needs, i.e., Uganda, Ethiopia, and Brazil. The solar andwind data for the locations are taken from National Solar
Radiation Database and NREL Wind Prospector respectively.

A. Proposed Benchmark
The block diagram of the proposed scalable andflexible system is shown in Fig. 1. The proposed
configuration has distributed generation and distributed
storage. To validate the flexibility of the system, threedifferent type of sources and loads are added.

II. CASE STUDIESThe three different case studies are implemented
which are described below.
Case Study 1: Modeling of the proposed system withdifferent sources and loadsCase Study 2: Implementation of the designed system
in HOMER.
III. CONCLUSION
This paper focuses on the design of a scalable and flexible
DC islanded microgrid for rural electrification. The results ofpower flow and the optimized cost of energy for the designed
system shows its effective implementation in rural areas with
future growth perspective.

REFERENCES[1] Nasir, M., Khan, H.A., Hussain, A., Mateen, L. and Zaffar, N.A.,
2018. Solar PV-Based Scalable DC Microgrid for Rural
Electrification in Developing Regions. IEEE Transactions on
Sustainable Energy, 9(1), pp.390-399.
[2] World Bank Group, Ahmed, A., Portale, E., Azuela, G., Liu, J., deWit, J., Sinton, J., Bazilian, M., Angelou, N., Banergee, S. andFoster, V., 2015. Sustainable Energy for All 2015: Progress Toward
Sustainable Energy. World Bank.
[3] Patel, A.M. and Singal, S.K., 2016, November. Off grid rural elec-trification
using integrated renewable energy system. In Power India International
Conference (PIICON), 2016 IEEE 7th (pp. 1-5). IEEE.

JOURNAL OF LATEX CLASS FILES, VOL. 6, NO. 1, JANUARY 2007

1

Synthetic Residential Load Models for Analysis of
Energy Management Impact on Smart Cities
Fernando B. dos Reis† , Timothy M. Hansen
Electrical Engineering and Computer Science Department
South Dakota State University
Brookings, South Dakota 57007
Email: † fernando.beretadosreis@sdstate.edu

Abstract—Demand response (DR), shifting load in response
to system conditions, can achieve defined goals, e.g., reducing
cost, losses, and curtailment of renewable resources. There areconsiderable challenges in obtaining customer load curves to beable to perform residential DR due to privacy concerns, lackof data, daily/seasonal variability, etc. This work focuses on thegeneration of synthetic load curves for residential customers
made of individual appliance and thermal loads. The aggregatedload of the individual customer synthetic load curves sum toa known system load curve that is now made of millions ofindividual appliances and thermal loads for city-sized DR studies.
Additionally, the synthetic load curves incorporate the ZIP load
model, allowing for realistic variations in active and reactive
power of loads in response to changes in voltage. As a first DR
study, large-scale DR considering distribution system limitationsin the presence of distributed generation, e.g., photovoltaic solar,
is studied.
Keywords—Residential load model, ZIP parameters, demand
response, home energy management systems, distributed generation.

I.

m (t) =l (t).
E [P ](2)

Substituting (2) into (1), the time-varying appliance rate into
the system with a Poisson process is described as
λ (t) =l (t + E [D]).
E [P ] E [D](3)

The expected home load l(t) is an input from the utility
data which contains residential, commercial, and industrial
costumers. Thus, it does not represent the behavior of a typicalresidential load, presented in Fig. 1. The distinct behaviorof residential customers, having its nadir near midday, iscrucial for studies with distributed photovoltaic generation, i.e.,
Appliance
at/near maximum generation
during low load periods. The ZIP
appliance parameters are presented in [3] with their respectcontribution in average residential costumers.

I NTRODUCTION

The current work significantly expands the queuing load
models presented in [1], which intrinsically considers the timevarying characteristics of load and makes use of only openly
available aggregated load data from electric utilities and system
operators. The queue model synthetically generates individual
appliances being turned on by customers with a defined power
rating and time duration, such that when summed together
equal a known system load curve.
Let λ(t) be the time-varying appliance rate into the systemdescribed by a Poisson process, D and P be random variables describing the duration and power rating of the set of
appliances, respectively, and m(t) be the average number ofappliances running at time t. Making use of the linear-withtime-shift (LIN-S) approximation from [2], λ(t) is expressedasλ (t) =m (t + E [D])
.
E [D]

(1)

Let l(t) be the expected aggregated household load at time t
so that the average number of ruining appliances is

Fig. 1: Daily expected behavior of residential and commercial
customers [3].

R EFERENCES[1]

T. M. Hansen, E. K. P. Chong, S. Suryanarayanan, A. A. Maciejewski,
and H. J. Siegel, “A partially observable markov decision process
approach to residential home energy management,” IEEE Transactions
on Smart Grid, vol. 9, no. 2, pp. 1271–1281, March 2018.
[2] S. G. E. Eick, A. M. William, and W. Whitt, “Mt/G/\infinity Queues
with sinusoidal arrival rates,” Management Science, vol. 39, no. 2, pp.
241–252, 1993.
[3] M. Diaz-aguiló, J. Sandraz, R. Macwan, F. D. León, S. Member,
C. Comack, and D. Wang, “Field Validated Load Model for the Analysisof CVR in Distribution Secondary Networks : Energy Conservation,”
IEEE Transactions on Power Delivery, vol. 28, no. 4, pp. 2428 – 2436,
2013.

Finite Element Analysis of Transformers in
Subterranean Vaults
Jaime Kolln, Robert B. Bass
Dept. of Electrical and Computer Engineering
Portland State University
Portland, Oregon, USA
kolln@pdx.edu

I. I NTRODUCTION

III. R ESULTS

Portland’s local utility, Portland General Electric (PGE) installs transformers below grade in vaults in the downtown area.
When a transformer is under electrical load, its temperaturetends to rise due to thermal losses caused by the circulationof electric current and magnetic flux. When installed in avault, a lack of sufficient ventilation may cause the transformer
temperature to rise above the transformer’s rated operating
temperature. Extended operation beyond this limit could leadto thermal damage, shortened asset lifetime, and possiblyinterruption in service.
All vaults have a removable lid for maintenance access, andoften these lids are vented to facilitate convective heat transfer
out of the vault. Vault lids with top-vent grates often requirea lifting force greater than 55 lbs and must be fitted with alift-assist mechanism which reduces the amount of ventilation
through the grate. Further, grated lids eventually get filled withleaves and debris, blocking the air passage and making the lid
even heavier. Regular clearing of debris from the grates also
adds a maintenance cost.
In order to alleviate these concerns, PGE asked the PowerEngineering Group at Portland State University to simulate
how various vault-mounted transformers behave within different climate conditions. The purpose of this work is to study
these models to determine under what conditions solid lids
may be used without endangering the transformer or causing
the vault lid to become too hot to step on.

Five transformer sizes were analyzed in a typical 660-PGE
vault from Oldcastle Precast. It was found the transformers
do not result in excessive transformer top oil temperatures or
lid temperatures under average mid-summer conditions whenthe vaults feature a vented or solid lid. In the model, thetransformers generally produce temperatures just less than
20 ◦ F higher with the solid lid. The models show lid surface
temperatures as high as 141 ◦ F on the larger transformer vaults
with solid lids under summer conditions

Fig. 1. 25 kVA Transformer, PGE-660 Vented Lid Vault Temp Profile.

II. T HERMAL M ODELING
Finite element analysis (FEA) is used to analyze the fluid
and thermal behavior of our vault-mounted transformer models. The transformers, vaults, lids, soil and air conditions
are represented as reasonably as possible without excessive
detail. A typical electrical load profile was used, with a focus
on warmer climate conditions, and only a handful of transformer/vault combinations were examined. Doing so allows
for the ability to concisely convey a set of results without anexcess of scenario details.
Over the course of this project several improvements havebeen made to the models. The most successful modification
was to include radiation, with smaller improvements including
a realistic ambient temperature profile and a 50% average load
profile.

Fig. 2. 25 kVA Transformer, PGE-660 Solid Lid Vault Temp Profile.

IV. VALIDATION
Portland State University’s research continues and is currently in the validation stage. As real world temperatures, load
profiles, and weather data is compared to the simulation results
the models can be improved. These results should be available
at the time of the conference and shall be presented then.

An Object Oriented Matlab Toolbox for PowerDistribution System Analysis
Hasala Dharmawardena, Student Member, IEEE ∗Ganesh K. Venayagamoorthy, Senior Member, IEEE ∗ †∗ Real-Time Power and Intelligent System Laboratory
Clemson University, South Carolina, USA† School of Engineering, University of KwaZulu-Natal, Durban, South Africa

hasala@ieee.org and gkumar@ieee.org
Abstract—This study describes a new Matlab based objectoriented simulation tool for power system analysis. It is applicable to any unbalanced condition, be it single phase loads,
un-transposed lines or single phase switching. The program
constructs the Bus admittance matrix by using the primitive
admittance matrices of the power system components. The
complexities associated with three-phase power flow are minimized by applying the Object Oriented Programming framework.
The toolbox supports studies related to three-phase power flow
solvers and short circuit analysis. It is also useful for classroom
education.
Index Terms—Bus Admittance Matrix, Distribution Power
Flow, Object Oriented Programming, Software, Three Phase
Distribution.

I. I NTRODUCTION
The modern power system, at the distribution level, ispresently undergoing a transformation in topology and dynamics. Given the complexity of the connected models and system
specific temporal dynamics, it is important to precisely modelthe distribution system to ensure safe, reliable and economical
operation of the system.
Modeling and simulation of the power system in thephase reference frame is much more complicated and timeconsuming than modeling in the balanced positive sequencereference frame. This is because of the size of the problem,at minimum increases by a magnitude of three. As a result,three-phase analysis requires more input data and computation
resources. The computational tools applied in the analysis need
to be extremely efficient and robust. Whereas in a balanced
system analysis, the system states are much closer to thenominal value, in the unbalanced system, the system states
have a larger possibility to stray away from the nominal value.
The existing commercial tools for distribution analysis
requires a significant investment in resources, to learn theThis study is supported by the National Science Foundation (NSF) of theUnited States, under grants IIP #1312260, #1408141 and #1638321, and theDuke Energy Distinguished Professorship Endowment Fund. Any opinions,findings and conclusions or recommendations expressed in this material arethose of author(s) and do not necessarily reflect the views of National Science
Foundation and Duke Energy.

operational idiosyncrasies, which is better invested in mastering the computation engine. Additionally, on the educational
front, there is a need for an easily understood, accessible, welldeveloped and maintained three power system toolbox.
Many free toolboxes such as MatPower are available forbalanced system analysis. However, the options for threephase system analysis are limited. The available Free andOpen Source (FOSS) software in this domain are written
in programming languages that are not widely used by thepower engineering community. Students and researchers are
already well versed in Matlab and have access to it. Thoughmany existing power system toolboxes are based on Matlab,
none of them support three-phase unbalanced system studies.
This could partly be attributed to the difficulty in using aprocedure-oriented programming framework to encapsulate
the complexities inherent to modeling the distribution system.
II. D ISCUSSION
The typical Kron reduced primitive impedance matrix of theunbalanced transmission line is given by (1).
Zaa Zab Zac(1)
Zabc =  Zba Zbb Zbc 
Zca Zcb Zcc
The modeling of the transformer is even more complicatedand dependent upon the connection type as well as the ground
impedance. Based on [1] the simplest model that can bepresented is in the form given by (2). Each of the four matrix
elements refers to a different 3x3 submatrix based on the typeof primary and secondary connections.
  
  
Ip
Y
Yps
V
= pp
= p
(2)
Is
Ysp Yss
Vs
The Matlab toolbox presented in this study addresses allthese complexities using the object-oriented programming
framework. It is a useful place to start developing newcomputational methods for power distribution system analysis.
R EFERENCES[1] J. Arrillage, C. Arnold, and B. Harker, “Computer modelling of electrical
power systems,” 1983.

Big Data: Application to Transmission andDistribution Co-Simulation for the Future Grid
Yaswanth “Nag” Velaga, P.K. Sen
Colorado School of Mines, Golden, CO
Abstract—Renewable energy is growing rapidly and isalready a significant source of electricity in many states in the
United States. Rapid integration of these sources is expected tochange the notions of power delivery in the near future. Withcurrent research efforts for the future grid focused heavily
towards Co-Simulation model, time has come to devise a practical
way to implement. This project aims at using big data to create areplica of the actual model for the distribution. Reliability
Coordinators (RC), Independent System Operators (ISO) andRegional Transmission Organizations (RTO) can use thesemodels to maintain the reliability of the system.
Keywords—Big Data, Co-Simulation, ISO, RTO, RC, Utilities

I. INTRODUCTION
As the grid becomes less centralized with the resource mix
integrates distributed energy resources (DERs), new tools are
required to study the interactions between T&D. Co-Simulation
is widely accepted by the industry as the future of grid
simulation. There are seven ISO/RTOs and eleven RCs in the
United States that coordinate the reliable power grid operations.
They run the transmission systems without distribution grid.
This model has been working because the penetration level of
DERs is low. With the reverse power flows, they need a
distribution model to prepare for the unexpected events.
Therefore, in order to create a virtual model, big data can beused.
II. CO-SIMULATION

III. SYSTEM MODEL
Approximately two-thirds of country’s transmission
system is operated by ISO/RTOs and Balancing Authorities.
Reliability coordinators keep oversight of bulk transmission
system to meet regulation standards. Models with these
operators cover upto 69kV with no visibility into distribution
grid. Transmission system is very accurately modeled andreflects the current state. Distribution system consists ofthousands of nodes and also not accurately modeled down to
the load. In order to co-simulate the T&D systems,
ISO/RTO/RCs need a model which can give the accurate
results and still capture all the characteristics of the system.
Either utilities need to share the model with the operators or
establish a communication platform to link the T&D systems.
These are not practical solutions because the model needs
constant updates and communication systems are notdependable every time. It is virtually impossible to integrate
T&D systems in the present framework. Only other way is tomodel the system based on constant data from field (Fig. 2).
Utilities often collect numerous amount of data from advanced
metering infrastructure (AMI) and sensors but not usedproperly. This data needs to be processed by big data
algorithms and change it to the way it can be used to build amodel. Current setup will relieve the utilities from the constantcommunication with operators. The idea was inspired fromthe IEEE T&D conference student poster contest feedback.

The traditional T&D systems are decoupled at theoperational level and solved separately using the legacy
software’s. In Co-Simulation approach T&D networks are
solved separately and the interactions are captured byinterchanging the solutions obtained for the two models. They
converge at every time step of the power flow and move
forward to the next (Fig. 1).

Fig. 2. Distribution System Model

Fig. 1. Co-Simulation Overview

Interdependent Electric and Water Infrastructure
Modeling, Optimization and Control:
System Simulations for Severe Contingencies andDrought Conditions
Scott Zuloaga, Student Member, IEEE, Vijay Vittal, Fellow, IEEE
School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA
Abstract— The water-energy nexus is a phrase commonlyused to describe the inherent and critical interdependencies
between the electric power system and the water delivery system(WDS). In the work presented here, the analytical framework
capturing the key interactions between these two criticalinfrastructures is explained and a summary of the mathematicalmodel used to describe the associated power and water system
dynamics is shown. The time scales for the system parameters ofinterest are known to be large, so the electric network behavior isrepresented using time-series power flows with inputs ofgenerator status determined from a short-term unit commitment
model and real power set-points given from a modified optimal
power flow solution. The WDS is represented within the
EPANET software with optimization of the systems’ operation
implemented via a genetic algorithm. The simulation engine
which integrates the different software used for modeling andoptimizing the operation of the interdependent infrastructure
systems will also be described. Results of the system responses toan overall WDS water shortage and severe power outages at a
pumping station are shown for a one-month long simulation.
Keywords— Critical Interdependent Infrastructures,
Combined Economic and Environmental Dispatch, Water
Delivery and Treatment System, Time-Domain Simulations,
Water-Energy Nexus.

I.

INTRODUCTION

The electric power system and the water distribution
system (WDS) are an example of critical infrastructure systems
which have interdependencies that arise in normal system
operation. These major interdependencies which exist havebeen recognized and studied at a high level for some time. The
main dependency of the electric system on the water
infrastructure results from the fact that thermoelectric power
plants require water for the cooling cycle. Water usage forpower plants is defined in terms of water withdrawal and water
consumption rates where the consumption is defined as thedifference between the water withdrawn and the water returned
to a water source. The rates of these factors for modern power
plants are dependent on many factors such as whether an open
or closed-loop cooling cycle is used, the type of emission
control schemes implemented and, of course, the plant’s
physical location. The water system dependency on the electric
infrastructure is clearly seen by the fact that water from its
source will most likely require electricity for pumping and/or
treatment before its final delivery to the end user.

II. Methodology and Initial Simulation Results
Because the interdependent simulation which has been
implemented has parameters of interest which can change
slowly with respect to time, long-term time-domain
simulations are considered here. For instance, since
thermoelectric power plants in the area of interest are usually
supplied with two or more independent sources for coolingwater and each plant typically has about two-weeks of water
storage onsite, the rate of change of this parameter can besmall. Since fast acting phenomena of the power system do notneed to be considered here, only time-series power flows areused for the network solution. The power system operation is
optimized with both a short-term unit commitment model
implementation as well as a modified version of an optimal
power flow which takes environmental factors into
consideration. Economic dispatch formulations that takeenvironmental constraints into account are sometimes referred
to as a combined economic and environmental dispatch(CEED). The CEED objective function contains in it anoperational water cost which is adjusted according to the rateof change of a given power plant’s onsite water storage tanklevel. Under severe drought and heat conditions, it has been
noted historically that some plants will have their maximum
output limited. Along these lines, two models have beendeveloped and are presented here as a method of de-rating
plants under extreme drought conditions.
Initial results for a one month-long simulation which had an
overall, system-wide shortage of water as well as a pumping
station power outage are shown below. A modified version of
the IEEE 14-bus test case and a test case representative of atypical city’s WDS were used for network modeling.

Fig. 1. Simulation Results for Normalized Tank Level and
De-Rating Schemes

Online Identification of Generator Participation Factors using Cellular
Computational Networks
U. A. Abu, Student Member, IEEE, 1P. Arunagirinathan, Student Member, IEEE,
G. K. Venayagamoorthy, Senior Member, IEEE, 3K. A. Folly, Senior Member, IEEE
1
Real-Time Power and Intelligent System Laboratory, Clemson University, SC, USA
2
School of Engineering, University of KwaZulu–Natal, Durban, South Africa
3
Department of Electrical Engineering, University of Cape Town, South Africa
uabu@clemson.edu, parani@ieee.org, gkumar@ieee.org and komla.folly@uct.ac.za
1,3

1,2

Abstract—Electromechanical oscillation modes are exhibited bysynchronous generators following a system disturbance.
Identifying the generator participation factors is important indesigning effective oscillation damping controller for the powersystem. A cellular computation network (CCN) based online
identification of generator participation factors is studied. IEEE
68 bus 16 generator New England-New York benchmark powersystem has been simulated on a real-time digital simulator.
Remote power system measurements are available at control
centers with synchrophasor devices installed. Synchrophasor
data driven online identification of generator participation
factors can be used in selective tuning of power system stabilizers
for different operating conditions of the power system.
Index Terms—: Cellular computational network, generator
participation factors, power system stabilizer, synchrophasordata.

A synchrophasor data driven online identification ofgenerator participation factors using a cellular computational
network (CCN) [1] is studied. CCN is a highly nonlinear,scalable computer architecture for learning the dynamics oflarge networked system. CCN based frequency prediction on amulti machine power system is presented in [2]. The CCN
diagram for the test power system used for this study is shownin Fig. 2. The test power system and controllers are simulated
on a real-time digital simulator (RTDS), which is capable ofsimulating power system in a 50µs time step. The
synchrophasor devices are receiving measurements from thesimulated power system at RTPIS laboratory, Clemson
University. Power system oscillation damping devices such as
power system stabilizers can be selectively tuned for changing
operating conditions based on the generator participation
factors identified online using CCN.

I. INTRODUCTION
The complexity of the power system increases with limited
generation options and increasing interconnections in thetransmission network, etc. Following a system disturbanceinter-area oscillation modes are present in the system. Powersystem becomes vulnerable when these oscillations are poorly
damped. A new methodology is presented for online
prediction of generator participation factors. The participation
factors give the information about the contribution ofgenerators to the electromechanical oscillation modes in a
power system. IEEE 68 bus 16 generator power system shownin Fig. 1 has been studied. The deployment of synchrophasor
devices brings the remote measurements to the electricutility’s control center hundred times faster rate compared toexisting SCADA technologies.

Fig. 2 CCN diagram

REFERENCES[1] B. Luitel and G. K. Venayagamoorthy, "Cellular computational networks A scalable architecture for learning the dynamics of large networked
systems," Neural Networks, vol. 50, pp. 120-123, February 2014.
[2] Y. Wei and G. K. Venayagamoorthy, "Cellular Computational
Genaralized Neuron Network for Frequency Situational Intelligence in a
Multi-machine Power System," Neural Networks, vol. 93, pp. 21-35,
Sept., 2017.

Fig. 1 IEEE 68 bus 16 generator five area power system

A Real-Time Hardware-in-the-Loop Simulation of
Sampled Value based Differential Protection
Jaya R A K Yellajosula, Sumit Paudyal, and Bruce A. Mork
Department of Electrical and Computer Engineering, Michigan Technological University, Houghton, USA
Email: jyellajo@mtu.edu, sumitp@mtu.edu, bamork@mtu.edu
Abstract—This work examines the transfer of sampled value
measurement data between different substations in a wide-area
network and utilize them for phasor estimation and transmission
protection. The main goal of this work is to develop an IEC61850
based Intelligent Electronic Device (IED), which can be utilized inwide-area protection. This IED is designed to subscribe sampledvalue measurement data from remote and local terminal, andestimate the required phasors, which will be used for protective
application. In case of fault, the IED executes the predefinedprotection logic and outputs trip signal in the form of GenericObject Oriented System Event (GOOSE) message. In relation
towards protection application, this work focuses on developing
adaptive alpha plane algorithm for transmission line differential
protection, with a focus on estimating phasors from sampledvalue measurements using kalman filter. In summary, this work
is intended to develop an IEC61850 based IED and perform realtime hardware in the loop simulation to test the effectiveness ofthe designed board and also protection algorithm for transmission line differential protection in a wide-area network.

Sampled Value
Measurements send to
IED

Real-Time Process
in Loop Simulation

GOOSE Based Trip signal
to the system

Fig. 3. HIL lab setup

I. K EY F IGURES

Fig. 1. System Level Schematics

Fig. 4. Line differential protection logic execution.

Fig. 2. Adaptive Alpha-Plane Algorithm
Fig. 5. Estimation algorithm output with some needed corrections to compensate the effect of network traffic

1

Optimal Routing and Charging of an Electric
Vehicle Fleet for High-Efficiency Dynamic
Transit Systems
Tao Chen, Student Member, IEEE, Bowen Zhang, Student Member, IEEE, Hajir Pourbabak, Student
Member, IEEE, Abdollah Kavousi-Fard, Member, IEEE, and Wencong Su, Member, IEEE

Abstract—This paper proposes a framework and its mathematical model for optimal routing and charging of an electric vehicle
fleet for high-efficiency dynamic transit systems, while taking
into account energy efficiency and charging price. Based on an
extended pickup and delivery problem, an optimization model isformulated from the transit service providers’ perspective and isapplied to an electric vehicle fleet with economically efficient but
small batteries in very urbanized areas.

Load (kWh)7$0.90/kWhLoad (kWh)6.5

7$0.95/kWh

6

6.5
5.5

6
5

5.5
4.5

5
4
0

5

10

15

20

25

30

35

4.5

Start Node 1

Delivery Node 7

4
0

5

10

15

20

25

30

35

Pickup Node 4
Delivery Node 9

Pickup Node 2
Load (kWh)Pickup Node 3

7Delivery Node 8

6.5

6

5.5

5

4.5

4
0

5

10

15

20

25

30

35

$0.80/kWh
Delivery Node 6

End Node 10
Pickup Node 5

I. I NTRODUCTION
Fig. 1. The optimal routing and charging decision for a single EV in an fleetW

E live in an increasingly urban world with abundanttransport and electricity infrastructure. Today, 54%of the world’s population lives in urban areas, a proportion
that is expected to increase to 66% by 2050. This poses
unprecedented environmental, economic and social challenges
to transportation systems that move the people, goods andservices of society [1]. Recently, advances in intelligent transportation systems and smart grid technologies offer great
promise to widely popularize electric vehicles (EVs), and havethe potential to revolutionize urban transportation systems andpower systems [2]. Besides, increased occupancy is anotherhigh-potential method for decreasing transportation energy
consumption, emissions and congestion. Urban mobility has
traditionally been restricted to privately owned vehicles or public transport. Innovative dynamic transit services are radicallychanging the traditional views of the transportation industry,
the social environment, and the business world.
On top of these services, this paper will take dynamic
commuter transit services a step further with its special focus
on high-efficiency EV fleet. Our proposed framework builds
upon the similar business model of an emerging commuter
service named ”MagicBus”, in San Francisco, California.
”MagicBus” offers unique services such as ”Book from yourphone ahead of time”, ”Choose when your ride will arrive”
and ”Pick up near home, drop off near work”. This paper
implements a similar idea and also considers an optimal EV
charging strategy and its potential impact on the average loadin the power distribution system, as illustrated in Figure 1.
The conventional traffic network Sioux Falls scenario withstatic origin-destination matrices is used for simulation purposes. The altered scenario aims at keeping the topologicalstructure, in which the length ratio between different routes
is not in proportion to the ones in the real map. The routing
results are shown in Figure 2.

(a) EV No. 1 and No. 2(b) EV No. 3 and No. 4(c) EV No. 5 and No. 6
Fig. 2. Optimal routing solutions for different EVs

R EFERENCES[1] W. Su, J. Wang, and Z. Hu, ”Planning, Control, and Management
Strategies for Parking Lots for PEVs,” Plug In Electric Vehicles in Smart
Grids, pp. 61-98. Springer Singapore, 2015.
[2] W. Su, H. Rahimi-Eichi, W. Zeng, and M.-Y. Chow, ”A Survey on theElectrification of Transportation in a Smart Grid Environment,” IEEE
Trans. on Industrial Informatics, vol.8, no. 1, pp.1-10, February 2012.

Analysis of “8•15” Blackout in Taiwan and the
Improvement Method of Contingency Reserve
Capacity Through Direct Load Control
Hongxun Hui, Student Member, IEEE, Yi Ding, Member, IEEE, Kaining Luan, Daoqiang Xu
Abstract—Electric power has been one of the most importantenergy for modern cities. Therefore, blackouts always havegreat impacts on our modern societies. This paper reviews theprocess of the large-area blackout in Taiwan on August 15th,2017. The direct cause of the accident is the incompetence of an
employee in the natural gas company, while the fundamental
cause is the shortage of the contingency reserve capacity (CRC).
Faced with the insufficient installed capacity in Taiwan, thispaper proposes an improvement method of CRC through direct
load control. A control framework, on account of the systemfrequency and the gas pipeline pressure, is developed to regulate
the power consumption of demand side resources (DSRs).
Besides, a droop controller is also proposed to determine theregulation capacity of DSRs. The effectiveness of the proposed
control strategy is verified by the numerical studies.
Index Terms—blackout; contingency reserve; direct load
control; demand side resource.

I.

IMPROVEMENT METHOD OF CONTINGENCY RESERVECAPACITY THROUGH DIRECT LOAD CONTROLThis paper proposes the improvement method of CRC
through direct load control (DLC), as shown in Fig. 1. Boththe system frequency and the pressure of the gas pipeline aremonitored by the controller, which can send signals to thedemand side resources (DSRs). When the system frequency
drops or the gas pipeline pressure decreases abnormally, thecontroller will reduce the power consumption of DSRs toprovide CRC for the system.

II.

CASE STUDIESThe test system (Fig. 2) is set to simulate an electric powersystem, which has a low level of operating reserve capacity,
just as the power system in Taiwan.

Fig. 2. Transfer function model of multi-sources single area system [12].

No loads provide CRC in Case 1. 10% loads can becontrolled to provide CRC by only monitoring the systemfrequency. 10% loads can be controlled to provide CRC bymonitoring both the system frequency and the pressure of thenatural gas pipeline. Fig. 3 shows the simulation results.

Fig. 3. Simulation results of the system frequency in the three cases.

III.
Fig. 1. The framework of DLC considering the gas pipeline pressure.

The controller is designed as the droop control theory,
which is described as:mPDLC   0 f   0 fdt     j PRESSGj dt
j 1

(1)

CONCLUSIONS

The abnormal gas pressure can be detected earlier than theabnormal system frequency, when the accident is caused by
the supply of primary energies. Simulation results show thatthe frequency deviation can decline 38.87% than another case,which only monitors the system frequency.

A Hierarchical Control Structure for Residential
Community Energy Optimization
Priti Paudyal, Prateek Munankarmi, Zhen Ni, and Timothy M. Hansen
Department of Electrical Engineering and Computer Science
South Dakota State University, Brookings, SD 57006
United States
Email: {priti.paudyal, zhen.ni}@sdstate.edu
Abstract—Demand response, change in energy consumption
pattern of the consumer in response to system conditions, canhelp to provide economic benefits to the utility. Residential sector
is one of the major sectors for electricity consumption in the
U.S. Thus, there is potential of energy management systems
in residential communities. However, there should be somemotivating factors such as financial incentives for the residential
consumers to participate in the energy optimization programs.
Moreover, ensuring the residents’ comfort will encourage thelong-term participation in such programs. This work presents ahierarchical control structure for residential community energy
optimization with reward for the participating consumers. The
framework is designed from utility’s perspective which seeks
to fulfill the demand reduction while minimizing the reward.
Meanwhile, the framework also considers the residents’ comfort.

min RWi,b =tRewarda,t(1)asubject to
X

Pa · δa,t ≤ Pi,b(2)

aand the appliances constraints.
The CC selects the optimal bid (Ri ) of each LC ’i’ for
maintaining the total power within its limit (Plimit ).
min

N
X

Ri

(3)

i=1

subject toN
X

I. K EY F IGURESThe proposed hierarchical control structure is shown in
Fig. 1. In case of the demand response (DR) event, central
controller (CC) in the upper level sends the demand reduction
request signal to the local controllers (LCs). Each LC performs
optimization, keeping its total power within different limits,and calculates the corresponding rewards. A reward corresponding to a demand limit comprises a bid. Then, each LC
submits its different bids to the CC. Finally, the CC selects the
optimal bids for each LC and the LCs issue the control signals
to the houses based on the selected bids. Home appliancesmodel adopted from [1].

XX

Pi ≤ Plimit

(4)

i=1

and constraints to ensure only 1 bid is selected from each LC.
III. K EY R ESULTS
Considering two LCs with 12 and 20 houses, the bids sentby the LCs are presented in Table I. The bids selected by theutility for 150 kW total demand limit are presented in Table II.
The CC selects the bids which provide the minimum reward
thereby satisfying the total demand limit.
TABLE IB IDS GENERATED BY LOCAL CONTROLLERS
Local controller-1

Local controller-2

Demand limit (kW)

Reward ($)

Demand limit (kW)

Reward ($)

65
60
55
50
45

1.95
3.14
5.07
26.14
210.46

120
115
110
105
100

6.67
7.02
9.84
123.0528.02

TABLE IIC ENTRAL CONTROLLER ’ S DECISION
Total demand limit= 150 kW
Fig. 1. System block diagram of the proposed hierarchical control structure
for residential community energy optimization.

II. K EY E QUATIONSEach LC performs optimization to generate several bids.
Each bid is generated by calculating the reward (RWi,b ) forthe corresponding power limit (Pi,b ). The objective function
for the LC is to minimize the reward which is expressedmathematically as:

Local controller

Reward ($)

Power limit (kW)12210.46123.0545
105

IV. C ONCLUSION
The proposed hierarchical framework achieves the demand
reduction by allocating demand limits to each LCs while providing minimum reward and considering consumer comfort.
R EFERENCES[1] M. Pipattanasomporn, M. Kuzlu, and S. Rahman, “An algorithm forintelligent home energy management and demand response analysis,”IEEE Transactions on Smart Grid, vol. 3, no. 4, pp. 2166–2173, Dec
2012.

Home Energy Management System in
Co-Simulation Framework
Prateek Munankarmi, Robert Fourney, and Timothy M. Hansen
Department of Electrical Engineering and Computer Science
South Dakota State University, Brookings, SD 57006
United States
Email: {prateek.munankarmi, robert.fourney, timothy.hansen}@sdstate.edu
III. K EY R ESULTS

Abstract—A home energy management system (HEMS)
changes the energy consumption pattern of the consumer basedon dynamic pricing programs or incentives. HEMS can utilizebuilding energy simulator such as EnergyPlus to accurately
model the energy consumption of the residential house considering finer details of building geometry and orientation, internal
heat gain of the appliances, and weather. As internal heat gain of
appliances and people have significant effect in the HVAC energy
consumption, an integrated HVAC and appliance schedulingis necessary to properly evaluate HEMS. This work presents
the formulation of HEMS considering detailed house model in
EnergyPlus, and combined scheduling of HVAC and appliancesin time-varying tariff. The proposed HEMS also considers theconsumer comfort while minimizing the electricity cost.

Different cases which are considered to evaluate HEMS areas follows.
1) Base Case: No optimization performed.
2) Case-I (upper bound): Optimization with perfect future
price information.
3) Case-II: Optimization with imperfect knowledge of future
price.
The total energy consumption of a house in all three cases
are comparable as shown in Table I.
TABLE IT OTAL ENERGY CONSUMPTION FOR JAN -2014 IN DIFFERENT CASES

I. K EY F IGURESThe system block diagram of the proposed home energy
management system (HEMS) is shown in Fig. 1. The detailed
model of the residential house is modeled in EnergyPlus.
HEMS optimization calculates the optimized HVAC setpoint
and the appliances schedules based on the price and consumer
preferences. HEMS+ represents a co-simulation framework
between HEMS and EnergyPlus. Consumer preferences, price,
and weather data are provided to HEMS optimization.

HVAC energy (kWh)Appliance energy (kWh)Total energy (kWh)BaseCase-I

Case-II

1488.72
1691.403180.121407.45
1691.99
3099.451460.45
1655.313115.76

The HEMS reduces the energy consumption during highprice and vice-versa to reduce the total electricity bill as shownin Fig 2. HEMS with imperfect price information (presented
in Case-II in Table II) saves 11.36% over status quo whereas
the upper bound (Case-I) of saving is 16.05%.
TABLE IIT OTAL ELECTRICITY ENERGY COST FOR JAN -2014 IN DIFFERENT CASESHVAC cost ($)
Appliance cost ($)
Total cost ($)
Saving (%)

Base

Case-I

Case-II

134.59125.98260.57-145.2573.49
218.74
16.05

143.6
87.35
230.95
11.36

Fig. 1. System block diagram of the proposed HEMS.

II. K EY E QUATIONS
The objective of the proposed HEMS is to minimize theelectricity bill as shown in (1). Here, λt represents the timevarying tariff rate such as real-time price (RTP), phvac,trepresents HVAC power (kW), pa represents rated appliance
power (kW), and δa,t is the on/off status of appliance a attime t.

min C =NTX
t=1

λt · T · (phvac,t +nX(pa · δa,t ))

(1)

a=1

subject to power limit, HVAC, and appliance constraints.

Fig. 2. Total house power for Jan-7, 2014

1

Distributed Locational Marginal Pricing based ofSmart Home Management
Chengzong Pang

Amin Mohsenzadeh

Department of Electrical and Computer Engineering
Wichita State University
Wichita, USA
Chengzong.pang@wichita.edu

Department of Electrical and Computer Engineering
Wichita State University
Wichita, USA
Axmohenzadeh@wichita.edu
Abstract— This paper proposes the application of Distributed

Locational Marginal Pricing (DLMP) concept as a price signal
to improve economic efficiency and system operations at both
power distribution system and end user levels. Results
demonstrate the advantage of DLMP over existing distribution
pricing schemes.
Index Terms— Distributed energy resources, DLMP,
Scheduling, Smart grid, Smart home.

I.

INTRODUCTION

Demand response can play a significant role to modify the
consumption patterns of customers by demand-side resources
considering technical and financial issues [1]. The utilizationof DR for residential consumers can affect approximately 40%
of energy consumption in the world[2]. In this paper, DLMP is
proposed as a price signal for end users instead of traditional
scheme such as TOU or RTP. It reflects the marginal cost ofdelivering incremental energy to a specific location in a
distribution grid and has effect on congestion and systemlosses. The superiority of using DLMP is providing a fair
allocation of power loss among nodes and connectedcustomers. To the best knowledge of aouthors, this is the firststudy in the literature considering PV, EV with capability of
two-way energy trading, residential scale of ESS, and smart
appliances in a single SHMS and distribution network in orderto decrease energy bill and power loss simultaneously.
II.

able to sell back energy to grid and gain revenue.
24

Total cos t   [ Pt grid .DLMPt node, i  Pt sell .tsellback ] (21)
t 1

IV.

NUMERICAL RESULTS

Fig. 1 shows decomposition of smart home power demand
via proposed SHMS strategy. The negative part represents theamount of injected energy to house or grid. The green line
shows how much energy should be bought from grid.

Figure 1. Decomposition of smart house power demand

DLMP CALCULATION IN POWER DISTRIBUTION SYSTEMThe DLMP is similar to LMP in transmission system inwhich the cost for one additional unit of energy to be served ata particular bus is obtained. DLMP of a node depends on load
level, transmission LMP, circuit diagram, and location of thenode which is given in (8)DLMPi ,t  LMPt (1 III.

Tplossi ,t
PLOSS

)

(8)

OBJECTIVE FUNCTION

The goal of this paper is minimizing total daily cost ofelectricity usage of a customer. In addition, smart homes are

Figure 2. Correlation between energy bough from gridt and DLMPV.

CONCLUSION

Based on simulations conducted, despite the extensive andnon-linear mathematical models, the proposed approachproved to be computationally efficient.

Impacts of Smart Inverters on Microgrids' Controls andProtections
Husam S. Samkari and Brian K. Johnson
University of Idaho
Department of Electrical and Computer Engineering
Moscow, Idaho USA
samk6783@vandals.uidaho.edu, bjohnson@uidaho.edu

Converter1i

VIabc_2

Smart inverters, basically, have the capability to control
both active and reactive power to improve their terminal
voltage. Inverters have several control schemes such as: fixed
reactive power, voltage-dependent reactive power, fixed
power factor, fixed power factor in terms of injected active
power, and volt/var control (terminal voltage regulation).
Additionally, papers show that having multi control modes
during different events can improve the voltage further [3].
In this project, a full smart inverter, and power system aremodeled using EMTP-RV program. Switch and averaged
inverter models are tested and compared to another model on adifferent EMTP-type program for validation. Different control
schemes are tested to characterize the impact of each control
type on the microgrid protection and control system.
Additionally, there are several ways to connect an inverter to a
distribution system. It can be connected directly to the powersystem, or through a power transform with different
configurations. These variations of connections andconfigurations are tested since they can impact the protection

[1]
[2]
[3]

Page Vabc_grid_V

Page Iabc_grid_A

1+
VIabc_1

2

Output_Bus

Adj_Trans_Param1?p
1000000M+
R_big1

Vref_grid Page

BUS_OUT

Measure_VSC

block_grid Page

R_small1+
1E-8?pfilter1

Fig. 1 Simplified one-line diagram of the smart inverter system
Characteristic function
0.6

0.4

0.2

0
f(x)

In literature, microgrids may use more renewable energy
resources, which are inverter based. In this work, the focus ison the inverter units, and the use of smart inverters. High
penetration of photovoltaic (PV) systems are causing reverse
power flow and voltage rise [1]. Several authors show benefitsof using smart inverters on controlling the voltage profile atthe distribution level such as [2]. However, having smart
inverters on a microgrid during island mode requires moreinvestigations [1].

vdc Page

INTRODUCTION

chopper Page

I.

scope
VDC_BUS+i

RLchoke#Rchoke_Ohm#,#Lchoke_H#v

Index Terms— Distribution System, Microgrid, Photovoltaic,
Smart Inverter, Power System.

Page switch_on

Page Iabc_line_A

scheme. The next step is testing the system during islandmode because the impact can be more severe. Moreover, thepercentage of penetration of inverter based sources will be
investigated. Finally, suggestions for choosing the appropriate
control scheme, and protection will be provided. The results inthis paper show only one scenario.

v

Abstract— This project investigates the impacts of using smart
inverters in microgrid systems. The impacts on protection andcontrol systems are evaluated. An EMTP-RV model is used torepresent the power system and the control system of a smart
inverter. Analysis of several types of smart inverters'
controllers, and different possible connections to power system
are investigated during symmetrical and unsymmetrical faults.

-0.2

-0.4

-0.6

-0.8

0

0.2

0.4

0.6

0.8

1
x

1.2

1.4

1.6

1.8

2

Fig. 2 Voltage-dependent reactive power characteristic (x is voltage in pu)

Fig. 3 Three phase voltage measured at the inverter’s terminal during a
SLG phase A fault beyond a Y-grounded/Δ transformer

F. Katiraei, C. Sun, and B. Enayati, “No Inverter Left Behind: Protection, Controls, and Testing for High Penetrations of PV Inverters on Distribution Systems,”
IEEE Power Energy Mag., vol. 13, no. 2, pp. 43–49, 2015.
P. Jahangiri and D. C. Aliprantis, “Distributed Volt/VAr control by PV inverters,” IEEE Trans. Power Syst., vol. 28, no. 3, pp. 3429–3439, 2013.
A. R. Malekpour and A. Pahwa, “A Dynamic Operational Scheme for Residential PV Smart Inverters,” IEEE Trans. Smart Grid, vol. 8, no. 5, pp. 2258–2267,
2016.

Multi-parameter Intelligent Energy Forecasting: From
Insight to Impact
Nazmus Sakib1, Eklas Hossain2, Sheikh Iqbal Ahamed1
1
Department of Mathematics, Statistics and Computer Science, Marqutte University, Milwaukee, WI-53233
2
Department of Electrical Engineering and Renewable Energy, Oregon Institute Technology, Klamath Falls, OR 97601
nazmus.sakib@marquette.edu, eklas.hossain@oit.edu, sheikh.ahamed@mu.edu
Abstract– This work is aimed at employing data science to develop
insights on demand-side management at Oregon Institute ofTechnology (OIT) by exploring historical load data, load profiles,
electricity consumption data, load demand data, time-of-use rates
data, critical-peak pricing data, and user behavior data obtained
from Oregon Renewable Energy Center. To achieve this, different
techniques of data science are compared and evaluated according totheir influence on different parameters of forecasting. The final
outcome of this work is a framework based on the data-driven
insights for intelligent decision-making and real-time interaction for
the OIT system. Along the course of this work, the state-of-the-art
Big Data collection, storage, and management techniques for energy
systems, along with their mining and analysis techniques are
explored.
Keywords– Big Data, Multi-parameter Intelligent Forecasting, Machinelearning, Demand-side management, Energy Forecasting.

I. RESEARCH OBJECTIVESIn this work, we seek to answer the following threeresearch questions: 1) how can we design advanced
computational sciences techniques in collecting, storing, andmanaging large-scale noisy energy data streams after exploring
the start-of-the-art techniques in this regard? 2) How can we
improve data computation, mining, and analysis in dealing withthe real-time, dynamic energy data addressing the salient featuresand limitation of the start-of-the-art computation techniques forthe existing system at OIT? 3) How can we come up with thevalid decisions, insights, and values for the existing system at
OIT, and test its efficacy, particularly by working withcollaborators in the energy domain? The scope of this researchfor both the theoretical study and analytics- will be limited to thespecific area requirement from the Oregon Renewable Energy
Center for demand-side management.
II. DATA SCIENCE INTERVENTION IN ENERGY SYSTEMSIn a popular conceptualization, Big Data is defined byits "3V" characteristics: Variety, Volume, and velocity. In the eraof Big Data, as Data Scientists, it is our onus to extract significant"Value" from the large "Volume" of wide "Variety," and high"Velocity" captured data. This "Value" can be characterized by a"3E" model: Energy, Exchange, and Empathy. Here, the "energy"
refers to the energy saving achieved from the analytics,"exchange" to the integration of other data sources for better
realization, and " empathy" to the better energy services and theconsequent satisfaction of the consumers. In this project, ourstep-by-step research plan is aligned to meet "3E" of energy
science domain leveraging the "Value" of "3V" of Data Science(Fig.1.). The primary initial result of our analytics is depicted
below in Fig. 2. It endeavors to find out a pattern in energy
consumption in different meters as well as in different time of
two different years (2013 and 2014). From the pattern, we aim to
predict and compare the consumption for 2015.

Fig. 1. Data science intervention in energy system: from insight to impact.

12000
10000
8000
6000
4000
2000
0

Sum of 0:30
Sum of 1:00
Sum of 1:30
2013

2014
Meter 1

2013

2014

2013

Meter 2
KWh
Electricity
Building 1

Fig. 2. Energy consumption as recorded by three different meters at different times of the day over two years.

2014
Meter 3Sum of 2:00
Sum of 2:30
Sum of 3:00
Sum of 3:30
Sum of 4:00

Adaptive Phasor Estimation Algorithm Using Improved
KFs under Steady-State/Dynamic Conditions
Yinfeng Wang, Chao Lu
Department of Electrical Engineering

Chen Fang, Ping Ling
Shanghai Electric Power Research InstituteState Grid Corporation of China
Shanghai, China

Tsinghua University
Beijing, China
Email: luchao@tsinghua.edu.cnT

d 2n  vˆkT HP(k | k  1)HT  R

SignalΔ SNR (dB)

0.1
0

KF
MAKF

0.1
0.05

0
-0.05
-0.1
-0.15
-0.2

-0.25
2

2.1

2.2

2.3

Time (s)

2.4

-0.3

2.5

2

2.1

1.1

0

2

68

64
62
60
58

56
2.05

2.1

Time (s)

2.15

2.2

2.4

2.5

True value
RTKF
TKF
KF
MAKF

66

54
2

1

0.5
0

-0.5
-1

2.05

2.1

Time (s)TKF
Mk×(5K2+11K+5) Mk×(4K2+12K+5)
RTKF
Mk×(2K2+2K+1)
Mk×(2K2+4K)
Frequency Ramp Test: To test the frequency tracki

Sampling rate:
6400 samples/s

2.15

2.2

1.38 s0.54 sIV. CONCLUSION

250

Fig. 2 Comparison of the theoretical and actual ΔSNR

Process model: X k  =ΑX k  1  wk

0.03
0.02
0.01
0
-0.01
-0.02
-0.03

2.3

Time (s)TABLE I. COMPUTATION COMPLEXITYIES OF THE TKF AND RTKF
Number of real
Number of real Time for 1 min
Complexity
addition
multiplication
estimations

-5

100
150
200
Number of measurements (N)True value
RTKF
TKF
KF
MAKF

2.2

Fig. 5 Test results of the amplitude and phase modulation.

N: 47
Δ SNR : 14.7

50

0.2

0.9

Theoretical results
Actual results

0

0.3

1.05

A. The S channel based on MAKF

-15

0.15

KF
MAKF

0.4

0.95

SelectioncommandIII. PERFORMANCE EVALUATION

0.5

1

Final
frequency

2πA1

Error (Degree)

Selection
CommandD channel-10

fc = f +Fig. 3 Structure of the frequency estimation module

1.15

Fig. 1 Framework of the proposed algorithm

15

Precis efrequency

Dynamic
I1cosφ1 - R1 sinφ1 frequency

Phase (Degree.)

RAKF

5

A1 = R1 + jI1
φ1 = atan  I1 / R1 

1.2

Final
outputDetector
Dynamic
phasorRoughfrequency

1-cycle Kay
windowdφ/dt

Dynamic
phasor &its derivatives

2-cycle Kay
windowdφ/dt

Error (p.u.)

Fundamental Residuals
frequency

10

Steady-statephasor

Amplitude (p.u.)

Signal1-cycleRDFT

Fig. 4 Test results based on the steady-state signal

Steady-statephasor(4)k

D. Frequency estimation module

-0.3

MAKF

1

steady-state results. d2n is the detection factor.

-0.2

S channel

 vˆ

with vˆk   sN (k )  HX  k  is the residual vector based on the-0.1

Frequency
estimation

T

C. Fast Change Detector

Amplitude Error (%)

II. PROPOSED ALGORITHM(3)

where Ri (t )   Ri (t ), Ri (t ), Ri ( K ) (t ) , Ii (t )   Ii (t ), Ii (t ), I i ( K ) (t )  .

I. INTRODUCTION
Recently, some researches have designed several algorithms
that attempts to simultaneously meet the requirements of bothP-class and M-class PMUs. However, this potential superiority
is achieved at expense of more complex structure and extra
computation burden.
In this paper, we continue the efforts to integrate the Mclass and P-class PMUs, and design an adaptive phasor
estimation algorithm via two improved KF and TKF methods.
This algorithm is focused on the accuracy improvement of the
traditional KF and the computational reduction of the
traditional TKF.

(2)

F( )cos  2 f  i  F( )sin  2 f  i   R i (t0 ) R (t )xi (t )   i    I i (t )   F( )sin  2 f  i  F( )cos  2 f  i    I i (t0 ) 
 Φi ( )xi (t0 )Selector

Index Terms—Phasor estimation, improved Kalman filter,moving average, computational complexity, fast change detector.

Measurement model: sN (k )  HX k   vk
B. The D channel based on RTKF

Phase Error (Degree)

Abstract—Devoted to balancing the performance of phasor
estimation under steady-state and dynamic conditions, this paper
proposes an adaptive phasor estimation algorithm based on two
improved Kalman filters (KF). A moving average method isadopted to increase the signal to noise ratio (SNR) of
measurements in traditional Kalman filter. Besides, a real-valued
operations based Taylor-Kalman filter (RTKF) is developed toreduce the computational complexity of traditional TKF. Then,the two KFs are initialized by rough frequency estimates andupdated by precise ones sequentially. The appropriate resultscorresponding to the current conditions are adaptively selectedby a fast change detector based on residuals. Tests under steadystate and dynamic conditions demonstrate the effectiveness of thewhole algorithm.

(1)This paper designs a KF based phasor estimation algorithm
to balance the dynamic and steady-state requirements.

Robust Unit Commitment and Dispatch Considering with
Atmospheric Pollutant Concentration Constraints
Yongcan Wang, Suhua Lou, Yaowu Wu, Mengxuan Lv
State Key Laboratory of Advanced Electromagnetic Engineering and Technology
Huazhong University of Science and Technology, Wuhan, 430074, China
Email: wangyc@hust.edu.cn

Abstract—With the increasing problem of air pollution,
improving atmospheric quality has been attracting more
attentions in the worldwide, especially in coal-burning areas. As
an important emission source of atmospheric pollutant, thescheduling strategy of coal-fired power plants has a significant
impact on atmospheric quality. It should be worthy to note thatthe relationship between atmospheric quality and the total
amount of pollutant emissions is non-linear. The pollutant
concentration in atmosphere, not only depends on pollutant
emissions, but also depends on meteorological conditions, i.e. theability of atmospheric diffusion. In this paper, a day-ahead unit
commitment and dispatch model of power system consideringwith pollutant concentration constraints is developed to improveatmospheric quality. In addition, robust optimization is adopted
to ensure the robustness of pollutant concentration constraints.
Keywords—Atmospheric diffusion model, Environmental
dispatch, Robust optimization, Pollutant concentration constraints.

( x - x0 - X )2
Cx =exp[ 
]
2 x2
2

(1)

Cz =exp[ ( z - z0 - Z )2
( z  z0 - Z )2
]  exp[ 
]
2
2 z
2 z2

(4)

B. Robust Unit Commitment and Dispatch Model
The proposed model aims to minimize total fuel cost in theworst-case, and immunize against all realizations of net load
uncertainty. The ground level pollutant concentration near
populated regions is considered as a particular constraint.
Pt

u, P

 ( F  ST )i 1 t 1

i ,t

i ,t

(7)

Qi,t  qi,t EUi / Tstup,i(8)qi ,t  max{ri ,t 1 , ri ,t 2 , ..., ri ,t Tstup,i }

(9)

(5)

Case 2 without pollutant
concentration constraint

0

4

8

12
Periods(h)

16

20

24

Fig. 1 The hourly pollutant concentration of controlled location

600

(3)

max
min
L

Qi,t  i Pi ,t   i ui ,t

500

800

( y - y0 - Y )
]
2 y2

T

(6)

Case 1 with 500 μg/m3 pollutant
concentration constraint

1000

(2)

C y =exp[N( x, y,2, t )  [Qi,t  Qi,t ]  Cmax

II. KEY RESULTS

Power output (MW)(2 )  x y z
3/2

C pollu ( x, y,z, t )  Q

pollu ,iBased on a three-unit power system, two cases (case 1 with
500μg/m3 pollutant concentration constraint, case 2 without
pollutant concentration constraint ) are simulated. Fig. 1 andFig.2 depict the hourly pollutant concentration and power
outputs of three units for two different cases, respectively.
Results show that the proposed mole is effective to control thepollutant concentration of concerned regions by changing
power outputs strategically.

0

Cx C y CzQtt 0 

i 1Pollutant concentration (μg/m3)

A. Gaussian Puff Model
Pollutant released into the atmosphere is transported by thewind, and mixed into the surrounding air by turbulent eddies
and molecular diffusion. The Gaussian puff model is ananalytical model to describe atmospheric pollutant transfer anddiffusion of non-continuous and no-constant pollutant source.
t 0 tN

1500

I. KEY EQUATIONS

C ( x, y,z,t )  

C

Unit1 in case 1
Unit1 in case 2

Unit2 in case 1
Unit2 in case 2

Unit3 in case 1
Unit3 in case 2

400

200

0

0

4

8

12
Periods(h)1620

Fig. 2 The hourly power outputs of three units

24Coordinated Power Allocation and Robust DC
Voltage Control of UPQC with Energy Storage
Unit During Source Voltage Sag
Ziwen Liu, Shihong Miao, Zhihua Fan, Kaiyun Chao, Yilong Kang
Abstract—Reasonable power allocation and robust DC
voltage response are essential requirements of unified
power quality conditioner (UPQC) during source voltage
sag. Motivated by this objective, this paper presents acontrol scheme for UPQC based on an improved UPQC-Q
power allocation strategy and sliding mode control of DCside voltage. The prominent features of the proposed
scheme are: 1) The active power needed for compensating
the voltage sag is eliminated and the feeder current isreduced during the source voltage sag; 2) The VA loadingof the series and shunt converters are controlled to be
maintained within the rated capacity range; 3) The sliding
mode control of DC voltage can effectively suppress theexternal disturbance and improve the robustness of UPQC
during source voltage sag. Simulations are presented tovalidate the effectiveness of the proposed control.
Index Terms—Unified power quality conditioner; Energy
storage unit; Coordinated power allocation; Robust control

D. The disturbance observation

 xˆ1  ue x2 C  wˆ wˆ =  k DO  x1  xˆ1 (3)

E. The proposed sliding-mode control
u=

u
a C 
 C

k1 e x2  k2 e  ktr sgn  s   
 k1wˆ (4)b bue C
 bue

III. KEY RESULTS

I. INTRODUCTION
A coordinated power allocation scheme and an improved
sliding mode DC voltage control of UPQC are proposed.
With the power allocation scheme, the active power needed
for compensating the voltage sag is eliminated and thefeeder current is reduced. Meanwhile, the VA loading of theseries and shunt converters can be maintained within the
rated capacity range. In addition, the improved sliding
mode DC voltage control can suppress external disturbancewithout any chattering.

(a) Source voltage, load voltage and feeder current(b) Active and reactive power of the source

II. KEY EQUATIONS AND SCHEMATIC DIAGRAM
A. The series compensation voltage
 V   V 2  V 2  V 1  k 2
sr
L
S



90


 sr

(1)

(c) Active and reactive power of the series converter

B. The shunt compensation current
 I sh  I sin  L.
1  sin  sh  180  sin  sin  L (2)(d) Active and reactive power of the shunt converter

C. Power control schematic diagram
V L

I sh

V sr

 sh 

I S



A

IS

o

L

I Lq

 sr

B
V S

VS (VL )I L
IL

Fig. 1. Power control strategy of the improved UPQC-Q(e) DC voltage with different controls
Fig. 2. Simulation results of case 1

During source voltage sag, the coordinated power
allocation among UPQC units is realized and the DC
voltage with the proposed control can be restored to normal
state with improved transient performance.

Distributed Voltage Control for Demand Response
Jinrui Guo, Balarko Chaudhuri

Shu Yuen Ron Hui

Imperial College London, United Kingdom
j.guo16@imperial.ac.uk
b.chaudhuri@imperial.ac.uk

The University of Hong Kong, Hong Kong, China
Imperial College London, United Kingdom
ronhui@eee.hku.hk

Distributed voltage control (DVC) could be realized using
a power electronic compensator (PEC) which decouples acluster of domestic customers (CDC) from the low voltage(LV) feeder. The supply voltage to each CDC could be reduced
independently to its stipulated limit which would offer more
flexibility for demand reduction (DR). The total DR through
DVC is given by (1) and compared against that from VCS.

DR with DVC (%)

DR with DVC (%)

4
2

6
4
2
0

0

3

6

9

12

15

18

21

24

0

Hour of day(c) Winter Weekdays

-20
-40
-60

-80
-100
6

9

12

15

18

21

9

12

15

18

21

24

21

24

Hour of day(d) Summer Weekdays

-60

-80

3

6

-40

-100
0

3

-20

24

0

3

6

Hour of day

9

12

15

18

Hour of day

Fig. 1. Results through a synthesized LV network (a) ∆DR and (b)DR Capability through DVC for winter weekdays; (c) ∆DR and (d) DR
Capability through DVC for summer weekdays(1)where, Nb is the total number of CDC, ∆PLL is the change
in network power loss and PLCi is the total loss of PECs.
Improved DR capability through DVC comes at the expenseof requirement of PECs at each CDC. To exercise maximum
reduction in CDC voltage, the voltage injected by PEC isdesigned in phase with the feeder-side voltage. The apparent
power processed by the PEC at ith CDC can be given as:q0
2 V 2(npi −1) + Q2 V 2(nqi −1) (1 + pf 0 )SCi = (VBi
− Vmin ) Pi0imini0 min(2)0where, VBi
and pfi0 are the modified feeder-side voltage andpower factor due to reduction in voltage of the ith CDC.
The 24 hour high-resolution demand and power exponent
profile of individual domestic customers, which would be aggregated to form CDCs at each LV feeder during DR capability
calculation, is generated using a demand model developed by
the Centre for Renewable Energy Systems Technology.

(a) Winter Weekdays

250

UB (95 pc)
Median
LB (5 pc)

200
150
100
50

(b) Summer Weekdays

250

DR with DVC (kW)

i=1

0

200
150
100
50
0

0

3

6

9

12

15

18

21

24

0

Hour of day(c) Winter Weekdays

-20

3

-40
-60
-80

6

9

12

15

18

21

24

21

24

Hour of day(d) Summer Weekdays-20

DR (%)

i=1

Nb
X
 n
npi 
Pi0 VBipi − Vmin
− ∆PLL −PLCi6(b) Summer Weekdays

8

UB (95 pc)
Median
LB (5 pc)

0

DR with DVC (kW)NbX(a) Winter Weekdays

8

DR (%)DR =Index ∆DR indicates the DR capability of VCS comparedwith that through DVC. The investment in PECs could berecovered by utilizing the DR capability from DVC to cut
down the necessity for conventional operating reserve.

DR (%)

I. M ETHODOLOGY AND K EY E QUATIONS

II. K EY R ESULTS

DR (%)

Abstract—Distributed voltage control (DVC) at the points ofconnection of individual loads could provide larger demand
flexibility compared with voltage control at substations (VCS).
This is especially true during high loading conditions as theallowable voltage reduction would be largely limited. This paper
quantifies the demand reduction capability through DVC andcompared with that of VCS using a 24 hour high-resolution
stochastic demand model. Implementing DVC would require
voltage compensators, the cost of which is carefully evaluatedto weigh the benefits. The results are firstly shown through asynthesized low voltage network with random distribution ofclusters of domestic customers and random length of feeder
sections. Then, conclusions are further justified using a Cigre
benchmark medium- and low-voltage (MV/LV) network. A casestudy on an islanded microgrid shows that DVC produces better
frequency control with wind power fluctuations.
Index Terms—Demand response, voltage control, demand reduction, microgrid

-40
-60
-80

-100

-100
0

3

6

9

12

15

Hour of day

18

21

24

0

3

6

9

12

15

18

Hour of day

Fig. 2. DR capability from DVC and comparison with that of VCS (∆DR)
through a Cigre benchmark MV/LV network for winter and summer weekdays

Progressive Time-differentiated Peak Pricing (PTPP)
for Aggregated Air-conditioning Load in Demand
Response Programs
Yunwei Shen, Fangxing Li
Department of Electrical Engineering and Computer Science, the University of Tennessee, Knoxville, TN 37996, USA;yshen26@utk.edu, fli6@utk.edu
Abstract—The continuous increase of peak load in China has
posed a significant challenge to the safety and stability of thepower grid. By reasonable control of the air-conditioning (AC)load, peak load could be reduced and balance could be achieved
between supply and demand at lower cost without affecting
customers’ comfort. In this paper, a baseline model of theaggregated AC load is introduced and simplified to describe therelationship between temperature setpoint and AC power and therelationship between the electricity price and the temperature
setpoint of AC is described based on the consumer psychology
theory. Then, a new demand response (DR) project called
Progressive Time-differentiated Peak Pricing (PTPP) for the ACload is designed. Finally, case study proves the feasibility of the
optimal price mechanism proposed in this paper. The influence ofdifferent price shapes and different compositions of consumers
on simulation results is analyzed, providing a theoretical basis
and reference data for the practical implementation of PTPP.
Keywords—Demand response, Critical peak pricing, Timedifferentiated peak pricing, Air-conditioning

Insensitive Area
Response Area
Saturation Area

ΔTset

a

b

Δp

Fig. 2. Relationship between Δp and ΔTset

C. Simplified method to calculate AC load variation
N agg

LACcut ,t    i  Ti ,t 2  i  Ti ,t   i

i  [1,, N agg ], t  [1,i 1LACrebound ,t  x1LACcut ,t 1  x2 LACcut ,t 2  x3 LACcut ,t 3 t  [4,

(6)

, ]

, ] (7)(8)
LAC ,t  LACbase,t  LACcut ,t  LACrebound ,t t  [1, , ]
II. PTPP OPTIMIZATION MODEL
The objective function of the PTPP optimization model is
set to minimize the difference between the response load
reduction and the target load reduction.
Min  ( LAC ,t  LACbase ,t  Lt arg et ,t ) 2

I. MODELING METHODOLOGIES
A. Thermal Dynamic models of a single AC

(9)

t 1

subject to:Equations (1)-(8)
00  pi ,t  aiTi ,t   K i  (pi ,t  ai ) ai  pi ,t  bi
i  [1, , N agg ], t  [1, , ] (10)Ti ,maxpi ,t  bi(ui ,t  ui ,t 1 )  (ui ,t  1  ui ,t  )  1 t  [2, ,   1],   [1, , NCPP  1] (11) u N

Fig. 1. Equivalent thermal parameters model of a single air-conditioning unit.
t

Ti t 1  Tot 1  (Tot 1  Ti t )e RC , s  0

t  [1,

t

n1

, ]

III.

(12)

, N agg ]i  [1,, N agg ], t [1,

, ]

(13)
(14)

SIMULATION AND DISCUSSIONS

Load / MW26
24
Baseline load
Load control target
Load under the PTPP
Load under the CCPP

22
20
10:30

11:00

11:30

12:00

12:30
Time / h

13:00

13:30

14:00

Fig. 3. Aggregated AC load under different pricing mechanisms(3)

Assume Tset represents the temperature setpoint and δ
denotes temperature deadbands. The indoor temperature limits[Tmin, Tmax] could be calculated by (4) and (5).
Tmin  Tset   / 2(4)
Tmax  Tset   / 2(5)
B. Consumer psychology theory

i  [1,

28

3

x 10

4

Original Cost
Cost under the PTPP
Cost under the CCPP

2.5
Cost / ￥Luncontrolled  Pn  sn ,t t  [1,
AC ,t

CPP

max( L  LAC  LACbase )  max( L  Lt arg et )Ti t 1  Tot 1   PR  (Tot 1   PR  Ti t )e RC , s  1

N

i ,t

pCPP,min  pi ,t  pbase  pCPP,max,   1] (1)t  [1, ,   1] (2)Where To is the ambient temperature, °C; Ti is the airtemperature inside the house, °C; C and R are the equivalentheat capacity, J/°C and the equivalent thermal resistance, °C/W
respectively; P is the cooling/heating power of the AC unit, kW;η is coefficient of performance (COP); s is the AC on/off statevariable, and 1 means on while 0 means off. Furthermore, theaggregated power consumption of N ACs can be obtained
without intentional interference such as manual control andprice signal:


t 1

2
1.5
1
10:30

11:00

11:30

12:00

12:30
Time / h

13:00

13:30

14:00

Fig. 4. Total electricity cost under different pricing mechanisms

A Multilevel Dual Converter fed Open end
Transformer Configuration for Hybrid AC-DC
Microgrid
Krishna Chaithanya Bandla, Student Member IEEE, Narayana Prasad Padhy, Senior Member IEEE
Department of Electrical Engineering, Indian Institute of Technology Roorkee, India
Abstract— Recent trends in renewable source integration andadvancements in electric loads led to the emerging of local DC
microgrids. However, due to majority of AC loads hybrid AC-DC
microgrids are formed, incorporating feasible power converters
as medium of power flow. Instead of conventional bidirectional
AC-DC and DC-DC converters as interlinking converters inhybrid AC-DC microgrid, an open-end transformer based
multilevel converter configuration is proposed as interlinking
converter in this paper. The converter configuration includes twoconverters concatenated with primary of transformer, is a uniquesubstitution for conventional multiple converters utilized. The
salient features of the configuration include reduced control
complexity, inherent isolation from AC grid, and able to provide
inter-grid bidirectional power flow. A modified control scheme is
proposed for precise power flow control in the system. Real time
simulation of the proposed configuration of hybrid AC-DC
microgrid is implemented in RSCAD/RTDS and experimented ona scaled hardware prototype. The outcomes depict the potency ofcontrol scheme in inter-grid power flow control.

Index Terms—Multilevel Inverter, Open-end winding transformer,
Power flow control, Hybrid AC-DC microgrid.

I.

AC Grid
DC Bus-1

DC Bus-2

a1

a2
b1

c1

c2

b2

Vbus=120v

Vbus=48v

Fig. 1. Open end transformer-based converter configuration for hybrid microgrid.

II.

POWER FLOW CONTROL SCHEME

The modified control scheme utilizes a primary and secondary
control of which primary control is a centralized control forboth VSCs and secondary control is a localized control for each
VSC. Primary control involves regulating the active andreactive power flow to AC grid form the DCMGs. The
secondary control modifies the output of primary control suchthat required response is achieved from the DCMGs.
III.

INTRODUCTION

In this paper, Multi-level converter configuration for Hybrid
AC-DC microgrids is proposed. Further, a modified power
flow control scheme to synthesize accurate power flow among
hybrid AC-DC microgrid is proposed. The converter topology
acts as interfacing converter between AC and DC micro-grids
as shown in Fig.1. This includes two 3-phase voltage source
converter (VSC) modules connected differentially to both theends of primary winding of the transformer. The VSC modules
utilized are easily available, thus reducing the effort ofdesigning new circuitry for prototype design. The secondary oftransformer is connected to AC grid. The key feature of theconverter configuration is its ability to interface dissimilar
voltage rated DCMGs to AC grid simultaneously withmultilevel output voltage. The advantages of transformer inthis configuration are suppression of DC component in current
fed to grid, isolation of DCMGs from AC grid and acts as filter
inductance at primary side.

VSC-2

VSC-1

VDCMG-1
50v/div

IDCMG-1
5A/div

EXPERIMENTAL RESULTS

VDCMG-2
20v/div

IDCMG-2
5A/div

VDCMG-1
50v/div

IDCMG-1
2A/div

VDCMG-2
20v/div

IDCMG-2
2A/divTime scale
1sec/div

Time scale
2sec/div

PDCMG-1 (200W/div)
PDCMG-1 (100W/div)

PDCMG-2 (50W/div)
CPQ
SchemePDCMG-2 (50W/div)

CPQ
SchemeMPFC
Scheme(a)

MPFC
Scheme(b)

Fig. 2. Experimental results of Hybrid AC-DC microgrid for different power
flow scenarios.

1

Reactive Power Support from EVs for Distribution
Grid Voltage Management
Jingyuan Wang, Student Member, IEEE, and Sumit Paudyal, Member, IEEE,
Michigan Technological University, USA
Email: jwang11@mtu.edu, sumitp@mtu.edu

Abstract—In this paper, we first developed optimal distribution
power flow and optimal EV charging models, which utilize
reactive power injection capability of the EVs to support the grid.
We demonstrated the benefits of dispatching reactive power from
EVs to the grid operation and also to the EV owners. Reactive
power dispatch from EVs could help manage distribution gridconstraints, e.g., undervoltage issues caused by the active power
consumption during the EV charging. We have shown that incoordinated charging scheme, if EVs agree to inject reactive
power into the grid, it benefits EVs by reducing the costs ofcharging the EVs in dynamic energy pricing schemes. We havealso demonstrated that the reactive power injection from EVs
can be coordinated with the load shifting and load curtailment
in demand response applications to help accommodate increased
number of EVs on constrained grids.

a)Distribution Grid
Control Center

Distribution GridEV
Aggregator

Active/ Reactive
Power Bounds
EV parameters
Q

Quadrant-II(P<0, Q>0)b)

Quadrant-I(P>0, Q>0)
Unity power factor

Index Terms—Electric vehicles, optimization, distribution grid,
demand response, reactive power control.

Quadrant-III Quadrant-IV(P<0, Q<0)(P>0, Q<0)P

Socket rating

I. K EY E QUATIONS
The optimization objective of this model is to minimize thetotal power loss on the distribution grid as well as the active
power shifting and curtailment of EV loads.
XshctΩ2 = P loss +(λ1 Pm,k+ λ2 Pm,k
)
(1)
m,k

where P sh stands for active power shifting and P ct stands
for active power curtailment.
EVs can provide reactive support back to distribution grid.
There are lower and upper limits for reactive power which
EVs could provide, which can be mathematically modeled asfollowing,
max
Qmin
(2)
m,k ≤ Qm,k ≤ Qm,k
where
Qmin
m,k = −

Xq

2

(3)

ev
2
Rm,e
− Pm,k,e

(4)

ev
2
Rm,e
− Pm,k,eFig. 1. a) High-level overview of the proposed active/reactive powerscheduling of EVs, and b) Charging region of EVs.

III. K EY R ESULTS
Voltage profile of node-18 is shown in Fig. 2. Voltages
at node-18 are within the limit from 0.95 p.u to 1.05 p.u.
when EVs operate in non-unity power factor mode. When EVs
operate at unity power factor mode, voltages at node-18 (andother nodes) are below 0.95 p.u. at some of the time intervals.

e

Qmax
m,k =

Xq

2

e

II. K EY F IGURES
Figure 1-a) shows a high-level schematic of the proposed
coordinated EV charging, where EVs support reactive power
to the grid while charging the EVs at lowest possible costs.
In this work, we consider EVs operating on the first andfourth P-Q quadrants to demonstrate the concept, i.e., EVs
can inject/withdraw reactive power while charging.

Fig. 2. Voltage profile at node-18 with and without reactive power support.

2018 IEEE PES General Meeting, Portland, OR USA
5-9 August 2018

Xiao, Hu
Shanghai Jiao Tong University
Shanghai, China

Li, Wang
Guangzhou Power Supply
Guangzhou, China

Introduction

Case StudyIn this paper, a joint planning method of battery energy storage
systems (BESS) and demand response (DR) designed for
exploring the feasibility of industrial consumers participating in
the peak-load shaving in IPs distribution system is proposed.
This bi-level optimization method considering both planningand operation provides a systemic solution which determines the
optimal installed capacity of BESS, layout of BESS and joint
economic dispatching of BESS and DR, concurrently. An 11buses IP distribution system located in Guangdong Province inthe south of China is utilized as the study case to verify thevalidity of proposed method.

An industrial park distribution system containing 11 industrial
enterprise consumers is utilized to evaluate the performance ofthe proposed method.

Fig 2. Layout of an 11-buses
industrial park distribution system.

Problem Formulation

 Joint peak-shaving strategy of BESS and D

Fig 3. Primary load curve.

Since the production of industrial enterprises will be adversely affected
by DR, we propose an equivalent cost CDR to represent the reduction
loss. A reasonable assumption is that CDR is in proportion to EDR, andthen EDR is more suitable to shave the shadow area between Lmax andLdiv with a relatively small area.
(b)

(a)

Fig 4. Dispatching results in the planning
Fig
5.
Trend
of
investment
profittarget year: (a) BESS output; (b) DR
varying
with
BESS
price
per
unit
output; (c) load curve after peak-shaving

Fig 1. Joint peak shaving of BESS and DR: (a) in annual durationload curve; (b) in annual time-series load curve.

Optimization Model
Total objective:

max 𝑓=𝐵𝑏𝑎𝑠𝑒 + 𝐵𝑤𝑎𝑡𝑡 + 𝐵𝑃𝑆𝐶 + 𝐵𝑠𝑢𝑏 − 𝐶𝐵𝐸𝑆𝑆_𝑖 − 𝐶𝐵𝐸𝑆𝑆_𝑚 − 𝐶𝐷𝑅 − 𝐶𝑙𝑜𝑠𝑠

Level-1 objective:min

Level-2 objective:max

𝑓1 = 𝐶𝐵𝐸𝑆𝑆_𝑖 + 𝐶𝐵𝐸𝑆𝑆_𝑚

𝑓2 = 𝐵𝑏𝑎𝑠𝑒 + 𝐵𝑤𝑎𝑡𝑡 + 𝐵𝑃𝑆𝐶 + 𝐵𝑔𝑜𝑣 − 𝐶𝐷𝑅 − 𝐶𝑙𝑜𝑠𝑠

Conclusion
In our method, the annual time-series and duration load curve are applied toensure the accuracy of optimization results instead of a typical scenario,
because the peak-load periods in IPs present stronger randomness anduncertainty than that in residential districts.At present, industrial consumers
participating in peak-load shaving via the joint investment of BESS and DR
are unable to get a profit in the investment cycle because of the high price of
BESS. But a good prospect for the investment of BESS is indicated.

Stochastic Optimal Transmission Switching
Considering N-1 Security Constraints
Heng Zhang
Haozhong Cheng
Department of Electrical Engineering
Shanghai Jiao Tong University
Shanghai, Chinahengzhang_ee@163.com

Jianping Zhang
Jianzhong Lu
East China Grid Company Limited
Shanghai, China

Abstract—A novel multi-scenario based two-level stochastic
optimal transmission switching model incorporating economic
and N-1 security constraints is presented considering wind farms
integration in this paper. The optimal strategy of switching lines
is obtained in the upper level, and N-1 security check is carriedout in the lower level to test whether the scheme meets the
security requirements. Corridor load rate (CLR) is used as index
to add new constraints to upper level to accelerate solving
process. The Affinity Propagation (AP) clustering algorithm isused to construct different operation scenarios for the sake ofhandling the short-term fluctuation of load and the intermittenceof wind power. The modified IEEE-RTS system is introduced totest the model.

The Upper Level: OTS Strategy

The Lower Level: N-1 Check

Yes

No

End

Fig.1 Flow chart of iterative process

II. NUMERCIAL EXAMPLESMin Op   LD （*  OCk * PG ,k +  SCb * Rsh ,b）


s.t.

P

kb

N
 ,c

G ,k

k
 ,c



Lines disconnected

Generator
fuel cost
(M$)

Load
shedding
cost(k$)AC
feasible
check

0

-

15.96

0

feasible

1

10-11

14.79

0

feasible2

9-11,10-11

13.84

617.6

feasible

3

9-11,10-11,17-18

13.73

534.5

feasible4

1-2,9-11
10-11,17-18

13.71

586.8

feasibleb  PRe, s   f mn ( i )  Pb ,c  Rsh ,,cbsb

TABLE I. OTS RESULTS OF DIFFERENT SWITCHABLE LINES
Number of
disconnected
lines

A. Upper Level
Aiming to minimize the generators fuel cost and load
shedding, the OTS model can be presented as follows (In order
to avoid isolated island, some lines are forbidden to be
switched off.):

Satisfy?

N e w C o n s t r a i n t : Sort the CLR andchoose the largest one as new constraint
line to upper level

Index Terms—optimal transmission switching, AP clustering
algorithm, N-1 security constraints, corridor load rate.

I. MATHEMATIC MODEL

Cong LiState Grid Beijing ElectricPower Company
Beijing, China

 ,c

(1)

m , nb

 ,c
c
 ,c
 ,c

f mn
( i )  rmn ( i ) (m  n )  M *(1  i li ) (i  ) (2) ,ci * PLi ,max  f mn
( i )  i * PLi ,max (i  )
 ,c

(3)

f mn (i )  PLi ,max *(i lti )(i  )

(4)

PG ,k min  PG,k,c  PG ,k max(k  )

(5)

 ,c

 ,c

0  Rsh ,b  b Pb

(b )(6)

B. Lower Level
In order to reduce solving time, the N-1 security could be
realized by adding lines one by one according to corridor loadrate (CLR) index until the model converges.The CLR can beobtained by (7):
CLR  f mn
(7)( i ) PLi ,max

Fig.2. Total operation cost and ratio change with different switchable lines

TABLE II. SOLVING TIME OF SINGLE AND TWO LEVEL MODEL
Number of disconnected lines
Single Level
Two Level

1
3682s
120s

2
11756s544s

321382s3885s

Peak Energy Management using Renewable Integrated
DC Microgrid
P Sanjeev, Student Member IEEE, Narayana Prasad Padhy, Senior Member IEEE
Department of Electrical Engineering, Indian Institute of Technology Roorkee, India
Abstract— Thirst for energy is increasing significantly withincrease in economy around the world which burdens
conventional grid (CG) due to peak demand, energy deficit andcarbon emission, but major part of continents has plentiful windand solar energy which can be harnessed locally to minimize it.
Major objective of this research is to reduce the peak power
deficit present in CG system and to provide a reliable power
supply even in case of grid failure or during blackout. In thispaper, development and evaluation of a small-scale gridinteractive DC microgrid (DCMG) for residential houses,
telecommunication systems and data center has been proposed.
The objective is being achieved by designing a comprehensive
power flow control strategy and explored for different practicalscenarios through real time simulation in RSCAD/RTDS
platform. A Prototype is developed to validate the simulation
results presented during grid connected and isolated mode.

Fig. 1. Grid interactive DCMG structure

III.

EXPERIMENTAL RESULTS

Index Terms— DC Microgrid, energy management, peak energy,
power flow control strategy, real time simulation.

I.

INTRODUCTION

In this paper, to eliminate the communication link between AC
and DC buses, a modified droop control strategy is used for
power transfer between AC and DC bus with simple relation
between voltage of DC bus and AC bus frequency in order tocompensate any power fluctuations on one side by using poweron other side. But it considers the synchronous generators andDC voltage sources as renewable sources inside microgrids
which unable to mimic intermittency and low inertia
characteristics of renewable sources. A simplified power flow
control strategy (PFCS) is proposed to mitigate above demerits
by considering all cases of grid interactive DCMG along withdepreciation of peak energy and energy deficit which
ultimately yields to releasing the stress on CG and low carbon
foot print.
II.

SYSTEM ARCHITECTURE & POWER FLOWCONTROL STRATEGY

Using a DC distribution network, it makes easier to include
local renewable energy sources and storage devices. Sincemodern electronic loads can be supplied with dc without
modifying the load itself, it would save the losses incurred
during conversions from AC to DC if they are fed directly fromAC grid. Grid interactive DCMG system contemplated in thispaper which is shown in Fig. 1.

Fig. 2.Experimental waveforms during off peak mode: DC bus voltage ( Vbus ),input currents ( i pv , ib , iuc ), load current ( iL ), grid side quantities ( Vgsrc , igsrc ).

A Two-Step Load Disaggregation Algorithm for
Quasi-static Time-series Analysis on Actual
Distribution Feeders
Electrical & Computer Engineering Department
FREEDM Systems CenterNorth Carolina State University, Raleigh, NC
jwang49@ncsu.edu

Nader Samaan

Brant Werts and Andrew Kling

Pacific Northwest National
Laboratory
Richland, WA

Duke Energy Carolinas
Charlotte, NC

Abstract—This paper focuses on developing a two-step load
disaggregation method for conducting quasi-static time-series
analysis using actual distribution feeder data. This can help
utilities conduct power flow studies using smart metermeasurements to assess the impact of high penetration of
distributed energy resources. In the first step, load profiles ofresidential and commercial buildings obtained from smart meterdata are used to match the load profile at the feeder head. This
step will determine the number of residential and commercial
loads on the feeder. The second step is to allocate the selected load
profiles to each load node based on its transformer rating. This
allows each load node to have its own load profile and theaggregation of those nodal load profiles matches closely to themetered feeder load shape at the substation. This algorithm isvalidated using smart meter data and the SCADA data of a real
feeder. We compared the performance of the proposed methodwith the traditional load allocation method (i.e. use the feeder load
shape for all subsequent load nodes scaling by the transformer
capacities) when conducting quasi-static power flow studies.
Results show that the proposed algorithm matches the utility datawell and the obtained voltage profiles reveal more voltage
dynamics than using the conventional load allocation method.

load types with the actual utility information. Second, wereformulated our load disaggregation algorithm into a 2-stepalgorithm that reflects the two main processes: load profile
selection and allocation.
II. KEY FIGURES
12

Weekday
Weekend10
8

Load [kW]Jiyu Wang, Xiangqi Zhu, David Mulcahy, Catie
McEntee, David Lubkeman, and Ning Lu

6
4
2
0
0

5

10

15

20

Time [hour]Fig. 1. Monthly energy consumption for a residential house
Day 2

Day 1

1600

Day 4

Day 3

Day 5

Day 6

Matching

Day 7

Pivot point 1

Day

1400

I. INTRODUCTION
Quasi-static time-series simulation [9] is now widely used toassess the operational characteristics (e.g. voltage profiles,
overloading conditions, etc.) of an electricity distribution system.
A critical step in the Quasi-static time-series simulation is the
allocation of nodal load profiles. In the past, the shape of thefeeder head load profile is used for each subsequent load nodes
and the load shape is scaled by letting its peak load be scaled
based on the distribution transformer capacity at the node [13].
In [16-17], we proposed a load disaggregation method using
a load profile database containing hundreds of load profiles. In
this paper, we further developed the methodology. The paper has
two major contributions. First, because we obtained a yearlyutility feeder head load profile with the corresponding yearlysmart meter data of all the residential and commercial buildingsin the area, we are able to validate the performance of thealgorithm by comparing the calculated number of houses andLoad [kW]1200

Index Terms—Load disaggregation, load shape, quasi-static
time-series analysis, distribution feeder, voltage profile

Pivot point 3

1000
800
600
Pivot point 2

400
0

24

48

72

Metered Feeder Head Profile

96

120

144

168

Time [hour]

Aggregated Feeder Head Profile

Fig. 2. Feeder head profile comparison
10
Aggregated Load Profile at One Node8

Three houses locate at this node
Load Profile for each house

6

4

2

0
0

5

10

15

20

25

Fig. 3. An example of load allocation disaggregation at a load nodeOptimal Water-Power Flow Problem:
Formulation and Distributed Optimal Solution
Ahmed S. Zamzam, Student Member, IEEE, Emiliano Dall’Anese, Member, IEEE,
Changhong Zhao, Member, IEEE, Josh A. Taylor, Member, IEEE, and Nicholas D. Sidiropoulos, Fellow, IEEE

Power and water networks are critical infrastructures. Thesesystems are predominantly planned and operated independently, although their operation is intrinsically coupled at multiple spatial and temporal scales. For example, in the UnitedStates the overall operation of drinking and wastewater networks represents 4% of the total electricity consumption [1].
Optimizing water pump operation has therefore significant
potential to save energy, reduce emissions, and enhance thereliability and efficiency of the power grid.
It is increasingly recognized that a joint optimization of
power and water infrastructures can bring significant benefits
from operational and economical standpoints [2]. Controllable
assets of water utilities can provide valuable services to powersystems to enhance reliability and efficiency, as well as to copewith the volatility of distributed renewable generation.
This paper formulates an optimal water-power flow (OWPF)
problem to minimize the (sum of the) cost functions associatedwith water and power operators while respecting relevant
engineering and operational constraints of the two systems
as well as pertinent intra-system coupling constraints. The
problem is tailored to coupled power distribution feeders andmunicipal water distribution networks, and it addresses thecontrollability of distributed energy resources (DERs) andwater pumps.
II. T HE OWPF P ROBLEM
Water and power networks are coupled because the power
consumed by a pump is proportional to the pump’s pressure
gain times its flow rate. Let xp and xw denote the control

9R

1J

2J

3J

10T

7J

6J

5J
4J

variables of the power and water networks, respectively. Then,the Optimal Water Power Flow problem can be formalized as
T
X

min

xp ,xwt
Cpt (xp ) + Cw
(xw )



t=1

xp ∈ Ωp ,s.t.

xw ∈ Ωw ,Ap x p − Aw x w = 0

where Ωp and Ωw are the nonconvex operational sets of thepower and the water systems, respectively. The last constraint
represent the coupling (consensus) constraint.
III. D ISTRIBUTED A LGORITHM
Algorithm 1: Distributed OWPF algorithm
Initialization: Systems use feasible points to initializes their variables.
repeat[S1a] Update power-related variables[S1b] Update water-related variables[S2] Update dual variables[S3] Update restriction points
until convergence criterion is met

IV. E XPERIMENTAL R ESULTS
The proposed approach is tested in a setting where theIEEE 13-node test feeder is coupled with a municipal water
distribution network in Fig. 1 which has three pumps.
1600

1600
PV Available
Uncotrollable Load
PV Injection
Pumps Consumption
Substation Set Point
Substation Injection

1400
1200
1000
800

PV Available
Uncotrollable Load
PV Injection
Pumps Consumption
Substation Set Point
Substation Injection

1400
1200

Power [kW]I. I NTRODUCTION

8R

Fig. 1: Municipal water network

Power [kW]Abstract—This paper formalizes an optimal water-power flow
(OWPF) problem to optimize the use of controllable assets acrosspower and water systems while accounting for the couplings
between the two infrastructures. Tanks and pumps are optimallymanaged to satisfy water demand while improving power grid
operations; for the power network, an AC optimal power flow
formulation is augmented to accommodate the controllability ofwater pumps. Unfortunately, the physics governing the operationof the two infrastructures and coupling constraints lead toa nonconvex (and, in fact, NP-hard) problem; however, afterreformulating OWPF as a nonconvex, quadratically-constrained
quadratic problem, a feasible point pursuit-successive convex
approximation approach is used to identify feasible and optimal
solutions. In addition, a distributed solver based on the alternating direction method of multipliers enables water and power
operators to pursue individual objectives while respecting thecouplings between the two networks. The merits of the proposed
approach are demonstrated for the case of a distribution feeder
coupled with a municipal water distribution network.

600

1000
800
600
400

400

200

200
0

0

-200

-200

-400

-400
7

8

9

10

11

12

13

14

15

16

17

18

7

8

Time

(a) OWPF Solution

9

10

11

12

13

14

15

16

17

18

Time

(b) Decoupled Solution

R EFERENCES[1] D. Denig-Chakroff, “Reducing electricity used for water production:
Questions state commissions should ask regulated utilities,” Water Research and Policy, 2008.
[2] E. Dall’Anese, P. Mancarella, and A. Monti, “Unlocking flexibility:
Integrated optimization and control of multi-energy systems,” IEEE Powerand Energy Magazine, vol. 15, no. 1, pp. 43–52, Jan 2017.

Real Time Tool to Characterize Power System
Communication Delays
Christoph Lackner

Felipe Wilches-BernalDavid A. Schoenwald

Brian J. Pierre

Rensselaer Polytechnic Institute Sandia National Laboratories Sandia National Laboratories Sandia National Laboratories
Albuquerque, NM
Albuquerque, NM
Albuquerque, NM
Troy, NYdaschoe@sandia.gov
bjpierr@sandia.gov
fwilche@sandia.gov
lacknc@rpi.edu
I. C OMMUNICATION D ELAYS IN S YNCHROPHASORM EASUREMENTS
Synchrophasor Measurement Units (PMUs) allow high resolution monitoring of power systems spanning large geographical areas [2]. One Advantage of PMU measurements is thatthey include a time tag according to a common and highly
accurate time reference. The IEEE C37.118 standard defines
the requirements for the transmission of PMU data acrosscommunication networks and specifies the accuracy of the
UTC time reference [1].
While a considerable amount of work has been done on
how to measure delays in general communication networks[3], very little work has been done specifically looking atdelays of PMU data. Because PMU data already has a very
accurate Timestamp it can be leveraged to determine delays
with a much higher accuracy.
Nowadays, PMU data is used in a number of real time
control and monitoring applications such as RT state estimation [4], HVDC modulation [5] and inter-area oscillation
damping [6]. The performance of these applications is affectedby delays of the incoming data, i.e. PMU data, if not properly
addressed [7]. Hence, understanding the nature of the delaysin the PMU data is critical to ensure the performance of PMU
based real time applications.
This work describes the implementation of a Real Time Tool
to measure the delays of PMU data with extreme accuracy. The
tool is named network characterizer (NC).
II. T OOL I MPLEMENTATIONThe NC consists of two parts:
1) A sychnronous part, which is implemented on a National
instrument RT-PXI.
2) An asynchronous part which is implemented on a PC
connected to the RT-PXI.
The synchronous part records all PMU data packages received
and records the delay associated with each packet by recording
a GPS time-stamp. The asynchronous part processes the PMU
data and record any anomalies such as packets that werereceived out of order or lost in the communication network.
Fig. 1 shows the setup of these two parts.
Sandia National Laboratories is a multimission laboratory managed andoperated by National Technology and Engineering Solutions of Sandia,
LLC., a wholly owned subsidiary of Honeywell International, Inc., for the
U.S. Department of Energys National Nuclear Security Administration undercontract DE-NA0003525.

ComputerGPS

RT-PXINC
PMU B
Network
PMU A

Fig. 1. Setup of the NC including the RT-PXI and the PC

III. R ESULTS
Fig. 2 shows the delays of a PMU connected to the tool
through a small dedicated local area network. The figure also
shows the measurement obtained by using Wireshark (WS), a
traditional network analysis tool. In this test UDP was usedby the PMU to send the phasor measurements taken every 33
ms. The full poster will contain a more detailed section onsome of the results obtained using this tool.
46
45.5
45
44.5
44
43.5
0

5

10

Fig. 2. Delays of a PMU measurement as determined by the NC and WS

R EFERENCES[1] IEEE Standard for Synchrophasor Data Transfer for Power Systems, IEEE
Std C37.118.2-2011 (Revision of IEEE Std C37.118-2005), pp. 153, Dec
2011.
[2] A. G. Phadke, Synchronized phasor measurements in power systems,
IEEE Computer Applications in Power, vol. 6, no. 2, pp. 1015, April
1993.
[3] R. Kay, Pragmatic network latency engineering fundamental facts andanalysis, cPacket Networks, White Paper, pp. 131, 2009.
[4] C. Lackner, Q. Zhang and J. H. Chow, ”Real-Time phasor-only state
estimation with topology processing as OpenPDC adapter,” 2017 IEEE
Power & Energy Society General Meeting, Chicago, IL, 2017, pp. 1-5[5] D. Trudnowski, et al, Initial closed-loop testing results for the Pacific DC
Intertie wide area damping controller, in Power and Energy Soc. General
Meeting, 2017. IEEE, Chicago, IL, 2017, pp. 15.
[6] F. Wilches-Bernal, et al, Effect of time delay asymmetries in powersystem damping control, in 2017 IEEE Power Energy Society General
Meeting, July 2017, pp. 15.
[7] H. Ali and D. Dasgupta, Effects of Time Delays in the Electric Power
Grid. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp. 139154.

1

Diagonal Quadratic Approximation for Decentralized
Collaborative TSO+DSO Optimal Power Flow
Ali Mohammadi, Student Member, IEEE, Mahdi Mehrtash, Student Member, IEEE, and Amin Kargarian, Member,IEEEis formulated and solved in the upper-level of hierarchy, thedistribution OPF is handled in the lower-level. Two coordination
strategies, namely diagonal quadratic approximation (DQA) andtruncated diagonal quadratic approximation (TDQA), are presented
to coordinate the local OPF problems in a parallel manner.
TSO level(level 1)v a  a

TSO

vb   b
DSO2

DSO1

DSO level(level 2)

va' a'vb' b'Initializationk  0, l  0, X (0) , (0) , w(0)l  l 1
Solve local OPF subproblems in parallel
Subproblem 1(TSO)

Inner loop

Abstract- Collaborative operation of electricity transmission anddistribution systems improves the economy and reliability of the entire
power system. However, this is a challenging problem given thattransmission system operators (TSOs) and distribution system
operators (DSOs) are autonomous entities that are unwilling to reveal
their commercially sensitive information. This paper presents a
decentralized decision-making algorithm for collaborative TSO+DSO
optimal power flow (OPF) implementation. The proposed algorithm isbased on analytical target cascading (ATC) for multilevel hierarchicaloptimization in complex engineering systems. A local OPF isformulated for each TSO/DSO taking into consideration interactions
between the transmission and distribution systems while respecting
autonomy and information privacy of TSO and DSOs. The local OPF
of TSO is solved in the upper-level of hierarchy, and the local OPFs of
DSOs are handled in the lower-level. A diagonal quadratic
approximation (DQA) and a truncated diagonal quadratic
approximation (TDQA) are presented to develop iterative
coordination strategies in which all local OPFs are solved in a parallelmanner with no need for a central coordinator. This parallel
implementation significantly enhances computations efficiency of thealgorithm. The proposed collaborative TSO+DSO OPF is evaluatedusing a 6-bus and the IEEE 118-bus test systems, and promising results
are obtained.

Subproblem 2(DSO1)

Subproblem n+1(DSOn)...

Update target variables t and response variables r (i.e., sharedvariables)

k=k+1
l=0

Update λ and w

I. INTRODUCTION
The transmission system is operated by a transmission system
operator (TSO), and the distribution system is controlled by a
distribution system operator (DSO). Since the transmission anddistribution grids are parts of an interconnected system, any
decisions made by TSO (DSOs) affects the DSOs’ (TSO’s)
operation and decisions. On the other hand, TSO and DSOs areautonomous control entities with their own rules, policies, andobjectives. While one entity aims at minimizing its own costs, theobjective of another entity might be reliability maximization withrespect to its local operational constraints. Furthermore, TSO and
DSOs might compete with each other to achieve their objectives.
Thus, although TSO and DSOs are parts of an interconnected
system, they are unwilling to share their commercially sensitive
data with each other.
In this paper, we present a decentralized collaborative two-level
TSO+DSO optimal power flow solution. The proposed algorithm isbased on analytical target cascading (ATC) method and allows afully parallel implementation TSO+DSO OPF. A local OPF
problem is formulated for TSO and each DSO which accounts forinteractions between the transmission and distribution systems. A
limited amount of information is exchanged among TSO and DSOs
which is in line with respecting the information privacy of theautonomous control entities. While the transmission OPF problem

X

k ,l

X

k ,l 1



 X

k ,l

X

k ,l 1

No

Inner loop
converged?
Yes

Outer loop

Index Terms- Collaborative transmission and distribution
operation, analytical target cascading, diagonal quadratic
approximation, decentralized optimization, parallel algorithm.

X k  X k ,l
Yes
Stop

outer loop
converged?

NoA Data-driven Voltage Control Framework forPower Distribution Systems
Hanchen Xu, Alejandro D. Domı́nguez-Garcı́a, and Peter W. Sauer
Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign
Email: {hxu45, aledan, psauer}@illinois.edu

Abstract—In this paper, we address the problem of coordinating a set of distributed energy resources (DERs) to regulate
voltage in power distribution systems to desired levels. To thisend, we formulate the voltage control problem as an optimization
problem, the objective of which is to determine the optimal
DER power injections that minimize the voltage deviations fromdesirable voltage levels subject to a set of constraints. The
nonlinear relationship between the voltage magnitudes and thenodal power injections is approximated by a linear model, theparameters of which can be estimated in real-time efficiently
using measurements. In particular, the parameter estimation
requires much fewer data by exploiting the structural characteristics of the power distribution system. As such, the voltage
control framework is intrinsically adaptive to parameter changes.
Numerical studies on the IEEE 37-bus power distribution test
feeder validated the effectiveness of the propose framework.

LP

Γi q (k) xi , and Rp(k) =i=1

Γi p(k) ri , where ri
i=1(km ) > >= αi xi .

Define φ = [(ũ(k1 ) )> , · · · , (ũ) ] , and


(k1 )
(k1 )
Γ1 (α1 p+q) · · · ΓL (αL p(k1 ) + q (k1 ) )


..
..
..
Φ=
.
.
.
.
Γ1 (α1 p(km ) + q (km ) ) · · ·ΓL (αL p(km ) + q (km ) )Then (1) is equivalent to the following:2

minimize kΦx − φk2 ,x(2)

the solution of which is given by x̂ = (Φ> Φ)−1 Φ> φ.
C. Voltage Controller
The voltage control problem is formulated as follows:
minimize

I. VOLTAGE C ONTROL F RAMEWORK

LP

pg ∈[pg ,pg ],q g ∈[q g ,q g ]c(pg , q g , u)s.t. u − u0 1N = R̂(pg − pd ) + X̂(q g − q d ).

A. Framework Overviewggwhere c(pg , q g , u) = 1>
N (p + q ) + γ([u − u]+ + [u − u]+ ).

II. N UMERICAL S IMULATIONS

Fig. 2. Line parameter estimation results.

Fig. 1. Data-driven voltage control framework.

B. Voltage Sensitivity Estimator
The sensitivity estimation problem is formulated as follows:
minimizer,xXkRp(k) + Xq (k) − ũ(k)

2

,
2

(1)

−1
where ũ = V 2 − V02 1N . Define Γi = 2(M −1 )> ei e>
,
i M
th
L
(k)
where ei is the i basis vector in R . Then, Xq=

Fig. 3. Voltage profiles under proposed control. (Disturbance at time 60.)

A Machine Learning Approach for Residential Load
Extraction using Low-resolution Smart Meter Data
Yao Meng, Student Member, IEEE, Ming Liang, Student Member, IEEE, Ning Lu, Senior Member, IEEE

Abstract— This paper presents a data-driven approach forload disaggregation. A 3-step machine learning algorithm isdeveloped to perform load disaggregation 15-minute, 30-minute,and hourly smart meter data. In the first step, a shared-learning
method is used to identify baseload days so that the baseline load
consumption of each customer (defined as the electricity
consumption without or with very little heating or cooling loads)
can be extracted. In the second step, the cooling and heating loads
are extracted for each out-door temperature cluster using an
averaging method followed by a trimming procedure. In the final
step, energy signatures are derived from the energy
disaggregation results for conducting energy disaggregation forfeeders without smart meter data. The method is developed,
tested, and validated using minute-by-minute sub-metered energy
consumption data collected from 145 household in the PECAN
street project.

for deriving energy signatures using 15-minute, 30-minute, andhourly smart meter measurements.
II. KEY EQUATIONS
Estimate the house cooling consumption
𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑃𝑃𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 = 𝑃𝑃ℎ𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜− 𝑃𝑃𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚

In mild days (transition seasons), HVAC system is notoperating, so that the smart meter data in this period does notinclude HVAC energy consumption, only non-temperaturesensitive load, which is regarded as base load.
𝑎𝑎𝑎𝑎𝑒𝑒𝑎𝑎𝑎𝑎𝑎𝑎− 𝑃𝑃𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚
𝑃𝑃ℎ𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 = 𝑃𝑃𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐

A common goal of the Load Disaggregation algorithms is
to extract time series load profiles of end use electricity
consumptions from measured total load consumptions. There
are two types of load disaggregation algorithms: power andenergy. The time series of the power consumptions of adisaggregated end use load can be used to develop load models
for power management programs such as peak shaving and load
shifting. The sampling rates of the meter data required for
power disaggregation algorithms are high. Normally, datacollected by power quality meters (usually higher than 60 Hz)
are used.
At the very minimum, minute-by-minutemeasurements are needed to identify the operation
characteristics of an end use load. The time series of the energy
consumptions of a disaggregated end use load can be used toderive load composition, which is a critical piece of information
required for power grid operation and planning. The sampling
rates of the meter data required for energy disaggregation
algorithms can be much lower than those for the power ones.
Traditionally, utilities use hourly, daily or even monthly energy
measurements to perform load disaggregation to extract theload composition data from a very high level (i.e. commercial,residential, agricultural, and industrial). The deployment of thesmart meters allows utilities to have access to a large amount of
intra-hour meter measurements. This creates an emerging needfor accurate, robust, and computationally efficient energy
disaggregation methods so that low-resolution smart meter data
can be used to derive energy related signatures. Thus, in thispaper, we will focus on the energy disaggregation algorithms(2)

Variations in the electricity usage of different day can lead tovery different calculation results. So this method is applied todaily average electricity load profiles.

Keywords-load modeling, load disaggregation, machinelearning, smart meter, data-driven methods, unsupervised learning.

I. INTRODUCTION(1)

𝑟𝑟𝑡𝑡𝑖𝑖 =𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝑡𝑡𝑖𝑖
�
𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑖𝑖(3)

Using a trimming method based on HVAC usage ratio is simple
and effective.
III. KEY SIMULATION RESULTS
Unit in following tables is kWh.
Table I Cooling and Heating Load Estimation Accuracy

Cooling Load
Heating Load

Overall
RMSE
0.29710.7559MAE

Best RMSE

0.6601
1.83500.08490.1524

Table II Cooling load accuracy comparison before and after trimming

Before trimming
After trimming

Overall
RMSE
0.29710.2190MAE
0.66010.5511Best
RMSE
0.08490.0542

Fig. 1 HVAC load before and after trimming for House 65

2.5

8
GKDE
DKDE

2

GKDE
DKDE

6

1.5
4
1
2

0.5
0
01-May07-May13-May19-MayTest Day

25-May 31-May
2017

001-May07-May13-May19-MayTest Day

25-May 31-May
2017

1.5

400

Avg Netload
Netload
CC
uCC

350
300

uCC
CC

1

250
0.5

200
0:00

4:00

8:00

12:00 16:00 20:00 24:00

Time

20

40

EV#

60

80

1

Detection of PMU Time Error Sources Using
Phase Angle Measurement Data
Ikponmwosa Idehen, Student Member, IEEE and Thomas J. Overbye, Fellow, IEEE

Abstract—In the event of data corruption or loss whichinvalidates time and message quality bits in IEEE C37.118
protocol, measurement data from phasor measurement units(PMUs) could be rendered incomprehensible. In this work, we
describe four time-based errors in PMU device operation, whileobserving the unique characteristic patterns formed. As a data
post-processing tool, a subsequence dynamic time warping
technique is utilized in a similarity-based, time-series pattern
query to identify instances of time-related errors in reported PMU
data. Results are demonstrated using transient stability data froma 2000-bus synthetic case and actual utility PMU data.
Index Terms—phasor measurement unit, error analysis, error
prototype, pattern similarity, pattern recognition, time-errors

I. INTRODUCTION
Fast-paced deployment of phasor measurement units (PMUs) is
considerably increasing operator situational awareness of thepower grid. Generated data are transmitted to control centerswhere they are used in short-term control schemes and longterm future planning. However, the unique operation of thesedevices makes them vulnerable to a new paradigm of errors
increasing the spectra of data quality issues tackled by control
centers.
Some of the synchronization and time-related issues
associated with PMU devices are attributed to intermittency inglobal positioning system (GPS) signals, total GPS signal loss
(GSL), drifting PMU internal clocks and signal spoofing [1].
Often, these translate to phase angle errors and manifest as
sporadic or periodic jumps in angles. Figures (1) and (2) show
voltage angles (VA) observed in four different prototype PMU
error simulations for a duration of 30 seconds.

red curve, which is a result of signal spoofing. Fig. (2) showsdifferent patterns in the VA profiles observed for a drifting
PMU clock. One pattern is due to an internal time offset (green
curve), and the other is a result of intermittent GPS signals (pinkcurve). Two key features are the non-divergence of the VA
measurements, and the short segment error duration prior to thenext data error.

Fig.2. Prototyped PMU voltage angles due to drifung clock and intermittent
GPS

In order to provide details on device time synchronizationand quality of measured data, the IEEE C37.118.2 protocol [2]utilizes flag bits to encode time and message quality
information in data frames. In the data lifecycle and archival
process involving data compression, migration and storage
activities, reported PMU data are vulnerable to possible data
loss, corruption or modification of flag bits, which in turnaffects intelligibility of the stored data. As part of a postprocessing stage, engineers can lose sight of the presence andcause of logical inconsistencies in the data, and result in a
reduced grid awareness level.
The purpose of this work is to provide a post-processing
technique for the source identification of PMU time-related
errors based on the sole use of reported phasor measurements.
It leverages on defined PMU error mechanisms to generate
prototype data error patterns, which are then used as training
sets in the error analysis and source error identification insynthetic and actual PMU data. A subsequence dynamic time
warping technique is employed in the search for distinct
patterns in PMU data error measurements.
REFERENCES[1]

Fig.1. Prototyped PMU voltage angles due to GSL and time signal spoofing

In Fig. (1), the black horizontal line represents the original,
steady state condition while the staircase-like VA profiles are
typical of continous drifting angles attributed to GSL and signal
spoofing events. For the same 5 µs time error, the blue curve,
which is a result of GSL, shows a larger VA deviation than the

[2]
[3]

Y, Wenxuan, Yong Liu, D. Zhou, Z. Pan, J. Zhao, M. Till, L. Zhu, L.
Zhan, Q. Tang, and Yilu Liu. "Impact of GPS signal loss and its mitigationin power system synchronized measurement devices." Smart Grid, IEEE
Transactions on, vol. PP, no.99, pp.1-1, 2016.
Q. Zhang, V. Vittal, G. Heydt, Y. Chakhchoukh, N. Logic and S. Sturgill,"The time skew problem in PMU measurements," 2012 IEEE Power andEnergy Society General Meeting, San Diego, CA, 2012, pp. 1-6.
NASPI and PNNL. (2017). NASPI-2016-TR-002 A Framework for the
Attributes of PMU Data Quality and a Methodology for Examining Data
Quality Impacts upon Synchrophasor Applications. [Online]. Available:
https://ws680.nist.gov/publication/get_pdf.cfm?pub_id=920627

Stability Robustness for Secondary Voltage Control in Autonomous
Microgrids With Consideration of Communication Delays
Guannan Lou, Wei Gu

Yinliang Xu

Department of Electrical Engineering
Southeast University
210096, Nanjing, Jiangsu, China
Guannan_lou@seu.edu.cn

Shenzhen Environmental Science and New Energy
Technology Engineering Laboratory
Tsinghua-Berkeley Shenzhen Institute518055, Shenzhen, China

I. INTRODUCTION

m

n

Qi
k PQ +

kiQ
s

MGCC

n

V

i

i=1

1

n

V*
k PV +

 Qi

kiV
s

Sampling
period T2

 Vi

ui

Primary Control

Droop control
ωiLC

Voltagecontroller

i*li

Pi
Filter
Qi

pi
qi

Power
calculation
Power controller

Current
controller

ili

R fi

vo1 io1
DG1

iline1Lc1 Vb 1

Rc1

iload1rline1
Rload1

L fi

C fi

voi ioiSampling
time T1

Rci

iline2

Vb 3

Lline1

iload3

Lload1V b 2 Lc2

Lline2

rline2

Rload3

Rload2

Lload3

Lload2

Rc2

io2

vo2
DG2

iload2

Fig. 2. Diagram of the MG test system.

A. Single time delay
The delay margin τd using different controller gains is
calculated for MG with a single delay. The root loci related
to critical stability with kiQ=0.02, kiV=20 is depicted in Fig.
3 and τd for different sets of controllers is shown in Fig. 4.
 increasing

300
200
100
0
60

 increasing

Communication
Time delays
channelvoi*

The stability robustness analysis of secondary voltage
control is performed by a typical MG in the presence ofboth single and independent multiple delays.

0.06

40
k IV

Fig. 3. Root loci of CEξ(s, ξ) related to
critical stability with kiQ = 0.02, kiV = 20.

0.04

20

0.02
0

0

k IQ

Fig. 4. Single delay margins fordifferent sets of controller gain

B. Independent multiple communication delays
The stability posture over the delay domain for unrelated
multiple delays τ1 and τ2 are investigated, where Kernel
curve in building block and the relationship among criticalω, τ1 and τ2 are shown in Fig. 5 and Fig. 6, respectively.

Lci

4.44
4.42

MicrogridFig. 1. Hierarchical control scheme for an inverter-based DG

II. DELAY-DEPENDENT STABILITY ANALYSIS
In the view of above analysis, the implementation of the
proposed approach includes the following steps:
Step 1: Establish an overall small-signal model of MG(rad/s)ii =1III. ANALYSIS RESULTS\

2   2

Q

Reactive Power SharingnVoltage Restoration

Secondary Voltage Control

The microgrid (MG) is emerging as a promising means of
distributed generations (DGs), loads and storages. The most
attractive advantage of MG concept is to island itself fromthe main grid and to provide an uninterrupted power supply,
where droop algorithm attracts attention to facilitate power
sharing among DGs. Typically, voltage droop exhibits apoor reactive power sharing due to the inconformity ofoutput impendences, and secondary voltage control (SVC) is
introduced to generate control signals for individual local
controllers (LCs) shown in Fig. 1. With the increasing usageof open communication networks, the time delays between
SVC and LCs are expected to degrade system performanceand even cause instability, which consequently cannot be
simply ignored. Therefore, secondary voltage control withconsideration of communication delays during control signaltransmission is addressed in this paper.

including inverters, network and loads, with considerationof time-delayed secondary voltage controller.
Step 2: Select sets of feasible PI controller parameters.
Step 3: For each set of PI control gains, obtain thecharacteristic equation. Furthermore, the delay-dependent
stability robustness is determined in the forms of delay
margin and stability regions, respectively.
Step 4: Investigate the qualitative impact of controller
gains on system stability robustness against time delays.
Step 5: Simulation is utilized to verify the accuracy of thetheoretical results by increasing the delay gradually whileobserving the system stability.

d(ms)

Abstract—The extensive usage of open communication
networks for secondary voltage control in autonomous
microgrids (MGs) inherently introduce time delays, which
would degrade the system performance. This paper proposes
an analytical method to determine the stability robustness of
secondary voltage control in MGs by taking into account thecommunication latency. A closed loop small-signal model of a
MG installed with PI secondary voltage controllers is derived
to include time delays between the MG centralized and local
controllers. Delay stability criterions based on tracing criticaleigenvalue and cluster treatment of characteristic roots arethus formulated to deliver delay margins for single andmultiple delays. By a series of trial declarations, the qualitative
impact of controller gains on delay-dependent stability can be
uncovered, to guide the controller design. The effectiveness ofthe proposed methodology is verified by a simulation study.

2
Building
block

4.4
4.38
4.36

2

4.34
0.15
0.15

0.1
1   1

2(s)

0.1

0.05

0.05
0

0

1(s)

Fig. 5. Kernel curve in building block Fig. 6. Kernel ϑSDS
(τ1, τ2) and its
0
and its offspring for an MG withcorresponding ω variations
unrelated delays (kiQ = 0.02, kiV = 20).
(kiQ = 0.02, kiV = 20).

Automated Distribution System Restoration with theMultiagent Q-Learning Based Algorithm
Inalvis Alvarez-Fernandez, Jungseok Hong, and Wei Sun
Department of Electrical and Computer Engineering
University of Central Florida
Orlando, Florida, United Statesalvarezfernandez@knights.ucf.edu, jasonhong@knights.ucf.edu, sun@ucf.edu

Abstract—This paper proposed using Q-Learning, multiagent
system (MAS), combination and battery algorithm for powersystem restoration considering the system’s operation constraints
in order to adapt to quick changes. The developed algorithm
considers voltage and current constraints, while finding systemswitching configuration to maximize the load pick-up after faults
happen to the given system. The algorithm consists of three parts.
First, it finds switching configurations using Q-learning. Second,the combination algorithm works as a back-up plan in case of thesolution from Q-learning violates system constraints. Third, thebattery algorithm is applied to determine the charging ordischarging schedule of battery systems. The obtained switching
configuration provides restoration solutions without violating
system constraints. Furthermore, the algorithm can adjust
switching configurations after the restoration. For example,
when renewable output changes, the algorithm provides anadjusted solution to avoid violating system constraints.
Simulation results demonstrate that the algorithm offers anefficient and effective restoration strategy for resilient
distribution system operation.

II.

RESULTS

The proposed algorithm is tested on the testbed. The testbed
is built based on the IEEE 9-Bus system. The model shouldhave a generator, load, switch, PV, and battery. There is nolimit to the number of components to run the proposed
algorithm. The testbed has two DGs, one battery, one PV, andthree loads. Each load has its own priority between 0 and 1. If aload has higher priority than other loads, it is restored first.
Typically, the voltage of distribution system ranges from 120V
to 35kV. The voltage of substation is 24.9kV, and the voltageof remaining network is 4.16kV in the testbed.

Keywords—Restoration, Q-Learning, MAS, Renewables

I.

PROPOSED RESTORATION ALGORITHM

Q-learning is one of the popular methods in reinforcement
learning. However, the simplest form of Q-learning has limited
applications to power system since every state needs to be
defined prior to the implementation. Therefore, Q-learning
without states is necessary to decrease the computationalburden and increase the efficiency of the algorithm. Modified
Q-learning from [1] is applied to power systems restoration.
From the experiment, it was found that Q-learning does notalways provide the solutions free from violating theconstraints. Therefore, it is necessary to have supplementaryalgorithms to provide solutions in case of failure of Q-learning.
This algorithm utilizes a combination approach to find the bestsolution to restore the given distribution system. However, it
only considers a reduced number of switch configuration
combinations to decrease the computational load of therestoration algorithm.
The goal of the battery algorithm is to use the most surplus
power from the given distribution system.

Fig.1. Overview of IEEE 9-Bus System
III.
[1]

REFERENCES

D. Ye, M. Zhang and D. Sutanto, "A Hybrid Multiagent
Framework With Q-Learning for Power Grid Systems
Restoration," in IEEE Transactions on Power Systems,
vol. 26, no. 4, pp. 2434-2441, Nov. 2011

Modelling and evaluation of flexible multi-energy
systems for low carbon environment
Ninoslav Holjevac, Student member, IEEE, Tomislav Capuder, Member, IEEE, Igor Kuzle, Senior Member, IEEE
Faculty of Electrical Engineering and Computing
University of Zagreb
Zagreb, Croatia
ninoslav.holjevac@fer.hr, tomislav.capuder@fer.hr, igor.kuzle@fer.hr
Abstract—With the increasing penetration of variable
renewable energy sources (RES) and distributed generation it is
becoming increasingly harder to maintain the optimal operationof the power system distribution networks. The paper presents acorrective receding horizon mixed integer linear programming(MILP) model of multi-energy microgrid (MEM) in which theoperational behavior of local, aggregated generating units,
renewable energy sources and customers is investigated throughdifferent modelling assumptions and multiple MEM system
configurations/layouts. This paper elaborates how theseassumptions affect MEM behavior in market driven environmentand demonstrates MEM capability to respond to intra-day
disturbances caused by variability and uncertainty inherent to
the production of the renewable energy sources (RES) whencontrolled by means of the proposed optimization model.

II. CORE EQUATION SET
At its core the corrective scheduling algorithm is guided
by the following equation set:241 S
t 1

24



Daily
cycle
end

N+1 cycle
startPlanning horizon

Missmatch

MIS 

(2)

241 S () short imp   sell  c mcp  long imp  M buy  c mcp   shorttexpt buy  ctmcpt  longtexp t  sell  ctmcpt
t 1

 F ng  c ng  F CCHP  c f  F D  c d  E imp  cimp  E exp  c exp

t
t
t
t
t
t
 t

  P  E w _ cur  P  H waste H binCCHP  cCCHP  H suCCHP  cCCHP 
UPD 
t
t
t
const
t
start


t  241 S DD E binD  cconst
 EtsuD  cstart emisst  cemiss
 t


(3)

24



(4)

III. KEY RESULTS
In addition to fact that MEM governed by the proposed
control strategy is achieving better cost and emission indicators
compared to traditional control strategies the proposed
optimization framework can be used to extract valuable
insights and information of the available flexibility resources
since this will present a key future of the modern distribution
system networks.
600

500

400

Predicted
reference

300

Realized costs

Unrealized
time steps

Updated plan

Missmatch

Predicted
reference


t
t
t
t
t
t
t

 ( P  E w _ cur  P  H waste )  ( H binCCHP  cCCHP  H suCCHP  cCCHP ) 
t
t
t
const
t
start


D
D
 ( EtbinD  cconst EtsuD  cstart)  emisst  c emiss )241 S 
t 1

Output power [kW]Planning horizon

Predicted daily costs – reference plan

Costs [EUR]

Control actions applied for the daily cycle only, lookahead prediction always for 24 hours horizon(1)

( F ng  c ng  F CCHP  c f  F D  c d )  ( E imp 0  c mcp  E exp0  c mcp )PRE 

I. INTRODUCTION AND CONTRIBUTIONS

Nth daily
cycle start "updated" predicted costs UPD)

t  241 S

Keywords—multi-energy systems; power system flexibility;
reeceding horizon corrective scheduling

Aggregating groups of consumers of different energy
vectors and generating units at the same location withcentralized control is known as the concept of multi-energy
microgrid. However, if those potentially flexible producers andconsumers do not have the ability to balance the variability anduncertainty of renewable energy sources production within
them, from the system perspective, they are seen as a source ofimbalances The work will present a MILP model expandedwith corrective receding horizon scheduling approach in orderto capture the value of integrating multiple energy vectors,
their optimal operation and their flexibility potential.

 predicted(PRE)  missmatchMISCOST 200

Realized costs

100

0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
5.5
6
6.5
7
7.5
8
8.5
9
9.5
10
10.5
11
11.5
12
12.5
13
13.5
14
14.5
15
15.5
16
16.5
17
17.5
18
18.5
19
19.5
20
20.5
21
21.5
22
22.5
23
23.5
24

0

t0

Fig. 1.

S time stepst t0 S

24 τ - S time stepst0 24

Time [hour]

Illustration of the developed receding horizon corrective
scheduling algorithm

CCHP unit realized operational point with deviations bars

Initial operation planhour [h]Fig. 2.
The operational point of the CCHP unit through out the wholedaily cycle (with deviation range bars) compared to the initial operation plan

Adoption of IoT framework for DER aggregationand control
Tylor Slay, Crystal Eppinger, Robert B. Bass
Dept. of Electrical and Computer Engineering
Portland State University
Portland, Oregon, USA
tslay@pdx.edu

I. I NTRODUCTION
The PSU team has worked with utility partner Portland General Electric over the past five years to develop an ad-hoc gridmanagement system for DER Aggregation. The Distributed
Energy Resources (DER) Aggregation System (DERAS) is abi-level control system that utilizes an IoT framework designed
to aggregate resources, optimize scheduling and provide utility
ancillary services. Figure 1 The multi-agent platform hosts
two tiers, the energy management system (EMS) and the
distributed management system (DMS). This provides the
security of a distributed network and the coordination ofa central control paradigm. The EMS aggregates DER and
can optimize dispatch to provide ancillary services, prioritize
renewable energy dispatch and ensure grid stability. The EMS
will use cloud-based asset models and characteristic asset
groupings to provide an alternative to previously-proposed,
low-latency, high-transfer systems.

forming a distributed network that can participate in gridrecovery and form power hubs following a potential largescale grid disturbance. The DMS can communicates withneighboring devices via a wi-fi mesh network to schedule
regional participation, effectively minimizing the data transfer
between each tier.
II. K EY C OMPONENTS
This research uses PJM’s ”Reg D” regulating resource
signal to confirm aggregated DER can participate in ancillary
services. The performance criteria is expressed in Equations
1-2 [1] and must result in 75% or greater to enroll in
regulation services. Once accepted the resource must maintaina performance level greater than 40%, else it will have to reapply to participate in the regulatory market. This effectively
sets the the bounds for DERAS testing.
P erf ormance(t) = maxi=0−5min[A ∗ Delay(t + i)
+ B ∗ (Correlation(t + i)]+ C ∗ P recision(t)(1)

where,
CorrelationScore = rsignal (δ, δ + 5minutes )δ − 5minutes|
DelayScore = |5minutes
response − signal|
Error = |signalhourly
1X
P recisionScore = 1 −|Error|n
A. Results(2)

At present, the DERAS system is in testing. Results will bepresented at the conference. Current tests include: simulated
DER model validation, communication resource requirements,
and PJM performance testing and validation.
R EFERENCES[1] PJM. Pjm manual 12: Balancing operations. Technical report, November
2017.
Fig. 1. DERAS Architecture

The DMS facilitates peer-to-peer interaction between assets,

A Distributed Energy Resource Aggregated System
for Providing Ancillary Services
Manasseh Obi, Kevin Marnell, Robert B. Bass
Dept. of Electrical and Computer Engineering
Portland State University
Portland, Oregon, USA
marnell@pdx.edu

Abstract—Stochastic renewable generation, like solar andwind, presents a new challenge to the electrical grid. These
generation assets can change rapidly due to clouds or a lull inwind. To maintain grid stability, fossil fuel generation assets like
combustion turbines and combined cycle gas turbines are used
to balance the differences between generation and load. Fossil
generation corrects these problems, but frequently must operate
inefficiently to do so. Distributed energy resources (DER) offer
a solution to traditional generation, especially when aggregatedas part of a distributed energy resource aggregation system(DERAS). A DERAS utilizes a vast number of DER assets
including solar generation, wind generation, battery storage,
and customer loads to compensate for stochastic renewablegeneration. DERAS provide ancillary services to compensate forgrid events. The presenters introduce a resource estimator locatedat the aggregator which combines emulated asset data from DER
agents using an Internet of Things architecture. This resource
estimator can predict the availability of fast acting DER assets
and use these to correct problems in the power grid by offering
ancillary services.

I. I NTRODUCTION
DERs include a wide range of small devices that may
be aggregated for use by the utility. DER includes small
generation assets like solar generators and diesel reserve
generators, which can be used to produce additional power.
DER includes demand response assets like water heaters,commercial refrigerators, and heat pumps, which can be used
to absorb excess power. DER also includes small storage assets
like batteries and electric vehicle chargers which can be used
to both discharge power to the grid or absorb power fromthe grid. The availability of these resources depends on boththe physical state of the device (a full battery cannot charge)
and its decision to be available for grid applications (owner
preferences).
By combining DER devices, an aggregator can provide
grid ancillary services that have previously been provided byfossil fuel generation. These services include load following
by adding and subtracting load to match generation, frequency
regulation by absorbing or discharging power in short bursts,
and reserve service by having additional generation available.
Aggregating many DER assets allows for the availability ofthese ancillary services.

II. K EY C OMPONENTS
For aggregating these DER, the presenters propose the
DERAS architecture outlined in Figure 1. Using the AllJoyn
IoT Framework DERAS can discover and aggregate tensof thousands of DER assets. DER assets that choose to be
managed by an aggregator will be connected into the DERAS
as agents. The physical DER assets will report their states,
both phyical states and availability states, to DERAS. The
agent hub continually reports and updates these states to theemulated asset models, also known as “digital twins.”
The digital twins are used to assess megawatt and megawatthour capacities, so the resource estimator can determine whichancillary services can be offered. The resource estimator
determines a percentage of certainty with which ancillary
services will be available. For instance, the resource estimator
may predict a 75% chance of having 400 kW available forreserve service and a 99% chance of 300 kW available for reserve service. The resource estimator will employ optimization
techniques to determine the ideal allocation of DER assets toprovide ancillary services. The aggregator or utility can thenuse this information to bid into power markets.

AllJOYN IOT
framework

firewallUTILITYor
3rd PARTY USERRESOURCE
ESTIMATOR

Turn on

EMULATEDASSETS

Digital Twins{agent models}Turn off
Updater

Agent HUB (I/O)

Agent 1

Agent 2Agent 3Agent...

X 104

Agent n

X 105

Fig. 1. DERAS system architecture.

1

Online Identification of Power System
Electromechanical Modes

P. Arunagirinathan, Student Member, IEEE, 1,2G. K. Venayagamoorthy, Senior Member, IEEE
1
Real-Time Power and Intelligent System Laboratory, Clemson University, SC, USA
2
School of Engineering, University of KwaZulu–Natal, Durban, South Africaparani@ieee.org and gkumar@ieee.org

Abstract—Poorly damped electromechanical oscillations
exhibited by synchronous generators have caused wide spread
blackouts in the past. The oscillation frequencies including theinter-area and intra area modes in the range from 0.3Hz to 2.0Hz
are studied carefully. The increasing penetration levels of
renewable energy generation sources produce more oscillations
due to its uncertainty. Therefore online identification of powersystem electromechanical modes is required in mitigating theseoscillations. Data driven techniques are commonly used for
power system stability analysis and stochastic subspace
identification (SSI) is used in this study. Synchrophasor devices
are installed in the electric power grid and streaming data to thecontrol centers. SSI based online identification of power systemelectromechanical modes using synchrophasor data is studied.
An IEEE 68 bus New England-New York benchmark powersystem has been simulated on a real-time digital simulator(RTDS) for this study.
Index Terms—Electromechanical modes, power system,
RTDS, stochastic subspace identification, synchrophasors

I. INTRODUCTION
The power system operation is challenged by low frequency
electromechanical mode oscillations exhibited by synchronous
generators for system disturbances. Poorly damped inter area
oscillations have caused wide spread blackouts of the systemin the past. Therefore identification of critical low frequency
electromechanical oscillation modes and mitigation of those isimportant for secure power system operation. The low
frequency oscillation modes in the range from 0.3 Hz to 2.0
Hz are considered for system stability analysis. In order todesign an effective adaptive controller it is required to have an
online model of the power system. Despite the power system
is highly complex and non-linear, the representation of powersystem through linear state space has been widely used forstability analysis and control. This is because, first, although
individual generators can only be best modeled by nonlinear
differential equations, modal analysis based on thelinearization of interconnected power system near a nominal
operating point can effectively uncover the characteristics ofoscillation modes; second, although power systems are subject
to changes of operating conditions, these variations arecomparatively small comparing to the major systematic
topology and generator parameters that remain unchanged;
thus, the modal analysis results can be well applied to a range
of operating points.
This work is supported in part by US National Science Foundation (NSF)
under grants #1312260, # 1408141 and #1232070 and the Duke Energy
Distinguished Professor Endowment Fund. Any opinions, findings andconclusions or recommendations expressed in this material are those of theauthor(s) and do not necessarily reflect the views of National Science
Foundation and Duke Energy.

State space representation of power system has been used invarious studies. Considering the stochastic nature of the powersystem, a stochastic subspace identification (SSI) [1] method
is used for online power system electromechanical oscillation
mode identification. SSI is a data driven system identification
technique and it has already been used in power systemapplications [2]. SSI can be applied to an input/output basedmodel or an output based model of the power system.
Synchrophasors deployed in the power system are
streaming remote measurements at a very fast rate to utility
control centers [3]. Power system model validation tools withsynchrophasor measurements are already available.
Synchrophasor measurement based adaptive controller designs
are proposed to damp the critical low frequency
electromechanicaloscillations
modes.
Therefore,
synchrophasor data driven SSI method for power systemelectromechanical modes has been studied. IEEE 68 bus New
England-New York benchmark power system shown in Fig.1
has been simulated on a real-time digital simulator and thesynchrophasor devices are configure to receive measurementsfrom RTDS using GTAO cards.

Fig. 1 IEEE 68 bus 16 generator five area power system

REFERENCES[1] N. J. Bershad, J. C. M. Bermudez and J. Y. Tourneret, "Stochastic analysisof the LMS algorithm for system identification with subspace," IEEE
Transactions on Signal Processing, vol. 56, no. 3, pp. 1018-1027, 2008.
[2] K. Tang and G. K. Venayagamoorthy, "Damping inter-area oscillations
using virtual generator based power system stabilizer," Electric Power
Systems Research, vol. 129, pp. 126-141, 2015.
[3] I. Kamwa and R. Grondin, "PMU configuration for system dynamic
performance measurement in large multiarea power systems," IEEE
Transactions on Power Systems, vol. 17, no. 2, pp. 385-394, May 2002.

A Power Waveform Classification Method for
Adaptive Synchrophasor Estimation
Cheng Qian, Graduate Student Member, IEEE, Mladen Kezunovic, Life Fellow, IEEE
Dept. of Electrical and Computer Engineering
Texas A&M University, College Station, TX
peterqiancheng@tamu.edu, kezunov@ece.tamu.edu
Abstract—An input waveform classification scheme is proposed, which is used to facilitate synchrophasor estimation. The
classification is achieved by scrutinizing the temporal trajectories
of input waveform frequency and amplitude features, which are
extracted by a uniquely designed multiresolution time-frequency
analysis technique using “pseudo-wavelet”. Such time-frequency
waveform features are further exploited to classify input waveform types. Contrary to traditional approaches for synchrophasor estimation, where a single algorithm is designed for all
types of input signal, a new framework is discussed where an
adaptive switching of algorithms is enabled, so that the mostsuitable algorithm can be used for the specific type of waveform.
The efficacy and efficiency of proposed methods are validatedwith standardized PMU testing waveforms, and simulated powersystem waveforms. Detailed work has been published in IEEE
Transactions on Instrumentation and Measurement.

II. KEY FIGURES

Fig. 1.

Framework for synchrophasor estimation utilizing proposed
waveform classification procedure.

Index Terms—Correlation, multiresolution analysis, powersystem measurements, spectral analysis, synchronized phasor
measurement

I. METHODOLOGY AND KEY EQUATIONS
The proposed method essentially utilizes a single cosine
wave as the mother wavelet in classic continuous wavelet
transform (CWT) definition, shown in (1).
∞𝛾(𝑥; 𝑎, 𝑏) = ∫ 𝑥̂(𝑡)𝜗(
−∞

𝑡−𝑏
)d𝑡
𝑎

(1)

where 𝛾(𝑥; 𝑎, 𝑏) is the correlation coefficient between 𝑥̂(𝑡) and𝜗(𝑡) for selected pair of 𝑎 and 𝑏 , 𝑥̂(𝑡) ≔ [𝑥(𝑡) −𝑥̅ ]/max⁡[𝑥(𝑡)]is the centered and normalized input signal, 𝜗(t)is a proposed pseudo-wavelet, which are unit cosine waves.
The resultant transform is able to reveal the time-frequency
composition of waveform on a multiresolution level. 𝛾(𝑥; 𝑎, 𝑏)
shows zero intensities when the instant waveform frequency
𝜔1 satisfies:
𝜔1 = 𝑘𝜔pw , 𝑘 = 0,2,3,4, …
2𝜋(2)
𝜔pw ≥ 𝜔pw,min ≡𝑇window
where 𝑇window is the length of data observation window inseconds.
This property can be leveraged to track the time-frequency
trajectory of input waveform, which can be further used to
identify and classify input waveform types.
This work was supported by the Power Systems Engineering Research Center(PSERC) Project T-57HI “Life-cycle management of mission-critical systems
through certification, commissioning, in-service maintenance, remote testing,and risk assessment”

Fig. 2.
Overall structure of proposed waveform classification method
employing the proposed PW time-frequency multiresolution analysis.

III. KEY RESULTS
The proposed methods are implemented in NI CompactRIO
and SEL-3355, where the performances are tested. The efficacy and efficiency of proposed methods are verified.
TABLE I.

SUMMARY OF COMPUTATION TIME

Platform
Procedure
Sampling Rate
Data Length
Time-Frequency Analysis
Freq. Trajectory Extraction
Total

MATLAB(SEL-3355)
1kHz
10 cycles
11ms
11ms
22ms

LabVIEW(NI cRIO-9082)
1kHz
10 cycles
4ms
11ms
16ms

Non-Invasive System Impedance Estimation Basedon Data Selection
Ori ZhangFrank Wang

Department of Electrical and Computer Engineering
University of Alberta
Edmonton, Canada
Shijia2@ualberta.ca

Department of Electrical and Computer Engineering
University of Alberta
Edmonton, Canada
Yang5@ualberta.ca

Abstract—Distribution network fault levels or equivalentcircuit impedances are important data for both utility companies
and their customers. The data has many uses, such as specifying
equipment rating, establishing protective relay setting, anddetermining the power quality impact of motor starting. In thisposter, a novel and practical method has been developed for nonintrusive measurement of network impedances using the
waveform data collected by power quality monitors. The method
has been verified using simulation studies and field cases. The
results show that the proposed method can serve as a useful tool
to support the impedance measurement task of utility companies.

In contrast, noninvasive methods use natural voltage andcurrent variations to obtain the impedance information without
any extra devices and negative impact.

Keywords— Power quality, fault level, impedance measurement

This poster shows an appropriate method for non-intrusive
measurement of equivalent circuit impedance using the
waveform data collected by power quality monitors. It beentested using various field data and its performance has beenfound acceptable for industry applications. The basic frame
work is shown as follows:
II. KEY EQUATIONS

I. INTRODUCTION
Distribution network fault levels are important data for both
utility companies and their customers. The data has many uses,
such as specifying equipment rating, establishing protective
relay setting, and determining the power quality impact ofmotor starting. The fault level data are typically obtained using
model based short-circuit calculations. The accuracy of theresults is highly dependent on the quality of input data and thestatus of equipment used by the model. Over the past many
years, utility companies have been looking for methods thatcan measure the actual fault level at a given point of a powersystem. In recent years, the need to measure network fault
levels has become more evident due to the concerns on power
quality and the proliferation of distributed generators.
Determining the fault level of a network is equivalent todetermining the Thevenin circuit impedance of the network or
the supply system impedance. After many years’ research
efforts, two broad approaches to estimating the equivalentcircuit impedance of a network have been proposed: invasive
and noninvasive methods. The first methods are usuallyoperated by injecting disturbances into the network in order tocause variations in the voltage and current and then determine
the impedance. Although these methods could provide reliable
results, the disturbances may cause negative impacts on thegrid operation. Furthermore, the devices used to generate thedisturbances can be quite expensive.

III. KEY FIGURESAn advanced maximum power point capturing
technique with supercapacitor for PV system

8
6
4
2
0

0

10

− 1]],

I = Isc [1 − C1 [e = |(V (k) − Vm )/(Vm )|.

∆V /V ≤ ((PP V 2 − PP V 1 )∆t)/(2CV 2 ).

(3)

0

10

20
(a)

Load 1
Load 330

Power (W)

Current (A)II. K EY FIGURES

200
150
100
50
0

0

0

10

20
(b)

Fig. 2: I-V and P-V curves under changing solar irradiance
1,000
800
600
0

2

4

6

8

10

12

14

0

2

4

6

8

10

12

14

Voltage (V) Power (W)

200
150
100
50

30
20
10

MPPC

P&O

0

2

4

6

8

10

12

14

Time (s)Fig. 3: Solar irradiance S has a rapid change.

In Fig. 4, the power provided by SuperC shown in green
line is a transient power. Specifically, this power has an ability
to stabilize output voltage of PV array.

(2)

When  ≤ th (th is error threshold), duty cycle stops varying.
When  > th , MPPC is triggered and Vm will be updated.
(3) The flicker is better prevented when SuperC is connectedin parallel with the PV system in Fig. 4. When RC/∆t >> 1,

8
6
4
2
0

30

Load Line(1)

where C1 and C2 are the relevant coefficients.
Through solving (1), MPP can be captured online in Fig. 3.
Therefore, its control strategy is designed a steady area :

S=1000
Load 2

20
(a)

200
150
100
50
0

Voltage (V)

10

20
(b)

30

Voltage (V)Fig. 1: I-V and P-V curves under a constant solar irradiance2Power (W) Voltage (V) S (W/m )( C VVoc )2

600

Load Line

I. K EY C ONTRIBUTIONS(1) Wrong perturbation under dynamic state is eliminated.
The proposed MPPC technique is designed on both steady anddynamic characteristics of PV system, respectively shown in
Fig. 1 and Fig. 2, instead of the conventional MPPT techniqueonly considering the steady characteristics of PV system. The
combination of Fig. 1(a) and Fig. 2(a) is defined as thecomplete I-V curves and the combination of Fig. 1(b) and
Fig. 2(b) is defined as the complete P-V curves.
(2) The trade-off for duty ratio step size does not exist.
MPPC technique is based on above complete I-V curves anda practical PV model with following equations:

800
200

Power (W)

1000
400

S (W/m2 )Abstract—In this paper, the characteristic curves considering
complete Current-Voltage (I-V) and Power-Voltage (P-V) relations for PV system are presented. Based on the I-V characteristic curve, a novel Maximum Power Point Capturing (MPPC)
technique is proposed to overcome the steady-state oscillationand slow dynamic response problems existing in the conventional
Maximum Power Point Tracking (MPPT) technique. In order tofurther mitigate the fluctuations for PV system, an appropriate
supercapacitor (SuperC) is selected to be connected in parallelwith the PV system. The simulation results demonstrate thedesigned MPPC-superC structure has a better performance thanthe conventional MPPT method and MPPC method in both
steady-state and dynamic responses.

Current (A)Lijun Zhang, Student Member, IEEE, Samson Shenglong Yu, Member, IEEE,
Herbert H.C. Iu, Senior Member, IEEE, Tyrone Fernando, Senior Member, IEEE,
Department of Electrical, Electronic and Computer Engineering. University of Western Australia

1,000
800
600
30
20
10
0

200
100
0
−100
−200

0

2

4

6

0

2

4

6

2

4

10

12

8

10

12

advanced MPPC

MPPC

0

8

6

8

10

14

14
SuperC

12

14

Time (s)Fig. 4: Comparison with a step change solar irradiance S.

30

Comparative Analysis of Demand Response and LoadManagement Algorithms
Divya T. Vedullapalli, Student Member, IEEE
Ramtin Hadidi, Member, IEEE
The Holcombe Dept. of ECE
Clemson University, SC, USA
dvedull@clemson.edu, rhadidi@clemson.edu

Abstract –The objective of this project is to quantify the efficacyof the Model Predictive Control (MPC) demand charge
management algorithm developed by Johnson Controls Inc. (JCI)against the existing demand side management strategies. Clemson
and JCI are working together to develop industry benchmarks
and define metrics that effectively evaluate the performance of
these algorithms in real world limitations. These algorithms areto be tested on a 50kW, 165kWh energy storage system installedbehind the meter at a Clemson University academic building. The
next phase of this work involves augmenting the mostadvantageous algorithms for the energy storage system with thebuilding management system to realize greater opportunities tothe end users.
Index Terms— Battery energy storage system (BESS), Demandcharge management, Load forecasting, Model Predictive Control(MPC), Optimization

I.

Bill Schroeder, Ryan Baumgartner
Johnson Controls, Inc.
507 E. Michigan St., P.O. Box 423
Milwaukee, WI, USA
s. to :t 1

SOC (t )  SOC (0) 

C  P1 ( )t 
 0

1 t 1

 P ( )tD  0 2

BCap

0  P1 (t )  PL (t )  P2 (t )  PShave
SOC min  SOC (t )  SOC max
0  Pi (t )  Pi ,max ; i  1, 2

III.

MPC FRAMEWORKINTRODUCTION

To avoid installation of capacity with increasing demand ofelectricity, utilities employ Time-of-Use (TOU) pricing
schemes for energy and/or include demand charges during
peak hours. A battery can be utilized to shift the load partiallyduring peak to off-peak time and reduce the electricity cost.
The schematic of the system is shown in Fig. 1, in whicharrows represent directions of power flows in the system.

Fig. 2 MPC Strategy(Courtesy of Wikipedia)

IV.

SAMPLE RESULTS

Grid
P3

P1

Battery

P2

LoadFig. 1: Schematic of load
management with battery

The goal of the demand side management algorithm is todetermine the optimal schedule of battery for minimizing
electricity cost while satisfying customers’ load and battery
operating conditions.
II. OPTIMIZATION FORMULATION
Minimize J    P1 (t )  PL (t )  P2 (t )  * t *  E (t ) 
P1 , P2 , PShavet 1

 max( P1 (t )  PL (t )  P2 (t )) *  D (t )Monthly Bill ($)

N

12000
10000
8000
6000
4000

Without BESS

2000

With BESS0

4 5 6 7 8 9 10 11 12 1 2 3
Month
Fig. 4 Grid demand and Electricity bill

Smart Grid-enabled CVR: An advanced application
for Distribution Management System
Shailendra Singh1,2, S. P. Singh1, Senior IEEE Member
1Power System Engineering Centre2
National Renewable Energy Laboratory
Golden, Colorado, USA

Department of Electrical Engineering
Indian Institute of Technology (BHU)
Varanasi, U. P., India

Abstract—In this paper, a multi-level multi-tasking smart
grid enabled conservation of voltage reduction (CVR) control
and optimization methodology has been proposed for advance
distribution management system (ADMS) application. First level
deals with centralized optimization of Volt/VAr optimization(VVO) devices and second one is voltage control in local domain
or decentralized control. The assessment of proposed method interms of energy saved and CVR factor has been analyzed in thirdlevel. The problem has been solved through particle swarm
optimization (PSO) and droop control method. The simulation
results reveal that enabling of CVR during presence of highphotovoltaic (PV) penetration is not only produces significant
power saving also mitigates the over/under voltage problem.

I.
SMART GRID ENABLED CVR AND OPTIMIZATION
Figure 1 shows the proposed framework that will fit into theexisting distribution grid. The ADMS is fed back throughadvance metering infrastructure and supervisory control anddata acquisition (SCADA) points which update the field
monitored measurements at regular intervals. The fundamental
task of VVO processor is to optimize the settings of VVO
devices according to CVR server. The range of CVR duration
and CVR voltage are set by control centre operator in CVR
server block according to requirement.
II.

MATHEMATICAL FORMULATION AND SOLUTION

The formulation of proposed CVR control is formulated inthree layers as delineated under:
A. First layer: Centralized control and optimization
In this layer, CVR is enabled using below objective functions.



n 1

f1  min   VCVR ,h Vk ,h  a ,b ,ca ,b , c

2

k






a ,b ,ca ,b ,c 
f2  min 
Ploss

j
Q
,h
loss ,h 


a
,
b
,
c





a ,b , cSantosh Veda2, Murali Baggu2 Senior IEEE Member

Power Distribution Network

ADMS

Control Set
Points OLTC

VVO
(CVR)

EV charging

DG
AVR

Measurements Capacitor(status)

Banks

Rooftop PVs

Fig.1 Closed loop framework for CVR operation

B. Second Layer: Local voltage control
If the voltage violation occurs in with defined time horizon of
first layer, then this layer operates and provides additional
reactive power compensation through PV inverters. The
compensation value is determined using Volt/VAR droop
control characteristics.
C. Third Layer: Calculation of Savings and CVR factor
W %(3)
CVR factor V %where W % is the percentage of reduction in quantity andV % is the percentage of voltage reduction.
III.

SIMULATIONS AND RESULTS

The proposed method has validated on modified IEEE 123
distribution test feeder in the presence of three PVs during peakdemand hour. Fig. 2 and Fig. 3 shows some key results using
OpenDSS and MATLAB environment.

(1)

Fig.2 % peak active power demanad reduction w.r. to different CVR voltages(2)

a ,b , cwhere, Vk,h , VCVR,h , Ploss , h and Qloss , h is the node voltage(p,u.) at kth node, expected CVR voltage (p,u.) , active power
loss and reactive power loss with a,b,c as phase notation
respectively and n is number of nodes.
The above problem has been solved through multi objective
PSO with system constraints.
Supported by Indo-US Science & Technology Forum, India under the
Bhaskara Advance Solar Energy Fellowship-2018

Fig.3 Lowest voltage profile during different % PV active power reduction

D2NDZ: Evaluating Unintentional Islanding Risks
in Distribution Grids with Deep Integration ofDistributed Energy Resources
Yan Li1 , Peng Zhang1 , Joseph N. Debs2 , David A. Ferrante2 ,Donald J. Kane2 , Samuel N. Woolard2 , Roderick S. Kalbfleisch2 , and Kenneth B. Bowes2
1 Dept. of Electrical and Computer Engineering, University of Connecticut, Storrs, CT 06269-4157, USA
2 Eversource Energy, Berlin, CT 06037, USA

Abstract—Efficiently calculating non-detection zones (NDZ)
becomes increasingly important when evaluating unintentionalislanding risks of distribution grids that are highly integrated
with distributed energy resources (DER). A rigorous theoreticalmethod, the learning-based DER-Driven Non-Detection Zone
(D2 NDZ), is presented to estimate NDZ for any given distribution
feeders. Numerical examples indicate that by using D2 NDZ, NDZ
can be quickly and effectively obtained while avoiding numerous
and time-consuming electromagnetic transient simulations.

II. T EST C ASES
A typical distribution feeder in Eversource Energy’s service
territory is used to validate D2 NDZ. Corresponding results areshown in Fig. 1 and Fig. 2.

I. M ETHODOLOGY OF D2 NDZThe basic idea of D2 NDZ is to efficiently figure out an
operation boundary by combining a theoretical analysis of
the steady state of a generic system with the learning-based
dynamic impact of DERs on a system. Specifically, it includes
that (1) a primary NDZ considering different types of loads isanalytically determined based on the steady state of a systemafter islanding and (2) the dynamic impact of DERs on theboundary of a primary NDZ is then analyzed and integrated
into the primary NDZ to obtain the desired result.
• Active power steady-state analysis(a) 1s NDZ Comparison in case 1(d) 1s NDZ Comparison in case 2(b) 2s NDZ Comparison in case 1(e) 2s NDZ Comparison in case 2(c) 3s NDZ Comparison in case 1(f) 3s NDZ Comparison in case 2S(1 + µ)2 (PI PC + PI PP + PP PC )
PDER
=
(1)
PL
(1 + µ)2 PI PC + (1 + µ)PI PP + PP PC• Reactive power steady-state analysis
S

φP V,L = βP V,L (1 − αP V,L e−NP V ),φP V,H = βP V,H (1 − αP V,H e−NP V ),(2)

Fig. 1 Comparisons between D2 NDZ and simulation-based method
Upper Boundary of NDZ
Lower Boundary of NDZ

(3)
(4)

This material is based upon work supported by the National Science
Foundation under Grant No. 1611095, Eversource Energy Center under Grant
No. 6367940, and UConn Academic Plan Level 1 under Award No. 2806600.

nt
ta
ns %)
Co d (
of oa
ge L
ta ce
en an
rc ed
Pe mp
I

As an estimation method, the performance of D2 NDZ
mainly depends on the parameters in each formula. A learningbased method is established to optimize these parameters fromthe experiments’ data.
NC
E
2
P
X
PDER
DER
min f =
(βP V,L , αP V,L )−mi
PL min,iPL min,ii=1(5)Generation/Load (%)

QDER= (1 + µ)2 (1 + ρ)
QL
• Exponential impact of DER dynamics on NDZPercentage of Constant Power Load (%)

Fig. 2 Impacts of loads on baseline NDZReal-time Operation Pattern Detection using
Measurements from a Network of PMUs
Ming Liang, Student Member, IEEE, Yao Meng, Student Member, IEEE, Ning Lu, Senior Member, IEEE

Abstract—This paper presents data-driven approach for realtime, operational pattern detection using measurements from a
network of PMUs. Real-time 3-phase PMU data from a networkof PMUs are used to derive multi-dimensional signatures thatbenchmark the operational signatures of frequency, voltage, andpower flow at different location of the transmission grid. Thosesignatures dynamically baseline the operation patterns of a given
system and can be used for detecting anomalies that are caused byequipment malfunctions, intentional and unintentional man-madeerrors, or cyber-attacks. Because the existing of inherent
operational patterns between system nodes, we demonstrated thatusing a combination of 1-dimensional threshold-based identifiersand 2-dimensional correlation-based operational regions, one canquickly establish a database that can be continuously updated andtrained using new data set. The method provides system operatorsmodel- and topology-free approach to monitor and detect anomaly
in grid operation.
Keywords-data driven approach, PMU, operation patterns,
anomaly detection, fluctuation, longest common subsequence.

on new datasets.
In this paper, we present our preliminary results on real-time
operation pattern detection using measurements from a networkof PMUs located in a regional power grid.
II. KEY EQUATIONS
Remove the DC offset of the original signal𝑦(𝑘) = ∑30
𝑖=1(𝑥𝑖 − 𝑥̅ )Conduct a linear regression for all the 𝑦(𝑘) data points during
the modelling period. The regression value at each time k iscalled as the trend and marked as 𝑦𝑛(𝑘). Calculate the error
between the signal y(k) and the trend 𝑦𝑛(𝑘).
𝑒(𝑘) = 𝑦(𝑘) − 𝑦𝑛(𝑘)(2)Finally, calculate fluctuation value, this process of detrending
followed by fluctuation measurement is repeated over a rangeof different sizes n.

I. INTRODUCTION
In the past, researchers rely mainly on synthetic PMU datafor deriving operational patterns and signatures because actual
data from a network of PMUs are not available to them.
Because of the lack of access to actual PMU data, especiallydata from a network of PMUs in actual power grid, IEEE test
systems are often used for generating the synthetic PMU data
sets for signature detection and validation. For example, in [1],events via simulation are carried out on an IEEE 39-bus system.
In [2], the authors present a data-driven real-time cyberphysical detection and defense strategy with distributed control
using real-time data from PMUs. Their strategy is tested on amodified IEEE 118-bus test system.
Because synthetic data lacks of actual system dynamics aswell as the true correlation between generation and load (i.e. thepower flow changes), it is sometime impossible for researchers
to derive and validate methods for real-time, operational patterndetection using measurements from a network of PMUs. The
true value of PMU data collected from a network of PMUs liesin the fact that it provides the grid operator close to real-time
snapshots of values from inherently correlated systemvariables, i.e. the system frequency, power flow (real andreactive), system voltage (angle and magnitude). Therefore,
investigating the data to reveal the characteristics of eachvariable or among variables can reveal the inherent system
operational patterns that are native to the system only. This
makes it possible for grid operators to develop a knowledge
database that can be dynamically updated and fine-tuned based(1)

1

𝐹(𝑛) = √ ∑𝑛𝑘=1[𝑒(𝑘)]2𝑛(3)III. KEY FIGURES

Fig. 1 The flow chart to build up operation patterns and events detection

REFERENCES
[1]

[2]

Yufei Tang, Jun Yang. Dynamic Event Monitoring Using Unsupervised
Feature Learning Towards Smart Grid Big Data. 2017 International Joint
Conference on Neural Networks (IJCNN), p 1480-1487.
Jin Wei. A Data-Driven Cyber-Physical Detection and Defense Strategy
Against Data Integrity Attacks in Smart Grid Systems. 2015 IEEE Global
Conference on Signal and Information Processing (GlobalSIP), p 667-671.

Nonlinear Dynamics Based On-line Detection ofOscillation Using Power System Measurement Data
Hwanhee Cho and Byongjun Lee∗

Suchul Nam

School of Electric Engineering
Korea University
Seoul, Repubilc of Korea
leeb@korea.ac.kr

Power Transmission Laboratory
Korea Electric Power Research Institute(KEPRI)
Daejeon, Repubilc of Korea
suchul.nam@kepco.co.kr

Abstract—This study proposes a time-series analysis approachand a nonlinear dynamics originated method to detect subsynchronous oscillation in power systems. Mathematical expressions of the fundamental instantaneous signal and sample discrete
signal of peak values are derived to examine the phenomenon ofinteraction between power system components. The results of thecirculating trajectory are shown in a two-dimensional map of thecalculated rms value and estimated Floquet multiplier when two
signals of different modes are mixed. Without applying a digital
filter or frequency decomposition, nonlinear oscillation detection
is possible by monitoring a nonlinear oscillatory index based on
the maximum Lyapunov exponent.
Index Terms—Phasor measurement units, Power system measurements, Series compensation, Subsynchronous resonance,
Time series analysis.

II. K EY R ESULTS
Figures below shows important results applying the proposed method of comprehensive application of nonlinear timeseries analysis.

I. K EY C ONCEPTS
The proposed method introduces the Floquet multiplier andprovides another perspective to analyze measured values. Rateof change(RoC) of data indirectly gives information of powersystem dynamics by linearizing Poincaré map. And then by
mapping RoC and magnitude in the same plane, power systemstatus can be monitored by using MLE. So that the proposed
method detects the abnormal nonlinear behavior of powersystems.

Fig. 2. Nonlinear oscillation detecting index based on the MLE (result of+0.3450).

Fig. 3. Nonlinear oscillation detection index for a compensation range from10 to 80%.

Fig. 1. Flowchart describing conceptual principle of the proposed algorithm.

This novel index is capable to express the unique phenomenon in power systems such as SSR, SSTI, and SSCI.
And its computation cost is relatively less and higher accuracy
comparing other digital filter based decomposing methods.

Frequency Response Estimation Following Large
Disturbances using Synchrophasors
Lucas Lugnani, Daniel Dotta

Joe H. Chow

Ildemar C. Decker

Joao M. F. Ferreira

RPI
UFSC
ONS
UNICAMPTroy, NY
Florianópolis, Brazil
Rio de Janeiro, Brazil
Campinas, Brazil
Email: lugnani@dsee.fee.unicamp.br Email: jmarco@ons.org.br Email: ildemar.decker@ufsc.br Email: chowj@rpi.edu
dottad@unicamp.br

Event Frequency
60
59.9

Hz

Abstract—One new challenge, with the massive penetrationof renewable generation (wind and solar), is the decline of theElectrical Power System’s (EPS) frequency response. In order todeal with this new scenario, the development of tools to estimate
key valuable parameters for frequency response is imperative.
The Wide Area Measurement Systems (WAMS) signals canbe useful to estimate the EPS frequency response after large
disturbances. The paper main goal is to estimate the frequency
response of the Brazilian Interconnected Power System (BIPS)
after large disturbances using synchrophasors. These events wererecorded at outlet voltage by a synchronized phasor measurement
prototype, the LVS (low voltage synchrophasor), with PMUs
(Phasor Measurement Units) installed in 26 universities throughout Brazil. The results show that high penetration of wind
generation contributes to change the inertial EPS response.
Index Terms—Inertial Response, Frequency Response, Electrical Power Systems, Phasor Measurement Units.

59.8
Filt. Sig. - SEeq
Filt. Sig. - N
Filt. Sig. - NE
Orig. Sig. - SEeq
Orig. Sig. - N
Orig. Sig. - NE

59.7
59.6
59.5

60

65

70

75

80

85

Time (s)I. K EY C ONCEPTS
The Frequency Response of a Generator to a power imbalance (here referred as disturbance) can be described as:
1
1
1(−∆G + (+ D)∆f )(1)
2H
RD 1 + sTG
During the first moments after the disturbance, the Frequency disturbance can be resumed to the Inertial Frequency
Response:∆G(2)∆f˙ = −
2H
In steady state, when Pacc = 0, the frequency deviation will
be given as:
1
∆G
(3)
∆f =
D + R1Ds∆f =Where D + R1D = β is the frequency response. Equations 2and (3) compose the response prior to nominal frequency
reestablishment and will be used to estimate the BIPS frequency response using PMU data.

Fig. 1: Original and Filtered Frequencies - Under and OverFrequency Events.

TABLE I: Estimated Parameters

II. K EY R ESULTS
Two real large disturbances are considered in this work:
Under-Frequency and Over-Frequency (high wind penetration)
Disturbances.
It becomes evident a relevant variation of Heq for the Ne
region between the First and Second Event. However, ananalysis of power generation source for both events shows thatFirst Event had a 2.25/2.5 GW hydro/wind regional generationand the Second Event had a 1.57/5.8 GW hydro/wind regional
generation.

SEeq
N
NE
SEeq
N
NE

First Event
ROCOF (Hz/s)
Heq (s)avg
max
avg-0.0603-0.0759
6.473-0.0989-0.06512.289-0.0404-0.1027
7.137
Second Event
0.08330.09245.716
0.0921
0.09272.016
0.11550.1196
5.691

β (MW/0.1Hz)
avg
978
992
980
1,251
1,236
1,189

1

A Hybrid Approach towards Event Detection in
Multi-machine Power Systems
Abhishek Banerjee, Student Member, IEEE, and Rajesh Kavasseri, Senior Member, IEEE

Index Terms—energy functions, event detection, singular value
decomposition, principal component analysis.

I. I NTRODUCTIONR

EAL time detection of events in a large scale powersystem requires the knowledge of the the entire dynamics
of the system. Analysis of fault in real time is a challenge
in case of a contingency, when the approach is completely
based on statistical methods applied on end data, collected post
events. Such statistical approaches pose a huge computationalburden as well it considers the entire system properties asa “black-box” problem. This data driven method is prone to
failure with the loss of data and does not provide an insight
into the physics of the problem. On the contrary, Structure
Preserving Energy Functions (SPEF’s) provide a way to derive
every component of the energy terms in a multi-machine
power system [1], [2], giving us complete knowledge of thesystem properties. Thus, motivated, our goal is provide a
hybrid approach where there can be a one-to-one mapping
between properties of energy function components and theoccurrence of the type of fault in the system.
II. S LIDING W INDOW P REDICTION FOR A NALYZINGS AMPLED DYNAMIC R ESPONSE
A sliding window based approach is used to determine theintrinsic properties of the SPEF’s subject to different faulted
scenarios. From [1], we know that the different types of faults
will leave different signatures on the wide area measurement
system. The sliding window approach is designed to detect
the sensitivity of the different energy function components tovarious types of faults. After the energy function components
are calculated, the total length of individual component is used
to determine the number of sliding windows. An appropriate
selection of window width needs to be done in order to capture
A. Banerjee and R. Kavasseri are with Department of Electrical & Computer Engineering, North Dakota State University, Fargo, ND, USA (e-mail:abhishek.banerjee@ndus.edu, rajesh.kavasseri@ndus.edu).

Loads
Generator 1
Generator 2

Transmission
lines

Generator 3
Generators
Generator 4

Fig. 1. Schematic representation of SPEF’s

maximum information from the trajectories of the individualenergy function components. The total number of windows(nw ) is calculated aslw − εnw =(1)∆where,
11P
W2i
lw = length of each component of W1 ,i=1

ε = window width, ∆ = slide increment
After the total number of windows are determined, the window
span is calculated usingnwX
j : (j + ε)(2)
j=1

In each window span, each energy component is subjected
to a singular value decomposition, ΦW = U ΣV T , where
maximum singular values (σ k ) are stacked up in a vector foreach window span. The sensitivity of the energy components
are evaluated by analyzing the stacked up maximum singularvalues.
III. F EATURE E XTRACTION USING PCA
5

W21 (pu)

Abstract—This paper presents a hybrid approach to determine
the type of fault occurrence in a large scale power system by
exploiting the underlying information of events in the structure
preserving energy function (SPEF) components. A sliding window
based method is proposed to determine the maximum singularvalues of these components. The resulting stacked up indicesprovide a metric for determining sensitivity of the energy function
components. The proposed approach encapsulates a two stage
process, i.e, firstly, a physical overview with a Sliding Window
Prediction (SWP) is presented and in the second stage a validation
is done by performing Principal component analysis (PCA) on
the SPEF components.

×10 -4a. load change

1

0

0

-5

-1

-10

-2

×10 -3b. Gen4 exc loss

-3

-15
1

2

1

2

P rincipal components

Fig. 2. PCA plots of W21 subject to a) sudden change in load, b) generator
4 excitation failure

It can be seen from Fig. 2, generator 4 excitation affects theW21 component more than sudden load change.
R EFERENCES[1] R. G. Kavasseri, Y. Cui and S. M. Brahma, ”A new approach for event
detection based on energy functions,” 2014 IEEE PES General Meeting,National Harbor, MD, 2014, pp. 1-5.
[2] K. R. Padiyar, Structure Preserving Energy Functions in Power Systems
CRC Press, 2013.

Security versus Computation Time in IV-ACOPF
with SOCP Initialization
Sayed Abdullah Sadat, David Haralson, and Mostafa Sahraei-Ardakani
Department of Electrical and Computer Engineering, University of Utahemail: sayed_abdullah@ieee.org

Abstract—Achieving the optimal solution to the security
constrained AC optimal power flow (SCACOPF) problem inreal-time is a computationally challenging task. Successive linear
programming (SLP) with a near optimal initialization has shownpromising potential for solving the problem. It is, however,essential to improve the computational performance of thealgorithm by only choosing the most critical contingencies,
instead of modeling the extensive list of all the possible
contingencies. This work examines the trade-off between
different levels of reliability, i.e., post-contingency network
violations, and computation time in the SCACOPF problem. It isshown that a similar level of reliability can be obtained by only
modeling the most critical contingencies, while substantially
improving the computation time. The work uses a combination of
second order conic programming (SOCP) relaxation of the
ACOPF and fast decoupled power flow to initialize the SLP
algorithm for solving the SCACOPF. The method is tested on a
range of test systems, using off-the-shelf solvers, i.e., Gurobi and
CPLEX, and the results are presented and compared.
Keywords—AC optimal power flow (ACOPF), computational
complexity, contingency constraints, second order conic
programming (SOCP), security constraints, stress metrics,
successive linear programming (SLP).

I.

INTRODUCTION

Power system operators in the United States are required tocomply with North American Electric Reliability Corporation(NERC)’s standards. NERC N-1 reliability standard requires
uninterrupted delivery of power even after a major disturbance(single outage of a line, transformer, or generator). To
guarantee N-1 reliability, security-constraints can be added to
the OPF problem, used for bulk power system operation [10].
Identifying the globally optimal solution to the security
constrained ACOPF (SCACOPF) problem within the limited
available computational time remains an open problem. This
work investigates a method to reduce the computational timeby employing a successive linear programming (SLP) method,
initialized through a combination of second order conic
programming (SOCP) and decoupled power flow. The SLP
method is developed upon the voltage and current in therectangular coordinate, “IV” formulation, rather than thecommon active and reactive power representation, P and Q.
Additionally, this work seeks to further reduce thecomputational time required for solving the SLP IV-ACOPF
by only selecting the most critical contingencies. System stressmetrics are used to guide the contingency selection procedureby filtering out the contingencies that are inconsequential as
shown in Fig.1. The resulting algorithm is more apt for large

systems, and will pave the way for potential employment of
SCACOPF in real-time operations. The contribution of thiswork is the development of an algorithm to reduce the overall
SCACOPF computation time, through (i) smart initialization of
the SLP method and (ii) appropriate selection of thecontingencies.

Fig. 1 Stress metrics and computation time, presented forthe IEEE 118-bus test case, with different number ofcontingencies modeled within the SCACOPF problem
II. RESULTS AND CONCLUSIONS
Finding the optimal solution to the security-constrained AC
optimal power flow in real-time is an extremely challenging
task, due to its heavy computational burden. Successive linear
programming with a near optimal initialization has shownpromising potential for solving this problem. This work
employs a combination of SOCP and decoupled power flow toinitialize SLP in an attempt to reduce the solution time,required for solving the SLP-SCACOPF. To further improvethe computational performance, this work develops acontingency selection method, based on system stress metrics,
and only models the most critical contingencies. Thus, a large
number of possible N-1 contingencies are eliminated from thesecurity constraints. This work shows that a trade-off exists
between different levels of reliability, measured in stressmetrics, and the computation time for the SCACOPF model.
The work further shows that the reliability saturates at somesystem-specific point and further inclusion of additional
security constraints will only increase the computation time,
without providing any reliability improvement. Thus, carefulselection of the contingency set represented in the SCACOPF
problem can play a significant role in reducing thecomputational burden of the problem.

Advanced Situational Awareness: Identification of
Localized and Widespread Events
Paroma Chatterjee, Student Member, IEEE, and Mojdeh Khorsand, Member, IEEE
School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA
pchatte6@asu.edu and mojdeh.khorsand@asu.edu

Abstract—Power system disturbances are initiated by aging
equipment, weather, cyber-attack, human error, and/or relay
mis-operation. After major disturbances, power system
response is highly dependent on protection schemes and system
dynamics. Improving situational awareness requires proper andsimultaneous assessment of protection scheme behavior anddynamic characteristics. This paper proposes a novel approachto enhance online situational awareness during disturbances by:
1) fast detection of critical protective relays prone to operationand 2) real-time generator coherency identification. The
proposed algorithm is tested using an equivalent WesternElectricity Coordinating Council (WECC) system.

I. INTRODUCTION
A single initiating event combined with an inappropriate
or lack of action of essential control and protective systems
can result in major blackouts. Prior analysis confirms thesignificant role of protection and control schemes during
blackouts [1]-[2]. Hidden failures of control and protection
schemes are often leading factors of widespread blackouts.
The ultimate goal of online dynamic security assessment is toimprove situational awareness and to provide observability
over system behavior after critical contingencies. However,this goal cannot be achieved without properly capturing the
responses of protective and control schemes: one of theunderlying factors of most blackouts. Most prior approaches
of online dynamic security assessment do not account foreither protection systems behavior or critical, automated andmanual control actions. To overcome these challenges, two
methods for online dynamic security assessment have been
proposed, to be able to ultimately hedge against cascades.
II. PROPOSED METHODOLOGY

would lead to an unstable power swing and the relatedtransmission line would be located along the electrical center.
(3)|𝑉𝑎𝑚𝑖𝑛 | ≤ 𝜀

In (3), |𝑉𝑎𝑚𝑖𝑛 | is the minimum voltage magnitude throughthe line, which occurs at 𝑎 𝑚𝑖𝑛 fraction of the line length.
B. Real-time Generator Coherency
In today’s power systems, system response based
corrective actions are an absolute necessity. One way ofdetermining the need of such an action is by observing
generator coherency. For this purpose, the method of
Dynamic Time Warping (DTW) has been employed here [3].
At every cycle, minimum DTW distance (𝑑𝐷𝑇𝑊 ) between
generator’s relative rotor angles are evaluated. The rate ofchange of 𝑑𝐷𝑇𝑊 is used to identify coherent group ofgenerators. Limited variation in rate of change of 𝑑𝐷𝑇𝑊 for
two generators identifies coherency of these generators. This
information can be leveraged to enable essential corrective
actions in case of loss of synchronism.
C. Identifying Critical Areas
The aforementioned methods can be used as parallel
metrics to combine the results and identify areas that are
critical and need to be monitored. For the purpose ofillustration, the WECC-179 bus system is used. A contingency(N-3) case was simulated which could be depicted as theequivalent of the outage of the California-Oregon corridor.
Use of the methodologies described in this paper ensue in theidentification of critical areas shown in fig. 1, for the casebeing investigated.

A. Minimum Voltage Evaluation (MVE)
This paper proposes to evaluate voltage dips acrosstransmission assets, as an attribute to identify whether the
impact of the initial event has spread through the system or is
local to the disturbance. A low voltage (without an actual
fault) is a flag for a local or widespread power swing and thelocation of the electrical center. Such detection is of criticalimportance in order to identify whether protection systems
may mis-operate, as this is one primary cause of uncontrolledislanding. Using PMU data, the minimum voltage magnitude
through a line is calculated at each time interval using the
optimization problem, (1)-(2).
22Minimize: |𝑉𝑎| = √(𝑉𝑥 − 𝑎𝑅𝐼𝑥 + 𝑎𝑋𝐼𝑦 ) + (𝑉𝑦 − 𝑎𝑋𝐼𝑥 − 𝑎𝑅𝐼𝑦 ) (1)Subject to: 0 ≤ 𝑎 ≤ 1(2)

In (1), 𝐼𝑥 , 𝐼𝑦 , 𝑉𝑥 , and 𝑉𝑦 are the real and imaginary parts of
the line current and bus voltage at either end bus. 𝑅 and 𝑋 are
the resistance and reactance of the line respectively and 𝑎 isthe fraction of the line length. |𝑉𝑎 | is voltage magnitude at 𝑎.
Given the special structure of this optimization problem,the optimal solution can be calculated at the rate of PMU datacollection. MVE via a pre-established threshold (𝜀) is shownin (3). If (3) holds for a very small value of 𝜀, the contingency

Fig. 1. An N-3 contingency case simulated in the WECC-179 bus system
for identifying critical areas to be monitored

REFERENCES
[1]
[2]

[3]

P. Pourbeik, P. S. Kundur, and C. W. Taylor, “The anatomy of a power
grid blackout,” IEEE Power and Energy Magazine, September 2006
M. K. Hedman, “Analytical Approaches for Identification and
Representation of Critical Protection Systems in Transient Stability
Studies,” Ph.D. dissertation, School of Elect. Comput. & Energy Eng.,
Arizona State Univ., Tempe, AZ, USA, May 2017
M. Muller, “Dynamic Time Warping” in Information Retrieval forMusic and Motion, Springer, 2007, pp. 69-84

Measurement-Based Voltage Stability Indicator
for Voltage Dependent and Induction Motor Loads
Mariana Kamel

Fangxing Li

Department of Electrical Engineering and Computer
Science
University of Tennessee
Knoxville, Tennessee
kvf552@vols.utk.edu

Department of Electrical Engineering and Computer
Science
University of Tennessee
Knoxville, Tennessee
fli6@vols.utk.edu

Abstract— This work presents a new voltage stability index
which takes into account the voltage dependency of system loads
in particular ZIP, exponential and induction motor loads. This
index is based on the idea that at the collapsing point, the system
PV curve and the load curve share the same tangent line. The
proposed index is evaluated based on local bus measurements ofvoltage and current which makes it suitable for real-timeassessment of the system stability. The performance of the
proposed index is to be tested on both the IEEE 14-bus system andthe 181-bus WECC system developed by the CURENT center.

2
𝑑𝑃𝑖 𝑉𝑖 𝑑𝑃𝑖
𝐸𝑇𝐻,𝑖− 2(𝑉𝑖2 + 𝑅𝑇𝐻,𝑖 𝑃𝑖 + 𝑋𝑇𝐻,𝑖 𝑄𝑖 )== 𝑉𝑖2 ( 2)𝑑𝑉𝑖 𝑃𝑖 𝑑𝑉𝑖
𝑍𝑇𝐻,𝑖 𝑆𝑖2 + 𝑉𝑖2 (𝑅𝑇𝐻,𝑖 𝑃𝑖 + 𝑋𝑇𝐻,𝑖 𝑄𝑖 )Where 𝐸𝑇𝐻,𝑖 𝑅𝑇𝐻,𝑖 , and 𝑋𝑇𝐻,𝑖 are the equivalent Thevenin
parameters at bus 𝑖 (voltage, resistance and reactancerespectively). The power of ZIP and exponential loads can be
expressed mathematically as follows.
𝑃 𝑍𝐼𝑃,𝑖 = 𝑃𝑍,𝑖 + 𝑃𝐼,𝑖 + 𝑃𝑃,𝑖 = 𝑎𝑉𝑖2 + 𝑏𝑉𝑖 + 𝑐∝
𝑃𝑒𝑥𝑝,𝑖 = 𝑉𝑖 𝑝
Where 𝑎, 𝑏, and 𝑐 are the ZIP load parameters. ∝𝑝 is the
exponential load parameter. Therefore, the normalized load𝑑𝑃 ⁄𝑑𝑉 can be calculated as:
𝑑𝑃𝑖
𝑉𝑖 𝑑𝑃𝑍𝐼𝑃,𝑖 2𝑃𝑍,𝑖 + 𝑃𝐼,𝑖==𝑑𝑉𝑖 𝑃𝑍𝐼𝑃,𝑖 𝑑𝑉𝑖
𝑃𝑍𝐼𝑃,𝑖𝑑𝑃𝑖
𝑉𝑖 𝑑𝑃𝑒𝑥𝑝,𝑖==∝𝑝𝑑𝑉𝑖 𝑃𝑒𝑥𝑝,𝑖 𝑑𝑉𝑖
For an induction motor load, as it can be seen from its
equivalent circuit, the motor load is equivalent to a constant P
load behind a transmission line. Therefore, when obtaining the
Thevenin equivalent at load bus 𝑖, the induction motor is to beconsidered as part of the system rather than a load connected tobus 𝑖Keywords— induction motor, voltage collapse, voltage instability

I. INTRODUCTION
Voltage instability and voltage collapse are identified as themain reason behind several major blackouts worldwide. The
severity of these blackout events has prompted significant
research effort in the area of monitoring and control of system
voltage stability. However, assessing the system stability andpredicting reliable security margins proves to be a challenge.
This is due to the complexity of the involved dynamics that leads
the system to eventually collapse. In order to simplify theassessment process several assumption are made. One of theseassumptions is to ignore load dynamics and assume a constant
PQ load model. For voltage dependent loads (ZIP and
exponential loads), such assumption works fine if the load bus
voltage is close to nominal. However, when the system isoperating close to its limit, such assumption does not hold welland will eventually lead to a more conservative and pessimistic
security margin. On the other hand, if the system load includes
a large percentage of induction motors then such assumption
will lead to highly optimistic and unreliable margins. Therefore,
in this work a novel voltage stability index which accounts forload characteristics is proposed. Such index will help provide apractical and a more accurate assessment of system voltage
security.
II. VOLTAGE STABILITY INDEX FORMUALTION
The index is based on the idea that at the collapsing point, thesensitivity 𝑑𝑃 ⁄𝑑𝑉 from the system PV curve is equal to 𝑑𝑃 ⁄𝑑𝑉from the load characteristic curve. The normalized system𝑑𝑃 ⁄𝑑𝑉 at a given load bus 𝑖 can be evaluated based on the
Thevenin equivalence at that bus as follows:

(a)

(b)

(c)

Fig. 1: (a) load bus 𝑖 (b) Induction Motor Equivalent Circuit (c) Thevenin
Equivalent at load bus 𝑖

The voltage stability index is defined as
𝑑𝑃𝑖
𝑑𝑃𝑖
𝑉𝑆𝐼𝑖 = tan−1 ( |) − tan−1 ( |)𝑑𝑉𝑖 𝐿𝑜𝑎𝑑𝑑𝑉𝑖 𝑆𝑦𝑠𝑡𝑒𝑚
III. RESULTS AND CONCLUSION
The performance of the proposed index is to be tested on boththe IEEE 14-bus system and the 181-bus WECC systemdeveloped by the CURENT center.

Advanced Situational Awareness: Identification of
Localized and Widespread Events
Paroma Chatterjee, Student Member, IEEE, and Mojdeh Khorsand, Member, IEEE
School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA
pchatte6@asu.edu and mojdeh.khorsand@asu.edu

Abstract—Power system disturbances are initiated by aging
equipment, weather, cyber-attack, human error, and/or relay
mis-operation. After major disturbances, power system
response is highly dependent on protection schemes and system
dynamics. Improving situational awareness requires proper andsimultaneous assessment of protection scheme behavior anddynamic characteristics. This paper proposes a novel approachto enhance online situational awareness during disturbances by:
1) fast detection of critical protective relays prone to operationand 2) real-time generator coherency identification. The
proposed algorithm is tested using an equivalent WesternElectricity Coordinating Council (WECC) system.

I. INTRODUCTION
A single initiating event combined with an inappropriate
or lack of action of essential control and protective systems
can result in major blackouts. Prior analysis confirms thesignificant role of protection and control schemes during
blackouts [1]-[2]. Hidden failures of control and protection
schemes are often leading factors of widespread blackouts.
The ultimate goal of online dynamic security assessment is toimprove situational awareness and to provide observability
over system behavior after critical contingencies. However,this goal cannot be achieved without properly capturing the
responses of protective and control schemes: one of theunderlying factors of most blackouts. Most prior approaches
of online dynamic security assessment do not account foreither protection systems behavior or critical, automated andmanual control actions. To overcome these challenges, two
methods for online dynamic security assessment have been
proposed, to be able to ultimately hedge against cascades.
II. PROPOSED METHODOLOGY

would lead to an unstable power swing and the relatedtransmission line would be located along the electrical center.
(3)|𝑉𝑎𝑚𝑖𝑛 | ≤ 𝜀

In (3), |𝑉𝑎𝑚𝑖𝑛 | is the minimum voltage magnitude throughthe line, which occurs at 𝑎 𝑚𝑖𝑛 fraction of the line length.
B. Real-time Generator Coherency
In today’s power systems, system response based
corrective actions are an absolute necessity. One way ofdetermining the need of such an action is by observing
generator coherency. For this purpose, the method of
Dynamic Time Warping (DTW) has been employed here [3].
At every cycle, minimum DTW distance (𝑑𝐷𝑇𝑊 ) between
generator’s relative rotor angles are evaluated. The rate ofchange of 𝑑𝐷𝑇𝑊 is used to identify coherent group ofgenerators. Limited variation in rate of change of 𝑑𝐷𝑇𝑊 for
two generators identifies coherency of these generators. This
information can be leveraged to enable essential corrective
actions in case of loss of synchronism.
C. Identifying Critical Areas
The aforementioned methods can be used as parallel
metrics to combine the results and identify areas that are
critical and need to be monitored. For the purpose ofillustration, the WECC-179 bus system is used. A contingency(N-3) case was simulated which could be depicted as theequivalent of the outage of the California-Oregon corridor.
Use of the methodologies described in this paper ensue in theidentification of critical areas shown in fig. 1, for the casebeing investigated.

A. Minimum Voltage Evaluation (MVE)
This paper proposes to evaluate voltage dips acrosstransmission assets, as an attribute to identify whether the
impact of the initial event has spread through the system or is
local to the disturbance. A low voltage (without an actual
fault) is a flag for a local or widespread power swing and thelocation of the electrical center. Such detection is of criticalimportance in order to identify whether protection systems
may mis-operate, as this is one primary cause of uncontrolledislanding. Using PMU data, the minimum voltage magnitude
through a line is calculated at each time interval using the
optimization problem, (1)-(2).
22Minimize: |𝑉𝑎| = √(𝑉𝑥 − 𝑎𝑅𝐼𝑥 + 𝑎𝑋𝐼𝑦 ) + (𝑉𝑦 − 𝑎𝑋𝐼𝑥 − 𝑎𝑅𝐼𝑦 ) (1)Subject to: 0 ≤ 𝑎 ≤ 1(2)

In (1), 𝐼𝑥 , 𝐼𝑦 , 𝑉𝑥 , and 𝑉𝑦 are the real and imaginary parts of
the line current and bus voltage at either end bus. 𝑅 and 𝑋 are
the resistance and reactance of the line respectively and 𝑎 isthe fraction of the line length. |𝑉𝑎 | is voltage magnitude at 𝑎.
Given the special structure of this optimization problem,the optimal solution can be calculated at the rate of PMU datacollection. MVE via a pre-established threshold (𝜀) is shownin (3). If (3) holds for a very small value of 𝜀, the contingency

Fig. 1. An N-3 contingency case simulated in the WECC-179 bus system
for identifying critical areas to be monitored

REFERENCES
[1]
[2]

[3]

P. Pourbeik, P. S. Kundur, and C. W. Taylor, “The anatomy of a power
grid blackout,” IEEE Power and Energy Magazine, September 2006
M. K. Hedman, “Analytical Approaches for Identification and
Representation of Critical Protection Systems in Transient Stability
Studies,” Ph.D. dissertation, School of Elect. Comput. & Energy Eng.,
Arizona State Univ., Tempe, AZ, USA, May 2017
M. Muller, “Dynamic Time Warping” in Information Retrieval forMusic and Motion, Springer, 2007, pp. 69-84