Student Poster Session
2020 IEEE PES General Meeting
Montreal, Canada (Held Remotely)
August 2020
Poster Categories:▪ Advanced Computational Methods for Power System Planning, Operation, and Control▪ Asset Management▪ Communication & Control in Energy Systems▪ Cyber & Physical Security of the Smart Grid
▪ Dynamic Performance and Control of Power Systems▪ Emerging Software Needs for the Restructured Grid
▪ Integrating Renewable Energy into the Grid
▪ Intelligent Monitoring & Outage Management▪ Market Interactions in Power Systems▪ Operation & Control▪ Power Electronics▪ Power System Modeling & Simulation▪ Smart Cities▪ Smart Grid Technology▪ Smart Sensors▪ Substation and Distribution Automation▪ System-Wide Events and Analysis Methods

IEEE PES Student Activities Subcommittee
Valentina Cecchi, Ph.D., Sridhar Chouhan, Ph.D., Anthony Deese, Ph.D., Paras Mandal, Ph.D., and Luke Dosiek, Ph.D.
1

Last Name
(Alphabetical Order)First Name

Paper Title

UG/GradResearch Area

Abdelmalak

MichaelDeep Learning Approach for Power System
Stability Assessment

Grad

Power System Modeling & Simulation

Abdollahi

Roghieh

A Hybrid Control Framework for Large- Scale
Battery Integration to the Power System for
Stability Analysis

Grad

Operation & Control

Abdollahi

RoghiehFrequency Control Using Tariff Regulation (AComparison Between the MPC Feedback Control
and Tariff Modification)

Grad

Market Interactions in Power Systems

Afrasiabi

Shahabodin

Wide-Area Measurement based Faulty Line
Detection using Robust Deep Learning Network

Grad

Akeyo,

Oluwaseun

Study of Large Solar PV Penetration on a Proposed
Generation and Transmission Benchmark System

Grad

Integrating Renewable Energy into the Grid

Akter

Homeyra

Are Electric Transportation, Big Data and Machine
Learning Transforming the Grid?

Grad

Smart Grid Technology

AlAshery

MohamedRisk Management via Second-Order Stochastic
Dominance Constraints: Benchmark Selection
Approaches

Grad

Alden

Rosemary

Tradeoffs in Forecasting of Residential Electric
Loads with Data from Smart Meters andIntelligent Circuit Breakers

UG

Emerging Software Needs for the Restructured
Grid

AmoatengDavid

A Deep Unsupervised Learning Approach to PMU
Event Detection in an Active Distribution Network

GradIntelligent Monitoring & Outage Management

BhusalNarayanSizing of Movable Energy Resources for Service
Restoration and Reliability Enhancement

Grad

Power System Modeling & Simulation

BossartMatthew

The Effect of Power Electronic Loads on WesternInterconnection Stability

Grad

Power System Modeling & Simulation
2

Chaulagain

Asim

Integration of Renewable Energy Systems to
Reduce Greenhouse Gas Emissions

Grad

Integrating Renewable Energy into the Grid

Chen

Yize

Learning to Solve Optimal Power Flow via Neural
Decoding

Grad

Operation & Control

Chen

HaotianDeveloping Bidding Strategy for Day- Ahead
Electricity Markets Using Reinforcement Learning
Methods

Grad

Market Interactions in Power Systems

da Cunha

Vinicius

Assessment of Capacitor Banks Control Practices
in Distribution Systems with High PV Penetration

Grad

Integrating Renewable Energy into the Grid

Das

Ronit

Cross-Market Price Difference Forecast Using
Deep Learning for Electricity Markets

Grad

Advanced Computational Methods for Power
System Planning

Doubleday

Kate

Benchmarks and Improvements in Probabilistic
Solar Forecasting

Grad

Integrating Renewable Energy into the Grid

Fan

He

A Dynamic Nonlinear Model of Once- ThroughBoiler- Turbine Units with Superheated Steam
Temperature

Grad

Integrating Renewable Energy into the Grid

Fernandes

ArnoldDistributed Dynamic State Estimation in Power
Systems under Cyber Attacks

Grad

Cyber & Physical Security of the Smart Grid

Fouad

Fouad

Real- Time Overload Detection Algorithm with
Combined Learning and Analytical Model

Grad

Advanced Computational Methods for Power
System Planning

Fu

Linbo

Distribution Market- Clearing and Pricing
Considering Coordination of DSO and ISO: An
MPEC Approach

GradMarket Interactions in Power Systems

Gangopadhyay

Soumyajit

Trading of Smart Grid Data: Advantages andChallenges

Grad

Market Interactions in Power Systems

Gautam,MukeshA Sensitivity- based Approach to Adaptive UnderFrequency Load Shedding

GradPower System Modeling & Simulation

Ghaljehei

MohammadA data- driven policy design for addressing theramping product nodal deliverability issue in realtime markets

Grad

Market Interactions in Power Systems

3

Ghorai

Anwesha

A comprehensive method to analyze powersystem resilience to extreme weather

Grad

Power System Modeling & Simulation

Gupta

Pooja

Coordinated Wide- Area Control of Multiple
Controllers in a Power System Embedded with
HVDC Lines

Grad

Communication & Control in Energy Systems

Guruwacharya

NischalDevelopment and Validation of Models to Assess
Dynamic Response of Converter- DominatedPower Systems Across Multiple Spatiotemporal
ScalesGrad

Dynamic Performance and Control of Power
Systems

Hasan

Fouad

Real- Time Overload Detection Algorithm with
Combined Learning and Analytical Model

Grad

Advanced Computational Methods for Power
System Planning

He

Mingyue

Socially- aware Day- ahead Distributed Energy
Resources Scheduling by Chance- constrained AC
Optimal Power Flow

Grad

Power System Modeling & Simulation

Herre

Lars

Impact of Imbalance Settlement System Design on
a Risk- Adverse Aggregator of Flexible Demand
Side Resources

Grad

Jan

WestmanOver Frequency Curtailment of PV Generation in
Low Inertia, Islanded Microgrids

Grad

Dynamic Performance and Control of Power
Systems

Johns

WilliamA Multi- function AAA Algorithm Applied toFrequency Dependent Line Modeling

GradPower System Modeling & Simulation

Kamel

Mariana

A Reinforcement Learning Approach for BranchOverload Relief in Power Systems

Grad

Operation & Control

KaymaneshAmirabbas

Industrial Smart Load Using Five- Level Packed UCell Converter

Grad

Power Electronics

KenyonRick Wallace

Operating the Maui Power System with 100%
Inverter Based Resources

Grad

Dynamic Performance and Control of Power
Systems

Khan

Mohammad A I

Efficient Hosting Capacity Computation forDistribution Circuit

Grad

Communication & Control in Energy Systems

Kim

Taehyung

PMU-Based Evaluation of Transmission Bus
Strength through Angle Sensitivity Metrics

Grad

4

Konstantinopoulos

Stavros

WTG Active Power Control for Transient Stability
Enhancement

Grad

Dynamic Performance and Control of Power
Systems

Kumar

AvinashLissajous Parameters based Islanding Detection ofMultiple DGs in Microgrid

Grad

Integrating Renewable Energy into the Grid

Ling

XiejinReinforcement Learning for Optimal Allocation ofSuperconducting Fault Current Limiters

UG

Advanced Computational Methods for Power
System Planning

Liu

Lu

SALSA-Based Method for Identifying Critical
Component and Critical Component Outage
Causality with Cascading Failure Data

Grad

Majidi

Majid

Integrating Small Pumped-Storage Hydropower in
the Operation of Power and Water Distribution
Systems

Grad

Mamun

M Al

Efficient Algorithm for Solving Combined T & DPower Flow and Dynamic Simulation

Grad

Dynamic Performance and Control of Power
Systems

Martins,

Daniella

Probabilistic short- circuit analysis: a new
approach integrating intermittent power injection

Grad

Power System Modeling & Simulation

Mehdipourpicha

HosseinRisk- constrained Bi- level Optimization for Virtual
Bidder Bidding Strategy in Day- Ahead Electricity
Markets

Grad

Power System Modeling & Simulation

MohammadMohammadComputationally Efficient Formulations for Fault
Isolation and Service Restoration in Distribution
Systems

Grad

Power System Modeling & Simulation

Mohiuddin

Sheik MA Unified Droop- Free Distributed Secondary
Control for Grid- Following and Grid- Forming
Inverters in AC Microgrids

Grad

Integrating Renewable Energy into the Grid

Mohseni

SoheilA Game- Theoretic Approach to Model
Interruptible Loads: Application to Micro- Grid
Planning

Grad

Advanced Computational Methods for Power
System Planning

MuhammadMuhammad

Overcurrent Protection Scheme for the IEEE 13Node Benchmark Test Feeder with Improved
Selectivity

GradIntelligent Monitoring & Outage Management

5Muthukaruppan

Valliappan

AMI Based Communication Scheme for
Decentralized Volt/VAR Control

Grad

Smart Grid Technology

NguyenBangDistributed Dynamic State Estimation forMicrogrids

Grad

System Wide Events & Analysis Methods

Patel

ShyamalEfficient Operation of Distribution Grids with High
Penetration of DER through Cluster Control

Grad

Operation & Control

PaudelAmrit

Peer- to- Peer Energy Trading in Smart Grids
Considering Network Utilization Fees

Grad

Market Interactions in Power Systems

Paul

Subho

Impact Assessment of Real Time Demand Control
on Active AC/DC Hybrid Distribution Networks

Grad

Operation & Control

Peralta Moarry

Dario

Ground Source Heat Pump Modeling andAggregation for Services Provision in Electricity
Markets

Grad

Market Interactions in Power Systems

Pfeifer

Nils

Coupling Effects and Impact of EHV AC Cable
Circuits on LV AC Systems within a Tunnel

Grad

Power System Modeling & Simulation

PincetiAndreaGeneration of Synthetic Multi- Resolution Time
Series Load Data via Generative Adversarial
Networks

Grad

Power System Modeling & Simulation

PoudelBikashA Dynamic Model of Small Modular Reactor Based
Nuclear Plant for Power System Studies

GradPower System Modeling & Simulation

PuvvadaNaha Yasasvi

Optimal Transmission Switching with Injection
Uncertainties

Grad

Operation & Control

RameshArun V

Security Constrained Unit Commitment with
Corrective Demand Response

Grad

Smart Grid Technology

RehmanWaqas Ur

Deadband Voltage Control and Power Buffering to
Enable Extreme Fast Charging of Electric Vehicles

Grad

Operation & Control

Ren

Junyu

A Method for Power System Transient Stability
Assessment Based on Transfer Learning

UG

6

Rong

Shuaiang

Impact of Photovoltaic Generation Integration on
Protection of Distribution Systems

Grad

Rosa de Jesus

Dan

Deep Learning Ensemble Based New Approach forVery Short- Term Wind Power Forecasting

Grad

Saida

MasaruSignal Dispatching Method for Secondary
Frequency Control Based on Area Requirement
Change and Merit- Order

Grad

Sang

LinweiA Scenario- adaptive Online Learning Algorithm
for Demand Response

Grad

Smart Grid Technology

Savasci

Alper

Computational Efficiency Comparison of UC- OPF
Formulations

Grad

Advanced Computational Methods for Power
System PlanningShirsat

AshwinSensitivity Analysis of Time- of- Use Rates on
Operations of Home Energy Management Systems

Grad

Operation & Control

Soltani,Zahra

Real- Time Topology Detection in Unbalanced
Distribution System Using Micro- PMU And Smart
Meter Data: A MIQP Approach

GradPower System Modeling & Simulation

Souto

Laiz

Comparison of Principal Component Analysis
Techniques for PMU Data Event Detection

Grad

System Wide Events & Analysis Methods

Subedi

Sunil

Model Predictive Voltage Support in Microgrids

Grad

Dynamic Performance and Control of Power
Systems

Sun

TingkaiN-k Security Assessment and Screening for LargeScale Random Equipment Faults in Bulk Power
Grid under Extreme Weather

Grad

Tang

Zefan

Quantum- Secure Microgrid

GradPower System Modeling & Simulation

VakiliRamin

Enhancing Situational Awareness: Prediction ofProtection Relay Operations After a Contingency

Grad

Dynamic Performance and Control of Power
Systems

Vedullapalli

Divya

MPC Based Battery and Thermostat set- point
Scheduling for Demand Response Applications in a
University Campus Building

Grad

Smart Grid Technology

Integrating Renewable Energy into the Grid

7

VenzkeAndreasVerification of Physics- Informed Neural Networks:
Formal Guarantees for Power System Applications

Grad

Operation & Control

Wang

Guofeng

An Online Energy Flow Control Strategy for
Regional Integrated Energy System

Grad

Integrating Renewable Energy into the Grid

Wang

Wenyu

Diversity Factor Prediction for Distribution
Feeders with Interpretable Machine Learning
Algorithms

Grad

Advanced Computational Methods for Power
System Planning

Wu

ChutianNonparametric Joint Chance Constraint for
Spinning Reserve Requirements

Grad

Power System Modeling & Simulation

Xu

ChengsiAnalysis of Radial Constraint Representation
Methods for Distribution Networks

Grad

Smart Grid Technology

Yan

Rong

Fast Transient Stability Batch Assessment using
Cascaded Convolutional Neural Networks

Grad

Dynamic Performance and Control of Power
Systems

Yang

Zhengwen

Virtual Synchronous Generator Control of Variable
Speed Pumped Storage Hydropower with Full-size
Converter

Grad

Integrating Renewable Energy into the Grid

YousafMuhammad

Hidden Protection Challenges of Unbalanced
Distributed Networks for Higher Concentration ofDistributed Energy Resources

Grad

Integrating Renewable Energy into the Grid

Yu

Yanghao

Estimating the Flexibility Benefit of Concentrating
Solar Plants in Electricity Markets

UG

Integrating Renewable Energy into the Grid

ZhihaoHuaDistributed Optimal Scheduling of Multi- region
Integrated Energy Systems Considering Regional
Heating Network

Grad

Operation & Control

8

Deep Learning Approach for Power System
Stability Assessment
Michael Abdelmalak, Student Member, IEEE, Bahram Parvin, Member, IEEE,
and Mohammed Benidris, Member, IEEE,
Abstract—The criticality to maintain the stability of powersystems has increased significantly as a result of introducing
various stochastic behaviors. The process to assess the dynamic
performance of power systems has become more important tomaintain reliability of the grid. Although time domain simulation
approach provide the most accurate results, its computationaltime is relatively large especially for very large systems whereas
direct methods, which usually consume less time, lacks theobservability of system states in some cases. In this paper, aconvolutional neural network approach is implemented to monitor and assess the system stability performance via time domain
simulation. In the proposed approach, the concept of heatmap
is proposed to be used as the input to the neural network. The
heatmap is a graphical representation for three main parametersof stability assessment; voltage profile, rotor angle, and rotor
angle deviation. Extensive simulations are carried for various
loading conditions, fault locations, fault scenarios, and system
topology. The integration of deep learning approach with time
domain simulation has the ability to provide faster means toforecast the state of power system dynamics.
Index Terms—Convolutional Neural Network, heatmap, time
domain simulation, transient stability.

I. I NTRODUCTION
Power system stability is one of the crucial characteristics
to maintain reliable operation, better performance, and maximum utilization [1]. Rotor angle stability has been studied
using three main approaches; Time domain simulation (TED),
Transient-energy function (TEF), and Equal Area Criteria(EAC). Time Domain simulation is based on solving nonlinear differential algebraic equations (DAE) that models the
dynamic performance of power systems. Several mathematical
approaches are implemented on TDS to enhance the results aswell as obtain solutions in a faster way. In Transient-energyfunction method, the potential and kinetic energy margins
are compared with threshold value to determine the system
state. TEF approaches require the observability of system statepre- and during failure in order to provide good results. The
implementation of Equal Area Criteria with TDS has provided
a less computationally involved simulation but less accurate
results. Thus, developing a method to determine the state ofrotor angle stability in a faster way with high accuracy levelis important to enhance power system stability.
Machine learning techniques have been applied in many
power system problems to provide faster, more efficient, andless computation methods. Although several neural network
methods have been studied in fault detection, classification,and rotor angle stability, most of them require the priorknowledge to the system state before, during, and after fault
The authors are with the Department of Electrical and Biomedical Engineering, University of Nevada-Reno, Reno, NV, 89557 USA e-mail: (mabdelmalak@nevada.unr.edu and mbenidris@unr.edu).

occurrence. Authors of [2] have proposed a support vector
machine (SVM) approach to transient stability prediction,
whereas in [3] another approaches based on decision tree
and multilayer perceptrons have been analyzed and comparedwith SVM. Most of these approaches have low accuracy when
implemented on larger systems and under noise measurements.
A more robust method that does not reply fully on fault
information can be integrated to enhance the methods ofmachine learning in rotor angle stability.
II. F OCUS OF THIS W ORK
In this work, a convolutional neural network (CNN) approach to assess the rotor angle stability is introduced. Several
system parameters are used to generate heatmap representationof the power system time simulation profiles. Time domain
simulations integrated on real-time simulators on dynamic
simulators are used to extract the needed information undervarious conditions. The extracted data is converted to an RGB
image representing the heatmap which will be used as an
input for CNN. The proposed method is demonstrated on
the Western Electricity Coordinating Council (WECC) 9-bus
transmission system as well as the IEEE-39 bus system.
In order to generate the heatmap, time domain simulation
is used to measure voltage magnitude, rotor angle, and rotor
angle deviation. Each parameter represents one layer of the
RGB heatmap image. To capture the dynamic system performance, heatmaps are generated based on varying loading
condition, fault locations, and system topology. The loading
condition is generated randomly based on statistical analysisof the load profile of IEEE RTS-24 system. The clearing time
is modeled to have a Normal distribution with mean equal thecontingency’s critical clearing time and 5% standard deviation.
Fault location is determined by selecting the impacted component (generator or transmission line) randomly. Two different
architectures for CNN are selected to be tested based on thesize of the system. Larger systems provide larger heatmap
and as a result deeper and wider convolution can be applied.
Finally, the trained CNN is tested with accurate real-time time
domain simulation for validation and comparison.
R EFERENCES[1] A. Gupta, G. Gurrala, and P. S. Sastry, “An online power systemstability monitoring system using convolutional neural networks,” IEEE
Transactions on Power Systems, vol. 34, no. 2, pp. 864–872, March 2019.
[2] F. R. Gomez, A. D. Rajapakse, U. D. Annakkage, and I. T. Fernando,“Support vector machine-based algorithm for post-fault transient stability
status prediction using synchronized measurements,” IEEE Transactions
on Power Systems, vol. 26, no. 3, pp. 1474–1483, Aug 2011.
[3] A. Gupta, G. Gurrala, and P. S. Sastry, “Instability prediction in powersystems using recurrent neural networks,” in Proceedings of the TwentySixth International Joint Conference on Artificial Intelligence, IJCAI-17,
2017, pp. 1795–1801.

Wide-Area Measurement based Faulty Line
Detection using Robust Deep Leraning Network

Shiraz, Iranshpower77@yahoo.com

Mousa Afrasiabi
Department of Power and Control
Engineering)
Shiraz University

Mohammad Mohammadi
Department of Power and Control
Engineering
Shiraz University

Shiraz, Iranmusa.afra@shirazu.ac.ir

Shiraz, Iranm.mohammadi@shirazu.ac.ir

Abstract— In this study, we aim to develop a real-time faulty
line detection in the transmission networks in wide-area powersystems. For this purpose, we design a robust deep Gabor filter
convolutional neural network (RDGCNN) to understand thefeatures of the complex and nonlinear signals during fault
occurrence directly from the raw measured signals by thephasor measurement units (PMUs), on the basis of which a
structure for fault detection, faulty line classification, and fault
location estimation in the noisy condition. The performance ofthe proposed RDGCNN is examined in the IEEE 68-bus system.

the modulated Gabor filter and CNN.
The proposed method is applicable in real-time
application with fewer parameters than CNN.
1000
Current (A)Shahabodin Afrasiabi
Department of Power and Control
Engineering
Shiraz University

Input

0

S ,1 ,1 , 2 ,1 
-1000

0

200

400 600
Sample800

1000

GConv 1

S ,1 ,1 , 2 , 512 

Keywords—component, formatting, style, styling, insert (keywords)Max pooling

I. INTRODUCTION (HEADING 1)

XXX-X-XXXX-XXXX-X/XX/$XX.00 ©20XX IEEE

Dropout 10 %GConv 2

S ,1 ,1 , 2 , 512 
Dropout 25%
GConv3

S ,1 ,1 , 2 , 512 
Dropout 10 %flatten.
.
.

Dense 1

S ,128 

Dropout
15%.
.
.

In this paper, we propose a robust Gabor filter based deep
CNN network, namely RDGCNN. The proposed method is
one module-based network, which benefits from Gabor filter
with less parameter in comparison with the standard CNN
and is able to handle the temporal features by the capacity totransformation with convolutional layer redesigning. In
contrast with hybrid methods that add additional feature
extraction to only generate features, RDGCNN benefits from
Gabor functions to modulate convolutional layers based
Gabor filter and promote understanding ability from the raw
signal. Furthermore, to improve the performance androbustness of the proposed method in noisy conditions, we
modified the conventional cross-entropy loss function andreformulate a new loss function. The proposed method uses
only half-cycle of the data measured by the PMUs and thenis able to detect fault, determine the faulty line, and estimate
fault location with high level of accuracy and reliability.
Numerical studies in a large IEEE 68-bus network show thehigh level of the accuracy in high noisy conditions comparedwith combinational shallow-based methods and the standard
CNN in the fault detection, faulty line classification, and fault
location less than 4 ms. We can summarize the contributionof the paper as follows: In this paper a comprehensive wide-area fault
detection and localization is proposed, where it
only requires half-cycle of the raw data and does
not need any additional feature extraction.
 The proposed method is highly robust in the
noisy condition due to the reformulation of a newloss function and the inherent characteristics ofS ,1 ,1 , 2 , 256 

Dense 2
S ,64 

Final Output

II. THE PROPOSED DEEP NETWORKThe designed RDGCNN network is visualized in Fig 1. To
implement, RDGCNN for the transmission lines fault
detection/localization the following procedure is used: Theexperimental data is firstly normalized, then converted from
1D- raw data measured by the set of PMUs, into 2D- signals
and formed as a set of tensors with size S ,1 ,1 , 2 ,1  . As
shown in Fig 1, the raw signals measured by the PMUs are
considered as the input for the first Gabor filter, namely
GConv 1 in Fig 1. This layer converts the input dataset into
the feature vector with size S ,1 ,1 , 2 , 512  .Max poolingpools the maximum over each time interval as its output
feature map. Thus, the length of max pooling outputs are
smaller than convolution layer, the feature vectors are formed
with S ,1 ,1 , 2 , 256  and 10% dropout probability. The
output of pooling layers is the input of the second and third
Gabor based convolutional layer, namely GConv 2 & 3,

respectively, with 10% and 25% dropout probability and thenboth of them generate the output with S ,1 ,1 , 2 , 512  .The
output of the GConv 2 should be converted from 2D-signals
to 1D- signals. To this end, the output of max-pooling layers
are flattened by a flatten layer and dropped out with 15%
probability. Two different dense layers have been considered,
which have outputs as a set of signals with S ,128  andS ,64  sizes, respectively. In the second dense layer, theoutputs are dropped out with 15% probability. In the final step,
the fault detection (fault/normal) conditions or faulty line, andfault location on the faulty line is determined.
III. RESULTS

Acc (%)

100
90
80

L-14

L-18

L-26

L-34

L-46
(a)

L-57

L-63

L-75

L-82 Non-fault

Spe (%)

100
RDGCNN80

CNN
ANN+SVM
WT+SVM

60
L-2

L-14

L-18

L-26

L-34

L-46
(b)

L-57

L-63

L-75

L-82 Non-fault

L-14

L-18

L-26

L-34

L-46
c

L-57

L-63

L-75

L-82 Non-fault

Ppr (%)

100

80

60
L-2

In the faulty line classification, the results of an 87-classclassification are assessed and show the superiority of the
proposed method with more than 97% and more than 7%
improvement in comparison with CNN and 13% improvementcompared to the hybrid shallow-based networks.
Consequently, the proposed method is at least 4, 11, and 14
times more accurate than CNN, ANN+SVM, and WT+SVM,
respectively, in the fault location estimation. Furthermore, theproposed method is able to perform less than 3.84 ms and,
therefore, validates its real-time application.
REFERENCES[1]

Overall, 43,000 different faulty conditions are generated.
In addition, 1,000 normal condition based on stochastic
characterization of the loads are produces. From the 44,000
different conditions, we devoted 70% of the dataset to thetraining of the proposed RDGCNN network and the rest of thedataset is used to test the performance of the proposed method
and comparison with other methods. We evaluated the
proposed method based on indices based confusion matrix, theaccuracy (Ac), specificity (Sp), and positive predictivity (Pp).
In the fault detection, we encounter with binary classification
method, while the class number is 87 (86 lines and one nonfault condition) in faulty line classification problem in IEEE
68-bus. The obtained metrics from the proposed RDGCNN,
CNN, ANN+SVM, and WT+SVM are given in Fig 2. The
superiority of the proposed robust deep network in terms ofaccuracy and reliability is clear in this Fig.

70
L-2

IV. CONCULSION

Fig. 2 – The results of faulty line detection of the proposed
method, CNN, ANN+SVM, WT+SVM, (a) Ac, (b) Sp, (c) Pp[2]

W. Li, D. Deka, M. Chertkov, and M. Wang, "Real-Time Faulted Line
Localization and PMU Placement in Power Systems
ThroughConvolutional Neural Networks," IEEE Transactions on
Power Systems, vol. 34, no. 6, pp. 4640-4651, 2019.
S. Luan, C. Chen, B. Zhang, J. Han, and J. Liu, "Gabor Convolutional
Networks," IEEE Transactions on Image Processing, vol. 27, no. 9, pp.
4357-4366, 2018.

Study of Large Solar PV Penetration on a Proposed
Generation and Transmission Benchmark System
Oluwaseun Akeyo, Student Member, IEEE, Aron Patrick1 , and Dan M. Ionel, Fellow, IEEE

Abstract—A framework for estimating the maximum PV hosting capacity of a transmission network. Contrary to conventional
approaches, in which the circuit maximum hosting capacity isevaluated for a fixed load level, the proposed scheme accounts
for the possible load level variations that may affect the highest
amount of PV system that can be connected to specified buses
within the transmission circuit. The impact of high PV penetration was studied on a modified benchmark test system developedin PSS R E power system simulation software. The response of thecircuit based on its bus voltages, branches power flow, PV points
of interconnection and penetration were analyzed and used toevaluate the maximum PV hosting capacity of the system withouta significant upgrade.
Index Terms—Hosting capacity, photovoltaic, PSS/E, Transmission network, Voltage violations, Thermal limits, PV penetration

I. H OSTING C APACITY FOR M ODIFIED T RANSMISSIONN ETWORK
The modified benchmark transmission system analyzed inthis work represents a small islanded power system networkwith 12 buses and four generating units (Fig. 1). At steadystate without renewable integration, the transmission network
total system load is approximately 65% of the total generation
capacity. The bus voltage voltages vary between 0.98pu to
1.03pu and the maximum loading for any of the transmission
lines is 71%, which is the power flow between buses 6 and 4.
The study focuses on the maximum capacity of a PV systemthat may be connected to any one of the buses in the example
transmission network without violating the bus voltages or thethermal limits of the circuit branches. Opposed to conventional
approaches, the proposed framework accounts for the systemload variation and employs an economic dispatch model tomeet the demand at the cheapest operation cost. The mathematical model for the economic dispatch model is as follows:
minimize,

CT =

N
X

Ci (Pi ),Figure 1. Single line diagram of the modified benchmark network, which has
a load of approximately 65% of total generation capacity. The transmission
circuit is completely assessed for PV connection at any of its buses.

II. S OLAR PV P ENETRATION I MPACT S TUDY
The network was analyzed at multiple load levels including
the base network loading. The estimated PV hosting capacity
for each bus at the base load level was compared withthe evaluated values as the system load increases and thegenerators real power outputs are updated.
The results show that the maximum amount of renewablethat may be connected to any of the system buses without
violating any thermal or voltage limits depends on the selectedbus and the total system load level.
For the example transmission network evaluated and PV
hosting capacity estimation framework employed, at thebaseload level, up to 200MW (approximately 10% of combined generation capacity) may be connected to any of the
network buses without voltage or thermal violations (Fig. 2).

 = |PT − Lc |, (1)i=1where, CT , represents the total operating cost at each specified
time; PT , combined generator output and Lc , the combinedload. The generator ramp constraint is expressed as:max[Pimin , Pit−1 + Ridown ] ≤ Pit ≤ min[Pimax , Pit−1 + Riup ].
(2)
where, Pit and Pit represents the generation units out power attime t and t − 1, respectively; Riup and Ridown , the generator
rising and falling ramp rates, respectively.
Oluwaseun Akeyo and Dan M. Ionel are with the SPARK Laboratory,
ECE Department, University of Kentucky, Lexington, KY. Aron Patrick iswith LG&E and KU, Louisville, KY.

(a)

(b)

Figure 2. (a) Minimum bus voltage at increasing network load levels.
For this example network and the generator response algorithm employed,
the minimum bus voltage reduces as the PV penetrations and load level
increases. (b) Maximum PV hosting capacity with respect to the circuit
solution convergence, voltage and thermal limits at 65% load level.

Are Electric Transportation, Big Data and Machine
Learning Transforming the Grid?
Homeyra Akter
Department of Electrical and Electronics Engineering
University of the Ryukyus
Okinawa, Japanhomairaakter08@gmail.com

Abstract—The electrical power system is moving towards thenext-generation smart grid system, including some advanced
technologies such as power electronics, renewable energy sources,
distributed generation, advanced monitoring and communication
systems, etc. These enable bi-directional communication andpower flow with enhancing security, reliability, and efficiencyof the power system. These advanced technologies contradict
the conventional power system. For example, renewable energy
sources produce DC power instead of AC power, such as solar
power. On the other hand, the AC power of renewable sources
like wind energy varies greatly. Power electronics-based loads
work separately, and electronic devices display constant power
load characteristics that unstable the system. Bi-directional communication occurs in decentralized distributed systems, which
opposed to conventional centralized systems. Connectivity andexchange of information are done by the Internet of things (IoT)
devices, which enable the grid components to exchange data. By
this time, a vast amount of data is generated, which can not be
handled by the conventional system. So, carry out the advanced
technologies in the traditional grid is challenging. That’s why
transforming the old power grid is attracted attention for movingon.
Electric transport systems are introduced to reduce the oil
dependency for transportation and to limit CO2 emissions related
to transportation. The integration of electric transport andrenewable energy is reducing the world’s dependence on fossil
fuels and greenhouse gas emissions significantly. There are somebarriers to implementing renewable energy generation. The
output power of renewable generation depends on the weather,not on demand. Electronic vehicle (EV) storage batteries are an
efficient solution to integrate the generation based on renewableenergy. Vehicle to grid (V2G) concept is the most promisingfeature of the electric transport sector, which helps to store
surplus energy and gives it back to the main grid duringhigh demand for electricity. The widespread implementation ofelectric transportation could play a significant role in integrating
renewable energy into transforming the existing power systems.
On the other hand, big data analysis and machine learningtechniques are very useful in transforming the grid. Big datacontains a large amount of data from the smart grid that collectsfrom monitoring transmission and distribution lines, generation
units, smart metering, and smart home. This data should be
stored in a cloud-based system for analyzing. Big data analysis
is considered as the main functionality for supply and demandside management for smart grids, control algorithms, and futureenergy market models. The management of dynamic energy
through this kind of data analysis is also attracted attention.
Dynamic energy management is necessary to process power
flow optimization, the massive amount of data from the systemmonitoring, real-time operation, etc. Big data-based researchstrategies such as power generation, optimization techniques,

and forecasting methodologies extend to renewable energy-based
systems like wind power and solar power systems. The datain the grid comes from different sources. Different types ofdata generated in the grid contain confidential information ofindividual users. Such data needs to be protected following legal
regulations. Besides, this data includes sensitive information of acountry’s organization or central grid. Manipulation of this data
can affect the safe operation of the grid. So security and privacy
is a critical issue with IoT integrated smart grid which is a cyberphysical system that makes it biased to cyber attacks. Anotheressential concern is to process and transfer of data within an
acceptable range of time.
Machine learning is an effective solution that applies to big
data processing and uses for security solutions. Machine learningtechniques provide an efficient way of analyzing and making theappropriate decision for running the grid. It consists of a combination of different algorithms that analyze the available data
through a set of instructions to generate data-driven predictions.
Machine learning functionality can predict the energy consumption and generation, appropriate price, optimum schedule, andsizing of generators, fault detection, etc. In renewable energybased grid systems, several machine learning methods are being
introduced at different stages, creating entirely new possibilities
for research. Support vector machines have provided many
optimization and forecasting techniques in the grid for solving
the problems of the renewable-based power system. Machinelearning-based economic optimization, monitoring power quality,
islanding detection are developed recently. Several algorithms are
implemented for predicting the load data such as support vector
regression, artificial neural network, deep neural network, etc.
For solving the security problems of grid, machine learning canbe considered as an effective solution.
Finally, we can say that the conventional electric grid is transforming to IoT based smart grid with the benefits of big data
analysis, machine learning techniques, and integrated electrictransportation with renewable energy.

Risk Management via Second-Order Stochastic
Dominance Constraints: Benchmark Selection
Approaches
Mohamed Kareem AlAshery, Student Member, IEEE, and Wei Qiao, Fellow, IEEE
Power and Energy Systems Laboratory
Department of Electrical and Computer Engineering
University of Nebraska−Lincoln
Lincoln, NE 68588-0511 USA
m.kareem@huskers.unl.edu; wqiao3@unl.edu

Abstract—This document includes the abstract for a poster
submitted to the 2020 IEEE PES GM student poster contest.

represents the decision-maker’s risk
maintaining the feasibility of the model.

Keywords—Decision-making
under
uncertainty;
risk
management; stochastic dominance; stochastic programming.

To overcome the obstacle of benchmark selection andunlock the potentials of the stochastic dominance models in
different power and energy applications, this poster discusses
two optimization-based benchmark selection approaches thatare applicable to any optimization problems with second-orderstochastic dominance constraints. The two approaches define a
fixed effective feasible region and an adaptive feasible region,
respectively, within which the benchmark distribution should bebounded to maintain the optimization problem’s feasibility.
These feasible regions of the benchmark distribution aredetermined by solving modified versions of the original
optimization problem. The first approach defines a fixed
effective feasible region for the whole benchmark distribution,
while the second approach defines the feasible regions forbenchmark’s scenarios sequentially, starting from the worst to
the best scenario. The approaches enable decision-makers todefine benchmark distributions rigorously that best represent
their risk preferences with the least number of scenarios, which
is desirable from the computational tractability point of view.
Compared to our work in [5], this poster discusses a newadaptive feasible region-based benchmark selection approachand the two benchmark selection approaches discussed in thisposter do not need to solve a different optimization problem, i.e.,the mean-risk model. A case study for the optimal bidding
problem of a wind power producer in the electricity market isused to validate and demonstrate the proposed two approaches.

I. POSTER’S ABSTRACT
Many decision-making problems, such as energy market
operation and participation, are accompanied with various
sources of uncertainty. A rational decision-making process
should account for those uncertainties while making decisions.
The stochastic programming technique has been applied tomodel such problems in three different ways [1]: expected utility
maximization models, mean-risk models, and stochastic
dominance models. The expected utility maximization models
determine the optimal outcome based on the optimal expectedvalue of the objective function, regardless of the objectivefunction’s variability. In contrast, the mean-risk models, firstly
introduced by Harry Markowitz [2], account for the objective’svariability by comparing the portfolios in two dimensions:
expected (mean) value and risk, where the risk can be quantified
via different risk measures, such as variance, value at risk (VaR),
and conditional value at risk (CVaR).
The stochastic dominance models, which optimize theobjective subject to stochastic dominance constraints that can beformed in different orders, have a solid theoretical basis formaking choices under risk [1]. In a stochastic dominance model,
the optimal solution dominates a predefined benchmark
distribution (or simply called “benchmark”) in the sense of thestochastic dominance concept. This benchmark ensures that thevalues and probabilities of the unfavorable outcome’s scenarios
are within the decision-maker’s risk preference. The secondorder stochastic dominance models proposed in [3] are the best
to represent the risk preferences of rational risk-averse decisionmakers [4]; and provide superior risk management performancecompared to mean-CVaR models as demonstrated in [5].
Nevertheless, the stochastic dominance models underlay two
major challenges. Firstly, they may be mathematicallyintractable for problems with large numbers of scenarios, as thenumber of a model’s constraints is proportional to the productof the number of the problem’s scenarios and the number ofscenarios of the imposed benchmark. The second challenge isthe difficulty in defining a benchmark distribution that best

preference

while

REFERENCES[1] D. Roman, K. Darby-Dowman, and G. Mitra, “Portfolio construction basedon stochastic dominance and target return distributions,” Math. Program.,vol. 108, no. 2–3, pp. 541–569, Sep. 2006.
[2] H. Markowitz, “Portfolio selection,” J. Finance, vol. 7, no. 1, pp. 77–91,
Mar. 1952.
[3] D. Dentcheva and A. Ruszczynski, “Risk-averse portfolio optimization via
stochastic dominance constraints,” in Handbook of Financial
Econometrics and Statistics, New York, NY, USA: Springer, 2015, pp.
2281–2302.
[4] M. Kallio and N. Dehghan Hardoroudi, “Second-order stochastic
dominance constrained portfolio optimization: Theory and computational
tests,” Eur. J. Oper. Res., vol. 264, no. 2, pp. 675–685, Jan. 2018.
[5] M. K. AlAshery, D. Xiao, and W. Qiao, “Second-order stochastic
dominance constraints for risk management of a wind power producer’s
optimal bidding strategy,” IEEE Trans. Sustain. Energy, pp. 1–10, Jul.,2019.

Tradeoffs in Forecasting of Residential Electric
Loads with Data from Smart Meters and Intelligent
Circuit Breakers
Rosemary E. Alden, Student Member, IEEE, Cristinel Ababei, Senior Member, IEEE , Dan M. Ionel, Fellow, IEEE

Abstract—Major recent developments in the smart grid include
the collection of large data sets through smart devices and energy
management systems. The need for improved understanding of
the current and future loads, motivated the emergence of machinelearning (ML) techniques, such as artificial neural networks, toforecast electrical loads. These ML are at the forefront of researchin the smart grids area and of interest to the industry. As devices
become more sophisticated, with higher sampling frequencies andthe IoT becomes more widespread, “big data” is facing renewed
challenges related to interpretation and considering portions ofdata while maintaining accuracy for electric load forecasting. Our
primary objective is studying data size and resolution needed toconstruct effective ML models for residential electric load forecasting. We are, specifically, interested in developing long shortterm memory (LSTM) models using energy consumption datafrom the most influential appliances and circuits such as HVAC,
water heater, and electric mains. Moreover, we are interested
in integrating these models with EnergyPlus to enable detailedbuilding energy simulations and optimizations for horizons of
24 hours. Our investigations will inform the big data collection
efforts in order to make it more focused and storage-conscious,
thus more meaningful.
Index Terms—Big data, EnergyPlus, variable loads, machinelearning, residential

I. E NERGY M ONITORING T ECHNOLOGIES AND B IG DATAIntelligent circuit breakers monitor the energy consumed
by large power appliances, such as heating, ventilation, andair conditioning (HVAC) systems and electric water heaters(EWH) at fine resolutions. Smart meters can report the totalenergy measured on a residential main circuit every 15 minutes. These developments offer unprecedented opportunities tomonitor residential loads at higher resolutions than ever before.
The natural consequence of closer monitoring is “big data”
from these IoT devices resulting in new formatting and interpretation challenges. We are working to identify a minimal
data set (a subset of the much larger data set) that provides
enough information to build accurate energy consumption
models in residencies. Our focus is on sampling frequency
and selection of specific measurements. The resolution drastically changes the experimental data and thus, the quality
and relevance of predictions as illustrated in Fig. 1. We willconsider other aspects related to big data, such as the impact
of variations in weather, occupancy, and high variability loads.
Rosemary E. Alden and Dan M. Ionel are with the SPARK Laboratory,
Electrical and Computer Engineering Dept., University of Kentucky, Lexington, KY. Cristinel Ababei is with the Electrical and Computer Engineering
Dept., Marquette University, Milwaukee, WI.

Figure 1. Power usage at different resolutions of the Honda Smart Home, on
December 15, 2018.

II. E XPERIMENTAL DATA AND ML T ECHNIQUES FOR
P REDICTION OF H IGH VARIABILITY L OADS
Our two primary data sets include TVA Robotic Houses
located in Knoxville, TN and the publicly available Honda
Smart Home (HSM) in Davis, CA. The HSM includes 230+
sensors collecting minutely data. We are producing 5-15
minute and hourly time resolutions using scripts in R. The
importance of sampling frequency for high variability loads
such as HVAC is shown in Fig. 2).
To handle such rapid changes a long short term memory(LSTM) model is under development for the 1 to 5 minuteresolutions. We chose LSTM as an RNN model to predict ahome’s daily energy load because, among the ML techniques
studied in literature, we find it one of the most promising.

(a)

(b)

Figure 2. (a) Total electric and (b) HVAC load for HSM over Dec- Feb,
2019. HVAC is a large consumer in homes and requires effective forecasting.

A Deep Unsupervised Learning Approach to PMU Event Detection
in an Active Distribution Network
David Ofosu Amoateng, Student Member, IEEE, Ruifeng Yan, Member, IEEE, Tapan Kumar Saha, Fellow, IEEE
School of ITEE, The University of Queensland, Australia

I. P ROBLEM F ORMULATION AND I NTRODUCTIONOUTPUT

4

2

3

CODINGS

4

RECONSTRUCTION
ERROR
(

1

,

3

DECODER̂2

ENCODER)

1

Trained Network

INPUT
Gaussian noise

Abstract—The increased deployment of advanced metering
devices such as PMUs in active distribution networks offers
network operators the potential to develop applications which
may help improve visibility regarding the operating state of
their network. Considering the high reporting rates of PMUs,
it is imperative to develop schemes capable of analysing PMU
data in near real-time, which will enable system operators taketimely actions. However, current methods used for situationalawareness in active distribution grids are heavily dependent ondata sample size or on detectors that require great effort intuning. By training deep autoencoders with PMU data froma real active distribution network, we develop and validate aframework which automatically learns a representation of the
network. With this approach, our framework can monitor thestate of the network and also detect events in a fraction of asecond, making it suitable for online analysis of PMU data frommultiple streams or analysis of large amounts of historical PMU
data. The performance of the proposed model is validated bycomparison with a sample based statistical anomaly detector.

>

DISCARDPASS TONETWORK2

Fig. 1: a) Data Outlier Filter Framework for Event Detection

II. M ODEL P ERFORMANCE
The figure below shows the performance of the proposedmodel in comparison with a statistical anomaly detector [1].

T

Let X = [Xi , Xi+1 , . . . , Xnl ] , X ∈ Rnl ×nv denote a
vector sequence of PMU measurements for a fixed window
size, where nl and nv are the window size and the numberof variables being analysed by the event detector respectively.
Xi ∈ R1×nl , which is an observation in the above vector:


abc
abc
, Vl1abc ,. . . ,Vjabc , Fj , I1j, Vljabc
Xi = V1abc , F1 , I11
where j represents the number of PMU devices providing
measurements. V abc , F are the three phase voltages andfrequency and Ilabc , Vlabc , Fl are the lag one differenceof the current magnitude, voltage magnitude, and frequency
respectively.
The input sample Xi ∈ R1×nl may contain data points
that may either be a subset of {DE }, the set of event data
points or {DN E }, the set of non-event data points. Using
recorded PMU data, our goal is to construct a model using
deep autoencoders that automatically learns a representation of
the network, reconstructs PMU data and predicts which set an
PMU data sample belongs to. To achieve this, we assume the
PMU dataset for building the model is unbalanced set where
the number of event data points DE  DN E , the numberof non-event data points. As such we aim to build a model
which is biased towards recognising and reconstructing normal
data points, such that a non event data point will yield a highreconstruction error when presented as input.
This Activity received funding from the Australian Renewable Energy
Agency (ARENA) as part of ARENA’s advancing renewables programme.

5.8%

(SAD)

Fig. 2: Event II (Static window size) : Failed event detection by thestatistical anomaly detector

III. C ONCLUSION
This paper presents a framework for detecting events indistribution network in order to improve situational awareness.
This would be instrumental in providing real time situation
awareness using high streaming PMU data. Future work
includes pairing this event detector with a machine learningclassifier, to automatically predict the type of event detected.
R EFERENCES[1] A. Shahsavari, M. Farajollahi, E. Stewart, E. Cortez, and H. MohsenianRad, “Situational awareness in distribution grid using micro-pmu data: A
machine learning approach,” IEEE Transactions on Smart Grid, 2019.

Model Predictive Voltage Support in Microgrids
Niranjan Bhujel† , Ujjwol Tamrakar, Timothy M. Hansen, and Reinaldo Tonkoski
Department of Electrical Engineering and Computer Science
South Dakota State University
Brookings, South Dakota, USA 57007
Email: † niranjan.bhujel@jacks.sdstate.edu

where J is the cost function, Q and R are weighting
matrices. Q = Q11 is the weight for voltage, R11 , andR22 are the weight for d-component and q-component of
the inverter current respectively. The MPC formulation was
implemented in pyomo. The weighting parameters used were
Q11 = 1, R11 = 0.01, andR22 = 0.001
II. K EY R ESULTS

Abstract—For the good performance and reliability of microgrids, voltage regulation is necessary. Since line resistance toreactance ratio is high in microgrids, it might be easier to regulate
voltage with active power than reactive power but the cost ofactive power is higher. Thus, an optimal control method shouldbe implemented. Model predictive control (MPC) is proposed forthis purpose.
Index Terms—Voltage support, model predictive control, optimal control, microgrid.

I. K EY E QUATIONS

Fig. 2: Waveform of voltage

Fig. 1: General diagram of the system

For the system shown in the Fig. 1, state differential
equations can be written as
xk+1 = Axk + Buk(1)

Let N be the horizon length (in terms of sample instant).
Our objective is to control the voltage at the PCC vc within
acceptable values. Then the objective of the controller is tosupport vc,d using the control inputs iinvd and iinvq . Next, let
us assume Γ = {1, 2, ...., N } represents the discrete sample
time instants in the forward-time horizon. Let yk = ∆vcdk
and uk = [iinvdk iinvqk ]> be the measured output of and be
the input respectively, then the proposed MPC formulation willtake the following form: N
X
min
JΓ =
(yk> Qyk + u>
k Ruk )
iinvd ,iinvq
k=1
s.t.

Fig. 3: Waveform of inverter current

Fig. 2 shows the plot of voltage when load changes by
0.75 pu. From the plot, we can see that the inverter is able toprovide support for voltage. Since weight for iinvd is higher
compared to that of iinvq , higher value of iinvq is used forvoltage support.
III. F UTUTE W ORK
The inverter was able to support voltage but there was stillsome oscillation that could be removed when PLL is improved.
They are part of future work.

xk+1 = Axk + Buk ∀ k ∈ Γ,
|iinvdk |≤ id,max ∀ k ∈ Γ,

(2)|iinvqk |≤ iq,max ∀ k ∈ Γ,
|iinvdk+1 − iinvdk |≤ Sd ∀ k ∈ Γ,
|iinvqk+1 − iinvqk |≤ Sq ∀ k ∈ Γ

1Sizing of Movable Energy Resources for Service
Restoration and Reliability Enhancement
Narayan Bhusal, Student Member, IEEE, Mukesh Gautam, Student Member, IEEE,
and Mohammed Benidris, Member, IEEE
Department of Electrical and Biomedical Engineering
University of Nevada, Reno, NV 89557, USA
Emails: bhusalnarayan62@nevada.unr.edu, mukesh.gautam@nevada.unr.edu, and mbenidris@unr.edu

Abstract—The dramatic increase of extreme events (e.g. hurricanes, earthquake, cyber-attacks) have severely impacted powersystems ranging from long outage times to major equipment
destruction. Distribution system failures and outages are major
contributors to power supply interruptions. This work proposes a
two-stage strategy to determine the minimum sizes of MERs with
network reconfiguration (NR) for distribution service restorationand supplying local and isolated loads. After a contingency, thefirst stage determines the NT based on the spanning tree searchalgorithm. In the second stage, if some system loads cannot be
fed by NR, MERs are deployed and the optimal routes to reachisolated areas are determined based on the Dijkstra’s shortest
path algorithm (DSPA). The traveling time obtained from theDSPA is incorporated with the proposed sequential Monte Carlosimulation-based approach to determine the sizes of MERs. The
results are demonstrated through case studies on IEEE-13 andIEEE-123 node test feeders.
Index Terms—Distribution service restoration, extreme events,
isolated loads, movable energy resources, network reconfiguration, and spanning tree search algorithm.

I. INTRODUCTION
The dramatic increase of the extreme events (e.g. hurricanes,
earthquakes, cyber-attacks) have led to large blackouts andmajor destructions of power grids resulting in economic losses
and more importantly, long outage duration times. Therefore developing control and operation method and planning
strategies to improve distribution service restoration (DSR)
is very vital. Network reconfiguration (NR), microgrid (MG)formation, and splitting power grid into several smaller andreliable MG, Distributed Generations (DGs), and movable energy resources (MERs) to form dynamic MGs are consideredas the most effective DSR approach. Mobile energy resources(MERs) are flexible and movable resources that can play animportant role in DSR, specifically when there are no othermeans of power supply during contingencies.
II. S IZING OF M OVABLE E NERGY R ESOURCES
This paper proposes a storchastic strategy to determine thesize of MER for DSR considering NR, travel times, andoutage duration of distribution components (DCs). Dijkstra’s
shortest path algorithm has been implemented to determine
the optimal route for MERs, spanning tree search algorithm
is used to determine the optimal NR, and sequential MonteCarlo simulations are used to model the outages of DC. andStart
Input all the system data
including reliability data
Generate outage history forlines, transformer, & switches
using MCS

Separate all the contingencies
for which reconfiguration is notpossible

Run power flow
For k=1:n0s

Record total power flow forcontingency hour kIs
k= n0s ?

k=k+1

No

Yes
Determine size of MERs, andaverage outage duration

Stop

Fig. 1. Flow chart of proposed approach

all the simulations are performed in MATLAB and OpenDSS
integrated environment. The procedure to determine the sizesof MERs is explained as follows and shown in Fig.1.
III. C ASE S TUDIESThe proposed approach is validated through case studies onIEEE 13- and IEEE 123-node test feeders. The MER sizes
and interruption times of the IEEE 13-node test feeder are
given in Table I. Even though both the feeders have same
TABLE IR ESULTS FOR ALL CASE STUDIES

Average Size (kWh)Average Size (kW)Maximum Size (kW)Average Duration Time (hours)

IEEE 13
3998
370
590
10.8

IEEE 123810
138.67198
5.84

level of loads, the average size of MERs in IEEE 13-node issignificantly larger than that of the IEEE 123-node becauseof the fact that reconfiguration is not possible for the case ofIEEE 13-node feeder.

The Effect of Power Electronic Loads on WesternInterconnection Stability
Matthew Bossart1,2 , Rick Wallace Kenyon1,2 , Dragan Maksimovic1 , Bri-Mathias Hodge1,2Electrical, Computer & Energy Engineering, 2 Renewable & Sustainable Energy Institute, University of Colorado Boulder
Boulder, CO{matthew.bossart, richard.kenyonjr, maksimov, brimathias.hodge}@colorado.edu

Abstract—The prevalence of power electronics in the bulk
power system is increasing rapidly in both the generation andconsumption of electricity. This work focuses on the effect ofchanging load composition - specifically the transition from single
phase air conditioner motors to power electronics backed air
conditioners - on power system stability. Various transmission
and generation contingency events for the Western Interconnection were simulated using Positive Sequence Load Flow softwareand planning models from the Western Electricity Coordinating
Council. In general, an increased proportion of power electronicload leads to more instability. For some specific faults resulting
in fault-induced delayed voltage recovery, transitioning to higher
proportions of power electronic loads helps expedite system
recovery. These results demonstrate that load composition should
be examined in conjunction with generation composition whenevaluating system stability.

60.00
Base, KDE=0.0
Highmix, KDE=0.0
Base, KDE=0.25
Base, KDE=0.50
Base, KDE=0.75
Base, KDE=1.00

59.95
System Frequency [Hz]

1

59.90

59.85

59.80
0

I. M ETHODSThis work focuses on the stability impact of one current
trend in the changing electrical load: the replacement of single
phase air conditioners with power electronics interfaced air
conditioners and/or air source heat pumps. This study uses
the 2023 Heavy Summer Planing Case (HS-Base) which was
originally developed by the Western Electricity Coordinating
Council and modified for use in the Western Wind andSolar Integration Study Phase 3 [1]. Two parameters, which
determine the proportion of single phase air conditioner andpower electronic load, were systematically adjusted to derive

10

20

30

40

Time [s]Fig. 2. System frequency after an N-2 contingency event: simultaneous loss of
two Palo Verde generators. Larger values of KDE indicate a more complete
shift from single-phase air conditioning to power electronic air conditioning.

additional cases from the HS-Base case. Positive sequence
type simulations of the entire Western Interconnection wererun for a set of five contingencies, including one generation
loss contingency and four transmission faults (Fig. 1).
II. M AIN R ESULTS & C ONCLUSIONS
Fig. 2 shows the frequency drop in the Western Interconnection due to an N-2 contingency event: simultaneous lossof two Palo Verde nuclear generators. The dotted lines show
the frequency nadir dropping significantly as the single-phase
air conditioning load is shifted to power electronic interfaced
air conditioning load. Unlike single-phase air conditioners,
power electronic load tends to consume constant power even as
frequency and voltage drop, leading to a more extreme system
response to a loss of real power.
The fundamental conclusion of this study is that load
composition is important to the stability of the bulk powersystem and should be studied in conjunction with changes ingeneration.
R EFERENCES

Fig. 1. The major transmission lines of the Western Interconnection with thelocation of the large disturbances considered. Circles indicate transmission
faults and the triangle indicates a generation loss.

[1] N. W. Miller, M. Shao, S. Pajic, and R. D’Aquila, “Western Windand Solar Integration Study Phase 3: Frequency Response and Transient
Stability,” National Renewable Energy Laboratory, Tech. Rep. December,
2014.

Optimal Transmission Switching with Injection
Uncertainties
Snehil Chandra, P. Naga Yasasvi, Abheejeet Mohapatra, and S. C. Srivastava
Department of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur, India 208016
Email: snehil0704@gmail.com; yasasvi@iitk.ac.in; abheem@iitk.ac.in; scs@iitk.ac.in

Abstract—This poster proposes a robust optimization modelfor the optimal transmission switching problem of the power
networks to cater to the short-term uncertainties in the power
injections of demands and renewable energy sources. The noveltyof the proposed model is that the base-point generation of theconventional generators and the switching statuses of the transmission lines are considered as robust variables. The proposed
approach uses the Benders Decomposition technique, with bothfeasibility and optimality cuts, to solve for the robust variables.
Index Terms—Transmission Switching, Mixed-Integer Programming, Robust Optimization, Benders Decomposition.

I. I NTRODUCTION
Optimal Transmission Switching (OTS) is typically an operational problem. The real-time operation of the system isusually heavily influenced by various uncontrollable factors
such as the uncertainty and variability in demand, uncertainty
in transmission and generation parameters, equipment failures,
weather, etc. Hence, it is important to develop a proficientapproach for OTS that considers the multi-fold uncertainties in
the power network, with base-point generation of conventional
generators and switching statuses of lines considered as robust
variables.
II. P ROPOSED APPROACH FOR ROBUST OTS
The robust OTS model (1)-(7) with demand and outputof RESs as major sources of uncertainty is considered. The
proposed approach for robust OTS intends to find the robust
values of the base-point generations (Pgbasei ∀i ∈ G) of theconventional generators and the transmission line switchingstatuses (αij ∀(i, j) ∈ L), which ensure feasible system operation for all load and RESs output scenarios (including theworst-case), as defined by the uncertainties.
X
X
Ci (Pgbasei + βi{ζdj − ζrj })
(1)
min
i∈G

s.t.

j∈B

− αij Pij ≤ Pij ≤ αij Pij , ∀(i, j) ∈ L(2)−2θ(1 − αij ) ≤ θi − θj − xij Pij ≤ 2θ(1 − αij ), ∀(i, j) ∈ L(3)X
X
Pij − Pgbasei − βi(ζdj − ζrj ) + Pdi + ζdij∈B(4)j∈δ(i)−Pri − ζri = 0, ∀i ∈ B
X(ζdj − ζrj ) ≤ Pgi , ∀i ∈ G (5)Pgi ≤ Pgbasei + βij∈B

To incorporate the injection uncertainties in loads and RESs
outputs, the real power generation P
of a conventional generator
is modified as Pgi = Pgbasei + βi j∈B (ζdj − ζrj )∀i ∈ G.
A. Solution methodology
The proposed formulation is solved by the BD technique[1]. The primal master problem aids in determining the robust
variables while the dual slave problem aids in worst-caserealization of the injection uncertainties. It is observed thatthe dual slave problem becomes unbounded as the base-point
generations of the conventional generators are chosen as robust
variables. To circumvent this, both optimality and feasibility
cuts are used in the proposed approach.
III. N UMERICAL R ESULTS
Effect of load and RESs uncertainty is considered withrespect to OTS operational problem for standard IEEE systems(Tables I, II). The robustness of the solution obtained isverified by MCS and is found to be feasible for all therealizations within the uncertainty set.
TABLE ID ETERMINISTIC OTS AND ROBUST OTS WITH LOAD UNCERTAINTY
System (Max. u%)IEEE 14 (70%) IEEE 30 (10%) IEEE 57 (5%)
Cost of
Deterministic16025.9
1693.2722815.125
Robust16044.277
1706.56
23165.823
generation ($)
TABLE IIROBUST OTS WITH LOAD AND RES S UNCERTAINTY
Uncertainty
GenerationLines which are(u%)
cost ($)
switched off
PdPr016031.130 1-2, 1-5, 2-5, 3-4, 6-11, 6-13, 7-9, 9-14
IEEE 14 70
1-2, 1-5, 2-5, 3-4, 6-11, 6-12, 9-145016035.6251
100 16044.27697
1-5, 2-5, 4-9, 6-13, 12-13
4-5, 9-10, 13-14, 3-15, 4-18,
21-20, 21-22, 24-25, 27-28,022616.6140
30-31, 41-43, 48-49, 49-50,
IEEE 57 5
44-45, 56-41, 56-42, 57-56, 9-55
2-3, 13-15, 1-15, 1-17, 4-18, 5-6, 10-12,
12-13, 14-15, 19-20, 24-26, 26-27,5022761.0281
34-32, 34-35, 37-38, 36-40, 38-44,
53-54, 11-43, 40-56, 56-42, 38-48
9-10, 9-13, 1-15, 3-15, 24-26,
100
23165.8228
41-43, 38-44, 56-42, 38-49
SystemR EFERENCES−θ ≤ θi ≤ θ , ∀i ∈ B, θref = 0(6)

αij ∈ {0, 1}, ∀(i, j) ∈ L(7)[1] R. A. Jabr, “Robust transmission network expansion planning withuncertain renewable generation and loads,”IEEE Trans. Power Syst.,vol.
28, no. 4, pp. 4558 – 4567, Nov. 2013.

Integration of Renewable Energy Systems to Reduce
Greenhouse Gas Emissions
Asim Chaulagain, Muhammad Khalil, Sejuti Fatima, Raynier Leyeza, Maisha Nasim, Muntasir Sauvik, Jason Pannell, Ramakrishna Gokaraju
Electrical and Computer Engineering, University of Saskatchewan, Sasakatoon, Canada
Email: chaulagain.asim@usask.ca, ahsan.khalil@usask.ca, sef526@mail.usask.ca, rjl048@mail.usask.ca, mnn525@mail.usask.ca,
sms804@mail.usask.ca, jason.pannell@usask.ca, rama.krishna@usask.ca

Abstract—With recent technological advancements in energy
storage systems, power production using wind and solar-based
renewable energy systems has become more viable. This poster
presents the integration of renewable energy systems as a powersource displacing GHG intensive electricity production in
Saskatchewan. For this, Federated Co-operatives Limited (FCL)
facilities, which are dispersed throughout Saskatchewan, aretaken as a case system. Wind and solar data from diverse parts ofthe province are collected and analyzed for choosing the best
renewable energy systems based on geographic location. Hosting
Capacity Analysis is performed in selected individual sites toensure that the current grid can accommodate the distributed
energy resources (DER). Finally, the results from the Cost-Benefit
Analysis reflects the viability of integrating renewable energies at
a specific location.

objectives of the proposed project are, therefore, to carry out an
in-depth cost-benefit analysis, hosting capacity analysis, andgrid integration studies with the renewables mentioned earlier
and energy storage, with primary focus to reduce GHG
emission. Various sites of Federated Co-operatives Limited(FCL) in Saskatchewan are used as case-studies.
II. KEY RESULTSTABLE I.

Monthly power bill of FCL Facilities

FCL Total Carbon
Footprint$285,197

2.1 kilo tonnes CO2 / year

TABLE II.

Keywords—Greenhouse Gas, Grid Integration, Distributed
Energy Sources, Hosting Capacity, Cost Benefit Analysis

I. INTRODUCTION
In Saskatchewan, the power is produced mostly through coalplants (about 32%). SaskPower, which is Saskatchewan’s
primary utility company, is taking the initiative of reducing
greenhouse gas emissions by 40% by the year 2030 [1]. The
utility’s plan to achieve this goal is by significantly increasing
renewable generation and supporting independent power
producers who can host renewables and modernizing the grid(adding energy storage, HVDC interconnections to Manitoba,etc.). During the past decade, several research papers andstandards have appeared explaining the grid interconnection ofrenewable energy sources [2]-[3]. Countries such as Australia,
Denmark, Germany, UK, and some states in the USA, such as
California, have adopted large amounts of solar photovoltaic
generation and wind generation. However, there is a very limited
amount of solar photovoltaic and wind generation currently
being used in Canada except for solar PVs in Ontario. A recent
work from the research group analyzing the hosting capacity ofphotovoltaic (PV) for a remote community in the northern partof Saskatchewan has been described in [4]. Solar PVs would
provide significantly reduced power during the winter months,
which creates hurdles in terms of providing a cost-benefit to the
IPPs. The challenge for the western Canadian provinces withthese two renewables is the inter-provincial transmission
connections are weak. So the utilities would need large amountsof energy storage to support more of these two renewables
sources within its jurisdiction. Putting large amounts ofelectrical energy storage also adds significantly to the capitalcost. Limited planning studies and research literature are dealing
with this subject in the Canadian context. The overarching

MONTHLY POWER BILL AND CARBON EMISSION DATACOST BENEFIT ANALYSIS (SOLAR)

SystemTotal
Cost ($)

Total Benefit($ bill saved /
year)

Break
Even
(years)25 kWh with battery

126081.63

4874.43

25.87

25 kWh without battery

97536.51

4874.43

20.00

292420.17

19699.69

14.80

178844.67

19699.69

9.1

197393.42

13452.22 gained
from PGPP

14.7

100 kWh with battery no
PGPP
100 kWh no battery no
PGPP
100 kWh with PGPPTABLE III.

REDUCTION IN GHG EMISSION

SystemPower Generation(kW/year)

Projection of GHGE
reduction in 20 years

25 kWh Solar

31098

560 tonnes

100 kWh Solar

1243042240 tonnes

III. REFERENCES
[1]
[2]

[3]

[4]

2030 Emission Reduction Goal Progressing, July. 2018.
R. K. Varma, M. Salama, R. Seethapathy and C. Champion, “ large-Scale
Photovoltaic Solar Power Integration in Transmission and Distribution
Networks”, 2009 IEEE PES General Meeting.
S. Narasimalu and B. Chelliah, "Integration of Energy Storage Systemwith Renewable Energy Source," 2018 Asian Conference on Energy,
Power and Transportation Electrification (ACEPT), Singapore, 2018, pp.
1-7.
K. Joshi and R. R. Gokaraju, "An Iterative Approach to Improve PV
Hosting Capacity for a Remote Community," 2018 IEEE Power & Energy
Society General Meeting (PESGM), Portland, OR, 2018, pp. 1-5.

Learning to Solve Optimal Power Flow via Neural
Decoding
Yize Chen∗∗ Department of Electrical Engineering, University of Washington, Seattle, USA

yizechen@uw.edu

Abstract—Many decision-making problems in engineering applications such as transportation, power system and operations
research require repeatedly solving large-scale linear programming problems with a large number of different inputs. For
example, in energy systems with high levels of uncertain renewable resources, tens of thousands of scenarios may need to besolved every few minutes. Standard iterative algorithms for linear
network flow problems, even though highly efficient, becomes abottleneck in these applications. In this work, we propose a novel
learning approach to accelerate the solving process. By leveraging
the rich theory and economic interpretations of LP duality, weinterpret the output of the neural network as a noisy codeword,
where the codebook is given by the optimization problem’s KKT
conditions. We propose a feedforward decoding strategy thatfinds the optimal set of active constraints. This design is error
correcting and can offer orders of magnitude speedup comparedto current state-of-the-art iterative solvers, while providing muchbetter solutions in terms of feasibility and optimality compared
to end-to-end learning approaches.

I. I NTRODUCTION
In many engineering applications, optimization programs
are solved repeatedly to find real-time decisions. Among them,
network flow problems form an important class and havebeen studied for decades with wide-ranging applications. Theyarise naturally in the context of transportation, networking,communication and energy systems. In many of these settings,
a network flow problem takes the form of a linear program(LP). Computational challenges exist in practice. Often thesechallenges are due to the increased stochasticity and the lowlatency requirements of real-time applications. The resource
allocation process in power systems is called the optimal
power flow (OPF) problem, where it takes the form of a
network flow problem that minimizes the cost of power
generation to satisfy the loads, subject to all of the physicalnetwork constraints (e.g., generator limits and line capacities).
Typically, a linear version called DCOPF–an LP problem–isoften applied in practice and solved periodically (e.g., every 5
minutes) to find the optimal operating conditions. Because of
the uncertainties brought on by the renewables on many of thenodes, the number of generation and load scenarios that need
to be considered are starting to grow exponentially. Despite theinherent similarity between scenarios, the OPF problem needs
to be resolved. Even if each one of the LPs takes 0.1 seconds
using modern solvers, not all of them can be completed within
the required time period. Therefore, using machine learning
to learn the mapping between variable input load profiles
and the corresponding optimal generation outputs has gained

significant attention, since making inference via a trainedarchitecture can offer orders of magnitude of computational
speedup compared to an iterative solver.
In this poster, we answer the question of whether a (standard) neural network can learn the solution of LP problems in
the affirmative. Instead of viewing it as an end-to-end learningtask, we leverage the rich algorithmic understanding of LPs
as well as the economic interpretation of the primal and dual
variables to offer a new solution architecture. Our workflow isshown in Figure 1. Concretely, we construct a neural networkthat takes the net load at each node as the input and the optimal
system costs as the output. However, we do not take these
outputs as the solution of the LP. Rather, using the neuralnetwork, we compute the gradient of the cost with respect tothe net loads. Identifying these as the dual variables, we usethem to predict the binding nodal and line constraints. Oncethese constraints are found, the optimal solutions are given
by solving a simple linear system of equations. The overall
procedure can be seen as an efficient surrogate learning modelfor optimization solvers.

Fig. 1: The schematic of our proposed Neural Decoder for solving LP
problems. During supervised training, a neural network (NN) is learned to
predict the optimal objective value; during implementation for solving LP, weinterpret the NN’s gradient as a noisy codeword to find active constraints,
and solve linear equations to get optimal solutions satisfying all constraintsof original problems.

Developing Bidding Strategy for Day-Ahead Electricity
Markets Using Reinforcement Learning Methods
Haotian Chen, Student Member, IEEE Rui Bo, Senior Member, IEEE
Missouri University of Science and Technology, Rolla, USA (rbo@mst.edu)

I.

INTRODUCTION

In electric markets, GENCOs submit offers to
independent system operators (ISOs), and ISO clears dayahead electricity markets by running Security Constrained
Unit Commitment (SCUC), and Security Constrained
Economic Dispatch (SCED). After the market clearing, ISOpublishes the results to market participants including
GENCOs. The results include energy clearing price andgeneration dispatch, which determines the revenue of the
GENCOs. In fully competitive markets, GENCOs are
expected to submit marginal cost as the offer price.
However, in electricity markets with limited number ofparticipants and with transmission congestions creating
market power opportunities, it is possible for a GENCO toplace strategic offer into the market, which may affect thecleared dispatch and/or market price and subsequently
change the profits of the GENCO. GENCOs may have the
incentive to develop bidding strategies in order to maximize
their profit.
In the literature, optimization approaches have beendeveloped to optimize bidding strategy based on electricity
market model and forecasted information. In recent years,
reinforcement learning based methods have been applied totackle the bidding strategy problem. The advantage of thismethod is it does not require as much information of themarket and other participants. However, it takes time tolearn and it needs to have the environment to interact withISO clearing process in order to create the learning process.
Although studies have been reported on this topic, thedetails are lacking. This paper intends to provide asignificant detail of the process for applying reinforcement
learning to develop bidding strategy, and provides insightful
discussion around how the performance of thereinforcement learning based method changes with thecomplexity of the domain problem, and the impact ofdifferent design on the performance.
II.

bids, generator offers and cost associated with constraint
violations. This model is implemented by using C++ and
CPLEX.
B. Bidding Strategy for Generators
Prior to day-ahead market clearing, market participants
submit offers/bids to the market for each hour of the next
operating day. Generator owners can apply bidding strategy
on the offer price in the hope to influence market price,which subsequently impact the revenue and profit of thegenerator.
C. Reinforcement Learning Model Structure
The sequence of market clearing and reinforcement
learning method for day i is:1.

The agent makes a decision on the offer pricebased on reinforcement learning method, andsubmit the offer to ISO;2.

ISO clears day-ahead SCED based on market
participant offers and various constraints.

3.

Participants compute their profit based onmarket clearing results;

4.

The agent updates reinforcement learningmodel. Return to 1
III.

In this section, episodic profits from three types ofreinforcement learning methods, which are Q-Learning,
Double Deep Q-Learning (DDQN) and Deep Deterministic
Policy Gradient (DDPG), are compared with results froman optimization method. As shown in figure 1, the final
profit from DDQN and DDPG can achieve the profit
nearly as high as that from the optimization method, andsignificantly better than Q-learning.
6,000,000
5,500,000
5,000,000
4,500,000
4,000,000
Q-Learning

3,500,000

DDQN

3,000,000

Optimization

2,500,000

DDPG

2,000,000
0METHODOLOGY

A. Day-ahead SCED Modelling
The goal of day-ahead SCED is to maximize hourlysocial welfare of the operating day, considering demand

RESULTS

Profit($)

Abstract—This paper introduces the detailed process ofapplying reinforcement learning to solve market participant
bidding strategy problem. The process includes the setup of
market clearing environment, reinforcement learning
structure. A comprehensive study demonstrates thereinforcement learning method can generate bidding
strategies that achieve profits close to those from optimization
methods. This study provides insights to the learning process
and performance of reinforcement learning and demonstrates
the performance varies with the changing condition of theenvironment, and tends to degrade with more complex
patterns or random disturbances in the environment.

100

200

300

400

500

600

Episode Index
Figure 1. Episodic aggregated profit from reinforcement learning
methods (Q-Learning, DDQN, DDPG) and an optimization method

Assessment of Capacitor Banks Control Practices inDistribution Systems with High PV Penetration
Vinicius C. Cunha, José Andrade, Tiago R. Ricciardi,
Fernanda C. L. Trindade, Walmir Freitas

Rafael A. G. Rosolen, Heliton O. Vilibor
Engineering Division
CPFL Energia
Campinas, SP, Brazil{rosolen, hdeoliveira}@cpfl.com.br

Department of Systems and Energy
University of Campinas
Campinas, SP, Brazil{vcunha, jcga, tiago, fernanda, walmir}@dsee.fee.unicamp.br
Abstract—This work evaluates the impact of photovoltaic (PV)generation on the operation of switched capacitor banks for an
entire utility. The number of switching operations must notsurpass four operations per day. In the simulations, the PV
generators considered are small units installed at low voltage(LV) systems. More than 760 switched capacitor banks areassessed, where only the banks controlled by current and power
factor experienced problems with the number of switching. The
proposed solution for these cases is to update their control mode
to reactive power, which, according to the simulation results,
respects the imposed limit for all banks considering scenarios ofhigh PV penetration. Therefore, such change in the control
philosophy represents a plausible solution to counteract theeffects of PV penetration increase.
Index Terms — Capacitor
photovoltaic generation.

I.

banks,

distribution

systems,

INTRODUCTION

The penetration of photovoltaic generation (PV) into
distribution energy systems has been increasing considerablyworldwide. Although the PV generators provide several
benefits, as more diversity of the energy matrix, thedistribution utilities are concerned about how the presence ofsuch generators will affect their established proceedings toplan and operate the distribution systems. Then, one topic tobe evaluated refers to the switched capacitor banks operation.
In this context, this work evaluates the impacts of distributed
PV generation in the switched capacitor banks operation for
all feeders from a Brazilian utility. The PV generators are
considered to be installed at low voltage (LV) systems. The
banks that experience operational problems (i.e., excessive
number of switching) have their control strategy altered as asimple solution to counteract the PV generation increase.
II.

Fig. 1. Percentage of capacitor banks per control mode for all 2,037 banksfrom a Brazilian utility.

III.

DESCRIPTION OF THE STUDIES AND RESULTS

The PV generators installed in the LV systems aredesigned to compensate the consumption of the customer,
based on the net metering tariff system. The radiance curve isacquired from real measurements with 1-minute resolution.
The number of generators installed is specified for each
substation, ranging from 5% to 35% of the total number ofcustomers from each substation evaluated.
The capacitor banks controlled by current and PF
experienced unacceptable increase in their switching number.
Fig. 2 (a) shows the statistical distribution of the number ofswitching operations per day in the form of boxplot for thecurrent and PF control modes. After the change in theoperational mode to reactive power, the number of switchingrespects the limit imposed, as shown in Fig. 2 (b).

(a)

(b)

Fig. 2. Distribution of capacitor banks operations per day (a) before and (b)after the changes in the operational mode.

CAPACITOR BANKS CHARACTERISTICS

For a brief contextualization of the problem size, thisutility provides energy for almost 4.5 million customers in 234
cities through 1,282 feeders. The number of capacitor banks
installed by the utility corresponds to 2,037, with 60% being
fixed. For the switched banks, the most common control modeis current, followed by time, power factor (PF) and reactive
power (kvar). Fig. 1 presents the percentage of capacitor banks
per control mode.
This work was supported in part by Sao Paulo Research Foundation(FAPESP) under Grants #2019/20186-8, #2017/10476-3, #2016/08645-9,
and #2015/24448-6, in part by the National Council for Scientific andTechnological Development (CNPq) under Grant #304783/2016-1 and inpart by CPFL Energia under ANEEL P&D program, Grant PD-000633047/2018.

IV. CONCLUSIONSThis work presents an assessment of the potential impactsof PV generation in the switched capacitor banks. The results
have shown that capacitor banks controlled by current and PF
have surpassed the utility predefined switching limits.
However, simply changing their operation mode for reactive
power control guarantees the proper operation for the banks.

Cross-Market Price Difference Forecast Using Deep
Learning for Electricity Markets
Ronit Das, Rui Bo, Waqas Ur Rehman, Haotian Chen, and Donald Wunsch
Department of Electrical and Computer Engineering
Missouri University of Science and Technology, Rolla USA (rbo@mst.edu)

Abstract—Price forecasting is in the center of decision making
in electricity markets. Many researches have been done inforecasting energy prices while little research has been reported
on forecasting price difference between day-ahead and realtime markets due to its high volatility, which however playsa critical role in virtual trading. To this end, this paper takes
the first attempt to employ novel deep learning architecture with
Bidirectional Long-Short Term Memory (LSTM) units to forecast
the price difference between day-ahead and real-time markets
for the same node. The raw data is collected from PJM market,
processed and fed into the proposed network. The Root Mean
Squared Error (RMSE) and customized performance metric areused to evaluate the performance of the proposed method. Casestudies show that it outperforms the traditional statistical models
like ARIMA, and machine learning models like XGBoost and
SVR methods in both RMSE and the capability of forecasting
the sign of price difference. In addition to cross-market pricedifference forecast, the proposed approach has the potential to be
applied to solve other forecasting problems such as price spread
forecast in DA market for Financial Transmission Right (FTR)trading purpose.
Index Terms—DA/RT price difference, forecasting, Long-ShortTerm Memory, LSTM, electricity markets, deep learning

TABLE IP ERFORMANCE C OMPARISON AMONG DIFFERENT F ORECASTINGT ECHNIQUES
Model
ARIMA(5, 0, 1)
SVR
XGBoostBidirectional LSTM

RMSE ($/MWh)45.18640.54440.19432.209Direction Accuracy (%)
48.36
55.1
56.05
57.82

Moreover, Fig.1 show in time series the cross-market pricedifference forecasting performance of the different models fordifferent time windows in 2016.

I. I NTRODUCTION
With the evolution of competitive deregulated power markets, forecasting of electricity prices has become a vital task
for market participants: accurate prediction of short-term electricity prices is crucial for generation companies (GENCOs) tomake optimal bidding strategies for participation in electricity
market, and thereby get maximum profit.
II. M ETHODOLOGY
The data is collected from the PJM Market from 1st January
till 31st December, 2016. The collected data is from both the
DA and RT markets on an hourly basis. For our purpose, agenerator node is chosen at random. Then price differences
are calculated for the node across the DA and RT markets. In
this paper, we stationarize the series by differencing [1].
The actual model used is the bi-directional variation of theLong-Short Term Memory, which is a kind of Recurrent Neural
Network which can capture both long-term and short-term
dependencies in the data, making is suitable for forecasting,speech recognition, etc. It was proposed by Hochreiter, et al.
[2] in 1997. In a bidirectional LSTM, the input is fed in bothforward and backward directions.
III. R ESULTS AND F UTURE W ORK
A. Results
The proposed model is compared against ARIMA, SVR and
XGBoost.

Fig. 1. Actual and forecasted cross-market price difference of different models
from Oct. 26 to Oct. 27, 2016 in PJM Market

B. Future Work
This paper only deals with a univariate time series model,
where one is looking at the price, or rather the price differencesat different time intervals.
Lastly, the proposed model
currently only forecasts price difference for the next hour. It
should be expanded to multi-hour (such as 24 hours in US
electricity markets) to align with industry practices.
IV. ACKNOWLEDGMENT
This material is based upon work supported by DARPA
under Grant D18AP00054, and by the U.S. Department ofEnergy’s Office of Energy Efficiency and Renewable Energy(EERE) under the Water Power Technologies Office Award
Number DE-EE0008781.
R EFERENCES[1] D. A. Dickey and S. G. Pantula, “Determining the order of differencing
in autoregressive processes,” Journal of Business & Economic Statistics,
vol. 5, no. 4, pp. 455–461, 1987.
[2] S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neuralcomputation, vol. 9, no. 8, pp. 1735–1780, 1997.

Benchmarks and Improvements in Probabilistic
Solar Forecasting
Kate Doubleday, Stephen Jascourt, Vanessa Van Sycoc Hernandez, William Kleiber, and Bri-Mathias Hodge

1200

1200

1000

1000

Irradiance (W/m2)

Irradiance (W/m2)

The field of probabilistic solar forecasting has grown significantly in the past few years as a variety of statistical,
machine learning, and numerical weather prediction (NWP)
post-processing methods have been proposed. These probabilistic forecasts can inform adaptive reserves and stochastic or
robust unit commitment/economic dispatch models. However,the developing field does not yet have a consistent validation
approach to compare novel methods to standard benchmark
methods, which would allow easy comparison among papers. This poster shows standard implementations of common
benchmark methods, then illustrates how advanced forecasting
techniques outperform two of those benchmarks.

800
600
400
200
0

IntervalInterval

0.98
800
0.75
600
0.50

0.98

6

12 18 24 30 36 42 48 54 60 66 72

Time (hours)

(a)

0.8

PeEn

Raw Ensemble

0.6
0.4
0.2

Interval

0.998

0.0

0.750
0.8

EMOS

BMA

0.500
0.250

0.6

0.002

0.4

0.75
0.50

400
0.25

0.25

0.02
200

0.02

0.2
0.0

0
0

prediction intervals, while the ECMWF ensemble has very
sharp intervals. The ECMWF ensemble, however, can be
unreliable with the observation frequently falling outside the
98% confidence interval.

Normalized PowerI. I NTRODUCTION

06

12 18 24 30 36 42 48 54 60 66 72

Time (hours)

6

12

18

24

30

36

42

48

6

12

18

24

30

36

42

48

Time [Hours](b)Fig. 1. Example hourly-resolution benchmark forecasts for 3 spring days for
the SURFRAD site in Boulder, CO using the (a) PeEn and (b) ECMWF NWP
ensemble methods. The fan plots show the 1% to 99% prediction intervals of
the probabilistic forecasts. The orange line shows the observed hourly average
irradiance.

II. F ORECAST B ENCHMARKING
A literature review of 31 probabilistic solar forecast papers
showed that 8 did not include any benchmark method at all,which does not allow the reader to easily compare among
papers. In the remaining papers, four common benchmark
classes were used, of which two are shown in Fig. 1: (1)a persistence ensemble (PeEn) of observations from the past
∼20 days at the same time of day and (2) an NWP ensemble,
shown here using the European Centre for Medium-Range
Weather Forecasts (ECMWF) 51-member ensemble. Thesebenchmarks have different characteristics: the PeEn has broad
K. Doubleday, V. Van Sycoc Hernandez, W. Kleiber, and B.-M. Hodge
are with the University of Colorado Boulder, Boulder, CO, 80309 USA andthe National Renewable Energy Laboratory (NREL), Golden, CO 80401. S.
Jascourt is with Maxar, Gaithersburg, MD 20878. This work was authoredin part by NREL, operated by Alliance for Sustainable Energy, LLC, for the
U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308.
Funding provided by DOE’s Office of Energy Efficiency and Renewable
Energy under Solar Energy Technologies Office Agreement Number 33505.
The views expressed in the article do not necessarily represent the views of
the DOE or the U.S. Government.

Fig. 2. 4-hour ahead power forecasts from the (clock-wise) PeEn, raw
ensemble, EMOS, and BMA methods for a utility-scale (MW’s) 1-axis
tracking plant over two days with mixed cloudiness.

III. A DVANCED NWP P OST-P ROCESSING
To improve upon these two benchmark approaches, two
NWP ensemble post-processing methods were explored forforecasting power from utility-scale photovoltaic plants: anensemble model output statistics (EMOS) method from theliterature [1] and a novel Bayesian model averaging (BMA)[2] method tailored to solar power. EMOS fits a parametrictruncated normal distribution to the ensemble, while BMA
uses a mixture model that also accounts for inverter “clipping.”
For a utility-scale plant in Texas, Fig. 2 compares the results
to the two benchmarks: EMOS and BMA are both sharper
than a basic PeEn, but EMOS can err on the side of too
sharp resulting in poorer reliability. BMA is can more flexibly
capture uncertainty in the ensemble and can improve upon thereliability deficiencies of the raw ensemble.
[1] A. E. Raftery, T. Gneiting, F. Balabdaoui, and M. Polakowski, “Using Bayesian model averaging to calibrate forecast ensembles,” Monthly
Weather Review, 133(5): 1155–1174, 2005.
[2] Simone Sperati and Stefano Alessandrini and Luca Delle Monache, “Anapplication of the ECMWF Ensemble Prediction System for short-term
solar power forecasting,” Solar Energy, 133: 437–450, 2016.

A Dynamic Nonlinear Model of Once-Through BoilerTurbine Units with Superheated Steam Temperature
He Fan, Student Member, IEEE, Zhi-gang Su, Pei-hong Wang
School of Energy and Environment
Southeast University
Nanjing, China
phfan@seu.edu.cn, phwang@seu.edu.cn, zhigangsu@seu.edu.cn

Abstract—With the rapid development of renewable energy inrecent years, the once-through boiler-turbine (OTBT) units in
China are required to improve operating flexibility in order toabsorb more renewable energy integrated into the power grid.
However, it is a challenge for control systems in OTBT units toprovide load following capability while maintaining stablesuperheated steam temperature. To this end, this paper proposesa dynamic nonlinear model of the OTBT units by introducing
superheated steam temperature into the model. Model structureis derived from mass and energy conservation laws by using a
lumped parameter method, and parameters and functions inmodel structure are identified by combining optimization
algorithm and running data. Validation results show that themodel has acceptable dynamic accuracies as well as a clear
physical structure, and, furthermore, it can be used forcontroller design to improve the performance of OTBT units.
Keywords—Controller design, nonlinear model, once-throughboiler-turbine, superheated steam temperature.

Kwang Y. Lee, Life fellow, IEEE
Department of Electrical and Computer Engineering
Baylor University
Waco, TX, USA
Kwang_Y_Lee@baylor.edu

x1  

1
e s
x1 
u1 ,
c0
c0

h d
d  lx3
lx  dxx2  ( fw 1 )(u2  u4 )  ( 1) f ( y1 , hst )u3  ( 3 1 ) x5  k1 1 ,
c1
c1
c1
c1
h d
d  lx3
lx  dxx3  ( fw 2 )(u2  u4 )  ( 2) f ( y1 , hst )u3  ( 3 2 ) x5  k1 1 ,
c2
c2
c2
c2
x4 

u2  u4 x4
 ,
c3
c3

x5  
x6 

1
1
x5 
u4 ,
csw
csw

k2 (hst  h fw ) f ( y1 , hst )u3
c5

h fw  h( x2 ), hst  lx3 



x6
,
c5

(lx3  h fw ) x5
x4  x5, csw  c4  c(u2 ),y1  x2  g ( x2 ),I.

INTRODUCTION

Nowadays, renewable energy in China has been developed
rapidly due to its clean energy merits, but the intermittence andrandomness of renewable energy pose a challenge to thestability of power grid. To reduce the negative influence, oncethrough boiler-turbine (OTBT) units are required to regulate
the unit power frequently. However, frequent load change is
not conductive to safe and economical operation of OTBT
units, especially for superheated steam temperature (SST)system. To capture the essential characteristics of OTBT units,
we propose a dynamic nonlinear model of OTBT units byincorporating SST into the model, where model has a clear
physical structure derived from mass and energy conservation
laws. Importantly, the model has satisfactory performances
suitable for controller design to track load command while
adjusting the SST.
II.

MODEL OF OTBT UNITS

According to the mechanism analysis, model structure of
OTBT units in the state-space form can be given as below:

This work is supported by National Natural Science Foundation of China(51676034).

y2  x3 ,y3  x6 ,y4  T ( y1 , hst ),where [x1, x2, x3, x4, x5, x6]=[rB, pm, hm, Ds, Dswr, Ne]; [u1, u2, u3,u4]=[uB, Dfw, ut, Dsw]; [y1, y2, y3, y4]=[pst, hm, Ne, Tst]; and h(.)
and T(.,.) are nonlinear functions.
III.

VALIDATONSTABLE I. MEAN RELATIVES ERRORS AND ROOT MEAN SQUARE ERRORS OFOUTPUT VARIABLES IN A WIDE LOAD RANGE
Index
MRE(%)RMSE

Output variables
pst (MPa)hm (kJ.kg-1)

Ne (MW)

Tst (oC)

0.66
0.18

0.62
24.12

0.76
7.8

0.36
2.66

IV.

CONCLUSION

The model proposed has a physical structure and isvalidated with a proper accuracy. The dynamic model can beused for controller design to track the load command andmaintain stable superheated steam temperature.

Distributed Dynamic State Estimation in Power
Systems under Cyber Attacks
Arnold Fernandes Student Member, IEEE, Rui Bo Senior Member, IEEE
Electrical and Computer Engineering Department
Missouri University of Science and Technology, Rolla MO, 65401

Abstract—A distributed dynamic framework for estimating thestates of the power system has been presented. This framework
employs distributed Kalman filters located at distributed control
centers that obtain locally available measurements and state estimates from neighboring control centers. The Kalman filters thenaggregate the information and obtain an estimate of the states.
A linearized state-space dynamical model of a power system
consisting of synchronous generators, buses and static loads has
been considered along with linear measurement functions. It has
been shown in the literature that Bayesian learning can be utilized
to filter out Gaussian attack signals. A similar technique has been
applied to secure the linearized power system.
Index Terms—Distributed estimation, security, attack mitigation

I. I NTRODUCTION
With the increasing usage of phasor measurement units(PMUs) it is possible to obtain high resolution measurements.
This measurement data could be given as input to the dynamic
state estimator which combines the measurement information
along with the power system dynamic model information to
give a temporally consistent state estimate.
A distributed state estimation framework is advantageous as
it reduces long distance communication, eliminates the needfor a centralized state estimation algorithm, and allows forparallel computation. Attack detection and mitigation in a
distributed estimation framework will be considered here.
II. P OWER S YSTEM DYNAMIC M ODEL
Di i
ωi
i
ω (t) + 0 i (Pm− Pei (t)),
i2H2H
Xqi − Xdi i
1
Ėqi (t) = − i Eqi (t) + bi0 z1i (t) + bi1 z2i (t) +Id (t),
T
Ti
i
i
i
i i
i i
i i
ż1 (t) =z2 (t), ż2 (t) = −c1 z2 (t) − c0 z1 (t) + u (y , t)

and the dynamical estimator is


l
l
l
l
X̂kl =Ad X̂k−1+ Bd Uk−1 + Kk−1
Yk−1
− Ŷk−1X ml+ Llk−1
X̂k−1− X̂k−1
.

(5)

m∈N l

IV. BAYESIAN L EARNING
In the event that an attacker tries to inject a Gaussian attacklsignal V k ∼ N (µa l , Ra l ) at the lth DCCl

Ykl = C l Xk + Vkl + V k ,(6)variational Bayesian learning is used to estimate the attacklmean µ̂a l and attack co-variance R̂a [1]. These values areprovided to the Kalman Filter when attack is detected so thatthe attack is filtered out along with the noise.
V. R ESULTS AND D ISCUSSIONω̇ i (t) = −(1)
A linearized version of the aforementioned model is obtainedin Equation (2) which is dicretized to give equation (3)Ẋ = AX + BU + W(2)

Xk = Ad Xk−1 + Bd Uk−1 + Wk−1 .

(3)

III. D ISTRIBUTED K ALMAN F ILTER

1
0.5
0
0

0.1

0.2

0.3

Time(s)Fig. 2: Attack detection at l = 2
A median χ2 detector is used to detect attacks at the DCC.
Here the measurements available to DCC 2 are attacked. Oncethe attacks are detected Bayesian learning can be used tomitigate the attack.
R EFERENCES

The measurement at the lth distributed control center (DCC)
and the estimate are given by
l
l
l
l
l
Yk−1
= C l Xk−1
+ Vk−1, Ŷk−1= C l X̂k−1,

β(M ed(Q2 ))δ̇ i (t) =ω i (t),Fig. 1: Distributed Power System under Attacks(4)[1] S. Zheng, T. Jiang, and J. S. Baras, “Robust state estimation under false
data injection in distributed sensor networks,” in 2010 IEEE Global
Telecommunications Conference GLOBECOM 2010, pp. 1–5, IEEE,2010.

[在此处键入]Distribution Market-Clearing and Pricing
Considering Coordination of DSO and ISO:
An MPEC Approach
Linbo Fu, Houhe Chen, Rufeng Zhang and Xue Li
Department of Electrical Engineering, Northeast Electric Power University, Jilin 132012, Jilin Province, China;
Email: 2201800164@neepu.edu.cn
ABSTRACT: Distribution-level electricity market provides a

Key results：

platform for trading energy and grid services from large-

Case 1: DSO market clearing without considering ISO market.

amount of small-scale distributed energy resources (DERs)

Case 2: DSO market clearing considering interactions with thelocated on distribution grids. The behavior of the DERs in

ISO market based on the proposed MPEC model.

the distribution electricity market may ultimately impact the

Figure 2 and Figure 3 verify the proposed method with themarket clearing and locational marginal prices (LMP) in the

coupled PJM5 and IEEE 33 system. Table I compares the results

wholesaleof distribution market-clearing in Cases 1 and 2.

market.

This

work

proposesabi-level

optimization model for distribution market clearing anddistribution

locational

marginal

pricing

TABLE I DISTRIBUTION MARKET-CLEARING RESULTS OF ADN(DLMP)

Case 1Case 2considering the interactions between distribution andPower from Trans./MW 18.72
DER1/MW24
DER2/MW24
Total cost /$1807.218.72
24
241732.32transmission wholesale markets. In the proposed model, theupper-level model represents the distribution system
operator (DSO) market clearing and the lower-level model
represents the wholesale market-clearing by the independent
system operator (ISO). The mathematical program withequilibrium constraints (MPEC) approach is applied to findthe equilibria among DSO and ISO. Results show that the
LMP at the substation will impact the DER dispatch, power
demands of the DSO as well as the DLMP. In turn, the power
demands of the DSO will further impact the ISO market andits LMPs. Using MPEC approach to clearing markets canimprove the economic benefits of the distribution network.
DER 1

...

DER N2Upper-level
DSO market clearing

Fig.2 Comparison of LMPs in transmission system
in Case 1 and Case 2

DSO

LMP ofcoupling point

Purchase active powerfrom the substation

Substation

ISO

Lower-level
ISO market clearing

Fig.1 DSO-ISO market clearing framework

Fig.3 Comparison of DLMPs in 33-node ADN
between Case 1 and Case 2

Trading of Smart Grid Data: Advantages andChallenges
Soumyajit Gangopadhyay, Sarasij Das
Department of Electrical Engineering
Indian Institute of Science
Bangalore, India
Email: soumyajitg@iisc.ac.in, sarasij@iisc.ac.in

Abstract—Smart grid data form the backbone of various
data driven power system applications like identification of low
frequency oscillation sources, instability prediction and postmortem analysis of blackouts. Currently, the access of smart
grid data is limited due to the lack of a data trading platform.
Trading of smart grid data may enable proper data exchange
across various entities of smart grids. This may help to exploit
the beneficial uses of the data. Besides data access, trading ofsmart grid data has several other advantages. However, several
challenges are also associated with trading of smart grid data.
This paper discusses the advantages and the challenges involvedin trading of smart grid data. Possible solutions are also discussed
that may help to address some of the challenges.

I. I NTRODUCTION
Smart grid data mining can help in understanding the dynamics of the power system during power system disturbancesalong with identification of low frequency oscillation sources
and instability prediction in power grids. The advantages
gained from trading smart grid data are outlined below.

B. Revenue generation
Trading of smart grid data shall lead to the generation ofrevenue. The obtained revenue can be used to install highly
accurate sensors in substations. This may help in substation
automation to a great extent.
C. Better data management practices
The revenue earned from trading of smart grid data can beused to improve the existing data management practices. This
may in turn help to improve the quality of the data.
III. C HALLENGES INVOLVED
A. Determining the price of data
Price of data may depend on various factors like data accuracy, completeness, measurement rate, cost of data collection,
demand for the data and age of the data. Determining theinfluence of each of these factors on the price of data is oneof the challenges associated with trading of smart grid data.
B. Balancing privacy concerns against the value of data

TABLE IS MART G RID DATA T YPES

Measurement data

Planning data
Operational data
Meteorological data
Financial data

Smart meters, EMS, DEMS,SCADA,
PMU,
power
quality meters, disturbancerecorders
Network and generation expansion planning data
Scheduling, congestion data
Weather, GIS data
Energy market data

II. A DVANTAGES GAINED

Privacy concerns of the data owners may limit the accessof smart grid data. Addressing the privacy concerns during
trading of smart grid data is a challenging task. Privacy
preserving algorithms may be used to mitigate the loss ofprivacy. However, the value of data reduces with increasing
privacy preservation. Therefore, balancing between protecting
privacy of the data owners and maintaining the value of data isa tough issue. Privacy pricing may help to address this issue.
C. Ensuring security
Security is an important aspect during smart grid data trading. Proper record of the transactions need to be maintained
within a secured database to increase the trust among thedata buyers. Using the blockchain technology for data access
control can help to ensure security.

A. Increased accessibility of smart grid data
Trading of smart grid data can help to access the various
types of smart grid data listed in Table I. This may help to
exploit beneficial uses of the data. For example, PMU data
may be used to identify the source generator responsible forlow frequency oscillations in power grids. Weather forecastingdata can be used along with historical energy consumption data
to increase the accuracy of load forecasting.

IV. C ONCLUSION
Advantages and challenges in smart grid data trading are
outlined in this paper. Research is needed to address thechallenges for successful trading of smart grid data.

A Sensitivity-based Approach to Adaptive Under-Frequency Load
Shedding
Mukesh Gautam, Student Member, IEEE, Mohammed Benidris Member, IEEE,
Department of Electrical & Biomedical Engineering,
University of Nevada, Reno, Reno, NV 89557(emails: mukesh.gautam@nevada.unr.edu, mbenidris@unr.edu)
Abstract—This paper proposes a Lagrange multipliers-based
adaptive under-frequency load shedding (UFLS) method for
frequency control in power systems. UFLS is one of key factors
for frequency stability especially in stressed power grids with highpenetration of renewable energy generation and deregulated electricity market. Conventional UFLS methods usually shed fixedamount of predetermined loads at predetermined locations which
can lead to over or under load curtailment. This paper presents
an adaptive method, based on Lagrange multipliers of power
balance constraints, to determine not only the amount of theload-step to be shed but also the best locations for load shedding.
This method applies a two-stage strategy for load-shedding. In
the first stage, the deficit/disturbance power is computed basedon the initial rate of change of frequency. In the second stage, theoptimization analysis is performed so as to calculate Lagrange
multipliers, which are used in the determination of the locationand the amount of load-step to be shed. The proposed method isimplemented on several test systems including the reduced 9-bus
3-machine Western Electricity Coordinating Council (WECC)
system and the New England 39-bus 10-machine system. The
results show that the proposed UFLS approach can effectively
bring the system back to normal state after contingencies.
Index Terms—Adaptive load shedding, frequency control, Lagrange multipliers.

I. I NTRODUCTION
Frequency stability is one the main concerns in modern
power systems due to electricity market deregulation andhigh penetration of renewable energy sources. Frequency drop
occurs usually when the generation deficit occurs in the powersystem. Load shedding is usually performed when the systemfrequency drops below predefined frequency setting. Sinceload shedding is performed as the last remedial action, it
should be quick, able to protect system from danger state, andable to avoid unnecessary load curtailment. Conventionally, toretain the power balance in case of generation deficit, a fixedamount of predetermined load at predetermined locations isgradually shed based on frequency deviations. Semi-adaptive
and adaptive approaches have been found more effective andefficient than fixed amount of predetermined load shedding
at certain predetermined locations. Among these schemes,
adaptive UFLS has advantage of optimizing the amount of load
to be shed. Adaptive load shedding is basically performed in
two steps: calculation of real deficit power in the system anddistributing the deficit power on certain load-shedding steps.
Therefore, a reliable and appropriate methodology that relates
the sensitivity of frequency to loads to determine the size andlocation of load to be shed is crucial for developing an adaptive
UFLS scheme.

Numerous methods have been proposed in the literature forthe control of frequency instability resulting from generation
deficit. Anderson and Mirheydar [1] have proposed a method
for setting under-frequency relays based on the initial rateof change of frequency. An under-frequency load shedding
scheme for islanded microgrids, which is not only independentof the microgrid parameters but also considers power generation variations during the process has been proposed in [2].
Although numerous adaptive UFLS schemes for determinationof deficit/disturbance power have been documented in the literature, determination of the locations and sizes of load-shedding
steps simultaneously using sensitivity based approach has
not been given much attention. The work proposed in [3]determines the location and step size of load shedding basedon the frequency sensitivity of the loads which does notcount for the change in Lagrange multipliers of power balance
constraints. This paper uses Lagrange multipliers to capture theimpact of power balance constraints on the power balance.
This paper proposes an adaptive method for the frequency
control of power system that sheds the optimal amount ofloads at optimal locations. A two-stage strategy is implemented for the adaptive load shedding. In the first stage,
the deficit/disturbance power is calculated based on the rateof change of frequency. In the second stage, deficit powers
calculated in first stage are distributed based on Lagrange
multipliers of the power balance constraints to determine
the location and amount of load to be shed. Therefore, theproposed method simultaneously determines the locations andamounts of load-shedding steps. The proposed approach isapplied on several systems including the reduced WesternElectricity Coordinating Council (WECC) system and the New
England 39 bus. The results show that the proposed UFLS
approach can effectively bring the system back to normal state
after contingencies.
R EFERENCES[1] P. Anderson and M. Mirheydar, “An adaptive method for setting underfrequency load shedding relays,” IEEE Transactions on Power Systems,
vol. 7, no. 2, pp. 647–655, May 1992.
[2] A. Ketabi and M. Fini, “An underfrequency load shedding scheme forislanded microgrids,” International Journal of Electrical Power & Energy
Systems, vol. 64, pp. 599–607, November 2014.
[3] C. P. Reddy, S. Chakrabarti, and S. C. Srivastava, “A sensitivity-based
method for under-frequency load-shedding,” IEEE Transactions on Power
Systems, vol. 29, no. 2, pp. 984–985, March 2014.

A data-driven policy design for addressing theramping product nodal deliverability issue in realtime markets
Mohammad Ghaljehei, Student Member,IEEE, and Mojdeh Khorsand, Member, IEEE
School of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, AZ 85287, USA
Email: mghaljeh@asu.edu and mojdeh.khorsand@asu.edu
New resource mix, e.g. renewable resources, are imposing
operational complexities to modern power systems byintensifying uncertainty and variability in the system net
load. The intensified uncertainty and variability lead to asignificant need for ramp capabilities as illustrated by the“duck curve” of the California independent system operator(CAISO) [1]. In the case of insufficient ramp capabilities inthe system, the power balance violation will occur, which
can not only jeopardize the system’s reliability, but can also
cause high penalty prices during the real-time (RT) market
processes and consequently create market inefficiency in thelong run [2]. To address this issue, some ISOs, e.g., CAISO
and Midcontinent ISO (MISO), have been augmenting theiroperating markets with flexible ramping products (FRPs) inorder attain higher responsiveness from the existing flexible
resources to meet the net load’s variability and uncertainty.
Since the FRP requirement policies proposed fromindustry are based on the proxy system wide or zonal
requirements, there is no guarantee that FRPs, which areprocured by the market, will actually be deliverable without
violating transmission line limits. The reason can simply be
associated with the fact that the deliverability for the postdeployment of FRPs within transmission limits isdisregarded when making decision on the FRPs allocation.
More advanced techniques to deal with the uncertainty
imposed to the power system include, but are not limited to,(i) stochastic programming, wherein the uncertainties are
explicitly represented and simultaneously solved in themodel [3] and (ii) robust optimization, which mitigates
worst-case consequences [4]. The ISOs in U.S. do not utilize
the stochastic programming and the robust optimization for
the generation scheduling since (i) these approaches arecomplex and computationally challenging for the large-scale
operational scheduling models [5], and (ii) pricing andmarket settlements of markets based on these approaches are
not well-acceptable by stakeholders.
Therefore, an approach is desirable in this situation that cancreate a proper balance between decisions efficiency (i.e.,deliverability of FRP awards) and complexity, while being
practically implementable. As the system operation can beconsiderably improved by exploiting the statisticalinformation of the scenarios, the focus of this paper is tointegrate data-mining techniques and enhanced FRP
requirement policies in a holistic way without disrupting theexisting practices and compromising computational
efficiency.
In this paper, in order to address the post-deployment
deliverability of FRPs, a computationally tractable dynamic
policy is proposed for FRPs awarded in fifteen-minutemarket (FMM), which improves upon the existing industry
models (as shown in Fig. 1). The key idea is to allocate the
FRPs to the flexible resources that can effectively deploy
their ramping capabilities in the corresponding locations
when needed without violating transmission line limits. To
do so, the proposed approach uses data-mining algorithms tospecify response factors for a set of generators that havehigher responsiveness given a set of ramping events.
Furthermore, the impacts of the FRPs post-deployment on
the transmission line flows are predicated so that the ramp
capabilities are allocated to the locations that are potentiallydeliverable. Finally, the performance of the enhanced FMM
market model, modified to include the proposed FRP policy
design, is compared against FMM market model withCAISO’s FRP design through a validation methodology.
This validation methodology mimics RT unit commitment(RTUC) of CAISO’s FMM.
Solve DA market with regulation andcontingency-based reserve

Solve FMM market
with CAISO s FRP
designSolve FMM market
with data-driven
FRP response set
policySolve... Solve
RTUC
RTUC
Net Load
Net Load
Scenario 1
Scenario n

Stochastic programming:
Monte Carlo Simulation
Solve... Solve
RTUC
RTUC
Net Load
Net Load
Scenario 1
Scenario n
Design FRP policy:
Using Data-mining algorithms
Offline phase

Comparing results
Out-of-sample validation phaseFig. 1. Flowchart of the proposed data-driven FRP policy.

REFERENCES[1] CAISO, “What the duck curve tells us about managing a green grid,”
2016.www.caiso.com/Documents/FlexibleResourcesHelpRenewables_
FastFacts.pdf.
[2] N. Navid and G. Rosenwald, “Market Solutions for Managing Ramp
Flexibility With High Penetration of Renewable Resource,” IEEE
Transactions on Sustainable Energy, vol. 3, no. 4, pp. 784–790, Oct.
2012, doi: 10.1109/TSTE.2012.2203615.
[3] J. M. Morales, A. J. Conejo, and J. Perez-Ruiz, “Economic Valuationof Reserves in Power Systems With High Penetration of Wind Power,”IEEE Transactions on Power Systems, vol. 24, no. 2, pp. 900–910,
May 2009, doi: 10.1109/TPWRS.2009.2016598.
[4] C. Zhao, J. Wang, J.-P. Watson, and Y. Guan, “Multi-Stage Robust
Unit Commitment Considering Wind and Demand Response
Uncertainties,” IEEE Transactions on Power Systems, vol. 28, no. 3,
pp. 2708–2717, Aug. 2013, doi: 10.1109/TPWRS.2013.2244231.
[5] M. Doostizadeh, F. Aminifar, H. Ghasemi, and H. Lesani, “Energy andReserve Scheduling Under Wind Power Uncertainty: An Adjustable
Interval Approach,” IEEE Transactions on Smart Grid, vol. 7, no. 6,
pp. 2943–2952, Nov. 2016, doi: 10.1109/TSG.2016.2572639.

A comprehensive method to analyze powersystem resilience to extreme weather
Anwesha Ghorai, Student Member, IEEE and Mojdeh Khorsand, Member, IEEE
School of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, AZ 85287, USA
Email: aghorai1@asu.edu and mojdeh.khorsand@asu.edu
Severe weather is considered as the leading cause of
power system outages in the United States. As per thereports from the Electric Power Research Institute(EPRI), Lawrence Berkeley National Laboratory(LBNL), and U.S. Department of Energy (DOE) [1],there is an estimated loss of $30 -$400 billion per year
due to these events. The change in weather pattern aswell as shift of energy resources from conventional
fossil fuel units to intermittent renewable resources
increase the necessity of quantifying system
performance in response to extreme events. The
concept of reliability in power systems has been
extensively researched; however, the notion of powersystem resilience to low probability, high impact
scenarios has not been explored extensively. The first
definition of resilience was given by [2] as the systemcompetency to sustain instabilities without itself
deteriorating to it, has been perceived by different otherdisciplines like Economy, Social, Medical sciences [34]. A more formal explanation of a resilient power gridis given by Federal Energy Regulatory Committee(FERC) as the “the ability to withstand and reduce themagnitude and/or duration of disruptive events, which
includes the capability to anticipate, absorb, adapt to,
and/or rapidly recover from such events” [5]. Several
methodologies have been proposed to measure the grid
resilience in terms of discrete metrics. A criticaldimension of the resilience, which is to quantify it
based on a time dependent approach considering
different phases of power system response to a severe
weather, has been introduced in [6].
Majority of these prior work rely on steady state
analysis of power to assess power system resilience toextreme events. However, power system response
during extreme events need to be evaluated from its
dynamic response perspective, which is governed byvarious control and protection assets responses.
Adequate analysis of system behaviour during extreme
events requires proper and simultaneous assessment ofboth protection scheme behaviour and dynamic
characteristics. Analysis that is conducted withinadequate consideration of these two aspects may
result in an inaccurate assessment of system behaviour.
This work proposes a framework to evaluate system
resilience while considering both transient stability as
well as steady state response. An overview of the
proposed method is shown in Figure 1.
The weather profile of the hurricane data is outlinedwith the fragility curve in order to obtain the failure
probability as a function of weather intensity for eachtransmission line. Monte Carlo Simulation is used to
identify the status of lines as a binary, i.e., ‘1’ meaning
the line is on and ‘0’ meaning the line is tripped, basedon the failure probability. After this, the system might

become unstable due to line outages; thus, it isessential to evaluate stability of power system via TDS
while including protection systems. The settings ofprotection devices are chosen according to the North
American Electric Reliability Corporation (NERC)
Protection and Control (PRC) standards. This paper
considers distance relays, underfrequency load
shedding (UFLS), undervoltage load shedding relays(UVLS), and under frequency generator tripping as theprotection models. Moreover, steady state response of
power system is evaluated using AC Optimal Power
Flow (ACOPF). The objective function for ACOPF is
minimizing amount of load shedding happening at eachsimulation step. In this paper, percentage of load shed
for the operational resilience and number of onlinetransmission lines for the infrastructure resilience aretaken as the resilience indicators and resilience metrics
are calculated based on the results of TDS and ACOPF.
The proposed algorithm is tested on the IEEE 145 test
system using PSSE Software tool.

Fig.1. The proposed approach

REFERENCES[1] Anya Castillo, “Risk analysis and management in power outage
and restoration: A literature survey,” Electric Power Systems
Research, vol.107, pp 9-15, 2014.
[2] C. S. Holling, “Resilience and stability of ecological systems,”
Annu. Rev. Ecol. Systematics, vol. 4, pp. 1–23, 1973.
[3] W. Neil Adger, “Social and ecological resilience: Are they relate”
Progr. Human Geography, vol. 24, pp. 347–364, 2000.
[4] A. Rose, “Economic resilience to natural and man-madedisasters: Multi- disciplinary origins and contextual dimensions,”
Environ. Hazards, vol. 7, no. 4, pp. 383-398, 2007.
[5] Federal Energy Regulation Committee, Grid Resilience in
RTOs/ISOs, June 2018.
[6] M. Panteli, P. Mancarella, D. N. Trakas, E. Kyriakides and N. D.
Hatziargyriou, “Metrics and Quantification of Operational andInfrastructure Resilience in Power Systems,” IEEE Trans. Power
Syst., vol. 32, no. 6, pp. 4732-4742, Nov. 2017.

Coordinated Wide-Area Control of Multiple
Controllers in a Power System Embedded with
HVDC Lines
Pooja Gupta

Anamitra Pal

Vijay Vittal

Arizona State University, USA

Arizona State University, USA

Arizona State University, USA

I. I NTRODUCTION
Low frequency oscillations (LFOs) are recognized as one of
the most challenging problems in electric grids, that can result
into system instability and limit power transfer capability.
Traditionally, LFO damping is provided by PSSs installed at
generator units. But they have to be carefully coordinated todamp both local and inter-area modes. HVDC systems that areprimarily used for power delivery, can rapidly alter their power
flows to enhance the damping of these modes. Additionally,
many other approaches have been attempted to improve thedamping of inter-area modes using the coordinated wide-area
controllers [1], [2], however, a thorough investigation using
detailed control models was not performed.
Addressing some of the issues identified in [2], the key contributions of this work are: a) evaluation of the improvementin small-signal stability that HVDC based SDC (DC-SDC) canachieve, b) use of detailed dynamic models of control devices
such as PSSs and SVCs to accurately determine the interactions between the controls and the network, c) development ofa robust coordinated wide-area damping controller (CWADC)
for a reduced-order polytopic system using LMI-based mixed
H2 /H∞ control with partial state feedback and regional poleplacement constraints, d) systematic study of the impact ofcommunication delays on the proposed approach and, e)
identification of alternate feedback signals for CWADC.
II. D EVELOPMENT OF C OORDINATED C ONTROLThe state-space model of the system corresponding toan operating condition (OC) posed with polytopic H2 /H∞
formulation is:ẋ = Ax + B1 w + B2 u
z∞ = C1 x + D11 w + D12 u(1)z2 = C2 x + D22 u
y = Cy x + Dy1 w + Dy2 uwhere x is the state of system, u is the control, w is thedisturbance, y is the output, z2 and z∞ are control signals forH2 and H∞ optimizations, respectively. The control law for
the LMI polytopic controller with gain matrix, KLM I−poly , isgiven by u = KLM I−poly xl , where
K11 K12 K13 .. K1l
K21 K22 K23 .. K2l 
KLM I−poly = 
(2)
 ..
..
..
..
.. 
Ki1 Ki2 Ki3 .. Kil
where xl denotes the reduced number of states obtained using
the enhanced SMA, while i signifies the number of controls
present in the system.
III. P RELIMINARY R ESULTS
To investigate the impact of proposed controller, a set ofthree different OCs for a 29 machine, 127 bus WECC system(with multiple controllers) is created: a) base case, b) increasein load at bus 6 by 25 MW and, c) increase in load at bus 55
by 50 MW. Modal analysis of the system at each of the threevertices (OCs) of polytope reveals multiple modes with poor
damping (< 5%). Fig. 1, provides the result of the applicationof the LMI control designed for a single polytopic system. All
modes have attained minimum damping of more than 15%.
15% damping

10

7.5% damping

Imaginary Axis

Abstract—This abstract assesses the stability improvementsthat can be achieved through the coordinated wide-area control ofpower system stabilizers (PSSs), static VAr compensators (SVCs)
and supplementary damping controllers (SDCs) for damping low
frequency oscillations (LFOs) in a power system embedded withmultiple high voltage DC (HVDC) lines. The improved damping
is achieved by designing a coordinated wide-area damping controller (CWADC) that employs partial state feedback. The design
methodology uses a linear matrix inequality (LMI)-based mixed
H2 /H∞ control with regional pole-placement that is robust for
multiple operating scenarios. To reduce the high computationalburden, an enhanced version of selective modal analysis (SMA)
is employed that reduces the number of required wide-area PMU
feedback signals. The studies are performed on a 29 machine,
127 bus equivalent model of the Western Electricity Coordinating
Council (WECC) system-embedded with two HVDC lines.
Index Terms—Coordinated Wide-Area Damping Controller(CWADC), Linear Matrix Inequality (LMI), Polytopic Control.

5

0

-5

-10
-7

-6

-5

-4

-3

-2

-1

0

Real Axis

Fig. 1. LMI control for 29 machine,127 bus system.
R EFERENCES[1] T. Wang, A. Pal, J. S. Thorp, and Y. Yang, “Use of polytopic convexity
in developing an adaptive interarea oscillation damping scheme,” IEEE
Trans. Power Syst., vol. 32, no. 4, pp. 2509–2520, 2017.
[2] P. Gupta, A. Pal, C. Mishra, and T. Wang, “Design of a coordinated wide
area damping controller by employing partial state feedback,” in PESGeneral Meeting (Atlanta), pp. 1–5, IEEE, 2019.

Development and Validation of Models to Assess
Dynamic Response of Converter-Dominated Power
Systems Across Multiple Spatiotemporal Scales
Nischal Guruwacharya† , Ujjwol Tamrakar, Timothy M. Hansen, and Reinaldo Tonkoski
Department of Electrical Engineering and Computer Science
South Dakota State University
Brookings, South Dakota, USA 57007
Abstract—In order to satisfy future electricity demand fromrenewable energy sources such as wind and photovoltaics, a significant amount of converter-based generation is being integrated
into a bulk power grid. The dynamics of such converter systems
can no longer be ignored as before. So, a data-driven, black-box
approach is used to model the dynamics of a power electronic
converter. System identification tools are used to identify the
dynamic models. The different linear transfer function models
thus obtained are combined through a statistical approach toderive a generalized non-linear model that captures the mostsignificant inverter dynamics.

representation for the analytical computation of transient performance parameters. The main focus is on designing reducedorder models for the aggregated power systems model that
accurately estimate system metrics such as the frequency
nadir, the rate-of-change-of-frequency, and the settling timeof frequency.
II. K EY F IGURES

I. I NTRODUCTION
With a growing interest in renewable energy and batteries,
power electronic converters are becoming a crucial part ofpower distribution networks [1]. As the future energy demandis met by converter-based generation, models that accurately
represent the interaction between the grid and the converters
are essential. To accurately model power electronic converters,
one needs to have detailed knowledge of various aspects of aconverter such as its physical topology, the complex modelsof the various voltage/current control loops, the models of thephase-locked-loop, the protection-scheme employed, etc.
Power electronic converters can operate in different states
based on the operating conditions of the grid. The dynamic
behavior of the converter under each of these operating states
can be significantly different. The dynamics of the switching
converters are modeled in a partitioned manner. The partitions
are based on their operating states and are captured by looking
into the input-output characteristics of the converter. This
segmentation divides the system into several simpler linear
models, which in combination covers most of the significant
converter dynamics. Markov switching techniques are used tocapture a wide array of system dynamics by switching between
different states.
Developing detailed mathematical models to capture theinherent dynamic characteristics of aggregated power systems
is critical to study power system reliability. Model reduction techniques (i.e., dominant pole, Routh-Pade, and Schurapproximations) are used to obtain reduced order models
that trade-off accuracy and computational complexity of thetransient performance parameters. The reduced order modelsof the frequency domain are then converted into time domain

Fig. 1: Partitioned modeling of converters.

Fig. 2: Description of the model reduction process.

III. C ONCLUSIONS AND F UTURE W ORK
A data-driven, black-box model for a grid-connected powerelectronic converter was developed from the logged datasetof grid voltage and current supplied by the inverter. In the
future, the dynamics of the inverter under several operating
conditions defined by the IEEE 1547 standard will be exploredand combined to captures the most significant dynamics of theinverter. In addition, we will focus on designing reduced ordermodels that accurately estimate system metrics.
R EFERENCES[1] R. H. Byrne, T. A. Nguyen, D. A. Copp, B. R. Chalamala, and I. Gyuk,“Energy management and optimization methods for grid energy storage
systems,” IEEE Access, vol. 6, pp. 13 231–13 260, 2018.

1

Real-Time Overload Detection Algorithm with
Combined Learning and Analytical Model
Fouad Hasan, Student Member, IEEE, Amin Kargarian, Senior Member, IEEE
II. LINEAR SENSITIVITY-BASED LEARNING ALGORITHM

Abstract—Reliable operation of the electric power system
highly depends on a transmission network that is immune to mixed
type contingencies and immediate actions against thosecontingencies. Operators run optimal power flow (OPF) in every
5-10 minutes resolution to determine transmission flow and toinvestigate any prospective overload in the system. However, forlarge scale systems, it becomes computationally costly andintractable. This paper presents a combined machine learning andanalytical model-based scheme to make an early prediction of line
congestion. The proposed method does not require solving OPF todetermine branch loading that makes it suitable for real-time
management of overload. Numerical results on the EPRI 39-bus
system, IEEE 57-bus system, and the IEEE 118-bus system show
the effectiveness of the proposed algorithm.

Input: 𝑃𝑑 and 𝑄𝑑

Using trained regression learnerPredict̃𝑔𝑃̃𝑔 − 𝐾𝑑 𝑃𝑑
𝑃𝐿 = 𝑆𝐹 × 𝐾𝑝 𝑃
Preventive or
corrective actions

Line overload?

Yes

No
Contingency?

No

Yes
List of contingencies

I. LEARNING-BASED BRANCH FLOW CALCULATION
𝑃𝐿𝑐𝑙 = 𝑃𝐿𝑙 + 𝐿𝑂𝐷𝐹𝑙,𝑘 × 𝑃𝐿𝑘

Key Equations:𝑇

Gen. Prediction: 𝑃̃𝑔 = [𝑝̃𝑔1 , 𝑝̃𝑔2 , 𝑝̃𝑔3 ⋯ 𝑝̃𝑔𝑛 ]Normal flow:
𝑃𝐿 = 𝑆𝐹 × 𝐾𝑃 . 𝑃̃𝑔 − 𝐾𝐷 . 𝑃𝑑
Cont. flow:∀𝑛 ∊ 𝑛𝑔Preventive or
corrective actions

Line overload?
Yes
No

𝑃𝐿𝐶𝑙 = 𝑃𝐿𝑙 + 𝐿𝑂𝐷𝐹𝑙,𝑘 × 𝑃𝐿𝑘

Stop

Fig. 2. Flowchart of the proposed algorithm.

TABLE I
Security classLine loading (%)
Low risk
𝑃𝐿 ≤ 25%
Normal25% < 𝑃𝐿 ≤ 50%
Alert
50% < 𝑃𝐿 ≤ 75%
Emergency
75% < 𝑃𝐿 ≤ 95%
Congested (or overloaded)
𝑃𝐿 > 95%

III. FINDINGS: CONFUSION MATRIX OF IEEE 118 SYSTEM
True state

Low

Predicted state|𝑃𝐿|
𝑃𝑟𝑒 − 𝑐𝑜𝑛𝑡𝑖𝑛𝑔𝑒𝑛𝑐𝑦 𝐿𝑜𝑎𝑑𝑖𝑛𝑔% =× 100|𝑃𝐿𝑚𝑎𝑥 ||𝑃𝐿𝑐 |
𝑃𝑜𝑠𝑡 − 𝑐𝑜𝑛𝑡𝑖𝑛𝑔𝑒𝑛𝑐𝑦 𝐿𝑜𝑎𝑑𝑖𝑛𝑔% =× 100|𝑃𝐿𝑐𝑚𝑎𝑥 |The branch flow state is classified into five classes, as shown in
Table I.

Normal97.9 97.6

96.5 96.7

Contingency

Fig. 1. Overall accuracy of classification by the combined algorithm.

As shown in Fig. 3, there is no mis-grouping with a multi-stepjump (e.g., “Emergency” lines predicted to be in the “Low risk”
category). An operator needs some actions if lines are either inemergency or congested zones. Other misclassifications are notcrucial as loading conditions are in a safe operating zone.

Congested

0

0

0

0

0

301
0.2%
7512
4.0%
109
0.1%

0

0

Congested

0

0

0

99.2%

95.1%

94.8%

TPR

16
0.0%
3566
1.9%
1000
0.5%
77.8%

0
3
0.0%
14
0.0%
82.4%

Accuracy=98.1%, Misclassification=1.9% (IEEE 118-bus)(a)True statePredicted state

Normal

825
0.4%
17735
9.5%
92
0.0%

Emergenc
y0

Low

EPRI 39-bus system IEEE 57-bus systemIEEE 118-bus system

153599
82.6%
1228
0.7%

Alert

Alert

98.1 97.8

95

NormalEmergencyFigure 1 shows the promising accuracy of the proposed
algorithm for early congestion/overload forecast during normal
and contingency situations.
100

Low

Normal

Low
15276
6
82.1%
2037
1.1%

NormalAlert

Emergency

Congested

626
0.3%

0

0

0

0

0

17875
9.6%
86
0.0%

362
0.2%
7253
3.9%
410
0.2%

Alert

0

Emergency

00Congested

0

0

0

98.7%

96.2%

90.4%

TPR

12
0.0%
4010
2.2%
547
0.3%
87.8%

0
1
0.0%
15
0.0%
93.8%

Accuracy=97.8%, Misclassification=2.2% (IEEE 118-bus)(b)Fig. 3. Prediction accuracy of line flow classification during a) normal and b)
contingency IEEE 118-bus system.

1Real-Time Overload Detection Algorithm with
Combined Learning and Analytical Model
Fouad Hasan, Student Member, IEEE, Amin Kargarian, Senior Member, IEEE
II. LINEAR SENSITIVITY-BASED LEARNING ALGORITHM

Abstract—Reliable operation of the electric power system
highly depends on a transmission network that is immune to mixed
type contingencies and immediate actions against thosecontingencies. Operators run optimal power flow (OPF) in every
5-10 minutes resolution to determine transmission flow and toinvestigate any prospective overload in the system. However, forlarge scale systems, it becomes computationally costly andintractable. This paper presents a combined machine learning andanalytical model-based scheme to make an early prediction of line
congestion. The proposed method does not require solving OPF todetermine branch loading that makes it suitable for real-time
management of overload. Numerical results on the EPRI 39-bus
system, IEEE 57-bus system, and the IEEE 118-bus system show
the effectiveness of the proposed algorithm.

Input: 𝑃𝑑 and 𝑄𝑑

Using trained regression learnerPredict̃𝑔𝑃̃𝑔 − 𝐾𝑑 𝑃𝑑
𝑃𝐿 = 𝑆𝐹 × 𝐾𝑝 𝑃
Preventive or
corrective actions

Line overload?

Yes

No
Contingency?

No

Yes
List of contingencies

I. LEARNING-BASED BRANCH FLOW CALCULATION
𝑃𝐿𝑐𝑙 = 𝑃𝐿𝑙 + 𝐿𝑂𝐷𝐹𝑙,𝑘 × 𝑃𝐿𝑘

Key Equations:𝑇

Gen. Prediction: 𝑃̃𝑔 = [𝑝̃𝑔1 , 𝑝̃𝑔2 , 𝑝̃𝑔3 ⋯ 𝑝̃𝑔𝑛 ]Normal flow:
𝑃𝐿 = 𝑆𝐹 × 𝐾𝑃 . 𝑃̃𝑔 − 𝐾𝐷 . 𝑃𝑑
Cont. flow:∀𝑛 ∊ 𝑛𝑔Preventive or
corrective actions

Line overload?
Yes
No

𝑃𝐿𝐶𝑙 = 𝑃𝐿𝑙 + 𝐿𝑂𝐷𝐹𝑙,𝑘 × 𝑃𝐿𝑘

Stop

Fig. 2. Flowchart of the proposed algorithm.

TABLE I
Security classLine loading (%)
Low risk
𝑃𝐿 ≤ 25%
Normal25% < 𝑃𝐿 ≤ 50%
Alert
50% < 𝑃𝐿 ≤ 75%
Emergency
75% < 𝑃𝐿 ≤ 95%
Congested (or overloaded)
𝑃𝐿 > 95%

III. FINDINGS: CONFUSION MATRIX OF IEEE 118 SYSTEM
True state

Low

Predicted state|𝑃𝐿|
𝑃𝑟𝑒 − 𝑐𝑜𝑛𝑡𝑖𝑛𝑔𝑒𝑛𝑐𝑦 𝐿𝑜𝑎𝑑𝑖𝑛𝑔% =× 100|𝑃𝐿𝑚𝑎𝑥 ||𝑃𝐿𝑐 |
𝑃𝑜𝑠𝑡 − 𝑐𝑜𝑛𝑡𝑖𝑛𝑔𝑒𝑛𝑐𝑦 𝐿𝑜𝑎𝑑𝑖𝑛𝑔% =× 100|𝑃𝐿𝑐𝑚𝑎𝑥 |The branch flow state is classified into five classes, as shown in
Table I.

Normal97.9 97.6

96.5 96.7

Contingency

Fig. 1. Overall accuracy of classification by the combined algorithm.

As shown in Fig. 3, there is no mis-grouping with a multi-stepjump (e.g., “Emergency” lines predicted to be in the “Low risk”
category). An operator needs some actions if lines are either inemergency or congested zones. Other misclassifications are notcrucial as loading conditions are in a safe operating zone.

Congested

0

0

0

0

0

301
0.2%
7512
4.0%
109
0.1%

0

0

Congested

0

0

0

99.2%

95.1%

94.8%

TPR

16
0.0%
3566
1.9%
1000
0.5%
77.8%

0
3
0.0%
14
0.0%
82.4%

Accuracy=98.1%, Misclassification=1.9% (IEEE 118-bus)(a)True statePredicted state

Normal

825
0.4%
17735
9.5%
92
0.0%

Emergenc
y0

Low

EPRI 39-bus system IEEE 57-bus systemIEEE 118-bus system

153599
82.6%
1228
0.7%

Alert

Alert

98.1 97.8

95

NormalEmergencyFigure 1 shows the promising accuracy of the proposed
algorithm for early congestion/overload forecast during normal
and contingency situations.
100

Low

Normal

Low
15276
6
82.1%
2037
1.1%

NormalAlert

Emergency

Congested

626
0.3%

0

0

0

0

0

17875
9.6%
86
0.0%

362
0.2%
7253
3.9%
410
0.2%

Alert

0

Emergency

00Congested

0

0

0

98.7%

96.2%

90.4%

TPR

12
0.0%
4010
2.2%
547
0.3%
87.8%

0
1
0.0%
15
0.0%
93.8%

Accuracy=97.8%, Misclassification=2.2% (IEEE 118-bus)(b)Fig. 3. Prediction accuracy of line flow classification during a) normal and b)
contingency IEEE 118-bus system.

1

Impact of Imbalance Settlement System Design on
a Risk-Averse Aggregator of Flexible Demand Side
Resources
Lars Herre
School of Electrical Engineering and Computer Science
KTH Royal Institute of Technology, 10044 Stockholm, Sweden
Email: lherre@kth.se

Abstract—To maintain power balance, system operators havedesigned a spectrum of ancillary services acting on different
time scales to maintain the frequency at its nominal value. In
balancing markets, balance responsible parties can sell or buy
additional energy. The system aims to minimize the real-time
deviation of a balance responsible party from their day-ahead
position. Real-time imbalance is today settled in different ways
depending on the specific regulations. However, the design ofthe imbalance settlement system can have a high impact on thebidding strategy of a balance responsible party. In this paper, thebidding strategy of a balance responsible party under different
imbalance settlement system designs is modeled. Specifically,
the risk-aversion of a balance responsible party managing an
aggregation of flexible loads under uncertainty and the impact
on its profitability as well as overall power system balancing isinvestigated.
Index Terms—demand side management, energy arbitrage,
imbalance settlement, risk management.

I. I NTRODUCTION
To maintain power balance, system operators have designed
a variety of ancillary services acting on different time scales tomaintain the frequency at its nominal value. This is to ensurebalance by penalizing the real-time deviation of a balance
responsible party (BRP) from their day-ahead position. Realtime imbalance is today settled in different ways dependingon the specific regulations. The design of the imbalancesettlement system can have a high impact on the bidding
strategy of actors.
In the Nordic Electricity Market (Nord Pool AS [1]), theimbalance is calculated by eSett OY [2] and broadcast ex-post.
Therefore, a BRP such as an aggregator of flexible controllable
loads, does not know the imbalance settlement price before thedecision about the real-time consumption is made.
In some countries, a one-price imbalance settlement system
is used, where the BRP benefits if it helps the system. For
instance, a BRP would benefit from less consumption thanthe day-ahead bid at times when the system is up-regulating
the supply side, or from more consumption when the system
is up-regulating the supply side.On the other hand, when the
BRP does not help the system, a disadvantageous imbalancesettlement price results in a loss for the BRP. Other countries
use a two-price imbalance settlement system, where the BRP
can never make a profit from being in imbalance. If the BRP
helps the direction of system up-or down-regulation, then thensame price as the day-ahead price is used. If it does nothelp the system, a disadvantageous imbalance settlement priceresults in a loss for the BRP.
These two imbalance settlement systems can result in different day-ahead bidding strategies, as it is shown in thispaper. The BRP faces uncertainty from imbalance settlement
prices and needs to take an optimal decision on the dayahead in a given imbalance settlement system. Furthermore,
the risk-aversion of the BRP is an important parameter that
affects the bidding strategy and the expected profits. A riskaverse BRP would expect lower profits, and be less prone
to risk, whereas a risk-taking BRP can expect higher profits
while taking higher risk. From another perspective, the type of
imbalance settlement system also affects the risk-aversion of
a BRP that wants to keep its conditional value at risk (CVaR)
constant.
Today, in some markets like Sweden, a producer’s imbalance is calculated by a two-price system while a consumer’s
imbalance is calculated by a one-price system. This means that
consumers can profit from being in imbalance. A consuming
BRP with flexible controllable loads can therefore strategically arbitrage energy between the day-ahead market and theimbalance settlement given perfect price information. Since
price forecasts are uncertain, a risk-averse bidding strategy
can support the BRP to maximize the expected profits. The
cost minimization problem of the risk-averse aggregator under
uncertainty is modeled withmin.

(1 − β) · E [cω ] + β · CVaR

(1a)

s.t.

Cost and revenues(1b)Electric power & energy constraints(1c)

Risk-aversion(1d)

II. K EY TAKE -AWAYS
The uncertainty from prices has a major impact on thebidding strategy of the BRP. Different degrees of risk-aversion
are analyzed. It is found that the yearly mean of expected
profits is higher in a one-price imbalance settlement system.
R EFERENCES[1] Nord Pool AS, “Historic Market Data,” October 2018. [Online].
Available: http://www.nordpoolspot.com/historical-market-data/[2] eSett Oy Settlement Service, “Nordic Imbalance Settlement,” 2018.
[Online]. Available: https://www.esett.com/

Computationally Efficient Formulations for
Fault Isolation and Service Restorationin Distribution Systems
Mohammad Mehdi Hosseini, Masood Parvania
Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, UT 84112
Emails: {mehdi.hosseini, masood.parvania}@utah.edu
Abstract—This paper proposes two computationally efficient
mixed integer optimization models, in quadratic-constrained(QC) and linear forms, for optimal real-time fault isolation andservice restoration (FISR) in distribution systems. The proposedmodels integrate an efficient integer programming formulation
to model the automated switching operation, which use only
one set of binary variables, and therefore are suitable to use inreal-time switching application because of their relatively lower
computational burden. The proposed models are implemented on
the 32-bus and 123-bus test distribution systems under multiple
fault scenarios in order to compare their accuracy and speedof computations. The simulations results show that the linear
model can provide FISR solutions in almost real-time, with anacceptable accuracy compared to the QC model.
Index Terms—Fault isolation and service restoration, automated switching, distribution system.

I. C ONTRIBUTIONS & H IGHLIGHTS
The automatic FISR systems rely on switching algorithms
and procedures to isolate the faulted line sections and restore
interrupted load points through main feeder and alternative
backup feeders. This paper investigates a mixed integer programming (MIP) approach for optimizing the real-time switching operation involved in automated fault isolation and service
restoration (FISR) in distribution system. More specifically,
integer programming is first utilized to formulate the automaticand real-time switching operation in distribution systems using
binary variables showing the status of line switching in the
network. The proposed real-time switching formulation is thenutilized to formulate the real-time FISR problem in two MIP
forms, namely, quadratic constrained (QC) and linear. The QC
formulation integrates an accurate branch flow formulation tomodel distribution power flow, while the linear model utilizes
the linear approximation of the formulas.
II. T HE FAULT I SOLATION AND R ESTORATION M ODELThe proposed algorithm finds the best real-time switchingsequence, based on the available automatic switches, and thefault location. Suppose the illustrative distribution system with
two feeders, shown in Fig. 1, undergoes a fault at line l23 .
The algorithm can find the second configuration in this figure,given the location of switches with automation capability,
using a binary variable eij that shows energizing conditionof lines, using the following real-time switching model:|sij − sdij | ≤ aij , ∀i, j,(1)

eij ≤ 1 − fij , ∀i, j,(2)3eij ≥ eki + sij + sji − 2, ∀i, j, k, j 6= k,Xeji ≤ 1, ∀i, j,

(3)
(4)

j|(i,j)∈L

By incorporating the proposed switching method in a grid
operation model, multiple faults may be isolated given any
fault location. In addition, depending on the objective of the
optimization model, states of switches will change to, forexample, maximize load restoration or reduce power loss. Bothquadratic and linear power flow models are tested with theswitching method, and it is shown that both models are highly
efficient in selecting switching sequences, while the linear
model is also fast enough to be used in real-time operations.
The accuracy and computation time of the proposed models
is compared with two common methods in Fig. 2, indicating
substantial saving in computational time.
a) Pre-fault

1

Upstream
Network

6
s56

1

Upstream
Network

6

5

l56

3

l23

s21 s23s12b) Post-fault

2

l12

s32
l45

2

3

l12

l23
s21 s23

5

l56
s56

l34

s54

s65 s45

s12

s34

4

s34

4

l45

s65 s45

Normally-closed switch
Normally-open switch
Automation capability

s32

l34

s54

Energized line (eij = 1)
De-energized line (eij = 0)
Line Fault

Fig. 1. FISR operation procedure

Switching
Accuracy

Proposed Linear Model

Time
Proposed QC Model

Model in [6]Iterative Method
0%

20%

40%

60%

80%

100%

120%

Fig. 2. Computation time and switching accuracy of four methods

A Multi-function AAA Algorithm Applied toFrequency Dependent Line Modeling
Lucas Monzón
Department of Applied Mathematics
University of Colorado, Boulder
Email: monzon@colorado.edu

William Johns, Spatika Iyengar, Matthew Reynolds,
Jonathan Maack, and Kumaraguru Prabakar
National Renewable Energy Laboratory
Email: matthew.reynolds@nrel.gov

Abstract—Modeling of power distribution system componentsthat are valid for a wide range of frequencies are crucial forhighly accurate modeling of electromagnetic transient (EMT)
events. This has recently become of interest due to the improvements needed for the resilient operation of distribution
systems. Vector fitting (VF) is used in electromagnetic transients
programs, like EMTP-RV, PSCAD, and ATP-EMTP, for wideband representations of power system components in EMT
simulations. In this research, we introduce a new multi-input
rational approximation algorithm (MIAAA) based upon [1] andillustrate its advantages with respect to VF. We show that MIAAA
not only outperforms VF in terms of achieving better accuracy
using fewer poles, but also has no numerical issues achieving
convergence. Also, in contrast to VF, MIAAA does not require
good estimates for the location of the approximation poles.

10−6 and fits the linear section of the function with error close
to double precision, while VF performs significantly worse.
Importantly, MIAAA also provides a better approximation
order since it only requires 36 common poles to reach thetarget accuracy in contrast with VF which requires 50 poles.
Although MIAAA was also faster than VF, we do not provide
speed comparisons since additional work is necessary for amore thorough comparison and, for the intended applications,the final number of poles used in the approximation is thedominant factor in running time and not the running time of
VF or MIAAA.

I. K EY M ETHODOLOGY
Starting from K functions {ϕk } K
k=1 with sample values
Nfrom a common set Z = {zn }n=1 ⊆ C, we iteratively build
approximations, by selecting a subset of M (support points)M{zn }n=1 and finding corresponding weights wn so that thequotient of barycentric representations
PM wn ϕk (zn )Bk (z) =

n=1

z−zn
wn
n=1 z−znPM, k = 1, . . . , K(1)

are used to approximate the functions ϕk , k = 1, . . . , K.
Since all Bk have a common barycentric denominator, thefinal rational approximations, written in partial fractions form,
have a common set of poles.
At each step of the iteration, a greedy choice is used to selectan additional support point zm from the initial sample set Z
which is responsible for the largest deviation. We then use
least squares to compute a set of common weights w1 , ..., wm
for the intermediate barycentric approximations.
We terminate the iteration when maxk |Bk (z) − ϕk (z)|z∈Z
is less than the desired tolerance.
II. R ESULTS
To illustrate the performance of the algorithm with respect
to VF, we consider a 6 by 6 terminal admittance matrix of apower distribution system already discussed in the literature[2]. The fitting errors achieved by both methods for two of
the matrix entries, f (1) and f (2), are displayed in Figure 1.
Results for other matrix entries are similar. Note that the
MIAAA approximation is always below the target accuracy

Fig. 1. Comparing vector fitting and MIAAA fitting errors on two entries,
f (1) and f (2), of an admittance matrix.

R EFERENCES[1] Y. Nakatsukasa, O. Sète, and L. Trefethen, “The aaa algorithm for rational
approximation,” SIAM Journal on Scientific Computing, vol. 40, no. 3,
pp. A1494–A1522, 2018.
[2] B. Gustavsen, “User’s guide for vectfit3.m,” SINTEF Energy Research, Trondheim, Norway, 2008. [Online]. Available:
www.sintef.no/projectweb/vectfit

A Reinforcement Learning Approach for BranchOverload Relief in Power Systems
Mariana Kamel
Dept. of Electrical Engineering and Computer Science
The University of Tennessee
Knoxville, TN, USA

Yawei Wang
Dept. of Computer Science
The George Washington University
Washington, DC, USA

Chen Yuan
GEIRI North America
San Jose, CA, USA

Fangxing Li
Dept. of Electrical Engineering and Computer Science
The University of Tennessee
Knoxville, TN, USA

Renchang Dai
GEIRI North America
San Jose, CA, USA

Guangyi Liu
GEIRI North America
San Jose, CA, USA

Abstract— This work presents a reinforcement learning (RL)
approach to the problem of branch overload relief. An agent istrained to re-dispatch generators’ real power output in order toadjust the power flow through the network so that none of its
branches is overloaded. The generation re-dispatch agent istrained using the deep deterministic policy gradient (DDPG)
algorithm. The proposed approach is tested on both the IEEE 14bus and 39-bus systems. Once trained, the performance of the redispatch agents was compared against that of the classical optimal
power flow (OPF) approach. The trained agents demonstrated
better performance with both having: (a) close to a 100% successrate for cases which had an OPF solution; and (b) 70.14% and46.48% success rates for cases which had no feasible OPF solution.
Such results conﬁrm the potential of the proposed DDPG-based
RL re-dispatch approach as a reliable method for managing
branch overloading.
Keywords—Branch overload, deep deterministic policy gradient,
generation re-dispatch, reinforcement learning

Fig. 1. Agent training results for the IEEE 14-bus system: episode reward.

Fig. 2. Testing results for the IEEE 14-bus system: display of solved test cases
according to the number of control steps taken to reach the solution.

I. CONTROL METHODOLOGY
In reinforcement learning, the agent interacts with theenvironment in discrete time-steps. At each time-step 𝑡 , theagent observes the system states 𝑠𝑡 , decides on an action a t andreceives a reward 𝑟𝑡 . The RL agent learns progressively as it
accumulates more and more experience until it learns an optimal
control law. In this work, the system states, actions, and rewards
are defined as follows: Given an N-bus system where 𝒢 is theset of system generators (excluding the slack unit), ℬ is the setof system branches, then; (1) System states are defined as: 𝑆 ={𝑠|𝑠𝑡 = {𝐼𝑖,𝑡 ∀ 𝑖 ∈ ℬ} ∪ {𝑃𝑔𝑗,𝑡 ∀ 𝑗 ∈ 𝒢} where 𝐼𝑖,𝑡 is the currentof the ith branch, 𝑃𝑔𝑗,𝑡 is the real output power of the jth generator.
(2) Action space is defined as: 𝐴 = {𝑎|𝑎𝑗,𝑡 = ∆𝑃𝑔𝑗,𝑡 ∀ 𝑗 ∈ 𝒢}
where ∆𝑃𝑔𝑗,𝑡 is the amount of change in the real output power ofthe jth generator. (3) The reward function is formulated as: 𝑟𝑡 =−1 + 𝑟𝑇𝑀𝐿 where 𝑟𝑇𝑀𝐿 is equal to: +20 if 𝐼𝑖,𝑡 ≤ 𝐼𝑖−𝑚𝑎𝑥 ∀ 𝑖 ∈ ℬ;
ore -20 if power flow diverges; or 0 otherwise.
II. RESULTS
The performance of the proposed RL-based control isinvestigated on both the IEEE 14-bus and 39-bus systems. The
following are the training and testing results:

Fig. 3. Agent training results for the IEEE 39-bus system: episode reward.

Fig. 4. Testing results for the IEEE 39-bus system: display of solved test cases
according to the number of control steps taken to reach the solution.

III. CONCLUSION
The results obtained show the potential of the proposed
DDPG-based RL control as a reliable tool for managing branchoverloading.

Industrial Smart Load Using Five-Level Packed UCell Converter
Amirabbas Kaymanesh, Student Member, IEEE and Ambrish Chandra, Fellow, IEEE
Department of Electrical Engineering
École de Technologie Supérieure (ÉTS), Montréal, Canada
Emails: amirabbas.kaymanesh.1@ens.etsmtl.ca and Ambrish.Chandra@etsmtl.ca

Abstract—By far, numerous comprehensive control methods
have been introduced for electric spring with versatile purposessuch as stabilizing point of common coupling voltage andfrequency. Although introduced controllers are technologicallymature, there are few researches focusing on the electric spring
topology. High switching frequency and voltage stress on
switches, low-power application, high harmonics content of theoutput waveforms are some of the inherent demerits of theconventional two-level converters already employed in theelectric spring configurations. This necessitates employment ofa multilevel converter as electric spring, especially for mediumvoltage industrial applications. Therefore, a five-level electricspring using PUC5 converter with a small-sized flying capacitor
and an open-loop sensor-less voltage balancing method
integrated into its modulation technique is introduced.
Comparing with a two-level electric spring, which has two bulky
capacitors, PUC5-based smart load has only one small size
capacitor. Besides, although it has four more switches, thevoltage and power rating of these switches are much lower. The
steady-state and dynamic performance of the proposed fivelevel electric spring has been also validated.
Keywords— Electric spring, PUC5, multilevel converter,
power quality.

I.

Vsr

Amplifier & sensor

Vs

A
D

PLL

RMS

-

+

│u│

ωt

ϕSL_ref

Vesr_refPI+

RCD ControllerƟNCL-Modulation Index

ϕNCL
S1
S2
S3

logical-operatorbased modulation
techniqueS6 S5 S4

Fig. 2. Schematic of the utilized radial-chordal decomposition controller
modified for PUC5-ES2 application

PUC5-ES2 CONFIGUTATION AND CONTROLLER

Topology of the proposed PUC5-based electric spring(PUC5-ES2) that should be connected in series with anoncritical load (NCL) to form an industrial smart load (SL)is shown in Fig. 1. Besides, a simplified diagram of theemployed controller can be seen in Fig. 2.
II.

Fig. 3. Steady-state waveforms in a grid with extra power generation

MODEL ANALISIS AND RESULTS

The aim of this section is to evaluate the steady-state anddynamic operation of the PUC5-ES2. The modelled system
considered here is a weak power system, which is powered bya conventional AC voltage source and a renewable energy
emulator that is able to inject various amount of active andreactive power to grid through a distribution line. A resistiveinductive load is considered as NCL (12.93+j10.18) and a
resistive load (19.225 Ω) is considered as critical load (CL).
Distribution line
ZT

S3

C1

Cf

-

Critical load Z2

BypassSwitch 1

Ves

S5

S6

+

Non-criticalload

BypassSwitch 2

+

V2

Z1

Incl

-

Vncl

T6

PUC5-based renewableenergy emulator

Zg
Vg

Weak grid

T3

Batteries-

V2PUC5+ C1PUC5

S2

-

T5

+

DC

+

T2

V1

Fig. 4. Captured RMS values and PF of the SL during a transient

Vs

T4
V1PUC5

Icl

S4

S1
T1

Lf1

Critical load Z3

PUC5-ES2

Lf2

Fig. 1. Diagram of the simulated weak grid using Simscape ElectricalFig. 3 depicts the recorded waveforms in the simulated
grid with redundant generated active power. As illustrated,
PUC5-ES2 has injected a five-level compensation voltage tothe grid and consequently PCC voltage has been stabilized toits nominal value (155.56⁄√2 = 110 𝑉 𝑅𝑀𝑆) and the PF of
SL has been modified from 0.786 to about 0.95. For checking
the dynamic performance, during this operation, resistance of
the CL has been suddenly changed to around 15 Ω at 𝑡 = 4 𝑠.
Fig. 4 shows the recorded measurements. As it is clear, bymodifying the NCL voltage, PUC5-ES2 has effectively
managed to keep the PCC voltage at its nominal value and themodified amount of PF stable in the desired value.

Operating the Maui Power System with 100%
Inverter Based Resources
Rick Wallace Kenyon1,2 , Anderson Hoke2 , Bri-Mathias Hodge1,2
1Electrical, Computer, and Energy Engineering; Renewable and Sustainable Energy Institute
University of Colorado Boulder, Boulder, USA
2
Power Systems Engineering Center, National Renewable Energy Laboratory, Golden, USA

As power systems achieve higher instantaneous levels of
renewable energy sources, the dynamical impacts of inverter
based resources (IBRs), the typical interfacing technology
for wind and solar energy, begins to play out [1]. This is
exceedingly prevalent at higher instantaneous penetrations,which are being experienced today; the Hawaiian island of
Maui achieved a peak instantaneous IBR penetration of 76%
in 2018 [2]. The traditional control strategy for transmission
connected IBRs is grid-following (GFL), where the IBR tracks
an existing voltage waveform and injects current to achieve
desired power set points. A valid question for relatively highshares of GFL IBRs is ‘what’s forming the grid?’, with an
anticipated instability at higher instantaneous penetrations.
Fig. 1 depicts this general concept of instability with a bears on
bicycles analogy. Grid-forming (GFM) inverters might solve
this stability issue as they do not rely on an existing voltage
waveform, but instead form the waveform frequency and magnitude directly. While GFM IBRs have existing, widespread
application to microgrids, the operation of a high voltage,
meshed transmission power system with only autonomously
synchronizing GFM and GFL IBRs has not yet occurred.
This work looks at the transient stability of the Maui powersystem with only GFM and GFL IBRs, a near future reality
considering Hawaii’s renewable energy goal of 100% by 2045.

run on seven cores during simulations. General data about thesystem is provided in Table I. Full order dynamical models
of GFM and GFL IBRs have been developed in PSCAD for
application to the Maui power system. The GFL have gridsupport capabilities, such as frequency droop and synthetic
inertia. The GFM is based on the droop control strategy.

AspectTABLE IP ERTINENT PARAMETERS OF M AUI S YSTEM .
*: FUTURE INSTALLATION , **: HYPOTHETICAL
Parameter
Quantity
Aggregate Rating

System

Buses
Loads

197
93

n/a
Peak: 200 MW

Generation

Wind: Type 3
Wind: Type 4
Distributed PV
Utility-Scale PV
GFL BESSGFM BESS**

2 Plants
2 Plants
171 Aggregate
2 + Plants
2 + 2* Plants
1–4** Plants

51 MW
24 MW
101 MW
5.4 MW
21 + 75* MW
20–60** MW

The simulations performed with the PSCAD Maui model
include standard transient stability scenarios such as load
steps, generation losses, and various types of faults. For eachsimulation, the penetration of GFL versus GFM resources isvaried in order to highlight the dependency of the system
on GFM assets. Fig. 2 shows the frequency response of thesystem for a fault and subsequent generation loss, which wasdeveloped during the model validation process.

Fig. 1. Bears on bicycles analogy of GFM vs. GFL operational limits. Notethat ‘grid-forming’ in this image implies any device forming the voltage
waveform.
Fig. 2. Frequency response traces of validation efforts for the Maui System.

A model of the Maui power system has been developed
and validated against field data, both in steady state and dynamically, with the electromagnetic transient program, powersystems computer aided design (PSCAD). The Maui system
PSCAD model incorporates parallel computing, allowing it toA special thanks to the Hawaiian Electric Company for their assistancewith the model development and operational information.

R EFERENCES[1] B. Kroposki, B. Johnson, Y. Zhang, V. Gevorgian, P. Denholm, B. Hodge,
and B. Hannegan, “Achieving a 100% Renewable Grid: Operating ElectricPower Systems with Extremely High Levels of Variable Renewable
Energy,” IEEE Power and Energy Magazine, vol. 15, no. 2, pp. 61–73,
Mar. 2017.
[2] R. W. Kenyon, A. Hoke, J. Tan, B. Kroposki, and B. M. Hodge, “GridFollowing Inverters and Synchronous Condensers: A Grid-Forming Pair?”Clemson University, Mar. 2020, p. 8.

Efficient Hosting Capacity Computation forDistribution Circuit
Mohammad Asif Iqbal Khan and Sumit Paudyal
Florida International University, Miami, FL, USA
Emails: {mkhan103, spaudyal}@fiu.edu

Abstract—Coordinating demand against variable whole-saleenergy market or wide-area control signals requires responsive
distributed energy resources (DERs) and at large scale it isvaluable to guarantee that the coordination does not violate
network measurements while maximizing the grid utilization.
Developing on the dynamic behavior of the distribution grid,
it is considered that the distribution feeder utilization canbe maximized using a smart coordinator that allows further
loads into the grid depending on the live grid measurements
used as constraints to determine whether the grid has
additional flexibility in real-time. Here, a demand dispatch
scheme to maximize distribution feeder utilization is developedthat guarantees network admissible DER/load coordination inpresence of diverse group of DERs. In specific, this work develops
a coordination scheme which evaluates the network parameters to
calculate available flexibility of the network and aggregates the
additional connections until the grid measurement constraints
are violated. By using IEEE 13 node test feeder, we could
demonstrate the maximization of DER connectivity using thedeveloped coordination scheme under defined constraints.
Index Terms—Distribution system, Power flow analysis,
Scheduling optimization

II. C ONSTRAINT- AWARE COORDINATOR LOGIC
The major constraints to the coordinator are:
• The voltage limit of the bus (V)• The active power sensitivity/flexibility (P)• The transformer rating/capacity limit (X)This coordinator logic in the coding environment can besummarized as in the Algorithm 1 below:
Result: Managing aggregated coordination at every
time iteration k:
Get Pflexibility [k];Get Vbus [k];Get Transformercapacity [k];if 0.95<Vbus [k] < 1.05 and Premaining flexibility [k] >0 and Transformerremaining capacity [k] > 0 thenAggregate network usage;
Update Premaining flexibility [k];Update Transformerremaining capacity [k];else
break;
end
Algorithm 1: Coordinator algorithm

I. K EY E QUATIONS

III. KEY R ESULTS
In the Fig. 1, the flexibility and the impact of flexible loads
at node 675 of IEEE 13 node feeder are shown for a day.

The Newton Raphson (NR) method can be built as:


ΔP
ΔQ

T


=

J1
J3

J2
J4

T 

Δδ
ΔV

T

where the Jacobian elements are as following:T " ∂P ∂P #T
J1 J2
∂δ
∂V
= ∂Q
∂Q
J3 J4
∂δ

(1)

(2)

∂V

Using the Jacobian element the active power grid flexibility
can be calculated per time step T:

T 
T
Flexibility = J2 ∗ (Vmeasurement – Vthreshold )(3)

where Vthreshold is defined as the under voltage floor ofgrid i.e. 0.95 (p.u.) and Vmeasurement is the actual measurementfrom the grid.

Fig. 1. Availability (top) and impact of addition of flexible loads (bottom)

PMU-Based Evaluation of Transmission Bus
Strength through Angle Sensitivity Metrics
Taehyung Kim, Alvaro Furlani Bastos, and Surya Santoso, The University of Texas at Austin
W. Mack Grady, Baylor University
Patrick Gravois, Megan Miller, Nemica Kadel, John Schmall, Shun Hsien (Fred) Huang, and Bill Blevins, ERCOT

Abstract—As the share of wind power generation increases,
the power system stability may be adversely affected. Wind
interconnection studies are conducted to evaluate and anticipate
the interactions between the wind project and the power system.
Various metrics have been developed to evaluate the systemstrength at the point of interconnection: short-circuit ratio,
voltage stability limit, and system inertia. These metrics, unfortunately, rely on network data which may not be readily available.
In this paper, a voltage phase angle based index is proposed
for evaluating a transmission bus strength. The proposed metricdescribes the sensitivity of the change of voltage phase angle tothe change of active power flow. The proposed metric requires
no network information but solely rely on PMU measurements.
The validity of the proposed metric is demonstrated by applying
it to analyzing the system strength of the Panhandle region of the
ERCOT transmission network. Through field data, it is shownthat the proposed metric correctly identifies buses as strong or
weak. Moreover, the metric can determine the system strength
caused by varying wind power production. It is shown that thesystem strength decreases during high wind power generation,and vice versa.

I. I NTRODUCTION
We propose a new method to estimate the system strength
using PMU measurements only. Instead of frequency andinertia, voltage angle and power measurements are used to
calculate the angle sensitivity to power. This method has beentested on the Panhandle region of the ERCOT network using
actual measurement data. This paper is organized as follows.
The theoretical background of angle sensitivity is discussed
in the first section. Then the approach to calculating the angle
sensitivity with PMU data is presented Finally, bus strengths of
the Panhandle region using the proposed method are presented
in the last section.

Read measured voltage
angle and power

Discard value

Apply filter and fill
missing data∆𝛿 > 0.005∆P > 0.25
R2 > 0.9

n =0

No

k
∆𝛿
(n)= AkP∆P
Yes

Get linear regression line No
and coefficients

n=n+1

n > Number of samples
Yes

End

Fig. 1. The flow chart to calculate the angle sensitivity.

III. E VALUATING PANHANDLE R EGION L INE S TRENGTH
Fig. 2 shows the relative angle sensitivity of buses in the
Panhandle region. It can be seen that Alibates and Tule Canyon
are the weakest buses, and Clear Crossing is the strongest bus.
IV. C ONCLUSION
This paper proposes a new method to evaluate transmission
bus strength. The angle sensitivity calculated using PMU
measurements is used to evaluate the system strength, and it
was applied to the Panhandle region of the ERCOT network.
From the case study, the results confirm those previously
evaluated using a WSCR method, i.e., buses at the remoteregion tend to be weaker than those closer to the center of the
ERCOT grid.

II. P ROPOSED M ETRIC
We propose a phase angle-based metric as follows:
∆δ
Xeq
=p
∆P
(V1 Vref )2 − (P0 Xeq )2(1)

The proposed metric is affected by Voltage magnitude, line
power flow, and equivalent reactance of bus. Fig. 1 shows thealgoritm to compute the angle sensitivity.

PMU 5@PMU 3@
PMU
5@
PMU
3@
WIND
MILLL
AJ
Swope
WIND MILLL AJ SwopePMU 5@PMU 5@
Rile y1L901@ EClk1@
PMU 5@ TULECA
PMU 5@
EClk1@
ALIBATESNYONRile y1L901@
EClk
Clr
Crossng
ALIBATES TULECA NYONEClk
Clr Crossng

Fig. 2. Angle sensitivities of the buses measured by selected PMUs

WTG Active Power Control for Transient Stability
Enhancement
Stavros Konstantinopoulos and Joe H. Chow

I. WTG AND SG I NFINITE B US S YSTEM
The system used for the design is the single machine infinite
bus system noted in Fig. 1

Fig. 1. SMIB and WTG System.

A. Active Power Control Design

Pord is ramped to the pre-fault power setpoint. This power
reduction will increase the electrical power demand on thefaulted SG, decelerating it. This is called the dynamic power
reduction (DPR). By monitoring the acceleration of the SG,
the ramp can be temporarily frozen (adaptive DPR) if the SG
starts to accelerate, improving swing mode damping.
B. Sliding Mode Reactive Power Control Design
The electromechanical model of the SG is adopted and thecontroller can be designed as the input is in affine form. The
swing equations areδ̇ = Ωω, 2H ω̇ = fˆ(δ) + ĝ(δ)IQ(1)
With fˆ(δ) = Pm − F (δ). With λ, k(δ) > 0, we choose [1]s = ω + λ(δ − δSEP )(2)−fˆ(δ) − λω
+ k(δ)sgn(s)
(3)
IQ =
ĝ(δ)
s2
L(s) ==⇒ L̇(s) = k(δ)ĝ(δ)|s| < 0(4)
2
since ĝ(δ) < 0. IQ denotes the reactive current injection of
the WTG, δ, ω the SG rotor angle and speed and δSEP thestable equilibrium’s rotor angle.
II. S INGLE M ACHINE I NFINITE B US S YSTEM R ESULTS
DPR
aDPR
aDPR + SMC Q

0.02

(pu)

Abstract—This work discusses the benefits of coordinating theactive and reactive power control of renewable resources, utilizing
their fast-acting converters and low inertia characteristics. The
proposed control scheme aims at improving the transient stability
margin of an adjacent large synchronous generator (SG). The
control design is developed for a Type-3 wind turbine generator(WTG) to act as a controllable braking resistor, slowing down
the acceleration of the SG after a fault. Simultaneously, thereactive power control uses a sliding-mode based design,/ to assist
in dynamic voltage recovery and damp the power swings. The
method is tested on a single-machine infinite-bus system.
Index Terms—DFIG, transient control, sliding mode control

0
-0.02
0

1

2

3

4

5

6

7

8

5

6

7

8

5

6

7

8

100

SG Power Output (pu)(deg.)

Time (s)Fig. 2. Transient Active Power Management Logic.

Fig. 2 shows that when a fault is detected, the powercommand Pord is reset to a low value. After the fault is cleared,
Stavros Konstantinopoulos and Joe H. Chow are with the Department ofElectrical and Systems Engineering, Rensselaer Polytechnic Institute, Troy,
NY, USA. e-mail:{konsts,chowj}@rpi.edu.

50
0
0

1

2

3

4

Time (s)
10
5
0
0

1

2

3

4

Time (s)Fig. 3. SG Response for Different Controller Configurations.

The critical clearing time of the system improved from 4.5
cycles with native controls, to 6.1 cycles with aDPR.
R EFERENCES[1] V. Utkin, “Variable structure systems with sliding modes,” IEEE Trans.
Autom. Control, vol. 22, no. 2, pp. 212-222, April 1977.

Lissajous Parameters based Islanding Detection ofMultiple DGs in Microgrid
Avinash Kumar, Rakesh Kumar Panda, Abheejeet Mohapatra, and S. N. Singh
Department of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur, India 208016
Email: kumarav@iitk.ac.in; rpanda@iitk.ac.in; abheem@iitk.ac.in; snsingh@iitk.ac.in

Abstract—This poster proposes a new passive technique forquick Unintentional Islanding Detection (UID) of multiple DGs,
even in the case of zero power mismatch/ power balance, by theuse of the Lissajous pattern. Point of Common Coupling (PCC)
voltage and current phasors are estimated by Moving Window
Discrete Fourier Transform (MWDFT). The Lissajous pattern
and the associated parameters, such as slant angle, major andminor axes, are evaluated from these phasors. Based on theseparameters, UI is detected. The proposed approach is tested on
Real-Time Digital Simulator (RTDS).
Index Terms——Islanding Detection, Lissajous pattern, Moving Window Discrete Fourier Transform, Microgrid Protection.

I. I NTRODUCTIONUID aids in seamless transition of DGs between gridfollowing and grid forming modes of operation and also assistsin control capabilities of the microgrid to regulate voltage
and frequency. The existing UID can be classified as passive,
active, and hybrid approaches. The passive approaches are
simple and commercially accepted, as they avoid power quality
and stability issues, and have decentralized control.
II. P ROPOSED M ETHOD USING L ISSAJOUS PARAMETERS
The proposed approach only requires the fundamental components of the voltage and current phasors at the PCC, which
are obtained from MWDFT. By using these estimated phasors,
the Lissajous pattern is constructed [1].
2î2pccv̂pcc
2îpcc v̂pcc cos(2ẑd )+−= sin2 (2ẑd )
2
2
Iˆ
V̂
Iˆmp1 V̂mp1

III. RTDS BASED S IMULATION R ESULTS
The proposed approach of UID of DGs in a microgrid istested in RTDS on a test microgrid. It consists of multiple
DGs, where DG1 is IIDG, DG2 is battery storage and DG3 is adiesel generator. The proposed UID is also tested for different
load quality factor Qf , PMC, Load Switching (LS), Capacitor
Switching (CS), faults, and DG out cases.
TABLE IS IMULATION R ESULTS OF VARIOUS L OAD Qf
Qf
0.5
1
1.77
2.5
3

bm =

2
Iˆmp1
+ {2Iˆmp1 cos(ẑd ) + V̂mp1 }V̂mp1 tan(θp )V̂ 2 Iˆ2

500

1000

1500

2000

2500

3000

2000

2500

3000

2000

2500

3000

(a)
0.3
0.25
0.2
0

500

1000

1500

(b)

(2)

mp1 mp12
V̂mp1+ {2V̂mp1 cos(ẑd ) + Iˆmp1 }Iˆmp1 tan(θp )The authors would like to thank the DST/Indo-US Science and Technology Forum (IUSSTF), New Delhi, India for providing financial support
to carry out this research work under projects IUSSTF/EE/2017282B andDST/EE/2018174D.

bm (pu)am =Pd =0.6 MW & Qd =0.2904 MVAr
Pd =0.7 MW & Qd =0.3388 MVAr
Pd =0.8 MW & Qd =0.3872 MVAr0am (pu)2V̂mp1 Iˆmp1 cos(ẑd )V̂ 2 − Iˆ2

mp1
2
2
V̂mp1
Iˆmp1

C (mF) fr (Hz) ∆amavg ∆bmavg
2.8778 60.01
0.04270.03175.7546 60.02
0.03370.029510.3444 60.1
0.03430.029114.3553 60.15
0.0487
0.036617.2120 60.2
0.03310.0252

0.6

The Lissajous parameters such as slant angle, major andminor axes are evaluated as

mp1

L (mH)
2.4442
1.22190.67790.48770.4061

0.8

A. Lissajous Parameterstan(2θp ) =R (Ω)
0.46080.46080.46080.46080.4608(1)mp1

3^p (rad)mp1

B. Operation of Proposed method
To discriminate UI from non-islanding events, slant angle
jump (SAJ = θppre − θpdis ) is inspected. SAJ 6= 0 indicatesthe occurrence of a disturbance in the microgrid. After one
precycle, ∆am = |apre
m − am (t)| and ∆bm = |bm − bm (t)|are evaluated.Thereafter, the average values of ∆am and ∆bmover a half cycle are monitored to discriminate UI fromfault/switching events. To discriminate UI from other events,∆amavg and ∆bmavg both should be less than 0.1pu.

0.3
0.25
0.2
0

500

1000

1500

No. of samples(c)Fig. 1. (a) θp (b) am and (c) bm for different PMC

R EFERENCES[1] B. D. Smith and S. H. Ward, “On the computation of polarization ellipse
parameters,” Geophysics, vol. 39, no. 6, pp. 867–869, 1974.

Reinforcement Learning for Optimal Allocation ofSuperconducting Fault Current Limiters
Xiejin Ling 1, Yinhong Li 1*
1

State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Hubei Electric Power Security and High Efficiency Key Laboratory, School of Electrical and Electronic Engineering, Huazhong Universityof Science and Technology, Wuhan 430074, China

Abstract-Superconducting fault current limiter (SFCL)
is a new type of current limiting equipment that can restrict
the short circuit current level in power systems without impacting on their normal operations. However, the existing
SFCL allocation methods have disadvantages of long computation and unstable convergence. Recent advances in thearea of artificial intelligence provide the possibility toachieve the optimal SFCL allocation with the considerationof both speed and convergence. To this end, the SFCL allocation is converted into a Reinforcement Learning (RL)
problem in this paper. A designer to find the optimal SFCL
allocation is viewed as an agent, and the power system isregarded as the environment. The agent adjusts the locationand size of SFCLs to respond to the feedback from the environment optimally. The Q-learning algorithm is applied tosolve the RL problem. The experimental results demonstrate
the effectiveness and superiority of the proposed method.
I.

II. Numerical Results

Fig. 1. (a): The curve of rewards, (b): The curve of the loss
function.

Key Model

A. Problem Formulation
N SFCLmin C    CZ l  zf l  CI l l 1s.t. zf l  zf max , l  1, 2, , N SFCL
Ik  Imax
k(1), k  1, 2, , N

Fig. 2. The reduction processes of the fault currents

B. Reinforcement Learningst   zf l 1 L  0  zf l  zf max (2)

at ,l   0, 0,, z,, 01L(3)

st 1  st  at ,l(4)

 M  C , if st 1 is Tsrt 1  st , at ,l   1000, if st 1 is T f 0, others(5)

Table I: The statistical results of ten experiments
MethodLowest Cost Mean Cost
MSE
Time
GA
3.217
4.823
1.386
65.349
PSO
3.217
4.478
1.344
4.119
IMINLP
3.217
3.217
0
1079.432
RL
3.217
3.217
0
39.581
III. Conclusion
This paper proposes an RL approach to solve the optimal SFCL allocation problem. The test results show that the
proposed method is superior to other algorithms in the calculation speed and the convergence performance. The method has great potential for application to other optimization
problems.

A Scenario-adaptive Online Learning Algorithm for
Demand Response
Linwei Sang, Qinran Hu, Member, IEEE, Yuan Zhao, Ruhuai Han, Zaijun Wu, Xiaobo Dou
Abstract—This paper introduces a scenario-adaptive online
learning algorithm for aggregating demand side resources. The
problem of dispatching demands is formulated under a contextual
combinatorial multi-armed bandit framework with the objective
to minimize the mismatch between actual aggregated demand
adjustment and the reduction target from system operator.
Comparing with previous online learning framework for demandaggregation, this paper considers temporal and spatial factors andcreates a scenario-adaptive mechanism to model the behavior of
demands. The simulation results show the proposed method
achieves less mismatch and more reliable aggregation
performance than conventional methods.
Keywords—Multi-armed bandit, demand response, online
learning, regret analysis, contextual bandit

I. INTRODUCTION
Demand side resources, as flexible grid assets, have beenplaying important roles in facilitating power system operations.
With the development of Internet of Things (IoT) technologies,
utility companies have started to explore the possibilities ofenabling real-time demand response.
In this paper, we extend the work in [1], and take thetemporal and special factors, which may influence the behavior
of demands, into consideration. Thus, this paper adopts
Contextual-CMAB algorithm and proposes scenario-based
probability to model users’ profile.
Contribution: 1) we propose the contextual combinatorial
upper confidence bound (Contextual-CUCB) algorithm basedon CMAB framework; 2) the regret of the proposed algorithm isanalyzed. We prove the upper regret bound of our algorithm
over T times, n users and |χ| scenarios are О(n3|χ|log(T/|χ|)); 3)
simulation results show Contextual-CUCB algorithm
outperforms CUCB-Avg which does not consider the scenarios.

We study the performance of the Contextual-CUCB
algorithm. Then, we compare its results to the offline optimalalgorithm and CUCB-Avg algorithm.
Accumulated mismatch

Instantaneous regret

Accumulated regret

Learning deviation

II. MAIN RESULTS
Denote the set of n users as [n]. At time t, only some of theusers Sk (belong to [n]) receive commands to realize theaggregated demand reduction. The aggregator generates thecommands based on the reduction target D from power system.
The loss at t time step is depicted by the square deviation of
aggregated demand reduction from the target (1).The problem
can be formulated as follows:
min E( å X iz,t - D) 2

S k Í[ n ]

s.t

iÎSk

(1)

X iz,t ~ Bern( piz )The Contextual-CUCB learning algorithm is proposed tosolve the problem, as following:

Fig.1 The accumulated mismatch, instantaneous regret, accumulated regret, andlearning deviation of the Contextual-CUCB, CUCB-Avg and oracle algorithm

REFERENCE[1] Y. Li, Q. Hu, and N. Li, “Learning and Selecting the Right Customers for
Reliability: A Multi-Armed Bandit Approach,” in 2018 IEEE Conferenceon Decision and Control (CDC), Miami Beach, FL, 2018, pp. 4869–4874

SALSA-Based Method for Identifying Critical
Component and Critical Component Outage Causality
with Cascading Failure Data
Lu Liu, Hao Wu
College of Electrical Engineering, Zhejiang University, Hangzhou, China

I. M ETHODOLOGY
A. Cascading Failure Structure
Cascading failure process can be described by fault chain (FC)(i)(i) (i)(i)
Li = {l1 ,· · ·, lj , lj+1 ,· · ·}, where lj is j-th component outages,(i)(i)
and component outage causality (COC) lj → lj+1 is propagation
relationship between two consecutive component outages.

C. Critical COC Identification
COC is quantified comprehensively considering factors ofcomponent criticalness, propagation probability and influence on
cascading failure blackouts. Critical COC is identified by risk
indicator using (4).
RIi→j = πh (i)W (i, j)Γ(i, j)πa (j),(4)
where Γ ∈ Rn×n is causality impact matrix, representing average
impact of COC i → j(i ∈ Vh , j ∈ Va ) on FCs.
Γ(i, j) =q∈Ωi→jVh = {l|l ∈ ν, O(l) > 0},
Va = {l|l ∈ ν, I(l) > 0},(1)

W (lh , la ) = Nlh→la /N, (lh ∈ Vh , la ∈ Va ),where O(l) is out-degree, and I(l) is in-degree. The adjacency
matrix W ∈ Rn×n presents propagation probability of COCs.
Two-step random walks are conducted on Gb for correlation
indicator calculation, by picking up different role-type component
randomly. One step walk starts from authority-component to hubcomponent, and another from hub to authority. Then two transition
matrices H and A, which correspond to two Markov chains
generated by two walks respectively, can be computed:
A = WcT Wr ,(2)
H = Wr WcT ,where Wr is row normalization matrix, Wc is column normalization matrix. A (H) reveals correlation between authority(hub)components. Principal eigenvalues πa and πh are calculated using
power method in (3), the higher entries of which can be used
for identifying critical components. Critical hub-component ismore likely to facilitate outage propagation, and critical authoritycomponent is more vulnerable to other outages.
πa(k+1)T = πa(k)T A,(k+1)T

πh

(k)T

= πh

H.

(3)

(5)

sj

where Ωi→j is FC collection containing i → j, Ni→j is FC numberin Ωi→j , dl(q) is accumulative load loss of q-th FC at stage sj .
sjII. C ASE S TUDY AND R ESULTS
177
173 175 176
171

Generator node
Load nodeBranch (Component)
Component in critical COC

163
167164
160
155
159
158

34

41 31

42
179

40

114 115

129

117
119
116

108

126

29
107

28

32
39
178

125
122
123
124
128
121
127

186
120
185
110

109

30

180
43 33

35

105 183

27
106

22
21

1
2

23
36

38
24
26

20
17

19
18

184
13
14
12
16
15
4
1110
8
3
6 5

37

10463

25

62
60

44
48

68
67
10266

52 53 55

65

69

96

49 50 51
46 47

7
9

64

61

59

45
54

56

162

154

161

144
146145
147
150
156
153
152
157
148
151
149

113
112
111

182
181

174

170
166
172169168
165

118

B. Critical COC Identification
This paper aims to identify high-risk COCs from massive
FCs based on SALSA approach. Define component outages
collection appeared in FCs as ν, hub-components collection as
Vh , authority-components collection as Va . Then bipartite graph
Gb = (Vh , Va , W ) can be constructed for describing COCs.

− dl(q)X dls(q)
1
sj
i−1
,
Ni→j
dl(q)

57 58

97

99
98

70 71

75
76
72
103
73
81
83
100
74
101
78 80
82
94
84 77
95
92
86 79
85
91 93
89
87
90 88

Fig. 1. IEEE 118-bus system graph.

143
142
141

140
139
138

137
136
135
131132131132
130

133
134

Risk (System Blackout Size, MW)

Abstract—Component outages in power system cascading failure
play different roles such as facilitating outage propagation or being
vulnerable to other component outages. The component outage
propagation relationship between two components can be describedby the component outage causality (COC). Recognizing the rolesof component outages and quantifying the high-impact COCs arehelpful for identifying critical components and critical COCs thatare crucial in cascading failure propagation. This paper proposes amethod based on Stochastic Approach for Link-Structure Analysis(SALSA) for identifying critical components and critical COCs withcascading failure data, in which component outage and causality arecompared to the web page and directed link respectively. Verificationon the IEEE 118-bus system with fault chains generated by cascading
failure simulation, demonstrates the mitigation measure on identified
critical COCs can effectively reduce cascading failure risk.

1000
800

Loss<10%
10%<Loss<20%776.0720%<Loss<30%
30%<Loss<40%

Loss>40%760.38664.54600
400

359.91200
0Original

RandomCritical

CFG

Fig. 2. System blackout size after
mitigation measures.

The proposed method have totally identified 2029 COCs with
non-zero risk indicators, and components of top 10 critical COCs
(e.g. 9 → 96, 7 → 96, 9 → 38, 7 → 38, 31 → 33, 33 → 30, 33 →31, 96 → 33, 31 → 30, 33 → 96) are presented in topologicalconnection graph of IEEE-118 bus system in Fig.1. It shows thatcritical COCs are not necessarily topologically connected, andcomponents of COCs are coincidentally located on important flowtransmission channels topologically.
The mitigation measures are adopted for validating identification results, which simulate relay operation in cascading failure process by reducing component outage probability by 90%
temporarily according to critical COCs information. Different
mitigation cases are simulated: 1) original system without mitigation, 2) mitigation measure with 10 randomly selected COCs,
3) mitigation measure with top 10 critical COCs, 4) mitigation
measure with comparison method, and results are shown in Fig.2.
Fig.2 shows that blackout risks decreases from 776.07MW to760.38MW, 359.91MW, 664.54MW in case2,3,4 respectively. The
mitigation effect on reducing system blackout risk is slight undercase2. Case3 and case4 have different mitigation effects, and case3
especially performs better in mitigation of large-scale blackouts
with system load losses greater than 10%. It verifies that critical
COCs are effectively identified by the proposed method.

Efficient Algorithm for Solving Combined T & DPower Flow and Dynamic Simulation
M Al Mamun

Arun Suresh

Mohammad Asif Iqbal Khan

Florida International University
mmamu011@fiu.edu

University of North Carolina at Charlotte
asuresh4@uncc.edu

Florida International University
mkhan103@fiu.edu

Sukumar Kamalasadan

Sumit Paudyal

University of North Carolina at Charlotteskamalas@uncc.edu

Florida International University
spaudyal@fiu.edu

I. P RELIMINARY R ESULTS
A test integrated T&D system is created with 9 bus
transmission system and a part of load at bus 8 is replaced byconnecting a 123 bus distribution system a shown in Fig. 1(a).
Simulation results of Decoupled and Unified approaches anderror between both approaches for A phase is shown in
Fig. 1(b).The dynamics using waveform relaxation for rotor
angles of three generators in the transmission network is shownin Fig. 1(c) .

32

250

29
30

33

350
51

251

28

31
26

46

102

66

101

40

22

38
19

18

20

135

62

39

9
52

152
7

3
2

9

8

7

3

12

3

5

55

6

78

56
76
80

84

76

95

93

88

90

92

17

16

85

79
77

54

96

34

89

91

81

86

87

82

83

195

4

6

5

53

13

15

2

74
73
72

610

94

1

149
150

67

160
57
61

8

75

60
58

59

10

69
68

37

2

71
70

97

14

11

99

98

197

36

35

450
100

63

41

114

451

103

65
105

42
21

113

104

106

64

43

44

23
24

112
107

108

45

27

110

300

151

109

47

48

111

50

49
25

9 Bus Transmission System
123 Bus Distribution System

411((a)) One Line Diagram

Voltage (pu)

Unified Approach
Decoupled Approach

1.04
1.02
1
0

20

40

60

80

100

120

Node
10

-7

Voltage error

Error (pu)

0
-5
-10
0

20

40

60

80

100

120

Node

((b)) Load flow Results Phase A
W2
W3
W1

378.5

Rotor frequency (rad/sec)

Abstract—Increased penetration of distributed energy resources
will lead to increased interaction of transmission and distribution system and have significant impact on the operationsof bulk transmission systems. This has lead to developmentof co-simulation platform for solving transmission and distribution (T&D) systems simultaneously for steady state as wellas dynamic studies. Most of co-simulation methods in current
literature follow a decoupled approach, where transmission anddistribution systems are decoupled at interface buses and solved
independently either one after other or simultaneously dependingon parallelizing capability of processing unit used. Since thebench-marking solutions to T&D co-simulation are not available, first this work presents a unified T&D co-simulation as abenchmark which can aid validating the results obtained using
other co-simulation approaches. However, this method may sufferfrom significant computational burden when the system becomeslarger. An approach to tackle this issue is to exploit the theparallel computational techniques on a accelerated platform tosolve and analyse unified system. Secondly, a decoupled dynamic
simulation is also performed on the transmission network where
the distribution network is considered as a lumped load connected to a node of the transmission network to observe thetransient stability. In this case, all the nonlinear differential
algebraic equations (DAEs) of the system are discretized using
the Backward Euler method first and, then, the network state
and algebraic variables are divided into two sub-systems. Afterthat the Gauss Jacobi Waveform Relaxation algorithm, which isparallel in nature, is used to determine the network variables foreach sub-system and, thus, the system dynamics is observed andanalyzed. Finally, rigorous MATLAB simulation results verify
the effectiveness of the proposed algorithms.

378

377.5

377

376.5
0

0.2

0.4

0.6

0.8

Time (sec)((c)) Generator rotor frequencies

Fig. 1: Preliminary Results

1Probabilistic short-circuit analysis: a new approach
integrating intermittent power injection
Daniella de Barcellos Martins, Wandry Rodrigues Faria, Benvindo Rodrigues Pereira Júnior
University of São Paulo, São Carlos, Brazil

Abstract—This paper presents a new approach for probabilistic
short-circuit analysis based on a Monte Carlo Simulation (MCS)
and a hybrid compensation technique to determine the fault
currents in an unbalanced distribution network with Distributed
Generators (DGs). The probabilistic short-circuit approach proposed considers the main uncertainties inherent to the fault
problem, such as the intermittent power production from DG,
load demand, fault location, fault type, and fault resistance. This
type of approach does not consider only extreme values in thefault current determination, allowing a better protection systemsizing and a better risk and costs analysis.

I. P ROBABILISTIC SHORT- CIRCUIT ANALYSIS
A probabilistic short-circuit approach does not consider only
extreme values in the fault current determination, allowing abetter protection system sizing and a better risk and costs
analysis. The flowchart in Fig. 1 illustrates the proposed
methodology applied to estimate the Probability Density Function (PDF) of fault currents.

weibull and gaussian distributions, respectively. For the fault
location, fault resistance, and fault type, the uniform distribution, the lognormal distribution, and different probabilities
of fault occurrence were considered, respectively. To evaluate
the methodology, 50,000 fault cases were simulated using the
MCS in a real distribution system.
II. R ESULTS
From Fig. 2, it is possible to verify the influence of DGs inshort-circuit levels; the currents without the generators present
higher values due to the contribution of the substation only.
When considering the generators, the substation contribution
decreases, and the frequency of the lower currents becomes
higher. From Fig. 3, it is possible to observe that the occurrence of currents lower than 1560 A represents 95% of all thesimulated cases.
2500
With DGs
Without DGs

2000

Begin

1500

FrequencyLine impedances, nominal
load power, type ofgenerators, power ofgenerator

Import system data

Generate random
values for power
production and loaddemand

1000

500

0

Backward-forward
sweep power flow

0

500

1000

1500

2000

2500

Current (A)Fig. 2. Fault current at the substation branch for both with and without DGs.

Store voltage andcurrent values
k = k+1
Generate random
values for faulted
point, fault type andfault resistance

1

1800

Fault location and type according
to uniform distribution and specificprobabilities. Fault resistanceaccording to lognormal distribution

1600

95%

CDF

0.9
0.8

1400

No

k > limit of
iterations?

0.7

Frequency

1200

Yes

Determine theProbability Density
Function of the fault
currents

End

0.6
1000
0.5
800

0.4

600

0.3

400

0.2

200
0

Fig. 1. Flowchart of the proposed method.

0

Cumulative probability

Estimate the fault
currents using hybrid
compensation
technique

0.1

500

1000

1500

2000

0
2500

Current (A)For the pre-fault conditions of the system, it was consideredthe variation of power production and load demands, using

Fig. 3. CDF for substation fault currents with DGs.

The authors would like to acknowledge the CNPq for the financial support(grants 134386/2018-3 and 424489/2018-0).

This probabilistic approach may be useful for distribution
companies in protection system planning and design.

Risk-constrained Bi-level Optimization for Virtual Bidder
Bidding Strategy in Day-Ahead Electricity Markets
Hossein Mehdipourpicha, Rui Bo, Senior Member, IEEE
Missouri University of Science and Technology, Rolla, USA (rbo@mst.edu)

II. CASE STUDY
The proposed model is tested on the 24-bus Reliability Test
System. One virtual bidder is considered in this system that canoffer from 5 different buses. Assume the maximum generation/
demand that virtual bidder can offer in the DAM is 100MW. We
assume the power quantities offered by other market participants(generators/demands) are known by the virtual bidder, and theiroffer/bid prices are unknown. These uncertain offer/bid prices aremodeled as 5 scenarios and RTM price uncertainty is modeled as
3 scenarios (A, B and C). Fig. 1 depicts the efficient frontier thatshows the expected profit decreases as the weighting factor β
increases. It means that, when the virtual bidder takes the risktaker position, the resulting optimal strategy allows the virtual

Expected Profit ($)

14200
β = 0.001
14100
14000
13900
13800
13700
13600
13500
13400
13300
-20000 -15000 -10000 -5000
0CVaR ($)

β =0.1

β=1
5000

10000

15000

Fig. 1. Efficient Frontier: expected profit vs CVaR
80000

RT Scenario A

RT Scenario B

RT Scenario C

60000
40000

Profit ($)

I. INTRODUCTION
Virtual bidders, as a purely financial players, can submit
incremental offers (INCs) or decremental bids (DECs) into the
day-ahead market without the obligation of providing/consuming
the physical energy in the real-time market. The net energy in DA
market and RT market must be zero, while the net profit iscalculated in the two-stage settlement process based on the pricedifference between DA and RT markets. Virtual bidders canmake profit by arbitraging the price difference between DA and
RT markets. However, they are exposed to the risk of profit
volatile due to the uncertainties of other market participants
strategies, which can change the DA market prices, and RT
market prices. In order to account for the virtual bidder’s impact
on DA market price and consider the uncertainties of the RT
market price forecast and other participants’ offers/bids, we
propose a risk-constrained bi-level optimization model tooptimize bidding strategy of virtual bidders in the DA market.
The upper-level problem seeks to maximize the profit of a virtual
bidder, whose revenue is calculated based on the cleared DA
market price obtained at the lower-level problem which
represents the market clearing process. The upper-level model
incorporates scenario-based uncertainties of RT market price andother market participants’ offers/bids. Lastly, the conditional
value-at-risk (CVaR) is included to quantify the risk of profit
associated with different bidding strategies. By virtue of KarushKuhn-Tucker optimality conditions, strong duality theorem and
Fortuny-Amat Transformation, the bi-level model is converted toa single-level mixed integer linear programming model.

bidder to make higher expected profit; however, it may incur
significant losses in some scenarios (such as RTM Scenario A
and C in conjunction with DAM scenario 2, as shown in Fig. 2).
On the contrary, when the virtual bidder takes the risk-averse
position, its expected profit is reduced, while the corresponding
optimal bidding strategy guarantees positive profits in all
scenarios (as seen in Fig. 3).

20000
0
1

2

3

4

5

-20000-40000DA Scenario Index

Fig. 2. Profit of risk-taker virtual bidder (β=0.001)

Profit ($)

Abstract—Virtual bidders place virtual offers/bids into dayahead (DA) electricity market, and the cleared energy is settled at
the price difference between day-ahead market and real-time (RT)market. With high volatility in the price difference, virtual bidders
face great uncertainties and financial risks in their decision making.
Therefore, this paper proposes a risk-constrained bi-level
optimization model for virtual bidders bidding strategy. In thismodel, uncertainties related to the other market participants
offers/bids and RT market prices are modeled through scenarios.
Financial risk associated with bidding decisions is modeled using
conditional value-at-risk (CVaR) metric. A case study is presented
to demonstrate the effectiveness of the proposed method.
Keywords— Bidding strategy, Bi-level optimization, Conditional
Value-at-Risk (CVaR), Uncertainties, Virtual bidder, Stochastic.

18000
16000
14000
12000
10000
8000
6000
4000
2000
0

RT Scenario A

RT Scenario B

1

3
4
DA Scenario Index

2

RT Scenario C

5Fig. 3. Profit of risk-averse virtual bidder (β=1)

III. CONCLUSION
Virtual bidders face a variety of uncertainties when making
bidding decisions in DAM. Scenario-based modelling is used tomodel these uncertainties, and CVaR metric is used to model thefinancial risk of the decisions. A risk-constrained bi-level
optimization model is proposed to derive the optimal bidding
strategy of a virtual bidder in participating in DAM market. A
case study shows that the proposed model allows virtual bidders
to choose a risk level that will balance between the expected
profit across all scenarios and the potential losses in certain
scenarios.

A Unified Droop-Free Distributed Secondary
Control for Grid-Following and Grid-Forming
Inverters in AC Microgrids
Sheik M. Mohiuddin and Junjian Qi
Department of Electrical and Computer Engineering
University of Central Florida
Orlando, Florida 32816 USA

Abstract—The inverter-based distributed generators (DGs) in
microgrids usually operate in either grid-following (GFL) or gridforming (GFM) control mode. In GFL mode the DG follows thevoltage and frequency of the microgrid while in GFM mode
it dictates voltage and frequency for the microgrid. In thispaper, we propose a unified droop-free distributed secondary
control for both GFL and GFM inverters based on distributed
optimization. The proposed control regulates the frequency tonominal frequency, regulates the global average voltage in the
microgrid to the rated voltage, and ensures accurate active andreactive power sharing among all GFL and GFM inverters. The
DGs can communicate through a sparse communication networkand share information about average voltage estimates andnominal active/reactive power. The effectiveness of the proposed
control is validated through simulations on an eight-DG test
microgrid with four GFL inverters and four GFM inverters.
Index Terms—AC microgrid, distributed control, distributed
optimization, frequency regulation, grid-following inverter, gridforming inverter, power sharing, voltage regulation.

I. S HORT S UMMARY
Most existing papers on microgrid control only consider
either GFL or GFM control mode. However, for exploiting
the plug and play features of the microgrid and enabling asmooth transition between grid-connected and islanded modes,
it is essential that DGs with both types of control capabilities
be properly coordinated. In this paper, we propose a unified
droop-free distributed secondary control for both GFL and
GFM inverters. The contributions are summarized as follows.
1) A novel unified droop-free distributed secondary control
framework utilizing a single communication network isdeveloped for both GFL and GFM inverters based on aformulated distributed optimization problem.
2) The proposed control has a very similar structure forboth GFL and GFM inverters and is very easy to implement. Both GFL and GFM inverters are coordinated toparticipate in frequency and voltage regulation.
3) Steady-state analysis shows that the proposed control
can regulate the frequency to nominal frequency, regulate the average voltage of all inverter output buses
to rated voltage, and achieve active and reactive power
sharing among all GFL and GFM inverters.
II. S ECONDARY C ONTROL FOR GFL AND GFM
I NVERTERS
The design objectives of the secondary frequency andvoltage control include 1) regulating the frequency back to

GFL-Contr ol

GFM -Contr ol

+
+

+
+

++

++

Fig. 1. Secondary control for the GFL and GFM inverters.

nominal frequency, 2) regulating the average voltage of theoutput buses of all inverters to the rated voltage, and 3) achieving proportional active and reactive power sharing among all
GFL and GFM inverters. Therefore, we define the following
objective function for the secondary control:NX
min f =
fi ,
(1)
i=1

with
fi =

2 1 XNαVr − V +aij (λPi − λPj )2
2
2
c
j∈Ni

1 X
+
aij (λQi − λQj )2 ,
(2)
2
j∈Nic
PN
where α > 0 is a design parameter, V = k=1 Vk /N is theaverage voltage of all inverter output buses, Vr is the rated
voltage (i.e., 1.0 in per unit), Nic denotes the neighbors of
DG i in G, and λPi = Pi /P i and λQi = Qi /Qi are the active
and reactive power utilization ratios for inverter i with P i and
Qi as the corresponding active and reactive power limits. The
proposed secondary control for the GFL and GFM inverters
are shown in Fig. 1.
The performance of the proposed unified distributed control
is validated through simulations on an eight-DG test microgrid implemented in Matlab/Simulink which demonstrates thecontroller success in frequency and voltage regulations as wellas achieving the objectives of equal active and reactive power
sharing among all the inverters.

A Game-Theoretic Approach to Model Interruptible
loads: Application to Micro-Grid Planning
Soheil Mohseni, Alan C. Brent, Daniel Burmester, Will N. Browne
Sustainable Energy Systems, Faculty of Engineering, Victoria University of Wellington, Wellington, 6140, New Zealand
Email: {soheil.mohseni, alan.brent, daniel.burmester, will.browne}@vuw.ac.nz

Abstract—Exploiting the flexibility offered by demand
response (DR) resources could have a significant impact on thelong-term economic viability of renewable and sustainable energy
systems (RSESs). While several mature DR programs exist in theliterature, the potential savings offered by forming coalitions in themodels of electricity sector decision-making is less well explored.
Accordingly, using insights from cooperative game theory thatprovides a platform to enforce and sustain cooperation among the
involved players, this study presents the first long-term demandside resource projection framework tailored towards the design
optimization of RSESs.
Keywords—Microgrids, Sizing, Demand response, Game theory.

I. KEY CONTRIBUTIONS
Notably, two novel generalizations of standard renewableenergy system planning are established: (1) a characterizationof the epistemic uncertainty associated with the delivery of
incentive-responsive loads in the long run, and (2) aquantitative analysis of the strategic, interacting choices of
economic agents involved in the game  the utility, demand
response aggregators (DRAs) that parcel up a set of smaller
active loads, and end-users  based on cooperative game theory.
II. KEY TEST SYSTEM AND MODEL
The conceptualized microgrid (MG), shown in Fig. 1,
consists of solar photovoltaic (PV), wind turbine (WT), andmicro-hydro (MH) power generation technologies, a supercapacitor (SC) bank, an inverter, an electrolyzer, a hydrogentank, a fuel cell (FC), and electric vehicle supply equipment(EVSE) to serve different customer classes mediated by DRAs.
A customer comfort-preserving optimization model that uses
the tit-for-tat strategy was developed for the long-term
equipment capacity planning of grid-independent MGs, theflowchart of which is shown in Fig. 2.

Fig. 2. Flowchart of the DR-integrated MG design optimization model.

III. KEY RESULTS AND DISCUSSION
The impact of the proposed game-theoretic DR schedulingframework (GT-DR) on the load curve fed into the optimization
model is compared to its non-game-theoretic counterpart(NGT-DR) and the baseline scenario (NO-DR) in Fig. 3 for the
day where the annual peak demand occurs. The figure reveals
that the cooperative arrangement of DR resources can play a
key role in peak load shaving. As Table I demonstrates, theimproved load factor, by leveling out seasonal demand
fluctuations in the light of identifying the best coalitions of theagents and an optimal distribution of the resulting total payoffs,
enables significant savings in the total net present cost (TNPC).

Fig. 3. Impact of demand response on the shape of the daily load profile.
TABLE I. MG DESIGN OPTIMIZATION RESULTS FOR DIFFERENT SCENARIOS
Scen.

Fig. 1. Schematic diagram of the conceptualized test-case micro-grid system.

GTDR
NGTDR
NODR

PV
(kW)

WT
(kW)

MH
(kW)

FC
(kW)

SC
(kWh)

Elec.
(kW)

Tank
(kg)

Inv.
(kW)

EVSE
(kW)

TNPC
($)

243

1.1k

588

860

77.1

1.1k

680

1.3k

66

9.7m

243

1.5k

784

920

84.1

1.3k

713

1.4k

88

243

1.6k

882

975

91.6

1.5k

749

1.5k

154

11.1
m
12.0
m

1

Overcurrent Protection Scheme for the IEEE 13-NodeBenchmark Test Feeder with Improved Selectivity
Muhammad Yousaf *, Kashem M. Muttaqi, Danny Sutanto
School of Electrical, Computer and Telecommunications Engineering
University of Wollongong, NSW 2500, Australia*my701@uowmail.edu.au
Abstract—The IEEE 13-node test system has been used widelyfor evaluating the protection strategies. This paper focuses on
the design of a robust overcurrent protection (OCP) scheme for
the IEEE 13-bus test feeder using standardized procedures toprovide a benchmark protection data for future research studies.
The protection strategy used in this paper employ thecoordination study among the relaying protective devices andfuses to improve the selectivity of the designed OCP scheme. The
designed protection strategy has been validated for all types offault events to demonstrate a realistic scenario. The proposed
protection data will be utilized for the future protection studies
performed on IEEE 13-node benchmark test system.
Index Terms—fault studies, overcurrent protection, IEEE 13node test feeder, protection devices.

I. INTRODUCTIONT

HE IEEE has developed the IEEE 13-node radial test
feeder for use by researchers for the verification of theirresearch outcomes, due to its semblance with a real
distribution network with unbalanced loading conditions.
Currently, no data is available in the literature for theprotection setting of this test feeder. This paper proposes an
OCP scheme for the IEEE 13-node radial test feeder by thecomparing the load flow and fault studies results with the
published literature. The paper presents n novel protection
strategy that need to be developed for the reliable operation of
the power networks using the IEEE 13-node radial test feeder
under different fault scenario. The proposed allocation of
optimal placement of the protective devices (PDs) and thedetermination of their protection setting can be utilized for
future protection studies conducted on this benchmark test
system as shown in Fig. 1.

II. PROPOSED OVERCURRENT PROTECTION SCHEME
The proposed protection strategy incorporates the impact of
future integration of distributed generations (DGs) on theoriginal protection setting as well. If the branch current lies
into the fault zone, this will lead to differentiate between fault
locations as shown in Fig. 2. Each zone of protection will betested to sort out the exact fault location to maximize theselectivity of the designed protection scheme. In case of lossof the coordination for recloser-fuse, the recloser’s fast
operation will be revised in the precise steps to restore thecoordination. If the fast operation of recloser reaches its
limitation, this will be followed by the upgradation of fuse.
For long laterals with series fuses, fuse-fuse coordination will
be restored by optimal upgradation of the fuse size unlike therelaying PDs.
F

Start

Measurement ofNode Voltage and Branch Current
Clear the DG internal Fault

X=1

Select protection zone X

Y=1

No

Measure the fault current contribution
from source Y (IF,Y)

Imeasured > 1.25 x IFL

X = X+1

From fault analysis data, find the fault current contributions
Yesfrom source
Y for identical fault type on section J (called
these values I MKJ and INKJ)

Yes
Fault detected

DG internal fault

Yes

Is IF,Y lies in between the range of
IMKJ and INKJ ?

Algebric sum of all thephase current is zero
Y=Y+1

No

Yes

No
Fault detected

No

650

Single Phase Distribution Line
Two Phase Distribution Line
Three Phase Distribution Line

RG60

If Y is total number of fault
current sources?

Determine the fault type

REC1

Yes

F

645
F646
L646

F645

633

632
R1

L645
F-DL

611
F-C611

F611

684
F684

F652

671
F671

F634

R2 F633

REC2
R5

XFM-1

Held

L634

Lost

Irecolser
TDSfast *Ifuse

Fuse Size Upgradation

692

675
F692
L692

F692-R

F675

F-C675

TDSfast  TDS min

L675 C675
No

652

Check the coordination
status?

TDSfast  TDS min

L671 F671-R

C611 L611

634

Distributed Load

646

680
L652

Fig. 1: The one line diagram of the IEEE 13-Node test feeder with optimal
placement of the PDs

Fuse-Fuse
coordination
exist?

Yes

Recloser-fuse Coordination
established?

No

Yes
DG Capacity for Coordination
Constraints

Stop

Fig. 2: Proposed Protection scheme for IEEE 13-node test feeder

Hidden Protection Challenges of Unbalanced Distributed Networks
for Higher Concentration of Distributed Energy Resources
Muhammad Yousaf *, Kashem M. Muttaqi, Danny Sutanto

School of Electrical, Computer and Telecommunications Engineering
University of Wollongong, NSW 2500, Australia*
E-mail: my701@uowmail.edu.au

I. INTRODUCTION
This research work focuses on quantifying the impacts of
DGs on the hidden protection challenges. Section II examines
the impact of DG penetration level on the overvoltage surges of
unfaulted phases during ground faults. Rise of zero-sequence
voltage has been explored in section III for unbalanced
allocation of photovoltaic (PV) sources. Section IV discusses
the grounding strategies for DERs units on the operation ofPDs.
II. VOLTAGE SURGES FOR UNFAULTED PHASES
According to IEEE Standards C62.92.3-1993, the voltage
increase in the unfaulted phase for a ground fault is equal to the
net value of fault voltage and voltage drop across the fault
impedance. The rise in the unfaulted phase is caused by the flowof the fault current through the capacitance to ground of thesephases. Furthermore, this issue also depends on the grounding
configuration and methodology of the network as depicted bysimulation results shown in Fig. 1.

unbalanced among the phases will result in the rise of neutral
potential rise (NPR) as illustrated in Fig. 2 generating highneutral current (IN) through the neutral grounding wire causing
health and safety hazards.
0.15

without IBDG

Zero-Sequence Voltage(p.u)

Abstract— Integration of the distributed energy resources(DERs) has empowered the electric power utilities to meet theincreasing load demands efficiently at the reduced cost. Despite
several benefits, there are some hidden protection challenges dueto the large-scale integration of DERs with unbalanced
distribution networks (DNs), which have not been properly
addressed in literature. This research work investigates theimpacts of higher concentration of DERs on the voltage swellingof the unfaulted phases during fault events for different DERs
penetration level. Increase in voltage unbalance of real DNs having
unbalanced voltage profile has been analyzed for unevenallocation of single-phase rooftop photovoltaic (PV) units. Impactof the grounding strategies of DERs on the operation of different
types of protection strategy has been assessed for different
grounding impedance followed by the mitigation strategy.
Keywords— Distributed energy resources, fault studies,overcurrent protection, distribution networks, grounding strategies.

633

0.1

646
611

0.05

0
0%

10% 25% 37% 50% 75% 100%
IBDG Penetration Level
Fig. 2: Zero-sequence voltage for different IBDG concentrations for bus 632

IV. GROUNDING SCHEME FOR DISTRIBUTED GENERATION
Grounding configuration of the DGs defines its fault current
contribution during the ground faults that may affect theoperation of overcurrent PDs as shown in Fig. 3.

Fig. 3: Fault current contribution from SBDG for different grounding strategies

The proposed hybrid grounding scheme can be illustrated by
the following flow chart as shown in Fig. 4. During gridconnected (GC) mode, the high impedance grounding (Z GC)
will be designed to limit the zero-sequence current through thegenerator rotor according to its thermal capability during the
ground faults. During the islanded (IL) mode of operation, thelow impedance grounding (ZIL) is recommended for the reliable
operation of the overcurrent PDs and the reduced overvoltage
stress on the unfaulted phases.
Run Fault Analysis
Optimum design of hybrid grounding scheme

Fig. 1: Comparison of the voltage rise of unfaulted phases for different
grounding strategies
III. IMPACT ON INCREASED NEUTRAL CURRENTHigh concentration of the uneven allocation of DER units
with the real unbalanced DNs may enhance the unbalanced
voltage profile among different phases. This enlarged

Detection of operating state

GC

IL
Insertion of ZIL for
Overcurrent PDs

Insertion of ZGC to protect
generator windings

Fault Clearance

Fig. 4: Proposed strategy for design of hybrid grounding scheme

AMI Based Communication Scheme for
Decentralized Volt/VAR Control
Valliappan Muthukaruppan

Mesut E. Baran

North Carolina State University
Raleigh, NC, USA
vmuthuk2@ncsu.edu

North Carolina State University
Raleigh, NC, USA
baran@ncsu.edu

Abstract—With the recent introduction of smart inverters toprovide reactive power (VAR) support, the number of voltage
support devices on the distribution system is expected to increase.
However, integrating these devices with current centralized
volt/var schemes is challenging due to increased real-time datarequirements. This study investigates the use of two communication schemes for implementing a decentralized volt-var control
scheme that can manage smart inverters along with traditional
voltage regulation devices using the existing automatic metering
infrastructure (AMI). A design guide is proposed to help with theplacement of additional collectors needed for Volt/VAR, and thelatency in communication is utilized to assess the performanceof the two schemes using the IEEE 123 Node prototype system.
Index Terms—Smart grid communication, volt/var control, radio mesh network, smart distribution system, automatic metering
infrastructure.

architecture of the VVO scheme. For a successful operation of
VVO, the master needs the load data from all the nodes in thesystem. Hence, the communication can be split into two layers:
layer-1 corresponds to data collection (upstream communication of load data) and layer-2 corresponds to the control loop
(downstream communication of control commands). Since thenumber of smart inverters is typically lower than the load
nodes in a distribution system and further grouping of thesesmart inverters under slaves makes the latency associated withlayer-2 communication negligible. Hence in this study, only
the layer-1 communication is investigated for performanceassessment.
The two data collection schemes analyzed in this study are:
Scheme 1: The smart meters at each house communicates
its load data directly back to the master through the local
collector.
• Scheme 2: The load data from the smart meters at eachhouse connected to a distribution transformer is aggregated at the distribution transformer using an explicit data
aggregator and then communicated back to the masterthrough the local collector.
•

I. I NTRODUCTION
Integrated volt/var control (IVVC) is the state-of-the-art
control scheme utilized by the utilities to maintain the voltage
on distribution systems within the ANSI standard. Current
approaches are central control schemes, and they are seriously
challenged by the increasing DER penetration in distribution
feeders, primarily due to the high volume of data that needs
to be sent to the controller. Recently, research is focused on
developing distributed and decentralized volt/var optimization
schemes that can address this challenge. However, very few
studies have looked into the feasibility of incorporating suchalgorithms in present distribution feeders. The issue is thatcurrent distribution systems have minimal communication
infrastructure.
The goal of this study is to develop a communication
scheme based on existing AMI infrastructure for implementing
a decentralized VVO scheme. The contribution of this paper
is as follows:• Provide a design guide for locating the collectors for agiven distribution system• Develop an analytical method to estimate the performanceof the proposed communication scheme considering latency in communication.
II. C OMMUNICATION S CHEME
Radio Frequency (RF) mesh based communication scheme
and a model-based master-slave architecture for volt/var optimization is adopted in this study. Figure 1 illustrates theFig. 1. The architecture of the adopted VVO scheme

Using a closed form equation for data generated by smart
meters and the overall bandwidth degradation of channels
inherent to RF mesh networks the overall latency in communication is estimated and utilized to compare the performanceof the proposed communication schemes.

Distributed Dynamic State Estimation for Microgrids
Bang L. H. Nguyen
ECE Department
Clarkson University
Potsdam, NY, USA
bangnguyen@ieee.org

Tuyen V. Vu
ECE Department
Clarkson University
Potsdam, NY, USA
tvu@clarkson.edu

Thomas H. Ortmeyer
ECE Department
Clarkson University
Potsdam, NY, USA
tortmeye@clarkson.edu

Abstract—Conventionally, the dynamic state estimation ofvariables in power networks is performed based on theforecasting-aided model of bus voltages. This approach is effectivein the stiff grids at the transmission level, where the bus voltages
are less sensitive to variations of the load. However, in microgrids,
bus voltages can fluctuate significantly under load changes, theforecasting-aided model may not sufficiently accurate. To resolve
this problem, this paper proposes a dynamic state estimation
scheme for microgrids using the state-space model derived fromdifferential equations of power networks. In the proposed scheme,
the branch currents are the state variables, whereas the bus
voltages become the inputs which can vary freely with loads. As aresult, the entire microgrids system can be partitioned into local
areas, where neighbor areas share the common inputs. The
proposed estimation scheme then can be implemented in a
distributed manner. A novel Kalman-based filtering method isderived to estimate both states and inputs simultaneously. Only
information of common inputs is exchanged between neighboring
estimators. Simulation results compare the proposed method(SIE) with the conventional weighted least squared (WLS) andtracking state estimation (TSE).
Keywords— Bad data detection, dynamic state estimation,
distributed scheme, data fusion, microgrids.

Tuan Ngo
Electric Power Engineers
EPE Consulting
Austin, TX, USA
ngotuan@utexas.edu

𝑧𝑥,𝑘 = 𝐶𝐴𝑑 𝑥𝑘−1 + 𝐶𝐵𝑑 𝑢𝑘−1 + 𝐶𝑤𝑘−1 + 𝑣𝑥,𝑘 . (3)Combining (3) with the previous state estimates and input
measurements, the following set of equations can be achieved.
𝑒𝑥,𝑘−1
𝑥̂𝑘−1
𝐼
0 𝑥
𝑘−1
𝑣
𝑢,𝑘−1
[𝑧𝑢,𝑘−1 ] = [ 0], (4)
𝐷 ] [𝑢 ] + [𝑘−1
𝐶𝑤𝑘−1 + 𝑣𝑥,𝑘
𝑧𝑥,𝑘
𝐶𝐴 𝐶𝐵
The WLS solution of (4) can be expressed as:𝑥̂𝑘−1𝑥̂[ 𝑘−1 ] = (𝒪 𝑇 𝑅−1 𝒪)−1 𝒪 𝑇 𝑅−1 [𝑧𝑢,𝑘−1 ],
𝑢̂𝑘−1
𝑧𝑥,𝑘

The predicted state vector and its covariance can be achieved as𝑥̂𝑘|𝑘−1 = 𝐴𝑑 𝑥̂𝑘−1 + 𝐵𝑑 𝑢̂𝑘−1 ,𝑃𝑥,𝑘|𝑘−1 = [𝐴𝑑𝐴
𝐵𝑑 ]𝑈𝑘−1 [ 𝑑 ] + 𝑄.
𝐵𝑑

(8)𝑃𝑥,𝑘 = (𝐼 − 𝐾𝑘 𝐶)𝑃𝑘|𝑘−1 .

(9)The main contributions of this paper are as follows: 1) This
work pioneer to resolve the problem of unknown inputs indynamic state estimation using state-space models, 2) A novel
Kalman-based filtering method is derived to simultaneously
estimates both states and inputs, 3) The distributed
implementation of proposed estimation scheme is introduced. 4)
The communication and bad data detection protocol arepresented.
II. FORMULATIONS

𝑑𝑖𝑖𝑗,𝑑𝑞𝑑𝑡+ 𝑗𝜔𝑜 𝑖𝑖𝑗,𝑑𝑞 = −

𝑅𝑖𝑗

1

𝑖
+ (𝑣𝑗,𝑑𝑞 − 𝑣𝑖,𝑑𝑞 ),𝐿𝑖𝑗 𝑖𝑗,𝑑𝑞
𝐿𝑖𝑗Figure 1. The comparison of estimates of 𝒗𝟑𝒅(1)

where i, j denote the bus index; •dq denotes as (•d + j•q); 𝜔𝑜 is theangular frequency of the dq-rotating frame. Combined with
PMU-based measurements, the state-space models of
microgrids can be described in discrete form as:
𝑥𝑘 = 𝐴𝑑 𝑥𝑘−1 + 𝐵𝑑 𝑢𝑘−1 + 𝑤𝑘−1 ,{𝑧𝑥,𝑘 = 𝐶𝑥𝑘 + 𝑣𝑥,𝑘 , 𝑧𝑢,𝑘 = 𝐷𝑢𝑘 + 𝑣𝑢,𝑘 .

(2)

The input-to-output relationship of the system can be derived(7)𝑥̂𝑘 = 𝑥̂𝑘|𝑘−1 + 𝐾𝑘 (𝑧𝑥,𝑘 − 𝐶𝑥̂𝑘|𝑘−1 ),where 𝐾𝑘 = 𝑃𝑥,𝑘|𝑘−1 𝐶 𝑇 (𝐶𝑃𝑥,𝑘|𝑘−1 𝐶 𝑇 + 𝑅𝑥 )The dynamic model of branch currents in dq-frame areexpressed as:(6)

The state estimate and its covariance can be obtained as−1

I. CONTRIBUTIONS(5)

Figure 2. The comparison of mean squared errors.

1

Efficient Operation of Distribution Grids with High
Penetration of DER through Cluster Control
Shyamal Patel, Student Member, IEEE, Adedoyin Adedokun, Student Member, IEEE, Krishna
Murari, Member, IEEE, Sukumar Kamalasadan, Senior Member, IEEE, Sumit Paudyal, Member, IEEE
Abstract—The proposed approach of distribution grid clustering and feeder-head error minimization was tested on IEEE
123-bus distribution feeder with 10 PVs and 10 energy storage
systems. The cluster controller primarily controls the energy
storage on the feeders to minimize the error on the tie-line flows.
The optimal number of clusters was determined using the eigen
gap analysis. managed by an optimal power flow framework.
Index Terms—Power balance, Resiliency, DER, Clustering

I. P ROPOSED A PPROACH
We propose a hierarchical approach for controlling DERs
connected to distribution feeders as shown in Fig. 1. At the top
hierarchy, Distribution System Operator (DSO) solves optimal
power flow (OPF) problem to find set points of legacy control
devices (tap changer, cap banks, network switches), and aggregate dispatch of the areas (set points to area controller).
The area controller solves a second OPF problem at finer
resolution using a distributed approach [1] and develops set
points for DER clusters. The distribution feeder with high DER
penetration is clustered optimally using spectral clustering [2].
The cluster boundaries are virtual and would be changing
dynamically with changing grid conditions. The loads withineach cluster are primarily addressed by the distributed generation present in that cluster. The measurement taken acrossfeeder connecting the clusters provides tracking error (i.e,dispatch minus response). These errors for each cluster areminimized by the individual cluster controllers. The remaining
error is aggregated and the area controller tries to minimize theaggregate error at the area level. If the further error persists,then it is the role of the DSO to dispatch resources to minimize
the net error seen from the sub-station. An OPF problem ingeneral can be formulated as:argminf (V, p, q)(1)∗
Sij = Vi (Vi∗ − Vj∗ ) yij

(2)

V,p,q

subject to :and with power and voltage limits. Spectral Clustering framework can be described as
L = DA(3)

Here, L is the lagrangian matrix, D is a diagonal matrix and
A is the powerflow based adjacency matrix.
Ln = D

− 21

LD

− 12

(4)

Here, Ln is the normalized lagrangian matrix
This material is supported by the U.S. Department of Energy under Award
Number DE-EE0008774. Authors are with the University of North Carolina
at Charlotte and Florida International University.

Fig. 1: Flowchart: Proposed approach

Fig. 2: Power error as observed at feeder head by area controller

II. R ESULTS
The proposed approach of distribution grid clustering andfeeder-head error minimization was tested on IEEE 123-bus
distribution feeder with 10 PVs and 10 energy storage systems.
The cluster controller primarily controls the energy storage on
the feeders to minimize the error on the tie-line flows. The
optimal number of clusters were determined using the eigen
gap analysis. Fig. 1 shows the distribution grid clusters for asnapshot of power flow. The resulting error observed by area
controller is shown in Fig. 2.
R EFERENCES[1] A. Thakallapelli and S. Kamalasadan, “Alternating direction method ofmultipliers (admm) based distributed approach for wide-area control,” in2017 IEEE Industry Applications Society Annual Meeting, 2017, pp. 1–7.
[2] R. J. Sánchez-Garcı́a, M. Fennelly, S. Norris, N. Wright, G. Niblo,
J. Brodzki, and J. W. Bialek, “Hierarchical spectral clustering of power
grids,” IEEE Transactions on Power Systems, vol. 29, no. 5, pp. 2229–
2237, 2014.

Peer-to-Peer Energy Trading in Smart Grids
Considering Network Utilization Fees
Amrit Paudel, Yang Jiawei, Hoay Beng Gooi
Nanyang Technological University, Singapore
88Price ($/MWh)

P2

P3

P1

Price ($/MWh)

P1

Abstract—In the P2P approach, each actor negotiates directlywith a set of trading partners. A proper market clearing
mechanism is necessary to facilitate energy transactions among
different parties. This work proposes a decentralized market
clearing mechanism for P2P energy trading, considering theprivacy of the agents as well as utilization fees for using thethird party owned network. Grid-related costs in P2P energy
trading are considered by calculating network utilization fees
using an electrical distance approach.

6
Without fees

4
2

6With fees

440
Iteration

60

20

40
Iteration

60

Fig. 4. Evolution of price of producers in 9-bus system
Demand1500

SupplyPower (MW)Power (MW)I. S YSTEM M ODEL

DemandSupply

1000

1000

1

500

Without fees

500
With fees

0

0
20

2

2

P3

2
20

1500

1

P2

40
Iteration

60

20

40
Iteration

60

Fig. 5. Evolution of total demand and supply in 9-bus system

Power (MW)nc
Consumers

Fig. 1. The schematic model of P2P energy market

4000
2000

9

8

7

3

C9

6

4
C5

C6

250

200

200

P2

P3

Without fees

500Power (MW)Power (MW)

250

P1

40
Iteration

60

5

400
200
0

200
400
Iteration

600

0

200
400
Iteration

600

III. C ONCLUSION
This work presented a fully decentralized market clearing
mechanism for P2P energy trading. It has been found that thetrading decision of consumers not only depends on the prices
offered by producers but also depends on the electrical distancefrom producers if network utilization fees are considered in
P2P trading.

150
100

P1

P2

P3

With fees

50
0

20

10

Fig. 7. Evolution of prices (left) and supply (right) without network fees in
39-bus system

Fig. 2. IEEE 9-bus system for simulation studies

100

15

0

P1

150

200 400 600 800 1000
Iteration
600

0

C4

1

With fees

5000

600

20
Price ($/MWh)

C8

10000

Fig. 6. Evolution of total demand and supply in 39-bus system
P3

5

Supply0200
400
Iteration

P2

C7

Without fees

Demand15000

0II. S IMULATION R ESULTS
2

SupplyPower (MW)

Producers

DemandPower (MW)

6000

np

20

40
Iteration

60

Fig. 3. Evolution of supply of producers in 9-bus system

1

Impact Assessment of Real Time Demand Control
on Active AC/DC Hybrid Distribution Networks
Subho Paul, SIEEE
Electrical Engineering, IIT Roorkee
Roorkee, India
spaul@ee.iitr.ac.in

Narayana Prasad Padhy, SMIEEEElectrical Engineering, IIT Roorkee
Roorkee, India
nppeefee@iitr.ac.in

Abstract— Large penetration of DC distributed energy
resources and DC electrical loads into the existing AC distribution
network transforms the conventional AC network to an AC/DC
hybrid distribution network (HDN). However, intermittent
renewable energy, uncertain market energy price and vulnerable
load demand causes serious real time power imbalance situation.
To address the above deficiency, this paper assesses the impact ofreal time demand control on AC/DC HDN by designing a multiobjective, non-linear, convex, real time energy management
algorithm based on Lyapunov optimization technique, whichworks only by knowing the updated present data of the uncertain
parameters rather than their probabilistic estimated values.

Fig. 1 Modified IEEE 33 bus AC/DC hybrid distribution network

I. INTRODUCTION
For the last few decades, exhaustive burning of fossil fuels
and increasing global warming threat forced the power sector to
integrate renewable energy resources in the electrical energy
generation portfolio. A significant share of green energy comesfrom battery collocated photovoltaic panels, which produces DC
power. These DC distributed energy resources and evolution of
DC loads necessitate the unification of DC networks with theexisting AC networks, and results AC/DC HDN. The proposed
real time algorithm aims to control the load demand at each busof the network against abrupt change in energy price and PV
generation.
The
proposed
multi-objective
strategysimultaneously minimizes the energy cost and maximizes theusefulness of the consumed power, designed as value function.
The strategy is formulated as non-linear convex optimization
problem. The defined real time demand control process is solved
by using merger of queuing theory and Lyapunov optimization
process. The proposed solution process does not require theprobabilistic estimated values of the uncertain parameters andworks only with their present values.

t

The main aim of the optimization problem is to minimize theenergy cost i.e. pay off to the main grid. However, it also has the
responsibility to satisfy its customers with less load curtailment
Therefore, objective function for the optimization problem isgiven by,(1)
Min Ot  t   Ct   Vn,t  X nL,t 
nN

grid
grid
bat
bat
L
L

t   X nPV,t , YnPV,t , X n ,t , Yn ,t , X n ,t , Yn ,t , X n ,t , Yn ,t , M nm ,t , Vn ,t , n ,t 

RTP
Here, Ct   X ngrid
t
,t t



nN



(2)





And Vn,t X nL,t   t X nL,,max
1  exp t X nL,tt

 ,0    1
tqueueing theory and Lyapunov optimization. The revised realtime optimization problem is given by,min

II. REAL TIME OPTIMIZATION FRAMEWORKt

Fig. 2 Simulated output- (a) load demand, (b) time cumulated net pay off of
the AC/DC HDN after applying proposed and greedy process(3)

Now, if the load control operation is implemented at each timespan separately, then overall benefit is acquired by minimizing
the long term value of the equation (1) as specified below,
1 T 1(4)
Min lim  E Ot  t 
T  T t  0tDifficulty regarding time average objective function, choice ofbattery charging/discharging time and load curtailment hours areresolved here by implementing the concept of merger of=V   Qn ,t X nbat,t t   
nN

nN

L X nL,tX

n ,tL ,maxn ,t X nL,,mint

 AOt  t 

(5)Subject to operating constraints of each network components
and the hybrid distribution network architecture.
III. SIMULATION RESULTS
The formulated real time demand control process is validated
on modified IEEE 33 bus AC/DC HDN as shown in Fig. 1. The
simulation outcomes depicted in Fig. 2(a) shows that the
proposed real time load control process reduces the demand ateach time step and that results the peak reduction of 6.04% (from4730.4 kW to 4444.39 kW). Comparisons of proposed processwith the greedy algorithm based solution technique are depicted
in Fig. 2. As can be seen from Fig. 2(a) that the demand reduction
is more in case of proposed one (4.1% for greedy process) andthat results low time accumulated net pay off for the distribution
network owner (Fig. 2(b)). Solution time at each time step forthe proposed strategy to reach the global optimum ranges from5.57 sec to 5.62 sec, which is quite low. Hence, the proposed
technique is a real time strategy as the solar generation, load andenergy price do not update within 5 to 6 sec.

Ground Source Heat Pump Modeling andAggregation for Services Provision inElectricity Markets
Dario Peralta, Student, IEEE, Kankar Bhattacharya, Fellow, IEEE, and Claudio Cañizares, Fellow, IEEE
Department of Electrical and Computer Engineering
University of Waterloo, Waterloo, Ontario, Canada{d2peralt, kankar, ccanizar}@uwaterloo.ca
Abstract—This poster presents a thermal load aggregatormodel to determine the optimal heating load dispatch to control
the house temperature for a community comprising many houses,
considering two heating alternatives: Heating Ventilation and
Air Conditioning (HVAC) system, and the proposed GroundSource Heat Pump (GSHP) system. The study presented here
considers a load aggregator that would submit demand bids to theutility operator and purchase electricity for its clients, considering
two different strategies: maximizing the customer comfort (BaseCase), and minimizing the aggregator’s total electricity cost.
Index Terms—Ancillary services, electricity market, groundsource heat pump, HVAC, load aggregator.

I. I NTRODUCTION
There are not many comparisons between aggregated GSHP
and HVAC loads for power consumption reduction, and noload aggregator models have yet been reported for optimal
power dispatch of heating/cooling loads with GSHP systems.
Therefore, a thermal load aggregation approach to minimize
the aggregator’s energy procurement costs is discussed in thiswork, based on a mathematical model that includes the GSHP
characteristics to optimize the electricity usage by end-users,
while considering household thermal comfort.
II. T EST S YSTEM AND R ESULTS
The load aggregator is assumed to provide electricity to 800
houses, as in [1], and that each house has the same thermal
characteristic. The aggregator collects information on theuncontrolled loads in the house, the house thermal and heating
system characteristics, and the current and forecasted ambient
temperature, in order to optimally schedule the heating load ofeach house. The energy purchase strategy of the aggregator is
implemented through its participation in the Day-ahead (DA)and Real-Time (RT) markets, as illustrated in Fig. 1.
Tables I and II present a summary of the aggregator’s total
procurement cost and the total heating energy over a 24-hour
period, also its total peak load demand and the time when
it occurred. Furthermore, the differences between HVAC and
GSHP systems are summarized in Table III, demonstrating theeffectiveness of the proposed two-stage strategy for optimal
aggregator load dispatch of HVAC and GSHP systems, andthe advantages of GSHP compared to HVAC.
This work has been supported by the NSERC Energy Storage Technology(NEST) Network, Canada, http://www.ryerson.ca/nestnet/

DA Market

RT Market

Utility Operator

Load AggregatorDA load aggregator model
HVAC constraints
GSHP constraints

RT load aggregator model
HVAC constraints
GSHP constraints

House
ThermalCharacteristics

Inputs
Outputs

Community

Fig. 1: Framework of the load aggregator participating in theelectricity market.
TABLE I: Aggregator with HVAC systems.
Total Cost ($)
Base Case
5,700
DA
4,944
Aggregator
RT
133
Total
5,078 (-10.9% *)* With respect to Base Case

Total Energy (kWh)Heating Load
68,64565,22965,625 (-4.4% *)Peak Demand (kW)
Demand
Hour
6,585
19:00
6,550 (-0.54% *) 17:00
6,303 (-4.3% *)17:00

TABLE II: Aggregator with GSHP systems.
Total Cost ($)
Base Case
4,999
DA
4,576
Aggregator
RT
68
Total
4,645 (-7.1% *)* With respect to Base Case

Total Energy (kWh)Heating Load
53,65552,09652,735 (-0.21% *)Peak Demand (kW)
Demand
Hour
5,800
8:00
5,750 (-0.86% *) 17:00
5,787 (-0.21% *)17:00

TABLE III: HVAC vs GSHP systems.
∆ Cost* ($)-12.3%
DA-7.4%
AggregatorRT-48.8%
Total-8.5%* With respect to HVAC
Base Case∆ Heating energy* (kWh)-21.8%-20.1%-19.6%∆ Peak load* (kW)-11.9%-12.2%-8.2%

R EFERENCES[1] S. Chen, Q. Chen, and Y. Xu, “Strategic bidding and compensation mechanism for a load aggregator with direct thermostat control capabilities,”
IEEE Transactions on Smart Grid, vol. 9, pp. 2327–2336, May 2018.

Coupling Effects and Impact of EHV AC Cable
Circuits on LV AC Systems within a Tunnel
Nils Pfeifer
Mustafa Kizilcay
Simon Papenheim
Pawel Malicki
University Siegen
Chair of Electrical Power Systems
Siegen, Germany
nils.pfeifer@uni-siegen.de
Abstract— The power generation by wind farms in the northand northeastern area of Germany has significantly increased
during the last ten years. Within the scope of transmission
system expansion plan of Germany new EHV transmission lines
at the 380-kV level will be erected as mixed lines consisting ofseveral underground cable and overhead line (OHL) sections.
The easiest way for the cable installation are trenches in the
ground, but this is not always possible, so that erection of cabletunnels is inevitable in certain areas. Within such a tunnel,
several transmission systems are intended to be placed, resulting
in a high electromagnetic field intensity and therefore coupling
effects. Due to the used equipment, it may happen that a lowvoltage system is also required within the tunnel to supply thoseequipment. In this investigation the influence from the EHVAC
systems to LV AC systems in different configurations isanalyzed and some measures to reduce those coupling effects are
examined.

lines are needed and can be placed within the tunnel. Due tothe narrow space inside the tunnel, there is high field strength
caused by EHV cables, which leads to strong coupling effects
that may cause high stress on LV cables and/or equipment.
Consequently, the field strength and the resulting couplingmust be examined to ensure proper operation of the low
voltage components inside the tunnel.
In this investigation the interaction of 380-kV AC cables
with a parallel LV cable system in a cable tunnel is analyzed.
The HVAC part consists of two independent three-phasesystems. For the full power transmission, two circuits with
two single-core cables per phase are required, so that overall
twelve EHV cables are installed in the tunnel. The schematiccross section of the tunnel is shown in Figure 1.
HVAC system 1

I.

HVAC system 2

LVAC

Index Terms—Coupling Effects, EMI, HVAC Tunnel System,
Magnetic Field

INTRODUCTION

In addition to the ever-increasing demand for energy, thechangeover to renewable energy sources in Germany requires
a transmission grid reinforcement to increase the transmission
capacity from North to South. Already during the planning,problems arise with laws and expectation of the ruralcommunities, which usually prefer underground cables to theerection of new overhead lines. Therefore, a “mixed” line withoverhead and underground cable sections will be thecompromise, because a complete implementation with cables
is technically not possible. In this investigation a 380-kV cablesection is studied, whereat the coupling within a tunnel due tothe electromagnetic field and the narrow space between thecables is the main focus.
There are two methods to place the cables in theunderground. The usual one uses a cable trench and protective
pipes. Hence, for this method the soil must be free forearthworks, which may not be possible in urban regions. The
alternative is a cable tunnel, whereby the digging over thecomplete length is not necessary anymore. In addition to theactual tunnel, integrated buildings will be constructed, which
are needed for the installation and for maintenance. Between
those buildings low-voltage (LV) supply or communication

GW

GW
LVAC
alternative position

Figure 1. Schematic tunnel layout

On each side of the tunnel an EHV AC system with six
cables belonging to the 380-kV transmission grid is located.
The positions of the LV systems and several mitigation
alternatives are studied with the aim to reduce the influence ofthe 380-kV AC system on the LV system. In addition,different cases with cross-bonding of cable sheaths and several
operational states are analyzed.
For this purpose steady-state and transient analyses will be
performed using EMTP-ATP (ElectroMagnetic Transients
Program, Version Alternative Transients Program).

Generation of Synthetic Multi-Resolution Time
Series Load Data via Generative Adversarial
Networks
Andrea Pinceti, Lalitha Sankar, and Oliver Kosut
School of Electrical, Computer and Energy Engineering
Arizona State University
Tempe, AZ, 85287
I. I NTRODUCTION
Historically, power system research has been based aroundthe development of simplified physical models and the study of
their interactions within a system. However, in recent years,
the field of machine learning has matured and improved tothe point where it can provide real value to power system
operations; for this reason, a large portion of the research
work focuses on applying machine learning techniques topower system applications. This change in direction represents
a major shift: moving away from physics-based device models
to a data-centric analysis of system behaviors.
Within this new paradigm, the availability of large amountsof real data is crucial. Unfortunately, while power systemmodels of all kinds are readily available, data is a much more
scarce resource. The few researchers who have relationships
with electric utilities can get access to real measurements
through long processes involving non-disclosure agreements;
in general though, the broader research community must relyon the very few and limited datasets that are publicly available.
The goal of our project is to develop an open source tool
for the generation of synthetic time-series load data at varying
sampling rates and for different time lengths. Leveraging aproprietary dataset of high resolution measurements from hundreds of phasor measurement units (PMUs) across many yearsof operation, we can model the behavior of real system loads
and subsequently generate realistic-looking data on demand.
The focus on load data is motivated by the fact that loads are
one of the main drivers of power system behaviors and they
represent a latent variable: loads depend on phenomena outsideof the power system itself (consumer behaviors, weather,etc.). Thus, realistic load profiles can be used as an input
to existing power system programs and, running dynamic
simulations, electric quantities such as voltages and currents
can be accurately determined.
II. L OAD G ENERATION S CHEME
Having access to a dataset of almost 100 TB of uncompressed PMU data allows us to observe load profiles at 30 samples per second for many consecutive years, thus capturing fast
dynamic behaviors as well as long term seasonal patterns. By
first decomposing and down-sampling the raw PMU load data
at different levels, we can use advanced generative adversarial

Fig. 1. Overview of the time-series load data generation scheme.

Fig. 2. Overview of the time-series load data generation scheme.

networks (GANs) to learn a generative model for each time
horizon and resolution. GANs are a powerful machine learningalgorithm in which a generator (usually a deep neural network)is trained to generate realistic data by making it “compete”
against a discriminator whose job is to distinguish between
real samples and those created by the generator.
Figure 1 shows an overview of the generation scheme. The
raw PMU load data is down-sampled at four different levels:
1) yearly profile, 2) weekly profile, 3) hourly profile, and 4)
minute profile. A GAN model is trained individually for eachlevel. The fully trained model can then be shared and usedby researchers to generate any type of data required by their
specific application. For example, for a dynamics study, twohours of data at 30 samples per second might be required;
GANs 3 and 4 will be used to generate the hourly patterns andsub-second patterns, respectively. Figure 2 shows examplesof real and generated load profiles at the weekly and hourly
levels; the GANs learn to create completely new realisticlooking profiles.

Turbine valve opening (p.u.)

0.7
0.55
0.4
0.25
0

200

400

600

A Dynamic Model of Small Modular Reactor Based
Nuclear Plant for Power System Studies
Time (sec)Frequency (Hz)

63

63.5

63.10762.5

62.873

61.5

60

60.5

58.5

Bikash Poudel∗ , Student Member, IEEE, Ramakrishna (Rama) Gokaraju∗ , Senior
57 Member, IEEE
0
200
400
600
∗ Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon,Time(sec)Canada
SK,
Email: bikash.poudel@usask.ca, rama.krishna@usask.ca
Steam pressure (MPa)
Mechanical power (p.u.)

1.05

Abstract—Small modular reactors (SMRs), an emerging nuclear power plant technology, are suitable for large grids as well
as remote load centers and offer load following and frequency
response capabilities. This poster presents a dynamic model of an
integral pressurized water reactor (iPWR)-type SMR, modeledin Siemens PTI PSS/E, to assess the contribution of the reactor
to the power system dynamics. The proposed SMR model mimics
the heat generation process and subsequent heat transfer processwith the inclusion of the reactor core based on point kinetics,
primary coolant based on natural circulation, and a simplified
three lump representation of the steam generator (SG). The SMR
model is integrated with the modified IEEE standard GGOV1
turbine-governor system and a power system study is conducted.
Results show the power system and reactor responses whensubjected to electrical variations of 20% rated electrical output(REO) with a valve rate limit of ±80% REO/min.

I. K EY F IGURES

Power
setpoint

2

1

Coolant node 1
5

Tc2

Cold leg

Hot leg4

Valve
opening (μ)

SpeedGovernor

VG

Valve
mapping

RcloseReference
power setpointμμmin=0

ṁcs

pref

psat
Reactor model

400

600

(3)

Hot leg temperature (C)

3

2

Cold leg temperature (C)

63.5
228

216
60.5 0200400Time (sec) 400
VP200 Time Flow
SF
(sec)

Model

Fig. 2. GGOV1 modified and integrated with the SMR model. Shaded region
shows the modifications with new modules.

400

600

70

120
Time (sec)(1+sTc)/(1+sTb)

170

Pmech
Thermalpower (MW-th)
Turbine180

GS160 Governor signal
VP Valve position
SF140 Steam flow

Pin
Reactor Model

120
200
400
200 Time (sec) 400
Time (sec)

600
600

0

200

Time (sec)

400

600

Fig. 3.Average
Dynamic
responses.
Legends: Case I-Without Reactor (.....), Case
coolant
temperature
Hot leg
temperature(C) (C)
II-WithUncontrolled Reactor (- - -), Case III-With(—).
288
PrimaryControlled
coolant flow Reactor(kg/s)
260
600

286
256
284

VG Governor signal
μ Valve positionṁcs Steam flow rate

Time (sec)

T3 T2

20

ref

Pref

200
T1

59.5

Kturb

Average coolant temperature (C)
Pressure (MPa)P Steam pressure

3.1
252
2.9

Pmech

600
600

T1 = 43 s
T2= 81 s
T3= 70 s

62.873

220
61.5

2.7
248
2.5 0
0

Turbine63.107224
62.5

Pressure 3.3
Reference256

pin
Pressurereference

Time (sec)= ηT ∆hṁcs

Steam pressure (MPa)Frequency (Hz)0

Steam flow(ṁcs )

Flow
model

P

III. K EY R288ESULTS
63.5
286 is implemented in a sinmodel
63.107
0.7
284
62.5
gle
machine based power systemmodel.
The electrical load
62.873
61.5
282
61.5
changes
from
100%
to
80%
REO
at
20
s. At 400 s, a
0.55
60
280
60.5 0 back to20%
step
increasebrings
the
load
100%
200
400REO. The
600
58.5
0.4
Time (sec)
T modelT T
results
show the importance of anfor an accurate
59.5 SMR
57
20
70
120
170
0.250
200
400
600
representationof(sec)systemdynamics.
Time (sec)0200Time
400
600

Valve
Mapping

GS

Reference
Power Output

μmax=1

200

2.960
2.7
58.5
2.5
570

Fig. 1. SMR block diagram. Shaded region shows the reactor components.

Ropen

0

400
600
Time (sec)mechFrequency (Hz)The Turbine
proposed
dynamic
valve opening (p.u.)
63

260
3.5

Flow model

200

61.5
3.1

Tp
SG metalTm
SG secondary
psat,Tsat

Time (sec)

0

3.5
3.363

THL

SG primary

0.75

0.25

232

Coolant node 2

TCL

3.1

II. K EY E QUATIONS
2.9
0.85
2.7
The core neutronics is describedby the average neutron
2.5
0.75 (φ) formulated as a point kinetics
flux
model.
The reactor
0
200
400
600
Time (sec)
0
200
400
600
(sec) ) is proportional to the average neutron
thermal powerTime(PthAverage(Pcoolant
temperature(C)
flux in the core. The mechanical power) is a function
mech
260
Mechanicalpower(p.u.)
of1.05the enthalpy difference (∆h) and steam flow rate (ṁcs ).
256
Turbine valve opening (p.u.)
ρ
β
dφ
0.7
0.95
= − 252 φ + λC
(1)
dt
Λ
Λ
0.55
0.85
r
Pth = 248
φP0th
(2)
200
400
600
0.4

Time (sec)

Tc1
3

3.3

1

Control rod
ρextρ(Tc2)
Fuel rodΦ, Pth,Tf
Core hydraulics

ρ(Tc1)

3.5

0.95

580

R EFERENCES
560

252
282

[1] B. Poudel, K. Joshi, and R. Gokaraju,”A Dynamic Model of Small
540
280
248
Modular
Reactor
Based
Nuclear
Plant for Power System Studies,” IEEE
00
200
400
600
200
400
600 520Time(sec)
TimeConvers.,(sec)Trans. EnergyIEEE Early0 Access,200Nov. 30,4002019. DOI:
600
Time (sec)
10.1109/TEC.2019.2956707[2] T. Inoue,
T. Ichikawa,P.
Kundur, and P. Hirsch, “Nuclear plant models for
Hot leg
leg temperaturetemperature(C)
(C)
Cold
232
288medium- to long-term power system stability studies,” IEEE Transactions
on Power Systems, vol. 10, no. 1, pp. 141–148, Feb 1995.
228
286
Averagetemperature(C) reactor[3] S. E. Arda and K. E. Holbert, “Implementing
a fuelpressurized
water
530
224
284
nuclear power plant model into grid simulations,” in 2014 IEEE PES
282General Meeting — Conference Exposition, July 2014, pp. 1–5.
220
480
280
216

00

200
400
200
400
Time(sec)
(sec)
Time

600
600

430
380

232
180
228
160
224

0

Cold leg temperature (C)
Thermal power (MW-th)
4

61.5

x 10-4200

Time (sec)400Core reactivity

600

T1

59.5
20

T3

70

Security Constrained Unit Commitment with
Corrective Demand Response
Arun Venkatesh Ramesh, Student Member, IEEE, and Xingpeng Li, Member, IEEE

Abstract—This work highlights the benefits of considering demand
response by considering flexibility offered by controllable loads asa corrective action for post-contingency emergencies in day-ahead
operations. A security-constrained unit-commitment (SCUC) with
explicit corrective demand response actions is proposed andsimulation results point cost savings. Since line and generator
contingencies are infrequent, economic benefits are achieved
without compromise in comfort level of customers in most cases.
Currently, system operators predominantly utilize demand
response program as a preventive action by shifting demand frompeak to non-peak hours for system reliability but neglect theimportance of such actions for post-contingency scenarios. The
proposed model can bridge this gap, and it was tested on IEEE 24bus system which shows significant cost savings in daily
operations.

I. INTRODUCTION
Smart grid technology advancements have brought two-way
communications possible through sensing and control signals.
Through the advanced metering infrastructure, system
operators can determine and send signals to enable demand
response (DR) for controllable loads. Typically, system
operators use security-constrained unit-commitment (SCUC) to
commit and obtain dispatch signals for generators to meet aforecasted day-ahead demand.
System operators often utilize preventive and corrective
control actions to maintain system reliability. Mostly, DR
through controllable loads are considered as a preventive
action. DR benefits the system by moving non-criticaldeferrable loads from peak hours to non-peak hours increase thesystem flexibility while also increasing demand side market
participation through financial incentives [1-2]. Though there
are emergency DR plans that several system operators
implement [3], they are solely based on supply and demandbalance for frequency regulations and typically this is factored
in the SCED process where operators handle DR throughcapacity markets and/or real-time markets [4-6].
Currently, the use of corrective demand response (CDR) in
response to contingency is mostly not considered in the SCUC
process, to the best knowledge of the authors. CDR can increase
power system operational performance by reducing theoperational costs when co-optimized with SCUC.
II. KEY MODELLING EQUATIONSSCUC objective considering cost of CDR:
(1)
Min: 𝑜𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛𝑎𝑙 𝑐𝑜𝑠𝑡 + ∑ , , (𝜋 ∗ 𝐿𝑐𝑜𝑠𝑡 ∗∑𝐷𝑆ℎ𝑒𝑑 , ) +, , (𝜋 ∗ 𝐺𝑐𝑜𝑠𝑡 ∗ 𝐷𝑆ℎ𝑒𝑑 , )Post-contingency modeling of nodal balance with CDR:
∑ ∈ ( ) 𝑃 , , + ∑ ∈ ( ) 𝑃 , , − ∑ ∈ ( ) 𝑃 , , = (2)𝑑 , − 𝐷𝑆ℎ𝑒𝑑 ,∑ ∈ ( )𝑃 , , +∑ ∈−∑ ∈𝑑 , − 𝐷𝑆ℎ𝑒𝑑 ,( )𝑃 , ,( )𝑃 , ,

=

(3)
(4)

𝐷𝑆ℎ𝑒𝑑 , & 𝐷𝑆ℎ𝑒𝑑 , ≤ 𝛽 ∗ 𝑑 ,where c is the contingent line; j is the contingent generator; tdenotes the time period; 𝑃 , , /𝑃 , , and 𝑃 , , /𝑃 , , denotes
the contingent generator dispatch of generator g and power flowon line k respectively; 𝑑 , is the demand at node n. Here, 𝜋 ∗𝐿𝑐𝑜𝑠𝑡 and 𝜋 ∗ 𝐺𝑐𝑜𝑠𝑡 represents the probabilistic penalty
cost associated CDR with respect line and generator
contingency as shown in (1); 𝐷𝑆ℎ𝑒𝑑 , and 𝐷𝑆ℎ𝑒𝑑 , denotes
the nodal demand curtailed as a corrective action for nodal
balance for contingency of line c and generator j, respectivelyin (2)-(3). CDR is implemented only on participating noncritical demand denoted by a fractional value, 𝛽 , of nodal
demand in (4).
III. RESULTS
The proposed SCUC-CDR was validated against traditional
SCUC on the IEEE 24-bus system. The mathematical model
was implemented using AMPL and solved using Gurobi solver
for a 24-hour (Day-Ahead) load period.
The difference in overall cost of SCUC-CDR and SCUC
demonstrates a cost saving of $ 15,348 (2.3% of total cost) byconsidering corrective demand curtailment of about 49.4 MW
cumulatively for all line contingencies and about 316.2 MW
cumulatively for all generator contingencies.
TABLE I. OPERATIONAL COST AND POST-CONTINGENCY DEMAND CURTAILED
SCUC
SCUC-CDR
Total Cost ($)
671,835
656,487
MIPGAP (%)
0.95%
0.84%
Solve time (s)3,814.59
4,489.34∑ , , , 𝐷𝑆ℎ𝑒𝑑𝑐𝑛,𝑡NA49.4 MW∑ , , , 𝐷𝑆ℎ𝑒𝑑𝑗𝑛,𝑡NA

316.2 MWIV. REFERENCES
[1]
[2]

[3]
[4]
[5]
[6]

C. Su and D. Kirschen, "Quantifying the Effect of Demand Response on
Electricity Markets," IEEE Transactions on Power Systems, vol. 24, no.
3, pp. 1199-1207, Aug. 2009.
H. Wu, M. Shahidehpour, and A. Al-Abdulwahab, “Hourly demand
response in day-ahead scheduling for managing the variability of
renewable energy,” IET Gen. Transm. Distrib., vol. 7, no. 3, pp. 226–234,
2013
M. H. Albadi and E. F. El-Saadany, “A summary of demand response inelectricity markets,” Elect. Power. Syst. Res., vol. 78, no. 11, pp. 1989–
1996, Nov. 2008.
Demandresponse
strategies,
PJM[Online].
Available:
https://www.pjm.com/markets-and-operations/demand-response.aspx
Load Resource Participation in the ERCOT Markets, ERCOT [Online].
Available: http://www.ercot.com/services/programs/load/laar
Demand response and load participation, CAISO [Online]. Available:
http://www.caiso.com/participate/Pages/Load/Default.aspx

Deadband Voltage Control and Power Buffering to
Enable Extreme Fast Charging of Electric Vehicles
Waqas ur Rehman, Student Member, IEEE, Rui Bo, Senior Member, IEEE
Missouri University of Science and Technology, Rolla, USA
Abstract— Integration of an extreme fast charging (XFC)
station with AC power grid may cause power quality issues thatadversely affect neighboring loads. It can introduce dynamic andsteady-state voltage violations in the host power grid. As EVs
arrive and depart, power swings of ±400 kW (as per the latest
CHAdeMO 2.0 protocol) are expected to be experienced by thepower grid. Consequently, transient over-voltage and undervoltage may occur, damaging other equipment on the grid.
Reactive power compensation capability of bi-directional electricvehicle (EV) chargers can prove essential in mitigating thesteady-state voltage violations caused by the EV charging itself orchanges in the neighboring loads. Use of stationary energy
storage system (ESS) for power buffering can be utilized toaddress the voltage transients (sags and swells) as a result of EV
charging at the XFC station. In order to address the controller‘hunting’ issue with conventional deadband voltage control (Vcontrol) methods, this paper proposes a Q-sign triggered
deadband V-control method at the point of common coupling(PCC). In addition, to ensure the ramp rate of power flow fromgrid is within the allowable limits set forth by the grid code, aramp rate control is proposed that uses the ESS as a ‘power
buffering’ device, which not only can help with voltage transient
mitigation but also can reduce the long-term wear and tear in theconventional voltage regulating devices. Lastly, different frommost reported work in the literature where no explicit limit of thepower electronic converters (PECs) is considered during the Vcontrol, this work takes into account a reasonable apparent
power capacity limit of the PECs when achieving the V-control.
This limit also affects the amount of active power that can beobtained from the power grid, and subsequently may require
ESS to function as ‘load sharing’ device to provide supplemental
active power to satisfy EV load. A case study simulated in
MATLAB/Simulink (interfaced with PLECS) is presented todemonstrate the efficacy of the proposed control approaches forthe operation of XFC station.

II. CASE STUDY RESULTS
Following cases are considered for the case study results:
Case 1: Without voltage and ramp rate control (base case)
Case 2: With deadband voltage control:a.
b.

Without ramp rate control (i.e., w/o power buffering)With ramp rate control (i.e., with power buffering)

Keywords—extreme fast charging, deadband voltage control,
ESS power buffering, ramp rate control.

I. XFC STATION CONTROL SCHEMATIC

Fig. 1. High-level control schematic of XFC station

Fig. 2. Case study results: (a) EVs load, (b) measured active power at PCC (c)measured reactive power at PCC (d) PCC voltage, (e) ESS power

A Method for Power System Transient Stability
Assessment Based on Transfer Learning
1Junyu Ren, 1Jinfu Chen, 2Benyu Li, 2Ming Zhao, 2Hengchu Shi, 2Hao You
1State Key Laboratory of Advanced Electromagnetic Engineering and Technology,2

Huazhong University of Science and Technology, Wuhan, China
Yunnan Electric Power Dispatching and Controlling Center, Kunming, China

Abstract—Traditional machine learning models are subject
to changes in the data distribution. Once the topology of power
grid changes, the distribution of power grid data will change,
resulting in the consequence that the former transient stability
assessment model based on machine learning is no longer
applicable and needs to be retrained. However, the power
network after the topology changes cannot provide enough data
for the training of the new model. To solve the problem above,
this paper proposes a method of power system transient stability
assessment based on transfer learning, which fully taps thepotential of past data and assists a small amount of newly
acquired data in training the model. The simulation results on
the CEPRI36 system show that the proposed method caneffectively improve the accuracy of original classification models,
especially for weak classifiers with insufficient generalization
ability. The disadvantage of transfer learning lies in the longtraining time, which still needs to be improved.
Keywords—transfer learning, power system, transientstability assessment

I. INTRODUCTION
Transient stability assessment (TSA) is significant to themaintenance of the stability of the whole power system.
However, the problem of power network topology change isstill an essential and unavoidable challenge in the research oftransient stability assessment based on machine learning forfollowing reasons: (1) The original machine learning model is
no longer applicable because of the change of data distribution
with the shift in power network topology.(2) Retraining themodel is inefficient because the grid with changed topology
often fails to provide enough newly labeled data in time.
Therefore, based on the idea of transfer learning, this paper
presents a new method of power system transient stability
assessment.
II. PROPOSED METHODOLOGY

III. CASE ANALYSISThe test is on CEPRI36 system, which is a 220 kV/500
kV AC power network with 8 generators and 26 AC lines.
TABLE I.

TEST RESULTS OF TRANSFER LEARNING MODEL

Target Data

DecisionTransfer

Increase

VolumeTree

Learning

(%)

3600

0.949

0.973

2.4

3000

0.951

0.971

2.0

…

…

…

…

400

0.924

0.936

1.2

TABLE II.

TEST RESULTS OF TRANSFER LEARNING MODEL WITH
DIFFERENT DATASET COMBINATION SCENARIOS

Dataset Scenarios

Accuracy of Accuracy for Target Data

SourceTargetOriginal

Original

Transfer

Data

Data

Model

Model

Model

DS1

TS1

TS2

0.964

0.776

0.960

DS2

TS1

TS3

0.964

0.651

0.966

…

…

…

…

…

…

DS10

TS5

TS4

0.959

0.833

0.967

No.

(DS: Dataset Scenario; TS: Topology Scenario)
TABLE III.

TEST RESULTS OF DIFFERENT MODELS
RF

TrRF

0.973

Increase
(%)
2.4

0.974

0.979

Increase
(%)
0.5

0.951

0.971

2.0

0.971

0.975

0.4

…

…

…

…

…

…

0.924

0.936

1.2

0.936

0.948

1.2

Target Data
Volume
3600

DT

TrDT

0.949

3000
…
400
TABLE IV.

TIME FOR TRAINING OF TRANSFER LEARNING MODEL
Target Data Volume

DT(s)TrDT(s)3600

0.00232

3.259

…

…

…

400

0.00058

0.941

IV. CONCLUSION(1) For the scenarios of power system topology change,transfer learning can improve the accuracy of machinelearning models by using data from similar fields.
(2) The transferability of the transfer learning model isrelated to the basic classifier and the amount of target data.
The strong generalization ability of the basic classifier itself
will restrict the transferability of the transfer learning model.
In a certain range, the larger the amount of target data, thebetter the transfer effect.
Fig. 1. Modeling process of TSA model based on transfer learning.

(3) The training time of the transfer learning model islonger than that of the basic classifier.

Impact of Photovoltaic Generation Integration on
Protection of Distribution Systems
Shuaiang Rong, Student Member, IEEE, Lina He, Member, IEEE
Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, U.S.A.
Email: srong4@uic.edu, lhe@uic.edu

Abstract— The increasing penetration of photovoltaic
distributed generation (PVDG) leads to significant challenges on
the protection of distribution systems. It is important to study
the impact of the integration of PVDG on the protection of
distribution systems and clarify the corresponding technicalchallenges to enhance their resilience. This paper develops a
generic distribution system integrated with a PVDG system, andidentified the resulting impact on the overcurrent protection of
distribution systems. A numerical study in this paper shows thatthe control of inverters can limit the fault current fed by PVDG
systems significantly during a short-circuit (SC) fault. This
makes the SC current too low to trigger the circuit breaker,
leading to a protection failure (should operate but does not). To
verify this conclusion, a comparison case with the connection ofa traditional synchronous generator (SG) is provided.

Load1

II. KEY DISTRIBUTION SYSTEM CONFIGURATION
Historically, the traditional distributed power supply
comes from diesel generators, which are SGs. With thedevelopment of solar PV, the distributed generation will bemostly replaced by the PVDG system. To study the impact of
the PVDG integration on the protection of the distribution
systems, a benchmark system is designed with the connectionof a SG as shown in Fig.1. A system with identical power
flows is designed where the power generated by the SG istaken over by a PVDG system, as shown in Fig.2.
In both systems, when the fault occurs at location F, thefault currents will flow from the substation and the distributed
generation system (SG or PVDG). Due to the same initial stateof the systems with same power flows, the fault current fed bysubstation If are identical in both cases, which can be detectedby relay R1. The circuit breaker CB1 will trip and isolate thefault from the substation. To study the difference of fault
current characteristics of the SG and PVDG, their fault
currents flow through the relay R2 are measured andcompared in the Fig.3.

Bus 2

Bus 1

Bus 3

CB1

If
Substation

R1

CB2

F

Ib

SG

Feeder1

R2

CB3

Load3
Feeder2

R3

Fig.1 The benchmark system with SG
Load1

Bus 3

CB1

If
Substation

Load2
Bus 2

Bus 1

I. INTRODUCTION
The emerging environmental issues and sustainable
concerns have inspired higher penetration of renewableenergy resources (RES) in distribution systems. Among these
RES, the PVDG is most prevailing due to its environmentalfriendly and ecological sustainable characteristics. The highlevel penetration of the PVDG can alleviate overloads andreduce the capacity of feeders and substation transformers
effectively. Yet, these power electronic-interfaced PVDG
systems pose challenges to the traditional protection of
distribution systems especially consider the control behaviorof the inverters. The traditional protection systems aredesigned based on the fault current characteristic of SGs,
which can no longer protect the distribution systems with
PVDG due to different fault current characteristics of PVDG
systems. To guide the upgrades of the protection of the future
distribution systems, this paper presents a quantitative
analysis of the SC fault characteristics of a PVDG system, anda comparison case with a distributed SG.

Load2

R1

CB2

F

R2

CB3
R3

Ib_pv

PV

Feeder1

Load3
Feeder2

Fig.2 Distribution system with PVDG system

III. KEY RESULTS

Fig.3 Fault currents generated from SG and PVDG

IV. CONCLUSION
As in Fig.3, the magnitude of the fault current fed by SG(Ib) can reach 5 times the current rating, which is large enoughto be detected by the overcurrent relays. The relay R2 can trip
the circuit breaker CB2, so that the fault current is interrupted
and drop to 0. However, in the case of the distribution system
integrated with the PVDG system, the current fed by the
PVDG (Ib_pv) will be limited by the current limiter in thecontroller of the PV inverter. This fault current has a low
magnitude (2 times the current rating) similar to the transientcurrents and overload currents, which can no longer be
detected by the relay R2. This protection failure can cause
damage of electrical equipment, leading to huge economicallosses to the power system.

Signal Dispatching Method for Secondary
Frequency Control Based on Area Requirement
Change and Merit-Order

Keywords—Load frequency control, Area requirement,
Control reserve market, Merit-order, Ramp-rate, Simulation

I. INTRODUCTION
Load Frequency Control (LFC) is a control method whichcalculates the Area Requirement (AR) from the frequency
deviation and dispatches the signal to multiple LFC generators
so that the output of these generators follow demand
fluctuations of cycles in several minutes to several ten minutes.
After establishing the control reserve market in Japan, an LFC
signal dispatching method based on the order of electricity
generation cost per kWh (Merit-Order (MO) method) will beapplied. One concern of using MO method is the increase infrequency fluctuation due to the decrease in total ramp-ratearising from that generators of lower cost tends to have lower
ramp-rate and the aggregated ramp-rate of all generators may
not be fully utilized.
Fig. 1 illustrates the time series change in LFC signal for
two types of LFC signal dispatching methods, i.e. Simplified
MO (S-MO) method, and proposed ΔAR-MO method. In
Figure 1, LAR(t), which is simply simulated as a triangularwave of four minutes cycles is dispatched to two generators,
L and H, having low and high cost per kWh respectively. In
S-MO method, incremental fuel cost is calculated from thepower output of each LFC generator every T [s] of LFC
control cycle to create an MO list. When LAR(t) is positive, theascending MO list is used to dispatch LAR(t). When LAR(t) is
negative, the descending MO list is used. As a result, thedifference between Lf_sum(t) (= Lf_L(t) + Lf_H(t)) and LAR(t) iscaused. It is indicated as “over” in Fig. 1(a).
In order to eliminate the over calculated Lf_sum(t) against
LAR(t) in S-MO method, we propose a method in which
ascending or descending MO list is switched over and LFC
signal is calculated in a different way depending on the
combinations of the signs of LAR(t) and the direction of change

XXX-X-XXXX-XXXX-X/XX/$XX.00 ©20XX IEEE

LFCsignal_L
LFCsignal_L
Lf_L
LFCsignal_L
LFCsignal_L
Lf_sum
LFCsignal
LFCsignal
LFCsignal
LFCsignal

60[MW]
LFCsignal[MW]
LFCsignal
LFCsignal [MW]signal [MW]LFC
[MW]
LFCsignal[MW]LFCsignal

over

40 60
60
60
20 60
40
40
400 40
20 20
-20
20 20
0 0
-40
0
0
-20
-60 -20
-20
-20 0
2
3
1
-40 -40
Time [min]-40 -40-60 -60 (a) S-MO method-60 -60

LFCsignal_H
LFCsignal_H
Lf_H LFCsignal_H
LFCsignal_H
Ⅰ
LAR
AR AR
AR AR 60

Ⅲ

Ⅳ

40

20
0

-20

Dispatchingorder change

-40

4

Ⅱ

-60

0

1

2
3
Time [min]4(b) ΔAR-MO method
Fig. 1. Examples of signal dispatching

0.3
0.2
0.1
0
-0.1
-0.2
-0.3
12:30

RR

ΔAR-MO

S-MO

Frequency deviation[Hz]

Abstract— In Japan, after establishing the control reserve
market in 2021, a Merit-Order (MO) method based on cost per
kWh of each generator will be applied to a Load Frequency
Control (LFC). A conventional Ramp-Rate (RR) method
dispatches LFC signal based on ramp-rate of each LFC
generator. In MO method, the frequency fluctuation may be
increased compared to RR method, because the aggregatedramp-rate of all LFC generators may not be fully utilized. To
solve this problem, we propose an LFC signal dispatching
method considering the change of Area Requirement (AR)calculated from the frequency deviation. The effectiveness of the
proposed method is evaluated using the IEEJ AGC30 model. As
a result, the proposed method is able to reduce the frequency
fluctuation compared to a simplified MO method, and theoperating cost compared to RR method.

Kouichiro Hata and Yoshiki Nakachi
Chubu Electric Power Co., Inc
Nagoya, Japan

LFCsignal [MW]signal [MW]LFC

Masaru Saida, Masaki Imanaka, Muneaki Kurimoto, Shigeyuki
Sugimoto and Takeyoshi Kato
Department of Electrical Engineering
Nagoya Univercity
Nagoya, Japansaida.masaru@a.mbox.nagoya-u.ac.jp

12:40
12:50
13:00
13:10
13:20
Fig. 2. Frequency deviation in each method

13:30

Table 1. Comparison of evaluation index (12:30~13:30)
Lave
Lave
Lave
CLFC
∆fMAD ∆fmax
𝐿𝑡𝑜𝑡
ave
method(Coal) (LNG) (LNG-CC)
3[10 yen] [Hz] [Hz][MW]
[MW] [MW][MW]
RR-96 0.0609 0.280

2.9

1.3

1.8

6.0

S-MO

-626 0.0812 0.300

73.1

-48.3

-17.9

139.2

ΔARMO

-200 0.0533 0.217

12.6

-7.7

-1.8

22.1

in LAR(t) (ΔLAR(t)). In Fig. 1(b), when LAR(t) and ΔLAR(t) areboth positive (time zone Ⅰ) or negative (zone Ⅲ), LAR(t) is
dispatched in the same way as in S-MO method. When LAR(t)is positive and ΔLAR(t) is negative (zone Ⅱ), ΔLAR(t) is
dispatched using the descending MO list. When LAR(t) is
negative and ΔLAR(t) is positive (zone Ⅳ), vice versa.
II. MAIN RESULTS
The effectiveness of ΔAR-MO method is evaluated byusing the IEEJ AGC 30 model. Fig. 2 shows the frequency
deviation result of each LFC method from 12:30 to 13:30. The
magnitude of frequency deviation in ΔAR-MO method is thesmallest among the three methods. Table 1 shows various
evaluation indexes regarding the performance of LFC. Fromleft to right: cost of LFC (CLFC), mean absolute deviation of
frequency ( ∆ fMAD), maximum frequency deviation ( ∆fmax),
average of LFC signal of each type of LFC generator (Lave_i),
the sum of absolute Lave_i (𝐿𝑡𝑜𝑡
ave ). The cost of LFC of ΔAR-MO
method is smaller than that of RR method.

1Computational Efficiency Comparison of
UC-OPF Formulations
Alper Savasci, Student Member, IEEE, Biswajit Dipan Biswas, Student Member, IEEE, Md Mahmud-Ul-Tarik
Chowdhury, Student Member, IEEE, Sumit Paudyal, Member, IEEE,
Sukumar Kamalasadan, Senior Member, IEEE
Abstract—The main objective of this paper is to investigate theexisting literature on Unit Commitment and Optimal Power Flow
(UC-OPF) optimization models and build a combined model that
improves the solution in terms of computational and monetary
cost leveraging the convex optimization techniques. The goal isto provide a unique UC-OPF framework that can be utilized byindependent system operators (ISOs) and Regional Transmission
Organization (RTOs).
Index Terms—Unit Committment, Efficiency Comparisons,
Optimal power flow

and extensive computational simulations to be conducted andcomparisons will be presented in terms of computation time,
exactness, and optimality on IEEE test systems.
II. K EY E QUATIONSPower Flow Model:
Pi = |Vi |Qi = |Vi |I. I NTRODUCTION
Generally, UC and OPF problems have high computational
complexity. As a combined problem, the UC-OPF problem isalso challenging because of the problem nature is nonconvex
and nonlinear due to the existence of integrality restrictions
from the UC part and nonlinear power flow equations fromthe OPF part. It can be even more difficult to obtain a highquality solution when the system scale becomes larger. The
UC-OPF, in its basic form, can be rendered as Mixed-Integer
Nonlinear Programming (MINLP) problem.
The drawbacks of this basic form are the lack of scalability
and tractability. To better scale and obtain high-quality solutions, different optimization techniques have been proposed in
the literature. These techniques can be classified into threegroups: decomposition techniques, linearization, and conic
optimization techniques. In the first category, a general MINLP
problem is decomposed into mixed-integer linear programming (MILP) and nonlinear programming (NLP) problems andsolved by an iterative algorithm such as branch-and-bound andcutting plane (outer approximation) methods. In the second
category, the MINLP problem is solved by linearizing theobjective and constraints, which are in nonlinear form, using
piece-wise linearization so that it can be formed as MILP,
which is generally more tractable than MINLP.
Even though using the linearization techniques results istractable optimization models, it might not capture challenging
nonlinear nature precisely. Therefore, third category techniques use conic optimization methods, which can guarantee
global UC-OPF solutions under specific network constraints
in polynomial time, capturing the nonconvex nature of thefeasible space in general [1], [2]. Mixed-Integer Semidefinite(MISDP) and Mixed-Integer Second-Order Cone Programming (MISOCP) are the main methods in the third category.
The following mixed-integer conic optimization methods, aswell as non-convex formulations, are planned to be consideredThis material is supported in part by the U.S. Department of Energy underAward Number DE-EE0008774. Authors are with the University of North
Carolina at Charlotte and Florida International University.

n
X
k=1
n
X

|Vk | (Gik cos (θi − θk ) + Bik sin (θi − θk ))(1)|Vk | (Gik sin (θi − θk ) − Bik cos (θi − θk ))

(2)

k=1

UC/OPF Model:XargminC(Pt,g )(3)

ut,g PgM in ≤ Pt,g ≤ ut,g PgM ax ,∀t ∈ T , ∀g ∈ G (4)inaxut,g QM≤ Qt,g ≤ ut,g QM,
gg∀t ∈ T , ∀g ∈ G (5)Pt,g − Pt−1,g ≤ RUg ut−1,g ,∀g ∈ G, ∀t ∈ T (6)Pt−1,g − Pt,g ≤ RDg ut,g ,∀g ∈ G, ∀t ∈ T (7)(xt−1,g − T Ug )(ut−1,g − ut,g ), ≥ 0,∀t ∈ T , ∀g ∈ G (8)(xt−1,g − T Dg )(ut−1,g − ut,g ), ≤ 0,∀t ∈ T , ∀g ∈ G (9)ut,g ∈ {0, 1},∀t ∈ T , ∀g ∈ G (10)ut,g ,Pt,g ,Qt,gt∈T ,g∈G

Power Flow Equations : Eq(1) - (2)III. K EY R ESULTS
An initial computational comparison of MINLP and MISDP
multi-period UC-OPF (t = 1 to 3) formulations on 3-generator
6-bus test network is shown in Table I. The shows that MISDP
formulation can ensure exact solution for this system. For
larger system if the MINLP fails to provide global optimal
solution, MISDP can ensure that.
TABLE I: MINLP vs. MISDP

Gen1 (MW)Gen2 (MW)Gen3 (MW)Demand
Loss

t=1
MINLP
MISDP
98.35
98.35
0
0
0
0
95
95
3.35
3.35

t=2
MINLP
MISDP
103.977 103.976
0
0
0
0
100
100
3.977
3.976

t=3
MINLP MISDP
161.6
161.6
150
150
0
0
300
300
11.6
11.6

R EFERENCES[1] X. Bai, H. Wei, K. Fujisawa, and Y. Wang, “Semidefinite programmingfor optimal power flow problems,” International Journal of ElectricalPower and Energy Systems, vol. 30, no. 6-7, pp. 383–392, 7 2008.
[2] J. Lavaei and S. H. Low, “Zero duality gap in optimal power flow
problem,” IEEE Transactions on Power Systems, vol. 27, no. 1, pp. 92–
107, 2012.

Sensitivity Analysis of Time-of-Use Rates on
Operations of Home Energy Management Systems
Ashwin Shirsat

Wenyuan Tang

Department of Electrical and Computer Engineering
North Carolina State University
Raleigh, NC 27695, USA
ashirsa@ncsu.edu

Department of Electrical and Computer Engineering
North Carolina State University
Raleigh, NC 27695, USAwtang8@ncsu.edu

80.29%

3.05%

87.53%

9.42%

63.67%

21.45%

0.21

32.40%

2.17%

Obj.

LF

P−LF

OP−LF

S−LF

PV−Exp.

BUF

Avg. SOC

LS

BGC

50

11.17$

0

Percent change

100

II. R ESULTS
Case(A)
Case(B)
Case(C)
Case(D)−50

Abstract—Widespread adoption of distributed energy resources, especially solar PV, is observed in present times forresidential applications. A similar trend is observed with distributed storage. Falling energy storage costs, the intermittent
nature of solar PV, and the mismatch between the solar output
and residential load demand are promoting the use of storage.
The self-sufficiency provided by the storage can lead to a strained
relationship with the utility. In this paper, we develop a model
predictive control based residential-scale DC-coupled PV-storage
system and analyze the sensitivity of its daily operations toperturbations in the start time, duration, and value of the timeof-use rate along with the variations in the flat PV sell backprice. The results can assist the process of designing effective
tariff schemes, such that the rate structure is of significant value
to the customers, as well as benefits the utility in achieving its
goals.

Base Value

Fig. 1. System sensitivity to variation in TOU rates.

I. I NTRODUCTION
Coupling storage with PV alleviates over-generation andexcessive curtailment but disrupts the relationship between thecustomers and the load serving entity (LSE). This strained
relationship is due to the self-sufficiency offered to the customers by the PV-storage systems. This results in the reduced
utilization of grid services by the customer, which leads toreduced compensation to the LSE, and can potentially disrupt
the utility business. However, the presence of the utility isnecessary since the PV-storage systems cannot be wholly
relied upon due to the volatile nature of solar energy. An
approach to address this problem is to design new tariff
schemes that can prove to be economical to the customers,
increase the residential PV-storage penetration, and also help
utilities run their business without losses.
Most of the existing literature is limited to the optimal
operations of the system given a specific tariff scheme. A
gap exists when it comes to the sensitivity of the PV-storage
system operations to variations in the time-of-use (TOU) tariff
schemes and rates. To bridge the gap in the literature on DCPVSS control algorithms and their sensitivity to TOU rates,
our contributions are as follows:
1) A realistic MPC based residential-scale DC-PVSS optimization model incorporating the converter losses, andthe flexibility to shift controllable loads.
2) Sensitivity analysis of the TOU tariff scheme on thedaily operational performance of the DC-PVSS by varying TOU rate value, start time, and duration.

(a) Obj(b) LF(c) P-LF(d) BGC(e) PV-Exp(f) BUF

Fig. 2. System sensitivity to variation in peak rate start time and duration.

Real-Time Topology Detection in Unbalanced
Distribution System Using Micro-PMU And Smart
Meter Data: A MIQP Approach
Zahra Soltani, Student Member, IEEE, and Mojdeh Khorsand, Member, IEEE

Absolute error(radian)Absolute error(p.u)

School of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, AZ 85287, USA
Email: zsoltani@asu.edu and mojdeh.khorsand@asu.edu
The information of distribution network topology issystem are considered. In each scenario, status of 12
crucial for the real-time (RT) operation and control
single-phase switches and measurement noise arestudies such as state estimation, volt/var control,
randomly chosen. The accuracy of the proposed iterative
distribution power flow study, and control of distributed
primary topology processor among all scenarios is 100%.
energy resources (DERs). However, the communication
Since, the proposed model is able to estimate powerlinks between switch devices and distribution
system states in the primary distribution system, themanagement system and the number of RT measurements
obtained voltage magnitude and angle values from case 1
are limited in the distribution network, which makes theare shown in Figures 2 and 3 using absolute error index.
knowledge of system operator from the distribution
The figures confirm that the errors in estimating systemsystem very restricted. Also, reconfiguration andstates are small. In the case 2, real world residential load
switching actions may occur more frequently withdata from Pecan Street Project with a time resolution of
integration of DERs in distribution system. Therefore, an
15 minutes for smart meter data is used [2]. The
efficient distribution system topology processor is criticalsimulations are conducted for 100 scenarios of various
for success of distribution management systems.
topologies and measurement noise. Monte Carlo
However, topology processor in the unbalanced
simulation is conducted to generate these 100 scenarios
distribution system is challenging due to single-phasewhich each of them includes 96 time intervals (one day
switches, switch cabinets, capacitor switches, mutual
every 15 minutes). In each time interval of each scenario,
impendences between phases in a line, and various phasestatus of 12 single-phase switches and measurement noise
configurations (i.e., single-phase, two-phase, and threeare randomly chosen. The accuracy of the proposed
phase) of feeders and laterals. This paper proposes a
topology processor among all scenarios is 99.95%.
Solve Power Flow Using
mixed-integer quadratic programming formulation based
OpenDSS
Updateon linearized rectangular IV formulation to determine theit=it+1
Measurement Noise (μPMU,
topology of the unbalanced primary distribution system.
No
Smart Meter, and Substation
measurments)
In the proposed formulation, untransposed distribution
Yes
IV-based Topology
Stop
It=1
lines, single-phase switches, and shunt elements ofEnd
Processor
Criteria
Flat Start
distribution lines are modeled. The power injection
formulations are linearized using iterative first-orderFigure 1: Topology Processor for Unbalanced Distribution Systems.
approximation of Taylor series. In order to eliminate
0.006
nonlinearity due to inclusion of binary variables
associated with status of single-phase switches, the big M
0.004
technique is utilized. The proposed model includes linear
0.002
constraints and convex objective function, which results
in a global optimal solution. The proposed model can0
1 5 9 13 17 21 25 29 33 37
handle identifying multiple three-phase and single-phase
Node number
switching actions at each time instant without
Figure 2: Absolute Error for Voltage Magnitude Estimation.
information of switch statuses in the prior time intervals.
The proposed model only requires measurement
0.006
information at each time snapshot to identify the topology
0.004
of the system correctly. Furthermore, the proposed
topology detection problem can estimate system states
0.002
simultaneously. Figure 1 shows the flowchart of the0simulation procedure for the proposed primary topology
1 5 9 13 17 21 25 29 33 37
Node number
processor in the unbalanced distribution system.
Figure 3: Absolute Error for Voltage Angle Estimation.
The model is simulated using a modified unbalanced
IEEE 13-bus distribution system [1]. Measurement data
REFERENCES
includes two μPMUs measurements, smart meter data on
load buses, and substation measurements. In order to[1] Distribution System Analysis Subcommittee Report, “Radial
distribution
test
feeders.”[Online].
Available:
show the performance of the proposed topology
http://www.ewh.ieee.org/soc/pes/dsacom/testfeeders/index.html.
processor, two case studies are investigated. In the case[2] “Pecan Street Inc. Dataport,” 2019. [Online]. Available:
1, 108 scenarios including all possible topologies of thehttps://dataport.pecanstreet.org/.

Comparison of Principal Component Analysis
Techniques for PMU Data Event Detection
L. Souto, J. Meléndez, S. Herraiz
Intelligent Systems and Control Engineering group
Universitat de Girona, Girona, Spainlaiz.souto, joaquim.melendez, sergio.herraiz AT udg.edu

Abstract—Principal component analysis (PCA) is a dimensionality reduction technique often applied to detect events in large
amounts of data collected by phasor measurement units (PMU)at transmission and distribution level. This article considers
five different approaches to select an appropriate number ofprincipal components and evaluates the accuracy of correct event
detections with use of two statistical tests in a 1−hour data filefrom the UT-Austin Independent Texas Synchrophasor Networkwith voltage magnitudes collected at different PMU substations.

I. S ELECTION OF PRINCIPAL COMPONENTS
Five different methods to select an appropriate numberof principal components with dimensionality reduction are
introduced as follows.
a) Kaiser criterion: This criterion retains all r principal
components containing at least as many information as a single
original variable in terms of variance.
mr := {max j ∈ {1, · · · , m} | λj ≥

1 X
λi }
m i=1

(1)

b) Automatic scree plot: In this method, the eigenvalues
are plotted decreasingly such that r corresponds to the eigenvalue whose distance to the origin of the coordinate system isthe shortest, as the idea is to search for an elbow in the plot.
r := {i ∈ {1, · · · , m} | miniq
λ2i + i2 }(3)d) Variance reconstruction error: In this method, theoptimal value of r is given by the minimum variance reconstruction error, considering a faulty observation as a unitaryvector ξi multiplied by a fault magnitude f and the correlation
matrix of reconstruction error R.
r := {j ∈ {1, · · · , m} | min ξi T Rξi }j2
r := {min i ∈ {1, · · · , m} | Tx2f ≥ Tlim
and Qxf ≥ Qlim }
(5)

r :=

r

1 X
λi × 100 ≥ V ar (%)
m i=1e) Statistical detectability: In this criterion, r is chosensuch that the smallest detectable events can be detected statistically in the projection subspace and residual subspace, suchthat (5) holds with a single PCA model or (6) holds with two
PCA models built separately for T2 and SPE statistics.

(2)c) Explained variance: In this method, a minimum percentage of the total variance is defined and r is taken as thesmallest integer satisfying it.
r :=TABLE IE VENT DETECTION RESULTS (Y ES /N O ) OVER A 10−S WINDOW
KaiserScree plot Variance
VRE
Detectability# r T2 Q r T2 Q r T2 Q r T2 Q r T2 r Q
1 3 Y Y 4 Y Y 3 Y Y 3 Y Y 2 Y 2 Y
2 2 N Y 3 N Y 3 N Y 1 N Y 4 Y 1 Y
3 2 Y Y 2 Y Y 3 N Y 2 Y Y 4 Y 1 Y
4 2 Y Y 2 Y Y 3 Y Y 1 Y Y 2 Y 2 Y
5 3 Y N 3 Y N 3 Y N 2 Y Y 3 Y 1 Y
6 2 Y Y 2 Y Y 3 Y Y 1 N Y 2 N 2 Y
7 2 N Y 3 N Y 3 N Y 2 N Y 2 N 2 Y
8 2 N Y 3 Y N 2 N Y 2 N Y 3 Y 3 N
9 3 Y Y 2 N Y 3 Y Y 1 N Y 3 Y 1 Y
10 2 Y Y 2 Y Y 2 Y Y 1 N Y 2 Y 2 Y
11 2 Y Y 5 Y Y 3 Y Y 1 N Y 2 Y 2 Y
12 2 Y Y 2 Y Y 3 Y Y 1 N Y 2 Y 2 Y
13 2 Y Y 3 Y N 3 Y N 2 Y Y 3 Y 1 Y
14 2 Y Y 2 Y Y 3 Y Y 1 Y Y 2 Y 2 Y
15 2 Y Y 2 Y Y 3 Y Y 1 N Y 2 Y 2 Y
16 2 Y Y 2 Y Y 2 Y Y 1 N Y 2 Y 2 Y

(4)



2
rT 2 := {min i ∈ {1, · · · , m} | Tx2f ≥ Tlim}rQ := {min i ∈ {1, · · · , m} | Qxf ≥ Qlim }(6)

II. C ASE STUDY
The procedures aforementioned have been tested with PMU
data from the UT-Austin Independent Texas Synchrophasor
Network. For illustration, Table I displays the detection results
and the corresponding r of each selection method, consideringa 10−second sliding window to build the PCA models.
III. C ONCLUSION
The results indicate that the PCA methodology is able toidentify different types of events, regardless of the approach
used to select the number of principal components. Nonetheless, the statistical detectability criterion is the most accuratewith both T2 and SPE statistics. The results calculated with T2
and SPE statistics are complementary in some cases, whereas
the time window also contributes to the task.

N-k Security Assessment and Screening for Large-Scale
Random Equipment Faults in Bulk Power Grid under
Extreme Weather
Tingkai Sun, Xue Li, Tao Jiang
Department of Electrical Engineering, Northeast Electric Power University, Jilin 132012, Jilin Province, China;
Email: suntingkai@aliyun.com

Key results：

ABSTRACT: Under extreme weather conditions such as
typhoon, ice disaster and rainstorm, the grid equipment
and

the

numberofhigh-orderFigure 2 and Figure 3 take the IEEE RTS79 system as an
example to verify the accuracy of the proposed method.
SE

Fault Number

N-k contingency states. This work proposes an N-k

Fault Number

M CS

Error/MW

Fault Number

impact increments of low order contingency states, and use

Fault Number

Fault Number

the independent relationship between equipment to further
that a large number of high order N-k contingency states
test case are employed to evaluate the performance of the
proposed methods. The results show that the N-k

Fault Number(c) N-4 contingency states(d) N-5 contingency states
Fig. 2 Precision analysis of IEEE RTS 79 test system

reduce the amount of calculation of contingency analysis so
can be quickly analyzed and screened. The IEEE RTS 79(b) N-3 contingency states

MCS

Impact/MW

corresponding high order contingency states by using theFault Number(a) N-2 contingency states

contingency analysis method based on impact increments,
states of grid analysis results, and calculates impact of the

Impact/MW
Fault Number

the computational efficiency of calculating the impact ofand this method can use limited low order contingency

SE

Impact/MW

N-k contingency analysis methods based on probability andImpact/MW

seriously affects the calculation accuracy of the traditional

Error/MW

contingency states will be greatly increased. Therefore, it

Error/MWprobability

Error/MW

faultsTab.1 Comparison of efficiency between State Enumeration
method and Impact Increments method
Examplecontingency analysis method proposed in this work can
rapidly and accurately calculate the impact of N-k

N-1
N-2
N-3

contingency states and accurately screen the high-impact

IISE

SE
NCTG=1
Time(s)
7.822
7.834
7.909

Time(s)
7.822
191
6809

NCTG=2
Time(s)199

N-k contingency states.
Impact/MW

NCTG

I s =   Iu

 s =  ( −1) s

n −k

k =0

 Iu

Fault Numberusk(a) N-2 contingency states sk = u | u  s, Card (u ) = kImpact/MW

Bus B1
OutageBranch 1(b) N-3 contingency states

MCS

Key figure：
Bus A1

Fault Number

MCS

Impact/MW

k =1 uks
ns

Impact/MWSE

Key equations：

Large Sca leTransm ission

OutageBranch 2

Fault Number

SystemBus A2Bus B2

Fig. 1 N-k contingency analysis method based on impact
increments

Fault Number(c) N-4 contingency states(d) N-5 contingency states
Fig. 3 Precision analysis of screening high-impact
N-k contingency states

Plaintext m

Shared Key k
Shared Key k
Basis Z 0 1
Plaintext m ClassicalCiphertext Ek(m)Encryptor
Channel

Decryptor

Loads

Laser

Local
Controller

Key, Dst Port =Generation

Microgrid Simulator

0 RTDS
1RSCAD X
Basis
Basis Z 0 1(Dst IP =GTNET1_IP

MGCC

)

②

Quantum-Secure Microgrid③ Quantum

Key Pool

Loads

GTNET1_PortGTNET2_IP

Key Pool

Key Consumption

GTNET2_PortKey Consumption

MGCC_IPMGCC_Port

PVs
Classical
Shared①Keyk
Shared Key kIP =, DstZhang
Port = ) Plaintext m
Zefan Tang, Yanyuan Qin, Zimin Jiang,
WalterO. Ciphertext
Krawec,E(Dstk(m)PengPlaintext m
Encryptor
Decryptor

Storage

Microgrid
Model

Quantum
Algorithm

MGCC
Microgrid Simulator
Abstract—Existing microgrid communication relies on classical(Dst IP =, Dst Port = )GTNET1_IP
RTDS
RSCAD
②
Rear View
public key systems, which are vulnerable to attacks by means of③ Quantum
quantum computers. In this paper, we present a potent solution
GTNET1_Port
Key Pool
Key Consumption
Rack 1 GTNET2_Portto the quantum era microgrid challenges by using quantum key
Control
MGCC
GTNET2_IP
MGCC_PortRack 3 MGCC_IP
Centerdistribution (QKD) for information-theoretically secure authenRack 2
Quantum-Secure
Quantum
Main Power Microgrid① Classical1
Modeltication. Specifically, three contributions have been made: Grid
1)
Basis X 0GTNET
Algorithm
ChassisMicrogrid(Dst IP =, Dst Port = )a novel QKD-based communication architecture is established
Basis Z 0 1
Classical
Microgrid Modelfor microgrid, 2) a quantum-secure microgrid testbed is built Fig. 2. Network topology for the quantum-secure Channel
microgrid
testbed. Two
in RSCAD
Loads
in RTDS environment using the practical decoy-state QKD GTNETLocal
Laser and quantum communications between
cards are used for the classicalalgorithm, and 3) different critical issues are analyzed based RTDSController
and the microgrid control center (MGCC).
RTDS Once the MGCC needs to
RTDS, it extracts a Key
given number of key bits Front
fromView
the key pool.
on the testbed and the QKD algorithm. The test results provide send data to
0 1
Basis
X
If
the
key
generation
is faster Generation
than the key consumption, there will be enoughRear View
valuable resources for building a quantum-secure microgrid.
Key
Pool
0 otherwise,1
bits;Zandthekeybits will be used up.
Loads
Index Terms—Quantum secure, microgrid, quantum key dis- keyBasisKey
Consumption
tribution, algorithm, cyber security, resilience
Rack 1

Storage

I. P RELIMINARY R ESULTS
In this paper, we investigate the feasibility of using QKD
for building a quantum-secure microgrid. Specifically, a novel
QKD-based communication architecture is established for microgrid. A quantum-secure microgrid testbed is built in RTDS
environment for evaluation and validation.
Based on the testbed and the QKD algorithm, the following
critical issues are further addressed: 1) the effect of the
distance (between two communication parties) on the QKDbased microgrid’s performance is evaluated; 2) the speed ofdata transmission is analyzed, such that it will be clear whichlevels of service the QKD system can provide for microgrid;and 3) the impacts of different attack levels on the QKD-based
microgrid system under various conditions (e.g., different
distances and data transmission speeds) are evaluated.

Quantum-Secure
Microgrid

Main Power
Grid

Local
Controller
Basis X 0
Basis Z 0

1
1

Basis X 0
Basis Z 0

Plaintext m

7.2 kmMicrogrid Simulator

0.6 kmRTDS
B3RSCAD, Dst Port =Storage

GTNET2_Port
MGCC_PortB7
GTNET2_IP

LD7

LD4

1.3 km②

B6

1.7 kmKey Consumption

Front
LD6View

LD5 MGCC_IP

Key Pool B5

Wind

DieselQuantum
Algorithm① Classical(Dst IP=, Dst Portmodel.
= )Fig. 3. One-line diagram of the
microgrid
The distributed energy
resources include a diesel
generator,
photovoltaic system, and a doubly-fed
1200aMVA
Grid
138 kVisBus
M1 A battery storage
induction generator wind turbine system.
also added. It
CAPrequires communication
kV Bus138/13.2 kV with the13.2
uses P-Q control, Banks
and
MGCC.

~

Transformer
Circuit Breaker
Load

8%

B1

Rear
View
LD1

7.2 kmLD2

0.6 kmRack 1

B3
PV
B7
LD4

B2
Storage
Rack 3

1.3 km
MGCC B6
LD6

Rack 2 1.4 km
1.7 kmGTNET Chassis
LD5
LD7
Wind
DieselMicrogrid Modelin RSCAD

Loads

Laser

MGCC

)

RTDS

GTNET1_Port
1.4
kmClassical
ChannelRTDS
Front View

Key
Generation
Key Pool(Dst IP =③ Quantum

B4
Microgrid
Model

LD2

GTNET1_IP

PVLD3
2.3 km1
1

Circuit BreakerMicrogrid Model
Loadin RSCAD
B2

B1

LD1

B4

Loads

Key Consumption
Storage

8%LD3
2.3 km

MGCC

MGCC

~

RackPVs
3
1200 MVA
Grid Shared
Key k
Shared Key k
Rack
138 kV Bus
M12
Plaintext
m Bus
Plaintext m
Ciphertext E138/13.2
k(m)CAP
13.2 kV
kV
Encryptor
GTNET
ChassisDecryptorBanks
Transformer

Fig. 4. Testbed setup for quantum-secure microgrid in RTDS environment.
The microgrid model is developed in RSCAD. The MGCC runs on a dedicatedcomputer, which receives data from and sends data back to RTDS.

PVs
Shared Key k
Shared Key k
Plaintext m
Ciphertext Ek(m)Encryptor
Decryptor

15

Voltage (kV)10

Fig. 1. QKD-based microgrid communication architecture. It contains two
types of communication: classical communication and quantum communication. Key bits are generated continuously via QKD, and stored in a key pool.

5

Phase A
Phase B
Phase CKey bits have been
consumed, and attack starts

0-5

System collapses-10This work was supported by the National Science Foundation under Grant
ECCS-1831811.
Z. Tang, Z. Jiang and P. Zhang are with the Department of Electrical andComputer Engineering, Stony Brook University, Stony Brook, NY 11794,
USA (e-mail: p.zhang@stonybrook.edu).
Y. Qin and W. O. Krawec are with the Department of Computer Scienceand Engineering, University of Connecticut, Storrs, CT 06269, USA.

-15
10

15

20

25

30

35

40
45
Time (s)

50

55

60

65

70

Fig. 5. Voltage response of bus 1 before and after the key bits are used
up. At t = 16 s, the key bits are used up, making the system vulnerable
to attacks. The magnitude of the voltage gradually decreases. The frequency
also decreases. At t = 59 s, the system collapses. It is therefore of great
importance to have enough key bits in the key pool.

B5Enhancing Situational Awareness: Prediction ofProtection Relay Operations After a Contingency
Ramin Vakili, Student Member, IEEE, and Mojdeh Khorsand, Member, IEEE
School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USArvakili@asu.edu and mojdeh.khorsand@asu.edu
Situational awareness plays a critical role in resilient smart
electric grids due to the increase in uncertain and unforeseen
events, e.g., the stochastic nature of renewable and distributed
energy resources (DERs) as well as cyber-attack threats. The
operation of modern electric grids near their security limit has
resulted in a reduced stability margin, making these grids
vulnerable to severe disturbances. Therefore, online monitoringof the static/dynamic stability of modern power systems is
known to be one of the most important tasks to enhancesituational awareness in the system and prevent blackouts [1].
Wide Area Measurements (WAMS) using synchronizedphasor measurement units (PMUs) are being widely used in theWestern Electricity Coordinating Council (WECC) system toprovide a wide range of applications such as situationalawareness for operational decision making. EasternInterconnection Phasor Project (EIPP) provides newopportunities to deploy the measurements from PMUs in realtime analysis to evaluate the system dynamic performance [2].
Using the online information provided by PMUs, many researchworks have developed different online Dynamic Security
Assessments (DSA) methods which can provide the operator
with an early prediction of system rotor angle or voltage stability
following various contingencies. As shown in [3], different
protection systems that exist in a power grid play a significant
role in defining the behavior of the system during disturbances.
Distance relays as well as Under Voltage Load Shedding(UVLS) and Under Frequency Load Shedding (UFLS) relays
are among the most important protection relays in the system.
However, in the DSA methods developed in previous researchworks, the behavior of these protection relays has not beenassessed during disturbances.
The proposed machine-learning-based algorithm equips theoperators with an online tool to predict the behavior of distance
relays as well as UFLS and UVLS relays in the system forseveral seconds after a disturbance, using the real-time dataobtained from the PMU units. This gives the operators (orautomatic control systems) enough time to initiate proper
control actions and enlightens the operators of how a
disturbance spreads in the grid and what areas are affected.
Besides its application as an online DSA method, thedeveloped algorithm can be used as a fast offline tool which uses
the results of early-terminated transient stability studies (e.g.
simulation results captured for one cycle after the disturbance)
to identify the critical protection relays which are essential to bemodeled in the transient stability studies, thereby addressing thecurrent challenges in modeling all the protection relays intransient stability studies.
The dataset needed for training and testing the proposedmachine learning (ML) algorithm is created by conducting
extensive offline transient stability studies considering different
types of contingencies and operating conditions. The problem
of predicting the operation of protection relays is a classification
problem. Different ML algorithms can be used for training aclassifier for predicting protection relay operations, among
which, the decision tree is used in this paper. Voltages of the 10
electrically closest high-voltage buses (i.e. the buses with thevoltage level of 345 kV and above, which are usually equippedwith PMU units) to the relay locations are captured for one cycle
after the disturbance and is used as the features of the algorithm.
I. RESULTS
Three decision trees for predicting distance relay as well as
UFLS and UVLS relay operations are trained and tested on the
WECC system representing the 2018 summer peak load caseconsidering different types of contingencies and operating
conditions. The performances of the algorithms are evaluatedusing four metrics of accuracy, the accuracy of predicting nooperation cases (ANO), recall, and precision. ANO andprecision show that what portion of no-operation and operation
predictions of the algorithm is correct, respectively. The recall
show that what portion of the total operation cases are correctly
identified as operation. The results are provided in Table. I.
Table I. Performance of the trained decision trees
The trained algorithm
Accuracy (%)
ANO (%)
RecallPrecision

Distance relays

97.31

98.17

0.943

0.945

UFLS relays

99.11

99.85

0.949

0.991

UVLS relays

97.82

98.71

0.929

0.929

The results show that the trained algorithms can predict theoperation or no-operation of the relays with high accuracy. The
precision values for all the algorithms are more than 0.9, whichindicates that when the algorithms predict the operation of arelay, the prediction is accurate in more than 90% of the time.
Also, the recall value is more than 0.9 for all the algorithms,
which shows that the trained algorithms can identify more than
90% of the operation cases. This makes the trained algorithms agreat planning tool for identifying the critical protection relays in
different contingencies, as well. The proposed algorithm predicts
operation of distance relay, UFLS relay, and UVLS in only
0.2746, 0.2718, and 0.3932 ms, respectively, which shows thatthe trained algorithms are fast enough to satisfy the need of an
online DSA method to provide an early alert of the pending relay
operations in the system.
REFERENCES
[1]

[2]

[3]

Y. Xu, Z. Y. Dong, K. Meng, R. Zhang, and K. P. Wong, “Real-timetransient stability assessment model using extreme learning machine,” in
IET Generation, Transmission & Distribution, vol. 5, no. 3, pp. 314-322,
Mar. 2011.
K. Sun, S. Likhate, V. Vittal, S. Kolluri, and S. Mandal, “An online
dynamic security assessment scheme using phasor measurements anddecision trees,” IEEE Power and Energy Society General Meeting Conversion and Delivery of Electrical Energy in the 21st Century,
Pittsburgh, PA, 2008.
M. Abdi-Khorsand, and V. Vittal, “Identification of Critical Protection
Functions for Transient Stability Studies,” IEEE Transactions on Power
Systems, vol. 33, no. 3, pp. 2940-2948, May 2018.

MPC Based Battery and Thermostat set-point
Scheduling for Demand Response Applications in a
University Campus Building
Divya T. Vedullapalli, Student Member, IEEE
Ramtin Hadidi, Member, IEEE
The Department of Electrical and Computer Engineering
Clemson University
Clemson, USA
dvedull, rhadidi @ clemson.edu
Abstract – In this work, we develop a demand
management controller in a building with a battery energy
storage system (BESS) and Heating Ventilation and Air
Conditioning (HVAC) set-point scheduling using the Model
Predictive Control (MPC). The outcome is the automation of thebattery allocation and thermostat settings of the HVAC for agiven set of building parameters, utility rate structure and battery
specifications. Battery operation is optimized with mixed
integer linear programming (MILP) and model predictive
control (MPC). It is observed that the grid limit changes
adaptively avoiding unnecessary peak reduction with MPC.
HVAC operation is modeled mathematically to optimize thepower bill. The platform has been simulated on a campus
university building modeled in EnergyPlus.
Index Terms— Battery energy storage system (BESS), Demandcharge management, HAVC Scheduling, Model Predictive Control(MPC), Optimization.

I.

3.

4.

5.

An optimizer for the HVAC operation that willcalculate setpoints to minimize the electricity bill forthe power consumed by HVAC by utilizing the thermal
mass of the building to store heat/cool energy without
sacrificing the comfort or productivity of building
occupants or the performance of devices andequipment in the building.
A Novel mathematical modeling of the HVAC for use
in optimization model has been developed to schedulethe thermostat set-points subjected to time-of-use
electricity pricing.
An optimizer for the BESS operation that will schedulethe battery to satisfy the predicted load of the building
in step (1) with its HVAC power determined in step (3).
The objective of this optimizer is to minimize themonthly electricity bill subjected to time-of-use
electricity pricing with demand charges.

INTRODUCTION

Peak power demand causes problems to the utilities, such as
the need of added capacity only for a fraction of 24 hrs., andstrain on the grid leading to blackouts. Utilities encourage
consumers through demand response programs to change theirpower usage habits and maintain a flat energy consumption
profile. In response to such programs, commercial consumers
can make use of batteries to shift the load partially from peak to
off-peak time and reduce the electricity cost. A building’s
thermal mass can also be used as a virtual battery to store theheat energy by pre-cooling or pre-heating during off-peak hours
and reducing the power consumption during peak hours.
This project involves the development of a demand
management controller for a commercial building. An accurate
forecast of the load consumption profile is necessary to find the
optimal operation schedule since forecast errors often lead to
sub-optimal or infeasible solutions. The following are the major
contributions of this work:
1. A forecaster that predicts the power from the facility
for the next 24 hours which will be the main input tothe optimizing algorithm.
2. Modeling of the university building in EnergyPlus andcomparison of the simulated building power withactual power consumption of the building

Fig 1. Schematic of the proposed algorithm

This algorithm is implemented on a single storied building
with an area of 52,000 square foot and 45 conditioned zones
located in Clemson University. Its power consumption is highly
dynamic, and its power prediction is exigent for developing ademand response algorithm.

Verification of Physics-Informed Neural Networks:
Formal Guarantees for Power System Applications
Andreas Venzke and Spyros Chatzivasileiadis

10−1
10−2
10−3
10−4

102

103

104

105

F : Max. viol. (MW)Abstract—This work presents a framework for verifying
physics-informed neural network behavior in power system applications. Machine learning approaches including neural networks
have shown significant promise, e.g., by performing power systemsecurity assessment at a fraction of the time required by conventional approaches. Neural networks are, however, treated so
far as black-box tools and trained physics-agnostic, i.e., without
knowledge of the underlying physical equations and constraints.
This represents a major obstacle towards their adoption in practice. For power system applications and leveraging mixed integer
linear reformulations of trained neural networks, this works
introduces a rigorous framework to provide formal guarantees ofneural network behaviour and to train physics-informed neuralnetworks. We demonstrate our methodology on a range of IEEE
test cases, illustrating that physics-agnostic training can lead to
substantial worst-case physical constraint violations.

L: RMSE (norm.)

Department of Electrical Engineering
Technical University of Denmark
Lyngby, Denmark{andven, spchatz}@elektro.dtu.dk

Number of data samples
Standard Training

80
60
40
20
0
102

103

104

105

Number of data samples
Physics-Informed Training

Fig. 1. We compare standard and physics-informed training of a neuralnetwork for predicting the solution to the DC-OPF problem for an IEEE
30 bus system as a function of the system loading. We show the accuracy interms of root mean square error (RMSE) and the worst-case bound on the
maximum physical constraint violation for the defined input domain D as afunction of the number of data samples used in training.

I. M OTIVATION
The increase of uncertainty in both generation and loadpresents challenges for secure power system operation. Machine learning, such as decision trees and neural networks,
has demonstrated significant potential for highly complex
classification tasks including the security assessment of powersystems. However, the inability to anticipate the behavior ofneural networks, which have been usually treated as a blackbox, has been posing a major barrier in the application of suchmethods in safety-critical systems, such as power systems.
In this work, we develop provable formal guarantees of the
behavior of neural networks for power system application,building on [1]. Our methods allow to evaluate the robustness
and improve the interpretability of neural networks and havethe potential to build the missing trust of system operators inneural networks, enabling their application in practice.
II. S UMMARY
Leveraging a mixed-integer reformulation of trained neuralnetworks from [2], we develop a rigorous framework to provide formal guarantees for both classification and regression
neural networks for power system applications.
For classification neural networks, we verify continuous
input regions for which we can guarantee the same classification, i.e. operating regions for which the classifiers predicts’safe’ operation with respect to defined security criteria. At thesame time, we can use this framework to identify so-called
adversarial examples. These are small changes to the neuralnetwork input which lead to a false change in classification, i.e.

from ’unsafe’ to ’safe’. By systematically including adversarial
examples in the training procedure we increase the robustnessof classification neural networks. We demonstrate our methods
on the IEEE 9-bus, 14-bus, and 162-bus systems, treating both
N-1 security and small-signal stability.
For regression neural networks, we train neural networks
to predict the solution to DC optimal power flow (DC-OPF)
problems with the system loading as input. We provide exact
worst-case guarantees on the violation of physical constraints
F for a defined neural network input domain D. To tighten
these guarantees, we train physics-informed neural networks.
To this end, we modify the objective function of neuralnetwork training to L + γF, where L is the loss function
associated with the accuracy, γ is a weighting term and theterm F penalizes the constraint violation. We demonstrate ourmethodology on range of PGLib OPF cases up to 300 buses,
illustrating that physics-agnostic training can lead to large
worst-case physical constraint violations, whereas physicsinformed training can reduce these substantially. Fig. 1 shows
the obtained results for the IEEE 30 bus test system.
R EFERENCES[1] A. Venzke and S. Chatzivasileiadis, “Verification of neural network
behaviour: Formal guarantees for power system applications,” arXiv
preprint arXiv:1910.01624, 2019.
[2] V. Tjeng, K. Xiao, and R. Tedrake, “Evaluating robustness ofneural networks with mixed integer programming,” arXiv preprint
arXiv:1711.07356, 2017.

An Online Energy Flow Control Strategy for
Regional Integrated Energy System
Guofeng Wang† , Chongbo Xu† , Chengpeng Hu† , Xiaodong Yang‡ ,Youbing Zhang∗††‡

Zhejiang University of Technology, Hangzhou 310023, China
Huazhong University of Science and Technology, Wuhan 430074, China

Abstract—In this paper, we propose an online strategy for
optimal energy flow control based on Lyapunov method inregional integrated energy system. First, consider the changes
in specific events. e.g., basic load, electricity price, queue lengthof controllable load, an event triggering mechanism is established
to automatically execute the scheduling signals. Without relying
on a priori knowledge, Lyapunov optimization method is thenused to control virtual energy queue of active load and thesystem stability in real time. Finally, the remaining renewableenergy resources are allocated in a pre-defined priority. The
theoretical deduction and numerical results prove the feasibilityof the proposed online algorithm.

It is obvious that the computational complexity will notincrease exponentially with the system dimension increase.
The proposed online algorithm can ensure that the schedulingof Xi (t) is applicable in the case of high-order system.
C. Comparison Analysis of Optimization Results
Through simulation, results under three different schemes
e.g. OALO (Online Algorithm with Lyapunov Optimization),
Online Algorithm OACI (Online Algorithm with Current Information), OALO+P2G are shown in Table I.
TABLE IS IMULATION R ESULTS I N T HREE C ASES

I. ENERGY FLOW CONTROL FRAMEWORK
A. Construction of the RIES System
We establish a typical architecture for regional integrated
energy system (RIES) in this paper (Fig. 1).
Main
Grid

EnergyManagement
SystemGas
Storage

RES Utilization (%)

Comfort Level (%)

631.74

54.46

79.76

B.OALO

520.66

55.38

67.38

C.OALO+P2G

457.90

98.08

67.38

HVACs

Operation Status

Electric Load
Electricity
Natural Gas
Information

CHP

Gas
Transmission

Fig. 1.

Cost (U)A.OACI

According to the comparison figure of running time shownin Fig. 2, the opening/closing frequency of HVAC in OACI is
obviously higher than that in OALO, which will substantiallyworsen the hardware of HVAC.

EnergyStorage
RES

Cases

Heat Load

Architecture for regional integrated energy system.

1

Operation Status of HVAC under OACI

0
Time

A stochastic programming problem is formulated as:
T −1
1X(P0) , min lim supE{C(t)}
T →∞
T t=0

(1)

Transform the original problem [1] (P0) into:N
X(P1) , min(Θ +[Qi (t)Li (t) + Xi (t)i=1Operation Status

B. Problem Formulation
1

Operation Status of HVAC under OALO

0
Time

Fig. 2.

(2)

Xi (t) − Si (t)(Vi R(t)− Qi (t))])Pi
The solution of minimizing objective function can be simplified to the following auxiliary theorems to determine thevalue of Xi (t),(i (t)0 , Vi Rt Xi (t)−S>Qi (t)Pi
Xi (t) =(3)i (t)Pi , Vi Rt Xi (t)−S≤Qi (t)Pi
This work was supported by National Natural Science Foundation of China(No. 51777193) and National Postdoctoral Program for Innovative Talents ofChina under Grants No. BX20190126.

Comparison of the HVAC operation status

II. C ONCLUSION
We propose an event-driven online energy optimization
algorithm for the regional integrated energy system incorporating P2G and CHP technology. The introduction of P2G and
CHP technology enhances the coupling degree between theelectrical network of the RIES, effectively reduces the gas costof users and significantly improves the utilization of RES.
R EFERENCES[1] M. J. Neely, “Stochastic network optimization with application to communication and queueing systems,” Synthesis Lectures on Communication
Networks, vol. 3, no. 1, pp. 1–211, 2010.

Diversity Factor Prediction for Distribution Feeders
with Interpretable Machine Learning Algorithms
Wenyu Wang, Nanpeng Yu, Jie Shi

Nery Navarro

Department of Electrical and Computer Engineering
University of California, Riverside
Riverside, USA

Grid Modernization
Southern California Edison
Pomona, USA

Abstract—The maximum diversified demand is an importantfactor to consider when utilities design new distribution systems.
To estimate the maximum diversified demand, engineers needto make an estimate of the diversity factor (DF). In practice,electricity utility companies usually estimate the DF using DF
tables, in which the DF changes with the number of customers.
However, besides the number of customers, DF also depends
on many other factors, such as customer type, weather, demographics, and socioeconomic conditions. Ignoring these factors,
DF tables have limited accuracy. In addition, engineers cannot
interpret or understand how various factors affect the DF. In thispaper, by leveraging supervised machine learning algorithms, webuild comprehensive DF prediction models that take a variety offactors into account. These models show high prediction accuracy and interpretabilty when applied to real-world distribution
feeders. Using the interpretation method called SHapley Additive
exPlanations, we quantify the importance of different features indetermining DFs. Finally, we offer more insights into how various
factors affect DFs.
Index Terms—Distribution circuit planning, diversity factor,
interpretable machine learning, SHapley Additive exPlanations,supervised machine learning.

Over Frequency Curtailment of PV Generation in
Low Inertia, Islanded Microgrids
Jan Westman
The Department of Electrical andComputer Engineering
Clemson University
Clemson, USA
jwestma@clemson.edu

Ramtin Hadidi
The Department of Electrical andComputer Engineering
Clemson University
Clemson, USArhadidi@clemson.edu

Abstract— The use of FREQ/WATT droop control of solar
photovoltaic (PV) inverters has been demonstrated to provide
effective mitigation of over frequency events in power systems withhigh penetrations of distributed PV resources. These
investigations have often been conducted in the context of
distribution and transmission systems with hundreds of MW ofrotating generation. In the case of islanded microgrids withrotating generation capacity on the order of tens of MW, thesystem inertia will be much lower and the generator dynamics
more tightly coupled to the PV inverter dynamics, leading torelatively less stable system frequency. This investigation proposes
to test the use of the FREQ/WATT function on the operation andcontrol of a small industrial, island microgrid grid with over 50%
penetration of PV resources by utilizing real-time digital
simulation with controller-hardware-in-the-loop (CHIL) testing ofcommercially available generator control systems. The results will
illustrate the dynamic response of the test system frequency toseveral load rejection events as well as the sensitivity of the systemfrequency to the FREQ/WATT function parameters. The
objective of the investigation is to determine the suitability of the
FREQ/WATT function to provide decentralized over frequency
curtailment of the microgrid test model PV system.
Keywords—controller-hardware-in-the-loop, droop control,
inverters, island microgrids, PV, real-time digital simulation

I. TEST SET-UP
The test model represents a three phase, 4160 V microgrid
composed of four 2.5 MW, 4160 V, synchronous generators, a
6.6 MW, 480 V solar photovoltaic (PV) system with a step up
transformer and an average value model (AVM) of the inverter,and five 1.65 MW induction motors driving compressor loads.
The test model is simulated using RTDS Technologies’ PB5
processor cards and a one line diagram of the test model isprovided in Fig. 1. The PV inverter controls with theimplementation of the FREQ/WATT function are included in
the simulated test system.

Curtiss Fox
The Department of Electrical andComputer Engineering
Clemson University
Clemson, USA
fox8@clemson.edu

Jesse Leonard
The Department of Electrical andComputer Engineering
Clemson University
Clemson, USA
jpleona@clemson.edu

Fig. 1. Microgrid test model implemented in the RTDS simulator.

II. TEST PROCEDURE
Sensitivity analysis of the system frequency response to asingle, full load induction motor load rejection event isperformed for the PV system FREQ/WATT function overfrequency deadband dof and droop slope Kof parameters. For the
analysis of each parameter, the microgrid is simulated for
multiple pre-event system operating conditions determined by
the number of induction motor loads connected to the microgrid.
For each test, the microgrid energy management system (EMS)
dispatches the PV system output and the number of synchronous
generators based on the maximum loading of the induction
motors and the maximum expected solar irradiance (i.e.
availability of full PV system output). As a result, eachparameter set is tested for varying amounts of system inertia dueto the differing numbers of generators dispatched by the EMS
with the maximum allowed contribution of the PV system.

The generator model voltage and speed control is performedby four Woodward easYgen 3400XT-P2 genset control systems.
The easYgens are interfaced to the RTDS simulation via thegigabit transceiver I/O (GTIO) cards. A diagram of thecontroller-hardware-in-the-loop (CHIL) test set-up is shown in
Fig. 2.
Fig. 2. The genset controller hardware connections to the RTDS simulator.

XXX-X-XXXX-XXXX-X/XX/$XX.00 ©20XX IEEE

1

Nonparametric Joint Chance Constraint for
Spinning Reserve Requirements
Chutian Wu, Student Member, IEEE, Amin Kargarian, Member, IEEE
Abstract—Probabilistic decision-making tools are needed for
power systems management as the penetration level of uncertain
renewable generation increases. These decisions-making tools
should be nonparametric, which meant agnostic to probability
density functions of renewable generation since probability
density functions of renewables may or may nor belong to any
known classes of probability distributions. This poster presents
data-driven nonparametric joint chance constraints (JCCs) for
economic dispatch. Solar generation uncertainty is considered asa random parameter with an unknown probability density
function that may not belong to any parametric class of probability
function. Since reserve provided by generators is dependent on
their ramp up/down capabilities that are time-dependent, reserve
up and down constraints at consecutive time intervals are
dependent. Thus, we propose to formulate reserve up and down
constraints as nonparametric JCCs that are joint over theconsidered scheduling horizon. Univariate and multivariate kernel
density estimators are used to estimate nonparametric probability
distributions of solar generation from historical data and thenconvert line flow and reserve JCCs into their closed-form solvableby standard solvers. Using joint chance constraints enhances
system reliability significantly as compared to using individual
chance constraints.

I. KEY EQUATIONS
1. The objective function is to minimize the operational costs,
including generation and reserve costs, of thermal units andbattery storage.
2
min ∑ ∑ 𝑐1𝑢 + 𝑐2𝑢 𝑝𝑢,𝑡 + 𝑐3𝑢 𝑝𝑢,𝑡
+ 𝑎𝑢 𝑟𝑢𝑢,𝑡
∀𝑡 ∀𝑢
c
𝑑
+ ∑ 𝛽𝑏 (𝑝𝑏,𝑡+ 𝑝𝑏,𝑡) + 𝛾𝑏 (𝑟𝑢𝑏,𝑡 + 𝑟𝑑𝑏,𝑡 )

(1)

∀𝑏

2. Reserve up chance constraint:
𝑑
𝑐
̃ 𝑟,𝑡
ℙ {∑(𝑝𝑢,𝑡 + 𝑟𝑢𝑢,𝑡 ) + ∑(𝑝𝑏,𝑡− 𝑝𝑏,𝑡+ 𝑟𝑢𝑏,𝑡 ) + ∑ 𝑃𝑅
∀𝑢

∀𝑏

∀𝑟

∀𝑡} ≥ 1 − 𝛼 𝐽𝐶𝐶≥ 𝑃𝑑,𝑡(2)

4. Introduce two auxiliary variables to reformulate (2) and (3):𝑑𝑐𝑃𝑑,𝑡 − ∑(𝑝𝑔,𝑡 + 𝑟𝑢𝑔,𝑡 ) − ∑(𝑝𝑏,𝑡− 𝑝𝑐,𝑡+ 𝑟𝑢𝑏,𝑡 )𝑔𝑏= 𝑔𝑗𝑢,𝑡

∀𝑡

(4)

𝑑
𝑐
𝑃𝑑,𝑡 − ∑(𝑝𝑔,𝑡 − 𝑟𝑑𝑔,𝑡 ) − ∑(𝑝𝑏,𝑡− 𝑝𝑏,𝑡− 𝑟𝑑𝑏,𝑡 )𝑔𝑏= 𝑔𝑗𝑑,𝑡∀𝑡(5)

5. Reformulated (2) and (3) as follows:𝑛𝑚𝑔𝑗𝑢,𝑡 (𝑥) − 𝑋𝑗,𝑖1∑ ∏ [1 − 𝜅𝑗 ()] ≥ 1 − 𝛼 ′
𝑛
ℎ𝑗

∀𝑡

𝑖=1 𝑗=1𝑛𝑚

𝑔𝑗𝑑,𝑡 (𝑥) − 𝑋𝑗,𝑖1∑ ∏ 𝜅𝑗 () ≥ 1 − 𝛼′
𝑛
ℎ𝑗

∀𝑡

II. KEY RESULTS
A six-bus system is used, and three cases are considered asfollows:• Case 1: Applying parametric chance constraint considering
known density functions (Gaussian) for solar generation• Case 2: considering reserved constraints as individual
chance constraints and applying nonparametric individual
chance constraints• Case 3: Applying the proposed nonparametric joint chanceconstraints
Although the third case has the highest operation cost andsolution time, applying nonparametric JCCs results in the
procurement of adequate reserve that ensures system security.
TABLE ICOMPARISON OF APPLYING PARAMETRIC, ICCS, AND 6 HOURS JCCS
Case 1Case 2
Case 39.3161 × 104
Total Cost ($)
9.3167 × 104
9.3197 × 104
Less than 1
Time (sec)Less than 1
64

𝑑
𝑐
̃ 𝑟,𝑡
ℙ {∑(𝑝𝑢,𝑡 − 𝑟𝑑𝑢,𝑡 ) + ∑(𝑝𝑏,𝑡− 𝑝𝑏,𝑡− 𝑟𝑑𝑏,𝑡 ) + ∑ 𝑃𝑅∀𝑏

≤ 𝑃𝑑,𝑡∀𝑟∀𝑡} ≥ 1 − 𝛼 𝐽𝐶𝐶

The authors are with the Department of Electrical and Computer
Engineering, Louisiana State University, Baton Rouge, LA 70803 USA(e-mail: cwu17@lsu.edu, kargarian@lsu.edu).

(7)𝑖=1 𝑗=1

3. Reserve down chance constraint:

∀𝑢

(6)

(3)

Fig.1. Hourly generation reserve up comparison between three cases.

Distributed Optimal Scheduling of Multi-region
Intergrated Energy Systems Considering Regional
Heating Network
Mingchao Xia, Zhihao Hua, Qifang Chen

Meijing Lv

School of Electrical Engineering
Beijing Jiaotong University
Beijing, China

Smart Campus Construction Center
University of Finance and Economics
Beijing, China

Abstract—This paper focuses on the joint optimization
scheduling of multi-region integrated energy systems (IES) andthe influence of regional heating networks on joint scheduling.
The IES model is firstly established, including the heating
network model and the internal equipment model. Then, inorder to ensure the independence of each IES, a bi-level modelof multi-region IESs joint scheduling considering regional
heating network is established, which is solved by distributed
algorithm - analysis target cascading method (ATC). Finally, thesimulation results show that the proposed model can effectively
optimize the energy scheduling between IESs while ensuring theindependence of each IES. The regional heating network can beused as heat storage equipment to participate in the joint
scheduling, and absorbs the excess heat energy in IES thatcannot be transferred to other IESs.
Index Terms-- integrated energy system; heating network;
distributed optimal scheduling; distributed algorithm

Gas Company

TABLE COMPARISON BETWEEN CENTRALIZED MODEL AND
DISTRIBUTED MODEL

t
GT ,i

t
PPV
,i
PV
t
PWT
,i

WT

G

ECC
t
in ,i

G

t
QGT
,i

Thermalenergy flow
Electric
energy flow
Gas energy
flow

GT

WH
t
GGB

IES#N

GB

HX

t
i
QGB
,i QHXsteam ,i

t
QHX
,i

Heating
networkf1 ($)

f2 ($)

f3 ($)

F ($)

35767.31
39090.50

52031.81
49449.41

48913.36
47997.61

100261.83
100423.01

t
Pload
,i

6

ECC
t
QWH
,i

Categories

Centralized
Distributed

t
in ,i

Q

t
load ,i

Q

100
95
90
85
80
75
24 70

IES3

4

2
Heat load
Average temperature of heating network

0

6

12

18

)

...

IES#2

t
PGT
,i

KEY RESULTS

When the objective function of ECC and IESi is tominimize the cost, the optimized cost is:

Temperature(

IES#1

(1)

where yi is the local specific variable of IESi; x is the local
specific variable of ECC; ζi is the shared variable between
ECC and IESi; μ*i is the reference value of the penalty term of
ECC, equal to the result value of the lower IESi optimization;
η*i is the reference value of the penalty term of IESi, equal to
the result value of the ECC; λ is the penalty coefficient; ηiand μi are shared variables of ECC and IESi.
III.

ECC

ECC
Pint ,imin f  y ,         2
 i i 2
i
i
i


N
2
min F  x,  1 ,  2 , ,  N     i  i 
2

i 1

INTRODUCTION

Power Grid

KEY EQUATIONS

According to ATC, the original objective functions (f1,f2, ···, fN for IESs; F for ECC) are transformed into:

Power(MW)I.

II.

Figure 1. Joint scheduling framework of Multi-region IESs.

Figure 2. Heat load and average temperature of heating network of IES3.

The upper layer is the energy coordination center (ECC),
and the lower layer is the multi-region IESs. ECC coordinates
the distribution of various energy sources and transfers thesurplus electric or thermal energy delivered by IESi to other
IESi with insufficient capacity.

Under the distributed modeling, different subjects canachieve respectively their optimality. The heating network canprovide a way for IES to absorb excess heat energy whichcannot be transmitted to other IESs, and reduce the impact ofload fluctuation on IES.

This work was supported by the National Science Foundation of China(Grant No. 51677003); this work was supported by the Project funded by
China Postdoctoral Science Foundation (Grant No. 2018M631326).

Analysis of Radial Constraint Representation
Methods for Distribution Networks
Chengsi Xu, Shufeng Dong
College of Electrical Engineering, Zhejiang University, Hangzhou, China
Abstract—In distribution network optimization problems,
radial topology is an important constraint to be considered. The
review of existing radial constraint representation methods(RCRMs) has not been reported yet, while appropriate methodshould be selected according to specific network to improvesolving efficiency. This paper is devoted to a new RCRM and asummary of the existing RCRMs. Firstly, based on the idea ofloop disconnection, a sufficient and necessary condition for
distribution network topology to be radial is proposed, and analgorithm for searching all loops in a network is introduced.
Then, the differences and characteristics of various RCRMs areanalyzed. Finally, the introduced RCRMs are applied to the
problem of service restoration reconfiguration. Simulation
result on an actual distribution network in Fuzhou, China
compares the computing performance of different methods.

Where L is the number of loops in the distribution network;
Ml is the number of branches in the lth loop; xlm is the state of
the mth branch in the lth loop.
II. CASE STUDY AND RESULTS
We take the service restoration reconfiguration problem as
an example to compare the computing performance ofdifferent RCRMs. The distribution network is shown in Fig. 1.
F5

D26

D28

D27
Z22

Z23

D23

D24

F3

Z20
D17

D18

D19
Z14

D20
Z15

D21

Z16

Z18

Z17

Z13

D12
D13
Z11
Z12
D1

B

(1)

b =1

Where B is the total number of branches in the distribution
network; b is the branch number; xb is the state of the bth
branch, whose value is 1 when the bth branch is closed and 0
when the bth branch is disconnected.
Many researchers have established the model of condition
2, including the spanning-tree model, the virtual demandmodel and the path-based model. However, condition 2
involves network connectivity, which is a property related to
the whole network, so its corresponding model is complex.
B. A new RCRM based on loop disconnection
Different from the existing methods, this paper takes
condition 3 as one of the conditions that radial topology
should satisfy, and proposes proposition 1, as follows:• Condition 3: All loops in the network are
disconnected.
• Proposition 1: condition 1 and condition 3 arenecessary and sufficient conditions to ensure
distribution network topology to be radial.
The formula for condition 1 is as follows:Ml xlm  M l − 1, l = 1, 2, , L

D15

D16

I. KEY METHODS
A. Existing RCRMs
The following two conditions are usually adopted toensure that the distribution network topology is radial.
• Condition 1: There are N-Ns closed branches in the
distribution network.
• Condition 2: The distribution network is connected.
Where N is the total number of nodes in the network; Ns is thenumber of substation nodes in the network.
The formula for condition 1 is as follows:

(10)

m =1

This work is supported by National Key R&D Program of China (No.
2016YFB0901300).

D2

Z1

F1

D3

Z2

D4

Z3

F4

D22

Keywords—radial constraint, distribution network, loop
disconnection, service restoration reconfiguration

 xb = N − Ns

D25

Z19

D5

Z4

Z21

D14

D6

D11

Z5
D7

Z6

D8
Z7

F2

D10

D9
Z8

Z9

Z10

Fig. 1. Diagram of an actual distribution network in Fuzhou, China

In the service restoration reconfiguration problem, using
different RCRMs to represent radial constraints. The variable
numbers of the spanning-tree model, the virtual demandmodel, the path-based model and the model proposed in thispaper are 112, 84, 187, 56 respectively. And the constraint
equation numbers are 140, 164, 238, 103 respectively.
Assume that the substations F1−F5 fault successively, andsolve the service restoration reconfiguration models based on
different RCRMs respectively. The computing time ofdifferent models is shown in TABLE I.
TABLE I.

COMPUTING TIME OF DIFFERENT MODELS

FaultySubstation

Computing Time (ms)Model in
This
Paper

Spanningtree Model

VirtualDemandModel

Path-based
Model

F1

12.5

14.7

16.7

26.6

F2

12.3

15.6

17.8

22.5

F3

7.2

8.2

10.6

18.6

F4

10.1

11.3

16.3

21.4

F5

7.4

8.7

9.8

16.6

The comparison result shows that the RCRM based onloop disconnection is applicable to the case where the numberof loops in the distribution network is not too large as the one
shown in Fig. 1. When the number of loops is large, thespanning tree model or virtual demand model should beselected, among which the spanning tree model is a better
choice. When the problem to be analyzed is closely related tothe power supply path and the number of loops in the networkis small, the power supply path model can be adopted.

1

Fast Transient Stability Batch Assessment using
Cascaded Convolutional Neural Networks
Rong Yan, Guangchao Geng, Quanyuan Jiang, and Yanglin Li

Abstract—Stability assessment is a regular routine in both
power system planning and operation. It is computationallydemanding using state-of-the-art approaches like time-domain
simulation (TDS), as numerous scenarios (e.g., N-1 contingencies)
have to be assessed for a large batch of contingencies. The
purpose of this work is to determine stability conclusion before
the simulator reaches the end of simulating time windows.
Therefore, TDS can be terminated early so as to reduce theaverage simulation time for batch assessment without losses ofaccuracy. The idea is to develop a data-driven methodology to“learn” from existing TDS results in the batch and “infer”
stability conclusions using current available TDS output. To
achieve this goal, cascaded convolutional neural networks (CNNs)
are designed to capture data from different TDS time intervals,
extract features, predict stability probability and determine
TDS termination. While accumulating more knowledge in batchprocessing, early termination criterion is refreshed continuously
via feedback learning to terminate TDS increasingly earlier, with
the increase of existing TDS results in the batch. Case studies inIEEE 39-bus and Polish 2383-bus system illustrate effectivenessof the proposed method.

Time-domain Simulator
Terminate TDS& Next Task
Y

RejectN
Reachconfidence
threshold?

Data Pre-processing
Feature extraction

Stability Assessment

Feature extraction

Stability Assessment

Confidence
Evaluation

Fig. 1. The application framework of fast transient stability batch assessment.
CNN-1

Stable：69.9%

Reject(Reject(Accept(, Stable)

Refreshing

CNN-2

Stable：85.2%, Stable)

Refreshing

CNN-3

STOP

Stable：99.9%, Stable)

Refreshing

CNN-4

Index Terms—Convolutional neural networks, data driven,machine learning, time-domain simulation, transient stability
assessment.

Simulator

Estimator

Updated Training Data

Fig. 2. Model refreshing mechanism.

I. P ROPOSED M ETHOD
In this paper, a novel fast transient stability batch assessment
framework using cascaded CNNs is proposed, in order todetermine stability conclusion using partial TDS output andearly terminate TDS to shorten the average simulation time
for a large batch of contingencies without losses of accuracy.
Considering the assessment time of training samples, we
proposed a model refreshing mechanism by the lights of semisupervised learning, so that early termination criterion can berefreshed continuously during batch assessment. In general,
this paper is highlighted with the following three contributions:1) Cascaded CNNs are designed to capture data from different TDS time intervals, extract features automatically, predictstability probability and determine TDS termination. It is a
novel framework to accelerate the process of security checkand enhancement in grid planning and day-ahead dispatch.
2) Early termination criterion refreshing mechanism via
feedback learning is proposed in this work. While accumulating more knowledge in batch processing, criterion can berefreshed continuously to terminate TDS increasingly earlier,R. Yan, G. Geng (Corresponding author), Q. Jiang and Y. Li are with
the College of Electrical Engineering, Zhejiang University, China (email:{yanrong052,ggc,jqy,21610189}@zju.edu.cn,). This work was supported by
National Natural Science Foundation of China (51677164), Fundamental
Research Funds for the Central Universities (2018QNA4018).

with the increase of existing TDS results in the batch. Moreover, mechanism also enhances the adaptability to changed
operating condition.
3) The proposed framework together with some algorithmic enhancements (e.g., misjudgment scenarios based loss
function) demonstrates accurate assessment results with lower
computational burden, compared with existing methods.
II. C ASE S TUDIES
Two typical systems with different scales are investigated:
IEEE 39-bus test system and Polish 2383-bus power system.
The following table illustrated the comparison test result ofPolish 2383-bus system.
TABLE II NDICES C OMPARISON BETWEEN THE P ROPOSED AND E XISTING M ETHODUSING P OLISH 2383- BUS S YSTEM
Method

AC(%)

FD(%)

FA(%)

RJ(%)

AEST(s)

MST(s)

EEM
TCC
SVM
DT
CNN
RNN
Proposed

96.07
99.10
91.01
98.26
98.26
98.31
99.82

3.93
0.88
0.09
0.94
0.32
0.33
0.11

0
0.02
0
0.84
1.42
0.27
0.07

0
0
0
0
0
1.09
0

5.407
2.047
4.057
2.566
2.279
2.072
1.118

5.502
2.030
≈0
≈0
0.400
0.069
0.697

* The accuracy requirement of online TSA frameworks is set to 98% (except SVM),
and training samples SVM: 15,000, DT: 9,500, single CNN and RNN: 7,500.

Paper Id: 20PESGM1191

Virtual Synchronous Generator Controlof Variable Speed Pumped Storage Hydropowerwith Full-size Converter

1191
PESGM 2020

Zhengwen Yang, Fen Tang, Xuezhi Wu, Wei Wang, Jingya Jiang
National Active Distribution Network Technology Research Center
Beijing Jiaotong University of Beijing, P.R. China

Abstract
To allow power electronics interfaced variable speed
pumped storage hydropower (PSH) providing adjustable
inertia as well as frequency and voltage support, a virtual
synchronous generator (VSG) control scheme of variable
speed PSH with the full-size converter is proposed. First, themathematical models of reversible pump-turbine are given.
On this basis, the proposed VSG control strategy is presented,
where the machine-side converter aims to provide a steadyDC-link voltage and meet the reversible pump-turbine
requirements while the grid-side converter conducts the VSG
control. In this way, the original fixed inertia of reversible
pump-turbine, which is wasted by the conventional control,
and the equivalent inertia of intermedia capacitor could becontrollable integrated into the adjustable system inertia togrid. Finally, the simulation study of a 5MW VSPSH iscarried out on Matlab/Simulink to verify the effectiveness ofthe proposed control method. The comparative results offixed speed PSH, variable speed PSH with conventional
control and the proposed control demonstrate that the
proposed control strategy has good reference tracking andpresents adjustable inertia to grid, which is meaningful tofacilitate frequency and voltage support of the power system.

C.

The proposed virtual synchronous generator control schematic diagram

The pump-turbine side adopts the closed-loop speed control as shown in Fig A, thus PSH could operate at
optimal target speed Ω* under a certain head. The machine-side converter control strategy is mainly composedof a DC-link voltage outer loop and a torque current inner loop to control the intermediate DC-link voltage asshown in Fig B. The VSG-based three-phase converter control method includes the outer loop VSG control andthe inner loop control as shown in Fig C, which can not only quickly respond to superior power schedulingcommands but also participate in power regulation when the grid frequency or voltage fluctuates.
D.

Simulation result

Keywords—variable speed pumped storage hydropower(VSPSH); full-size converter (FSC); virtual synchronous
generator (VSG)；rotor inertia and virtual inertia
A.

The basic structure of variable speed pumped
storage hydropower with full-size converter

Fig.1. Simulation results of C-PSH Fig.2.Simulation results of FSC-VSPSH2,
Jvsg=2 when △P0=+1MW at 15s andwhen △fgrid=-0.1Hz△fgrid=-0.1Hz at 30s.

The typical variable speed pumped storage hydropowerwith full-size grid-connected converter consists of areversible pump-turbine, a motor-generator i.e. electrically
excited synchronous generator (EESG), and a three-level fullsize converter (FSC) which is composed of the machine-side
converter (MSC) and grid-side converter (GSC). The GSC isconnected to the grid and is usually controlled to provide a
steady DC-link voltage and meet the power quality
requirements, while the MSC is connected to EESG andcontrol the active and reactive power injected into the grid.
B.

The model of reversible pump-turbine

The available hydraulic mechanical power PPT can be
expressed as formula (1),where η is the hydraulic turbine
efficiency, ρ is the volume density of water, g is theacceleration due to gravity, Q is the water flow rate passing
through the turbine, and H is the effective water head.For a
fixed water head, the pump-turbine hydrodynamic torquedepends mainly on the water flow rate and on the rotational
speed, which is given as formula (2). Neglecting themechanical friction effects, the pump-turbine motion equation
is expressed as formula (3). where J is the total inertia of thepump-turbine and motor-generator and Tem is theelectromagnetic torque of EESG.
(1)
PPT = Ph   gHQw
TPT  Qw ,    PPT / (2)

TPT  Qw ,    Tem  J  d  / dt (3)

Fig.3. Simulation results of FSCVSPSH when △fgrid=-0.1Hz.

Based on MATLAB/Simulink，the proposed control strategies are validated. C-PSH: Fixed speed PSH ; FSCVSPSH1:variable speed PSH with conventional control; FSC-VSPSH2:variable speed PSH with the VSG
control . The main waveforms of VSPSH2 with Jvsg = 2 when power command steps up and grid frequency steps
down are presented in Fig.2. At 15s, the power command P0 is changed from 2MW to 3MW. At 30s, the gridfrequency is reduced by 0.1Hz. It can be seen that VSPSH2 achieves good reference tracking and presents inertia
support to grid frequency changes, which proves the effectiveness of the VSG control strategy. The comparative
results of three cases when grid frequency steps down are shown in Fig.1 and Fig.3. It can be seen from Fig.1
that the C-PSH can respond to the grid frequency changes at 3MW power generation. However, the adjustment
range of the rotational speed is limited, and the kinetic energy released on the rotor is small.
The simulation results of FSC-VSPSH1 and FSC-VSPSH2 when the grid frequency changes by 0.1Hz at 30s
are shown in Fig. 3. FSC-VSPSH2 under the VSG control strategy, with different Jvsg (Jvsg=30, 10, 1), the entire
FSC-VSPSH2 can respond to the frequency changes of the grid, while the FSC-VSPSH1 does not change. Whenthe grid frequency drops by 0.1 Hz, FSC-VSPSH2 can automatically reduce the rotor speed to release the inertia
to support output energy and the DC-link capacitance releases the energy on it as well. As the governor controls
the opening output of guide vane to increase, the influent flow is larger and the speed rises, and the turbine
continues to provide energy to grid. Under different Jvsg, the active power and DC-link side voltages vary in
different magnitudes. Compared with VSPSH1, since the FSC completely decouples the pump-turbine from thegrid, in the face of grid frequency changes, there is no change in VSPSH1 under the traditional control strategy,
and the active power is still maintained at 3MW, and the speed and DC-link voltage do not change.
E、Conclusion
The comparative results of fixed speed HSP, variable speed HSP with conventional control and the proposed
control demonstrate that the application of the VSG control strategy in VSPSH is meaningful to facilitate
frequency and voltage support of the power system. The method is coordinated by the pump-turbine, thesynchronous motor, the machine-side converter, and the grid-side converter, and can not only respond to thesuperior power dispatching command but also provide the necessary active power and reactive power for the grid.
In addition, it is also possible to make full use of the pumping unit own inertia and DC capacitor inertia to provide
the adjustable virtual inertia by the virtual synchronous generator, which is conducive to grid stability.
ACKNOWLEDGMENT
This work was funded by the National Key R&D Program of China (2018YFB0905200).

Estimating the Flexibility Benefit of Concentrating
Solar Plants in Electricity Markets
Yanghao Yu, Ershun Du, Ning Zhang

Siwei Liu, Zhidong Wang

State Key Lab. of Power System, Dept. of ElectricalEngineering, Tsinghua University, Beijing, Chinaningzhang@tsinghua.edu.cn

State Grid Economic and Technological Research Institute,
Beijing, China

Abstract—When equipped with thermal energy storage (TES),
concentrating solar power (CSP) plants can provide both energy
benefit and flexibility benefit for power systems. It is capable ofshifting generation to periods with insufficient solar irradiation,
providing ancillary services, and supplying firm capacity at peakload periods. In this paper, we focus on the operation behavior of
CSP when simultaneously participating in three types of electricity
market—energy market, ancillary service (AS) market, andcapacity market. The results of case study show the quantified
flexibility benefit of CSP in electricity markets. The optimal
configuration of CSP in terms of TES and solar multiple (SM) is
also analyzed.
Index Terms—Concentrating solar power, thermal energy
storage, flexibility benefit, electricity market.

(a) Base Case

I. INTRODUCTION
The controllability of CSP brings the opportunity for CSP toprovide both electricity energy and operation flexibility. Fromthe view of power system planning and operation, the potential
benefits provided by CSP can be divided into three main parts
as energy value, ancillary services value and capacity value.
In this paper, we evaluate the flexibility benefit of CSP
through considering it to participate in energy market, ancillary
service market, and capacity market. The flexibility of CSP isreflected by the strategic generation curve and adjustable output
interval. The benefit model of CSP in joint electricity markets
is formulated as a mixed integer programming (MILP) problem.

(b) No Capacity Case

II. CASE STUDY
The case study analyzes how the CSP plant optimallyoperates in three types of markets and how much benefit isobtained when equipped with TES. The benefit results are
summarized in Table I. The average daily revenues arecompared for the base case participating in all the three markets,the case without capacity market, the case participating inenergy market only, and the case without TES.

(c) Energy-Only Case

TABLE IBENEFITS OF CSP CASES PARTICIPATING IN DIFFERENT MARKETS
Daily
Revenue(k$)
EnergyReserve
Regulation
Capacity

31.92
2.71
3.67
17.33

No
Capacity
31.40
3.21
4.46
0

EnergyOnly
34.39
0
0
0

Total Revenue

55.63

39.07

34.39

19.37

Flexibility
Benefit

36.26(65.2%)

19.70(50.4%)

15.02(43.7%)-

Base CaseNo TES19.37
00
0

Fig. 1 compares the operation of CSP in the four cases.
Typically, the optimal CSP output and solar power curve in thesummer week scenario are presented.

(d) No TES Case
Figure 1 The optimal operation of CSP plant in typical summer week.

III. CONCLUSION
Equipped with TES, CSP has the flexibility to shift
generation and address the uncertainty of solar energy. Besides
energy market, participating AS market and capacity market
can significantly improve the flexibility benefit of CSP.
Moreover, after sensitive analysis, the optimal configuration of
CSP is reached at an 8-hour-TES and a 2.3 SM.