Student Poster Book of Abstracts
201ͺ IEEE Power and Energy Society
Transmission and Distribution Conference and Exhibition
Chicago, Illinois April 14-17 201ͺ

IEEE PES Student Activities Subcommittee
Chair
Dr. Anurag K Srivastava
Assistant Professor
School of Electrical Engineering and Computer Science
Washington State University
PO Box 642752
EME 102
Spokane St Pullman
Washington 99164-2752, USA
asrivast@eecs.wsu.edu

Vice-Chair
Dr. Jignesh M. Solanki
Assistant Research Professor
Lane Department of Computer Science and Electrical EngineeringWest Virginia University
395 Evansdale Drive,
Morgantown, WV 26506-6109, USAjmsolanki@csee.wvu.edu

Secretary
Dr. Aaron St. Leger
Assistant Professor
Department Electrical Engineering and of Computer Science
United States Military Academy
646 Swift RoadWest Point, NY 10996-1905, USA
Aaron.StLeger@usma.edu

WebmasterDr. Jignesh M. Solanki
Assistant Research Professor
Lane Department of Computer Science and Electrical EngineeringWest Virginia University
395 Evansdale Drive,
Morgantown, WV 26506-6109, USAjmsolanki@csee.wvu.edu

Smart Sensors, Communication and Control in Energy Systems
No.

Student
Reg. No.

T&D
Reg. No.

Title of Posters

Student Name

Grad/Undergrad

1

1110

14-7976

Real-Time HIL Simulation of Shipboard Power System (SPS)
and Energy Storage Devices (ESD) Management

Dingyi Li

MS

2

1123

14-8303

Designing An Open-Source Phasor Measurement Unit with
C37.118 Compliance

Brad Tuffley

BS

3

1078

14-6641

Optimal PMU Placement for Fault Observability in Distributed
Power System by Using Simultaneous Voltage and Current
Measurements

Hamidreza
Nazaripouya

PhD

4

1087

14-7157

A MATLAB Based Fault Detection in Phasor Measurement Unit
(PMU) Measurements

AnupamMukherjee

MS

5

1140

14-8591

Distributed Excitation Control for Future Cyber Physical Energy
Systems

Meimanat
Mahmoudi

PhD

6

1143

14-8708

Investigating Synchrophasor Data Quality Issues

Kenta Kirihara

MS

7

1091

14-7430PMU Based Proportional-Integral Damping Controller To
Enhance Power System Stability

Saad Elasad

BS

Smart Grid Technology
No.

Student
Reg. No.

T & D Reg.
No.

Title of Posters

Student Name

Grad/Undergrad

8

1117

14-8027

Coordinated Electric Vehicle Charging Solutions Using
Renewable Energy Sources

Kumarsinh Jhala

MS

9

1017

14-4856

An Optimal Framework for Residential Load Aggregators

Qinran Hu

PhD

10

1086

14-6509

Agent based State Estimation in Smart Distribution GridS M Shafiul Alam

PhD

11

1107

14-7952

Burns & McDonnell - K-State Smart Grid Laboratory:
Protection, Communication & Power Metering -Integrated
Smart Grid Laboratory

Emilio C.Piesciorovsky

PhD

12

1137

14-8618

Incentive Based Demand Response in WECC 240-Bus
System to Minimize Price Variation

Ailin Asadinejad

PhD

13

1166

14-8825

Benefits of using PEV vehicles in PRT systems andmodifying vehicle routing problem

Ali Dehghan Banadaki

PhD

No.

Student Reg.
No.

T & D Reg.
No.

Title of Posters

Student Name

Grad/Undergrad

14

1162

14-8807

False Data Attack on State Estimation in Smart Grid

Deepak Tiwari

PhD

15

1165

14-8828

Effect of Stealth Data Attack on AC Optimal Power Flow

Behnam Khaki

PhD

16

1170

14-8834

Cyber-Security Valunerablity Assessment of the Smart
Grids

Reza Kazemi

PhD

17

1145

14-8297NISTIR 7628 Visualization Model

Matthew Harvey

BS

Cyber and Physical Security of Smart Grid

Geometric Disturbance in Electric Power Systems
No.

Student Reg.
No.

T & D Reg.
No.

Title of Posters

Student Name

Grad/Undergrad

18

1100

14-7970

Effects of Micro-Structured Surface Geometries on
Condensation Heat Transfer

Andrés Martínez

BS

No.

Student Reg.
No.

T & D Reg. No.

Title of Posters

Student Name

Grad/Undergrad

19

1073

14-5852

Optimal Strategy for Minimizing Load Shedding in
Microgrids subsequent to FaultTriggered Islanding

Shanshan Ma

MS

20

1013

14-3206

A Hybrid Dynamic Optimization Approach for Stability
Constrained Optimal Power Flow

Guangchao Geng

PhD

21

1026

14-4967

Reactive Power Planning Considering High Penetration
Wind Energy

Xin Fang

PhD

22

1032

14-4907

Interval Power Flow Using Linear Relaxation and
Optimality-based Bounds Tightening (OBBT) Methods

Tao Ding

PhD

23

1033

14-5003

Exploration of Multifrontal Method with GPU in Power
Flow Computation

Xue Li

PhD

24

1036

14-4904

Multi-objective Robust Optimization Model for
Microgrids Considering Tie-line Power
Constraints and Wind Uncertainty

Linquan Bai

PhD

25

1105

14-8063

Impact of Load Models on the Statistics of Voltages in
Stochastic Power Systems

Goodarz Ghanavati

PhD

26

1126

14-8396

Power Management of Remote Microgrids Considering
Stochastic Behavior of Renewable
Energy Sources

Santosh Chalise

PhD

27

1146

14-8654

Flue Gas Desulfurization Wastewater Treatment Using
Constructed Wetlands

Jose M. Paredez

BS

28

1157

14-8798

Optimal Power Flow Including Magnetic Amplifier byUsing Sequential Quadratic Programming

Xiaohu Zhang

PhD

No.

Student
Reg. No.

T & D Reg.
No.

Title of Posters

Student Name

Grad/Undergrad

29

1150

14-8777

NMSU Power Generating Capacity: Vision and Roadmap for 2050

Aaron Rosenthal

MS

No.

Student Reg.
No.

T & D Reg.
No.

Title of Posters

Student Name

Grad/Undergrad

30

1159

14-8804

Power System Events Identification using Feature
Selection and Extraction Techniques

Prem Alluri

MS

31

1011

14-3077

Locating Sub-Cycle Faults in Distribution Network
Applying Half-Cycle DFT Method

Po-Chen Chen

PhD

32

1175

14-8840

A Lyapunov Function Based Remedial Action Screening
Tool Using Real-Time Data

Mohammed Benidris

PhD

Advanced Computational Methods for Power System Planning, Operation, and Control

Emerging Software Needs for the Restructed Grid

System-Wide Events and Analysis Methods

Intelligent Monitoring and Outage Management
No
.

Stude
nt
Reg.
No.

T&D
Reg.
No.

Title of Posters

Student Name

Grad/Undergr
ad

33

1004

14-3041Development of an FPGA based controller for islanding and antiislanding of microgrid

Kumaraguru
Prabakar

PhD

34

1080

146797Development of Graphical User Interface for Reactive Power Planningand VAR Benefit Assessment for Power Systems with High Penetrationof Renewable Energy

Riyasat Azim

PhD

35

1183

1414831

Synchrophasor monitoring of line outages in WECC with an angle acrossan area

Atena Darvishi

PhD

No.

Student
Reg. No.

T&D
Reg. No.

Title of Posters

Student Name

Grad/Undergrad

36

1181

14-8717

Optimization Active Power Curtailment (APC) with
Load Tap changing (OLTC) Regulators to Control
Voltage fluctuating in Renewable supply

Hameed R. Atia

PhD

37

1031

14-3842A Wavelet-Based Method for High Resolution
Multi-Step PV Generation Forecasting

Ahlmahz Negash

PhD

38

1042

14-4901

Emission-based Optimal Dispatch Framework with
DR and Volatile Wind Power

Hantao Cui

PhD

39

1055

14-3212

Energy Resource Scheduling in Real Time
Considering a Real Portuguese Generation and
Demand Scenario

Pedro Faria

PhD

40

1089

14-7142

Renewable Energy Forecasting using Hybrid
Artificial Neural Network Technique

Chiranjeevi Bharadwaj Kotharu

MS

41

1124

14-8255

Harmonic Analysis in Distribution System with
Electric Vehicles and Wind Generators

Ritam Misra

MS

42

1125

14-8369

Optimization of a Solar-Diesel-Battery Hybrid
Energy System for Remote Regions

Mu Chai

BS

43

1131

14-8480

Integration of Photovoltaic with Hydro only System
in an Isolated Network

Pratiksha Tiwari

MS

44

1149

14-8177

Locational Dependence of PV Hosting Capacity

Kyle Coogan

MS

45

1179

14-8843

An Analytical Method for Constructing a
Probabilistic Model of a Wind FarmSamer Sulaeman

PhD

Integrating Renewable Energy into the Grid

Substation and Distribution Automation
No.

Student Reg.
No.

T & D Reg.
No.

Title of Posters

Student Name

Grad/Undergrad

46

1034

14-4886

Dynamic Compensation of Electronic Transformer Phase
Delay

Can Huang

PhD

47

1070

14-5870

A Multi-Agent Design for Power Distribution Systems
Automation

M. Jawad Ghorbani

PhD

48

1184

14-14660

An Online Algorithm for Health Diagnosis and Prognosis
of Circuit Breaker

Saugata Biswas

PhD

Dynamic Performance and Control of Power Systems
No.

Student Reg.
No.

T & D Reg.
No.

Title of Posters

Student Name

Grad/Undergrad

49

1127

14-8429

Improvement of Transient Stability of PV-Hydro System
Using Virtual Synchronous Machines

Ujjwol Tamrakar

MS

50

1156

14-8543

An Out-Of-Step Detection of Multi-machine Power Systemusing PMU Data

Yawei Wei

MS

51

1164

14-8816

Sequential Learning Function applied to the design and tuningof a Fuzzy Controller for VSC

Ivan Riaño

BS

52

1039

14-5078

PMU-Based Wide Area PSSs to Enhance the Damping ofInter-Area Oscillations

Duaa Abumaali

MS

No.

Student
Reg. No.

T&D
Reg. No.

Title of Posters

Student Name

Grad/Undergrad

53

1035

14-4988

LMP Step Pattern Detection based on Real-Time Data

Haoyu Yuan

PhD

54

1098

14-7931

Customer Incentive Pricing Mechanism in the Smart Grid

Timothy Hansen

PhD

55

1132

14-8483

Distribution Locational Marginal Price using Optimal Three-phase
Current Injection Power Flow

Jie WeiMS

No.

Student Reg.
No.

T & D Reg.
No.

Title of Posters

Student
Name

Grad/Undergrad

56

1173

14-8750

Discharge Energy Based Stage Classification of Cavity Partial
Discharge in Oil-Paper Insulation

Zhenze Long

MS

57

1101

14-5279

Impact of Voltage Reduction and Ambient Temperature on
Power Consumption, Line Lossand Generation

Sushanta Paul

PhD

Market Interactions in Power Systems

Asset Management

Flexible AC Transmission Systems
No.

Student Reg. No.

T & D Reg. No.

Title of Posters

Student Name

Grad/Undergrad

58

1029

14-4895

Adaptive PI Control of STATCOM for Voltage Regulation

Yao XuPhD

No.

Student
Reg. No.

T & D Reg.
No.

Title of Posters

Student Name

Grad/Undergrad

59

1046

14-3464

Control of Hybrid Tripole HVDC Based on LCC and F-MMC

Feng Xu

PhD

60

1088

14-7154

A Total Harmonic Distortion (THD) computation using
improved Shunt Active Power Filter Design in Energy
Management Systems

Ranganath Vallakati

MS

61

1171

14-8714

PV Current Harmonic Suppression Under Unbalanced Fault
Based on Factor Selection Control

Xiaoxuan Lou

MSPower Electronics

Electric Machine and Drives
No.

Student Reg.
No.

T & D Reg.
No.

Title of Posters

Student
Name

Grad/Undergrad

62

1079

14-6719

Harmonic filtering phenomenon in single phase induction
motorsGaurav Singh

PhD

No.

Student Reg.
No.

T & D Reg.
No.

Title of Posters

Student Name

Grad/Undergrad

63

1054

14-0404

Modeling and Simulation of a High-Head Pumped Hydro
SystemHang Li

MS

64

1108

14-8075

Developing Integrated Load Modeling Framework forCampus Microgrids with Large Buildings

Sayonsom Chanda

PhD

65

1172

14-8837

Dynamic Modeling and Analysis on Cut-in Grid of Doublyfed Induction Wind Power System

Sibei MiMS

Power System Modeling and Simulation

Real-Time HIL Simulation of Shipboard Power System (SPS)
and Energy Storage Devices (ESD) Management
Dingyi Li and Dr. Noel N. Schulz
Department of Electrical and Computer Engineering
Kansas State University
Manhattan, KS
Email: dingyi@ksu.edu
Abstract - There are many situations that cancause a fault on a shipboard power system,
especially in naval battleships. Batteries andultra-capacitors are simulated to be thebackup energy storage devices (ESDs) topower the shipboard power system when an
outage and damage happen. ESDs arecommonly being used for electrical ship
applications, due to carious advantages, suchas: guaranteed load leveling, good transient
operation and energy recovery during
braking operation. To fulfill theserequirements converters with specific control
algorithm are required to connect the ESDs
to the dc link of the motor drive system. SPS
is required to function in three different
modes namely: steady-state (normal) mode,
acceleration mode and braking (regenerative)mode. During acceleration mode the power
flow is from ESDs to SPS whereas during
regenerative mode the kinetic energy of the
motor is converted into electrical energy andfed back to battery. Also, generators inside of
SPS can charge the battery packs undernormal mode when the State-Of-Charge(SOC) level of battery packs are below thestandard level. The SPS and ESDs which
include battery packs and ultra capacitors
are simulated and verified through thesimulations by using Matlab Simulink. In thisresearch, the real-time hardware-in-the-loop(HIL) simulation of shipboard power system(SPS), bidirectional DC-DC converter, and
ESDs are being designed, implemented andcontrolled on OPAL-RT simulator to test theperformance of SPS and ESDs in different
fault locations.
Key words – HIL; real-time; SPS; ESDs
management; bidirectional DC-DC converter

Designing An Open-Source Phasor Measurement Unit
with C37.118 Compliance
Brad Tuffley, Student Member, IEEE
Douglas Rapier, Student Member, IEEE,
Anurag K. Srivastava, Senior Member, IEEE
Smart Grid Demonstration and Research Investigation Lab, Washington State University

Email: btuffley@eecs.wsu.edu, drapier@eecs.wsu.edu, asrivast@eecs.wsu.edu
Abstract— IEEE Standard C37.118 outlines
standards for collecting time aligned voltage andcurrent phasors by using a Phasor Measurement
Unit (PMU). Some of the performance criterion
include Total Vector Error (TVE), Frequency
Error (FE), and Rate of Change of Frequency
Error (RFE) are defined by this Standard.
However, due to technologically different
approaches, each manufacturer uses different
estimation algorithms to derive their PMU data.
The purpose of this project is to develop a prototype
of advanced open source PMU, which can be used
as a platform to test various phasor estimation
algorithms for different power system operating
conditions. The open source nature will allow forversatile PMU testing given unique algorithms andspecifications. Support for this project comes from
Washington State University (WSU) and the U.S.
Department of Energy (DOE).

I.

II.

Key Figures

Figure 1 Shows the typical PMU components used today inorder to capture and relay phasor data.

KEY EQUATIONS

Total Vector Error (TVE).

TVE n =   (!! ! !!! ! )! !(!! ! !!! ! )!
(!! ! )! !(!! ! )!

.

(1)

Frequency Measurement Error (FE).

FE =    𝑓!"#$ − 𝑓!"#$%&"' = ∆𝑓!"#$ − ∆𝑓!"#$%&"'(2)

ROCOF Measurement Error (RFE).

RFE =    (𝑑𝑓/𝑑𝑡)!"#$ − (𝑑𝑓/𝑑𝑡)!"#$%&"'(3)

Figure 2. Outlines the basic process diagram that iscurrently used for PMU Testing.

Optimal PMU Placement for Fault Observability in Distributed
Power System by Using Simultaneous Voltage and Current
Measurements
H. Nazaripouya1 and S. Mehraeen2, Member, IEEE

Abstract—This paper proposes a new strategy to achieve fault
observability of power systems while aiming minimum required
number of Phasor Measurement Units (PMUs) in the network.
The proposed method exploits the nodal voltage and meshcurrent analyses where the impedance and admittance matricesof the network and its dual circuit are developed and utilized for
fault location. The criterion of determining the number and theplaces of PMUs is to be able to obtain the fault location and
impedance in a unique manner (i.e., without multi estimation.) In
addition, the method considers faults along the lines as opposedto the faults only on system buses available in the literature. The
proposed approach provides an economical solution to decreasemeasurement costs for large power networks, distributed
generation networks, and micro grids. Simulation results forIEEE 7-bus, 14-bus, and 30-bus systems verify the effectivenessof the proposed approach.

II. KEY FIGURES

Fig. 1. Power network a) before the fault b) after the fault on point p

Index Terms – Fault Location, Phasor Measurement Unit (PMU),
Optimal Placement, Fault Observability.

I. METHODOLOGYPMU placement at each bus helps measure the voltage
phasor at that bus and the current phasors in all the branches
connected to that bus. When a fault occurs in the system,
according to the location and impedance of the fault all
voltages and currents of the network change including at
PMU nodes (busses.) But the problem arises when more than
one fault (with possibly different impedances and locations)cause the same change in the voltages and currents at PMU
busses. This problem is called multi estimation. The focus of
this paper is to find the proper locations for PMUs that reduce
multi estimation when faults occur, and to use measured
voltage and current changes at the PMU locations to uniquely
identify a fault as opposed to the available methods that only
rely on voltage measurement/estimation. Here, the concept of“mesh impedance,” namely Z ′ , matrix is developed and themesh equations of the network are obtained. Introducing
current measurement can reduce the number of required
PMUs.

Fig. 2. Part of dual circuit of the power system a) before the fault b) after thefault on point P

III. KEY RESULTS

Test
SystemIEEE 7BusIEEE 14bus
IEEE 30bus

Authors are with the school of Electrical Engineering and Computer
Science, Louisiana State University, Patrick F. Tailor Hall, Dr.,
Baton Rouge, LA 70803. Contact authors: 1hnazar1@tigers.lsu.edu.
2smehraeen@lsu.edu,
This work was supported in part by NSF CAREER ECCS#1151141

Table I. Results of applying proposed approach
Based onVoltage
Based on both Voltage and current changechange only
# undesired# undesired# PMUsLocation
sectors
sectors
Bus 1 or 2 or 4 or
30
2
1
5 or 7
Bus 1 or 2 or 4 or
115
40
1
5
405

162

2

Bus 10 and 25

Table II. Comparison between proposed approach and prior approach
Test
Number of PMUs
SystemPrior Approach
Proposed Approach
IEEE 7 Bus

51IEEE 14 bus

81IEEE 30 bus

172A MATLAB Based Fault Detection in Phasor
Measurement Unit (PMU) Measurements
Anupam Mukherjee, IEEE student member and Prakash Ranganathan, IEEE Senior Member
Electrical Engineering Department, University of North Dakota, Grand Forks, USAemail: anupam.mukherjee@my.und.edu
Abstract—Fault detection, isolation and correction in anelectrical grid have always been the critical tasks for ensuring
maximum transfer of electric power to loads. The phasor
measurement units (PMU) or Digital Fault Recorders (DFR) has
been in the forefront to monitor the phasor voltages, current andfrequency parameters in the transmission lines for detection
faults[1]. With increase in frequency of outages, weather related
disturbances; the percentage of fault occurrence has increased
dramatically. This poster work focuses on the detection oftransmission line errors using the PMU data set. A MATLAB
based PMU testbed is developed, and tested for generation anddetection of faults affecting an electric signal [2]. The design
parameters include 1) amplitude, 2) frequency and 3) phasedeviations.

function. In the frequency deviations of the voltage signal canthen be monitored. The FFT tool of Simulink represents thevoltage signal in the frequency domain. It represents themagnitude of the signal(𝐷𝑓 ) as a percentage of that of thefundamental frequency (𝑓𝑟𝑒𝑓 ) and plots it as a function of thevarying frequency(𝑓). Value of (𝐷𝑓 ) is constant for a given
frequency. If the value of (𝐷𝑓 ) at (𝑓𝑟𝑒𝑓 )is considered tobe(𝐷𝑓𝑟𝑒𝑓 ), then any deviation of(𝐷𝑓 )from (𝐷𝑓𝑟𝑒𝑓 )will give us
an equivalent deviation of (𝑓)from(𝑓𝑟𝑒𝑓 ), as shown in (3).
(𝐷𝑓 − 𝐷𝑓𝑟𝑒𝑓 ) ≡ (𝑓 − 𝑓𝑟𝑒𝑓 )(3)

Keywords: PMU, Fault detection, MATLAB

I. INTRODUCTION
In our experiments, simulation test beds were developed
to detect deviation of the PMU parameters. The simulations
have been discussed briefly.
A. Amplitude Deviation Setup
A functional representation of the three phase AC signal
can be shown as in (1) and (2).
𝑉 = 𝑉𝑎 ∗ sin(2𝜋𝑓𝑡 + 𝜃)
(1)
𝐼 = 𝐼𝑎 ∗ sin(2𝜋𝑓𝑡 + 𝜃)
(2)
We consider the voltage signal (𝑉), where (𝑉𝑎 ) is theamplitude, (𝑓)is the frequency and(𝜃)is the phase angle. In
the transmission line (𝑉𝑎 ) can be monitored for any deviationfrom a reference value(𝑉𝑟𝑒𝑓 ), which is considered to be valueof (𝑉𝑎 )under no fault condition.
If, |(𝑉𝑎 − 𝑉𝑟𝑒𝑓 )| ≥ 0.05 ∗ (𝑉𝑟𝑒𝑓 ) at any instance, then thedeviation is considered to be due to a fault in the line. Here,0.05 ∗ (𝑉𝑟𝑒𝑓 ) is a randomly chosen value.

Fig. 2 Test Bed to monitor frequency deviation

C. Phase Deviation Setup
The deviation in phase (∆𝜃) can be calculated by
performing a cross correlation function between the line
voltage signal (𝑉) and a reference voltage signal (𝑉𝑟𝑒𝑓 ),shown in (4).
𝑉𝑟𝑒𝑓 = 𝑉𝑎𝑟𝑒𝑓 ∗ sin(2𝜋𝑓𝑟𝑒𝑓 + 𝜃𝑟𝑒𝑓 )(4)
Considering (f= 𝑓𝑟𝑒𝑓 ) and𝑉𝑎 = 𝑉𝑎𝑟𝑒𝑓 , the cross correlated
signal can be represented as shown in (5).
𝑅𝑥𝑦 = (𝑉𝑎𝑟𝑒𝑓 ∗ 𝑉𝑎 /2) ∗ [cos(𝜃𝑟𝑒𝑓 − 𝜃) − sin(2 ∗ 2𝜋𝑓𝑡 +𝜃𝑟𝑒𝑓 + 𝜃](5)
From this signal, the higher order frequencies can be filteredusing a low-pass filter (LPF). Let us denote the resulting
frequencies by (𝑆𝜃 ). The term remaining after filtering
is(𝑉𝑎 ∗ 𝑉𝑎 /2) ∗ cos(𝜃𝑟𝑒𝑓 − 𝜃). Thus, the phase deviation(|∆𝜃|)can be calculated as shown in (6).
|∆𝜃| = 𝑎𝑟𝑐𝑜𝑠(2 ∗ 𝑆𝜃 /(𝑉𝑎 ∗ 𝑉𝑎 /2))
(6).

Fig 1. Test Bed to monitor amplitude deviation

B. Frequency Deviation Setup
Faults in the system can cause the frequency of the signal
to deviate from the acceptable value(𝑓𝑟𝑒𝑓 ). The frequency (𝑓)of the voltage signal can be obtained from the frequency
domain representation of the time domain voltage signal. Thiscan be achieved through the application of Fourier transfer

Fig, 3. Test Bed to monitor Phase Deviation

II. REFERENCES[1] A.G.Phadke and Bogdan Kasztenny, “Synchronized Phasor and
Frequency Measurement Under Transient Conditions,” IEEE Trans. Power
Delivery, vol. 24, no.1, pp. 89-95, 2009.
[2] www.mathworks.org

1

Distributed Excitation Control for Future Cyber
Physical Energy Systems
Meimanat Mahmoudi, Kevin Tomsovic, Seddik Djouadi and Husheng Li
Department of Electrical Engineering and Computer Science
University of Tennessee, Knoxville, 37996

Abstract--Integration of cyber technologies capable ofmonitoring, communicating, and controlling the electric powersystem, renders the smart grid one of the most complex cyber
physical systems. For the smart grid to be resilient to disturbances,wide-area monitoring and control is a necessity. While local
controllers are blind to the widespread effects of disturbances andcentralized controllers are challenging to implement, distributed
controllers can achieve a trade-off between performance andassociated cost. In this work, the design and performance of two
distributed excitation control schemes are presented. We showthat distributed control can help prevent disturbances frompropagating in large power networks. Moreover, we demonstrate
the effectiveness of distributed control in damping inter-area
oscillations. In order to highlight the advantages of the proposed
controllers, results are compared with local and centralized
control schemes.

area four machine test system which has been widely used foranalysis of inter-area modes. Due to the distributed nature of
the design, communication between local controllers helps toimprove the damping of inter-area modes, which are not readily
apparent for the local controller. Moreover, there is no need tomodel a large-scale system as a whole and deal with thecomplexities arising in a centralized scheme.
II. DISTRIBUTED EXCITATION CONTROL FOR POWER
NETWORKS
A. Distributed LQR Excitation Control Design

Index Terms--Cyber-physical systems, distributed control,
smart grid, and wide-area control.

I. INTRODUCTIONT

HE next generation of electricity grid, also known as the“Smart Grid” , is one of the most complex cyber physicalsystems (CPS) due to its extreme dimension, geographic reachand high reliability requirements. For the smart grid to beresilient, wide-area monitoring and control is a necessity. Theinterconnected structure of the power grid poses a great risk ofsystem-wide failures in the network. A local disturbance can
influence the system over a wide area and lead to cascading
failures and blackouts. Prior to the introduction of real-time
phasor measurement units (PMUs) power system control was
primarily local. Except for the very slowest of controllers and a
few specialized schemes, engineers have designed systems
largely through local decisions based on local measurements.
Due to the lack of observability in local measurements ofcertain inter-area modes, existing control schemes may fail todeliver the expected performance and reliability. Therefore,
using wide-area measurements becomes a key element indesigning control.
The purpose of this research is twofold. First, we show that
distributed control can help prevent disturbances frompropagating in large power networks. In doing so, we used thecontinuum model test systems. Second, we demonstrate theeffectiveness of distributed control in damping inter-area
oscillations and compare the results with the performance of
local and centralized control schemes. For this, we use a two-Fig. 1. Distributed LQR control agent and information exchanges for part of aradial structure.

B. Distributed Excitation Control with 𝐻∞ PerformanceFig. 2. Physical layer (in blue) and cyber layer (in green) for Two-area fourmachine test system.

ACKNOWLEDGMENT
The authors gratefully acknowledge the support of the
National Science Foundation through grant CNS-1239366 andsupport by the Engineering Research Center Program of the
National Science Foundation and the Department of Energy
under NSF Award Number EEC-1041877 and the CURENT
Industry Partnership Program.

Investigating Synchrophasor Data Quality Issues
Kenta Kirihara, Student Member, IEEE, Andy K. Yoon, Student Member, IEEE,
Karl E. Reinhard, Student Member, IEEE,and Peter W. Sauer, Fellow, IEEE
Department of Electrical and Computer Engineering
University of Illinois at Urbana-Champaign
Email: kirihar1@illinois.edu

Abstract—Synchrophasor data is a real-time measurementof electrical quantities from across the power grid taken by
Phasor Measurement Units (PMUs), which can substantially
improve real-time situational awareness and control of the events
happening on the power system. Currently, more than 1000
PMUs are installed across North America, computing and storingsynchrophasor data at select locations. However, the efforts toprocess the synchrophasor data to meet very high operator
trust expectations has been challenging. This paper describes
Synchrophasor Data Quality (SDQ) Activity, Trustworthy Cyber
Infrastructure for the Power Grid (TCIPG) Project efforts toinvestigate synchrophasor data archived from American Transmission Company’s system including both observations fromthe field and evolving efforts to characterize synchrophasor
data quality. Methods to identify and utilize synchrophasor
characteristics for systems events is investigated with the aidof statistical techniques.

I. OVERVIEW
Since April 2012, the SDQ Activity has been working
with American Transmission Company (ATC) to investigate its
archived synchrophasor data. Fig. 1 shows an example nominal
synchrophasor data network from the point of measurement tothe control room and data archive with four levels and connecting transmission paths. Each level has an identifiable rolein computing, processing, and forwarding data to meet powersystem requirements and as such becomes a possible pointof failure. Attributing defective synchrophasor data sources
and corresponding error rates is key to prioritizing efforts toimprove data quality.
During summer 2013, an SDQ Activity research student
worked as an EMS intern working for ATCs synchrophasor
system project engineer. His experience working with fieldsynchrophasor data in the field highlighted fundamental
challenges to providing very reliable, high integrity, and very
accurate synchrophasor data to the system control room. The
first challenge was analyzing synchrophasor data flow to
identify and classify data error types. There were occurrencesin which a single PMU data failed to report to the PDC,
or when a group of PMUs that are associated with a PDC
failed to report to the data archives. To aid understanding
the issues, he developed and implemented an application
showing real-time data status at each PMU location on aservice area map. This tool enabled the control center to havecontinuous, real-time situational awareness of data availability.

Fig. 1. Example PMU Data Flow [1]Another challenge was productively using synchrophasor
data. The volume of the PMU data collected, which is over
1GB of relevant measurement information per hour, contains
a plethora of information. For instance, during an unusual
event in a system, the voltage phase may present signature
behavior. In order to investigate this, a tool to visualize
the phase angle difference through system in both real-timeand post-event was created. Having a tool which allows
the information to be presented immediately adds value
to the data. The tool was also used to compare the PMU
angle measurements to the State Estimator derived from the
SCADA measurements.
Although both of these solutions were significant steps
in developing a utilization of PMUs, the full potential ofsynchrophasor measurements is far from fully trustworthy.
Through the summer internship, ATC and TCIPG researchers
gained valuable insights into ATC, which subsequently proved
to be invaluable to setting up practical, secure data sharing
arrangements between ATC and TCIPG that enable further
investigation of synchrophasor data quality.
R EFERENCES[1] Research Activity Fact Sheet, Trustworthy Cyber Infrastructure for thePower Grid, Champaign, IL, 2012, pp. 57-59

3) Third, we have modeled the interactions between different decision making entities in energy markets, assuming
a game-theoretic framework and considering different
objectives for system operators and generation companies [5].
4) Fourth, a bi-level optimization problem that supports
renewable energy integration and other social, physical,
and technical stakeholders’ objectives has been established.
To analyze the proposed CPSS framework, we are currently
integrating these cyber, physical, and social aspects of the
problems discussed. The development of this CPSS framework
will further enhance our understanding of large-scale complex
systems and thus build better mechanisms, strategies, andprotocols to improve these systems’ reliability and welfare.
The proposed framework will be used to analyze different
scenarios, such as the analysis of consumer behavior. The
main objective of the consumer behavior analysis scenario is
to study the behavior of large scale consumers given energy
market price fluctuations, under the CPSS framework. We
assume that the objective of the consumer is to minimize acertain real-time cost function, that models a utility functionof consumers (which encapsulates the social component of
the model) when they backlog their energy consumption fora period of time. This cost function also incorporates the costof energy paid to utilities in each time-period, given certain
price and energy dynamics for each large-scale consumer.
Furthermore, the control input in this scenario is the amountof energy to be backlogged in each time-period, which is apart of the possible shiftable demand. The cyber-physical component is depicted by the means of communication between
the consumers and utilities, and different physical propertiesand dynamics that govern and characterize the operation of
the grid. Under this framework, the following questions andanalysis scenarios arise:• For a consumer utilizing optimal control problem forenergy backlogging, how does the consumer behavior
change during subsequent time-periods in response to
price signals from utilities or retailers?
• How are locational marginal prices affected by energy
backlogging problems of large-scale consumers?
• How does the social utility function for consumers affectthe optimal decisions from the energy backlogging problem, and what are different models of consumer utility
function in terms of energy backlogging?
Furthermore, other research scenarios can be analyzed using
this framework. Examples include a) policy decision scenarios
such as the effect of subsidy prices on the optimal generation
quantities from different plants on the stability of the power
grid, and b) system disturbances and robustness scenario suchas the effects of cyber-attack mechanisms in the DNCS levelon the social welfare.
III. ACKNOWLEDGMENT
The work presented in this abstract is funded in part by the
NSF CMMI grant 1201114.

R EFERENCES[1] D. Hristu-Varsakelis and W. S. Levine (Ed.): Handbook of networked andembedded control systems, 2005
[2] International Energy Agency, “Technology roadmap, Smart Grids,” IEA,
Tech. Rep., 2011.
[3] A. Elmahdi, A. F. Taha, S. Hui and S. H. Żak ,“A hybrid schedulingprotocol to improve quality of service in networked control systems,”
2012 50th Annual Allerton Conference on Communication, Control, andComputing, pp. 98–105, 1–5 Oct. 2012
[4] A. Elmahdi, A.F. Taha, and Dengfeng Sun, “Observer Based Controlof Decentralized Networked Control Systems”. Submitted to the IEEE
Transactions on Automatic Control. September, 2013. Under Review.
[5] Taha, A. and Panchal, J. H., “Multilevel decision-making in decentralized energy systems with multiple technologies and uncertainty, IEEE
Transactions on Systems, Man and Cybernetics: Systems, 2013, in press.

IEEE PES T&D 2014, Chicago, Illinois, USA, April 2014

PMU Based Proportional-Integral Damping
Controller To Enhance Power System Stability
Saad Elasad, Member, IEEE Ebrahim Al Agi, Member, IEEE
Ihab Aljayyousi, Member, IEEE Khaled Ellithy, Senior Member, IEEE
Department of Electrical Engineerinng, Qatar University
Abstract—With the mature of synchrophassor new technology,
developing phasor measurement units (PMUs) based wide-area
damping control system becomes feasible. PMUs are devices thatprovide synchronized measurements of real-time phasors ofvoltages and currents. Synchronization is achieved by same-timesampling of voltage and current waveforms using timing signals
from the global positioning system satellite (GPS). A number of
PMUs are already installed in several power utilities around theworld for various applications. Real-time information fromremote PMUs offers the power system operators the opportunity
to avoid catastrophic outages, improve system stability, andpredicts the status of the system under varying conditions. The
objective of this study is to design a Proportional-Integral (PI)damping controller based on PMU to enhance power systems
stability. The remote PMUs with satellite synchronized clock will
generate the stabilizing signal with high speed and accuracy to
the PI controller. The PI controller was tuned through poleplacement technique. The impact of the designed PI damping
controller on the system dynamic performance is assessed using
modal analysis (eigenvalues analysis) and nonlinear time-domain
simulations. The salient significance of this study is to gain
experience on this new technology and its applications to smart
power systems. The power system shown in Fig. 1 was used to
study the effectiveness of the proposed PMU-based PI damping
controller. Fig. 2 shows the developed architecture of the overallsystem. The system state-space representation was formed andthe dominant eigenvalues corresponding to the electro-mecanicaloscillation mode were obtained as listed in Table 1. It can be seenthat the damping of electro-mecanical oscillation mode was
improved with the proposed PMU-based PI controller. The
system dynamic model was developed using Real-Time Digital
Simulator (RTDS). Time-domain simulations using the RTDS
model was carried out to verify the the eigenvalues results and totest the performance of the proposed PI controller under large
disturbances. Fig. 3 shows the system time-response with andwithout the designed PI controller; it can be seen that the
proposed PMU-based PI controller improves the damping of thepoorly damped oscillation modes and hence enhancing the systemstability

Fig.2 Real-time system modeling using RTDS with PMUs
Table 1: Eigenvalues of electro-mechanical osccilations mode
Test Power
SystemWithout
Controller
With PI
Based PMU

Eigenvalues
Operating Point 1
Operating Point 2
P = 0.9pu, Q= 0.3pu
P = 0.6pu, Q= 0.3pu
0.5318 ± j7.2102−0.0899 ± j7.705−2.7285 ± j15.0657−3.8022 ± j13.7008

Fig. 3 System time-response for a three phase fault at point F(Operating point 1: P = 0.9 pu , Q= 0.3pu)

REFERENCES[1]

Fig. 1 Single-line diagram of power system under study[2]________________________________________________________

This project was sponsored by Qatar National Resarch FundUndegradute Research Experience Program UREP 15-148-2- 046[3]Aboul-Ela, A. Sallam, J. McCalley, and A. Fouad, “Damping
controller design for power system oscillations using global
signals,” IEEE Trans. On Power Syst., vol. 11, No. 2, pp. 767–
773, May 1996.
Jaime De La Ree, Virgilio Centeno, James S. Thorp, and A. G.
Phadke, “Synchronized Phasor Measurement applications inPower Systems IEEE Trans. On Smart Grid, pp. 20-27,Vol. 1,
No. 1, June 2010.
C. W. Liu, “Phasor Measurement Applications in Taiwan” Proc..
Transmission and Distribution Conference and Exhibition, IEEE Asia Pacific, pp. 490-493, 2002.

1

Coordinated Electric Vehicle Charging Solutions
Using Renewable Energy Sources
Kumarsinh Jhala, Student Member, IEEE, Balasubramaniam Natarajan, Senior Member, IEEE
Anil Pahwa, Fellow, IEEE and Larry Erickson
WiCom Group, Department of Electrical and Computer Engineering, Kansas State University
Manhattan, KS-66502, USA
kumarsinh@k-state.edu, bala@k-state.edu

Abstract—The growing concerns about global warming, air
pollution and oil shortage have motivated the research anddevelopment of electric vehicles (EVs), which are energy efficient.
The US government has stated its goal of putting 1 million EVs
on the road by 2015. Due to the anticipated increase in numberof EVs, EV charging is emerging as a significant challenge forcustomers and utilities. In this work, we propose coordinatedEV charging strategies for residential chargers and commercial
charging stations at parking lots. In residential charging, themain focus is on the investigation of distribution transformer
overloading. In commercial charging stations, the focus is on
minimizing energy drawn from grid while utilizing energy fromrenewable energy resources in order to maximize benefits tothe parking lot owners. We propose an optimal control strategy
for EV charging that maximizes the use of renewable energy
sources. The coordination strategies are discussed for both ofthese scenarios.

I. I NTRODUCTION
Electric vehicle charging poses significant challenges dueto the anticipated increase in number of electric vehicles on
the road. The anticipated increased penetration of EVs, if
not properly managed, can have an adverse impact on theoperation and stability of the power system. While it is evident
the EVs can reduce CO2 emissions and improve air quality, it
is important to note that emissions are actually shifted from avehicle tailpipe to the electric power generation that supports
EV charging. Therefore, to have a tangible environmental
impact, we have to consider renewable energy (RE) sources(e.g., wind and solar) and their role in EV charging solutions.
Therefore, developing the charging strategy to incorporate
maximum use of renewable energy is one of the major goalsof this research. In this work, we are interested in two primarychallenges related to EV charging.
1) Residential charging - Distribution transformer overloading:According to [1], the current distribution system is incapableof supporting large number of EVs. In [1], a typical residential
situation with worst-case uncoordinated charging scenario is
considered where all users charge their EV simultaneously. Insuch cases, as few as two charging EVs can force transformer
to operate above it’s rating. This may eventually result intransformer failures. A probabilistic model of EV charging
was developed in [2]. It considers the case that not all

EVs need a full recharge and they will not be charging at
the same time. The results in [2] show that uncoordinatedcharging can exceed the transformer rating. So, we need somemethod of coordination to avoid transformer’s loss of life.
The goal of this research is to develop charging strategies
that avoid transformer’s overloading while ensuring fairness tocustomers. Some charging methods that prevents transformer’s
overloading and loss of life like Staggered Charging andProportional Charging are presented in this poster.
2) Commercial Parking lot charging - Coordinating Distributed Generation (DG) and EVs:
We consider a parking lot with M charging stations. In
addition to grid power the lot also uses solar and wind energy
to charge plug-in EVs. EV charging at commercial parking
lots need to have variable charging rates as time available forcharging is random. We assume that, each charging station
is capable of charging vehicles at five distinct charging rates.
The goal of our work is to coordinate the charging rate inorder to minimize the power drawn from grid while providing
the desired level of charging within the available time frame.
The charging rate of each vehicles is the control variable inthis model. The constraints on the control problem include;(1) the time available for charging; (2) demand of charge foreach vehicle in the charging station, and (3) the amount of
renewable energy available from DGs(solar or/and wind generators). We propose an optimal control strategy for EV charging
while maximizing the use of renewable energy sources inorder to provide for maximum benefit to the charging station
operator/lot owner.
ACKNOWLEDGEMENT
This work is supported in part by a grant awarded by The
K-State Electric Power Affiliates Program.
R EFERENCES[1] M. Rutherford and V. Yousefzadeh, “The impact of electric vehicle battery
charging on distribution transformers,” in Applied Power Electronics
Conference and Exposition (APEC), 2011 Twenty-Sixth Annual IEEE,
2011, pp. 396–400.
[2] S. Argade, V. Aravinthan, and W. Jewell, “Probabilistic modeling of ev
charging and its impact on distribution transformer loss of life,” in Electric
Vehicle Conference (IEVC), 2012 IEEE International, 2012, pp. 1–8.

1

An Optimal Framework for Residential Load Aggregators
Qinran Hu, Fangxing Li
The Department of Electrical Engineering and Computer Science, the University of Tennessee, Knoxville, TN 37996, USA,
Email: {qhu2, fli6}@utk.edu
Abstract— With the development of intelligent demand-side
management with automatic control, distributed populations oflarge residential loads, such as air conditioners (ACs), have theopportunity to provide effective demand-side ancillary services
while reducing the network operating costs. This work presents
an optimal framework for residential load aggregators. Under
this framework, the load aggregators are able to: 1) have an
optimal control strategies over ACs and electrical water heaters(EWHs); 2) quickly and automatically response to the requestfrom load serving entities. To residents, the framework is
designed with probabilistic model of comfortableness, which
ensures the residents’ daily life will not be affected. Also, theframework fairly and strategically rewards residents who
participated in the demand response program, which may
stimulate the potential capability of loads optimized andcontrolled by the load aggregator. In addition, the proposed
framework will be validated on several numerical case studies.

I.
MOTIVATION
The development of communication and sensor
technologies provide the platform for consumers and suppliers
to interact with each other. This creates an opportunity forload aggregators to play an increasingly vital role in demand
response by applying optimal control strategies over loads.
According to Monthly Energy Review April 2012 made by
U.S. Energy Information Administration, the residential
electricity use in US in 2011 is 1,423,700 million kWhconsisting 38% of the total electricity use. Therefore, it isreasonable to consider designing an optimal framework forresidential load aggregators to provide effective demand-side
ancillary service by strategically controlling the residential
loads and rewarding the residents.
Based on several pilot trial runs by utilities, ACs and
EWHs are especially critical here, because they are
increasingly predominant and can provide fast responses withminimal impact to residents in a short time period. Moreover,in most of the countries, ACs and EWHs typically account for
one half of the total peak demand in residential aspects.
Therefore, this work considers the aggregate demand of
populations of ACs and EHWs.
II. INTRODUCTION AND PRE-STUDY
There are several assumptions for the proposed
framework, 1)ACs and EWHs have two-way communication
with load aggregators; 2) Users provide a temperature range
in which they feel comfortable with; and 3) Users decide
whether they are willing to compromise about the temperature
range. Following the above assumptions, the load aggregatoris able to use the proposed framework to dispatch the loads
need to be reduced without affecting residents daily life, while
sending out the rewards to residents according to thecontributions they made.

Fig.1 is the brief schematic figure of the control andrewards mechanism. Based on the proposed framework, somepreliminary studies have been carried out. For example, here
Fig.2 and TABLE I show the preliminary results of a 10-usercase study regarding the residents’ satisfaction as well as the
reward distribution.
Ambient
Temperature

AC_1

EWH_1

Controller

AC_2

EWH_2

AC_n

EWH_n

Dr

Dothe rDag

Dtotal-User_1
Preferences
Sensor Data

Status
Total
RewardController

User_2
User_nFig.1 Schematics of the control and rewards mechanism
Temperature (F)Temperature (F)Room T

85
80

80

75

75

…

70

…

70

65
60

Room T

85

65

User 1

User 2User 3User 10……
Temperature (F)60

User 1

User 2User 3……

User 10

Room T

85
80

75

…

70
65
60

User 1

User 2User 3……

User 10

Fig 2. The operating points of ACs under some typical scenarios
TABLE I. PRELIMINARY RESULT (LENGTH: ONE WEEK)
User
1
2
3
10

Temperature (F) Compromise Satisfaction (%)
62-73
No
100
78-85
Yes
84
64-82
No
100
67-80
Yes
96

Rewards ($)
3.2
3.0
4.5
3.7

III. CONTENT TO INCLUDE IN THE WORKWith the above motivation, introduction and pre-study,
this work proposes an optimal structure for residential load
aggregators with both effective demand-side ancillary service
and strategic rewarding mechanism. Also, the detailed of theframework design, including optimization problem
formulation, probabilistic comfortableness model, and therewarding mechanism will be detailed in the work. In addition,
the proposed framework will be validated on numerical casestudies.

Agent based State Estimation in Smart DistributionGrid
S M Shafiul Alam, Student Member, IEEE Balasubramaniam Natarajan, Senior Member, IEEEand Anil Pahwa, Fellow, IEEE
WiCom Group, Department of Electrical and Computer Engineering, Kansas State University
Manhattan, KS-66502, USA
alam@k-state.edu, bala@k-state.edu, pahwa@k-state.edu
Abstract—In this paper, a novel agent based static state estimation strategy is proposed. Electric power distribution network is
well modeled using decentralized measurements and distributed
state-space formulations. In such systems, sensor nodes acting
as agents estimate only a subset of states, instead of evaluating
local estimates of global states. We consider the case when foreach agent, (1) the local measurement model is underdeterminedand (2) all state elements for a particular agent is completely
shared with its neighbors. We propose a state estimation strategy,

which effectively integrates the principles of local consensus
and least squares solution through the use of system specificbinary projection matrices. The potency of the proposed method
is illustrated using a radial power distribution network. A
rigorous analysis of convergence is also presented to motivate
its application for smart grid with arbitrary topology.
Index Terms—Nonlinear underdetermined system, distributed
state estimation, local consensus.

Burns & McDonnell - K-State Smart Grid Laboratory:
Protection, Communication & Power Metering Integrated Smart Grid Laboratory
Emilio C.Piesciorovsky and Noel N. Schulz
Electrical and Computer Engineering Department
College of Engineering, Kansas State University
Manhattan, U.S.A.
Abstract— The Burns & McDonnell - Kansas State University
Smart Grid Laboratory was created because of the need of theenergy industry (electrical utilities, equipment manufacturers,
and contractors) and engineering universities to recruit highly
skilled power engineers and develop a state-of-the-art
educational program. The smart grid laboratory integrates
communication, protection, metering and control equipment
together to demonstrate engineering and electric power concepts
for smart grid research and educational activities. The smart
grid lab objectives are defined for pre-college, undergraduate,
and graduate students based on Bloom’s taxonomy levels,
providing hands-on experiences and demonstrations. By
coupling the laboratory experiences with computer-based
simulations, students gain a deeper understanding of theengineering principles and real-world challenges.

I. SMART GRID LABORATORY OBJECTIVESThe Burns & McDonnell - K-State Smart Grid Laboratory
objectives are defined for pre-college, undergraduate, andgraduate students based on Bloom’s taxonomy levels, as
shown in Figure 1.

substation-power system, power meter K-State facility andcommunication system. The Burns & McDonnell - K-State
Smart Grid Laboratory is a multidisciplinary laboratory
integrating power and protection systems at the substationpower system area, communications at the networking area,and power meters at the power meter K-State facility. Figure 2
shows the network (A), protection (B, E, and F), control (C),and computer-based simulator (D) panels.

Figure 2. Smart Grid Laboratory Panels.

III. COMPUTER-BASED SIMULATORS, ACTIVITIES & RESULTS

Figure 1. Smart Grid Laboratory Objectives.

II. NETWORK ARQUITECTURES & EQUIPMENT
The smart grid laboratory is based on three smart gridarchitectures used in research and education. They are the

The authors gratefully acknowledge the contributions of Burns &McDonnell, Schweitzer Engineering Laboratories Inc., Fishnet Security,
NovaTech, and Biotronics.

This laboratory allows visitors and students to explore
different issues in power system protection, communications
and control. By coupling the laboratory experiences withcomputer based simulations, students gain a deeper
understanding of the engineering principles and real-world
challenges. The smart grid laboratory was integrated touniversity and college events, engineering courses, researchand projects, and KSU facilities, for educational outreach andscholarly advances. The smart grid laboratory is an integrated
facility focusing on the combination of protection,communication, and power metering areas to educate thepublic, to spark the interest of K-12 students in engineering
careers, and to advance knowledge for current K-State
students. In this laboratory, students can integrate equipment
and software with course knowledge to better understandelectric utility challenges in smart grid applications.

Incentive Based Demand Response in WECC
240-Bus System to Minimize Price Variation
Ailin Asadinejad

Kevin Tomsovic

Electrical and computer science engineering dept.
University of Tennessee
Knoxville, US
aasadine@utk.edu
Abstract
In this poster, an incentive based DR program is
proposed. The required load reduction and adequate
incentive payment are designed by a Load Serving
Entity (LSE). This program is simple to implement for
both customer and LSE while it brings considerable
saving. To equitably share the benefits of DR, LSE candecrease the wholesale price for all customers,
including those who do not participate in DR program.
Different thresholds above market variable price areconsidered as trigger for DR programs. We compare aconstant threshold and an optimal threshold based on aanalysis of historical load and generation. In thisoptimization savings for both LSE and the customer are
optimized but consumer inconvenience is limited byconstraining the number of interruptions per day andseason. Results show a constant threshold has a greater
impact on average LMP and price volatility in thesummer (peak summer load system) while greater valueof DR can be realized throughout the year by
optimizing the threshold.
Data and parameter that are required for numericaltest is derived from a WECC 240 bus reduced model.
Note that the load profiles of each device in residential
and commercial sectors are used to find reducible partof load at each hour.

Electrical and computer science engineering dept.
University of Tennessee
Knoxville, US

tomsovic@utk.edu
40.00%
30.00%
20.00%
10.00%
0.00%

constant threshold

optimum threshold

Figure 2: costumer saving from DR
450400

without DR

350

constant threshold

300
optimum threshold

250
200
150
100
50
0

250.00%

1

3

5

7

9

11 13 15 17 19

21 23

200.00%Figure 3: LMP curve in worse day of summer in Nevada region

150.00%

References:100.00%[1] U.S. Government Accountability Office, “Electricity Markets:
Consumers could benefit from demand programs, but challenges
remain,” Report to Senate Committee on Governmental Affairs,
2004.

50.00%
0.00%[2] The Brattle Group, “Quantifying Demand Response Benefits in
PJM” report for PJM Interconnection and Mid Atlantic Distributed
Resource Initiative (MADRI), 2007.
constant threshold

optimum threshold[3] Talukdar, Sarosh, “The Power Systems Engineering ResearchCenter and Some Grand Challenges,” Seminar presented to theCarnegie Mellon Electricity Industry Center, 2002, available at
http://www.cmu.edu/electricity.

Figure1: LSE benefit from both methods

1Benefits of using PEV vehicles in PRT systems andmodifying vehicle routing problem
Ali Dehghan Banadaki, Student Member, IEEE,
Co-Author: Venkata Satish Kasani, Student Member, IEEE
Dr. Sarika Khushalani-Solanki, Member, IEEE, Dr. Jignesh Solanki, Member, IEEE

Figure1 2 - Physical structure and the corresponding
electrical model of PRT

Figure 1 – PRT in Morgantown
Abstract — Personal Rapid Transit (PRT) is a public driverlesstransport system which has been used since 1975 in
Morgantown. These small vehicles are controlled by computer
center, therefore, eliminating human errors made them safer
than usual transport systems like buses or taxies. Moreover PRT
is based on demand response unlike the other mass transit
vehicles which work on scheduled time; therefore, they are moreenergy efficient systems. Safety and energy efficiency are two
important factors that made these vehicles to be more common
these days and spreading through other countries like Englandand Netherlands. PRT uses electricity power instead of gas
which is another privilege of PRT systems but still researchers
propose new methods to make this kind of transportation evenbetter than today. In this poster I try to show that converting
power rail propelling to battery vehicle would make thistransport system better for two reasons. First, if we model eachvehicle of PRT as a current load, each power supply as a voltage
source and each line with resistors, then we understand that inthis kind of system some portion of power are always lost in theresistors of lines, therefore, by using battery vehicles there is no
power loss in the lines and it improves the efficiency.

Second, if we analyze the demand response of all stations of PRT
in Morgantown we realize that the peaks are occurred aroundthe daily peak in the morning and evening. Again we can handle
this situation by using battery vehicles and setting the time forcharging them in the hours which is not the peak load.

In this case we should not forget to add a constraint to vehicle
routing problem which makes a new optimization formula. This
constraint limits the number of vehicles in each cycle to those
who are not in charge station, i.e., we do not always have all of
our vehicles ready to use. Thus a new constraint will be added to
the formula to optimize the new vehicle routing problem.
Reference:
1- Optimal Position Finding Algorithm for the Power Sources in the
PRT system; IEEE Industry Applications Conference; 2003

1

False Data Attack on State Estimation in Smart
Grid
Deepak Tiwari, Student Member, IEEE, Jignesh Solanki, Member, IEEE and Sarika Khushalani
Solanki, Member, IEEE
Abstract—Analysis of vulnerability of power grid is done incase of malicious data attack on state estimation. State estimation
plays very vital role in determining the power grid variablethrough meter measurements and the available power systemmodel, which is very important for the reliable operation of power
grid. A special class of attack goes unobserved by the bad datadetection algorithm, which is also discussed here in this poster.
Main aim of the attacker is to construct such an attack vector
which bypasses the detection algorithm.
Index Terms—Malicious Data Attack, Optimization
Technique, Smart grid.

II. KEY EQUATIONS
A. Cardinality minimization problem
min ‖𝐻∆𝜃‖0∆𝜃∈𝑅𝑛𝑠𝑢𝑏𝑗𝑒𝑐𝑡 𝑡𝑜 𝐻(𝑘, : )∆𝜃 = 1

There are certain ‘I’ meters which could not be attacked due tothe protection, so this model becomesmin ‖𝐻∆𝜃‖0∆𝜃∈𝑅𝑛𝑠𝑢𝑏𝑗𝑒𝑐𝑡 𝑡𝑜 𝐻(𝑘, : )∆𝜃 = 1
𝐻(𝐼, : )∆𝜃 = 0

I. INTRODUCTION

M

easurement of vulnerability is defined by the impact of an
attack and difficulty of detecting the attack. Smart grid isa transformed electrical grid which includes advanced censors,robust two way communication, and distributed computers. In
a power grid, the Supervisory Control and Data Acquisition(SCADA) plays and important role. The state estimator takes
the measured data from SCADA, which are power flows, bus
power injections etc.
This poster also discusses the class of cyber security problem
in smart grid [1]. In this paper, analysis of vulnerability of
power grid to false data is done considering the cyber security
problem as a cardinality minimization problem. Also themodeling of attacks have been done by various authors [2], [3].
In [3], the attack is considered as the two cases 1) random false
data injection attack 2) targeted false data injection attack andconstruct an attack vector for both cases. The malicious
measurements 𝑍𝑎 can bypass the bad data detection (BDD) test
if and only if attack is a linear combination of measurement
matrix.
[4]- [6] discuss the unobserved coordinated attack as
cardinality minimization problem, the optimal solution towhich eases the task of vulnerability analysis of network. To
detect the presence of bad data, the two norm of the residual
calculated and limited by a threshold. If the residual is too big,
the BDD alarm will be on.

B. False data attack model
The measurement vector.
𝑍 = ℎ(𝑥) + 𝑒

a is the attack vector injected into the system..
𝑍𝑎 = ℎ(𝑥) + 𝑒 + 𝑎III. SUMMARY
This work is based on the solution to false data attack problem
on the state estimation in smart grid. This cyber security
problem was considered as cardinality minimization problem.
Also the various optimization tools for the cyber-security needs
to be developed in near future for the efficient operation ofsmart grid.
IV. REFERENCES
Kin Cheong Sou, Henrik Sandberg, and Karl Henrik Johansson “On theexact solution to a smart grid cyber-security problem,” IEEE Trans. Smart
Grid, vol. 4,no 2, June 2011
Jinsub Kim and Lang Tong, “On Topology Attack of a Smart Grid” IEEE
Trans. Smart Grid Communication, vol 31,issue 7, pp 1294-1305, June
2013
Y. Liu, M. K. Reiter, and P. Ning, “False data injection attacks against
state estimation in electric power grids,” 16th ACM Conf. on Computerand Communications Security, New York, NY, USA, 2009.
H. Sandberg, A. Teixeira, and K. H. Johansson, “On security indices forstate estimators in power networks,” First Workshop on Secure Control
Systems, Stockholm, Sweden, April 2010.
Oliver Kosut, Liyan Jia, Robert J. Thomas , and Lang Tong, “Malicious
Data Attacks on the Smart Grid”. IEEE Trans. Smart Grid, vol.2 ,no 4,
Dec 2011
R. Bobba, K. Rogers, Q. Wang, H. Khurana, K. Nahrstedt, and T.
Overbye, “Detecting false data injection attacks on dc state estimation,”in Proc. 1st Workshop Secure Control Syst. (CPSWEEK 2010).

[1]

[2]

[3]

[4]

[5]

[6]

Authors are with Computer Science and Electrical Engineering Department,
West Virginia University, Morgantown, WV 26505 USA (e-mails:
detiwari@mix.wvu.edu, jignesh.solanki@mail.wvu.edu, Sarika.KhushalaniSolanki@mail.wvu.edu.

1

Effect of Stealth Data Attack on AC Optimal
Power Flow
B. Khaki, Student Member, IEEE, J. Solanki, Member, IEEE, S. K. Solanki, Senior Member, IEEE

Abstract--as the main characteristic of smart grid is thecommunication between different parts of the grid in order toprovide a real-time monitoring and control system, this type ofgrids is considered a cyber-physical system which its cyber layer
is vulnerable to malicious data attacks. The data corruption canaffect the performance of the physical system and make it
unstable or compel it to operate under the conditions with is not
optimal from the economic point of view. In the cases of dataattacks, especially stealth attacks, the manipulated data remains
unobservable to the operator and may affect the optimal
operation of the power grid. In this paper, malicious data attack
effect on AC optimal power flow and its consequences on
operator’s decisions are investigated through the formulation of
the system and simulation results.

stealth attacks which are able to deceive the operator andsatisfy the adversary's goal. [8] introduces a method ofdesigning an unobservable data attack based on AC power
flow model.
In this paper, considering [7], the effect of stealth attack,based on the nonlinear optimal power flow, on the operator
and closed loop control is examined.
II. KEY EQUATIONS
A. OPF Equation
min 𝑐�𝑃𝑔 �𝑃𝑔

𝑠. 𝑡. ℎ�𝑃𝑔 , 𝑃𝑑 � = 1𝑇 𝑃𝑔 + 1𝑇 𝑃𝑑 = 0
𝑓�𝑃𝑔 , 𝑃𝑑 � = 𝐹𝑔 𝑃𝑔 + 𝐹𝑑 𝑃𝑑 + 𝐹0 ≤ 0Index Terms-- Data Attack, Smart Grid, Power Flow.

𝑤𝑖𝑡ℎ

����𝑓
𝐺𝑔
𝐹𝑑
−𝑃
����𝑓
−𝐹
−𝐺
𝐹𝑔 = � 𝑔 � , 𝐹𝑔 = � 𝑑 � , 𝐹0 = �−𝑃 �
����
0
𝐼
−𝑃 𝑔
0
−𝐼
0

I. INTRODUCTIONSMART grid is an idea introduced as a controllable power
grids so that the costs of producing electricity decreases asmuch as possible. To satisfy this goal, electricity resources andloads are communicated with control centers where in addition
to monitoring the system, real-time decisions made by thecontrollers and operators.
According to the communication backbone of the smart
grid, it is considered a cyber-physical system which its cyberlayer is vulnerable to malicious data attacks. Corrupted dataattacks, particularly unobservable attacks, may affect thecontrollers and operators performance, as they manage thesystems according to the manipulated measurements.
Therefore, many researchers have investigated the effect ofthese attacks on power flow and energy market studies. [1,2]studies the effect of data attack on power market, considering
linearized power flow model. [3] considers the nonlinear
model in studying the effect of data attack on energy markets
and illustrates the nonlinearity alleviate the effect of attack
designed based on the linear model. Then in [4], the authors
propose partitioned state space of the system into priceregions, and they investigated the effect of meter data andtopology data to find the worst data attack by defining relative
price perturbation criteria.
[5, 6] demonstrate that Bad Data Detection (BDD) is notcapable to detect stealth attacks in estate estimators. [7]considers the linear optimal power flow model to explore theB. Khaki, J. Solanki, S. K. Solanki are with Computer Science andElectrical Engineering Department, West Virginia University, Morgantown,
WV
26505
USA
(e-mails:
bekhaki@mix.wvu.edu,
jignesh.solanki@mail.wvu.edu, Sarika.Khushalani-solanki@mail.wvu.edu).

B. Attack Model
Z , in the following equation, is the measurement vector..
𝑍 = ℎ(𝑥) + 𝑊

A is the attack vector injected into the system..
𝑍𝑎 = ℎ(𝑥) + 𝑊 + 𝐴(𝑍0 )III. REFERENCES

[1]
[2]

[3]

[4]

[5]

[6]

[7]

[8]

L. Xie, Y.Mo, and B. Sinopoli, “Integrity data attacks in power market
operations,” IEEE Trans. Smart Grid, vol. 2, no. 4, pp. 659–666, 2011.
L. Jia, R. J. Thomas, and L. Tong, “Impacts of malicious data on realtime price of electricity market operations,” in Proc. HICSS, 2012, pp.
1907–1914.
J. Liyan, R. J. Thomas, and L. Tong, “On the nonlinearity effects on
malicious data attack on power system,” PESociety General Meeting
2012, pp. 1-6, July 2012.
L. Jia, J. Kim, R. J. Thomas, and L. Tong, “Impact of Data Quality on
Real-Time Locational Marginal Price, “ IEEE Trans on Power Systems,
issue: 99, Nov. 2013.
Y. Liu, M. K. Reiter, and P. Ning, “False data injection attacks against
state estimation in electric power grids,” 16th ACM Conf. on Computerand Communications Security, New York, NY, USA, 2009.
H. Sandberg, A. Teixeira, and K. H. Johansson, “On security indices forstate estimators in power networks,” First Workshop on Secure Control
Systems, Stockholm, Sweden, April 2010.
A. Teixeira, H. Sandberg, G. Dan, and K. H. Johansson, “Optimal power
flow: Closing the loop over corrupted data, “ American Control
Conference (ACC) 2012, pp. 3534 - 3540, July 2012.
K.R. Davis, K. L. Morrow, R. Bobba, and E. Heine, “Power flow cyberattacks and perturbation-based defense, “ IEEE International Conference
on Smart Grid Communications (SmartGridComm) 2012, pp. 342 - 347,
Nov. 2012.

Cyber-Security Vulnerability Assessment of Smart
Grids
Jignesh. M. Solanki, Member IEEE
Sarika K. Solanki, Senior Member IEEE

Reza Kazemi, Student Member, IEEE
Lane Dept. of Electrical Engineering and Computer Science,West Virginia University, Morgantown, WV

Abstract— The development and expansion of new smart
electrical grids brings increased capacity, reliability andefficiency through the merging of new communication andcomputer technologies with the existing electricity network. This
integration, however, creates a new host of vulnerabilities
stemming from cyber intrusion and injection of corrupted data
in an attempt to subject the electric grid to physical andfinancial losses. Analysis of these new threats calls for amodeling framework that is able to model the effects of thesecyber threats on the physical components of the power system
by taking into account the interactions between the cyber andphysical components of the smart grids.
Index Terms—Smart Grid, Cyber-security, Cyber Intrusion.

I.

INTRODUCTION

A smart grid is defined as the integration of real-timemonitoring, advanced sensing, and communications, utilizing
analytics and control, enabling the dynamic flow of both
energy and information to accommodate existing and newforms of supply, delivery, and use in a secure and reliable
electric power system, from generation source to end-user [1].
Wide integration of communication networks and intelligent
devices in the smart grids makes it more vulnerable to cyberthreats compared to old power systems. For smart gridattackers, a general goal is to maximize the effect of their
attacks with lower cost and risk, which usually means their
attacks, will aim at triggering cascading failures by just taking
down a few components whose failure can lead to blackouts in
power grids [2]. In general these intrusions can lead to loss ofload, loss of information, economic loss or equipment damage
according to scale and intention of the intrusion. Intruders canalso affect critical activities required for operation and controlof the power system like state estimation and contingency
analysis by manipulating the data readings from smart metersin the grid [3]-[6], or conduct a topology attack by transferring
false information about in service transmission lines to thecontrol center [7]Lane Dept. of Electrical Engineering and Computer Science,West Virginia University, Morgantown, WV

II.

MODELING THE CYBER INTRUSION IMPACT ON THE
SMART GRIDS

There has been some research work to evaluate the effectof the cyber intrusions on the electrical smart grids in recent
years [1], [8]. One of the major issues in simulation of theseeffects is coming up with a method to take the interactions ofcyber and physical parts of the smart grids in to account in a
way that enables us to accurately predict the potential impacts
of a cyber attack plot on the stability of the network as well as
safety of the components. In order to reach this goal for
simulating such interactions and dependencies within cyber
physical power systems can be considered as a graph withevery electrical component like transformers, generators,
loads, plug-in hybrids and circuit breakers considered as anode. Cyber parts of the smart grid such as switches andcontrol centers, sensors and breaker actuator controls are also
assumed as a node. The dependencies between the nodes are
represented by edges.
REFERENCES
[1]

[2]

[3]

[4]

[5]

[6]

[7]
[8]

D. Kundur, F. Xianyong S. Mashayekh, S. Liu, T. Zourntos, and K.L.
Butler-Purry. "Towards modelling the impact of cyber attacks on asmart grid." International Journal of Security and Networks 6, no. 1,
2011, pp. 2-13.
J. Yan , Y. Zhu , H. He and Y. Sun "Revealing temporal features ofattacks against smart grid", 2013 IEEE PES Innovative Smart Grid
Technologies (ISGT).
A. Giani, E. Bitar, Garcia, M. M., McQueen, P. Khargonekar, and K.
Poolla, "Smart Grid Data Integrity Attacks: Characterizations andCountermeasure," IEEE SmartGridComm, 2011.
S. Zonouz, C. M. Davis, K. R. Davis, R. Berthier, R. B. Bobba, and W.
H. Sanders, "SOCCA: A Security-Oriented Cyber-PhysicalContingency Analysis in Power Infrastructures." ,” IEEE Trans. Smart
Grid, vol. 3, no. 4, pp. 1790–1799, 2014.
M. Esmalifalak, G Shi, Z Han, L Song. "Bad Data Injection Attack andDefense in Electricity Market Using Game Theory Study." IEEE Trans.
Smart Grid, vol. 4, no. 1, pp.160 -169, 2013.
L. Xie , Y. Mo and B. Sinopoli "Integrity data attacks in power market
operations", IEEE Trans. Smart Grid, vol. 2, no. 4, pp.659 -666
2011.
J. Kim and L. Tong, "On Topology Attack of a Smart Grid," 2013
IEEE PES Innovative Smart Grid Technologies (ISGT), 2013.
W. Eberle and L. Holder, “Insider threat detection using graph-based
approaches,” in Proc. Cybersecurity Applications and Technology
Conference for Homeland Security, 2009, pp. 237–241.

1

NISTIR 7628 Visualization Model
Matthew Harvey, Student Member, IEEE, Dan Long, Student Member, IEEE, Karl Reinhard,
Student Member, IEEE
Trustworthy Cyber Infrastructure For the Power Grid (TCIPG)
Department of Electrical Engineering, University of Illinois Urbana-ChampaginAbstract-- NISTIR 7628 is a report that lists out guidelines onwhat should be done to secure the Smart Grid from cyberattacks. This document stands 600 pages and can be difficult tounderstand and conceptualize due to the density of information.
This poster lays out new ways to visualize the NISTIR 7628 in a
number of different models that are currently being created.
Research is currently being done to see how we can use this newmodel to data mine all sorts of information that is currently in
NISITR 7628. Some goals of this research is to compare actual
utility firewall systems with such suggested in the guidelines tosee how they match up, as well as coming up with what resources
are most vulnerable and how many resources need to be spared tosecure certain aspects of the power grid.

I. INTRODUCTIONI

N August of 2010, the National Institute of Standards andTechnology Computer Security Division, NIST, released
NISTIR 7628 Guidelines for Smart Grid Cyber Security. This
document was written in response to this new set of problems
that has arisen and is accompanied by the “NIST Framework
and Roadmap for Smart Grid Interoperability Standards”,
which came out in 2008. The report is written as a 600 page,
three volume document that is meant to serve as an “analyticalframework” and with the hope that individual organizations
will be able to reference it. The document is not only very
long, but it tends to repeat itself a good amount. The NISTIR
7628 document does attempt to combat this with a series ofdiagrams, many of which work to illustrate the entire power
infrastructure. One focus of many of these illustrations is toshow and explain the relationships between different parts of
the infrastructure. These are two dimensional “maps” of thegrid that are still hard to understand and require a good amountof page flipping.
The document focuses mainly on the cyber-security aspectsof this new digital grid. That means most physical threats areleft out. The framework built from NISTIR 7628 focusesintensely on the IT and telecommunication aspects of theinfrastructure. It discusses, with security being the paramountpoint, the ways to integrate these systems that previously may
have not been involved, or least have not been as important,
into the new grid.
One question raised is, how can one take the information
already represented within the NISITR 7628, and make a more

[1] NISTIR 7628: Guidelines to Smart Grid Cyber Security, NISTIR 7628,
2013.

concise tool that industry and researchers could use to better
understand the guidelines, and research the connections morein depth? The information provided is very thorough anduseful, but the problem is its inaccessibility. This documentthat many professionals try to reference is not well understood.
The goal of the NISTIR 7628 Visualization is to provide
visual aid to the document that will make the information canbe accessed much more easily and therefore can be more
helpful in making our next-generation grid more secure.
II. MODELING NISTIR 7628
Within the NIST document, a number of visual
representation and diagrams are used to illustrate thecomplexity of the power grid infrastructure by drawing
security connections between different aspects of the power
grid. NIST divides the power grid into seven different domains
including, Markets, Operations, Service Provider, Bulk
Generation, Transmission, Distribution and Customers. The
visualizations included in the NIST document have beencombined together into a computer model to allow for a great
functionality and easy grasp of the information being
represented in the diagrams. Please refer to the NISTIR 7628
to view the current visualizations provided.
A. 3D Model
Two models have been developed, the first an HTML web
page, second a 3D MATLAB model. These have bothreflected the diagrams already in the NIST document.
Currently, a newer model is being developed to incorporate
more information form the NISTIR 7628 as well as a welllabeled 3D model that allows any user to easily gather
information about security requirements and different actors,
or components of the power grid infrastructure. The two
platforms that are being explored for this updated model use
JAVA and HTML5. Both allow for better visual aspects andbetter functionality.
B. Goals
The tool has proven to have potential after feedback
received from industry, academia, and government partners.
We are seeking effective methods to label actors, logicalinterfaces, and interfaces compatible with interactive
perspective changes, seeking ways to easily zoom-in and panout, and seeking ways to increase information content
available to user; user selects and controls the information
displayed.

Effects of Micro-Structured Surface Geometries on
Condensation Heat Transfer
Andrés Martínez, Caleb Chiroy, and Amy R. Betz
Department of Mechanical and Nuclear Engineering
Kansas State University
Manhattan, Kansas, USA
Email: marmot91@ksu.edu

Abstract—The purpose of this research is to experimentally
study how micro-structured geometries affect the heat transfer
coefficient on a surface under filmwise condensation conditions.
Filmwise condensation is a major concern when designing steam
condensers for thermoelectric power plants. These plants
currently account for 40% of freshwater withdrawal and 3% offreshwater usage in the United States. Filmwise condensation
averages five times lower heat transfer coefficients than thosepresent in dropwise condensation. Currently, filmwise
condensation is the dominant condensation regime inthermoelectric power plants due to their prolonged usage. The
film thickness is directly proportional to the condenser’s overall
thermal resistance. This investigation focuses on optimizing
surface geometries with sinusoidal patterns to reduce film
thickness and maximize filmwise heat transfer. The experimental
setup allows us to control the cooling load, pressure, and steam
quality in order to measure the steam-side surface temperature
under steady state conditions. The test apparatus allows controlof the cooling load and steam properties while being able tointerchange the surface structures. The heat transfer coefficientis determined by measuring surface temperature. By comparing
the heat transfer coefficients, we can find the optimal surface
geometries by varying fin height, fin pitch, and surface
curvature. A four axis micro milling machine with precision of
one micron was used to machine the desired surface patterns.
Aluminum was chosen based on its machinability, cost, andthermal properties. Type T thermocouples, hygrometers, andpressure transducers were used for data acquisition. A vacuum
pump creates low pressure conditions and temperature steamthat mimics condenser conditions. The steam-surface
temperature is measured once the system achieves steady state
conditions.

1

Optimal Strategy for Minimizing Load
Shedding in Microgrids subsequent to FaultTriggered Islanding
Shanshan Ma, Student Member, IEEE, and Wei Sun, Member, IEEE
Abstract–Microgrid (MG) is defined as part of distribution
system, which consists of load, storage sources and distributed
generations (DGs) or renewable energy sources. With the large
penetration of DGs, MG can operate in two modes, gridconnected mode or islanded mode. In the grid-connected
mode, the outputs of DGs are set at a certain level, and themain grid compensates any insufficient power requirement ofload. In the islanded mode, MG is disconnected from the main
grid and makes full use of DGs and storage resources tosustain automatically and independently.
A smooth transition from grid-connected to islanded modeis desirable but still a challenge for MG operation. Islanding
could be caused by disturbances such as an intentional switch
incident or a sudden fault happens in the main grid. In the caseof intentional switching, the power sharing of each DG andstorage resource is scheduled ahead with smooth transition.
However, a fault in the main grid with subsequent tounintentional islanding may result MG in voltage andfrequency instability. Without appropriate control strategies,
the severe voltage and frequency variation can lead DGs tooperate abnormally or be damaged. Consequently, MG can’t
operate automatically after islanding occurs.
While many control strategies have been proposed to solve
the transient stability issue, most of them focus on voltage andfrequency control of DGs rather than directly minimizing thepower imbalance. As renewable energy sources must be
interfaced with MG through inverters, advanced inverter
control strategies are required to maintain stable frequency andvoltage. Therefore, directly minimizing power imbalance may
help to avoid the complex control strategy, but requires system
to shed some load.
Network reconfiguration is able to minimize the load
shedding and keep system stable. Considering the possible
imbalance between DGs and load in MG after the islanding,
network reconfiguration can directly transfer power from one
DG to another load by changing the state of the sectionalizing
and tie switches. In this way, MG can make full use of existing
power and minimize power imbalance between generation and
S. Ma and W. Sun are with the Department of Electrical Engineering,South Dakota State University, Brookings, SD 57006 USA (e-mail:shanshan.ma@sdstate.edu, wei.sun@sdstate.edu).

load. Furthermore, network reconfiguration considers thesystem level constraints, which helps to avoid any voltage orcurrent violation. However, reconfiguration may notcompletely avoid load shedding. The generation re-dispatch
will be applied to achieve an automatic and seamless transition
from grid-connected to islanded mode.
In this paper, it is assumed that power imbalance exist in theislanding mode. A novel optimization strategy that combines
network reconfiguration with generation re-dispatch is
proposed to minimize the load shedding, as shown in Fig. 1.
When MG detects the signal of islanding, the optimization
process will provide a strategy to reconfigure network and redispatch generations in real time. The inputs of the
optimization process are network model of MG, monitored
data from system and islanding detection. The outputs are thestrategies of generation re-dispatch and network
reconfiguration. The constraints include DG capacity limits,transient stability constraints, and the statistic model ofrenewable energy source outputs. A novel solution
methodology is proposed to solve this stochastic optimization
problem. The modified CERTS Microgrid test system is used
to demonstrate the advantage of developed strategy.
Based on the proposed strategy, MG is able to run
automatically in islanded mode subsequent to fault-triggered
incidents or unscheduled outage of main grid. The developed
strategy can also be integrated to future distributed energy
management systems and achieve a resilient microgrid.

Fig. 1: The structure of developed optimal strategy.

Index Terms—Generation re-dispatch, islanding, load
shedding, Microgrid, reconfiguration.

A Hybrid Dynamic Optimization Approach for
Stability Constrained Optimal Power Flow
Guangchao Geng1, Venkataramana Ajjarapu2, and Quanyuan Jiang1
College of Electrical Engineering1
Zhejiang University
Hangzhou, China
Email: ggc@zju.edu.cn, jqy@zju.edu.cn
Abstract — Stability constrained optimal power flow (SOPF) isan effective and economic tool to enhance stability performanceby adjusting initial steady-state operating conditions, with theconsideration of rotor angle and short-term voltage performance
criteria. SOPF belongs to the category of dynamic optimization
problems which are computational expensive. In order to reduce
its computational complexity, a hybrid dynamic optimization
approach is proposed for efficient and robust solving SOPF
problems. Based on direct multiple shooting method, thisapproach combines the algorithmic advantages from existing
direct sequential and simultaneous approaches. Coarse-grained
parallelism among multiple shooting intervals is explored. A
modular-based implementation architecture is designed to takeadvantage of the loose coupling between time-domain simulationand optimization. Case studies on various test systems indicate
the proposed approach is able to reduce computation timecompared with other direct approaches for dynamic optimization. Also, the investigated parallelization are effective to achieve
acceleration on a symmetric multiprocessing platform.

I.

Department of Electrical and Computer Engineering2
Iowa State University
Ames, IA, United States
Email: vajjarap@iastate.edu
III.

Test
System
CASE9
CASE39CASE57
CASE118
CASE162
CASE300

KEY RESULTSTABLE ICOMPARISON OF CONVERGENCE AND OBJECTIVE VALUES
SEQ
DMS
SIM
Iter

Obj ($/hr)

Iter

Obj ($/hr)

Iter

Obj ($/hr)

79
26
21
60

3,994.859
26,230.732
40,525.660
119,757.520
FAIL-C
FAIL-C

11
22
20
62
34
113

4,014.751
26,230.738
40,525.665
119,756.949
12,756.558
670,651.911

10
18
12
53
20

4,018.629
26,230.759
40,525.667
119,755.967
12,765.106
FAIL-M

TABLE IICOMPARISON OF COMPUTATIONAL PERFORMANCE
SEQ
DMS
SIM

Test
System CPU (sec) Mem (MB) CPU (sec) Mem (MB) CPU (sec) Mem (MB)
0.284
322
0.456
333
0.154
363
CASE9
0.389
333
0.728
340
1.381
360
CASE39
0.296
335
0.552
338
0.583
382
CASE57
2.881
380
9.953
886
101.2446,051
CASE118
343
3.914
448
8.304
1,253
CASE162 FAIL-C
453
23.416
887
FAIL-M >7,680
CASE300 FAIL-C
Note: The symbol ‘FAIL-C’ denotes failure to converge, ‘FAIL-M’ denotes out of memory.

KEY EQUATIONS
64

min Φ( u)min Φ ( u)

x ,u

s ,u

II.

i  [1... N S ]KEY FIGURES

Accelerating ratio si  Si  si 1 , u Ψ  Ψ ( si )  Ψs.t  H ( u)  0
 Γ  Γ ( u)  Γ F ( x, x, u)  0
Ψ  Ψ ( x )  Ψs.t  H ( u)  0
 Γ  Γ ( u)  Γ

CASE39CASE57
CASE118
CASE162
CASE300

32

16

8

4

2

1

1

2

4

8

16

32

64

Number of CPU coresFig. 3. Accelerating ratio curves for the proposed approach.
120

Fig. 1. Algorithm principle demonstration for direct multiple shooting method

relative rotor angles (degree)

100
80
60
40
20
0
-20
-40
-60

Fig. 2. Modular-based framework for dynamic optimization

0

0.5

1

1.5
t (sec)2

2.5

Fig. 4. Time-domain simulation verification of SOPF solution.

3

Reactive Power Planning Considering High Penetration
Wind Energy
Xin Fang, Fangxing Li

Yan XuDepartment of EECSThe University of Tennessee
Knoxville, TN, USA
Emails: xfang2@utk.edu, fli6@utk.edu

Power & Energy Systems GroupOak Ridge National Laboratory
Oak Ridge, TN, USA
Email: xuy3@ornl.gov
method for providing optimized quantity and location for Vars is
proposed.

Abstract— This paper addresses the optimal placement of reactive
power (Var) sources under the paradigm of high penetration windenergy. The reactive power planning (RPP) investment in thisparticular condition involves a high level uncertainty due to the wind
power characteristic. The correlation between wind speeds ofdifferent wind farms should also be considered when there are
multiple wind farms. To properly model the wind generation
uncertainty, a multi-scenario framework optimal power flow thatconsiders the voltage stability constraints is developed. The objectiveof RPP is to minimize the total cost (Var cost and the expected
generation cost). So the RPP under this condition is to optimize the
Var location and size, meanwhile minimizing the fuel cost andtherefore find the global optimal RPP results. The proposed
approach is based on two sets of variables (TSV) combined withmulti-scenario model. A Benders decomposition technique is used tosolve this model with an upper problem (Master problem) for Var
allocation optimization and a lower problem (subproblem) for
generation cost minimization. Impact of the potential reactive power
support from DFIG is also analyzed. Case studies are provided toverify the proposed method.

This paper will present a Benders decomposition based multiscenario, two sets of variables (TSV) optimization model to solve
RPP problem with high penetration wind energy. The wind poweris modeled as a multi-level generation resource. The correlation
among different wind farms is modeled and the reactive power
support from DFIG are considered and analyzed. The objective of
the optimization problem in this paper is the total cost of Var
compensation and the generation cost (fuel cost) after integrating
wind energy.
II. WIND POWER MODEL
The wind speed in a single wind farm can be easily generated
utilizing Monte Carlo simulation. When there are multiple windfarms, the correlation should be considered. To solve this problem,
at first the correlated normally distributed series are generated, andthen transform these normally distributed series into Weibull
distributed wind speed series.

Index Terms- Voltage stability constrained optimal power flow
(VSCOPF), reactive power planning (RPP), wind power, Weibull
distribution, Benders decompositionI.

For instance, in the case of a normal distribution variable x, thecumulative probability Fx of CDF is obtained from (4) and (5).

Fx INTRODUCTION1xu(1  erf (
))
2
2

(1)
Wind power is known as an intermittent energy resource thatx
often has a negative correlation with load peaks [1]. The
22
erf ( x) e t dt(2)
increasing penetration of wind energy presents significant
 0challenges to the operation and planning of the bulk powertransmission systems. At the same time, conventional
So the transformation from normal distribution to Weibull
synchronous-machine-based power generation, currently the main distribution can be shown as:source of reactive power of the system, is being exhausted as a11x
result of increasing wind penetration. Therefore, reactive power(3)w  [ ln(1  (1  erf ( )))] k(Var) is needed to accormadate the wide operation ranges of the22wind generation and maintain appropriate voltage profiles because
Fig. 1 shows the transformation of Normal distribution ( u =0,
voltage collapse is often associated with reactive power=1) to Weibull distribution (  =7, k =2.2).
deficiencies.



The target of reactive power planning (RPP) or Var planning is
to optimize the reactive resources (both by location and size). This
optimization problem is a mixed integer nonlinear programming
problem. Determination of the buses that should be considered as
potential candidates for Var resources and their size is complex
and often be treated as a heuristical problem. The problem isfurther complicated with the integration of wind energy, due to theuncertainty of wind power plant output. In this paper, a systematic

1

1

0.7
Probability

Weibull
Distribution
CDF

NormalDistribution
CDF

0.8

Fx(x)

Probability

0.9

Fw(w)

0.6
0.5
0.4

NormalDistribution
PDF

correlation coefficient 0
correlation coefficient 0.4
correlation coefficient 0.8
correlation coefficient 1

0.30
0.25
0.20
0.15
0.10
0.05
0.00
0

0.3

Weibull
Distribution
PDF

0.2
0.1
0
-5

0

x

Percent of wind active power output
Fig. 3 Wind power output bar figure for different wind speed correlation

w

5

10

15

20

Fig. 3 shows that the correlation coefficient changes the wind
active power output distribution significantly. TABLE I is the Var
allocation results for different correlation and reactive power
control strategies.

Variables (x, w)
Fig. 1. Transformation of Normal distribution to Weibull Distribution

III. PROBLEM FORMULATION AND DECOMPOSITION
The purpose of the RPP in this paper is to maintain the system
voltage stability under high penetration wind energy. Here theload margin is employed as the voltage stability margin.
Therefore, the objective is to minimize the reactive compensation
cost under the specific operating point and the fuel cost under thenormal operating point. Two sets of variables (TSV) are specified
for these two operating points.
Min: c  c  Q  u   p  f ( P )
1

kN

Q

2

*
ck

k

t

tW

N

10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Gi ,t

iN

TABLE I. Var allocation under different wind speed correlation
Correlation
Fuel
VAR
Total
Bus 5
Bus 9
Coefficient
cost ($)
cost($)
cost($)

(4)

0

16.4

30

11464.4

129.6

11594

0.4

16.4

30

11496.8129.611626.4

0.8

16.4

30

11525.9129.611655.5

1

16.4

30

11535.2

129.611664.8

G

From the objective function in (4), the optimization model canbe decomposed into two levels. The upper-level (the masterproblem) is to optimize Var compensation with the voltage
stability constraints. The lower-level (the subproblem) is the
ACOPF with given Var compensation allocation and gets theminimum expected generation cost considering all different windscenarios[1]

IV. CASE STUDIES AND RESULTS
The proposed voltage stability constrained optimal power flow
(VSCOPF) model is studied on IEEE 14 bus and IEEE 118 bus
system.
A. IEEE 14-Bus system
Two wind farms with total capacity of 77MW are connected tobus 2, the capacity penetration is 30%. One wind farm is 30MW,
and the other one is 47MW, in which all the wind turbines are
DFIG which have three reactive power control strategies: unity
power factor, D curve reactive power capability and power factor
0.98. In the simulation, the system load margin  is 50%. Fig. 2
is the wind active power output bar figure. In all cases, fixed cost

c1 is 33.47$/hr and varying cost c 2 is 1.23$/(MVar*hr).

2

V. REFERENCE“U.S. Renewable Electricity: How Does Wind Generation Impact
Competitive Power Market,” CRS Report for Congress, Nov. 2012
[Online]. Available at : www.fas.org/sgp/crs/misc/R42818.pdf, accessed in
Dec. 2012.

1

Interval Power Flow Using Linear Relaxation and
Optimality-based Bounds Tightening (OBBT) Methods
Tao Ding*, Student Member, IEEE, Rui Bo, Senior Member, IEEE, Fangxing Li, Senior Member, IEEE
AbstractWith increasingly large scale of intermittent and nondispatchable resources being integrated into power systems, thepower flow problem presents greater uncertainty. In order toobtain the upper and lower bounds of power flow solutions,
Cartesian coordinates-based power flow is utilized to formulate
the interval power flow problem as a quadratically constrained
quadratic programming (QCQP) model. This non-convex
QCQP model can be relaxed to linear programming problem byintroducing convex and concave enclosures of the original
feasible region. To improve interval solution that includes thetrue interval solution, optimality-based bounds tightening(OBBT) method is employed to find a better outer hull of thefeasible region.
In interval power flow analysis, power injections are volatileand can be represented using intervals, which can be formulated
as a standard QCQP problem without the loss of generality:(QCQP)(1)
max / 2minxT Q0 x+c0T xnwhere A, B R 2 n2 n , A  B=i =1  j =1 aij bij .
2n2n

For minimization problem of the original QCQP, the
QCQP-LR is a relaxed linear programming model and therefore
provides a lower bound of the original QCQP. Similarly, it
provides an upper bound for the maximization problem of theoriginal QCQP. It can be observed that the relaxed IPF-LR
model is dependent on the hyper-rectangular variable domain.
At the beginning, the initial domain is usually so large that leads
to a conservational interval solution, so it is desirable toschedule a more rigorous hyper-rectangle domain to achieve an
optimal interval solution. For this reason, the OBBT method is
employed in Fig.1 to solve the IPF-QCQP model, which willtighten the relaxed model IPF-LR based on linear optimality by
cycling through each participating variable until the volume
fails to improve.
LR

xR

s.t. d j  xT Q j x +c Tj x  d j

j =1,...,m

(2)

(3)
i =1,...,2nli  xi  ui
where m denotes the number of quadratic constraints, Qj(j=1,…,m) are indefinite 2n2n matrices, cj (j=0,…,m) aren-dimensional vectors, the set  l , ui  is assumed to bei =1 inonempty and bounded.
Let dummy variable Z=xxT  R 2 n2 n and substitute Z for xxT
in QCQP model, the objective and constraints become bilinear.
Furthermore, the convex envelopes and concave envelopes as
defined in (6)-(9) are introduced to obtain the tractable linear
relaxation (LR) model of the non-convex problem QCQP,
formulated as follows:/ min
c0T x +Q0  Z(QCQP-LR) max(4)2n
2 n2 n
xR

k

k- 1

k+1

x

2n

,zR

Fig.1 Gradually tightened convex hull using OBBT-based method

The proposed method was verified on the IEEE 57-bus
system, with a ±20% variation on load and generator powers.
The results were compared with those of Monte Carlo (MC)simulation with 5000 sample size. Fig.2 depicts the interval
lower and upper bounds for voltage magnitudes. The
comparison between QCQP and MC simulation illustrates theeffectiveness of the proposed method, and the maximum errorof voltage magnitude is no more than 3% and average error isabout 1%.

s.t. d j  c x +Q j  Z  d j(5)

zij  li x j  l j xi  li l j  0

(6)

1.06

zij  ui x j  u j xi  ui u j  0

(7)

1.04

zij  li x j  u j xi  li u j  0(8)

zij  ui x j  l j xi  ui l j  0(9)li  xi  ui

(10)

Tao Ding (e-mail: dingt12@mails.tsinghua.edu.cn) is with the Department
of Electrical Engineering, Tsinghua University, Beijing, China. T. Ding is also
a visiting scholar with the Department of Electrical Engineering and Computer
Science, The University of Tennessee, Knoxville, TN 37996 USA.
Rui Bo is with the Midwest Independent Transmission System Operator(Midwest ISO), St.Paul, USA. (e-mail: rui.bo@ieee.org).
Fangxing Li is with the Department of Electrical Engineering and Computer
Science, The University of Tennessee, Knoxville, TN 37996 USA. (e-mail:fli6@utk.edu)

1.08

Voltage Magnitude [p.u.]

T
j

1.02
1
0.98
0.96
0.94
Upper bound for QCQP
Upper bound for MC
Lower bound for QCQP
Lower bound for MC

0.92
0.9
5

10

15

20

25
30
35
Bus Number

40

45

50

55

Fig.2 Bounds of voltage magnitudes under 20% variation

Exploration of Multifrontal Method with GPU in
Power Flow Computation
Xue Li, Fangxing Li, Joshua M. Clark
The Department of Electrical Engineering and Computer Science, the University of Tennessee, Knoxville, TN 37996, USA,
Email: {xli44, fli6, jclark74}@utk.edu
Abstract— Solving sparse linear equations is the key part ofIII.

power system analysis. The Newton-Raphson and its
variations require repeated solution of sparse linear equations;
therefore improvement in efficiency of solving sparse linear
equations will accelerate the overall power system analysis.
This work integrates multifrontal method and graphic
processing unit (GPU) linear algebra library to solve sparselinear equations in power system analysis. Multifrontal
method converts factorization of sparse matrix to a series ofdense matrix operations, which are the most computational
intensive part of multifrontal method. Our work develops
these dense kernel computations in GPU. Example systems
from MATPOWER and random matrices are tested. Results
show that performance improvement is highly related to thequantity and size of dense kernels appeared in thefactorization of multifrontal method. Overall performance,
quantity and size of dense kernels from both cases arereported.

I.
(
(

)
)

Fig. 2. Sizes of dense computation kernels of case3012wp

KEY EQUATIONS[II.

KEY RESULTS

]

[

][

Fig. 3. a) Sizes of dense computation kernels of studied MATPOWER
cases; b) Sizes of dense computation kernels of random generated sparse
matrices with the same dimension and sparsity as MATPOWER cases
accordingly]

KEY FIGURESGEMM 3%
CPU 8%CUDA Execution
Control 13%
cudaStream
Sync 18%
CUDA Memory Copy 59%

Fig. 4. Dense kernel run-time break down

Fig. 1. Example of Multifrontal Method
TABLE I
case2383wp

PERFORMANCE SUMMARY FOR MATPOWER CASES
case2736sp

case2737sop

case2746wp

case3012wp

case3120sp

case3375wp

Size

4438

5237

5280

5127

5725

5991

6357

Sparsity

0.9986

0.99880.99880.99880.99890.99900.9990Multifrontal w/BLAS(s)0.05640.0676

0.06820.07150.07740.07570.0722Multifrontal w/CUBLAS(s)0.05910.07010.07120.06730.07560.08020.0773

Improvement-4.77%-3.69%-4.43%

5.82%

2.31%

-5.90%-7.07%

1

Multi-objective Robust Optimization Model for
Microgrids Considering Tie-line Power
Constraints and Wind Uncertainty
Linquan Bai, Fangxing Li, Tao Ding, Hongbin Sun
Extended Abstract:
As an important clean energy source, wind power develops
very fast in recent years, including microgrids. The wind
power prediction techniques have been significantly studied.
However, it is very difficult to accurately predict the wind
power due to the intermittent nature of wind, which brings
negative effects on the utilization of wind power.
When the microgrids operate at grid-connected mode, thetie-line power is always scheduled within a certain range toensure the grid’s security and stability in case of a contingency
event.
The objectives of the energy management in microgrids
may include maximizing the lifetime of energy storage (ES)and minimizing the utilization of power produced by thethermal units from the grid.
To address the above challenges, this paper proposes amulti-objective robust optimization model for microgrids withthe consideration of tie-line constraints and wind uncertainty.
The proposed method can effectively identify the optimalscheduling of ES and thermal power from the grid under the
worst wind power forecast scenarios.
The objective function is given by:

min
 Pg
f = min max
s
Pw ≤∀Pw( ) ≤ Pwmax
max
E∑C (P )∑C (E)
1g

the microgrid; Pwf, k (t ) is the forecasted wind power; PE , j (t ) is
the ES output; and Pd , r (t ) is the load in the microgrid.
It should be noted that (2) is different from the energybalance constraints of traditional economic dispatch model,
since the microgrid could exchange energy with the gridminmax
within a scheduled range  Pline .
, Pline(ii) Ramping constraint:DRi ≤ Pg ,i (t ) − Pg ,i (t − 1) ≤ URi(3)

where URi and DRi are the upper and lower ramping power,
respectively, for a thermal unit.
(iii) Transmission line constraint:
Pl −ij (t ) ≤ Pl max
− ij

(4)

where Pl −ij (t ) is the power flow on the transmission line andPl max− ij indicates the capacity for each transmission line.

(iv) Energy storage constraints:
In this work, the power output of ES is positive when it
discharges and negative when it is charged.
(5)− Ee ⋅ β C ≤ E (t ) − E (t − 1)(1 − σ ) ≤ Ee ⋅ β D(6)SOCmin ≤ E (t ) / Ee ≤ SOCmax
where
β
is
the
maximumcharge
rate,
β
is
the
maximumCD
2discharge rate, σ is the self-discharge rate of ES; Ee is thewhere Pg is the power produced by thermal units from the grid;
rated capacity of ES; SOCmax is the upper limit of SOC; andE is the energy in the ES; Pwmax and Pwmin denote the upper and SOC is the lower limit.
minlower bound of wind power output with the consideration of
The equations with superscript (s) indicate all possible(s)the wind power forecast errors; and Pw is the wind power in scenarios within the given wind power output range.
a specific scenario.
N
N
N
N
 min
(s)
(s)
(s)
max
In order to solve the multi-objective problem, the Pareto
 Pline (t ) ≤ ∑ Pg ,i (t ) + ∑ Pw, k (t ) + ∑ PE , j (t ) − ∑ Pd , r (t ) ≤ Pline (t )=i 1 =
k 1 =j 1 =r 1
frontier is employed by transforming the multi-objective into a
 DR ≤ P ( s ) (t ) − P (t − 1) ≤ UR
g ,i
g ,i
i
single one via the dualistic factor contrast method.
 i
(7)
 (s)
max
The constraints of the model can be expressed as (2)-(7).
 Pl −ij (t ) ≤ Pl −ij(i) Tie-line power constraint:
(s)
(1)

g

Ng

Nw

Ne

Nd

min
max
Pline
(t ) ≤ ∑ Pg ,i (t ) + ∑ Pwf, k (t ) + ∑ PE , j (t ) − ∑ Pd , r (t ) ≤ Pline(t ) (2)=i 1 =
k 1 =j 1 =r 1

where Pg ,i (t ) is the power supplied by the ith thermal unit to
L. Bai and F. Li are with the EECS Department, The University ofTennessee at Knoxville (UTK), TN, USA.
T. Ding is with the Department of Electrical Engineering, Tsinghua
University, Beijing, China and the EECS Department at UTK, TN, USA.
H. Sun is with the Department of Electrical Engineering, Tsinghua
University, Beijing, China.
Contact: F. Li, fli6@utk.edu.

w

e

d

 SOCmin ≤ E (t ) / Ee ≤ SOCmax(s)− Ee ⋅ β C ≤ E (t ) − E (t − 1)(1 − σ ) ≤ Ee ⋅ β Dwhere the second and the fifth sub-equations in (7) are theramping constraints to deal with the uncertain wind power
next time period.
To solve the above optimization problem, robust
optimization method is very suitable to be introduced to findthe optimal solutions of scheduling ES and thermal power inthe worst scenario.

1

Impact of Load Models on the Statistics of Voltagesin Stochastic Power Systems
Goodarz Ghanavati, Paul D. H. Hines and Taras I. Lakoba
College of Engineering andMathematical Sciences
University of Vermont
Burlington, VT

II. S IMULATION AND RESULTS
We model the system with a set of stochastic differentialalgebraic equations:(1)ẋ = f x, y

0 = g x, y, η
(2)

−4

x 10
Case 1Case 2
6
Case 37

0.3
0.25

Autocorrelation of ∆V

I. I NTRODUCTION
Power systems are vulnerable to large scale failures such
August 2003 blackout in the U.S. and Canada [1]. Integration of renewable energy sources into power systems results
in increased stochasticity, which may increase the system’svulnerability. Also, power systems are increasingly operated
near their stability limits. Therefore, it is important to enhancesituational awereness to reduce the risk of blackouts.
Stochastic nonlinear dynamical systems research showsthat there are early-warning signs (EWS) that indicate theapproach of critical transitions [2]. Increase in variance andautocorrelation of state variables are two generic EWS. The
increase in these statistics is sign of a phenomena known as
critical slowing down (CSD), which is the slower recovery ofdynamical systems from prerturbations as they approach critical transitions. These EWS are suggested in [2] as measuresfor determining the distance of a dynamical system to criticaltransitions.
Earlier research [3] has shown that CSD occurs in powersystems as they approach voltage or oscillatory instability.
Therefore, CSD signs may be useful indicators of stability inpower systems. Also, the authors have previously shown that
CSD signs are more clearly observable in voltage magnitudes
than in other state variables [3]. In this paper, we examine
how these statisitics change with load characteristics.

where f, g represent the set of differential and algebraic equations of the system, x, y represent the vectors of differential
and algebraic variables, η is the vector of gaussian randomvariables added to the load η ∼ N (0, 0.01).
We studied the impact of load characteristics on the statistics
of bus voltage magnitudes in a small three-bus system [4].
In this system, by increasing the load, the system moves
toward the Hopf bifurcation. Fig. 1 shows the variance andautocorrelation of the load bus voltage magnitude versus theload level for three ZIP loads with different parameters. The
variance and autocorrelation of the voltage magnitude increaseas the system approaches instability. In the poster, we will
discuss how varying the proportions of Z, I, P affects thevariance and autocorrelation of voltages.

5
4

2
σ ∆V

Abstract—This paper presents a study of the impact of different load models on the statistics of bus voltage magnitudes instochastic power systems. Prior research shows that the stastistics
of voltage magnitudes such as variance and autocorrelation riseas a power system approaches a critical transition such as voltage
collapse or oscillatory instability. The increase in these statistics isdue to critical slowing down which occurs in nonlinear stochastic
dynamical systems. In this paper, we examine how these statistics
vary with changes in load models. The results indicate thatload characteristics can impact bus voltage statisitcs significantly.
Therefore, if these statistics are to be used for power systemstability monitoring, it is important to consider the uncertainty
introduced by changes in load characteristics.

0.2

0.15

3
2

0.1

0.05

1
0
0

Case 1Case 2
Case 3

0.5

1

1.5

Load level

2

0
0

0.5

1

1.5

Load level2

Figure 1. Variance and autocorrelation of the load bus voltage magnitude
versus load level, the ratio of the load power to its nominal value. The
parameters of ZIP load for cases 1,2,3 are respectively [100%P], [50%P,
50%I], [25%P, 50%I, 25%Z]. Vertical lines show the bifurcation points forthree cases.

R EFERENCES[1] S. Abraham and J. Efford, “Final report on the August 14, 2003 blackoutin the United states and Canada: causes and recommendations,” US–
Canada Power Syst. Outage Task Force, Tech. Rep., 2004.
[2] M. Scheffer, J. Bascompte, W. A. Brock, V. Brovkin, S. R. Carpenter,
V. Dakos, H. Held, E. H. Van Nes, M. Rietkerk, and G. Sugihara, “Early–
warning signals for critical transitions,” Nature, vol. 461, no. 7260, pp.
53–59, Sep. 2009.
[3] G. Ghanavati, P. D. Hines, T. I. Lakoba, and E. Cotilla-Sanchez, “Understanding early indicators of critical transitions in power systems fromautocorrelation functions,” arXiv preprint arXiv:1309.7306, 2013.
[4] H. Ghasemi, “On-line monitoring and oscillatory stability margin prediction in power systems based on system identification,” Ph.D. dissertation,
University of Waterloo, 2006.

Power Management of Remote Microgrids
Considering Stochastic Behavior of Renewable
Energy Sources
Santosh Chalise and Reinaldo Tonkoski
Electrical Engineering and Computer Science Department, South Dakota State University, Brookings, SD, USA
Email: santosh.chalise@sdstate.edu, reinaldo.tonkoski@sdstate.edu
Abstract—Traditionally, diesel generators are being used as a
primary source of electricity generation in remote Microgrids.
However, the overall fuel costs (including transportation andstorage), and high maintenance, have resulted in high costs ofenergy. Renewable energy sources (RES) are good candidates toreduce fuel consumption, however also adds complexity in the
power management system (PMS) due to the stochastic behaviorof generated power output. Varying power output of RES cause apower imbalance in the system which ultimately affects thesystem’s voltage and frequency. In addition, RES into the diesel
and natural gas based microgrids reduces the loading of thegenerator. This power unloading forces generators to operate in
the low efficiency region. The primary objective of PMS is toensure the stable operation of microgrid system and also to
optimize the energy generation with the objective to minimize theoverall cost of energy. This task is of high complexity due to thepresence of highly variable power generation. This research
proposed an PMS framework for optimization on the day ahead
as well as in the real time scale using particle swarm optimization(PSO). Hybrid control method was considered for the microgrid
control. Key optimization objectives of proposed PMS are to
reduce fuel consumption, maximize the use of renewable energy
sources, and coordinate available resources to ensure microgrid
reliability. Simulation of microgrid in different scenarios were
performed and results were compared.

I.

50

75 kW
Total Power
Load
PV
Battery

40

30Power (kW)

Keywords—microgrid; renewable; optimization; PSO; scheduleFig 2: Microgrid optimization framework

20

KEY FIGURES
10CentralController

0

2

LC

LC
LC

Load

RES

Battery

Fig 1: Hybrid control method

6

8

10

12
14
Time (hr)

16

18

20

22

24

Fig. 3: Scheduled output of Generator and Battery

Table 1: Fuel consumption comparison

LC

Rotating Generator

4

Configuration
Single Generator
Single Generator withBattery

Fuel consumption, ft3/hr
10050
9700

FLUE GAS DESULFURIZATION WASTEWATERTREATMENT USING CONSTRUCTED WETLANDSJose M. Paredez1, Natalie Mladenov1, Madhubhashini B. Galkaduwa2, Ganga M.
Hettiarachchi2, Stacy Hutchinson3
1
Department of Civil Engineering, 2Department of Agronomy,
3
Department of Biological and Agriculture Engineering,Kansas State University
Manhattan, KS, USA

Abstract--BACKGROUND AND PURPOSE: Coalfired power plants emit harmful pollutants such as sulfur
dioxide, nitrogen oxides, and carbon dioxides. To
combat these harmful emissions the Clean Air Act wasestablished in 1970. To comply with these federal
regulations, coal-fired power plants implemented Flue
Gas Desulfurization (FGD) to reduce sulfur dioxide
emissions. Although FGD systems reduce atmosphericemissions they create wastewater containing harmful
pollutants such as selenium, arsenic, and mercury.
Constructed wetlands are increasingly being employed
for the removal of these toxic trace elements from FGD
wastewater. This study investigates constructedwetland's native microbes' dependency on organiccarbon and the competitive interactions between
selenium and arsenic in column experiments thatsimulate FGD wastewater treatment by constructedwetlands. Laboratory column outflow experiments usingsoils with natural FGD wastewater are underway.
ANTICIPATED RESULTS: It is anticipated native
microbes will be capable of treatment but require acarbon source and the competitive interactions between
selenium and arsenic will rapidly fill sorption sites that
may lead to a shorter treatment cell life.
CONCLUSION: FGD technology has great potential
for reducing harmful air pollutants from being emitted.
Now the challenge remains to design treatment systems
for the sequestration of toxic trace elements that are
released in the FGD wastewater stream. Therefore ourevaluation of the sustainability and most effective
process parameters of constructed wetland systems isimportant for power providers.

Optimal Power Flow Including Magnetic Amplifier
by Using Sequential Quadratic Programming
Xiaohu Zhang1 , Kevin Tomsovic1 and Aleks Dimitrovski2
1

Dept. of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, Tennessee, USA
2
Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
Email: xzhang46@utk.edu

Abstract—Magnetic Amplifier (MA), a series reactor
with continuous reactance regulation, is recently proposed to control the power flow. The cost of a MA is
far less than a comparative FACTS device because it
uses a simple and low power AC/DC power electronics
to control its output reactance. Therefore, multiple
devices can be installed in one system to maximize theutilization of the existing transmission system. Optimal
Power Flow (OPF) is the fundamental approach for
efficient power dispatch. To incorporate MA, standard
OPF should be modified because the transmission
line reactance will be considered as the optimization
variable. This increases the nonlinearity of the OPF
problem. In this study, the Sequential Quadratic Programming (SQP) [1] is adopted to solve the nonlinear
and nonconvex problem. Numerical simulation results
based on IEEE 9 bus, IEEE 30 bus are given to show
the effectiveness of the method.

I. Key Equations
The modified OPF can be formulated as:
To minimize
f (w)subject tog(wk ) + ∇g(wk )T ∆w = 0
k

(7)

k T

h(w ) + ∇h(w ) ∆w ≤ 0(8)

W k is the Hessian of the Lagrangian at iteration k:
W k = ∇2 L(wk , λk , µk )

(9)
k

k+1

k+1

The solution of the subproblem is [∆w λµ].
The vector gives the search direction and the new value ofthe multiplier for next iteration. This process will continue
until the solution converges.
II. Key Results
The simulation test is based on IEEE 9 bus, and IEEE
30 bus system. Assume every non-transformer lines are installed with MA, the following table shows the comparisonof generation cost.
TABLE I
Generation Cost

(1)

subject tog(w) = 0(2)h(w) ≤ 0(3)

Cost $/hwithout MA
12881
964.4966

9 bus
30 busw is the optimization vector which includes the transmission line reactance x:

Cost $/hwith MA
12717
949.3825

1060

1040

w = [θ V Pg Qg x]T(4)

L(w, λ, µ) = f (w) + λT g(w) + µT h(w)(5)

The QP subproblem at iteration k is formulated as:
To minimize
1∇f (wk )T ∆w + ∆wT W k ∆w
(6)2

Cost [$/h]1020

f (w) is the objective function, the total generation
cost is selected.
• g(w) is the real and reactive power balance equation
at each bus.
• h(w) is the limits on line flows, voltage magnitude,
real and reactive power output for each generator andthe reactance output for the MA.
The Lagrangian is given by:
•

1000

980

960

940

0

Fig. 1.

0.5

1

1.5

2
iteration

2.5

3

3.5

4

Iteration Process for IEEE 30 bus system

References[1] J. Nocedal and S. Wright, Numerical Optimization. Springer,
2006 (2nd edition)

1

NMSU Power Generating Capacity:
Vision and Roadmap for 2050
Craig Bear, Shawn Benoit, Luis Caldera, Alejandro Castro Jr., Andrew Garcia,
and Marcus Wolschlager, Student Members, IEEE
Satish J. Ranade, Nadipuram Prasad
Abstract--Energy sustainability has become a key
consideration in planning for future infrastructure during thepast few decades. As campus loads are projected to increase wellinto the future, there is an abundant need for upgrading current
infrastructure as well as researching and implementing novel
energy harvesting technologies for the purpose of increasing
campus sustainability. This poster presents a visionary roadmap
of how such sustainability-minded programs can be implemented
by the year 2050.

benefits to the University by increasing generation capacity,
lowering tuition, and enabling the University to use the profits
to make campus improvements. The bottom line is that if
NMSU can become sustainable, it could become a localized
self-sufficient microgrid and eventually produce even more
power than that required by the campus.

I. INTRODUCTION
New Mexico State University's power generating capacity
must increase to meet the energy demands of campus facilities
by the year 2050. In its current configuration, NMSU’s cogeneration plant can only supply around 40% of the present
campus load. The remainder of the demand is provided by the
El Paso Electric Company, primarily from the Tortugas
substation. With the student population on the rise and theprice of electrical energy increasing due to political climates,
inflation, and growing scarcity of resources, NMSU faces aclassic challenge seen in the utility industry: current power
generation is being utilized at its economic maximum and isbeing rapidly outpaced by growing demand. This poster
explores potential solutions that would help NMSU keep up
with growing demands.
At a minimum, if present power generating capacity is
maintained, all campus facilities must employ alternate means
to generate power in order to contribute towards meeting
increased growth in campus energy requirements. New
building construction must integrate energy harvesting
capabilities to harness energy from air exhaust systems, utilize
available space on rooftops for solar energy, and explore other
innovative ideas in energy storage and generation toward
developing sustainable environments. The goal is to create
environments that are self-sufficient in their energy
requirements. The integration of such novel energy harvesting
technologies will transform NMSU’s campus into a true
microgrid configuration.
Simple modifications to current infrastructure are also
explored in this poster. Currently, NMSU’s co-generating
station is comprised of an AC generator driven by a natural gas
fired turbine through a gearbox operating at 1800 RPM. A
potential solution that could improve sustainability would beto replace the AC generator with a direct shaft connected DC
generator. Such a DC generation could generate sufficientamounts of power to create an energy surplus, which could besold back to El Paso Electric. This would provide long-term

Fig 1: Concept for harvesting energy frombuilding air exhaust systems

Fig 2: Proposed configuration of a variable speed alternatorwithout a gearbox and a synchronous generator
While predominantly a visionary outlook on campus
sustainability, the roadmap presented in this poster may serve
as a model for other campuses seeking to implement similarchanges. Microgrid and energy sustainability projects areactively being researched by students at NMSU under the
direction of Dr. Satish Ranade and Dr. Nadipuram Prasad.

1Power System Events Identification using
Feature Selection and Extraction Techniques
Prem Alluri, Student Member, IEEE, Sarika Khushalani Solanki, Senior Member, IEEE,
Jignesh Solanki, Member, IEEE
Lane Department of Computer Science and Electrical EngineeringWest Virginia University (WVU)
Morgantown, WV, USAe-mail: skhushalanisolanki@mail.wvu.edu
Abstract— An Electric Power Grid is a vastinterconnected network which delivers electricity fromsuppliers to consumers. Large number of dynamic eventsoccur in the grid on daily basis with some having negligible
effect whereas others leading to Catastrophic Failures.
Recent trends in wide area measurement systems (WAMS)
have brought techniques that can be used to identify andprevent these events in turning into catastrophic failures.
This paper presents a novel technique to identify dynamic
events in the grid by considering only minimal set ofsystem parameters (measurements). Symbolic Aggregate
Approximation (SAX) is used to discretize the time series
non-linear measurements and the obtained symbolic data
is clustered into predominant groups. Feature extraction
and selection techniques are used on each of the clusteredgroups to identify minimal sets of measurements that arecapable to identify specific power system events. The
proposed methodology recognizes Power System events ina minimal time with accuracy around 89%.
I. SYMBOLIC AGGREGATE APPROXIMATIONSAX is a process of symbolic representation of time series
data with an approximate distance function with the lower
bound of the Euclidean distance [1]. It uses PAA in which
each sequence of time-series data is divided into s segments ofequal length (Sliding Window segments) and the averagevalue of each segment is used as a coordinate of an sdimensional feature vector. Figure 1, shows an example oftime series to symbolic string conversion

The division point on the numeric axis is decided to equalize
the cumulative probability in the division section according to
the number of digitization (break point size β). In Figure 1,
sliding window length is 11 and the break point size β is 5 i.e(a,b,c,d,e).
II. METHODOLOGY OF IMPLEMENTATIONThe Proposed approach has two steps a) Offline training modeand b) Online classification mode. In the offline mode,
training data set is built using all the instances (events)
generated and this data set is grouped into coherent clusters
based on the information it gives out. In the Online
classification mode, each newly arrived test instances arecompared with the clusters built in the offline mode and thenearest observed cluster gives out the information of the event
occurred. Once the classification of the new instances is made,these classified ones are then added to their respective clusterin the training data set.
III. KEY RESULTS
The proposed methodology is performed on IEEE 24 bus
Reliability test system with 11 generator units.

Actual Time Series

e
d

0.97
0.43

c

-0.43

b
a

0

-0.97

Figure 2. Event Location accuracy with resepct to the number of clusters.
2

4

6

8

10

12

Figure 1. Time series discretization using SAX. The SAXified output of theabove time series is a symbolic string ‘c e e e c a a b d d e’After transforming time series using PAA, discretization stepis performed to produce symbols of equal probability.

REFERENCES[1]

J. Lin, E. Keogh, S. Lonardi, J.P. Lankford and D.M. Nystrom,“Visually Mining and Monitoring Massive Time Series,” proceedings ofthe tenth ACM SIGKDD International Conference on Knowledge
Discovery and Data Mining. Seattle, WA, Aug 22-25, 2004.

Locating Sub-Cycle Faults in Distribution
Network Applying Half-Cycle DFT Method
Po-Chen Chen, Vuk Malbasa, and Mladen Kezunovic
Power System Control and Protection Laboratory, Department of Electrical and Computer Engineering,Texas A&M University, TX 77843
Email: pchen01@neo.tamu.edu, vmalbasa@tamu.edu, kezunov@ee.tamu.edu
Abstract— It is necessary to accurately detect andlocate sub-cycle faults in order to prevent
unexpected outages. However, conventional fault
location methods cannot locate these faults as
typically data windows longer than the fault’s
signature are used for phasor extraction. This
paper presents an overall analysis of how the
single-phase-ground sub-cycle fault in the
distribution network can be located using voltage
sag. The half-cycle Discrete Fourier transform isused for phasor extraction in the time-domain
simulations. Our results reveal that the proposed
approach is capable of accurately locating subcycle faults whose duration is between 0.5 and 1.0
cycles. The results also suggest that the placementof meters may significantly affect the capability ofthe proposed approach to locate sub-cycle faults.
I.

KEY FIGURES

Figure 3. The procedure of identifying fault starting time andfaulty phase.

II.

KEY RESULTS

Figure 1. Saskpower network, Canada.

Figure 4. Results of cases, meters at all nodes: fault at L002A.

Figure 2. Comparison between data extracted from full-cycle andhalf-cycle DFT.

A Lyapunov Function Based Remedial Action
Screening Tool Using Real-Time Data
Mohammed Benidris, Student Member, IEEE and Joydeep Mitra, Senior Member, IEEE

Abstract—This work introduces a fast transient stability screening tool to classify a designated set of contingencies into stableand unstable subsets using direct methods. The proposed method
is based on the conservativeness of the transient stability direct
methods. The classification processes of the contingencies are
performed along the solution trajectory towards the Controlling
Unstable Equilibrium Point, controlling UEP. The proposed
screening tool is intended to reliably capture the unstable contingencies and efficiently reduce the number of contingencies thatneed further analyses. If a numerical problem is encounteredduring the computation, the proposed scheme applies homotopybased approaches to find the desired solution. If the numericalproblem can not be solved using homotopy-based methods, thecontingency is sent to a time-domain simulator for further analysis. The method is applied to the Western System Coordinating
Council (WSCC) test system and results are presented.
Keywords—Transient stability, screening, Lyapunov function,
homotopy.

I.

I NTRODUCTION

As a result of market forces, increased renewable generation,and recent advances in power flow control technologies, powersystems are increasingly being operated closer to their stability
limits. On-line transient stability assessment, TSA, has become
one of the important features for systems that operate insuch stressed environment. Attempts to reach on-line transientstability assessment have been facing high computation burden
and low calculation speed. Most of the current strategies drop
off the non-severe contingencies using screening tools andperform detailed simulations on the severe contingencies. The
most important factors of any screening tool are the absolute
capture of the unstable events and the efficiency of capturing
the stable events.
The existing on-line transient stability analysis tools basedon transient stability direct methods have introduced several
system dependent thresholds for classifications. These thresholds are used to classify a set of contingencies to stable, unstable or undecided subsets. The undecided contingencies are
the contingencies that have numerical convergence problems
such as failure to calculate the controlling unstable equilibrium point, controlling UEP. In order for these thresholds
not to fail in classifying the contingencies, off-line transientstability analyses are required. Also, for the unstable or undecided contingencies, detailed time-domain simulations are
performed. Therefore, we can see that these methods need offThe authors are with the Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, 48823 USA e-mail:(benidris@msu.edu andmitraj@msu.edu).

line transient stability assessments and may excessively use
time-domain simulations.
II. F OCUS OF THIS W ORK
In this work, we propose a fast screening tool to classify
a designated set of contingencies into stable and unstable
subsets using transient stability direct methods. The proposed
method utilizes the advantages of the conservativeness ofthe direct methods to initially classify the designated set ofcontingencies into stable, “potentially” unstable and undecided
subsets. The potentially unstable subset is further divided into
stable, potentially unstable and undecided subsets along thesolution trajectory towards the controlling UEP. The undecided
subset which has numerical problem is checked again using
homotopy-based approaches.
The effective scheme utilized in this work is to check thestability of different points by transient stability direct methods
to screen out a large number of stable contingencies, andthen apply time-domain simulation to check those potentiallyunstable contingencies. The excessive use of time-domain
simulations is considerably reduced by applying homotopybased approaches in the case iterative methods fail to find asolution.
Depending on the speed of the computation and the extent
of conservation, this procedure checks stability by sequentiallychecking energy margin at exit point, minimum gradientpoint and controlling UEP. If numerical problems, such as
convergence issues, are encountered during the computation,
the studied contingency is sent to time-domain simulation forstability check. However, before sending the contingency forthe time domain simulator, if the numerical problem is related
to the calculation of the post-fault stable equilibrium point,
SEP, the minimum gradient point or the controlling UEP,
we use homotopy-based approaches to find the controlling
UEP. On the other hand, if the numerical problem is related
to the exit point or the homotopy-based approaches fail tofind the desired solution, the contingency is sent to the timedomain simulator for further analysis. The homotopy-based
approaches are utilized in this work to increase the efficiencyof the screening tool and to reduce the number of contingencies
that need further analyses using time-domain simulations. The
strategy of using direct method to filter out a large number ofcontingencies first and conducting further analysis on a muchsmaller number of contingencies makes the on-line transientstability screening feasible.

DOCUMENT SUBMITTED FOR 2014 T AND D POSTER CONTEST

1

Development of an FPGA based controller for
islanding and anti-islanding of microgrid
Kumaraguru Prabakar, Student Member, IEEE, Yan Xu, Member, IEEE, and Fangxing Li, Senior Member, IEEE

Abstract—Microgrids are typically systems with distributed
energy resources and loads. They have the capability to connect
or disconnect from the grid through a switch. This differentiates
the microgrids from a typical distribution system. The microgrid
switch is usually located at the point of common coupling toenable the microgrid connect and disconnect itself from the main
grid. The microgrid switch controller should have the capability
to identify the black out and disconnect from the grid. Moreover,it should also connect back to the grid once the grid comes back.
One such controller is developed using an FPGA based device.
The logic of the controller is explained in this article.

Start

Is
|fG −fµG | &|VG −VµG | &|φG − φµG |≤ errorNo

Yes

Is µ grid in
islanded
mode?

No

Yes

I. I NTRODUCTION
ICROGRIDS are systems which have distributed energy
resources (DERs) and loads with the capability toconnect or disconnect from the main grid [1]. In order toconnect or disconnect from the grid, the microgrid controller
should be able to identify the status and health of the grid andeither connect or disconnect from the grid.

M

II. M ICROGRID MODEL USED FOR TESTING
The microgrid model used for testing is shown in Figure
1. The microgrid model has four buses and it is connected tothe main grid through bus number 1. The microgrid switch
is located before bus number 3. The microgrid controller will
decide when to open or close the switch to enable islanded or
anti-islanded mode respectively.
III. I SLANDING AND ANTI - ISLANDING REQUIREMENTS
The controller should check the operating characteristics on
both sides of the switch. Parameters such as 3−φ rms voltage,

Open theµgrid switch

Fig. 2: Flowchart for operation of microgrid switch controller

frequency and phase. If these parameters are not within a
specific limit, the controller will island the system. Otherwise,
if these parameters are within the limit under an islanded mode,the controller will anti-island the microgrid. This is shown asa flow chart in Figure 2.
IV. C ONCLUSION
This work discusses the development of FPGA based controller to island the microgrid from the main grid when theparameters shown above do not lie within the limits. Also, thecontroller will have the capability to resynchronize back to
the grid, when the system parameters lie within the operating
limits prescribed by the utility.
R EFERENCES[1]

N. Hatziargyriou, H. Asano, R. Iravani, and C. Marnay, “Microgrids,”
Power and Energy Magazine, IEEE, vol. 5, no. 4, pp. 78–94, 2007.
Inverter 1

Bus 3
RMain grid

Bus 1

13.8 : 2.4 kv

2.4 : 0.48 kv

Transformer

Transformer

Bus 2

SwitchLBus 4Inverter 2

PCC
Cable
3 to 4R
L

Fig. 1: Microgrid model used for switch controller test

Yes

No

Close theµgrid switch

Keywords—Microgrid, Microgrid controller, FPGA.

Is|fG −fµG | or
|VG −VµG |or|φG − φµG |≤ errorDevelopment of Graphical User Interface for Reactive
Power Planning and VAR Benefit Assessment for Power
Systems with High Penetration of Renewable Energy
Riyasat Azim, Student Member, IEEE, Xin Fang, Student Member, IEEE, Weihong Huang, Student Member, IEEE,
Fangxing ‘Fran’ Li, Senior Member, IEEE, Yan Xu, Member, IEEE

Abstract—The ever increasing renewable energy penetration inmodern power systems tends to pose complex operational andplanning challenges for the system operators. Reactive powerplanning and benefit sensitivity analysis of reactive powersources have become essential part of modern power system
operation and planning. This Poster presents the functionalities
of a graphical user interface under development which intends toprovide a useful platform for voltage stability constrained
reactive power planning and VAR benefit sensitivity analysis
suitable for budget constrained reactive power planning. The
graphical user interface integrates the capabilities of powerful
optimization and power system analysis tools in order to provide
a common platform for static VAR optimization, dynamic VAR
optimization and VAR sources benefit sensitivity analysis withvisualization and analysis capabilities.

Synchrophasor monitoring of line outages in WECC with an angle across an area
Atena Darvishi

Ian Dobson

Electrical and Computer Engineering Department
Iowa State University, Ames IA USA
darvishi@iastate.edu, dobson@iastate.edu
Abstract—The area angle is a scalar measure of power systemarea stress that responds to line outages within the area and is acombination of synchrophasor measurements of voltage angles
around the border of the area. Both idealized and practicalexamples are given to show that the variation of the area angle
for single line outages can be approximately related to changes in
the overall susceptance of the area and the line outage severity.

q1 22.3%

q2 0.6%

q3 0.8%

q4 1%
q5 2%
q 6 18%
q7 59%

I. I NTRODUCTION
The angle across an area of a power system is a weighted
combination of synchrophasor measurements of voltage phasor
angles around the border of the area [?]. The weights arecalculated from a DC load flow model of the area in sucha way that the area angle satisfies circuit laws. We previously
showed how area angle responded to single line outages inside
the area in some simple Japanese test cases [?]. Here we want
to show and explain how the area angle responds to single line
outages in an area of the WECC system. The changes in area
angle can usually distinguish the single line outage severity,and can be related to changes in the overall susceptance of
the area. We give some simple idealized examples to explain
how the area angle works and then show results for an areaof WECC.

Boundary Buses
with Weights

q1218%

q11 3%

q8 39%
q9 41%

q10 0.4%
8

Fig. 1. An area of WECC system with area lines in black, north border
buses in red and south border buses in blue. Layout detail is not geographic.

of the outage confirm the relationship between area angle andthe severity of the outage.

II. R ESULTS FOR ANGLES ACROSS AN AREA OF WECC
We illustrate the use of area angles to monitor single,
non-islanding line outages inside two areas of the WECC
system. The WECC area shown in Figure ?? covers roughly
Washington and Oregon. The northern (and western) border isnear the borders of Canada-Washington, Washington-Montana
and Oregon-Idaho, and the south border is near the OregonCalifornia border. North-south transfers through this area areof considerable economic importance. The area angle isArea Susceptance

Maximum Power into the area

Area Angleθarea2 = 0.223 θ1 + 0.006 θ2+ 0.008 θ3 + 0.01 θ4 + 0.02 θ5 + 0.18 θ6 + 0.59 θ7− 0.39 θ8 − 0.41 θ9 − 0.004 θ10 − 0.03 θ11 − 0.18 θ12
We are interested in monitoring the north-south area stresswith the area angle when there are single non-islanding line
outages, and relating changes in the area angle to the areasusceptance and the outage severity. We take out each linein the system in turn and calculate the monitored area angle(i)(i)θarea and the area susceptance barea in each case. We consider
the maximum power coming to the area as a measure of theseverity of the outage.
Both the area angle and the area susceptance track theseverity of the outage for each line outage in Figure ??. The
similar patterns of changes in the area angles and the severity

Line Outages(i)(i)
Area angle θarea , area susceptance barea , and max power into theFig. 2.
area for each line outage in WECC area . Base case (the point at extreme
right) is θarea = 52.9o , barea = 66.7 pu, max power = 66.0 pu.

R EFERENCES[1] I. Dobson, Voltages across an area of a network, IEEE Transactions on
Power Systems, vol. 27, no. 2, May 2012, pp. 993-1002.
[2] A. Darvishi, I. Dobson, A. Oi, C. Nakazawa, Area angles monitor area
stress by responding to line outages, NAPS North American Power
Symposium, Manhattan KS USA, September 2013.

Optimization Active Power Curtailment (APC) with
Load Tap changing (OLTC) Regulators to Control
Voltage fluctuating in Renewable supply
Hameed R. Atia
Electrical and Computer Engineering
South Dakota State University
Brookings, SD
Hameed.atia@sdstate.edu

Abstract—Abstract: The electric power grid has been growing
constantly by high penetration of distributed generators (DGs)such as photovoltaic, wind turbines, and microturbines. Voltage
Fluctuating is one of many challenges faced. This paper presents
a novel method to optimize active power curtailment (APC) with
Load Tap changing (OLTC) regulators to control voltage
fluctuating in Microgrid. With significant level of solar or windenergy penetration, overvoltage can occur and the voltage at
the load end may be greater than the voltage at the substation.
On Load Tap changing (OLTC) regulators maybe not detect theovervoltage at the end of the feeder, optimized active power
curtailment (APC) will control of voltage. Coordination of OLTC
and APC with DG operation mode can control voltage rise andimprove voltage profile, and ultimately minimize power loss.

circuits and so, over the years, techniques have been developed
to make the maximum use of distribution circuits to supply
customers within the required voltages.

Index Terms - Power curtailment, on load tap changing,
distributed generators, voltage drop, objective function,
constrain.
Index Terms— Power curtailment, on load tap changing,
distributed generators, voltage drop, and objective function,
constrain. Linear programing.

Fig. 1.

Typical Microgrid Level

I. INTRODUCTIONR

ENEWABLE energy is regularly growth worldwide

because of energy [1]. At this day, distributed generation isseen mainly as a means of producing electrical energy.
Conventionally, distribution systems have been designed totake power from the transmission system and to distribute it tocustomers [2]. Real power (P) impact to the voltage angle (θ)
and reactive power (Q) impact to the voltage magnitude.
Therefore the flowing of real power and reactive power has
been from the higher to the lower voltage levels with angle (θ).
This is shown in Figure1. However, with high penetration ofrenewable electrical energy the power flows may convert
reversed and the distribution system is no longer a supplying
loads. Distribution system operator has an responsibility tosupply its customers at a voltage within specified limits(typically around _5% of nominal). This requirement often
determines the design and capital cost of the distribution

Fig. 2. Voltage Variation down a radial Feeder

The voltage profile of a radial distribution feeder is shownin Figure 2 with the key volt drops identified:1

A Wavelet-Based Method for High Resolution
Multi-Step PV Generation Forecasting
Ahlmahz Negash, Ali Hooshmand, Ratnesh Sharma
Abstract-- Forecasts play a vital role in integrating renewable

Index Terms—High Resolution Forecasting, NARX Model,
Multi-Step Prediction, Photovoltaics, Solar Irradiance

I. RESULTS (CLOUDY)
Cloudy4actualforecast
persistence

3.5

actualforecast
persistence

4.5
4
3.5

Power, (kW)energy while maintaining power system stability and maximizing
economic benefits of various energy resources. The issue with PV
generation forecasting is that it relies on forecasts of solar
irradiation which, due to the complex, nonlinear relationshipbetween meteorological variables such as humidity, pressure,temperature, and cloud transients, can be quite difficult to model.
Two important decisions in the forecasting process are selectionof the forecasted variable (model output) and selection of
explanatory variables (model inputs). This paper proposes a new
method to forecast PV generation using wavelet based input
selection and an output variable that directly represents clouds
transients. We model this cloud effect by first determining a clear
sky model (CSM) and forecasting the difference between the
CSM and actual measurements of GHI. Potential model inputs
are first decomposed using wavelet multi resolution analysis andfinal input selection is based on the correlation between the
inputs and output at various timescales. Separate neural network
structures are designed to separately forecast sunny and cloudy
days. Using the high resolution forecast of GHI (20 min
increments), the next day’s PV generation is determined. The
wavelet-based input selection step improves average PV power
forecast accuracy by up to 15% on cloudy days and 25% on
sunny days.

Sunny
5

3
2.5
2
1.5
1
0.5
0
8am

9am

10am

11am

12pm
time

1pm

2pm

3pm

4pm

III. PROPOSED METHODOLOGY
Step 1:
Determine the clear sky model (CSM)

Step 2:
Subtract CSM from measured
irradiance (to focus on effect of clouds)

Step 3:
Perform wavelet-based input selection

3

Power, (kW)

2.5

Step 4:
Perform NARX neural network forecast

2
1.5
1
0.5
0
8am

9am

10am

11am

12pm
time

1pm

2pm

3pm

4pm

Step 5:
Subtract NARX output from the CSM
to obtain final irradiance forecast

II. RESULTS (SUNNY)This work was supported in part by the National Science Foundation
Graduate Research Fellowship Program (GRFP) and the Engineering
Innovation Fellows Program (EIFP).

Step 6:
Calculate PV power from final
irradiance forecast

Emission-based Optimal Dispatch Frameworkwith DR and Volatile Wind Power
Hantao Cui, Fangxing Li
The Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN 37996, USA
Email: {hcui7, fli6}@utk.edu
Abstract—The increasing penetration of wind power aims

at reducing fuel consumption and the consequent emissions.
On the contrary, volatility of wind power, especially theprediction error, increases the number of unintended
startups or shutdowns of the prevailing thermal units thus
elevates the emission level. This paper presents aframework for ISO and LSE to handle emission-based
security-constrained optimal dispatch that accommodates
demand response (DR) and volatility of wind power (WP)
output. Expected results for the established model would
demonstrate the effectiveness of minimizing emissions.

the actual fuel consumed, respectively. K(·) is the kernel ofsmoothing function and h the bandwidth by which weight is
exercised on neighboring observations.
 f  f gn  n g , ph nNg , p ( f )  f  f gn K h 
nN K 

Voluntary DR are modeled with fixed price elasticity anddetermined at early stage of the iterative scheduling process.
Elasticity is given as(2)  Dk / DkkI. FRAMEWORK DESCRIPTIONIn the proposed framework, for day-ahead market, ISO
commits the units according to emissions bids fromgenerators, predicted WP output and informed load level byload side entity (LSE). ISO also checks network security ofeach commitment result to rule out violations. LSE, as anaggregator of demand side, performs voluntary DR programs
internally and reviews load interrupting requests outside from
ISO. LSE can be viewed as the sole demand side participant, on
behalf of all loads, in the market operated by ISO.
Two types of incentive-based DR programs are included:
voluntary and mandatory. Voluntary programs are managed by
LSE with the forecasted electricity price. Mandatory programs
are called for by ISO during network security check andverified by LSE. Volatility of WP is also incorporated into theframework, derived from the prediction error described by Beta
distribution [1].
This framework adopts the flexibility at demand side, takes
into account the impact of volatile WP and aims at minimizing
emissions. Through nodal prices, balance of demand side andgeneration is achieved. It also clarifies the duties and steps for
ISO and LSE, which is amenable for day-ahead optimal
dispatch. Scheme of the framework is shown in Fig. 1.
Voluntary
DR

Price

WP
Volatility
Conventional
Security Check

Price
Correction

MandatoryDR

Fig. 1. Scheme of the presented framework

II. MODELING FORMULATION
An input-based model is used to capture the emission
characteristics of units. With a non-parametric NadarayaWatson kernel estimator of SOx and NOx emissions level[2], anestimate function that specifies the pollution level of pollutant
p as a function of generator g’s burned fuel f is given as (1) ,where g , p and f g are the observed actual emission level andn(1)PRk / PRk

Mandatory DR supplied by generators with high marginalemissions rate would be demanded curtailment by ISO, rather
those located within high LMP zones. However, ISO faces thedilemma that each location differs in the sensitivity of price andemission. To identify the nodes where curtailment of DR is both
economically and environmentally beneficial, emissions price(in $/ton SOx and NOx) is defined to convert emissions
reduction to value in dollar. Overall value of DR at bus jcomposes of LMP reduction and emissions reduction value, isgiven as(3)
 j  f ( j )  g( j )where  j and  j are the LMP and marginal emissions rate atbus j, respectively[3].
Finally, with Bender’s decomposition, UC with SC for ISO
is decomposed into two iterative problems. Master problem
solves unit commitment and eliminates Bender’s cut from sub
problem, while sub problem checks network security andgenerates Bender’s cuts. The whole framework is modeled as
mixed-integer programming problem and solved by CPLEX.
III. EXPEDTED RESULTS
To illustrate the validity and effectiveness of the presented
framework and model, case study will be performed on ISO
New England 39-bus system with 10 generators. Volatility ofwind power will be identified with fixed Beta distribution
parameters, followed with Monte-Carlo sampling and Cholesky
decomposition. Voluntary DR resources will also follow a fixedelasticity.
Design of cases will focus on the configuration of
Mandatory DR resources, i.e. locations, maximum curtailment
power and duration. Successful solving the iterative model willshow the validity of the model. A more promising result would
be that under certain circumstances DR will be dispatched foremissions reduction purpose at the cost of higher LMP,
although it could be used to reduce the latter. Meanwhile, time
consumption to solve the model needs concern as an evaluationof its practical use.

Energy Resource Scheduling in Real Time Consideringa Real Portuguese Generation and Demand Scenario
Pedro Faria, Marco Silva, Zita ValeGECAD - Knowledge Engineering and Decision Support Research Center
IPP - Polytechnic Institute of Porto
Porto, Portugal
pnfar@isep.ipp.pt, marsi@isep.ipp.pt, zav@isep.ipp.pt
Abstract— The development power systems and the introductionof distributed generation and Electric Vehicles (EVs), bothconnected to distribution networks, bring several hugechallenges in the operation and planning tasks. This newparadigm requires improved energy resources management
approaches in order to accomplish not only the generation, butalso the consumption management through demand response
programs, energy storage units, EVs and other players in aliberalized electricity markets environment. The work here
presented includes a methodology to be used by Virtual Power
Players (VPPs), concerning the energy resource scheduling insmart grids, considering day-ahead, hour-ahead and real-timeenergy resources scheduling. The case study considers a 33-bus
distribution network with huge penetration of distributed
energy resources. The wind generation profile is based on a realPortuguese wind farm. The obtained results concerns four
scenarios taking into account 0, 1, 2 and 5 periods (hours,
minutes, or other) ahead of the scheduling period, in the hourahead and real-time scheduling.

II.

KEY FIGURE

Index Terms— Demand response, distributed generation, wind
generation, remuneration tariffs.

I.

KEY EQUATIONS NL
MPLoad ( L ,t )  PLoad ( L ,t )  MPSell (t )  PSell (t )  
T 
L 1

In    N
NV
ST


t 1
  MPCh ( ST ,t )  PCh ( ST ,t )   MPCh (V ,t )  PCh (V ,t ) V 1
 ST 1

N SP
 N DGcP  DG ( DG ,t ) DG ( DG ,t )  cSP ( SP ,t )  PSP ( SP ,t )  SP 1
 DG 1NV
 N ST  cDch ( ST ,t )  PDch ( ST ,t )   cDch (V ,t )  PDch (V ,t )  TV 1C    STN 1
NL


L
t 1
  cCut ( L ,t )  PCut ( L ,t )   cRed ( L,t )  PRed ( L,t )  L 1

L 1
 NL

N DG
  cNSD ( L ,t )  PNSD ( L ,t )   cGCP DG ,t  PGCP ( DG ,t ) 


DG 1
 L 1(1)Figure 1. ERM proposed methodology.

III.

KEY RESULTS(2)

Figure 2. Day-ahead load demand forecast and EVs charge scheduling.

This work is supported by FEDER Funds through COMPETE
program and by National Funds through FCT under the projects
FCOMP-01-0124-FEDER: PEst-OE/EEI/UI0760/2011, PTDC/SENENR/122174/2010 and SFRH/BD/80183/2011 (P. Faria PhD), andby the GID-MicroRede, project nº 34086, co-funded by COMPETE
under FEDER via QREN Programme.

1

Renewable Energy Forecasting using Hybrid
Artificial Neural Network Technique.
Chiranjeevi Bharadwaj Kotharu1*, Student Member, IEEE, Dipesh Patel1**, Student Member, IEEE,
Ziyad M. Salameh1**, Life Fellow, IEEE
Abstract— Renewable energy forecasting presented a hugedemand for better and accurate prediction models in the electricutilities market. Being able to predict future energy generation
not only helps energy traders but also helps utility, schedulers
and operators of electricity. This poster presents the applicationof an iterative hybrid Artificial Neural Network (ANN) method
known as Genetic Algorithm based Back-Propagation (GA-BP)
neural network on energy forecasting. For analyzing this modelthe data was taken from a hybrid renewable energy data
monitoring system at Renewable Energy Laboratory (REL) of
University of Massachusetts Lowell. The results were validated
against the available data and other methods of forecasting. The
proposed method was implemented in Matlab & Simulink. The
proposed method shows a significant improvement in efficiency
with a mean relative error of 20% which is better whencompared to those other prediction models with Mean relative
error of up to 30%
Index Terms— Hybrid Artificial Neural Networks, ShortTerm Forecasting, Power Forecasting, reliability, Utility.

I. PROPOSED ARCHITECTURE

Fig.1 Algorithm for GA-BP used for power forecasting.

n  ni  no  a(1)

The latest 24-hr data of wind speed, irradiance, wind powerand solar power generated by the RES are used as inputs for
network to predict the following 1-hr data. The input nodes of
the network are 144 i.e. (24X6=144) equals the 24-hr data
count. The output readings count for 6 as for 1-hr. The hidden
layer nodes are judged according to the equation 1.
Where n is the number of hidden layer nodes, ni and no are
the number of input and output nodes respectively and 1≤ a≥10. The fitness evaluation is formulated in order to obtain
minimum value of the root mean square error (RMS). The
objective function is the reciprocal of the RMS value as shownin equations 2, 3 and 4.

Ea  (Ta  Oa ) 2

 x kd  2 10 d  2  x kd 310 d 3  .....x( k 1) d, if 5  x kd 1  9
10 d  2
Wk  
d 2
d 3
 x kd  2 10  x kd 310  .....x( k 1) d, if 0  x kd 1  5
10 d  2(5)
Where x1,x2…, xd represent chromosomes and xkd+1,
xkd+2,…….,x(k+1)d represent the kth gene in chromosome. While
the actual weight is Wk, Ta is the actual output, Oa is thecalculated output, E is the error and Fn is the objectivefunction.
I. KEY RESULT
8
7
Total Power (KWH)

The advent of Artificial Neural Networks into forecastinghas improved the performance of prediction models to a great
extent. The GA-BP methodology is well known for its
accuracy and performance. The entire system is coded in
Matlab and a Simulink model was created for it.

(2)

n

E

 En
a

;

1
E

(4)

5
4
3
2
1

(3)

0

n

Fn 

6

.

ActualPredicted

10 20 30 40 50 60 70 80 90 100 110 120
Time (minutes)

Fig.2 Predicted Vs Actual measured total power generated by the hybrid RE.

Harmonic Analysis in Distribution System with Electric
Vehicles and Wind Generators
Ritam Misra

Sumit Paudyal, Member IEEE

Department of Electrical and Computer Engineering
Michigan Technological University
Houghton, USArmisra@mtu.edu

Department of Electrical and Computer Engineering
Michigan Technological University
Houghton, USA
sumitp@mtu.edu

Abstract— Harmonic distortion on voltages and currents
increases with the increased penetration of Electric Vehicle (EV)loads in distribution systems. Wind Generators (WGs), which aresource of harmonic currents, have some common harmonic
profiles with EVs. Thus, WGs can be utilized in careful ways to
subside the effect of EVs on harmonic distortion. This paper
studies the impact of EVs on harmonic distortions and integrationof WGs to reduce it. A decoupled harmonic three-phaseunbalanced distribution system model is developed, where EVs
and WGs are represented by harmonic current loads and sources,
respectively. The developed model is first used to solve harmonic
power flow on IEEE 34-bus distribution system with 100%
penetration of EVs are modeled in OPENDSS, and its effect on
current/voltage total harmonic distortions (THDs) is studied.
Next, carefully sized WGs are selected at different locations in the
34-bus distribution system to demonstrate reduction in thecurrent/voltage THDs.

II.

WG

Ah Bh
Ch Dh

Base Load

III.

ℎ
ℎ
∠𝑉𝑝,𝐿
− ∠𝐼𝑝,𝐿=

𝐼0 ℎ𝑝,𝐿

ℎ
∠𝜃𝑝,𝐿
(1)

To represent the line current balance in each case after the
integration of wind and PEV the following equation is used:
𝐼𝑤 ℎ𝑝,𝑛 +ℎ𝛼 𝐼𝑝,𝑙,𝑟 =ℎ𝛽 𝐼𝑝,𝑙,𝑠 +ℎℎ2
1
𝐿 𝐼𝑝,𝑙 + 𝐼𝑒𝑣 𝑝,𝑛

𝑇𝐻𝐷𝑣 =

𝑇𝐻𝐷𝑖 =

13ℎ 2 1/2ℎ =2 |𝑉𝑛 |13ℎ 2 1/2ℎ =2 |𝐼𝑛 |Case A(2)

The total harmonic distortion of voltage (THDv) andcurrent (THDi) are defined as

Case
B|𝑉𝑛1 | × 100%(3)|𝐼𝑛1 | × 100(4)

KEY RESULTS

Table I. THD of Line Currents

The L wye-connected constant power loads on a per-phasebasis has been designed as:ℎ𝐼𝑝,𝐿EV LoadFig. 1 Integration of EVs and WGs in the system

Case
Studies

KEY EQUATIONS

node

Serieselement

Index Terms--Total harmonic distortion, Electric Vehicles, Wind
energy, Distributed generation, Load management.

I.

KEY FIGURE

Case
C

MonitorsLine
Current
THD ofphase aLine
Current
THD ofphase bLine
Current
THD ofphase cM1

40.5245

33.827333.5022M2

10.2019.01887.4814M3

6.76187.94537.6573M4

9.8209

5.237

9.261

M1

4.89696.13605.1628M2

35.3252

46.544137.8092

M3

17.0135

32.193137.6465M4

8.82016.01447.7061

M1

6.20277.56087.3108M2

5.23436.42926.5506M3

7.95857.8224

6.9416M4

6.3561

5.067

6.3421

Optimization of a Solar-Diesel-Battery Hybrid Energy
System for Remote Regions
Mu Chai, Zichun Tang and Moonyoung Lee
Department of Engineering Science, University of Toronto, Toronto, ON, M5S 3G4 Canada
Email: mchai@ieee.org, zichun.tang@mail.utoronto.ca, moonyoung.lee@utoronto.ca
Abstract – The cost of Photovoltaic (PV) panels has gone
down rapidly in the last ten years. Although rooftop
installations in Canadian urban areas have yet to reach grid
parity without government incentives, PVs among otherrenewables are increasingly being considered to meet theload demands of Canadians in remote northern areas due totheir high cost of electricity. While many qualitative studies
have been done, this paper aims to both quantify andoptimize the cost of a Solar-Diesel-Battery Hybrid Energy
System to power a remote research centre. In order toachieve high model accuracy, the on-site actual annual
irradiance will be used in conjunction with an approved PV
model to simulate PV generations. Through the use of
accurate PV modelling and Mixed Integer Linear
Programing (MILP), the optimal number of batteries,
number of solar panels as well as the types of panel out of 20
off-the-shelf models will be determined. Only actual data
and manufacturers’ fact sheets are used in this model, whilecertain underlying assumptions are made.

[

]
[

[

]

]

Or, simply:
[
[

] [

]
]

I. Key Equations and Graphs
Objective function: The following function represents the costfunction that the optimization algorithm desires to minimize:

Figure 1 Annual Power Curve

Figure 2 Annual Power Curve (Clearly shows)Integration of Photovoltaic with Hydro only System
in an Isolated Network
Pratiksha Tiwari, Reinaldo Tonkoski and David. Galipeau
Department of Electrical and Computer Science
South Dakota State University
Brookings, SD, USA
pratiksha.tiwari@sdstste.edu

Fig. 1 shows a block diagram of the PV/hydro system which
includes PV panels with a grid tied micro- inverter, a hydro system
and loads connected to a common 400 VAC bus. Fig. 2 shows thedaily load profile, PV generation, hydro generation and frequency
response of the PV/hydro system for a sunny day. The frequency ofthe system was within ±2.5% of the nominal frequency of 50 HZ up
to 160 seconds. After that the load decreased from 95 to 10 kW(89%) in 20 seconds which increased the frequency above 2.5% but
returned back to the steady state. This indicates that the system wasstable but may need additional inertia to maintain frequency within±2.5% of nominal frequency.

PV (kW)

Load (kW)Fig. 1 Block diagram for PV/hydro system
100
50
0

H (kW)

20

40

80

120

160

200

240

20

40

80

120

160

200

240

20

40

80

120

160

200

240

20

40

80

120

160

200

240

40
20
0
100
50
0

F (Hz)

Abstract— In an isolated remote areas of developing countries,
micro hydro is one of the main energy sources. Availability of water
decreases significantly in winter season which directly effects thehydro power generation. Combining solar photovoltaics with microhydro can be an effective solution for isolated areas where grid
extension is not feasible. Photovoltaics (PV) can supply power during
day and hydropower supply power during night and cloudy days.
Increase in photovoltaic power reduces hydro generation. However,injecting photovoltaic power can cause disturbance in systemfrequency due to its intermittent nature. Previous work has shown thesimulation of hydro and photovoltaic with battery backup system canmeet the power demand. But, the ability of hydro to handle frequency
deviations due to photovoltaic variability has not addressed.
Frequency response of the system with photovoltaic and governor
controlled hydro was investigated, but was not validated in a realsystem. The goal of this research is to analyze the frequency
transients in an isolated network with solar photovoltaics andhydropower as power sources. Hydro model was developed in
Matlab/Simulink with synchronous machine, excitation system andits governor system and photovoltaic power was injected as a current
source to the system. The change in frequency due to variations inphotovoltaics power output and load curve was analyzed. Simulation
was done for different irradiance data obtained experimentally in the
microgrid lab. Simulation results show that, hydro can handle thefrequency transients due to photovoltaic power fluctuation and load
variation if existing hydro has the sufficient inertia. Further tovalidate the system, real time simulation of developed model will bedone in the real time digital simulator. An inverter based hydroemulator will be developed to have the same characteristics of thehydro system and a real photovoltaics system will be implemented.
Control signals from real time digital simulator will drive hydroemulator. A power hardware in the loop simulation with real
photovoltaics and inverter based hydro system emulator will be used
to investigate the frequency response of hybrid photovoltaics andhydro system.

55
50
45

Time (S)Fig. 2 Frequency response of PV/hydro system with daily load profile

Locational Dependence of PV Hosting Capacity
Kyle Coogan, Matthew J. Reno, and Santiago Grijalva
Georgia Institute of Technology
Atlanta, Georgia USA

II.
RESULTS
Maximum feeder bus voltage was analyzed for each PV
size and location by finding the maximum bus voltage on thefeeder (in pu). The combination of these 200 locations foreach of these 101 system sizes (including base case 0 MW)
yields the distribution shown in Figure 1. For example,
consider the case of a 10 MW PV system. For this systemsize, approximately 25% of the 200 potential three-phasebuses will result in a max bus voltage above 1.05 pu given aload of 50% of peak.
The same data used for Figure 1 was analyzed using the
distance of the PV interconnection to the substation as anindependent variable. Each data point in Figure 2 represents a
power flow solution with the maximum feeder bus voltage
plotted as a function of this distance and colored to indicate
the PV system size. Figure 2 validates the difference in slopes
Sandia National Laboratories is a multi-program laboratory managed andoperated by Sandia Corporation, a wholly owned subsidiary of Lockheed
Martin Corporation, for the U.S. Department of Energy’s National
Nuclear Security Administration under contract DE-AC04-94AL85000.

Max Bus Voltage in Each Scenario(pu)

1.11Percentiles

1.1Median95th and up
90th to 95th
75th to 90th
50th to 75th
25th to 50th
10th to 25th
5th to 10th
5th and below

1.09
1.08
1.07
1.06
1.05
1.04
1.03

0

1

2

3

4

5

6

7

8

9

10

PV Size (MW)Figure 1. Effect of PV size on max bus voltage under 50% load.
1.11

10
9

1.1

8
1.09
7
1.08

6

1.07

5
4

1.06

PV Size (MW)I.
METHODOLOGY
A real, 12.47 kV feeder with mostly industrial customers
was used for this analysis. To examine the impact of central
PV installations on the feeder, an extensive process is used tostep through all considered locations, storing data from thepower flow solutions for each scenario. The set of scenarios
include a significant range of system sizes and locations. Dueto the fact that Ckt7 is an industrial feeder, the focus of theanalysis is on single, large-scale, central PV plants. The PV
systems were sized ranging from 0 to 10 MW in 100 kW
increments, and all three-phase buses were considered.
The unbalanced three-phase power flow is solved using
the distribution system software OpenDSS with GridPV toperform analysis in MATLAB. The solutions from the power
flow simulations for each PV study case are analyzed todetermine any violations or limitations in the distribution
system that would not allow the particular interconnection.

between percentile ranges in Figure 1. Interconnections
toward the end of the feeder exhibit a wider range ofmaximum voltages, and therefore a larger response todifferences in system size. We looked at the maximum
system size allowed at each of the buses before any voltage orthermal violations occurred, shown in Figure 3, varying bothmarker size and color to correspond to maximum system size.

Max Bus Voltage in Each Scenario(pu)Abstract — With rising adoption of solar energy, it isincreasingly important for utilities to easily assess potential
interconnections of photovoltaic (PV) systems. In this analysis,
we show the maximum feeder voltage due to various PVinterconnections and provide visualizations of the PV impact tothe distribution system. We investigate the locational
dependence of PV hosting capacity by examining the impact of
PV system size on these voltages with regard to PV distance andresistance to the substation. We look at the effect of increasingsystem size on feeder violations. The magnitude of feeder load isalso considered as an independent variable with repeatedanalyses to determine the effect on the PV impact analysis. A
technique is presented to determine and visualize the maximum
capacity for possible PV installations for distribution feeders.
Ultimately, these methods will serve as a basis for continuing toformulate a technique for identifying key criteria and circuit
parameters to establish the likelihood of feeder impact due tohigh penetrations of PV.

Robert J. Broderick
Sandia National Laboratories
Albuquerque, NM, USA

3
1.05
2
1.04
1.03

1
0

0.5

1

1.5

2

2.5

3

3.5

4

0

PV Distance (km)Figure 2. PV size and distance effect on max bus voltage under 50% load.

Figure 3. Maximum allowed PV size at a single bus under 50% load.

An Analytical Method for Constructing a
Probabilistic Model of a Wind FarmSamer Sulaeman, Student Member, IEEE, Sirisha Tanneeru, Mohammed Benidris, Student Member, IEEE
and Joydeep Mitra, Senior Member, IEEE
Department of Electrical and Computer Engineering
Michigan State University
East Lansing, Michigan 48824, USA(samersul@msu.edu, sirisha.tanneeru@xcelenergy.com, benidris@msu.edu and mitraj@msu.edu)
Keywords—Wind power, reliability of wind turbine, adequacy
assessment.

I.
Band1

M ODELING A PPROACH
Band m CO  0

Band3

0

0

0

0

C11

C21

C31

Cm1

C 12

C22

C32

Cm2

C13

C23

C33

Cm3

C1n

C2n

C3n

Cmn

CO  C11
CO  C21

Transition

Band2

Co due to FOR

Abstract—Wind energy penetration levels have increased inrecent years all over the world. Despite the advantages of wind
power, wind power introduces complexity to the planning andoperation of power system due to output fluctuations. In addition,
maintaining the efficiency, reliability and operation of the main
power grid in present of intermittent resources has become avital challenge. For power systems, the intermittent nature anduncertainty of wind turbine generators (WTG) output power
introduce complexity of applying traditional reliability methods toevaluate system reliability for planning and operation. In contrast
to conventional generators, the operational characteristics of
WTG add complexity to the reliability assessment methods
applied on conventional generators. Since the output power of
WTG depends mainly on wind speed regime in a particular
wind farm geographical location, and mechanical availabilityof WTG, wind power output will exhibit variation due to theintermittent nature of wind speed which adds complexity ofapplying traditional techniques used for adequacy assessmentof power system. Therefore, it is important to investigate theexpected output power of wind turbines due to wind speed andmechanical availability. In addition, a model represents wind
power should consider the intermittent nature of power output,
and should also be applicable to meet with adequacy assessment
techniques used for conventional generation with inclusion ofvariability and intermittency nature of wind power. This poster
introduces a new method to model the output power of windfarms in reliability evaluation. The proposed model is presented
in terms of capacity outage probability table (COPT) considering
the mechanical failure of WTG and the correlation between
the outputs of turbines on the same farm. Normal convolution
methods are not applicable because the correlation between theturbines. Based on the proposed model, the COPT of windfarm has been constructed and applied on the IEEE RTS-79
to calculate the well known reliability indices. Furthermore, acomparison of the reliability indices with and without considering
the mechanical failures of WTG is shown. The results indicate
the importance of inclusion WTG mechanical availability inestimating reliability of wind power. The results were validated
using Monte Carlo simulation.

Co due to Band
Transition

Fig. 1.

Wind power transition between bands
TABLE I.
Unit
Commitment

R ELIABILITY INDICES
LOLP

EDNS
MW/yr

LOEE
MWh/yrLOLE
Hr/yr

Using Discrete Convolution
Base Case

0.001070.135211181.169.3681Wind power only

0.00073

0.08903

777.726

6.3910Including wind (FOR)0.000810.09842859.823

7.0347

Using Monte Carlo State Sampling
Base Case

0.001170.13840

1209.05

10.2211Wind power only

0.00076

0.08592750.606.6394

Including wind (FOR)0.00083

0.10900

952.227.2509

1

Dynamic Compensation of Electronic
Transformer Phase Delay
Can Huang and Fangxing Li
EECS Department, University of Tennessee, Knoxville
Knoxville, TN 37996, USA
Email: chuang16@utk.edu
Abstract— Accuracy and timeliness of sampled values is a
crucial issue in smart substations. To improve the phaseaccuracy of electronic transformers, a dynamic phasecompensation method for electronic transformers is
proposed. It calibrates the phase errors by the real-timemeasured delays, instead of the fixed estimated values in
traditional compensation methods. In order to minimize
the additional errors generated in the calibration process, aCoordinate Rotation Digital Computer (CORDIC)
algorithm is applied in rotating plane coordinates andcalculating square root. Then, the iterative architecture of
CORDIC is optimized as pipelined and implemented in
FPGA. The total delay caused by the algorithm is 111.32 ns
under 100MHz system clock. Finally, experimental results
verify the validity of the proposed method.

Jun Mei
School of Electrical Engineering, Southeast University
Nanjing, 210096, China

 tan    x0 
 x1 
 1
 y   cos   tan 
1   y0 

 1
 1
Sn 2 n   1 S1 21   x0  xn  n

(

cos

)
 
 
i 
 y  i 1n1   S1 21
1   y0  n
 Sn 2(1)(2)

II. EXPERIMENTAL RESULTS
A/D Converters

AC Signal Generator

LPVT

LPCT

I. DYNAMIC COMPENSATION OF PHASE DELAY
In general, the phase delay of electronic transformers is
modeled as two components: a rated phase delay 2 f  td (fmeans sampling frequency and t d is the rated time delay) andan uncertain phase error  e . In this poster,  e is real-timemeasured and compensated in Merging Unit (MU) using
CORDIC algorithm.
y( x1 , y1 )y1y0

( x0 , y0 )x0xFigure 1. Circle rotation diagram of the CORDIC algorithm.

yx2  y 2  r 2

t  

r sinh(t )

t
O

r

x

r cosh(t )t 0Merging Unit

Optical fiber

Figure 3. Test bed of calibration system.
Table1 Experimental results of traditional compensation method

Rate/%

Ratio/%

Delay/usPhase difference/degree

99.99480.0462-7.4

7’58’’

99.9950
99.9946
99.9951
99.99450.04640.04680.04700.0480

-4.8
-5.2
-4.6
-5.4

5’10’’
5’’39’’
4’’57’’
5’’49’’

Table2 Experimental results of dynamic compensation methodx1

Calibration Systemt  

Figure 2. Hyperbolic vector diagram of the CORDIC algorithm.

Rate/%

Ratio/%

Delay/usPhase difference/degree

99.99470.0457-6.5

1’15’’

99.9946
99.9949
99.9950
99.9950

0.04670.04770.04540.0455

-4.2
-5.1
-7.7
-6.2

0’13’’
0’37’’
1’16’’
0’55’’

III. ACKNOWLEDGEMENTS
The authors would like to thank NARI Group Corporation(NARI) for providing the technical support for this project.

1

A Multi-Agent Design for Power Distribution
Systems Automation
M. Jawad Ghorbani, Muhammad A. Choudhry, and Ali Feliachi,

Abstract—A new Multi Agent System (MAS) design for fault
location, isolation and restoration in power distribution systems ispresented. When there is a fault in the Power Distribution System(PDS), MAS quickly isolates the fault and restores the service
to the fault-free zones. Hierarchical coordination strategy isintroduced to manage the agents which integrate the advantagesof both centralized and decentralized coordination strategies.
Proposed MAS is composed of Zone Agents (ZA), Feeder Agents(FA) and Substation Agents (SA). In this framework, ZAs locate
and isolate the fault based on the locally available information
and assist the FA for reconfiguration and restoration. FA cansolve the restoration problem using the existing algorithms for
the 0-1 Knapsack problem. A novel Q-learning mechanism isalso introduced to support the FAs in decision making forrestoration. The design is illustrated by the use of case studies offault location, isolation and restoration on West Virginia Super
Circuit (WVSC). The results from the case studies indicate theperformance of proposed MAS design.
Index Terms—Fault location and isolation, multi agent system,
Power Distribution, Q-learning, restoration, self-healing.

I. I NTRODUCTION
A self-healing PDS, when subject to a contingency (fault),
is able to automatically perform remedial actions to restore
the system to the best possible state. The objective of selfhealing PDS is to minimize the duration of outages as well as
the customers interruptions, in favor of overall PDS reliability.
Basically, PDS is in the normal state while system parameters are within normal operating range. System enters theabnormal state when an outage happens. In this paper various
aspects of a self-healing PDS are discussed, including:
1) Fault Location: when a fault occurs in the PDS, the selfhealing system is triggered by an automatic re-closer trip in
real-time operation, and starts to detect the fault location andidentifies the fault type by monitoring data along the feeder.
2) Isolation: the first corrective action after locating thefault is to open the relevant switches (in arbitrary order) inorder to isolate the fault from the rest of the system.
3) Reconfiguration & Restoration: finding a reconfiguration strategy which determines the switching sequence necessary to reconfigure the system in order to allow the broadest
possible power access.
In this paper, authors compare the possible MAS frameworks considering the reliability and communications requirements and proposed a hierarchically decentralized MAS
architecture composed of upper and lower level agents. In
the bottom-up order, agents are classified as Zone Agents(ZA), Feeder Agents (FA) and Substation Agents (SA). In
the proposed framework, ZAs are responsible for locating thefault and isolating it. While reconfiguration and restoration are
handled by FAs.
In that framework, ZAs have access to the voltage andcurrent phasor data of themselves and their immediate neighbors to a defined neighborhood through communication. Using
these data and by real-time monitoring of indices introducedin this paper, ZAs can decide whether the fault is in theirneighborhood or not. Following fault location and isolation
by ZAs and informing the corresponding FA about the fault
information, FA first restores the upstream zones of faulted
area and starts negotiating with alternative FAs, that have tie
switches to faulty feeder. FAs evaluate the possible reconfiguration and choose the best reconfiguration using deterministic
optimization problem solving techniques to supply as manyloads as possible.
Although agents in MAS can solve the optimization techniques to find the near optimal configuration, it is also
necessary that they learn new behaviors online, such thatthe performance of the agents and MAS gradually improves.
Therefore, a learning methodology (Q-Learning) is used toteach agents to use their experiences and prevent them fromdoing the time consuming optimization process each time afault happens. The Q-Learning technique introduced in thispaper is modified for power distribution systems and uses
defined Q-Matrices as the repositories for all the information
learned through previous restoration experiences.
The main contributions of this work can be summarized asfollows.
• MAS architecture: A hierarchical distributed MAS that
perform the FLIR using less communicated messages
than the existing methods.
• Fault Location and Isolation Algorithm: Proposed FLI algorithm is fully distributed and ZAs along the feeder canlocate and isolate the faulty section by communicatingwith their immediate neighbors.
• Restoration Learning algorithm: A reinforcement learningalgorithm is developed in conjunction with restoration
mathematical programming to take the advantage ofrestoration experiences and use the learning knowledge
for future restorations.
The performance and feasibility of proposed framework isdemonstrated on West Virginia Super Circuit (WVSC) whichis composed of 5 distribution feeders.

An Online Algorithm for Health Diagnosis andPrognosis of Circuit Breaker
Saugata S. Biswas, Student Member, IEEE and Anurag K. Srivastava, Senior Member, IEEE,
Smart Grid Demonstration & Research Investigation Lab (SGDRIL),
Washington State University, Pullman, WA, USA

Abstract — With ongoing efforts to make the power grid
smarter, utilities are upgrading the substations withenhanced automation. One of the important aspects ofsubstation automation is online monitoring and predictionof the health of the substation assets. Circuit breaker is oneof the most vital components in a substation for tripping
action during the occurrence of a fault. It is of highimportance to ensure that the circuit breaker is in healthy
condition, and can operate as per expectation wheneverrequired. There is requirement of keeping a continuous
watch on the health of all the components within circuit
breaker itself including trip coil and detect and predict thepossibility and cause of potential health problem. This
research work involves the development of an online
algorithm that runs in a substation and continuously
monitors and predicts the health condition of a circuit
breaker and suggests proper control actions if and whennecessary. Arrangement has also been made such that thealgorithm results can be remotely accessed for engineering
access. Results obtained by online implementation of the
proposed algorithm using actual circuit breaker datashows successful operation.	
  remote substation that would take in data from substation fortripping of circuit breaker to analyze the health condition of
the trip coil automatically. Also, analyzed information can beused to inform the engineer about the real time healthcondition of the monitored trip coil. This kind of online
algorithm could lead to the saving of time, effort, and money,at the same time ensuring that an actual deterioration of trip
coil health would not go unnoticed or unattended. This poster
discuss about the development of an online algorithm forcarrying out diagnostic and prognostic analysis of a circuit
breaker trip coil in real time. This algorithm cannot only detecta health problem, but can also categorize them as either
mechanical or electrical problem depending on the root cause.
Further, it can suggest maintenance actions based on analyzed
results by the algorithm.
II. KEY FIGURE
Figure 1 shows the trip coil current profile, which is usedby the proposed algorithm to analyze the health condition of atrip coil. Individual sections in this profile (as marked in the
figure) are analyzed by the algorithm to analyze diagnostic
and prognostic information regarding the circuit breaker
health.

I. INTRODUCTION
High voltage or medium voltage circuit breakers are
extremely vital components in substations. As one of theimportant functions of these circuit breakers involve
disconnecting faulty part of power system circuits during thefaults. Reliability of their operation should be guaranteed at all
times. The component inside a circuit breaker that is
responsible for actuating a tripping action is the trip coilassembly. Thus, there is need to ensure that this trip coilarrangement is in proper health condition at all times, and has
not deteriorated due to repeated operations. One way ofensuring this is by doing regular periodic maintenance of
circuit breakers. However, the biggest disadvantage of thisapproach is that, most of the times, utility personnel are sent tothese unmanned substations for maintenance, when actually
there is no need for maintenance at that time. This would
result in loss of time, effort and money. On the other hand,sometimes an actual deterioration of trip coil condition may
even remain undetected and unnoticed unless this deterioration
leads to a catastrophic failure leading to a non-trip conditionof the trip coil and hence the circuit breaker power contacts.
An alternative approach is to design an algorithm to run online
in an IED (intelligent electronic device) stationed at theFig. 1. Trip Coil current profile showing various sections that are analyzed

III. ACKNOWLEDGEMENT
We would like to thank Schweitzer Engineering Lab (SEL)
for sponsoring this research project.

IMPROVEMENT OF TRANSIENT STABILITY OF PV-HYDRO SYSTEMUSING VIRTUAL SYNCHRONOUS MACHINES
Ujjwol Tamrakar, David W.Galipeau and Reinaldo Tonkoski
Department of Electrical Engineering
South Dakota State University
Brookings, SD, USA
ujjwol.tamrakar@sdstate.edu

The integration of hydro can improve the reliabilityof photovoltaic (PV) systems without the need of theexpensive and toxic storage. Hydro plants have limited
inertia and damping property while PV systems do not.
Increased PV penetration in these PV–Hydro integrated
systems can have significant impact on microgrid
dynamic performance and transient stability, especiallyin the isolated mode of operation due to insufficient
inertia and damping from conventional synchronous
generators of the microgrid. A solution to improve thetransient stability of such integrated PV-Hydro systems
is to add virtual inertia and damping to the systemthrough Virtual Synchronous Machines (VSM).
When there is a change in the load ( PL), the systemfrequency drops or rises. The governor system responds
to this change in frequency; however the response timeof the governor will be slow. If the change in load issignificantly large, inertia and damping of the hydrounit might not be sufficient and the integrated PVHydro system may lose stability. A VSM can inject orabsorb the active power during the transient period
when there is a mismatch between generation and load.
Depending upon the change in system frequency ( f),the active power to be injected or absorbed by the VSM
is calculated based on:
(

)

(

)

(1)

where,
is the VSM’s steady state active power,
is the gain for transient power supported by VSM
due to virtual inertia andis the gain for transient
power supported by VSM due to virtual damping
If the VSM is designed to supply the transient active
power only, equation (1) can be further simplified as:
(

)

(

)

(2)

Ki is the inertia emulating characteristics and can be
represented by; where H represents theamount of inertia, P g0 represents the nominal output
power of the generator and f0 is the nominal systemfrequency. The Kp emulates the effect of damper
windings of a real synchronous generator.

PV System
PV
InverterDC

AC

Hydro System
Excitation
SystemSG
Governer
SystemVirtual Synchronous Machine

Inverter

PL

D PL

PVSM

DC

AC
EnergyStorage
VSM
Algorithm
Hysteresis BandPWM Generator

Fig. 1 Proposed PV-Hydro Integrated System with Virtual
Synchronous Machine

The transient stability of the PV–Hydro system without
the VSM will be analyzed first in MATLAB\Simulink
model. The system will then be implemented using a
Real Time Digital Simulator (OPAL-RT). A PV- Hydrosystem with a VSM can improve the stability of thesystem improving frequency regulation and outages due
to cascaded trippings of relays. This allows theintegration of PV systems and other intermittent
renewable sources with high penetration in a microgrid
system.

An Out-Of-Step Detection of Multi-machine Power
System using PMU Data
Yawei Wei, Sumit Paudyal, and Bruce A.Mork
Department of Electrical and Computer Engineering, Michigan Technological University, Houghton,Michigan,USA
Email: yawweiw@mtu.edu,sumitp@mtu.edu, bamork@mtu.edu,

Abstract— This research proposes an algorithm forout-of-step detection of multi-machine power system by using
PMU devices and which is based on Zubov’s method.By
exploring the voltage, power angle and the rate of frequencychange data from PMU during transient, the proposed method
could generate required Zubov formula’s parameters
corresponding to the general power system swing function.The
crux of this algorithm is to construct a Lyapunov function andthen solve a linear partial differential equation (p.d.e), which
will give a precise stability region for detection and control
decision. First,using the proposed PMU data,it could give amore accurate decision. Second,it is suitable for real-time PMU
data on short time detection or judgment in typically 1 second
time period. What’s more, the implementation is easy toachieve if the power swing equations nonlinearity is expressed
as a power series function. This proposed algorithm isdeveloped in MATLAB and tested by standard IEEE-9 andIEEE-14 bus system, both contain multi-machine in the gridconfiguration.By changing the fault happened locations, fault
duration time and damping factors in both cases, this detection
algorithm simulation result shows clear stable or unstable curve
for analysis.By adjusting the data sampling rate, the presented
work could detect the out-of-swing situation for both absolutestable or asymptotic stability in short time period.The results
demonstrate the proposed detection algorithm has a better
performance than the conventional relay protection scheme.

I. Key Figure
In Fig.1 and Fig.2, they depicts the result of IEEE-9
system stable and unstable situation with initial single
variable of different fault clearing time. It gives an insight
look on the behavior’s changing under different situation,
which is also visualized for system detection.

Fig. 2. 3LG fault unStable curve in IEEE-9

Zubovs function. It combines with power swing Equation(2)togive a full map for analyzing. Pesin is the electrical power
and Pm is the mechanical power input. is power angle withs is the stable point and u is the unstable point.Equation(2) isnormalized by adding time factorτ which yields in equation(3).
n
X
∂V
i=1

M

∂xi

fi (x) = −φ(x)(1 − V(x))

dδd2 δ+ D(δ)= P − Pe sinδ − P s sin2δ
2
dt
dt

(1)

(2)

d2 δ
dδ
+ D0 (δ)= P0 − sinδ − P0s sin2δ
(3)
2
dτ
dτ
Defining x = δ − δ s which transfers the stable equilibrium
point(S.E.P) to the initial, then equation(3) becomesd2 x
dx+ D0 (x) + R(x) = 0
2
dτ
dτ

(4)

As the standard D(τ) could be presented as D(τ) = a1 sin2 δ +a2 cos2 δ, the D’(x) and R(x) composed of sine and cosine
terms can be expanded in a power series as
D0 (x) = D0 + D1 x + D2 X 2 + . . .

Fig. 1. 3LG fault Stable curve in IEEE-9.

II. Key Equations
Equation (1) give the general Zubovs critical formula forstability analysis.In this equation it define the basic structure of(5)

Equation(6) represents for the transformation function bydefining x1 = x = δ − δ s , x2 = ẋ = ωδ. Thus the equation (6)could be formulated below.
1Dx˙1 = x2 , x˙2 = − x2 −f (x1 )
(6)
M
M

Sequential Learning Function applied to the design andtuning of a Fuzzy Controller for VSC
Ivan Riaño1
1

Universidad Distrital Francisco José de Caldas, Bogotá, Colombia.

Abstract—This paper present the develop of a Sequential
Learning Function to improve the tuning process of a fuzzy basis
function expansions (FBFE) for the control of reactive power andthe DC Voltage of the rectifier station of a VSC based on
Differential Evolution, results are compared with a static fitness
evaluation and classical control techniques.
Index Terms— Differential evolution, FBFE, AC/DC Converter,
Voltage Source Converter, supervised learning.

I.

.
II.

DIFERENTIAL EVOLUTION ALGORITHM

The fuzzy control model of the VSC was developed as an
FBFE whose means and deviations were tuned using a
differential evolution algorithm Fig. 1. The control variables
and operation ranges were found through a switch basedmodel of VSC System.
III.

INTRODUCTION

Thanks to provides the necessary features for embedded
applications in AC Grids, Voltage Source Converter (VSC) arehighly used in today distributed generation systems and willplay an important role in tomorrow transmission andDistribution system.
Looking for improve the response of VCS, was introduceda Sequential Learning function applied together with adifferential evolution algorithm. Here is presented a short
introduction and main characteristics of the tested algorithm,
finally are presented a response comparison with linear control
strategies, and a comparison with a static learning technique.

SECUENCIAL LEARNING

During the tuning process, the Fitness Function was defined
following the characteristics of the VSC. First was obtained asteady state at the equilibrium point, next was performed thereactive control and finally was reduced the first peak andsetting time. The best fuzzy control system was validated inthe VSC model against some changes in load, and comparedwith a strategy of classic control linearization.
IV.

RESULTS

The proposed method increases the speed of the learning
process Fig. 3 in comparison with static fitness functions. In
the DC Voltage control Fig. 4 it can be seen that the sequential
learning function present a setting time reduction of 60% and asteady-state error reduction of 50% compared with the linear
control strategies proposed in precedent works.

Fig.2 Learning Process

Sequential
Learning
Function0
Fig. 1 Diagram flow of the differential evolution algorithmwith the Sequential Learning function implemented.

Fig.3 Dc Voltage control

IEEE PES T&D 2014, Chicago, Illinois, USA, April 2014

PMU-Based Wide Area PSSs to Enhance theDamping of Inter-Area Oscillations
Duaa AbuMaali, Member, IEEE Faisal Khan, Member, IEEE Hussein Taoube; Member, IEEE
Mohamad Mahjoub, Member, IEEE Khaled Ellithy, Senior Member, IEEE

Fig. 2 Real-time system modeling using RTDS with PMUs
Table 1: Eigenvalues of inter-area and local oscillations modes
Two-area test
systemWithout PSS
With PSSlocal signals
With proposed
PSS-global signals

Real Power of Tie-Line,MW

Abstract— The operation of interconnected power systems issubject to low frequency inter-area oscillations significantly.
These oscillations are undesirable as they result inefficient
operation of the power systems. The stability of these oscillations
is of vital concern. Adequate damping of the inter-area
oscillations is a pre-requisite for secure operation of powersystems. An available solution to damp out these inter-area
oscillations is the addition of power system stabilizers (PSSs) to
the generators excitation systems. Although conventional PSSs
exist on many generators, their effect is only on the oscillations oflocal area and do not effectively damp out inter-area oscillations.
The general problem is that the locally measured signals input tothe conventional PSSs often are insufficient for damping interarea oscillations mode. In this paper PSSs using wide-areameasurements based phasor measurement units (PMUs) is
proposed. The global stabilizing signals input to the proposed
PSSs are detected and transmitted by the PMUs. The proposed
PSS is designed to ensure satisfactory damping of the inter-area
oscillations which results in maximize power transfer capacityand enhancing system stability. Two-area power system shown in
Fig. 1 is used to test the effectiveness and performance of the
proposed PSSs. The system state-space model with the PMUs wasdeveloped to obtain the eigenvalues associated to the inter-areaand local oscillations modes. The participation factors were also
determined to identify these modes. The eigenvalues results arelisted in Table 1. It can be seen that the system is unstablewithout PSSs; the instability is caused by the inter-area mode(+0.06±j3.73). The inter-area damping was improved with theproposed PSS compared to the conventional PSS. The systemmodel with physical PMUs loop was developed as shown in Fig. 2and nonlinear time-domain simulations are carried out to verify
the eigenvalues results and to test the effectiveness of the
proposed PSS. Figure 3 shows the response of the tie-line real
power with and without the designed PSS; it can be seen that the
proposed PSSs based PMUs improves the damping of inter-area
oscillation and enhancing the system stability. The proposed PSSs
will be implemented in practice to the smart power grid of Gulf
Cooperation Council (GCC).

Inter-Area+0.06 ± j 3.73

Eigenvalues
Local 1-0.14 ± j 6.5

Local 2-0.23 ± j5.99-0.43 ± j3.77-0.11± j6.51-1.86 ± j6.75-1.05 ± j3.74-0.14± j6.52-1.7± j 6.65

PSS with Local Signals
PSS with Global PMU Signals
No Pss

800
600
400
200
2

4

6

8

10

12

14

16

18

20

Time (s)Fig. 3 Time-response of the real power of the tie-line for 3-phasefault on bus 8
REFERENCES
[1]
[2]

[3]

Fig. 1 Two-area power system with PMUs loop

P. Kundur, Power Systems Stability and Control, McGraw-Hill, 1994.
Yanfend Gong, Armando Guzman, Synchrophasor-Based Online
Modal Analysis to Mitigate Power System Interarea Oscillation,
Journal of Reliable Power, Vol. 2, no.2, pp. 4-47, 2011.
Kamwa, R. Grondin, Y. Hebert, “Wide-area measurement basedstabilizing control of large power systems—A decentralized/
hierarchical approach,” IEEE Trans. On Power Syst., Vol. 16, no. 1,
pp. 136–153, Feb. 2001.

LMP Step Pattern Detection based on
Real-Time Data
Haoyu Yuan, Fangxing Li, and Yanli Wei
Department of Electrical Engineering and Computer Science
The University of Tennessee, Knoxville
Knoxville, TN 37996
USA{hyuan2, fli6, ywei9}@utk.edu

Abstract—Locational marginal pricing (LMP) methodology has
been widely adopted by most independent system operators(ISOs) and regional transmission organizations (RTOs) in
today’s electricity markets. Previous studies show that LMP has
a step change characteristic with varying load. This can be usedby market participants to predict the future electricity priceand potential step change of LMP. In this paper, an effective
algorithm using quality threshold (QT) clustering is proposed
to detect the step change pattern of the hourly LMP. A set ofindices to differentiate various patterns is introduced.
Furthermore, a web-based tool is built to demonstrate the pricebehavior of different locations based on the 5 minutes real-time
LMP data from ISOs/RTOs. The user friendly design withclustering functionality ensures easy statistical study over alarge amount of historical data.
Index Terms—Locational marginal price (LMP), critical load
level (CLL), QT clustering, step change pattern detection,market participant, price prediction.

I. INTRODUCTION
Most Independent System Operators (ISOs) and Regional
Transmission Organizations (RTOs) in North America haveadopted an economic dispatch-based model to clear dayahead energy market. Locational marginal price (LMP) canreflect impacts of transmission congestion and losses basedon network models, which contribute to the economic
operation of the electricity market.

an easy-to-use web based tool for market participants toperform statistical analysis of the LMP over their interested
time and locations.
Quality threshold (QT) clustering algorithm has been
widely applied in research areas such as oncology for tumor
classification [9-10]. The characteristic of not knowing
cluster number in tumor classification is similar to our study
in clustering LMP, which is the inspiration to this research
work to adopt the QT clustering. In addition, two indices,
outlier and overlap indicator (OI), are defined for efficientdescription of the fitness of a LMP pattern.
Despite the importance of detecting and visualizing thestep pattern in real time LMP data, there is no web
application in providing such information to market
participants. Thus, we develop a web-based application on the
JAVA EE platform to dynamically generate detected step
pattern diagram to users of interest.
Historical data from ISO is studied in this work. An
illustration of the step pattern detection algorithm is shown in
Fig. 1Given the known unit commitment decisions, the stepchange characteristic of LMP has been well studied. Criticalload level (CLL) is introduced to indicate the point where theprice spike occurs. When the load level is close to the CLL,
there will be higher risk of having volatile price. This
becomes more common especially when the system has alarge amount of intermittent renewable generations.
Uncertainty analysis associated with CLL has been studied
from the perspective of both market participants and theoperators.
Market participants, who are actively involved in the
power trading business, need to investigate all the possible
causes for LMP volatility. It is clear that CLL is an importantsignal for predicting price spikes. It is also desirable to have

Figure 1. Illustration of Outlier and Overlap Indicator (OI).

Customer Incentive Pricing Mechanism in the Smart Grid
Timothy Hansen, Graduate student member, IEEE
Colorado State University
Department of Electrical and Computer Engineering, Fort Collins, CO 80523, USA
Email: timothy.hansen@colostate.edu
Abstract—As the Smart Grid introduces profound changes in
the operation of the electric power industry, the need for
efficient and robust resource allocation (RA) algorithms arise.
This is especially difficult due to the increasingly stochastic
nature of the availability of massively distributed resources.
Similar challenges exist in resource allocation in the realm ofcomputing. Based on the experiences in the computing domain,
this work solves the Smart Grid RA (SGRA) problem using aheuristic framework implemented as a genetic algorithm. The
SGRA problem is defined as: given a set of end-users andinformation about their respective assets, subject to end-userand system constraints, determine how to control assets (e.g.,
adjusting thermostats, rescheduling smart appliances) tomaximize a given objective. To encourage increased customer
participation in SGRA, a new pricing methodology known ascustomer incentive pricing (CIP) is introduced to offset the enduser inconvenience of controlling their assets.
If theinconvenience of giving up control of a particular asset is not
worth the incentive, the end-user can eschew participation inthat time interval. This approach — enabled by a fully
deregulated market structure including aggregators — offers
the end-user more choices to participate in the deregulated
electricity market at regular intervals.
1

I. KEY EQUATIONS

II. KEY FIGURE

Figure 1. Cyber-physical social system (CPSS) for the SGRA
(based on [1]).

III. KEY RESULTS

Aggregator profit components (S, N, B) and objectivefunction (P) from [1]:

∑[

∑

]

(1)

∑[

∑

]

(2)

∑[

∑

]

.

(3)
(4)

Figure 2. Comparison of real-time pricing, spot marking pricing,and customer incentive pricing [1].

REFERENCE
This work was supported by the National Science Foundation under grant
numbers CNS-0905399 and CCF-1302693, and the Colorado State
University George T. Abell Endowment.

[1]. T. Hansen, et al., “Heuristic optimization for an aggregator-based
resource allocation in the Smart Grid,” under review, 8 pp., Dec 2013.

Distribution Locational Marginal Price using
Optimal Three-phase Current Injection Power Flow
Jie Wei, Farshid Sahriatzadeh and Anurag K. Srivastava
The School of Electrical Engineering and Computer Science, Washington State University
Pullman, WA 99164, USA
Abstract – With ongoing smart grid development, objective
is to use available information to make power system more
efficient, environmental friendly, reliable, economic and secure.
Major part of smart grid investment is going towards
distribution systems. One of the goals within smart grid isdevelopment of infrastructure to avail price signal to end users
in distribution system. This price signal is currently based on
estimate of nodal price in transmission system. Transmission
nodal price does not reflect fair pricing signal for distribution
system without consideration of distribution system constraints
such as unbalanced loads and distribution lines flows. Solving
optimal power flow (OPF) with applicable constraints isnecessary to obtain locational marginal price (LMP) for eachnode that can be used in real-time pricing (RTP). Formulationand implementation of distribution LMP (DLMP) is needed forfair pricing. Although Newton-Raphson based PQ mismatch
approach can be used to solve three-phase OPF in distribution
system, it may lead to divergence due to large R/X ratio of
distribution lines in large scale systems. In this work, threephase current injection method (TCIM) formulation has beenused to develop OPF formulation and calculate DLMP.
Developed formulation is able to deal with distributed
generations (DG) and suitable to implement in advanced
distribution management system (DMS).

II. KEY FIGURES
System Information - IEEE 13bus

TCIM OPF Flow Chart
System Data Input
TCIM Power Flow

Optimal Power Flow model

I. KEY EQUANTIONSSolve OPF problem using
Interior Point Method

Power injection equations:P   P  PQ   Q  Q
sp s
k

s
gk

s
lk

sp s
k

s
gk

s
lk

Run TCIM Power
Flow Again

Three-Phase Power Flow Equations: P   (V  I )  (V  I )Q   (V  I )  (V  I )calc s
k

s
rk

s
rk

s
mk

s
mk

Check criteria

calc s
k

s
mk

s
rk

s
rk

s
mk

yes

no

End

Current injection equations:  G  V  B  V n

I rk 
s

i 1

st

t

st

t

ki

ri

ki

mi

t 

Developed algorithms uses the database of the input test
system to run TCIM power flow first. The results obtained
from TCIM power flow is used as initial values to establish
an OPF model. Interior Point Method was used to solve the
OPF problem.

  G  V  B  V n

I mk 
s

st

t

st

t

ki

mi

ki

ri

i 1 t Three-phase current mismatches: P   j Q  I  Y E
E 
sp

2

k

s

sp

k

k

sp

k

s

s

st

t

ki

i

Discharge Energy Based Stage Classification of
Cavity Partial Discharge in Oil-Paper Insulation
1, 2

2

1

Zhenze Long , Student Member IEEE, Weigen Chen and David Yu , Senior Member IEEE
1. College of Engineering and Applied Science, University of Wisconsin Milwaukee, WI, USA;2. School of Electrical Engineering, Chongqing University, Chongqing, China
Email: zlong@uwm.edu
INTRODUCTION

In earlier studies, only the maximum PD magnitude andrepetition rate are regarded as PD information. This simple
information is not reliable to determine the developing stagefor PD, which is an extremely stochastic event. Recently,
fingerprint identification has been regarded as a useful tool in
the recognition and classification of PDs. Phase resolved
partial discharge (PRPD) pattern is the most widely usedmethod. However, the 3-D φ-q-n pattern, whose y axis
represents the apparent discharge, is not the perfect solution.
Since new research indicates that when the insulation is about
to break down, the apparent discharge would not change verymuch.
In this poster, apparent discharge in PRPD pattern is replacedby discharge energy consumption. Then twenty-ninestatistical parameters are extracted from the new PRPD
patterns and using Kernel principle component analysis(KPCA), eight new feature parameters are refined from theoriginal statistical parameters. In addition, based on
experimental data of cavity discharge, the discharge stages
are classified into four stages which are incipient discharge,
unstable discharge, consistent discharge, and disruptive
discharge, according to cluster analysis.
II.

KEY FIGURES AND TABLES

Average Discharge Energy
per second 0.1mJ/s

12
10
8
Wavg

6

4

n(times)

3

n(次 )

I.

Partial discharge is one of the main reasons for aging anddisruption of the oil-paper insulation. As a result, theinvestigation of PDs’ characteristics is important in detecting
and diagnosing the internal failure of transformer oil-paper
insulation.

2

1

0
4
3

400
300

2

-7

x×10

200

1
W(J)

100
0

0

phi(°)

Figure 2. The images of φ-W-n at 8h hour
Table 1. Statistical Parameters at 8th hour

SkSk+
Sk
KuKu+
Ku
PePe
Pe+
Asy
α
β

Hqmax(φ)
0.76270.8238N/A
3.12453.0018N/A
45
39
0.98450.8174
N/A
N/A

Hqn(φ)
0.0226
0.0367N/A
1.8433
1.7275N/A
41
42
1.06840.0851
N/A
N/A

Hn(φ)
0.23190.2249N/A
2.36112.5882N/A
23
24
1.01670.3123
N/A
N/A

Table 2. Contribution Rates of Principle Component

Component

Value
of λi

Contribution
rate (%)

λ1
λ2
λ3
λ4
λ5
λ6
λ7
λ8

6.96701.11471.08880.17950.14000.0416
0.03890.0007

83.3
13.14
2.31
0.95
0.19
0.041
0.053
0.016

Cumulative
contribution
(%)
83.3
96.44
98.75
99.70
99.89
99.93199.984
100

4
2
0
0

5

10

15

20
25
Discharge Time /h30

35

Figure 1. The variation of average discharge energy per second

Wavg 

Hn(q)
N/A
N/A
1.2278N/A
N/A
4.8508
N/A
52
N/A
N/A
0.13451.3895_1
qU  n2

Figure 3. Cluster Analysis Result

1Impact of Voltage Reduction and Ambient
Temperature on Power Consumption, Line Lossand Generation
Sushanta Paul, Student Member, IEEE and Dr. Ward Jewell, Fellow, IEEE
Abstract— A voltage-reduction program is one strategy appliedby utilities to reduce energy consumption and peak demand.
This paper presents an analysis of how consumer energy
consumption, line loss, and thus total consumption andgeneration are affected by voltage reduction. Since bus voltage
depends on line resistance, which varies with ambient
temperature, the impact of temperature on power consumption,line loss and generation is discussed as well. Joint effect of voltage
and ambient temperature is also analyzed by two-factor factorial
design. The results are presented based on the types of loads toshow how load models play an important role in the analysis. All
the analyses are tested on the IEEE 13-bus system.
Index Terms—Energy conservation, energy consumption,
power generation, reactive power

I. IMPACT OF VOLTAGE REDUCTION ON LOAD AND LINE LOSS
Load Model

TABLE I
Load Power
Consumption
Constant
Decrease
Decrease
Decrease ()

Const. Z
Const. I
Const. P
Composite load(i) Const. P dominant(ii)Const. I dominant Decrease(iii)Const.Z dominant Decrease

Line Loss
Decrease
Constant
Increase
Increase
Increase(if % of const. P load > %of const. Z load)orDecrease(if % of const. P load < %of const. Z load)
Decrease

II. IMPACT OF REDUCED VOLTAGE ON POWER GENERATION
Variation in active power generation (∆P Gen) is thesummation of variations in load ∆P Load and line loss (∆PLoad),
as shown in (1). ∆PGen = ∆PLoad + ∆PLoss(1)
If, decrement and increment are considered positive, andnegative respectively, thenTherefore, ∆PLoad + ∆PLoss > 0∆PGen > 0. Hence, the active
power supply from the substation will decrease with areduction in voltage for all composite load models.
This work was supported by the U.S. Department of Energy under grant
DOEDE-FG36-08GO88149.

III. IMPACT OF TEMPERATURE ON LINE LOSS, LOAD AND
GENERATION
We know that at a higher temperature the resistance of aline will increase and consequently line loss (I2R) will
increase, but with the voltage-dependent loads such as
constant impedance and constant current, load will decrease
because the line drop (IZ) increases with the increase inresistance at a higher temperature thus leading to a decrease in
the node voltage, and finally, the voltage-dependent load willconsume less power at a higher temperature. As the voltage
drops at a higher temperature, the reduced voltage again
affects the line losses, and whether line losses will increase or
decrease at that reduced voltage depends on the type of load asshown in Table I. For the system with a dominant constant
impedance load, line losses decrease at a reduced voltage
caused by a rise in temperature, and again, line losses increase
for an increase in resistance due to temperature rise, but anincrement in line losses for an increase in resistance due totemperature rise is greater than a decrement in line losses due
to voltage reduction caused by a temperature rise, so the line
losses will increase at a higher temperature. For the systemwith a dominant constant power load, line losses increase at a
reduced voltage caused by temperature rise, and again line
losses increase for an increase in resistance due to temperaturerise; therefore, line losses will increase to a greater degree.
Similarly, for the system with a dominant constant current
load, due to a temperature rise, line losses will increase by anamount that is less than the increment in line losses for thesystem with a dominant constant power load but greater than
the increment in line losses for the system with a dominantconstant impedance load. A comparison in the increase in line
loss is shown in (3)(3)
Impact of temperature on generation: ∆PGen = ∆PLoad +∆PLoss . Generation will increase if, ∆PLoss > ∆PLoad , ordecrease if ∆P Load > ∆PLoss . System with dominant constant
power load,, therefore generation increases.
System with dominant constant impedance load,, therefore generation decreases. For the system withdominant constant current load,, therefore
generation increases.
IV. JOINT EFFECT OF VOLTAGE AND AMBIENT TEMPERATURE
The interaction effect of temperature and voltage is given by
(4).
2
 a b

y 
 yij yi. y. j  y..  SS A  SS B  .. ab 
 i 1 j 1
SS N  
abSS A SS B

with 1 degree of freedom.

2(4)Adaptive PI Control of STATCOM for
Voltage Regulation
Yao Xu and Fangxing Li
The Department of Electrical Engineering and Computer Science, the University of Tennessee (UT), Knoxville, TN37996, USA,
Email: yxu25@utk.edu and fli6@utk.edu
Abstract—STATCOM can provide fast and efficient
reactive power support to maintain power system voltage
stability. In the literature, various STATCOM control
methods have been discussed including many applicationsof proportional–integral (PI) controllers. However, theseprevious works obtain the PI gains via a trial and error
approach or extensive studies with a tradeoff ofperformanceandapplicability.
Hence,
controlparameters for the optimal performance at a given
operating point may not be effective at a different
operating point. This paper proposes a new control model
based on adaptive PI control, which can self-adjust thecontrol gains during disturbance such that theperformance always matches a desired response,
regardless of the change of operating condition. Since theadjustment is autonomous, this gives the plug-and-playcapability for STATCOM operation. In the simulation
test, the adaptive PI control shows consistent excellence
under various operating conditions such as different
initial control gains, different load levels, change oftransmission network, and consecutive disturbances. As acomparison, the conventional STATCOM control with
tuned, fixed PI gains usually perform fine in the original
system, but may not perform as efficient as the proposed
control method when there is a change of the system
conditions.

I.

L1=200 km

B1

B3

L3=180 km

L2=75 kmB2
500 kV,
8500 MVA

Load
300 MW

500 kV,
6500 MVA

Load
200 MW

500 kV,
9000 MVASTATCOM

A

Fig. 2. The studied system.

III.

KEY RESULTS

Fig. 3. Voltages with changed Kp and Ki in the original control.

KEY EQUATIONS

Voltage regulator PI gains can be computed by thefollowing equations:
( )

( )
(

( )

)

∫

( )

(1)
(2)

( )

Current regulator PI gains can be obtained by:
( )

( )
(

( )

∫

( )

II.
Measured
Voltage

Vm -

+

Vref
Reference
Voltage

Imax

Iqref
-

Imin

Voltage Regulator
block (outer loop)

(3)
(4)

Fig. 4. Results of measured voltage with change of transmission
network.

KEY FIGURESPI Controller
Adjust Kp
and Ki

)

( )

+
Iq

amax

PI Controller
amin

Adjust Kp
and KiCurrent Regulator
block (inner loop)

Fig. 5. Results of measured voltage in two consecutive disturbances.
Fig. 1. Adaptive PI control block for STATCOM.

Control of Hybrid Tripole HVDC Based on
LCC and F-MMC
Feng Xu, Zheng Xu
Department of Electric Engineering, Zhejiang University, Hangzhou 310027, Zhejiang Province, China
Email: xuf_1988@zju.edu.cn; xuzheng007@zju.edu.cn
Abstract—In order to overcome some inherent defects ofthe conventional tripole HVDC, a hybrid tripole HVDC
structure is proposed, which replaces the thyristor converters of pole 3 with the modular multilevel converters(F-MMC) based on cascaded full bridge sub-modules(FBSM). To increase the transmission capacity, a special
current modulation is adopted. In order to meet the demands of the reactive power (voltage) balance, the DC
current balance and the sub-module capacitor voltage
balance in the transition process, the corresponding control
strategies are also proposed. The performance of the proposed hybrid tripole HVDC structure is evaluated throughtime-domain simulation studies in PSCAD/EMTDC. The
results show that a satisfactory response of the system canbe achieved with the proposed control strategies.
I. KEY FIGURES

Idmes

sig

+

U3ref

- I

+

I3ref

sig

-

3

U 3 M
U
X

PI
Controller

isd*

sc

Udmes

Udref/ 2

PI parameters

MCC

udiffm +
Q0

Q1
Q2

-

+
+



2/3

isq'

usmes
-

usref

+

PI
Controller+

upmref-+Inner
controll
er

isq*

isq

Mod
FiringulatioSignals
n

emref

AVC

U3ref

+

PI
Controller

sig

-

Udmes

udiffm +
-

isd*

DVC

unmref+Udref/ 2

Fig. 2. Control block diagram of the F-MMC.

II. KEY RESULTS

Pole 2Zac

Zac
AC system-1

AC system-2

P1&P2

1.0
0.5
0.0
-0.5
-1.0

DC voltage/pu

Pole 1

P3
2

3

4

5

t/s

6

7

8

9

10

Fig. 3. DC voltages of three poles.
Pole 3PCC-2(a) Proposed hybird HVDC structure
SMn

SMn

SM2

SM2

SM2

SM1

SM1

SM1

SM1

SM1

SM1

SM2

SM2

SM2

SMn

SMn

SMn

2

P1

1

P2

0
-1

P3
2

3

4

5

t/s

6

7

8

9

10

Fig. 4. DC currents of three poles.

T1
A
T3

+
Uc

-

0.02
0.01
0.00
-0.01
-0.02
-0.03

current/pu

SMn

DC current/pu

PCC-1

T2
T4

B

(b) Topology of F-MMC
Fig. 1. Proposed hybrid tripole HVDC system.

(c) 12-pulse LCC

3.0

3.2

3.4

3.6

3.8

3.4

3.6

3.8

t/s

Fig. 5. Ground current.

voltage/kV

Filters &condenser

2.1
2.0
1.9
3.0

Fig. 6. Capacitor voltage.

3.2

t/s

1

A Total Harmonic Distortion (THD) computation using improved Shunt
Active Power Filter Design in Energy Management Systems
Ranganath Vallakati, IEEE Student Member, Prakash Ranganathan, IEEE Senior Member
Email: ranganath.vallakati@my.und.edu
Department of Electrical Engineering, University of North Dakota, Grand Forks, ND 58202
Abstract—Power factor correction is an important task in
power system field. The advent of power electronics andnonlinear converters in recent days has increased theconsumption of non-sinusoidal current significantly. The
modern day power electronic devices with a very highswitching frequency represent a significant amount of reactive
power and harmonic components compared to traditional
linear loads. Harmonic or sub-harmonic components need tobe reduced in the power system, because they create additional
problems such as voltage harmonics, and disturbances
affecting equipment and sub-systems interconnected to thepower network. The consumption of reactive power by
nonlinear loads presents critical problems such as higher Root
Mean Square (RMS) current value; increase in temperature oftransmission lines and equipment causing losses and larger
inefficiencies in the system.
This research work studies a filter named ‘Shunt
Active Power Filter’ (SAPF) based on a PQ theory for
reducing such harmonics caused due to non-linear loads. Our
implementation uses a MATLAB/Simulink modelingtechnique to design the SAPF and PQ theory concepts. The
filter models the active and reactive powers in time domain.
Through our simulation and analysis, we observe that theresults show significant reduction in harmonic components
and can eliminate the power system stability problems
yielding better efficiency.

II. KEY FIGURE

Figure 1. System Block Diagram of SAPF [1]III. THD COMPUTATION & SIMULATIONIn the simulink design, we modeled the sources using virtual
elements and bridge rectifier is treated as a non-linear load.
The shunt active power filter is connected in parallel to thetransmission line as shown in Figure 1. The simulation ran fora total period of 0.5 seconds, during which the SAPF isconnected to the grid after 0.1 seconds. Thus, we understandthe system behaviors in two conditions; 1) THD calculation
with SAPF and 2) THD computation without SAPF. For thesimulated model without SAPF, THD is 30.90% at t=0.3
seconds. With SAPF included, the computed THD is 2.91%.
THD = 30.90%
Without SAPF

Keywords: Shunt Active Filter, Harmonics, PQ theory
I. INTRODUCTIONT

he use of non-linear loads in power electronic (PE)devices has been increasing continuously from the past 3
decades. With more and more dependency on PE devices,
power system is being subjected to dynamic oscillations due tothe non-linearity conditions. Traditionally, many types ofsolutions were proposed and they were somewhat helpful in
reducing the level of impact on the system [1][2]. Some ofthem include in-line reactors, Static compensators, Passive
filters, resonant filters and high pass filters. Power quality
problem requires continuing monitoring and new methods toreduce the percentage of harmonics or inter-harmonics in the
network.
Due to the limitations of classical filters and capacitors, active
power filters are now seen as a viable alternative tocompensate harmonics and reactive power in non-linear loads.
The objective of this active power filter design is tocompensate the harmonic currents and voltages in addition to
selective reactive power compensation such that the gridcurrent or voltage waves can conserve the sinusoidal form.

THD = 2.91%
With SAPF

Figure 2. THD Simulation
REFERENCES[1]. “Instantaneous Power Theory and Applications to Power Conditioning”
by Hirofumi Akagi, Edson Hirokazu Watanabe and Mauricio Aredes.
[2]. H. Akagi, Y. Kanazawa and A. Nabae, “Generalized Theory of
Instantaneous Reactive Power and its Applications,” Transactions of the IEEJapan, Part B, vol. 103, no.7, 1983

PV Current Harmonic Suppression Under Unbalanced
Fault Based on Factor Selection Control
Niancheng Zhou, Xiaoxuan Lou, Student Member, IEEE, David Yu, Member, IEEE, Qiang Fu,
Qianggang Wang.


II. KEY EXPRESSIONS

Abstract—Fault ride-through operation of three-phase
photovoltaic (PV) inverters will produce apparent current
harmonics under unbalanced grid fault. This paper mainly
researches on power control of PV under unbalanced voltage, andhas analyzed the total harmonic distortion (THD) and the featureof each order of the harmonics in the inverter currents. Then theanalytic formulas of power fluctuation, current THD and odd
harmonics in the current of the PV inverter were derived, and twoalgorithms of the adjustment coefficients of PV power control
were proposed. The control strategy model of current harmonic
suppression under unbalanced grid voltage was built. In addition,
the feasibility of this method is verified by the simulation result
produced by PSCAD/EMTDC.

The current reference with coefficients can be expressed as,
P [u (t )   u (t )]  Qr [u (t )   u (t )]ir (t )  r(1)
| u (t ) |2 (1   )  u (t )u (t )   | u (t ) |2
A. Power Fluctuation Suppression p(t ) 
(1   )(1   )np  2 p(t )-p(t )  max 1   (1   )n   n
 q(t ) 
(1   )nq  2 q(t )-q(t )  max 1   n

I3 

T

Cdc
L
C

Current Tracking
Control

MPPT

I7 

uabc,
iLabc

Power
Reference
Qr
Calculation

Current Reference
Calculation

uabc(a) The Structure of PV Generation System

udc
PI

Pr

Ur

Constant

tanj Power Factor

Signal ofControl
Method

U
+

-

PI
U

Constant
Voltage

u2

++-

Qr

Current Reference
Calculation

-+

V

Pr

Pr (1   n) 4 B 2 A2  2 B A  1 4 B
[
 2]
3U 
A
C ( A  B)

(6)

Pr (1   n) 8B3 A3  4 B 2 A2  4 B A  1 2 8B 2[
  3 ] (7)3U A A
C ( A  B)

mabc

1
0.6
0.2

wC

I9=0.0014pu
THD=5%

I5=0.0041pu



udcr

P

Current Tracking Control

idc, udc(5)

The international standard for the PV generation system witha rated voltage of 380V and a rated capacity of 10MVA has
formulated that odd current harmonics from 3rd to 9th should belower than 0.0041pu、0.0041pu、0.0029pu and 0.0014pu.
Then the selection region of the coefficients α and β should bein the shadow area, shown in figure 2:

iabcr

Pr

udc

(4)

P (1   n) 2 B A  1 2
1
E32  F32  r  [ ]3U
2
C ( A  B) A

I5 

udcr

A2
1
2C ( B  C )2
2

Where A=(1+)n, B=1+n and C= B  A2 .

HE grid-connected PV system and its power control
structure are shown in figure 1.

idc, udc

I2
1 
I12

THD 

I. INTRODUCTION

PWM
mabc

(3)

B. Odd Harmonics and THD Suppression

Index Terms—unbalanced voltage; photovoltaic; harmonic
suppression; power fluctuation; odd harmonic

PV Array(2)

I7=0.0029pu-0.2(b) Power Control of PV system
Fig. 1 Grid-connected photovoltaic system

-0.6
-1
0

Feasible
region0.2

I3=0.0041pu

0.4



0.6

0.8

1

Fig. 2 The contour lines of odd current harmonics and current THD
This work was supported by National Natural Science Foundationof China (51277184); Scientific Research Foundation of State Key
Lab. of Power Transmission Equipment and System Security(2007DA10512711209)...

III. SIMULATIONThe control method is verified in PSCAD/EMTDC with a
rated capacity of 2.5kVA, L=12mH, C=0.7μH, Cdc=1800μH.
1

1Harmonic filtering phenomenon in single phaseinduction motors
Gaurav Singh, Student Member, IEEE, and E.R.(Randy) Collins, Jr., Senior Member, IEEE

Abstract—Single phase induction motors are widely used inhousehold applications. This follows from their various attributes
such as their simple and rugged construction, wide availability
and low cost. For use in household devices such as refrigerators,
air conditioners and pumps, capacitor start capacitor run motors
are preferred, because of their higher efficiency and lower
acoustical noise. This, coupled with the increasing usage of single
phase rectifier front end devices in recent years, leads to aninteresting scenario where single phase capacitor run motors areconnected in shunt with a single phase full wave rectifier across
a common feeder bus. Intuitively it is expected that this motorrectifier combination will draw an extremely distorted harmonicrich current. However, recent work by the authors has shownthat this is not the case. It has, in fact, been observed that thesingle phase induction motor absorbs a large quantity of theharmonic current, leading to a relatively harmonic free current
being absorbed by the combination. This poster attempts tooutline this harmonic filter like behaviour of the single phasecapacitor run motor and it’s implications for the power grid.

Full wave rectifier with filter capacitor andresistive load

1 phase120V
60 Hz
supplyAC

1 phase motor

M
Fig. 1. Experimental circuit showing single phase cap run motor connectedin parallel with single phase full wave rectifier

done using three motors of representative sizes. Results froman experiment carried out with a 2HP motor have been shownin Fig. 2

Index Terms—Single phase, Induction motor, Rectifier, Filter,
harmonics.

40

30

I. I NTRODUCTION
RADITIONALLY utilities have considered single large
loads to be the main contributors to harmonics in the
power grid. Household loads were not considered to be aserious threat to power quality. However, the recent adventof devices such as Compact Fluoroscent Lamps (CFL), personal computers and other consumer electronic devices, which
utilize rectifier front ends has changed this scenario drastically.
Single phase full wave rectifiers, employed by these devices,
are rich sources of odd harmonics. The growing numbers ofthese devices is causing an accumulation of harmonics indistribution systems.
Another electrical device present in abundance in a modern
household is the single phase capacitor run Induction motor.
This leads to an interesting scenario where the single phase
Induction motor is connected to a supply voltage which is richin harmonics. This scenario was simulated in the laboratory
and it’s results have been shown here.

20

T

A. Experimental work and results
The experimental setup utilized for this scenario has beenshown in Fig. 1.
We expect this motor rectifier combination to draw a hugeamount of harmonic current. However, the results obtained
were unexpected in that the total current drawn by the combination is relatively harmonic free. The experimental work was
Gaurav Singh and E.R.Collins, Jr. are with the Department of Electricaland Computer Engineering, Clemson University

10
Motor current
Rectifier current

0
-0.02

-0.01

0

0.01

0.02

0.03

Total current
Volts (scaled by 0.2)

-10

-20

-30

-40

Fig. 2. Plot of voltage, motor current, rectifier current and total current whena 2HP motor is connected into the circuit. Note that voltage has been scaled
down by a factor of 0.2 for illustration purposes.

II. C ONCLUSION
Because of the presence of a running capacitor, the auxiliary
winding circuit of a single phase induction motor is essentiallyan RLC circuit with a resonant point. As a result, at dominantharmonic frequencies in households, the single phase induction motor represents a much lower impedance for harmonic
currents produced by rectifier front end devices, as comparedto source impedance. This causes harmonic cancellation in the
total current absorbed by the house load. Cheap single phasemotor drives that are sometimes employed by these devices,
negate this characteristic. Thus, it turns out that single phasemotor drives end up doing more harm than good to the power
quality of a household.

Modeling and Simulation of a High-Head Pumped
Hydro System
Hang Li, Student Member IEEE, Gregory Parker
Brian K. Johnson, Senior Member IEEE,
Joseph D. Law, Member IEEE

Kyle Morse
Donald F. Elger
Department of Electrical and Computer Engineering
University of Idaho
Moscow, Idaho USA

Department of Electrical and Computer Engineering
University of Idaho
Moscow, Idaho USA
b.k.johnson@ieee.org

MODELING

Pump Storage Project

Setup

OVERVIEWII.

Define simulation
time limit

If Reachedsimulation time
limit

FunctionI.

4) The tail water can be a lake or river at a lower elevation.

Simulation and Calculation

Abstract— Increasing penetration of intermittent renewablegeneration, such as solar or wind, will lead to the deviation of thepower grid frequency from normal due to the randomness of theoutput renewable energy sources. Pumped storage can be used tobalance the renewable output in a manner to regulate thefrequency of the grid. This paper presents a feasibility study of a
proposed pumped storage system used to regulate the frequencyof power grid to meet the NERC CPS2 standard in response tovariable renewable energy output. Studies are performed using
dynamic electrical and hydraulic models of the pumped hydrosystem.

no

Simulate withdynamic
modelsIncrease timeyes
Calculate
CPS2

Plot theResults

Fig. 2 Overall simulation flow diagram
Fig. 1 Pumped storage system diagram

Fig. 1 shows a diagram of the pumped storage system. The
points listed below illustrate the operation of the pumped storage
system.
1) Energy is stored in the upper reservoir in the form ofpotential energy associated with the increased elevation of
the water in the reservoir relative to the elevation of the tailwater.
2) When the system is generating power, water flows down thepenstock. When the system is pumping water, the waterflows up the same penstock. Only one penstock is used tosimplify the pump storage system design.
3) Electric machine located at the bottom of the penstock forgeneration and pumping

III.

SIMULATION RESULTS

There were four CPS2 violations during the total time
period, giving a mean score of 92%.

Fig. 3 Simulation result

Developing Integrated Load Modeling Framework forCampus Microgrids with Large Buildings
Sayonsom Chanda, Student Member, IEEE and Anurag K Srivastava, Senior Member, IEEE
Smart Grid Demonstration and Research Investigation Lab (SGDRIL)
Washington State University, Pullman, WA, USA
sayonsom.chanda@email.wsu.edu
Abstract--- With ongoing smart grid efforts, electricity
consumers of the future are expected to opt for energy
conservation through demand response, establishing energy
consumption patterns for trending analysis and microgrid
implementation for higher reliability. Customers may also havean option to choose from which utility they draw their power. All
these ongoing changes may lead to significant changes in
distribution system power flows. To analyze and understandconsumer behavior, it is required to develop detailed load
models. This will help with the smart grid implementation
process, and will provide better insights for developing global or
local control systems.
Index Terms--- ETP Model, ZIP
SynerGEE, GridLAB-D, energy usage

I.

Model,

information will be put in the correct syntax, and added to theexisting one-line power distribution model converted from
SynerGEE, to obtain the detailed GridLAB-D model. For
future needs, several buildings or other information can be
added as and when necessary.

microgrid,

INTRODUCTION & MOTIVATION

Load modeling is critical for analyzing the functionality ofthe electrical grid, as loads eventually drive the need for power
generation, transmission and distribution. The advent of smart
grid and bilateral information exchange, has also broughtnewer kinds of power sources like fuel cells, and newer kindsof loads like electric vehicles, which will become increasingly
mainstream. This raises a need of a framework to betterorganize the task of load modeling to expedite the process ofdevelopment of detailed load models to help with power flow
studies, planning, development and implementation of modern
distribution systems. Developing a detailed load model isusually a tedious process, and requires inputs from contractors,
maintenance and construction engineers. Hence, most
campuses or large buildings do not have detailed load models,despite its evident benefits. However if a systematic automatedapproach, as shown in this poster (Fig.1), can be followed detailed load models of campuses with large buildings can be
developed.
II.

METHODOLOGY

The task is carried out using Open Source software,
GridLAB-D, developed by Pacific Northwest National
Laboratories (PNNL) [1]. Currently, utilities model theirdistribution systems using proprietary software like SynerGEE.
So, the first step of this work is to develop a converter totranslate the SynerGEE files (detailed enough to include
building transformers or building data) into GridLAB-D.
To this "Detailed Load Model Developer" program, civilengineering data about building construction, weather data,obtained from different sources may be added. Additionally,
information about distributed storage and distributed oncampus generators may be added in appropriate fields. All thisFigure 1. Detailed Load Modeling Developer FrameworkIII.

APPLICATIONS OF THIS WORK(i) Developing and testing of restoration strategies forhigher reliability of power supply. (ii) This work may be
extended to large commercial buildings as well. (iii) Modelingof generators and distributed energy resources for peakshaving, islanded microgrid operation. (iv) Facilitate thedevelopment of better control systems and algorithms tooptimize the distribution system. (iv) Studying of newer kindsof distributed energy storage options like battery charging,electric vehicles and their impact on distribution system power
flow.
REFERENCE[1]

Chassin, D.P, Schneider, K., Gerkensmeyer, C., "GridLAB-D: An opensource power systems modeling and simulation environment",
Transmission and Distribution Conference and Exposition, 2008. T&D.

Dynamic Modeling and Analysis on Cut-in Grid of
Doubly-fed Induction Wind Power System
1, 2

1

Sibei Mi , Student Member IEEE, Jingqi Su and David Yu , Senior Member IEEE
1. College of Engineering and Applied Science, University of Wisconsin Milwaukee, WI, USA;2. School of Electrical Engineering, Chongqing University, Chongqing, China
Email: sibeimi@uwm.edu
This poster introduces doubly-fed induction generator in grid side converter operates in PWM Rectifier state while thethe rotary dq coordinate system dynamic mathematical rotor-side converter operates in PWM inverter status. When it
model based on the basic principles of wind turbines. By operates in super-synchronous state, part of the slip powerstudying on the grid control strategy of Doubly-fed induction drives back from the exciter rotor winding through grid, andgenerator, analysis vector control technology for dual PWM, rotor-side converter operates in PWM Rectifier state while theand adopt PSCAD / EMTDC to establish the dynamic grid side converter operates in PWM inverter status
simulation model.
Simulation analysis of operating characteristics shows in
Figure2. Before 0.5 seconds, generator is controlled by the
From (1), when wind speed remains unchanged, the output
speed,
andafter that it is controlled by pitch angle. In steadypower of wind turbine varies while the wind speed changes.
state operation, set the voltage to be 0.69p.u.. As showing in
To track Pmax , the adjustments of m must be in time when Figure 2, the simulation result is between 0.6p.u.and 0.7p.u.
wind speed changes, and maintain the optimal tip speed ratio. which is quite close to the set-point. Grounding fault happened
Fan speed m can be adjusted by varying the pitch of wind at 3rd second, and the voltage dips to about 0.25p.u.. After 0.2
seconds, failure is excluded. Then after a very short time ofturbine blades or by controlling the output power of the oscillation, the voltage quickly runs back to the normal
generator.
working value. The simulation shows that the unit has a strong
1
1
R 3 3
Pmax  Cp max Av 3  Cp max A (
) m
2
2
opt

(1)

resiliency.
KEY FIGURES

There is the rotary dq coordinate system dynamic
mathematical model of the doubly-fed induction generator.
Equations of the rotor
Tl  Te 

TJ d r3N P
 Tl 
Lm ( I sd I rq  I sq I rd )N P dt
2

Rs

jLs

'

(2)

When study the electromechanical transient of the paralleloperate doubly-fed induction wind turbines, the dynamic
process of the stator winding circuit can be not taken into
consideration.

USE  Ed  j Eq

Figure 1. Equivalent circuit of the doubly-fed induction generator

Therefore, the voltage equations of the stators turn into:'U sd  Rs ( I sd )  1 Ls ( I sq )  E d'U sq  Rs ( I sq )  1 Ls ( I sd )  E q

Is

(3)

Differential equation of stator inner potential:









Lm
Rr
 dE d' dt   L E d  1 ( Ls  Ls ) I sq  s1 E q  L U rqrr
 dE q   Rr E d  1 ( Ls  L's ) I sd  s1 Ed  Lm U rd
Lr
Lr
 dt

(4)

(2),(3)and(4)constitute the dynamic model of the doublyfed induction generator. From (3) we can get the equivalentcircuit of the doubly-fed induction generator showing in
Figure 1.
Under the state of different energy flow of the rotor, thetwo converter achieve the functions of rectifier and inverter
alternatively. For AC excited generator, when it operates in
sub-synchronous state, rotor winding absorbs slip power, andFigure 2. Three-phase voltage of generator outlet