Grounding grids break diagnosis withconsideration of underground pipe
Cigong Yu, Zhihong Fu, Xiaorui Hu, Minghui Bao, Yongliang Ji and Shenwu Yu

Abstract—Grounding grid is an essential means for thesafety of operators and power apparatus in substation. Many
approaches including transient electromagnetic method havebeen applied to diagnose faults in grounding grids. This
paper investigated break point diagnosis of grounding gridswith consideration of underground pipe. Based on theequivalent resistivity distribution characteristics, thegrounding fault can be accurately located in the grounding
grids. The influence of underground pipe on the distributionof equivalent resistivity is analyzed. Factors including
underground pipe made of different materials and buried indifferent depth are discussed in detail. The results show thatthe underground pipe puts a great impact on fault diagnosisof grounding grids.
Index Terms—Grounding grid, transient electromagnetic
method, fault diagnosis, underground pipe, substation

Fig. 1 Contour map of equivalent resistivity in the presence ofunderground pipe made of (a) steel, and (b) concrete.

r (ohm·m)

Hr (m)(a)r (ohm·m)

Hr (m)(b)r (ohm·m)

Hr (m)

I. INTRODUCTION
In China, the grounding grids are made of flat or roundsteel and buried about 1 m under the earth surface. Due tothe years of soil erosion, lightning and short-circuit current
damage, the grounding faults occur frequently. Recently,
there are many research literatures focusing on fault
diagnosis of grounding grids. All the diagnosis methods
can be divided into four categories as method based on thetheory of circuit, based on the electromagnetic theory,
based on the electrochemical detection theory, and thetransient electromagnetic method (TEM).
This paper is a follow-up work of fault diagnosis ofgrounding grids using transient electromagnetic method.
In this study, we devote to investigating the effect ofunderground pipe on fault diagnosis of grounding grids
based on transient electromagnetic method. Factors thatplay the role on diagnosis results including undergroundpipe made of different materials and buried in different
depth will be discussed.
II. KEY FIGURESr (ohm·m)

Hr (m)(a)r (ohm·m)

Hr (m)(b)(c)
Fig. 2. Contour maps of equivalent resistivity at the situation the pipe isburied (a) 0.5m, (b) 1.15m, and (c) 1.75m beneath the earth surface. A
break cut occurs at point B(4,5) (close to point 9).

III. CONCLUSION
In this paper, we have conducted simulation tests toinvestigate the influence of underground pipe on fault
diagnosis of grounding grids. The contour map ofequivalent resistivity displays regular distribution
characteristics. When the faults occur, the equivalent
resistivity calculated at locations above the broken
grounding mesh increases considerably. From the typicalchange of equivalent resistivity distribution, the grounding
fault can be accurately located.
Analyses show that the underground pipe has great
impact on the fault diagnosis of grounding grids. The
influence of underground pipe made of different materials
and buried in different depth has been surveyed. The
conclusions drawn are:1) The pipe with high conductivity buried above thegrounding girds has significant influence on the fault
diagnosis of grounding grids so that the fault cannot be
pinpointed, whereas that of poor conductivity has no
impact on the diagnosis results.
2) The high conductivity pipe buried above thegrounding grids has significant influence on fault
diagnosis of grounding grids, whilst that buried under the
grounding grids has no impact on diagnosis results.

A Game Theoretic Approach to Analyze theDynamic Interactions of Multiple Residential
Prosumers Considering Power Flow Constraints
Tao Chen, Student Member, IEEE
Hajir Pourbabak, Student Member, IEEE
Department of Electrical and Computer Engineering
University of Michigan-Dearborn

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

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

II. K EY EQUATIONS
M in Ji = Ci

(1)

M ax Ji = (Ri − Ci )(2)

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

(4)

j

(5)

i

Z(x) = argmax Ψ(x, y) x, Z(x) ∈ X

(6)

y∈X

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

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

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

A Game Theoretic Approach to Analyze theDynamic Interactions of Multiple Residential
Prosumers Considering Power Flow Constraints
Tao Chen, Student Member, IEEE
Hajir Pourbabak, Student Member, IEEE
Department of Electrical and Computer Engineering
University of Michigan-Dearborn

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

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

II. K EY EQUATIONS
M in Ji = Ci

(1)

M ax Ji = (Ri − Ci )(2)

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

(4)

j

(5)

i

Z(x) = argmax Ψ(x, y) x, Z(x) ∈ X

(6)

y∈X

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

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

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

A Game Theoretic Approach to Analyze theDynamic Interactions of Multiple Residential
Prosumers Considering Power Flow Constraints
Tao Chen, Student Member, IEEE
Hajir Pourbabak, Student Member, IEEE
Department of Electrical and Computer Engineering
University of Michigan-Dearborn

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

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

II. K EY EQUATIONS
M in Ji = Ci

(1)

M ax Ji = (Ri − Ci )(2)

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

(4)

j

(5)

i

Z(x) = argmax Ψ(x, y) x, Z(x) ∈ X

(6)

y∈X

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

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

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

A Game Theoretic Approach to Analyze theDynamic Interactions of Multiple Residential
Prosumers Considering Power Flow Constraints
Tao Chen, Student Member, IEEE
Hajir Pourbabak, Student Member, IEEE
Department of Electrical and Computer Engineering
University of Michigan-Dearborn

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

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

II. K EY EQUATIONS
M in Ji = Ci

(1)

M ax Ji = (Ri − Ci )(2)

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

(4)

j

(5)

i

Z(x) = argmax Ψ(x, y) x, Z(x) ∈ X

(6)

y∈X

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

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

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

Application of Microgrids in Addressing Distribution
Network Load Ramping
Alireza Majzoobi, Amin Khodaei
Dept. of Electrical and Computer Engineering
University of Denver
Denver, CO, USA
Alireza.Majzoobi@du.edu, Amin.Khodaei@du.edu
Abstract—In spite of all advantages of solar energy, thedeployment of this type of the renewable energy will significantly
change the typical electric load profile, thus necessitating achange in traditional distribution grid management practices. In
this paper, microgrids are utilized to offer a viable and local
solution to this challenge while preventing costly investmentsfrom the electric utility. A microgrid optimal scheduling model isdeveloped to ensure that the microgrid excess generation can becoordinated with the distribution grid net load to remove theassociated significant variability and ramping. Numericalsimulations exhibit the effectiveness of the proposed model.
Keywords— Microgrid, Solar energy, duck curve, optimalscheduling, grid-connected operation.

R

I. INTRODUCTION

ENEWABLE ENERGY resources have been significantly
developed over the past few decades, due to significant
advantages that they offer, such as reduced operation cost, air
pollution reduction, and benefiting from ubiquitous source ofenergy. Despite these benefits, renewable energy resource
challenge the traditional grid management practices, thus theirlikely impacts on the grid should be also considered. For
instance, rapid growth of solar energy as one of the mostfavorable generation technologies adopted by end-use
customers, has changed the typical daily demand curves. A
typical daily demand curve rises in the morning and peaks inthe afternoon, (especially in the summers as air conditioners
are extensively used) and it hits a second highest peak in early
evening. The solar energy resources, however, usually
generate the highest amount of power in the afternoon anddecrease toward sunset, hence they offer the capability ofsupplying the around-noon power demand but a marginal
effect on early evening peaks. Therefore, rapid growth of solar
energy has led to changing traditional afternoon peaks toafternoon valleys which followed by a steep and problematic
peak in the late afternoon [1, 2].

companies as they may require additional fast response assets
to respond quickly to this load ramp-up. This ramping effectbecomes more severe as the solar energy penetration increases
in the power grid. Fig. 1 illustrates the current and future
estimates of this ramping effect in California [1, 2]. This paper
proposes a new model, leveraging microgrid’s ramping
capability, to address this challenge.
II. MODEL OUTLINE
Microgrid is considered in this paper as a viable solution
for changing the demand curve and mitigating the ramping
effects in distribution grids. Microgrids consist of Distributed
Energy Resources (DER) and interconnected loads which canoperate in both grid-connected and islanded modes [3]. In
grid-connected mode, as considered here, the microgrid can
export its extra generation to the grid to mitigate the severramping, while ensuring that the power seen by the utility has
manageable ramps. To do this, two problems are modeled:first, the maximum generation ramping capability of the
microgrid, defined as the microgrid generation change in two
consecutive hours, is calculated, i.e., the objective (1) subject
to prevailing operational constraints. Second, a model for
optimal scheduling problem of microgrid in grid-connectedmode is developed with the objective of minimizing thesystem operational cost and subject to ramping limitation
constraint (2). This constraint ensures that the desirable
amount of ramping, which could be managed by the utility, isachieved. The problems are modeled using mixed-integer
programming and solved using CPLEX.

PMax ramp up  Max PMt  PM (t 1)(1)

PMt   Pjt   t(2)

jIII. DICSUSSION AND CONCLUSION
The microgrid has been used as a solution for mitigation of
net load ramping in distribution grids, which occurs due toconcurrent decrease in solar generation and increase in
consumers’ loads. The maximum ramping of the microgrid isobtained and an optimal scheduling model is further
developed for coordination of calculated maximum ramping
capability with connected loads in order to alleviate the steep
ramp of new demand curves.
REFERENCES[1]

Fig1. Steep ramping in California duck curve [1].

This sever ramp-up in the new demand curve, also called“duck curve”, would be a pressing issue for the utility

[2]
[3]

P. Denholm, M. O’Connell, G. Brinkman, and J. Jorgenson,“Overgeneration from Solar Energy in California: A Field Guide to the
Duck Chart”, National Renewable Energy Laboratory technical report,
Nov. 2015.
https://www.caiso.com/Documents/FlexibleResourcesHelpRenewables_
FastFacts.pdf
S. Parhizi, H. Lotfi, A. Khodaei, S. Bahramirad, “State of the Art in
Research on Microgrids: A Review”, IEEE Access, Vol. 3, 2015.

Consensus-based Distributed Control for Economic
Operation of Distribution Grid with Multiple
Consumers and Prosumers
Hajir Pourbabak, Student Member, IEEE
Tao Chen, Student Member, IEEE
Wencong Su, Member, IEEE

Department of Electrical and Computer Engineering
University of Michigan-Dearborn
wencong@umich.edu
Abstract— This paper investigates the economic operation of
distribution grid with multiple consumers and prosumers
using consensus-based distributed control algorithms. This
paper formulates the utility and cost functions of a variety of
consumers and prosumers and model the interactions among
multiple consumers and prosumers. The distributed control
algorithms contained in this paper can apply to many othersmart grid applications.

I. INTRODUCTION

The next-generation grid is a level playing field in termsof electricity transactions, where all customers have an equal
opportunity. As a result, there is a need to investigate theinteractions among a number of consumers and prosumers.
The existing control approaches can be divided into three
categories, namely, centralized control, decentralized
control, and distributed control.

Prosumers

Consumers

The distributed control has the potential to solve theeconomic operation problems of multiple consumers andprosumers. The envisioned electricity market consists ofmultiple consumers and prosumers, as shown in Fig. 1. We
will consider the mathematical models of utility and costfunctions for various consumers and prosumers.
1

P2,1
1

P2,2

Pn ,1

2

2

P2,i
Pn ,2

3

..........

,

=

,

4
,

−

,

,

=

,

,

+

<
≥
,

+

2

(1)

2

(2)

As our first case, we scale up the test system to consider
four consumers and four prosumers.
The Lagrange multipliers (incremental costs) for subproblems are shown in Fig. 2. Table II compares the
proposed distributed method with the centralized method.
The results of distributed method are the same as that using
centralized method.
45
40
35
30
25
20
15

sub-problem 1
sub-problem 2
sub-problem 3
sub-problem 4

10
5
0
0

200

400

600

Iteration

800

1000

Fig. 2. Generation Output Power of Prosumers for Sub-problems in Casen

Table II: Benchmarking Comparison with Distributed methods

3

Pn,3 . . . . . . . . . .

iFig. 1. The schematic model of multi consumer and prosumer

1200

Pn ,i

Equation(1)
modelsthequadratic
utilityfunction ( , ) for different consumers and Equation (2)shows the quadratic cost function for the -th prosumer.

Prosumers
1
2
3
4
1
2
3
4

Optimal Incremental Cost (cent/kW)
Distributed Method
9.32029.97258.76858.2310Centralized Method
9.3247
9.97858.76848.231299.4230
97.4708
90.0391
106.4299

99.1408
97.8692
90.0300
106.4100

Optimal Output Power (kW)Synchrophasor Reference Algorithm for
PMU Calibration System
Cheng Qian, Graduate Student Member, IEEE, Mladen Kezunovic, Fellow, IEEE
Dept. of Electrical and Computer Engineering
Texas A&M University, College Station, TX
peterqiancheng@tamu.edu, kezunov@ece.tamu.edu
Abstract— This paper describes a reference algorithm
specifically designed for PMU Calibration System. Contrary to
existing DFT-based and curve fitting-based methods, which use
a single signal model, the proposed algorithm applies anadaptive mechanism, and switches signal models according to
specific signal input. Signals are modeled with parameters withapparent and certain physical meaning, fundamentally
avoiding error magnification from derivative calculation forfrequency and rate of change of frequency estimation.
Levenberg-Marquardt algorithm is used for solving the signal
parameters. The test results show that the proposed algorithm
has a much higher accuracy than the requirements of IEEE
standard C37.118.1, and hence can serve as a reference
algorithm in a PMU Calibration System.

Fig. 2.

Procedure of synchrophasor estimation with proposed algorithm

Keywords—
Levenberg-Marquardt
algorithm,
PMU
calibration system, reference synchrophasor algorithm,
synchrophasor estimation, power system measurements.

I. METHODS AND KEY EQUATIONS
Levenberg-Marquardt algorithm is the key to thealgorithm, as shown in (1)−1∆𝒙(𝑖) = [𝑱(𝑖)𝑇 𝑱(𝑖) + 𝜇 (𝑖) diag(𝑱(𝑖)𝑇 𝑱(𝑖) )] 𝑱(𝑖)𝑇 [𝒃 − 𝑓(𝒕, 𝒙(𝑖) )] (1)Signal models determine the Jacobian matrix J, as listed.
A. Model for Static , Step, and Frequency Ramp Signals𝑥(𝑡) = √2𝑋𝑟𝑚𝑠 cos(2𝜋𝑓0 𝑡 + 2𝜋∆𝑓𝑡 + 𝜋𝑅𝑓 𝑡 2 + 𝜑0 ) (2)B. Model for Harmonic Distorted and Out-of-Band Signals𝑥(𝑡) = √2𝑋𝑟𝑚𝑠 cos(2𝜋𝑓0 𝑡 + 2𝜋∆𝑓𝑡 + 𝜋𝑅𝑓 𝑡 2 + 𝜑0 ) +(3)√2𝑋𝑟𝑚𝑠,ℎ𝑎𝑟 cos(2𝜋𝑘𝑓0 𝑡 + 2𝜋𝑘∆𝑓ℎ𝑎𝑟 𝑡 + 𝜑0,ℎ𝑎𝑟 )C. Model for Modulation Signals𝑥(𝑡) = √2𝑋𝑟𝑚𝑠 [1 + 𝑘𝑚 cos(2𝜋𝑓𝑚 𝑡)]cos[2𝜋𝑓0 𝑡 + 2𝜋∆𝑓𝑡 + 𝑘𝑎 cos(2𝜋𝑓𝑚 𝑡 − 𝜋) + 𝜑0 ] (4)Fig. 3. Flowchart of Reference AlgorithmIII. KEY RESULTS

II. KEY FIGURESTABLE I.
Test Type

Fig. 1.

Reference PMU Test System at Texas A&M University

This work was supported by the Power Systems Engineering ResearchCenter (PSERC) Project T-57HI “Life-cycle management of missioncritical systems through certification, commissioning, in-service
maintenance, remote testing, and risk assessment”

Steady-state
test
Harmonic test
OOB test
Frequency
ramp test
Modulation
test

ALGORITHM ACCURACY IN IMPLEMENTATION TESTS
TVE/IEEE(%)

Frequency/IEEE(Hz)

ROCOF/IEEE(Hz/s)

0.01%/1%

0.0004/0.005

5×10-4/0.1

0.01%/1%
0.01%/1%

0.002/0.005
0.002/0.005

10-3/0.4 (P)
10-3/0.4 (P)

0.04%/1%

0.002/0.01

4×10-4/0.2

0.09%/3%

0.012/0.06

0.2/2

Synchrophasor Reference Algorithm for
PMU Calibration System
Cheng Qian, Graduate Student Member, IEEE, Mladen Kezunovic, Fellow, IEEE
Dept. of Electrical and Computer Engineering
Texas A&M University, College Station, TX
peterqiancheng@tamu.edu, kezunov@ece.tamu.edu
Abstract— This paper describes a reference algorithm
specifically designed for PMU Calibration System. Contrary to
existing DFT-based and curve fitting-based methods, which use
a single signal model, the proposed algorithm applies anadaptive mechanism, and switches signal models according to
specific signal input. Signals are modeled with parameters withapparent and certain physical meaning, fundamentally
avoiding error magnification from derivative calculation forfrequency and rate of change of frequency estimation.
Levenberg-Marquardt algorithm is used for solving the signal
parameters. The test results show that the proposed algorithm
has a much higher accuracy than the requirements of IEEE
standard C37.118.1, and hence can serve as a reference
algorithm in a PMU Calibration System.

Fig. 2.

Procedure of synchrophasor estimation with proposed algorithm

Keywords—
Levenberg-Marquardt
algorithm,
PMU
calibration system, reference synchrophasor algorithm,
synchrophasor estimation, power system measurements.

I. METHODS AND KEY EQUATIONS
Levenberg-Marquardt algorithm is the key to thealgorithm, as shown in (1)−1∆𝒙(𝑖) = [𝑱(𝑖)𝑇 𝑱(𝑖) + 𝜇 (𝑖) diag(𝑱(𝑖)𝑇 𝑱(𝑖) )] 𝑱(𝑖)𝑇 [𝒃 − 𝑓(𝒕, 𝒙(𝑖) )] (1)Signal models determine the Jacobian matrix J, as listed.
A. Model for Static , Step, and Frequency Ramp Signals𝑥(𝑡) = √2𝑋𝑟𝑚𝑠 cos(2𝜋𝑓0 𝑡 + 2𝜋∆𝑓𝑡 + 𝜋𝑅𝑓 𝑡 2 + 𝜑0 ) (2)B. Model for Harmonic Distorted and Out-of-Band Signals𝑥(𝑡) = √2𝑋𝑟𝑚𝑠 cos(2𝜋𝑓0 𝑡 + 2𝜋∆𝑓𝑡 + 𝜋𝑅𝑓 𝑡 2 + 𝜑0 ) +(3)√2𝑋𝑟𝑚𝑠,ℎ𝑎𝑟 cos(2𝜋𝑘𝑓0 𝑡 + 2𝜋𝑘∆𝑓ℎ𝑎𝑟 𝑡 + 𝜑0,ℎ𝑎𝑟 )C. Model for Modulation Signals𝑥(𝑡) = √2𝑋𝑟𝑚𝑠 [1 + 𝑘𝑚 cos(2𝜋𝑓𝑚 𝑡)]cos[2𝜋𝑓0 𝑡 + 2𝜋∆𝑓𝑡 + 𝑘𝑎 cos(2𝜋𝑓𝑚 𝑡 − 𝜋) + 𝜑0 ] (4)Fig. 3. Flowchart of Reference AlgorithmIII. KEY RESULTS

II. KEY FIGURESTABLE I.
Test Type

Fig. 1.

Reference PMU Test System at Texas A&M University

This work was supported by the Power Systems Engineering ResearchCenter (PSERC) Project T-57HI “Life-cycle management of missioncritical systems through certification, commissioning, in-service
maintenance, remote testing, and risk assessment”

Steady-state
test
Harmonic test
OOB test
Frequency
ramp test
Modulation
test

ALGORITHM ACCURACY IN IMPLEMENTATION TESTS
TVE/IEEE(%)

Frequency/IEEE(Hz)

ROCOF/IEEE(Hz/s)

0.01%/1%

0.0004/0.005

5×10-4/0.1

0.01%/1%
0.01%/1%

0.002/0.005
0.002/0.005

10-3/0.4 (P)
10-3/0.4 (P)

0.04%/1%

0.002/0.01

4×10-4/0.2

0.09%/3%

0.012/0.06

0.2/2

1

Flexible Implementation of Power System
Topology Control in Smart Electricity Grids
Payman Dehghanian, Student Member, IEEE, Yaping Wang, Gurunath Gurrala, Member, IEEE,
Erick Moreno-Centeno, and Mladen Kezunovic, Fellow, IEEE
Abstract—This paper proposes a novel framework for optimaltransmission switching satisfying the AC feasibility, stability andcircuit breaker (CB) reliability requirements needed for practicalimplementation. The proposed framework can be employed as acorrective tool in day to day operation planning scenarios, in
response to possible contingencies. The switching options are
determined using an efficient heuristic algorithm taking into
account the DC optimal power flow, resulting in a binary tree
structure. Then, the AC feasibility and stability checks are
conducted and the CB condition monitoring data is employed toperform a CB reliability and line availability assessment.
Ultimately, the operator will be offered multiple AC feasible andstable switching options with associated benefits. The operator
can use this information, other operating conditions not explicitly
considered in the optimization, and his/her own experience toimplement the best and most reliable switching action(s). The
effectiveness of the proposed approach is validated on the IEEE118 bus test system.
Index Terms— Circuit breaker (CB); heuristic; load shedrecovery (LSR); optimization; reliability; transmission switching.

criterion such as minimum generation cost or maximum Load
Shed Recovery, etc. This paper also proposes a decision
making support tool based on a CB failure probability
assessment technique using condition monitoring data. Thisquantifies the benefits due to each switching action in the tree.
The operator can use this information, other operating
conditions not explicitly considered in the optimization, andhis/her own experience to select the best and most reliableswitching action at each level of the switching tree for
implementation. Therefore, this paper aims at bridging the gap
between the theoretical advancements in transmission line
switching and the day to day implementation practices.
II. PROPOSED FRAMEWORKThe proposed framework is generally demonstrated in Fig.
1. A simple example shown in Fig. 2 is used to illustrate how
a three-level Binary Switching Tree (BST) is constructed. In
this figure, each box denotes a tree node (i.e. a system state);
the node number and the corresponding load shed recovery(LSR) percentage (included in parentheses) are provided ineach box.

I. INTRODUCTIONTHOUGH being performed for decades on a very limited
scale with rather focused aims, transmission switchinghas recently gained further importance with the increased
penetration of renewable energy resources and the growing
demand for more reliable operation of power systems. It has
been shown that various operating conditions can be tackled
through transmission switching: voltage violations andoverloading conditions as a result of possible contingencies,
network losses and congestion management, security
enhancement, reliability improvements, and also cost
reduction for economic benefits.
The focus of this paper is to propose a corrective
transmission switching framework to be used in day to day
operation planning scenarios in response to system probablecontingencies. It incorporates AC feasibility, stability and CB
reliability requirements for practical implementation ofswitching actions. The proposed framework in this paper
provides several switching options per contingency in a treelike structure. At each level of the switching tree, theframework suggests the operator with a set of AC feasible andstable switching plans based on a selected optimization
This work was financially supported by the Advanced Research Project
Agency-Energy (ARPA-E) under the Green Electricity Network Integration(GENI) project “Robust Adaptive Transmission Control”.

Fig. 1. The overall framework proposed for transmission line switching.

Fig. 2. Example illustrating the BST construction.

Optimal Generation Expansion Plan Under CO2
Emission Reduction Mechanisms
Yuqian Song, and Jin Zhong
Department of Electrical and Electronic Engineering
The University of Hong Kong,Hong Kong SAR
E-mail: yqsong@eee.hku.hk
Abstract—Generation Expansion Planning (GEP) problem is acomplex, computational consuming problem in power systems.
Conventionally, several factors such as investment cost,
operation and maintenances cost is taken into consideration
when formulating this problem. As global-warming effect is now
drawing more and more concerns, and a series of regulatory
instruments of emission reduction are implemented around theworld, the cost of emission abatement has now become a factor
that is not to be neglected. This paper develops a model with 20year horizon, takes account the features of different generation
technologies, and investigates different CO2 emission reduction
mechanisms including carbon tax, feed-in tariff, and cap &trade mechanism. The optimal investment plans under eachmechanism are obtained. By comparing the simulation result,
how the regulatory instruments influence the GEP problem andthe effectiveness of each mechanism is discovered.

PROBLEM FORMULATION

By minimizing the cost of 20-year GEP problem, the optimal
investment strategies are obtained corresponding to different
emission reduction mechanisms as well as different
implementing scenarios of each mechanism (slack and strictcases). The simulation result shows some interesting
conclusions of the effectiveness of each mechanism.

𝟐𝟐𝟐𝟐 𝑵𝑵𝑵𝑵

𝟐𝟐𝟐𝟐 𝑵𝑵𝑵𝑵

𝒚𝒚=𝟏𝟏 𝒊𝒊=𝟏𝟏

𝒚𝒚=𝟏𝟏 𝒊𝒊=𝟏𝟏

𝐦𝐦𝐦𝐦𝐦𝐦 � � 𝐹𝐹 = 𝐦𝐦𝐦𝐦𝐦𝐦 � ��𝑪𝑪(𝑷𝑷𝑮𝑮𝑮𝑮,𝒚𝒚 ) + (𝒘𝒘𝒊𝒊,𝒚𝒚 × 𝑷𝑷𝑰𝑰𝑰𝑰𝑰𝑰 𝒊𝒊
× 𝑳𝑳𝑳𝑳𝑳𝑳𝑳𝑳𝒕𝒕𝒕𝒕𝒕𝒕𝒕𝒕 )�

(𝟏𝟏)

𝐦𝐦𝐦𝐦𝐦𝐦 � ��𝑭𝑭 + 𝑻𝑻𝑻𝑻𝑻𝑻 × 𝑬𝑬(𝑷𝑷𝑮𝑮𝑮𝑮,𝒚𝒚 )�

(𝟐𝟐)

𝐦𝐦𝐦𝐦𝐦𝐦 � ��𝑭𝑭 − 𝑭𝑭𝑭𝑭𝑭𝑭𝒕𝒕𝒕𝒕𝒕𝒕𝒕𝒕 (𝑷𝑷𝑮𝑮𝑮𝑮,𝒚𝒚 )�

(𝟑𝟑)

𝐦𝐦𝐦𝐦𝐦𝐦 � ��𝑭𝑭 + (𝑷𝑷𝒆𝒆𝒆𝒆 𝒚𝒚 × 𝝆𝝆𝒆𝒆𝒆𝒆 𝒚𝒚 ) − (𝑸𝑸𝒂𝒂𝒂𝒂 𝒚𝒚 × 𝝈𝝈𝒂𝒂𝒍𝒍 𝒚𝒚 )�

(𝟒𝟒)

𝒚𝒚=𝟏𝟏 𝒊𝒊=𝟏𝟏𝟐𝟐𝟐𝟐 𝑵𝑵𝑵𝑵

𝒚𝒚=𝟏𝟏 𝒊𝒊=𝟏𝟏𝟐𝟐𝟐𝟐 𝑵𝑵𝑵𝑵

𝒚𝒚=𝟏𝟏 𝒊𝒊=𝟏𝟏

III.

SIMULATION RESULTS

Table I. –Investment Plan under Different Emission Reduction
Mechanisms

Year
1

B
0

LT
0

0
0
0
0
re
re
re
re
0
0
0
0
0
0
ct
0
cl+ct
ct

0
0
0
0
0
0
0
0
0
0
0
cl+ct
ct
0
ct
ct
0
2*cl

20

2*cl

ct

ct

ct

0
0
0
0
0
0
0
0
0
0
0
cl
ct
ct
0
ct
ct
cl
2*cl
+ct

0
0
0
0
0
0
0
0
0
0
0
0
ct
re+ct
ct
cl
ct
3*re

re
re
re
re
0
0
0
cl
re
2*re
0
0
0
0
0
ct
ct
ct

2*cl

2*cl

In the table, re, cl and ct represent for the investment decision of renewable energy unit, clean
energy unit and conventional generation unit respectively. The items in table head represent forbase case, low tax, high tax, low FIT, medium FIT, high FIT, and cap & trade respectively.

KEY EQUATIONSObjective:

𝟐𝟐𝟐𝟐 𝑵𝑵𝑵𝑵

0
0
0
0
0
0
0
0
0
0
0
2*cl
ct
cl+ct
ct
0
ct
0

HT
2*cl

LF
0

MF
0

HF
cl

CAP
2*cl

Total Amount of CO2
Emission (unit)

II.

0
0
0
0
0
0
0
0
0
0
0
cl+ct
ct
0
ct
ct
ct
cl

74000
72000
70000
2

4

5

6

10

Carbon Taxes （$/unit）

20

Figure I. Carbon Dioxide Emission under Different Carbon Taxes

Total Amount of CO2
Emission (unit)

I.

2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19

105000
95000
85000
75000
65000

Carbon Taxes （$/unit）
Figure II. Carbon Dioxide Emission under Different Cap

Optimal Generation Expansion Plan Under CO2
Emission Reduction Mechanisms
Yuqian Song, and Jin Zhong
Department of Electrical and Electronic Engineering
The University of Hong Kong,Hong Kong SAR
E-mail: yqsong@eee.hku.hk
Abstract—Generation Expansion Planning (GEP) problem is acomplex, computational consuming problem in power systems.
Conventionally, several factors such as investment cost,
operation and maintenances cost is taken into consideration
when formulating this problem. As global-warming effect is now
drawing more and more concerns, and a series of regulatory
instruments of emission reduction are implemented around theworld, the cost of emission abatement has now become a factor
that is not to be neglected. This paper develops a model with 20year horizon, takes account the features of different generation
technologies, and investigates different CO2 emission reduction
mechanisms including carbon tax, feed-in tariff, and cap &trade mechanism. The optimal investment plans under eachmechanism are obtained. By comparing the simulation result,
how the regulatory instruments influence the GEP problem andthe effectiveness of each mechanism is discovered.

PROBLEM FORMULATION

By minimizing the cost of 20-year GEP problem, the optimal
investment strategies are obtained corresponding to different
emission reduction mechanisms as well as different
implementing scenarios of each mechanism (slack and strictcases). The simulation result shows some interesting
conclusions of the effectiveness of each mechanism.

𝟐𝟐𝟐𝟐 𝑵𝑵𝑵𝑵

𝟐𝟐𝟐𝟐 𝑵𝑵𝑵𝑵

𝒚𝒚=𝟏𝟏 𝒊𝒊=𝟏𝟏

𝒚𝒚=𝟏𝟏 𝒊𝒊=𝟏𝟏

𝐦𝐦𝐦𝐦𝐦𝐦 � � 𝐹𝐹 = 𝐦𝐦𝐦𝐦𝐦𝐦 � ��𝑪𝑪(𝑷𝑷𝑮𝑮𝑮𝑮,𝒚𝒚 ) + (𝒘𝒘𝒊𝒊,𝒚𝒚 × 𝑷𝑷𝑰𝑰𝑰𝑰𝑰𝑰 𝒊𝒊
× 𝑳𝑳𝑳𝑳𝑳𝑳𝑳𝑳𝒕𝒕𝒕𝒕𝒕𝒕𝒕𝒕 )�

(𝟏𝟏)

𝐦𝐦𝐦𝐦𝐦𝐦 � ��𝑭𝑭 + 𝑻𝑻𝑻𝑻𝑻𝑻 × 𝑬𝑬(𝑷𝑷𝑮𝑮𝑮𝑮,𝒚𝒚 )�

(𝟐𝟐)

𝐦𝐦𝐦𝐦𝐦𝐦 � ��𝑭𝑭 − 𝑭𝑭𝑭𝑭𝑭𝑭𝒕𝒕𝒕𝒕𝒕𝒕𝒕𝒕 (𝑷𝑷𝑮𝑮𝑮𝑮,𝒚𝒚 )�

(𝟑𝟑)

𝐦𝐦𝐦𝐦𝐦𝐦 � ��𝑭𝑭 + (𝑷𝑷𝒆𝒆𝒆𝒆 𝒚𝒚 × 𝝆𝝆𝒆𝒆𝒆𝒆 𝒚𝒚 ) − (𝑸𝑸𝒂𝒂𝒂𝒂 𝒚𝒚 × 𝝈𝝈𝒂𝒂𝒍𝒍 𝒚𝒚 )�

(𝟒𝟒)

𝒚𝒚=𝟏𝟏 𝒊𝒊=𝟏𝟏𝟐𝟐𝟐𝟐 𝑵𝑵𝑵𝑵

𝒚𝒚=𝟏𝟏 𝒊𝒊=𝟏𝟏𝟐𝟐𝟐𝟐 𝑵𝑵𝑵𝑵

𝒚𝒚=𝟏𝟏 𝒊𝒊=𝟏𝟏

III.

SIMULATION RESULTS

Table I. –Investment Plan under Different Emission Reduction
Mechanisms

Year
1

B
0

LT
0

0
0
0
0
re
re
re
re
0
0
0
0
0
0
ct
0
cl+ct
ct

0
0
0
0
0
0
0
0
0
0
0
cl+ct
ct
0
ct
ct
0
2*cl

20

2*cl

ct

ct

ct

0
0
0
0
0
0
0
0
0
0
0
cl
ct
ct
0
ct
ct
cl
2*cl
+ct

0
0
0
0
0
0
0
0
0
0
0
0
ct
re+ct
ct
cl
ct
3*re

re
re
re
re
0
0
0
cl
re
2*re
0
0
0
0
0
ct
ct
ct

2*cl

2*cl

In the table, re, cl and ct represent for the investment decision of renewable energy unit, clean
energy unit and conventional generation unit respectively. The items in table head represent forbase case, low tax, high tax, low FIT, medium FIT, high FIT, and cap & trade respectively.

KEY EQUATIONSObjective:

𝟐𝟐𝟐𝟐 𝑵𝑵𝑵𝑵

0
0
0
0
0
0
0
0
0
0
0
2*cl
ct
cl+ct
ct
0
ct
0

HT
2*cl

LF
0

MF
0

HF
cl

CAP
2*cl

Total Amount of CO2
Emission (unit)

II.

0
0
0
0
0
0
0
0
0
0
0
cl+ct
ct
0
ct
ct
ct
cl

74000
72000
70000
2

4

5

6

10

Carbon Taxes （$/unit）

20

Figure I. Carbon Dioxide Emission under Different Carbon Taxes

Total Amount of CO2
Emission (unit)

I.

2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19

105000
95000
85000
75000
65000

Carbon Taxes （$/unit）
Figure II. Carbon Dioxide Emission under Different Cap

1

Frequency Scanning Techniques for
Subsynchronous Resonance Screening Analysis
Hardik Parikh1, Student Member, IEEE and Billy Yancey2, Member, IEEE
Abstract—Subsynchronous Resonance (SSR) is a well known
phenomenon that is observed in the conventional energy
generation and wind farm sites. SSR is more prone to generationsites that are directly connected to the series compensatedtransmission lines. Series compensation is required to improvethe voltage profile and reduce transmission losses of thetransmission lines. The work presented here aims to highlight thepotential causes, the system configurations and the methods thatare used to identify SSR. The main focus of the poster is tooutline frequency scan techniques that are based on the small
signal perturbation method. The analysis generally employs
Electro Magnetic Transient Program (EMTP). This techniqueprovides the spectrum of frequency response to resistance andreactance. Finally, based on that we deduce the damping
provided by the system, and thereby the SSR risk to the project.
Index Terms—Frequency Scan; Subsynchronous Resonance(SSR); Series compensation; Grid; Contingency; Perturbations;Electro Magnetic Transient Program (EMTP)

WITH

II. FREQUENCY SCANNING TECHNIQUES FOR SSR
The frequency scan technique is one of the method to identify
SSR susceptibility. This technique considers small signal
perturbation method. Small signal current signals are injected
into the system under study. The screening analysis isperformed for turbine side and grid side to identify the natural
frequency response. The analysis considers the resistance andreactance vs. frequency plots. The turbine side scan is shownin Figure 1 and the grid side scan is shown in Figure 2. The
analysis focuses on reactance zero crossing event. The system
must have sufficient resistance to damp out any oscillationthat may happen at that point. If the system doesn’t havesufficient resistance at that point; it may be subject to higher
SSR vulnerability. Detailed EMTP type models are also
developed for SSR study that considers system contingencies,
faults, shunt compensation and series compensation
configurations.

I. INTRODUCTION

the development and implementation of Series
compensation techniques improve the voltage profile,
the SSR phenomenon has attracted a lot attention.
It is further classified in classical SubsynchronousResonance (SSR), Subsynchronous Torsional Interactions(SSTI) and Subsynchronous Control Interaction (SSCI).
When a series capacitor is added into a transmission line, it
creates a resonant frequency. This is not a problem as long as
energy is not injected at the resonant frequency. However, aninterconnection which injects power at the resonant frequency
can lead to severe damage to generation equipments
connected to the main grid system. Following the increased
events of SSR many regulatory authorities have mademandatory screening studies to identify the possibility of
exposure to SSR and protection techniques to avoid any
possibilities for such occurrence. In general screening analysis
includes the frequency scan techniques [1], Small signal
analysis, Electromagnetic transient analysis, Admittancematrix [2] methods.

Fig. 1. Turbine side frequency scan (100% dispatch)

Fig. 2. Grid side frequency scan (N-1 contingency)

REFERENCES
[1]

1

2

The University Of Texas at Arlington, Arlington, TX 76013,USA
Email: hardik.parikh@mavs.uta.edu
Electric Power Engineers Inc., Austin, TX 78738, USA
Email:byancey@epeconsulting.com[2]

Sahni, M., Muthumuni, D., Badrzadeh, B., Gole, A., & Kulkarni, A.,“Advanced screening techniques for sub-synchronous interaction inwind farms,” in Transmission and Distribution Conference andExposition (T&D), 2012 IEEE PES (pp. 1-9).
Badrzadeh, B., Sahni, M., Muthumuni, D., Zhou, Y., & Gole, A.,“Sub-synchronous interaction in wind power plants—part I: Study tools
and techniques,” in Power and Energy Society General Meeting, 2012
IEEE (pp. 1-9).

1Distance Relay Settings Adequacy Assessment
for an Evolving Network Topology
M. Tasdighi, Student Member, IEEE, and M. Kezunovic, Fellow, IEEE
Abstract—A challenge raised in today’s power system isassessing the system protection security and dependability after
the topology has been changed due to relay operation uponoccurrence of cascading faults or intentional operator switchingactions. This paper proposes an automated setting calculation
module which could be used to review the adequacy of the
distance relay settings following network topology changes. The
calculation procedure is broken down into blocks which could beprocessed in parallel in order to improve the computation speed.
A new concept called distance-of-impact (DoI) is proposed toreduce the computational burden by conducting the calculations
on a limited portion of the network affected by the topology
change. The module performance is tested on synthetic IEEE 118bus and real-life Alberta transmission operator systems. A
sensitivity analysis in the form of N-2 contingency impact on the
network relay settings coordination is conducted on the test
systems.
Keywords—Distance-of-impact (DoI), power system protection
security and dependability, N-2 contingency, phase distancesettings, relay ranking, topology control, vulnerability.

I. KEY FIGURES
Figure 2. General implementation of parallel computation for N tasks

DoI = 2
DoI = 1

e

i

a
j

c

g

d

h

b

f

Figure 3. Illustrating the concept of DoI

II. KEY RESULTS

Simulation Time (s)

1500

1000

500
for the whole network
for DoI=5 from the switching action

Figure 1. General flowchart of the relay setting calculation module

M. Tasdighi and M. Kezunovic are with Department of Electrical andComputer Engineering, Texas A&M University, Texas, USA. (E-mail:m.tasdighi@tamu.edu; kezunov@ece.tamu.edu).
The financial support for this research comes from the Advanced
Research Projects Agency-Energy (ARPA-E), U.S. Department of Energy(DOE), under the Green Electricity Network Integration (GENI) project“Robust Adaptive Topology Control”, ARPA-E award no. DE-AR0000220.

0

1 2 3 4 5 6 7 8 9 10
The Rank of N-2 Contingency Case (Table III)

Figure 8. Simulation time comparison between with and without
implementing DoI while deploying 30 processors on Alberta System

REFERENCES[1]

M. Tasdighi and M. Kezunovic, "Automated Review of Distance Relay
Settings Adequacy After the Network Topology Changes," IEEE
Transactions on Power Delivery, DOI: 0.1109/TPWRD.2016.2524654.

Optimal Power Flow-Based Generation Sheddingfor Dynamic Remedial Action Scheme
Arun Shrestha, Student Member, IEEE, Valentina Cecchi, Member, IEEE, and Robert W. Cox, Member, IEEE

Abstract—An optimal power flow (OPF)-based generation
shedding for dynamic remedial action scheme (RAS) is proposedin this paper. A straightforward generation-shedding cost function is developed using pre-RAS and post-RAS OPF costs. The
proposed method updates RAS control actions online based on
the system operating conditions and cost functions. The proposed
method is tested on a modified IEEE 39-Bus system. Test results
demonstrate effectiveness of the proposed generation-shedding
method.

I. I NTRODUCTION
In North America, the power systems are planned andoperated to withstand the loss of any single or multiple
elements without violating North American Electric Reliability
Corporation (NERC) system performance criteria. For a contingency resulting in the loss of multiple elements (CategoryC), emergency transient stability controls like load shedding or
generation shedding may be necessary to stabilize the powersystem. Emergency control is designed to sense abnormal
conditions and subsequently take pre-determined remedialactions to prevent instability. Commonly known as Remedial
Action Schemes (RAS), these emergency control approaches
have been extensively adopted by utilities.
Previous research on generation-shedding RAS schemes
are focused towards determining the minimum amount ofgeneration to shed. In this paper, an OPF-based generationshedding RAS is proposed. This scheme uses online stability
calculations and generator cost functions to determine appropriate remedial actions. Unlike conventional RAS, which aredesigned using offline simulations, online stability calculations
make the proposed RAS dynamic and adapting to any powersystem configuration and operating state. The generationshedding cost is calculated using pre-RAS and post-RAS OPF
costs. The criteria for selecting generators to trip is based on
the minimum cost rather than minimum amount of generation
to shed. For an unstable Category C contingency, the RAS
control action that results in stable system with minimum
generation-shedding cost is selected among possible candidate
solutions. The RAS control actions update whenever there isa change in operating condition, system configuration, or costfunctions.

where,f (PGi,P re−RAS ) =

NG
X

Min

FGi

(2)

i=1

f (PGi,P ost−RAS ) =

NG
X

Min

FGi

(3)

i=1, i6=CS

Hence, the critical generators to shed (CS) are selected suchthat the generation-shedding cost is minimized, the systemremains stable following the contingency, and the operating
constraints are not violated in the post-RAS period.
Min FGSi (i ∈ C)

(4)

The RAS choice, MW shed, post-RAS OPF cost, andgeneration-shedding cost for Test Case 2 are tabulated inTable I. Fig. 1 shows the OMIB P-δ and generator rotor angles
for the G71 RAS choice. The RAS choice of G71 results ina stable system with minimum generation-shedding cost. It
is noted that the MW shed during G71 generation shedding
is greater than the G51. This shows that shedding minimum
generation for system stability might not always be the mosteconomical choice.
TABLE IG ENERATION -S HEDDING C OST F OR T EST C ASE 2 ($/ H )RASChoiceMW
Shed

Post-RASOPF Cost ($/h)

Gen Shed
Cost($/h)

Transient
Stability

G71

227.1867,452.70

1322.10

StableG51

197.4367,646.50

1515.90

StableG41

220.92

67,969.10

1838.50

Unstable

G71, G72

454.36

68,674.60

2544.00

Unstable

G51, G52

394.8669,021.70

2891.10

StableG61418.52

69,275.20

3144.60

StableG41, G42

441.8469,695.60

3565.00

Unstable

G51, G52, G53

592.2970,524.80

4394.20

StableG41, G42, G43

662.76

71,582.80

5452.20

Unstable

G61, G62

837.0472,729.10

6598.50

Unstable

II. OPF-BASED G ENERATION -S HEDDING S ELECTION
The generation-shedding cost FGSi is defined as:
FGSi = f (PGi,P ost−RAS ) − f (PGi,P re−RAS )(1)

A. Shrestha, V. Cecchi, and R. Cox are with the Electrical and Computer
Engineering Department and the Energy Production and Infrastructure Center(EPIC) at the University of North Carolina at Charlotte, Charlotte, NC 28223
USA (e-mail: ashrest1@uncc.edu, vcecchi@uncc.edu, robert.cox@uncc.edu).

Fig. 1. OMIB P-δ curve and generator rotor angles for G71 RAS choice.
Shedding generators G71 stabilizes the system.

Real Time Data Mining Techniques for Anomaly
Detection in Syncrophasor Data at PDC
D.Williams, S.Warner, T.Golberg, T. McGrew, S.Thomas, Y. Wu, A. Srivastava
Washington State University, Pullman, Washington, USA

Abstract—Phasor Measurement Units (PMU) have become
the forefront of smart grid measuring devices stemming fromtheir high rate of data sampling and signal processing for realtime monitoring of power system operation. Although important
for the future of power system automation and protection, PMUspresent challenges for Wide Area Monitoring System (WAMS)
implementation arising from network security, signal noise andcommunication issues. We evaluate two techniques that allow for
anomaly detection in PMU data streams to enable real time
control and dynamic event detection seen during power system
operation. Developed techniques also enable high data quality
utilized for substation level applications and Energy
Management System (EMS) applications running in control
centers.
Keywords— PMU, PDC, WAMS, Dynamic Event Analysis,
Smart Grid, Data Anomaly

I.

INTRODUCTION

With a need for data cleaning applications in WAMS forearly stage bad data and event detection, we present two
methods for anomaly detection in PMU data streams
implemented in decentralized Phasor Data Concentrator(PDC) at substations. Our research aims to validate theefficiency and accuracy that can be achieved from data mining
algorithms used in time series applications and provide new
methods that ultimately will further our ability to detect bad
data and monitor our power system in real time reliably.
Developed data mining approaches implemented in OpenPDC
are validated using synthetic data generated using an industrial
PMU connected to simulated data anomalies generated bymodeled power system test cases in Real Time Digital
Simulator (RTDS).
II.

METHODOLOGY“Anytime Algorithms” provide the ability to terminate
application at any given point in a data stream, allowing foranalysis to be conducted at any level of completedcomputation. Combined with wavelet based properties, we areable to enhance the level of computational analysis to achieve
the highest accuracy of anomalies flagged in the evaluateddata set. This presents the ability to detect variations ofdifferent events seen in PMU data streams, ranging fromincomplete data to dynamic events in power system operation.
We evaluate “Haar Wavelet and K-means” which is aclustering approach and “Haar Wavelet and Chebyshev”
which is a linear regression method; both give us the ability todetect data anomalies in time series applications, therefore
This work is supported by the US Department of Energy (DOE).

they are optimal choices for our use for data cleaning.
III.

FIGURES & KEY EQUATIONSTABLE I. Haar and K-Means Algorithm
1.)
2.)

Read synchrophasor Data Measurement Set X

3.)

Choose random data from X as initial centroid

4.)
5.)

Initialization of “k”. n is the number of points in data points in set X. 𝒌𝒌 ≈ �Compute the distance d(x) of your initial centroid and all other data points x

𝒏𝒏
𝟐𝟐

Determine a new centroid from the remaining centroids using the probability
equation. D(x) denotes the shortest distance between a data point and closest center.
𝑫𝑫(𝒙𝒙)𝟐𝟐6.)∑ 𝒙𝒙𝜺𝜺𝒙𝒙𝑫𝑫(𝒙𝒙)7.)Starting at next level i, use the Euclidean Distance formula to calculate the distance8.)

between 𝒙𝒙 𝒊𝒊 And the determined centroid 𝒄𝒄𝒊𝒊 .

9.)
10.)
11.)

Update d(x) as the distance between a data point and the nearest centroid

Calculate mean of clusters based on data convergence towards centroids, to update
cluster value.
Using final centers from previous level as new initial centers, we project centersreturned by using 𝟐𝟐𝒊𝒊 space in the 𝟐𝟐𝒊𝒊+𝟏𝟏 space.
Check membership changes.

Output flagged outliers determined from multi-level clustering. Move to next window.

TABLE II. Haar and Chebyshev Algorithm
1.)
2.)
3.)

Read in X data series from window.
Determine initial level one probability 𝑷𝑷𝒊𝒊 .
For level one, solve for k using Chebyshev’s inequality for upper and lower bounds. X isthe data in the read in, 𝝁𝝁 is the data mean, σ is the standard deviation of the data, and kis the number of standard deviations from the mean.
𝟏𝟏

4.)

5.)
6.)
7.)

𝟏𝟏

Upper: 𝑷𝑷 �|𝑿𝑿 − 𝝁𝝁| ≥ 𝒌𝒌𝒌𝒌 ≤ 𝒌𝒌𝟐𝟐 � Lower: 𝑷𝑷 �|𝑿𝑿 − 𝝁𝝁| ≤ 𝒌𝒌𝒌𝒌 ≥ (𝟏𝟏 − 𝒌𝒌𝟐𝟐 � k=

𝟏𝟏

�𝒑𝒑𝟏𝟏

After determining σ and µ, solve for initial ODV (Outlier Detection Values) from the set
for upper and lower bounds.
ODV 𝟏𝟏𝟏𝟏 = 𝝁𝝁 + 𝒌𝒌 + 𝝈𝝈
ODV 𝟏𝟏𝟏𝟏 = 𝝁𝝁 − 𝒌𝒌 + 𝝈𝝈
Determine Level two probability using the multiple of stage one probability.
Ex. 𝑷𝑷𝟏𝟏 =. 𝟏𝟏𝟏𝟏 → 𝑷𝑷𝟐𝟐 =. 𝟎𝟎𝟎𝟎𝟎𝟎

Using the new probability value, determine bounds and solve for ODV’s of level two as
performed in the initial stages. Repeat 3-5 Until Desired level of stages are derived.
Flag data anomalies found outside of derived bounds. Continue to next window.

IV.

FUTURE WORK

We are prepared to conduct a three stage testing sequenceon our chosen methods to validate their accuracy when appliedon PMU data sets. Stage one consists of offline testing,applying predefined data sets consisting of PMU anomalies
and events. The final two stages will evaluate their accuracy toflag anomalies being fed “live” data streaming packets in
OpenPDC. The results concluded from these tests will
ultimately determine which method responds the best to
anomaly detection in PMU data streaming.

Learning Scheme for Microgrid Islanding andReconnection
Carter Lassetter∗ , Eduardo Cotilla-Sanchez† , Jinsub Kim‡
College of Electrical Engineering and Computer Science
Oregon State University
Corvallis, Oregon
Email: ∗ lassettc@oregonstate.edu, † ecs@oregonstate.edu, ‡ kimjinsu@oregonstate.edu

Abstract—The future electrical power systems tend toward
smarter and greener technology. Microgrids go hand in handwith the building of this smarter grid, helping to integrate
renewable resources as well as aiding in the mitigation ofcascading blackouts. The main benefit of microgrids may also
be their biggest detriment: A microgrid may operate in an
islanded or interconnection mode; the ability to optimally andsafely reconnect a microgrid is not well understood. In thisposter, the use of a machine learning algorithm is demonstrated
to determine timings for stable reconnection of a microgrid in
real-time. With several PMU data streams, machine learningtechniques can be utilized on the plethora of data created todetermine safe reconnections of microgrids dynamically. Utilizing
machine learning techniques for microgrids to predict the state ofa reconnect will also give way to future applications of artificial
intelligence on the grid. A microgrid armed with this prediction
method could dynamically learn from its own predictions for
future operation.

Fig. 1. A stable reconnect w.r.t. Bus 101.

I. I NTRODUCTION
As we approach a smarter grid which implements an everincreasing amount of microgrids, it becomes important to havethe ability to determine safe points in which one can reconnect
an island or disconnect one from the main grid. With large
variability of bus voltages and angles in the power grid, it
becomes difficult to create a standard rule mathematically todepict the outcome of the disconnection or connection of asubset of the main grid; As a result, artificial intelligence techniques can be useful in creating a prediction algorithm for griddisconnects/reconnects. Support vector machines, or SVMs,
have made large strides in the machine learning communityand can be applied to a plethora of problems. Making useof bus voltages and angles, it is possible to create operating
vectors which represent the current state of the system. With
Phaser Measurement Units, one can gather data in near realtime making dynamic monitoring and action schemes morereasonable.
II. P RELIMINARY R ESULTS
The IEEE RTS-96 is a well tested case in the power system
research community, thus it was selected for benchmarking
this approach. The time-domain simulation is performed with
Siemens PSS/E and a Python customized interface. Various
operating points with different initial conditions were used asbaseline cases to ensure diversity when training and testing.
Using SVM, we trained a classifier which divides two classes.

Fig. 2. An unstable reconnect w.r.t. Bus 101.

In our case, the two classes represent stable and unstable
reconnections of a microgrid as shown in Figure 1 and Figure
2 respectively. It is important to note that only bus 101
is shown, all buses must abide by convergence rules to beconsidered stable. The operating vectors within a time-frame
before reconnection of an island were used along with theoutcome of the reconnect to train a classifier. After training
our classifier, we could provide it with a different state of the
network it hasn’t seen before and obtain a prediction pertaining
to the stability of the case if it were to be reconnected. The
first test involved using the bus angles and voltages at the time
step before reconnection. Even with limited temporal data,
accuracies of the predictions averaged in the mid 80 percent
range.

Multi-Area State Estimation with
Synchrophasor Measurements
Xiaoyuan Fan, Student Member, IEEE, Dongliang Duan, Member, IEEE
Department of Electrical and Computer Engineering
University of Wyoming
Laramie, Wyoming, USA
Email: xfan3@uwyo.edu, dduan@uwyo.edu.

Abstract—Motivated by the advancements in synchronizedphasor measurement units and the urgent need of a better state
estimator to address the corresponding system complexity andcomputational burden, we focus on a potential solution for thestate estimation in the control room: multi-area state estimation(MASE) with synchrophasor measurements. Two algorithms havebeen proposed to tackle this problem, namely synchrophasoronly linear MASE and synchrophasor-assisted hybrid MASE.
Numerical simulations have been implemented in IEEE 14-bus
system to evaluate and compare the performance of these twoalgorithms.

I. I NTRODUCTION
Power system state estimation (SE) is one of the mostfundamental applications at the control center, since it helps
the system operator to monitor, control and optimize the performance of the power grid. Shortly after the centralized state
estimator being integrated into the control room in the late1970s, researchers started to develop a more efficient estimator
due to computational resource deficiency and communication
capability limitation at that time.
To address the afore-mentioned two issues, multi-area state
estimation was proposed and its core idea is to distribute thestate estimation process at local control centers, which could
significantly shorten the measurement collecting process andreduce the computational stress. Moreover, different areas ofa huge power grid are connected and coupled by the so-called
tie lines, which are originally designed to prevent the systemfrom collapsing as a single area during emergency situations.
In addition, privacy and policy issues render the reluctance ofdifferent ISOs to share the information of their operations andphysical infrastructures. All these make MASE a necessity ofbetter power system control and operations.

linear relationship between synchrophasor voltage and current
measurements, and thus the estimation result of local SE
could be acquired in one iteration. Moreover, synchrophasor
measurements not only have synchronization over a huge
geographical area with the GPS signal, but also provide highaccuracy on the magnitude and phase angle of the voltage andcurrent.
Due to the observability issue with the current PMU placement profile, synchrophasor-assisted hybrid MASE would
be more practical during this transition period, while
synchrophasor-only linear MASE is more promising in the
future where more PMUs are installed.
III. K EY E QUATIONSWith the estimated boundary bus state, the optimization
problem for the decentralized hybrid MASE is given as
follows:
arg min J(xi ) = arg min[z i − hi (xi )]T C −1
i [z i − hi (xi )]
xi
xi
int
s. t. f (x̂bi ) = x̂2,biCompared with hybrid MASE, the synchrophasor-only
MASE provides a linear system model due to linearity between PMU measurements and system state, the optimization
problem is given as follows:arg min J(xi ) = arg min[z i − Ai xi ]H C −1
i [z i − Ai xi ]xi
xi
s. t. f (x̂bi ) = x̂bi int
IV. N UMERICAL S IMULATIONS

II. MASE WITH S YNCHROPHASOR M EASUREMENTS
In general, there are two ways to incorporate PMU measurements (or synchrophasor measurements) into the MASE,
which are the synchrophasor-assisted hybrid MASE and thesynchrophasor-only linear MASE.
In the first case, PMU measurements can be processed withSCADA measurements hybridly in a single state estimator, orbe processed to a separate state estimator in order to keep
the traditional state estimator intact. The equivalency between
these two scenarios has been proved in [1]. For the second
case, the Jacobian matrix will be a constant matrix due to theFig. 1. Decomposing IEEE 14-bus system into two areas.
[1] M. Zhou, V. Centeno, J. Thorp, and A. Phadke, “An alternative forincluding phasor measurements in state estimators,” Power Systems, IEEE
Transactions on, vol. 21, no. 4, pp. 1930–1937, Nov 2006.

Risk-based stress indicators for
cascading contingencies
Xian Guo, Siddhartha Khaitan, and James McCalley
Dept. of Electrical and Computer Engineering
Iowa State University
Ames, IA, USA
xianguo@iastate.edu
Abstract— In order to assess power systems for exposure to
successive “cascading” contingencies, this poster proposes a riskbased stress indicator, which measures the system’s ability to avoid
cascading contingencies. The successive line outage distribution
factor (LODF) is adopted to compute power flows following the K
successive outage. The Kth-order risk indicator provides the ability tocontinuously monitor the power system for cascading exposure. The
IEEE 30-bus system is used to illustrate application of the risk-based
indicator. Ongoing work includes implementing on a large-scalemodel of the Western U.S.
Index Terms—Transmission, risk, stress, cascading, contingency.

I. MOTIVATION
Stress monitoring for cascading contingencies provides thepower system with the ability to guard against extreme events,
recognizing possible severe events, enhancing situationalawareness, and enabling early identification of corrective action
when a severe event occurs. In this poster, we describe andillustrate the utilization of a risk-based cascading contingency
stress indicator based on information available from a steady-state
power flow solution.

represents the risk of an additional outage 𝑖𝐾 following a specificsuccessive contingency ⁡(𝑖1 , … , 𝑖𝐾−1 ) , in order to evaluate thepropagation of a particular cascading contingency. We also use
total circuit risk (includes all K successive contingency leading
to 𝑖𝐾 ), which is the aggregated stress exposed to a particular circuit
𝑖𝐾 , in order to identify weak area in power system.
C. Conditional probability
We estimate the probability of occurrence of outaging circuit𝑖2 given outage of circuit 𝑖1 , Pr⁡(𝑖2 |𝑖1 ), to be proportional to theratio of the increase in flow for circuit 𝑖2 following the outage tothe increase in flow for circuit i2 necessary to trip it with certainty.
III. RESULTS
The figure shows the tree structure associated with the Kthorder risk for the IEEE 30 bus system under a specific operating
point. The number in red indicates conditional circuit risk; thenumber in black shows conditional probability of a particular
circuit outage.

II. METHODOLOGYth

A. K -order system risk
We compute Kth-order system risk to indicate the exposure ofa particular initiating event to further propagation of up to K
successive contingencies; this risk is computed as the weighted
summation over the expectation of the consequences (or“severities”) of all likely successive contingencies. It is calculated
by applying the probability of a particular successive contingency
Pr⁡(. ) and the corresponding consequences evaluated by Sev(. ),both of which are computed as functions of power flow levels.
B. Circuit risk
We denote 𝑖𝐾 as the Kth successive contingency. Circuit risk
includes conditional circuit risk and total circuit risk. Circuit risk
differs from Kth-order system risk in that, whereas Kth-ordersystem risk quantifies all likely K successive contingencies,
circuit risk quantifies K successive contingencies, all of whichlead to 𝑖𝐾 ⁡. We use conditional circuit risk (which covers one
particular K successive contingency leading to 𝑖𝐾 ), which

IV. CONCLUSION
The tree structure of Kth-order risk identifies likely cascading
events and weak areas in the system, which provides early warning
information for system operators to take action in risk mitigation.
The utilization of successive-LODF can realize calculation
efficiency when successive cascading events are involved. We will
apply this indicator in the WECC system, to verify the efficiency
and efficacy of our method.

Distribution System Low-Voltage Circuit Topology
Estimation using Smart Metering Data
Jouni Peppanen, Santiago Grijalva

Matthew J. Reno, Robert J. Broderick

School of Electrical and Computer Engineering
Georgia Institute of Technology
Atlanta, GA, USA

Sandia National Laboratories
Real Circuit Topology
Albuquerque, NM, USA
TLV5

0.0400+j0.0400Real Circuit Topology

Abstract— Operating distribution systems with a growing
number of distributed energy resources requires accurate feeder
models down to the point of interconnection. Many of the new
resources are located in the secondary low-voltage distribution
circuits that typically are not modeled or modeled with low levelof detail. This poster and the related paper present a practicaland computational efficient approach for estimating thesecondary circuit topologies from historical voltage and power
measurement data provided by smart meters and distributed
energy resource sensors. The computational infeasibility of
exhaustive topology estimation is demonstrated and an efficientalgorithm is presented for verifying whether two radial
secondary circuits have identical topologies.

I. DISTRIBUTION SECONDARY CIRCUIT PARAMETER AND
TOPOLOGY ESTIMATION PROBLEM
The objective of the distribution secondary circuit topology
and parameter estimation problem is to simultaneously identify
topology and component series impedance parameters of agiven secondary circuit leveraging the measurements from
AMI and distributed energy sources (DER).
V,P,Q
Service
Transformer

R,X
...

Primary
SystemR,X

True
Secondary
Circuit 8

5-10.0100+j0.0250
5-2 0.0100+j0.0250
5-3 0.0100+j0.0250
5-4 0.0100+j0.0250
VB1
8-2
8-3
8-4
8-1
8-01

0.0413+j0.0378
0.0100+j0.0250
Circuit Based0.0404+j0.0382
on Meter Pairing
8-02Mock0.0397+j0.0414
0.0393+j0.0415
0.0405+j0.0394
5-10.0100+j0.0250
5-2 0.0100+j0.0250
5-3 0.0100+j0.0250
5-4 0.0100+j0.0250
VB0

8-2

8-3

8-4

8-1

VB1

Estimated
Secondary
Circuit 8

Mock
Circuit Based on Meter Pairing
0.0102+j0.0256
VB0

0.0106+j0.0244 0.0106+j0.0241 0.0102+j0.0244 0.0104+j0.0255
8-2

8-3

8-4

VB1

8-1

Fig. 2 True and estimatedtopologies for a secondary circuit
0.0102+j0.0256
VB0

The DSTE
algorithm 0.0106+j0.0241
estimates the0.0102+j0.0244
circuit series0.0104+j0.0255
impedance
0.0106+j0.0244
8-3
8-1
parameters8-2 with a highaccuracy8-4 both without
and withpractical levels of measurement error.
TABLE II. THE AVERAGE RELATIVE ERRORS OF THE ESTIMATED R AND X

R,X
V,P,Q

...

V

5-0

approximation of voltage drop magnitude. The algorithm
0.0400+j0.0400 0.0400+j0.0400 0.0400+j0.0400 0.0400+j0.0400
processes 5-1
each secondary
circuit 5-3
separately in
5-2
5-4 an iterative
TLV5
manner. For each meter
pair at each iteration, the algorithm
0.0400+j0.0400
True
Mock for
Circuitparallel/series
Based on Meter Pairing
5-0
estimates
the parameters
meters circuit
Secondary
Real Circuit Topology
0.0400+j0.0400
0.0400+j0.0400
0.0400+j0.0400
0.0400+j0.0400
subsection
typeswith linear
regression.
The DSTE
algorithm
Circuit 5 5-1
5-2
5-3
5-4
results in a mock circuit
thatincludesallthemetersin theVB1original secondary 0.0413+j0.0378
circuit.
topology
8-01
MockTheCircuitestimated
Based on Meter
Pairing is correct
Estimated that at
Real Circuit
VB0each iteration,
provided
the Topology
correct meter pair and
0.0100+j0.0250
Secondary
8-02
0.0397+j0.0414
0.0393+j0.0415
0.0404+j0.0382 0.0405+j0.0394
regression
model
type
are
selected.
Circuit 5

R,X

Fig. 1 Secondary circuit topology and parameter estimation problem: topology
unknown, the unknown parameters in red, available measurements in blue

Theoretically, the topology of a secondary circuit can be
estimated by performing an exhaustive search of all possible
topologies. However, as illustrated in Table 1, to evaluate all
the alternative topologies would be a computationallydemanding task even with 5 to 7 meters.

Meas. Error?
No
Yes

[%]
0.45
3.31

[%]
0.36
3.84

[%]
2.80
17.57

[%]
1.44
11.66

The DSTE algorithm is executed offline without the needof modifying any existing information systems. The algorithm
is executed within seconds for each secondary circuit even if
thousands of measurement samples are used to counteract theaccuracy, granularity, and time synchronization issues related
to AMI and DER measurements.

TABLE I. THE NUMBER OF POSSIBLE SECONDARY CIRCUIT TOPOLOGIES [16]SCADA
DeviceMeasurements & States# Meters 1 2 3 4
5
6
7
8
9
10
# Topologies 1 3 22 262 4,336 91,984 2.38e6 72.8e6 2.57e9 1.03e11

II. PARAMETER AND TOPOLOGY ESTIMATION ALGORITHM
Motivated by the vast number of possible topologies, we
propose a computationally efficient greedy-type joint
parameter and topology estimation algorithm (DSTE) that isbased on linear regression and a well-known linear

AMI
P, Q, V, I
Measurements

DER

Distribution
System Model

Components,
Parameters &Connectivity

GIS

RefinedSecondary Circuit
Secondary Circuit
Component Connectivity
Topology
Estimator

Fig. 3 Practical utility implementation of the DSTE algorithm

Q-learning Algorithm to Prevent Cascading Failure
in Smart Grids: Experimental Implementation
Sina Zarrabian, Student Member, IEEE, Rabie Belkacemi, Member, IEEE
Department of Electrical and Computer Engineering
Tennessee Technological University
Cookeville, Tennessee, USAszarrabia42@students.tntech.edu

Abstract—This research work proposes an adaptive learning
approach based on the Q-learning algorithm to preventcascading failure and blackout in real-time. This novel work
utilizes the Q-learning algorithm to train the system for optimal
action selection of power re-dispatch based on the obtained
rewards. The trained system then is able to relieve thecongestion of transmission lines by optimal power re-dispatch of
the generators to prevent consecutive line outages and blackout
after N-1 contingency condition. The proposed Q-learning
control is validated by experimental results. The obtained results
show the accuracy and robustness of the proposed approach inpreventing blackout after contingency condition.

III.

KEY FIGURESLine Currents (A)1086Blackout

2
0

0

10

0

10

20

30
(a)

50

60

40

50

60

Bus Voltage (p.u)

1.2
1
0.8
0.6
0.4

G1

G2
20

G3
30
(b)

Frequency (Hz)

61.5
G1

G2

G3

61
Fig. 2. Blackout
without control: (a) line currents (b) bus voltages

10

20

3
2

TL2
1

0

-2

0
-1

0

500
Iteration

-2

1000

0

500
Iteration

20

30
(a)

40

20

30
50 (d)60
Time (s)

1.1
G1

G2

G3

1
0 40

50

0

500
Iteration

2

2

1

1

-1
-2

1000

0

500
Iteration

0.9
61

10

20

30
(b)

40

50

60

0
-1
-2

0

500
Iteration

1000

Fig. 3. N-1 with Q-learning: (a) line currents (b) bus voltages
G1

G2

G3

60.5

IV.

60

CONCLUSION

59.5 work presents an adaptive intelligent method based on
This
0
10
20
30
40
50
60
(c)
the Q-learning algorithm
to prevent cascading failure and0
blackout
after G2N-1 G3contingency. The results show the
G1-10robustness and accuracy of the proposed intelligent method in-20
providingthe stability for the system after contingency while-30
retainingtheline 30currents,
frequency
and voltage values
0
10
20
40
50
60
(d) limits. Our results demonstrate that thewithin the operation2
proposedQ-learning method is accurate, fast, and promising.
1.5
G1
1

0

10

20

30
(e)
Time (s)

G2
40

G3
50

60

1

0

0

500
Iteration

1

TL12

2

1

RewardReward0500
Iteration

-1
-2

1000

TL11

2

1

0

TL8

0
60

-1
-2

-2

1000

TL7

2

10

0
-1

TL6

0

TL3
2

1

-1

-90

10

60
2

0

-180

0

G1
G2
G3

90

TL1 50

1

Reward

4

0

180

40
2

Reward0

5

Reward6

Reward

TL1
TL3
TL5
30
TL6
(c)
TL7
TL9
TL12
TL13

Reward59

1

Bus Voltage (pu)

60
59.5

Rotor Angle (deg)Line Currents (A)

7

60.5

Reward8

Internal Voltage E (pu)Fig. 1. Single line diagram of the testbed and experimental system

40

1.4

Angle  (deg)

II.

12

4

Frequency (Hz)𝑎Line Tripping

14

INTRODUCTION

The complexity of the power grid, the large number ofcomponents, and the different time scales dynamics andinteractions have made modeling and analysis of cascading
failure (CF) events extremely complicated. The recent
methods proposed in this area involve either the use of load
shedding or preventive measures that are aimed at reducing
the probability of occurrence of CF.
In this paper, we utilize a novel application of the Qlearning in preventing cascading failure by estimating an
optimal policy to select the best action-value (Q-value) ofgenerated power re-dispatch by:
𝑟𝑡+1 +
𝑄𝑡+1 (𝑠𝑡 , 𝑎𝑡 ) = (1 − 𝛼)𝑄𝑡 (𝑠𝑡 , 𝑎𝑡 ) + 𝛼 [ 𝛾 max 𝑄 (𝑠 , 𝑎)]
𝑡
𝑡+1

TL13
TL3
TL5
TL6
TL7
TL9
TL12
TL1

16

Index Terms—Blackouts, Cascading Failures, Experimental
Systems, Q-learning, Real-Time, Smart GridsI.

KEY RESULTS

18

1
0
-1
-2

0

500
Iteration

1

Selecting and Redesigning Distribution Feeders for
CVR Investment Benefits
Kaveh Rahimi, Himanshu Jain,
and Abhineet Parchure
ECE Department, Virginia Tech
Virginia, USA

Tamer Rousan

Robert Broadwater

ECE Department, University of
Illinois at Urbana-Champaign
Illinois, USA

Electrical Distribution Design (EDD)
Virginia, USA

Abstract— Conservation Voltage Reduction (CVR) is employedfor peak load reduction and energy savings by electric utilities.
Selecting feeders where the most benefit is realized from CVR isof interest. In the work here the theoretical CVR performance ofover 1000 distribution feeders is evaluated based on circuit
models and available load data. The feeders with the best CVR
performance are identified, and characteristics of the good
performing feeders are described. In identifying good performing
feeders, load-voltage dependency factors for summer and winterare used in quasi-steady state power flow analysis. In addition,
the Volt/VAR Control (VVC) scheme of a feeder with poor CVR
performance is redesigned to improve its CVR performance.
Results show that there can be considerable energy savings frominvestments in control schemes to improve CVR performance.
Index Terms— Energy conservation, Conservation Voltage
Reduction, Power Distribution, SCADA Systems, Volt/VAR
Control
TABLE I. CHARACTERISTICS AND SAVING OF THE SELECTED FEEDERS FOR
CVR IMPLEMENTATION

Feeder Name
1
2
3
4
5
6
7
8
9
10
11

Annual MWh Annual MWh with CVR Percentage
Feeder Length
Saving (MWh)Control Category(Base Case) (Coordinated Control) Improvement(Mile)
Urban-Rural 23728
22609
4.72%
1119
18.4
VVC Devices
Urban-Rural 23885
22794
4.57%
1091
22.9
VVC Devices
Urban
20567
19493
5.22%
1074
13.5
Flat VP
Urban
18336
17350
5.38%
986
9.4
Flat VP
Urban
18668
17690
5.24%
977
9.4
Flat VP
Urban-Rural 20245
19291
4.71%
954
11.1
VVC Devices
Urban
17931
16979
5.31%
953
14.5
Flat VP
Urban-Rural 20365
19433
4.58%
932
18.7
VVC Devices
Urban-Rural 17402
16614
4.53%
788
15.6
Flat VP
Urban-Rural 14279
13615
4.65%
664
13.0
Flat VP
Urban-Rural 13498
12840
4.87%
658
4.1
Flat VP

Fig. 1. Percent voltage drop before and after redesigning the VVC system
for the selected poor performing feeder during summer

Type

Table II. CHARACTERISTICS AND SAVING OF THE MODIFIED FEEDER AFTERCVR IMPLEMENTATION

Annual MWh Annual MWh with CVR Percentage
Feeder Length
Saving (MWh)Control Category(Base Case) (Coordinated Control) Improvement(Mile)
Urban-Rural 27130
26148
3.62%
983
25.2
VVC Devices

Feeder Name Type
12

Fig. 2. Percent voltage drop before and after redesigning the VVC system
for the selected poor performing feeder during winterMechanical and Aerospace Engineering

Optimal Energy Management for Microgrid with Stationary and Mobile Storages
Yubo Wang, Bin Wang, Tianyang Zhang, Hamidreza Nazaripouya, Chi-Cheng Chu and Rajit Gadh

Introduction and backgroundThe studied system

Increasing PV penetration and EV integration to smart grid in recent years
They add uncertainties to the microgrid Energy Management System (EMS)The state-of-art EMS either uses deterministic methods or robust methods,
they either ignore the uncertainties or results in too conservative results
This paper models the uncertainties in EV availability, energy demands,
solar generation and loads with two-stage stochastic optimization.
The studied grid-connected MG is a common scenario in a distribution
system and we use real collected data to evaluate the performance of the
proposed method
Extensive analysis is made on the performance of the method

Model development
Two-stage stochastic optimization:

Grid, BESS, and EV constraints

Sample Average Approximation (SAA)
Monte Carlo simulation

Numerical results (1)Numerical results (2)Conclusions and future studies
A comprehensive cost function is considered
and the problem is formulated as a MILP problem
To evaluate the proposed method, we use real
data for SAA Monte Carlo simulation
The optimum is also tightly bounded within 1
percent optimality gap
Two interesting observations are made:(1) the proposed stochastic optimization method
outperforms its deterministic counterpart by 5.5
percent in expectation at the cost of higher
computational time;(2) Increase of EVs in the MG helps to cut down
the operation cost at first after which the cost
starts to increase

Three user behavior modeling using KDE

Shed light on future EV integration to the grid.
Future study will include more EV users, andstochastic modelling in EV numbers

Upper and lower bounds estimation of SAA
2016

Reserve Margin Policy for Scheduling Wind
Energy and Stochastic Reserve
Mojgan Hedayati, Student Member, IEEE, Kory W. Hedman, Member, IEEE, and Junshan Zhang, Fellow, IEEE
School of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, AZ85287, USA
Email: mhedayat@asu.edu, khedman@asu.edu, Junshan.Zhang@asu.edu
Abstract— The rapid increase in the integration of renewable
resources has given rise to challenges in power system operations.
One aspect is the impact of integrating wind generation on
operating reserve procurement. Due to the uncertainty andvariability of renewable generation, additional reserves may beneeded to maintain system reliability. Uncertainty complicates theprocess of economic dispatch and renders the deterministic
optimization approach less effective. Existing optimization
solutions for handling uncertainty, such as scenario-based
stochastic programming and robust programming, are also
computationally expensive, especially when there are multiple
wind farms. Such approaches are less practical for large-scale
systems during real-time operations. This paper investigatescombining deterministic and stochastic approaches by exploiting
offline stochastic calculations for training deterministic operation
policies. Such deterministic policies are then added to real-time
operational models. This paper focuses on deriving such policies
for energy and reserve. One approach for handling the uncertainty
of wind generation is to leave a reserve margin when schedulingthe wind power. The extra available wind will then be accessible
for providing operational reserve. This paper proposes an offline
policy generation technique to provide a stochastic reserve margin
to hedge against the real-time uncertainty of (multiple) wind farm
generation. The proposed policy generation structure uses aforecast-based framework that accounts for wind generation andsystem loading conditions. A co-optimized energy and operating
reserve market model is developed to embody leaving a reserve
margin for wind power to obtain a more flexible energy andreserve scheduling. The proposed approach is tested on the
Reliability Test System 1996 (RTS96) with consideration of wind
uncertainties. The operation costs are compared to those obtainedusing the existing reserve rules. Wind generation data is used toanalyze the effectiveness of the presented model in handling
uncertainty and achieving a more secure system operation.

I. KEY EQUATIONSMin ∑𝑔,𝑡(𝑐𝑤𝑒 𝑃𝑤𝑡 + 𝑐𝑤𝑟 𝑟𝑤𝑡 ) + ∑𝑔,𝑡(𝑐𝑔𝑒 𝑃𝑔𝑡 + 𝑐𝑔𝑟 𝑟𝑔𝑡 )𝑒𝑒+ ∑ 𝑝𝑟𝑜𝑏𝑠 ( ∑ (𝑐𝑔1
𝑟𝑔𝑠𝑡 ) + ∑ (𝑐𝑤1
𝑟𝑤𝑠𝑡 )
𝑠,𝑡𝑔𝑤

𝑝𝑒𝑛𝑎𝑙𝑡𝑦𝑒𝑟+ ∑𝑤 (𝑐𝑤1∆𝑤,𝑠,𝑡 ) +∑𝑤(𝑐𝑝 + 𝑐𝑤1
)𝑃𝑤𝑠𝑡
+
−
+ ∑ 𝑐𝑐𝑛 (𝐿𝑆𝑛𝑡+ 𝐿𝑆𝑛𝑡) )𝑛Subject to:Base case constraints:∑𝑔∈𝑔(𝑛) 𝑃𝑔,𝑡 + ∑𝑘∈𝛿+ (𝑛) 𝑃𝑘,𝑡 − ∑𝑘∈𝛿 −(𝑛) 𝑃𝑘,𝑡 + ∑𝑤∈𝑤(𝑛) 𝑃𝑤,𝑡 =𝑑𝑛,𝑡 , ∀𝑛, 𝑡(1)
𝑃𝑔𝑚𝑖𝑛 𝑢𝑔,𝑡 ≤ 𝑃𝑔𝑡 , 𝑃𝑔𝑡 + 𝑟𝑔𝑡 ≤ 𝑃𝑔𝑚𝑎𝑥 𝑢𝑔,𝑡 ∀𝑔, 𝑡(2)∑𝑞∈𝐺 𝑟𝑞𝑡 ≥ 𝑃𝑔𝑡 + 𝑟𝑔𝑡 + ∑𝑤 𝜉𝑤𝑡 , ∀𝑔, 𝑡(3)𝑓

𝜉𝑤𝑡 ≥ 𝑃𝑤𝑡 − (𝑃𝑤𝑡 − 𝛼𝜎𝑤𝑡 ), ∀𝑤, 𝑡(4)

𝑓
𝑃𝑤𝑡 + 𝑟𝑤𝑡 ≤ 𝛼𝑃𝑤𝑡 , ∀𝑤, 𝑡(5)Second Stage constraints (for scenarios):∑𝑔∈𝑔(𝑛)(𝑃𝑔,𝑡 + 𝑟𝑔𝑠𝑡 ) + ∑𝑘∈𝛿+ (𝑛) 𝑃𝑘,𝑠,𝑡 − ∑𝑘∈𝛿−(𝑛) 𝑃𝑘,𝑠,𝑡 ++−∑𝑤∈𝑤(𝑛)(𝑃𝑤,𝑡 + 𝑟𝑤,𝑠,𝑡 ) + 𝐿𝑆𝑛𝑡
− 𝐿𝑆𝑛𝑡= 𝑑𝑛,𝑡 , ∀𝑛, 𝑡(6)−𝑟𝑔𝑡 ≤ 𝑟𝑔,𝑠,𝑡 ≤ 𝑟𝑔𝑡 , ∀𝑔, 𝑠, 𝑡

(7)

𝑃𝑔𝑚𝑖𝑛 ≤ 𝑃𝑔,𝑡 + 𝑟𝑔𝑠𝑡 ≤ 𝑃𝑔𝑚𝑎𝑥 , ∀𝑔, 𝑠, 𝑡

(8)

𝑝𝑒𝑛𝑎𝑙𝑡𝑦−𝑟𝑤,𝑠,𝑡 ≤ 𝑟𝑤𝑡 + 𝑃𝑤𝑡𝑠

, ∀𝑔, 𝑠, 𝑡

(9)References[1] M. He, L. Yang, J. Zhang, V. Vittal, “A Spatio-Temporal Analysis
Approach for Short-Term Forecast of Wind Farm Generation,” IEEE Trans.
Power Syst., vol. 29, no. 4, July 2014.

II. METHODOLOGY
2 load levels(weekday &weekend)

Initialize wind
policy level α

Phase I
12 month &8 time epochs:
96 Markov
models for wind

Generatevariousscenario sets

i=1

Select the ith
set of {epoch,load/windlevel}

Wind scenario
tree
construction

Scenario
reduction

Flexible dispatch andreserve schedulingPhase II
10 different
representative
wind states

Wind scenario 1

Risk costassessment

Update αWind scenario n

Clustering
wind states

i=i+1

Select and savethe optimal
policy for set iFig. 1. Offline policy training procedureNo

α < αmaxYes

Optimal Sizing and Placement of Battery Energy Storage inDistribution System Based on Solar Size for Voltage Regulation
H. Nazaripouya1, Y. Wang1, P. Chu1, H. R. Pota2, and R. Gadh1 Members, IEEE

Abstract—This paper proposes a new strategy to achieve
voltage regulation in distributed power systems in the presenceof solar energy sources and battery storage systems. The goal is
to find the minimum size of battery storage and its
corresponding location in the network based on the size andplace of the integrated solar generation. The proposed method
formulates the problem by employing the network impedance
matrix to obtain an analytical solution instead of using arecursive algorithm such as power flow. The required
modifications for modeling the slack and PV buses (generator
buses) are utilized to increase the accuracy of the approach. The
use of reactive power control to regulate the voltage regulation is
not always an optimal solution as in distribution systems R/X islarge. In this paper the minimum size and the best place ofbattery storage is achieved by optimizing the amount of bothactive and reactive power exchanged by battery storage and its
grid-tie inverter (GTI) based on the network topology and R/Xratios in the distribution system. Simulation results for the IEEE
14-bus system verify the effectiveness of the proposed approach.

Vk  Vk0  Z eqki I i  Z eqkj I j(7)

I j kmin  ( Vk0  Z eqki I i  Vmax )I j kmin  ( Vk0  Z eqki I i  Vmin )Z eqkjZ eqkj(Vk0  Z eqki I i )Vk0  Z eqki I i(Vk0  Z eqki I i )Vk0  Z eqki I i

(8)

(9)

I j min  max{real((I j ) kmin ) |0k n }  j. max{image((I j ) kmin ) |0k n }(10)
II. KEY RESULTS
The IEEE 14-bus benchmarks are considered as the casestudies as shown in Figs. 1.

I. KEY EQUATIONS AND FIGURE
The mathematical relationship between location of BESS
and solar and amount of exchanging power with specific node
voltage developed in this paper is presented as follows:
V  Z bus ( I 0  I )  Z bus I 0  Z busI  V 0  V
 V1   Z11     
 Vi    Z i1     V   Z
 n   n1

 Z1i
 
 Z ii 
 Z ni V1   Z11  V2   Z 21
     Vm    Z m1
      Vi   Z i1      Vn   Z n1

Z12
Z 22

Z m2

Zi2

Z n2

 Z1n   0       Z in    I i      
 Z nn   0 

 Z1m
 Z 2m Z mm Z im Z nmVi  Vi0  ( Z i 2  Z i1

 Z1i
 Z 2i 
 Z mi
 
 Z ii 
 Z niZZ
 Z
 Z1n   1i I i  12 I 2    1m I m 
Z
Z11
Z11 

 Z 2 n   11
I2

 (3)

  


 

 Z mn 
Im










 Z in  

I
i




 



 Z nn  

0



Z1i
Z13
Z12

 
 ( Z 21 Z  Z 2i ) I i   ( Z 22  Z 21 Z ) ( Z 23  Z 21 Z )
11
11
11

 
 ( Z Z1i  Z ) I   ( Z  Z Z12 ) ( Z  Z Z13 )3i i   
32
31
33
31
 31 Z
Z11
Z11
11

 




 
Z
Z
Z
1
i
12
( Z m1
 Z mi ) I i  ( Z m 2  Z m1
) ( Z m 2  Z m1 13 )

 
Z11
Z11
Z11

1

Fig. 1. 14 buses IEEE benchmark

(2)

Z
Z
Z12
) I 2    ( Z im  Z i1 1m ) I m  ( Z ii  Z i1 1i ) Ii
Z11
Z11
Z11

I i  S  Vi   Ppv Vi 

(1)

(4)

Z1m 
)
Z11   I 2  Z ( Z 3m  Z 31 1m )   I 3 
Z11       I Zm ( Z mm  Z m1 1m )  
Z11 

 ( Z 2m  Z 21

(5)

(6)

Authors are with University of California, Los Angeles Smart Grid
Energy Research Center (UCLA SMERC), Contact authors:
hnazari@g.ucla.edu,
ybwang@ucla.edu,
chichengwi@gmail.com,
gadh@ucla.edu.
2Author is with The University of NSW h.pota@adfa.edu.au

Tables I and II demonstrate the results.
Table I. Results after BESS integration with one unit solar source
Bus number 9Best Location for BESS
0.1196 pu
Minimum Active power (BESS)
0.3566 pu
Minimum Reactive Power (GTI)
Voltage profile
Bus
#
P=0pu
P=0.2pu
P=0.4pu
P=0.6pu P=0.8pu P=1p.u
11.06001.06001.06001.06001.06001.06001.04501.04501.04501.04501.04501.04502
0.99780.99890.99981.00071.00141.00173
0.99821.00031.00231.00411.00571.00654
51.0032
1.00651.00961.01271.01541.0173
1.0362
1.03801.0396
1.0413
1.04241.04206
1.01571.01581.01591.01601.01561.01347
1.04501.0452
1.04531.04541.04491.042880.9964
0.99550.9946
0.9937
0.99220.988490.99550.99500.9946
0.9941
0.99300.9898101.01181.01241.01301.01351.01351.011711
1.0202
1.02181.02331.02481.02581.025212
1.01431.01571.01701.01831.01901.018113
0.98790.98800.98800.98810.98750.984914
Table II. Results after BESS integration with scattered solar source
Bus number 7
Best Location for BESS
0.0845 pu
Minimum Active power (BESS)
0.4338 pu
Minimum Reactive Power (GTI)

Screening Algorithms for Large-Scale Stochastic Transmission Capacity
Expansion Planning with reliability Constraints
Mohammad Majidi-Qadikolai, Ross Baldick
Abstract
Increasing interest in utilizing more renewable energy resources raises the need for new transmission lines tointegrate them to the gird on one hand, and increasing environmental concerns limits building new transmission
lines on the other had. These issues makes transmission planning a challenging issue. To cope with environmental
concerns and permitting issues, transmission expansion planning is required over a longer term that will increase
uncertainties in load and generation. A large number of scenarios and candidate lines are proposed in theearly stage of transmission capacity expansion planning (TCEP) to capture these uncertainties, and it makes
transmission planning a very challenging task. Stochastic TCEP with reliability constraints is formulated as a
two-stage mixed-integer optimization problem to capture uncertainties and network reliability. We develop acandidate line reduction algorithm, which is a heuristic method to screen candidate lines list and remove lines
for which their closure will increase flow on heavily loaded lines in the network. It will reduce the number ofbinary variables in the optimization problem that can significantly decrease computational time. In the next step,
variable contingency list algorithm is used to screen reliability constrains to further decrease the problem size.
The proposed framework is summarized as the following steps:
Step 1 : Load Input Data and data cleaning.
Load, Generation, current and candidate transmission components etc. The base case system that contains
existing lines, load, buses and generators is referred to as So . The initial candidate lines list (CLL) in this stepis referred as CLLo .
Step 2 : Solve a relaxed version of OPF problem.
In this step, a relaxed version of OPF, in which constraints related to line capacity limits are ignored, is run.
Step 3 : Create monitored lines list (MLL).
In this step, lines with flows more than 50% of their nominal capacity will be added to Monitoring Lines List
(MLL).
Step 4 : Reduce candidate lines list (RCL).
Line Closure Distribution Factor (LCDF) is calculated for candidate lines in CLLr to evaluate the impact ofclosing each candidate line on the lines in MLL.
Step 5 : Solve a relaxed version of TCEP problem.
In this step, we solve a relaxed version of the original integrated TCEP, in which all constraints related tocontingency analysis are ignored (for the base case So ) with updated CLL.
Step 6 : Create variable contingency list (VCL).
Modified Line Outage Distribution Factor (LODF) matrix is calculated for single outage of all lines in Sr .
Step 7 : Solve TCEP optimization problem with updated CLL and selected contingencies.
Step 8 : Run DC-SCOPF with all contingencies for the selected plan in step 7 to check feasibility of results.
The proposed method is applied to a reduced ERCOT case study with 317 buses, 427 existing branches, 489
conventional power plants, 36 wind farms, 182 load centers, and 427 candidate lines. Two cases A and B are
considered with 5 and 10 scenarios respectively. By using the RCL algorithm, the number of candidate lines isreduced to 78 and 90 for cases A and B (from 427). The VCL algorithm decreases the number of operation states
to 28 and 62 for cases A and B (more than 99% problem size reduction). Sub-optimality of the proposed method
for these case studies is less than 10.5% for both cases that shows the accuracy of the proposed model. The
simulation run time is 5.1 and 87.4 minutes for cases A and B for the proposed method in this poster, and noanswer is achieved for the original problem after 34 days.

1

1

Screening Algorithms for Constraint reduction in
Large-Scale Stochastic Transmission Capacity
Expansion Planning
Mohammad Majidi-Qadikolai, Ross Baldick
University of Texas at Austin

Abstract—In this paper, a heuristic method is proposed forsolving large-scale transmission capacity expansion planning(TCEP) problem under uncertainties. The proposed method usesa modified version of candidate line reduction algorithm (CLR)
to select a subset of candidate lines for expansion and the variable
contingency lists algorithm (VCL) to select a subset of lines thatshould be considered for contingency analysis. The problem isformulated as a two-stage stochastic optimization problem, anda reduced ERCOT case study is used to demonstrate capabilitiesof the proposed method.

I. I NTRODUCTION
Increasing interest in utilizing more renewable energy resources raises the need for new transmission lines to integratethem to the gird on one hand, and increasing environmental
concerns limits building new transmission lines on the otherhad. These issues makes transmission planning a challenging
issue. To cope with environmental concerns and permitting
issues, transmission expansion planning is required over alonger term that will increase uncertainties in load and generation. A large number of scenarios and candidate lines areproposed in the early stage of transmission capacity expansion
planning (TCEP) to capture these uncertainties, and it makes
transmission planning a very challenging task. Stochastic
TCEP with reliability constraints is formulated as a two-stage
mixed-integer optimization problem to capture uncertainties
and network reliability. We develop a candidate line reduction
algorithm, which is a heuristic method to screen candidatelines list and remove lines for which their closure will increase
flow on heavily loaded lines in the network. It will reduce thenumber of binary variables in the optimization problem thatcan significantly decrease computational time. In the next step,
variable contingency list algorithm is used to screen reliability
constrains to further decrease the problem size.
II. T HE PROPOSED METHOD
The proposed framework is summarized as the following
steps:
Step 1 : Load Input Data and data cleaning.
Load, Generation, current and candidate transmission components etc. The base case system that contains existing lines,
load, buses and generators is referred to as So . The initial
candidate lines list (CLL) in this step is referred as CLLo .
Step 2 : Solve a relaxed version of OPF problem.
In this step, a relaxed version of OPF, in which constraints
related to line capacity limits are ignored, is run.

Step 3 : Create monitored lines list (MLL).
In this step, lines with flows more than 50% of their nominal
capacity will be added to Monitoring Lines List (MLL).
Step 4 : Reduce candidate lines list (RCL).
Line Closure Distribution Factor (LCDF) is calculated forcandidate lines in CLLr to evaluate the impact of closing
each candidate line on the lines in MLL.
Step 5 : Solve a relaxed version of TCEP problem.
In this step, we solve a relaxed version of the original integrated TCEP, in which all constraints related to contingency
analysis are ignored (for the base case So ) with updated CLL.
Step 6 : Create variable contingency list (VCL).
Modified Line Outage Distribution Factor (LODF) matrix is
calculated for single outage of all lines in Sr .
Step 7 : Solve TCEP optimization problem with updated CLL
and selected contingencies.
Step 8 : Run DC-SCOPF with all contingencies for the selected plan in step 7 to check feasibility of results.
III. N UMERICAL R ESULTS
The proposed method is applied to a reduced ERCOT casestudy with 317 buses, 427 existing branches, 489 conventional
power plants, 36 wind farms, 182 load centers, and 427
candidate lines. Two cases A and B are considered with 5
and 10 scenarios respectively. By using the RCL algorithm,
the number of candidate lines is reduced to 78 and 90 forcases A and B (from 427). The VCL algorithm decreases thenumber of operation states to 28 and 62 for cases A and B(more than 99% problem size reduction). Sub-optimality ofthe proposed method for these case studies is less than 10.5%
for both cases that shows the accuracy of the proposed model.
The simulation run time is 5.1 and 87.4 minutes for cases A
and B for the proposed method in this poster, and no answer
is achieved for the original problem after 34 days.

1

A Model for Demand Response Aggregators
Participating through Demand Response Exchange
Venkat Durvasulu, Graduate Student Member, IEEE, Timothy M. Hansen, Member, IEEE

Abstract—With grids evolving into smart grids and communication systems deeply integrated into the grid, demand side
management has gained greater significance in the operation of
the electrical power system. This work presents an incentivebased demand response (DR) model for demand response aggregators (DRA) to participate in the bulk power market througha new entity Demand Response Exchange market (DRX). With
the proposed model, we study the economic impact of DR on thesystem clearing price and the total benefit obtained. This model
provides the amount each entity has to pay for the benefit they getout of the DR program. We tested the proposed model on PJM
5-bus system under various loading conditions, and comparedeach entities surplus before and after implementing DR.

on the duration and the quantity of DR the aggregator chooses
the most economical assets to be used for that particular event.
The model also addresses the compensation to the DRA bycharging the benefited entity proportional to the gain obtained
by DR service.
III. K EY F IGURES
Fig. 1 shows the flow of power, money, and bids with theaggregators in the market. Fig. 2 shows the time scale eventson a typical day-ahead market with the DRX.

I. I NTRODUCTION
With ever increasing demand and limited transmission capacity, the electric power system is pushed to its security limits
during peak hours. To avoid such situations, the independent
system operator (ISO) usually resorts to use expensive peaking
generators to meet the load. With advancement in communication systems and distributed generation, the grid is evolving
into smart grids allowing the ISO to implement demand side
management techniques to manage peak loads. The regulatory
bodies (FERC) have made necessary amendments to theexisting structure to facilitate demand response (DR).
Even though the FERC orders 719 and 745 have madeprovisions for the ISOs in the U.S. to accept DR bids like anyother resource in the market [1], the ISOs have reserved a capon the minimum accepted bid. This cap discourages potentiallysmall customers from participating in the DR program. Also,
it is largely debated and acknowledged by FERC that thecompensation for DR service is not encouraging which maylead to under investment in DR services. The proposed model
aims to provide an encouraging environment for the DRA toparticipate as they don’t directly bid into the ISO, but interact
through a non-profit entity DRX who aggregates all the bids
and provides the information as a service to the ISO.
II. P ROPOSED M ODELThe DRA in the proposed model is an entity with a setof customers who are willing to curtail or shift their loads.
The aggregator has to bid into the DRX a non-profit entity,
which is organized by the ISO in a fully deregulated market[2]. During an event of market inefficiency, the ISO triggers
the DRX, calling for DR bids for the day-ahead market. The
DRA bid into the market with the aggregated power available
from their customers who are willing to shift or curtail theirloads. The most cost effective bids are cleared by the DRX
and the curtailment information is sent to the ISO. Depending

Fig. 1: Proposed Model of Demand Response Aggregator
interacting with Demand Response Exchange Market

Fig. 2: Time scale events for day-ahead market with demand
response exchange

R EFERENCES[1] Federal Energy Regulatory Commission, “Order no. 719, wholesalecompetition in regions with organized electric markets,” 2008.
[2] T. M. Hansen, R. Roche, S. Suryanarayanan, A. A. Maciejewski, and
H. J. Siegel, “Heuristic optimization for an aggregator-based resource
allocation in the smart grid,” IEEE Transactions on Smart Grid, vol. 6,
no. 4, pp. 1785–1794, 2015.

Impact of Transmission Tariff on Price Arbitrage
Operation of Energy Storage System in Alberta
Electricity Market
A.I. Adebayo, H. Zareipour, A.M. Knight
University of Calgary, Canada. abiola.adebayo@ucalgary.ca, andy.knight@ucalgary.ca

Abstract—this work investigates the impact of transmission
tariff on arbitrage operational profitability of energy storage in
the Alberta electricity market. While the tariff policy is underreview to ascertain its applicability on energy storage facilities,
we review the impact of the application of existing tariffstructures to understand the potential impact on the economics ofoperating energy storage facilities for arbitrage.

STS Rate: This tariff, applicable to ESS while discharging, isthe product of metered energy, price and loss factor which isdetermined by the Independent System Operator (ISO).
Table 1: DTS tariff breakdown[1]Keywords—tariff; arbitrage; energy storage; electricty market

I.

INTRODUCTION

Increasing commercial interest in investment into energy
storage system (ESS) in Alberta has made it necessary toinvestigate important factors that can affect the profitability of
their arbitrage operation in Alberta electricity market. Factors
such as capital cost, operation and maintenance cost, pricevariation, storage and conversion efficiency are some of thefactors known to impact operational profitability of ESS. One
other factor, however, which is unique to each electricity
market is the transmission tariff. The current tariff structure inAlberta was not formulated with consideration of bulk energy
storage facilities. However, the fact their operation modes canbe regarded as either generation or load has led to suggestions
that the current tariff structure for demand and supply may besuitable.
II. TRANSMISSION TARIFF IN ALBERTA
Alberta’s electric system is legislated to be congestion free,requiring continuous investment in transmission facility suchthat there is always enough transmission capacity available forany device to connect to the grid irrespective of the location.
The costs incurred by ensuring that the system always meetthis legislative requirement are significant, and this cost isrecovered from the participants in the electricity market
through transmission tariffs. The existing tariff rates that may
be applicable to ESS are[1]:DTS Rate: This tariff is applicable to all demand and may be
applied to a storage facility during charging operation, giventhat an ESS draws power from the grid just like any otherload. The details of this tariff are given in the table 1. The
most significant component of the DTS is the coincident
metered demand. This charge is applied to the metereddemand during the 15 minute interval of peak system demandin each month.

III Operational Impact of DTS
A self–scheduling optimization model that optimizes arbitrage
profit, is used to estimate operational profit, metered energy,
operating hours and storage capacity utilization. The model is
run with and without transmission tariff scenarios. Figure 1
shows the average power flow at a given hour of the week, fora case study evaluating operation over a 5-year period.

Figure 1: 5 year average hourly schedules
IV

References[1]“ISO Tariff.” [Online]. Available:
http://www.aeso.ca/downloads/AESO_2015_ISO_Tariff_(2016-01-01).pdf. [Accessed: 19-Feb-2016].

Guide to Selecting Transmission Lines for Dynamic
Line Rating
Leanne Dawson, Andrew M. Knight
Department of Electrical and Computer Engineering, University of Calgary
Abstract— With an increased focus on renewable power
generation, dynamic line rating is being investigated as a way toconnect the new intermittent generation to the grid without
needing to build additional infrastructure.

calculation of the environmental conditions impacts both thethermal limit and the voltage quality limit.
7
Voltage Quality Limit
VQ Limit with R-69kV
VQ Limit with R-138kV
VQ Limit with R-240kV
Thermal Limit-69kV
Thermal Limit-138kV
Thermal Limit-240kV

6Power Transfer Limit (pu of SIL)

I. INTRODUCTION
Presently, a static thermal rating is typically used to
calculate the maximum ampacity of transmission lines, basedon nominal environmental conditions. Dynamic line rating is
an alternative approach that takes into account changing
environmental conditions to update the rating. Both the static
rating and the dynamic line rating are calculated based on a setof thermal equations described in IEEE Standard 738[1]. The
potential rating increase is based on line length andenvironmental conditions, most notably wind speed. The
maximum length of line that is subject to the thermal ampacity
limit is defined using the transmission line loadability curve,
otherwise known as the St. Clair curve[2]. For longer lines, thetransmission line loadability curve is based on the voltage drop
across the line.

5

4

3

2

1

0
0

50
100
Length of Transmission Line (km)150

Fig. 1: Impact of Resistance of Line Loadability
1000

II. RESEARCH

900

Initial research to identify lines suitable for dynamic
thermal rating considers the intercept between the thermal
ampacity limit and the voltage quality limit. Including thenominal line resistance in the calculations of the voltage
quality (VQ) curve has a significant impact on the VQ curve.
The maximum length of transmission line that is thermallylimited decreases significantly when resistance is included in
the voltage drop equation. Fig. 1 demonstrates the effect ofincluding a constant resistance for different voltage levels,
calculating the ampacity at nominal environmental conditions.

800

I-Static Limit
I-Thermal Limit
I-VQ Limit with R
I-Voltage Quality Limit
I-VQ Limit with wind 2m/s

Current (A)

700
600
500
400
300
200
100

For longer lines, the transmission line loadability curve isdependent on voltage drop and resistance. However, theresistance of a conductor is based on conductor temperature,
and as demonstrated in IEEE Standard 738[1], the current
rating is related to convection cooling due to wind. This
research investigates the effect on resistance if the voltage drop
is constant and convection cooling is considered. The
resistance decreases due to convection cooling, allowing morecurrent to flow through the line. The increase in current would
subsequently increase the resistance, creating a new current
rating. This process is repeated until an equilibrium point isfound. Fig. 2 shows the increase in current rating due toconvection cooling for longer lines, compared to the curveconsidering constant resistance. It demonstrates that dynamic

0

0

50

100

150

Length (km)Fig. 2: Impact of Convection Cooling on Line Loadability for 138 kV

REFERENCES[1]

IEEE, Standard 738 Standard for Calculating the Current-[2]

H. St Clair, “Practical Concepts in Capability and Performance ofTemperature Relationship of Bare Overhead Conductors. 2013.
Transmission Lines,” IEEE Trans. Power Appar. Syst., vol. 72, no.
2, pp. 1152–1157, 1953.

Using Optimal Potential of Incentive and Price Base
Demand Response to Reduce Costs and Price
Volatility
Ailin Asadinejad

Kevin Tomsovic

Department of Electrical Engineering and Computer Science
University of Tennessee
Knoxville, Tennessee
aasadine@utk.edu
Abstract --- There are two general categories of demand
response (DR): price-based and incentive-based (IB) DR
programs. Each one has its own benefits taking advantage ofdifferent aspects of flexible demand. In this paper, bothcategories of DR are modeled based on the demand-priceelasticity concept to design an optimum scheme for achieving the
maximum benefit of DR. The objective is to not only reduce costs
and improve reliability but also to increase customer acceptanceof a DR program by limiting price volatility. Time of use (TOU)
programs are considered for a price-based scheme designed
using a monthly peak and off-peak tariff. For the incentivebased DR, a novel optimization is proposed that in addition to
calculation of an adequate and a reasonable amount of loadchange for the incentive, the best times to realize the DR isfound. This optimum threshold maximizes benefit considering
the comfort level of customers as a constraint. Results from a
reduced model of the WECC show the proposed DR program
leads to a significant benefit for both the load serving entities(LSEs) and savings in customer’s electricity payment. It also
reduces both the average and standard deviation of the monthly
locational marginal price (LMP). The proposed DR scheme
maintains simplicity for a small customer to follow and for LSEs
to implement. key result

1

2

3

4

5

6

7

8

9

10 11 12

50.0
45.0
40.0
35.0
30.0
25.0
20.0

TOU and IB progran

TOU programbase case
Figure 2: San Diego monthly average LMP

45.00

2.50

40.00
2.00

35.00

1.50

30.00
25.00

1.00

20.00
0.50

15.00
SMUD

North West

Idaho

Rocky MT

FresnoBay area

LADWPSan Francisco

San Diego

SCE

South West

CNTcoast

10.00
PGE

0.00

Figure 1: LSE benefit and customer saving per total load

average LMP $/MWh

50.00

Nevada

LSE benefit $/MW

3.00

Proposed program scheme according to Figure 1 brings comparable
benefit for both customers and load aggregators that makes it fair
and appropriate program for all participants. In addition, as it shownin Figure 2 this DR program has considerable effect on reducing
market peak price.

North WestSMUD
Bay area
Rocky MT
FresnoSan Francisco
Idaho
San Diego
SCE
LADWP
CNTcoast
South WestPGE
Nevadabase case
TOU program
IB program

0.00

2.00

4.00

6.00

Figure 3: LSE benefit per total load

8.00

10.00

Day Ahead Solar Irradiance Forecasting using
Markov Switching Model for Remote Microgrids
Ayush Shakya, Semhar Michael, and Reinaldo Tonkoski
Department of Electrical Engineering and Computer Science, South Dakota State University, Brookings, SD
Email: ayush.shakya@sdstate.edu

Time of day in hour,longitude and latitude

Abstract—Photovoltaic (PV) systems integration is increasingly
being used to reduce fuel consumption in diesel based remote
microgrids. However, low correlation of PV power availability
with load and uncertainty reduces the benefits of PV integration.
These challenges can be handled by introducing reserve, whichcauses an increment in the operational cost. Solar irradiance forecasting helps in reducing the reserve requirement. A new solar
irradiance forecasting method for remote microgrids based on
Markov Switching Model is presented here. This method makes
use of open source, historical irradiance data to predict one day
ahead solar irradiance for scheduling remote microgrids energy
management systems. The model considers clear sky irradiance(CSI) and Fourier basis functions for three regimes or states:
high, medium and low energy regimes for a day corresponding
to sunny, mildly cloudy and extremely cloudy day, respectively.
A case study for validation of the algorithm for Brookings, SD
showed Root Mean Square Error (RMSE) of 50 W/m2 for June
14, 2008. The error assessment showed that RMSE was higher
during summer season and lower during winter season. Thismethod is expected to benefit energy management system of theremote microgrids.
Index Terms—Clear Sky Irradiance (CSI), Fourier Basis Expansion, Global horizontal irradiance (GHI), Markov Switching
Model, Root Mean Square Error (RMSE)

Fourier basis
expansion fordaily variation ofirradiance

Computation of Clear
Sky Irradiance (CSI)

Fourier basis
expansion for
yearly variation ofirradiance

Markov Switching Model fitting using Expectation Maximization (EM) Algorithm

Model Selection
Daily Irradiance Forecasts
Error Assessment

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

1200

High Energy State (Sunny day)Medium Energy State (Mildly cloudy day)Low Energy State (Extremely Cloudy day)Actual Irradiance
Forecasted Irradiance

I. K EY E QUATION
1000

Pp
yt (St = k) = βk,1 CSI(t) + i=1 βk,1i φ1i (t)Pq+ j=1 βk,2j φ2j (t) + k (t),Past solar irradiance data
(www.solaranywhere.com)

Irradiance (W/m2)

800

(1)

II. K EY F IGURES

600

400

Apr

May

June

July

200

Aug
Sept

Mar
0
1

Oct
Feb
Nov
Jan

2

3

4

5

6

7

8

9

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Time (hour)

CSI

Fig. 3. Irradiance variation for June 14, 2008.
Dec

Fig. 1. CSI for a year along with actual irradiance for the year 2001.

1

Implementation of an IDMS for Volt/VAR
Optimization Testing
Paul E. Rankin, Student Member, IEEE, Jonathan A. Tribble, Student Member, IEEE, and Estefano
Joniaux, Student Member, IEEE
Department of Electrical and Computer Engineering
University of North Carolina at Charlotte, Charlotte, NC
prankin4@uncc.edu, jtribble@uncc.edu, ejoniaux@uncc.edu
Abstract—An Integrated Distribution Management System(IDMS) is the next big step in grid monitoring, control, andoptimization. An IDMS is able to measure and read the grid
continuously in real-time in order to achieve a variety of goals.
The functions of an IDMS are many; its applications include
fault identification and isolation, real-time monitoring,
calculation, and optimization of parameters within the grid. This
research, as part of a senior design project, focused oninvestigating effects of load volt/var management (LVM).
Through the development and testing via simulation of various
LVM formulations, a business case for IDMS is proposed.

Main Gen
15 MW

Bus 1
13.8 kV

Bus 3
4.16 kVBus 4
4.16 kV

Bus 5
4.16 kVBus 6
0.208 kVBus 7
0.208 kV

545 kW
348 kVAR
To Other
Loads

T1

T2

Load 1
50 kVA

Load 19100 kVA

Load 17300 kVA

Load 23350 kVA

Fig. 1 Baseline Feeder for Volt/VAR Optimization
Main Gen
15 MW

Bus 1
13.8 kV

Bus 2
4.16 kV

Bus 3
4.16 kVBus 4
4.16 kV

Bus 5
4.16 kVBus 6
0.208 kVBus 7
0.208 kV

588 kW
64.8 kVAR

I. INTRODUCTION

To OtherLoads

O

NE way an IDMS can be used to optimize the grid is byproviding Volt/Var support to various buses in the grid.
An IDMS will do this by monitoring the parameters
within a distribution system, such as voltage, power factor,
and current, and then adjust capacitor banks, transformer taps,
and line voltage regulators in order to ensure that numerous
conditions are met. This support can achieve a variety of
purposes from improving power factor to reduce line losses toreducing overall demand if there is concern about a possible
power shortage or brownout. Even though these operations are
performed separately now, they can be performed quickly andcontinuously when an IDMS [1] is utilized. The purpose of the
project is to set up a model of a distribution system uponwhich one can observe the effects of an IDMS utilizing Load
Volt/VAR Management (LVM) problem formulations todetermine if such a system would be beneficial [2]. The
analysis is performed using software provided by GE Alstom.
The problem formulations developed is robust and powerfulenough to realize significant changes within the distribution
system. A business case based on the results is also being
devised to support implementation of such a system.

Bus 2
4.16 kV

T1

T2

Load23350 kVA

Cap5
300 kVAR

Load17200 kVA

Cap7
150 kVARCap3
50 kVARLoad19100 kVA

Load1
50 kVACap1
15 kVAR

Fig. 2. Model with Capacitors for Power Factor Correction
Main Gen
15 MW
Bus 1
13.8 kV

Bus 2
4.16 kV

Bus 3
4.16 kVBus 4
4.16 kV

Bus 5
4.16 kVBus 6
0.208 kVBus 7
0.208 kV

554 kW
61 kVARTo Other
Loads

T1

T2

Load23350 kVA

Cap5
300 kVAR

Load17200 kVA

Cap7
150 kVAR

Load19100 kVACap3
50 kVARLoad1
50 kVACap1
15 kVAR

Fig. 3. Model with Adjusted Transformer Taps

II. KEY FIGURES
A preliminary study case of Volt/VAR optimization on amodel adapted from the IEEE 13 bus distribution system was
performed. This was done by taking a baseline model asshown in Figure 1, adding capacitors to correct power factor
as shown in Figure 2, and finally adjusting the transformer
taps to reduce bus voltages for conservation voltage reduction(CVR), as seen in Figure 3.
III. KEY RESULTS
The system voltage profiles for the different cases areshown in Figure 4. It is noted how a flatter voltage profile,higher power factor (from 0.84 in the baseline to 0.94) andoverall lower voltages as desired for CVR are achieved.

Fig. 4. Feeder Voltage Profile (Voltage [pu] vs Node Distance [pu])IV. CONCLUSIONSThe proposed method shows that the voltage profile can be
optimized in coordination with control devices resulting in
better voltage profile. The algorithms developed using Alstom
software also indicates that a business model can be
developed.
V. REFERENCES[1][2]

GE Alstom Distribution Network Analysis Functions User’s Guide, 2015
Volt/VAR Control and Optimization Concepts and Issues, Bob Uluski, EPRI,
Available Online,
http://cialab.ee.washington.edu/nwess/2012/talks/uluski.pdf, 2011.

Microgrid Testbed for the Development of Energy
Management Systems using Commercial
Off-The-Shelf (COTS) Inverter/Charger
Shaili Nepal, Ayush Shakya, and Reinaldo Tonkoski
Department of Electrical Engineering and Computer Science, South Dakota State University, Brookings, SD
Email: ayush.shakya@sdstate.edu
Abstract—In this paper, a methodology of real time controlof battery inverter/charger is implemented in OPAL-RT system.
This system is a hardware based real time system. Microgrid test
bed is composed of 5 kW PV, 3.5 kW natural gas generator, 6 kW
inverting/charging system, 8 kWh lithium iron battery system andprogrammable loads upto 5 kW . The inverter/charger control
algorithm is tested in the microgrid test bed. The protocol used incontrol of inverter/charger and battery system is Controller Area
Network (CAN) protocol. This methodology is for commercial
microgrids using a COTS inveter making the system economical.
The laboratory scaled microgrid test bed is located in South
Dakota State University, Brookings, SD.
Index Terms—Battery, CAN, COTS, Hybrid Inverter/charger,
Microgrid

I. K EY F IGURESPower Flow
Data Flow

OPAL-RTCAN Bus

240 V
HybridInverter/Charger

240 V

AC BusCAN Bus

CAN bus48 V, 8.2 kWhBattery Bank
with BMS

Fig. 1. Block diagram of experimental set up for real-time control of hybrid
inverter/charger.

1

Frequency-Dependent Impedance Calculation forElectric Power Lines
Layton Hall, Student Member, IEEE, Hunter Damewood, Student Member, IEEE,
Bikash Poudel, Student Member, IEEE, Valentina Cecchi, Member, IEEE
Electrical and Computer Engineering Department
University of North Carolina at CharlotteCharlotte, North Carolina, United Stateslhall67@uncc.edu, hdamewoo@uncc.edu

Abstract—An approach for calculating frequencydependent impedance of electric power lines has been the focusof this undergraduate research project, presented in thisabstract. Frequency-dependent characteristics due to the effectof ground return and the skin effect are taken into account. The
calculated impedances are used in frequency-dependent line
models to study the propagation of non-fundamental frequency
components of power systems voltages and currents. Commonlyused ACSR cable types are considered. Line model frequency
response from 1 to 1500 Hz is calculated and presented
graphically for 25 types of 1-mile long ACSR cables. These
values can be used in frequency-dependent line models or
tabulated for further analysis of harmonics in the power grid.

PI or exact PI model structures [1], which can be easily
integrated in system level analysis tools, such as Harmonic
Power flow. A simplified flowchart of the proposed approach
used to identify frequency-dependent line impedance isshown in Figure 1 below.

Keywords—frequency-dependent impedance, skin effect,
power system, harmonics

Find the constant for Bessel function
using these measured values

Start

Obtain dc resistance and 60 Hz ac resistance *I. INTRODUCTION
The electrical parameters of the line, i.e. series resistance,
series inductance, shunt capacitance and shunt conductanceare frequency dependent [1]. Frequency dependent
characteristics due to the skin effect and the effect of groundreturn are more noticeable for series resistance and series
inductance [2]. Manufacturers provide only the dc resistanceand ac resistance of the cables at 60 Hz. The ac resistancecalculated from the Bessel functions [3] differ from the value
provided by the manufacturer. In this work, the constants usedin the Bessel functions are derived based on thegiven/empirically determined dc resistance and 60 Hz ac
resistance; the newly calculated constant is used to determine
the conductor ac resistance at other harmonic frequencies.
Calculations on several standard ACSR cables are performed.
The work is part of an undergraduate research project insupport of the development of highly accurate frequencydependent line models to be used for harmonic power flow.
II. CALCULATIONS
The given dc resistance and 60 Hz ac resistance are utilized
to find the constant applied in the Bessel functions [3]. Bessel
functions are then used to calculate the ac resistance of theline at multiple frequencies. The focus of this particular work
has been on single-phase lines, thus far. The ac resistance ofthe cable is then used in Carson’s equations [4] to include theeffect of ground return. The determined per unit length
frequency-dependent series impedance can be used in simple

Use the calculated constant in Bessel function to
calculate ac resistance at harmonic frequencies

Use Carson’s equations to find thefrequency-dependent series impedances per unit length

Embed the impedances into the PI
or Exact PI model for system level analysis

End*This information is the measured value given by the
manufacturer at a conductor temperature of 55o Celsius.
Fig. 1. Flow Chart of the Proposed Approach Used to Determine
Frequency-Dependent Line Impedance

REFERENCES
[1]
[2]

[3]
[4]

J. Grainger, W. Stevenson and W. Stevenson, Power system Analysis.
Central Station Engineers of the Westinghouse Electric Corporation,
Electrical Transmission and Distribution Reference Book. East
Pittsburgh: Westinghouse Electric Corporation, 1950.
H. Dwight, Electrical Elements of Power Transmission Lines. New
York: The Macmillan Company, 1954.
W. Kersting, Distribution system modeling and analysis. Boca Raton:
CRC Press, 2002.

MICROGRID DEVELOPMENT IN AFRICA
Yin Mak, Alexandra Schleicher, McLean Sloughter PhD
Department of Electrical Engineering and Mathematics, Seattle University
Abstract- Energy poverty is an unfamiliar concept to many
people, but can be a serious impediment to economic mobility inthe areas it affects. In Muhuru Bay, Kenya, as in many areas without
regular access to electricity, when the sun sets, all activities would
come to a halt. In 2014 KiloWatts for Humanity (KWH) traveled to
Kenya to work with local partners and installed an energy kiosk
powered by solar panels and wind turbines. Portable battery kits
paired with LED lights were rented out and returned for bi-weekly
recharging. Using statistical analysis, our project aims to inform thedevelopment of best practices for the installation of microgrids indeveloping countries.

representations of the data for easy consumption. Through analysis
we were able to represent the community’s current electricalsituation. We found that a majority of the villagers owned cell
phones and nothing else, with a small percentage also owningradios. Different households were compared to determine which
type of individual had access to more electrical devices, and thus,
wealth. The electrical desires of the villagers was also surveyed,
and among other things it was found that the demand was highest
for security lighting. The results of the surveys were presented to
KWH’s business team and microgrid design team to inform theplans for this new kiosk.

The first phase of our research involved data retrieval from
KWH’s Linux cloud server. A data logger installed in Muhuru Bay
records and transmits data from the kiosk to the server every minute.
The transmission includes information ranging from total energy
accumulation and solar power to temperature. Our primary interest
was to use cumulative energy consumption data to track energy
usage trends at the kiosk. We utilized Matlab to format the raw data
and transform it into a workable form. The first significant outcome
was formed when we applied a linear regression line with theenergy consumption data. The result showed that power was being
consumed at a relatively constant rate over the study period, whichis to say the coefficient of determination was relatively close to 1.
This conflicted with our prediction of an eventual drop in usage as
initial interest waned. A second significant outcome was produced
when the energy consumption data was parsed by week and then byday. We discovered that all days displayed the same generalbehavior, despite what day of the week it was. As a result, anaverage day in the Muhuru Bay kiosk was computed. As shown infigure 1, we calculated the average energy consumption at eachminute of the day and plotted it on a graph. This produced a load
profile which is useful for predictions as well as daily comparison.
Changes in energy usage habits are easily observed by referencing
the load profile. We determined how power was being usedthroughout the day, the week and the month.

The third and final phase of our research was a culmination of
our earlier work. At this phase the microgrid design team wasdetermining the specifications for the electrical system in Chalokwa.
As part of the team, we used the HOMER software package todetermine the most efficient way to meet the predicted energy needsof the village. The primary interest was balancing the number ofbatteries and solar panels to purchase within the budget limitations.
The survey results told us what the Chalokwa villagers wanted, andthe data from Muhuru Bay provided real world context to reference.
As shown below in figure 2, the energy load profile from Muhuru
Bay was used as one of the baselines for predicted usage in
Chalokwa. Generally speaking a higher overnight load resulted in asystem requiring more batteries, whereas a higher day time loadrequired more solar panels. The cost of each component wasentered along with information about the location itself and the
expected energy usage of the site. The cost to develop such a system
was provided by the software. These results will be used to develop
a system proposal for the new kiosk. Using this analysis KWH
designed an optimal system given the total budget for the project.
In June 2016 a team will travel to Chalokwa to complete theinstallation we have all been preparing for.

Fig 2. Load profile in HOMER
Fig 1. Average day
The second phase of our research was in preparation for anotherenergy kiosk installation in Chalokwa, Zambia. In addition, an
assessment team conducted 48 surveys and 11 detailed interviews
with community members in Chalokwa. We compiled theinformation in Excel and used SPSS to create graphical

Projects such as this one are still a relatively new endeavor, andbest practices are evolving or have yet to be established. Our work
aims to bring information full-circle, allowing experiences fromprevious installations to inform the development of each new
project. This work can facilitate evidence-based implementations
for future projects in both our own and other organizations.

Optimal Planning of Urban Microgrids with an
Energy Management System
Mike Quashie, Student Member IEEE
Geza Joós, Fellow IEEE
Department of Electrical and Computer Engineering
McGill University
Montreal, Canada. H3A 0E9
Email: mike.quashie@mail.mcgill.ca, geza.joos@mcgill.ca

Abstract—This paper proposes a bi-level planning strategy that
optimally configures an urban microgrid to maximize its benefits.
The work implores the karush- Kuhn-tucker (KKT) condition
to transform the two level formulation into a single level mixed
integer linear programing. The optimization strategy receives themodeled thermal and electric load as input and simultaneously
size and optimize the output of the distributed energy resources
through the implementation of an energy management system(EMS). It further analyzes the available investment options of
the microgrid using capital budgeting techniques to determine thereturn on investment to a microgrid stakeholder. The approach,though applicable to all microgrids, is developed within thecontext of an urban microgrid. Results obtained through its
application show significant savings in energy cost and related
microgrid benefits to stakeholders.

UPPER LEVEL
Minimize Upper-level Objective (Investment cost , annualized
operation cost and cost of energy unserved)
Determine:

DER Design Configuration/generation mix (X)
X

LOWER LEVEL
Minimize Lower level Objective (houly Generation Cost, Emission
Cost)

Determine:

Determine Operating point of energy resources (P)P

I. K EY E QUATIONS

Fig. 1. Schematic diagram of the bilevel design-operating model.

Design – Upper Level Problem:The main of objective is tominimize annualized investment cost, operation cost and costof energy unserved.
(
)
X
X
X
X
inv
eus
min γ
%y
Ci xi +Cy (x, p, t) +Cy (t)
x≥0

y∈Y

i∈B

t∈T

II. K EY R ESULTS
The base unit for cost in table I is 1000 whiles in fig. 2
the cost of energy in the base case is the base unit.

t∈T

(1)

TABLE IO PTIMAL M ICROGRID C ONFIGURATION

Main Constraints:••

Budget constraint
Maximum available capacity of DERs to be installed

EMS – Lower Level Problem: The main of objective isto hourly fuel (Cif ) and emmission (Ciz )cost, utility electric
power (Pge (t)) and heat (Pgh (t))cost .

Wind (kW)CHP (kW)

Storage (kWh)Cost (p.u)

2900

1200

1740

1333

Cy (x, y, t) =X
min
(Cif + Ciz )Pie (t) + Cge Pge (t)Pi∈D+Cgh Pgh (t) +mi∈B Ci xi

P(2)

Main constraints:
Electric and Thermal Power balance
Generation limit• Operational limit of storage resource• Limit on energy avaialable for demand response••

Fig. 2. Effect of penetration level of energy resources on cost of energy

PMU-based Reduced-order Modeling of Power
System Dynamics via Selective Modal Analysis
Benjamin P. Wiseman, Yang Chen, Student Member, IEEE, Le Xie, Member, IEEE and P. R. Kumar, Fellow, IEEE
Department of Electrical and Computer Engineering
Texas A&M University
College Station, USA

Abstract—This poster investigates how to perform online
system identification employing synchrophasor data. Two
approaches to identifying a reduced-order model are presented:
a purely data-driven approach, and an approach that integrates
online data-driven dynamic system identification with firstprinciple offline selective modal analysis. With prior knowledgeof the frequency range interesting to power system operators, it
is shown that the second approach recovers the key modes of theoriginal system and produces a much reduced-order model ofgrid-level dynamics. Even with the presence of uncertainty about
the actual modes of interest, an automatic tuning scheme isdevised to adaptively adjust the frequency range to improvesystem identification. Numerical examples with synthetic
synchrophasor data demonstrate the efficacy of the proposed
identification approach.
Keywords—Phasor measurement unit (PMU), data-driven
modeling, system identification, selective modal analysis (SMA).

modes [2]. This matrix can be used to determine the lG relevant
states that contribute most to the h modes. SMA is then applied
to obtain a low-order system G that retains the desired hmodes.
If the dynamic model for G can be achieved accuratelyfrom first-principle offline modeling, then online data-driven
identification targeting a specific frequency range can bedirectly applied to recover a mode of interest. This process
assumes that the input/output data for the identification is
selected from the relevant states of the mode of interest.
Since changing operating conditions in real power systems
prevent offline studies from obtaining an accurate dynamic
model, we have also proposed an automatic tuning scheme toadaptively adjust the frequency range of online data-driven
identification to account for small errors in the offline model.

I. INTRODUCTION
This work is motivated by the increasing complexity of
power system dynamics and the need for reducing suchcomplexity in online monitoring and control. On one hand, thegrid structure is undergoing significant changes as more andmore alternative resources are integrated into the system. On
the other hand, new monitoring devices, such assynchrophasors, provide improved capability for online
adaptive modeling and control of power system dynamics.
Combined with continued advances in computing, theavailability of large quantities of synchrophasor data could
enhance monitoring and control for large power systems.

Fig. 1. Topology of simulated system [2].

This integrated approach was applied to synthetic
synchrophasor data from a 4-generator, 28th-order system [2]modeled in MATLAB. A reduced-order model was obtainedusing SMA, and a 2nd-order system was estimated from thedata. This model accurately contained the mode of interest.

II. NOVEL INTERGRATED APPROACH
Our key contribution is the development of an integrated
approach combining online data-driven dynamic system
identification with first-principle-based offline selective modal
analysis, described in [1].
Let a linear state-space model of a dynamic system beobtained from first-principle offline modeling:

III. CONCLUSIONSThis work demonstrates the significant potential to use
synchrophasor data for online identification of power system
dynamics. Future work will validate this approach by using
real-world measurements, as well as investigating piecewise
system dynamic modeling for frequency ranges of interest.
REFERENCES

ẋ = Ax + Bu.

(1)

[1]

From the eigenvalues of A, h modes can be selectedaccording to either a frequency range or a damping ratio ofinterest. Then, the participation matrix P can be calculated,measuring the association between the state variables and the[2]This material is based upon work partially supported by NSF underContract No. CPS-1239116, ECCS-1150944, DGE-1303378, and NSF
Science & Technology Center Grant CCF-0939370.

I. J. Pérez-Arriaga, G. C. Verghese, and F. C. Schweppe, “Selective
modal analysis with applications to electric power systems, part I:
Heuristic introduction,” Power Apparatus and Systems, IEEE
Transactions on, no. 9, pp. 3117–3125, 1982.
P. Kundur, N. J. Balu, and M. G. Lauby, Power system stability andcontrol. McGraw-hill New York, 1994, vol. 7.

1Frequency and Voltage Control of Isolated
Micro-grid Using Droop Control Approach
Lekhnath Kafle, Student Member, IEEE, Reinaldo Tonkoski, Member, IEEE and Zhen Ni, Member, IEEE
Department of Electrical Engineering and Computer Science, South Dakota State University, Brookings, SD
Email: lekhnath.kafle@sdstate.edu

Abstract—Micro-grid can be operated in grid connected andisolated mode. In grid connected mode, frequency and voltage
can be maintained by utility grid whereas in isolated mode,
frequency and voltage fluctuation are major problem becauseof the intermittent nature of renewable resources like wind andsolar. Our work focuses on frequency and voltage control ofhybrid wind and Photovoltaic (PV) isolated micro-grid using
battery inverter droop control technique. In droop control of
inverter, active power can be controlled by regulating differencein frequency and reactive power can be regulated by regulatingchange in voltage magnitude of micro-grid. Droop control helps togenerate reference voltage and frequency for the inverter control
mechanism. The instantaneous active and reactive power can be
determined by using measured micro-grid voltage and current
which is then fed to low pass filter to obtain average active andreactive power. Then, reference parameters are generated using
droop equations. With this, inverter outputs required power in
addition to wind and solar PV to balance load and generation.

Fig. 2. Power circuit and control block diagram of inverter interfaced battery
system with droop conrol

Index Terms—droop control, frequency fluctuation, hybrid
generation, isolated mode, micro-grid, voltage fluctuation

I. P RINCIPLE

Fig. 3 shows the power balance between generation andconsumption in presence of utility grid. We expect similarbalance in load and generation in absence of grid with help of
inverter interfaced droop controlled battery storage system.

1200
Grid PowerPV Injected Power
Load PowerWind Power

1000
800

Power (kW)The proposed system module of AC micro-grid consists ofwind generation, PV generation and battery storage system
as shown in Fig. 1. During isolated mode, it is required
to maintain balance between load and generation. This canbe achieved by the help of a voltage source inverter (VSI)
with droop control technique. The simplified block diagram of
overall control system for VSI with droop technique is shownin fig. 2.

II. PRELIMINARY RESULTS

600
400
200
0
-200
0.5

1

1.5

2

2.5

3

3.5

4

Time (s)Fig. 3. Validation of total power generation and consumption in variable windspeed, variable irradiance and temperature during grid connected mode

Fig. 1. A proposed system model representing micro-grid with battery storage
system.

Wide-area Control Design for Damping Inter-area
Oscillations in Power Networks
May Mahmoudi and Kevin Tomsovic
Department of Electrical Engineering and Computer Science
University of Tennessee, Knoxville, Tennessee 37996-2250
Email: {mmahmoud, tomsovic}@utk.edu
Abstract— The next generation of the electricity grid, also
known as “Smart Grid”, is one of the most complex cyberphysical systems (CPS) due to its extreme dimension, geographic reach, and high reliability requirements. One of themain concerns for secure and reliable operation of powersystems is the small signal stability problems caused by interarea oscillations. In the future grid, enhancing the transfer
capability while maintaining system stability requires damping
these oscillations. In this poster, we proposed a wide-area
optimal control framework using group sparse regularization
functions. The proposed control aims to optimize a standard costcriterion while penalizing the number of communication links.
The group sparse regularization approach is used to induce adesired communication structure and encode prior information
about the underlying system into the control design. We present
two applications of the proposed algorithm for damping interarea modes in power networks. Our results suggest that the
proposed method provides flexibility in designing wide-area typecontrols by allowing for a pre-defined communication structure.
It can also act as an alternative approach to modal analysis
methods in finding effective measurement-control loops in thesystem. The ability to encode system constraints in the control
design objective is another major advantage of the method.

1

Risk Analysis of Weather Impacts on Outage
Management in Distribution System
Po-Chen Chen, Tatjana Dokic, and Mladen Kezunovic
Department of Electrical and Computer Engineering
Texas A&M University
College Station, Texas, 77840, USA
pchen01@tamu.edu, tatjana.djokic@tamu.edu, kezunov@ece.tamu.edu
Abstract— Weather impacts are one of the main causes of
distribution outages. The proposed risk analysis allows utility
operators to achieve more precise outage prediction and optimize
real time operation. The experiment results show that thesummer rainfall amount is highly correlated with thegeographical locations.

W

This study focuses on the Hazard and Vulnerability part of therisk analysis, which comprises the following part of the risk
expression:

R = P[T ] ⋅ P[C T ](1.b)

III. HISTORICAL WEATHER DATA ANALYSIS AND OUTAGEZONE CLASSIFICATIONS

I. INTRODUCTION

eather impacts are the main causes of electrical outages
in the US. The incidence and severity of weather
conditions and major outages show a growing trend since
1992, and projections are that they will to increase in the
future due to climate change. For instance, the number ofoutages grew by more than 12% from 2013 to 2014.
Mitigating weather impacts to improve outage management is
a complex task. The complexity of distribution systems may
prolong the time to locate faults. The outage risk mapping
techniques become critical in reducing the search range for the
maintenance crew in locating faults. Without utilizing weather
data or relying on manual predictions only, the outage
mapping results can be unreliable.

Despite the richness of global meteorologicalmeasurements, the weather data may be too sparse to provide
the initial and boundary conditions for doing numerical
weather predictions. Given the large amount of available
weather data, the key issue is how to interpret the most
relevant input data from the available data pool. Fig. 1 shows
the linkage between Nino 3.4 and rainfall over Harris Countyof Texas in a seasonal scale. Fig. 2 shows the outage zoneclassifications based on the risk analysis results.

II. RISK ANALYSIS FRAMEWORK FORMULATIONThe risk analysis framework for improving OM may be
defined as a stochastic process using

R = P[T ] ⋅ P[C T ] ⋅ u(C )(1,a)

where R is associated risk index for each component of thesystem, Hazard P[T] is a probability of a Threat T affecting
the system; P[C|T] is the vulnerability of the component or the
probability that Consequences C will happen if system isunder the impact of a Threat T, and u(C) is the utility (cost) of
the consequences. In the outage management processes, thisframework is applied such that R represents the overall
estimate of expected risk associated with power outages, T
represents an intensity of weather impact that may cause an
outage event, C represents an outage event. Hazard P[T]represents a probability of a weather impact with intensity T.
In this work, Hazard is represented spatially through theoutage zone classifications where probability of a weather
associated threat is quantified in different areas. Vulnerability
P[C|T] represents the probability of an outage under occurring
weather conditions. Worth of Loss u(C) represents thefinancial loss utility is experiencing due to the outage event.

Fig. 1. The geospatial distribution of 4 extreme year summer rainfall data in each gridcell of Harris County.

Fig. 2 Predictions of outage zone classifications.

1Placement of Transmission Line Surge Arresters
Based on Risk Analysis
Tatjana Dokic, Po-Chen Chen, and Mladen Kezunovic
Department of Electrical and Computer Engineering
Texas A&M University
College Station, Texas, 77840, USA
tatjana.djokic@tamu.edu, pchen01@tamu.edu, kezunov@ece.tamu.edu
Abstract—This study describes placement of additional line
surge arresters on transmission towers in order to reduce numberof lightning caused outages and associated costs. The study isbased on risk analysis for insulator breakdown.

𝑗=1

(2)

∙ 𝑢(𝐶(𝑖, 𝑗, 𝑡)), 𝑖 = 1, … , 𝐾)

I. INTRODUCTION

I

nsulator failures are the main factor contributing to line
maintenance cost accounting for more than 70% of outages
and 50% of cost. Due to the climate change the number ofextreme and severe weather events including lightning strikes
is increasing in recent years. In addition, aging infrastructure
makes the network more vulnerable to these events. With risk
analysis, the accumulated impact of past lightning-caused
disturbances in the network can be estimated. The risk study
results than can be used to determine the best placement
strategy for line surge arresters that would improve overall line
lightning protection scheme.
II. RISK ANALYSIS FRAMEWORK FORMULATIONThe risk assessment framework can be defined as follows:
𝑅(𝑋, 𝑡) = 𝑃[𝑇(𝑋, 𝑡)] ∙ 𝑃[𝐶(𝑋, 𝑡)|𝑇(𝑋, 𝑡)]∙ 𝑢(𝐶(𝑋, 𝑡))𝑁1
min (𝑓𝑖 = ∑ 𝑃[𝑇(𝑖, 𝑗, 𝑡)] ∙ 𝑃[𝐶(𝑖, 𝑗, 𝑡)|𝑇(𝑖, 𝑗, 𝑡)]𝑁where N is a total number of insulator locations, and K is a total
number of line surge arrester configurations.
IV. STUDY RESULTS
The study analyzes a portion of the power network
consisting of 10 substations interconnected with 12
transmission lines with 170 insulated transmission towers. The
limited number of 10 line surge arresters is considered forplacement in the network. The optimal location of surgearresters is presented in Fig. 2. In Fig. 3. the risk map after thesurge arrester instalment is presented. Based on the study, thechosen line surge arrester configuration is expected to decrease
the overall risk of insulator breakdown by 36%.

(1)

where R is the State of Risk for insulator breakdown, T is thelightning peak current, Hazard P[T] is a probability of alightning strike with intensity T, Vulnerability P[C|T] is theprobability of an insulation total failure, Economic Impact, u(C)is an estimate of financial losses in case of insulation total
failure, X represents the spatial parameter (longitude andlatitude) and t represents the time parameter obtained usingGPS.
The State of Risk value is associated with every network
towers geospatially referenced as presented in Fig. 1. The map
represents risk values for a selected moment in time. Each time
step has a different risk map representing current state of risk
value.

Fig. 1. Risk map.

Fig. 2. Locations of surge arresters.

III. PLACEMENT OF LINE SURGE ARRESTERS
Based on the developed risk maps the optimal placement
strategy for line surge arresters that minimizes the probabilityof insulator breakdown and associated economic losses with thefollowing cost function can be made:Fig. 3. Reduced risk map

Predictive Scheduling for Electric Vehicles
Considering Uncertainty of Load and User Behaviors
Bin Wang, Rui Huang, Yubo Wang, Hamidreza Nazaripouya, Charlie Qiu, Chi-Cheng Chu, Rajit Gadh
Department of Mechanical and Aerospace Engineering, UCLA
{wangbin, gadh}@ucla.edu
Abstract— Un-coordinated Electric Vehicle (EV) charging
can create unexpected load in local distribution grid, which may
degrade the power quality and system reliability. The
uncertainty of EV load, user behaviors and other baseload in
distribution grid, is one of challenges that impedes optimal
control for EV charging problem. Previous researches did not
fully solve this problem due to lack of real-world EV charging
data and proper stochastic model to describe these behaviors. In
this paper, we propose a new predictive EV scheduling algorithm(PESA) inspired by Model Predictive Control (MPC), which
includes a dynamic load estimation module and a predictive
optimization module. The user-related EV load and base load are
dynamically estimated based on the historical data. At each timeinterval, the predictive optimization program will be computedfor optimal schedules given the estimated parameters. Only the
first element from the algorithm outputs will be implemented
according to MPC paradigm. Current-multiplexing function ineach Electric Vehicle Supply Equipment (EVSE) is considered
and accordingly a virtual load is modeled to handle theuncertainties of future EV energy demands. This system isvalidated by the real-world EV charging data collected on UCLA
campus and the experimental results indicate that our proposedmodel not only reduces load variation up to 40% but also
maintains a high level of robustness. Finally, IEC 61850 standard
is utilized to standardize the data models involved, which brings
significance to more reliable and large-scale implementation.

I.

Scheduling Algorithm:
Algorithm 1: Predictive EV Scheduling Algorithm (PESA)
Calculate average baseload by averaging historical data
Estimate EV demand for each EVSE
𝑡=1
Do
Estimate baseload profile with error
For each vehicle
Estimate leave time and energy consumption
End
Estimate virtual load parameters
Solve problem (1) ,subject to constraints
For each vehicle:
Implement new scheduleEnd
𝑡 =𝑡+1
While 𝑡 ≤ 𝑇

III.

Table I Comparison of Load Variation
Load Variation

SYSTEM OVERVIEWPower Supply
Charging Request
Data Flow/Operation
Control Signal

MobileAppControl Center

RESULTS AND CONCLUSIONSWith PESA

Without PESA

40.141370.7471

EVSE ID

Table II ASER Values for Different EVSEs
EVSE 1 EVSE 2 EVSE 3 EVSE 4 Overall

ASER(%)

7.406124.6687

1.6531

19.628114.9745

ASER is defined as:
𝐿

IEC 61850 Interface

Mobile Service

Data Collector

𝐴𝑆𝐸𝑅 =

Internet

Buidling Load1𝑒𝑛 − 𝑒𝑛,𝑐⋅∑⋅ 100%
𝐿𝑒𝑛𝑛

Scheduling Service

Solar Panels

Gateway
with IEC 61850 Interface
AC

EVSE(Level I & II)

Fig. 1 System Overview

II.

PROBLEM FORMULATIONKey Equation:
𝜏=𝑇min ∑(𝑃𝑏 (𝜏) + ∑ 𝑟𝑛 (𝜏) −
𝜏=𝑡

𝑛∈𝑁

1
⋅ (𝑃𝑏 (𝜏) + ∑ 𝑟𝑛 (𝜏)))2
𝑇−𝑡+1
𝑛∈𝑁

Fig. 1 Predictive Scheduling Results with PESA

(1)

9/20/2015

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SMARTKEY Remote FOB Programming New or Used Toyota
Highlander / Prius
I saw this question all over with no solid answer so I am here to put this torest and collect all the info in one place.
You need:

CLICK TO CHANGE COLOR­ Techstream software­ MINI VCI cable or compatible cable (Mongoose MFC)
­ Compatible remote­ Used Remotes need a PASS CODE from TIS (can get it from online services
or an authorized locksmith/dealer with access to TIS, or someone with theKeygen)Build YourPATHFINDER
Learn More­­­­­­­­­­ Techstream software v9 (you get it with the cable or you can find it aroundthe net)­ Cable: just search for techstream cable on ***removed source***)­ The drivers for the cable ­ works well on XP but for win 64bit you need tomanually install x64 drivers.
­The remote with working batteries of course. needs to have the Same FCC
ID and the same transmitter ID which is printed on the inside on the circuitboard.
Mine for 2008 hybrid highlander with hatchback power
FCC ID HYQ14AAB
Toyota part# 8990448110
transmitter ID: 271451­0140
If your remote is virgin and has never been programmed you are good to go,
if not perhaps if you bought a used one: you need to do smartkey reset via
techstream and it will ask you for a passcode.
it gives you a 96 character code + vin # of the car you can get thepasscode using those 2 on some website (20­30$) or from a dealer/locksmith

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If the smartkey is brand new you can just use techstream and program itturn on the car like you are going to drive it but leave it in park, this way
your battery will not drain while you work, make sure your laptop is fully
charged.

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2JZ and LS1 Harnesses
Plug and Play, Lifetime Warranty, Toll­Free

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1/8

9/20/2015

SMARTKEY Remote FOB Programming New or Used Toyota Highlander / Prius ­ Toyota Nation Forum : Toyota Car and Truck Forums
Install the drivers for the cable and install techstream make sure it runs
connect the cable to obdII socket and your USB and in techstream connect tothe carmake sure that is your car in the menu and chose WITH SMARTKEY option.
in techstream: body electrical> smart

Premium Vendor Showcase

key> utility>

New remote> key code registration and follow the prompt very easy.
Used remote> Smartkey reset with the drivers door open for the whole
duration of programing and the car on fully so the battery does not die. Get
the SEED code , do not close the techstream till you get the passcode , if you
close it the smarkey reset code will change.
get the passcode and put it in .. wait 16 minutes .. the program is slow do
not freak out if the screen disappears jus put your own timer for 16 min.
then register the remote.. using Register Key CLASSIC.. chose 3rd option " I
have already reset the smart key"program your used key firstthen your original key and you are done.

FOR PEOPLE WHO NEED THE TIS PASSCODE: it goes for around 30$ online
or you can buy the program for 400$ from china to generate it. the program
comes on a USB dongle and only works from the dongle, I have not foundthe hack for that.
If you have a dealer friend or locksmith friend they should be able to hook
you up.
I might be able to hook you up with SEED code for 15$ from a local
locksmith friend of mine. send me a pm if you need it.

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I have the Passcode calculator program but it does not work without the USB
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03­18­2015, 07:40 PM

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And how much of this is legal, illegal, cracked, or warez?
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03­18­2015, 07:47 PM

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Originally Posted by Rolling ThunderAnd how much of this is legal, illegal, cracked, or warez?

A production
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How you get techstream is your business .. but it comes with the cablethat you buy. the rest should be out of the gray area.

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99% of the techstream's out there are pirated software sold illegally.
As per forum

rules, no posting where to obtain said software/cables.

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03­18­2015, 08:15 PM

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post #5 of 18

Anyhow TEchstream works
you do not need your dealer for new remotes .. the question is how we canget the passcode for free or cheap for used remotes.

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SweenyP can you add this post to the stickies?

Quote

post #6 of 18

03­18­2015, 08:16 PM

funman1
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Thanks!! Glad to know it CAN be done DIY
Mine is not Smart Key so I could never test or play with this...
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doctorman (03­18­2015)03­24­2015, 09:45 AM

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post #7 of 18

I did the Highlander programing after I got hte batteries for the remote
CR1632 from walmart/ they are tough to find, check by jewelry dept. ororder it online and wait.
I had to downgrade my techstream form v10 to v9 so the seed code
calculator would recognize the code.
Used remote> Smartkey reset with the drivers door open for the whole
duration of programing and the car on fully so the battery does not die. Get
the SEED code , do not close the techstream till you get the passcode , if you
close it the smarkey reset code will change.
get the passcode and put it in .. wait 16 minutes .. the program is slow do
not freak out if the screen disappears just put your own timer for 16 min.
then register the remote.. using Register Key CLASSIC.. chose 3rd option " I
have already reset the smart key"program your used key firstthen your original key and you are done.
if you are getting your codes online make sure you are connected to internet
before you start, and make sure the laptop is full and can handle 30min off
the charger.
I ended up buying the Seed code form an online source and I got 3 more
Seed code credit with them ,, who ever needs them PM me.
now ordering the cut keys;
get a Torx30 screwdriver
follow this https://www.youtube.com/watch?v=TzLDWzuuAZc
get your code
you can order it online from ovnisf or maybe your local lock smith 10­20$ per
key.

==========
PRIUS
the prius was more complicated since the immobilizer and the smartkey are
not together in techstream like highlander,
so first erase all keys in immobilizer then register them all

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then go do the smartkey Reset as above. you do not need to Reset theimobiliser, just the smart key in the prius/camry
Last edited by doctorman; 03­24­2015 at 09:47 AM.
Quotepost #8 of 18

06­17­2015, 10:28 AM

beachbummm
New TN User

Quick Reply

just got my china mini vci for Toyota ..
trying to program used sks smart key fob.
like everyone else I need the pass code, I have no problem paying the $20
bucks for it just not sure what this dongle is mentioned above ..
isn't that the cable that connects the laptop to the car?

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06­17­2015, 08:04 PM

funman1
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What pass code are you referring to?
If you are referring to the pass code to open the illegal software we can't help
you...
­

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06­17­2015, 08:44 PM

doctorman

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post #10 of 18

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Originally Posted by beachbummm

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just got my china mini vci for Toyota ..
trying to program used sks smart key fob.
like everyone else I need the pass code, I have no problem paying
the $20 bucks for it just not sure what this dongle is mentioned
above ..
isn't that the cable that connects the laptop to the car?
I have one seed code left PM me if you need it, you need to have the cableand techstream installed on your computer
Quote

06­17­2015, 08:44 PM

doctorman

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post #11 of 18

Quote :
Originally Posted by funman1
What pass code are you referring to?
If you are referring to the pass code to open the illegal software we

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can't help you...
pass code is not illegal.. it is what is needed to get the seed

Quotepost #12 of 18

06­17­2015, 09:20 PM

funman1
Got Pyro?

Quick Reply

Ahh from what I have seen you have to pay like $20 bucks to a shadywebsite to get the seed code...
There was a guy on here that found a website where he paid a small amountand got what he needed.
I originally thought you were looking for the crack to open the program..
My mistake­

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post #13 of 18

09­12­2015, 05:16 PM

sugarland

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Originally Posted by funman1

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Ahh from what I have seen you have to pay like $20 bucks to a
shady website to get the seed code...
There was a guy on here that found a website where he paid a small
amount and got what he needed.
I originally thought you were looking for the crack to open the
program..
My mistake
Just buy a virgin key and you wont have to worry about resetting yourcomputer.
Quote

post #14 of 18

09­12­2015, 05:17 PM

sugarland
New TN User

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Doctorman... I see you like my video on how to get the key code. Thanks forreminding me what torx number it is.

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doctorman (09­12­2015)09­12­2015, 08:18 PM

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post #15 of 18

Quote:
Originally Posted by sugarland
Just buy a virgin key and you wont have to worry about resetting
your computer.

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I have dealt with the website that give you the seed code like 6 times so far
and so far very good interactions, the first time is annoying to figure thingsout but now I can do the reset for any of my 3 toyota cars and for family aswell save money on remote and feel good that I know how to take care of
my car when I want instead of needing someone else's help.
thanks for your video .. I still did not do it lol .. got lazy as I did not reallyneed it but I have to get it done eventually to have a backup.
I still have some seed codes left if anyone needs it.
Quote

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Toyota Nation Forum : Toyota Car and Truck Forums > Toyota Truck, SUV and Van Forums > Highlander Forum > 2nd Generation (2008­2013) > SMARTKEY Remote FOB
Programming New or Used Toyota Highlander / Prius

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03­18­2015, 07:07 PM

doctorman

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post #1 of 18

SMARTKEY Remote FOB Programming New or Used Toyota
Highlander / Prius
I saw this question all over with no solid answer so I am here to put this torest and collect all the info in one place.
You need:

CLICK TO CHANGE COLOR­ Techstream software­ MINI VCI cable or compatible cable (Mongoose MFC)
­ Compatible remote­ Used Remotes need a PASS CODE from TIS (can get it from online services
or an authorized locksmith/dealer with access to TIS, or someone with theKeygen)Build YourPATHFINDER
Learn More­­­­­­­­­­ Techstream software v9 (you get it with the cable or you can find it aroundthe net)­ Cable: just search for techstream cable on ***removed source***)­ The drivers for the cable ­ works well on XP but for win 64bit you need tomanually install x64 drivers.
­The remote with working batteries of course. needs to have the Same FCC
ID and the same transmitter ID which is printed on the inside on the circuitboard.
Mine for 2008 hybrid highlander with hatchback power
FCC ID HYQ14AAB
Toyota part# 8990448110
transmitter ID: 271451­0140
If your remote is virgin and has never been programmed you are good to go,
if not perhaps if you bought a used one: you need to do smartkey reset via
techstream and it will ask you for a passcode.
it gives you a 96 character code + vin # of the car you can get thepasscode using those 2 on some website (20­30$) or from a dealer/locksmith

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If the smartkey is brand new you can just use techstream and program itturn on the car like you are going to drive it but leave it in park, this way
your battery will not drain while you work, make sure your laptop is fully
charged.

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SMARTKEY Remote FOB Programming New or Used Toyota Highlander / Prius ­ Toyota Nation Forum : Toyota Car and Truck Forums
Install the drivers for the cable and install techstream make sure it runs
connect the cable to obdII socket and your USB and in techstream connect tothe carmake sure that is your car in the menu and chose WITH SMARTKEY option.
in techstream: body electrical> smart

Premium Vendor Showcase

key> utility>

New remote> key code registration and follow the prompt very easy.
Used remote> Smartkey reset with the drivers door open for the whole
duration of programing and the car on fully so the battery does not die. Get
the SEED code , do not close the techstream till you get the passcode , if you
close it the smarkey reset code will change.
get the passcode and put it in .. wait 16 minutes .. the program is slow do
not freak out if the screen disappears jus put your own timer for 16 min.
then register the remote.. using Register Key CLASSIC.. chose 3rd option " I
have already reset the smart key"program your used key firstthen your original key and you are done.

FOR PEOPLE WHO NEED THE TIS PASSCODE: it goes for around 30$ online
or you can buy the program for 400$ from china to generate it. the program
comes on a USB dongle and only works from the dongle, I have not foundthe hack for that.
If you have a dealer friend or locksmith friend they should be able to hook
you up.
I might be able to hook you up with SEED code for 15$ from a local
locksmith friend of mine. send me a pm if you need it.

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Wtb s51­s54 5th gear...

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Anyone have experience...

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3'87 camry now what??

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06­08 RAV4 Oil Burners...

========
I will update this post soon with more detail.

1

Who would be able...

thanks to all who helped me in this post FYI ­ Keyless Remote Programming

6

XLE_Philly's Build Thread

14

2000 4 cyl camry will...

5

Need Help­ No...

I have the Passcode calculator program but it does not work without the USB
dongle if anyone can crack it and extract the algorithm or make it workwithout the dongle we can all get our passcodes for free

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03­18­2015, 07:40 PM

Rolling Thunder
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And how much of this is legal, illegal, cracked, or warez?
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03­18­2015, 07:47 PM

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post #3 of 18

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Latest Toyota News

Originally Posted by Rolling ThunderAnd how much of this is legal, illegal, cracked, or warez?

A production
decision on the
jointly developed
sports car betweenBMWfunman1 likes this.

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Toyota RAV4 Recalled for Faulty
Windshield Wipers
The Toyota RAV4and RAV4 EV
models are being
recalled for an
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post #4 of 18

03­18­2015, 07:59 PM

Resident Nutcase

Fate of Toyota­BMW Sports Car to be
Decided by Yearâ€™s End

How you get techstream is your business .. but it comes with the cablethat you buy. the rest should be out of the gray area.

Quotesweeneyp

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CLICK TO CHANGE COLOR

99% of the techstream's out there are pirated software sold illegally.
As per forum

rules, no posting where to obtain said software/cables.

New Toyota C­HR Concept Brings thePrius Crossover Closer to Reality
Toyota took
another step
towards making a
Prius crossover areality

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03­18­2015, 08:15 PM

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post #5 of 18

Anyhow TEchstream works
you do not need your dealer for new remotes .. the question is how we canget the passcode for free or cheap for used remotes.

http://www.toyotanation.com/forum/122­2nd­generation­2008­2013/1065282­smartkey­remote­fob­programming­new­used­toyota­highlander­prius.html

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SweenyP can you add this post to the stickies?

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post #6 of 18

03­18­2015, 08:16 PM

funman1
Got Pyro?

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Thanks!! Glad to know it CAN be done DIY
Mine is not Smart Key so I could never test or play with this...
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doctorman (03­18­2015)03­24­2015, 09:45 AM

doctorman

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post #7 of 18

I did the Highlander programing after I got hte batteries for the remote
CR1632 from walmart/ they are tough to find, check by jewelry dept. ororder it online and wait.
I had to downgrade my techstream form v10 to v9 so the seed code
calculator would recognize the code.
Used remote> Smartkey reset with the drivers door open for the whole
duration of programing and the car on fully so the battery does not die. Get
the SEED code , do not close the techstream till you get the passcode , if you
close it the smarkey reset code will change.
get the passcode and put it in .. wait 16 minutes .. the program is slow do
not freak out if the screen disappears just put your own timer for 16 min.
then register the remote.. using Register Key CLASSIC.. chose 3rd option " I
have already reset the smart key"program your used key firstthen your original key and you are done.
if you are getting your codes online make sure you are connected to internet
before you start, and make sure the laptop is full and can handle 30min off
the charger.
I ended up buying the Seed code form an online source and I got 3 more
Seed code credit with them ,, who ever needs them PM me.
now ordering the cut keys;
get a Torx30 screwdriver
follow this https://www.youtube.com/watch?v=TzLDWzuuAZc
get your code
you can order it online from ovnisf or maybe your local lock smith 10­20$ per
key.

==========
PRIUS
the prius was more complicated since the immobilizer and the smartkey are
not together in techstream like highlander,
so first erase all keys in immobilizer then register them all

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9/20/2015

SMARTKEY Remote FOB Programming New or Used Toyota Highlander / Prius ­ Toyota Nation Forum : Toyota Car and Truck Forums
then go do the smartkey Reset as above. you do not need to Reset theimobiliser, just the smart key in the prius/camry
Last edited by doctorman; 03­24­2015 at 09:47 AM.
Quotepost #8 of 18

06­17­2015, 10:28 AM

beachbummm
New TN User

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just got my china mini vci for Toyota ..
trying to program used sks smart key fob.
like everyone else I need the pass code, I have no problem paying the $20
bucks for it just not sure what this dongle is mentioned above ..
isn't that the cable that connects the laptop to the car?

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Quotepost #9 of 18

06­17­2015, 08:04 PM

funman1
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What pass code are you referring to?
If you are referring to the pass code to open the illegal software we can't help
you...
­

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No really; I get paid to blow stuff up...

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06­17­2015, 08:44 PM

doctorman

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post #10 of 18

Thread Starter

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Official TN Member

Originally Posted by beachbummm

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just got my china mini vci for Toyota ..
trying to program used sks smart key fob.
like everyone else I need the pass code, I have no problem paying
the $20 bucks for it just not sure what this dongle is mentioned
above ..
isn't that the cable that connects the laptop to the car?
I have one seed code left PM me if you need it, you need to have the cableand techstream installed on your computer
Quote

06­17­2015, 08:44 PM

doctorman

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post #11 of 18

Quote :
Originally Posted by funman1
What pass code are you referring to?
If you are referring to the pass code to open the illegal software we

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9/20/2015

SMARTKEY Remote FOB Programming New or Used Toyota Highlander / Prius ­ Toyota Nation Forum : Toyota Car and Truck Forums

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can't help you...
pass code is not illegal.. it is what is needed to get the seed

Quotepost #12 of 18

06­17­2015, 09:20 PM

funman1
Got Pyro?

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Ahh from what I have seen you have to pay like $20 bucks to a shadywebsite to get the seed code...
There was a guy on here that found a website where he paid a small amountand got what he needed.
I originally thought you were looking for the crack to open the program..
My mistake­

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post #13 of 18

09­12­2015, 05:16 PM

sugarland

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Quote:New TN User

Originally Posted by funman1

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Ahh from what I have seen you have to pay like $20 bucks to a
shady website to get the seed code...
There was a guy on here that found a website where he paid a small
amount and got what he needed.
I originally thought you were looking for the crack to open the
program..
My mistake
Just buy a virgin key and you wont have to worry about resetting yourcomputer.
Quote

post #14 of 18

09­12­2015, 05:17 PM

sugarland
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Doctorman... I see you like my video on how to get the key code. Thanks forreminding me what torx number it is.

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doctorman (09­12­2015)09­12­2015, 08:18 PM

doctorman

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post #15 of 18

Quote:
Originally Posted by sugarland
Just buy a virgin key and you wont have to worry about resetting
your computer.

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I have dealt with the website that give you the seed code like 6 times so far
and so far very good interactions, the first time is annoying to figure thingsout but now I can do the reset for any of my 3 toyota cars and for family aswell save money on remote and feel good that I know how to take care of
my car when I want instead of needing someone else's help.
thanks for your video .. I still did not do it lol .. got lazy as I did not reallyneed it but I have to get it done eventually to have a backup.
I still have some seed codes left if anyone needs it.
Quote

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Optimal Scheduling of Integrated Microgrids withHigh Resolution Islanding
Guoshun Zhao, and Amin Khodaei
Dept. of Electrical and Computer Engineering
University of Denver
Denver, CO, USA
Rocky.Zhao@du.edu, Amin Khodaei@du.edu
Abstract—The increasing penetration of microgrids indistribution networks, as a viable option for both end-use
customers and electric utilities, will result in formation of many
interconnected microgrids in a not so far future. This paper
considers the case in which integrated microgrids are
geographically close and electrically connected, and further
studies anticipated interactions between the microgrids and theutility grid, during grid-connected and islanded modes.
Accordingly, a model for the optimal scheduling of integrated
microgrids considering both grid-connected and islanded
operation modes will be proposed. The microgrids capability inoperating in the islanded mode, for multiple hours is scrutinized
by a T- 𝝉 islanding criterion. Numerical simulations study test
integrated microgrids for exploring merits, as well as
demonstrating benefits when compared to deployment of anindividual microgrid.
Index Terms—Integrated microgrids, distributed energy
resource, optimal scheduling, islanded operation, grid-connected
operation.

I. INTRODUCTIONM

ICROGRIDS, assmall-scale power systems integrating
distributed generators, energy storage systems, andcontrollable loads, are significantly deployed over the past few
years and are anticipated to grow more in the near future. This
growing penetration will result in emergence of networks of
microgrids which not only are connected and exchange power
during the grid-connected mode, but also provide support forother microgrids during the islanded mode. This integration canpotentially provide considerable benefits for the system andalso for individual customers, such as (1) reducing the system
operation cost and customer payments, (2) reducing power
losses at the distribution level and increasing energy efficiency,(3) reducing load curtailment, and (4) supporting renewablegeneration.

MG1

MG2

MG3
Fig 1. Integrated microgrids are connected to the same upstream substation

To achieve this, the inter-microgrids power transfer ispenalized with the coefficient  and the problem isdecomposed accordingly. The equation (1) will be checked
after each Lagrangian iteration until a value of zero is obtained,i.e., the problem is converged. If not zero, the associatedLagrangian multiplier (2) will be updated for revising thesolution in the next iteration.
(1)
Pmnt  Pnmt  0 new   old   ( Pmnt  Pnmt )(2)III. DISCUSSION AND CONCLUSION
The optimal scheduling model is developed for demonstrating
the merits of integrated microgrids. Individual schedulingproblem for each microgrid is discussed using Lagrange
Relaxation as a decomposition method. Each microgrid’s excess
generation, beyond its load, would help other microgrids tosupply local loads during islanded operation. The proposedmodel analyzes through numerical simulations, where it showsthat integrated microgrids would reduce great amount of power
losses.
REFERENCES

II. MODEL OUTLINE[1]

The main contribution of this paper is twofold. First, a modelfor the optimal scheduling problem of integrated microgrids isdeveloped, with the objective of minimizing the system
operational cost. Second, as microgrids should be controlledindividually and the optimization problem cannot be solved
centrally, Lagrange Relaxation is applied to decompose theintegrated problem to a set of smaller and individual schedulingproblems for each microgrid. Consequently, each microgrid
would benefit by transfering its excess power to othermicrogrids and they could reliably supply its local loads, reduce
power losses, and increase operational reliability.

[2]
[3]
[4]

[5]

A. Khodaei, “ Provisional Microgrids”, Smart Grid, IEEE Transactionson (Volume:6 , Issue: 3 ), 2014
A. Khodaei, “ Provisional Microgrid Planning ”, Smart Grid, IEEE
Transactions on (Volume:PP , Issue: 99 ), 2015
Parhizi, S, Lotfi, H. ; Khodaei, A. ; Bahramirad, S. “State of the Art in
Research on Microgrids: A Review”, Access, IEEE, (Volume:3), 2015
A. Khodaei, “Microgrid optimal scheduling with multi-period islanding
constraints,” IEEE Trans. Power Syst., vol. 29, no. 3, pp. 1383-1392, May
2014.
A. Khodaei, “Resiliency-oriented microgrid optimal scheduling,” IEEE
Trans Smart Grid, vol. 5, no. 4, pp. 1584-1591, July 2014.

Modeling and Assessment of PV Solar Plants for
Composite System Reliability Considering
Radiation Variability and Component Availability
Samer Sulaeman, and Joydeep Mitra
Department of Electrical and Computer Engineering
Michigan State University
East Lansing, Michigan 48824, USA(samersul@msu.edu and mitraj@msu.edu)

Abstract—This paper presents a method to model the output
power of large PV systems in composite system reliability assessment. Grid level PV systems are usually constructed from a large
number of power electronic components and PV panels. Modelingof these systems in power system reliability is a complex task
due to the dependency of the output power on the intermittent
source (solar) and the availability of a large number of systemcomponents. An analytical method to construct a capacity outage
probability and frequency table (COPAFT) that captures boththe intermittency of the input source and component failures is
proposed to model PV systems. The intermittency of the input
source and components availabilities are modeled separately andthen convolved to construct a single COPAFT. The resulting
COPAFT forms multi-state reliability model of the entire solar
facility. The proposed method reduces the complexity of modelingand evaluating large PV systems in composite system reliability
assessment. The method is demonstrated on IEEE RTS. Considering the PV farm location with a view to enhance system
reliability, sensitivity study was conducted to measure the effectof the location of the PV farm on overall system reliability. The
results confirm that connecting PV farms to the buses that are
at high risk enhances the overall system reliability.
Index Terms—PV systems, reliability of composite systems,
renewable energy.

and the functionality of a large number of system components.
This paper proposes an analytical method to model the outputof large PV power plants in a manner that considers both the
failure of system components and input dependency.
II. T HE P ROPOSED M ETHOD
The purpose of this work is to construct a model that sorts
the output power of PV farms into clusters considering theeffect of the input variability and failures of system components. Two COPAFTs are constructed: (1) the dependency on
the output on the variability of the radiation and (2) the outage
due to failure of system components. These two COPAFTs
are then combined to produce a single COPAFT that can beviewed as a single source with multi–states. The proposed
method is depicted in figure 1.
Input(Hourly Solar
Radiation Data)

PV Panels

System Outage
Model

Convolution

Output Power
Curve

Cluster bands of the
Output PowerI. I NTRODUCTION
The global movement towards renewable energy resources
has gained significant momentum due to environmental concerns and economic incentives of fossil fueled generating
units. Despite the advantages of renewable energy resources,
the reality of input uncertainty and output variability adds
to the complexity of modeling these resources in planningand operation studies. One of the important aspects in thereliability evaluation of composite systems is assessment of
the effect of such variable resources on the reliability of themain grid. Grid connected PV systems are usually constructedfrom a large number of components such as power electronic
circuits and solar panels. The reliability of such components ishighly dependent upon the operation and ambient conditions.
Operational and environmental factors may lead to overall
reduction in the output power and failure of system components [1], [2]. Modeling of these systems in power system
reliability is a complex task due to the dependency of theoutput power on the intermittent source (solar) main input

System Components
Failure Data

Combined outage
Model

Reliability
Assessment Model

Figure 1. The Proposed Method

III. C ONCLUSION
This paper proposes an analytical approach that comprehensively models large PV systems in power system reliability
studies. The proposed method takes into account both thevariability of solar radiation and failures of system components
such as power electronic converters.
R EFERENCES[1] Forman, S. E. ”Performance of experimental terrestrial photovoltaic
modules.” Reliability, IEEE Transactions on, vol. 31, no. 3, pp. 235–245,
1982.
[2] Fanney, A. Hunter, et al. ”Comparison of photovoltaic module performance measurements.”Journal of Solar Energy Engineering, vol. 128,
no. 2, pp. 152–159, 2006.

Economic Analysis, Optimal Sizing and Managementof Energy Storage for PV Grid Integration
Bananeh Ansari1, 2, Di Shi1, Ratnesh Sharma1, and Marcelo G. Simoes2
1

Energy Management Department, NEC Laboratories America, Inc., Cupertino, CA
2
EECS Department, Colorado School of Mines, Golden, CO
Email: bansari@mymail.mines.edu; dshi@nec-labs.com

Abstract—This paper investigates the optimal procurement andscheduling of battery storage in distribution systems with highphotovoltaic (PV) penetration. The battery under study is assumed
to be owned by private entities with the objective of minimizing theelectricity bill (or, interchangeably, maximizing the PV systemyields). Voltage regulation is achieved by controlling the net
metered real power at the point of common coupling of (PCC) of
each entity. Dynamic energy conversion equation is used to model
the battery with the impact of cyclic aging considered. The
procurement-scheduling problem is formulated as a nonlinear
programming (NLP) problem. A recursive method is proposed todetermine the lifetime of the battery. The proposed methodology isvalidated on the IEEE 33-bus feeder system. A thorough economic
analysis is conducted to quantify the financial benefits brought inby battery.
Index Terms—Energy storage system (ESS), distribution network,
PV generation, nonlinear programming, voltage regulation.

I.  

INTRODUCTION

Traditionally, power flow in distribution system has always
been unidirectional, from substation to end-users. Penetration of
distributed energy resources has transformed the unidirectional
distribution networks into bidirectional networks, where power
can also flow in the “reverse” direction, from the downstream
network toward the substation. Bidirectional power flow has
introduced new operational issues to the grid, one of which isvoltage rise along the feeders when power flows toward the
upstream network. This is particularly significant in distribution
networks with high photovoltaic (PV) penetration. The existing
solution to this issue is to curtail PV generation to stop reverse
power flow. However, generation curtailment causes considerable
energy loss in the most productive hours of a day. The othersolution is to use voltage regulation devices, i.e. on-load tapchanging transformers (OLTC), and step voltage regulators(SVR). The main drawback is that this will significantly increase
the mechanical stress and maintenance cost of these devices.
Reactive power control from PV inverters is another possible
solution but current utility practices (IEEE 1547 and UL 1741)
only allow PV to operate at unity power factor, and further,
reactive power provision reduces the lifetime of PV inverters [1]Energy storage system (ESS) has shown potentials to help
mitigate the voltage rise issue in distribution networks with highPV penetration [2],[3]. Instead of curtailment, the excess energy
from PV can be directed to the ESS, and, can be either selfconsumed or injected back to the grid at a later time whennecessary. This mechanism not only mitigates overvoltage, butalso saves a considerable amount of energy, and shaves the peak.
In this paper, we propose an optimization-based framework to
determine the optimal size and dispatch of end-user-owned
battery storage with the objective of minimizing the end-user’s
energy bill taking battery degradation into account. Constraints
are placed on the net power metered at PCC, so that voltage

always remains within the specified limits. Moreover, theproposed approach determines the lifetime of the battery toensure the procurement of battery is economically viable.
II.  

OPTIMAL SIZING AND DISPATCH OF ESS

A.   The optimization framework
The main objective of purchasing energy storage system froman end-user’s perspective is to minimize the energy costs bystoring excess PV generation, self-consuming or injecting it backto the grid during peak-pricing hours. However, the battery
degrades when it goes through charge/discharge cycles, and its
effective capacity reduces. Degradation cost must to be taken into
account when determining whether it makes economic sense topurchase the battery storage. Hence, the objective function will beT∑ ⎡⎣ P .π
net ,tt =1

net ,t+ λB ΔCB ,t ⎤⎦(1)where Pnet,t is the net metered (purchased) power at the PCC intime interval t, πnet,t is the time-of-use (TOU) price (hourly), λB is
the unit cost of storage, and ΔCB,t is capacity degradation.
Minimizing objective function (1) is subject to the following
constraints: 1) power balance at PCC 2) battery dynamics, 3)battery charge/discharge status, 4) battery energy limits, 5)battery capacity degradation, 6) battery initial energy andcapacity condition, and 7) dynamic voltage regulating limitsß on
net metered power.
III.   RESULTS

Figure 1 top: voltage profile w/o ESS, bottom: voltage profile w ESS

Resilience-Constrained Unit Commitment to Reduce
the Aftermath of Hurricanes
Rozhin Eskandarpour, Amin Khodaei
Department of Electrical and Computer Engineering
University of Denver
Denver, CO 80210, USA
rozhin.eskandarpour@du.edu, amin.khodaei@du.edu
Abstract—Hurricanes frequently cause damage to electric powersystems, leading to widespread and prolonged loss of electric
services. Resilience-constrained unit commitment (RCUC) can beused to schedule available generation resources for satisfying theforecasted load in case of multiple component outages as a resultof hurricanes. Accurate estimation of the duration of power
outages caused by hurricanes prior to landfall is an importantfactor for an efficient RCUC solution. In this paper, a regression
and data mining method is used to estimate and model the power
grid critical components, here generation units and transmission
lines, that will fail during a predicted hurricane. The model istrained on synthetic damage data from past storms and theprediction is used in the developed RCUC problem. The role ofthe microgrids in improving the grid response and supporting thesystem operator in improving grid resilience is further
investigated via accurate grid-connected and islanded operation
models. The RCUC problem is formulated using mixed-integer
linear programming (MILP). The merits and effectiveness of the
proposed models are demonstrated using the standard IEEE 118bus system for different storm intensities and locations.

II. MODEL OUTLINE AND RESULTS
Fig. 1 depicts the outline of the proposed RCUC model. The
proposed RCUC problem is applied to the standard IEEE 118bus test system [5]. In order to exhibit the effectiveness of the
proposed model, two cases are studied as follows: Case 1:
proposed RCUC with N-m criterion trained on synthetic data,
and Case 2: proposed RCUC with N-m criterion andmicrogrids.
Forecasting the extreme event- Determine impacted generation units and transmission
lines based on the trained model
Component outages
Resilience-Constrained Unit Commitment- Optimal scheduling of available resources- Preventive commitment and corrective dispatch
Fig. 1 Proposed RCUC model

I. INTRODUCTION
The importance of improving resilience in power systems is
widely discussed in the literature [1], [2]; however, themathematical modeling of optimal scheduling of available
resources based on resilience considerations and efficientmodeling of weather related incidents is limited. In particular,
the power grid response to hurricanes, as one of the major
extreme natural events in the U.S., requires additional
discussions in the research community.
A hurricane is typically assigned a “category” of one
through five based on its maximum 1-minute sustained windspeed according to the Saffir-Simpson Hurricane Scale [3].
Predicting hurricane, its impact, and strength plays a vital role
in resiliency studies of power systems [4]. In this paper, kernel
density estimation is applied on synthetic data to estimate theprobability of failure for each unit based on the center ofhurricane and the category of the hurricane. Then, a resilienceconstrained unit commitment (RCUC) problem is developed
which considers the probabilistic failure model of the systemcomponents, and determines the commitment and dispatch ofavailable generation units to ensure an economic operation
under normal conditions and a resilient operation undercontingency cases. The proposed formulation further efficiently
models the microgrid grid-connected and islanded operation
modes while ensuring a practical and dynamic response to
extreme events in case of upstream network outages.

The numerical simulations exhibit the merits andapplicability of the proposed RCUC model compared toexisting work on SCUC. It is shown that having proper data to
train the system can improve prediction of the impact of
hurricane and resiliency of the system. Numerical simulation
further indicate that the microgrids have a complimentary value
proposition in power system resiliency as they can efficiently
lower the possibility of load shedding by utilizing local
generation.
REFERENCES
[1]
[2]
[3]

[4]

[5]

A. Khodaei, “Resiliency-oriented microgrid optimal scheduling,”
Smart Grid IEEE Trans. On, vol. 5, no. 4, pp. 1584–1591, 2014.
S. Parhizi, H. Lotfi, A. Khodaei, and S. Bahramirad, “State of the Art
in Research on Microgrids: A Review.”
T. Schott, C. Landsea, G. Hafele, J. Lorens, A. Taylor, H. Thurm, B.
Ward, M. Willis, and W. Zaleski, “The Saffir-Simpson hurricane
wind scale,” Natl. Weather Serv., 2012.
A. Arab, A. Khodaei, S. K. Khator, K. Ding, V. Emesih, Z. Han, andothers, “Stochastic Pre-hurricane Restoration Planning for ElectricPower Systems Infrastructure,” Smart Grid IEEE Trans. On, vol. 6,
no. 2, pp. 1046–1054, 2015.
“IEEE 118-Bus System - Illinois Center for a Smarter Electric Grid
(ICSEG).” [Online]. Available:
http://publish.illinois.edu/smartergrid/ieee-118-bus-system/.