Smart Grid Modeling Approach for Wide Area

8/13/2019 Smart Grid Modeling Approach for Wide Area

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Abstract This paper describes an approach for modeling

smart grids for wide area control applications. More specifically,

it is proposed to model smart grids as a set of interdependent

composite networks. A composite network is one whose evolution

in time and/or space is described as a composition of more than

one category of networks. This work proposes an interconnection

of a communication network, information network, and a power

system network to model smart grids. More specifically this work

proposes a quantitative model focusing on bulk generation and

transmission. The resulting model will be used for studying and

simulating wide area measurement and control techniques and

contingencies of components. The modeling methodology isbased on the initial partitioning by the National Institute of

Standards and Technology (NIST) of the smart grid domains. A

basic example with control of load (demand response) and

generator set points over a communication link is presented.

Index TermsSmart Grid, Communication Network

Modeling, Power System Modeling, Wide Area Measurement

and Control

I. INTRODUCTION

he power grid consists of physical components, which

generate and transmit power, and cyber components

which transmit data and control signals. Currently,

operation and control of bulk power generation andtransmission network occurs at centralized control centers and

relies mostly on operator in the loop control and analysis. For

example, results from state estimation and contingency

analysis will be reviewed by operators and adjustments systemoperation made accordingly by the system operator. This

control loop relies on human intervention and the time scale is

on the order of minutes. In addition, some automatic wide areacontrol, such as automatic generation control (AGC), has been

implemented and relies on a slow response. More specifically,

AGC acts slowly and deliberately over tens of seconds or a

few minutes [1]. Current analytical techniques and models

make assumptions that communication lines are in service and

any latency or bandwidth constraints are negligible and/orhave no effect on system operation. With the slow response

and dynamics of current wide area control techniques these

assumptions are adequate. However, the advancement andimplementation of smart grid technology requires more

advanced models that factor in the status and performance of

A. St. Leger and John James are with the Electrical Engineering and

Computer Science department at the United States Military Academy, WestPoint, NY 10996 USA (e-mail: [email protected] and

[email protected] ). Dean Frederick is with Saratoga Control Systems,

Inc.

communication and information networks. For example,

results presented in [2] show that an increase in time delay can

cause degradation of frequency control using decentralized

intelligent loads. Furthermore, too much latency can lead tosystem instability. As a result, the present state and dynamics

of communication networks can be a critical contingency for

current and future smart grid applications. The objective of

this work is to develop a modeling methodology for analyzingsmart grids, control techniques, and identifying important

contingencies within cyber and physical elements of the

system specifically for wide area control applications. These

contingencies could be malicious, for example a cyber orphysical attack on a network, or non-malicious. A critical

component for modeling smart grids is modeling the

interdependencies and interactions between the cyber andphysical components.

Vulnerability analysis of power systems and information

networks is a continuing field of research [3-5]. The focus of

many research efforts have been placed on large cascadingfailures due to impacts of such disruptions. Historically, much

research has focused on either the power grid or information

networks [6, 7]. Recently the interdependencies of the twoinfrastructures been studied [3, 8, 9]. The current state-of-the-

art techniques rely on qualitative analysis of the systems and

interdependencies [5] and, as a result, develop approximateresults and estimations of the real interdependencies of the two

systems. Some work is moving towards a quantitativeapproach more suitable for analyzing smart grid applications

and contingencies [10]. The approach described in this paper

is to develop a unified quantitativemethodology of modeling

both the cyber and physical components of the system, and theinterdependencies between the two. More specifically the

focus is on a unified cyber/physical system model suitable for

simulation and analysis of the following:

Physical contingencies in HV transmissionnetwork/bulk power generation and sensors

Cyber contingencies in smart grid componentsrelated to HV transmission/bulk Generation

Decentralized local and wide area control Centralized wide area control

Some specific examples of wide area control techniques of

interest are voltage control, frequency control, and small

signal stability control for inter-area modes. Of particular

interest is how advanced wide area control techniques are

susceptible to contingencies of physical and/or cybercomponents.

Smart Grid Modeling Approach for Wide Area

Control ApplicationsAaron St. Leger,Member, IEEE, John James, Senior Member, IEEE,Dean Frederick, Senior Member,

IEEE

T

978-1-4673-2729-9/12/$31.00 2012 IEEE


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Fig. 2. Smart Grid Model Structure

This general framework of LCNC and RSOC linked to

physical model of the power system/communication network

is generalized to allow for modeling of a wide range of smart

grid devices and controls. The next sections discuss the powersystem, communication network, and control components in

more detail.

A. Power System Network Components

The power system network, which consists of aninterconnection of transmission lines and transformers, is

modeled by an interconnection of impedances modeling each

component. Network equations in terms of the nodal

admittance can be written for an n bus system from this asfollows [12]:

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where busY is the bus admittance matrix, Iis a column vector of

current injection at the network nodes and V is a columnvector of nodal voltages. Generators and loads are modeled as

power injections into the system nodes.

Generators are modeled as synchronous machines with

governors, exciters and power system stabilizers. The

mechanical model of the generator is based on the swingequation and governors, exciters and power system stabilizers

are based on standard models used in power system analysis.

Details on these models can be seen in [12]. Loads aremodeled as constant power. Enhancement of this work is

ongoing to incorporate ZIP and dynamic load models based on

induction machines.

B. Communication Network Components

Communication network modeling consists of two

approaches. The first is to model physical devices and

communication links (e.g. modems, fiber optic networks, etc).Initial work has incorporated a frequency shift key (FSK)

modem to transmit control signals between components. The

second approach is a generic communication link model

incorporating bandwidth and latency which are the two mostinherent properties for smart grid communication as discussed

in [13]. The initial model incorporates a variable time-delay to

the data sent over a communication link. This allows variationof the time-delay to model constant or variable latency in data

transmission. Work is ongoing to develop time-delay based

models to represent specific communication hardware and

protocols. However, the initial time-delay model can be used

to study the effects of latency on wide area control techniquesand other smart grid functions.

C. Control Components

Modeling of control components is broken down intoLCNC and RSOC models. RSOC models are used for wide

are controllers such as Static Var Compensation (SVC) control

proposed in [14]. A model for a SVC in our approach is shown

in Fig. 3. Communication links transmit measurements from a

phasor measurement unit (PMU) unit embedded in the powersystem model and deliver it to an algorithm which processes

the data, updates the discrete state of the SVC and send a

control signal over a communication link. Different controlalgorithms, communication links, SVC models, etc. can be

modeled with this approach.

Fig. 3. RSOC Model of Wide Area Control SVC

LCNC models are used for localized control which can bebased on local and/or wide area measurements. For example,

power system stabilizer controls are implemented via local

measurement and feedback at the generators. Smart gridcomponents requiring wide area measurement or transfer of

information between components embedded in the network

are modeled as intelligent agents. An intelligent agent is an

autonomous, goal-oriented entity that can interact with its

environment [13]. This is modeled here as an algorithmdictating the behavior of the agent with local and wide area

measurement as inputs while local control actions and

communication with other agents as outputs. Latency of localcontrol actions and measurements for LCNC is assumed to be

zero. Latency and transmission of information to and from

LCNC is represented by the communication network model.

The following section discusses initial efforts towardconstructing and simulating the proposed smart grid model.

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and their associated cyber and physical contingencies. TheMATLAB/Simulink simulation environment is flexible to

allow a wide range of modeling and simulation of smart grid

components and control schemes. Future work will include

analyzing the scalability of the proposed approach andrefining models. Simulating very large systems in detail will

likely be impractical, however, the developed framework

allows for implementation of refined or simplified modelsmore appropriate for large system simulation.

V. ACKNOWLEDGMENT

This work was supported by the Defense Threat Reduction

Agency MIPR# 10-2693M, to the United States MilitaryAcademy.

VI. REFERENCES

[1] N. Jaleeli, et al., "Understanding Automatic Generation Control," IEEETransactions on Power Systems, vol. 7, pp. 1106-1122, Aug 1992.

[2] D. Trudnowski, et al., "Power-system frequency and stability control

using decentralized intelligent loads," Proceedings of the 2006 IEEE

Power Engineering Society T&D Conference adn Expo,pp. 1453-1459,

May 2006.[3] S. Chiaradonna, et al., "On a modeling framework for the analysis of

interdepenedencies in electric power systems," Proceedings of the 37th

Annual IEEE/IFIP International Conference on Dependable Systems

and Networks, 2007.

[4] J. C. Laprie, et al., "Modeling cascading and escalating outages in

interdependent critical infrastructures," Proceedings of the 2006 IEEEInternational Conference on Dependable Systems and Networks, pp.

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[5] J. C. Laprie, et al., "Modelling interdependencies between the electricity

and information infrastructures," Proceedings of the Internationalconference on Computer Safety, Reliability and Security (SAFECOMP),

pp. 54-67, 2007.

[6] P. Crucitti, et al., "Model for cascading failures in complex networks,"

Physical Review E, vol. 69, Apr 2004.[7] P. Task Force on Understanding, Mitigation and Restoration of

Cascading Failures in Electric Power Systems, "Vulnerability

Assessment for Cascading Failures in Electric Power Systems,"Proceedings of the 2009 IEEE Power Systems Conference and

Exposition,pp. 1-9, 2009.

[8] A. Z. Faza, et al., "Reliability Modeling for the Advanced ElectricPower Grid: A Proposal for Doctoral Research," Proceedings of the

33rd Annual IEEE International Computer Software and Applications

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[9] J. Lin, et al., "A General Framework for Quantitative Modeling of

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Computer Software and Applications Conference,pp. 668-671, 2009.

[10] J. Nutaro, "Designing power system simulators for the smart grid:

combining controls, communications, and electro-mechanicaldynamics," Proceedings of the 2011 IEEE Power Engineering Society

General Meeting,pp. 1-5, July 2011 2011.

[11] "NIST Interagency Report 7628: Guidelines for Smart Grid Cyber

Security: Smart Grid Cyber Security Strategy, Architecture, and High-

Level Requirements," 2010.

[12] P. Kundur, Power System Stability and Control. New York: McGrawHill, 1994.

[13] C. P. Nguyen and A. J. Flueck, "Modeling of communication latency in

smart grid," Proceedings of the 2011 IEEE Power and Energy Society

General Meeting, 24-29 July 2011.

[14] J. Quintero and V. Venkatasubramanian, "SVC Compensation on aReal-Time Wide-Area Control for Mitigating Small-Signal Instability in

Large Electric Power Systems," Proceedings of the 2006 International

Conference on Power System Technology,pp. 1-8, Oct 2006.

[15] "MATLAB," http://www.mathworks.com, Ed., ed: The Mathworks, Inc.

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