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Distributed Software Emulator forCyber-Physical Analysis in Smart Grid

Song Tan WenZhan Song Dan Huang Qifen Dong Lang Tong

Abstract—A Smart Grid is a highly complex cyber-physical electrical power system that uses two-way digital communication andintelligent embedded devices to achieve sensing, control, computation and communication within power network. To validate thefunctionality, security and reliability of such an system requires the modeling and emulation of both communication network and powernetwork, as well as the interactions between them. In this paper, we present Smart-Grid Common Open Research Emulator (SCORE),a distributed software emulator for cyber-physical analysis in Smart Grid. SCORE integrates the emulations of both power networkand communication network, and it is highlighted by the following features: firstly, SCORE is the first software emulation platform forSmart Grid, which means that the same application program running in SCORE can be directly ported to embedded devices with littleor no migration issues. Secondly, for one Smart Grid instance, SCORE supports distributed emulation when the instance is in verylarge scale. Thirdly, for multiple Smart Grid emulation instances running on different networked computers, SCORE allows them todynamically connect or disconnect with each other in run time, such that each instance can capture its own interior system dynamicseven without a prior knowledge of the entire Smart Grid topology.

Index Terms—Cyber-Physical System, Distributed Emulation, Smart Grid

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1 INTRODUCTION

Smart Grid is a highly complex cyber-physical sys-tem envisioned to modernize the power network byleveraging the two-way communications of the dataand information from the deployed embedded devices.Integrating the power system closely with communica-tion system and intelligent control system, Smart Gridpromotes customers’ choice by enabling them to managetheir energy usage and choose the most economicallyefficient offering. It also improves the safety of the powersystem by using automation and alternative resources tomaintain delivery system reliability and stability. More-over, through integrating renewable, storage, and gener-ation alternatives, it brings sustainable and environment-friendly energy to the society.

The smart grid innovation brings many new chal-lenges and research issues to cyber-physical analysis,such as demand response algorithms, routing protocolsin Advanced Metering Infrastructure (AMI), market poli-cy programs, and countermeasures against cyber attacks,etc. However, due to the system complexity, reliabilityand integrity requirements of smart grid, especially the

• Song Tan, WenZhan Song, Dan Huang are with Sensorweb ResearchLaboratory, Department of Computer Science, Georgia State UniversityEmail: stan,songwz,dhuang@cs.gsu.edu

• Qifen Dong is with College of Information Engineering, Zhejiang Univer-sity of Technology, Zhejiang, China Email: qdong@cs.gsu.edu

• Lang Tong is with School of Electrical and Computer Engineering, CornellUniversity Email: ltong@ece.cornell.edu

. Our research is supported by NSF-CPS-1135814, and partial resultshave been published in SmartGridComm2012.

high cohesion of communication and power system, val-idating, verifying or demonstrating those critical SmartGrid applications are not easy tasks. It is highly desirablein the Smart Grid research community to have a reliableand efficient platform to tackle this issue, especially in alab environment.

In this paper, we present Smart-Grid Common OpenResearch Emulator (SCORE). It is built upon CORE[1], an open source communication network emulatorfrom Naval Research Laboratory. SCORE emulates bothcommunication networks and power networks and theirinteractions. The same Smart Grid application programrunning in SCORE can be directly ported to embeddeddevices in a real Smart Grid with little or no mod-ification. Specifically, SCORE highlights itself by thefollowing features:

• SCORE is a software emulation platform withinwhich the Smart Grid applications execute insideeach emulated virtual node. Each virtual node isa light weighted Linux virtual machine such thatthe tested Smart Grid applications, such as demandresponse algorithms, routing protocols, and marketpolicy programs, and countermeasures against cy-ber attacks etc, can be directly ported from SCOREplatform to real Smart Grid embedded devices withno or little migration issue.

• For a given Smart Grid instance, SCORE supportsdistributed emulation using general PCs, providingthe capability to deal with large-scale Smart Gridinstances.

• For multiple Smart Grid emulation instances run-ning on different networked computers, SCOREallows them to dynamically connect or disconnect

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with each other in run time when the interfacesbetween each other are specified. Each instancecan capture its own interior system dynamics evenwithout a prior knowledge of the entire Smart Gridtopology.

2 RELATED WORK

Creating test platform for cyber-physical analysis inSmart Grid is challenging and it has been studied foryears. From our literature reviews, the approaches tosolve this issue generally fall into two categories: realhardware testbed and software simulation.

2.1 Real hardware testbed approach

Real hardware testbeds are further classified into twocategories: flat-out hardware platforms and hardware-in-the-loop platforms.

2.1.1 Flat-out hardware platform

Flat-out hardware platforms are the ones consisting ofpure hardware devices. The Korean government selectedthe whole Jeju Island to build the Smart Grid testbedto allow the testing of Smart Grid technologies andbusiness models [2]. Renewable Energy Laboratory inGreece set up a central-controlled microgrid testbedconsisting of PV-panels, battery banks and inverter-s to investigate the proposed Smart Grid topologies[3]. Sensorweb Reserach Laboratory from Georgia StateUniversity designed SmartGridLab testbed to test thedistributed demand response algorithm. It consists ofintelligent power switch, power generator, renewableenergy sources, smart appliances, and power meter [4].

2.1.2 Hardware-in-the-loop platform

Hardware-in-the-loop platform mixed hardware deviceswith software simulators to achieve the cyber physicalanalysis of Smart Grid. Stanovich et al. in [5] integrateshardware from energy field, such as Remote TerminalUnit (RTU), fiber optical cables within the testbed. Hahnet al. in [6] employs devices like Programmable LogicUnits (PLUs) and Intelligent Electronic Devices (IEDs)for communication networks and Real-Time Digtial Sim-ulators for power network simulation.

2.2 Software simulation approach

The software simulation tools for Smart Grid analysiscan be further classified into two categories: individualsimulation platforms and co-simulation platforms.

2.2.1 Individual simulation platforms

Individual simulation platforms are those which en-capsulate the simulation features for Smart Grid intoone entity. In other words, it is one single simulator tocomplete the job. These platforms usually focus on oneparticular aspect of interests for Smart Grid. In 2008,Guo et al. designed and developed an energy demandmanagement simulator (EDMS) to predict the responsefrom different deployment strategies of distributed do-mestic energy management [7]. A self-adaptive demand

management strategy is simulated and analyzed withinthis platform. In 2009, Molderink et al. built a sim-ulation environment from scratch to analyze controlalgorithms for energy efficiency [8]. Micro-generators,energy buffers and appliances are modeled and differentenergy streams like heat and gas are considered. In2010, Faria et al. presents Demsi, a simulator for demandresponse in the context of competitive electricity marketsand intensive use of distributed generation [9]. Demisis extended from power system analysis tool PSCAD[10]. Energy service provider and demand side playerare modeled and strategic decisions are supported. In2011, Narayan et al. presents GridSpice [11], a cloud-based simulation package for Smart Grid. Leveraging thepowerful component of Gridlab-D [12] and Matpower[13], GridSpice is being developed iteratively with anultimate goal of modeling the interactions between allparts of the electrical network, including generation,transmission, distribution, storage and loads. Currently,GridSpice includes the ability to perform distributionsimulations along with one-shot optimal power flowsimulations and demand response features. All the in-dividual software platforms can complete their owntasks in the specific application domain, but they alljust concentrate on the power network simulation. TheCommunication network, as another critical componentof Smart Grid is not considered in these platforms. Thisis why the co-simulation platform comes to the picture.

2.2.2 Co-simulation platforms

Co-simulation (co-operative simulation) is a simulationmethodology that allows individual components to besimulated by different simulation tools running simul-taneously and exchanging information in a collabora-tive manner [14]. In [15], Hopkinson et al. introduce afederated simulation combing NS2 [16], a discrete eventnetwork simulator with PSCAD [10], a continuous timepower network simulator. In [17], Godfrey et al. simulatethe Smart Grid using NS2 and OpenDSS [18], a powernetwork simulator. In [19], Mallouhi et al. set up a co-simulation testbed specifically for security analysis ofSCADA system by employing PowerWorld simulatorand OPNET. In [20] and [21], Lin et al. introduces a globalevent queue to synchronize NS2 and PSLF [22]. The co-simulation approach typically needs iteratively runningseparate electrical and communication network simula-tion. The performance is affected by putting extra over-head of an intermediary of synchronization. Meanwhile,the interaction between communications and power sys-tem models is usually restricted to fixed synchronizationinterval. Mismatches can occur between the real dy-namics and the simulated one, which exposes reliabilityissues of such systems. An improvement about this issueis to integrate one simulation component into the other.In [23], electric network is made into a component withinOMNET++ [24], a network simulator. In [25], the adevssimulation tools are integrated into NS2 to provide ahybrid modeling of the continuous time power system

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and discrete event communication system through thediscretization of the continuous power dynamics.

2.3 Remarks about related work

From the above discussion, we can see the characteristicsof the real hardware testbed approach and the softwaresimulation approach for cyber-physical analysis in SmartGrid.

The real hardware testbed approach achieves highfidelity by employing dedicated devices as part of thetestbeds. The critical control programs, such as demandresponse algorithms, routing protocols, and security s-trategies, can be tested in real hardware testbeds andthey could be directly migrated to the actual Smart Gridembedded devices. However, the problems with the realhardware testbed approach is that since the dedicatedand specialized hardware are the integral parts of thetestbeds, they cannot be easily accessed and used bythe public research community and difficult to be scaledwhen the test case becomes quite large.

The software simulation approach, on the other hand,achieves better scalability and can be easily accessedand distributed. The software simulation tools typi-cally run on a single PC and abstract the operatingsystem, communication protocols and power dynamicsinto mathematical simulation models to produce overallstatistical analysis. In other words, they just duplicatethe behaviours of the Smart Grid system but not theexecution environment. Therefore, the critical controlprograms of Smart Grid applications either cannot betested or can be tested but cannot be migrated to physicalSmart Grid devices directly.

2.4 Our approach: software emulation

SCORE bridges the gaps between real hardware ap-proach and software simulation approach. The key ad-vantages of SCORE are:• First, software emulation achieves high fidelity by

duplicating the execution environment, such thatthe programs running in the emulation platformcan be directly ported to the embedded devices asfirmware.

• Second, SCORE enables distributed emulation fea-ture such that very large scale test case can also besupported.

• Finally, SCORE supports dynamic connection anddisconnection between multiple Smart Grid emula-tion instances in real time. The significance of thisfeature is two folds. First, when users from multipleparties in different locations want to conduct theintegration testing together, but want to preservethe privacy of power and communication networksconfigurations, this feature would make it happenwithout requiring explicit synchronization from allparties. Second, our in-progress research is integrat-ing SCORE with real hardware testbeds. In this case,the dynamic feature would be necessary since wewant to give the freedom of SCORE to dynamically

connect and disconnect with the testbed at any time.SCORE also has its own limits: the power network modelis static DC power flow model such that we cannot useSCORE to capture transient power dynamics or renew-able grid integrations, etc. The strengths and limitationsof our approach compared with related works are listedin Table 1.

TABLE 1Summary of features for Real hardware testbed,

Software Simulation and SCOREReal hardware testbed Software Simulation SCORE

Accessibility Difficult Easy EasyScalability Low High HighCode migration Yes No YesTime step Real time Real time/discrete time Real timeTransient analysis of power system Yes Yes NoRenewable integration Yes No No

Our previous conference paper in [26] has draw greatattention in research community and our open sourcerelease now has more than one hundred downloads fromhttp://sourceforge.net/projects/score-sensorweb/.Inthis paper, we make substantial extensions by:• Presenting implementation details of GUI, services

layer, lighted weighted virtual nodes, communica-tion module, power module.

• Adding domain decomposition based algorithmsand implementations for dynamic connect and dis-connect feature.

• Developing energy model programming API library.• Conducting extensive survey of related work.

3 SYSTEM DESIGN AND IMPLEMENTATION

3.1 Overview

Our design of SCORE takes advantage of CORE’s struc-ture. Figure 1 provides an abstract overview of SCORE’sarchitecture and our integration approach. As shown,SCORE consists of GUI, Service Layer, CommunicationModule and Power Module.

3.2 GUI

The SCORE GUI is built using Tcl/Tk. The Tk toolkitprovides almost sufficient widgets for all the X windowsystem interface needs. The Tcl/Tk GUI provides aneasily drag-and-draw canvas with various Smart Griddevices (Appliances, Solar Panel, and Wind Turbine,etc),which can be placed and linked together with commu-nication links or power lines. Also, the communicationinterfaces, power interfaces and energy model parame-ters of each node can be configured. During emulationexecution, a bash shell can be popped out when doubleclicking the selected node. Users can navigate the localfile system or execute bash script through the interactiveshell window. Distributed emulation can be conductedby assigning a selection of nodes to another emulationserver in GUI. The message broker in Service Layer isused to forward messages from the GUI to the appro-priate emulation server.

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Virtual Nodes

Smart Grid

Applications

Communication

interfacePower

interface

Power

ModuleCommunication

Module

Protocol

library

Wire/

Wireless

model

Initializer

Energy

models

Incremental

update

handler

GUI

Power

network

model

Synchronization

/interaction

Service Layer

Start-UP

daemon

Linux

containers

support

Session

manager

Smart Grid

Services

$Dynamic

connection/

disconnection

add-on

Fig. 1. SCORE Architecture

3.3 Service Layer

The Service Layer are python frameworks that is re-sponsible for creating sessions, instantiating the virtualnodes, communication and power interfaces, communi-cation links and power lines, based on the GUI inputor a batch-mode configuration file. Note that start-updaemon in service layer interacts with GUI using aTCP socket-based API such that the emulation can runon a different machine with the GUI or even withouta GUI. Pre-defined energy models and communicationprotocols, which are usually daemonized in the Linuxoperating system of emulation server, are all wrapped asSmart Grid services in this layer. These communicationand energy services can all be employed to developvarious Smart Grid Applications. Users are also allowedto add their own customized services to SCORE byproviding their own implementations. Last but not theleast, as our newly developed feature, the Dynamicconnection/disconnection add-on in service layer canprocess the dynamic emulation request by employing thedynamic features in communication modules and powermodules.

3.4 Light weighted virtualization

The emulation feature of SCORE are implemented us-ing Linux namespace techniques, which is a recentlight weighted paravirtualization technique supportedby mainstream Linux kernel. Different from the typicalvirtual machines techniques like the ones in VMwareor Virtual Box, each emulated virtual node in SCOREonly has its separated copy of network interface, protocolstack and process control group. Other resources likeoperating system and local file system, are shared by allthe virtual nodes. This light-weighted virtualization ap-proach enables the scalability of SCORE. Moreover, from

the perspective of codes running inside the virtual node,each emulated node is just another piece of hardwareplatform controlled by Linux OS, which equips SCOREwith the property of portability that the emulated nodecan execute unmodified Smart Grid application codesrunning inside a real physical Linux-running hardwaredevices, and vice versa.

3.5 Communication Module

The communication module in SCORE leverages thecomprehensive support of various wired and wirelesscommunication network models and protocols fromCORE. Each emulated node has its own instance of OSimplemented TCP/IP stack supported by Linux names-paces, from the perspective of Open Systems Intercon-nection (OSI) model, thus SCORE allows high fidelityemulation of network layer and above. By default, a sim-plified simulation of link and physical layers is enabled,which is implemented using netem with Ethernet bridg-ing in Linux. Statistical network effects such as band-width, loss rate, bit error rate and noise level can be con-figured and applied. For higher-fidelity link and physicallayer emulation, other network simulation tools, such asEMANE [1], can be easily integrated. In addition, thevirtualized Ethernet interface can be directly mappedto a physical Ethernet interface on the emulation host,such that all the traffic passing through that physicalport can be transferred to the emulation environment,allowing real time communication between the virtualnodes inside a running emulation and external physicalnetworks. By using the virtualized interfaces on eachemulation host, the communication network emulatedon different hosts can directly connect with each otherin run time, which enables the dynamic emulation of thecommunication networks. Meanwhile, we employ thisfeature to enable the interactions and synchronizationbetween the communication module and the powermodule discussed in the next section. The idea is thatthe power module is running on a host physically inthe same network with the communication emulationhost so that the power module can receive and react tothe queued-up messages sent by all the emulated virtualnode in real time.

3.6 Power Module

The power module in SCORE emulates the power flowsanalysis within Smart Grid and also provides implemen-tations of pre-defined energy models. We use currentmodel in circuit theory to emulate the real power flowwithin power network. Figure 2 shows the data flowdiagram of power module. The power module receivesinitial power network topology, energy model config-uration information and the dynamic connection/dis-connection request from service layer to formulate thepower network model, which will be introduced later.The model is updated when the corresponding modelupdates are received from the interactions with commu-nication module.

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The power network module of SCORE is highlightedby the following features:

• First, SCORE adopt incremental model updating incomputation to react more efficiently to the systemstatus changes.

• Second, as the size and order of the power net-work increase, distributed computation for powernetwork becomes a necessity for efficient Smart Gridemulation. SCORE highlights itself in scalability byenabling the user to conduct the emulation in adistributed way when a single PC cannot provideenough computation capabilities. We choose to splitthe power network model into several subdomainsand let each subdomains be computed and updatedseparately in parallel. With appropriate coordinatingamong those separate computing and updating pro-cesses, the merged result of power flows in SmartGrid is solid without any loss of precision comparedwith centralized computation.

• Finally, by only specifying the interfaces betweeneach power network, SCORE allow dynamic con-nections and disconnections of multiple Smart Gridinstances running on different hosts. The signifi-cance of this feature are two-folded: first, in thesituation when each user is reluctant to expose theirown Smart Grid topology details to others, theycan still conduct the combined emulation with eachother to see the impact of external networks on theirown network. Privacy is protected while Smart Gridanalysis is conducted. Second, even though we havenot implemented in our platform, by adding an ap-propriate hardware interface serving as the gateway,this feature enables SCORE to communicate andexchange energy directly with testbeds or even thereal power grid. This cyber-physical interaction isvery valuable to the scalable and reliable testings ofSmart Grid technologies [27].

Multiple instances

Parsing

Partitioning

Incremental

updatingComputing

Coordinating

Power network

topology and Energy

model information

Initailized

power network

model

Power network

model updates

Parallel power

network model

Power flows in

Smart Grid

Topology

updates

Updates result

Computation result

in each subdomain

Power network models in

each subdomain

Dynamic Connection/

disconnection request

Fig. 2. Data flow diagram of Power Module

3.6.1 Power network model

Now let us introduce the power network model. Assumea power grid is composed of n nodes and b branches.Since the power network dynamics is subject to Kirch-hoff’s current and voltage laws (KCL and KVL), in orderto calculate the voltages of all nodes, we apply nodalanalysis to the grid and get the linear equations for thewhole system:

AV = I (1)

where coefficient matrix A is the (n − 1) × (n − 1)reduced nodal admittance matrix since we have chosena reference node. Let Nbr(i) represents the neighbor setof node i in the power network, we get:

aij =

∑

s∈Nbr(i)

gis i = j.

−gij j ∈ Nbr(i)

0 otherwise

(2)

gij is the admittance between node i and node j, V andI are the unknown node voltage vector and the knownnodal current injection vector, respectively.

3.6.2 Incremental updating

Based on previous model, let’s consider the situationwhen the power network topology changes. Supposethe power grid status changes, such as the admittancebetween node i and node j is changed by ∆gij . So thenew coefficient matrix A can be written as:

A = A + U∆gijUT (3)

where

U =[

0 · · · 1 · · · −1 · · · 0]T

i j

Particularly, the changed admittance ∆gij equals to −gijwhen the branch is removed and ∆gij = gij when a newbranch is added. Notice that [28]

A−1 = A−1 −A−1U(∆gij

−1 + UTA−1U)−1

UTA−1 (4)

then we can get the A−1 with a much lower computationcost when we store previously computed A−1.

3.6.3 Power network model: domain decomposition

Power network is naturally suitable for emulation indistributed paradigms, since due to the economy andgeographical reasons, power network is generally a net-work of loosely coupled sub power networks. Each subnetwork is a relatively independent partition of the w-hole energy system and only few in-between connectionlines join them together. Inside each sub network, wedivide the nodes into two sets:• Internal nodes: nodes that only have connections

with the nodes inside the same sub network.• Boundary nodes: nodes that have connections with

the nodes in other sub networks.

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Fig. 3. The general architecture of power network

The architecture of the power network is illustrated inFigure 3. Based on the previous analysis, we apply theSchur complement domain decomposition method [29]to our power network model. Specifically, suppose thereare k sub networks, by grouping the internal nodes ofeach sub network and putting all the boundary nodes ofthe network in the back, we formulate the nodal analysismodel for the whole power network as the following:

YA1A10 · · · 0 YA1B

0 YA2A2· · · 0 YA2B

......

. . ....

...0 0 · · · YAkAk

YAkB

YBA1 YBA2 · · · YBAkYBB

VA1

VA2

...VAk

VB

=

IA1

IA2

...IAk

IB

(5)

Notice that B is the set of all boundary nodes in thewhole network, consisting of B1, B2, ..., Bk. Therefore,YAiB only has non zero entries in its submatrix YAiBi

,for all i = 1, 2, ...k.

From (5), if the voltages for boundary nodes set VB

is known, then the voltages for the nodes in each subnetwork can be calculated as the following:

YAiAiVAi = IAi − YAiBVB ,∀i ∈ {1, 2, ..., k}. (6)

Meanwhile, if we keep the corresponding part for theboundary node set B in equation (5), we can get:

YBBVB = IB (7)

where

YBB = YBB −k∑

i=1

YBAiYAiAi

−1YAiB (8)

IB = IB −k∑

i=1

YBAiYAiAi

−1IAi (9)

Definexi = YBAiYAiAi

−1YAiB (10)

yi = YBAiYAiAi

−1IAi(11)

for all i = 1, 2, ...k. Notice that xi and yi only requireslocal information for sub system i, we employ thisfeature to provide the following two power networkemulation paradigms in our platform.

3.6.4 Distributed computation

Power network is usually involved with a huge amoun-t of entities and complex interactions between them.Emulating such a system efficiently requires a largeamount of computation resources. By decomposing theemulation instance of one power network into severalsub power networks, distributed computing providesthe approach to balance the computation resources a-mong several computation hosts and increase the overallemulation performance of the system. Figure 4 demon-strates an example of decomposing one whole networkinto two subnetworks, which are interconnected by theconnection line c with impedance z. Specifically, suppose

Sub network2Sub network1

b1 b2

connection line c

Z

Sub network1

b1

Vb1

Sub network2

b2

b1 b2

connection line c

Z

Vb1 Vb2

Vb2

Fig. 4. The decomposition of one power network emula-tion instance into two.

the power network is decomposed into k sub powernetworks and each sub power network is assigned toone computation host. We choose one of them as theCoordinator and it also caches YBB and IB for theboundary node set. Based on equations (6) ∼ (11), thedecomposition process for each computation host i, i =1, 2, ..., k, is executed as the following:• Compute xi and yi based on equations (10) and

(11) respectively. Send the results to the Coordinatorhost.

• The coordinator collects xi and yi from each host,and calculate VB based on equations (7) (8) (9). Afterthat, it sends VB back to each host.

• Each host i receives VB from the coordinator andcalculate VAi based on equation (6).

3.6.5 Dynamic connection/disconnection between mul-tiple instances

Our platform also highlights itself by enabling the dy-namic connections and disconnections of several inde-pendent emulation instances. The emulation instancesof different power networks running on different hostscan interact with each other in run time to evaluate theimpact on system dynamics when other energy systemsjoin in. This kind of analysis is critical for the transientstability analysis of power network when the networktopology is reconfigured [30]. Figure 5 illustrates thecomposition example of two independent power net-

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work dynamically connected with each other by a con-nection line c with impedance z. Specifically, suppose

Sub network2Sub network1

b1 b2

connection line c

Z

Sub network1

b1

Vb1

Sub network2

b2

Vb2

Fig. 5. The composition of two power network emulationinstances into one.

there are already k connected emulation instances andanother emulation instance k + 1 joins in run time. Alsoassume that the k+1 instance connects with the instanceset E directly, E ⊆ {1, 2, ..., k}. Then the compositionprocess for each computation host i, i = 1, 2, ..., k, k + 1,is executed as the following:• If i ∈ E, then adjust the boundary node set Bi

by adding the new boundary nodes connected withinstance k + 1 and also adjust the internal node setAi by removing the corresponding boundary nodesconnected with instance k + 1. Compute xi and yibased on equations (10) and (11) respectively. Sendthe results to the coordinator host.

• The coordinator first reforms the boundary node setB by adding the new boundary nodes in Bi, i ∈ Eand Bk+1, then rebuilt YBB and IB for the wholesystem. Secondly, it collects xi and yi from eachhost, and calculate VB based on equations (7) (8)(9). Finally, it sends YBBVB back to each host.

• Each host i receives YBBVB from the coordinatorand calculate VAi based on equation (6).

The dynamic disconnection steps are similar with theabove steps except that instead of adding boundarynodes to the boundary set, the coordinator will removethe boundary nodes related with the exiting instances.

3.7 Energy model programming API

In order to support complicated power flow modelsfor various objects in power network, we provide theprogramming APIs for the user to create various load dy-namics, switch configurations and storage charging/dis-charging configurations. The users invoke the APIs intheir own Smart Grid application program to interactwith the power module, such that the system powerdynamics could be updated. Since the power moduleis essentially a socket server, all the programming APIsare implemented as socket client and their requestsare messages queued up in the server side in a FIFObasis. These APIs are wrapped as a static library anddistributed with the SCORE software. The following isa code snippet from examples using the APIs.

/∗ inc lude header f i l e . ∗/# include ”EnergyDaemon . h”i n t main ( i n t argc , char ∗∗ argv ) {

. . ./∗∗ This s e t s the des ired energy

r a t e of the energy model .∗/

double desiredEnergy=rand ()%50+1;ed . setDesiredEnergy ( desiredEnergy ) ;}

4 EVALUATION

In this section, we first employ a simple case toevaluate the basic features of the building-in function-alities of SCORE, by presenting the results of powerflow calculations, distributed emulation and dynamicconnection/disconnection. Then, a comprehensive largescale cyber-physical test case is employed to demonstratethe complete capabilities of SCORE to support cyber-physical analysis.

The first case is created as a Smart Grid powerdistribution network with one power plant and fivehouses. Each house is connected with the power networkthrough an intelligent power switch, which serves as theenergy control center for the house. Within each house,there are four kinds of nodes: loads (represented bywasher), renewable resources (represented by solar paneland wind turbine), and power storages (representedby battery). Wireless networks (the cloud icon) wlan32equips each node with communication capabilities. Weconfigure the wireless channel to be 54Mbps bandwidth,200ms delay, 0.1% packet loss rate and the resistances ofall the power lines to be the same constant value. Figure6 shows the setting up of the study case and the initialpower flow without any demand response involved. Thethick lines means the power flow is relatively heavy atthat moment. For each virtual time period h in a virtual

Fig. 6. The basic case in SCORE

day, h = 0, 1, 2, 3, ...23, each kind of nodes in the studycase behaves as the following:

8

30

40

50

60

70

80

90

100

110

120

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223

400

600

800

1000

1200

1400E

ne

rgy P

rice

(U

S c

en

ts p

er

kw

h)

To

tal e

ne

rgy s

up

ply

fro

m p

ow

er

pla

nt

(kw

)

Period (h)

Real time energy priceTotal energy supply from power plant

Fig. 7. Real time price and Total energy supply frompower plant

• Power Plant: Broadcasting its real time energyprices to all the intelligent power switches. Theenergy price has only two values: HIGH (100 centsper kwh) and LOW (50 cents per kwh).

• Intelligent Power Switch: Receiving the energyprice information from the power plant and thenrelay the messages to the other nodes within thesame house immediately.

• Load: Receiving energy price information from in-telligent power switch. When the price is HIGH, itlowers its load from 200w to 150w. When the priceis LOW, it increases its load from 150w to 200w.

• Solar Panel: When h ∈ H1 = {h|h =0, 1, 2, 3, 4, 5, 19, 20, 21, 22, 23}, setting its maximumoutput to 0. When h ∈ H2 = {h|h =6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18}, settingits maximum output to 30w.

• Wind Turbine: Randomly setting its maximum out-put to 30w or 60w.

• Power Storage: Its capacity is set to 50kwh andinitial energy is 10kwh. After receiving energy priceinformation from intelligent power switch. Whenthe price is HIGH, it start discharging if there isstill enough residual energy in the storage. Whenthe price is LOW, it stops discharging. The batterykeeps discharging until all the energy is used up.

Note that here the virtual hour is a test case iterationclock, not an hour in real time.

4.1 Power flow calculation

SCORE captures the power flow analysis using currentmodel in circuit theory for Smart Grid applications inreal time. Figure 7 shows the real time energy price andthe total energy supply from power plant in a 24 hoursvirtual day. The impact of the demand response strategyon total energy supply is clearly illustrated by the twotrend curves in the test case: when the price goes up, thetotal energy supply goes down. And when the price goesdown, the total energy supply goes up correspondingly.Renewable resources result in the fluctuations of totalenergy supply curve. More specifically, table 2 presents

the power values passing through the nodes and thepower lines between intelligent power switches duringexecution periods. For example, when the virtual clockh = 6, the energy price is low, so that the washer isin 200w mode, the storage stops discharing. The powerflows of all power lines conform to Kirchhoff’s circuitand voltage laws in power network nodal analysis.

4.2 Distributed emulation

SCORE extends its scalability by distributed emulation.We evaluate SCORE’s scalability using the previouslyintroduced test case and extend it to a much largerscale. Our testing machines are 64 bits HP destop withPentium(R) Dual-Core CPU E5700 @ 3.00GHz and 4GiBmemory. Figure 8 and Figure 9 shows the peak CPUusage and memory usage of SCORE running on one,two and four machines when the scale of the Smart Gridincreases. We can see from the figures that as the numberof emulation servers increases, the CPU and memoryusage of each machine is decreased since each of themcan take care of the instantiation, computation and com-munication in parts of the Smart Grid. A single instanceof our PC can support about 100 virtual nodes effectively.SCORE’s distributed emulation capability greatly releasethe burden of each emulation server and enable largescale emulation.

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Figure 10 shows the execution time of the study casefor 24 clocks. Note that when the scale of power grid issmall, which means the computation cost is relativelylow, less machines can finish the emulation relativelyfaster because there is less communication overhead be-tween emulation servers. However, when computationtime dominates the execution time, the advantages ofparallel computation begin to emerge.

4.3 Dynamic connections/disconnection betweeninstances

SCORE supports dynamic connections between multipleSmart Grid emulation instances. We evaluate this featurelike shown in Figure 11. Here we use two independen-t emulation instances running separately on different

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TABLE 2Test Case result

Period h=0 h=2 h=6 h=8 h=12 h=14 h=16 h=18 h=20 h=22Washer(n9) 200w 200w 200w 150w 150w 150w 200w 150w 150w 150wSolar Panel(n15) 0w 0w 21.4286w 8.82353w 9.375w 8.82353w 21.4286w 9.375w 0w 0wWind Turbine(n21) 42.8571w 42.8571w 21.4286w 17.6471w 9.375w 17.6471w 21.4286w 9.375w 18.75w 18.75wStorage(n27) 0w 0w 0w 17.6471w 18.75w 17.6471w 0w 18.75w 18.75w 18.75wPower line (n2,n3) 334.286w 334.286w 334.286w 137.647w 146.25w 137.647w 334.286w 146.25w 146.25w 146.25wPower line (n2,n4) 411.429w 411.429w 411.429w 169.412w 180w 169.412w 411.429w 180w 180w 180wPower line (n4,n7) 540w 540w 540w 222.353w 236.25w 222.353w 540w 236.25w 236.25w 236.25wPower line (n5,n6) 77.1429w 77.1429w 77.1429w 31.7647w 33.75w 31.7647w 77.1429w 33.75w 33.75w 33.75wPower line (n5,n7) 180w 180w 180w 74.1176w 78.75w 74.1176w 180w 78.75w 78.75w 78.75wPower line (n4,n6) 102.857w 102.857w 102.857w 42.3529w 45w 42.3529w 102.857w 45w 45w 45wPower line (n5,n6) 77.1429w 77.1429w 77.1429w 31.7647w 33.75w 31.7647w 77.1429w 33.75w 33.75 33.75wPower line (n3,n6) 231.429w 231.429w 231.429w 95.2941w 101.25w 95.2941w 231.429w 101.25w 101.25w 101.25w

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servers and dynamically connect with each other. Noticethat the combined instance of the two is the same as thesingle instance in subsection 4.1, which is running in asingle emulation server. Figure 12 shows the dynamicsof the load of washer (n12). The two instances areconnected at virtual clock h = 9. Before h = 9, thepricing messages broadcast by the power plant cannot bereceived by the nodes in Smart Grid instance Two, so thedesired maximum load of washer(n12) is always 200w.However, Smart Grid instance Two forms a microgridby itself and there is no power flows from the power

plant. So the load of n12 totally depends on the solarenergy , random generated wind energy and the storageenergy. The desired load cannot be met. After h = 9,the behavior of n12 is almost the same as it is insubsection 4.1.until h = 17, when the two emulationinstances are disconnected. After h = 17, Smart Gridinstance Two forms a microgrid again, and the energyconsumption behavior is similar as the one before h = 9.The fluctuations are resulted from the random generatedwind energy.

Fig. 11. Dynamic connections of two Smart Grid emula-tion instances in SCORE

4.4 Comprehensive cyber-physical analysis: undercyber attacks

This testing case we created is based on the AMI net-work test case from American Electric Power Company[31] and the IEEE PES 37 bus distribution system testfeeders [32]. Through this case, we further illustrate theadvantages of our platform over software simulators: 1)the actual control program written in C language (eithercorrect or malicious modified) can be directly run oneach virtual node; 2) the real time cyber-physical impacts(altered system routing table entries and power flow per-turbations) of the control programs can be demonstrated.Figure 13 shows the building blocks of our experimentalscenario:• Operation layer: The control program in control cen-

ter broadcasts real-time energy prices every 5 min-utes and also collects meter reading data through

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AMI Head-End. Meanwhile, the control center alsocalculates the bills for each house, based on the realtime price and the collected energy consumptiondata.

• Customer layer: The IEEE 37 bus distribution testfeeders is set up to provide power for 200 residentialhouses. Each house is equipped with loads includ-ing a water heater, a dryer, a PHEV, a solar panel,and a storage. Moreover, a smart meter is employedto serve as the interface between the power networkand AMI for each house. The program running insmart meter responses to the real time prices toadjust the setpoint of appliances within each housecorrespondingly based on the price-responsive con-trol model in [33].

• AMI network: AMI enables communications andinteractions between/within the operation layer andthe customer layer. The control center and A-MI Head-End is connected through Internet. AMIHead-End, the relay nodes and the smart metersare formed as a IEEE 802.11 Radio Frequency Meshnetwork.

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Suppose the customer under smart meter X wants tomanipulate his energy bill without being caught. In or-der to achieve this, he launches a Distributed Denial-of-service attack to the bi-direction data flow within AMI,which consists of the energy consumption data from thesmart meters to the AMI Head-End, and the energy pricedata from the AMI Head-End to the smart meters. Forthe energy consumption data, the attacker modifies theones from smart meter X and his targeted neighbors,such that each targeted neighbor has an increase in thereported energy consumption compared to the actualconsumption, and the smart meter X has a decreaseequal to the total increase of its targeted neighbors inthe reported energy consumption. In this way, from theperspective of utility company, the total energy providedstill conforms to the total energy being billed. For theenergy price data, the attacker modifies the price to alower value, such that based on the demand responsemodel, the actual energy consumption of each targeted

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neighbors will also increase. In this case, from the per-spective of each targeted neighbor, the minor increase inthe reported energy consumption data due to attack willbecome even less noticeable.

Specifically, as shown in Figure 14, the customer ofsmart meter X attacks three relay nodes at the same time:its own direct cluster head (Relay 2) and two neighborcluster heads (Relay 1 and 3). Originally, Relay 1 andRelay 3 will directly interact with Relay 4 for the bi-direction data. We can see this from the result of routecommand in the terminal of Relay 1. To reach 192.169.0.4,which is the IP address of Relay 4, no intermediategateway is needed and packets can be simply forwardedthrough interface eth0. However, after attack, there is oneextra high priority entry in the routing table of Relay1 such that the packets designated to 192.169.0.4 willbe forwarded to 192.169.0.2 first instead of the originalone hop reach. As a result, for Relay 2, besides the datapackets of the 10 customers within its own cluster, it willalso intercept the data packets of the other 20 customerswithin the clusters of Relay 1 and 3. By making thethree Relay nodes working in concert to compromisethe data, customer X could dramatically reduces its ownreported energy usage. As shown in Figure 15, for smartmeter X, even though the actual energy usage acrossthe day is 64kwh, the reported data is manipulated to35kwh. The remaining 64−35 = 29kwh are evenly addedto the other 29 customers’ reported data. In this way,from the perspective of utility company, the total energyconsumed still conforms with the total energy beingbilled. Moreover, from the perspective of each of theother 29 customers’, since only 29/29 = 1kwh is added totheir energy consumption, which usually results in about0.1$ increase in their bills,it is very much likely thatthe customer will just let it go. Also note that since the

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energy price is modified to a lower value after the attack,the real power consumption paradigm of the attackedneighbors changes dramatically, compared to the normalsituation when the correct real time energy price is given.As shown in Figure 16, the real power consumption ofthe attacked neighbors stays at a relatively higher levelall the time after the attack and the demand responsethrough real time pricing is not working any more.If more neighbors are involved in the attack, this willseverely increase load of the system, which can result ina higher cost of power transmission or even an outage.An effective approach to detect this kind of attack is

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by monitoring the network traffic. As shown in Figure17, since the routing path of the packets is changedand much more data packets are forwarded to Relay 2,the throughput of Relay 2 will be increased unusuallyfrom the moment of attack. Also, the network trafficcongestion at Relay 2 will result in an increase in thecommunication delay from Relay 1 to the AMI meterhead.

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5 CONCLUSION AND FUTURE WORK

In this paper, we present the design, implementationand operation of SCORE for Smart Grid emulation. Onefuture direction would be integrating SCORE with realhardware testbed to create a uniform cyber-physicalanalysis platform. Also, a cloud based deployment forour platform could be built to provide universal accessfor the users.REFERENCES

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Song Tan received his BSc degree in ComputerScience from both Northeast Normal University(China) and Southern Polytechnic State Univer-sity (USA) in 2010. He is currently a PhD Can-didate in Sensorweb Laboratory, Georgia StateUniversity, USA. His research is about SmartGrid emulation, testbed design and power sys-tem modeling.

WenZhan Song is a tenured associate profes-sor in Georgia State University. His researchmainly focuses on sensor web, smart grid andsmart environment where sensing, computing,communication and control play a critical roleand need a transformative study. His researchhas received 6 million+ research funding fromNSF, NASA, USGS, Boeing and etc since 2005,and resulted in 80+ journal articles, conferencearticles and book chapters in this area.

Qifen Dong received her BE degree fromthe department of Electronics Information En-gineering, Zhejiang University of Technology,Hangzhou, China, in 2007. She has been work-ing toward the PhD degree in the department ofControl Theory and Control Engineering, Zhe-jiang University of Technology since 2007. Herresearch interests including Wireless SensorNetworks, Smart Grids and Embeded system.

Dan Huang received his MS in computer sci-ence from Southeast University, China. He iscurrently a graduate student in computer sci-ence department at Georgia State Universityand he works in Sensorweb Lab. His researchinterests include distributed computing, socialnetworks, smart grid.

Lang Tong joined Cornell University in 1998where he is now the Irwin and Joan JacobsProfessor in Engineering and the Cornell sitedirector of the Power Systems Engineering Re-search Center (PSerc). Lang Tong is a Fellowof IEEE and received Best Paper Award (withMin Dong) from the IEEE Signal ProcessingSociety, the Leonard G. Abraham Prize PaperAward from the IEEE Communications Society(with Parvathinathan Venkitasubramaniam andSrihari Adireddy).