1

Novel Smart Grid and SCADA System

Interdependency Networks for Future’s Clean,

Sustainable and Green Energy

Pravin Chopade1, Dr. Serap Karagol

2, Dr. Marwan Bikdash, Dr. Ibraheem Kateeb, Dr. Numan Dogan

Computational Science and Engineering Department-COE, CST-SOT, ECEN-COE

North Carolina A&T State University, Greensboro, NC, USA

pvchopad@ncat.edu, serap.karagol@omu.edu.tr, bikdash@ncat.edu, kateeb@ncat.edu, dogan@ncat.edu.

(1 Ph.D. candidate, NCA&TSU, USA and Associate Professor, Bharati Vidyapeeth Deemed University College of Engineering, Pune, India, 2 Assistant

Professor, Electrical and Electronics Engineering, Ondokuz Mayis University, Samsun, Turkey)

Abstract— A novel approach of interdependency modeling of

Smart Grid and Supervisory Control and Data Acquisition

(SCADA) networks is introduced. Methodological approach for

vulnerability reduction considering both structural and

functional vulnerability is introduced in this paper. The analysis

of cyber-attacks on Smart Grid and SCADA network is

discussed. Paper discusses importance and need of clean,

sustainable and green energy for the future. Paper also analyzes

interdependent Smart Grid and SCADA network under severe

emergency situations. The contribution of this paper provides

novel network with integrated infrastructures for active and

survivable network operation. Thus novel Smart Grid and

SCADA interdependency network will act as future’s clean,

sustainable and Green energy model.

Keywords- Smart Grid, SCADA, Vulnerability, Emergency,

Interdependency modeling, Green Energy.

I. INTRODUCTION

Past historic blackouts highlight the vulnerability of the

Smart electric grid infrastructures and their interdependencies.

The large geographic extension of power failures effects is

related to the high interconnectivity of power grid

transmission and distribution infrastructures and the multiple

interdependencies existing between these infrastructures and

Supervisory Control and Data Acquisition (SCADA) i.e. the

information infrastructures supporting the control, the

monitoring, the maintenance and the exploitation of power

supply systems. The blackout is an example of the potential

ramifications of a failure or attack on the Smart Power grid

and SCADA systems. SCADA systems and their components

can be found in a number of national infrastructures including

the water, oil, and gas industries. SCADA systems are

computer controlled devices that perform and relay physical

changes in infrastructure systems to technical operators. They

are capable of monitoring millions of data points

simultaneously, and can therefore be manipulated by a cyber-

attack [1]. An adversary thus can penetrate the electrical

power grid, or other control system, with little more than a

laptop and an Internet connection. This is a major threat [2].

The source of vulnerability includes natural disasters,

equipment failures, human errors, or deliberate sabotage and

attacks. Increased electricity demand and transmission

bottlenecks make the complex power system even more

vulnerable and, with triggering disruption at opportune time,

topple it over to blackouts [3], [4].

Clearly there is a need to analyze and model critical

infrastructures in the presence of interdependencies in order:

i) To understand how such interdependencies may

contribute to the occurrence of large outages and

blackouts and

ii) To develop architectural solutions those are well

suited to improve the dependability and resilience of

power grid infrastructures.

In this paper we aim to address these objectives focusing

attention on two interdependent infrastructures: the Smart

Power Grid infrastructure and SCADA i.e. the information

infrastructures supporting management, control and

maintenance functionality.

Section II addresses cyber-attacks on smart grid and

SCADA network. Section III discuses need of clean,

sustainable and green energy for future. In Section IV, we

discuss novel Smart Grid and SCADA network modeling.

Section V analyses interdependent Smart Grid and SCADA

network under severe emergencies.

II. CYBER-ATTACKS ON SMART GRID AND SCADA

NETWORKS

Smart Grid design and deployment must take into account

the current cyber vulnerabilities in the legacy of power grid.

The known vulnerabilities in the existing legacy power grid

should continue to be addressed and mitigated in concert with

the implementation of Smart Grid technologies [5]. Resistance

to attack is one of the seven principle characteristics of the

Smart Grid vision [6]. However, implementation of a Smart

Grid that is resistant to attack is particularly difficult for

several reasons. The Smart Grid deployment will increase the

This work is supported by The Defense Threat Reduction Agency (DTRA) and Pennsylvania State University under contract DTRA01-03-D-

0010/0020 and sub-contract S03-34.

978-1-4799-2546-9/13/$31.00 ©2013 IEEE

2

complexity of the existing system and will include the addition

of many new communication paths. The increased complexity

and expanded communication paths can easily lead to an

increase in vulnerability to cyber-attack [3]. The size (millions

of nodes) of a fully implemented Smart Grid and an

unpredictable intelligent adversary make it difficult to

anticipate how attacks may be manifested [7], [8]. Smart Grid

technology that has known vulnerabilities has already been

deployed in some parts of the current power grid [9].

Furthermore, the goal of “resistance to attack” is in

competition with some of the other desired characteristics of

the Smart Grid, e.g. the goal of “optimizing assets and

operating efficiently”. The desire to minimize costs and to

provide services tend to take priority over the desire for

security in the face of a threat that is not well understood.

Chronology of reported cyber-attacks on electric and other

utilities including data obtained by Motter [10] are given

below and other sources, reports some incidents and attacks

that affected electricity and other critical utilities during the

last decade. It is noteworthy that due to the high sensitivity of

such data, a complete historical data base of these events is not

publicly available (and probably it will never be in the future).

Nevertheless, these examples show that the threat is real and it

will be increasing due to market deregulation and the

increased complexity and openness of the future SCADA

architectures for power infrastructures. Actually, several

working groups and initiatives dedicated to the analysis and

assessment of security related vulnerabilities and threats in the

context of power system infrastructures and the proposal of

appropriate solutions to mitigate them have been created

recently.

Chronology of reported cyber-attacks on SPGN and SCADA

network [10].

1994: Salt River Project: A water facility in Arizona

was breached by a cyber-attack. The hacker

trespassed in critical areas that could have caused

significant damage.

1997: A teenager remotely disabled part of the public

switching network in Massachusetts, which shutdown

telephone service to 600 customers.

2000: A disgruntled employee of an Australian

company used his laptop car computer to remotely

hack into the controls of a sewage treatment system,

which caused 264,000 gallons of raw sewage to be

released into public waterways of Australia over a

period of two months. This caused marine life to die

and creek water to turn black, producing an

unbearable stench to nearby residents, among other

impacts.

2000: On October 13, the Control System of Ertan

Hydro Station received unexpected signals, and then

they reduced generation 900 MW within 7 seconds,

almost causing Sichuan power system to collapse.

2001: Hackers attacked the California Independent

System Operator managing the electricity supply of

California. The Los Angeles Times reported that the

cyber hackers “got close” to disrupting power flow

during the California rolling blackouts in May 2001.

2001: October 1, many Fault Recorders

dysfunctioning were caused by a Timer Logical

Bomb, this type of device had been installed on 146

sets in China.

2003: The SQL Slammer worm infected and disabled

internal systems at nuclear power plant in Ohio,

Safety was never compromised, but a safety

parameter display system and the plant process

control computer were knocked off-line by the cyber

worm for several hours.

2003: On December 30, several viruses were found in

the control systems of 3 HVDC convert stations

(Longquan, Zhengping, Ercheng), which transfer

total 6000 MW from Three Gorge to East and South

of China.

2006: May 18/17/15, Malware Infection LEAKS

Japanese Power Plant Data. A malware infection has

being blamed for the leak of sensitive Japanese

power plant information onto the Internet. The

information included key facility location and

operation procedures for the Chubu Electric Power

Company’s thermal power plant in Owase, Mie

Prefecture; some employee data were also

compromised. A sub-contractors use of file sharing

software is suspected to have caused the malware

infection.

Securing the assets of electric power delivery systems,

from the control center to the substation, to the feeders and

even to customer meters, requires an end-to-end security

infrastructure that protects the myriad of communication

assets (control center-based) SCADA, RTUs (Remote

Terminal Units), PLCs (Programmable logic controllers),

power meters, digital relays, and bay controls) used to operate,

monitor, and control power flow and measurement.

III. NEED OF CLEAN, SUSTAINABLE AND GREEN ENERGY

FOR THE FUTURE

Today, an electricity disruption such as a blackout can have

a domino effect- a series of failures that can affect banking,

communications, traffic, and security. This is a particular

threat in the winter, when homeowners can be left without

heat. A smarter grid will add resiliency to our electric power

system and make it better prepared to address emergencies

such as severe storms, earthquakes, large solar flares, and

terrorist attacks. Because of its two-way interactive capacity,

the Smart Grid will allow for automatic rerouting when

equipment fails or outages occur. This will minimize outages

and minimize the effects when they do happen [11].

When a power outage occurs, Smart Grid technologies will

detect and isolate the outages, containing them before they

become large-scale blackouts. The new technologies will also

help ensure that electricity recovery resumes quickly and

strategically after emergency- routing electricity to emergency

services first, for example. In addition, the Smart Grid will

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take greater advantage of customer-owned power generators to

produce power when it is not available from utilities. By

combining these “distributed generation” resources, a

community could keep its health center, police department,

traffic lights, phone system, and grocery store operating

during emergencies [12].

In addition, the Smart Grid is a way to address an aging

energy infrastructure that needs to be upgraded or replaced.

It’s a way to address energy efficiency, to bring increased

awareness to consumers about the connection between

electricity use and the environment. And it’s a way to bring

increased national security to our energy system- drawing on

greater amounts of home-grown electricity that is more

resistant to natural disasters and attack [13].

The Smart Grid is not just about utilities and

technologies; it is about giving you the information

and tools you need to make choices about your

energy use. If you already manage activities such as

personal banking from your home computer, imagine

managing your electricity in a similar way. A smarter

grid will enable an unprecedented level of consumer

participation.

For example, you will no longer have to wait for your

monthly statement to know how much electricity you

use. With a smarter grid, you can have a clear and

timely picture of it. “Smart meters”, and other

mechanisms, will allow you to see how much

electricity you use, when you use it, and its cost.

Combined with real-time pricing, this will allow you

to save money by using less power when electricity is

most expensive.

While the potential benefits of the Smart Grid are

usually discussed in terms of economics, national

security, and renewable energy goals, the Smart Grid

has the potential to help you save money by helping

you to manage your electricity use and choose the

best times to purchase electricity. And you can save

even more by generating your own power.

IV. NOVEL SMART GRID AND SCADA NETWORK

MODELING

The complex network theory has been successfully applied

in the analysis of various technological networks. As the

power grid increases in size and complexity, it is becoming

more important to understand the emergent behaviors that can

take place in the system. We try to apply complex network

theory to smart grid and SCADA network analysis. Both these

interdependent networks can often be represented in a useful

way as networks; the structure (topology) of networks is

mathematically described in terms of graphs, i.e., sets of

vertices (nodes) and edges (links). For a smart electric power

grid, the vertices can be power plants, stations and power

users, and the edges power lines [14] and for SCADA

networks, Master Control Station (MCS), Remote Terminal

Units (RTUs), Intelligent Electronics Devices (IEDs),

Programmable Logic Controllers (PLC) can be described as

nodes and information and communication lines can be

modeled by links (as adopted in this work), or vice versa [13],

[15]. Consider an example of Smart Grid and SCADA system

shown in Fig. 1, where the electrical needs of a node in the

SCADA network can be supplied by one or more nodes in the

Smart Grid. Fig. 1 shows extracted graph topology (Structural

Topology) of Smart Grid, SCADA network and their

interconnections.

Figure 1. The interdependent Smart Grid and SCADA network.

A novel or methodological approaches to comprehensively

analyze the vulnerability of interdependent infrastructures are

required, two types of vulnerability are considered:

Structural vulnerability and

Functional vulnerability

For structural vulnerability, infrastructures topologies are the

only information while operating regimes of different

infrastructures are further taken into consideration to analyze

functional vulnerability. The vulnerability analysis process of

interdependent infrastructures can be seen in Fig. 2. From the

figure, the first step is to extract the topology of each

infrastructure, i.e. what are described by nodes and what are

modeled by links. When infrastructure topologies have been

extracted, their operating regimes can be further considered

for analysis on functional vulnerability. However, no matter it

is the analysis on structural vulnerability or functional

vulnerability, the most important thing is to model

interdependences between two infrastructures. There are

several types of interdependences between infrastructures.

Different scholars have different view on the classification [7].

A. Structural vulnerability Analysis

Vulnerability is related to attacks and can be described as

the decrease of system efficiency after an attack. To analyze

structural vulnerability, infrastructure topologies are only

considered and the most important thing is to determine what

are used to describe structural efficiency. There are many

definitions on structural efficiency, such as average shortest

distance, network diameter, and cluster efficiency, but they all

have some limitation. Usually, the average reciprocal shortest

path lengths of networks are used to measure the structural

efficiency and it is generally accepted [8], [16].

4

Figure 2. The vulnerability analysis process of interdependent infrastructures

Smart Grid and SCADA network.

The topology is represented as a graph },{ EVG with N

nodes, }{ ivV is the set of vertices and E is the set of edges,

denote ),( ji vvd by the shortest path lengths connecting two

nodes in the network or it indicates the minimum number of

edges that one crosses to traverse from node i to node j , then

the structural efficiency )(GX of one infrastructure can be

defined as follow:

(1) 1

.

1)( ∑

∈∈ , SP GjGi ijSP dNNGX

Where PN is the number of resource nodes (such as

generator nodes in the smart grid network and RTU nodes in

the SCADA network) and SN is the number of load nodes

(nodes offer services to other systems). This distance matrix

ijd has the same dimensions as the adjacency matrix A .

The adjacency matrix A of size NN be written as

(2) vertex to vertex from edge no is thereif0

vertex to vertex from edgean is thereif1

ji

jiAij

edges. parallel by connecteddirectly are and if kjikAij

When two nodes are not connected at all, or become

disconnected due to attacks, their shortest path length

),( ji vvd becomes infinite, and then ),(

1

ji vvd is zero. If

)(GX is large, it is indicated that the network is well

connected and has high efficiency.

In order to fully understand the structure of a power grid,

one needs to know not only its topology, but also the structure

that results from the physical properties that govern flow. To

understand the electrical structure of a given smart grid we

need a measure of electrical distance. Electrical distance does

not perfectly represent all of the ways in which components in

a grid are connected; it is a useful starting point for structural

analysis. The electrical distance, is the absolute value of the

inverse of the system admittance matrix given by Eq. (3).

(3) 11

YYEd

Taking electrical distance into consideration the structural

efficiency )(GX is given by,

(4) 1

.

1)( ∑

∈∈ , SP GjGi ijSP ZNNGEX

(5) .

1)( ∑

∈∈ , SP GjGi

ijSP

YNN

GEX

Where, )(GEX gives electrical structural efficiency. Zij is

the absolute value of the series impedance of the shortest

electrical path between buses i and j.

We consider the power from a generator to be accessible to

a consumer if there is a path of transmission lines between the

two. In practice, the existence of a connection between two

substations does not always imply that power can be

transferred across it as there may be capacity or other

constrains present. In addition, another important thing is to

model the structural interdependence. For analysis on

structural vulnerability, when one smart grid node is attacked,

all SCADA nodes connected by this power node will be

deleted. Similarly, when a SCADA node is attacked, the

corresponding load-based power generators will be also

removed. This will change network topologies. The structural

efficiency can be calculated and structural vulnerability can be

further analyzed.

B. Functional Vulnerability Analysis

To analyze the functional vulnerability, operating regimes

of different infrastructures should be further considered.

While the connectedness of the smart grid allows for the

transmission of power over large distances, it also implies that

local disturbances propagate over the whole grid. The failure

of a power line due to lightning strike or short-circuit leads to

the overloading of parallel and nearby lines. Power lines are

guarded by automatic devices that take them out of service

when the voltage on them is too high. Generating substations

are designed to switch off if their power cannot be transmitted;

this protective measure has the unwanted effect of diminishing

5

power for all consumers. Another possible consequence of

power line failure is the incapacitation of transmission

substations, possibly causing that the power from generators

cannot reach distribution substations and ultimately

consumers.

In order to account for such functional vulnerability of the

system we need to consider power network dynamics. The

power network dynamics are coupled by its network Eq. (6).

(6) .VYI bus

The bus admittance of system is given by

(7) /1 ijijijij jBGZY

In the system, there is a complex power injected into the thi

bus is given by [17]

Ni

jQPIVS iiiii

,.........3,2,1

(8) *

Real or active power coming out of bus i :

(9) )sin()cos(

1

N

jjiijjiijjii BGVVP

Reactive power coming out of bus i :

(10) )cos()sin(

1

N

jjiijjiijjii BGVVQ

Where ,iP iQ and ,iV are the active power, reactive power

and complex voltage at bus ,i respectively. )( ji is the

difference in angles of the voltage phasors at two buses i and

.j

For a medium length or a long transmission line .RX For

ease of calculation we can thus ignore .R After simplification

active and reactive power flow equations are given by

(11) )sin(.

)sin(. jiij

jijiijjiij

X

VVBVVP

))cos((. jijiiijij VVVBQ

(12) ))cos((

ij

jijiiij

X

VVVQ

Power balance equation is given by [18]

(13)

11

11

Loss

N

iD

N

iG

Loss

N

iD

N

iG

QQQ

PPP

ii

ii

Under different vulnerability or attacks conditions above

mentioned system parameters changes which affect the power

flow in the system and sometimes it leads to complete system

collapse or blackout situation.

V. ANALYSIS OF INTERDEPENDENT SMART GRID AND

SCADA NETWORKS UNDER SEVERE EMERGENCIES

The increased complexity and expanded communication

paths can easily lead to an increase in vulnerability to cyber-

attack [5,13]. The size (millions of nodes) of a fully

implemented smart grid and an unpredictable intelligent

adversary make it difficult to anticipate how attacks may be

manifested [14]. Smart Grid technology that has known

vulnerabilities has already been deployed in some parts of the

current power grid [9, 14].

We modeled IEEE-30 bus [19] combined smart grid with

Microgrid as spinning reserve capacity and SCADA network

using MATLAB [20]. Some of the details of this model are

given in Table I.

Table I. Configuration of IEEE-30 bus Smart Grid and SCADA Network.

Smart Grid network SCADA Network

No of Buses or

Nodes

30 No of RTUs

(Nodes)

25

No of lines or

branches

41 No of IEDs

(Nodes)

30

No of Generator

Buses

06

At Bus 1, 2,

5, 8, 11, 13

PLC 06

Slack Bus Bus 1 MTU 01

We tested Interdependent network performance for structural

and functional vulnerability. Under failure conditions i.e. the

case of structural vulnerability case shown in Fig. 3 which

shows average distance under random vertex failures of

combined Smart Grid and SCADA network. The functional

behavior is shown in Fig. 4. Fig. 4 shows the analysis of

interdependency network with Microgrid as spinning reserve.

Figure 3. Average distance under random vertex failures of interdependent

Smart Grid and SCADA network.

When 5 Units with 710 MW tripped down without

interdependent and Microgrid network frequency drops to

59.886 Hz within 5.8 seconds. If there is no immediate

spinning reserve capacity available from Microgrid then

complete grid may reach to failure or blackout stage but with

0 0.2 0.4 0.6 0.8 10

1

2

3

4

5

fv-fraction of removed vertices

lv-a

vera

ge d

ista

nce

Random vertex failures of combined Smart Grid and SCADA network

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interdependent and Microgrid network frequency excursion

arrests at 59.950 within 0.7 seconds and complete system

survive. Smart Grid and SCADA interdependency network

combined with Microgrid can reduce Grid congestion and thus

it will help to avoid complete system failure or blackouts

situations.

Figure 4. Simulation test results for Novel Smart Grid and SCADA System

Interdependency network.

VI. CONCLUSIONS

Smart grid technologies will enable higher percentages of

centralized and distributed renewable generation to be

integrated into the grid efficiently and reliably, so they can

become significant contributors to our overall energy

platform. Renewable power can become as mainstream as coal

is today, thereby reducing carbon emissions, natural resource

depletion, and dependence on foreign oil -ultimately helping

us improve our energy security. Structural and functional

vulnerability analysis can be used to analyze the vulnerability

of interdependent infrastructures. Our results indicate that

interdependent smart grid and SCADA network is more

vulnerable. Microgrid can share large portion of the load and it

will reduce pressure of main power grid. Thus it will provide

better economical solution. Novel network will reduce carbon

footprint which will provide efficient and sustainable energy

solutions.

ACKNOWLEDGMENT

The authors gratefully acknowledge The Defense Threat Reduction Agency (DTRA) and Pennsylvania State University for their support and finance for this Project.

The Authors of this paper are greatly thankful to the Management of Bharati Vidyapeeth Pune, India, Bharati Vidyapeeth Deemed University Pune, Dr. Anand R. Bhalerao, Principal and Dean, Bharati Vidyapeeth Deemed University

College of Engineering, Pune, India, for their support and constant inspiration.

REFERENCES

[1] C. Ten; C. Liu; G. Manimaran, “Vulnerability Assessment of Cybersecurity for SCADA Systems,” Power Systems, IEEE Transactions on , vol.23, no.4, pp.1836,1846, Nov. 2008 doi: 10.1109/TPWRS. 2008.2002298

URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4652578

& isnumber=4652575

[2] Tai-hoon Kim, “Securing Communication of SCADA Components in Smart Grid Environment”, International Journal of Systems Applications, Engineering & Development, Issue 2, vol. 5, 2011, pp. 135-142.

[3] A. Murray, and T. Grubesic, “Critical Infrastructure-Reliability and Vulnerability”, Advances in Spatial Science, Springer Publications, 2007, ISBN 978-3-540-68055-0.

[4] F. R. Spellman and R.M. Bieber, Energy Infrastructure Protection and Homeland Security, Government Institution (GI), The SCARECROW Press, Inc, 2010, ISBN 978-1-60590-678-2.

[5] NISTIR 7628, U. S. Department of Commerce, “Guidelines for Smart Grid Cyber Security: Vol.1, Smart Grid Cyber Security Strategy, Architecture, and High-Level Requirements”, The Smart Grid Interoperability Panel- Cyber Security Working Group, August 2010.

[6] U.S. Department of Energy Office of Electricity Delivery and Energy Reliability, “Study ofSecurity Attributes of Smart Grid Systems - Current Cyber Security Issues”, April 2009.

[7] P. Pederson, D. Dudenhoeffer, S. Hartley, M. Permann, “Critical Infrastructure Interdependency Modeling: A Survey of U.S. and International Research”, INL Report, U.S. Department of Energy

National Laboratory and Battelle Energy Alliance, August 2006.

[8] S. Sun, Z. Liu, Z. Chen, Z. Yuan, “Error and attack tolerance of evolving networks with local preferential attachment”, Physica A 373, 2007, pp. 851-860, Elsevier, doi:10.1016/j.physa.2006.05.049.

[9] “Smart Grid Legislative and Regulatory Policies and Case Studies”, U.S. Energy Information Administration, U.S. Department of Energy, Washington, DC 20585, December 2011.

[10] A. E. Motter, “Cascade Control and Defense in Complex Networks”, Phys. Rev. Lett. 93, 098701, 2004.

[11] T. G. Lewis, Critical Infrastructure Protection in Homeland Security Defending a Networked Nation, A John Wiley & Sons, Inc., Publications, 2006, ISBN-13: 978-0-471-78628-3.

[12] Cisco Smart Grid Security Solutions, 2009.

[13] Clark W. Gellings, The Smart Grid: Enabling Energy Efficiency and Demand Response, CRC Press, 2009, ISBN-10: 0-88173-623-6.

[14] T. Flick, J. Morehouse and C. Veltsos, Securing the Smart Grid: Next Generation Power Grid Securiy, Elsevier Publications, 2011, ISBN 978-1-59749-570-7.

[15] Ronald L. Krutz, Securing SCADA Systems, Wiley Publishing, Inc., 2006, ISBN-13: 978-0-7645-9787-9.

[16] Charles J. Kim, Obinna B. Obah, “Vulnerability Assessment of Power Grid Using Graph Topological Indices”, International Journal of Emerging Electric Power Systems, vol. 8, Issue 6, 2007, Article 4, pp. 1-17.

[17] Leonard L. Grigsby, Electric Power Engineering Handbook, Second Edition, CRC Press, 2006.

[18] Yoshihide Hase, Handbook of Power System Engineering, John Wiley & Sons Ltd, 2007, ISBN-13: 978-0-470-02742-4.

[19] University of Washington, power systems test case archive. http://www.ee.washington.edu/research/pstca.

[20] http://www.mathworks.com/