Energy and Power Engineering, 2013, 5, 807-810
doi:10.4236/epe.2013.54B155 Published Online July 2013 (http://www.scirp.org/journal/epe)
Preliminary Analysis of Smart Grid Risk Index System
and Evaluation Methods
Ruihua Liu
Sichuan Electric Vocational and Technical College, Sichuan Chengdu, China
Received February, 2013
ABSTRACT
Combined the purpose and requirements of security and stability economic operations of the smart grid, a more com-
prehensive, risk indicator system of smart grid is established from five aspects of the smart grid strategic risk, external
risk, financial risk, compliance risk, operational risks. On this basis, it is conducted smart grid risk assessments by inte-
grated of the use of Borda sequence value method of the original risk matrix and AHP (hierarchical analysis method), in
order to assess risk facing the smart grid and enterprise development more comprehensively, objectively and systemati-
cally and conveniently, and prompt companies continue to reduce risk, improve economic efficiency, continuously im-
prove the targeted improvement measures and continue to improve the level of grid development.
Keywords: Smart Grid; Information; the Borda Sequence Value Method; the Analytic Hierarchy Process
1. Introduction
In recent years, China's power grid construction has
made great strides, basically meet the needs of economic
and social development of the demand for electricity.
Based on independent innovation, China speeds up the
construction of UHV backbone frame for a strong smart
grid [1 -4] with characteristics of information, digitization,
and automation, interactive. But in the situation of fre-
quent natural disasters, the current complex economic
and changing environment, electricity sales growth is
slowing down; the risk of power grid is increasingly
complex and diverse. It is necessary scientifically to as-
sess the risks facing the grid development and enterprise
development, determine the impact of the main risks in
order to propose effective measures to reduce risk and
promote the power grid development scientifically,
healthy and sustainably.
Due to th e complexit y of the power system, the p ower
grid risk is not likely to be characterized by a single in-
dicator, it is necessary to use multiple risk indicators in
order to more fully reflect the overall level of relation-
ship from many different angles at the same time.
Therefore, how to establish a scientific risk index system
and taking targeted measures are crucial.
Based on the existing power grid risk assessment re-
search [5-9], this p aper put forward a more co mplete risk
assessment index system for the smart grid, initially
identified summarized, including strategic risk, external
risk, financial risk, compliance risk, running risk about
19 indicators in five aspects. On this basis, describe the
Borda sequence value method and AHP (Analytic Hier-
archy Process) based on the original risk matrix, and fuse
the two methods in the process of power grid enterprise
risk assessment in order to play a certain role in guiding
the development of smart grid.
2. Smart Grid Risk Index System
The construction and development of the smart grid is a
complex system engineering with multi-objective, multi-
dimensional and the whole process, accordingly, the risk
assessment index system is also a multi-level index sys-
tem. According to the role and requirements of the de-
velopment of the smart grid, the risk index system is built
from five aspects, grid enterprise strategic risks, external
risk, financial risk, compliance risk, and op erational risks,
every aspect contains a number of subordinate ind icators,
in order to quantify from different angles. The five as-
pects constitute a whole system, which can fully and ef-
fectively reflected the risk facing the grid development
and enterprise development, in order to reduce the major
risk, improve the development of smart grid.
According to the correlation of risk and value chain
management of power grid enterprise, initially identified
and summarizes, the major risks include strategic risk,
external risk, financial risk, compliance risk, and opera-
tional risks , the four aspects by analyzing the power grid
enterprise within the external environment, internal in-
terviews and expert assessment, and the architecture is

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808
shown in Table 1.
The risk indicators set is more targeted, can help pow-
er grid find main reason which affects grid development
and enterprise development to grasp the direction of grid
development, and targets research to propose risk reduc-
tion measures.
3. Smart Grid Risk Assessment Methods
3.1. Original Risk Matrix Method
Original risk matrix method primarily examine from the
needs of the project and technology, based on the two
aspects, analyze and identify whether there will be a risk,
and take in some way.
Table 1. The architecture of power grid risk index system.
Risk matrix method takes into account the risk impact
and risk probability factors, and can conduct the most
direct assessment of the risk factors that impact on the
project. This method does not directly derived from ex-
pert opinion judgment matrix, but grading, through a
more intuitive experience, judge by experts to quantify
the level of risk impact and risk probability, determine
prior impact on risk and risk probability, and then apply
the Borda sort by analysis of the importance of various
risk factors to assess the risk of the project. This method
is feasible in specification decision-making process and
can better integrate of the views of the groups, and there-
fore more and more widely appreciated. The concrete
used in the risk assessment of the enterprise information
project, we must first identify the potential risks of the
project, identify, further analyze: assess risk potential
impact on power grid, assess risk probability of occur-
rence to identify the risks in the risk matrix location , and
then set the appropriate preventive measures or contin-
gency plans. The likelihood of risk is shown in Table 2.
3.2. Borda Order Value Method
Borda order value method introduces risk matrix to de-
termine the level of risk, by sorting the risk of risk matrix.
This method quantifies the level of risk and makes the
final produce results at the same level of risk signifi-
cantly less than the original risk matrix, which is condu-
cive to the identification of key risk and more scientific
for powe r g rid.
On the other hand, the superiority of Borda order value
method is that it can rate in roses-category based on mul-
tiple evaluation criteria of the importance of risk sort,
which works as follow.
Assuming there is a total of N risk, assuming a risk is i,
k is the one of the criteria (specifically applicable to the
original matrix, two risk criteria: risk impact criteria and
probabilistic risk criteria), and the level of the i risk in
the criteria k expresses as risk, so the Borda number of
the risk i is:
2
1()
ii
k
bNr


k
A risk Borda sequence value is the risk level, and the
risk Borda sequence value is he number of the Borda
large number of risk than the risk, so the Borda is much
bigger and the risk level is much lower.
3.3. Analytic Hierarchy Process
Analytic Hierarchy Process (AHP, the Analytic Hierar-
chy Process) is a multi-objective decision-making method
combined qualitative and quantitative evaluation. The
basic idea of the method is a complex issue first decom-
posed into a certain level and a certain composition part,
comparison and calculation of each component part of
the piece, in order to obtain the weight of the different
components, so as to focus on which integral part will be
Table 2. The architecture of power grid risk index system.
Risk levelThe likelihood of risk
(probability of occurring within one year)
Lowest 0.01 below
Lower 0.01-0.3
low 0.3-0.7
Higher 0.7-0.9
Highest 0.9 above

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the basis of providing decision-making. Mathematics of
the decision-making process to achieve a combination of
qualitative and quantitative will be more intuitive. The
main steps of the analytic hierarchy process are as fol-
lows:
 Modeling
Analyzing by AHP, in order to make the problem
more conditioning, to build a hierarchical model is
needed. The model can be clearly demonstrated the com-
plex part of the problem. The level of the model can be
divided into three categories:
One is the highest level, only one element, the ultimate
goal of the plunge classification, in risk assessment, this
element is the ultimate risk of information technology.
Second but the middle layer, criteria layer in the hier-
archy, the middle layer are composed of several levels.
The third category is the lowest level, and also meas-
ures layer, the layer is used to implement measures and
programs to achieve their goals. The most senior of the
analytic hierarchy model established in the risk assess-
ment of power grid enterprise are risk weights, the mid-
dle layer and the bottom is the index system of risk as-
sessment information technology.
 Building a judgment matrix
Modeling the analytic hierarchy process, need to de-
termine the importance of each factor to determine the
importance of secondary and tertiary indicators, and can
be drawn on the relative importance of the level, that is
the weight, and then come to the overall risk weight of
the index weights. With Arabic numerals 1-9 will deter-
mine quantifiable results form a judgment matrix, quan-
titative criteria such as Table 3:
Judgment matrix of the structure is as follows:
Table 3. The importance of quantitative criteria.
Judging
system two indexes evaluation rules
1 Equally important
3 A slightly important th an another
5 Another obvious
7 A strongly than the other
9 Extremely important one than another
2,4,6,8 Between the two adjacent judgments
Reciprocal The index i and j comparison deter-
mines
ij
a
The matrix A is formed by pairwise comparison of the
n number of risk factors, and the matrix elements of the
quantized values is the importance of the element i and j.
After building the judgment matrix based on expert
scoring data, need for expert judgment matrix consis-
tency test, if not meet the consistenc y test, need to adjust
the matrix, last need to average expert data in accordance
with the authority of experts level.
 Single-stage sort of risk factors and the consis-
tency
Through various risk factors, determine the degree of
importance of the various factors on levels of risk, and
sort. The specific method is based on the matrix theory,
the first judgment matrix is obtained feature vector W,
and then proceed to the normalization process, obtaining
the weight.
Eigenvectors:

*** *
12
, ,...,T
n
Wwww,
and,
*
1
1
=, 1,2,...,
nij
in
jij
i
a
wij
a





，n
Normalizing a treatment:

12
**
1
, ,...,,
=/
T
n
n
ii i
i
Www w
ww w



After calculating the eigenvectors of the judgment ma-
trix, use eigenvectors to represent the impact of risk fac-
tors on the upper risk pairs. Check the consistency to
judgment matrix, in order to ensure the reasonableness of
the conclusions of the Analytic Hierarchy Process. Con-
sistency of judgment matrix test method is as follows:
Firstly, obtain matrix eigenvalue max

Secondly, seeking the consistency index
max
(- )/(1
I
Cnn  )

,
it indicates that the judgment matrix has complete con-
sistency, test end;
If 0
I
C

, calculate the random consistency ratio
=/
R
II
, CCR
I
R value judgments average random con-
sistency index of the matrix .
At last, by calculating, if , then the consis-
tency of judgment matrix and risk factors sort results can
be accepted, and if not, the consistency of the judgment
matrix and risk factors Sort results can not be unaccept-
able and need to make the appropriate changes to the
ju d gment ma trix .
0.1
R
C
 Sort of total risk factors
Total risk factors sort is calculating a combination of
weight relative to the total final goal the results for each

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810
focus all the elements by using results of the risk factors
mentioned above, i.e. the risk factor for all levels on an
element of risk importance weights weight. This step is
carried out from the top down, can eventually obtain the
combination weight of the overall corporate risk at the
lowest level of power grid enterprise risk.
Considering that both of Borda value method and AHP
have lack, integrate the two methods, we can make risk
assessment more practical and more reasonable, and thus
provide a more scientific basis for the next step to control
risk. The specific method is:
Considering the likelihood of risk size and risk impact,
first score risk and likelihood of occurrence probability
for scoring and decided the risk level by risk matrix me-
thod, and then sort the importance of risk factors order by
Borda sequence value method and inform the results to
experts, after experts understanding of the relative im-
portance of the risk situation, then pairwise comparison
and get judgment matrix, comprehensively assess power
grid risk by the traditional method of AHP-level analyze.
4. Summary
With the construction of the strong and smart grid in our
country, information technology has been deepening and
more sophisticated, this means a greater risk of conse-
quential, and to assess various risk correctly will be able
to make the decision-making to make quick and accurate
decisions for the development of power grid enterprises
by reducing the risk of occurrence or reduce the risk of
occurrence which will bring negative effects and enable
enterprises to more successful information technology.
The smart grid information risk index system covers
five aspects of the grid enterprise: strategy risk, external
risk, financial risk, compliance risk, operational risks.
The assessment index system is relatively comprehensive
and can fully reflect the grid risk in practical applica-
tions.
At the same time, assess risk of smart grid by the inte-
gration of the Borda sequence value method and on AHP
(hierarchical analyze method), in order to be comprehen-
sive, objective and systematic to assess, convenience for
the grid enterprise early to detect the major risks facing
the grid development, enable companies to continue to
reduce risk, improve economic efficiency, continuing
enhance the level of power grid development.
REFERENCES
[1] A. Ipakchi and F. Grid, “Albuyeh of the Future,” IEEE
Power and Energy Magazine, 2009.
doi:10.1109/MPE.2008.931384
[2] J. M. Wang and T. Shi, “Construction of Evaluation In-
dex System for Smart Grid,” East China Electric Power,
Vol. 40, No. 2, 2012, pp. 0194-0196.
[3] Z. D. Wang, Y. Huang, ect., “Study on Evaluation In-
dexes of Power Grid Development,” Journal of Northeast
Dianli University Natural Science Edition, Vol. 29, No. 4,
2009, p. 84.
[4] “Research Focuses and Advance Technologies of Smart
Grid in Recent Years,” Chinese Science Bulletin, 2012,
Vol. 5722, pp. 2879-2886.
[5] J. Gao and J. K. Wang, “Research on Communication
Network Architecture of Smart Grid,” Proceedings of
2012 International Conference on Future Electrical
Power and Energy Systems, Hong Kong Education Soci-
ety, Hong Kong:, 2012, 6.
[6] Z. S. Hua, B. G. Gong and X. Y. Xu, “A DS- AHP Ap-
proach for Multi-attribute Decision Making Problem with
in Complete Inform Ation,” Expert Systems with Applica-
tions, Vol. 34, No. 3. 2008.
[7] X. M. Niu, “Comparison of Several Regular Comprehen-
sive Evaluation Method,” Statistics and Decision.
[8] C. Zhang, H. Z. Cheng, X. Xi, etc. “A Study of Distribu-
tion Network Feeding Modes Selection Based on Ana-
lytic Hierarchy Process and Fuzzy Comprehensive
Evaluation,” Power System Technology, Vol. 30, No. 22,
2006, pp. 67-70.
[9] X. R. Chang, J. F. Xing and J. Zhang, “Dispatcher Train-
ing Evaluation Based On Multi-grade Fuzzy Comprehen-
sive Evaluation Method,” Power System Technology, Vol.
29, No. 17, 2005, pp. 31-33.