International Core Journal of Engineering 2020-26 | Page 37

O a
 O a
L
, O a M , O a U 
(4)
Establish risk factor matrix
IV. R ANKING OF RISK FACTORS MATRIX BASED ON
Sort elements of each factor
layers
TRIANGULAR FUZZY NUMBERS
A. Risk factor AHP model
Risk factor AHP model classifies different risk factors
on the basis of risk identification to form different levels.
Among them, the power market settlement belongs to the
target layer, that is, the indicator that need to be sorted by
risk. The severity when the risk occurs, the recovery speed
after the risk occurs, the frequency of the risk, and whether
the risk is easily detected, which are the criterion layer, that
is, the assessment of the various possibilities of risk. The
specific risks that may arise are the factor layer, which are
the data risk, credit risk, tax risk and policy risk involved in
risk identification. The power market settlement risk factor
AHP model is shown in Figure 2 [8] .
Use fuzzy operator to calculate
weighted average of each layers
Calculate the risk factor
deviation degree
Normalize and sort the deviation
degree
Fig.3. Figure for sequencing of risk factor
ķ First, the matrix A ( a ij j ) n composed of limited risk
factors is established, according to the AHP model of
settlement risk factors in power market and the tab.1
Settlement of Competitive electricity market
Severity
Recovery speed
ĸ Secondly, the average value of each row element
Monitoring
difficulty
Frequency
a ij  O  is calculated in the risk matrix and b i ik has been
proposed according to the rank of average value from large
to small.
Data risk
Credit risk
Tax risk
Policy risk
Fig. 2. AHP model for power market settlement risk
a ij  O 
B. Determination of risk factor matrix based on triangular
fuzzy numbers
A risk factor matrix is constructed based on the power
yp
market settlement risk factor AHP model. Hypothetically
matrix A ( a ij j ) n is a matrix of risk factors, a ij a ij L , a ij M , a ij U is a

Where, O is the attitude of decision makers. O
 O 
usually taken in industrial production. If a ij

0.5 is
 O 
a il
,the
a ij  O  and a il  O  .
Ĺ
Next, the weight vector Z
T
 Z 1 , Z 2 , Z n 
is
determined. The value of Z j can be determined by Equation
(6).
assessment and a ij U is the most optimistic assessment.
M
ij
(5)
average value is obtained to be b i ik is the average values of
fuzzy number formed by the factor layer for the comparison
of the two important degrees of the criterion layer. Where,
a ij L is the most pessimistic assessment. a ij M is the most likely
L
ij
1
ª  1  O  a ij L  a ij M  O a ij U º ¼ , 0 d O d 1
2 ¬
U
ij
a , a , a are all between 0 and 1, and the value of risk
factors are shown in Table I.
Q  j n   Q   j  1  n 
Z j
(6)
TABLE I. T ABLE FOR VALUE OF RISK FACTOR
definition
Extremely important
Strongly important
Obviously important
Slightly important
Equally important
Slightly unimportant
Obviously not important
Strongly unimportant
Extremely unimportant
Where, Q   is the fuzzy quantization operator, its value
is given by Equation (7).
Value
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
Q  r 
C. Ranking of risk factors
The ranking of risk factors is an important method to
determine the comprehensive severity of risk factors.
According to the ranking results, decision makers can focus
on controlling more important risks in each risk. The
ranking steps for risk factors are shown in Figure 3 [9] .
­ 0 , r  a
° r  a
°
, a d r d b
®
° b  a
°̄ 1 , r ! b
> @
(7)
Where, a , b , r  0,1 .The values corresponding to the
fuzzy quantum are shown in Table II.
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