International Core Journal of Engineering 2020-26 | Page 39

Finally, according to the content shown in Table VI, the
average value is normalized, and the relevant opinions of the
two experts are assembled, and finally the ranking vector is
V =  0.276,0.252,0.250,0.222  , that is, data risk>credit
risk>tax risk>policy risk.
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VI. C ONCLUSION
A method based on triangular fuzzy numbers for risk
factor ranking is used in this paper. This method can
effectively compare the severity of settlement risk factors in
power market. And analysis results can provide a basis for
more accurately and effectively selecting the corresponding
control measures in power market.
The case analysis in the paper shows that when most of
the language fuzzification descriptions are selected
during analysis, the values of the four power market
settlement risks calculated by the triangular fuzzy numbers
are not much different, which indicates data risk, credit risk,
tax risk and policy risks significantly affect all the security
of power market settlements.
The calculation results show that when most of the
language fuzzification descriptions are selected, the ranking
results are that data risk is greater than credit risk, credit risk
is greater than tax risk, and tax risk is greater than policy
risk. Therefore, in the power market settlement process, data
risk is the most important risk and the one that needs to be
controlled most.
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