Credit risk analysis of electricity retailers based on cloud model and intuitionistic fuzzy analytic hierarchy process

With the continuous opening of China's electricity market, a large number of third-party electricity retailers have poured into the electricity market. Electricity retailers are the bridge between the main player of the electricity market. In view of the large number and variety of electricity retailers in the current electricity market, this paper divides the electricity retailers that have been publicized in China into five categories. In view of the uncertainty of the credit evaluation standard of electricity retailers, this paper determines the cloud model scale of the risk level of the electricity retailers; selects three primary indicators, eight secondary indicators, and 20 tertiary indicators to construct the credit risk evaluation indicator system of the electricity retailer. This paper also builds a computing model combining intuitionistic fuzzy analytic hierarchy process (IFAHP) with cloud model; obtains the final evaluation score by combining the certainty of cloud model with intuitionistic fuzzy weight, the qualitative to quantitative change of the indicator is realized, and makes the fuzzy concept of credit rating of electricity retailers get a quantitative change. From the model results, it can be seen that the risk score of the electricity grid asset retailers is 14.8666, which is the lowest risk; the independent electricity retailers has a risk score of 26.9706, which is the highest risk. Compared with the traditional AHP-cloud model, this method is not only more objective, but also can obtain more information such as the distribution of index values from entropy and hyper entropy.

Automated summary

This paper studies the credit rating of electricity retailers in China, divides them into types, and establishes an indicator system. By combining cloud model and intuitionistic fuzzy analytic hierarchy process, a risk assessment model is constructed. The results show significant differences in risk scores among electricity retailers.