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Research on credit rating and risk measurement of electricity retailers based on Bayesian Best Worst Method-Cloud Model and improved Credit Metrics model in China's power market

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  • Zhang, Yuanyuan
  • Zhao, Huiru
  • Li, Bingkang
  • Zhao, Yihang
  • Qi, Ze

Abstract

With the advancement of Power Market reform and the opening of the Demand Side market, the dynamic risk management of electricity retailers has attracted more attention. This paper designs a risk prevention linkage mechanism of credit evaluation-risk measurement for retailers. Firstly, this paper constructs a retailers' credit evaluation index system containing 18 indexes, and proposes a retailers' credit rating technology based on Bayesian Best Worst Method (BBWM)- Cloud model. On the basis of Best Worst Method (BWM), BBWM uses Multinomial Distribution to model the input indexes, so as to obtain more reliable weights in the group decision-making environment. Secondly, a credit risk measurement model based on improved Credit Metrics model and CVaR method for electricity retailers is constructed. The Credit Metrics model adopts the Marked-to-Market system, which can consider the retailers' default risk and the risk of credit rating changes, and CVaR method can accurately describe the retailers’ credit risk. Finally, four electricity retailers are taken as examples to verify the effectiveness and scientificity of the model. This paper can provide guarantee and theoretical basis for the credit management of Power Sales market, standardize the behavior of electricity retailers and reduce the transaction risk of Power Market.

Suggested Citation

  • Zhang, Yuanyuan & Zhao, Huiru & Li, Bingkang & Zhao, Yihang & Qi, Ze, 2022. "Research on credit rating and risk measurement of electricity retailers based on Bayesian Best Worst Method-Cloud Model and improved Credit Metrics model in China's power market," Energy, Elsevier, vol. 252(C).
    • Handle: RePEc:eee:energy:v:252:y:2022:i:c:s0360544222009914
    DOI: 10.1016/j.energy.2022.124088
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