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Abstract Since the reformation of the electricity market of China is increasing and the modernization of the national governance system is being promoted, the electricity market management system needs to be gradually improved. Credit evaluation is an essential part of the electricity market management system. Therefore, a complete credit evaluation system plays a fundamental role in ensuring that the electricity market can operate safely, stably, efficiently, and reliably. The traditional credit evaluation methods focus on the financial situation and credit records of the generating companies. With the gradual development of the electricity market transactions, the market behavior needs to be included in the credit evaluation indicator system to standardize the behavior of the generating companies and reduce the risks of the market transactions. This study establishes a complete credit evaluation system from three perspectives: market behavior, financial situation, and social responsibility. In addition, to determine the weight of indicators in the credit evaluation, the analytic hierarchy process (AHP) algorithm, fuzzy theory, and the non‐dominated sorted genetic algorithm II (NSGA‐II) algorithm are combined. The variance of the evaluation results and the deviation degree of the indicator weight are considered the objective functions of the NSGA‐II algorithm to optimize the indicator weight. Finally, comparing as an example the results of the credit evaluation before and after the optimization of an electricity market shows that the credit evaluation method can effectively distinguish the generating companies that adopt different market behaviors and has a high practical significance. |
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