面向电力现货市场事中监管的市场力行为识别与溯源方法OA

To address the challenges of timely identification of abnormal market participants before electricity spot market clearing and quantitative attribution of the underlying causes of abnormal behaviors,a market power behavior identification and attribution method for intra-market supervision is proposed.First,a market power indicator system covering physical and economic behaviors and an indicator-behavior mapping relationship are established.Second,an isolation forest is used for unsupervised detection of multidimensional features,including bidding,quantity declaration,maintenance declaration,and energy storage state of charge,thereby obtaining anomaly scores and abnormal states for market participants.Then,a causal forest estimates the conditional average treatment effect of anomalous states on market outcomes,while CatBoost extracts the explanatory weights of individual indicators for the heterogeneity of these effects.Finally,typical market power behaviors are attributed through the indicator-behavior mapping relationship.Case studies using actual data from a regional electricity market in China show that the model can accurately identify various abnormal patterns,including physical withholding,economic withholding,and collusive behaviors,achieving high precision and recall.The proposed method can provide a basis for identifying abnormal market participants and analyzing the underlying causes of their behaviors for intra-market supervision of electricity spot markets.