当前我国电力市场正处于深化改革阶段，发电侧与售电侧的串谋行为加剧了市场力滥用的风险，严重危害了我国电力市场化进程。为了加强对电力市场化进程中发售两侧之间串谋问题的防范，提出了一种基于考虑主客观因素的递进式双边匹配的发售两侧市场力风险识别模型，该模型适用于电力市场中长期交易场景，旨在识别该场景下发电商与售电商的市场力风险。该方法通过融合分析市场主体主客观行为特征，构建“风险特征筛选-异常交易锁定”的全流程识别框架。通过利用Borda计数法量化发电侧与售电侧在报价、报量、合作稳定性3种维度下的主观偏好特征，并引入熵平衡匹配算法对出清结果分布集中度进行客观量化，最终实现风险主体的精准识别。算例结果表明，该方法在准确识别高风险交易主体的同时可使市场监管效率得到有效提升。

Currently, China′s electricity market is in a phase of deepening reforms, where collusion between the generation side and the retail side intensifies the risk of market power abuse, severely hindering the market-oriented development of the power sector. To enhance the prevention of collusion between the generation and retail sides in the electricity marketization process, a progressive bilateral matching model for market power risk identification is proposed. This model is applicable to medium- and long-term trading scenarios in the electricity market and aims to identify market power risks among power generation companies and retail suppliers. By integrating the analysis of subjective and objective behavioral characteristics of market entities, the method establishes a comprehensive identification framework of "risk feature screening - abnormal transaction locking." It employs the Borda counting method to quantify subjective preference features of the generation and retail sides across three dimensions—pricing, volume bidding, and cooperative stability—while introducing an entropy-balanced matching algorithm to objectively quantify the concentration of clearing results. This ultimately enables precise identification of at-risk entities. Case study results demonstrate that the method not only accurately identifies high-risk trading entities but also effectively enhances market supervision efficiency.