The identification of generating units that abuse market power is an essential part of risk prevention in a spot market, especially in the early stage of the construction of the spot market. In this study, a model for identifying generating units that abuse market power is designed based on the AdaBoost-DT algorithm. It is targeted at the imbalance between samples of generating units that abuse market power and normal generating units in the spot market. First, the four main methods by which market power is abused by generating units in the spot market are described: collusion, economic withholding, physical withholding, and extreme quotation. Second, the specific characteristics of the four methods are analyzed, and the identification indexes for generating units that abuse market power are established. Thereafter, a sample set of generating units that abuse market power using different methods is constructed. Furthermore, a training set is formed with samples of normal generating units to construct a model based on the AdaBoost-DT algorithm, for identifying generating units that abuse market power. Finally, the spot market data of a certain region are used for an example analysis. The results show that the accuracy of model identification is 97%, which validates the method.

中文翻译：

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基于AdaBoost-DT算法的电力现货市场滥用市场力量的发电机组辨识

识别滥用市场支配力的发电机组是预防现货市场风险的重要组成部分，尤其是在现货市场建设的早期阶段。在这项研究中，基于AdaBoost-DT算法设计了一个模型，用于识别滥用市场支配力的发电机组。它针对的是滥用市场支配力的发电机组样本与现货市场中正常发电机组之间的不平衡。首先，描述了现货市场中发电机组滥用市场支配力的四种主要方法：串谋，经济预提，实物预提和极端报价。其次，对这四种方法的特点进行了分析，建立了滥用市场支配力的发电企业的识别指标。之后，构建了使用不同方法滥用市场支配力的发电机组的样本集。此外，使用正常发电机组的样本形成训练集，以基于AdaBoost-DT算法构建模型，以识别滥用市场支配力的发电机组。最后，以某地区的现货市场数据为例进行分析。结果表明，模型识别的准确率为97％，证明了该方法的有效性。