首页> 外文OA文献 >Identification of Generating Units That Abuse Market Power in Electricity Spot Market Based on AdaBoost-DT Algorithm
【2h】

Identification of Generating Units That Abuse Market Power in Electricity Spot Market Based on AdaBoost-DT Algorithm

机译:基于Adaboost-DT算法的电力点市场滥用市场力量的发电机构的识别

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务,本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文,但由于OA文献来源多样且变更频繁,仍可能出现获取不到、文献不完整或与标题不符等情况,如果获取不到我们将提供退款服务。请知悉。

摘要

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.
机译:鉴定滥用市场权力的产生单位是现货市场风险预防的重要组成部分,特别是在现货市场建设的早期阶段。在本研究中,基于ADABOOST-DT算法设计了滥用市场功率的产生单元的模型。它是针对滥用市场功率和正常发电机的发电单元的样本之间的不平衡。首先,描述了在现货市场中产生了市场权力的四种主要方法:勾结,经济扣缴,物理扣留和极限报价。其次,分析了四种方法的具体特征,并建立了用于产生滥用市场权力的单位的识别指标。此后,构建了使用不同方法滥用市场功率的制定单元的样本集。此外,训练集形成有正常产生单元的样本,以构建基于Adaboost-DT算法的模型,用于识别滥用市场功率的生成单元。最后,某个区域的现货市场数据用于示例分析。结果表明,模型识别的准确性为97%,验证了该方法。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号