价格制定者在信息不全时，用历史数据预测系统状态并优化报价。
A Data-Driven Pool Strategy for Price-Makers Under Imperfect Information
- 基于历史数据和SVM分类，将报价曲线映射到系统运行状态
- 在3个不同规模电网上验证，可准确识别系统模式组合
- 适合电力市场中缺乏实时参数的发电企业决策参考
本文研究在信息不完整条件下，价格制定者的最优投标策略。由于市场参与者无法获取电力系统的关键传输参数，价格制定者需利用历史数据，基于自身报价曲线估计市场结果。通过结合环形多参数线性规划（rim-MPLP）理论分析经济调度的线性规划模型，揭示了发电机组与输电线路状态标志组合所构成的系统模式特征。采用支持向量机（SVM）构建多类分类模型，将报价曲线映射至对应系统模式，并嵌入价格制定者的决策框架。该方法在IEEE 30节点系统、伊利诺伊合成200节点系统及南卡罗来纳合成500节点系统上进行了验证，展现出良好的预测与适应能力。
原文摘要 · Abstract (English)
This paper studies the pool strategy for price-makers under imperfect information. In this occasion, market participants cannot obtain essential transmission parameters of the power system. Thus, price-makers should estimate the market results with respect to their offer curves using available historical information. The linear programming model of economic dispatch is analyzed with the theory of rim multi-parametric linear programming (rim-MPLP). The characteristics of system patterns (combinations of status flags for generating units and transmission lines) are revealed. A multi-class classification model based on support vector machine (SVM) is trained to map the offer curves to system patterns, which is then integrated into the decision framework of the price-maker. The performance of the proposed method is validated on the IEEE 30-bus system, Illinois synthetic 200-bus system, and South Carolina synthetic 500-bus system.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。