基于多智能体深度确定策略梯度算法的火力发电商竞价策略

Bidding Strategy for Thermal Power Generation Companies Based on Multi-agent Deep Deterministic Policy Gradient Algorithm

火电是新型电力系统的重要支撑,研究火力发电商的竞价策略以及不同出清机制的影响,对保障其低碳高效运营具有重要意义。构建基于多智能体深度确定策略梯度算法的竞价策略模型,分析不同火力发电商组合的竞价差异化策略,优化多主体报价报量策略,探究边际统一出清、按报价支付出清和随机匹配出清3种典型出清机制的市场影响。结果表明,该策略模型可引导火力发电商采取合理的竞价方式以提高市场效率;在新能源渗透率较低时,不同出清机制对各类型机组的影响有所不同;随着新能源渗透率的提高,采用按报价支付出清机制可以兼顾经济和环境效益;当新能源渗透率达到较高水平时,采用随机匹配出清机制可有效应对市场波动风险。

Thermal power is an important support for the new power system.It is of great significance to study the bidding strategy for thermal power generation companies and the influence of different clearing mechanisms to ensure their low-carbon and efficient operation.A bidding strategy model is constructed based on the multi-agent deep deterministic policy gradient algorithm to analyze the differential bidding strategies for different combinations of thermal power generation companies.The multi-agent price and quantity bidding strategy is optimized,and the market impact of different market clearing mechanisms is explored.The simulation results indicate that the proposed bidding strategy model can guide the thermal power generation companies to optimize their bidding methods and improve the market efficiency.When the penetration rate of new energy is low,the applicability of different clearing mechanisms varies for various types of units;with the increase of the penetration rate of new energy,the pay as bid mechanism can be used to enhance the economic and environmental efficiency of the electricity market;when the penetration rate of new energy reaches a high level,the random matching clearing mechanism can effectively address market volatility risks.