摘要
为解决多市场主体参与电力优化的问题,提出了基于多能源新型市场主体动态聚合模型的电力优化调度。首先,概述了多类型市场主体的发展阶段,包括政策导向与市场适应期以及市场化运行与技术创新期。然后,通过运行相似性分析和聚类分析等方法,提出了基于递归神经网络(RNN)与强化学习的动态聚合模型,旨在预测各市场主体的未来行为并通过聚合模型实现系统的动态调度。进而构建了基于多主体聚合的电力调度优化模型,以总成本最小化为目标,考虑了网络的基本参数、负荷需求、可再生资源的不确定性等因素,并通过仿真分析进行了验证。结果表明,与基础情景比较,聚合后的优化调度总成本减少56%,网络损耗减少73%,有效提高了系统的运行效率和可再生能源的渗透率。
To address the issues of multi-market entities participation and electricity optimization,an electricity optimization dispatch based on a dynamic aggregation model of multi energy new market participants was proposed.Initially,the development stages of multiple types of market participants were outlined,including the policy orientation and market adaptation period as well as the market operation and technology innovation period.Subsequently,through methods such as operation similarity analysis and clustering analysis,a dynamic aggregation model based on Recurrent Neural Networks(RNN)and reinforcement learning was proposed,aimed at predicting the future behavior of each market participant and implementing the system′s dynamic disptach through the aggregation model.Furthermore,an electricity disptach optimization model based on multi-agent aggregation was constructed,with the objective of minimizing total costs,taking into consideration factors such as the network′s basic parameters,load demand,and the uncertainty of renewable resources,and was validated through simulation analysis.The results show that compared to the baseline scenario,the total optimization dispatch cost after aggregation was reduced by 56%,and network losses were reduced by 73%,effectively enhancing the system′s operational efficiency and the penetration rate of renewable energy.
作者
于丹文
王庆玉
张青青
李付存
Yu Danwen;Wang Qingyu;Zhang Qingqing;Li Fucun(Electric Power Science Research Institute,State Grid Shandong Electric Power Company,Jinan 250003,China)
出处
《能源与环保》
2025年第2期203-208,共6页
CHINA ENERGY AND ENVIRONMENTAL PROTECTION
基金
国网山东省电力公司科技项目资助(52062623000Z)。
关键词
多能源市场主体
聚合模型
调度优化
电力系统运行
multi energy market entities
aggregation model
dispatch optimization
operation of power system
