山东电力技术2024，Vol.51Issue(6)：27-35,9.DOI:10.20097/j.cnki.issn1007-9904.2024.06.004
基于强化学习算法的微电网优化策略
Optimization Strategy for Microgrid Based on Reinforcement Learning Algorithm
摘要
Abstract
Distributed energy has the characteristics of small-scale fluctuations and intermittency,making it difficult to formulate operational strategies for microgrids.As an effective way to integrate multiple distributed energy sources and external grids,multi-energy microgrid management is becoming a very complex task.A microgrid real-time optimal operation strategy was proposed under the influence of comprehensive factors such as load demand,renewable energy sources and energy storage devices.Firstly,based on the reinforcement learning framework,the microgrid operation problem was modeled as a Markov decision process,and then a microgrid strategy optimization model was constructed with the aim of minimizing voltage fluctuations and operational losses in the microgrid.In order to effectively utilize the interconnection structure of the distribution network,a graph attention proximal policy optimization(GT-PPO)algorithm was designed on the basis of the proximal policy optimization algorithm.This algorithm uses an attention mechanism and a graph neural network to learn the correlation of distribution network nodes to formulate the optimal scheduling strategy for multi-energy distribution networks at different times under various environments.Simulation experiments were conducted using two specifications of the improved IEEE 33 node and IEEE 118 node distribution networks.The experimental results show that the optimization strategy can achieve real-time optimization of microgrids,and the results are better than the traditional proximal policy optimization(PPO)algorithm and double deep Q network(DDQN)algorithm.关键词
微电网/近端策略优化/图自注意力网络/策略优化Key words
microgrid/proximal policy optimization/graph attention networks/strategy optimization分类
信息技术与安全科学引用本文复制引用
李子凯,杨波,周忠堂,张健,徐明珠..基于强化学习算法的微电网优化策略[J].山东电力技术,2024,51(6):27-35,9.基金项目
国网山东省电力公司科技项目资助项目"电动两轮车换电业务建设运营关键技术研究"(520607220008).State Grid Shandong Electric Power Company Science and Technology Project Funding Project"Research on Key Technologies for the Construction and Operation of Electric Two wheeled Vehicle Power Exchange Business Fund"(520607220008). （520607220008）
