摘要
针对微电网中分布式发电设备存在输出不确定性和间歇性问题,以及传统的深度确定性策略梯度算法存在收敛速度慢、鲁棒性差、容易陷入局部最优的缺点。本文提出了一种基于优先经验回放的深度确定性策略梯度算法,以微电网系统运行成本最低为目标,实现微电网的能量优化调度。首先,采用马尔可夫决策过程对微电网优化问题进行建模;其次,采用Sumtree结构的优先经验回放池提升样本利用效率,并且应用重要性采样来改善状态分布对收敛结果的影响。最后,本文利用真实的电力数据进行仿真验证,结果表明,提出的优化调度算法可以有效地学习到使微电网系统经济成本最低的运行策略,所提出的算法总运行时间比传统算法缩短了7.25%,运行成本降低了31.5%。
In view of the output uncertainty and intermittent problems of distributed power generation equipment in microgrid,and the shortcomings of traditional deep deterministic policy gradient algorithm,such as slow convergence speed,poor robustness,and easy to fall into local optimum.In this paper,a deep deterministic policy gradient algorithm based on prioritized experience replay is proposed,aiming at the lowest operating cost of the microgrid system,to realize the energy optimal scheduling of the microgrid.First,the Markov decision process is used to model the microgrid optimization problem;secondly,the prioritized experience replay pool with Sumtree structure is used to improve the efficiency of sample utilization,and importance sampling is applied to improve the influence of state distribution on the convergence results.Finally,this paper uses real power data for simulation verification.The results show that the proposed optimal scheduling algorithm can effectively learn the operation strategy that minimizes the economic cost of the microgrid system.At the same time,the introduction of prioritized experience replay and importance sampling improves the performance of the algorithm.
作者
李瑜
张占强
孟克其劳
魏皓天
Li Yu;Zhang Zhanqiang;Meng Keqilao;Wei Haotian(The College of Information Engineering,Inner Mongolia University of Technology,Hohhot 010080,China;The College of Energy and Power Engineering,Inner Mongolia University of Technology,Hohhot 010051,China)
出处
《电子测量技术》
北大核心
2023年第2期73-80,共8页
Electronic Measurement Technology
基金
内蒙古自治区科技重大专项计划项目(2020ZD0016,2021ZD003)
内蒙古自治区科技计划项目(2020GG0281)资助
关键词
优先经验回放
微电网能量优化调度
深度确定性策略梯度算法
prioritized experience replay
microgrid energy optimization scheduling
deep deterministic policy gradient algorithm
