南方电网技术2024，Vol.18Issue(9)：106-116,11.DOI:10.13648/j.cnki.issn1674-0629.2024.09.012
基于深度强化学习的微电网日前日内协调优化调度
Day-Ahead and Intra-Day Coordinated Optimal Scheduling of Microgrid Based on Deep Reinforcement Learning
摘要
Abstract
Due to the randomness of renewable energy generation and the time series coupling characteristics of energy storage systems,it is necessary to properly model uncertain variables and develop optimization algorithms that can efficiently handle multi-objective problems when constructing economic scheduling models for microgrids.In this context,an efficient multi-time scale dispatching method for microgrids based on deep reinforcement learning and heuristic algorithms is proposed,which can take into ac-count uncertain factors and achieve economic and environmental protection operation.The proposed method optimizes the microgrid from two time scales:day-ahead and intra-day.The day-ahead optimization phase utilizes short-term forecast data for initial decision making to minimize the operating cost.For the intra-day dispatching phase,it utilizes the day-ahead optimization scheme as a refer-ence and revises the day-ahead operation scheme if necessary to cope with the real-time fluctuations of renewable energy.The process of intra-day optimization is decoupled into global and local two phases,the global stage is modeled as a non-convex nonlinear optimization problem and solved using heuristic algorithms,while the local stage is modeled as a Markov decision process and solved using deep reinforcement learning methods.Combining deep reinforcement learning with heuristic algorithms improves the training speed and convergence performance of reinforcement learning,avoiding the difficulty of designing reward functions in complex environments.Finally,the case analysis verifies that the proposed scheme achieves optimization of scheduling cost and computing speed,and is suitable for real-time scheduling of microgrids.关键词
微电网/多时间尺度/经济调度/深度强化学习/群搜索算法Key words
microgrid/multi-time scale/economic scheduling/deep reinforcement learning/group search algorithm分类
信息技术与安全科学引用本文复制引用
徐钰涵,季天瑶,李梦诗..基于深度强化学习的微电网日前日内协调优化调度[J].南方电网技术,2024,18(9):106-116,11.基金项目
国家自然科学基金资助项目(52104149).Supported by the National Natural Science Foundation of China(52104149). （52104149）
