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基于深度强化学习的综合能源系统优化调度OA北大核心CSTPCD

Optimal Dispatch of Integrated Energy System Based on Deep Reinforcement Learning

中文摘要英文摘要

针对综合能源系统中可再生能源和负荷的不确定性,提出一种基于深度强化学习的优化调度方法.首先,阐述了深度强化学习方法的基本原理;然后,提出了基于深度强化学习的综合能源系统优化调度模型,并对其中的状态空间、动作空间和奖励函数进行设计;继而,设计了基于异步优势策略梯度算法的模型求解流程;最后,通过算例仿真验证表明,所提方法能自适应源、荷不确定性,达到与传统数学规划方法相近的优化效果.

In allusion to the uncertainty of renewable energy and load in integrated energy system,an optimal dispatch meth-od based on deep reinforcement learning was proposed.Firstly,the methodology of the deep reinforcement learning was ex-pounded,and an optimal dispatch model based on the deep re-inforcement learning,in which the state space,action space and reward function were designed,was proposed.Secondly,the model solving process based on asynchronous advantag…查看全部>>

刘必晶

国网电力科学研究院,北京市海淀区 100192

动力与电气工程

综合能源系统优化调度深度强化学习不确定性源荷

integrated energy systemoptimal dispatchdeep reinforcement learninguncertainty sources and loads

《现代电力》 2024 (4)

710-717,8

10.19725/j.cnki.1007-2322.2022.0296

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