内蒙古电力技术2025，Vol.43Issue(4)：21-29,9.DOI:10.19929/j.cnki.nmgdljs.2025.0045
基于深度强化学习的综合能源系统优化调度
Optimized Scheduling of Integrated Energy System Based on Deep Reinforcement Learning
摘要
Abstract
In order to reduce the number of training rounds required for agent convergence,improve the utilization efficiency of experience samples,and optimize the energy scheduling of the integrated energy system(IES),this paper introduces deep reinforcement learning(DRL)algorithm and proposes an improved deep deterministic policy gradient(DDPG)algorithm based on multi-environment instances and data feature score experience sampling.Firstly,multiple environment instances are introduced to enable extensive interactions between the agent and the environment,thereby obtaining informative experience.Secondly,feature quantization is applied to different data and experience sampling is performed according to feature to enhance sample utilization efficiency.Finally,the improved DDPG algorithm is compared with the classical soft actor-critic(SAC)and twin delayed deep deterministic policy gradient(TD3)algorithms,validating the effectiveness of the proposed method in improving convergence speed and sample utilization efficiency.Furthermore,the performance improvement of the model through incremental learning is verified through case simulations.关键词
综合能源系统/深度强化学习/改进深度确定性策略梯度算法/多环境实例/特征分数Key words
integrated energy system(IES)/deep reinforcement learning(DRL)/improved deep deterministic policy gradient(DDPG)algorithm/multi-environment instances/feature scores分类
信息技术与安全科学引用本文复制引用
梁海峰,闫峰,尚隽,王楚通..基于深度强化学习的综合能源系统优化调度[J].内蒙古电力技术,2025,43(4):21-29,9.基金项目
河北省自然科学基金青年项目"新型电力系统下共享储能市场多元主体互动机制研究"(E2024502048) （E2024502048）
