南方电网技术2026，Vol.20Issue(8)：89-103,15.DOI:10.13648/j.cnki.issn1674-0629.2026.08.008
基于改进TD3的农村微电网源网荷储优化运行策略
Optimal Operation Strategy for Source-Grid-Load-Storage in Rural Microgrids Based on Improved TD3
摘要
Abstract
As a crucial carrier for supporting the implementation of the"dual carbon"goals and empowering rural revitalization,rural microgrids can effectively enhance overall system operational efficiency by integrating distributed energy with traditional power generation resources.To this end,this paper proposes a optimal operation strategy for source-grid-load-storage in rural microgrids based on an improved twin delayed deep deterministic policy gradient(TD3)algorithm.Firstly,an optimal operation model for rural microgrids incorporating biogas is constructed by comprehensively considering operating costs and renewable energy utilization.Secondly,a compound assessment scored attention-weighted critic(CASA-Critic)is designed to dynamically extract and focus on key state features,thereby enhancing the evaluation accuracy of the value network.Then,a dynamic perception strategy is introduced to adaptively adjust the intensity of action exploration based on the temporal progression and environmental state uncertainties during training,improving the balance between exploration and exploitation.Finally,simulation analysis is conducted based on a typical rural microgrids source-grid-load-storage operation scenario.The results demonstrate that,compared to optimization strategies based on other deep reinforcement learning,the proposed method exhibits better convergence and optimization performance,while also showing significant superiority in improving system operational economy and promoting renewable energy accommodation.关键词
农村微电网/优化运行/注意力加权评论家网络/动态感知策略/深度强化学习Key words
rural microgrid/optimal operation/CASA-critic/dynamic perception strategy/deep reinforcement learning分类
信息技术与安全科学引用本文复制引用
陈国齐,魏颖豪,刘毅,李梦锐,熊马超,王灿..基于改进TD3的农村微电网源网荷储优化运行策略[J].南方电网技术,2026,20(8):89-103,15.基金项目
国家自然科学基金资助项目(5237791).Supported by the National Natural Science Foundation of China(5237791). （5237791）
