Multi-Agent Deep Reinforcement Learning Approach for Temporally Coordinated Demand Response in Microgrids

Chunchao Hu; Zexiang Cai; Yanxu Zhang

doi:10.17775/CSEEJPES.2021.05090

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Multi-Agent Deep Reinforcement Learning Approach for Temporally Coordinated Demand Response in Microgrids

Regular Papers | 更新时间：2026-10-15

- Multi-Agent Deep Reinforcement Learning Approach for Temporally Coordinated Demand Response in Microgrids
- Multi-Agent Deep Reinforcement Learning Approach for Temporally Coordinated Demand Response in Microgrids
- 中国电机工程学会电力与能源系统学报(英文) 2025年11卷第4期页码：1512-1522
- 作者机构：
  
  1. School of Electrical and Electronic Engineering, South China University of Technology,Guangzhou,China
  2. China Southern Power Grid Electrical Technology Co., LTD.
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2021.05090
  中图分类号：
- 纸质出版：2025-07-01
- 稿件说明：
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Chunchao Hu, Zexiang Cai, Yanxu Zhang. Multi-Agent Deep Reinforcement Learning Approach for Temporally Coordinated Demand Response in Microgrids[J]. 中国电机工程学会电力与能源系统学报(英文), 2025,11(4):1512-1522.

Chunchao Hu, Zexiang Cai, Yanxu Zhang. Multi-Agent Deep Reinforcement Learning Approach for Temporally Coordinated Demand Response in Microgrids[J]. CSEE Journal of Power and Energy Systems, 2025, 11(4): 1512-1522.
Chunchao Hu, Zexiang Cai, Yanxu Zhang. Multi-Agent Deep Reinforcement Learning Approach for Temporally Coordinated Demand Response in Microgrids[J]. 中国电机工程学会电力与能源系统学报(英文), 2025,11(4):1512-1522. DOI： 10.17775/CSEEJPES.2021.05090.

Chunchao Hu, Zexiang Cai, Yanxu Zhang. Multi-Agent Deep Reinforcement Learning Approach for Temporally Coordinated Demand Response in Microgrids[J]. CSEE Journal of Power and Energy Systems, 2025, 11(4): 1512-1522. DOI： 10.17775/CSEEJPES.2021.05090.

摘要

Abstract

Price-based and incentive-based demand response (DR) are both recognized as promising solutions to address the increasing uncertainties of renewable energy sources (RES) in microgrids. However

since the temporally optimization horizons of price-based and incentive-based DR are different

few existing methods consider their coordination. In this paper

a multi-agent deep reinforcement learning (MA-DRL) approach is proposed for the temporally coordinated DR in microgrids. The proposed method enhances micrigrid operation revenue by coordinating day-ahead price-based demand response (PBDR) and hourly direct load control (DLC). The operation at different time scales is decided by different DRL agents

and optimized by a multi-agent deep deterministic policy gradient (MA-DDPG) using a shared critic to guide agents to attain a global objective. The effectiveness of the proposed approach is validated on a modified IEEE 33-bus distribution system and a modified heavily loaded 69-bus distribution system.

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