Deep Reinforcement Learning Based Optimal Energy Management of Multi-Energy Microgrids with Uncertainties

Yang Cui; Yang Xu; Yang Li; Yijian Wang; Xinpeng Zou

doi:10.17775/CSEEJPES.2023.05120

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Deep Reinforcement Learning Based Optimal Energy Management of Multi-Energy Microgrids with Uncertainties

Regular Papers | 更新时间：2026-04-16

- Deep Reinforcement Learning Based Optimal Energy Management of Multi-Energy Microgrids with Uncertainties
- Deep Reinforcement Learning Based Optimal Energy Management of Multi-Energy Microgrids with Uncertainties
- 中国电机工程学会电力与能源系统学报(英文) 2026年12卷第2期页码：1002-1014
- 作者机构：
  
  Northeast Electric Power University,Jilin,China
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2023.05120
  中图分类号：
- 纸质出版：2026
- 稿件说明：
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Yang Cui, Yang Xu, Yang Li, 等. Deep Reinforcement Learning Based Optimal Energy Management of Multi-Energy Microgrids with Uncertainties[J]. 中国电机工程学会电力与能源系统学报(英文), 2026,12(2):1002-1014.

Yang Cui, Yang Xu, Yang Li, et al. Deep Reinforcement Learning Based Optimal Energy Management of Multi-Energy Microgrids with Uncertainties[J]. CSEE Journal of Power and Energy Systems, 2026, 12(2): 1002-1014.
Yang Cui, Yang Xu, Yang Li, 等. Deep Reinforcement Learning Based Optimal Energy Management of Multi-Energy Microgrids with Uncertainties[J]. 中国电机工程学会电力与能源系统学报(英文), 2026,12(2):1002-1014. DOI： 10.17775/CSEEJPES.2023.05120.

Yang Cui, Yang Xu, Yang Li, et al. Deep Reinforcement Learning Based Optimal Energy Management of Multi-Energy Microgrids with Uncertainties[J]. CSEE Journal of Power and Energy Systems, 2026, 12(2): 1002-1014. DOI： 10.17775/CSEEJPES.2023.05120.

摘要

Abstract

Multi-energy microgrid (MEMG) offers an effective approach to deal with energy demand diversification and new energy consumption on the consumer side. In MEMG

it is critical to deploy an energy management system (EMS) to efficiently utilize energy and ensure reliable system operation. To help EMS formulate optimal dispatching schemes

a deep reinforcement learning (DRL)-based MEMG energy management scheme with renewable energy source (RES) uncertainty is proposed in this paper. To accurately describe the operating state of the MEMG

the off-design performance model of energy conversion devices is considered in scheduling. The nonlinear optimal dispatching model is expressed as a Markov decision process (MDP) and is then addressed by the twin delayed deep deterministic policy gradient (TD3) algorithm. In addition

to accurately describe the uncertainty of RES

the conditional-least squares generative adversarial networks (C-LSGANs) method based on RES forecast power is proposed to construct the scenario set of RES power generation. The generated data of RES is used to schedule the acquisition of caps and floors for the purchase of electricity and natural gas. Based on this

the superior energy supply sector can formulate solutions in advance to tackle the uncertainty of RES. Finally

the simulation analysis demonstrates the validity and superiority of the method.

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