Optimal Secondary Control of Islanded AC Microgrids with Communication Time-delay Based on Multi-agent Deep Reinforcement Learning

Yang Xia; Yan Xu; Yu Wang; Suman Mondal; Souvik Dasgupta; Amit. K. Gupta

doi:10.17775/CSEEJPES.2021.08680

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Optimal Secondary Control of Islanded AC Microgrids with Communication Time-delay Based on Multi-agent Deep Reinforcement Learning

Regular Papers | 更新时间：2026-10-15

- Optimal Secondary Control of Islanded AC Microgrids with Communication Time-delay Based on Multi-agent Deep Reinforcement Learning
- Optimal Secondary Control of Islanded AC Microgrids with Communication Time-delay Based on Multi-agent Deep Reinforcement Learning
- 中国电机工程学会电力与能源系统学报(英文) 2023年9卷第4期页码：1301-1311
- 作者机构：
  
  1. School of Electrical and Electronic Engineering, Nanyang Technological University,Singapore,639798
  2. Rolls-Royce@NTU Corporate Lab, Nanyang Technological University,Singapore
  3. School of Electrical Engineering, Chongqing University,Singapore
  4. Rolls-Royce Singapore Pte. Ltd.,Singapore
  5. Rolls-Royce Solutions GmbH,Germany
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2021.08680
  中图分类号：
- 纸质出版：2023-07-01
- 稿件说明：
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Yang Xia, Yan Xu, Yu Wang, 等. Optimal Secondary Control of Islanded AC Microgrids with Communication Time-delay Based on Multi-agent Deep Reinforcement Learning[J]. 中国电机工程学会电力与能源系统学报(英文), 2023,9(4):1301-1311.

Yang Xia, Yan Xu, Yu Wang, et al. Optimal Secondary Control of Islanded AC Microgrids with Communication Time-delay Based on Multi-agent Deep Reinforcement Learning[J]. CSEE Journal of Power and Energy Systems, 2023, 9(4): 1301-1311.
Yang Xia, Yan Xu, Yu Wang, 等. Optimal Secondary Control of Islanded AC Microgrids with Communication Time-delay Based on Multi-agent Deep Reinforcement Learning[J]. 中国电机工程学会电力与能源系统学报(英文), 2023,9(4):1301-1311. DOI： 10.17775/CSEEJPES.2021.08680.

Yang Xia, Yan Xu, Yu Wang, et al. Optimal Secondary Control of Islanded AC Microgrids with Communication Time-delay Based on Multi-agent Deep Reinforcement Learning[J]. CSEE Journal of Power and Energy Systems, 2023, 9(4): 1301-1311. DOI： 10.17775/CSEEJPES.2021.08680.

摘要

Abstract

In this paper

an optimal secondary control strategy is proposed for islanded AC microgrids considering communication time-delays. The proposed method is designed based on the data-driven principle

which consists of an offline training phase and online application phase. For offline training

each control agent is formulated by a deep neural network (DNN) and trained based on a multi-agent deep reinforcement learning (MA-DRL) framework. A deep deterministic policy gradient (DDPG) algorithm is improved and applied to search for an optimal policy of the secondary control

where a global cost function is developed to evaluate the overall control performance. In addition

the communication time-delay is introduced in the system to enrich training scenarios

which aims to solve the time-delay problem in the secondary control. For the online stage

each controller is deployed in a distributed way which only requires local and neighboring information for each DG. Based on this

the well-trained controllers can provide optimal solutions under load variations

and communication time-delays for online applications. Several case studies are conducted to validate the feasibility and stability of the proposed secondary control.

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