Deep Reinforcement Learning Based Bi-layer Optimal Scheduling for Microgrids Considering Flexible Load Control

Zitong Zhang; Jing Shi; Wangwang Yang; Zhaofang Song; Zexu Chen; Dengquan Lin

doi:10.17775/CSEEJPES.2021.06120

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Deep Reinforcement Learning Based Bi-layer Optimal Scheduling for Microgrids Considering Flexible Load Control

Regular Papers | 更新时间：2026-10-15

- Deep Reinforcement Learning Based Bi-layer Optimal Scheduling for Microgrids Considering Flexible Load Control
- Deep Reinforcement Learning Based Bi-layer Optimal Scheduling for Microgrids Considering Flexible Load Control
- 中国电机工程学会电力与能源系统学报(英文) 2023年9卷第3期页码：949-962
- 作者机构：
  
  State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electronic Engineering, Huazhong University of Science and Technology,Wuhan,China
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2021.06120
  中图分类号：
- 纸质出版：2023-05-01
- 稿件说明：
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Zitong Zhang, Jing Shi, Wangwang Yang, 等. Deep Reinforcement Learning Based Bi-layer Optimal Scheduling for Microgrids Considering Flexible Load Control[J]. 中国电机工程学会电力与能源系统学报(英文), 2023,9(3):949-962.

Zitong Zhang, Jing Shi, Wangwang Yang, et al. Deep Reinforcement Learning Based Bi-layer Optimal Scheduling for Microgrids Considering Flexible Load Control[J]. CSEE Journal of Power and Energy Systems, 2023, 9(3): 949-962.
Zitong Zhang, Jing Shi, Wangwang Yang, 等. Deep Reinforcement Learning Based Bi-layer Optimal Scheduling for Microgrids Considering Flexible Load Control[J]. 中国电机工程学会电力与能源系统学报(英文), 2023,9(3):949-962. DOI： 10.17775/CSEEJPES.2021.06120.

Zitong Zhang, Jing Shi, Wangwang Yang, et al. Deep Reinforcement Learning Based Bi-layer Optimal Scheduling for Microgrids Considering Flexible Load Control[J]. CSEE Journal of Power and Energy Systems, 2023, 9(3): 949-962. DOI： 10.17775/CSEEJPES.2021.06120.

摘要

Abstract

In this paper

the bi-layer scheduling method for microgrids

based on deep reinforcement learning

is proposed to achieve economic and environmentally friendly operations. First

considering the uncertainty of renewable energy

the framework of day-ahead and intra-day scheduling is established

and the implementation scheme for both price-based and incentive-based demand response (DR) for the flexible load is determined. Then

comprehensively considering the operating characteristics of the microgrid in the day-ahead and intra-day time scales

a bi-layer scheduling model of the microgrid is established. In terms of algorithms

since day-ahead scheduling has no strict requirement for dispatching time

the particle swarm optimization (PSO) algorithm is used to optimize the time-of-use electricity price and distributed power output for the next day. Considering the environmental fluctuations and requirements for rapidity of intra-day online scheduling

the deep reinforcement learning (DRL) algorithm is adopted for optimization. Finally

based on the data from the actual microgrid

the rationality and effectiveness of the proposed scheduling method is verified. The results show that the proposed bi-layer scheduling based on the PSO and DRL algorithms achieves the optimization of scheduling cost and calculation speed

and is suitable for microgrid online scheduling.

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