期刊
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
卷 13, 期 4, 页码 1982-1993出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSTE.2022.3178415
关键词
Microgrids; Frequency control; Safety; Power generation; Load modeling; Renewable energy sources; Reinforcement learning; Decentralized control; Networked-microgrid; economic frequency control; multi-agent deep reinforcement learning; soft actor-critic; safety policy learning
资金
- RR@NTU Corporate Lab Phase II, Nanyang Technological University, Singapore
In this paper, a data-driven decentralized economic frequency control method is proposed for isolated networked-microgrid systems. Each agent in the multi-agent deep reinforcement learning framework is trained offline to generate optimal control actions based on local information in online application. Additionally, a safety model is designed to support each agent and can be used for online monitoring and guidance.
Multiple microgrids can be interconnected to form a networked-microgrid (NMG) system. In this paper, a data-driven decentralized economic frequency control method is proposed for isolated NMG systems. Based on multi-agent deep reinforcement learning (MA-DRL) framework, each DRL agent controls the generator and energy storage system (ESS) in each microgrid of the NMG. For offline training stage, the optimal control strategy is learned by soft actor-critic (SAC) algorithm with a global reward, which aims to restore system frequency while considering economy. Besides, to satisfy system constraints and avoid high learning costs, a safety model scheme is designed and trained to support each DRL agent. Since the agents are trained in the centralized learning process at offline stage, they are able to coordinate in a decentralized manner for online application, which only requires local information to generate optimal control actions. Also, the trained safety model can be applied for online stage to monitor and guide online actions. Finally, numerical tests are conducted to demonstrate the feasibility and effectiveness of the proposed method under the variation of renewable generation and load demand.
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