A Multi-Agent Reinforcement Learning Method for Cooperative Secondary Voltage Control of Microgrids

Abstract

This paper proposes a novel cooperative voltage control strategy for an isolated microgrid based on the multi-agent advantage actor-critic (MA2C) algorithm. The proposed method facilitates the collaborative operation of a distributed energy system (DES) by adopting an attention mechanism to adaptively boost information processing effectiveness through the assignment of importance scores. Additionally, the algorithm we propose, executed through a centralized training and decentralized execution framework, implements secondary control and effectively restores voltage deviation. The introduction of an attention mechanism alleviates the burden of information transmission. Finally, we illustrate the effectiveness of the proposed method through a DES consisting of six energy nodes.

Multiagent-Based Control for Plug-and-Play Batteries in DC Microgrids with Infrastructure Compensation

“…The second feature of the MARL-based control is the more precise stability and balance that can be accomplished (in principle) in the control process due to the several cascade-correction stages, as shown in the general structure of the conventional multiagent- Multiagent primary-secondary management was a prominent recent MARL application for solving power-flow management in modern decentralized networks, especially managing power-storage flow. Thus, many recent power-management research works have suggested the active and successful applications of managing energy-storage systems based on MARL, where the management of battery-energy-storage systems was the most successful [10,26,27]. The success of the abovementioned MARL-based control is promoted by three fundamental features.…”