Multi-Agent Reinforcement Learning for the Energy Optimization of Cyber-Physical Production Systems

Bakakeu, Jupiter; Kißkalt, Dominik; Franke, Jöerg; Baer, Shirin; Klos, Hans-Henning; Peschke, J.

doi:10.1109/ccece47787.2020.9255795

Cited by 9 publications

(1 citation statement)

References 22 publications

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“…The equipment in the microgrid usually belongs to different equipment agents. Agents need to efficiently collect and process different sensor data, and deploying multiple synchronized sensors in the system that can monitor system data such as frequency or power flow is essential for a multi-agent environment [36].…”

Section: Microgrid Voltage Secondary Controlmentioning

confidence: 99%

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

Wang

Tang

et al. 2023

Energies

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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.

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Section: Microgrid Voltage Secondary Controlmentioning

confidence: 99%