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Fully Distributed Reinforcement Learning for Efficient Networked Microgrids Resource Management: Source-Load-Storage Coordination
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DOI：10.1109/jas.2025.125636.png)
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Abstract
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This letter proposes a fully distributed multi-agent reinforcement learning (DMARL) algorithm for the coordinated optimization and scheduling of source-load-storage in networked microgrids. To accommodate the rapid development of networked microgrids, we have designed a DMARL algorithm with an event-triggered mechanism (ETM). Unlike centralized approaches, DMARL empowers individual agents to cooperatively learn and optimize based only on local observations, while reducing the communication burden. Simulation results validate the performance of the proposed algorithm, demonstrating its effectiveness in efficient microgrid resource management.
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