An Efficient Distributed Reinforcement Learning for Enhanced Multi-Microgrid Management

Conference · Fri Sep 30 04:00:00 EDT 2022

DOI:https://doi.org/10.1109/IJCNN55064.2022.9892754· OSTI ID:1900414

Das, Avijit ^[1]; Ni, Zhen ^[2]; Wu, Di ^[1]

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1. BATTELLE (PACIFIC NW LAB)
2. Florida Atlantic University

Economic dispatch in multi-microgrid (MMG) systems requires coordinating distributed energy resources (DERs) of different microgrids, which leads to a significant increase in the number of states for energy management. In these cases, traditional reinforcement learning (RL) approaches become computationally expensive or output a solution that causes extra-operating costs for the system. This paper proposes an RL approach that employs local learning agents to interact with microgrid environments in a distributed manner and aggregates the outcomes to train the global agent to learn the policy for the MMG system. This distributed exploration and aggregation process provides an effective solution and guides the global agent to learn the dispatch policy efficiently. Case studies are performed on a system with three microgrids with different types of DERs. Results obtained using the proposed RL and comparisons with conventional methods substantiate the effectiveness of the proposed approach in terms of operation costs, computation time, and peak-to-average ratio.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1900414
• Report Number(s): PNNL-SA-170345
• Country of Publication: United States
• Language: English