An Efficient Distributed Reinforcement Learning for Enhanced Multi-Microgrid Management
An Efficient Distributed Reinforcement Learning for Enhanced Multi-Microgrid Management
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DOI：
10.1109/ijcnn55064.2022.9892754
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
Avijit Das;Z. Ni;Di Wu
中科院分区：
文献类型：
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作者：
Avijit Das;Z. Ni;Di Wu
与单个微电网相比，多微电网（MMG）系统中的经济调度涉及越来越多的分布式能源（DER）状态。在这些情况下，传统的强化学习 (RL) 方法可能会变得计算成本高昂，或者在寻找成本最低的解决方案时效率较低。本文提出了一种新颖的强化学习方法，该方法采用本地学习代理以分布式方式与各个微电网环境进行交互，并使用全局代理来搜索行动以最大限度地降低 MMG 系统级别的系统成本。与传统方法相比，所提出的分布式强化学习框架在学习调度策略方面更加有效。案例研究在具有不同类型 DER 的 3 微电网系统上进行。与传统方法相比，结果证实了该方法在运行成本、计算时间和峰均比方面的有效性。
Economic dispatch in a multi-microgrid (MMG) system involves an increasing number of states from distributed energy resources (DERs) compared to a single microgrid. In these cases, traditional reinforcement learning (RL) approaches may become computationally expensive or less effective in finding the least-cost solution. This paper presents a novel RL approach that employs local learning agents to interact with individual microgrid environments in a distributed manner and a global agent to search for actions to minimize system cost at the MMG system level. The proposed distributed RL framework is more efficient in learning the dispatch policy compared to conventional approaches. Case studies are performed on a 3-microgrid system with different types of DERs. Results substantiate the effectiveness of the proposed approach in comparison with conventional methods in terms of operation costs, computation time, and peak-to-average ratio.