This paper proposes an online dynamic multi-microgrid formulation (DIVIMF) method using Deep Reinforcement Learning. It aims to reconfigure the microgrid into several self-supplied islands and minimize total operation...
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ISBN:
(纸本)9781665464413
This paper proposes an online dynamic multi-microgrid formulation (DIVIMF) method using Deep Reinforcement Learning. It aims to reconfigure the microgrid into several self-supplied islands and minimize total operation cost at the same time. Spanning Tree Algorithm is used to reduce the total number of microgridformulation. Proximal -Policy Optimization is implemented to train the agent which determines the status of sectionalizing switches in microgrid in real-time. To show the effectiveness of the proposed DMMF method, a case study was conducted in the modified cigre-14 bus test network. The results demonstrated that the proposed DMMF method reduced the total operation cost compared to the operation cost derive from original Cigre 14 bus formulation.
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