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- W4387005291 abstract "This paper proposes an online Dynamic Multi-Microgrid Formulation (DMMF) 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 microgrid formulation. 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." @default.
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- W4387005291 date "2023-07-16" @default.
- W4387005291 modified "2023-09-26" @default.
- W4387005291 title "Cost Effective Dynamic Multi-Microgrid Formulation Method Using Deep Reinforcement Learning" @default.
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- W4387005291 doi "https://doi.org/10.1109/pesgm52003.2023.10253225" @default.
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