Cost Effective Dynamic Multi-Microgrid Formulation Method Using Deep Reinforcement Learning

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...

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Bibliographic Details
Main Authors: Jung, YoonGun, Chang, Minhyeok, Kang, Sungwoo, Jang, Gilsoo, Lee, Hojun, Yoon, Minhan, Song, Sungyoon, Han, Changhee
Format: Conference Proceeding
Language:English
Subjects:
Costs
Deep learning
Deep Reinforcement Learning (DRL)
Distributed generation (DG)
Dynamic Multi-Microgrid Formulation
Force
Heuristic algorithms
Microgrid (MG)
Microgrids
Reconfiguration
Reinforcement learning
Spanning Tree Algorithm
Switches
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Summary: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.
ISSN:1944-9933
DOI:10.1109/PESGM52003.2023.10253225