Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks

• URL: http://arxiv.org/abs/2110.14300v1
• Date: Wed, 27 Oct 2021 09:31:22 GMT
• Title: Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks
• Authors: Jianhong Wang, Wangkun Xu, Yunjie Gu, Wenbin Song, Tim C. Green
• Abstract summary: The emerging trend of decarbonisation is placing excessive stress on power distribution networks. Active voltage control is seen as a promising solution to relieve power congestion and improve voltage quality without extra hardware investment. This paper formulates the active voltage control problem in the framework of Dec-POMDP and establishes an open-source environment.
• Score: 2.992389186393994
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a problem in power networks that creates an exciting and yet challenging real-world scenario for application of multi-agent reinforcement learning (MARL). The emerging trend of decarbonisation is placing excessive stress on power distribution networks. Active voltage control is seen as a promising solution to relieve power congestion and improve voltage quality without extra hardware investment, taking advantage of the controllable apparatuses in the network, such as roof-top photovoltaics (PVs) and static var compensators (SVCs). These controllable apparatuses appear in a vast number and are distributed in a wide geographic area, making MARL a natural candidate. This paper formulates the active voltage control problem in the framework of Dec-POMDP and establishes an open-source environment. It aims to bridge the gap between the power community and the MARL community and be a drive force towards real-world applications of MARL algorithms. Finally, we analyse the special characteristics of the active voltage control problems that cause challenges for state-of-the-art MARL approaches, and summarise the potential directions.

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