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Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks
Jianhong Wang · Wangkun Xu · Yunjie Gu · Wenbin Song · Tim C Green

Fri Dec 10 08:30 AM -- 10:00 AM (PST) @

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 (e.g. interpretability) for state-of-the-art MARL approaches, and summarise the potential directions.

Author Information

Jianhong Wang (Imperial College London)
Wangkun Xu (Imperial College London)
Yunjie Gu (University of Bath)
Wenbin Song (Shanghaitech University)
Tim C Green (Imperial College London)

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