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

Jianhong Wang, Wangkun Xu, Yunjie Gu, Wenbin Song, Tim C Green

Keywords: multi agent reinforcement learning, power distribution network, active voltage control

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

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TL;DR: This paper presents and solves a real-world problem called active voltage control in power distribution networks using multi-agent reinforcement learning.

Code: https://github.com/Future-Power-Networks/MAPDN

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/multi-agent-reinforcement-learning-for-active/code)