Multi-agent reinforcement learning for active voltage control on power distribution networks

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

Research output: Contribution to journalArticlepeer-review

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. © 2021 Neural information processing systems foundation
Original languageEnglish
Pages (from-to)3271-3284
Number of pages14
JournalAdvances in Neural Information Processing Systems
Volume34
Publication statusPublished - 2021

Fingerprint

Dive into the research topics of 'Multi-agent reinforcement learning for active voltage control on power distribution networks'. Together they form a unique fingerprint.

Cite this