@inproceedings{e14d006948c747829035384a8eebea4a,
title = "Multi-agent reinforcement learning for active voltage control on power distribution networks",
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. {\textcopyright} 2021 Neural information processing systems foundation",
author = "Jianhong Wang and Wangkun Xu and Yunjie Gu and Wenbin Song and Green, {Tim C}",
year = "2021",
month = dec,
day = "31",
language = "English",
volume = "34",
series = "Advances in Neural Information Processing Systems",
publisher = "MIT Press",
pages = "3271--3284",
booktitle = "Advances in Neural Information Processing Systems 34 (NeurIPS 2021)",
}