Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks
Jianhong Wang, Wangkun Xu, Yunjie Gu, Wenbin Song, Tim C Green
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.
BibTeX
@inproceedings{
wang2021multiagent,
title={Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks},
author={Jianhong Wang and Wangkun Xu and Yunjie Gu and Wenbin Song and Tim C Green},
booktitle={Advances in Neural Information Processing Systems},
editor={A. Beygelzimer and Y. Dauphin and P. Liang and J. Wortman Vaughan},
year={2021},
url={https://openreview.net/forum?id=hwoK62_GkiT}
}