Name
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. |
Year | Venue | DocType |
|---|---|---|
2021 | Annual Conference on Neural Information Processing Systems | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianhong Wang | 1 | 6 | 7.92 |
| Wangkun Xu | 2 | 0 | 0.34 |
| Yunjie Gu | 3 | 0 | 0.68 |
| Wenbin Song | 4 | 0 | 0.34 |
| Tim C. Green | 5 | 0 | 0.34 |