Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks
This addresses voltage control problems in power networks for utility operators, but it is incremental as it focuses on environment setup and analysis rather than new algorithmic breakthroughs.
The paper tackles the challenge of active voltage control in power distribution networks due to decarbonization stress by formulating it as a Dec-POMDP and creating an open-source environment, aiming to bridge the gap between power and MARL communities for real-world applications.
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.