Under voltage load shedding has been considered as a standard approach to recover the voltage stability of the electric power grid under emergency conditions, yet this scheme usually trips a massive amount of load inefficiently. Reinforcement learning (RL) has been adopted as a promising approach to circumvent the issues; however, RL approach usually cannot guarantee the safety of the systems under control. In this paper, we discuss a couple of novel safe RL approaches, namely constrained optimization approach and Barrier function-based approach, that can safely recover voltage under emergency events. This method is general and can be applied to other safety-critical control problems. Numerical simulations on the 39-bus IEEE benchmark are performed to demonstrate the effectiveness of the proposed safe RL emergency control.
@article{arxiv.2112.01484,
title = {Safe Reinforcement Learning for Grid Voltage Control},
author = {Thanh Long Vu and Sayak Mukherjee and Renke Huang and Qiuhua Huang},
journal= {arXiv preprint arXiv:2112.01484},
year = {2021}
}
Comments
Workshop on Safe and Robust Control of Uncertain Systems at the 35th Conference on Neural Information Processing Systems (NeurIPS) 2021. arXiv admin note: substantial text overlap with arXiv:2103.14186, arXiv:2011.09664, arXiv:2006.12667