Deriving fast and effectively coordinated control actions remains a grand challenge affecting the secure and economic operation of today's large-scale power grid. This paper presents a novel artificial intelligence (AI) based methodology to achieve multi-objective real-time power grid control for real-world implementation. State-of-the-art off-policy reinforcement learning (RL) algorithm, soft actor-critic (SAC) is adopted to train AI agents with multi-thread offline training and periodic online training for regulating voltages and transmission losses without violating thermal constraints of lines. A software prototype was developed and deployed in the control center of SGCC Jiangsu Electric Power Company that interacts with their Energy Management System (EMS) every 5 minutes. Massive numerical studies using actual power grid snapshots in the real-time environment verify the effectiveness of the proposed approach. Well-trained SAC agents can learn to provide effective and subsecond control actions in regulating voltage profiles and reducing transmission losses.
@article{arxiv.2012.06458,
title = {On Training Effective Reinforcement Learning Agents for Real-time Power Grid Operation and Control},
author = {Ruisheng Diao and Di Shi and Bei Zhang and Siqi Wang and Haifeng Li and Chunlei Xu and Tu Lan and Desong Bian and Jiajun Duan},
journal= {arXiv preprint arXiv:2012.06458},
year = {2020}
}