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Workshop: Machine Learning for Engineering Modeling, Simulation and Design

On Training Effective Reinforcement Learning Agents for Real-time Power Grid Operation and Control

Ruisheng Diao · Di Shi · Bei Zhang · Siqi Wang · Haifeng Li · Chunlei Xu · Tu Lan · Desong Bian · Jiajun Duan · Zheng Wu


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 (<20 ms) control actions in regulating voltage profiles and reducing transmission losses.

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