Autonomous Voltage Control for Grid Operation Using Deep Reinforcement Learning
This addresses the problem of secure and economic grid operation for power grid operators by introducing a novel autonomous control paradigm, though it is incremental as it builds on existing deep reinforcement learning methods applied to a specific domain.
The paper tackles the challenge of adapting power grid voltage control to the stochastic nature of renewable energy and demand response by proposing Grid Mind, a deep reinforcement learning agent that learns control policies through offline simulations and adapts to load/generation variations and topological changes, demonstrating promising performance on the IEEE 14-bus system with tens of thousands of scenarios.
Modern power grids are experiencing grand challenges caused by the stochastic and dynamic nature of growing renewable energy and demand response. Traditional theoretical assumptions and operational rules may be violated, which are difficult to be adapted by existing control systems due to the lack of computational power and accurate grid models for use in real time, leading to growing concerns in the secure and economic operation of the power grid. Existing operational control actions are typically determined offline, which are less optimized. This paper presents a novel paradigm, Grid Mind, for autonomous grid operational controls using deep reinforcement learning. The proposed AI agent for voltage control can learn its control policy through interactions with massive offline simulations, and adapts its behavior to new changes including not only load/generation variations but also topological changes. A properly trained agent is tested on the IEEE 14-bus system with tens of thousands of scenarios, and promising performance is demonstrated in applying autonomous voltage controls for secure grid operation.