Deep Reinforcement Learning for Electric Transmission Voltage Control
This work addresses the challenge of grid complexity for electric transmission operators, though it is incremental as it builds on existing DRL methods.
The paper tackled the problem of automating electric transmission voltage control by applying deep reinforcement learning (DRL) to systems with up to 500 buses, demonstrating its promise but noting that more research is needed to consistently outperform conventional methods.
Today, human operators primarily perform voltage control of the electric transmission system. As the complexity of the grid increases, so does its operation, suggesting additional automation could be beneficial. A subset of machine learning known as deep reinforcement learning (DRL) has recently shown promise in performing tasks typically performed by humans. This paper applies DRL to the transmission voltage control problem, presents open-source DRL environments for voltage control, proposes a novel modification to the "deep Q network" (DQN) algorithm, and performs experiments at scale with systems up to 500 buses. The promise of applying DRL to voltage control is demonstrated, though more research is needed to enable DRL-based techniques to consistently outperform conventional methods.