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Deep Reinforcement Learning for Electric Transmission Voltage Control

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Document pages: 8 pages

Abstract: Today, human operators primarily perform voltage control of the electrictransmission system. As the complexity of the grid increases, so does itsoperation, suggesting additional automation could be beneficial. A subset ofmachine learning known as deep reinforcement learning (DRL) has recently shownpromise in performing tasks typically performed by humans. This paper appliesDRL to the transmission voltage control problem, presents open-source DRLenvironments for voltage control, proposes a novel modification to the "deep Qnetwork " (DQN) algorithm, and performs experiments at scale with systems up to500 buses. The promise of applying DRL to voltage control is demonstrated,though more research is needed to enable DRL-based techniques to consistentlyoutperform conventional methods.

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