The electric power system is an essential cornerstone of modern society, enabling everything from the internet to refrigeration. Due to a variety of forces including climate change, changing economics, and the digital computer revolution, the electric grid is undergoing a period of major change. In order to overcome current and upcoming challenges in the electric power system, such as integrating renewable resources into a system that was not designed for intermittent power sources, researchers and industry practitioners must simulate the electric grid, its component devices, and its operation. PowerWorld Simulator is a commercial power systems simulation tool that contains a suite of modeling and simulation features including power flow simulation, contingency analysis, transient stability simulation, and more (PowerWorld Corporation, 2020). The Simulator Automation Server (SimAuto) add-on for PowerWorld provides an application programming interface (API) that operates in-memory, allowing users to rapidly configure, run, and obtain results for simulations. PowerWorld and SimAuto are commonly used throughout the research community as well as in industry.
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.
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