Deep Reinforcement Learning for Voltage Control in Power Systems - Barg, Mauricio W.; Rodrigues, Barbara S.; Justino, Gabriela T.; Cotta, Kleyton Pontes; Portuita, Hugo R. V.; Loução Jr., Flávio L.; Abreu, Iran Pereira; Pigossi Jr., Antônio Carlos (Anais da Brazilian Conference on Intelligent Systems (BRACIS); 2023: Anais da XII Brazilian Conference on Intelligent Systems, 2023)
The great complexity and size of electrical power systems makes their operation and control a challenging task. Maintaining a stable voltage profile to assure the security and stability of the system is one of many tasks that must be conducted daily by power system operators and its automatic control equipment. This work proposes a deep reinforcement learning framework for controlling the equipment responsible for keeping the voltages across the system buses within their limits. More specifically, a smart agent that is capable of deciding the best course of action in order to keep the system’s voltages within a specified range while taking into account system’s conditions is proposed. Besides the traditional deep reinforcement learning approach, three novel reinforcement learning variations named windowed, ensemble and windowed ensemble Q-Learning, which alter the agent’s learning process for voltage control, are presented and tested on IEEE 13, 37 and 123 bus systems, simulated on OpenDSS.