Abstract
Traditionally, the distribution system is operated to improve revenue, reliability, and power quality. Extensive optimal operation strategies are researched to solve model-based distribution system operation problems. However, the penetration of distributed generations (DGs) and renewable energy resources (RESs) such as photovoltaic and wind generation has increased recently. The RES has uncertainties and variability, which cause the complexity of the distribution system operation. In this sense, the time-consuming and complex formulation is becoming a challenge to an optimal distribution system operation. Furthermore, unbalanced voltage is occurred in the distribution system, causing lower power quality. Deep reinforcement learning (DRL) is a powerful model-free technique to overcome the challenges of operating with optimized strategies. The energy storage system (ESS) and voltage regulators are the control variables to optimize the cost and power quality of the distribution system. This paper uses the DRL model to describe the distribution system optimization problem.
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