Deep Reinforcement Learning Based Approach for Optimal Power Flow of Microgrid with Grid Services Implementation

Abstract

Electric vehicles (EVs) have rapidly grown in popularity, and the number of inverter-based EV chargers increases promptly due to their high efficiency and capabilities of providing grid services. EV and other distributed energy resources (DER) would become a crucial part of the resilience and performance of the microgrid. Optimizing the EV-interfaced microgrid is challenging due to the non-linearity and uncertainty. In this paper, we propose a method based on deep reinforcement learning (DRL) with Twin Delayed Deep Deterministic Policy Gradients (TD3) to optimize the microgrid. The proposed method can be used to optimize different objectives. An example objective of stabilizing the voltage fluctuations in a power system modified from the IEEE 30-bus system is presented. The proposed system can provide grid service policies for reactive power control according to the requirements specified in the IEEE 1547 standard. This model-free DRL approach can be adapted to other microgrid systems.