Optimizing Microgrid Operations Using Deep Reinforcement Learning for Battery and Load Management

The fundamental element of the future smart grid is the renewable source microgrid system. This paper presents a reinforcement learning (RL) framework for the optimization of a dynamic microgrid’s operation. This study used data from Nissan Leaf battery modules and the main component of the microgrid is battery; in this case, the primary challenge is managing the battery storage leads to minimizing the net demand. This framework utilized Multi-Layer Perceptron to predict net demand using historical data. These predicted values support proactive decision-making in the RL stage. Consequently, the Deep Deterministic Policy Gradient (DDPG) algorithm, a model-free off-policy actor-critic RL method, is applied here. The simulation environment, designed for a microgrid with real-time data of microgrid uses, and the reward function designed to serve the purpose of minimizing the net demand. The RL agent’s training iterated over a couple of episodes, and the final result demonstrates 92.84% enhanced operational performance by maintaining the total net demand near zero through the battery storage balance and shifting load. This assessment indicates the agent’s policy converges to a nearly balanced solution while running optimally, whereby the level of reward is close to zero, reflecting the effectiveness of demand reduction. Therefore, the assessments confirmed the potential of the developed framework in microgrid system energy optimization, and the outcomes are elaborated on learning characteristics and feature impacts with Explainable Artificial Intelligence (XAI).