Reinforcement learning-based energy storage management in smart grids

Abstract

This study investigates the use of reinforcement learning (RL) techniques as a dynamic control mechanism to enhance the management of energy storage in smart grid systems. The research aims to optimize the efficiency of energy storage operations by analyzing collected data from different time intervals in a simulated smart grid scenario. An evaluation of the energy storage status reveals a consistent upward trend in the quantity of stored energy, with a 30% cumulative growth across time intervals. An examination of the demand and supply of the grid indicates a persistent insufficiency of energy, with an average shortfall of 15% in meeting the requirements of the system. Through the use of reinforcement learning (RL) methodologies, the system exhibits a remarkable 450% improvement in cumulative rewards, providing substantiation of its capacity to acquire knowledge and adjust its behavior over time. The system's actions indicate a purposeful shift in strategy, with 75% of instances involving charging procedures, emphasizing a commitment to energy preservation and the buildup of stored energy. Despite a shift in approach, persistent disparities between grid demand and supply need the implementation of more accurate technologies for effective energy management. The findings highlight the effectiveness of using reinforcement learning (RL) for managing energy storage in smart grids. This approach improves energy reserves and optimizes energy storage by altering actions accordingly. These insights contribute to the advancement of adaptive energy management strategies, resulting in the development of sustainable and resilient smart grid infrastructures.