Enhancing grid stability and renewable energy integration with reinforcement learning for optimized demand response

Abstract

Demand response (DR) is a strategy that encourages customers to adjust their energy usage during periods of peak demand, aiming to enhance the reliability of the power grid and reduce operational costs. The optimal DR scheme utilizes both distribution system operators and consumers within the energy network to achieve optimal results. The integration of renewable energy sources into smart grids poses significant challenges due to their intermittent and unpredictable nature. DR strategies, coupled with reinforcement learning techniques, have emerged as promising approaches to address these challenges and optimize grid operations where traditional methods fail to meet such kind of complex requirements. This article presents a reinforcement learning-based strategy to optimize DR and energy management in smart grids, focusing on battery-photovoltaic integrated systems. The proposed method employs the soft actor-critic with automated adjustment of temperature algorithm to enhance load-shifting flexibility and grid stability. Experimental results, using the CityLearn environment, demonstrate significant reductions in energy costs 3% and 15% compared to rule-based control and soft actor-critic-based strategies, respectively.