Deep Reinforcement Learning-Based Demond Response Management in Residential Microgrids

Demand response (DR) strategies are essential for balancing electricity supply and demand, especially in the context of residential microgrids that rely heavily on renewable energy sources such as solar demand response (DR) strategies are essential for balancing electricity supply and demand, especially in the context of residential microgrids that rely heavily on renewable energy sources such as solar and wind. As global energy systems shift toward decarbonization and decentralization, microgrids have emerged as a critical solution for enhancing energy resilience and reducing dependency on centralized power generation. However, the inherent intermittency and unpredictability of renewable energy generation pose significant challenges to maintaining a stable supply-demand equilibrium. Traditional demand-side management techniques often fall short in dynamically adapting to these fluctuations, necessitating the development of more intelligent, adaptive, and autonomous control mechanisms. This paper presents a novel deep reinforcement learning (DRL)-based demand response management framework tailored specifically for residential microgrids. The core motivation behind this study lies in overcoming the limitations of rule-based and optimization-based DR models that require explicit modeling of the environment and lack real-time adaptability. By leveraging DRL, a subset of machine learning that combines deep neural networks with reinforcement learning principles, the proposed approach enables a residential microgrid to autonomously learn optimal load scheduling policies through continuous interaction with the environment. This results in improved energy efficiency, reduced electricity costs for consumers, and enhanced utilization of locally generated renewable power.The proposed system is designed to intelligently manage and schedule flexible household loads—such as electric water heaters, washing machines, HVAC systems, and electric vehicle chargers—based on real-time electricity prices, user preferences, and grid conditions. Unlike traditional methods that rely on predefined rules or deterministic models, the DRL agent is trained to make decisions that maximize long-term rewards, which may include minimizing total energy costs, reducing peak demand, and maximizing user comfort.