Demand response optimization strategy of electric water heater based on RL

In the face of global climate challenges, energy conservation and demand response have become crucial. Electric water heaters (EWHs), with their excellent heat storage capacity, are an ideal choice for household demand response. However, individual households often have relatively small and variable hot water demands, and they may struggle to respond promptly to fluctuations in electricity prices. To address this challenge, we have developed a small-scale community model, which includes a centralized water supply center, a group of price-sensitive loads, centralized energy storage, and rooftop photovoltaic panels. Initially, we employ a Multi-Layer Perceptron (MLP) to model the EWHs, enhancing their adaptability to dynamic environmental changes. Subsequently, we formalize the problem as a Markov Decision Process (MDP) and apply a Reinforcement Learning (RL) algorithm to centrally manage multiple electric water heaters and energy storage devices. The results demonstrate that the RL algorithm effectively avoids high electricity price periods and actively utilizes electricity for heating to accumulate thermal energy reserves during low electricity price periods. Simultaneously, it ensures that users meet their water heating needs, thus reducing overall energy costs. This approach contributes to better addressing climate challenges, improving energy efficiency, and offering more flexible energy management.