An effective energy management Layout-Based reinforcement learning for household demand response in digital twin simulation

With the growth in energy consumption, demand response (DR) programs in the power network have gained popularity and can be expected to become more widespread in the future. Through DR programs, users are encouraged for utilizing renewable energy and reducing their power consumption at peak times, thereby helping to balance supply and demand on the grid, as well as generating revenue from the sale of excess power. This paper presents an effective energy management layout (EML) for household DR employing Reinforcement Learning (RL) and Fuzzy Reasoning (FR). RL would be a model-free control method that consists of doing measures and assessing the outcomes as it interacts with the environments. Through direct integration of customer feedback into its control logic, the suggested method takes into account user satisfaction by utilizing FR as a reward function. Through the shift of controllable devices from peak hours, whenever energy cost is higher, to off-peak periods, whenever energy cost is low, Q-learning, an RL method according to a reward scheme, has been applied for scheduling the execution of smart home devices. With the suggested method, 14 home devices can be controlled by one agent, and many status-action pairs as well as fuzzy logic for the reward function are used to assess the actions taken for a particular status. Simulations are implemented in the digital twin envi-ronment and demonstrate that the suggested device planning method smooths the energy usage and minimizes the energy price by taking into account the consumers' satisfaction, the consumers' feedback, and their satis-faction settings. The Home EML has been presented with a consumer interface in MATLAB/Simulink for demonstrating the suggested DR approach. The simulation tools include smart devices, energy price signals, smart meters, solar photovoltaics, batteries, electric vehicle, and grid supply.