Optimal Energy Consumption Optimization in a Smart House by Considering Electric Vehicles and Demand Response via a Hybrid Gravitational Search and Particle Swarm Optimization Algorithm

Buildings are the main energy consumers across world, especially in urban communities. Building smartization, or smartification of housing, therefore, is a major step towards grid smartization too. By controlling consumption lighting, heating, and cooling systems, can be optimized. All some part consumed future smart buildings must supplied by renewable sources (RES), which mitigates environmental impacts reduces peak demand for electrical energy. In this paper, new optimization algorithm applied to solve optimal problem considering electric vehicles response homes. way, large power stations that work with fossil fuels will no longer developed. The current study modeled evaluated performance house presence (EVs) bidirectional exchangeability grid, an storage system (ESS), solar panels. Additionally, RES ESS predicting solar-generated prediction uncertainty have been considered work. Different case studies, including sales resulting from PV panels’ generated time-variable loads such as washing machines, different (DR) strategies based on price variations were taken into account assess economic technical effects EVs, BESS, proposed model was simulated MATLAB. A hybrid particle swarm (PSO) gravitational search (GS) utilized optimization. Scenario generation reduction performed via LHS backward methods, respectively. Obtained results demonstrate minimizes supply cost stochastic time use (STOU) loads, EV, ESS, system. Based results, markedly reduced electricity costs house.