Intelligent Energy Management for Prosumer Smart Home System with Multi-Energy Resources

Abstract

This article presents a hybrid particle swarm optimization-linear programming (PSO-LP) approach for smart home intelligent energy management systems (SHIEMS). The proposed method formulates the objective function as the minimization of total daily electricity costs, considering various home appliances, electricity from a rooftop photovoltaic (PV) system, and wind power generation, as well as a battery energy storage system (BESS) and vehicle-to-home (V2H) integration. The optimal BESS and V2H scheduling obtained from PSO is then incorporated into LP to complete the optimal home appliance scheduling. Seven case studies under a time-of-use tariff were conducted to evaluate the effectiveness of the proposed method in solving the problem under different conditions and benefiting prosumers by optimizing and managing energy in smart homes. The results demonstrate that the proposed hybrid PSO-LP-based SHIEMS effectively minimizes daily electricity costs for prosumers by 16.48% compared to the non-scheduling. Additionally, the proposed framework maximizes PV electricity sales for prosumers by 17.941 kWh and minimizes energy usage from the grid by 0.608 kWh, which helps alleviate the grid burden during peak hours.