Smart home energy management using demand response with uncertainty analysis of electric vehicle in the presence of renewable energy sources

In this article, smart home energy management is proposed using real-time pricing (RTP) based demand response for effective utilization of renewable -based distributed generation (DGs), battery, and electric vehicle (EV) to reduce grid dependency. The smart home consists of eighteen smart appliances and EV. The EV can be operated either in vehicle -to -home or home -to -vehicle. The smart appliances in the smart home are categorized into two types: electrical and thermal loads. Each smart appliance's operating time slots and durations are scheduled based on the tariff, availability of DGs, and storage devices using the Binary Particle Swarm Optimization (BPSO) algorithm without affecting user preferences. Due to the proposed method, the grid dependency of the smart home in each time slot is reduced, which shall reduce the net electricity cost. Different cases are analyzed based on the various combinations of renewable -based DGs such as wind power, solar photovoltaic (PV), and battery in the smart home to showcase the effectiveness of the proposed method. The performance of the proposed system is analyzed under uncertain EV and DG operations. A detailed comparison is made with and without appliance scheduling in the proposed smart home to showcase the necessity of appliance scheduling and reduce the grid dependency. The simulation results show the profit of |13.046 and the grid dependency reduced by 9.636 kW in case 5 with appliance scheduling compared to case 5 without appliance scheduling. Therefore, the electricity cost and grid dependency are reduced using the BPSO algorithm for appliance scheduling in the smart home under normal and uncertain conditions.