Integration of Electric Vehicles and Energy Storage System in Home Energy Management System with Home to Grid Capability

Arshad Mohammad, Mohd Zuhaib, Imtiaz Ashraf, Marwan Alsultan, Shafiq Ahmad, Adil Sarwar and Mali Abdollahian
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Arshad Mohammad: Department of Electrical Engineering, Zakir Husain College of Engineering and Technology, Aligarh Muslim University, Aligarh 202002, India
Mohd Zuhaib: Department of Electrical Engineering, Zakir Husain College of Engineering and Technology, Aligarh Muslim University, Aligarh 202002, India
Imtiaz Ashraf: Department of Electrical Engineering, Zakir Husain College of Engineering and Technology, Aligarh Muslim University, Aligarh 202002, India
Marwan Alsultan: Industrial Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
Shafiq Ahmad: Industrial Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
Adil Sarwar: Department of Electrical Engineering, Zakir Husain College of Engineering and Technology, Aligarh Muslim University, Aligarh 202002, India
Mali Abdollahian: School of Science, College of Sciences, Technology, Engineering, Mathematics, RMIT University, Melbourne, VIC 3001, Australia

Energies, 2021, vol. 14, issue 24, 1-27

Abstract: In this paper, we proposed a home energy management system (HEMS) that includes photovoltaic (PV), electric vehicle (EV), and energy storage systems (ESS). The proposed HEMS fully utilizes the PV power in operating domestic appliances and charging EV/ESS. The surplus power is fed back to the grid to achieve economic benefits. A novel charging and discharging scheme of EV/ESS is presented to minimize the energy cost, control the maximum load demand, increase the battery life, and satisfy the user’s-traveling needs. The EV/ESS charges during low pricing periods and discharges in high pricing periods. In the proposed method, a multi-objective problem is formulated, which simultaneously minimizes the energy cost, peak to average ratio (PAR), and customer dissatisfaction. The multi-objective optimization is solved using binary particle swarm optimization (BPSO). The results clearly show that it minimizes the operating cost from 402.89 cents to 191.46 cents, so that a reduction of 52.47% is obtained. Moreover, it reduces the PAR and discomfort index by 15.11% and 16.67%, respectively, in a 24 h time span. Furthermore, the home has home to grid (H2G) capability as it sells the surplus energy, and the total cost is further reduced by 29.41%.

Keywords: home energy management system; Demand Side Management; real-time pricing; binary particle swarm optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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