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

Author

Listed:
  • 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)

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%.

Suggested Citation

  • Arshad Mohammad & Mohd Zuhaib & Imtiaz Ashraf & Marwan Alsultan & Shafiq Ahmad & Adil Sarwar & Mali Abdollahian, 2021. "Integration of Electric Vehicles and Energy Storage System in Home Energy Management System with Home to Grid Capability," Energies, MDPI, vol. 14(24), pages 1-27, December.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:24:p:8557-:d:705895
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    Citations

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    Cited by:

    1. José Luis Ruiz Duarte & Neng Fan, 2022. "Operation of a Power Grid with Embedded Networked Microgrids and Onsite Renewable Technologies," Energies, MDPI, vol. 15(7), pages 1-24, March.
    2. Sheng Ding & Chengmei Xu & Yao Rao & Zhaofang Song & Wangwang Yang & Zexu Chen & Zitong Zhang, 2022. "A Time-Varying Potential Evaluation Method for Electric Vehicle Group Demand Response Driven by Small Sample Data," Sustainability, MDPI, vol. 14(9), pages 1-21, April.
    3. Masoud Alilou & Gevork B. Gharehpetian & Roya Ahmadiahangar & Argo Rosin & Amjad Anvari-Moghaddam, 2022. "Day-Ahead Scheduling of Electric Vehicles and Electrical Storage Systems in Smart Homes Using a Novel Decision Vector and AHP Method," Sustainability, MDPI, vol. 14(18), pages 1-19, September.
    4. Reda El Makroum & Ahmed Khallaayoun & Rachid Lghoul & Kedar Mehta & Wilfried Zörner, 2023. "Home Energy Management System Based on Genetic Algorithm for Load Scheduling: A Case Study Based on Real Life Consumption Data," Energies, MDPI, vol. 16(6), pages 1-18, March.
    5. Jonas Sievers & Thomas Blank, 2023. "A Systematic Literature Review on Data-Driven Residential and Industrial Energy Management Systems," Energies, MDPI, vol. 16(4), pages 1-21, February.
    6. Senthil Prabu Ramalingam & Prabhakar Karthikeyan Shanmugam, 2022. "Hardware Implementation of a Home Energy Management System Using Remodeled Sperm Swarm Optimization (RMSSO) Algorithm," Energies, MDPI, vol. 15(14), pages 1-24, July.

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