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Optimal Demand Response Using Battery Storage Systems and Electric Vehicles in Community Home Energy Management System-Based Microgrids

Author

Listed:
  • Ayesha Abbasi

    (Department of Electrical and Computer Engineering, International Islamic University Islamabad, Islamabad 44000, Pakistan)

  • Kiran Sultan

    (Department of CIT, The Applied College, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Sufyan Afsar

    (Department of Electrical Engineering, Bahria University, Islamabad 44000, Pakistan)

  • Muhammad Adnan Aziz

    (Faculty of Information Technology & Computer Science, University of Central Punjab, Lahore 54000, Pakistan)

  • Hassan Abdullah Khalid

    (Center for Advanced Studies in Energy, National University of Science and Technology, Islamabad 44000, Pakistan)

Abstract

Demand response (DR) strategies are recieving much attention recently for their applications in the residential sector. Electric vehicles (EVs), which are considered to be a fairly new consumer load in the power sector, have opened up new opportunities by providing the active utilization of EVs as a storage unit. Considering their storage capacities, they can be used in vehicle-to-grid (V2G) or vehicle-to-community (V2C) options instead of taking power in peak times from the grid itself. This paper suggests a community-based home energy management system for microgrids to achieve flatter power demand and peak demand shaving using particle swarm optimization (PSO) and user-defined constraints. A dynamic clustered load scheduling scheme is proposed, including a method for managing peak shaving using rules specifically designed for PV systems that are grid-connected alongside battery energy storage systems and electric vehicles. The technique being proposed involves determining the limits of feed-in and demand dynamically, using estimated load demands and profiles of PV power for the following day. Additionally, an optimal rule-based management technique is presented for the peak shaving of utility grid power that sets the charge/discharge schedules of the battery and EV one day ahead. Utilizing the PSO algorithm, the optimal inputs for implementing the rule-based peak shaving management strategy are calculated, resulting in an average improvement of about 7% in percentage peak shaving (PPS) when tested using MATLAB for numerous case studies.

Suggested Citation

  • Ayesha Abbasi & Kiran Sultan & Sufyan Afsar & Muhammad Adnan Aziz & Hassan Abdullah Khalid, 2023. "Optimal Demand Response Using Battery Storage Systems and Electric Vehicles in Community Home Energy Management System-Based Microgrids," Energies, MDPI, vol. 16(13), pages 1-22, June.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:13:p:5024-:d:1182049
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    References listed on IDEAS

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    1. Tommaso Schettini & Mauro dell’Amico & Francesca Fumero & Ola Jabali & Federico Malucelli, 2023. "Locating and Sizing Electric Vehicle Chargers Considering Multiple Technologies," Energies, MDPI, vol. 16(10), pages 1-16, May.
    2. Sai Sudharshan Ravi & Muhammad Aziz, 2022. "Utilization of Electric Vehicles for Vehicle-to-Grid Services: Progress and Perspectives," Energies, MDPI, vol. 15(2), pages 1-27, January.
    3. Md. Rayid Hasan Mojumder & Fahmida Ahmed Antara & Md. Hasanuzzaman & Basem Alamri & Mohammad Alsharef, 2022. "Electric Vehicle-to-Grid (V2G) Technologies: Impact on the Power Grid and Battery," Sustainability, MDPI, vol. 14(21), pages 1-53, October.
    4. Tao Lei & Zhihao Min & Qinxiang Gao & Lina Song & Xingyu Zhang & Xiaobin Zhang, 2022. "The Architecture Optimization and Energy Management Technology of Aircraft Power Systems: A Review and Future Trends," Energies, MDPI, vol. 15(11), pages 1-37, June.
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    1. Dariusz Kurz & Damian Głuchy & Michał Filipiak & Dawid Ostrowski, 2023. "Technical and Economic Analysis of the Use of Electricity Generated by a BIPV System for an Educational Establishment in Poland," Energies, MDPI, vol. 16(18), pages 1-23, September.
    2. Yinghao Shan & Liqian Ma & Xiangkai Yu, 2023. "Hierarchical Control and Economic Optimization of Microgrids Considering the Randomness of Power Generation and Load Demand," Energies, MDPI, vol. 16(14), pages 1-23, July.
    3. Gyuhyeon Bae & Ahyun Yoon & Sungsoo Kim, 2024. "Incentive Determination for Demand Response Considering Internal Rate of Return," Energies, MDPI, vol. 17(22), pages 1-15, November.
    4. Olga Bogdanova & Karīna Viskuba & Laila Zemīte, 2023. "A Review of Barriers and Enables in Demand Response Performance Chain," Energies, MDPI, vol. 16(18), pages 1-33, September.
    5. E. Poongulali & K. Selvaraj, 2024. "Improved load demand prediction for cluster microgrids using modified temporal convolutional feed forward network," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 87(3), pages 561-574, November.

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