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Energy Internet-Based Load Shifting in Smart Microgrids: An Experimental Study

Author

Listed:
  • Ali M. Jasim

    (Electrical Engineering Department, University of Basrah, Basrah 61001, Iraq)

  • Basil H. Jasim

    (Electrical Engineering Department, University of Basrah, Basrah 61001, Iraq)

  • Soheil Mohseni

    (Sustainable Energy Systems, Wellington Faculty of Engineering, Victoria University of Wellington, Wellington 6140, New Zealand)

  • Alan C. Brent

    (Sustainable Energy Systems, Wellington Faculty of Engineering, Victoria University of Wellington, Wellington 6140, New Zealand
    Department of Industrial Engineering and the Centre for Renewable and Sustainable Energy Studies, Stellenbosch University, Stellenbosch 7600, South Africa)

Abstract

This study investigated a grid-connected smart microgrid (MG) system integrating solar photovoltaic (PV) panels and a battery energy storage system (BESS) as distributed energy resources (DERs) to locally serve residential loads. The load-shifting demand-side management (DSM) technique was employed to effectively manage the load appliances. The proposed load-shifting algorithm relies on minimum price incentives to allow customers to allocate their load appliances economically during minimum price periods. The algorithm considers the waiting times and minimum tariff periods for appliances, calculates precise operating durations for each appliance, and prioritizes powering the appliances from the MG first, followed by the main grid. The system comprises two non-shiftable and three shiftable loads. When the MG power is insufficient to activate all shiftable loads, the system transfers the remaining unsupplied shiftable appliances to periods with low-priced energy. The Energy Internet concept is adopted to manage energy and monitor usage when a customer is unable to check the accuracy of their energy meter by supervising the system’s features on-site. The proposed comprehensive system enables load management, continuous monitoring, customer awareness, and energy cost saving. Six cases were studied, both numerically and experimentally, with varying MG power generation and load pre-scheduling periods, with and without DSM application. In all adopted cases, the implemented system save energy costs by at least 50%.

Suggested Citation

  • Ali M. Jasim & Basil H. Jasim & Soheil Mohseni & Alan C. Brent, 2023. "Energy Internet-Based Load Shifting in Smart Microgrids: An Experimental Study," Energies, MDPI, vol. 16(13), pages 1-26, June.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:13:p:4957-:d:1179727
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    References listed on IDEAS

    as
    1. Zhou, Kaile & Yang, Shanlin & Shao, Zhen, 2016. "Energy Internet: The business perspective," Applied Energy, Elsevier, vol. 178(C), pages 212-222.
    2. Gonçalves, Ivo & Gomes, Álvaro & Henggeler Antunes, Carlos, 2019. "Optimizing the management of smart home energy resources under different power cost scenarios," Applied Energy, Elsevier, vol. 242(C), pages 351-363.
    3. Bilal Naji Alhasnawi & Basil H. Jasim & Zain-Aldeen S. A. Rahman & Josep M. Guerrero & M. Dolores Esteban, 2021. "A Novel Internet of Energy Based Optimal Multi-Agent Control Scheme for Microgrid including Renewable Energy Resources," IJERPH, MDPI, vol. 18(15), pages 1-24, July.
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    Cited by:

    1. Yeferson Lopez Alzate & Eduardo Gómez-Luna & Juan C. Vasquez, 2024. "Innovative Microgrid Services and Applications in Electric Grids: Enhancing Energy Management and Grid Integration," Energies, MDPI, vol. 17(22), pages 1-28, November.
    2. 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.

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