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Quantifying Peak Load-Carrying Capability: A Comprehensive Reliability Analysis of Grid-Connected Hybrid Systems

Author

Listed:
  • Osamah H. Almgbel

    (Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Mustafa M. A. Seedahmed

    (Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Abdullah Ali Alhussainy

    (Department of Electrical Engineering, College of Engineering, University of Prince Mugrin, Madinah 42241, Saudi Arabia)

  • Sultan Alghamdi

    (Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
    Smart Grids Research Group, Center of Research Excellence in Renewable Energy and Power Systems, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Muhyaddin Rawa

    (Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
    Smart Grids Research Group, Center of Research Excellence in Renewable Energy and Power Systems, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Yusuf A. Alturki

    (Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
    Smart Grids Research Group, Center of Research Excellence in Renewable Energy and Power Systems, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

Abstract

Energy leaders around the world are constantly looking into feasibility and opportunities in renewable energy to diversify their energy sources. This study examines the reliability of a grid-connected microgrid consisting of solar energy, wind energy, and storage batteries to supply the required load and share the surplus with the grid. As the reliability of each component separately has an impact on system reliability, in this study, the loss of load expectation (LOLE) technique was used to estimate the peak load-carrying capability (PLCC) of the systems and the duration of outages as a means of analyzing the reliability of these systems and selecting the optimal combination among the cases. Moreover, this study used the load data of the area under study as the primary load and considered the grid as a secondary load to share the surplus after fulfilling the demand requirements. Furthermore, ten cases of grid-connected system configurations were considered to conduct this research, incorporating various combinations of solar panels, wind turbines (WTs), and batteries. The results revealed that, while maintaining an acceptable risk level represented by an LOLE of 0.1 days per year, the WT (850 MW) case emerged as the leading power producer compared to the other cases. It was able to produce 840.245 MW and 818.345 MW as the total power produced and the amount of surplus power that will be delivered to the grid after meeting the primary load needs in the area under study, respectively. This analysis can be informative for administrators in charge of planning and policy-making, helping them to take appropriate action.

Suggested Citation

  • Osamah H. Almgbel & Mustafa M. A. Seedahmed & Abdullah Ali Alhussainy & Sultan Alghamdi & Muhyaddin Rawa & Yusuf A. Alturki, 2024. "Quantifying Peak Load-Carrying Capability: A Comprehensive Reliability Analysis of Grid-Connected Hybrid Systems," Sustainability, MDPI, vol. 16(22), pages 1-21, November.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:22:p:10107-:d:1524651
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    References listed on IDEAS

    as
    1. Cai, Baoping & Liu, Yonghong & Ma, Yunpeng & Huang, Lei & Liu, Zengkai, 2015. "A framework for the reliability evaluation of grid-connected photovoltaic systems in the presence of intermittent faults," Energy, Elsevier, vol. 93(P2), pages 1308-1320.
    2. Iris, Çağatay & Lam, Jasmine Siu Lee, 2021. "Optimal energy management and operations planning in seaports with smart grid while harnessing renewable energy under uncertainty," Omega, Elsevier, vol. 103(C).
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