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Microgrid Reliability Incorporating Uncertainty in Weather and Equipment Failure

Author

Listed:
  • Sakthivelnathan Nallainathan

    (School of Engineering and Energy, Murdoch University, Perth, WA 6150, Australia)

  • Ali Arefi

    (School of Engineering and Energy, Murdoch University, Perth, WA 6150, Australia)

  • Christopher Lund

    (School of Engineering and Energy, Murdoch University, Perth, WA 6150, Australia)

  • Ali Mehrizi-Sani

    (The Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA 24061, USA)

Abstract

Solar photovoltaic (PV) and wind power generation are key contributors to the integration of renewable energy into modern power systems. The intermittent and variable nature of these renewables has a substantial impact on the power system’s reliability. In time-series simulation studies, inaccuracies in solar irradiation and wind speed parameters can lead to unreliable evaluations of system reliability, ultimately resulting in flawed decision making regarding the investment and operation of energy systems. This paper investigates the reliability deviation due to modeling uncertainties in a 100% renewable-based system. This study employs two methods to assess and contrast the reliability of a standalone microgrid (SMG) system in order to achieve this goal: (i) random uncertainty within a selected confidence interval and (ii) splitting the cumulative distribution function (CDF) into five regions of equal probability. In this study, an SMG system is modeled, and loss of load probability (LOLP) is evaluated in both approaches. Six different sensitivity analysis studies, including annual load demand growth, are performed. The results from the simulations demonstrate that the suggested methods can estimate the reliability of a microgrid powered by renewable energy sources, as well as its probability of reaching certain levels of reliability.

Suggested Citation

  • Sakthivelnathan Nallainathan & Ali Arefi & Christopher Lund & Ali Mehrizi-Sani, 2025. "Microgrid Reliability Incorporating Uncertainty in Weather and Equipment Failure," Energies, MDPI, vol. 18(8), pages 1-23, April.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:8:p:2077-:d:1636843
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    References listed on IDEAS

    as
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    2. Shanmugarajah Vinothine & Lidula N. Widanagama Arachchige & Athula D. Rajapakse & Roshani Kaluthanthrige, 2022. "Microgrid Energy Management and Methods for Managing Forecast Uncertainties," Energies, MDPI, vol. 15(22), pages 1-22, November.
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    4. Zhai, Xiangyu & Li, Zening & Li, Zhengmao & Xue, Yixun & Chang, Xinyue & Su, Jia & Jin, Xiaolong & Wang, Peng & Sun, Hongbin, 2025. "Risk-averse energy management for integrated electricity and heat systems considering building heating vertical imbalance: An asynchronous decentralized approach," Applied Energy, Elsevier, vol. 383(C).
    5. Javier Faulin & Angel A. Juan & Sebastián Martorell & José-Emmanuel Ramírez-Márquez (ed.), 2010. "Simulation Methods for Reliability and Availability of Complex Systems," Springer Series in Reliability Engineering, Springer, number 978-1-84882-213-9, July.
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