Microgrid Reliability Incorporating Uncertainty in Weather and Equipment Failure

Sakthivelnathan Nallainathan, Ali Arefi (), Christopher Lund and Ali Mehrizi-Sani
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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

Energies, 2025, vol. 18, issue 8, 1-23

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.

Keywords: renewable energy; standalone microgrid; reliability evaluation; Monte Carlo simulation; cumulative distribution function (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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