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Published (Version of Record)CC BY V4.0, Open Access
Journal article
Microgrid Reliability Incorporating Uncertainty in Weather and Equipment Failure
Energies (Basel), Vol.18(8), 2077
2025
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.
Details
- Title
- Microgrid Reliability Incorporating Uncertainty in Weather and Equipment Failure
- Authors/Creators
- Nallainathan SakthivelnathanAli Arefi - Murdoch UniversityChristopher Lund - Murdoch UniversityMehrizi-Sani Ali
- Publication Details
- Energies (Basel), Vol.18(8), 2077
- Publisher
- MDPI AG
- Number of pages
- 23
- Identifiers
- 991005773428307891
- Copyright
- © 2025 by the authors
- Murdoch Affiliation
- Centre for Water, Energy and Waste
- Language
- English
- Resource Type
- Journal article
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- Collaboration types
- Domestic collaboration
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- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.18 Power Systems & Electric Vehicles
- 4.18.204 Smart Grid Optimization
- Web Of Science research areas
- Energy & Fuels
- ESI research areas
- Engineering