Enhancing the Reliability of Photovoltaic Systems in Microgrid at Campus Area

Hanadi; Hadi  Christian; Syafril  Tomoyahu; Virara  Faniama; Justin  Pradipta; Irsyad N. Haq Haq; Edi  Leksono

doi:10.5614/joki.2024.16.1.3

Authors

Hanadi Energy Management Laboratory, Engineering Physics Department, Faculty of Industrial Technology, Institut Teknologi Bandung, Indonesia
Hadi Christian Energy Management Laboratory, Engineering Physics Department, Faculty of Industrial Technology, Institut Teknologi Bandung, Indonesia
Syafril Tomoyahu Energy Management Laboratory, Engineering Physics Department, Faculty of Industrial Technology, Institut Teknologi Bandung, Indonesia
Virara Faniama Energy Management Laboratory, Engineering Physics Department, Faculty of Industrial Technology, Institut Teknologi Bandung, Indonesia
Justin Pradipta (1) Energy Management Laboratory, Engineering Physics Department, Faculty of Industrial Technology, Institut Teknologi Bandung, Indonesia ; (2) Engineering Physics Research Group, Engineering Physics Department, Faculty of Industrial Technology, Institut Teknologi Bandung, Indonesia
Irsyad N. Haq Haq (1) Energy Management Laboratory, Engineering Physics Department, Faculty of Industrial Technology, Institut Teknologi Bandung, Indonesia ; (2) Engineering Physics Research Group, Engineering Physics Department, Faculty of Industrial Technology, Institut Teknologi Bandung, Indonesia
Edi Leksono (1) Energy Management Laboratory, Engineering Physics Department, Faculty of Industrial Technology, Institut Teknologi Bandung, Indonesia ; (2) Engineering Physics Research Group, Engineering Physics Department, Faculty of Industrial Technology, Institut Teknologi Bandung, Indonesia

Keywords:

Photovoltaic Systems, Microgrid Reliability, LOLP (Loss of Load Probability), Markov Model, Machine Learning in Power Systems

Abstract

This paper assesses the reliability of photovoltaic systems within a microgrid, considering the system's operational mode and monthly data on solar radiation and load demand. The evaluation encompasses various reliability metrics, including microgrid failure rate, interruption duration, system unavailability, EENS, EIR, LOLE, and LOLP, with the objective of minimizing these parameters. The methodologies applied involve the Markov model and artificial intelligence algorithms such as Naive Bayes and Support Vector Machine (SVM). Results indicate that the microgrid exhibits enhanced reliability in an on-grid mode configuration, with a LOLP value of 0.0008. Furthermore, employing machine learning, specifically SVM, for LOLP calculation based on solar radiation yields a more precise value of 0.7245. This study offers valuable insights for policymakers and system designers in determining the optimal configuration for microgrids.

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