Application of Monte Carlo simulation and stochastic fractional search algorithm for solar PV placement considering diverse solar radiations

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dc.contributor.authorBarutcu, Ibrahim Cagri
dc.contributor.authorSharma, Gulshan
dc.contributor.authorBokoro, Pitshou N.
dc.contributor.authorCelik, Emre
dc.date.accessioned2025-10-11T20:48:34Z
dc.date.available2025-10-11T20:48:34Z
dc.date.issued2025
dc.departmentDüzce Üniversitesien_US
dc.description.abstractThis study employs Monte Carlo Simulation (MCS) within the structure of the Stochastic Fractional Search Algorithm (SFSA) to address circumstances involving uncertainty. The goal is to improve the system's performance by creating probability distribution functions for bus voltages and branch currents. We will use the resultant distribution in chance-constrained stochastic scheduling. The objective of the present research is to analyze the impact of uncertainties in the operation of photovoltaic (PV) systems, specifically in relation to different solar radiation conditions, on the amount of power loss. The approach focuses on including stochastic constraints in distribution systems instead of depending solely on precise deterministic boundaries. The goal is to enhance efficiency and ensure optimal consumption of power. This research enhances the knowledge base on PV unit positioning in distribution systems by integrating meta-heuristic optimization and MCS into a comprehensive framework. The investigation centers on the implementation of a chance-constrained method. We evaluate the optimization results using MCS under various uncertainty scenarios to demonstrate the effectiveness of the recommended approach. Furthermore, we conduct an analysis to assess the likelihood of exceeding the system's boundaries. The strategy's effectiveness is assessed by comparing the results of the SFSA with the Firefly algorithm (FA) utilizing probabilistic evaluation and simulation.en_US
dc.identifier.doi10.1016/j.egyr.2024.12.070
dc.identifier.endpage902en_US
dc.identifier.issn2352-4847
dc.identifier.scopus2-s2.0-85213281884en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage885en_US
dc.identifier.urihttps://doi.org/10.1016/j.egyr.2024.12.070
dc.identifier.urihttps://hdl.handle.net/20.500.12684/21993
dc.identifier.volume13en_US
dc.identifier.wosWOS:001412454800001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofEnergy Reportsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKA_WOS_20250911
dc.subjectDistribution griden_US
dc.subjectOptimal lossesen_US
dc.subjectProbabilistic constraintsen_US
dc.subjectSolar power plantsen_US
dc.subjectStochastic planningen_US
dc.titleApplication of Monte Carlo simulation and stochastic fractional search algorithm for solar PV placement considering diverse solar radiationsen_US
dc.typeArticleen_US

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