Studies on effective solar photovoltaic integration in distribution network with a blend of Monte Carlo simulation and artificial hummingbird algorithm

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dc.authoridSharma, Prof. Gulshan/0000-0002-4726-0956;
dc.contributor.authorBarutcu, Ibrahim Cagri
dc.contributor.authorSharma, Gulshan
dc.contributor.authorCelik, Emre
dc.contributor.authorBokoro, Pitshou N.
dc.date.accessioned2025-10-11T20:48:24Z
dc.date.available2025-10-11T20:48:24Z
dc.date.issued2024
dc.departmentDüzce Üniversitesien_US
dc.description.abstractIn this paper, the two level stochastic optimisation approach has been suggested. In the lower level, the probability distribution functions (pdfs) for bus voltages and branch currents have been determined using the Monte Carlo simulation (MCS) to be employed in chance-constrained probabilistic optimisation by taking into account solar radiation and power consumption uncertainties in the distribution networks (DNs). In the upper level, artificial hummingbird algorithm (AHA) handles the expected power loss minimisation subjected to chance constraints, which are related to bus voltages and branch currents, by optimising photovoltaic (PV) system capacities. This research examines the effect of uncertainties in PV system performing under diverse solar radiation and varying PV penetration level scenarios on expected power losses with stochastic DN limits. The stochastic optimisation approach has been compared with the deterministic method for observing the efficiency with optimal power usage. This research improves the knowledge base for optimal PV installation in DN by combining AHA with MCS and emphasising chance-constrained methods. To indicate the efficacy of proposed strategy, the optimisation outcomes are tested utilising MCS under various uncertainty circumstances and DN parameters are assessed in terms of probabilities of exceeding limitations. The results are compared with the application of firefly algorithm (FA) using stochastic assessment and simulations. The simulation results show that the AHA technique outperforms the FA method in terms of effectively minimising power losses with less simulation time.en_US
dc.identifier.doi10.1049/esi2.12175
dc.identifier.endpage890en_US
dc.identifier.issn2516-8401
dc.identifier.scopus2-s2.0-85208219815en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage862en_US
dc.identifier.urihttps://doi.org/10.1049/esi2.12175
dc.identifier.urihttps://hdl.handle.net/20.500.12684/21903
dc.identifier.volume6en_US
dc.identifier.wosWOS:001354430900001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherWileyen_US
dc.relation.ispartofIet Energy Systems Integrationen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKA_WOS_20250911
dc.subjectdistribution networksen_US
dc.subjectoptimisationen_US
dc.subjectpower generation planningen_US
dc.subjectrenewable energy sourcesen_US
dc.subjectsolar power stationsen_US
dc.titleStudies on effective solar photovoltaic integration in distribution network with a blend of Monte Carlo simulation and artificial hummingbird algorithmen_US
dc.typeArticleen_US

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