Probabilistic optimal planning of multiple photovoltaics and battery energy storage systems in distribution networks: A boosted equilibrium optimizer with time-variant load models

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dc.authoridElseify, Mohamed A/0000-0001-5108-6236
dc.authoridSeyyedabbasi, Amir/0000-0001-5186-4499;
dc.authorwosidElseify, Mohamed A/JQJ-5609-2023
dc.authorwosidSeyyedabbasi, Amir/HJH-7387-2023
dc.authorwosidKamel, Salah/C-8567-2019
dc.contributor.authorElseify, Mohamed A.
dc.contributor.authorSeyyedabbasi, Amir
dc.contributor.authorDominguez-Garcia, Jose Luis
dc.contributor.authorKamel, Salah
dc.date.accessioned2024-05-19T14:40:47Z
dc.date.available2024-05-19T14:40:47Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractIn recent years, there has been a rapid increase in distributed generation (DG) technologies incorporated into distribution networks (DNs) to meet the challenge of load growth. However, the stochastic nature of renewable energy, such as photovoltaic (PV), makes the amount of energy produced uncertain. This uncertainty, along with changes in generation and load demand, can increase energy losses and voltage instability. To address this issue, energy storage systems can be integrated to decrease the effects of the intermittency associated with renewable technologies. This paper proposes a new variant of an equilibrium optimizer (EO) based on reinforced learning, named RLEO, for optimal incorporation of multiple battery energy storage (BES) units integrated synchronously with solar PVs into distribution systems while minimizing energy loss. The RLEO algorithm employs reinforced learning mechanisms to prevent premature convergence of the EO and improve its exploration and exploitation capabilities. The performance of the RLEO algorithm is assessed using standard CEC 2017 benchmark functions and compared with the original EO and other popular algorithms using various statistical criteria. The RLEO algorithm is also applied to determine the optimal size and position of multiple PV units in IEEE 69-bus and 118bus DNs with single and multi-objective optimization problems. Using the developed algorithm, the optimal arrangement of three non-dispatchable PVs results in a slight increase in the percentage reduction of energy loss across various load profiles: 53.0035 % for commercial, 19.6372 % for industrial, and 28.1783 % for residential. In contrast, by employing the optimal configuration of three PV + BES units, the reduction in energy loss percentage experiences a remarkable surge to 68.3466 % for commercial, 68.0917 % for industrial, and 68.1779 % for residential load scenarios using the proposed algorithm. This clearly indicates that the proposed RLEO algorithm significantly surpasses recent optimization methods documented in the literature when it comes to addressing the challenge of optimal allocation for multiple DGs. Furthermore, its applicability extends to more intricate optimization problems.en_US
dc.description.sponsorshipEuropean Union [801342]; Government of Catalonias Agency for Business Competitiveness (ACCIO)en_US
dc.description.sponsorshipThis project has received funding from the European Union's Horizon 2020 research and innovation programme under Marie SklodowskaCurie grant agreement No. 801342 (Tecniospring INDUSTRY) and the Government of Catalonias Agency for Business Competitiveness (ACCIO) .en_US
dc.identifier.doi10.1016/j.est.2023.108986
dc.identifier.issn2352-152X
dc.identifier.issn2352-1538
dc.identifier.scopus2-s2.0-85171330593en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1016/j.est.2023.108986
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5015
dc.identifier.volume73en_US
dc.identifier.wosWOS:001165580300001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofJournal of Energy Storageen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectDg Optimal Allocationen_US
dc.subjectBattery Energy Storageen_US
dc.subjectPhotovoltaicen_US
dc.subjectUncertaintyen_US
dc.subjectEnergy Lossen_US
dc.subjectDistribution Networksen_US
dc.subjectReinforcement Learningen_US
dc.subjectEquilibrium Optimizeren_US
dc.titleProbabilistic optimal planning of multiple photovoltaics and battery energy storage systems in distribution networks: A boosted equilibrium optimizer with time-variant load modelsen_US
dc.typeArticleen_US

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