Advanced Restoration Management Strategies in Smart Grids: the Role of Distributed Energy Resources and Load Priorities

dc.authorscopusid56487372400
dc.authorscopusid26665865200
dc.authorscopusid7006505111
dc.contributor.authorAhmadi, B.
dc.contributor.authorCeylan, O.
dc.contributor.authorOzdemir, A.
dc.date.accessioned2025-03-15T20:06:54Z
dc.date.available2025-03-15T20:06:54Z
dc.date.issued2025
dc.departmentKadir Has Universityen_US
dc.department-tempAhmadi B., Faculity of Electrical Engineering, Mathematics and Computer Science, University of Twente, Enschede, Netherlands; Ceylan O., Department of Management Information Systems, Kadir Has University, Istanbul, Türkiye; Ozdemir A., Department of Electrical and Electronics Engineering, Kadir Has University, Istanbul, Türkiyeen_US
dc.description.abstractFast restoration following long outages is a challenge in the smart city management process. It is necessary to accurately characterize the real operating conditions of the system for optimal restoration. This study focuses on two key factors of a practical distribution system restoration. The first factor is cold load pickup (CLPU), which commonly occurs after an outage and is caused by thermostatically controlled loads. A time-dependent CLPU is modeled to accurately describe the restored load behaviors. The second factor is the effect of the distributed generators (DG), energy storage systems (ESSs), and load priority factors on the system's restoration process. To address this challenge, a robust optimization model is proposed that fully considers the effect of DG, and ESS units and uncertainty of CLPU. The proposed models are tested on the IEEE 33-node and 69-node test systems using the Advanced Grey Wolf Algorithm (AGWO). The simulation scenarios are designed to uncover optimal scheduling strategies for the restoration process corresponding to each Pareto solution of a previous study. The results are discussed for several distinct initial conditions. Moreover, a comparative evaluation is done, contrasting the outcomes achieved through the AGWO algorithm with those stemming from alternative heuristic methods. © 2025 The Authorsen_US
dc.description.sponsorshipTürkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK; Horizon 2020, (957682, 117E773); Horizon 2020en_US
dc.identifier.doi10.1016/j.compeleceng.2025.110196
dc.identifier.issn0045-7906
dc.identifier.scopus2-s2.0-85218891617
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.compeleceng.2025.110196
dc.identifier.urihttps://hdl.handle.net/20.500.12469/7221
dc.identifier.volume123en_US
dc.identifier.wosqualityQ2
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.relation.ispartofComputers and Electrical Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectLarge-Scale Blackouten_US
dc.subjectPower System Restorationen_US
dc.subjectRobust Optimizationen_US
dc.subjectSelf-Healingen_US
dc.subjectSmart Griden_US
dc.titleAdvanced Restoration Management Strategies in Smart Grids: the Role of Distributed Energy Resources and Load Prioritiesen_US
dc.typeArticleen_US
dspace.entity.typePublication

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