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

dc.authoridAhmadi, Bahman/0000-0002-1745-2228
dc.authoridOzdemir, Aydogan/0000-0003-1331-2647
dc.authorscopusid56487372400
dc.authorscopusid26665865200
dc.authorscopusid7006505111
dc.authorwosidAhmadi, Bahman/Gsd-7380-2022
dc.authorwosidCeylan, Oguzhan/Aag-1749-2019
dc.authorwosidOzdemir, Aydogan/A-2223-2016
dc.contributor.authorAhmadi, Bahman
dc.contributor.authorCeylan, Oguzhan
dc.contributor.authorOzdemir, Aydogan
dc.date.accessioned2025-03-15T20:06:54Z
dc.date.available2025-03-15T20:06:54Z
dc.date.issued2025
dc.departmentKadir Has Universityen_US
dc.department-temp[Ahmadi, Bahman] Univ Twente, Facul Elect Engn Math & Comp Sci, Enschede, Netherlands; [Ceylan, Oguzhan] Kadir Has Univ, Dept Management Informat Syst, Istanbul, Turkiye; [Ozdemir, Aydogan] Kadir Has Univ, Dept Elect & Elect Engn, Istanbul, Turkiyeen_US
dc.descriptionAhmadi, Bahman/0000-0002-1745-2228; Ozdemir, Aydogan/0000-0003-1331-2647en_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.en_US
dc.description.sponsorshipEU [957682]; The Scientific and Technological Research Council of Turkey TUBITAKen_US
dc.description.sponsorshipThe funding for this research is provided by the EU HORIZON 2020 project SERENE, grant agreement No 957682, and the "117E773 Advanced Evolutionary Computation for Smart Grid and Smart Community" project, conducted under the 1001 Project framework organized by "The Scientific and Technological Research Council of Turkey TUBITAK".en_US
dc.description.woscitationindexScience Citation Index Expanded
dc.identifier.doi10.1016/j.compeleceng.2025.110196
dc.identifier.issn0045-7906
dc.identifier.issn1879-0755
dc.identifier.scopus2-s2.0-85218891617
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.compeleceng.2025.110196
dc.identifier.volume123en_US
dc.identifier.wosWOS:001435820400001
dc.identifier.wosqualityQ2
dc.language.isoenen_US
dc.publisherPergamon-elsevier Science Ltden_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectLarge-Scale Blackouten_US
dc.subjectPower System Restorationen_US
dc.subjectSmart Griden_US
dc.subjectRobust Optimizationen_US
dc.subjectSelf-Healingen_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

Files