DC FieldValueLanguage
dc.contributorDepartment of Logistics and Maritime Studiesen_US
dc.creatorWang, Zen_US
dc.creatorXu, Zen_US
dc.creatorWang, Xen_US
dc.creatorXie, Men_US
dc.date.accessioned2021-11-19T05:58:01Z-
dc.date.available2021-11-19T05:58:01Z-
dc.identifier.issn2213-1388en_US
dc.identifier.urihttp://hdl.handle.net/10397/91613-
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectCleaningen_US
dc.subjectGenetic algorithm (GA)en_US
dc.subjectPhotovoltaic power plantsen_US
dc.subjectRoutingen_US
dc.subjectSchedulingen_US
dc.subjectTraveling salesman problemen_US
dc.titleA temporal-spatial cleaning optimization method for photovoltaic power plantsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume49en_US
dc.identifier.doi10.1016/j.seta.2021.101691en_US
dcterms.abstractCleaning is critical for photovoltaic (PV) systems, as it can remove dust deposition and keep the systems operating efficiently. Existing studies on PV cleaning focus predominately on determining optimal cleaning frequencies or intervals. In practice, however, route planning is also an indispensable decision to be made when cleaning large-scale PV plants. In this work, we study a temporal-spatial cleaning optimization problem for large-scale PV plants. A two-stage cleaning optimization policy that consists of a periodic planning stage and a dynamic adjustment stage is proposed. In the former stage, tentative cleaning intervals are determined; in the latter stage, the temporal scheduling and spatial routing problems are jointly optimized in a dynamic fashion, so as to minimize the total economic loss. We model the problem as an extended version of the classical traveling salesman problem (TSP), that is, a production-driven traveling salesman problem with time-dependent cost (PD-TSP-TC). Genetic algorithm is employed to solve the corresponding nonlinear 0–1 integer programming problem. A case study on a real PV plant is conducted to demonstrate the performance of the proposed method, which shows that the temporal-spatial cleaning optimization method results in a better performance than the one that ignores route planning and that models route planning as classical TSP.en_US
dcterms.accessRightsembargoed accessen_US
dcterms.bibliographicCitationSustainable energy technologies and assessments, Feb. 2022, v. 49, 101691en_US
dcterms.isPartOfSustainable energy technologies and assessmentsen_US
dcterms.issued2022-02-
dc.identifier.scopus2-s2.0-85118858040-
dc.identifier.eissn2213-1396en_US
dc.identifier.artn101691en_US
dc.description.validate202111 bchyen_US
dc.description.oaNot applicableen_US
dc.identifier.FolderNumbera1067-n01-
dc.identifier.SubFormID43870-
dc.description.fundingSourceSelf-fundeden_US
dc.description.fundingTextThe author(s) received no specific funding for this work.en_US
dc.description.pubStatusPublisheden_US
dc.date.embargo2024.02.29en_US
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