dc.creatorGrisales-Noreña L.F.
dc.creatorMontoya O.D.
dc.creatorGil-González W.
dc.date.accessioned2020-03-26T16:32:50Z
dc.date.accessioned2022-09-28T20:12:10Z
dc.date.available2020-03-26T16:32:50Z
dc.date.available2022-09-28T20:12:10Z
dc.date.created2020-03-26T16:32:50Z
dc.date.issued2019
dc.identifierJournal of Energy Storage; Vol. 25
dc.identifier2352152X
dc.identifierhttps://hdl.handle.net/20.500.12585/9050
dc.identifier10.1016/j.est.2019.100891
dc.identifierUniversidad Tecnológica de Bolívar
dc.identifierRepositorio UTB
dc.identifier55791991200
dc.identifier56919564100
dc.identifier57191493648
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/3722457
dc.description.abstractThis paper presents a method to find the optimal location, selection, and operation of energy storage systems (ESS- batteries-) and capacitors banks (CB) in distribution systems (DS). A mixed-integer non-linear programming model is proposed to formulate the problem. In this model, the minimization of energy loss in the DS is selected as an objective function. As constraints are considered: the active and reactive energy balance, voltage regulation, the total number energy storage devices that can be installed into network, as well as the operative bounds associated with the ESS (time of charge-discharge and energy capabilities). Three operating scenarios for the DS are analyzed by adopting the method proposed in this work. The first scenario is an evaluation of the base case (without batteries and CB), in which the initial conditions of the DS are determined. The second scenario considers the location of the ESS composed by redox flow batteries. Finally, the third scenario includes the installation of REDOX flow batteries with CB in parallel to correct operating problems generated by battery charging, and improve their impact on the grid. A master-slave strategy is adopted to solve the problem here discussed, implementing a Chu & Beasley genetic algorithm in both stages as an optimization technique. The proposed method is tested in a 69-node test feeder, where numerical results demonstrate its effectiveness. © 2019 Elsevier Ltd
dc.languageeng
dc.publisherElsevier Ltd
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.rightsAtribución-NoComercial 4.0 Internacional
dc.sourcehttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85070824336&doi=10.1016%2fj.est.2019.100891&partnerID=40&md5=aceaad3c7b8331c512531903381e9477
dc.titleIntegration of energy storage systems in AC distribution networks: Optimal location, selecting, and operation approach based on genetic algorithms


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