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Heuristic Methodology for Planning AC Rural Medium-Voltage Distribution Grids

dc.contributor.authorDanilo Montoya, Oscar
dc.contributor.authorMartín Serra, Federico
dc.contributor.authorHernan De Angelo, Cristian
dc.contributor.authorChamorro Vera, Harold Rene 
dc.contributor.authorAlvarado Barrios, Lázaro 
dc.date.accessioned2023-11-22T08:06:47Z
dc.date.available2023-11-22T08:06:47Z
dc.date.issued2021
dc.identifier.citationMontoya OD, Serra FM, De Angelo CH, Chamorro HR, Alvarado-Barrios L. Heuristic Methodology for Planning AC Rural Medium-Voltage Distribution Grids. Energies. 2021; 14(16):5141. https://doi.org/10.3390/en14165141es
dc.identifier.issn1996-1073
dc.identifier.urihttps://hdl.handle.net/20.500.12412/4714
dc.description.abstractThe optimal expansion of AC medium-voltage distribution grids for rural applications is addressed in this study from a heuristic perspective. The optimal routes of a distribution feeder are selected by applying the concept of a minimum spanning tree by limiting the number of branches that are connected to a substation (mixed-integer linear programming formulation). In order to choose the caliber of the conductors for the selected feeder routes, the maximum expected current that is absorbed by the loads is calculated, thereby defining the minimum thermal bound of the conductor caliber. With the topology and the initial selection of the conductors, a tabu search algorithm (TSA) is implemented to refine the solution with the help of a three-phase power flow simulation in MATLAB for three different load conditions, i.e., maximum, medium, and minimum consumption with values of 100%, 60%, and 30%, respectively. This helps in calculating the annual costs of the energy losses that will be summed with the investment cost in conductors for determining the final costs of the planning project. Numerical simulations in two test feeders comprising 9 and 25 nodes with one substation show the effectiveness of the proposed methodology regarding the final grid planning cost; in addition, the heuristic selection of the calibers using the minimum expected current absorbed by the loads provides at least 70% of the calibers that are contained in the final solution of the problem. This demonstrates the importance of using adequate starting points to potentiate metaheuristic optimizers such as the TSA.es
dc.language.isoenges
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleHeuristic Methodology for Planning AC Rural Medium-Voltage Distribution Gridses
dc.typearticlees
dc.identifier.doi10.3390/en14165141
dc.issue.number16es
dc.journal.titleEnergieses
dc.page.initial5141es
dc.relation.projectIDThis work was supported in part by the Centro de Investigación y Desarrollo Científico de la Universidad Distrital Francisco José de Caldas under grant 1643-12-2020 associated with the project “Desarrollo de una metodología de optimización para la gestión óptima de recursos energéticos distribuidos en redes de distribución de energía eléctrica” and in part by the Dirección de Investigaciones de la Universidad Tecnológica de Bolívar under grant PS2020002 associated with the project “Ubicación óptima de bancos de capacitores de paso fijo en redes eléctricas de distribución para reducción de costos y pérdidas de energía: Aplicación de métodos exactos y metaheurísticos”.es
dc.rights.accessRightsopenAccesses
dc.subject.keywordDistribution system planninges
dc.subject.keywordTabu search algorithmes
dc.subject.keywordMinimum spanning treees
dc.subject.keywordHeuristic optimization methodologyes
dc.subject.keywordRural distribution networkses
dc.volume.number14es


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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