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This paper proposes an alternative methodology to optimally design an electrical distribution system in a real urban area. A medium voltage (MV) network polygon is extracted from the web page of a distribution company (GIS platform), which serves as a comparison pattern. A redesign of the medium voltage network is made to the extracted polygon. Taking as a premise that it is a new urbanization that needs to be provided with electric power, with all the technical requirements imposed by the regulating entity, good power quality and at minimum cost. To achieve this goal, two mathematical optimization models are used. The methodology begins by delimiting the project area in a geo-referenced manner; in this area 36 possible sites are located where the distribution transformers that will provide electricity to the new urbanization can be placed. The first optimization model determines how many transformers will be installed, by means of a minimization objective function, the constraints of the model are: transformer capacity (KVA) and coverage (number of subscribers connected to the low voltage network of each transformer). Once the number of transformers to be installed and their respective geo-referenced optimal locations are determined, the model is run. The second heuristic optimization model, based on graph theory, calculates the mine spanning tree for the connection of the medium voltage electrical network, thus interconnecting the transformers of the project with the minimum distance, thus optimizing the construction cost. These optimization models are implemented and solved with Matlab and LpSolve software. The topology found with the two proposed models is electrically evaluated by running power flows with CYMEDIST software. Finally, the proposed model is evaluated by comparing the electrical parameters obtained with the electrical parameters of the polygon extracted from the GIS system of the distribution company.

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Amaya Vásquez L, Campaña Molina M Ángel. Optimal Design of Electrical Distribution Networks Using Optimization Models. inycomp [Internet]. 2023 Jan. 15 [cited 2024 Dec. 21];25(1):e-20311572. Available from: https://revistaingenieria.univalle.edu.co/index.php/ingenieria_y_competitividad/article/view/11572

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Received 2021-08-31
Accepted 2022-10-31
Published 2023-01-15