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Optimal Planning of Primary Feeders in Underground Distribution Networks using Heuristic Algorithms

Planificación Óptima de Alimentadores Primarios en Redes de Distribución Subterráneas empleando Algoritmos Heurísticos




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SISTEMAS ELÉCTRICOS DE POTENCIA

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Optimal Planning of Primary Feeders in Underground Distribution Networks using Heuristic Algorithms. (2021). Revista Técnica "energía", 17(2), PP. 1-7. https://doi.org/10.37116/revistaenergia.v17.n2.2021.421

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How to Cite

Optimal Planning of Primary Feeders in Underground Distribution Networks using Heuristic Algorithms. (2021). Revista Técnica "energía", 17(2), PP. 1-7. https://doi.org/10.37116/revistaenergia.v17.n2.2021.421

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This paper presents a methodological proposal for Greenfield Planning of underground electrical distribution systems, using Dynamic Programming and optimization techniques based on heuristic algorithms. The goal of the methodology is the search a local optimal solution that allows determining the minimum necessary number of switchgear equipment, together with the associated number of primary circuits in Open Loop (OL) topological configuration and the optimal layout in the geo-referenced urban plane.

To solve the different optimization problems, various heuristic algorithms are used, such that: to determine the optimal number of switchgear equipment and its associated primary circuits  Genetic Algorithm (AG) is used, in the layout of the OL primaries Ant Colony Optimization algorithm (ACO), and for the spatial sectorization of the primary circuits the K-medoids algorithm is applied.

The geographic coordinates and the power of the medium voltage to low voltage transformation centers (TC MV / LV), as well as the geo-referenced graph of the roads in the study area, are required as input parameters.

For the practical implementation of the methodological proposal, a computational tool has been developed in Matlab, which was used to prepare the planning of the underground electrical network of a large urban sector in the city of Ambato with a surface of 2.97 km².


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