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Optimizing Photovoltaic Generation Placement and Sizing Using Evolutionary Strategies Under Spatial Constraints

Author

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  • Carlos Henrique Silva

    (Electrical, Mechanical and Computer Engineering School, Federal University of Goiás (UFG), Av. Universitária No. 1488, Goiânia 74605-010, Brazil)

  • Saymon Fonseca Santos Mendes

    (Electrical, Mechanical and Computer Engineering School, Federal University of Goiás (UFG), Av. Universitária No. 1488, Goiânia 74605-010, Brazil)

  • Lina P. Garcés Negrete

    (Electrical, Mechanical and Computer Engineering School, Federal University of Goiás (UFG), Av. Universitária No. 1488, Goiânia 74605-010, Brazil)

  • Jesús M. López-Lezama

    (Research Group on Efficient Energy Management (GIMEL), Department of Electrical Engineering, Universidad de Antioquia (UdeA), Calle 67 No. 53–108, Medellín 050010, Colombia)

  • Nicolás Muñoz-Galeano

    (Research Group on Efficient Energy Management (GIMEL), Department of Electrical Engineering, Universidad de Antioquia (UdeA), Calle 67 No. 53–108, Medellín 050010, Colombia)

Abstract

This study presents a methodology for optimizing the placement and sizing of photovoltaic generation in power distribution networks. In addition to technical and budgetary constraints, the proposed approach incorporates georeferenced spatial restrictions to determine the optimal location and capacity of the generation units. These spatial constraints are not commonly considered in similar studies, which make them the main contribution in the proposed methodology. The proposed approach is divided into three stages and utilizes simulations in OpenDSS and QGIS, which employ optimization strategies such as the Hybrid Evolutionary Strategy and the Hybrid Genetic Algorithm. The methodology was evaluated on the IEEE 34-bus system and a real feeder. The results demonstrate the effectiveness of the proposed approach, which achieves significant reductions in system losses—14.48% for the IEEE 34-bus system and 14.08% for the real feeder—while also improving voltage profiles. These findings validate its applicability in the efficient and sustainable planning of power distribution systems.

Suggested Citation

  • Carlos Henrique Silva & Saymon Fonseca Santos Mendes & Lina P. Garcés Negrete & Jesús M. López-Lezama & Nicolás Muñoz-Galeano, 2025. "Optimizing Photovoltaic Generation Placement and Sizing Using Evolutionary Strategies Under Spatial Constraints," Energies, MDPI, vol. 18(8), pages 1-34, April.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:8:p:2091-:d:1637644
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    References listed on IDEAS

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    1. Mehigan, L. & Deane, J.P. & Gallachóir, B.P.Ó. & Bertsch, V., 2018. "A review of the role of distributed generation (DG) in future electricity systems," Energy, Elsevier, vol. 163(C), pages 822-836.
    2. Marco Segreto & Lucas Principe & Alexandra Desormeaux & Marco Torre & Laura Tomassetti & Patrizio Tratzi & Valerio Paolini & Francesco Petracchini, 2020. "Trends in Social Acceptance of Renewable Energy Across Europe—A Literature Review," IJERPH, MDPI, vol. 17(24), pages 1-19, December.
    3. Tolabi, H.B. & Ara, A. Lashkar & Hosseini, R., 2020. "A new thief and police algorithm and its application in simultaneous reconfiguration with optimal allocation of capacitor and distributed generation units," Energy, Elsevier, vol. 203(C).
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