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Heuristic Methodology for Planning AC Rural Medium-Voltage Distribution Grids

Author

Listed:
  • Oscar Danilo Montoya

    (Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas, Bogotá 110231, Colombia
    Laboratorio Inteligente de Energía, Universidad Tecnológica de Bolívar, Cartagena 131001, Colombia)

  • Federico Martin Serra

    (Laboratorio de Control Automático (LCA), Facultad de Ingeniería y Ciencias Agropecuarias, Universidad Nacional de San Luis—CONICET, San Luis 5730, Argentina)

  • Cristian Hernan De Angelo

    (Grupo de Electrónica Aplicada (GEA), Facultad de Ingeniería, Instituto de Investigaciones en Tecnologías Energéticas y Materiales (IITEMA)—CONICET, Universidad Nacional de Rio Cuarto, Córdoba 5800, Argentina)

  • Harold R. Chamorro

    (Department of Electrical Engineering at KTH, Royal Institute of Technology, SE-100 44 Stockholm, Sweden)

  • Lazaro Alvarado-Barrios

    (Department of Engineering, Universidad Loyola Andalucía, 41704 Sevilla, Spain)

Abstract

The 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.

Suggested Citation

  • Oscar Danilo Montoya & Federico Martin Serra & Cristian Hernan De Angelo & Harold R. Chamorro & Lazaro Alvarado-Barrios, 2021. "Heuristic Methodology for Planning AC Rural Medium-Voltage Distribution Grids," Energies, MDPI, vol. 14(16), pages 1-20, August.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:16:p:5141-:d:618098
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    References listed on IDEAS

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    1. Bharath Varsh Rao & Friederich Kupzog & Martin Kozek, 2019. "Three-Phase Unbalanced Optimal Power Flow Using Holomorphic Embedding Load Flow Method," Sustainability, MDPI, vol. 11(6), pages 1-16, March.
    2. José Luis Picard & Irene Aguado & Noemi G. Cobos & Vicente Fuster-Roig & Alfredo Quijano-López, 2021. "Electric Distribution System Planning Methodology Considering Distributed Energy Resources: A Contribution towards Real Smart Grid Deployment," Energies, MDPI, vol. 14(7), pages 1-18, March.
    3. Brandon Cortés-Caicedo & Laura Sofía Avellaneda-Gómez & Oscar Danilo Montoya & Lazaro Alvarado-Barrios & Harold R. Chamorro, 2021. "Application of the Vortex Search Algorithm to the Phase-Balancing Problem in Distribution Systems," Energies, MDPI, vol. 14(5), pages 1-35, February.
    4. Fei Tang & Huizhi Zhou & Qinghua Wu & Hu Qin & Jun Jia & Ke Guo, 2015. "A Tabu Search Algorithm for the Power System Islanding Problem," Energies, MDPI, vol. 8(10), pages 1-27, October.
    5. M Haouari & J Chaouachi & M Dror, 2005. "Solving the generalized minimum spanning tree problem by a branch-and-bound algorithm," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(4), pages 382-389, April.
    6. Rui Li & Wei Wang & Zhe Chen & Jiuchun Jiang & Weige Zhang, 2017. "A Review of Optimal Planning Active Distribution System: Models, Methods, and Future Researches," Energies, MDPI, vol. 10(11), pages 1-27, October.
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    Cited by:

    1. Benedetto-Giuseppe Risi & Francesco Riganti-Fulginei & Antonino Laudani, 2022. "Modern Techniques for the Optimal Power Flow Problem: State of the Art," Energies, MDPI, vol. 15(17), pages 1-20, September.
    2. Julián David Pradilla-Rozo & Julián Alejandro Vega-Forero & Oscar Danilo Montoya, 2023. "Application of the Gradient-Based Metaheuristic Optimizerto Solve the Optimal Conductor Selection Problemin Three-Phase Asymmetric Distribution Networks," Energies, MDPI, vol. 16(2), pages 1-29, January.
    3. Brandon Cortés-Caicedo & Luis Fernando Grisales-Noreña & Oscar Danilo Montoya, 2022. "Optimal Selection of Conductor Sizes in Three-Phase Asymmetric Distribution Networks Considering Optimal Phase-Balancing: An Application of the Salp Swarm Algorithm," Mathematics, MDPI, vol. 10(18), pages 1-34, September.

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