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Slime Mold Inspired Distribution Network Initial Solution

Author

Listed:
  • Verner Püvi

    (Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland)

  • Robert J. Millar

    (Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland)

  • Eero Saarijärvi

    (Trimble Solutions, 02130 Espoo, Finland)

  • Ken Hayami

    (Principles of Informatics Research Division, National Institute of Informatics, Tokyo 101-8430, Japan)

  • Tahitoa Arbelot

    (Ensimag-National School of Computer Science and Applied Mathematics, Grenoble Institute of Technology, 38402 Saint Martin D’Heres, France)

  • Matti Lehtonen

    (Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland)

Abstract

Electricity distribution network optimisation has attracted attention in recent years due to the widespread penetration of distributed generation. A considerable portion of network optimisation algorithms rely on an initial solution that is supposed to bypass the time-consuming steps of optimisation routines. The aim of this paper is to present a nature inspired algorithm for initial network generation. Based on slime mold behaviour, the algorithm can generate a large-scale network in a reasonable computation time. A mathematical formulation and parameter exploration of the slime mold algorithm are presented. Slime mold networks resemble a relaxed minimum spanning tree with better balance between the investment and loss costs of a distribution network. Results indicate lower total costs for suburban and urban networks.

Suggested Citation

  • Verner Püvi & Robert J. Millar & Eero Saarijärvi & Ken Hayami & Tahitoa Arbelot & Matti Lehtonen, 2020. "Slime Mold Inspired Distribution Network Initial Solution," Energies, MDPI, vol. 13(23), pages 1-17, November.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:23:p:6278-:d:452896
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    References listed on IDEAS

    as
    1. Ali Ahmadian & Ali Elkamel & Abdelkader Mazouz, 2019. "An Improved Hybrid Particle Swarm Optimization and Tabu Search Algorithm for Expansion Planning of Large Dimension Electric Distribution Network," Energies, MDPI, vol. 12(16), pages 1-14, August.
    2. Shin Watanabe & Atsuko Takamatsu, 2014. "Transportation Network with Fluctuating Input/Output Designed by the Bio-Inspired Physarum Algorithm," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-11, February.
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    Cited by:

    1. Edy Quintana & Esteban Inga, 2022. "Optimal Reconfiguration of Electrical Distribution System Using Heuristic Methods with Geopositioning Constraints," Energies, MDPI, vol. 15(15), pages 1-20, July.
    2. Samar Fatima & Verner Püvi & Ammar Arshad & Mahdi Pourakbari-Kasmaei & Matti Lehtonen, 2021. "Comparison of Economical and Technical Photovoltaic Hosting Capacity Limits in Distribution Networks," Energies, MDPI, vol. 14(9), pages 1-23, April.

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