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A Decentralized Solution for Transmission Expansion Planning: Getting Inspiration from Nature

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Listed:
  • Sara Lumbreras

    (Escuela Técnica Superior de Ingeniería (ICAI), Instituto de Investigación Tecnológica, Universidad Pontificia Comillas, 28015 Madrid, Spain)

  • Sonja Wogrin

    (Escuela Técnica Superior de Ingeniería (ICAI), Instituto de Investigación Tecnológica, Universidad Pontificia Comillas, 28015 Madrid, Spain)

  • Guillermo Navarro

    (Escuela Técnica Superior de Ingeniería (ICAI), Instituto de Investigación Tecnológica, Universidad Pontificia Comillas, 28015 Madrid, Spain)

  • Ilaria Bertazzi

    (University of Turin, 10124 Turin, Italy)

  • Maria Pereda

    (Administración de Empresas y Estadística, Departamento Ingeniería de Organización, Escuela Superior de Ingenieros Industriales, Escuela Politécnica de Madrid, 28015 Madrid, Spain
    Unidad Mixta Interdisciplinar de Comportamiento y Complejidad Social (UMICCS), 28015 Madrid, Spain)

Abstract

Transmission expansion planning is a problem of considerable complexity where classical optimization techniques are unable to handle large case studies. Decomposition and divide-and-conquer strategies have been applied to this problem. We propose an alternative approach based on agent-based modeling (ABM) and inspired by the behavior of the Plasmodium mold, which builds efficient transportation networks as result of its search for food sources. Algorithms inspired by this mold have already been applied to road-network design. We modify an existing ABM for road-network design to include the idiosyncratic features of power systems and their related physics, and test it over an array of case studies. Our results show that the ABM can provide near-optimal designs in all the instances studied, possibly with some further interesting properties with respect to the robustness of the developed design. In addition, the model works in a decentralized manner, using mostly local information. This means that computational time will scale with size in a more benign way than global optimization approaches. Our work shows promise in applying ABMs to solve similarly complex global optimization problems in the energy landscape.

Suggested Citation

  • Sara Lumbreras & Sonja Wogrin & Guillermo Navarro & Ilaria Bertazzi & Maria Pereda, 2019. "A Decentralized Solution for Transmission Expansion Planning: Getting Inspiration from Nature," Energies, MDPI, Open Access Journal, vol. 12(23), pages 1-17, November.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:23:p:4427-:d:289438
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    References listed on IDEAS

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    1. Wook-Won Kim & Jong-Keun Park & Yong-Tae Yoon & Mun-Kyeom Kim, 2018. "Transmission Expansion Planning under Uncertainty for Investment Options with Various Lead-Times," Energies, MDPI, Open Access Journal, vol. 11(9), pages 1-19, September.
    2. Koesrinartoto, D. & Sun, Junjie & Tesfatsion, Leigh, 2005. "An agent-based computational laboratory for testing the economic reliability of wholesale power market designs," ISU General Staff Papers 200501010800001043, Iowa State University, Department of Economics.
    3. Li, Gong & Shi, Jing, 2012. "Agent-based modeling for trading wind power with uncertainty in the day-ahead wholesale electricity markets of single-sided auctions," Applied Energy, Elsevier, vol. 99(C), pages 13-22.
    4. José Manuel Galán & Luis R. Izquierdo & Segismundo S. Izquierdo & José Ignacio Santos & Ricardo del Olmo & Adolfo López-Paredes & Bruce Edmonds, 2009. "Errors and Artefacts in Agent-Based Modelling," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(1), pages 1-1.
    5. Fichera, Alberto & Pluchino, Alessandro & Volpe, Rosaria, 2018. "A multi-layer agent-based model for the analysis of energy distribution networks in urban areas," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 710-725.
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