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

Lumbreras, Sara and Wogrin, Sonja and Navarro, Guillermo and Bertazzi, Ilaria and Pereda García, María (2019). A Decentralized Solution for Transmission Expansion Planning: Getting Inspiration from Nature. "Energies", v. 12 (n. 23); pp. 4427-4444. ISSN 1996-1073. https://doi.org/doi.org/10.3390/en12234427.

Description

Title: A Decentralized Solution for Transmission Expansion Planning: Getting Inspiration from Nature
Author/s:
  • Lumbreras, Sara
  • Wogrin, Sonja
  • Navarro, Guillermo
  • Bertazzi, Ilaria
  • Pereda García, María
Item Type: Article
Título de Revista/Publicación: Energies
Date: 21 November 2019
ISSN: 1996-1073
Volume: 12
Subjects:
Freetext Keywords: transmission expansion planning; agent-based modeling
Faculty: E.T.S.I. Industriales (UPM)
Department: Ingeniería de Organización, Administración de Empresas y Estadística
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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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

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainENE2016-79517-RUnspecifiedUnspecifiedPlanificación estratégica bajo incertidumbre del transporte y la generación en el mercado eléctrico

More information

Item ID: 64295
DC Identifier: http://oa.upm.es/64295/
OAI Identifier: oai:oa.upm.es:64295
DOI: doi.org/10.3390/en12234427
Official URL: https://www.mdpi.com/1996-1073/12/23/4427
Deposited by: Memoria Investigacion
Deposited on: 02 Oct 2020 13:38
Last Modified: 02 Oct 2020 13:38
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