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

Sara Lumbreras, Sonja Wogrin, Guillermo Navarro, Ilaria Bertazzi and Maria Pereda
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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

Energies, 2019, vol. 12, issue 23, 1-17

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.

Keywords: transmission expansion planning; agent-based modeling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
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