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A Benders’ Decomposition Approach for Renewable Generation Investment in Distribution Systems

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  • Sergio Montoya-Bueno

    (Department of Applied Mechanics and Project Engineering, University of Castilla-La Mancha, 13071 Ciudad Real, Spain)

  • Jose Ignacio Muñoz-Hernandez

    (Department of Applied Mechanics and Project Engineering, University of Castilla-La Mancha, 13071 Ciudad Real, Spain)

  • Javier Contreras

    (Department of Applied Mechanics and Project Engineering, University of Castilla-La Mancha, 13071 Ciudad Real, Spain)

  • Luis Baringo

    (Department of Applied Mechanics and Project Engineering, University of Castilla-La Mancha, 13071 Ciudad Real, Spain)

Abstract

A model suitable to obtain where and when renewable energy sources (RES) should be allocated as part of generation planning in distribution systems is formulated. The proposed model starts from an existing two-stage stochastic mixed-integer linear programming (MILP) problem including investment and scenario-dependent operation decisions. The aim is to minimize photovoltaic and wind investment costs, operation costs, as well as total substation costs including the cost of the energy bought from substations and energy losses. A new Benders’ decomposition framework is used to decouple the problem between investment and operation decisions, where the latter can be further decomposed into a set of smaller problems per scenario and planning period. The model is applied to a 34-bus system and a comparison with a MILP model is presented to show the advantages of the model proposed.

Suggested Citation

  • Sergio Montoya-Bueno & Jose Ignacio Muñoz-Hernandez & Javier Contreras & Luis Baringo, 2020. "A Benders’ Decomposition Approach for Renewable Generation Investment in Distribution Systems," Energies, MDPI, vol. 13(5), pages 1-19, March.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:5:p:1225-:d:329480
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    References listed on IDEAS

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    1. Jeremy A. Bloom & Michael Caramanis & Leonid Charny, 1984. "Long-Range Generation Planning Using Generalized Benders' Decomposition: Implementation and Experience," Operations Research, INFORMS, vol. 32(2), pages 290-313, April.
    2. Viral, Rajkumar & Khatod, D.K., 2012. "Optimal planning of distributed generation systems in distribution system: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 5146-5165.
    3. Ghasemi, Ahmad & Mortazavi, Seyed Saeidollah & Mashhour, Elaheh, 2015. "Integration of nodal hourly pricing in day-ahead SDC (smart distribution company) optimization framework to effectively activate demand response," Energy, Elsevier, vol. 86(C), pages 649-660.
    4. Baringo, L. & Conejo, A.J., 2013. "Correlated wind-power production and electric load scenarios for investment decisions," Applied Energy, Elsevier, vol. 101(C), pages 475-482.
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    Cited by:

    1. Javier Contreras & Gregorio Muñoz-Delgado, 2021. "Distributed Power Generation Scheduling, Modeling, and Expansion Planning," Energies, MDPI, vol. 14(22), pages 1-2, November.

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