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Representative Days for Expansion Decisions in Power Systems

Author

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  • Álvaro García-Cerezo

    (Escuela Técnica Superior de Ingeniería Industrial, Universidad de Castilla-La Mancha, Campus Universitario s/n, 13071 Ciudad Real, Spain)

  • Luis Baringo

    (Escuela Técnica Superior de Ingeniería Industrial, Universidad de Castilla-La Mancha, Campus Universitario s/n, 13071 Ciudad Real, Spain)

  • Raquel García-Bertrand

    (Escuela Técnica Superior de Ingeniería Industrial, Universidad de Castilla-La Mancha, Campus Universitario s/n, 13071 Ciudad Real, Spain)

Abstract

Short-term uncertainty needs to be properly modeled when analyzing a planning problem in a power system. Since the use of all available historical data may lead to problems of computational intractability, clustering algorithms may be applied in order to reduce the computational effort without compromising accurate representation of historical data. In this paper, we propose a modified version of the traditional K-means method, seeking to represent the maximum and minimum values of input data, namely, electricity demand and renewable production in several locations of a power system. Extreme values of these parameters must be represented as they are high-impact decisions that are taken with respect to expansion and operation. The method proposed is based on the K-means algorithm, which represents the correlation between demand and wind-power production. The chronology of historical data, which influences the performance of some technologies, is characterized through representative days, each made up of 24 operating conditions. A realistic case study, applying representative days, analyzes the generation and transmission expansion planning of the IEEE 24-bus Reliability Test System. Results show that the proposed method is preferable to the traditional K-means technique.

Suggested Citation

  • Álvaro García-Cerezo & Luis Baringo & Raquel García-Bertrand, 2020. "Representative Days for Expansion Decisions in Power Systems," Energies, MDPI, vol. 13(2), pages 1-18, January.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:2:p:335-:d:307155
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    References listed on IDEAS

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    1. Bell, William Paul & Wild, Phillip & Foster, John & Hewson, Michael, 2015. "Wind speed and electricity demand correlation analysis in the Australian National Electricity Market: Determining wind turbine generators’ ability to meet electricity demand without energy storage," Economic Analysis and Policy, Elsevier, vol. 48(C), pages 182-191.
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    3. 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.
    4. Zhou, Ying & Wang, Lizhi & McCalley, James D., 2011. "Designing effective and efficient incentive policies for renewable energy in generation expansion planning," Applied Energy, Elsevier, vol. 88(6), pages 2201-2209, June.
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    Cited by:

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    2. Baringo, Luis & Boffino, Luigi & Oggioni, Giorgia, 2020. "Robust expansion planning of a distribution system with electric vehicles, storage and renewable units," Applied Energy, Elsevier, vol. 265(C).
    3. Victor H. Hinojosa & Joaquín Sepúlveda, 2020. "Solving the Stochastic Generation and Transmission Capacity Planning Problem Applied to Large-Scale Power Systems Using Generalized Shift-Factors," Energies, MDPI, vol. 13(13), pages 1-15, June.
    4. García-Cerezo, Álvaro & Baringo, Luis & García-Bertrand, Raquel, 2021. "Robust transmission network expansion planning considering non-convex operational constraints," Energy Economics, Elsevier, vol. 98(C).
    5. Tostado-Véliz, Marcos & Rezaee Jordehi, Ahmad & Amir Mansouri, Seyed & Jurado, Francisco, 2022. "Day-ahead scheduling of 100% isolated communities under uncertainties through a novel stochastic-robust model," Applied Energy, Elsevier, vol. 328(C).
    6. James H. Merrick & John E. T. Bistline & Geoffrey J. Blanford, 2021. "On representation of energy storage in electricity planning models," Papers 2105.03707, arXiv.org, revised May 2021.

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