IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i2p335-d307155.html
   My bibliography  Save this article

Representative Days for Expansion Decisions in Power Systems

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/2/335/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/2/335/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    2. 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.
    3. 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.
    4. Baringo, L. & Conejo, A.J., 2011. "Wind power investment within a market environment," Applied Energy, Elsevier, vol. 88(9), pages 3239-3247.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jia, Rui & Du, Kun & Song, Zhigang & Xu, Wei & Zheng, Feifei, 2024. "Scenario reduction-based simulation method for efficient serviceability assessment of earthquake-damaged water distribution systems," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    2. Yuhong Wang & Xu Zhou & Yunxiang Shi & Zongsheng Zheng & Qi Zeng & Lei Chen & Bo Xiang & Rui Huang, 2021. "Transmission Network Expansion Planning Considering Wind Power and Load Uncertainties Based on Multi-Agent DDQN," Energies, MDPI, vol. 14(19), pages 1-28, September.
    3. Merrick, James H. & Bistline, John E.T. & Blanford, Geoffrey J., 2024. "On representation of energy storage in electricity planning models," Energy Economics, Elsevier, vol. 136(C).
    4. 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).
    5. 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.
    6. Navarro, Andrés & Favereau, Marcel & Lorca, Álvaro & Olivares, Daniel & Negrete-Pincetic, Matías, 2024. "Medium-term stochastic hydrothermal scheduling with short-term operational effects for large-scale power and water networks," Applied Energy, Elsevier, vol. 358(C).
    7. 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).
    8. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Ian M. Trotter & Torjus F. Bolkesj{o} & Eirik O. J{aa}stad & Jon Gustav Kirkerud, 2021. "Increased Electrification of Heating and Weather Risk in the Nordic Power System," Papers 2112.02893, arXiv.org.
    3. Xenophon, Aleksis Kazubiernis & Hill, David John, 2019. "Emissions reduction and wholesale electricity price targeting using an output-based mechanism," Applied Energy, Elsevier, vol. 242(C), pages 1050-1063.
    4. Arjmand, Reza & Rahimiyan, Morteza, 2016. "Statistical analysis of a competitive day-ahead market coupled with correlated wind production and electric load," Applied Energy, Elsevier, vol. 161(C), pages 153-167.
    5. Jadidoleslam, Morteza & Ebrahimi, Akbar & Latify, Mohammad Amin, 2017. "Probabilistic transmission expansion planning to maximize the integration of wind power," Renewable Energy, Elsevier, vol. 114(PB), pages 866-878.
    6. Fan, Vivienne Hui & Dong, Zhaoyang & Meng, Ke, 2020. "Integrated distribution expansion planning considering stochastic renewable energy resources and electric vehicles," Applied Energy, Elsevier, vol. 278(C).
    7. Henrik C. Bylling & Salvador Pineda & Trine K. Boomsma, 2020. "The impact of short-term variability and uncertainty on long-term power planning," Annals of Operations Research, Springer, vol. 284(1), pages 199-223, January.
    8. Yu, Hao & Wei, Yi-Ming & Tang, Bao-Jun & Mi, Zhifu & Pan, Su-Yan, 2016. "Assessment on the research trend of low-carbon energy technology investment: A bibliometric analysis," Applied Energy, Elsevier, vol. 184(C), pages 960-970.
    9. Exizidis, Lazaros & Kazempour, S. Jalal & Pinson, Pierre & de Greve, Zacharie & Vallée, François, 2016. "Sharing wind power forecasts in electricity markets: A numerical analysis," Applied Energy, Elsevier, vol. 176(C), pages 65-73.
    10. Adel F. Alrasheedi & Ahmad M. Alshamrani & Khalid A. Alnowibet, 2023. "Investing in Wind Energy Using Bi-Level Linear Fractional Programming," Energies, MDPI, vol. 16(13), pages 1-14, June.
    11. Després, Jacques & Hadjsaid, Nouredine & Criqui, Patrick & Noirot, Isabelle, 2015. "Modelling the impacts of variable renewable sources on the power sector: Reconsidering the typology of energy modelling tools," Energy, Elsevier, vol. 80(C), pages 486-495.
    12. Simshauser, P., 2019. "On the impact of government-initiated CfD’s in Australia’s National Electricity Market," Cambridge Working Papers in Economics 1901, Faculty of Economics, University of Cambridge.
    13. 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).
    14. Faezeh Akhavizadegan & Lizhi Wang & James McCalley, 2020. "Scenario Selection for Iterative Stochastic Transmission Expansion Planning," Energies, MDPI, vol. 13(5), pages 1-18, March.
    15. Cheng, Yaohua & Zhang, Ning & Kirschen, Daniel S. & Huang, Wujing & Kang, Chongqing, 2020. "Planning multiple energy systems for low-carbon districts with high penetration of renewable energy: An empirical study in China," Applied Energy, Elsevier, vol. 261(C).
    16. Rintamäki, Tuomas & Siddiqui, Afzal S. & Salo, Ahti, 2020. "Strategic offering of a flexible producer in day-ahead and intraday power markets," European Journal of Operational Research, Elsevier, vol. 284(3), pages 1136-1153.
    17. William Paul Bell & John Foster, 2017. "Using solar PV feed-in tariff policy history to inform a sustainable flexible pricing regime to enhance the diffusion of energy storage and electric vehicles," Journal of Bioeconomics, Springer, vol. 19(1), pages 127-145, April.
    18. Martelli, Emanuele & Freschini, Marco & Zatti, Matteo, 2020. "Optimization of renewable energy subsidy and carbon tax for multi energy systems using bilevel programming," Applied Energy, Elsevier, vol. 267(C).
    19. Toka, Agorasti & Iakovou, Eleftherios & Vlachos, Dimitrios & Tsolakis, Naoum & Grigoriadou, Anastasia-Loukia, 2014. "Managing the diffusion of biomass in the residential energy sector: An illustrative real-world case study," Applied Energy, Elsevier, vol. 129(C), pages 56-69.
    20. Shahnazi, Rouhollah & Dehghan Shabani, Zahra, 2020. "Do renewable energy production spillovers matter in the EU?," Renewable Energy, Elsevier, vol. 150(C), pages 786-796.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:2:p:335-:d:307155. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.