IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v271y2018i1d10.1007_s10479-018-3003-z.html
   My bibliography  Save this article

Large-scale unit commitment under uncertainty: an updated literature survey

Author

Listed:
  • W. Ackooij

    (EDF R&D)

  • I. Danti Lopez

    (EDF R&D
    University College Dublin)

  • A. Frangioni

    (Università di Pisa)

  • F. Lacalandra

    (The Italian Regulatory Authority for Energy, Networks and Environment (ARERA))

  • M. Tahanan

    (Supply Chain Consultants)

Abstract

The Unit Commitment problem in energy management aims at finding the optimal production schedule of a set of generation units, while meeting various system-wide constraints. It has always been a large-scale, non-convex, difficult problem, especially in view of the fact that, due to operational requirements, it has to be solved in an unreasonably small time for its size. Recently, growing renewable energy shares have strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex and uncertain (stochastic, robust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focussing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, while providing entry points to the relevant literature on optimization under uncertainty. This is an updated version of the paper “Large-scale Unit Commitment under uncertainty: a literature survey” that appeared in 4OR 13(2):115–171 (2015); this version has over 170 more citations, most of which appeared in the last 3 years, proving how fast the literature on uncertain Unit Commitment evolves, and therefore the interest in this subject.

Suggested Citation

  • W. Ackooij & I. Danti Lopez & A. Frangioni & F. Lacalandra & M. Tahanan, 2018. "Large-scale unit commitment under uncertainty: an updated literature survey," Annals of Operations Research, Springer, vol. 271(1), pages 11-85, December.
    • Handle: RePEc:spr:annopr:v:271:y:2018:i:1:d:10.1007_s10479-018-3003-z
    DOI: 10.1007/s10479-018-3003-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-018-3003-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-018-3003-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Rasku, Topi & Miettinen, Jari & Rinne, Erkka & Kiviluoma, Juha, 2020. "Impact of 15-day energy forecasts on the hydro-thermal scheduling of a future Nordic power system," Energy, Elsevier, vol. 192(C).
    2. Bismark Singh & Bernard Knueven & Jean-Paul Watson, 2020. "Modeling flexible generator operating regions via chance-constrained stochastic unit commitment," Computational Management Science, Springer, vol. 17(2), pages 309-326, June.
    3. van Ackooij, Wim & De Boeck, Jérôme & Detienne, Boris & Pan, Stefania & Poss, Michael, 2018. "Optimizing power generation in the presence of micro-grids," European Journal of Operational Research, Elsevier, vol. 271(2), pages 450-461.
    4. Motta, Vinicius N. & Anjos, Miguel F. & Gendreau, Michel, 2024. "Survey of optimization models for power system operation and expansion planning with demand response," European Journal of Operational Research, Elsevier, vol. 312(2), pages 401-412.
    5. Guelpa, Elisa & Bischi, Aldo & Verda, Vittorio & Chertkov, Michael & Lund, Henrik, 2019. "Towards future infrastructures for sustainable multi-energy systems: A review," Energy, Elsevier, vol. 184(C), pages 2-21.
    6. David Rebollal & Mónica Chinchilla & David Santos-Martín & Josep M. Guerrero, 2021. "Endogenous Approach of a Frequency-Constrained Unit Commitment in Islanded Microgrid Systems," Energies, MDPI, vol. 14(19), pages 1-22, October.
    7. Abdi, Hamdi, 2021. "Profit-based unit commitment problem: A review of models, methods, challenges, and future directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    8. Ruilin Pan & Qiong Wang & Zhenghong Li & Jianhua Cao & Yongjin Zhang, 2022. "Steelmaking-continuous casting scheduling problem with multi-position refining furnaces under time-of-use tariffs," Annals of Operations Research, Springer, vol. 310(1), pages 119-151, March.
    9. Mínguez, R. & van Ackooij, W. & García-Bertrand, R., 2021. "Constraint generation for risk averse two-stage stochastic programs," European Journal of Operational Research, Elsevier, vol. 288(1), pages 194-206.
    10. Wim Ackooij & Pedro Pérez-Aros, 2020. "Gradient Formulae for Nonlinear Probabilistic Constraints with Non-convex Quadratic Forms," Journal of Optimization Theory and Applications, Springer, vol. 185(1), pages 239-269, April.
    11. Cristian Camilo Marín-Cano & Juan Esteban Sierra-Aguilar & Jesús M. López-Lezama & Álvaro Jaramillo-Duque & Juan G. Villegas, 2020. "A Novel Strategy to Reduce Computational Burden of the Stochastic Security Constrained Unit Commitment Problem," Energies, MDPI, vol. 13(15), pages 1-19, July.
    12. Angelina D. Bintoudi & Lampros Zyglakis & Apostolos C. Tsolakis & Paschalis A. Gkaidatzis & Athanasios Tryferidis & Dimosthenis Ioannidis & Dimitrios Tzovaras, 2021. "OptiMEMS: An Adaptive Lightweight Optimal Microgrid Energy Management System Based on the Novel Virtual Distributed Energy Resources in Real-Life Demonstration," Energies, MDPI, vol. 14(10), pages 1-19, May.
    13. Alexia Marchand & Michel Gendreau & Marko Blais & Jonathan Guidi, 2019. "Optimized operating rules for short-term hydropower planning in a stochastic environment," Computational Management Science, Springer, vol. 16(3), pages 501-519, July.
    14. Suradej Duangpummet & Jessada Karnjana & Waree Kongprawechnon, 2021. "State-of-charge estimation based on theory of evidence and interval analysis with differential evolution optimization," Annals of Operations Research, Springer, vol. 300(2), pages 399-414, May.
    15. Schäffer, Linn Emelie & Helseth, Arild & Korpås, Magnus, 2022. "A stochastic dynamic programming model for hydropower scheduling with state-dependent maximum discharge constraints," Renewable Energy, Elsevier, vol. 194(C), pages 571-581.
    16. Wim Ackooij & Debora Daniela Escobar & Martin Glanzer & Georg Ch. Pflug, 2020. "Distributionally robust optimization with multiple time scales: valuation of a thermal power plant," Computational Management Science, Springer, vol. 17(3), pages 357-385, October.
    17. Basu, Mousumi, 2023. "Fuel constrained commitment scheduling for combined heat and power dispatch incorporating electric vehicle parking lot," Energy, Elsevier, vol. 276(C).

    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:spr:annopr:v:271:y:2018:i:1:d:10.1007_s10479-018-3003-z. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.