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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
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Lejeune, Miguel A. & Dehghanian, Payman & Ma, Wenbo, 2024. "Profit-based unit commitment models with price-responsive decision-dependent uncertainty," European Journal of Operational Research, Elsevier, vol. 314(3), pages 1052-1064.
    6. 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.
    7. 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.
    8. 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).
    9. 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.
    10. Tsay, Calvin, 2024. "A Quantile Neural Network Framework for Twostage Stochastic Optimization," DES - Working Papers. Statistics and Econometrics. WS 43773, Universidad Carlos III de Madrid. Departamento de Estadística.
    11. 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.
    12. 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).
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. Basu, Mousumi, 2023. "Fuel constrained commitment scheduling for combined heat and power dispatch incorporating electric vehicle parking lot," Energy, Elsevier, vol. 276(C).

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