IDEAS home Printed from https://ideas.repec.org/a/inm/orijoc/v32y4i2020p857-876.html
   My bibliography  Save this article

On Mixed-Integer Programming Formulations for the Unit Commitment Problem

Author

Listed:
  • Bernard Knueven

    (Discrete Math & Optimization, Sandia National Laboratories, Albuquerque, New Mexico 87185;)

  • James Ostrowski

    (Industrial and Systems Engineering, University of Tennessee, Knoxville, Tennessee 37996;)

  • Jean-Paul Watson

    (Data Science & Cyber Analytics, Sandia National Laboratories, Livermore, California 94551)

Abstract

We provide a comprehensive overview of mixed-integer programming formulations for the unit commitment (UC) problem. UC formulations have been an especially active area of research over the past 12 years due to their practical importance in power grid operations, and this paper serves as a capstone for this line of work. We additionally provide publicly available reference implementations of all formulations examined. We computationally test existing and novel UC formulations on a suite of instances drawn from both academic and real-world data sources. Driven by our computational experience from this and previous work, we contribute some additional formulations for both generator production upper bounds and piecewise linear production costs. By composing new UC formulations using existing components found in the literature and new components introduced in this paper, we demonstrate that performance can be significantly improved—and in the process, we identify a new state-of-the-art UC formulation.

Suggested Citation

  • Bernard Knueven & James Ostrowski & Jean-Paul Watson, 2020. "On Mixed-Integer Programming Formulations for the Unit Commitment Problem," INFORMS Journal on Computing, INFORMS, vol. 32(4), pages 857-876, October.
  • Handle: RePEc:inm:orijoc:v:32:y:4:i:2020:p:857-876
    DOI: 10.1287/ijoc.2019.0944
    as

    Download full text from publisher

    File URL: https://doi.org/10.1287/ijoc.2019.0944
    Download Restriction: no

    File URL: https://libkey.io/10.1287/ijoc.2019.0944?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
    ---><---

    References listed on IDEAS

    as
    1. Maurice QUEYRANNE & Laurence A. WOLSEY, 2017. "Tight MIP formulations for bounded up/down times and interval-dependent start-ups," LIDAM Reprints CORE 2876, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Matthias Nowak & Werner Römisch, 2000. "Stochastic Lagrangian Relaxation Applied to Power Scheduling in a Hydro-Thermal System under Uncertainty," Annals of Operations Research, Springer, vol. 100(1), pages 251-272, December.
    3. Samer Takriti & Benedikt Krasenbrink & Lilian S.-Y. Wu, 2000. "Incorporating Fuel Constraints and Electricity Spot Prices into the Stochastic Unit Commitment Problem," Operations Research, INFORMS, vol. 48(2), pages 268-280, April.
    4. Yongpei Guan & Kai Pan & Kezhuo Zhou, 2018. "Polynomial time algorithms and extended formulations for unit commitment problems," IISE Transactions, Taylor & Francis Journals, vol. 50(8), pages 735-751, August.
    5. Yang, Linfeng & Zhang, Chen & Jian, Jinbao & Meng, Ke & Xu, Yan & Dong, Zhaoyang, 2017. "A novel projected two-binary-variable formulation for unit commitment in power systems," Applied Energy, Elsevier, vol. 187(C), pages 732-745.
    6. René Brandenberg & Matthias Huber & Matthias Silbernagl, 2017. "The summed start-up costs in a unit commitment problem," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 5(1), pages 203-238, March.
    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. Kim, Donghun & Wang, Zhe & Brugger, James & Blum, David & Wetter, Michael & Hong, Tianzhen & Piette, Mary Ann, 2022. "Site demonstration and performance evaluation of MPC for a large chiller plant with TES for renewable energy integration and grid decarbonization," Applied Energy, Elsevier, vol. 321(C).
    2. Tumbalam Gooty, Radhakrishna & Ghouse, Jaffer & Le, Quang Minh & Thitakamol, Bhurisa & Rezaei, Sabereh & Obiang, Denis & Gupta, Raghubir & Zhou, James & Bhattacharyya, Debangsu & Miller, David C., 2023. "Incorporation of market signals for the optimal design of post combustion carbon capture systems," Applied Energy, Elsevier, vol. 337(C).
    3. O’Malley, Cormac & de Mars, Patrick & Badesa, Luis & Strbac, Goran, 2023. "Reinforcement learning and mixed-integer programming for power plant scheduling in low carbon systems: Comparison and hybridisation," Applied Energy, Elsevier, vol. 349(C).
    4. Aliakbari Sani, Sajad & Bahn, Olivier & Delage, Erick, 2022. "Affine decision rule approximation to address demand response uncertainty in smart Grids’ capacity planning," European Journal of Operational Research, Elsevier, vol. 303(1), pages 438-455.
    5. Luis Montero & Antonio Bello & Javier Reneses, 2022. "A Review on the Unit Commitment Problem: Approaches, Techniques, and Resolution Methods," Energies, MDPI, vol. 15(4), pages 1-40, February.
    6. Skolfield, J. Kyle & Escobedo, Adolfo R., 2022. "Operations research in optimal power flow: A guide to recent and emerging methodologies and applications," European Journal of Operational Research, Elsevier, vol. 300(2), pages 387-404.
    7. Kai Pan & Ming Zhao & Chung-Lun Li & Feng Qiu, 2022. "A Polyhedral Study on Fuel-Constrained Unit Commitment," INFORMS Journal on Computing, INFORMS, vol. 34(6), pages 3309-3324, November.
    8. Layon Mescolin de Oliveira & Ivo Chaves da Silva Junior & Ramon Abritta, 2023. "A Space Reduction Heuristic for Thermal Unit Commitment Considering Ramp Constraints and Large-Scale Generation Systems," Energies, MDPI, vol. 16(14), pages 1-15, July.
    9. de Mars, Patrick & O’Sullivan, Aidan, 2021. "Applying reinforcement learning and tree search to the unit commitment problem," Applied Energy, Elsevier, vol. 302(C).
    10. Castelli, Alessandro Francesco & Pilotti, Lorenzo & Monchieri, Alessandro & Martelli, Emanuele, 2024. "Optimal design of aggregated energy systems with (N-1) reliability: MILP models and decomposition algorithms," Applied Energy, Elsevier, vol. 356(C).
    11. Gao, Xian & Knueven, Bernard & Siirola, John D. & Miller, David C. & Dowling, Alexander W., 2022. "Multiscale simulation of integrated energy system and electricity market interactions," Applied Energy, Elsevier, vol. 316(C).
    12. Panagiotis Andrianesis & Dimitris Bertsimas & Michael C. Caramanis & William W. Hogan, 2020. "Computation of Convex Hull Prices in Electricity Markets with Non-Convexities using Dantzig-Wolfe Decomposition," Papers 2012.13331, arXiv.org, revised Oct 2021.
    13. Jalving, Jordan & Ghouse, Jaffer & Cortes, Nicole & Gao, Xian & Knueven, Bernard & Agi, Damian & Martin, Shawn & Chen, Xinhe & Guittet, Darice & Tumbalam-Gooty, Radhakrishna & Bianchi, Ludovico & Beat, 2023. "Beyond price taker: Conceptual design and optimization of integrated energy systems using machine learning market surrogates," Applied Energy, Elsevier, vol. 351(C).
    14. Tuyen Nguyen-Duc & Linh Hoang-Tuan & Hung Ta-Xuan & Long Do-Van & Hirotaka Takano, 2022. "A Mixed-Integer Programming Approach for Unit Commitment in Micro-Grid with Incentive-Based Demand Response and Battery Energy Storage System," Energies, MDPI, vol. 15(19), pages 1-26, September.
    15. Stevens, Nicolas & Papavasiliou, Anthony, 2022. "Application of the Level Method for Computing Locational Convex Hull Prices," LIDAM Discussion Papers CORE 2022002, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

    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. Luis Montero & Antonio Bello & Javier Reneses, 2022. "A Review on the Unit Commitment Problem: Approaches, Techniques, and Resolution Methods," Energies, MDPI, vol. 15(4), pages 1-40, February.
    2. Kai Pan & Ming Zhao & Chung-Lun Li & Feng Qiu, 2022. "A Polyhedral Study on Fuel-Constrained Unit Commitment," INFORMS Journal on Computing, INFORMS, vol. 34(6), pages 3309-3324, November.
    3. Jianqiu Huang & Kai Pan & Yongpei Guan, 2021. "Multistage Stochastic Power Generation Scheduling Co-Optimizing Energy and Ancillary Services," INFORMS Journal on Computing, INFORMS, vol. 33(1), pages 352-369, January.
    4. Suvrajeet Sen & Lihua Yu & Talat Genc, 2006. "A Stochastic Programming Approach to Power Portfolio Optimization," Operations Research, INFORMS, vol. 54(1), pages 55-72, February.
    5. Wim Ackooij & Jérôme Malick, 2016. "Decomposition algorithm for large-scale two-stage unit-commitment," Annals of Operations Research, Springer, vol. 238(1), pages 587-613, March.
    6. 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.
    7. Wim Ackooij & Jérôme Malick, 2016. "Decomposition algorithm for large-scale two-stage unit-commitment," Annals of Operations Research, Springer, vol. 238(1), pages 587-613, March.
    8. Yang, Linfeng & Li, Wei & Xu, Yan & Zhang, Cuo & Chen, Shifei, 2021. "Two novel locally ideal three-period unit commitment formulations in power systems," Applied Energy, Elsevier, vol. 284(C).
    9. Alexis Tantet & Philippe Drobinski, 2021. "A Minimal System Cost Minimization Model for Variable Renewable Energy Integration: Application to France and Comparison to Mean-Variance Analysis," Energies, MDPI, vol. 14(16), pages 1-38, August.
    10. Hongling, Liu & Chuanwen, Jiang & Yan, Zhang, 2008. "A review on risk-constrained hydropower scheduling in deregulated power market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(5), pages 1465-1475, June.
    11. Thomas Bittar & Pierre Carpentier & Jean-Philippe Chancelier & Jérôme Lonchampt, 2022. "A decomposition method by interaction prediction for the optimization of maintenance scheduling," Annals of Operations Research, Springer, vol. 316(1), pages 229-267, September.
    12. Schulze, Tim & McKinnon, Ken, 2016. "The value of stochastic programming in day-ahead and intra-day generation unit commitment," Energy, Elsevier, vol. 101(C), pages 592-605.
    13. Skolfield, J. Kyle & Escobedo, Adolfo R., 2022. "Operations research in optimal power flow: A guide to recent and emerging methodologies and applications," European Journal of Operational Research, Elsevier, vol. 300(2), pages 387-404.
    14. Ying-Yi Hong & Gerard Francesco DG. Apolinario, 2021. "Uncertainty in Unit Commitment in Power Systems: A Review of Models, Methods, and Applications," Energies, MDPI, vol. 14(20), pages 1-47, October.
    15. Moritz Nobis & Carlo Schmitt & Ralf Schemm & Armin Schnettler, 2020. "Pan-European CVaR-Constrained Stochastic Unit Commitment in Day-Ahead and Intraday Electricity Markets," Energies, MDPI, vol. 13(9), pages 1-35, May.
    16. Philip J. Neame & Andrew B. Philpott & Geoffrey Pritchard, 2003. "Offer Stack Optimization in Electricity Pool Markets," Operations Research, INFORMS, vol. 51(3), pages 397-408, June.
    17. Klein Haneveld, W.K. & Vlerk, M.H. van der, 2000. "Optimizing electricity distribution using two-stage integer recourse models," Research Report 00A26, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    18. Ke, Xinda & Wu, Di & Rice, Jennie & Kintner-Meyer, Michael & Lu, Ning, 2016. "Quantifying impacts of heat waves on power grid operation," Applied Energy, Elsevier, vol. 183(C), pages 504-512.
    19. Nogata, Daisuke, 2022. "Determinants of household switching between natural gas suppliers: Evidence from Japan," Utilities Policy, Elsevier, vol. 76(C).
    20. Chyong, Chi Kong & Newbery, David, 2022. "A unit commitment and economic dispatch model of the GB electricity market – Formulation and application to hydro pumped storage," Energy Policy, Elsevier, vol. 170(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:inm:orijoc:v:32:y:4:i:2020:p:857-876. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.