IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v319y2024i2p427-441.html
   My bibliography  Save this article

Security-constrained unit commitment: A decomposition approach embodying Kron reduction

Author

Listed:
  • Constante-Flores, Gonzalo E.
  • Conejo, Antonio J.

Abstract

We address the day-ahead scheduling of electricity production units throughout a network imposing N-1 security constraints, which ensures uneventful operation under any single-branch failure. For realistic electric energy systems, this optimization problem, which is mixed-integer linear or nonlinear but convex, involves millions of continuous variables, millions of constraints, and thousands of binary variables. This problem is intractable if state-of-the-art branch-and-cut solvers are used. As a solution methodology, we propose a Benders-type decomposition technique with a dynamically enriched master problem. Such master problem incorporates scheduling (binary) decisions and decisions pertaining to under-contingency operating conditions. The subproblems represent the operation of the system under no failure and single-branch failure. As the algorithm progresses, the master problem incorporates additional under-contingency operating conditions, which increases its computational burden. We use Kron reduction to compact (reducing variables and constraints) the description of the under-contingency operating conditions in the master problem without losing accuracy, which renders major computational gains. The methodology proposed allows solving, within reasonable computing times, instances intractable with state-of-the-art branch-and-cut solvers and decomposition algorithms.

Suggested Citation

  • Constante-Flores, Gonzalo E. & Conejo, Antonio J., 2024. "Security-constrained unit commitment: A decomposition approach embodying Kron reduction," European Journal of Operational Research, Elsevier, vol. 319(2), pages 427-441.
  • Handle: RePEc:eee:ejores:v:319:y:2024:i:2:p:427-441
    DOI: 10.1016/j.ejor.2023.06.013
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221723004642
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ejor.2023.06.013?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.

    References listed on IDEAS

    as
    1. Denise D. Tönissen & Joachim J. Arts & Zuo-Jun Max Shen, 2021. "A column-and-constraint generation algorithm for two-stage stochastic programming problems," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(3), pages 781-798, October.
    2. Wang, Jiadong & Wang, Jianhui & Liu, Cong & Ruiz, Juan P., 2013. "Stochastic unit commitment with sub-hourly dispatch constraints," Applied Energy, Elsevier, vol. 105(C), pages 418-422.
    3. Teodor Gabriel Crainic & Mike Hewitt & Francesca Maggioni & Walter Rei, 2021. "Partial Benders Decomposition: General Methodology and Application to Stochastic Network Design," Transportation Science, INFORMS, vol. 55(2), pages 414-435, March.
    4. Mads Almassalkhi & Sarnaduti Brahma & Nawaf Nazir & Hamid Ossareh & Pavan Racherla & Soumya Kundu & Sai Pushpak Nandanoori & Thiagarajan Ramachandran & Ankit Singhal & Dennice Gayme & Chengda Ji & Enr, 2020. "Hierarchical, Grid-Aware, and Economically Optimal Coordination of Distributed Energy Resources in Realistic Distribution Systems," Energies, MDPI, vol. 13(23), pages 1-40, December.
    5. Antonio J. Conejo & Miguel Carrión & Juan M. Morales, 2010. "Decision Making Under Uncertainty in Electricity Markets," International Series in Operations Research and Management Science, Springer, number 978-1-4419-7421-1, January.
    Full references (including those not matched with items on IDEAS)

    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. Taulant Kërçi & Juan S. Giraldo & Federico Milano, 2020. "Sensitivity Analysis of the Impact of the Sub- Hourly Stochastic Unit Commitment on Power System Dynamics," Energies, MDPI, vol. 13(6), pages 1-17, March.
    2. Yasemin Merzifonluoglu & Eray Uzgoren, 2018. "Photovoltaic power plant design considering multiple uncertainties and risk," Annals of Operations Research, Springer, vol. 262(1), pages 153-184, March.
    3. Pandžić, Hrvoje & Kuzle, Igor & Capuder, Tomislav, 2013. "Virtual power plant mid-term dispatch optimization," Applied Energy, Elsevier, vol. 101(C), pages 134-141.
    4. Wang, Dongxiao & Qiu, Jing & Reedman, Luke & Meng, Ke & Lai, Loi Lei, 2018. "Two-stage energy management for networked microgrids with high renewable penetration," Applied Energy, Elsevier, vol. 226(C), pages 39-48.
    5. Sadeghian, Omid & Mohammadpour Shotorbani, Amin & Mohammadi-Ivatloo, Behnam & Sadiq, Rehan & Hewage, Kasun, 2021. "Risk-averse maintenance scheduling of generation units in combined heat and power systems with demand response," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    6. Christos N. Dimitriadis & Evangelos G. Tsimopoulos & Michael C. Georgiadis, 2021. "A Review on the Complementarity Modelling in Competitive Electricity Markets," Energies, MDPI, vol. 14(21), pages 1-27, November.
    7. Mohagheghi, Erfan & Gabash, Aouss & Alramlawi, Mansour & Li, Pu, 2018. "Real-time optimal power flow with reactive power dispatch of wind stations using a reconciliation algorithm," Renewable Energy, Elsevier, vol. 126(C), pages 509-523.
    8. Grover-Silva, Etta & Heleno, Miguel & Mashayekh, Salman & Cardoso, Gonçalo & Girard, Robin & Kariniotakis, George, 2018. "A stochastic optimal power flow for scheduling flexible resources in microgrids operation," Applied Energy, Elsevier, vol. 229(C), pages 201-208.
    9. Jaber Valinejad & Mousa Marzband & Michael Elsdon & Ameena Saad Al-Sumaiti & Taghi Barforoushi, 2019. "Dynamic Carbon-Constrained EPEC Model for Strategic Generation Investment Incentives with the Aim of Reducing CO 2 Emissions," Energies, MDPI, vol. 12(24), pages 1-35, December.
    10. Hernán Gómez-Villarreal & Miguel Carrión & Ruth Domínguez, 2019. "Optimal Management of Combined-Cycle Gas Units with Gas Storage under Uncertainty," Energies, MDPI, vol. 13(1), pages 1-29, December.
    11. Alfredo Alcayde & Raul Baños & Francisco M. Arrabal-Campos & Francisco G. Montoya, 2019. "Optimization of the Contracted Electric Power by Means of Genetic Algorithms," Energies, MDPI, vol. 12(7), pages 1-13, April.
    12. Hanif, Sarmad & Alam, M.J.E. & Roshan, Kini & Bhatti, Bilal A. & Bedoya, Juan C., 2022. "Multi-service battery energy storage system optimization and control," Applied Energy, Elsevier, vol. 311(C).
    13. Thibaut Th'eate & S'ebastien Mathieu & Damien Ernst, 2020. "An Artificial Intelligence Solution for Electricity Procurement in Forward Markets," Papers 2006.05784, arXiv.org, revised Dec 2020.
    14. Xie, Kaigui & Dong, Jizhe & Singh, Chanan & Hu, Bo, 2016. "Optimal capacity and type planning of generating units in a bundled wind–thermal generation system," Applied Energy, Elsevier, vol. 164(C), pages 200-210.
    15. Savelli, Iacopo & Morstyn, Thomas, 2021. "Electricity prices and tariffs to keep everyone happy: A framework for fixed and nodal prices coexistence in distribution grids with optimal tariffs for investment cost recovery," Omega, Elsevier, vol. 103(C).
    16. Nielsen, Maria Grønnegaard & Morales, Juan Miguel & Zugno, Marco & Pedersen, Thomas Engberg & Madsen, Henrik, 2016. "Economic valuation of heat pumps and electric boilers in the Danish energy system," Applied Energy, Elsevier, vol. 167(C), pages 189-200.
    17. Wei, Wei & Liu, Feng & Wang, Jianhui & Chen, Laijun & Mei, Shengwei & Yuan, Tiejiang, 2016. "Robust environmental-economic dispatch incorporating wind power generation and carbon capture plants," Applied Energy, Elsevier, vol. 183(C), pages 674-684.
    18. Paolo Falbo & Carlos Ruiz, 2021. "Joint optimization of sales-mix and generation plan for a large electricity producer," Papers 2110.02016, arXiv.org.
    19. Liu, Chuanju & Lin, Shaochong & Shen, Zuo-Jun Max & Zhang, Junlong, 2023. "Stochastic service network design: The value of fixed routes," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 174(C).
    20. Guanglei Wang & Hassan Hijazi, 2018. "Mathematical programming methods for microgrid design and operations: a survey on deterministic and stochastic approaches," Computational Optimization and Applications, Springer, vol. 71(2), pages 553-608, November.

    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:eee:ejores:v:319:y:2024:i:2:p:427-441. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    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.