IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i14p5370-d1194017.html
   My bibliography  Save this article

A Space Reduction Heuristic for Thermal Unit Commitment Considering Ramp Constraints and Large-Scale Generation Systems

Author

Listed:
  • Layon Mescolin de Oliveira

    (Laboratory of Power Systems, Department of Electrical Energy, Federal University of Juiz de Fora, José Lourenço Kelmer St., São Pedro, Juiz de Fora 36036-900, Brazil)

  • Ivo Chaves da Silva Junior

    (Laboratory of Power Systems, Department of Electrical Energy, Federal University of Juiz de Fora, José Lourenço Kelmer St., São Pedro, Juiz de Fora 36036-900, Brazil)

  • Ramon Abritta

    (Department of Geoscience and Petroleum, Norwegian University of Science and Technology, PTS Paviljong, 540, Valgrinda, S.P. Andersens veg 15, 7031 Trondheim, Norway)

Abstract

This paper expands the research around a recently proposed method to reduce the search space region for thermal unit commitment problems. The importance of such techniques comes from the combinatorial explosion regarding the variables of the problem when there are a large quantity of generating units in the system. The proposed heuristic approach utilizes sensitivity indices to gather information about the system and fix many of the binary decision variables over the planning horizon. This work further explores the method by demonstrating its effectiveness in large-scale systems subjected to ramp constraints. Despite the significantly increased complexity, the results of this paper indicate that the method can achieve high quality solutions notably faster than other approaches from the literature.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:14:p:5370-:d:1194017
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/14/5370/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/14/5370/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wei Han & Hong-hua Wang & Xin-song Zhang & Ling Chen, 2013. "A Unit Commitment Model with Implicit Reserve Constraint Based on an Improved Artificial Fish Swarm Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-11, December.
    2. Shahbazitabar, Maryam & Abdi, Hamdi, 2018. "A novel priority-based stochastic unit commitment considering renewable energy sources and parking lot cooperation," Energy, Elsevier, vol. 161(C), pages 308-324.
    3. Aml Sayed & Mohamed Ebeed & Ziad M. Ali & Adel Bedair Abdel-Rahman & Mahrous Ahmed & Shady H. E. Abdel Aleem & Adel El-Shahat & Mahmoud Rihan, 2021. "A Hybrid Optimization Algorithm for Solving of the Unit Commitment Problem Considering Uncertainty of the Load Demand," Energies, MDPI, vol. 14(23), pages 1-21, November.
    4. 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.
    5. Zhu, Xiaodong & Zhao, Shihao & Yang, Zhile & Zhang, Ning & Xu, Xinzhi, 2022. "A parallel meta-heuristic method for solving large scale unit commitment considering the integration of new energy sectors," Energy, Elsevier, vol. 238(PC).
    6. Layon Mescolin de Oliveira & Ivo Chaves da Silva Junior & Ramon Abritta, 2022. "Search Space Reduction for the Thermal Unit Commitment Problem through a Relevance Matrix," Energies, MDPI, vol. 15(19), pages 1-16, September.
    7. 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.
    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. Layon Mescolin de Oliveira & Ivo Chaves da Silva Junior & Ramon Abritta, 2022. "Search Space Reduction for the Thermal Unit Commitment Problem through a Relevance Matrix," Energies, MDPI, vol. 15(19), pages 1-16, September.
    2. Ebrie, Awol Seid & Kim, Young Jin, 2024. "Reinforcement learning-based optimization for power scheduling in a renewable energy connected grid," Renewable Energy, Elsevier, vol. 230(C).
    3. Haiyan Zheng & Liying Huang & Ran Quan, 2023. "Mixed-Integer Conic Formulation of Unit Commitment with Stochastic Wind Power," Mathematics, MDPI, vol. 11(2), pages 1-16, January.
    4. 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.
    5. Ali, E.S. & Elazim, S.M. Abd & Balobaid, A.S., 2023. "Implementation of coyote optimization algorithm for solving unit commitment problem in power systems," Energy, Elsevier, vol. 263(PA).
    6. Ceran, Bartosz, 2019. "The concept of use of PV/WT/FC hybrid power generation system for smoothing the energy profile of the consumer," Energy, Elsevier, vol. 167(C), pages 853-865.
    7. 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.
    8. Wilberforce, Tabbi & El Hassan, Zaki & Durrant, A. & Thompson, J. & Soudan, Bassel & Olabi, A.G., 2019. "Overview of ocean power technology," Energy, Elsevier, vol. 175(C), pages 165-181.
    9. Dong, Jizhe & Han, Shunjie & Shao, Xiangxin & Tang, Like & Chen, Renhui & Wu, Longfei & Zheng, Cunlong & Li, Zonghao & Li, Haolin, 2021. "Day-ahead wind-thermal unit commitment considering historical virtual wind power data," Energy, Elsevier, vol. 235(C).
    10. Zhu, Xiaodong & Zhao, Shihao & Yang, Zhile & Zhang, Ning & Xu, Xinzhi, 2022. "A parallel meta-heuristic method for solving large scale unit commitment considering the integration of new energy sectors," Energy, Elsevier, vol. 238(PC).
    11. Junjie Jia & Nan Yang & Chao Xing & Haoze Chen & Songkai Liu & Yuehua Huang & Binxin Zhu, 2019. "An Improved Constrained Order Optimization Algorithm for Uncertain SCUC Problem Solving," Energies, MDPI, vol. 12(23), pages 1-19, November.
    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. Georgios Semertzidis & Dimitrios Stamatakis & Vasilios Tsalavoutis & Athanasios I. Tolis, 2022. "Optimized electric vehicle charging integrated in the unit commitment problem," Operational Research, Springer, vol. 22(5), pages 5137-5204, November.
    14. Wei, Lishen & Ai, Xiaomeng & Fang, Jiakun & Cui, Shichang & Gao, Liqian & Li, Kun & Wen, Jinyu, 2025. "Data-augmentation acceleration framework by graph neural network for near-optimal unit commitment," Applied Energy, Elsevier, vol. 377(PD).
    15. Pavlos Nikolaidis & Harris Partaourides, 2021. "A Model Predictive Control for the Dynamical Forecast of Operating Reserves in Frequency Regulation Services," Forecasting, MDPI, vol. 3(1), pages 1-14, March.
    16. 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.
    17. 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).
    18. 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).
    19. Iram Parvez & Jianjian Shen & Ishitaq Hassan & Nannan Zhang, 2021. "Generation of Hydro Energy by Using Data Mining Algorithm for Cascaded Hydropower Plant," Energies, MDPI, vol. 14(2), pages 1-28, January.
    20. Huang, Jingwei & Qin, Hui & Shen, Keyan & Yang, Yuqi & Jia, Benjun, 2024. "Study on hierarchical model of hydroelectric unit commitment based on similarity schedule and quadratic optimization approach," Energy, Elsevier, vol. 305(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:gam:jeners:v:16:y:2023:i:14:p:5370-:d:1194017. 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.