IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i19p7153-d928458.html
   My bibliography  Save this article

Search Space Reduction for the Thermal Unit Commitment Problem through a Relevance Matrix

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

Given the combinatorial explosion related to the operation decisions in the thermal unit commitment problem, this paper presents a new strategy to reduce the search space and to start the multi-modal optimization process. To achieve such goals, a relevance matrix is obtained to indicate how important each generating unit is at each hour of the operational planning. This matrix is generated through the results of a constructive heuristic based on sensitivity indexes that account for operational and economic characteristics of the generating units and of the system under planning. The proposed method is shown to reduce the complexity of the problem, thus decreasing the combinatorial explosion and, consequently, the computational burden. Its effectiveness is verified by performing optimizations with and without its utilization. The results achieved with the proposed space-reduction approach enable solutions that present good quality. Furthermore, these solutions are retrieved with significantly reduced processing time.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:7153-:d:928458
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/19/7153/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/19/7153/
    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. Sirote Khunkitti & Neville R. Watson & Rongrit Chatthaworn & Suttichai Premrudeepreechacharn & Apirat Siritaratiwat, 2019. "An Improved DA-PSO Optimization Approach for Unit Commitment Problem," Energies, MDPI, vol. 12(12), pages 1-23, June.
    3. Maria Acuna & Carlos Silva & Andrés Tocaruncho & Diana Vargas & Diego Patiño & David Barrera & Johan Peña, 2021. "Operational Planning of Energy for Non-Interconnected Zones: A Simulation-Optimization Approach and a Case Study to Tackle Energy Poverty in Colombia," Energies, MDPI, vol. 14(10), pages 1-16, May.
    4. 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.
    5. Rosane Santos & André Luiz Diniz & Bruno Borba, 2022. "Assessment of the Modeling of Demand Response as a Dispatchable Resource in Day-Ahead Hydrothermal Unit Commitment Problems: The Brazilian Case," Energies, MDPI, vol. 15(11), pages 1-15, May.
    6. Khanmohammadi, S. & Amiri, M. & Haque, M. Tarafdar, 2010. "A new three-stage method for solving unit commitment problem," Energy, Elsevier, vol. 35(7), pages 3072-3080.
    7. Pascale Bendotti & Pierre Fouilhoux & Cécile Rottner, 2019. "On the complexity of the Unit Commitment Problem," Annals of Operations Research, Springer, vol. 274(1), pages 119-130, March.
    8. Saber, Navid Abdolhoseyni & Salimi, Mahdi & Mirabbasi, Davar, 2016. "A priority list based approach for solving thermal unit commitment problem with novel hybrid genetic-imperialist competitive algorithm," Energy, Elsevier, vol. 117(P1), pages 272-280.
    9. 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.
    10. Dang, Chuangyin & Li, Minqiang, 2007. "A floating-point genetic algorithm for solving the unit commitment problem," European Journal of Operational Research, Elsevier, vol. 181(3), pages 1370-1395, September.
    11. 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).
    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. 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).
    2. 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.

    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, 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.
    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. Vasilios A. Tsalavoutis & Constantinos G. Vrionis & Athanasios I. Tolis, 2021. "Optimizing a unit commitment problem using an evolutionary algorithm and a plurality of priority lists," Operational Research, Springer, vol. 21(1), pages 1-54, March.
    4. 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).
    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. Donovin D. Lewis & Aron Patrick & Evan S. Jones & Rosemary E. Alden & Abdullah Al Hadi & Malcolm D. McCulloch & Dan M. Ionel, 2023. "Decarbonization Analysis for Thermal Generation and Regionally Integrated Large-Scale Renewables Based on Minutely Optimal Dispatch with a Kentucky Case Study," Energies, MDPI, vol. 16(4), pages 1-23, February.
    7. Awol Seid Ebrie & Chunhyun Paik & Yongjoo Chung & Young Jin Kim, 2023. "Environment-Friendly Power Scheduling Based on Deep Contextual Reinforcement Learning," Energies, MDPI, vol. 16(16), pages 1-12, August.
    8. Xiaohua Zhang & Jun Xie & Zhengwei Zhu & Jianfeng Zheng & Hao Qiang & Hailong Rong, 2016. "Smart Grid Cost-Emission Unit Commitment via Co-Evolutionary Agents," Energies, MDPI, vol. 9(10), pages 1-13, October.
    9. George E. Halkos & Panagiotis-Stavros C. Aslanidis, 2023. "Addressing Multidimensional Energy Poverty Implications on Achieving Sustainable Development," Energies, MDPI, vol. 16(9), pages 1-30, April.
    10. Basma Salah & Hany M. Hasanien & Fadia M. A. Ghali & Yasser M. Alsayed & Shady H. E. Abdel Aleem & Adel El-Shahat, 2022. "African Vulture Optimization-Based Optimal Control Strategy for Voltage Control of Islanded DC Microgrids," Sustainability, MDPI, vol. 14(19), pages 1-26, September.
    11. Glotić, Arnel & Glotić, Adnan & Kitak, Peter & Pihler, Jože & Tičar, Igor, 2014. "Optimization of hydro energy storage plants by using differential evolution algorithm," Energy, Elsevier, vol. 77(C), pages 97-107.
    12. 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.
    13. Shukla, Anup & Singh, S.N., 2016. "Advanced three-stage pseudo-inspired weight-improved crazy particle swarm optimization for unit commitment problem," Energy, Elsevier, vol. 96(C), pages 23-36.
    14. Harun Or Rashid Howlader & Oludamilare Bode Adewuyi & Ying-Yi Hong & Paras Mandal & Ashraf Mohamed Hemeida & Tomonobu Senjyu, 2019. "Energy Storage System Analysis Review for Optimal Unit Commitment," Energies, MDPI, vol. 13(1), pages 1-21, December.
    15. Borche Postolov & Nikolay Hinov & Atanas Iliev & Dimitar Dimitrov, 2022. "Short-Term Hydro-Thermal-Solar Scheduling with CCGT Based on Self-Adaptive Genetic Algorithm," Energies, MDPI, vol. 15(16), pages 1-25, August.
    16. L. A. C. Roque & D. B. M. M. Fontes & F. A. C. C. Fontes, 2014. "A hybrid biased random key genetic algorithm approach for the unit commitment problem," Journal of Combinatorial Optimization, Springer, vol. 28(1), pages 140-166, July.
    17. Qun Niu & Lipeng Tang & Litao Yu & Han Wang & Zhile Yang, 2024. "Unit Commitment Considering Electric Vehicles and Renewable Energy Integration—A CMAES Approach," Sustainability, MDPI, vol. 16(3), pages 1-28, January.
    18. Yang, Zhile & Li, Kang & Guo, Yuanjun & Feng, Shengzhong & Niu, Qun & Xue, Yusheng & Foley, Aoife, 2019. "A binary symmetric based hybrid meta-heuristic method for solving mixed integer unit commitment problem integrating with significant plug-in electric vehicles," Energy, Elsevier, vol. 170(C), pages 889-905.
    19. Shengli Liao & Zhifu Li & Gang Li & Jiayang Wang & Xinyu Wu, 2015. "Modeling and Optimization of the Medium-Term Units Commitment of Thermal Power," Energies, MDPI, vol. 8(11), pages 1-17, November.
    20. Erica Ocampo & Yen-Chih Huang & Cheng-Chien Kuo, 2020. "Feasible Reserve in Day-Ahead Unit Commitment Using Scenario-Based Optimization," Energies, MDPI, vol. 13(20), pages 1-17, October.

    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:15:y:2022:i:19:p:7153-:d:928458. 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.