IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v8y2015i11p12345-12864d58715.html
   My bibliography  Save this article

Modeling and Optimization of the Medium-Term Units Commitment of Thermal Power

Author

Listed:
  • Shengli Liao

    (Institute of Hydropower System & Hydroinformatics, Dalian University of Technology, Dalian 116024, China)

  • Zhifu Li

    (Institute of Hydropower System & Hydroinformatics, Dalian University of Technology, Dalian 116024, China)

  • Gang Li

    (Institute of Hydropower System & Hydroinformatics, Dalian University of Technology, Dalian 116024, China)

  • Jiayang Wang

    (Institute of Hydropower System & Hydroinformatics, Dalian University of Technology, Dalian 116024, China)

  • Xinyu Wu

    (Institute of Hydropower System & Hydroinformatics, Dalian University of Technology, Dalian 116024, China)

Abstract

Coal-fired thermal power plants, which represent the largest proportion of China’s electric power system, are very sluggish in responding to power system load demands. Thus, a reasonable and feasible scheme for the medium-term optimal commitment of thermal units (MOCTU) can ensure that the generation process runs smoothly and minimizes the start-up and shut-down times of thermal units. In this paper, based on the real-world and practical demands of power dispatch centers in China, a flexible mathematical model for MOCTU that uses equal utilization hours for the installed capacity of all thermal power plants as the optimization goal and that considers the award hours for MOCTU is developed. MOCTU is a unit commitment (UC) problem with characteristics of large-scale, high dimensions and nonlinearity. For optimization, an improved progressive optimality algorithm (IPOA) offering the advantages of POA is adopted to overcome the drawback of POA of easily falling into the local optima. In the optimization process, strategies of system operating capacity equalization and single station operating peak combination are introduced to move the target solution from the boundary constraints along the target isopleths into the feasible solution’s interior to guarantee the global optima. The results of a case study consisting of nine thermal power plants with 27 units show that the presented algorithm can obtain an optimal solution and is competent in solving the MOCTU with high efficiency and accuracy as well as that the developed simulation model can be applied to practical engineering needs.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:8:y:2015:i:11:p:12345-12864:d:58715
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/8/11/12345/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/8/11/12345/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hyeon-Gon Park & Jae-Kun Lyu & YongCheol Kang & Jong-Keun Park, 2014. "Unit Commitment Considering Interruptible Load for Power System Operation with Wind Power," Energies, MDPI, vol. 7(7), pages 1-19, July.
    2. 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.
    3. Niknam, Taher & Khodaei, Amin & Fallahi, Farhad, 2009. "A new decomposition approach for the thermal unit commitment problem," Applied Energy, Elsevier, vol. 86(9), pages 1667-1674, September.
    4. Liu, Liwei & Zong, Haijing & Zhao, Erdong & Chen, Chuxiang & Wang, Jianzhou, 2014. "Can China realize its carbon emission reduction goal in 2020: From the perspective of thermal power development," Applied Energy, Elsevier, vol. 124(C), pages 199-212.
    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. Feng, Zhong-kai & Niu, Wen-jing & Wang, Wen-chuan & Zhou, Jian-zhong & Cheng, Chun-tian, 2019. "A mixed integer linear programming model for unit commitment of thermal plants with peak shaving operation aspect in regional power grid lack of flexible hydropower energy," Energy, Elsevier, vol. 175(C), pages 618-629.
    2. Shengli Liao & Hongye Zhao & Gang Li & Benxi Liu, 2019. "Short-Term Load Dispatching Method for a Diversion Hydropower Plant with Multiple Turbines in One Tunnel Using a Two-Stage Model," Energies, MDPI, vol. 12(8), pages 1-18, April.
    3. Gang Wang & Daihai You & Suhua Lou & Zhe Zhang & Li Dai, 2017. "Economic Valuation of Low-Load Operation with Auxiliary Firing of Coal-Fired Units," Energies, MDPI, vol. 10(9), pages 1-20, September.
    4. Alok K. Tripathi, 2021. "Crisis of Survival of Thermal Power Plants in India due to Consistently Falling Capacity Utilization Factors Responsible and Future Outlook," International Journal of Energy Economics and Policy, Econjournals, vol. 11(3), pages 328-337.
    5. Ying-Yi Hong, 2016. "Electric Power Systems Research," Energies, MDPI, vol. 9(10), pages 1-4, October.

    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. Li, Xi & Yu, Biying, 2019. "Peaking CO2 emissions for China's urban passenger transport sector," Energy Policy, Elsevier, vol. 133(C).
    2. Xindong Wang & Chun Yan & Wei Liu & Xinhong Liu, 2022. "Research on Carbon Emissions Prediction Model of Thermal Power Plant Based on SSA-LSTM Algorithm with Boiler Feed Water Influencing Factors," Sustainability, MDPI, vol. 14(23), pages 1-26, November.
    3. Chen, Yen-Haw & Lu, Su-Ying & Chang, Yung-Ruei & Lee, Ta-Tung & Hu, Ming-Che, 2013. "Economic analysis and optimal energy management models for microgrid systems: A case study in Taiwan," Applied Energy, Elsevier, vol. 103(C), pages 145-154.
    4. Alexander Franz & Julia Rieck & Jürgen Zimmermann, 2019. "Fix-and-optimize procedures for solving the long-term unit commitment problem with pumped storages," Annals of Operations Research, Springer, vol. 274(1), pages 241-265, March.
    5. Glotić, Arnel & Zamuda, Aleš, 2015. "Short-term combined economic and emission hydrothermal optimization by surrogate differential evolution," Applied Energy, Elsevier, vol. 141(C), pages 42-56.
    6. Cheng, Rui & Xu, Zhaofeng & Liu, Pei & Wang, Zhe & Li, Zheng & Jones, Ian, 2015. "A multi-region optimization planning model for China’s power sector," Applied Energy, Elsevier, vol. 137(C), pages 413-426.
    7. 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.
    8. Goudarzi, Arman & Swanson, Andrew G. & Van Coller, John & Siano, Pierluigi, 2017. "Smart real-time scheduling of generating units in an electricity market considering environmental aspects and physical constraints of generators," Applied Energy, Elsevier, vol. 189(C), pages 667-696.
    9. Xiaopeng Guo & Xiaodan Guo & Jiahai Yuan, 2014. "Impact Analysis of Air Pollutant Emission Policies on Thermal Coal Supply Chain Enterprises in China," Sustainability, MDPI, vol. 7(1), pages 1-21, December.
    10. Pereira, Sérgio & Ferreira, Paula & Vaz, A.I.F., 2014. "Short-term electricity planning with increase wind capacity," Energy, Elsevier, vol. 69(C), pages 12-22.
    11. Fallahi, Farhad & Nick, Mostafa & Riahy, Gholam H. & Hosseinian, Seyed Hossein & Doroudi, Aref, 2014. "The value of energy storage in optimal non-firm wind capacity connection to power systems," Renewable Energy, Elsevier, vol. 64(C), pages 34-42.
    12. 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.
    13. Mingquan Wang & Lingyun Zhang & Xin Su & Yang Lei & Qun Shen & Wei Wei & Maohua Wang, 2019. "Assessing the technology impact for industry carbon density reduction in China based on C3IAM-Tice," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 99(3), pages 1455-1468, December.
    14. Liu, Dunnan & Liu, Mingguang & Xu, Erfeng & Pang, Bo & Guo, Xiaodan & Xiao, Bowen & Niu, Dongxiao, 2018. "Comprehensive effectiveness assessment of renewable energy generation policy: A partial equilibrium analysis in China," Energy Policy, Elsevier, vol. 115(C), pages 330-341.
    15. Wei Wang & Hualin Xie & Tong Jiang & Daobei Zhang & Xue Xie, 2016. "Measuring the Total-Factor Carbon Emission Performance of Industrial Land Use in China Based on the Global Directional Distance Function and Non-Radial Luenberger Productivity Index," Sustainability, MDPI, vol. 8(4), pages 1-19, April.
    16. Chen, Xiuzhi & Liu, Chang & van Oel, Pieter & Mergia Mekonnen, Mesfin & Thorp, Kelly R. & Yin, Tuo & Wang, Jinyan & Muhammad, Tahir & Li, Yunkai, 2022. "Water and carbon risks within hydropower development on national scale," Applied Energy, Elsevier, vol. 325(C).
    17. Sen, Parag & Roy, Mousumi & Pal, Parimal, 2016. "Application of ARIMA for forecasting energy consumption and GHG emission: A case study of an Indian pig iron manufacturing organization," Energy, Elsevier, vol. 116(P1), pages 1031-1038.
    18. Teng, Minmin & Lv, Kunfeng & Han, Chuanfeng & Liu, Pihui, 2023. "Trading behavior strategy of power plants and the grid under renewable portfolio standards in China: A tripartite evolutionary game analysis," Energy, Elsevier, vol. 284(C).
    19. Khodr, H.M. & El Halabi, N. & García-Gracia, M., 2012. "Intelligent renewable microgrid scheduling controlled by a virtual power producer: A laboratory experience," Renewable Energy, Elsevier, vol. 48(C), pages 269-275.
    20. Jun Hao & Xiaolei Sun & Qianqian Feng, 2020. "A Novel Ensemble Approach for the Forecasting of Energy Demand Based on the Artificial Bee Colony Algorithm," Energies, MDPI, vol. 13(3), pages 1-25, January.

    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:8:y:2015:i:11:p:12345-12864:d:58715. 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.