IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i5p922-d212556.html
   My bibliography  Save this article

Interval Optimization-Based Unit Commitment for Deep Peak Regulation of Thermal Units

Author

Listed:
  • Yinping Yang

    (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China)

  • Chao Qin

    (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China)

  • Yuan Zeng

    (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China)

  • Chengshan Wang

    (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China)

Abstract

The deep peak regulation of thermal units is an important measure for coping with significant wind power penetration. In this paper, based on interval optimization, a novel multi-objective unit commitment method is proposed considering the deep peak regulation of thermal units. In the proposed method, a thermal power cost model was developed to accurately determine the economic performance of three different peak regulation scenarios, particularly of the deep peak regulation scenario. The midpoint and width of the cost interval are simultaneously considered in the optimization process. The non-dominated sorting GA-II (NSGA-II) algorithm was incorporated into the model for a coordinated control of the midpoint and width of the obtained cost interval for further optimization. Considering that significant wind penetration results in greater nodal variations, the affine arithmetic was employed to solve nodal uncertainties, so that all system variations can be addressed. The method proposed in this paper was validated by a modified IEEE-39 bus system. The results showed that it serves as a useful tool for power dispatchers to obtain robust and economic solutions at different wind power prediction accuracies.

Suggested Citation

  • Yinping Yang & Chao Qin & Yuan Zeng & Chengshan Wang, 2019. "Interval Optimization-Based Unit Commitment for Deep Peak Regulation of Thermal Units," Energies, MDPI, vol. 12(5), pages 1-21, March.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:5:p:922-:d:212556
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/5/922/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/5/922/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Li, Longxi & Mu, Hailin & Li, Nan & Li, Miao, 2016. "Economic and environmental optimization for distributed energy resource systems coupled with district energy networks," Energy, Elsevier, vol. 109(C), pages 947-960.
    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. Quanhui Che & Suhua Lou & Yaowu Wu & Xiangcheng Zhang & Xuebin Wang, 2019. "Optimal Scheduling of a Multi-Energy Power System with Multiple Flexible Resources and Large-Scale Wind Power," Energies, MDPI, vol. 12(18), pages 1-14, September.
    2. Xiaolong Yang & Dongxiao Niu & Meng Chen & Keke Wang & Qian Wang & Xiaomin Xu, 2020. "An Operation Benefit Analysis and Decision Model of Thermal Power Enterprises in China against the Background of Large-Scale New Energy Consumption," Sustainability, MDPI, vol. 12(11), pages 1-19, June.
    3. Hongwei Li & Qing Xu & Shitao Wang & Huihui Song, 2022. "Peak Shaving Methods of Distributed Generation Clusters Using Dynamic Evaluation and Self-Renewal Mechanism," Energies, MDPI, vol. 15(19), pages 1-17, September.
    4. Lin, Boqiang & Liu, Zhiwei, 2024. "Assessment of China's flexible power investment value in the emission trading system," Applied Energy, Elsevier, vol. 359(C).

    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. Xinxin Liu & Nan Li & Feng Liu & Hailin Mu & Longxi Li & Xiaoyu Liu, 2021. "Optimal Design on Fossil-to-Renewable Energy Transition of Regional Integrated Energy Systems under CO 2 Emission Abatement Control: A Case Study in Dalian, China," Energies, MDPI, vol. 14(10), pages 1-25, May.
    2. Alqahtani, Mohammed & Hu, Mengqi, 2022. "Dynamic energy scheduling and routing of multiple electric vehicles using deep reinforcement learning," Energy, Elsevier, vol. 244(PA).
    3. Li, Ruonan & Mahalec, Vladimir, 2022. "Integrated design and operation of energy systems for residential buildings, commercial buildings, and light industries," Applied Energy, Elsevier, vol. 305(C).
    4. Xiaofeng Liu & Shijun Wang & Jiawen Sun, 2018. "Energy Management for Community Energy Network with CHP Based on Cooperative Game," Energies, MDPI, vol. 11(5), pages 1-18, April.
    5. Ziemele, Jelena & Gravelsins, Armands & Blumberga, Andra & Blumberga, Dagnija, 2017. "Sustainability of heat energy tariff in district heating system: Statistic and dynamic methodologies," Energy, Elsevier, vol. 137(C), pages 834-845.
    6. Zheng, Xuyue & Wu, Guoce & Qiu, Yuwei & Zhan, Xiangyan & Shah, Nilay & Li, Ning & Zhao, Yingru, 2018. "A MINLP multi-objective optimization model for operational planning of a case study CCHP system in urban China," Applied Energy, Elsevier, vol. 210(C), pages 1126-1140.
    7. Rui Gao & Hongxia Guo & Ruihong Zhang & Tian Mao & Qianyao Xu & Baorong Zhou & Ping Yang, 2019. "A Two-Stage Dispatch Mechanism for Virtual Power Plant Utilizing the CVaR Theory in the Electricity Spot Market," Energies, MDPI, vol. 12(17), pages 1-18, September.
    8. Petrelli, Marina & Fioriti, Davide & Berizzi, Alberto & Bovo, Cristian & Poli, Davide, 2021. "A novel multi-objective method with online Pareto pruning for multi-year optimization of rural microgrids," Applied Energy, Elsevier, vol. 299(C).
    9. Li, Longxi & Cao, Xilin & Wang, Peng, 2021. "Optimal coordination strategy for multiple distributed energy systems considering supply, demand, and price uncertainties," Energy, Elsevier, vol. 227(C).
    10. Qin Wang & Jincan Zeng & Beibei Cheng & Minwei Liu & Guori Huang & Xi Liu & Gengsheng He & Shangheng Yao & Peng Wang & Longxi Li, 2024. "A Cooperative Game Approach for Optimal Design of Shared Energy Storage System," Sustainability, MDPI, vol. 16(17), pages 1-19, August.
    11. Longxi Li, 2020. "Optimal Coordination Strategies for Load Service Entity and Community Energy Systems Based on Centralized and Decentralized Approaches," Energies, MDPI, vol. 13(12), pages 1-22, June.
    12. Rigo-Mariani, Rémy & Chea Wae, Sean Ooi & Mazzoni, Stefano & Romagnoli, Alessandro, 2020. "Comparison of optimization frameworks for the design of a multi-energy microgrid," Applied Energy, Elsevier, vol. 257(C).
    13. Lucas Feksa Ramos & Luciane Neves Canha & Josue Campos do Prado & Leonardo Rodrigues Araujo Xavier de Menezes, 2022. "A Novel Virtual Power Plant Uncertainty Modeling Framework Using Unscented Transform," Energies, MDPI, vol. 15(10), pages 1-13, May.
    14. Kong, Xiangyu & Xiao, Jie & Wang, Chengshan & Cui, Kai & Jin, Qiang & Kong, Deqian, 2019. "Bi-level multi-time scale scheduling method based on bidding for multi-operator virtual power plant," Applied Energy, Elsevier, vol. 249(C), pages 178-189.
    15. Fonseca, Juan D. & Commenge, Jean-Marc & Camargo, Mauricio & Falk, Laurent & Gil, Iván D., 2021. "Multi-criteria optimization for the design and operation of distributed energy systems considering sustainability dimensions," Energy, Elsevier, vol. 214(C).
    16. Alberto Fichera & Mattia Frasca & Rosaria Volpe, 2020. "A cost-based approach for evaluating the impact of a network of distributed energy systems on the centralized energy supply," Energy & Environment, , vol. 31(1), pages 77-87, February.
    17. Jichun Liu & Jianhua Li & Yue Xiang & Xin Zhang & Wanxiao Jiang, 2019. "Optimal Sizing of Cascade Hydropower and Distributed Photovoltaic Included Virtual Power Plant Considering Investments and Complementary Benefits in Electricity Markets," Energies, MDPI, vol. 12(5), pages 1-23, March.
    18. Gao, Datong & Li, Jing & Ren, Xiao & Hu, Tianxiang & Pei, Gang, 2022. "A novel direct steam generation system based on the high-vacuum insulated flat plate solar collector," Renewable Energy, Elsevier, vol. 197(C), pages 966-977.
    19. Karmellos, M. & Georgiou, P.N. & Mavrotas, G., 2019. "A comparison of methods for the optimal design of Distributed Energy Systems under uncertainty," Energy, Elsevier, vol. 178(C), pages 318-333.
    20. Kang, Jing & Wang, Shengwei & Yan, Chengchu, 2019. "A new distributed energy system configuration for cooling dominated districts and the performance assessment based on real site measurements," Renewable Energy, Elsevier, vol. 131(C), pages 390-403.

    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:12:y:2019:i:5:p:922-:d:212556. 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.