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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
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    References listed on IDEAS

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    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.
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    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).
    5. Wang, Kaiyan & Zhu, Hengtao & Dang, Jian & Ming, Bo & Wu, Xiong, 2024. "Short-term optimal scheduling of wind-photovoltaic-hydropower-thermal-pumped hydro storage coupled system based on a novel multi-objective priority stratification method," Energy, Elsevier, vol. 309(C).

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