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Power System Day-Ahead Unit Commitment Based on Chance-Constrained Dependent Chance Goal Programming

Author

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  • Zhiwei Li

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China)

  • Tianran Jin

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China)

  • Shuqiang Zhao

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China)

  • Jinshan Liu

    (Dispatching and Control Center of State Grid Qinghai Electric Power Company, Xining 810008, China)

Abstract

In the context of large-scale renewable energy integrated into an electrical power system, the effects of power forecast errors on the power balance equation of the power system unit commitment model is considered. In this paper, the problem of solving the power balance equation with uncertain variables was studied. The unit commitment model with random variables in the power balance equation was solved by establishing a power system day-ahead optimisation unit commitment model based on chance-constrained dependent chance goal programming. First, to achieve the solution of the power balance equation with random variables, the equality constraint is loosened into an inequality constraint, and the power balance equation constraint is transformed into a dependent chance programming model aimed at maximising the probability of occurrence of random events in an uncertain environment. Then, the dependent chance programming model is proposed to ensure the economy and security of the scheme, and the goal programming model is introduced to facilitate an efficient solution. By combining dependent chance programming and goal programming, a power system day-ahead unit commitment model based on chance-constrained dependent chance goal programming is established. Finally, an example is discussed to demonstrate the effectiveness of the proposed model.

Suggested Citation

  • Zhiwei Li & Tianran Jin & Shuqiang Zhao & Jinshan Liu, 2018. "Power System Day-Ahead Unit Commitment Based on Chance-Constrained Dependent Chance Goal Programming," Energies, MDPI, vol. 11(7), pages 1-20, July.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:7:p:1718-:d:155536
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    References listed on IDEAS

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    Cited by:

    1. Tong Guo & Yajing Gao & Xiaojie Zhou & Yonggang Li & Jiaomin Liu, 2018. "Optimal Scheduling of Power System Incorporating the Flexibility of Thermal Units," Energies, MDPI, vol. 11(9), pages 1-17, August.
    2. Alireza Amani & Hosein Alizadeh, 2021. "Solving Hydropower Unit Commitment Problem Using a Novel Sequential Mixed Integer Linear Programming Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(6), pages 1711-1729, April.
    3. Kun Yang & Kan Yang, 2021. "Short-Term Hydro Generation Scheduling of the Three Gorges Hydropower Station Using Improver Binary-coded Whale Optimization Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(11), pages 3771-3790, September.
    4. Wang, Zizhao & Wu, Feng & Li, Yang & Shi, Linjun & Lee, Kwang Y. & Wu, Jiawei, 2023. "Itô-theory-based multi-time scale dispatch approach for cascade hydropower-photovoltaic complementary system," Renewable Energy, Elsevier, vol. 202(C), pages 127-142.

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