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Two-Stage Stochastic Optimization for the Strategic Bidding of a Generation Company Considering Wind Power Uncertainty

Author

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  • Gejirifu De

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Beijing 102206, China)

  • Zhongfu Tan

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Beijing 102206, China
    School of Economics and Management, Yan’an University, Yan’an 716000, China)

  • Menglu Li

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Beijing 102206, China)

  • Liling Huang

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Beijing 102206, China)

  • Xueying Song

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Beijing 102206, China)

Abstract

With the deregulation of electricity market, generation companies must take part in strategic bidding by offering its bidding quantity and bidding price in a day-ahead electricity wholesale market to sell their electricity. This paper studies the strategic bidding of a generation company with thermal power units and wind farms. This company is assumed to be a price-maker, which indicates that its installed capacity is high enough to affect the market-clearing price in the electricity wholesale market. The relationship between the bidding quantity of the generation company and market-clearing price is then studied. The uncertainty of wind power is considered and modeled through a set of discrete scenarios. A scenario-based two-stage stochastic bidding model is then provided. In the first stage, the decision-maker determines the bidding quantity in each time period. In the second stage, the decision-maker optimizes the unit commitment in each wind power scenario based on the bidding quantity in the first stage. The proposed two-stage stochastic optimization model is an NP-hard problem with high dimensions. To tackle the problem of “curses-of-dimensionality” caused by the coupling scenarios and improve the computation efficiency, a modified Benders decomposition algorithm is used to solve the model. The computational results show the following: (1) When wind power uncertainty is considered, generation companies prefer higher bidding quantities since the loss of wind power curtailment is much higher than the cost of additional power purchases in the current policy environment. (2) The wind power volatility has a strong negative effect on generation companies. The higher the power volatility is, the lower the profits, the bidding quantities, and the wind power curtailment of generation companies are. (3) The thermal power units play an important role in dealing with the wind power uncertainty in the strategic bidding problem, by shaving peak and filling valley probabilistic scheduling.

Suggested Citation

  • Gejirifu De & Zhongfu Tan & Menglu Li & Liling Huang & Xueying Song, 2018. "Two-Stage Stochastic Optimization for the Strategic Bidding of a Generation Company Considering Wind Power Uncertainty," Energies, MDPI, vol. 11(12), pages 1-21, December.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:12:p:3527-:d:191423
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    References listed on IDEAS

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    Cited by:

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    2. Jing Wu & Zhongfu Tan & Keke Wang & Yi Liang & Jinghan Zhou, 2021. "Research on Multi-Objective Optimization Model for Hybrid Energy System Considering Combination of Wind Power and Energy Storage," Sustainability, MDPI, vol. 13(6), pages 1-15, March.
    3. Heng Yang & Ziliang Jin & Jianhua Wang & Yong Zhao & Hejia Wang & Weihua Xiao, 2019. "Data-Driven Stochastic Scheduling for Energy Integrated Systems," Energies, MDPI, vol. 12(12), pages 1-21, June.
    4. Li, Wanying & Dong, Fugui & Ji, Zhengsen & Xia, Meijuan, 2023. "Analysis of the compound differential evolution game of new energy manufacturers’ two-stage market behavior under the weight of consumption responsibility," Energy, Elsevier, vol. 264(C).
    5. Gejirifu De & Xinlei Wang & Xueqin Tian & Tong Xu & Zhongfu Tan, 2022. "A Collaborative Optimization Model for Integrated Energy System Considering Multi-Load Demand Response," Energies, MDPI, vol. 15(6), pages 1-26, March.

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