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Two-Stage Optimization Model for Two-Side Daily Reserve Capacity of a Power System Considering Demand Response and Wind Power Consumption

Author

Listed:
  • Jun Dong

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Anyuan Fu

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Yao Liu

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Shilin Nie

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Peiwen Yang

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Linpeng Nie

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

Abstract

Today, wind power is becoming an important energy source for the future development of electric energy due to its clean and environmentally friendly characteristics. However, due to the uncertainty of incoming wind, the utilization efficiency of wind energy is extremely low, which means the problem of wind curtailment becomes more and more serious. To solve the issue of wind power large-scale consumption, a two-stage stochastic optimization model is established in this paper. Different from other research frameworks, a novel two-side reserve capacity mechanism, which simultaneously takes into account supply side and demand side, is designed to ensure the stable consumption of wind power in the real-time market stage. Specifically, the reserve capacity of thermal power units is considered on the supply side, and the demand response is introduced as the reserve capacity on the demand side. At the same time, the compensation mechanism of reserve capacity is introduced to encourage generation companies (GENCOs) to actively participate in the power balance process of the real-time market. In terms of solution method, compared with the traditional k-means clustering method, this paper uses the K-means classification based on numerical weather prediction (K-means-NWP) scenario clustering method to better describe the fluctuation of wind power output. Finally, an example simulation is conducted to analyze the influence of reserve capacity compensation mechanism and system parameters on wind power consumption results. The results demonstrate that with the introduction of reserve capacity compensation mechanism, the wind curtailment quantity of the power system has a significant reduction. Besides, the income of GENCOs is gradually increasing, which motivates their enthusiasm to provide reserve capacity. Furthermore, the reserve capacity mechanism designed in this paper promotes the consumption of wind power and the sustainable development of renewable energy.

Suggested Citation

  • Jun Dong & Anyuan Fu & Yao Liu & Shilin Nie & Peiwen Yang & Linpeng Nie, 2019. "Two-Stage Optimization Model for Two-Side Daily Reserve Capacity of a Power System Considering Demand Response and Wind Power Consumption," Sustainability, MDPI, vol. 11(24), pages 1-22, December.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:24:p:7171-:d:298052
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    References listed on IDEAS

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    1. Jun Dong & Yaoyu Zhang & Yuanyuan Wang & Yao Liu, 2021. "A Two-Stage Optimal Dispatching Model for Micro Energy Grid Considering the Dual Goals of Economy and Environmental Protection under CVaR," Sustainability, MDPI, vol. 13(18), pages 1-28, September.
    2. Al-Lawati, Razan A.H. & Crespo-Vazquez, Jose L. & Faiz, Tasnim Ibn & Fang, Xin & Noor-E-Alam, Md., 2021. "Two-stage stochastic optimization frameworks to aid in decision-making under uncertainty for variable resource generators participating in a sequential energy market," Applied Energy, Elsevier, vol. 292(C).

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