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Peaking Compensation Mechanism for Thermal Units and Virtual Peaking Plants Union Promoting Curtailed Wind Power Integration

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  • Tianliang Wang

    (School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China)

  • Xin Jiang

    (School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China)

  • Yang Jin

    (School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China)

  • Dawei Song

    (State Grid Henan Economic Research Institute, Zhengzhou 450052, China)

  • Meng Yang

    (State Grid Henan Economic Research Institute, Zhengzhou 450052, China)

  • Qingshan Zeng

    (School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China)

Abstract

As the installed capacity of wind power increases rapidly, how to promote wind power curtailment (WPC) integration has become a concern. The surface and underlying causes of wind power curtailment are insufficient peaking capability of the power system and imperfect peaking compensation mechanisms, respectively. Therefore, this paper proposes a peaking compensation mechanism uniting supply side and demand side to enhance system peaking capability. Firstly, through incentive and fairness analysis, the interest relationship of peaking subjects is researched based on game theory, and the peaking contribution on supply/demand side is quantified by Pearson correlation coefficients. Secondly, based on clustering analysis, the potential of system peaking providers are explored adequately, supply-side thermal units are divided into deep peaking clusters, and demand-side demand response (DR) resources are integrated into virtual peaking plants (VPP). Accordingly, a two-stage wind-thermal-VPP coordination optimization model is built to dispatch peaking providers. Furtherly, a two-layer peaking compensation allocation method considering peaking contribution and peaking enthusiasm is proposed to encourage peaking providers and mitigate “combination explosion”. Simulation results indicate that the proposed mechanism effectively promotes the enthusiasm of union peaking and the integration of WPC.

Suggested Citation

  • Tianliang Wang & Xin Jiang & Yang Jin & Dawei Song & Meng Yang & Qingshan Zeng, 2019. "Peaking Compensation Mechanism for Thermal Units and Virtual Peaking Plants Union Promoting Curtailed Wind Power Integration," Energies, MDPI, vol. 12(17), pages 1-20, August.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:17:p:3299-:d:261334
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    References listed on IDEAS

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    1. He, Yongxiu & Xu, Yang & Pang, Yuexia & Tian, Huiying & Wu, Rui, 2016. "A regulatory policy to promote renewable energy consumption in China: Review and future evolutionary path," Renewable Energy, Elsevier, vol. 89(C), pages 695-705.
    2. Tang, Rui & Wang, Shengwei & Li, Hangxin, 2019. "Game theory based interactive demand side management responding to dynamic pricing in price-based demand response of smart grids," Applied Energy, Elsevier, vol. 250(C), pages 118-130.
    3. Zhang, Sufang & Jiao, Yiqian & Chen, Wenjun, 2017. "Demand-side management (DSM) in the context of China's on-going power sector reform," Energy Policy, Elsevier, vol. 100(C), pages 1-8.
    4. Siqing Sheng & Qing Gu, 2019. "A Day-ahead and Day-in Decision Model Considering the Uncertainty of Multiple Kinds of Demand Response," Energies, MDPI, vol. 12(9), pages 1-26, May.
    5. Monyei, C.G. & Adewumi, A.O., 2017. "Demand Side Management potentials for mitigating energy poverty in South Africa," Energy Policy, Elsevier, vol. 111(C), pages 298-311.
    6. Dong, Changgui & Qi, Ye & Dong, Wenjuan & Lu, Xi & Liu, Tianle & Qian, Shuai, 2018. "Decomposing driving factors for wind curtailment under economic new normal in China," Applied Energy, Elsevier, vol. 217(C), pages 178-188.
    7. Seungmi Lee & Jinho Kim, 2018. "Analytical Assessment for System Peak Reduction by Demand Responsive Resources Considering Their Operational Constraints in Wholesale Electricity Market," Energies, MDPI, vol. 11(12), pages 1-15, November.
    8. Craparo, E.M. & Sprague, J.G., 2019. "Integrated supply- and demand-side energy management for expeditionary environmental control," Applied Energy, Elsevier, vol. 233, pages 352-366.
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