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Estimating ramping requirements with solar-friendly flexible ramping product in multi-timescale power system operations

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  • Cui, Mingjian
  • Zhang, Jie

Abstract

The increasing solar power penetration causes the need of additional flexibility for power system operations. Market-based flexible ramping services have been proposed in several balance authorities to address this issue. However, the ramping requirements in multi-timescale power system operations are not well defined and still challenging to be accurately estimated. To this end, this paper develops a multi-timescale unit commitment and economic dispatch model to estimate the ramping requirements. Furthermore, a solar power ramping product (SPRP) is developed and integrated into the multi-timescale dispatch model that considers new objective functions, ramping capacity limits, active power limits, and flexible ramping requirements. To find the optimal ramping requirement based on the level of uncertainty in netload, a surrogate-based optimization model is developed to approximate the objective function of the multi-timescale dispatch model that considers both economic and reliability benefits of the balancing authorities. Numerical simulations on a modified IEEE 118-bus system show that a better estimation of ramping requirements could enhance both the reliability and economic benefits of the system. The use of SPRP can reduce the flexible ramping reserves provided by conventional generators.

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  • Cui, Mingjian & Zhang, Jie, 2018. "Estimating ramping requirements with solar-friendly flexible ramping product in multi-timescale power system operations," Applied Energy, Elsevier, vol. 225(C), pages 27-41.
    • Handle: RePEc:eee:appene:v:225:y:2018:i:c:p:27-41
    DOI: 10.1016/j.apenergy.2018.05.031
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    2. Sreekumar, Sreenu & Yamujala, Sumanth & Sharma, Kailash Chand & Bhakar, Rohit & Simon, Sishaj P. & Rana, Ankur Singh, 2022. "Flexible Ramp Products: A solution to enhance power system flexibility," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    3. Fan, Shuai & Liu, Jiang & Wu, Qing & Cui, Mingjian & Zhou, Huan & He, Guangyu, 2020. "Optimal coordination of virtual power plant with photovoltaics and electric vehicles: A temporally coupled distributed online algorithm," Applied Energy, Elsevier, vol. 277(C).
    4. Yuanyuan, Zhang & Huiru, Zhao & Bingkang, Li, 2023. "Distributionally robust comprehensive declaration strategy of virtual power plant participating in the power market considering flexible ramping product and uncertainties," Applied Energy, Elsevier, vol. 343(C).
    5. Keeratimahat, Kanyawee & Bruce, Anna & MacGill, Iain, 2021. "Analysis of short-term operational forecast deviations and controllability of utility-scale photovoltaic plants," Renewable Energy, Elsevier, vol. 167(C), pages 343-358.
    6. Li, Binghui & Feng, Cong & Siebenschuh, Carlo & Zhang, Rui & Spyrou, Evangelia & Krishnan, Venkat & Hobbs, Benjamin F. & Zhang, Jie, 2022. "Sizing ramping reserve using probabilistic solar forecasts: A data-driven method," Applied Energy, Elsevier, vol. 313(C).
    7. Zhang, Jian & Cui, Mingjian & He, Yigang, 2020. "Robustness and adaptability analysis for equivalent model of doubly fed induction generator wind farm using measured data," Applied Energy, Elsevier, vol. 261(C).
    8. Abdollahi, Elnaz & Wang, Haichao & Lahdelma, Risto, 2019. "Parametric optimization of long-term multi-area heat and power production with power storage," Applied Energy, Elsevier, vol. 235(C), pages 802-812.
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    10. Chen, Xiaoyang & Du, Yang & Lim, Enggee & Fang, Lurui & Yan, Ke, 2022. "Towards the applicability of solar nowcasting: A practice on predictive PV power ramp-rate control," Renewable Energy, Elsevier, vol. 195(C), pages 147-166.

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