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Characterizing and analyzing ramping events in wind power, solar power, load, and netload

Author

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  • Cui, Mingjian
  • Zhang, Jie
  • Feng, Cong
  • Florita, Anthony R.
  • Sun, Yuanzhang
  • Hodge, Bri-Mathias

Abstract

One of the biggest concerns associated with integrating a large amount of renewable energy into the power grid is the ability to handle large ramps in the renewable power output. For the sake of system reliability and economics, it is essential for power system operators to better understand the ramping features of renewables, load, and netload. In this paper, an optimized swinging door algorithm (OpSDA) is adopted and extended to accurately and efficiently detect ramping events. For wind power ramps detection, a process of merging “bumps” (that have a different changing direction) into adjacent ramping segments is integrated to improve the performance of the OpSDA method. For solar ramps detection, ramping events that occur in both clear-sky and measured (or forecasted) solar power are removed to account for the diurnal pattern of solar generation. Ramping features are extracted and extensively compared between load and netload under different renewable penetration levels (i.e., 9.77%, 15.85%, and 51.38%). Comparison results show that: (i) netload ramp events with shorter durations and smaller magnitudes occur more frequently when renewable penetration level increases, and the total number of ramping events also increases; and (ii) different ramping characteristics are observed in load and netload even at a low renewable penetration level.

Suggested Citation

  • Cui, Mingjian & Zhang, Jie & Feng, Cong & Florita, Anthony R. & Sun, Yuanzhang & Hodge, Bri-Mathias, 2017. "Characterizing and analyzing ramping events in wind power, solar power, load, and netload," Renewable Energy, Elsevier, vol. 111(C), pages 227-244.
    • Handle: RePEc:eee:renene:v:111:y:2017:i:c:p:227-244
    DOI: 10.1016/j.renene.2017.04.005
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    Citations

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    Cited by:

    1. Lee, Joseph C.Y. & Draxl, Caroline & Berg, Larry K., 2022. "Evaluating wind speed and power forecasts for wind energy applications using an open-source and systematic validation framework," Renewable Energy, Elsevier, vol. 200(C), pages 457-475.
    2. Marta Poncela & Arturs Purvins & Stamatios Chondrogiannis, 2018. "Pan-European Analysis on Power System Flexibility," Energies, MDPI, vol. 11(7), pages 1-19, July.
    3. Dhruv Suri & Jacques de Chalendar & Ines Azevedo, 2024. "What are the real implications for $CO_2$ as generation from renewables increases?," Papers 2408.05209, arXiv.org.
    4. EunJi Ahn & Jin Hur, 2022. "A Practical Metric to Evaluate the Ramp Events of Wind Generating Resources to Enhance the Security of Smart Energy Systems," Energies, MDPI, vol. 15(7), pages 1-16, April.
    5. Keller, Victor & Lyseng, Benjamin & English, Jeffrey & Niet, Taco & Palmer-Wilson, Kevin & Moazzen, Iman & Robertson, Bryson & Wild, Peter & Rowe, Andrew, 2018. "Coal-to-biomass retrofit in Alberta –value of forest residue bioenergy in the electricity system," Renewable Energy, Elsevier, vol. 125(C), pages 373-383.
    6. Feng, Cong & Sun, Mucun & Cui, Mingjian & Chartan, Erol Kevin & Hodge, Bri-Mathias & Zhang, Jie, 2019. "Characterizing forecastability of wind sites in the United States," Renewable Energy, Elsevier, vol. 133(C), pages 1352-1365.
    7. Han, Shuang & Zhang, Lu-na & Liu, Yong-qian & Zhang, Hao & Yan, Jie & Li, Li & Lei, Xiao-hui & Wang, Xu, 2019. "Quantitative evaluation method for the complementarity of wind–solar–hydro power and optimization of wind–solar ratio," Applied Energy, Elsevier, vol. 236(C), pages 973-984.
    8. Naegele, S.M. & McCandless, T.C. & Greybush, S.J. & Young, G.S. & Haupt, S.E. & Al-Rasheedi, M., 2020. "Climatology of wind variability for the Shagaya region in Kuwait," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    9. Xin Fang & Venkat Krishnan & Bri-Mathias Hodge, 2018. "Strategic Offering for Wind Power Producers Considering Energy and Flexible Ramping Products," Energies, MDPI, vol. 11(5), pages 1-19, May.
    10. Shang, Chuanfu & Wei, Pengcheng, 2018. "Enhanced support vector regression based forecast engine to predict solar power output," Renewable Energy, Elsevier, vol. 127(C), pages 269-283.
    11. 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.
    12. Jin, Xiaoyu & Liu, Benxi & Liao, Shengli & Cheng, Chuntian & Zhang, Yi & Zhao, Zhipeng & Lu, Jia, 2022. "Wasserstein metric-based two-stage distributionally robust optimization model for optimal daily peak shaving dispatch of cascade hydroplants under renewable energy uncertainties," Energy, Elsevier, vol. 260(C).
    13. Jie Wan & Yanjia Wang & Guorui Ren & Jinfu Liu & Wei Wang & Jilai Yu, 2020. "An Integrated Evaluation Method of the Wind Power Ramp Event Based on Generalized Information of the Source, Grid, and Load," Energies, MDPI, vol. 13(24), pages 1-19, December.
    14. Cui, Yang & He, Yingjie & Xiong, Xiong & Chen, Zhenghong & Li, Fen & Xu, Taotao & Zhang, Fanghong, 2021. "Algorithm for identifying wind power ramp events via novel improved dynamic swinging door," Renewable Energy, Elsevier, vol. 171(C), pages 542-556.
    15. Fan, Huijing & Zhen, Zhao & Liu, Nian & Sun, Yiqian & Chang, Xiqiang & Li, Yu & Wang, Fei & Mi, Zengqiang, 2023. "Fluctuation pattern recognition based ultra-short-term wind power probabilistic forecasting method," Energy, Elsevier, vol. 266(C).
    16. Stavros-Andreas Logothetis & Vasileios Salamalikis & Bijan Nouri & Jan Remund & Luis F. Zarzalejo & Yu Xie & Stefan Wilbert & Evangelos Ntavelis & Julien Nou & Niels Hendrikx & Lennard Visser & Manaji, 2022. "Solar Irradiance Ramp Forecasting Based on All-Sky Imagers," Energies, MDPI, vol. 15(17), pages 1-17, August.

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