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Sizing ramping reserve using probabilistic solar forecasts: A data-driven method

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  • Li, Binghui
  • Feng, Cong
  • Siebenschuh, Carlo
  • Zhang, Rui
  • Spyrou, Evangelia
  • Krishnan, Venkat
  • Hobbs, Benjamin F.
  • Zhang, Jie

Abstract

Ramping products have been introduced or proposed in several U.S. power markets to mitigate the impact of load and renewable uncertainties on market efficiency and reliability. Current methods often rely on historical data to estimate the requirements of ramping products and fail to take into account the effects of the latest weather conditions and their uncertainties, which could lead to overly conservative or insufficient requirements. This study proposes a k-nearest-neighbor-based method to give weather-informed estimates of ramping needs based on short-term probabilistic solar irradiance forecasts. Forecasts from multiple sites are employed in conjunction with principal component analysis to derive numerical classifiers to characterize system-level weather conditions. In addition, we develop a data-driven method to optimize the model parameters in a rolling-forward manner. By using real-world data from the California Independent System Operator, we design two metrics to evaluate method performance: 1) frequency of shortage and 2) oversupply of ramping product. Our proposed method presents advantages in comparison with the baseline and a set of benchmark methods: without compromising system reliability, it reduces system ramping requirements by up to 25%, therefore improving both system reliability and economics.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:313:y:2022:i:c:s0306261922002574
    DOI: 10.1016/j.apenergy.2022.118812
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    References listed on IDEAS

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    1. Wang, Qin & Tuohy, Aidan & Ortega-Vazquez, Miguel & Bello, Mobolaji & Ela, Erik & Kirk-Davidoff, Daniel & Hobbs, William B. & Ault, David J. & Philbrick, Russ, 2023. "Quantifying the value of probabilistic forecasting for power system operation planning," Applied Energy, Elsevier, vol. 343(C).
    2. Liu, Jingxuan & Zang, Haixiang & Ding, Tao & Cheng, Lilin & Wei, Zhinong & Sun, Guoqiang, 2023. "Harvesting spatiotemporal correlation from sky image sequence to improve ultra-short-term solar irradiance forecasting," Renewable Energy, Elsevier, vol. 209(C), pages 619-631.
    3. Shi, Jiantao & Guo, Ye & Shen, Xinwei & Wu, Wenchuan & Sun, Hongbin, 2024. "Multi-interval rolling-window joint dispatch and pricing of energy and reserve under uncertainty," Applied Energy, Elsevier, vol. 356(C).

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