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

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

Applied Energy, 2022, vol. 313, issue C, No S0306261922002574

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.

Keywords: Probabilistic forecast; k-nearest neighbors; Flexible ramping product; Solar power forecast; Flexibility; Reliability (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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DOI: 10.1016/j.apenergy.2022.118812

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