Sizing ramping reserve using probabilistic solar forecasts: A data-driven method. (1st May 2022)

• Record Type: Journal Article
• Authors: Li, Binghui; Feng, Cong; Siebenschuh, Carlo; Zhang, Rui; Spyrou, Evangelia; Krishnan, Venkat; Hobbs, Benjamin F.; Zhang, Jie
• Is Part Of: Applied energy. Volume 313(2022)
• Publication Date: 2022-05-01
• Subjects: Probabilistic forecast -- k-nearest neighbors -- Flexible ramping product -- Solar power forecast -- Flexibility -- Reliability; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2022.118812
• Languages: English
• ISSNs: 0306-2619

Abstract

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. Highlights: This paper gives weather-informed estimates of ramping needs in electricity markets. We develop a k-nearest-neighbor-based method to characterize weather information. A multi-objective optimization model is developed to select best model parameters. Our results show better results in terms of both system reliability and economics.