Using the analog ensemble method as a proxy measurement for wind power predictability. (February 2020)

• Record Type: Journal Article
• Authors: Shahriari, M.; Cervone, G.; Clemente-Harding, L.; Delle Monache, L.
• Is Part Of: Renewable energy. Volume 146(2020)
• Page Start: 789
• Page End: 801
• Publication Date: 2020-02
• Subjects: Wind power forecasting -- Probabilistic forecast -- Forecast uncertainty -- Analog ensemble -- Wind output volatility; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2019.06.132
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: Wind power forecast uncertainty exposes wind farms to volatile real-time electricity prices and increases wind power integration costs. Wind power forecast uncertainty could address these challenges and facilitate the process of siting suitable wind farm locations. In this study, the Analog Ensemble (AnEn) is employed to generate probabilistic wind speed forecasts at 80-m height using past forecast and analysis fields from the Global Forecast System (GFS). The AnEn predictions are used as proxy measurements for how difficult it is to estimate wind speed at different locations in the contiguous United States. The results show significant spatial variations in the wind speed error over the domain. This measure of uncertainty is paramount when determining the most suitable locations for large wind farms. We observed that locations with higher average wind speed are associated with larger degrees of forecast uncertainty which increases the difficulty to predict wind speed at these locations. Our analysis showed high correlation between forecast uncertainty and wind power output volatility which indicates higher risk of operating in real time electricity markets for wind farms located in areas with higher wind speeds. Further, a simple risk analysis using Sharpe ratio was performed to evaluate the riskiness of wind farms in the U.S. Highlights: Introducing a measure to quantify the degree of difficulty to predict wind power. Standard deviation of power is an appropriate measure for riskiness of wind farms. Higher wind power forecast uncertainty in areas with higher average wind speed. Higher wind speed could justify the higher risk associated with wind power forecast.