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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 789-801 |
| Number of pages | 13 |
| Journal | Renewable Energy |
| Volume | 146 |
| DOIs | |
| State | Published - Feb 1 2020 |
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All Science Journal Classification (ASJC) codes
- Renewable Energy, Sustainability and the Environment
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Using the analog ensemble method as a proxy measurement for wind power predictability. / Shahriari, M.; Cervone, Guido; Clemente-Harding, L.; Delle Monache, L.
In: Renewable Energy, Vol. 146, 01.02.2020, p. 789-801.Research output: Contribution to journal › Article
TY - JOUR
T1 - Using the analog ensemble method as a proxy measurement for wind power predictability
AU - Shahriari, M.
AU - Cervone, Guido
AU - Clemente-Harding, L.
AU - Delle Monache, L.
PY - 2020/2/1
Y1 - 2020/2/1
N2 - 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.
AB - 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.
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U2 - 10.1016/j.renene.2019.06.132
DO - 10.1016/j.renene.2019.06.132
M3 - Article
VL - 146
SP - 789
EP - 801
JO - Renewable Energy
JF - Renewable Energy
SN - 0960-1481
ER -