Generating wind power scenarios for probabilistic ramp event prediction using multivariate statistical post-processing
2018
Abstract. Wind power
forecasting is gaining international significance as more regions promote
policies to increase the use of renewable energy. Wind ramps, large
variations in wind power production during a period of minutes to hours,
challenge utilities and electrical balancing authorities. A sudden decrease
in wind-energy production must be balanced by other power generators to meet
energy demands, while a sharp increase in unexpected production results in
excess power that may not be used in the power grid, leading to a loss of
potential profits. In this study, we compare different methods to generate
probabilistic ramp forecasts from the High Resolution Rapid Refresh (HRRR)
numerical weather prediction model with up to 12 h of lead time at two
tall-tower locations in the United States. We validate model performance
using 21 months of 80 m wind speed observations from towers in Boulder,
Colorado, and near the Columbia River gorge in eastern Oregon. We employ four statistical post-processing methods, three of which are not
currently used in the literature for wind forecasting. These procedures
correct biases in the model and generate short-term wind speed scenarios
which are then converted to power scenarios. This probabilistic enhancement
of HRRR point forecasts provides valuable uncertainty information of ramp
events and improves the skill of predicting ramp events over the raw
forecasts. We compute Brier skill scores for each method with regard to predicting up-
and down-ramps to determine which method provides the best prediction. We
find that the Standard Schaake shuffle method yields the highest skill at
predicting ramp events for these datasets, especially for up-ramp events at
the Oregon site. Increased skill for ramp prediction is limited at the
Boulder, CO, site using any of the multivariate methods because of the poor
initial forecasts in this area of complex terrain. These statistical methods
can be implemented by wind farm operators to generate a range of possible
wind speed and power scenarios to aid and optimize decisions before ramp
events occur.
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