Volume 3, issue 1 | Copyright
Wind Energ. Sci., 3, 371-393, 2018
https://doi.org/10.5194/wes-3-371-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research articles 14 Jun 2018

Research articles | 14 Jun 2018

Generating wind power scenarios for probabilistic ramp event prediction using multivariate statistical post-processing

Rochelle P. Worsnop1, Michael Scheuerer2,3, Thomas M. Hamill3, and Julie K. Lundquist1,4 Rochelle P. Worsnop et al.
  • 1Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, Colorado, USA
  • 2Cooperative Institute for Research in the Environmental Sciences, University of Colorado Boulder, Boulder, Colorado, USA
  • 3NOAA/ESRL, Physical Sciences Division, Boulder, Colorado, USA
  • 4National Renewable Energy Laboratory, Golden, Colorado, USA

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 12h of lead time at two tall-tower locations in the United States. We validate model performance using 21 months of 80m 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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This paper uses four statistical methods to generate probabilistic wind speed and power ramp forecasts from the High Resolution Rapid Refresh model. The results show that these methods can provide necessary uncertainty information of power ramp forecasts. These probabilistic forecasts can aid in decisions regarding power production and grid integration of wind power.
This paper uses four statistical methods to generate probabilistic wind speed and power ramp...
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