Offshore Wind Resource Assessment for the California Pacific Outer Continental Shelf (2020)

Optis, Mike; Rybchuk, Alex; Bodini, Nicola; Rossol, Michael; Musiał, W.

doi:10.2172/1677466

Cited by 31 publications

(55 citation statements)

References 21 publications

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“…The main data source is the network of buoy-based wind speed measurements from the National Data Buoy Center, maintained by the National Oceanic and Atmospheric Administration (National Data Buoy Center, 1971). These data have been used to characterize the wind resource in offshore California (Wang et al, 2019;Optis et al, 2020c), the US offshore Atlantic (Optis et al, 2020b), and the Great Lakes (Doubrawa et al, 2015). These buoys generally provide years worth of wind speed measurements at heights of less than 5 m and are of high quality.…”

Section: Introductionmentioning

confidence: 99%

New methods to improve the vertical extrapolation of near-surface offshore wind speeds

et al. 2021

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Abstract. Accurate characterization of the offshore wind resource has been hindered by a sparsity of wind speed observations that span offshore wind turbine rotor-swept heights. Although public availability of floating lidar data is increasing, most offshore wind speed observations continue to come from buoy-based and satellite-based near-surface measurements. The aim of this study is to develop and validate novel vertical extrapolation methods that can accurately estimate wind speed time series across rotor-swept heights using these near-surface measurements. We contrast the conventional logarithmic profile against three novel approaches: a logarithmic profile with a long-term stability correction, a single-column model, and a machine-learning model. These models are developed and validated using 1 year of observations from two floating lidars deployed in US Atlantic offshore wind energy areas. We find that the machine-learning model significantly outperforms all other models across all stability regimes, seasons, and times of day. Machine-learning model performance is considerably improved by including the air–sea temperature difference, which provides some accounting for offshore atmospheric stability. Finally, we find no degradation in machine-learning model performance when tested 83 km from its training location, suggesting promising future applications in extrapolating 10 m wind speeds from spatially resolved satellite-based wind atlases.

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Section: Introductionmentioning

confidence: 99%

New methods to improve the vertical extrapolation of near-surface offshore wind speeds

et al. 2021

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“…Contrary to popular belief, wind speeds are not always higher at 500 m compared to 100 m (Bechtle et al 2019), which was confirmed by workshop participants (Weber et al 2021), as well as an analysis conducted at NREL with WRF simulations over Hawaii, the Pacific Northwest, and the mid-Atlantic Coast (the methodology used for California in Optis et al [2020] was replicated for Hawaii and the Pacific Northwest). Figure 12 (left panels) shows the 20-year average maximum wind speed in the layer between 100 m and 500 m, and (right) the 20-year average difference between 100-m and 500-m wind speeds.…”

Section: Wind Shear and Airborne Wind Energy Resource In The United Statesmentioning

confidence: 92%

Airborne Wind Energy

Weber

Marquis

Cooperman

et al. 2021

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This section briefly describes the way in which this report addresses the congressional request regarding focus and scope, the consideration of AWE in relation to traditional wind energy, and the approach taken to develop the report. Addressing the Congressional Request: Focus and ScopeThis report describes technical analyses of various aspects of AWE provided to the U.S. Department of Energy (DOE) Wind Energy Technologies Office to underpin DOE's response to the congressional request for a report on AWE in the Energy Act of 2020, which states:-Not later than 180 days after the date of the enactment of this Act, the Secretary shall submit to the Committee on Science, Space, and Technology of the House of Representatives and the Committee on Energy and Natural Resources of the Senate a report on the potential for, and technical viability of, airborne wind energy systems to provide a significant source of energy in the United States.

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“…We consider a 20-year numerical data set recently developed by the National Renewable Energy Laboratory to provide accurate cost estimates for floating wind in the California OCS (Figure 1). As described in detail in Optis et al (2020), this product includes a single WRF setup that is run for a 20-year period (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019), and an additional 15 WRF ensemble members 100 run over a single year (2017), which were selected because of strong data coverage from the network of buoy and coastal radar observations used for model validation. All of the simulations are run with the common attributes in Table 1 -Reanalysis forcing product: selected between ERA5, developed by the European Centre for Medium-Range Weather Forecasts (Hersbach et al (2020)), and the Modern-Era Retrospective analysis for Research and Applications, Version 2 ( Gelaro et al (2017)), developed by the National Aeronautics and Space Administration.…”

Section: Numerical Simulation Setupmentioning

confidence: 99%

Assessing Boundary Condition and Parametric Uncertainty in Numerical-Weather-Prediction-Modeled, Long-Term Offshore Wind Speed Through Machine Learning and Analog Ensemble

Bodini

Optis

et al. 2021

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Abstract. To accurately plan and manage wind power plants, not only does the time-varying wind resource at the site of interest need to be assessed, but also the uncertainty connected to this estimate. Numerical weather prediction (NWP) models at the mesoscale represent a valuable way to characterize the wind resource offshore, given the challenges connected with measuring hub height wind speed. The boundary condition and parametric uncertainty associated with modeled wind speed is often estimated by running a model ensemble. However, creating an NWP ensemble of long-term wind resource data over a large region represents a computational challenge. Here, we propose two approaches to temporally extrapolate wind speed boundary condition and parametric uncertainty using a more convenient setup where a mesoscale ensemble is run over a short-term period (1 year), and only a single model covers the desired long-term period (20 year). We quantify hub-height wind speed boundary condition and parametric uncertainty from the short-term model ensemble as its normalized across-ensemble standard deviation. Then, we develop and apply a gradient-boosting model and an analog ensemble approach to temporally extrapolate such uncertainty to the full 20-year period, where only a single model run is available. As a test case, we consider offshore wind resource characterization in the California Outer Continental Shelf. Both the proposed approaches provide accurate estimates of the long-term wind speed boundary condition and parametric uncertainty across the region (R2 > 0.75), with the gradient-boosting model slightly outperforming the analog ensemble in terms of bias and centered root-mean-square error. At the three offshore wind energy lease areas in the region, we find a long-term median hourly uncertainty between 10 % and 14 % of the mean hub-height wind speed values. Finally, we assess the physical variability of the uncertainty estimates. In general, we find that the wind speed uncertainty increases closer to land. Also, neutral conditions have smaller uncertainty than the stable and unstable cases, and the modeled wind speed in winter has less boundary condition and parametric sensitivity than summer.

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Offshore Wind Resource Assessment for the California Pacific Outer Continental Shelf (2020)

Abstract: Department of Energy (DOE) reports produced after 1991 and a growing number of pre-1991 documents are available free via www.OSTI.gov.

Cited by 31 publications

References 21 publications

New methods to improve the vertical extrapolation of near-surface offshore wind speeds

New methods to improve the vertical extrapolation of near-surface offshore wind speeds

Airborne Wind Energy

Assessing Boundary Condition and Parametric Uncertainty in Numerical-Weather-Prediction-Modeled, Long-Term Offshore Wind Speed Through Machine Learning and Analog Ensemble

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