The Cost of Floating Offshore Wind Energy in California Between 2019 and 2032

Beiter, Philipp; Musiał, W.; Duffy, Patrick; Cooperman, Aubryn; Shields, Matt; Heimiller, D.; Optis, Mike

doi:10.2172/1710181

Cited by 41 publications

(68 citation statements)

References 24 publications

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“…The second novel method is based on machine learning, which has emerged as a promising approach for the vertical extrapolation of wind speeds. Bodini and Optis (2020a) and Bodini and Optis (2020b) explored this concept using four lidars and surface flux stations dispersed around the Southern Great Plains site, operated by Argonne National Laboratory. They found that a relatively simple random forest algorithm, trained on near-surface atmospheric variables, considerably outperformed the conventional power-law and logarithmic wind profiles.…”

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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“…The four vertical extrapolation models presented in the previous section are all validated against lidar data from NYSERDA Buoys E05 and E06 over the full period of record. For each lidar, we consider only the time periods where wind speeds are reported at every height from 20 m to 200 m. Based on recommendations from Section 2, we validate using the REWS calculated from each extrapolation model based on the 10-MW offshore reference wind turbine (Beiter et al 2020).…”

Section: Methodsmentioning

confidence: 99%

“…These data sources provide the best means for robust validation of the offshore wind resource, with the ability to assess wind shear and wind veer (i.e., change in wind direction with height) across the entire rotor-swept area of expected 8-to 12-MW offshore turbines and the majority of the 15-MW wind turbines (Beiter et al (2020); Table 5). Furthermore, a growing body of validation exercises are showing strong agreement between lidar-measured and anemometer-measured wind speeds, giving confidence to the exclusive use of floating lidar for offshore wind profile measurements (Carbon Trust 2018).…”

Section: Floating Lidar Measurementsmentioning

confidence: 99%

“…Offshore wind turbines will be considerably larger than their land-based counterparts. Recently, Beiter et al (2020) compared four future build-out scenarios for offshore California with floating wind turbine capacities ranging from 8 MW to 15 MW (Table 5). Large wind turbine performance is influenced by the high vertical wind shear across the rotor (Barthelmie, Shepherd, and Pryor 2020) and research has shown that weighting the wind speed over the rotor swept area provides a more accurate estimate of kinetic energy passing through the rotor than the simple consideration of hub-height wind speeds (Wagner et al 2014;Redfern et al 2019).…”

Section: Wind Profiles and Rotor-equivalent Wind Speedmentioning

confidence: 99%

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Best Practices for the Validation of U.S. Offshore Wind Resource Models

Optis

Bodini

Debnath

et al. 2020

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“…Details for calculating REWS are provided in (Wagner et al, 2014). To calculate REWS, we assume a 10-MW offshore reference turbine as described in Beiter et al (2020) and summarized in Table 6. We also assess model performance using the four recommended performance metrics from Optis et al (2020a), summarized 230 in Table 7.…”

Section: Single-column Modelmentioning

confidence: 99%

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Optis¹

2021

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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 machinelearning model. These models are developed and validated using 1 year of observations from two floating lidars deployed in U.S. 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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The Cost of Floating Offshore Wind Energy in California Between 2019 and 2032

Abstract: We also want to thank the NREL contributors and reviewers, including Eric Lantz, Paul Veers and Brian Smith, as well as Tiffany Byrne, who coordinated the project schedule and deliverables. Technical editing was provided by Deanna Cook and Sheri Anstedt.

Cited by 41 publications

References 24 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

Best Practices for the Validation of U.S. Offshore Wind Resource Models

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