the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Onshore and Offshore Wind Resources and Operating Conditions in the Eastern U.S.
Rebecca Foody
Jacob Coburn
Jeanie A. Aird
Rebecca J. Barthelmie
Abstract. A major issue in quantifying potential power generation from prospective wind energy sites is the lack of observations from heights relevant to modern wind turbines, particularly for offshore where tip heights are projected to increase beyond 250 m. We present analyses of uniquely detailed datasets from LiDAR (Light Detection And Ranging) deployments in New York State and on two buoys in the adjacent New York bight to examine the relative power generation potential and power quality at these on- and off- shore locations. Time series of 10-minute wind power production are computed from these wind speeds using the power curve from the International Energy Agency 15 MW reference wind turbine. Energy density at 150 m height at the offshore buoys is more than 40 % higher and the Weibull scale factor is 2 ms-1 higher than at all but one of the land sites. Given the relatively close proximity of these LiDAR deployments, they share a common synoptic scale meteorology and hence the seasonal variability of wind speeds is similar with lower wind speeds in July and August. Accordingly, time series of power production from the on- and off- shore location are highly spatially correlated with the Spearman rank correlation coefficient dropping below 0.4 for separation distances of approximately 350 km, but careful planning of on- and off- shore wind farms can reduce the system-wide probability of lower wind energy power production. Analyses of the power production time series indicate AEP is almost double for the two offshore locations. Further, electrical power production quality is higher from the offshore sites that exhibit a lower amplitude of diurnal variability, plus a lower probability of wind speeds below the cut-in and of ramp events of any magnitude. Despite this and the higher resource, the estimated Levelized Cost of Energy (LCoE) is higher from the offshore sites mainly due to the higher infrastructure costs. Nonetheless, the projected LCoE is highly competitive from all sites considered.
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Rebecca Foody et al.
Status: final response (author comments only)
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RC1: 'Comment on wes-2023-95', Anonymous Referee #1, 15 Sep 2023
The comment was uploaded in the form of a supplement: https://wes.copernicus.org/preprints/wes-2023-95/wes-2023-95-RC1-supplement.pdf
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RC2: 'Comment on wes-2023-95', Anonymous Referee #2, 19 Sep 2023
General comments:
The paper presents data from a set of onshore and offshore lidars. The paper mostly presents statistics based on these data and the results of the analysis are as expected, as the wind blows more and more steady offshore compared to onshore. There is no new methods, concepts or ideas introduced in the manuscript, so I have rated the scientific significance as low. Nonetheless, the paper could be useful for somebody that is looking specifically for information about the wind climate in this region.Â
My main comment on the analysis itself is about the low data recovery percentage of the lidar data. It is not demonstrated that there is no correlation of when data recovery is low and what the wind climate is. For example, one would expect that the lidars return 'not available' when a measurement cannot be obtained. Most of these data will be during low wind speed conditions when there is not enough aerosols to measure the wind. You have your long-term measurement time series from ERA5, so you could correct for this. Also in general I miss some discussion of the type of lidars you are using, because they are not the same offshore (zephyr) and onshore (windcube). What kind of filtering was done (precipitation? CNR?).Technical comments:
l13: factor -> parameter
l166: conventional usage would be capital gamma
l211-215: since the lidar signal depends on aerosol concentration this method will likely miss many low-level jets as the lidar will simply not return a signal above the jet. Would be good to discuss this.
In addition: I am not quite sure how to interpret the comment about the comparibility with the 500 m height: did you use data up to 500 m? It would be good to show what the recovery percentage is at this height, related to the remark above.
l232: move bracket from before Barthelmie to before 2023.Citation: https://doi.org/10.5194/wes-2023-95-RC2
Rebecca Foody et al.
Rebecca Foody et al.
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