Assessment of surface wind datasets for estimating offshore wind energy along the Central California Coast

Abstract

In the United States, Central California has gained significant interest in offshore wind energy due to its strong winds and proximity to existing grid connections. This study provides a comprehensive evaluation of near-surface wind datasets in this region, including satellite-based observations (QuikSCAT, ASCAT, and CCMP V2.0), reanalysis (NARR and MERRA), and regional atmospheric models (WRF and WIND Toolkit). This work highlights spatiotemporal variations in the performance of the respective datasets in relation to in-situ buoy measurements using error metrics over both seasonal and diurnal time scales. The two scatterometers (QuikSCAT and ASCAT) showed the best overall performance, albeit with significantly less spatial and temporal resolution relative to other datasets. These datasets only slightly outperformed the next best dataset (WIND Toolkit), which has significantly greater temporal and spatial resolution as well as estimates of winds aloft. Considering tradeoffs between spatiotemporal resolution of the underlying datasets, error metrics relative to in-situ measurements, and the availability of data aloft, the WIND Toolkit appears to be the best dataset for this region. The framework and tradeoff analysis this research developed and demonstrated to assess offshore wind datasets can be applied in other regions where offshore wind energy is being considered.

Spatial and temporal variation of offshore wind power and its value along the Central California Coast

“…com/NREL/hsds-examples). WIND Toolkit's 10 m wind speed and direction data were validated against buoy measurements along the Central California Coast, and WIND Toolkit was determined to be the best dataset for offshore wind energy production estimates for the region (Wang et al 2019).…”

“…To guide the evaluation and optimal planning of offshore wind energy, it is critical to consider both spatial and temporal variability in energy production across a range of scales (Lee et al 2018). Offshore winds, such as along the California Coast, vary on interannual, seasonal (peaks in the spring), synoptic, and daily time scales (peaks in the early evening), in addition to being spatially variable (Walter et al 2018, Wang et al 2019. This spatiotemporal variability becomes critical in estimating power production since the power produced by a turbine depends on the cube of the wind speed, a nonlinear relationship that amplifies the effects of small changes in wind speed.…”

“…The approach of averaging higher-frequency temporal variability has the potential to lead to significantly biased power estimates (Karnauskas et al 2018). Considering the role of seasonal and daily variations in power production in the grid system, spatial patterns of offshore wind speed across seasonal and daily time scales are documented by Wang et al (2019); however, realistic power generation estimates are missing, making it difficult to assess their value. Previous studies that consider variations in offshore wind power generation either considered the spatial patterns of temporal variability on time scales longer than the diurnal cycle (e.g.…”

Wind Power Cogeneration to Reduce Peak Electricity Demand in Mexican States Along the Gulf of Mexico

“…The resolution of every point downloaded is 50 km covering all the Mexican states along the Gulf of Mexico including offshore points, which can all be observed in Figure 2. Several studies have used MERRA-2 to determine wind characteristics, as did Reference [22] in the Central Californian Coast, assessing offshore conditions. A study about wind and rainfall areas of tropical cyclones making landfall over South Korea was examined for the period 1998-2013 using MERRA-2 data.…”

The Effect of Averaging, Sampling, and Time Series Length on Wind Power Density Estimations

“…Despite their low sampling frequency, satellite data have been used in various studies [1][2][3][4][5][6][7][8]. A review of the various data sources from remote sensing, reanalysis, and mesoscale models was presented by Wang et al [9]. Mesoscale models are frequently used in wind resource characterization [10][11][12].…”