What can surface wind observations tell us about interannual variation in wind energy output?

Abstract

The past decade of wind power growth was supported by capacity factor improvements and associated cost reductions. But are higher capacity factors a technology success story or, as suggested by recent research, has the influence of technology been overstated by ignoring positive surface wind speed trends? The answer could influence estimates of wind energy's cost and even future deployment rates. We find that US surface wind speed observations imply a 2.6% improvement in capacity factors from 2010 to 2019. Yet newer vintages of wind plants have recorded capacity factors that are 25% larger than plants built close to 2010. It follows that technological factors and improved site quality, not higher wind speeds, drove most of the improvement in capacity factors. Additionally, we match hundreds of meteorological stations to nearby (<25 km) wind plants and compare annual estimated generation, based on a function of surface wind speed observations, to annual recorded generation. Researchers rely on this publicly available surface data because measurements co-located with wind plants are generally considered proprietary. Our analysis addresses a research gap: interannual variation in observed surface wind speeds is rarely compared to observed data at wind plant locations and turbine heights. We find that despite its common use for this purpose, generation estimates based on publicly available surface observational data provide a poor proxy for interannual variability in recorded wind generation. These findings suggest that caution is generally needed when researchers use surface wind speed measurements to investigate longterm wind energy trends.

Limitations of reanalysis data for wind power applications

“…Validation of higher fields has shown stability is a predictor of mean errors 18 . Recent work has also found that interannual variability in recorded wind generation is only weakly correlated with observed surface wind speeds, suggesting that wind turbines are subject to meteorological phenomena above the surface boundary layer 19 . Furthermore, ground observations of surface heat fluxes (an important diurnally varying signature of stratification) do not provide sufficient geographic coverage to usefully constrain reanalysis models 20 .…”

“…18 Recent work has also found that interannual variability in recorded wind generation is only weakly correlated with observed surface wind speeds, suggesting that wind turbines are subject to meteorological phenomena above the surface boundary layer. 19 Furthermore, ground observations of surface heat fluxes (an important diurnally varying signature of stratification) do not provide sufficient geographic coverage to usefully constrain reanalysis models. 20 Together, these points indicate that while reanalysis outputs, such as wind speed, are most often validated at the surface (10 m above ground), that surface validation may not provide much insight into accuracy at wind turbine heights as different phenomena affect meteorology at hub heights.…”

An assessment of observed wind speed and wind power density over China for 1980–2021

“…In future studies, it is necessary to detect the breakpoints of NWS series both considering the inhomogeneities in the mean state and variability. 71,72 Another limitation in this study is that we use the simple power law to relate the observed NWS and HWS instead of fully understanding their relationship; as suggested by Millstein et al, 73 this could affect the results for long-term WPD trends. This problem also deserves some indepth studies.…”

Global assessment of spatiotemporal changes of frequency of terrestrial wind speed

“…These data were subject to a series of quality control procedures, including duplicate checks, neighbor outliers and distribution gap checks, to eliminate bad data and maintain data continuity (Dunn et al 2016). The HadISD has been used for the annual monitoring of wind in the Bulletin of the America Meteorological Society State in recent years (Dunn et al 2016) and has been widely used in previous studies (Woolway et al 2019, Zhou et al 2021, Millstein et al 2022. It is noteworthy that Dunn et al (2022a) reported on erroneously missing calm winds (SWS = 0 m s −1 ) in the ISD and hence the HadISD since May 2013 for many stations outside of North America, which has an impact on the magnitude of the reversal in winds occurring approximately at the same time (see text S1 and figure S1 in the supplementary information for more detail, Dunn et al 2022a).…”

“…The power curve of wind turbines is helpful for wind energy forecasting without further technical details of wind power operating conditions (Lydia et al 2014). It is widely used in wind power assessment (Wang et al 2016, Pryor et al 2020, Millstein et al 2022. We assume the wind turbine GE 2.5-120 was installed around each observation site and use its power curve to derive the wind power output under the wind regime at observation sites.…”