Article: Pryor, Sara C.; Shepherd, Tristan J.; Barthelmie, Rebecca J.; “Interannual Variability of Wind Climates and Wind Turbine Annual Energy Production”, Wind Energy Science, 3 (2):651-665
Abstract: The interannual variability (IAV) of expected annual energy production
(AEP) from proposed wind farms plays a key role in dictating project financing. IAV in preconstruction projected AEP and the difference in 50th and 90th percentile (P50 and P90) AEP derive in part from variability in wind climates. However, the magnitude of IAV in wind speeds at or close to wind turbine hub heights is poorly defined and may be overestimated by assuming annual mean wind speeds are Gaussian distributed with a standard deviation (sigma) of 6 %, as is widely applied within the wind energy industry. There is a need for improved understanding of the long-term wind resource and the IAV therein in order to generate more robust predictions of the financial value of a wind energy project. Long-term simulations of wind speeds near typical wind turbine hub heights over the eastern USA indicate median gross capacity factors (computed using 10 min wind speeds close to wind turbine hub heights and the power curve of the most common wind turbine deployed in the region) that are in good agreement with values derived from operational wind farms. The IAV of annual mean wind speeds at or near typical wind turbine hub heights in these simulations and AEP computed using the power curve of the most commonly deployed wind turbine is lower than is implied by assuming sigma = 6 %. Indeed, rather than 9 out of 10 years exhibiting AEP within 0.9 and 1.1 times the long-term mean AEP as implied by assuming a Gaussian distribution with sigma of 6 %, the results presented herein indicate that in over 90% of the area in the eastern USA that currently has operating wind turbines, simulated AEP lies within 0.94 and 1.06 of the long-term average.
Further, the IAV of estimated AEP is not substantially larger than IAV in mean wind speeds. These results indicate it may be appropriate to reduce the IAV applied to preconstruction AEP estimates to account for variability in wind climates, which would decrease the cost of capital for wind farm developments.
Funding Acknowledgement: US Department of Energy [DE-SC0016438]; Cornell University’s Atkinson Center for a Sustainable Future [ACSF-sp2279-2016]; NSF [ACI-1541215]; NSF Extreme Science and Engineering Discovery Environment, XSEDE [TG-ATM170024]; Office of Science of the US Department of Energy [DE-AC02-05CH11231]
Funding Text: This research was funded by the US Department of Energy (DE-SC0016438) and Cornell University’s Atkinson Center for a Sustainable Future (ACSF-sp2279-2016). This research was enabled by access to a range of computational resources supported by the NSF (ACI-1541215 and those made available via the NSF Extreme Science and Engineering Discovery Environment, XSEDE; award TG-ATM170024) and those of the National Energy Research Scientific Computing Center, a DOE Office of Science user facility supported by the Office of Science of the US Department of Energy under contract no. DE-AC02-05CH11231. The authors gratefully acknowledge stimulating conversations with Ken Westrick and Ken Davies, the work of Peter Cook in undertaking initial processing of the EIA data, and Brandon Barker and Bennett Wineholt for maintaining the Aristotle cloud system. We also gratefully acknowledge the many people who have contributed to the development of the WRF model and the two reviewers who provided h elpful feedback on our original submission.