Articles | Volume 5, issue 4
Wind Energ. Sci., 5, 1435–1448, 2020
https://doi.org/10.5194/wes-5-1435-2020
Wind Energ. Sci., 5, 1435–1448, 2020
https://doi.org/10.5194/wes-5-1435-2020
Research article
31 Oct 2020
Research article | 31 Oct 2020

Operational-based annual energy production uncertainty: are its components actually uncorrelated?

Nicola Bodini and Mike Optis

Related authors

The sensitivity of the fitch wind farm parameterization to a three-dimensional planetary boundary layer scheme
Alex Rybchuk, Timothy W. Juliano, Julie K. Lundquist, David Rosencrans, Nicola Bodini, and Mike Optis
Wind Energ. Sci., 7, 2085–2098, https://doi.org/10.5194/wes-7-2085-2022,https://doi.org/10.5194/wes-7-2085-2022, 2022
Short summary
Can reanalysis products outperform mesoscale numerical weather prediction models in modeling the wind resource in simple terrain?
Vincent Pronk, Nicola Bodini, Mike Optis, Julie K. Lundquist, Patrick Moriarty, Caroline Draxl, Avi Purkayastha, and Ethan Young
Wind Energ. Sci., 7, 487–504, https://doi.org/10.5194/wes-7-487-2022,https://doi.org/10.5194/wes-7-487-2022, 2022
Short summary
Assessing boundary condition and parametric uncertainty in numerical-weather-prediction-modeled, long-term offshore wind speed through machine learning and analog ensemble
Nicola Bodini, Weiming Hu, Mike Optis, Guido Cervone, and Stefano Alessandrini
Wind Energ. Sci., 6, 1363–1377, https://doi.org/10.5194/wes-6-1363-2021,https://doi.org/10.5194/wes-6-1363-2021, 2021
Short summary
Extreme wind shear events in US offshore wind energy areas and the role of induced stratification
Mithu Debnath, Paula Doubrawa, Mike Optis, Patrick Hawbecker, and Nicola Bodini
Wind Energ. Sci., 6, 1043–1059, https://doi.org/10.5194/wes-6-1043-2021,https://doi.org/10.5194/wes-6-1043-2021, 2021
Short summary
Approaches for predicting wind turbine hub-height turbulence metrics
Hannah Livingston, Nicola Bodini, and Julie K. Lundquist
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2021-68,https://doi.org/10.5194/wes-2021-68, 2021
Preprint withdrawn
Short summary

Related subject area

Design methods, reliability and uncertainty modelling
Effectively using multifidelity optimization for wind turbine design
John Jasa, Pietro Bortolotti, Daniel Zalkind, and Garrett Barter
Wind Energ. Sci., 7, 991–1006, https://doi.org/10.5194/wes-7-991-2022,https://doi.org/10.5194/wes-7-991-2022, 2022
Short summary
Efficient Bayesian calibration of aerodynamic wind turbine models using surrogate modeling
Benjamin Sanderse, Vinit V. Dighe, Koen Boorsma, and Gerard Schepers
Wind Energ. Sci., 7, 759–781, https://doi.org/10.5194/wes-7-759-2022,https://doi.org/10.5194/wes-7-759-2022, 2022
Short summary
Fast yaw optimization for wind plant wake steering using Boolean yaw angles
Andrew P. J. Stanley, Christopher Bay, Rafael Mudafort, and Paul Fleming
Wind Energ. Sci., 7, 741–757, https://doi.org/10.5194/wes-7-741-2022,https://doi.org/10.5194/wes-7-741-2022, 2022
Short summary
A simplified, efficient approach to hybrid wind and solar plant site optimization
Charles Tripp, Darice Guittet, Jennifer King, and Aaron Barker
Wind Energ. Sci., 7, 697–713, https://doi.org/10.5194/wes-7-697-2022,https://doi.org/10.5194/wes-7-697-2022, 2022
Short summary
Influence of wind turbine design parameters on linearized physics-based models in OpenFAST
Jason M. Jonkman, Emmanuel S. P. Branlard, and John P. Jasa
Wind Energ. Sci., 7, 559–571, https://doi.org/10.5194/wes-7-559-2022,https://doi.org/10.5194/wes-7-559-2022, 2022
Short summary

Cited articles

ANSI C12.1-2014: Electric Meters – Code For Electricity Metering, Standard, National Electrical Manufacturers Association, Virginia, available at: https://webstore.ansi.org/preview-pages/NEMA/preview_ANSI+C12.1-2014.pdf (last access: 1 October 2020), 2014. a
Brower, M.: Wind resource assessment: a practical guide to developing a wind project, John Wiley & Sons, Hoboken, New Jersey, https://doi.org/10.1002/9781118249864, 2012. a, b
Cameron, J.: Post-construction Yield Analysis, European Wind Energy Association Technical Workshop, available at: http://www.ewea.org/events/workshops/wp-content/uploads/proceedings/Analysis_of_Operating_Wind_farms/EWEA Workshop Lyon - 2-3 Jessica Cameron Natural Power.pdf (last access: Last access: 1 October 2020), 2012. a
Clifton, A., Smith, A., and Field, M.: Wind Plant Preconstruction Energy Estimates: Current Practice and Opportunities, Tech. rep., available at: https://www.nrel.gov/docs/fy16osti/64735.pdf (last access: 1 October 2020), 2016. a
Download
Short summary
Calculations of annual energy production (AEP) and its uncertainty are critical for wind farm financial transactions. Standard industry practice assumes that different uncertainty categories within an AEP calculation are uncorrelated and can therefore be combined through a sum of squares approach. In this project, we show the limits of this assumption by performing operational AEP estimates for over 470 wind farms in the United States and propose a more accurate way to combine uncertainties.