Articles | Volume 4, issue 2
https://doi.org/10.5194/wes-4-211-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-4-211-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 May 2019
Research article |
| 08 May 2019
| 08 May 2019
Polynomial chaos to efficiently compute the annual energy production in wind farm layout optimization
Andrés Santiago Padrón
Department of Aeronautics & Astronautics,
Stanford University, Stanford, CA 94305, USA
Jared Thomas
Department of Mechanical Engineering, Brigham Young University, Provo, UT 84602, USA
Andrew P. J. Stanley
Department of Mechanical Engineering, Brigham Young University, Provo, UT 84602, USA
Juan J. Alonso
Department of Aeronautics & Astronautics,
Stanford University, Stanford, CA 94305, USA
Department of Mechanical Engineering, Brigham Young University, Provo, UT 84602, USA
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Cited
15 citations as recorded by crossref.
- Optimizing wind farms layouts for maximum energy production using probabilistic inference: Benchmarking reveals superior computational efficiency and scalability A. Dhoot et al. 10.1016/j.energy.2021.120035
- Increasing wind farm efficiency by yaw control: beyond ideal studies towards a realistic assessment U. Ciri et al. 10.1088/1742-6596/1618/2/022029
- Machine learning enables national assessment of wind plant controls with implications for land use D. Harrison‐Atlas et al. 10.1002/we.2689
- A simplified, efficient approach to hybrid wind and solar plant site optimization C. Tripp et al. 10.5194/wes-7-697-2022
- Speeding up large-wind-farm layout optimization using gradients, parallelization, and a heuristic algorithm for the initial layout R. Valotta Rodrigues et al. 10.5194/wes-9-321-2024
- Stochastic gradient descent for wind farm optimization J. Quick et al. 10.5194/wes-8-1235-2023
- Impact of yaw misalignment on turbine loads in the presence of wind farm blockage F. Bernardoni et al. 10.1002/we.2899
- Constrained optimization of offshore wind turbine positions under uncertain wind conditions with correlated data P. Pettersson et al. 10.1088/1742-6596/2626/1/012056
- Uncertainty quantification of offshore wind farms using Monte Carlo and sparse grid P. Richter et al. 10.1080/15567249.2021.2000520
- Wake steering optimization under uncertainty J. Quick et al. 10.5194/wes-5-413-2020
- Uncertainty quantification of forecast error in coupled fire–atmosphere wildfire spread simulations: sensitivity to the spatial resolution U. Ciri et al. 10.1071/WF20149
- Requirements for the application of the Digital Twin Paradigm to offshore wind turbine structures for uncertain fatigue analysis J. Jorgensen et al. 10.1016/j.compind.2022.103806
- Real-time identification of clusters of turbines F. Bernardoni et al. 10.1088/1742-6596/1618/2/022032
- FLOW Estimation and Rose Superposition (FLOWERS): an integral approach to engineering wake models M. LoCascio et al. 10.5194/wes-7-1137-2022
- Efficient ultimate load estimation for offshore wind turbines using interpolating surrogate models L. van den Bos et al. 10.1088/1742-6596/1037/6/062017
14 citations as recorded by crossref.
- Optimizing wind farms layouts for maximum energy production using probabilistic inference: Benchmarking reveals superior computational efficiency and scalability A. Dhoot et al. 10.1016/j.energy.2021.120035
- Increasing wind farm efficiency by yaw control: beyond ideal studies towards a realistic assessment U. Ciri et al. 10.1088/1742-6596/1618/2/022029
- Machine learning enables national assessment of wind plant controls with implications for land use D. Harrison‐Atlas et al. 10.1002/we.2689
- A simplified, efficient approach to hybrid wind and solar plant site optimization C. Tripp et al. 10.5194/wes-7-697-2022
- Speeding up large-wind-farm layout optimization using gradients, parallelization, and a heuristic algorithm for the initial layout R. Valotta Rodrigues et al. 10.5194/wes-9-321-2024
- Stochastic gradient descent for wind farm optimization J. Quick et al. 10.5194/wes-8-1235-2023
- Impact of yaw misalignment on turbine loads in the presence of wind farm blockage F. Bernardoni et al. 10.1002/we.2899
- Constrained optimization of offshore wind turbine positions under uncertain wind conditions with correlated data P. Pettersson et al. 10.1088/1742-6596/2626/1/012056
- Uncertainty quantification of offshore wind farms using Monte Carlo and sparse grid P. Richter et al. 10.1080/15567249.2021.2000520
- Wake steering optimization under uncertainty J. Quick et al. 10.5194/wes-5-413-2020
- Uncertainty quantification of forecast error in coupled fire–atmosphere wildfire spread simulations: sensitivity to the spatial resolution U. Ciri et al. 10.1071/WF20149
- Requirements for the application of the Digital Twin Paradigm to offshore wind turbine structures for uncertain fatigue analysis J. Jorgensen et al. 10.1016/j.compind.2022.103806
- Real-time identification of clusters of turbines F. Bernardoni et al. 10.1088/1742-6596/1618/2/022032
- FLOW Estimation and Rose Superposition (FLOWERS): an integral approach to engineering wake models M. LoCascio et al. 10.5194/wes-7-1137-2022
Latest update: 18 Apr 2024
Short summary
We propose the use of a new method to efficiently compute the annual energy production (AEP) of a wind farm by properly handling the uncertainties in the wind direction and wind speed. We apply the new ideas to the layout optimization of a large wind farm. We show significant computational savings by reducing the number of simulations required to accurately compute and optimize the AEP of different wind farms.
We propose the use of a new method to efficiently compute the annual energy production (AEP) of...
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