Articles | Volume 8, issue 5
https://doi.org/10.5194/wes-8-865-2023
© Author(s) 2023. 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-8-865-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Jun 2023
Research article |
| 01 Jun 2023
| 01 Jun 2023
A comparison of eight optimization methods applied to a wind farm layout optimization problem
Department of Mechanical Engineering, Brigham Young University, Provo, UT 84602, USA
National Renewable Energy Laboratory, Golden, CO 80401, USA
Nicholas F. Baker
Department of Mechanical Engineering, Brigham Young University, Provo, UT 84602, USA
Paul Malisani
Applied Mathematics Department, IFP Energies nouvelles, 1 et 4 avenue de Bois-Préau, 92852 Rueil-Malmaison, France
Erik Quaeghebeur
Uncertainty in AI Group, Eindhoven University of Technology, 5612 AZ Eindhoven, the Netherlands
Sebastian Sanchez Perez-Moreno
RWE Renewables GmbH, 20354 Hamburg, Germany
John Jasa
National Renewable Energy Laboratory, Golden, CO 80401, USA
Christopher Bay
National Renewable Energy Laboratory, Golden, CO 80401, USA
Federico Tilli
TU Delft, 2628 CD Delft, Netherlands
David Bieniek
RWE Renewables GmbH, 20354 Hamburg, Germany
Nick Robinson
UL Renewables, British Columbia, Kelowna, Canada
Andrew P. J. Stanley
National Renewable Energy Laboratory, Golden, CO 80401, USA
Wesley Holt
Department of Mechanical Engineering, Brigham Young University, Provo, UT 84602, USA
Andrew Ning
Department of Mechanical Engineering, Brigham Young University, Provo, UT 84602, USA
National Renewable Energy Laboratory, Golden, CO 80401, USA
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Wind farms produce energy and to do so have to occupy a non-trivial amount of space. Understanding how much energy a proposed wind farm will make (and at what cost) is technically challenging, especially when turbines are packed closely together. Plus, there's a key tradeoff in how much space a farm occupies and how cheap the energy it can produce might be: less space means more costly energy. This work shows an novel way to run computational simulations efficiently to understand that tradeoff.
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Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-10, https://doi.org/10.5194/wes-2023-10, 2023
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Andrew P. J. Stanley and Andrew Ning
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Wind Energ. Sci., 4, 595–618, https://doi.org/10.5194/wes-4-595-2019, https://doi.org/10.5194/wes-4-595-2019, 2019
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Wind Energ. Sci., 4, 355–368, https://doi.org/10.5194/wes-4-355-2019, https://doi.org/10.5194/wes-4-355-2019, 2019
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Paul Fleming, Jennifer King, Katherine Dykes, Eric Simley, Jason Roadman, Andrew Scholbrock, Patrick Murphy, Julie K. Lundquist, Patrick Moriarty, Katherine Fleming, Jeroen van Dam, Christopher Bay, Rafael Mudafort, Hector Lopez, Jason Skopek, Michael Scott, Brady Ryan, Charles Guernsey, and Dan Brake
Wind Energ. Sci., 4, 273–285, https://doi.org/10.5194/wes-4-273-2019, https://doi.org/10.5194/wes-4-273-2019, 2019
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Wake steering is a form of wind farm control in which turbines use yaw offsets to affect wakes in order to yield an increase in total energy production. In this first phase of a study of wake steering at a commercial wind farm, two turbines implement a schedule of offsets. For two closely spaced turbines, an approximate 14 % increase in energy was measured on the downstream turbine over a 10° sector, with a 4 % increase in energy production of the combined turbine pair.
Andrés Santiago Padrón, Jared Thomas, Andrew P. J. Stanley, Juan J. Alonso, and Andrew Ning
Wind Energ. Sci., 4, 211–231, https://doi.org/10.5194/wes-4-211-2019, https://doi.org/10.5194/wes-4-211-2019, 2019
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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.
Christopher J. Bay, Jennifer King, Paul Fleming, Rafael Mudafort, and Luis A. Martínez-Tossas
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2019-19, https://doi.org/10.5194/wes-2019-19, 2019
Preprint withdrawn
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Andrew P. J. Stanley and Andrew Ning
Wind Energ. Sci., 4, 99–114, https://doi.org/10.5194/wes-4-99-2019, https://doi.org/10.5194/wes-4-99-2019, 2019
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We show that optimizing wind turbine design and wind turbine layout at the same time is superior to doing so sequentially. This coupled optimization can reduce the cost of energy by 2–5 % compared to sequential optimization. We also demonstrate that wind farms with closely spaced wind turbines can greatly benefit from different turbine designs throughout the farm. Heterogeneous turbine design can reduce the cost of energy by up to 10 % compared to farms with all identical turbines.
Andrew Ning
Wind Energ. Sci., 1, 327–340, https://doi.org/10.5194/wes-1-327-2016, https://doi.org/10.5194/wes-1-327-2016, 2016
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Wind turbines rarely operate in isolation but rather in close proximity within wind farms. Currently analysis methods are designed for analyzing turbines in isolation, or within a waked region. Actuator cylinder theory is extended to handle multiple vertical axis wind turbines in close proximity. We find good agreement in power predictions as compared to available higher-fidelity simulation data. The corresponding code may be useful for conceptual design and has been fully open-sourced.
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Short summary
Short summary
Though large wind turbines are appealing to reduce costs, larger blades are prone to aero-elastic instabilities due to their long, slender, highly flexible nature. New rotor concepts are emerging including two-bladed rotors and downwind configurations. We introduce a comprehensive evaluation of flutter behavior including classical flutter and edgewise vibration for large-scale two-bladed rotors. The study aims to provide designers with insights to mitigate flutter in future designs.
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Short summary
This work compares eight optimization algorithms (including gradient-based, gradient-free, and hybrid) on a wind farm optimization problem with 4 discrete regions, concave boundaries, and 81 wind turbines. Algorithms were each run by researchers experienced with that algorithm. Optimized layouts were unique but with similar annual energy production. Common characteristics included tightly-spaced turbines on the outer perimeter and turbines loosely spaced and roughly on a grid in the interior.
This work compares eight optimization algorithms (including gradient-based, gradient-free, and...
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