Articles | Volume 2, issue 1
https://doi.org/10.5194/wes-2-115-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/wes-2-115-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
10 Mar 2017
Research article |
| 10 Mar 2017
Optimization of wind plant layouts using an adjoint approach
Ryan N. King
CORRESPONDING AUTHOR
University of Colorado, Boulder, Colorado, USA
National Renewable Energy Laboratory, Golden, Colorado, USA
Katherine Dykes
National Renewable Energy Laboratory, Golden, Colorado, USA
Peter Graf
National Renewable Energy Laboratory, Golden, Colorado, USA
Peter E. Hamlington
University of Colorado, Boulder, Colorado, USA
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- WRF Prediction of Operational Wind Farm Data and Influence of Upwind Farms on Power Production G. Cuevas-Figueroa et al. 10.2139/ssrn.3923485
- Variable neighborhood search for large offshore wind farm layout optimization D. Cazzaro & D. Pisinger 10.1016/j.cor.2021.105588
- Modified Beetle Annealing Search (BAS) Optimization Strategy for Maxing Wind Farm Power through an Adaptive Wake Digraph Clustering Approach Y. Chen et al. 10.3390/en14217326
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- Continuous adjoint formulation for wind farm layout optimization: A 2D implementation E. Antonini et al. 10.1016/j.apenergy.2018.07.076
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- Combining shallow-water and analytical wake models for tidal array micro-siting C. Jordan et al. 10.1007/s40722-022-00225-2
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- Simulated Annealing Algorithm for Wind Farm Layout Optimization: A Benchmark Study K. Yang & K. Cho 10.3390/en12234403
- Systems Engineering for Lidar-Assisted Control: A Sequential Approach D. Schlipf et al. 10.1088/1742-6596/1102/1/012014
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- Data-Driven Machine Learning for Wind Plant Flow Modeling R. King et al. 10.1088/1742-6596/1037/7/072004
- Offshore wind farm wake modelling using deep feed forward neural networks for active yaw control and layout optimisation S. Anagnostopoulos & M. Piggott 10.1088/1742-6596/2151/1/012011
38 citations as recorded by crossref.
- Dynamic Flow Modelling for Model-Predictive Wind Farm Control M. van den Broek & J. Wingerden 10.1088/1742-6596/1618/2/022023
- WRF Prediction of Operational Wind Farm Data and Influence of Upwind Farms on Power Production G. Cuevas-Figueroa et al. 10.2139/ssrn.3923485
- Variable neighborhood search for large offshore wind farm layout optimization D. Cazzaro & D. Pisinger 10.1016/j.cor.2021.105588
- Modified Beetle Annealing Search (BAS) Optimization Strategy for Maxing Wind Farm Power through an Adaptive Wake Digraph Clustering Approach Y. Chen et al. 10.3390/en14217326
- 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
- Continuous adjoint formulation for wind farm layout optimization: A 2D implementation E. Antonini et al. 10.1016/j.apenergy.2018.07.076
- Adjoint-based calibration of inlet boundary condition for atmospheric computational fluid dynamics solvers S. Akbarzadeh et al. 10.5194/wes-4-619-2019
- Well layout optimization for groundwater heat pump systems using the adjoint approach S. Halilovic et al. 10.1016/j.enconman.2022.116033
- Stochastic gradient descent for wind farm optimization J. Quick et al. 10.5194/wes-8-1235-2023
- Data-driven optimisation of wind farm layout and wake steering with large-eddy simulations N. Bempedelis et al. 10.5194/wes-9-869-2024
- Wind farm layout optimization for wake effect uniformity K. Yang et al. 10.1016/j.energy.2019.07.019
- Combining shallow-water and analytical wake models for tidal array micro-siting C. Jordan et al. 10.1007/s40722-022-00225-2
- A novel approach for wind farm micro-siting in complex terrain based on an improved genetic algorithm W. Hu et al. 10.1016/j.energy.2022.123970
- Machine learning enables national assessment of wind plant controls with implications for land use D. Harrison‐Atlas et al. 10.1002/we.2689
- Optimal design of wind farms in complex terrains using computational fluid dynamics and adjoint methods E. Antonini et al. 10.1016/j.apenergy.2019.114426
- Micro-Siting of Wind Turbines in an Optimal Wind Farm Area Using Teaching–Learning-Based Optimization Technique M. Hussain et al. 10.3390/su14148846
- 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
- Adjoint-based model predictive control for optimal energy extraction in waked wind farms M. Vali et al. 10.1016/j.conengprac.2018.11.005
- Realistic Wind Farm Layout Optimization through Genetic Algorithms Using a Gaussian Wake Model N. Kirchner-Bossi & F. Porté-Agel 10.3390/en11123268
- A review of physical and numerical modeling techniques for horizontal-axis wind turbine wakes M. Amiri et al. 10.1016/j.rser.2024.114279
- Accuracy of WRF for prediction of operational wind farm data and assessment of influence of upwind farms on power production G. Cuevas-Figueroa et al. 10.1016/j.energy.2022.124362
- Wind Farm Layout Optimization (WindFLO) : An advanced framework for fast wind farm analysis and optimization S. Reddy 10.1016/j.apenergy.2020.115090
- An Unsteady Actuator Line Solver to Enable Adjoint Sensitivity Studies for Wake Steering E. Young et al. 10.1088/1742-6596/1618/4/042038
- Multi-scale optimization of the design of offshore wind farms D. Cazzaro et al. 10.1016/j.apenergy.2022.118830
- Blade planform design optimization to enhance turbine wake control J. Allen et al. 10.1002/we.2699
- Robust wind farm layout optimization M. Sinner & P. Fleming 10.1088/1742-6596/2767/3/032036
- Pseudo-2D RANS: A LiDAR-driven mid-fidelity model for simulations of wind farm flows S. Letizia & G. Iungo 10.1063/5.0076739
- Mesh‐adaptive simulations of horizontal‐axis turbine arrays using the actuator line method G. Deskos & M. Piggott 10.1002/we.2253
- Bayesian Optimisation of a Two‐Turbine Configuration Around a 2D Hill Using Large Eddy Simulations C. Jané‐Ippel et al. 10.1002/we.2946
- Simulated Annealing Algorithm for Wind Farm Layout Optimization: A Benchmark Study K. Yang & K. Cho 10.3390/en12234403
- Systems Engineering for Lidar-Assisted Control: A Sequential Approach D. Schlipf et al. 10.1088/1742-6596/1102/1/012014
- Wind Farm Simulation and Layout Optimization in Complex Terrain J. Allen et al. 10.1088/1742-6596/1452/1/012066
- Multi-scale design optimization of modern offshore wind farms D. Cazzaro et al. 10.2139/ssrn.3952581
- Review on optimisation methods of wind farm array under three classical wind condition problems F. Azlan et al. 10.1016/j.rser.2020.110047
- Multi-objective turbine allocation on a wind farm site A. Dinçer et al. 10.1016/j.apenergy.2023.122346
- A comprehensive analysis of grid-based wind turbine layout using an efficient binary invasive weed optimization algorithm with levy flight I. Koc 10.1016/j.eswa.2022.116835
- Wind farm layout optimization to minimize the wake induced turbulence effect on wind turbines L. Cao et al. 10.1016/j.apenergy.2022.119599
- Data-Driven Machine Learning for Wind Plant Flow Modeling R. King et al. 10.1088/1742-6596/1037/7/072004
Latest update: 23 Nov 2024
Short summary
This paper demonstrates optimization of wind turbine locations within a utility-scale wind plant using a nonlinear flow model and gradient-based optimization techniques made possible through the use of adjoints. This represents a groundbreaking improvement in model fidelity and optimization efficiency for wind energy applications. The optimized wind farms demonstrate significant improvements in annual energy production with turbine layouts that take advantage of nonlinear flow curvature effects.
This paper demonstrates optimization of wind turbine locations within a utility-scale wind plant...
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