Articles | Volume 4, issue 4
https://doi.org/10.5194/wes-4-663-2019
© 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-4-663-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Dec 2019
Research article |
| 16 Dec 2019
Massive simplification of the wind farm layout optimization problem
Andrew P. J. Stanley
CORRESPONDING AUTHOR
Department of Mechanical Engineering, Brigham Young University, 701 E University Pkwy, 350 EB, Provo, UT 84602, USA
Andrew Ning
Department of Mechanical Engineering, Brigham Young University, 701 E University Pkwy, 350 EB, Provo, UT 84602, USA
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Cited
26 citations as recorded by crossref.
- Optimizing the Wind Farm Layout for Minimizing the Wake Losses A. Bellat et al. 10.25046/aj060135
- A machine learning approach for modeling irregular regions with multiple owners in wind farm layout design S. Reddy 10.1016/j.energy.2020.119691
- Wind Farm Layout Optimization (WindFLO) : An advanced framework for fast wind farm analysis and optimization S. Reddy 10.1016/j.apenergy.2020.115090
- A simplified, efficient approach to hybrid wind and solar plant site optimization C. Tripp et al. 10.5194/wes-7-697-2022
- Wind farm layout optimization with load constraints using surrogate modelling R. Riva et al. 10.1088/1742-6596/1618/4/042035
- Wind farm layout optimization with uncertain wind condition Y. Wen et al. 10.1016/j.enconman.2022.115347
- Evaluation of the potential for wake steering for U.S. land-based wind power plants D. Bensason et al. 10.1063/5.0039325
- Wake expansion continuation: Multi‐modality reduction in the wind farm layout optimization problem J. Thomas et al. 10.1002/we.2692
- Evaluating the Performance of various Algorithms for Wind Energy Optimization: A Hybrid Decision-Making model A. Ala et al. 10.1016/j.eswa.2023.119731
- Turbine scale and siting considerations in wind plant layout optimization and implications for capacity density A. Stanley et al. 10.1016/j.egyr.2022.02.226
- Realistic Optimization of Parallelogram-Shaped Offshore Wind Farms Considering Continuously Distributed Wind Resources A. Gonzalez-Rodriguez et al. 10.3390/en14102895
- Objective and algorithm considerations when optimizing the number and placement of turbines in a wind power plant A. Stanley et al. 10.5194/wes-6-1143-2021
- A comparison of eight optimization methods applied to a wind farm layout optimization problem J. Thomas et al. 10.5194/wes-8-865-2023
- Reliability‐based layout optimization in offshore wind energy systems C. Clark et al. 10.1002/we.2664
- The Coriolis force and the direction of rotation of the blades significantly affect the wake of wind turbines R. Nouri et al. 10.1016/j.apenergy.2020.115511
- An efficient method for modeling terrain and complex terrain boundaries in constrained wind farm layout optimization S. Reddy 10.1016/j.renene.2020.10.076
- Towards sequential sensor placements on a wind farm to maximize lifetime energy and profit A. Yildiz et al. 10.1016/j.renene.2023.119040
- Control-oriented model for secondary effects of wake steering J. King et al. 10.5194/wes-6-701-2021
- FLOW Estimation and Rose Superposition (FLOWERS): an integral approach to engineering wake models M. LoCascio et al. 10.5194/wes-7-1137-2022
- Machine learning enables national assessment of wind plant controls with implications for land use D. Harrison‐Atlas et al. 10.1002/we.2689
- Optimizing the physical design and layout of a resilient wind, solar, and storage hybrid power plant A. Stanley & J. King 10.1016/j.apenergy.2022.119139
- 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
- Topology optimization of wind farm layouts N. Pollini 10.1016/j.renene.2022.06.019
- Effectively using multifidelity optimization for wind turbine design J. Jasa et al. 10.5194/wes-7-991-2022
- Wind farm layout optimization using adaptive evolutionary algorithm with Monte Carlo Tree Search reinforcement learning F. Bai et al. 10.1016/j.enconman.2021.115047
- A framework for simultaneous design of wind turbines and cable layout in offshore wind J. Pérez-Rúa & N. Cutululis 10.5194/wes-7-925-2022
26 citations as recorded by crossref.
- Optimizing the Wind Farm Layout for Minimizing the Wake Losses A. Bellat et al. 10.25046/aj060135
- A machine learning approach for modeling irregular regions with multiple owners in wind farm layout design S. Reddy 10.1016/j.energy.2020.119691
- Wind Farm Layout Optimization (WindFLO) : An advanced framework for fast wind farm analysis and optimization S. Reddy 10.1016/j.apenergy.2020.115090
- A simplified, efficient approach to hybrid wind and solar plant site optimization C. Tripp et al. 10.5194/wes-7-697-2022
- Wind farm layout optimization with load constraints using surrogate modelling R. Riva et al. 10.1088/1742-6596/1618/4/042035
- Wind farm layout optimization with uncertain wind condition Y. Wen et al. 10.1016/j.enconman.2022.115347
- Evaluation of the potential for wake steering for U.S. land-based wind power plants D. Bensason et al. 10.1063/5.0039325
- Wake expansion continuation: Multi‐modality reduction in the wind farm layout optimization problem J. Thomas et al. 10.1002/we.2692
- Evaluating the Performance of various Algorithms for Wind Energy Optimization: A Hybrid Decision-Making model A. Ala et al. 10.1016/j.eswa.2023.119731
- Turbine scale and siting considerations in wind plant layout optimization and implications for capacity density A. Stanley et al. 10.1016/j.egyr.2022.02.226
- Realistic Optimization of Parallelogram-Shaped Offshore Wind Farms Considering Continuously Distributed Wind Resources A. Gonzalez-Rodriguez et al. 10.3390/en14102895
- Objective and algorithm considerations when optimizing the number and placement of turbines in a wind power plant A. Stanley et al. 10.5194/wes-6-1143-2021
- A comparison of eight optimization methods applied to a wind farm layout optimization problem J. Thomas et al. 10.5194/wes-8-865-2023
- Reliability‐based layout optimization in offshore wind energy systems C. Clark et al. 10.1002/we.2664
- The Coriolis force and the direction of rotation of the blades significantly affect the wake of wind turbines R. Nouri et al. 10.1016/j.apenergy.2020.115511
- An efficient method for modeling terrain and complex terrain boundaries in constrained wind farm layout optimization S. Reddy 10.1016/j.renene.2020.10.076
- Towards sequential sensor placements on a wind farm to maximize lifetime energy and profit A. Yildiz et al. 10.1016/j.renene.2023.119040
- Control-oriented model for secondary effects of wake steering J. King et al. 10.5194/wes-6-701-2021
- FLOW Estimation and Rose Superposition (FLOWERS): an integral approach to engineering wake models M. LoCascio et al. 10.5194/wes-7-1137-2022
- Machine learning enables national assessment of wind plant controls with implications for land use D. Harrison‐Atlas et al. 10.1002/we.2689
- Optimizing the physical design and layout of a resilient wind, solar, and storage hybrid power plant A. Stanley & J. King 10.1016/j.apenergy.2022.119139
- 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
- Topology optimization of wind farm layouts N. Pollini 10.1016/j.renene.2022.06.019
- Effectively using multifidelity optimization for wind turbine design J. Jasa et al. 10.5194/wes-7-991-2022
- Wind farm layout optimization using adaptive evolutionary algorithm with Monte Carlo Tree Search reinforcement learning F. Bai et al. 10.1016/j.enconman.2021.115047
- A framework for simultaneous design of wind turbines and cable layout in offshore wind J. Pérez-Rúa & N. Cutululis 10.5194/wes-7-925-2022
Latest update: 25 Sep 2023
Short summary
When designing a wind farm, one crucial step is finding the correct location or optimizing the location of the wind turbines to maximize power production. In the past, optimizing the turbine layout of large wind farms has been difficult because of the large number of interacting variables. In this paper, we present the boundary-grid parameterization method, which defines the layout of any wind farm with only five variables, allowing people to study and design wind farms regardless of the size.
When designing a wind farm, one crucial step is finding the correct location or optimizing the...
