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
| 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
54 citations as recorded by crossref.
- Optimizing the Wind Farm Layout for Minimizing the Wake Losses A. Bellat et al. https://doi.org/10.25046/aj060135
- A machine learning approach for modeling irregular regions with multiple owners in wind farm layout design S. Reddy https://doi.org/10.1016/j.energy.2020.119691
- FLOWERS AEP: An Analytical Model for Wind Farm Layout Optimization M. LoCascio et al. https://doi.org/10.1002/we.2954
- Optimal Wind Farm Layout in a Complex Terrain by Varying Turbine Hub Heights: Case Study of Yeongdeok, South Korea J. Lee et al. https://doi.org/10.3390/en19041109
- Beyond a single solution: Multimodal wind farm layout optimization via cluster annealing elite search Y. Chen et al. https://doi.org/10.1016/j.apenergy.2026.127852
- Wind farm layout optimization with uncertain wind condition Y. Wen et al. https://doi.org/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. https://doi.org/10.1063/5.0039325
- Wake expansion continuation: Multi‐modality reduction in the wind farm layout optimization problem J. Thomas et al. https://doi.org/10.1002/we.2692
- Wind farm layout optimization based on dynamic Levy sparrow search algorithm: A multi-parameter analysis with active yaw control L. Shen et al. https://doi.org/10.1016/j.energy.2025.135989
- Evaluating the Performance of various Algorithms for Wind Energy Optimization: A Hybrid Decision-Making model A. Ala et al. https://doi.org/10.1016/j.eswa.2023.119731
- Dynamic web-based GIS tool for pre-feasibility evaluation of renewable energy projects E. Sainz-Ortiz et al. https://doi.org/10.1016/j.enconman.2024.119162
- An optimization framework for wind farm layout design using CFD-based Kriging model Z. Wang et al. https://doi.org/10.1016/j.oceaneng.2023.116644
- A method for fast and accurate prediction of wind turbine thrust coefficients using classical momentum theory and power curve V. Tai et al. https://doi.org/10.1088/1755-1315/1372/1/012021
- Objective and algorithm considerations when optimizing the number and placement of turbines in a wind power plant A. Stanley et al. https://doi.org/10.5194/wes-6-1143-2021
- A comparison of eight optimization methods applied to a wind farm layout optimization problem J. Thomas et al. https://doi.org/10.5194/wes-8-865-2023
- Reliability‐based layout optimization in offshore wind energy systems C. Clark et al. https://doi.org/10.1002/we.2664
- An efficient method for modeling terrain and complex terrain boundaries in constrained wind farm layout optimization S. Reddy https://doi.org/10.1016/j.renene.2020.10.076
- Direct integration of non-axisymmetric Gaussian wind-turbine wake including yaw and wind-veer effects K. Ali et al. https://doi.org/10.5194/wes-10-511-2025
- Towards sequential sensor placements on a wind farm to maximize lifetime energy and profit A. Yildiz et al. https://doi.org/10.1016/j.renene.2023.119040
- Control-oriented model for secondary effects of wake steering J. King et al. https://doi.org/10.5194/wes-6-701-2021
- Alignment-based layout optimization of a floating offshore wind farm J. Park et al. https://doi.org/10.1007/s12206-026-2101-0
- A Two-Step Grid–Coordinate Optimization Method for a Wind Farm with a Regular Layout Using a Genetic Algorithm G. Huang et al. https://doi.org/10.3390/en17133273
- Topology optimization of wind farm layouts N. Pollini https://doi.org/10.1016/j.renene.2022.06.019
- Data-driven optimisation of wind farm layout and wake steering with large-eddy simulations N. Bempedelis et al. https://doi.org/10.5194/wes-9-869-2024
- Effectively using multifidelity optimization for wind turbine design J. Jasa et al. https://doi.org/10.5194/wes-7-991-2022
- Optimization of a wind farm layout to mitigate the wind power intermittency T. Kim et al. https://doi.org/10.1016/j.apenergy.2024.123383
- Data-driven wake model parameter estimation to analyze effects of wake superposition M. LoCascio et al. https://doi.org/10.1063/5.0163896
- Stochastic black-box optimization using multi-fidelity score function estimator A. Agrawal et al. https://doi.org/10.1088/2632-2153/ad8e2b
- Wind farm layout optimization using adaptive evolutionary algorithm with Monte Carlo Tree Search reinforcement learning F. Bai et al. https://doi.org/10.1016/j.enconman.2021.115047
- Effect of cost elements on optimum layout of an offshore wind farm P. Ziyaei & M. Khorasanchi https://doi.org/10.1016/j.apor.2025.104537
- Offshore wind farm layout optimization with alignment constraints P. Malisani et al. https://doi.org/10.5194/wes-10-1611-2025
- Wind Farm Layout Optimization (WindFLO) : An advanced framework for fast wind farm analysis and optimization S. Reddy https://doi.org/10.1016/j.apenergy.2020.115090
- A simplified, efficient approach to hybrid wind and solar plant site optimization C. Tripp et al. https://doi.org/10.5194/wes-7-697-2022
- Wind farm layout optimization with load constraints using surrogate modelling R. Riva et al. https://doi.org/10.1088/1742-6596/1618/4/042035
- Characterization of Multimodality in Wind Farm Layout Optimization D. Poole https://doi.org/10.1002/ese3.70377
- A General Parallelization Framework for Speeding up Wind Farm Layout Optimization: Using Random Search as an Example R. Wang et al. https://doi.org/10.1088/1742-6596/3224/3/032046
- Turbine scale and siting considerations in wind plant layout optimization and implications for capacity density A. Stanley et al. https://doi.org/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. https://doi.org/10.3390/en14102895
- The Coriolis force and the direction of rotation of the blades significantly affect the wake of wind turbines R. Nouri et al. https://doi.org/10.1016/j.apenergy.2020.115511
- Wind Farm Layout Optimization Using Multiobjective Modified Electric Charged Particles Optimization Algorithm Based on Game Theory Indexing in Real Onshore Area T. Hidayat et al. https://doi.org/10.3390/su162310222
- Efficient wind farm layout optimization with the FLOWERS AEP model and analytic gradients M. LoCascio et al. https://doi.org/10.1063/5.0237778
- Metocean conditions at two Norwegian sites for development of offshore wind farms E. Cheynet et al. https://doi.org/10.1016/j.renene.2024.120184
- A two-step topology and layout wind farm optimization approach N. Pollini https://doi.org/10.1088/1742-6596/2767/9/092003
- Speeding up large-wind-farm layout optimization using gradients, parallelization, and a heuristic algorithm for the initial layout R. Valotta Rodrigues et al. https://doi.org/10.5194/wes-9-321-2024
- FLOW Estimation and Rose Superposition (FLOWERS): an integral approach to engineering wake models M. LoCascio et al. https://doi.org/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. https://doi.org/10.1002/we.2689
- Optimizing the physical design and layout of a resilient wind, solar, and storage hybrid power plant A. Stanley & J. King https://doi.org/10.1016/j.apenergy.2022.119139
- A novel metric to compare wind farm layouts and identify trends in the layout optimization problem M. Baricchio et al. https://doi.org/10.1088/1742-6596/3224/3/032020
- Optimizing wind farms layouts for maximum energy production using probabilistic inference: Benchmarking reveals superior computational efficiency and scalability A. Dhoot et al. https://doi.org/10.1016/j.energy.2021.120035
- A neighborhood search integer programming approach for wind farm layout optimization J. Pérez-Rúa et al. https://doi.org/10.5194/wes-8-1453-2023
- Evaluating wind-farm power generation using a new direct integration of axisymmetric turbine wake K. Ali et al. https://doi.org/10.1088/1742-6596/2767/9/092015
- Need For Speed: Fast Wind Farm Optimization M. Sarcos et al. https://doi.org/10.1088/1742-6596/2767/9/092088
- A framework for simultaneous design of wind turbines and cable layout in offshore wind J. Pérez-Rúa & N. Cutululis https://doi.org/10.5194/wes-7-925-2022
- Achieving Power-Noise Balance in Wind Farms by Fine-Tuning the Layout with Reinforcement Learning G. Guo et al. https://doi.org/10.3390/en18185019
54 citations as recorded by crossref.
- Optimizing the Wind Farm Layout for Minimizing the Wake Losses A. Bellat et al. https://doi.org/10.25046/aj060135
- A machine learning approach for modeling irregular regions with multiple owners in wind farm layout design S. Reddy https://doi.org/10.1016/j.energy.2020.119691
- FLOWERS AEP: An Analytical Model for Wind Farm Layout Optimization M. LoCascio et al. https://doi.org/10.1002/we.2954
- Optimal Wind Farm Layout in a Complex Terrain by Varying Turbine Hub Heights: Case Study of Yeongdeok, South Korea J. Lee et al. https://doi.org/10.3390/en19041109
- Beyond a single solution: Multimodal wind farm layout optimization via cluster annealing elite search Y. Chen et al. https://doi.org/10.1016/j.apenergy.2026.127852
- Wind farm layout optimization with uncertain wind condition Y. Wen et al. https://doi.org/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. https://doi.org/10.1063/5.0039325
- Wake expansion continuation: Multi‐modality reduction in the wind farm layout optimization problem J. Thomas et al. https://doi.org/10.1002/we.2692
- Wind farm layout optimization based on dynamic Levy sparrow search algorithm: A multi-parameter analysis with active yaw control L. Shen et al. https://doi.org/10.1016/j.energy.2025.135989
- Evaluating the Performance of various Algorithms for Wind Energy Optimization: A Hybrid Decision-Making model A. Ala et al. https://doi.org/10.1016/j.eswa.2023.119731
- Dynamic web-based GIS tool for pre-feasibility evaluation of renewable energy projects E. Sainz-Ortiz et al. https://doi.org/10.1016/j.enconman.2024.119162
- An optimization framework for wind farm layout design using CFD-based Kriging model Z. Wang et al. https://doi.org/10.1016/j.oceaneng.2023.116644
- A method for fast and accurate prediction of wind turbine thrust coefficients using classical momentum theory and power curve V. Tai et al. https://doi.org/10.1088/1755-1315/1372/1/012021
- Objective and algorithm considerations when optimizing the number and placement of turbines in a wind power plant A. Stanley et al. https://doi.org/10.5194/wes-6-1143-2021
- A comparison of eight optimization methods applied to a wind farm layout optimization problem J. Thomas et al. https://doi.org/10.5194/wes-8-865-2023
- Reliability‐based layout optimization in offshore wind energy systems C. Clark et al. https://doi.org/10.1002/we.2664
- An efficient method for modeling terrain and complex terrain boundaries in constrained wind farm layout optimization S. Reddy https://doi.org/10.1016/j.renene.2020.10.076
- Direct integration of non-axisymmetric Gaussian wind-turbine wake including yaw and wind-veer effects K. Ali et al. https://doi.org/10.5194/wes-10-511-2025
- Towards sequential sensor placements on a wind farm to maximize lifetime energy and profit A. Yildiz et al. https://doi.org/10.1016/j.renene.2023.119040
- Control-oriented model for secondary effects of wake steering J. King et al. https://doi.org/10.5194/wes-6-701-2021
- Alignment-based layout optimization of a floating offshore wind farm J. Park et al. https://doi.org/10.1007/s12206-026-2101-0
- A Two-Step Grid–Coordinate Optimization Method for a Wind Farm with a Regular Layout Using a Genetic Algorithm G. Huang et al. https://doi.org/10.3390/en17133273
- Topology optimization of wind farm layouts N. Pollini https://doi.org/10.1016/j.renene.2022.06.019
- Data-driven optimisation of wind farm layout and wake steering with large-eddy simulations N. Bempedelis et al. https://doi.org/10.5194/wes-9-869-2024
- Effectively using multifidelity optimization for wind turbine design J. Jasa et al. https://doi.org/10.5194/wes-7-991-2022
- Optimization of a wind farm layout to mitigate the wind power intermittency T. Kim et al. https://doi.org/10.1016/j.apenergy.2024.123383
- Data-driven wake model parameter estimation to analyze effects of wake superposition M. LoCascio et al. https://doi.org/10.1063/5.0163896
- Stochastic black-box optimization using multi-fidelity score function estimator A. Agrawal et al. https://doi.org/10.1088/2632-2153/ad8e2b
- Wind farm layout optimization using adaptive evolutionary algorithm with Monte Carlo Tree Search reinforcement learning F. Bai et al. https://doi.org/10.1016/j.enconman.2021.115047
- Effect of cost elements on optimum layout of an offshore wind farm P. Ziyaei & M. Khorasanchi https://doi.org/10.1016/j.apor.2025.104537
- Offshore wind farm layout optimization with alignment constraints P. Malisani et al. https://doi.org/10.5194/wes-10-1611-2025
- Wind Farm Layout Optimization (WindFLO) : An advanced framework for fast wind farm analysis and optimization S. Reddy https://doi.org/10.1016/j.apenergy.2020.115090
- A simplified, efficient approach to hybrid wind and solar plant site optimization C. Tripp et al. https://doi.org/10.5194/wes-7-697-2022
- Wind farm layout optimization with load constraints using surrogate modelling R. Riva et al. https://doi.org/10.1088/1742-6596/1618/4/042035
- Characterization of Multimodality in Wind Farm Layout Optimization D. Poole https://doi.org/10.1002/ese3.70377
- A General Parallelization Framework for Speeding up Wind Farm Layout Optimization: Using Random Search as an Example R. Wang et al. https://doi.org/10.1088/1742-6596/3224/3/032046
- Turbine scale and siting considerations in wind plant layout optimization and implications for capacity density A. Stanley et al. https://doi.org/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. https://doi.org/10.3390/en14102895
- The Coriolis force and the direction of rotation of the blades significantly affect the wake of wind turbines R. Nouri et al. https://doi.org/10.1016/j.apenergy.2020.115511
- Wind Farm Layout Optimization Using Multiobjective Modified Electric Charged Particles Optimization Algorithm Based on Game Theory Indexing in Real Onshore Area T. Hidayat et al. https://doi.org/10.3390/su162310222
- Efficient wind farm layout optimization with the FLOWERS AEP model and analytic gradients M. LoCascio et al. https://doi.org/10.1063/5.0237778
- Metocean conditions at two Norwegian sites for development of offshore wind farms E. Cheynet et al. https://doi.org/10.1016/j.renene.2024.120184
- A two-step topology and layout wind farm optimization approach N. Pollini https://doi.org/10.1088/1742-6596/2767/9/092003
- Speeding up large-wind-farm layout optimization using gradients, parallelization, and a heuristic algorithm for the initial layout R. Valotta Rodrigues et al. https://doi.org/10.5194/wes-9-321-2024
- FLOW Estimation and Rose Superposition (FLOWERS): an integral approach to engineering wake models M. LoCascio et al. https://doi.org/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. https://doi.org/10.1002/we.2689
- Optimizing the physical design and layout of a resilient wind, solar, and storage hybrid power plant A. Stanley & J. King https://doi.org/10.1016/j.apenergy.2022.119139
- A novel metric to compare wind farm layouts and identify trends in the layout optimization problem M. Baricchio et al. https://doi.org/10.1088/1742-6596/3224/3/032020
- Optimizing wind farms layouts for maximum energy production using probabilistic inference: Benchmarking reveals superior computational efficiency and scalability A. Dhoot et al. https://doi.org/10.1016/j.energy.2021.120035
- A neighborhood search integer programming approach for wind farm layout optimization J. Pérez-Rúa et al. https://doi.org/10.5194/wes-8-1453-2023
- Evaluating wind-farm power generation using a new direct integration of axisymmetric turbine wake K. Ali et al. https://doi.org/10.1088/1742-6596/2767/9/092015
- Need For Speed: Fast Wind Farm Optimization M. Sarcos et al. https://doi.org/10.1088/1742-6596/2767/9/092088
- A framework for simultaneous design of wind turbines and cable layout in offshore wind J. Pérez-Rúa & N. Cutululis https://doi.org/10.5194/wes-7-925-2022
- Achieving Power-Noise Balance in Wind Farms by Fine-Tuning the Layout with Reinforcement Learning G. Guo et al. https://doi.org/10.3390/en18185019
Saved (final revised paper)
Latest update: 09 Jun 2026
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...
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