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Wind farm power density optimization according to the area size using a novel self-adaptive genetic algorithm

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  • Kirchner-Bossi, Nicolas
  • Porté-Agel, Fernando

Abstract

This work studies the power density (PD) optimization in wind farms, and its sensitivity to the available area size. A novel genetic algorithm (PDGA) is introduced, which optimizes PD and the turbine layout, by self-adapting to the PD and to the solutions diversity. PDGA uses the levelized cost of energy (LCOE) as cost function, which in turn employs the EPFL analytical wake model to derive the power output. For the baseline area size, PDGA reduces 2.25% the original LCOE, 2.6 times more than optimizing with constant PD. PDGA-driven solutions provide 11% and 6% LCOE reductions against the default layout for the smallest (6.4 km2) and largest (386 km2) scaled wind farm areas, respectively. Specially relevant for the industry, PDGA solutions depict convex fronts for area vs. LCOE or vs. PD, which allows determining the required area or turbine number given a target LCOE. Unlike default layouts, optimized ones reveal a linear relationship between LCOE and PD. The mean turbine spacing tends to 8-9D for very large areas. The economics-optimized PDs are below the estimated PD available in the atmosphere. This work is limited to a simplified, offshore wind climatology, a specific wind turbine model, and the LCOE specifications used herein.

Suggested Citation

  • Kirchner-Bossi, Nicolas & Porté-Agel, Fernando, 2024. "Wind farm power density optimization according to the area size using a novel self-adaptive genetic algorithm," Renewable Energy, Elsevier, vol. 220(C).
    • Handle: RePEc:eee:renene:v:220:y:2024:i:c:s0960148123014398
    DOI: 10.1016/j.renene.2023.119524
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    References listed on IDEAS

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    1. Hoogwijk, Monique & de Vries, Bert & Turkenburg, Wim, 2004. "Assessment of the global and regional geographical, technical and economic potential of onshore wind energy," Energy Economics, Elsevier, vol. 26(5), pages 889-919, September.
    2. Feng, Ju & Shen, Wen Zhong, 2017. "Design optimization of offshore wind farms with multiple types of wind turbines," Applied Energy, Elsevier, vol. 205(C), pages 1283-1297.
    3. Yu-Ting Wu & Fernando Porté-Agel, 2012. "Atmospheric Turbulence Effects on Wind-Turbine Wakes: An LES Study," Energies, MDPI, vol. 5(12), pages 1-23, December.
    4. Siyal, Shahid Hussain & Mörtberg, Ulla & Mentis, Dimitris & Welsch, Manuel & Babelon, Ian & Howells, Mark, 2015. "Wind energy assessment considering geographic and environmental restrictions in Sweden: A GIS-based approach," Energy, Elsevier, vol. 83(C), pages 447-461.
    5. Dupont, Elise & Koppelaar, Rembrandt & Jeanmart, Hervé, 2018. "Global available wind energy with physical and energy return on investment constraints," Applied Energy, Elsevier, vol. 209(C), pages 322-338.
    6. Bastankhah, Majid & Porté-Agel, Fernando, 2014. "A new analytical model for wind-turbine wakes," Renewable Energy, Elsevier, vol. 70(C), pages 116-123.
    7. Kusiak, Andrew & Song, Zhe, 2010. "Design of wind farm layout for maximum wind energy capture," Renewable Energy, Elsevier, vol. 35(3), pages 685-694.
    8. Nicolas Kirchner-Bossi & Fernando Porté-Agel, 2021. "Wind Farm Area Shape Optimization Using Newly Developed Multi-Objective Evolutionary Algorithms," Energies, MDPI, vol. 14(14), pages 1-25, July.
    9. Nicolas Kirchner-Bossi & Fernando Porté-Agel, 2018. "Realistic Wind Farm Layout Optimization through Genetic Algorithms Using a Gaussian Wake Model," Energies, MDPI, vol. 11(12), pages 1-26, November.
    10. Li, Wenwen & Özcan, Ender & John, Robert, 2017. "Multi-objective evolutionary algorithms and hyper-heuristics for wind farm layout optimisation," Renewable Energy, Elsevier, vol. 105(C), pages 473-482.
    11. Ju Feng & Wen Zhong Shen, 2015. "Modelling Wind for Wind Farm Layout Optimization Using Joint Distribution of Wind Speed and Wind Direction," Energies, MDPI, vol. 8(4), pages 1-18, April.
    12. Amin Niayifar & Fernando Porté-Agel, 2016. "Analytical Modeling of Wind Farms: A New Approach for Power Prediction," Energies, MDPI, vol. 9(9), pages 1-13, September.
    13. Fernando Porté-Agel & Yu-Ting Wu & Chang-Hung Chen, 2013. "A Numerical Study of the Effects of Wind Direction on Turbine Wakes and Power Losses in a Large Wind Farm," Energies, MDPI, vol. 6(10), pages 1-17, October.
    14. Antonini, Enrico G.A. & Caldeira, Ken, 2021. "Atmospheric pressure gradients and Coriolis forces provide geophysical limits to power density of large wind farms," Applied Energy, Elsevier, vol. 281(C).
    15. Chowdhury, Souma & Zhang, Jie & Messac, Achille & Castillo, Luciano, 2012. "Unrestricted wind farm layout optimization (UWFLO): Investigating key factors influencing the maximum power generation," Renewable Energy, Elsevier, vol. 38(1), pages 16-30.
    16. Feng, Ju & Shen, Wen Zhong, 2015. "Solving the wind farm layout optimization problem using random search algorithm," Renewable Energy, Elsevier, vol. 78(C), pages 182-192.
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