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Global-local analysis of cost-optimal onshore wind turbine configurations considering wind classes and hub heights

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  • Satymov, Rasul
  • Bogdanov, Dmitrii
  • Breyer, Christian

Abstract

This study aims to contribute to the field of energy systems modelling with high-resolution cost-optimised onshore wind turbine configurations and an openly available hourly data of wind electricity yield on a global-local scale. It introduces a more rigorous and novel methodology to estimate the wind electricity yield for lowest cost electricity generation by considering different wind classes and hub height related capital expenditures along with limitations induced by extreme wind gusts. Based on up-to-date financial and technical assumptions, including the latest power curves for ENERCON wind turbines, the results of this study show that there exists a certain hub height that enables wind turbines to deliver the lowest cost electricity and growing beyond that height does not pay-off the rise in capital expenditures needed for stronger foundations and taller and sturdier towers. Class III turbines provide higher full load hours and more stable hourly generation profiles, but in some areas higher cost does not pay-off or wind gusts become a limiting factor. The application of this novel multi-turbine multi-hub height high resolution optimisation results to energy system modelling would significantly increase the quality of modelling by improved estimation of the wind generation cost at different locations.

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  • Satymov, Rasul & Bogdanov, Dmitrii & Breyer, Christian, 2022. "Global-local analysis of cost-optimal onshore wind turbine configurations considering wind classes and hub heights," Energy, Elsevier, vol. 256(C).
    • Handle: RePEc:eee:energy:v:256:y:2022:i:c:s0360544222015328
    DOI: 10.1016/j.energy.2022.124629
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    2. Yan, Bowen & Shen, Ruifang & Li, Ke & Wang, Zhenguo & Yang, Qingshan & Zhou, Xuhong & Zhang, Le, 2023. "Spatio-temporal correlation for simultaneous ultra-short-term wind speed prediction at multiple locations," Energy, Elsevier, vol. 284(C).
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    5. Ravi Kumar Pandit & Davide Astolfi & Isidro Durazo Cardenas, 2023. "A Review of Predictive Techniques Used to Support Decision Making for Maintenance Operations of Wind Turbines," Energies, MDPI, vol. 16(4), pages 1-17, February.

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