Simulating wind characteristics through direct optimization procedures: illustration with three Russian sites
- Northern (Arctic) Federal University, Arkhangelsk, 1632002, RU
- UiT The Arctic University of Norway, Narvik, 8515, NO
- Northern (Arctic) Federal University, Arkhangelsk, 1632002, RU Laboratoire Gestion des Risques et Environnement, University of Haute-Alsace, Mulhouse, 68093, FR
Published in Issue 2022-01-29
How to Cite
Kangash, A., Virk, M. S., Maryandyshev, P., & Brillard, A. (2022). Simulating wind characteristics through direct optimization procedures: illustration with three Russian sites. International Journal of Energy and Environmental Engineering, 13(2 (June 2022). https://doi.org/10.1007/s40095-021-00470-5
Abstract
Abstract Wind energy assessment of a territory where a wind park is planned to be built is important. This can be performed through an appropriate evaluation of the wind characteristics in this territory. To simulate the wind speeds, a Weibull function is recommended whose parameters are classically determined either applying logarithms or using one of the formulas proposed in the literature. In the present study, direct optimization procedures are applied, which consist to minimize the squared difference between the experimental and simulated densities or probabilities. These procedures are applied on the wind characteristics collected from the ERA5 website during 41 years at three Russian sites close to Arkhangelsk. These direct optimization procedures are proved to give lower errors than the classical one or the formulas of the literature. They also lead to lower values of the estimated Annual Energy Production for a Vestas V90-2.0 wind turbine. Direct optimization procedures are also applied to determine the optimal parameters associated with a unique or a superposition of two von Mises distribution functions to simulate the wind directions in these three Russian sites.Keywords
- Wind characteristics,
- Weibull distribution function,
- von Mises distribution function,
- Direct optimization procedure,
- Optimal parameters
References
- Weis and Ilinca (2010) Assessing the potential for a wind power incentive for remote villages in Canada (pp. 5504-5511) https://doi.org/10.1016/j.enpol.2010.04.039
- Souba and Mendelson (2018) Chaninik Wind Group: lessons learned beyond wind integration for remote Alaska (pp. 40-47) https://doi.org/10.1016/j.tej.2018.06.008
- Ghani et al. (2019) Wind energy at remote islands in arctic region—a case study of Solovetsky islands https://doi.org/10.1063/1.5110756
- ERA5 hourly data on single levels from 1979 to present. Accessed October 12th, 2021.,
- https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=form
- Freitas de Andrade et al. (2019) Four heuristic optimization algorithms applied to wind energy: determination of Weibull curve parameters for three Brazilian sites (pp. 1-12) https://doi.org/10.1007/s40095-018-0285-5
- Kollu et al. (2012) Mixture probability distribution functions to model wind speed distributions https://doi.org/10.1186/2251-6832-3-27
- Vestas: Vestas V90 wind turbine.
- https://www.vestas.com/en/products/2%20mw%20platform/v90%202_0_mw#
- !. Accessed October 5th, 2021,
- https://www.vestas.com/en/products/2%20mw%20platform/v90%202_0_mw#
- !
- International Electrotechnical Commission: Wind energy generation systems. Part 12–1. (2017)
- Khalid Saeed et al. (2019) Comparison of six different methods of Weibull distribution for wind power assessment: A case study for a site in the Northern region of Pakistan https://doi.org/10.1016/j.seta.2019.100541
- Jung and Schindler (2019) The role of air density in wind energy assessment—a case study from Germany (pp. 385-392) https://doi.org/10.1016/j.energy.2019.01.041
- Masseran et al. (2013) Fitting a mixture of von Mises distributions in order to model data on wind direction in Peninsular Malaysia (pp. 94-102) https://doi.org/10.1016/j.enconman.2012.11.025
- Diyoke (2019) A new approximate capacity factor method for matching wind turbines to a site: case study of Humber region, UK (pp. 451-462) https://doi.org/10.1007/s40095-019-00320-5
- Sunderland et al. (2013) Small wind turbines in turbulent (urban) environments: a consideration of normal and Weibull distributions for power prediction (pp. 70-81) https://doi.org/10.1016/j.jweia.2013.08.001
- Quan and Leephakpreeda (2015) Assessment of wind energy potential for selecting wind turbines: an application to Thailand (pp. 17-26) https://doi.org/10.1016/j.seta.2015.05.002
10.1007/s40095-021-00470-5