Evaluation of obstacle modelling approaches for resource assessment and small wind turbine siting: case study in the northern Netherlands
-
Published:2022-06-02
Issue:3
Volume:7
Page:1153-1169
-
ISSN:2366-7451
-
Container-title:Wind Energy Science
-
language:en
-
Short-container-title:Wind Energ. Sci.
Author:
Phillips CalebORCID, Sheridan Lindsay M., Conry Patrick, Fytanidis Dimitrios K., Duplyakin Dmitry, Zisman Sagi, Duboc Nicolas, Nelson Matt, Kotamarthi RaoORCID, Linn Rod, Broersma Marc, Spijkerboer Timo, Tinnesand Heidi
Abstract
Abstract. Growth in adoption of distributed wind turbines for energy generation is significantly impacted by challenges associated with siting and accurate estimation of the wind resource. Small turbines, at hub heights of 40 m or less, are greatly impacted by terrestrial obstacles such as built structures and vegetation that can cause complex wake effects. While some progress in high-fidelity complex fluid dynamics (CFD) models has increased the potential accuracy for modelling the impacts of obstacles on turbulent wind flow, these models are too computationally expensive for practical siting and resource assessment applications. To understand the efficacy of available models in situ, this study evaluates classic and commonly used methods alongside new state-of-the-art lower-order models derived from CFD simulations and machine learning approaches. This evaluation is conducted using a subset of an extensive original dataset of measurements from more than 300 operational wind turbines in the northern Netherlands. The results show that data-driven methods (e.g. machine learning and statistical modelling) are most effective at predicting production at real sites with an average error in annual energy production of 2.5 %. When sufficient data may not be available de novo to support these data-driven approaches, models derived from high-fidelity simulations show promise and reliably outperform classic methods. On average these models have 6.3 %–11.5 % error compared with 26 % for classic methods and 27 % baseline error for reanalysis data without obstacle correction. While more performant on average, these methods are also sensitive to the quality of obstacle descriptions and reanalysis inputs.
Funder
Wind Energy Technologies Office
Publisher
Copernicus GmbH
Subject
Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment
Reference52 articles.
1. Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Dean, A. J., Devin, M. ,Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jozefowicz, R., and Zheng, X.: Tensorflow: Large-scale machine learning on heterogeneous distributed systems, arXiv preprint: arXiv:1603.04467, 2015. 2. Astroup, P. and Larsen, S. E.: WAsP Engineering Flow Model for Wind over
Land and Sea, Riso National Laboratory, Roskilde, Denmark, ISBN 87-550-2529-3, August 1999. 3. Bieringer, P. E., Piña, A. J., Lorenzetti, D. M., Jonker, H. J. J., Sohn, M. D., Annunziao, A. J., and Fry, R. N.: A graphics processing unit (GPU) approach to large eddy simulation (LES) for transport and contaminant dispersion, Atmosphere, 12, 890, https://doi.org/10.3390/atmos12070890, 2021. 4. Brown, M., Gowardhan, A., Nelson, M., Williams, M., and Pardyjak, E.: QUIC
transport and dispersion modelling of two releases from the joint urban 2003
field experiment, Int. J. Environ. Pollut., 52, 263–287, 2013. 5. Bruse, M. and Fleer, H.: Simulating surface–plant–air interactions inside
urban environment with a three dimensional numerical model, Environ. Model.
Softw., 13, 373–384, https://doi.org/10.1016/S1364-8152(98)00042-5, 1998.
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|