Next Article in Journal
Pollution Transport Patterns Obtained Through Generalized Lagrangian Coherent Structures
Previous Article in Journal
Characterization of Wind-Sea- and Swell-Induced Wave Energy along the Norwegian Coast
Previous Article in Special Issue
The Significance of Wind Turbines Layout Optimization on the Predicted Farm Energy Yield
Open AccessArticle

A Four Dimensional Variational Data Assimilation Framework for Wind Energy Potential Estimation

1
Applied Math and Computer Science Laboratory, Department of Computer Science, Universidad del Norte, Barranquilla 080001, Colombia
2
Department of Computer Science, Universidad Simon Bolivar, Barranquilla 080001, Colombia
3
Industrial Engineering Department, Universidad del Norte, Barranquilla 080001, Colombia
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Atmosphere 2020, 11(2), 167; https://doi.org/10.3390/atmos11020167
Received: 6 November 2019 / Revised: 20 December 2019 / Accepted: 2 January 2020 / Published: 5 February 2020
(This article belongs to the Special Issue Climate Modeling for Renewable Energy Resource Assessment)
In this paper, we propose a Four-Dimensional Variational (4D-Var) data assimilation framework for wind energy potential estimation. The framework is defined as follows: we choose a numerical model which can provide forecasts of wind speeds then, an ensemble of model realizations is employed to build control spaces at observation steps via a modified Cholesky decomposition. These control spaces are utilized to estimate initial analysis increments and to avoid the intrinsic use of adjoint models in the 4D-Var context. The initial analysis increments are mapped back onto the model domain from which we obtain an estimate of the initial analysis ensemble. This ensemble is propagated in time to approximate the optimal analysis trajectory. Wind components are post-processed to get wind speeds and to estimate wind energy capacities. A matrix-free analysis step is derived from avoiding the direct inversion of covariance matrices during assimilation cycles. Numerical simulations are employed to illustrate how our proposed framework can be employed in operational scenarios. A catalogue of twelve Wind Turbine Generators (WTGs) is utilized during the experiments. The results reveal that our proposed framework can properly estimate wind energy potential capacities for all wind turbines within reasonable accuracies (in terms of Root-Mean-Square-Error) and even more, these estimations are better than those of traditional 4D-Var ensemble-based methods. Moreover, large variability (variance of standard deviations) of errors are evidenced in forecasts of wind turbines with the largest rate-capacity while homogeneous variability can be seen in wind turbines with the lowest rate-capacity. View Full-Text
Keywords: wind turbine generator; 4D-Var; ensemble based data assimilation; hybrid methods wind turbine generator; 4D-Var; ensemble based data assimilation; hybrid methods
Show Figures

Figure 1

MDPI and ACS Style

Nino-Ruiz, E.D.; Calabria-Sarmiento, J.C.; Guzman-Reyes, L.G.; Henao, A. A Four Dimensional Variational Data Assimilation Framework for Wind Energy Potential Estimation. Atmosphere 2020, 11, 167.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop