A Four Dimensional Variational Data Assimilation Framework for Wind Energy Potential Estimation
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-02-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/11/2/167 |
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| author | Elias D. Nino-Ruiz Juan C. Calabria-Sarmiento Luis G. Guzman-Reyes Alvin Henao |
| author_facet | Elias D. Nino-Ruiz Juan C. Calabria-Sarmiento Luis G. Guzman-Reyes Alvin Henao |
| author_sort | Elias D. Nino-Ruiz |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-20T02:01:47Z |
| format | Article |
| id | doaj.art-b6ac318f0c9448b7adf6cf16f2108003 |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-12-20T02:01:47Z |
| publishDate | 2020-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-b6ac318f0c9448b7adf6cf16f21080032022-12-21T19:57:19ZengMDPI AGAtmosphere2073-44332020-02-0111216710.3390/atmos11020167atmos11020167A Four Dimensional Variational Data Assimilation Framework for Wind Energy Potential EstimationElias D. Nino-Ruiz0Juan C. Calabria-Sarmiento1Luis G. Guzman-Reyes2Alvin Henao3Applied Math and Computer Science Laboratory, Department of Computer Science, Universidad del Norte, Barranquilla 080001, ColombiaApplied Math and Computer Science Laboratory, Department of Computer Science, Universidad del Norte, Barranquilla 080001, ColombiaApplied Math and Computer Science Laboratory, Department of Computer Science, Universidad del Norte, Barranquilla 080001, ColombiaIndustrial Engineering Department, Universidad del Norte, Barranquilla 080001, ColombiaIn 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.https://www.mdpi.com/2073-4433/11/2/167wind turbine generator4d-varensemble based data assimilationhybrid methods |
| spellingShingle | Elias D. Nino-Ruiz Juan C. Calabria-Sarmiento Luis G. Guzman-Reyes Alvin Henao A Four Dimensional Variational Data Assimilation Framework for Wind Energy Potential Estimation Atmosphere wind turbine generator 4d-var ensemble based data assimilation hybrid methods |
| title | A Four Dimensional Variational Data Assimilation Framework for Wind Energy Potential Estimation |
| title_full | A Four Dimensional Variational Data Assimilation Framework for Wind Energy Potential Estimation |
| title_fullStr | A Four Dimensional Variational Data Assimilation Framework for Wind Energy Potential Estimation |
| title_full_unstemmed | A Four Dimensional Variational Data Assimilation Framework for Wind Energy Potential Estimation |
| title_short | A Four Dimensional Variational Data Assimilation Framework for Wind Energy Potential Estimation |
| title_sort | four dimensional variational data assimilation framework for wind energy potential estimation |
| topic | wind turbine generator 4d-var ensemble based data assimilation hybrid methods |
| url | https://www.mdpi.com/2073-4433/11/2/167 |
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