Realistic Wind Farm Layout Optimization through Genetic Algorithms Using a Gaussian Wake Model
Wind Farm Layout Optimization (WFLO) can be useful to minimize power losses associated with turbine wakes in wind farms. This work presents a new evolutionary WFLO methodology integrated with a recently developed and successfully validated Gaussian wake model (Bastankhah and Porté-Agel mode...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-11-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/11/12/3268 |
| _version_ | 1828351183225356288 |
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| author | Nicolas Kirchner-Bossi Fernando Porté-Agel |
| author_facet | Nicolas Kirchner-Bossi Fernando Porté-Agel |
| author_sort | Nicolas Kirchner-Bossi |
| collection | DOAJ |
| description | Wind Farm Layout Optimization (WFLO) can be useful to minimize power losses associated with turbine wakes in wind farms. This work presents a new evolutionary WFLO methodology integrated with a recently developed and successfully validated Gaussian wake model (Bastankhah and Porté-Agel model). Two different parametrizations of the evolutionary methodology are implemented, depending on if a baseline layout is considered or not. The proposed scheme is applied to two real wind farms, Horns Rev I (Denmark) and Princess Amalia (the Netherlands), and two different turbine models, V80-2MW and NREL-5MW. For comparison purposes, these four study cases are also optimized under the traditionally used top-hat wake model (Jensen model). A systematic overestimation of the wake losses by the Jensen model is confirmed herein. This allows it to attain bigger power output increases with respect to the baseline layouts (between 0.72% and 1.91%) compared to the solutions attained through the more realistic Gaussian model (0.24⁻0.95%). The proposed methodology is shown to outperform other recently developed layout optimization methods. Moreover, the electricity cable length needed to interconnect the turbines decreases up to 28.6% compared to the baseline layouts. |
| first_indexed | 2024-04-14T01:35:53Z |
| format | Article |
| id | doaj.art-93ac822f63694b6186cd4e0843059e03 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-14T01:35:53Z |
| publishDate | 2018-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-93ac822f63694b6186cd4e0843059e032022-12-22T02:19:56ZengMDPI AGEnergies1996-10732018-11-011112326810.3390/en11123268en11123268Realistic Wind Farm Layout Optimization through Genetic Algorithms Using a Gaussian Wake ModelNicolas Kirchner-Bossi0Fernando Porté-Agel1Wind Engineering and Renewable Energy Laboratory (WiRE), École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, SwitzerlandWind Engineering and Renewable Energy Laboratory (WiRE), École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, SwitzerlandWind Farm Layout Optimization (WFLO) can be useful to minimize power losses associated with turbine wakes in wind farms. This work presents a new evolutionary WFLO methodology integrated with a recently developed and successfully validated Gaussian wake model (Bastankhah and Porté-Agel model). Two different parametrizations of the evolutionary methodology are implemented, depending on if a baseline layout is considered or not. The proposed scheme is applied to two real wind farms, Horns Rev I (Denmark) and Princess Amalia (the Netherlands), and two different turbine models, V80-2MW and NREL-5MW. For comparison purposes, these four study cases are also optimized under the traditionally used top-hat wake model (Jensen model). A systematic overestimation of the wake losses by the Jensen model is confirmed herein. This allows it to attain bigger power output increases with respect to the baseline layouts (between 0.72% and 1.91%) compared to the solutions attained through the more realistic Gaussian model (0.24⁻0.95%). The proposed methodology is shown to outperform other recently developed layout optimization methods. Moreover, the electricity cable length needed to interconnect the turbines decreases up to 28.6% compared to the baseline layouts.https://www.mdpi.com/1996-1073/11/12/3268wind farm layout optimizationGaussian wake modelgenetic algorithmsevolutionary computationHorns RevPrincess Amalia |
| spellingShingle | Nicolas Kirchner-Bossi Fernando Porté-Agel Realistic Wind Farm Layout Optimization through Genetic Algorithms Using a Gaussian Wake Model Energies wind farm layout optimization Gaussian wake model genetic algorithms evolutionary computation Horns Rev Princess Amalia |
| title | Realistic Wind Farm Layout Optimization through Genetic Algorithms Using a Gaussian Wake Model |
| title_full | Realistic Wind Farm Layout Optimization through Genetic Algorithms Using a Gaussian Wake Model |
| title_fullStr | Realistic Wind Farm Layout Optimization through Genetic Algorithms Using a Gaussian Wake Model |
| title_full_unstemmed | Realistic Wind Farm Layout Optimization through Genetic Algorithms Using a Gaussian Wake Model |
| title_short | Realistic Wind Farm Layout Optimization through Genetic Algorithms Using a Gaussian Wake Model |
| title_sort | realistic wind farm layout optimization through genetic algorithms using a gaussian wake model |
| topic | wind farm layout optimization Gaussian wake model genetic algorithms evolutionary computation Horns Rev Princess Amalia |
| url | https://www.mdpi.com/1996-1073/11/12/3268 |
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