Wind Farm Area Shape Optimization Using Newly Developed Multi-Objective Evolutionary Algorithms
In recent years, wind farm layout optimization (WFLO) has been extendedly developed to address the minimization of turbine wake effects in a wind farm. Considering that increasing the degrees of freedom in the decision space can lead to more efficient solutions in an optimization problem, in this wo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/14/4185 |
| _version_ | 1797527243447074816 |
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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 | In recent years, wind farm layout optimization (WFLO) has been extendedly developed to address the minimization of turbine wake effects in a wind farm. Considering that increasing the degrees of freedom in the decision space can lead to more efficient solutions in an optimization problem, in this work the WFLO problem that grants total freedom to the wind farm area shape is addressed for the first time. We apply multi-objective optimization with the power output (PO) and the electricity cable length (CL) as objective functions in Horns Rev I (Denmark) via 13 different genetic algorithms: a traditionally used algorithm, a newly developed algorithm, and 11 hybridizations resulted from the two. Turbine wakes and their interactions in the wind farm are computed through the in-house Gaussian wake model. Results show that several of the new algorithms outperform NSGA-II. Length-unconstrained layouts provide up to 5.9% PO improvements against the baseline. When limited to 20 km long, the obtained layouts provide up to 2.4% PO increase and 62% CL decrease. These improvements are respectively 10 and 3 times bigger than previous results obtained with the fixed area. When deriving a localized utility function, the cost of energy is reduced up to 2.7% against the baseline. |
| first_indexed | 2024-03-10T09:40:16Z |
| format | Article |
| id | doaj.art-884058f4a5f545ebbe544b882ae76d33 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T09:40:16Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-884058f4a5f545ebbe544b882ae76d332023-11-22T03:41:27ZengMDPI AGEnergies1996-10732021-07-011414418510.3390/en14144185Wind Farm Area Shape Optimization Using Newly Developed Multi-Objective Evolutionary AlgorithmsNicolas 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, SwitzerlandIn recent years, wind farm layout optimization (WFLO) has been extendedly developed to address the minimization of turbine wake effects in a wind farm. Considering that increasing the degrees of freedom in the decision space can lead to more efficient solutions in an optimization problem, in this work the WFLO problem that grants total freedom to the wind farm area shape is addressed for the first time. We apply multi-objective optimization with the power output (PO) and the electricity cable length (CL) as objective functions in Horns Rev I (Denmark) via 13 different genetic algorithms: a traditionally used algorithm, a newly developed algorithm, and 11 hybridizations resulted from the two. Turbine wakes and their interactions in the wind farm are computed through the in-house Gaussian wake model. Results show that several of the new algorithms outperform NSGA-II. Length-unconstrained layouts provide up to 5.9% PO improvements against the baseline. When limited to 20 km long, the obtained layouts provide up to 2.4% PO increase and 62% CL decrease. These improvements are respectively 10 and 3 times bigger than previous results obtained with the fixed area. When deriving a localized utility function, the cost of energy is reduced up to 2.7% against the baseline.https://www.mdpi.com/1996-1073/14/14/4185wind farm layout optimizationwind farm area shapegenetic algorithmsgaussian wake modelmulti-objective optimizationpareto front |
| spellingShingle | Nicolas Kirchner-Bossi Fernando Porté-Agel Wind Farm Area Shape Optimization Using Newly Developed Multi-Objective Evolutionary Algorithms Energies wind farm layout optimization wind farm area shape genetic algorithms gaussian wake model multi-objective optimization pareto front |
| title | Wind Farm Area Shape Optimization Using Newly Developed Multi-Objective Evolutionary Algorithms |
| title_full | Wind Farm Area Shape Optimization Using Newly Developed Multi-Objective Evolutionary Algorithms |
| title_fullStr | Wind Farm Area Shape Optimization Using Newly Developed Multi-Objective Evolutionary Algorithms |
| title_full_unstemmed | Wind Farm Area Shape Optimization Using Newly Developed Multi-Objective Evolutionary Algorithms |
| title_short | Wind Farm Area Shape Optimization Using Newly Developed Multi-Objective Evolutionary Algorithms |
| title_sort | wind farm area shape optimization using newly developed multi objective evolutionary algorithms |
| topic | wind farm layout optimization wind farm area shape genetic algorithms gaussian wake model multi-objective optimization pareto front |
| url | https://www.mdpi.com/1996-1073/14/14/4185 |
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