Xiangyu Li
Optimisation of Large Offshore Wind Farm Layout Considering Reliability and Wake Effect
Li, Xiangyu; Dao, Cuong D.; Kazemtabrizi, B.; Crabtree, Christopher J.
Authors
Cuong D. Dao
Dr Behzad Kazemtabrizi behzad.kazemtabrizi@durham.ac.uk
Associate Professor
Professor Christopher Crabtree c.j.crabtree@durham.ac.uk
Professor
Abstract
Nowadays, the increasing demand of electricity and environmental hazards of the greenhouse gas lead to the requirement of renewable energies. The wind energy has been proved as one of the most successful sustainable energies. Recently, the development trend of the wind energy is to build large offshore wind farms (OWFs) with hundreds of wind turbines, which could generates more power in one wind farm. In the large OWF, the wake effect is a very important impact factor to the wind farms, especially for those with close spacing. Therefore, the wind farm layout, the location of the wind turbines (WTs) is very essential to the performance of the whole wind farm, especially for large OWFs. In this research, we focus on the optimization of the large OWF layout by considering performance of the OWF, such as the total output energy. Firstly, the model for wind farm performance evaluation is established by incorporating historical wind speed data and the wake effect which can affect the total wind farm output. Then, by using the metaheuristic algorithms, the genetic algorithm (GA), the OWF layout is optimized. This study can offer useful information to the wind farm manufactures in the large OWF design phase.
Citation
Li, X., Dao, C. D., Kazemtabrizi, B., & Crabtree, C. J. (2020). Optimisation of Large Offshore Wind Farm Layout Considering Reliability and Wake Effect. . https://doi.org/10.1115/gt2020-15495
Presentation Conference Type | Conference Paper (Published) |
---|---|
Conference Name | ASME Turbo Expo 2020 |
Acceptance Date | Feb 18, 2020 |
Online Publication Date | Jan 11, 2021 |
Publication Date | Jan 1, 2020 |
Deposit Date | Mar 11, 2020 |
Publicly Available Date | Mar 13, 2020 |
DOI | https://doi.org/10.1115/gt2020-15495 |
Public URL | https://durham-repository.worktribe.com/output/1142625 |
Files
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(1.1 Mb)
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