An international team of scientists have developed a new design approach with the aim of increasing the efficiency of wind farms.
The work has been carried out by scientists at Penn State Behrend and the University of Tabriz, Iran. The new design approach is basically an algorithm to design more efficient wind farms, helping to generate more revenue for builders and more renewable energy for their customers.
Though wind turbines are efficient, wind farm layouts can reduce this efficiency if not properly designed. Builders do not always put turbines in the places with the highest wind speeds, where they will generate the most power. Turbine spacing is also important — because turbines create drag that lowers wind speed, the first turbines to catch the wind will generate more power than those that come after.
To build more efficient wind farms, designers must take these factors into account wind speed and turbine spacing, as well as land size, geography, number of turbines, amount of vegetation, meteorological conditions, building costs, and other considerations, according to the researchers. Balancing all of these factors to find an optimum layout is difficult, even with the assistance of mathematical models.
The researchers focused on one approach, called “biogeographical-based optimization.” Created in 2008 and inspired by nature, BBO is based on how animals naturally distribute themselves to make the best use of their environment based on their needs. By creating a mathematical model from animal behavior, it is then possible for the researchers to calculate the optimal distribution of objects in other scenarios, such as turbines on a wind farm.
The researchers from Penn State and the University of Tabriz completed the approach by incorporating additional variables, including real market data, the roughness of the surface — which affects how much power is in the wind — and how much wind each turbine receives.
The research team also improved the BBO approach by incorporating a more realistic model for calculating wakes — areas with slower wing speeds created after the wind blows past a turbine, similar to the wake behind a boat — and testing how sensitive the model was to other factors such as interest rates, financial incentives, and differences in energy production costs. They report their results online in the Journal of Cleaner Production, which will be published in November.