“Wind energy is on the rise, and not just in the US,” said Mohammad Rasouli, assistant professor of electrical engineering at Penn State Erie, the Behrend College.
Rasouli points out that efficiency ratings on solar panels is still only 25 percent, while wind turbines are much more efficient and “convert over 45 percent of the wind energy to electricity.” Though wind turbines are more efficient, if the layout of a wind farm is not properly designed, this will reduce the efficiency of the turbines.
Sometimes, builders may not put turbines in places with the highest wind speeds where they can generate the most power. Another consideration is spacing, and this is important because turbines create drag that lowers wind speed. Simply put – the first turbines to catch the wind will generate more power than those that are behind them.
In order to build more efficient wind farms – besides wind speed and turbine spacing – other factors such as land size, geography, number of turbines, amount of vegetation, meteorological conditions, building costs, as well as other considerations are also important, according to the researchers.
When balancing all the factors using mathematical models, it still can be difficult to design an optimum layout. “This is a multi-objective approach,” said Rasouli. “We have a function and we want to optimize it while taking into account various constraints.”
Biogeographical-based optimization
Biogeographical-based optimization (BBO) was one approach the researchers focused on in finding the best way to optimize a wind farm. BBO was created in 2009 and inspired by nature. Basically, 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 based on animal behavior, it is then possible to calculate the optimal distribution of objects in other scenarios, such as turbines on a wind farm. “Analytical methods require a lot of computation,” said Rasouli. “This BBO method minimizes computation and gives better results, finding the optimum solution at less computational cost.”
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 – Optimal design of wind farm layout using a biogeographical based optimization algorithm – was published in the Journal of Cleaner Production November 10, 2018.
By incorporating more data, such as updated meteorological records and manufacturer information, the researchers will be able to use the BBO approach to optimize wind farm layouts in many different locations, helping wind farm designers across the world make better use of their land and generate more energy to meet future energy demands from consumers.
“This is a more realistic optimization approach compared to some of the simplifying methods that are out there,” said Rasouli. “This would be better to customers, to manufacturers, and to grid-style, larger-size wind farms.”
