Virtually all wind turbines, which produce more than 5% of the world’s electricity, are controlled as if they were individual, free-standing units. The vast majority, however, are part of larger wind farms with dozens or even hundreds of turbines, whose wakes can affect each other.
The energy output of such installations could be increased by modelling the wind flow of the entire farm and optimising the control of individual units, said the researchers, including engineers from the Massachusetts Institute of Technology (MIT), the California Institute of Technology and Siemens Gamesa.
Without investing in new equipment, the team said a given installation’s energy output could increase by about 1.2% overall, and 3% during optimal wind speeds. While that “may seem modest”, the researchers said, the algorithm-based approach could be deployed at any wind farm. If that 1.2% energy increase were applied to all existing wind farms, it would be the equivalent of adding more than 3,600 new wind turbines, or enough to power about 3m homes – all for basically no cost.
“Essentially all existing utility-scale turbines are controlled ‘greedily’ and independently,” said MIT civil and environmental engineer Michael F Howland.
Turbines are normally controlled to maximise only their own power production, as if they were isolated units, but they are deliberately spaced close together in wind farms to optimise land use and infrastructure access. This proximity means they are often strongly affected by the turbulent wakes produced by others that are upwind – a factor that individual turbine control systems do not currently consider.
“From a flow physics standpoint, putting wind turbines close together in wind farms is often the worst thing you could do,” said Howland. “The ideal approach to maximise total energy production would be to put them as far apart as possible,” but that would increase associated costs.
Today, each turbine constantly senses the incoming wind direction and speed, and uses its internal control software to rotate its yaw and align as closely as possible to the wind direction.
In the new system, based on a flow model, the team found that by turning one turbine slightly away from its own maximum output position – 20º away from its peak output angle, for example – the resulting increase in power output from one or more downwind units will more than make up for the slight reduction in output from the first unit.
The flow model predicts the power production of each turbine in the farm depending on the incident winds in the atmosphere and the control strategy of each turbine. While based on flow physics, the model learns from operational wind farm data to reduce predictive error and uncertainty.
By using a centralised control system that takes all of the interactions into account, a wind farm was operated at power output levels that were as much as 32% higher under some conditions.
During tests at a real utility-scale installation in India, the system achieved a 1.2% increase in energy output at all wind speeds, and a 3% increase at speeds of 6-8 metres per second (22-29km/h).
The researchers said the model and cooperative control strategy could be implemented at any existing or future wind farm. Translated to the world’s existing fleet of wind turbines, Howland estimated that a 1.2% overall energy improvement would produce more than 31 Terawatt-hours of additional electricity per year, approximately equivalent to installing an extra 3,600 wind turbines at no cost. This would translate into roughly $950m in extra revenue for wind farm operators each year.
The amount of energy that could be gained varies widely from one wind farm to another, depending on factors including the spacing of the units and variations in wind patterns. The model can provide a clear prediction of the potential gains in all cases, Howland said.
By reducing wake losses, the algorithm could make it possible to place turbines closer together in future wind farms, increasing power density and reducing installation footprints.
The research was published in Nature Energy.
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