Wind Power

23 Aug 2022

MIT Data Modeling Tool Optimises Wind Farm Output

23 Aug 2022  by   

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Research engineers have found that the energy output of multi-turbine wind farms can be increased by modeling the wind flow of the entire array of turbines and optimising the control of individual units.

The output can be increased without the need for equipment upgrades, the researchers said.

They said that the increase in energy output from a given installation is about 1.2% overall, and as much as 3% at optimal wind speeds.

But if their algorithm were applied to all the world’s existing wind farms, it would be like adding more than 3,600 new wind turbines and a total gain to power producers of almost $1 billion year.

The research was published in the journal Nature Energy in a study led by MIT Civil and Environmental Engineering assistant professor Michael F. Howland.

“Essentially all existing utility-scale turbines are controlled ‘greedily’ and independently,” said Howland. The term “greedily” refers to the fact that they are controlled to maximize only their own power production, as if they were isolated units with no detrimental impact on neighboring turbines.

Turbines are spaced close together in wind farms to achieve economic benefits related to land use and to infrastructure such as access roads and transmission lines. This proximity means that turbines are often strongly affected by the turbulent wakes produced by others that are upwind from them. Individual turbine-control systems typically do not currently take this factor into account.

From a flow-physics standpoint, putting wind turbines close together in wind farms is often the “worst thing you could do,” Howland said. A better approach to maximise total energy production would be to put them as far apart as possible, but that would increase the associated costs.

The researchers developed a new flow model that 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.

The model is designed to learn from operational wind farm data to reduce predictive error and uncertainty. The approach uses the physics-based, data-assisted modeling of the flow within the wind farm and the resulting power production of each turbine to find the optimal orientation for each turbine at any given moment. Doing so allows operators to maximise the output from the whole farm, not just the individual turbines.

Turbines today sense the incoming wind direction and speed and use internal control software to adjust its yaw (vertical axis) angle position to align as closely as possible to the wind. The research team found that by turning one turbine just slightly away from its own maximum output position — perhaps 20 degrees away from its individual peak output angle — the resulting increase in power output from one or more downwind units more than made up for the slight reduction in output from the first unit.

In separate work from 2014, engineers review velocity (blue) and turbulence (yellow) in a simulation of the Lillgrund Wind Farm in Denmark at a visualization lab at the National Renewable Energy Laboratory. Credit: NREL/ Dennis Schroeder.

By using a centralised control system that takes all of these interactions into account, the collection of turbines was operated at power output levels that were as much as 32% higher under some conditions.

In a months-long experiment using a utility-scale wind farm in India, the predictive model was first validated by testing a range of yaw orientation strategies, most of which were intentionally suboptimal.

By testing many control strategies the researchers could identify the true optimal strategy. They said the model was able to predict the farm power production and the optimal control strategy for most wind conditions tested. They said this would enable use of the model to design optimal control strategies for new wind conditions and new wind farms without needing to perform fresh calculations.

A second months-long experiment at the same farm implemented only the optimal control predictions from the model. That test, the researchers said, proved that the algorithm’s real-world effects could match the overall energy improvements seen in simulations.

They said that averaged over the entire test period, the system achieved a 1.2% increase in energy output at all wind speeds, and a 3% increase at speeds between 6 and 8 meters per second (about 13 to 18 miles per hour).

The amount of energy to be gained typically would vary from one wind farm to another, depending on factors including the spacing of the units, the geometry of their arrangement, and the variations in wind patterns at that location over the course of a year.

But in all cases, the model can provide a clear prediction of exactly what the potential gains are for a given site, Howland said. “We’re really just making a software change,” he said, “and there’s a significant potential energy increase associated with it.”

The research team included Jesús Bas Quesada, Juan José Pena Martinez, and Felipe Palou Larrañaga of Siemens Gamesa Renewable Energy Innovation and Technology in Navarra, Spain; Neeraj Yadav and Jasvipul Chawla at ReNew Power Private Limited in Haryana, India; Varun Sivaram formerly at ReNew Power Private Limited in Haryana, India and presently at the Office of the U.S. Special Presidential Envoy for Climate, United States Department of State; and John Dabiri at California Institute of Technology. The work was supported by the MIT Energy Initiative and Siemens Gamesa Renewable Energy.


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