Big Data Helps Engineers Design SMART Wind Farms
Tom Lombardo posted on October 15, 2017 |

Look at a typical wind farm and you'll see dozens, if hot hundreds, of identical turbines. Why are they all the same size and design? I mean, if you're forming a rock-and-roll band, you wouldn't hire four lead guitarists. If you're assembling a football team, you don't draft fifty-three quarterbacks. Why should wind farms be any different?


The obvious answer is that you want the wind farm to maximize energy production with minimal cost, so you find the most efficient turbine available and get a volume discount by purchasing a bunch of them. It sounds good intuitively, but science often astonishes intuition. That's why the US Department of Energy's Atmosphere to Electrons (A2e) program exists: to improve wind plant performance and reduce the cost of wind power.


A2e is a collaboration between the private sector, academia, and the federal government. In conjunction with the A2e initiative, the National Renewable Energy Laboratory (NREL) released a report entitled Enabling the SMART Wind Power Plant of the Future Through Science-Based Innovation, which discusses the science and technology that will spawn the next generation of wind power plants. The fifty-seven-page report isn't exactly a breeze to read, so I thought I'd summarize the labcoat lingo for you.



SMART Wind Power

Engineers continue to make improvements in wind turbine design, but we're approaching the point of diminishing returns, where it takes a lot of costly research and development just to achieve a slight increase in turbine output. It makes sense, then, to focus not on the turbine itself, but the wind farm as a whole.


The Department of Energy envisions a future with wind power plants based on the SMART (System Management of Atmospheric Resource through Technology) principle. (Side note: SMART is a textbook example of a "backronym" if I've ever seen one.) One of the ways that engineers will develop SMART wind farms is to design turbines that communicate and cooperate, allowing them to work as a team rather than a collection of individuals. Currently, wind plants consist of large turbines spaced far apart, with each turbine facing directly into the wind and absorbing as much wind as it can. (This is seen in the top part of the following image.) As these farms have been studied over the years, researchers discovered that when the turbines in the front row grab wind as much as possible, the entire operation produces less electricity overall. (Football analogy: a selfish player who tries to pad his own stats is usually a detriment to the team.) Conversely, when the leading edge turbines let more wind get through to the ones behind, the whole wind farm achieves greater efficiency and production. (That concept may have a broader lesson for humanity, but I'll stick to wind farms.)


The SMART wind farm controller, shown in the bottom half of the above image, will respond to real-time weather conditions, telling certain turbines to turn slightly away from the wind and/or adjust their blade pitch in order to steer the turbulent wake winds away from downstream turbines, allowing those turbines to perform at their best. Furthermore, the new plant will incorporate turbines of different sizes and tower heights, with each optimized for its unique location on the wind farm.


High-Fidelity Modeling

High-Fidelity Modeling (HFM), made possible by advances in sensor and computer technology, uses a set of transducers to measure parameters such as wind speed, direction, and turbulence, as well as the condition of the turbines themselves. Using data from these sensors, wind farm controllers can maximize electricity production by directing wind flow based on current conditions. By monitoring each turbine's operation, HFM can measure turbine loads, predict component failures, and optimize maintenance schedules, which lowers the cost of operating a wind farm.



Wind as Baseload Power

The grid still relies on non-renewable (and often dirty) sources of energy, largely due to the perceived unpredictability of the wind and solar resources. It turns out that the "unreliable" reputation is only valid on a small scale. While renewable energy sources fluctuate in a specific location, they're relatively stable over a large geographical area. Case in point: Remember how the grid crashed because of all those solar panels being blacked out by the August 2017 eclipse? No, you don't remember that, because the grid handled the eclipse with no problems. Granted, solar currently makes up a small percentage of the grid's power, but overall solar electricity production barely dipped at all during the eclipse, because at any given moment, the moon's shadow only covered a small chunk of land.   


High-fidelity modeling helps scientists more accurately measure, describe, and predict wind patterns. Armed with this scientific data, engineers can design wind and solar farms so that renewable energy can provide baseload power as well as ancillary services such as peak demand response, power factor correction, and frequency regulation.



We can end our dependence on fossil fuels; all it takes is a little SMART thinking.



Images and Video courtesy of NREL

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