By - Jim Vess

The cost of wind power could be cut in half with better modeling and management, NREL study says

Energize Weekly, September 20, 2017

Technological improvements could cut the generating costs of wind power in half, making it as economical as natural gas generation—and the key isn’t just a new turbine, blade or tower, but a supercomputer.

A new report by the National Renewable Energy Laboratory (NREL), in Golden, Colo., outlines an approach to maximizing wind farm efficiency through better modeling, management and design driven by supercomputers and sensors.

“The collection of intelligent and novel technologies that comprise this next-generation technology can be characterized as ‘System Management of Atmospheric Resource through Technology,’ or SMART strategies,” the report said.

A SMART wind power plant is projected to have an unsubsidized, levelized cost of energy of $23 a megawatt-hour or less by 2030, a 50 percent drop from the current cost, the report said.

At these lower prices, wind energy installations in the United States could increase to more than 200 gigawatts by 2030 and 500 gigawatts by 2050, supplying respectively 20 percent and 47 percent of U.S. electricity with wind, report said.

Compared to a “business-as-usual scenario,” these investments could lead to as much as $150 billion in cumulative electric sector cost savings from 2017 to 2050.

The country’s wind resource is big enough to generate more than 7.5 times the country’s total electricity generation in 2016. It supplied about 5.5 percent of the nation’s electricity in 2016.

The key to these cost efficiencies is a better understanding and management of atmospheric processes, wind power plant physics and electricity dispatch to the grid.

In each case, what is needed are models “requiring the world’s largest computers and advanced computationally efficient algorithms.”

“The ability to truly understand, control, and predict the performance of the future wind plant relies on understanding and tying together a range of physical phenomena from regional weather systems to the wind flow that passes over individual wind turbine rotor blades,” the report said.

An advanced wind generating facility, using these technologies, would be able respond to atmospheric conditions and control airflow within the plant to maximize power production.

Among the elements in these future wind power plants would be:

  • High-resolution modeling and state-of-the-art sensors to precisely estimate wind power plant energy production, reducing uncertainty and increasing predictability of electricity production
  • Integrated wind plant design with real-time active control of turbines, and operational strategies to increase reliability and extend turbine lifetimes
  • Innovative design of wind turbines and components such as rotors and drivetrains to optimize performance and enhance energy capture, including larger rotors and taller towers to capture higher-potential wind energy in the Earth’s upper atmosphere
  • Better management of the interface with the grid with controllable, dispatchable and predictable grid support services. This will enhance grid resilience and stability, including precise forecasting of wind energy production for short-term grid operation and planning.

Among the types of cost reductions the NREL study anticipates from better management and efficiencies are a 21 percent drop in capital expenditures through scaling the size of turbines and other turbine innovations. This would also lead to a 22 percent annual increase in energy production.

The efficiencies should also cut operational expense by a quarter as a result of advanced modeling, sensing and data analysis.

This work is being done through U.S. Department of Energy’s “Atmosphere to Electrons” project, an applied research program that is combining national laboratory researchers and computing power with industry partners.

“Fundamental scientific advances are needed to drive broad-based wind power competitiveness and support a new era of abundant low-cost energy,” the report said.

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