系級：心理一姓名：何柔漪 學號：06135047
New control
strategy helps reap maximum power from wind farms
Date: April 23, 2018
Source: University of Texas at Austin, Texas
Advanced Computing Center
A team of
researchers from The University of Texas at Dallas (UT Dallas) has developed a
new way to extract more power from the wind. This approach has the potential to
increase wind power generation significantly with a consequent increase in
revenue. Numerical simulations performed at the Texas Advanced Computing Center
(TACC) indicate potential increases of up to six to seven percent.
According to
the researchers, a one percent improvement applied to all wind farms in the
nation would generate the equivalent of $100 million in value. This new method,
therefore, has the potential to generate $600 million in added wind power
nationwide.
The team
reported their findings in Wind Energy in
December 2017 and Renewable Energy in
December 2017.
In the branch
of physics known as fluid dynamics, a common way to model turbulence is through
large eddy simulations. Several years ago, Stefano Leonardi and his research
team created models that can integrate physical behavior across a wide range of
length scales -- from turbine rotors 100 meters long, to centimeters-thick tips
of a blades -- and predict wind power with accuracy using supercomputers.
"We
developed a code to mimic wind turbines, taking into account the interference
between the wake of the tower and the nacelle [the cover that houses all of the
generating components in a wind turbine] with the wake of the turbine
rotor," said Leonardi, associate professor of mechanical engineering and
an author on the Wind Energy paper,
which was selected for the cover.
Beyond the
range of length scales, modeling the variability of wind for a given region at
a specific time is another challenge. To address this, the team integrated
their code with the Weather Research and Forecasting Model (WRF), a leading
weather prediction model developed at the National Center for Atmospheric
Research.
"We can
get the wind field from the North American Mesoscale Model on a coarse grid,
use it as an input for five nested domains with progressively higher resolution
and reproduce with high fidelity the power generation of a real wind
farm," Leonardi said.
The growing
power of computers allows Leonardi and his team to accurately model the wind
field on a wind farm and the power production of each single turbine. Testing
their model's results against data from a wind farm in North Texas, they saw a
90 percent agreement between their predictions and the turbine's efficiency.
They will present their results at Torque 2018, a major wind energy research
conference.
TAKING THE TURBULENCE OUT OF THE OPTIMIZATION
CONTROL ALGORITHM
Wind doesn't
simply flow smoothly in one direction. It contains turbulence and wakes which
are magnified when turbines are grouped together as they are on a wind farm.
Wake
interactions lead to losses of up to 20 percent of annual production, according
to the U.S. Department of Energy. Understanding how turbulence impacts energy
generation is important to adjust the behavior of the turbines in real-time to
reap maximum power.
Using their
modeling capabilities, they tested control algorithms that are used to manage
the operation of dynamic systems at wind farms. This included the control
algorithms known as extremum seeking control, a model-free way of getting the
best performance out of dynamic systems when only limited knowledge of the
system is known.
"Many
thought it would not be possible to use this approach because of turbulence and
the fact that it provides a situation where turbines are changing all the
time," Leonardi said. "But we did a huge number of simulations to
find out a way to filter turbulence out of the control scheme. This was the
major challenge."
With extremum
seeking control, the system increases and reduces the rotational speed of a
spinning turbine blade, all the while measuring the power, and calculating the gradient.
This is repeated until the controller finds the optimal operating speed.
"The
important thing is that the control algorithm does not rely on a physics-based
model," Leonardi said. "There are many uncertainties in a real wind
farm, so you cannot model everything. The extremum seeking control can find the
optimum no matter if there is erosion or icing on the blades. It's very robust
and works despite uncertainties in the system."
SIMULATING THE WIND
To test their
new approach, the team ran virtual wind experiments using supercomputers at the
TACC, including Stampede2 and Lonestar5 -- two of the most powerful in the
world. They were able to use these systems through the University of Texas
Research Cyberinfrastructure (UTRC) initiative, which, since 2007, has provided
researchers at any of the University of Texas System's 14 institutions access
to TACC's resources, expertise and training.
Access to
powerful supercomputers is important because wind turbines are expensive to
build and operate and few wind research facilities are available to
researchers.
"The
benefits of using high performance computing to create a virtual platform for
doing analyses of proposed solutions for wind energy are enormous," said
Mario Rotea, professor of mechanical engineering at UT Dallas, and site
director of the National Science Foundation-supported Wind-Energy Science,
Technology and Research (WindSTAR) Industry-University Cooperative Research
Center (IUCRC). "The more we can do with computers, the less we have to do
with testing, which is a big part of the costs. This benefits the nation by
lowering the cost of energy."
While the
application of extremum seeking control to wind farms is yet to be field
tested, the UT Dallas team already applied the method to a single turbine at
the National Renewable Energy Laboratory (NREL).
"The NREL
test gave us experimental data supporting the value of extremum seeking control
for wind power maximization," said Rotea. "The experimental results
show that extremum seeking control increases the power capture by 8-12%
relative to a baseline controller."
Given the
encouraging experimental and computational results, the UT Dallas team is
planning an experimental campaign involving a cluster of turbines in a wind
farm.
COLLABORATIONS AND NEXT STEPS
The
development of the fluid dynamics model for wind turbines was part of an
international collaboration between four U.S. institutions (Johns Hopkins
University, UT Dallas, Texas Tech and Smith College) and three European
institutions (Technical University of Denmark, École polytechnique fédérale de
Lausanne and Katholieke Universiteit Leuven) funded by the National Science
Foundation.
Through the
WindSTAR center, they collaborate with nine leading wind energy companies and
equipment manufacturers. These companies are interested in adopting or
commercializing the work.
"The
members of our center do not have access to a lot of horsepower in terms of HPC
[high-performance computing]," said Rotea. "The computers at TACC are
an asset for us and give us a competitive advantage over other groups. In terms
of solving actual problems, we create control systems that they may
incorporate, or they may use HPC to develop new tools for forecasting wind
resources or determine if there are turbines that are not performing."
In addition to
developing the new turbulence algorithms and control strategies, members of the
WindSTAR team have introduced methods to predict accurate results on
less-powerful computers (work that appeared in the March 2018 issue of Wind Energy) and to
determine how closely to place turbines to maximize profits, depending on the
cost of land (presented at the 2018 Wind Symposium).
The long-term
effects of the work go beyond the theoretical.
"The
research allows us to optimize wind energy power production and increase the
penetration of renewable energy in the grid," Leonardi said. "There
will be more power generated by the same machines because we understand more
about the flow physics in a wind farm, and for the same land use and
deployment, we can get more energy."
心得：
Wind power is the use of air flow through
wind turbines to mechanically power generators for electric power. Wind power,
as an alternative to burning fossil fuels, is plentiful, renewable, widely
distributed, clean, produces no greenhouse gas emissions during operation,
consumes no water, and uses little land. The net effects on the environment are
far less problematic than those of nonrenewable power sources. It'd be
phenomenal if it can be implemented widely, it'd create job and great economy
as well as sustainable energy!
