Improving control of wind resources
Or, at least develop appropriate ways to manage wind intermittency.
Unlike conventional methods of energy production, where output can be accurately evaluated out to the very last kilowatt-hour, wind, like any element of weather, is far less predictable. Dominguez-Garcia and Gross are the recent recipients of a three-year grant from the National Science Foundation to study and develop a methodology that can better handle the intermittent electricity production of wind-based production and integrate the resources into the existing power grid.
“The power system is a ‘just-in-time’ manufacturing process since we do not have at present the ability to store large amounts of energy, and with wind, you really have no control over how much power you can generate,” said Dominguez-Garcia, the principal investigator on the study.
“The ultimate problem is that the wind blows when it wants to and not when the system needs it,” said Gross.
“The challenge is how to manage a resource which has no control knobs,” continued Dominguez-Garcia.
The intermittent nature of wind energy raises logistical problems that Dominguez-Garcia and Gross will address in their investigations. For example, power system operators whose systems include wind energy currently need to compensate for the uncertainty of wind power by requiring higher power reserve levels. This frequently translates into committing additional generation capacity to ensure adequate power capacity should the wind fail to blow.
In other words, such a strategy may result in either “an issue of commission whenever too many controllable units are committed, which makes production uneconomic, or a risk of omission if the reserves are too low for the slow winds, resulting in the undesirable loss of load,” said Gross.
To combat this problem, Dominguez-Garcia and Gross are developing a detailed statistical analysis to help create models of wind farm output, taking into account technology, location, wind variability, and potential for forecasting error on a given farm. In addition to aiding power system operators, the models will reduce the costs of building and maintaining a wind farm by better predicting the output of a given site or a group of sites.
Their research is especially timely as more and more states begin to set higher standards for sustainable energy and green technology in their energy portfolios. California recently enacted a target of 30% of energy to be generated via renewable resources by 2020. Currently, the United States has the largest wind-based generation capacity installed in the world, though not on a per capita basis.
“There’s a huge hype about sustainable technology right now, obviously, “said Dominguez-Garcia. “The National Science Foundation and others are putting a lot of resources into this research right now. Of course, it is just research right now, but we’re confident that the technology will materialize. “
Energy production modeling is just one aspect of the greater, nationwide push for sustainable technology. Issues of green energy storage, transport, and construction still loom. For instance, the best locations for wind generation typically lay in areas of low population density. Predicting wind production is the first half of the solution, transporting that sustainable energy to the New Yorks, Chicagos, and Champaign-Urbanas of the world is the other. Taking this into account, Dominguez-Garcia and Gross are specifically using graduate and undergraduate research assistants to get the next generation of green energy engineers off to a running start. “We have a key responsibility in educating and training the new generation of engineers to ensure the sustainability of the nation for the future,” said Gross.
After all, when it comes to predicting the weather, we can use all the help we can get.