Penn State, NCAR researchers aim to better predict renewable energy production

Guido Cervone makes adjustments to one of the National Center for Atmospheric Research’s Total Sky Imager machines. Cervone and his research team will spend three months at NCAR’s facilities in Boulder, Colorado, researching the uncertainties associated with renewable energy production. Credit: Guido CervoneAll Rights Reserved.

UNIVERSITY PARK, Pa. -- During the summer of 2015, Penn State researchers are partnering with the National Center for Atmospheric Research (NCAR) to investigate a major obstacle facing renewable energy — uncertainty in energy production due to atmospheric conditions like cloud cover or wind speed. The team, led by Guido Cervone, associate professor of geography and associate director of the Penn State Institute for CyberScience, seeks to develop new algorithms that better predict the amount of energy produced by solar and wind sources. Their goal is to increase the use of renewable energy on a daily basis and reduce costs.

“With traditional sources of energy production, such as diesel, it is possible to calculate with certainty how much energy can be generated. Given a known quantity of diesel and the characteristics of the generator, it is possible to accurately regulate when and how much energy can be output. With renewable energy, though, there’s uncertainty because of atmospheric factors -- rapidly changing cloud cover and variable wind speeds can lead to an under- or over-generation of power,” said Cervone.

This uncertainty makes it difficult and sometimes inefficient to fully take advantage of available renewable sources. With improved predictive algorithms, however, any smart grid operator -- a person or organization that is both a user and producer of energy -- may optimize the use of renewable sources, and make optimal decisions of when to purchase or sell energy on the main grid. Cervone’s team will primarily be looking at the “day ahead” market, or how much will be produced within the next 24 hours from a given point in time.

To develop their new algorithms, the researchers will spend three months, from May to August, analyzing atmospheric data from across the United States in NCAR’s Boulder, Colorado facilities. There, they also will put their models to the test by comparing predictions with actual data made available through NCAR’s Yellowstone supercomputer, which gives access to meteorological data from around the United States.

“NCAR is really the prime research center in the world to study atmospheric science. We’re excited to collaborate with a team that does so much to take atmospheric research from theory to operational use,” said Cervone, who has worked with NCAR for four years as an affiliate scientist and is one of only 36 researchers worldwide serving in this capacity today.

The research will be funded by through a competitive NCAR Advanced Study Program grant. In addition to Cervone, the research team includes Gabriella Ferruzzi, a postdoctoral researcher in the Department of Geography and Institute for CyberScience; Anna De Angelis, a visiting international student; Yanni Cao, a geography master of science student; and Laura Harding, Elena Sava and Carolynne Hultquist, all geography doctoral students.

NCAR is sponsored by the National Science Foundation and managed by the University Corporation for Atmospheric Research (UCAR), a consortium of 105 North American universities, including Penn State, with Ph.D. programs in the atmospheric sciences and related disciplines.

Last Updated April 21, 2017