Overall Rating Gold - expired
Overall Score 67.08
Liaison Michelle Perez
Submission Date May 18, 2011
Executive Letter Download

STARS v1.0

University of California, San Diego
IN-3: Innovation 3

Status Score Responsible Party
Complete 1.00 / 1.00 Dave Weil
Director of Building Commissioning and Sustainability
Facilities Management
"---" indicates that no data was submitted for this field

A brief description of the innovative policy, practice, program, or outcome:

California’s goal of generating 33 percent of its power from renewable energy sources by 2020 will be challenging on days when clouds shade acres of solar photovoltaic panels or when thousands of wind turbines spin more slowly during calm weather. However, researchers at UC San Diego have developed sophisticated forecasting tools that give California electricity distributors advance notice of meteorological changes that affect solar output. The technology allows energy suppliers to more efficiently schedule their fossil-fuel fired plants or energy-storage facilities to meet the state’s demand for electricity.

“If we can correctly forecast the distribution of solar irradiation and wind patterns within a small margin of error, we could rely more on alternative energy sources and less on power generated by fossil fuels,” said Jim Blatchford, senior policy representative for the California Independent System Operator (CAISO), the non-profit organization that coordinates, controls and monitors most of California’s electrical power grid.

UC San Diego researchers led by Jan Kleissl, assistant professor of environmental engineering in the Department of Mechanical and Aerospace Engineering, have used 16 weather stations on the 1,200-acre campus to measure solar radiation every second and map clouds continuously. The data can verify a predictive model that uses satellite data to predict how moving clouds will affect the power output of 6,000 photovoltaic panels on the campus.

“While California tries to encourage the development of renewable sources of electricity, the current state of the art does not provide the modeling fidelity needed to optimally integrate all the photovoltaic capacity that will be deployed in California over the next decade,” said Byron Washom, principal investigator of the DOE-funded project and director of Strategic Energy Initiatives at UC San Diego. “As more and more home owners, small businesses and other non-utility groups install photovoltaic systems and connect them to the energy grid, fluctuations in cloudiness over relatively small areas can potentially have significant negative impact on the associated electricity distribution system if not adequately addressed.”

The university is an ideal place to analyze clouds because it is home to engineering and climate researchers, a 1.2-megawatt photovoltaic system, a self-contained energy grid and one of the world’s densest collections of roof-mounted weather-monitoring stations that can be used to study meteorological effects on the electrical output of photovoltaic panels.

But Kleissl is using a “total sky imager” to understand clouds much better. The imager records their movements across the entire sky throughout the day. “Some clouds grow or shrink and change shape, while others move across the sky without changing much,” said Kleissl. “So far, we’ve found that cloud shape changes are difficult to predict, but we will be able to model the cloud movement and its impact on shading of photovoltaic systems with 90 percent accuracy.” The sky above UC San Diego photographed by the imager can be viewed in real time during the daytime at http://maeresearch.ucsd.edu/kleissl/demroes/live/TSI.jpg .

Kleissl’s experiences have calibrated the sudden, dramatic effects of clouds on the output of photovoltaic panels.

“Once a cloud arrives, the power output from our photovoltaic panels can decrease 40 to 80 percent within a few seconds, and when the cloud leaves the power output increases just as dramatically,” said Kleissl. “Utilities and operators of large power plants want to be able to predict the timing of these transitions so that they can charge up energy storage systems in advance to ‘smooth’ the clear-cloudy transition or prepare other generation to make up for the lost solar power.”

“In the future, electricity generators and energy storage facilities of all scales will likely be able to bid into an hourly market, and the ability to accurately forecast photovoltaic output 1 to 3 hours in advance, despite the variability of the solar resource, would be of immense economic, environmental and operational value,” said Washom.

This technology is expected to eventually be integrated into the management of any grid, large or small.

A letter of affirmation from an individual with relevant expertise:
The website URL where information about the innovation is available:

The information presented here is self-reported. While AASHE staff review portions of all STARS reports and institutions are welcome to seek additional forms of review, the data in STARS reports are not verified by AASHE. If you believe any of this information is erroneous or inconsistent with credit criteria, please review the process for inquiring about the information reported by an institution and complete the Data Inquiry Form.