Monitoring Clouds Foretells Solar Power

One problem often found with solar power systems and output are issues due to weather changes. When it clouds up, solar power output goes down. Sighting a solar power plant in the right geographical location is therefore extremely important to maximize best weather conditions, etc for maximized solar power potential. Now a new system developed by researchers at the Sandia National Laboratories could help make this site placement process a whole lot easier.

The system, according to Sandia researchers, lets observation of cloud shape, size and movement occur more easily to provide “a way for utility companies to predict and prepare for fluctuations in power output due to changes in weather.” It is believed that models generated via this data will help these companies “assess potential power plant locations, ramp rates and power output.” It is designed more to study larger scale plant issues where it is “less well understood what happens when only part of a large system is covered by a moving cloud shadow, while the rest stays in sunlight.”

Sandia Forecast system

image via Sandia National Laboratories

The test pilot for this forecasting system is currently under way at at the 1.2-megawatt La Ola Solar Farm on the Hawaiian island of Lana’i, which is the state’s largest solar power system and one that can produce enough power to supply up to 30 percent of the island’s peak electric demand. The plant currently sells power to Maui Electric Company.

Sandia said its researchers connected 24 small, nonintrusive sensors to the plant’s PV panels and used a radio frequency network to transmit data. The sensors took readings at one-second intervals to provide researchers with “unprecedented detail about cloud direction and coverage activity.”

“Currently, a utility company that wants to build a large solar PV power plant might have a lot of questions about the plant’s output and variability at a proposed site. Work being done at the La Ola plant is leading to new methods that eventually can be used to answer these questions,” said Sandia researcher Josh Stein in a statement. “These techniques will allow a developer to place a sensor network at a proposed site, make measurements for a period of time and use that to predict plant output variability.”

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I am the editor-in-chief and founder for EarthTechling. This site is my desire to bring the world of green technology to consumers in a timely and informative matter. Prior to this my previous ventures have included a strong freelance writing career and time spent at Silicon Valley start ups.