Solar developers are scrambling to meet Puerto Rico utility company PREPA’s new ramp-rate requirement. Any new utility-scale power plant operator must commit to limit changes in output (“ramps”) to 10% per minute — a tall order for PV, as a single PV panel could fluctuate over 70% per second.
What if solar developers could predict how passing clouds affect fluctuations in power output — and plan their plants accordingly?
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Solar power shortfalls due to elevation in the Twin Peaks neighborhood of San Francisco for one year.
A Google Earth map of shadows generated from sky imagery. The cloud and shadow layers are superimposed on Google Earth along with weather station and PV output data. Combined with cloud motion tracking, this information will next be used to make solar irradiance forecasts in the sky imager coverage area.
Shadow Mapping Movie 1
Shadow Mapping Movie 2
by Bryan Urquhart
Using the Google Earth file, one can easily determine the optimum tilt and azimuth angles for any site in California, as well as the average annual increase in radiation at the optimum tilt and azimuth versus horizontally flat. These maps were created using the SUNY 10km Gridded Dataset and an algorithm described by J. Page (in Practical Handbook of Photovoltaics: Fundamentals and Applications) for transforming horizontal global and diffuse radiation into radiation on an inclined surface.
California Maps in Google Earth
USA Maps in Google Earth
by Matt Lave
Google Earth KMZ download…
This color map illustrates the Mean Global Horizontal Solar Energy Density across the state of California, USA during a typical meteorological year (TMY). This is the energy that a horizontally oriented solar panel would receive in one year. The data for this map comes from the corrected National Solar Radiation Database, SUNY 10km Gridded Dataset.
Reference: Nottrott and Kleissl, 2010
by Anders Nottrott