DOE OSTI2019
Over the last ten years, lower costs and government incentives for rooftop and utility scale solar photovoltaic (PV) systems have led to a rapid growth in the amount of power generated from this renewable energy source. For example, the cumulative PV generating capacity of 1,160 MW in New York State at the end of 2018 represents a growth of more than 4,000% over the past seven years and almost 400% over the past five years. But increased dependence on this inherently variable energy source introduces the need to anticipate, i.e., forecast, changes in energy production and manage them appropriately. Utilities and grid operators can then use this forecast information to balance changes and maintain stability of the electric grid by adding additional sources or diverting excess supplies for sale or storage, as needed. Current techniques based on persistence modeling are not accurate enough to provide reliable forecasts. With support from the U.S. Department of Energy’s Solar Energy Technology Office (DOE SETO) and the New York Power Authority (NYPA), Brookhaven National Laboratory (BNL), the National Center for Atmospheric Research (NCAR), and the Electric Power Research Institute (EPRI) formed a collaborative team to provide reliable forecasting over a range of time intervals. In addition, the forecasting area was scaled up to about 50 km2, a factor of 20 times larger than previously achieved, so that available solar irradiance could be forecast throughout an entire region including both large generating facilities and distributed rooftop solar arrays. In its role, BNL developed a technology for predicting changes in available solar energy from zero to 30 minutes ahead. This “nowcasting” approach uses inexpensive, off-the-shelf, high definition (HD) imagers with custom software to identify and track the movement of approaching clouds and predict their impact on available solar irradiance. The BNL Nowcasting model improves forecasting accuracy by 20 to 50% over current techniques. Forecasting reliability for low clouds (worst case scenario since they move over solar panels more rapidly than high clouds) was calculated to be 95% accurate for forecast periods of five minute ahead. Reliability decreases with longer forecast time intervals and was calculated to be about 80% for 10 minute forecasts, and about 70% for 20 minutes ahead. Clouds were still reliably tracked by the imager network at 60% accuracy for forecast lead times up to about 30 minutes ahead, and this was determined to be the practical nowcasting limit for low clouds in a network of this scale. This work represents the second phase of a multi-phase effort to develop and deploy solar forecasting at a regional scale so that contributions from distributed rooftop solar resources can be forecast along with solar generating facilities, giving utilities and stakeholders an accurate assessment of future energy production. Additional work is planned to operate the forecasting network in an area upstate New York to assess system reliability under diverse meteorological conditions and to monitor system performance over a full range of seasonal variability.