TY - GEN
T1 - Comparison of solar irradiance smoothing using a 45-sensor network and the wavelet variability model
AU - Dyreson, Ana
AU - Morgan, Eric
AU - Monger, Sam
AU - Acker, Tom L
N1 - Publisher Copyright:
Copyright © (2014) by American Solar Energy Society.
PY - 2014
Y1 - 2014
N2 - With increasing penetrations of solar photovoltaic (PV) power in the electricity grid, the variability of the irradiance, and therefore power, is important to understand because variable resources can challenge grid operations. Predicting PV variability using one irradiance sensor, as is commonly done, does not account for the smoothing of irradiance over the extent of the power plant. This smoothing is examined using two methods: averaging measurements from many irradiance sensors, and using a model developed by Lave, Kleissl, and Stein [1] called the Wavelet Variability Model. The results show the similarities and differences between two irradiance smoothing models. These two models both show that the smoothing effect is significant for large PV power plants, which means the power plant output has less variability and is easier to integrate into the electricity grid than might have been expected using a single point sensor measurement to predict variability.
AB - With increasing penetrations of solar photovoltaic (PV) power in the electricity grid, the variability of the irradiance, and therefore power, is important to understand because variable resources can challenge grid operations. Predicting PV variability using one irradiance sensor, as is commonly done, does not account for the smoothing of irradiance over the extent of the power plant. This smoothing is examined using two methods: averaging measurements from many irradiance sensors, and using a model developed by Lave, Kleissl, and Stein [1] called the Wavelet Variability Model. The results show the similarities and differences between two irradiance smoothing models. These two models both show that the smoothing effect is significant for large PV power plants, which means the power plant output has less variability and is easier to integrate into the electricity grid than might have been expected using a single point sensor measurement to predict variability.
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M3 - Conference contribution
AN - SCOPUS:84944738620
T3 - 43rd ASES National Solar Conference 2014, SOLAR 2014, Including the 39th National Passive Solar Conference and the 2nd Meeting of Young and Emerging Professionals in Renewable Energy
SP - 249
EP - 256
BT - 43rd ASES National Solar Conference 2014, SOLAR 2014, Including the 39th National Passive Solar Conference and the 2nd Meeting of Young and Emerging Professionals in Renewable Energy
PB - American Solar Energy Society
T2 - 43rd ASES National Solar Conference 2014, SOLAR 2014, Including the 39th National Passive Solar Conference and the 2nd Meeting of Young and Emerging Professionals in Renewable Energy
Y2 - 6 July 2014 through 10 July 2014
ER -