Modeling solar irradiance smoothing for large PV power plants using a 45-sensor network and the Wavelet Variability Model

Local short-term variability in solar irradiance

“…While the PDFs of single-sensor observations all exhibit such tails, these features are much less prominent for the spatially averaged k * increment distributions. This result is consistent with previous findings of e.g., Lave and Kleissl (2010), Marcos et al (2011b), Dyreson et al (2014), andvan Haaren et al (2014). Comparing the distributions of increments from multiple pyranometers and PV plants of different capacities for various temporal resolutions, these previous studies all showed high magnitude changes with increasing time lag, and fewer high magnitude changes with increasing PV plant size (or numbers of sensors).…”

“…size (Dyreson et al, 2014;Lave et al, 2012;Marcos et al, 2011b;van Haaren et al, 2014). Furthermore, spatial autocorrelation structures and decorrelation length scales of increments in irradiance and clear-sky index (i.e., irradiance normalized to clear-sky conditions), and PV power output have also been studied for a range of spatial scales and increment values (Arias-Castro et al, 2014;Elsinga and van Sark, 2014;Hinkelman, 2013;Hoff and Perez, 2012;Mills, 2010;Perez et al, 2012;Perpiñán et al, 2013).…”

“…For example, the few studies that have been based on high-resolution 1 Hz PV power observations only had measurements from a maximum of six PV systems (Marcos et al, 2011a, b) at their disposal. Though irradiance measurements with this high temporal resolution have on occasion been conducted with more sensors, the spatial coverage remains strongly confined (e.g., up to 45 sensors spread across ∼ 2.5 km 2 ; Dyreson et al, 2014). Some studies have used artificially generated data to overcome these restrictions, either by simulating simple cloud shapes (Arias-Castro et al, 2014;, or by constructing virtual networks based on time-shifted single-sensor measurements .…”

Irradiance Variability Quantification and Small-Scale Averaging in Space and Time: A Short Review

“…Early ground-based analyses have characterized single-site irradiance on time scales ranging from hours to months [19,20], while later studies have often been geared towards increasing temporal resolutions of, e.g., 300 s [21,22], 60 s [23][24][25][26], 20 s [11,27,28], 10 s [10,12,29], 5 s [30,31], 1 s [13,[32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49], 2 of 19 and occasionally even down to 0.1 s [50], 0.04 s [51] and 0.01 s [15,16,52,53]. Yet, the spatial coverage of many of these data sets has remained very confined due to the typically small number of pyranometers in simultaneous operation.…”

“…• the analysis of power spectra and non-linear characteristics of PV power and solar irradiance time series [16,17,32,35,42,43,48,[64][65][66][67][68], • the comparison of power fluctuations from specific PV plants with corresponding irradiance measurements [25,43,57,69], • the characterization of power variability as a function of PV plant size [13,25,36,44], • the assessment of the effects of partial shadowing on performance reduction from single modules to entire PV systems [51,[70][71][72], • the development of methods to infer irradiance and PV power estimates from whole sky images [47,[73][74][75][76][77][78][79][80][81], • the proposition of classification schemes and measures to quantify variability [25,38,39,[82][83][84][85][86][87], • the estimation of spatial smoothing, including correlation structures and decorrelation length scales of irradiance and PV power, as well as their increments…”