Cloud speed impact on solar variability scaling – Application to the wavelet variability model

Cloud Speed Impact on Solar Variability Scaling - Application to the Wavelet Variability Model Matthew Lave Jan Kleissl University of California, San Diego 9500 Gilman Dr. #0411 La Jolla, CA 92093 mlave@ucsd.edu jkleissl@ucsd.edu Abstract The wavelet variability model (WVM) for simulating solar photovoltaic (PV) powerplant output given a single irradiance sensor as input has been developed and validated previously. Central to the WVM method is a correlation scaling coefficient ( ) that calibrates the decay of correlation of the clear sky index as a function of distance and timescale, and which varies by day and geographic location. Previously, a local irradiance sensor network was required to derive . In this work, we determine from cloud speeds. Cloud simulator results indicated that the value is linearly proportional to the cloud speed ( ): . Cloud speeds from a numerical weather model (NWM) were then used to create a database of daily values for North America. For validation, the WVM was run to simulate a 48MW PV plant with both NWM values and with ground values found from a sensor network. Both WVM methods closely matched the distribution of ramp rates (RRs) of measured power, and were a strong improvement over linearly scaling up a point sensor. The incremental error in using NWM values over ground values was small. The ability to use NWM-derived values means that the WVM can be used to simulate a PV plant anywhere a single high- frequency irradiance sensor exits. This can greatly assist in module siting, plant sizing, and storage decisions for prospective PV plants. 1. Introduction The variable nature of power produced by PV power plants can be of concern to electric operators. For example, the Puerto Rico Electric Power Authority (PREPA) requires that utility-scale PV plants in Puerto Rico limit ramps (both up and down) to 10% of capacity per minute (PREPA). At short timescales such as 1-minute, the variability of solar PV power production is mostly caused by the movement of clouds across the PV plant. While a single PV module can produce highly variable output due to the instantaneous crossing of cloud edges, geographic diversity of modules within a PV plant will lead to smoothing of the total power output. Geographic diversity can be quantified through the correlation coefficients between the timeseries of power output of different PV modules within the plant. This correlation generally decreases with distance and increases with fluctuation timescale. Irradiance and power measurements have been used to quantify the relative reduction in aggregate variability for a combination of sites. Sites a few to hundreds of kilometers apart were shown to lead to a smoothed aggregate output and the amount of smoothing varied based on the distances between sites and local meteorological conditions (Curtright and Apt, 2008, Lave and Kleissl, 2010, Otani, et al., 1997, Wiemken, et al., 2001). Other investigators (Mills and Wiser, 2010, Perez, et al., 2011, Perez, et al., 2012) calculated the correlation of irradiance fluctuations between sites and found decorrelation distances – the distances over which sites become independent of one another – to vary based on fluctuation timescale and distance between sites. Accounting for cloud speed further enhanced the accuracy of these correlation models (Hoff and Perez, 2012). Correlation was also shown to depend on orientation relative to the direction of cloud motion (Hinkelman, et al., 2011).