High-frequency irradiance fluctuations and geographic smoothing

High-frequency irradiance fluctuations and geographic smoothing Matthew Lave 1 , Jan Kleissl 1 , Ery Arias-Castro 2 Dept. of Mechanical & Aerospace Eng., University of California, San Diego Dept. of Mathematics, University of California, San Diego Abstract Using six San Diego solar resource stations, clear-sky indices at 1-sec resolution were computed for one site and for the average of six sites separated by less than 3 km to estimate the smoothing of aggregated power output due to geographic dispersion in a distribution feeder. Ramp rate (RR) analysis was conducted on the 1-sec timeseries, including moving averages to simulate a large PV plant with energy storage. Annual maximum RRs of up to 60% per second were observed, and the largest 1-sec ramp rates were enhanced over 40% by cloud reflection. However, 5% per second ramps never occurred for a simulated 10 MW power plant. Applying a wavelet transform to both the clear-sky index at one site and the average of six sites showed a strong reduction in variability at timescales shorter than 5-min, with a lesser decrease at longer timescales. Comparing these variability reductions to the Hoff and Perez (2010) model, good agreement was observed at high dispersion factors (short timescales), but our analysis shows larger reductions in variability than the model at smaller dispersion factors (long timescales). 1. Introduction The variable nature of solar radiation is a concern in realizing high penetrations of solar photovoltaics (PV) into an electric grid. High frequency fluctuations of irradiance caused by fast moving clouds can lead to unpredictable variations in power output on short timescales. Short-term irradiance fluctuations can cause voltage flicker and voltage fluctuations that can trigger automated line equipment (e.g. tap changers) on distribution feeders leading to larger maintenance costs for utilities. Given constant load, counteracting such fluctuations would require dynamic inverter VAR control or a secondary power source (e.g. energy storage) that could ramp up or down at high frequencies to provide load following services. Such ancillary services are costly to operate, so reducing short-term variation is essential. Longer scale variations caused by cloud groups or weather fronts are also problematic as they lead to a large reduction in power generation over a large area. These long-term fluctuations are easier to forecast and can be mitigated by slower ramping (but larger) supplementary power sources, but the ramping and scheduling of power plants also adds costs to the operation of the electric grid. Grid operators are often concerned with worst-case scenarios, and it is important to understand the behavior of PV power output fluctuations over various timescales. Many previous studies have shown the benefit of high-frequency irradiance data. Suehrcke and McCormick (1989) and Gansler et al. (1995) found 1-min data to have different statistics from lower- frequency data, including a much more bi-modal distribution than 1-hour or 1-day data. Gansler et al. (1995) mention that while using 1-hour data may be acceptable for space and water heating systems, where the thermal capacitance effects dampen out short-term variations, the time response of PV systems is much faster and using 1-hour data will likely lead to errors. Understanding that high-frequency fluctuations are important, further studies have looked to characterize these fluctuations, often by comparing fluctuations at one site to fluctuations at the average of multiple sites. Otani et al. (1997) use a fluctuation factor defined as the root mean squared (RMS) value of a high-pass filtered 1-min time series of solar irradiance to demonstrate a 2-5 times reduction in