Publications

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Sandia National Laboratories has developed a modeling approach to simulate time-synchronized, 1-minute power output from large PV plants in locations where only hourly irradiance measurements are available via satellite sources. The approach uses 1-min irradiance measurements from analogue sites in a similar geographic area. PV output datasets generated for 2007 in southern Nevada are being used for a Solar PV Grid Integration Study to estimate the integration costs associated with various utility-scale PV generation levels. Plant designs considered include both fixed-tilt thin-film, and singleaxis- tracked polycrystalline Si systems ranging in size from 5 to 300 MWAC. Simulated power output profiles at 1-min intervals were generated for five scenarios (149.5 MW, 222 WM, 292 MW, 492 MW, and 892 MW) each comprising as many as 10 geographically separated PV plants. Copyright© (2011) by the American Solar Energy Society.

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This report describes in-depth analysis of photovoltaic (PV) output variability in a high-penetration residential PV installation in the Pal Town neighborhood of Ota City, Japan. Pal Town is a unique test bed of high-penetration PV deployment. A total of 553 homes (approximately 80% of the neighborhood) have grid-connected PV totaling over 2 MW, and all are on a common distribution line. Power output at each house and irradiance at several locations were measured once per second in 2006 and 2007. Analysis of the Ota City data allowed for detailed characterization of distributed PV output variability and a better understanding of how variability scales spatially and temporally. For a highly variable test day, extreme power ramp rates (defined as the 99th percentile) were found to initially decrease with an increase in the number of houses at all timescales, but the reduction became negligible after a certain number of houses. Wavelet analysis resolved the variability reduction due to geographic diversity at various timescales, and the effect of geographic smoothing was found to be much more significant at shorter timescales.

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The 1.2-MW La Ola photovoltaic (PV) power plant in Lanai, Hawaii, has been in operation since December 2009. The host system is a small island microgrid with peak load of 5 MW. Simulations conducted as part of the interconnection study concluded that unmitigated PV output ramps had the potential to negatively affect system frequency. Based on that study, the PV system was initially allowed to operate with output power limited to 50% of nameplate to reduce the potential for frequency instability due to PV variability. Based on the analysis of historical voltage, frequency, and power output data at 50% output level, the PV system has not significantly affected grid performance. However, it should be noted that the impact of PV variability on active and reactive power output of the nearby diesel generators was not evaluated. In summer 2011, an energy storage system was installed to counteract high ramp rates and allow the PV system to operate at rated output. The energy storage system was not fully operational at the time this report was written; therefore, analysis results do not address system performance with the battery system in place.

We present an approach to simulate time-synchronized, one-minute power output from large photovoltaic (PV) generation plants in locations where only hourly irradiance estimates are available from satellite sources. The approach uses one-minute irradiance measurements from ground sensors in a climatically and geographically similar area. Irradiance is translated to power using the Sandia Array Performance Model. Power output is generated for 2007 in southern Nevada are being used for a Solar PV Grid Integration Study to estimate the integration costs associated with various utility-scale PV generation levels. Plant designs considered include both fixed-tilt thin-film, and single-axis-tracked polycrystalline Si systems ranging in size from 5 to 300 MW{sub AC}. Simulated power output profiles at one-minute intervals were generated for five scenarios defined by total PV capacity (149.5 MW, 222 WM, 292 MW, 492 MW, and 892 MW) each comprising as many as 10 geographically separated PV plants.

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This report investigates the effects that increased distributed photovoltaic (PV) generation would have on the Kauai Island Utility Co-op (KIUC) system operating requirements. The study focused on determining reserve requirements needed to mitigate the impact of PV variability on system frequency, and the impact on operating costs. Scenarios of 5-MW, 10-MW, and 15-MW nameplate capacity of PV generation plants distributed across the Kauai Island were considered in this study. The analysis required synthesis of the PV solar resource data and modeling of the KIUC system inertia. Based on the results, some findings and conclusions could be drawn, including that the selection of units identified as marginal resources that are used for load following will change; PV penetration will displace energy generated by existing conventional units, thus reducing overall fuel consumption; PV penetration at any deployment level is not likely to reduce system peak load; and increasing PV penetration has little effect on load-following reserves. The study was performed by EnerNex under contract from Sandia National Laboratories with cooperation from KIUC.

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High frequency irradiance variability measured on the ground is caused by the formation, dissipation, and passage of clouds in the sky. If we can identify and associate different cloud types/patterns from satellite imagery, we may be able to predict irradiance variability in areas lacking sensors. With satellite imagery covering the entire U.S., this allows for more accurate integration planning and power flow modeling over wide areas. Satellite imagery from southern Nevada was analyzed at 15 minute intervals over a year. Methods for image stabilization, cloud detection, and textural classification of clouds were developed and tested. High Performance Computing parallel processing algorithms were also investigated and tested. Artificial Neural Networks using imagery as inputs were trained on ground-based measurements of irradiance to model the variability and were tested to show some promise as a means for predicting irradiance variability.

We propose and examine several statistical criteria for characterizing time series of solar irradiance. Time series of irradiance are used in analyses that seek to quantify the performance of photovoltaic (PV) power systems over time. Time series of irradiance are either measured or are simulated using models. Simulations of irradiance are often calibrated to or generated from statistics for observed irradiance and simulations are validated by comparing the simulation output to the observed irradiance. Criteria used in this comparison should derive from the context of the analyses in which the simulated irradiance is to be used. We examine three statistics that characterize time series and their use as criteria for comparing time series. We demonstrate these statistics using observed irradiance data recorded in August 2007 in Las Vegas, Nevada, and in June 2009 in Albuquerque, New Mexico.

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