Publications

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

High frequency irradiance variability measured on the ground is caused by the formation, dissipation, and passage of clouds in the sky. Variability and ramp rates of PV systems are increasingly important to understand and model for grid stability as PV penetration levels rise. Using satellite imagery to identify cloud types and patterns can 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 modelling 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. Artificial Neural Networks, cloud texture analysis, and cloud type categorization can be used to model the irradiance and variability for a location at a one minute resolution without needing many ground based irradiance sensors. © 2012 IEEE.

Ota City, Japan and Alamosa, Colorado present contrasting cases of a small rooftop distributed PV plant versus a large central PV plant. We examine the effect of geographic smoothing on the power output of each plant. 1-second relative maximum ramp rates are found to be reduced 6-10 times for the total plant output versus a single point sensor, though smaller reductions are seen at longer timescales. The relative variability is found to decay exponentially at all timescales as additional houses or inverters are aggregated. The rate of decay depends on both the geographic diversity within the plant and the meteorological conditions (such as cloud speed) on a given day. The Wavelet Variability Model (WVM) takes into account these geographic smoothing effects to produce simulated PV powerplant output by using a point sensor as input. The WVM is tested against Ota City and Alamosa, and the WVM simulation closely matches the distribution of ramp rates of actual power output. © 2012 IEEE.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Clear sky models estimate the terrestrial solar radiation under a cloudless sky as a function of the solar elevation angle, site altitude, aerosol concentration, water vapor, and various atmospheric conditions. This report provides an overview of a number of global horizontal irradiance (GHI) clear sky models from very simple to complex. Validation of clear-sky models requires comparison of model results to measured irradiance during clear-sky periods. To facilitate validation, we present a new algorithm for automatically identifying clear-sky periods in a time series of GHI measurements. We evaluate the performance of selected clear-sky models using measured data from 30 different sites, totaling about 300 site-years of data. We analyze the variation of these errors across time and location. In terms of error averaged over all locations and times, we found that complex models that correctly account for all the atmospheric parameters are slightly more accurate than other models, but, primarily at low elevations, comparable accuracy can be obtained from some simpler models. However, simpler models often exhibit errors that vary with time of day and season, whereas the errors for complex models vary less over time.

Abstract not provided.

Sandia National Laboratories (Sandia) and SunPower Corporation (SunPower) have completed design and deployment of an autonomous irradiance monitoring system based on wireless mesh communications and a battery operated data acquisition system. The Lanai High-Density Irradiance Sensor Network is comprised of 24 LI-COR{reg_sign} irradiance sensors (silicon pyranometers) polled by 19 RF Radios. The system was implemented with commercially available hardware and custom developed LabVIEW applications. The network of solar irradiance sensors was installed in January 2010 around the periphery and within the 1.2 MW ac La Ola PV plant on the island of Lanai, Hawaii. Data acquired at 1 second intervals is transmitted over wireless links to be time-stamped and recorded on SunPower data servers at the site for later analysis. The intent is to study power and solar resource data sets to correlate the movement of cloud shadows across the PV array and its effect on power output of the PV plant. The irradiance data sets recorded will be used to study the shape, size and velocity of cloud shadows. This data, along with time-correlated PV array output data, will support the development and validation of a PV performance model that can predict the short-term output characteristics (ramp rates) of PV systems of different sizes and designs. This analysis could also be used by the La Ola system operator to predict power ramp events and support the function of the future battery system. This experience could be used to validate short-term output forecasting methodologies.