Publications

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It is important to be able to accurately simulate the variability of solar PV power plants for grid integration studies. We aim to inform integration studies of the ease of implementation and application-specific accuracy of current PV power plant output simulation methods. This report reviews methods for producing simulated high-resolution (sub-hour or even sub-minute) PV power plant output profiles for variability studies and describes their implementation. Two steps are involved in the simulations: estimation of average irradiance over the footprint of a PV plant and conversion of average irradiance to plant power output. Six models are described for simulating plant-average irradiance based on inputs of ground-measured irradiance, satellite-derived irradiance, or proxy plant measurements. The steps for converting plant-average irradiance to plant power output are detailed to understand the contributions to plant variability. A forthcoming report will quantify the accuracy of each method using application-specific validation metrics.

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Imagery from GOES satellites is analyzed to determine how solar variability is related to the NOAA classification of cloud type. Without using a model to convert satellite imagery to average insolation on the ground, this paper investigates using cloud categories to directly model the expected statistical variability of ground irradiance. Hourly cloud classified satellite images are compared to multiple years of ground measured irradiance at two locations to determine if measured irradiance, ramp rates, and variability index are correlated with cloud category. Novel results are presented for ramp rates grouped by the cloud category during the time period. This correlation between satellite cloud classification and solar variability could be used to model the solar variability for a given location and time and could be used to determine the variability of a location based on the prevalence of each cloud category.

Photovoltaic (PV) modules with attached microinverters are becoming increasingly popular in PV systems, especially in the residential system market, as such systems offer several benefits not found in PV systems utilizing central inverters. PV modules with fully integrated microinverters are emerging to fill a similar market space. These 'AC modules' absorb solar energy and produce AC energy without allowing access to the intermediate DC bus. Existing test procedures and performance models designed for separate DC and AC components are unusable when the inverter is integrated into the module. Sandia National Laboratories is developing a new set of test procedures and performance model designed for AC modules. © 2013 IEEE.

Module temperature is modeled using a transient heat-flow model. Module temperature predicted in this fashion is important in the calculation of cell temperature, a vital input in performance modeling. Parameters important to the model are tested for sensitivity, and optimized to a single day of measured module temperature using simultaneous non-linear least squares regression. These optimized parameters are then tested for accuracy using a year's worth of data for one location. The results obtained from this analysis are compared with modeled data from a different site, as well as to results obtained using a steadystate model. We find that the transient model best captures the variability in module temperature, and that the transient model works best when calibrated for a specific location.

Variability of photovoltaic (PV) power output is a potential concern to utilities because it can lead to voltage changes on the distribution system and have other adverse impacts on power quality unless additional equipment is added or operational practices are changed to mitigate these effects. This paper develops and evaluates a simple yet novel approach for quantifying irradiance variability over various timescales. The approach involves comparison between measured irradiance and a reference, clear sky irradiance, determined from a model. Conceptually, the "Variability Index" is the ratio of the "length" of the measured irradiance plotted against time divided by the "length" of the reference clear sky irradiance signal. Adjustments are proposed that correct for different measurement intervals. By evaluating the variability index at several sites, we show how annual and monthly distributions of this metric can help to classify sites and periods of time when variability is significant. Copyright © (2012) by American Solar Energy Society.

Module temperature is modeled using a transient heat-flow model. Module temperature predicted in this fashion is important in the calculation of cell temperature, a vital input in performance modeling. Parameters important to the model are tested for sensitivity, and optimized to a single day of measured module temperature using simultaneous non-linear least squares regression. These optimized parameters are then tested for accuracy using a year's worth of data for one location. The results obtained from this analysis are compared with modeled data from a different site, as well as to results obtained using a steadystate model. We find that the transient model best captures the variability in module temperature, and that the transient model works best when calibrated for a specific location.

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.

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.

Most photovoltaic (PV) performance models currently available are designed to use irradiance and weather data and predict PV system output using a module or array performance model and an inverter model. While these models can give accurate results, they do so for an idealized system. That is, a system that does not experience component failures or outages. We have developed the Photovoltaic Reliability and Performance Model (PV-RPM) to more accurately model these PV systems by including a reliability component that simulates failures and repairs of the components of the system, as well as allow for the disruption of the system by external events such as lightning or grid disturbances. In addition, a financial component has also been included to help assess the profitability of a PV system. In this report we provide some example analyses of three different PV system designs using the PV-RPM.

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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.

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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.

The Sandia Array Performance Model (SAPM) [1] describes the power performance of photovoltaic (PV) modules under variable irradiance and temperature conditions. Model parameters are estimated by regressions involving measured module voltage and current, module and air temperature, and solar irradiance. Measurements are made under test conditions chosen to isolate subsets of parameters and which improve the quality of the regression estimates. Uncertainty in model parameters results from uncertainty in each measurement as well as from the number of measurements. Uncertainty in model parameters can be propagated through the model to determine its effect on model output. In this paper we summarize the process for estimating uncertainty in model parameters for flat-plate, crystalline silicon (cSi) modules from measurements, present example results, and illustrate the effect of parameter uncertainty on model output. Finally, we comment on how analysis of parameter uncertainty can inform model developers about the presence and impacts of model uncertainty. © 2011 IEEE.

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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 99^th 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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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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