Publications

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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. 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.

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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.

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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 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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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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During the development of a solar photovoltaic (PV) energy project, predicting expected energy production from a system is a key part of understanding system value. System energy production is a function of the system design and location, the mounting configuration, the power conversion system, and the module technology, as well as the solar resource. Even if all other variables are held constant, annual energy yield (kWh/kWp) will vary among module technologies because of differences in response to low-light levels and temperature. A number of PV system performance models have been developed and are in use, but little has been published on validation of these models or the accuracy and uncertainty of their output. With support from the U.S. Department of Energy's Solar Energy Technologies Program, Sandia National Laboratories organized a PV Performance Modeling Workshop in Albuquerque, New Mexico, September 22-23, 2010. The workshop was intended to address the current state of PV system models, develop a path forward for establishing best practices on PV system performance modeling, and set the stage for standardization of testing and validation procedures for models and input parameters. This report summarizes discussions and presentations from the workshop, as well as examines opportunities for collaborative efforts to develop objective comparisons between models and across sites and applications.

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Design and operation of the electric power grid (EPG) relies heavily on computational models. High-fidelity, full-order models are used to study transient phenomena on only a small part of the network. Reduced-order dynamic and power flow models are used when analysis involving thousands of nodes are required due to the computational demands when simulating large numbers of nodes. The level of complexity of the future EPG will dramatically increase due to large-scale deployment of variable renewable generation, active load and distributed generation resources, adaptive protection and control systems, and price-responsive demand. High-fidelity modeling of this future grid will require significant advances in coupled, multi-scale tools and their use on high performance computing (HPC) platforms. This LDRD report demonstrates SNL's capability to apply HPC resources to these 3 tasks: (1) High-fidelity, large-scale modeling of power system dynamics; (2) Statistical assessment of grid security via Monte-Carlo simulations of cyber attacks; and (3) Development of models to predict variability of solar resources at locations where little or no ground-based measurements are available.

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Photovoltaic systems are often priced in $/W{sub p}, where Wp refers to the DC power rating of the modules at Standard Test Conditions (1000 W/m{sup 2}, 25 C cell temperature) and $ refers to the installed cost of the system. However, the true value of the system is in the energy it will produce in kWhs, not the power rating. System energy production is a function of the system design and location, the mounting configuration, the power conversion system, and the module technology, as well as the solar resource. Even if all other variables are held constant, the annual energy yield (kWh/kW{sup p}) will vary among module technologies because of differences in response to low-light levels and temperature. Understanding energy yield is a key part of understanding system value. System performance models are used during project development to estimate the expected output of PV systems for a given design and location. Performance modeling is normally done by the system designer/system integrator. Often, an independent engineer will also model system output during a due diligence review of a project. A variety of system performance models are available. The most commonly used modeling tool for project development and due diligence in the United States is probably PVsyst, while those seeking a quick answer to expected energy production may use PVWatts. In this paper, we examine the variation in predicted energy output among modeling tools and users and compare that to measured output.

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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.

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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.

Most system performance models assume a point measurement for irradiance and that, except for the impact of shading from nearby obstacles, incident irradiance is uniform across the array. Module temperature is also assumed to be uniform across the array. For small arrays and hourly-averaged simulations, this may be a reasonable assumption. Stein is conducting research to characterize variability in large systems and to develop models that can better accommodate large system factors. In large, multi-MW arrays, passing clouds may block sunlight from a portion of the array but never affect another portion. Figure 22 shows that two irradiance measurements at opposite ends of a multi-MW PV plant appear to have similar irradiance (left), but in fact the irradiance is not always the same (right). Module temperature may also vary across the array, with modules on the edges being cooler because they have greater wind exposure. Large arrays will also have long wire runs and will be subject to associated losses. Soiling patterns may also vary, with modules closer to the source of soiling, such as an agricultural field, receiving more dust load. One of the primary concerns associated with this effort is how to work with integrators to gain access to better and more comprehensive data for model development and validation.

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This is a blind modeling study to illustrate the variability expected between PV performance model results. Objectives are to answer: (1) What is the modeling uncertainty; (2) Do certain models do better than others; (3) How can performance modeling be improved; and (4) What are the sources of uncertainty? Some preliminary conclusions are: (1) Large variation seen in model results; (2) Variation not entirely consistent across systems; (3) Uncertainty in assigning derates; (4) Discomfort when components are not included in database - Is there comfort when the components are in the database?; and (5) Residual analysis will help to uncover additional patterns in the models.

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A reliability and availability model has been developed for a portion of the 4.6 megawatt (MWdc) photovoltaic system operated by Tucson Electric Power (TEP) at Springerville, Arizona using a commercially available software tool, GoldSim{trademark}. This reliability model has been populated with life distributions and repair distributions derived from data accumulated during five years of operation of this system. This reliability and availability model was incorporated into another model that simulated daily and seasonal solar irradiance and photovoltaic module performance. The resulting combined model allows prediction of kilowatt hour (kWh) energy output of the system based on availability of components of the system, solar irradiance, and module and inverter performance. This model was then used to study the sensitivity of energy output as a function of photovoltaic (PV) module degradation at different rates and the effect of location (solar irradiance). Plots of cumulative energy output versus time for a 30 year period are provided for each of these cases.

The island of Lanai is currently one of the highest penetration PV micro grids in the world, with the 1.2 MWAC La Ola Solar Farm operating on a grid with a peak net load of 4.7 MW. This facility interconnects to one of Lanai's three 12.47 kV distribution circuits. An initial interconnection requirements study (IRS) determined that several control and performance features are necessary to ensure safe and reliable operation of the island grid. These include power curtailment, power factor control, over/under voltage and frequency ride through, and power ramp rate limiting. While deemed necessary for stable grid operation, many of these features contradict the current IEEE 1547 interconnection requirements governing distributed generators. These controls have been successfully implemented, tested, and operated since January 2009. Currently, the system is producing power in a curtailed mode according to the requirements of a power purchase agreement (PPA).

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Photovoltaic (PV) system performance models are relied upon to provide accurate predictions of energy production for proposed and existing PV systems under a wide variety of environmental conditions. Ground based meteorological measurements are only available from a relatively small number of locations. In contrast, satellite-based radiation and weather data (e.g., SUNY database) are becoming increasingly available for most locations in North America, Europe, and Asia on a 10 x 10 km grid or better. This paper presents a study of how PV performance model results are affected when satellite-based weather data is used in place of ground-based measurements.

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Four approaches to modeling multi-junction concentrating photovoltaic system performance are assessed by comparing modeled performance to measured performance. Measured weather, irradiance, and system performance data were collected on two systems over a one month period. Residual analysis is used to assess the models and to identify opportunities for model improvement. Large photovoltaic systems are typically developed as projects which supply electricity to a utility and are owned by independent power producers. Obtaining financing at favorable rates and attracting investors requires confidence in the projected energy yield from the plant. In this paper, various performance models for projecting annual energy yield from Concentrating Photovoltaic (CPV) systems are assessed by comparing measured system output to model predictions based on measured weather and irradiance data. The results are statistically analyzed to identify systematic error sources.

Four approaches to modeling multi-junction concentrating photovoltaic system performance are assessed by comparing modeled performance to measured performance. Measured weather, irradiance, and system performance data were collected on two systems over a one month period. Residual analysis is used to assess the models and to identify opportunities for model improvement.

This report documents the various photovoltaic (PV) performance models and software developed and utilized by researchers at Sandia National Laboratories (SNL) in support of the Photovoltaics and Grid Integration Department. In addition to PV performance models, hybrid system and battery storage models are discussed. A hybrid system using other distributed sources and energy storage can help reduce the variability inherent in PV generation, and due to the complexity of combining multiple generation sources and system loads, these models are invaluable for system design and optimization. Energy storage plays an important role in reducing PV intermittency and battery storage models are used to understand the best configurations and technologies to store PV generated electricity. Other researcher's models used by SNL are discussed including some widely known models that incorporate algorithms developed at SNL. There are other models included in the discussion that are not used by or were not adopted from SNL research but may provide some benefit to researchers working on PV array performance, hybrid system models and energy storage. The paper is organized into three sections to describe the different software models as applied to photovoltaic performance, hybrid systems, and battery storage. For each model, there is a description which includes where to find the model, whether it is currently maintained and any references that may be available. Modeling improvements underway at SNL include quantifying the uncertainty of individual system components, the overall uncertainty in modeled vs. measured results and modeling large PV systems. SNL is also conducting research into the overall reliability of PV systems.

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Preliminary evaluation of deep borehole disposal of high-level radioactive waste and spent nuclear fuel indicates the potential for excellent long-term safety performance at costs competitive with mined repositories. Significant fluid flow through basement rock is prevented, in part, by low permeabilities, poorly connected transport pathways, and overburden self-sealing. Deep fluids also resist vertical movement because they are density stratified. Thermal hydrologic calculations estimate the thermal pulse from emplaced waste to be small (less than 20 C at 10 meters from the borehole, for less than a few hundred years), and to result in maximum total vertical fluid movement of {approx}100 m. Reducing conditions will sharply limit solubilities of most dose-critical radionuclides at depth, and high ionic strengths of deep fluids will prevent colloidal transport. For the bounding analysis of this report, waste is envisioned to be emplaced as fuel assemblies stacked inside drill casing that are lowered, and emplaced using off-the-shelf oilfield and geothermal drilling techniques, into the lower 1-2 km portion of a vertical borehole {approx}45 cm in diameter and 3-5 km deep, followed by borehole sealing. Deep borehole disposal of radioactive waste in the United States would require modifications to the Nuclear Waste Policy Act and to applicable regulatory standards for long-term performance set by the US Environmental Protection Agency (40 CFR part 191) and US Nuclear Regulatory Commission (10 CFR part 60). The performance analysis described here is based on the assumption that long-term standards for deep borehole disposal would be identical in the key regards to those prescribed for existing repositories (40 CFR part 197 and 10 CFR part 63).

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Military test and training ranges operate with live-fire engagements to provide realism important to the maintenance of key tactical skills. Ordnance detonations during these operations typically produce minute residues of parent explosive chemical compounds. Occasional low-order detonations also disperse solid-phase energetic material onto the surface soil. These detonation remnants are implicated in chemical contamination impacts to groundwater on a limited set of ranges where environmental characterization projects have occurred. Key questions arise regarding how these residues and the environmental conditions (e.g., weather and geostratigraphy) contribute to groundwater pollution. This final report documents the results of experimental and simulation model development for evaluating mass transfer processes from solid-phase energetics to soil-pore water.

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The Bryan Mound salt dome, located near Freeport, Texas, is home to one of four underground crude oil-storage facilities managed by the U. S. Department of Energy Strategic Petroleum Reserve (SPR) Program. Sandia National Laboratories, as the geotechnical advisor to the SPR, conducts site-characterization investigations and other longer-term geotechnical and engineering studies in support of the program. This report describes the conversion of two-dimensional geologic interpretations of the Bryan Mound site into three-dimensional geologic models. The new models include the geometry of the salt dome, the surrounding sedimentary units, mapped faults, and the 20 oil-storage caverns at the site. This work provides an internally consistent geologic model of the Bryan Mound site that can be used in support of future work.

Military test and training ranges operate with live fire engagements to provide realism important to the maintenance of key tactical skills. Ordnance detonations during these operations typically produce minute residues of parent explosive chemical compounds. Occasional low order detonations also disperse solid phase energetic material onto the surface soil. These detonation remnants are implicated in chemical contamination impacts to groundwater on a limited set of ranges where environmental characterization projects have occurred. Key questions arise regarding how these residues and the environmental conditions (e.g., weather and geostratigraphy) contribute to groundwater pollution impacts. This report documents interim results of a mass transfer model evaluating mass transfer processes from solid phase energetics to soil pore water based on experimental work obtained earlier in this project. This mass transfer numerical model has been incorporated into the porous media simulation code T2TNT. Next year, the energetic material mass transfer model will be developed further using additional experimental data.

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The West Hackberry salt dome, in southwestern Louisiana, is one of four underground oil-storage facilities managed by the U. S. Department of Energy Strategic Petroleum Reserve (SPR) Program. Sandia National Laboratories, as the geotechnical advisor to the SPR, conducts site-characterization investigations and other longer-term geotechnical and engineering studies in support of the program. This report describes the conversion of two-dimensional geologic interpretations of the West Hackberry site into three-dimensional geologic models. The new models include the geometry of the salt dome, the surrounding sedimentary layers, mapped faults, and a portion of the oil storage caverns at the site. This work provides a realistic and internally consistent geologic model of the West Hackberry site that can be used in support of future work.

The geologic model implicit in the original site characterization report for the Bayou Choctaw Strategic Petroleum Reserve Site near Baton Rouge, Louisiana, has been converted to a numerical, computer-based three-dimensional model. The original site characterization model was successfully converted with minimal modifications and use of new information. The geometries of the salt diapir, selected adjacent sedimentary horizons, and a number of faults have been modeled. Models of a partial set of the several storage caverns that have been solution-mined within the salt mass are also included. Collectively, the converted model appears to be a relatively realistic representation of the geology of the Bayou Choctaw site as known from existing data. A small number of geometric inconsistencies and other problems inherent in 2-D vs. 3-D modeling have been noted. Most of the major inconsistencies involve faults inferred from drill hole data only. Modem computer software allows visualization of the resulting site model and its component submodels with a degree of detail and flexibility that was not possible with conventional, two-dimensional and paper-based geologic maps and cross sections. The enhanced visualizations may be of particular value in conveying geologic concepts involved in the Bayou Choctaw Strategic Petroleum Reserve site to a lay audience. A Microsoft WindowsTM PC-based viewer and user-manipulable model files illustrating selected features of the converted model are included in this report.

[1] We used a two-dimensional coupled heat and fluid flow model to investigate large-scale, lateral heat and fluid transport on the eastern flank of the Juan de Fuca Ridge. Cool seawater in the natural system is inferred to enter basement where it is exposed close to the spreading center and flow laterally beneath thick sediments, causing seafloor heat flow to be depressed relative to that input at the base of the plate. The flow rate, and thus the properties of permeable basement (the flow layer), controls the efficiency of lateral heat transport, as quantified through numerical modeling. We simulated forced flow in this layer by pumping water through at a fixed rate and quantified relations between flow rate, thickness of the permeable basement, and the extent of suppression of seafloor heat flow. Free flow simulations, in which fluid flow was not forced, match heat flow constraints if nonhydrostatic initial conditions are used and flow layer permeabilities are set to the high end of observed values (10-11 to 10-9 m2). To match seafloor heat flow observations, the models required lateral specific discharge of 1.2 to 40 m/yr for flow layer thicknesses of 600 to 100 m, respectively. The models also replicate differences in fluid pressures in basement, and the local distribution of pressures above and below hydrostatic. Estimated lateral flow rates are 10× to 1000× greater than estimates based on radiocarbon ages of basement pore waters. Estimated lateral flow rates based on thermal and chemical constraints can be reconciled if diffusion from discrete flow zones into less permeable stagnant zones in the crust is considered. © 2003 by the American Geophysical Union.

The Big Hill salt dome, located in southeastern Texas, is home to one of four underground oil-storage facilities managed by the U. S. Department of Energy Strategic Petroleum Reserve (SPR) Program. Sandia National Laboratories, as the geotechnical advisor to the SPR, conducts site-characterization investigations and other longer-term geotechnical and engineering studies in support of the program. This report describes the conversion of two-dimensional geologic interpretations of the Big Hill site into three-dimensional geologic models. The new models include the geometry of the salt dome, the surrounding sedimentary units, mapped faults, and the 14 oil storage caverns at the site. This work provides a realistic and internally consistent geologic model of the Big Hill site that can be used in support of future work.

Existing paper-based site characterization models of salt domes at the four active U.S. Strategic Petroleum Reserve sites have been converted to digital format and visualized using modern computer software. The four sites are the Bayou Choctaw dome in Iberville Parish, Louisiana; the Big Hill dome in Jefferson County, Texas; the Bryan Mound dome in Brazoria County, Texas; and the West Hackberry dome in Cameron Parish, Louisiana. A new modeling algorithm has been developed to overcome limitations of many standard geological modeling software packages in order to deal with structurally overhanging salt margins that are typical of many salt domes. This algorithm, and the implementing computer program, make use of the existing interpretive modeling conducted manually using professional geological judgement and presented in two dimensions in the original site characterization reports as structure contour maps on the top of salt. The algorithm makes use of concepts of finite-element meshes of general engineering usage. Although the specific implementation of the algorithm described in this report and the resulting output files are tailored to the modeling and visualization software used to construct the figures contained herein, the algorithm itself is generic and other implementations and output formats are possible. The graphical visualizations of the salt domes at the four Strategic Petroleum Reserve sites are believed to be major improvements over the previously available two-dimensional representations of the domes via conventional geologic drawings (cross sections and contour maps). Additionally, the numerical mesh files produced by this modeling activity are available for import into and display by other software routines. The mesh data are not explicitly tabulated in this report; however an electronic version in simple ASCII format is included on a PC-based compact disk.