Publications

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This report provides performance data and analysis for two Stion copper indium gallium selenide (CIGS) module types, one framed, the other frameless, and installed at the New Mexico, Florida and Vermont RTCs. Sandia looked at data from both module types and compared the latter with data from an adjacent monocrystalline baseline array at each RTC. The results indicate that the Stion modules are slightly outperforming their rated power, with efficiency values above 100% of rated power, at 25degC cell temperatures. In addition, Sandia sees no significant performance differences between module types, which is expected because the modules differ only in their framing. In contrast to the baseline systems, the Stion strings showed increasing efficiency with increasing irradiance, with the greatest increase between zero and 400 Wm -2 but still noticeable increases at 1000 Wm -2 . Although baseline data availability in Vermont was spotty and therefore comparative trends are difficult to discern, the Stion modules there may offer snow- shedding advantages over monocrystalline-silicon modules but these findings are preliminary.

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High-frequency solar variability is an important to grid integration studies, but ground measurements are scarce. The high resolution irradiance algorithm (HRIA) has the ability to produce 4-sceond resolution global horizontal irradiance (GHI) samples, at locations across North America. However, the HRIA has not been extensively validated. In this work, we evaluate the HRIA against a database of 10 high-frequency ground-based measurements of irradiance. The evaluation focuses on variability-based metrics. This results in a greater understanding of the errors in the HRIA as well as suggestions for improvement to the HRIA.

As PV and wind power penetrations in utility balancing areas increase, it is important to understand how they will impact net load. We investigate daily and seasonal trends in solar power generation, wind power generation, and net load. Quantitative metrics are used to compare scenarios with no PV or wind, PV plus wind, only PV, or only wind. PV plus wind scenarios are found to have a larger reduction in maximum net load and smaller ranges between maximum and minimum load than PV only or wind only scenarios, showing that PV plus wind can be a beneficial combination.

Short circuit current (Isc) depends on the effective irradiance incident upon a PV module. Effective irradiance is highly correlated with broadband irradiance, but can vary slightly as the spectral content of the incident light changes. We explore using a few spectral wavelengths with broadband irradiance to predict Isc for ten modules of varying technologies (silicon, CIGS, CdTe). The goal is to identify a few spectral wavelengths that could be easily (and economically) measured to improve PV performance modeling.

Accurately representing the local solar variability at distribution timescales (30-seconds and shorter) is essential to modeling the impact of solar photovoltaics (PV) on distribution feeders. Previous works have examined variability at single locations, but this may not be useful to an operator whose distribution feeder is in a different climate region. In this work, we compare high-frequency variability from 8 locations in the United States. We define a variability metric for quantifying variability and use this metric to quantify and compare the variability at each of the 8 locations. We also explore the relationship between high-frequency and low-frequency (hourly) variability to see if widely-available low-frequency data (e.g., satellite data) may be used to determine variability climate zones. The end goal is to provide high-frequency solar inputs with climatologically representative solar variability for use in distribution studies.

To address the lack of knowledge of local solar variability, we have developed and deployed a low-cost solar variability datalogger (SVD). While most currently used solar irradiance sensors are expensive pyranometers with high accuracy (relevant for annual energy estimates), low-cost sensors display similar precision (relevant for solar variability) as high-cost pyranometers, even if they are not as accurate. In this work, we present evaluation of various low-cost irradiance sensor types, describe the SVD, and present validation and comparison of the SVD collected data. The low cost and ease of use of the SVD will enable a greater understanding of local solar variability, which will reduce developer and utility uncertainty about the impact of solar photovoltaic (PV) installations and thus will encourage greater penetrations of solar energy.

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A 9.6 kW test array of Prism bifacial modules and reference monofacial modules installed in February 2016 at the New Mexico Regional Test Center has produced six months of performance data. The data reveal that the Prism modules are out-performing the monofacial modules, with bifacial gains in energy over the six-month period ranging from 18% to 136%, depending on the orientation and ground albedo. These measured bifacial gains were found to be in good agreement with modeled bifacial gains using equations previously published by Prism. The most dramatic increase in performance was seen among the vertically tilted, west-facing modules, where the bifacial modules produced more than double the energy of monofacial modules and more energy than monofacial modules at any orientation. Because peak energy generation (mid-morning and mid-afternoon) for these bifacial modules may best match load on the electric grid, the west-facing orientation may be more economically desirable than traditional south-facing module orientations (which peak at solar noon).

This data shows the PV power by neighborhood for Oahu, Hawaii.

This data shows the PV power for all Oahu during the year 2014.

Distributed solar photovoltaic (PV) power generation in Oahu has grown rapidly since 2008. For applications such as determining the value of energy storage, it is important to have PV power output timeseries. Since these timeseries of not typically measured, here we produce simulated distributed PV power output for Oahu. Simulated power output is based on (a) satellite-derived solar irradiance, (b) PV permit data by neighborhood, and (c) population data by census block. Permit and population data was used to model locations of distributed PV, and irradiance data was then used to simulate power output. PV power output simulations are presented by sub-neighborhood polygons, neighborhoods, and for the whole island of Oahu. Summary plots of annual PV energy and a sample week timeseries of power output are shown, and a the files containing the entire timeseries are described.

This report contains the written footprint of a Sandia-hosted workshop held in Albuquerque, New Mexico, June 22-23, 2016 on “Complex Systems Models and Their Applications: Towards a New Science of Verification, Validation and Uncertainty Quantification,” as well as of pre-work that fed into the workshop. The workshop’s intent was to explore and begin articulating research opportunities at the intersection between two important Sandia communities: the complex systems (CS) modeling community, and the verification, validation and uncertainty quantification (VVUQ) community The overarching research opportunity (and challenge) that we ultimately hope to address is: how can we quantify the credibility of knowledge gained from complex systems models, knowledge that is often incomplete and interim, but will nonetheless be used, sometimes in real-time, by decision makers?

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The research presented in this report compares several real - time control strategies for the power output of a large number of PV distributed throughout a large distribution feeder circuit. Both real and reactive power controls are considered with the goal of minimizing network over - voltage violations caused by large amounts of PV generation. Several control strategies are considered under various assumptions regarding the existence and latency of a communication network. The control parameters are adjusted to maximize the effectiveness of each control. The controls are then compared based on their ability to achieve multiple objectiv es. These objectives include minimizing the total number of voltage violations , minimizing the total amount of PV energy curtailed or reactive power generated, and maximizing the fairness of any control action among all PV systems . The controls are simulat ed on the OpenDSS platform using time series load and spatially - distributed irradiance data.

In this project, an integrated solution to measuring and collecting solar variability data called the solar variability datalogger (SVD) was developed, tested, and the value of its data to distribution grid integration studies was demonstrated. This work addressed the problem that high-frequency solar variability is rarely measured – due to the high cost and complex installation of existing solar irradiance measuring pyranometers – but is critical to the accurate determination of the impact of photovoltaics to electric grid operation. For example, up to a 300% difference in distribution grid voltage regulator tap change operations (a measure of the impact of PV) [1] has been observed due solely to different solar variability profiles.

To address the lack of knowledge of local solar variability, we have developed, deployed, and demonstrated the value of data collected from a low-cost solar variability sensor. While most currently used solar irradiance sensors are expensive pyranometers with high accuracy (relevant for annual energy estimates), low-cost sensors display similar precision (relevant for solar variability) as high-cost pyranometers, even if they are not as accurate. In this work, we list variability sensor requirements, describe testing of various low-cost sensor components, present a validation of an alpha prototype, and show how the variability sensor collected data can be used for grid integration studies. The variability sensor will enable a greater understanding of local solar variability, which will reduce developer and utility uncertainty about the impact of solar photovoltaic installations and thus will encourage greater penetrations of solar energy.

Since solar PV power generation is growing rapidly, it is important to accurately model solar power production in renewable generation integration studies which look at the impact of variable renewable generation on electric grid operations. However, solar irradiance or power measurements are often sparse both spatially and temporally, making it difficult to simulate PV power output. Here, we describe the technique used to simulate generation from up to 40 utility-scale PV plants and 9 areas of distributed PV in the state of New Mexico given only five hourly irradiance measurements plus a sixth irradiance measurement at 1-minute resolution that was used as a lookup library. Spatial smoothing based on the plant size was applied, then this area-average irradiance was converted to PV power output using irradiance to power models. In this way, PV power output profiles for each location, and for the aggregate of all locations, were produced and supplied to the integration study. Also for use in the study, day-ahead solar power output forecasts were created by adding errors representative of the current state of solar forecasting to the actual power output values.

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Increasing the penetration of distributed renewable sources, including photovoltaic (PV) sources, poses technical challenges for grid management. The grid has been optimized over decades to rely upon large centralized power plants with well-established feedback controls, but now non-dispatchable, renewable sources are displacing these controllable generators. This one-year study was funded by the Department of Energy (DOE) SunShot program and is intended to better utilize those variable resources by providing electric utilities with the tools to implement frequency regulation and primary frequency reserves using aggregated renewable resources, known as a virtual power plant. The goal is to eventually enable the integration of 100s of Gigawatts into US power systems.

Increasing number s of PV on distribution systems are creating more grid impacts , but it also provides more opportunities for measurement, sensing, and control of the grid in a distributed fashion. This report demonstrates three software tools for characterizing and controlling distribution feeders by utilizing large numbers of highly distributed current, voltage , and irradiance sensors. Instructions and a user manual is presented for each tool. First, the tool for distribution system secondary circuit parameter estimation is presented. This tool allows studying distribution system parameter estimation accuracy with user-selected active power, reactive power, and voltage measurements and measurement error levels. Second, the tool for multi-objective inverter control is shown. Various PV inverter control strategies can be selected to objectively compare their impact on the feeder. Third, the tool for energy storage for PV ramp rate smoothing is presented. The tool allows the user to select different storage characteristics (power and energy ratings) and control types (local vs. centralized) to study the tradeoffs between state-of-charge (SOC) management and the amount of ramp rate smoothing.

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We report an evaluation of the accuracy of combinations of models that estimate plane-of-array (POA) irradiance from measured global horizontal irradiance (GHI). This estimation involves two steps: 1) decomposition of GHI into direct and diffuse horizontal components and 2) transposition of direct and diffuse horizontal irradiance (DHI) to POA irradiance. Measured GHI and coincident measured POA irradiance from a variety of climates within the United States were used to evaluate combinations of decomposition and transposition models. A few locations also had DHI measurements, allowing for decoupled analysis of either the decomposition or the transposition models alone. Results suggest that decomposition models had mean bias differences (modeled versus measured) that vary with climate. Transposition model mean bias differences depended more on the model than the location. When only GHI measurements were available and combinations of decomposition and transposition models were considered, the smallest mean bias differences were typically found for combinations which included the Hay/Davies transposition model.

Accurately representing the local solar variability at timescales relevant to distribution grid operations (30-s and shorter) is essential to modeling the impact of solar photovoltaics (PV) on distribution feeders. Due to a lack of available high-frequency solar data, some distribution grid studies have used synthetically-created PV variability or measured PV variability from a different location than their study location. In this work, we show the importance of using accurate solar PV variability inputs in distribution studies. Using high-frequency solar irradiance data from 10 locations in the United States, we compare the ramp rate distributions at the different locations, use a quantitative metric to describe the solar variability at each location, and run distribution simulations using representative 1-week samples from each location to demonstrate the impact of locational solar variability on the number of voltage regulator tap change operations. Results show more than a factor of 3 difference in the number of tap change operations between different PV power variability samples based on irradiance from the different locations. Errors in simulated number of tap changes of up to -70% were found when using low-frequency (e.g., 15-min) solar variability.

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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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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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