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.
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.
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).
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.
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.
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).