Clifford W. Hansen

Global horizontal irradiance clear sky models : implementation and analysis.

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.

Evaluation of Global Horizontal Irradiance to Plane-of-Array Irradiance Models at Locations Across the United States

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.

Modeling the Irradiance and Temperature Dependence of Photovoltaic Modules in PVsyst

In order to reliably simulate the energy yield of photovoltaic (PV) systems, it is necessary to have an accurate model of how the PV modules perform with respect to irradiance and cell temperature. Building on a previous study that addresses the irradiance dependence, two approaches to fit the temperature dependence of module power in PVsyst have been developed and are applied here to recent multi-irradiance and temperature data for a standard Yingli Solar PV module type. The results demonstrate that it is possible to match the measured irradiance and temperature dependence of PV modules in PVsyst. Improvements in energy yield prediction using the optimized models relative to the PVsyst standard model are considered significant for decisions about project financing.

Introduction to the open source PV LIB for python Photovoltaic system modelling package

The proper modeling of Photovoltaic(PV) systems is critical for their financing, design, and operation. PV_LIB provides a flexible toolbox to perform advanced data analysis and research into the performance modeling and operations of PV assets, and this paper presents the extension of the PV_LIB toolbox into the python programming language. PV_LIB provides a common repository for the release of published modeling algorithms, and thus can also help to improve the quality and frequency of model validation and inter comparison studies. Overall, the goal of PV_LIB is to accelerate the pace of innovation in the PV sector.

Simulation of one-minute power output from utility-scale photovoltaic generation systems.

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.

Statistical criteria for characterizing irradiance time series.

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.

Estimation of parameters for single diode models using measured IV curves

Many popular models for photovoltaic system performance (e.g., [1], [2]) employ a single diode model (e.g., [3]) to compute the IV curve for a module or string of modules for given irradiance and temperature conditions. Most commonly (e.g., [4]), parameters are determined using only current and voltage at short circuit, open circuit and maximum power from a single IV curve at standard test conditions, along with reported temperature coefficients. In contrast, module testing frequently records IV curves at a wide range of irradiance and temperature conditions, such as those specified in IEC 61853-1 [5], which, when available, should also be used to parameterize the performance model. We propose a parameter estimation method that makes use of the full range of available IV curves, and demonstrate the accuracy of the resulting performance model.

Uncertainty and sensitivity analysis in performance assessment for the proposed high-level radioactive waste repository at Yucca Mountain, Nevada

Abstract Extensive work has been carried out by the U.S. Department of Energy (DOE) in the development of a proposed geologic repository at Yucca Mountain (YM), Nevada, for the disposal of high-level radioactive waste. As part of this development, a detailed performance assessment (PA) for the YM repository was completed in 2008 and supported a license application by the DOE to the U.S. Nuclear Regulatory Commission (NRC) for the construction of the YM repository. The following aspects of the 2008 YM PA are described in this presentation: (i) conceptual structure and computational organization, (ii) uncertainty and sensitivity analysis techniques in use, (iii) uncertainty and sensitivity analysis for physical processes, and (iv) uncertainty and sensitivity analysis for expected dose to the reasonably maximally exposed individual (RMEI) specified the NRC’s regulations for the YM repository.

Conceptual structure and computational organization of the 2008 performance assessment for the proposed high-level radioactive waste repository at Yucca Mountain, Nevada

Abstract Extensive work has been carried out by the U.S. Department of Energy (DOE) in the development of a proposed geologic repository at Yucca Mountain (YM), Nevada, for the disposal of high-level radioactive waste. This presentation describes the overall conceptual structure and computational organization of the 2008 performance assessment (PA) for the proposed YM repository carried out by the DOE in support of a licensing application to the U.S. Nuclear Regulatory Commission (NRC). The following topics are addressed: (i) regulatory background, (ii) the three basic entities underlying a PA, (iii) determination of expected, mean and median dose to the reasonably maximally exposed individual (RMEI) specified in the NRC regulations for the YM repository, (iv) the relationship between probability, sets and scenario classes, (v) scenario classes and the characterization of aleatory uncertainty, (vi) scenario classes and the determination of expected dose to the RMEI, (vii) analysis decomposition, (viii) disjoint and nondisjoint scenario classes, (ix) scenario classes and the NRC’s YM review plan, (x) characterization of epistemic uncertainty, and (xi) adequacy of Latin hypercube sample size used in the propagation of epistemic uncertainty. This article is part of a special issue of Reliability Engineering and System Safety devoted to the 2008 YM PA and is intended as an introduction to following articles in the issue that provide additional analysis details and specific analysis results.

Use of replicated Latin hypercube sampling to estimate sampling variance in uncertainty and sensitivity analysis results for the geologic disposal of radioactive waste

The 2008 performance assessment (PA) for the proposed repository for high-level radioactive waste at Yucca Mountain (YM), Nevada, used a Latin hypercube sample (LHS) of size 300 in the propagation of the epistemic uncertainty present in 392 analysis input variables. To assess the adequacy of this sample size, the 2008 YM PA was repeated with three independently generated (i.e., replicated) LHSs of size 300 from the indicated 392 input variables and their associated distributions. Comparison of the uncertainty and sensitivity analysis results obtained with the three replicated LHSs showed that the three samples lead to similar results and that the use of any one of three samples would have produced the same assessment of the effects and implications of epistemic uncertainty. Uncertainty and sensitivity analysis results obtained with the three LHSs were compared by (i) simple visual inspection, (ii) use of the t-distribution to provide a formal representation of sample-to-sample variability in the determination of expected values over epistemic uncertainty and other distributional quantities, and (iii) use of the top down coefficient of concordance to determine agreement with respect to the importance of individual variables indicated in sensitivity analyses performed with the replicated samples. The presented analyses established that an LHS of size 300 was adequate for the propagation and analysis of the effects and implications of epistemic uncertainty in the 2008 YM PA.