Joshua S. Stein

A Wavelet-Based Variability Model (WVM) for Solar PV Power Plants

A wavelet variability model (WVM) for simulating solar photovoltaic (PV) power plant output given a single irradiance point sensor timeseries using spatio-temporal correlations is presented. The variability reduction (VR) that occurs in upscaling from the single point sensor to the entire PV plant at each timescale is simulated, then combined with the wavelet transform of the point sensor timeseries to produce a simulated power plant output. The WVM is validated against measurements at a 2-MW residential rooftop distributed PV power plant in Ota City, Japan and at a 48-MW utility-scale power plant in Copper Mountain, NV. The WVM simulation matches the actual power output well for all variability timescales, and the WVM compares well against other simulation methods.

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.

Lanai High-Density Irradiance Sensor Network for characterizing solar resource variability of MW-scale PV system

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® 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 photovoltaic Performance Modeling Collaborative (PVPMC)

Sandia National Laboratories is forming the PV Performance Modeling Collaborative (PVPMC). This effort is aimed at improving confidence in PV performance model predictions by bringing traceability and transparency to the modeling process and encouraging third party validation of existing algorithms. Activities currently being pursued in this collaborative include: (1) developing a website (http://pvpmc.org), (2) developing a Matlab™ PV Performance Modeling Toolbox (PV_LIB), and (3) sponsoring periodic PV performance modeling workshops and events. Contributions are welcomed from stakeholders in PV or related industries.

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.

Technology development needs for integrated grid-connected PV systems and electric energy storage

Researchers at Sandia National Laboratories and the U.S. Department of Energys Solar Energy Technologies Program assessed status and needs related to optimizing the integration of electrical energy storage and grid-connected photovoltaic (PV) systems. At high levels of PV penetration on our electric grid, reliable and economical distributed energy storage will eliminate the need for backup utility generation capacity to offset the intermittent nature of PV generation. This paper summarizes the status of various storage technologies in the context of PV system integration, addressing applications, benefits, costs, and technology limitations. It then discusses further research and development needs, with an emphasis on new models, systems analysis tools, and even business models for high penetration of PV-storage systems on a national scale.

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.

A standardized approach to PV system performance model validation

PV performance models are used to predict how much energy a PV system will produce at a given location and subject to prescribed weather conditions. These models are commonly used by project developers to choose between module technologies and array designs (e.g., fixed tilt vs. tracking) for a given site or to choose between different geographic locations, and are used by the financial community to establish project viability. Available models can differ significantly in their underlying mathematical formulations and assumptions and in the options available to the analyst for setting up a simulation. Some models lack complete documentation and transparency, which can result in confusion on how to properly set up, run, and document a simulation. Furthermore, the quality and associated uncertainty of the available data upon which these models rely (e.g., irradiance, module parameters, etc.) is often quite variable and frequently undefined. For these reasons, many project developers and other industry users of these simulation tools have expressed concerns related to the confidence they place in PV performance model results. To address this problem, we propose a standardized method for the validation of PV system-level performance models and a set of guidelines for setting up these models and reporting results. This paper describes the basic elements for a standardized model validation process adapted especially for PV performance models, suggests a framework to implement the process, and presents an example of its application to a number of available PV performance models.

Ota City : characterizing output variability from 553 homes with residential PV systems on a distribution feeder.

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.