Solar Consulting Services

Colebrook, NH, United States

Solar Consulting Services

Colebrook, NH, United States
SEARCH FILTERS
Time filter
Source Type

News Article | May 4, 2017
Site: www.eurekalert.org

BOULDER, Colo. -- Expanding its work in renewable energy, the National Center for Atmospheric Research (NCAR) is launching a three-year project to develop specialized forecasts for a major wind and solar energy facility in Kuwait. "We're putting our expertise and technology to work around the world," said NCAR Senior Scientist Sue Ellen Haupt, the principal investigator on the project. "This landmark project meets our mission of science in service to society." The $5.1 million project will focus on developing a system to provide detailed forecasts of wind and solar irradiance at Kuwait's planned 2-gigawatt Shagaya renewable energy plant. After NCAR develops the system, the technology will be transferred to the Kuwait Institute for Scientific Research (KISR) for day-to-day operations. The forecasts will help Kuwait reach its goal of generating 15 percent of its energy from renewable sources by 2030. With the ability to anticipate the amount of electricity that sun and wind will produce hours to days in advance, energy operators will be able to power up or down traditional plants as needed to meet demand. "This technology will provide us with important benefits," said Salem Al-Hajraf, manager of KISR's Renewable Energy Program. "We are providing green energy to the grid using abundant sources of energy, which are sun and wind." When electric utilities integrate power from intermittent sources such as wind or solar into the grid, they temporarily reduce or shut off traditional sources such as oil or natural gas. But if weather conditions fail to come together as expected, the utility may not be able to power up traditional plants in time to meet their customer needs. To help utility managers anticipate renewable wind energy more reliably, NCAR has designed and is constantly improving a wind energy prediction system for Xcel Energy that has saved tens of millions of dollars for the utility's customers in Colorado and nearby states. The specialized system relies on a suite of tools, including highly detailed observations of atmospheric conditions, advanced computer modeling, and artificial intelligence techniques that enable Xcel Energy to issue high-resolution forecasts for wind farm sites. With funding from the U.S. Department of Energy, NCAR has also led a national team of scientists who have developed a cutting-edge forecasting system with the potential to save the solar energy industry hundreds of millions of dollars in the United States alone through improved forecasts. The new Sun4Cast™ system, unveiled last year, greatly improves predictions of clouds and other atmospheric conditions that influence the amount of energy generated by solar arrays. In Kuwait, the NCAR team will build on these technologies to develop both wind and solar energy forecasts. The scientists will customize the system to predict dust storms that can blot out sunlight and damage wind turbines. They will also incorporate the influence of nearby mountain ranges and the Persian Gulf on local weather patterns. "This is a great opportunity to do research into dust and other particulates, which we haven't previously needed to focus on to this extent for wind and solar energy prediction," Haupt said. "This kind of work will pay multiple dividends for energy forecasting as well as better understanding and predicting of weather in certain desert environments." Haupt and her team will collaborate with researchers at Pennsylvania State University and Solar Consulting Services in Florida, as well as with KISR. "This is an exciting international partnership that will both generate significant economic benefits and advance our understanding of the atmosphere," said Antonio J. Busalacchi, president of the University Corporation for Atmospheric Research. "In addition to reducing energy costs for our partners in Kuwait, the knowledge that we gain will help us further improve weather prediction skills here in the United States." The University Corporation for Atmospheric Research is a nonprofit consortium of 110 North American colleges and universities that manages the National Center for Atmospheric Research under sponsorship by the National Science Foundation. KISR leads and partners internationally to develop, deploy, and exploit the best science, technology, knowledge, and innovation for public and private sector clients, for the benefit of Kuwait and others facing similar challenges and opportunities.


Gueymard C.A.,Solar Consulting Services | Ruiz-Arias J.A.,University of Jaén | Ruiz-Arias J.A.,University of Malaga
Renewable and Sustainable Energy Reviews | Year: 2015

In this study, a detailed review of the performance of 24 radiative models from the literature is presented. These models are used to predict the clear-sky surface direct normal irradiance (DNI) at a 1-min time resolution. Coincident radiometric and sunphotometric databases of the highest possible quality, and recorded at seven stations located in arid environments, are used for this analysis. At most sites, an extremely large range of aerosol loading conditions and high variability in their characteristics are noticed. At one site (Solar Village), DNI was measured routinely with an active cavity radiometer with very low uncertainty compared to field pyrheliometers, which makes its dataset exceptional. The reviewed models are categorized into 5 classes, depending on the number of aerosol-related inputs they require. One of the models (RRTMG) is considerably more sophisticated (and thus less computationally efficient) than the other models-which are all of the parametric type currently in use in solar applications, and specifically devised for cloudless conditions. RRTMG is more versatile and is selected here for benchmarking purposes. The results show good consistency between the different stations, with generally higher prediction uncertainties at sites experiencing larger mean aerosol optical depth (AOD). Disaggregation of the performance results as a function of two aerosol optical characteristics (AOD at 1 j.im, ft, and Angstrom exponent, a) shows that the simplest parametric models' performance decreases when subjected to turbidity conditions outside of what is "normal" or "typical" under temperate climates. Only a few parametric models perform well under all conditions and at all stations: REST2, CPCR2, MMAC, and METSTAT, in decreasing order of performance. The Ineichen and Hoyt models perform adequately at low AODs, but diverge beyond a specific limit. REST2 is the only parametric model that performs similarly to the RRTMG benchmark under all AOD regimes observed here-and even sometimes better. The inspection of the models' performance when considering the simultaneous effects of both ft and a reveals a clear pattern in the models' error, which is influenced by the frequency distribution of a values. This suggests most models may have difficulty in correctly capturing the effect of a, and/or that observational and instrumental issues at high AOD values may also enhance the apparent model prediction errors. A study of the specific sensitivity of DNI on AOD confirmed previous findings. It is concluded that, assuming a "perfect" model, DNI can be modeled within 5% accuracy only if ft is known to within =t 0.02. © 2015 Elsevier Ltd. All rights reserved.


Ruiz-Arias J.A.,University of Jaén | Ruiz-Arias J.A.,U.S. National Center for Atmospheric Research | Dudhia J.,U.S. National Center for Atmospheric Research | Gueymard C.A.,Solar Consulting Services | Pozo-Vazquez D.,University of Jaén
Atmospheric Chemistry and Physics | Year: 2013

The daily Level-3 MODIS aerosol optical depth (AOD) product is a global daily spatial aggregation of the Level-2 MODIS AOD (10-km spatial resolution) into a regular grid with a resolution of 1° × 1° . It offers interesting characteristics for surface solar radiation and numerical weather modeling applications. However, most of the validation efforts so far have focused on Level-2 products and only rarely on Level 3. In this contribution, we compare the Level-3 Collection 5.1 MODIS AOD dataset from the Terra satellite available since 2000 against observed daily AOD values at 550 nm from more than 500 AERONET ground stations around the globe. Overall, the mean error of the dataset is 0.03 (17%, relative to the mean ground-observed AOD), with a root mean square error of 0.14 (73%, relative to the same), but these errors are also found highly dependent on geographical region. We propose new functions for the expected error of the Level-3 AOD, as well as for both its mean error and its standard deviation. Additionally, we investigate the role of pixel count vis-à-vis the reliability of the AOD estimates, and also explore to what extent the spatial aggregation from Level 2 to Level 3 influences the total uncertainty in the Level-3 AOD. Finally, we use a radiative transfer model to investigate how the Level-3 AOD uncertainty propagates into the calculated direct normal and global horizontal irradiances. © 2013 Author(s).


Gueymard C.A.,Solar Consulting Services
Journal of Solar Energy Engineering, Transactions of the ASME | Year: 2011

The design, energy output, and cost effectiveness of solar projects using concentrators critically depend on the resource in direct normal irradiance (DNI). Many modeled DNI datasets now exist, and a recent preliminary study has shown some areas of serious disagreement in Europe. So far, no rigorous performance assessment has been undertaken for other parts of the world. The present contribution focuses on North Africa and bordering regions, which have great solar potential for power plants based on thermal or photovoltaic concentration systems. The mean monthly and annual performance of eight different modeled datasets providing DNI is analyzed here, with respect to measured radiation data at 14 sites, which are used as "ground-truth". Relatively good results are generally obtained for sites in southern Europe. Serious problems, however, are found at various sites in North Africa or the Middle East. Most of these problems appear linked to inadequate aerosol optical depth data used by the models, and to the dust storms from the Sahara that regularly, and strongly, modify the aerosol regime. A method that can potentially correct these problems, or allow for model benchmarking based on a reference aerosol database, is proposed. The bankability of current datasets is questioned. © 2011 American Society of Mechanical Engineers.


Gueymard C.A.,Solar Consulting Services | Wilcox S.M.,National Renewable Energy Laboratory
Solar Energy | Year: 2011

The US National Renewable Energy Laboratory (NREL) is responding to a growing demand for high-accuracy solar resource data with uncertainties significantly lower than those of existing solar resource datasets, such as the National Solar Radiation Database (NSRDB). Measurements for long-term solar resource characterizations require years to complete, which is an unacceptable timeline for the rapidly emerging needs of renewable energy applications. This contribution seeks methods of reducing the uncertainty of existing long-term solar resource datasets by incorporating lower-uncertainty site-specific ground measurements of a limited period of record. In particular, various techniques are being explored to make full use of the existing high-resolution radiation data available in the NSRDB and other sources, and extrapolate them over time using locally measured data and other supportive information. The interannual variability in global and direct radiation is studied here using long-term data at various sites. NSRDB's modeled data for the 1998-2005 period are compared to quality-controlled measurements to assess the performance of the model, which is found to vary greatly depending on climatic condition. The reported results are encouraging for applications involving concentrators at very sunny sites. Large seasonal biases are found at some cloudy sites. Various improvements are proposed to enhance the quality of the existing model and modeled data. The measurement of solar radiation to characterize the solar climate for renewable energy and other applications is a time consuming and expensive operation. Full climate characterization may require several decades of measurements-a prospect that is not practical for an industry intent on rapid deployment of solar technologies. This study demonstrates that the consistency of the solar resource in both time and space varies widely across the United States. The mapped results here illustrate regions with high and low variability and provide readers with quick visual information to help them decide where and how long measurements should be taken for a particular application. The underlying data that form these maps are also available from NREL to provide users the opportunity for more detailed analysis. © 2011.


Gueymard C.A.,Solar Consulting Services
AIP Conference Proceedings | Year: 2010

The prediction of the circumsolar augmentation of the direct normal irradiance incident on a CPV collector is difficult because it depends on many factors, such as solar position (air mass), atmospheric conditions, wavelength, and collector's opening angle. A general assessment of the circumsolar effect is described in this study, based on recently introduced instrumentation to measure the aureole's radiance, and appropriate radiative transfer modeling. Results of parametric simulations obtained with the SMARTS radiative code using variable air mass, aerosol conditions, and concentrator geometries are presented. © 2010 American Institute of Physics.


Gueymard C.A.,Solar Consulting Services
Solar Energy | Year: 2012

The aerosol optical depth (AOD) is known to be a critical input for radiation modeling purposes, and partially determines the accuracy of modeled direct normal irradiance (DNI) and global horizontal irradiance (GHI). This contribution examines to what extent time variations in AOD also determine the observed variability in DNI, particularly at the daily and longer time scales. Two measures of variability are introduced: the Aerosol Variability Index (AVI) characterizes the magnitude of the variability in AOD over specific periods, from daily to yearly, whereas the Aerosol Sensitivity Index (ASI) relates the magnitude of relative variations in irradiance to absolute variations in AOD. AOD measurements at 180 Aeronet sites over the world are used to obtain clear-sky irradiances with the REST2 radiative model, as well as determinations of ASI and AVI. Large geographic variations exist in AVI, whose largest values are found over western Sahara. The variations of ASI follow a different pattern because it decreases when AOD increases. The variability in GHI is typically 2-4 times lower than that in DNI. On a long-term basis, the normal aerosol-induced variability in DNI is less than ±5% at most sites, but some areas might experience a much larger variability, comparable to that created by large volcanic eruptions. The latest such events predate most current modeled DNI or GHI datasets, making resource assessments potentially too optimistic for bankability if based on such limited data series alone. © 2012 Elsevier Ltd.


In the context of the current rapid development of large-scale solar power projects, the accuracy of the modeled radiation datasets regularly used by many different interest groups is of the utmost importance. This process requires careful validation, normally against high-quality measurements. Some guidelines for a successful validation are reviewed here, not just from the standpoint of solar scientists but also of non-experts with limited knowledge of radiometry or solar radiation modeling. Hence, validation results and performance metrics are reported as comprehensively as possible. The relationship between a desirable lower uncertainty in solar radiation data, lower financial risks, and ultimately better bankability of large-scale solar projects is discussed. A description and discussion of the performance indicators that can or should be used in the radiation model validation studies are developed here. Whereas most indicators are summary statistics that attempt to synthesize the overall performance of a model with only one number, the practical interest of more elaborate metrics, particularly those derived from the Kolmogorov-Smirnov test, is discussed. Moreover, the important potential of visual indicators is also demonstrated. An example of application provides a complete performance analysis of the predictions of clear-sky direct normal irradiance obtained with six models of the literature at Tamanrasset, Algeria, where high-turbidity conditions are frequent. © 2014 Elsevier Ltd.


Various types of precipitable water (PW) measurement are compared for different sites around Tucson, Arizona, where arid conditions prevail, and the sensitivity of irradiance to PW variations is largest. The accuracy of some determinations of this quantity is assessed by comparison with routine GPS meteorology data. Large scatter is obtained with all types of empirical functions relating PW to surface temperature and humidity data, but the climate sensitivity of this kind of determination is found lowest when relating PW to the surface specific humidity, rather than the more usual vapor pressure or dew point temperature. The impact on the accuracy of predicted direct normal irradiance (DNI) and global horizontal irradiance (GHI) of various sources of PW data, at either low or high temporal resolution, is assessed using predictions from the REST2 radiative model, in combination with co-located sunphotometric and radiometric data at Tucson during a 7-month period. Results suggest that the accuracy of the predicted DNI and GHI is only lightly sensitive to the uncertainty in the input PW data. In case PW is not measured locally, a convenient source of data is provided by reanalysis, such as from the MERRA model. © 2013 Elsevier Ltd.


The intrinsic performance of 18 broadband radiative models is assessed, using high-quality datasets from five sites in widely different climates. The selected models can predict direct, diffuse and global irradiances under clear skies from atmospheric data, and have all been (or still are) involved in large-scale applications, for instance to prepare solar resource maps and datasets, or to evaluate solar radiation in GIS software. The input data to the models include accurate aerosol and water vapor measurements by collocated sunphotometers, if needed. Cloud occurrences are meticulously scrutinized through the use of various tools to avoid cloud contamination of the test data. The intrinsic performance of the models is evaluated by comparison between their predictions and measurements at high frequency (1-minute time step at four sites, 3-minute at one site). The total expanded uncertainty of these measurements is estimated at 3% for direct irradiance, and 5% for diffuse and global irradiance.Various statistics are calculated to evaluate the systematic and random differences between the data series, as well as the agreement between the cumulative distribution functions. In the latter case, stringent statistics based on the Komolgorov-Smirnov (KS) test are used. Large differences in performance are apparent between models. Those that require more atmospheric inputs perform usually better than simpler models. Whereas many models can predict the global horizontal irradiance within uncertainty limits similar to those of the radiation measurements, the prediction of direct irradiance is less accurate. Moreover, the prediction of diffuse horizontal irradiance is particularly deficient in most models. The cumulative distribution functions also denote areas of concern.A ranking of all models is proposed, based on four statistical indicators: mean bias difference (MBD), root mean square difference (RMSD), total uncertainty with 95% confidence limits (U 95), and the newly introduced Combined Performance Index (CPI), which optimally combines two KS indices with RMSD. For direct irradiance, consistently high rankings are obtained with five models (REST2, Ineichen, Hoyt, Bird, and Iqbal-C, in decreasing order of performance) that require a relatively large number of atmospheric inputs. The inferior performance of models requiring little or no atmospheric inputs suggests that large-scale solar resource products derived from them may be inappropriate for serious solar applications. Additionally, prediction uncertainties under ideal clear-sky conditions can propagate and affect all-sky predictions as well-resulting in potential biases in existing solar resource maps at the continent scale, for instance. © 2011 Elsevier Ltd.

Loading Solar Consulting Services collaborators
Loading Solar Consulting Services collaborators