Solar resource assessment involves a collection of methods to characterize the solar radiation normally available in a location of interest. In this sense, it is not a simple quantification of the available solar irradiance, but an exhaustive description that includes a detailed statistical analysis and a thorough study of associated uncertainty. In addition, it is an interdisciplinary field in which several disciplines should work together, as solar irradiance modelling, radiometry, metrology, meteorology, climatology, geography, engineering, remote sensing, statistics, and financing. In this sense, a reliable assessment of the solar resource along with a correct analysis of the uncertainty is fundamental to favor the bankability of the project. Furthermore, the reduction of the uncertainty gives the promoter confidence in the project, since it provides more reliable estimations of the annual production, which facilitates to seek investors and to face bidding processes. Nevertheless, solar resource assessment has had a slow development until recent years, when the impetus of the industry to meet its needs has led to faster and more consistent progress.
To characterize the solar resource with the lowest uncertainty, on- site long-term high-quality solar irradiance measurements are required. However, these measurements are never available in practice. Thus, satellite-based models (Perez et al., 2013a; Miller et al., 2013) are extensively used today since they can provide both full spatial coverage and long-term historical time series of data. Alternatively, it is possible to use more sophisticated physically-based methods which make use of
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radiative transfer and atmospheric modeling (Jones and Fletcher, 2013). This is the case, for instance, of atmospheric reanalyses, such as MERRA2 of NASA, ERA-Interim of ECMWF or CFSR of NOAA. However, these models are generally less accurate that the satellite- based ones (Boilley and Wald, 2015). But, even the satellite-based models present most often higher uncertainty than direct observations, with a large range of variation depending on local features of the site of interest. Thus, to better characterize the solar resource, a variety of methods is used, by combining both onsite measurements and long- term satellite-derived modeled data. Several factors influence in the selection of the methods to apply, such as the purpose of the study, requirements and the availability of data, among others. Ruiz-Arias and Gueymard (2015b) propose an ensemble of best practices for solar resource assessment. These are: the selection of a proper location for the installation of the radiometric station, the collection of at least 12 months of data, acquisition of long-term historical data of solar irradiance from satellite, filling of gaps due to erroneous and missing data, assessment of measured data quality and uncertainty, evaluation of satellite against ground observations, long-term bias correction of satellite data and derivation of useful information for solar resource characterization, as the statistical parameters of the long-term dataset, TMY and the total uncertainty.
Satellite-based estimations
To cover long-term periods (over 15 years) and almost any part of the globe, satellites are the best option to estimate the surface solar irradiance. They transport radiometric instruments that sense the multispectral radiation reflected and emitted by the Earth, both from the surface and the atmosphere, with high spatial and temporal resolutions. To derive the solar irradiance received by the earth surface from the reflectance measured by the satellite sensor, several approaches have been proposed along the years. Today, the most common methods can be classified according to their nature. Thus, there are physically founded methods and semi-empirical based methods –also known as cloud-index methods- (Ruiz-Arias and Gueymard, 2015b). The first type of methods has probably more potential for improvement in the
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forthcoming years, although it is more complex and computational demanding. In addition, like CSRT models, they require precise information of the actual atmospheric state, which is not always available with the required accuracy (Habte et al., 2013; Miller et al., 2013). On the other hand, semi-empirical methods still are the most widely used for solar energy applications (Perez et al., 2002, 2013a; Polo et al., 2008; Rigollier et al., 2004; Lefèvre et al., 2007). In these methods, GHI is estimated superimposing the cloud optical amount derived from the satellite measured radiance on the clear-sky GHI obtained from a CSRT model. DNI is then derived by means of all-sky separation model. Usually these methods are additionally adapted by researchers and solar resource providers according to their own criterion in order to reduce the uncertainty of the estimations. Ineichen (2014) presented a benchmarking study of seven semi-empirical approaches from different databases. Anyhow, several sources of uncertainty still remain (Cebecauer et al., 2011). Most recently other approaches have been proposed, which are based on artificial intelligence (AI) techniques, such as artificial neural networks (ANN) (Quesada-Ruiz et al. 2015; Linares-Rodríguez et al., 2015). Unlike the semi-empirical methods, AI-based methods can provide directly both GHI and DNI, and are constrained by the amount of information available for carrying out the model training.
Site adaptation
Also referred to as dataset merging or measured record extension, the term site adaptation designates an ensemble of methods applied to correct the systematic error in the long-term dataset used to characterize the solar resource in a location of interest by means of short-term local ground measurements (Suri and Cebecauer, 2011; Bender et al., 2011; Thuman et al., 2012). The aim is to reduce the original uncertainty of the satellite estimation. To this end, the different methods calibrate the long-term satellite data against local observations during the overlapping period. This strategy reduces the random errors and, overall, the bias or systematic errors. Today this is a standard requirement for bankability for large solar projects.
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Nowadays, there are several approaches to carry out site adaptation. They all are based on the use of short term solar irradiance ground observations datasets –generally at least one year is required-. These methods can be statistically or physically founded. Polo et al. (2016) present a review of the state-of-the-art of site adaptation methods.
Typical meteorological years
Typical meteorological years (TMYs) are artificial annual time series -normally of hourly or sub-hourly values- of useful variables for solar energy systems, which aims to condensate all the long-term information into a single year. Thus, it has the natural diurnal and seasonal variations and theoretically represents a year of typical climatic conditions in the location of interest. In the context of solar resource applications, the aim of TMY is to preserve the statistical features that characterized the solar resource in the location of interest, in order to describe the expected behavior of the solar system, ideally during the lifetime of the facility (Stoffel et al., 2010a; Sengupta et al., 2015). In this sense, TMY representativeness directly benefits from the use of improved long-term data. Originally it was introduced in 1978 by the Sandia National Lab and later by NREL, as a solution to the limitations of the computational capabilities that made difficult solar energy simulations. Since then it has become a tool of common use in the solar energy industry. It is necessary for complementing the bankability analysis of solar energy projects, such as CSP and PV. However, nowadays its use for plant design is not recommended.
From a different perspective, TMYs are fundamental in comprehensive solar resource assessment studies. The theoretical features of this synthetic year facilitate to obtain the objective quantitative estimation of the expected solar energy production in the location of interest. In this sense, this is a key property of TMYs, since they allow specialists to estimate the values of the expected annual solar irradiation at different probability scenarios of available solar energy amount. Such scenarios are usually measured in terms of the probability of exceedance -habitually also referred to by the name of its complementary concept, the percentile-, and the corresponding
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estimated uncertainty, which includes the uncertainty associated to the estimation of the long-term data. These valuable properties are of major interest for the solar energy industry, because they are key information to assess the economic risk of the solar project, which finally is translated into financial interest. Thus, TMYs of several scenarios are demanded to carry out the feasibility analysis, usually the average - probability of exceedance of 50%- and low-energy or pessimistic cases -probability of exceedance of 75%, 90%, 95% or even 99% (Cebecauer and Suri, 2015).
TMY does not correspond to any particular year of a certain period, but it is a calendar year artificially built as a statistical-based weighted composition of twelve months selected from the historical long-term time series of the location of interest. Customarily, weights are used to take into account not only the main variable of interest for generating the TMY -GHI or DNI, depending on the application-, but also ancillary meteorological variables, such as temperature, wind speed, relative humidity, etc., which are of interest for production analysis. Nonetheless, the experts do not agree in the appropriateness of taking into account the meteorological variables and how to do it in order to adjust the composite of weights, since there are not conclusive evidences of such convenience. Thus, Habte et al. (2014) from NREL have proposed a new configuration of weights in which the main variables for PV or CSP applications, that is, GHI and DNI respectively, have the 100% of the weight. They name the resulting TMY as typical global (horizontal irradiance) year (TGY) and typical direct (normal irradiance) year (TDY). In the context of this research work, to refer to both at the same time the term typical solar year (TSY) is used. It should be noted that TMY may not have necessarily more information than TSYs, but slightly different, since the last can be completed simply adding the corresponding meteorological information. The difference is that TMY takes into account –in a non- consensual way- the meteorological variables to generate the year, while TSYs only consider the solar irradiance component of interest to form it.
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Consequently, unfortunately there is no scientific consensus on a standard method to generate TMYs or TSYs. On the contrary, there is a varied set of methods and strategies followed by different authors and providers to generate their own TMYs. Thus, both methods and weights may result in different TMYs for the same location and long-term dataset, which is an unwanted situation. In this context, some research efforts analyze the performance of different TMY approaches in solar energy applications (Ineichen, 2011b; Realpe et al., 2016).
Uncertainty
Uncertainty is probably the most important individual concept in solar resource assessment. It is essential to evaluate the quality of the assessments and key for the bankability of solar projects. Uncertainty is, thus, indispensable for any rigorous analysis in solar energy applications in order to obtain comprehensive conclusions. The uncertainty in the solar resource is directly related to the uncertainty in the expected performance of the solar plant (Sengupta et al., 2015). Furthermore, the uncertainty in the estimation of the solar resource is the largest source of the overall energy production uncertainty.
Nevertheless, it is a parameter that sometimes is not completely clear in the solar energy industry. It should not be confused with the variability of the data. Actually the total uncertainty is a composition of individual contributions of all the elements involved in the characterization of the solar irradiance: measurements, modelling, interannual variability, spatial variability, representativeness of the period and site adaptation approach, (Meyer et al., 2009). In addition to all these, for TMY it should be also take into account the uncertainty derived from the method itself (Fernandez-Peruchena et al., 2016). The characteristics of each component of the uncertainty depend on the specific aspect of the solar resource assessment. To add all these contributions (ui) to obtain the total uncertainty (U) it is common to use
the Gaussian law of error propagation (equation 1.2).
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