THE EFFECT OF SOLAR RADIATION DATA TYPES ON CALCULATION OF TILTED AND SUNTRACKING SOLAR RADIATION

THE EFFECT OF SOLAR RADIATION DATA TYPES ON CALCULATION OF TILTED AND SUNTRACKING SOLAR RADIATION Tomáš Cebecauer, Artur Skoczek, Marcel Šúri GeoModel Solar s.r.o., Pionierska 15, 831 02 Bratislava, Slovakia, Tel: +421 2 492 12 422, fax: +421 2 492 12 423; corresponding author: tomas.cebecauer@geomodel.eu ABSTRACT: Energy yield assessment of photovoltaic systems is inherently non-linear. The calculation chain starts with simulation of solar radiation, where many factors are considered: sun s position, terrain, properties of the atmosphere and cloudiness to determine the absolute value of global irradiance and the ratio of diffuse and direct components. The ratio of direct and diffuse components influences the global irradiance received by the inclined surface of PV modules, it changes during a day and also during seasons and it is highly dependent on the geographical location. Therefore, optimally multiyear time series should be used for proper modeling of energy performance of a PV generator. However, for historical reasons, several solar radiation data products with different level of simplification are used in PV simulation tools long-term monthly-averaged daily profiles, synthetic time series, and typical meteorological years (TMYs). A new simplified approach is based on the statistical aggregation of solar radiation into relevant groups (bins) according to distribution of irradiance values. This paper seeks to provide a benchmark for four solar radiation data products used in the contemporary software packages for performance assessment of photovoltaic systems. The results of calculation the in-plane irradiation show different performance of data products, when compared to full time series, with error amplitude up to ±3%. 1. APPROACH Photovoltaic systems perform have non-linear response to weather parameters, especially global irradiance and air temperature. The calculation chain starts with simulation of solar radiation, which is determined by sun position, properties of the atmosphere and cloudiness, which in turn control the ratio of the diffuse and direct components. This ratio changes dynamically during a day, type of weather and during the year, and it is highly dependent on a geographical location. In PV simulation, the best results can be achieved using high-resolution multiyear time series (e.g. several years of 15-minute data) of solar radiation with subsequent timeintegration of the calculated PV instantaneous values. However, for historical reasons, PV simulation packages use only simplified climate data products on the input: (i) typical (average) daily profiles for each month, (ii) synthetic time series derived from 12 monthly averages by a stochastic weather generator, or (iii) Typical Meteorological Year (TMY). The reduction of full time series to any of the above mentioned data products has an effect on the data accuracy, due to distorted ratio of diffuse/global irradiance and probability distribution of air temperature and solar radiation data pairs. Thus, simplified data products may not describe appropriately the regional climate, which in turn affects PV performance simulation results. This paper compares impact of four solar radiation data products (including global horizontal irradiance and diffuse horizontal irradiance) used in the contemporary software packages for the assessment of tilted and suntracking global radiation and evaluates the results for different climates. For calculation of irradiance impinging on a tilted or suntracking receiver, diffuse and direct components have to be known. These components can be directly measured on a site; they can be calculated by clear-sky models (e.g. SOLIS [1]) or most typically they are derived from global irradiance by empirical or more advanced models, e.g. [2, 3]. Further, the models transposing diffuse horizontal to diffuse tilted irradiance are needed to calculate irradiance received by tilted or suntracking surfaces (e.g., the model by Perez [4]. In this paper, we analyze three items: 1. We compare global horizontal irradiation as calculated from the 6-years data and 17-years data. 2. We compare simulation results of annual global tilted radiation as received from using different global horizontal (GHI) and direct normal (DNI) radiation data products (full time series, aggregated statistics, TMY, monthly averaged daily profiles and synthetic time series generated in PVSYST). As reference, time series covering period of 1994 to 2010 of 15-minute and 30-minute GHI and DNI irradiance data are used from the SolarGIS database [5]. The in-plane global irradiation is calculated for each data product using Perez tilted model and compared to the result of using the full time series. 3. Next, the simulation in selected software packages was carried out for three configurations: south-oriented tilted surface at 30, for one-axis tracker, and for two-axis tracker. The gain of annual Global In-plane Irradiation (GII) compared to Global Horizontal Irradiation (GHI) is presented for eight different combinations of data and models. 2. COMPARISON OF IRRADIATION DATA FROM THE LAST 6 YEARS AND 17 YEARS For the selected test sites, the comparison is shown between Global Horizontal Irradiation calculated from the recent 6 years (irradiance derived from Meteosat Second Generation satellite) and the recent 17 years (combination 1

of irradiance derived from Meteosat First and Second Generation satellites). The percentual difference of annual sums of Global Horizontal Irradiance is shown in Fig. 4. The negative values represent sites where 6-years sum is higher than 17-years sum. It can be observed that the majority of 6-years annual sums are higher than the average of 17 years (last 6 years were sunnier), and the difference is usually below 2%. Only 5 sites, out of 35 tested have higher value of the 17-year sum, but there is no obvious geographical dependence in the distribution of the results. Average daily profiles based on long-term monthly averaged values (e.g., ESRA, PVGIS, RETScreen); Synthetic time series (e.g., Meteonorm, PVSYST) Typical Meteorological Year (e.g., PVWATTS, PVSYST, SAM); Aggregated probability statistics (SolarGIS pvplanner) Full multiyear series (e.g. SolarGIS offline version). To obtain the most accurate simulations, the full multiyear time series have to be used, typically as 15-, 30-minute or hourly data. However, full time series are not used in most of the available software packages for historical reasons, and also due to higher demand on computational resources. Calculation of in-plane solar radiation for tilted and suntracking surfaces assumes availability of diffuse and direct irradiance; but the resulting global in-plane irradiance reacts non-linearly to these components. Therefore data sets that provide good compromise between the needs for computing power and accuracy are still desirable. The characteristics of solar radiation data products are presented below. Fig 1. The difference between GHI calculated from the last 6 and from the last 17 years of data. The negative values represents sites where 6-years average is higher than the average of 17 years. 3. USE OF SOLAR DATA PRODUCTS IN THE SIMULATION PROGRAMS The engineering software packages use various data products to generate solar radiation for tilted and suntracking surfaces. Some of those used in photovoltaics are: Fig. 2: Distribution of daily sums of global horizontal irradiation within months does not follow Gaussian (normal) distribution. Monthly averages most often do not represent a typical weather (data for Payerne (CH) and Seville (ES). 2

1. Monthly averages of daily profiles For fast estimate of solar potential of a site, monthly averages of daily profiles are used. These are represented by hourly or even 15-minute values. It is to be noted that most often average value do not represent weather patterns in a particular month. Fig. 2 shows distribution of daily values of GHI for two different sites (Seville in Spain and Payerne in Switzerland). It can be observed that daily values within a month are not distributed normally, usually there is strong asymmetry and monthly averages do not represent the most typical days. In Payerne, for example, the distribution of days in summer is very flat with wide scatter of days, while for Seville the probability distribution has clear peaks but it is notably asymmetric. In the majority of cases, the value of median (the most probable day) is higher than the average value. This leads to an important statement that average day occurs very rarely and aggregated statistics (median and percentiles) better characterize climate of a site. In some older software (e.g., RETScreen, ESRA, PVGIS), algorithms are implemented, based on the assumption of monthly-averaged sky conditions [6, 7, 8]) and the use of the average daily profiles. This approach was developed in times of limited data availability and computing options, though they are still popular and widely used. 2. Synthetic hourly time series A method for generating synthetic hourly time series from long-term monthly averages by Aguiar and Collares- Pereira [9] is another widely used approach. Synthetic time series are practical as they generate hourly time series from just 12 monthly values. The implementation of the mathematical models for generating synthetic time series may be of different complexity and performance in various climates. The method used in this paper is implemented in PVSYST. 3. Typical Meteorological Year Typical Meteorological Year (TMY) constitutes another approach to real climate characterization, and a number of data set has been developed for several countries of Europe and America. For decades, TMYs have been used by engineers to simulate building energy performance or solar systems. TMYs replace many years of data with a single typical year, and they are used in applications such as PVWATTS or PVSYST. TMYs are generally built by assembling the most representative months from the long-term time series into a typical composite year. Weighting factors are applied to provide selective emphasis on the meteorological parameters of interest (including, but not limited to irradiance components). TMYs can be constructed by many methods, optimally in a way to represent real time climate as close as possible to the needed applications - PV in our case. Most often, TMY includes 12 fragments of real data that describe a climate most realistically based on the given selection criteria. The typical year is constructed on the monthly basis, comparing months of individual years with longterm monthly characteristics: cumulative distribution function and mean. The selection of the most representative month takes into account different weights of individual weather parameters (e.g., GHI, DNI, air temperature, humidity, wind speed, and wind direction) and completeness of time series. The representative months are concatenated into a typical year. In the selection criteria, the higher weight is given to solar radiation parameters, thus the RMY can be tuned for a required solar energy application (PV, CPV or CSP). 4. Multi year time series Times series will very likely dominate in future solar energy simulations. These can be on-site measured or satellite-derived data, or their combination. Satellitederived solar radiation data offer unsurpassed performance in terms of availability, high-quality, completeness, and timeliness of delivery. Nowadays satellite-based solar time series can be calculated for almost any location on the Earth, at high spatial and temporal resolution, and they have a potential to represent weather patterns for 13 up to 25 years. Compared to any other data product, the strength of multiyear time series resides in their ability to describe the probability of occurrence of extreme and typical events for tuning the design of solar energy systems and risk analysis of the investment. 5. Aggregated statistics For PV simulations, high resolution solar and air temperature data pairs can be organized into percentiles and statistical bins according to their probability of occurrence. Organizing data this way aims to use more effectively storage space for fast access and online calculation for any selected site - enabling to consider non-linearity in simulations and preserving speed and accuracy of computation. The simulation is realized for each bin separately, thus approximating different types of weather (defined by the combination of GHI, DNI and air temperature). This approach [10] allows significant reduction of data volume while preserving information about occurrence of different weather situations individually for each month. This is especially important for designing high-speed web-based calculators, interactive maps and analytical tools. 4. INCLINED IRRADIATION CALCULATED FROM THE STUDIED PRIMARY DATA PRODUCTS To better understand the possible impact of a particular data product in different climatic zones a number of sites have been selected in Europe and Africa. The distribution of the sites is shown in Fig. 3. For each site a series of global inclined irradiances has been calculated, with inclination varying from 0 to 90, with a step of 15. The reference data are time series of satellite derived GHI and DNI irradiance comprising of 17 years data. The original time step of data is 30 minutes for years 1994-2004 (Meteosat First Generation) and 15 minutes for years 2005-2010 (Meteosat Second Generation). The data was linearly interpolated to work with the homogenous set of 15-minute irradiance data. 3

Fig 3. The distribution of the test sites. The inclined radiation was calculated using: Monthly average profiles (15 minute resolution) - AVERAGE, Monthly percentile distribution (15-minute resolution) - PERCENTILE, Typical Meteorological Year (hourly resolution) TMY, Synthetically generated one year of data using monthly averages of GHI and diffuse radiation (hourly resolution) using PVSYST method SYNTHETIC TS. The inclined irradiation was calculated for each data product using Perez tilted model. The sum of inclined radiation was compared to corresponding sum of inclined radiation calculated form primary full time 15-minute time series data as a reference. The results show that energy yield assessment is sensitive to the selection of solar radiation data product. The data representation and choice of the model significantly influence the calculated in-plane irradiation output. The differences between the approaches are not systematic, but vary between the sites. If full time series are considered as a reference, as they represent the most detailed description of the site climate conditions, then both the TMY and aggregated statistics give results very similar to the reference. The differences are below 1% in most cases for all configurations and sites, and only in the extreme situations this threshold is exceeded. The aggregated statistics in the form of percentiles shows clear geographical pattern with increasing latitude the inclined radiation becomes slightly overestimated. But the difference from time series is small and maximum of 1.5% is found only for vertical surfaces. For the surfaces close to optimum angle the difference is below 1%. Fig 4. Deviation of simulated inclined irradiation of different data products compared to inclined irradiance calculated from full time series. Tilt angles: 0, 15, 30, 45, 60, 75, 90. 4

The TMY data product performs slightly worse than aggregated statistics. The deviation reaches the level of the 1.5% more, but similarly to the previous product this occurs for vertical position. No geographical pattern was found and both overestimation and underestimation of inclined irradiation is present. These TMY results are probably linked to the way of data product creation, where assimilation of months (that mostly resemble the average and frequency distribution of full time series) from various years may lead to slightly different characteristics of the resulting data products. When monthly-averaged daily profiles are used, the inplane irradiation becomes overestimated in all locations. The deviation in extreme cases surpasses the 2.5%, the highest overestimation is found in European sites and Central Africa. The synthetic data series reach highest differences (over 3.0%), especially in extreme cases of vertical surfaces. The difference to the use of multiple time series is relatively high with both positive and negative amplitude (no geographical pattern). The monthly averages of global and diffuse irradiation provide too simplified solar characterization of a site and the results confirm that synthetic data generator has been tuned for the climate of Central, West Europe and for Mediterranean Europe. The generator fails to produce higher errors in climate regions in North Europe and South of the Mediterranean region. It is possible that more complex methods of synthetic data generation may provide better results. Four sites are analyzed with different climate conditions. Fig. 5 shows that the data representation and the choice of the model determine the calculated of the in-plane radiation. 5. SIMULATION USING DIFFERENT MODELS Simulation shows the results when using different data products: full time series, TMY, aggregated statistics, long-term averaged daily profiles, synthetic time series and different implementations of in-plane solar radiation models (SolarGIS, PVGIS and PVSYST) for three configurations: South oriented tilted at 30, one-axis tracking and for two axis tracking system. The gain of Global In-plane Irradiation compared to Global Horizontal Irradiation is presented for eight different combinations of data products and numerical models. All data are derived from the SolarGIS satellitebased database: A. Time series of 15-minute data for 6 years, SolarGIS [10] in-plane model. This simulation is considered as a reference B. Typical Meteorological Year, hourly data, SolarGIS in-plane model C. Aggregated statistics, 15-minute data, SolarGIS in-plane model D. Average monthly profiles, 15-minute data, SolarGIS in-plane model E. Synthetic hourly data, PVSYST in-plane model synthetic data are derived from monthly averages of global and diffuse and irradiation in PVSYST F. PVGIS CM-SAF satellite-based data, 12 monthly averages, PVGIS in-plane model G. PVGIS-3 interpolated ground station data, 12 monthly averages, PVGIS in-plane model [7, 11] Fig. 5: Comparison of different data aggregations and inplain models. Irradiance gain in comparison to horizontal plane (GHI) for 2 axis tracker, 1 axis tracker with rotation axis oriented north-south and inclined at an angle of 30 and a fix plane inclined at angle of 30. The simulation of in-plane global irradiation using various packages with different implementation of the inclined radiation calculation shows even higher difference (Fig. 5) when compared to the Fig. 4. It is interesting to note that horizontal-to-inclined surface gains for trackers obtained from synthetic dataset generated by PVSYST are higher than for the reference data input. On the other hand, these gains are lower for a fixed inclined surface. This may be a result of distorted statistics of ratio DIF/GHI when using synthetically-generated data. The difference of in-plane radiation from synthetic data calculated by SolarGIS and PVSYST models points to possible differences in the applied tilted irradiance model. 5

6. CONCLUSIONS This contribution confirms some of the previous partial findings and by the more detailed analysis aims to trigger discussion about the impacts on the data models on the accuracy of simulation of the PV systems performance. Three key findings have been observed: GHI calculated from the last 6 years of data overestimates the long-term average calculated from the last 17 years of data, typically by 2%; In the next step, we compared a calculation of inplane global irradiation for South-facing PV array, fixed-mounted at an angle from 0 to 90 degrees using different data products. It is considered that the reference calculation is based on the use of full time series. The analysis of four data sources has shown that the closest to the full time series is aggregated statistics, followed by TMY. TMY data may be generated using various approaches and therefore the results are method-dependent. The long-term monthly-averaged daily profiles and synthetic time series show higher deviation with changing geographic pattern (up to ±3%). Monthly averaged daily profiles have tendency to generate systematic error. In case of synthetic data generation, the results may depend on the method used (in this study the method implemented in PVSYST). Comparison of 8 combinations of data products, data types, representation period, and software implementation shows that deviations when comparing various tracking system and model implementation can result in deviations of the annual global in-pane irradiation. This is valid especially for older data formats, such as synthetic time series and long-term monthly-averaged long-term daily profiles. The best performance has been found with the statistically aggregated solar radiation, and this data representation has a good potential for fast calculations at marginal compromises in the quality (error is less than ±0.5). The above-mentioned differences in annual value of global irradiation affect energy simulation in PV models, which can contribute by another 2% of uncertainty or more. [5.] Cebecauer T., Šúri M., Perez R., 2010. High performance MSG satellite model for operational solar energy applications, ASES National Solar Conference, Phoenix, USA. [6.] ESRA, 2000. Greif J., Scharmer K., Eds., European Solar Radiation Atlas, 4th edition. Scientific advisors: Dogniaux R., and Page J.; Authors: Wald L., Albuisson M., Czeplak G., Bourges B., Aguiar R., Lund H., Joukoff A., Terzenbach U., Beyer H.- G., Borisenko E. P., Paris: Presses de l'ecole des Mines de Paris [7.] Šúri M., Huld T., Cebecauer T., Dunlop E.D., 2008. Geographic Aspects of Photovoltaics in Europe: Contribution of the PVGIS Web Site. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 1, 34-41. [8.] RETScreen, 2010. Clean Energy Project Analysis Software, http://www.retscreen.net/ [9.] Aguiar R., Collares-Pereira M., 1992. TAG: a timedependent, autoregressive, Gaussian model for generating synthetic hourly radiation. Solar Energy, 49, 167 174. [10.] Šúri M., Cebecauer T., Skoczek A., SolarGIS: solar data and online applications for PV planning and performance assessment. Proceeding of the EUPVSEC 2011 Conference, Hamburg, Germany, Sept 2011. [11.] Muneer, T., 1990, Solar radiation model for Europe. Building services Engineering Research and Technology, 11, 153-163 References [1.] Ineichen P., 2008. A broadband simplified version of the Solis clear sky model. Solar Energy, 82, 8, 758-762. [2.] Perez R., Ineichen P., Maxwell E., Seals R. and Zelenka A., 1992. Dynamic Global-to-Direct Irradiance Conversion Models. ASHRAE Transactions-Research Series, pp. 354-369. [3.] Skartveit A., Olseth J.A., Tuft M.A., 1998, An hourly diffuse fraction model with correction for variability and surface albedo, Solar Energy, 63, 173-183. [4.] Perez R., Seals R., Ineichen P., Stewart R., Menicucci D., 1987, A new simplified version of the Perez diffuse irradiance model for tilted surfaces, Solar Energy, 39, 221-231. 6