HOW TYPICAL IS SOLAR ENERGY? A 6 YEAR EVALUATION OF TYPICAL METEOROLOGICAL DATA (TMY3)

HOW TYPICAL IS SOLAR ENERGY? A 6 YEAR EVALUATION OF TYPICAL METEOROLOGICAL DATA (TMY3) Matthew K. Williams Shawn L. Kerrigan Locus Energy 657 Mission Street, Suite 401 San Francisco, CA 94105 matthew.williams@locusenergy.com shawn@locusenergy.com ABSTRACT The US solar industry makes bets on system performance using NREL s TMY3. TMY3 is a statistically generated 1- year dataset of typical meteorological elements, which is used to simulate solar power production. Industry uses TMY3 to evaluate potential sites, calculate performance benchmarks, and make performance guarantees. Solar irradiance naturally varies on a temporal and climactic basis, thus creating uncertainty from using TMY3. The level of this uncertainty and its bias is not well understood by the solar community. Through analysis of TMY3, the solar industry can more effectively manage their irradiance risk. Using solar irradiance observations from NOAA s Surface Radiation Network (SurfRad) from 2006-2011, TMY3 is analyzed to understand recent irradiance deviations. Understanding irradiance variance is important to understanding the risk associated with TMY3 data. This paper discusses the magnitude of temporal variation in irradiance, how TMY3 s accuracy is impacted by climactic differences, and the irradiance risk associated with using TMY3. 1. INTRODUCTION The US solar industry has experienced explosive growth in recent years, including a 109% year-over-year increase in photovoltaic (PV) installations in 2011 [1]. While there are a variety of factors influencing the rise of solar, falling costs play a large role in making solar financially viable. During the period of high growth in 2011, the cost of PV installation fell by 20% year-over-year [1]. Improving the understanding of solar resource availability and variability will also help reduce the cost of solar PV systems, since financiers will be better able to quantify risks and can therefore reduce the embedded risk premiums in loans. Solar insolation drives the generation of PV energy, thus variability in insolation causes variability in the project s income generation. Weather naturally varies over time and long term changes in solar resource have been repeatedly observed [2], [3]. To establish the financial viability of a project, the solar potential of the site must first be assessed to understand risk. There are a variety of methods available to determine a site s solar potential and resource variability. The methodology used for assessment typically depends on the scale of the planned system. For large scale projects, a bankable dataset consisting of a combination of pyranometer measurements and historical satellite-based solar insolation estimates is required. Statistical techniques are then applied to determine P50, P90, P95, and P99 production thresholds [4]. A P99 production level indicates that solar power generation will be above this level with 99% probability. While this method of solar resource assessment is effective to determine long term variability, the cost of acquiring the data is often prohibitive for smaller scale projects. To aid the development of solar power systems (in addition to energy modeling applications for buildings) for which the aforementioned methodology is too expensive, the National Renewable Energy Laboratory (NREL) developed free typical meteorological data from records from the National Solar Radiation Database (NSRDB). This data 1

includes key solar meteorological elements, such as global horizontal irradiance (GHI), direct normal irradiance (DNI), and diffuse horizontal irradiance (DHI). The most current iteration of these files is TMY3, which covers 1020 sites across the US using data from 1976-2005 or 1991-2005 when a complete history is unavailable [5]. Figure 1 shows the locations for which TMY3 data is available. NSRDB [7], physical instrumentation to measure solar irradiance does not exist at a large number of TMY3 locations. This is because of the high cost of measurement equipment, as well as the costs associated with operating and maintaining this equipment over long time periods. To properly analyze TMY3, comparison ground station site data should come from diverse geographies and climates. The National Oceanic and Atmospheric Administration (NOAA) operates the Surface Radiation Network (SurfRad), a network of seven high quality solar irradiance monitoring sites across the continental US [8]. Of these seven monitored locations, five have corresponding TMY3 stations. Figure 2 shows the location of the 5 sites that were used to analyze TMY3. Fig. 1: Map of TMY3 Stations across US. Credit NREL [6]. TMY3 data has gained widespread usage in the solar industry because it has enabled small and medium scale project developers to evaluate a site s solar resource without cost. Additionally, the data is being used for benchmarking PV system production and calculating system performance guarantees. While TMY3 data can be used for benchmarking and performance guarantees, this is not the target use case TMY3 was designed to address, and using the data for these purposes involves risk. According to the Users Manual for TMY3 Data Sets Because they represent typical rather than extreme conditions, they are not suited for designing systems to meet the worst-case conditions occurring at a location [5]. Usage of TMY3 data for the aforementioned purposes introduces a risk from the natural variation of solar irradiance. This risk causes the solar industry to behave more cautiously when using TMY3 and consequentially the cost of this risk is priced into installation cost. Providing a better understanding of this uncertainty will assist the solar industry in using TMY3 in a more effective manner. 2. DATA To determine the uncertainty behind TMY3, the typical solar resource data must be compared to ground station solar irradiance measurements at analogous locations. However, due to the fact that many of the TMY3 stations were generated from modeled solar data coming from the Fig. 2: Map of SurfRad sites with equivalent TMY3 stations. These five locations are well dispersed geographically, allowing for a robust analysis of TMY3 that is not locationally dependent. This dispersion also results in the fact that the TMY3/SurfRad stations are in a variety of climates [9], allowing for solar resource variation to be explored based on climate. Additionally, the elevations of these sites are widely varied. Table 1 shows each site s respective location, elevation, and climate. Table 1: TMY3/SURFRAD SITE DESCRIPTION Site Name Latitude Longitude Elevation Climate Bondville, IL 40.05-88.37 213 m Continental Boulder, CO 40.13-105.24 1689 m Semi-Arid Desert Rock, NV 36.63-116.02 1007 m Arid Fort Peck, MT 48.31-105.1 634 m Semi-Arid Penn State, PA 40.72-77.93 376 m Humid Continental The following subsections further describe these sites in further detail, covering the data acquired from both TMY3 and SurfRad. 2

2.1 Typical Meteorological Year 3 As was mentioned previously, TMY3 data is generated from meteorological records coming from the NSRDB. The NSRDB classifies each station based on uncertainty of the data, with classes ranging from the most certain (Class I) to the least certain (Class III) [7]. Class III stations have some incomplete records. To address missing data, TMY3 datasets do not include any partial months in their statistical derivation (i.e., partial months were not used as input data). [5]. For the TMY3/SurfRad sites, NSRDB records from 1991-2005 were used to generate TMY3 for these locations. However due to the eruption of Mount Pinatubo (Philippines) in June 1991, three anomalous years were excluded from the pool of records available for TMY3 creation [5]. Table 2 shows the details of NSRDB data used for TMY3 generation at the TMY3/SurfRad sites. Table 2: TMY3 CREATION SITE DETAILS Site Name NSRDB Class Pool Years Years Bondville, IL II 12 1991-2005 Boulder, CO III 12 1991-2005 Desert Rock, NV I 12 1991-2005 Fort Peck, MT II 12 1991-2005 Penn State, PA II 12 1991-2005 Based on the above datasets, the Sandia method was used to select a typical month of records for each month of the year [5]. This monthly selection method is completed by: 1. Comparing the cumulative distribution function (CDF) of meteorological elements against the respective long-term average CDF using the Finkelstein-Schafer (FS) statistic [10]. 2. Weighing each meteorological element s FS statistic by its importance (solar resource accounts for half of the weights in TMY3). 3. Identifying candidate months based on the lowest weighted FS statistic. 4. Filtering candidate months for persistence of temperature and GHI conditions. 5. Selecting the filtered candidate month with the lowest weighted FS statistic for each month of the To complete the Sandia method, the typical months are then chained together and smoothed to result in a TMY record. This methodology was applied to generate the TMY data that will be analyzed at each of the five TMY3/SurfRad sites. 2.2 Surface Radiation Network NOAA s Surface Radiation Network was created in 1993 to understand the amount of energy reaching the Earth s surface. Since 1995 SurfRad has had scientific instrumentation monitoring solar irradiance and atmospheric conditions impacting radiation transmission at a high time resolution across the US. SurfRad monitors GHI and DNI (in W/m 2 ), as well as other meteorological elements. The data was originally collected at 3-minute intervals, but in 1999 the data acquisition was upgraded to record measurements a 1-minute resolution [8]. SurfRad has created one of the highest quality solar irradiance datasets available in the world. While solar irradiance data at most of the TMY3/SurfRad sites is available back to 1995, data from 2006-2011 was chosen to evaluate TMY3, as these are years outside of the NSRDB dataset used to generate TMY3. Additionally, these years contain the most recent solar conditions available, as well as the time period in which TMY3 has been used. SurfRad has a very high monitoring uptime, including a 99.5% daily uptime for this dataset (10900 days out of 10955) [8], resulting in limited missing data. Similar to the creation of TMY3, months with gaps in data are excluded from this analysis. Table 3 shows the number of months with incomplete data at each site. Table 3: SURFRAD MONTHS WITH INCOMPLETE DATA Site Name SurfRad s dataset is ideal for evaluating TMY3 due to station location overlap, high quality measurement instrumentation, high temporal resolution data, and limited missing data. 3. ANALYSIS Number of Months Bondville, IL 7 Desert Rock, NV 9 Fort Peck, MT 6 Penn State, PA 6 Boulder, CO 7 The solar variability of the five sites was examined by comparing TMY3 solar resource values against observed solar resource data from SurfRad during 2006-2011. The solar irradiance values (W/m 2 ) from SurfRad were converted to solar insolation (Wh/m 2 ) by accounting for time interval in which the observations where taken. Insolation values from both SurfRad and TMY3 were aggregated on a temporal basis for comparison. 3

Due to the variation in the amount of solar insolation across sites and months on it is difficult to directly compare solar resource differences in Wh/m 2, thus the anomalies from TMY3 must be put into relative terms for analysis. The formula below shows how anomalies were calculated. In this formula i represents the site, j represents the time period of the data (e.g. January or summer), and k represents the year of the data. Solar resource variability differs depending on a number of factors (e.g. latitude, climate, elevation, etc.), so for a robust analysis of solar resource deviation from TMY3 the anomalies were aggregated across the five TMY3/SurfRad sites for temporal analysis and aggregated based on Köppen Geiger climate classification for climactic analysis. The following subsections discuss the solar resource anomalies at the five TMY3/SurfRad sites on a temporal and climactic basis. 3.1 Monthly Variation Since TMY3 data is generated by finding typical months, analyzing the anomalies at a monthly level provides the greatest insight into departures from typical solar resource conditions. Monthly anomalies from the five TMY3/SurfRad sites from 2006-2011 were averaged and a 95% confidence interval of this average was calculated to show the mean solar resource variation from TMY3 for each month of the Additionally, the extrema of the monthly anomalies were determined to examine the range of possible deviations from TMY3. Figures 3 and 4 present this analysis graphically for GHI and DNI, respectively. Fig. 4: Graph of monthly DNI anomaly extrema, mean, and 95% confidence interval of mean for each month of the The mean monthly anomalies and their respective 95% confidence intervals show on average how observed solar resource compares to typical values and the statistical significance of the mean anomalies. Due to the small sample size and unknown underlying distribution of anomalies, the 95% confidence interval was calculated using a bias-corrected and accelerated (BCa) bootstrap technique [11] with 1000 samples of monthly anomalies taken for each month. The confidence intervals showed that the mean observed GHI was statistically different from TMY3 GHI in six months of the year and mean observed DNI was statistically different from TMY3 DNI in five months of the year with a significance level of 5%. Of the statistically significant mean monthly anomalies, all six of the GHI anomalies were positive and four out five of the DNI anomalies were positive. The anomaly extrema and mean anomaly analysis at the five TMY3/SurfRad sites both indicate that TMY3 has a tendency towards underpredicting typical solar conditions. Despite this tendency, TMY3 accurately describes typical monthly GHI for 50% of the year and typical monthly DNI for 58% of the Overall, TMY3 generally presents a good picture of typical monthly solar resource for a location. 3.2 Seasonal Variation Fig. 3: Graph of monthly GHI anomaly extrema, mean, and 95% confidence interval of mean for each month of the To examine cyclical solar resource trends within a year, TMY3 was analyzed on a seasonal basis. Seasonal anomalies from the five TMY3/SurfRad sites from 2006-2011 were aggregated. Anomaly extrema, means, and 95% confidence intervals of the means were calculated to determine seasonal solar resource volatility and tendencies. Figures 5 and 6 graphically display the seasonal values for GHI and DNI, respectively. 4

different from TMY3 for GHI or DNI, while both winter and summer were statistically significant for both GHI and DNI. For all five statistically seasons the mean observed solar conditions were greater than TMY3 typical conditions. Fig. 5: Graph of seasonal GHI anomaly extrema, mean, and 95% confidence interval of mean for each season of the Fig. 6: Graph of seasonal DNI anomaly extrema, mean, and 95% confidence interval of mean for each season of the The analysis of seasonal variation from TMY3 displayed similar results as the monthly analysis. Observed GHI and DNI are usually greater than TMY3, again showing the predilection to underestimate typical solar resource. TMY3 correctly assesses typical GHI for 25% of seasons in the year and typical DNI for 50% of seasons in the Notwithstanding, TMY3 realistically predicts typical solar conditions, as even statistically different seasonal means are reasonably close to TMY3. 3.3 Climactic Variation The five TMY3/SurfRad sites are distributed across four climates, allowing for TMY3 to be evaluated in a variety of different climactic conditions. Arid, continental, and humid continental climates have data available from one site each and the semi-arid climate has data aggregated from two sites. Monthly solar resource anomalies for each climate from 2006-2011 were used to understand the impact of climate on the accuracy of TMY3values. Due to missing months of data for each climate, monthly anomalies were examined in aggregate rather than a month by month analysis used in section 3.1. Figures 7 and 8 graphically show the distributions of monthly anomalies by climate for GHI and DNI, respectively. As can be seen in the graphs, observed GHI and DNI can vary from TMY3 by large amounts within a season. When compared to the equivalent monthly graphics, it can be seen that the range of solar resource deviations is smaller on a seasonal basis than on a monthly basis. This reduction in volatility is due to smoothing effect of aggregating multiple months of insolation. As was found in the monthly analysis, DNI has a larger anomaly range and has greater volatility than GHI. For both GHI and DNI, all mean seasonal anomalies were positive, indicating TMY3 underestimates typical solar resource. The statistical significance of the average seasonal anomalies was determined by calculating the mean anomaly from TMY3 and its 95% confidence interval for each season. The confidence intervals were constructed using the BCa bootstrap technique with 1000 samples of seasonal anomalies taken per season. In 3 seasons for GHI and 2 seasons for DNI, observed solar resource was statistically different from TMY3 at a 5% level of significance. Fall was the only season not statistically Fig. 7: Histograms of monthly GHI anomaly by climate. 5

varying levels of precipitation received in each climate, which ultimately impacts solar resource due to cloud cover. The statistical significance of anomalies by climate was tested using the BCa bootstrap technique with 500 samples to build a 95% confidence interval. At a 5% level of significance, none of the observed GHI or DNI values for any climate were statistically different from TMY3 typical GHI and DNI. Fig. 8: Histograms of monthly DNI anomaly by climate. As can be seen in the graphics, the sample distributions of GHI and DNI monthly anomalies for each climate are asymmetric and positively skewed with the exception of the arid climate s GHI anomaly distribution. The means of the monthly anomalies for both GHI and DNI are positive with the exception of GHI in the humid continental climate. Tables 4 and 5 contain summary statistics by climate of GHI and DNI monthly anomalies, respectively. Table 4: MONTHLY GHI ANOMALY STATISTICS BY CLIMATE Site Name Mean Standard Deviation Max Min Arid 3.5% 7.8% 23.4% -19.6% Continental 9.9% 17.9% 64.2% -21.5% Humid Continental -0.2% 19.0% 57.1% -42.9% Semi-Arid 6.6% 14.4% 50.7% -27.3% Table 5: MONTHLY DNI ANOMALY STATISTICS BY CLIMATE Site Name Mean Standard Deviation Max Min Arid 3.9% 16.2% 53.4% -33.6% Continental 20.7% 34.6% 127.2% -37.7% Humid Continental 1.7% 42.5% 171.6% -60.7% Semi-Arid 9.1% 34.6% 161.7% -52.0% As can be seen in the tables, each climate has a wide range of monthly GHI and DNI anomalies, with DNI anomalies having a greater size of deviation from TMY3. Noticeably, the arid climate has a much smaller variance than other climates, while the humid continental climate has the greatest level of variation. It is hypothesized that the differences in climactic anomaly deviations occur due to the While none of the GHI or DNI anomalies were found to be statistically significant, the analysis still revealed much about the impact of climate upon solar resource variation and TMY3. Again, the analysis shows TMY3 tends to underpredict solar conditions and DNI is more volatile than GHI. Climate also drives the dispersion of solar resource anomalies, with wetter climates experiencing more deviation than drier ones. 4. CONCLUSIONS This study provided great insight into the natural variation of solar resource and in particular the deviation from the typical conditions of TMY3. GHI and DNI vary greatly across a large range in all climates and time frames tested. DNI was seen to have a greater volatility that GHI in every climate and time frame tested. On average, observed GHI and DNI are significantly different from TMY3 values for approximately half of the months and seasons within a Solar resource was observed to have greater variability in climates with higher levels of precipitation. Despite the variability seen in this study, TMY3 is a good representation of typical solar conditions. Even in months and seasons with statistically significant differences from TMY3, average insolation was usually close to typical insolation. Within in the context of this study, TMY3 tended to be conservative in estimating typical solar conditions. In the event of large deviations from TMY3, positive anomalies had a larger potential magnitude than negative anomalies. In general, TMY3 provides a good view of a location s solar potential and because it errs on the safe side, it is ideal for use in the solar industry. While this study was revealing of how typical solar energy is, further study of this topic is warranted. The quantity of sites evaluated should be expanded to better comprehend the impact of locations and climates upon solar variability. Additionally, increasing the number of years of observational data available for analysis will allow for improved detection of long term trends in solar resource variance and a greater understanding of extreme deviations from typical solar conditions. 6

5. ACKNOWLEDGEMENTS This work was done under funding from Locus Energy. 6. REFERENCES (1) Solar Energy Industries Association and GTM Research, (2012). U.S. Solar Market Insight Report: 2011 Year-In-Review. http://www.greentechmedia.com/research/ussmi (2) Wild, M. (2009). Global dimming and brightening: A review, J. Geophys. Res., 114, D00D16, doi:10.1029/2008jd011470 (3) Wild, M. et al. (2005). From dimming to brightening: Decadal changes in solar radiation at the Earth s surface. Science 308, 847 850 (4) Vignola F., C. Grover, and N. Lemon, (2011). Building a Bankable Solar Radiation Dataset. Proc. Of American Solar Energy Society s Annual Conference, Raleigh, NC (5) S. Wilcox and B. Marion, Users Manual for TMY3 Data Sets, Technical Report NREL/TP-581-43156, Revised May 2008 (6) NREL. Map of TMY3 Stations. http://rredc.nrel.gov/solar/old_data/nsrdb/1991-2005/tmy3/ustmymaps3medium.gif (7) National Solar Radiation Database 1991 2005 Update: User s Manual, Technical Report NREL/TP-581-41364, April 2007 (8) Surface Radiation Network (SurfRad): http://www.srrb.noaa.gov/surfrad/ (9) Peel, M.C., B.L. Finlayson and T.A. McMahon. (2007). Updated world map of the Köppen Geiger climate classification. Hydrol. Earth Syst. Sci. 11: 1633 1644. (10) Finkelstein J.M., Schafer R.E (1971). Improved goodness to fit tests. Biometrica 58, 641 645. (11) Efron, B. (1987). Better Bootstrap Confidence Intervals. Journal of the American Statistical Association Vol. 82, No. 397, 171 185 7