- Email: [email protected]
Performance of roof-top PV systems in Germany from 2012 to 2018
Solar Energy 194 (2019) 128–135
Contents lists available at ScienceDirect
Solar Energy journal homepage: www.elsevier.com/locate/solener
Performance of roof-top PV systems in Germany from 2012 to 2018 a,⁎
b
Henrik te Heesen , Volker Herbort , Martin Rumpler
T
c
a
Trier University of Applied Sciences, Environmental Campus Birkenfeld, Institute for Operations and Technology Management, Campusallee, 55768 HoppstädtenWeiersbach, Germany Technische Hochschule Ulm, University of Applied Sciences, Prittwitzstrasse 10, 89075 Ulm, Germany c Trier University of Applied Sciences, Environmental Campus Birkenfeld, Institute for Software Systems, Campusallee, 55768 Hoppstädten-Weiersbach, Germany b
A R T I C LE I N FO
A B S T R A C T
Keywords: PV system Performance Yield Roof-top
The photovoltaic (PV) sector is a central pillar for the global energy transition process which aims to reach the climate change mitigation goals. Roof-mounted systems in particular can make a significant contribution to greenhouse gas-free energy generation. By the end of 2018, more than 1.5 million rooftop systems have been installed in Germany. The yield data measured by monitoring systems and provided by web-based online systems from 2012 to 2018, as well as the configuration information of 23,944 PV systems, are evaluated in this publication. Long-term indicators are derived from the spatial and temporal distribution of the yield data. The yield analysis shows a typical south-north gradient in Germany. 2018 was the year with the highest yield, 2013 the year with the lowest. The annual specific yield varies between 816 kWh/kWp in 2017 at the Baltic Sea and 1049 kWh/kWp in 2018 in Bavaria. If analysis of data evaluation is narrowed to one to two years, a deviation of five percent and more from the long-term yield can be observed. In order to obtain detailed information on the quality of PV systems, at least five years of data are required. The yield data evaluation can be used by PV system operators and owners to identify an undersupply of their PV system and to initiate countermeasures.
1. Introduction Photovoltaic (PV) systems of various sizes and capacities have been installed globally in recent years. At the end of 2018, the total capacity worldwide reached 480.4 GWp with an average annual growth of about 30% (International Renewable Energy Agency, 2019). In the coming years, the PV sector will continue to play an important role in the global transition from a fossil-based energy system to a renewable and sustainable one (Teske et al., 2015; Ram et al., 2017; Jacobson et al., 2017; Breyer et al., 2018). There are several groups which have analyzed the performance and yield of PV systems with a systematic approach that focuses on the specific yield and performance ratio of these systems. Country specific evaluations have been published e. g. for Germany (te Heesen et al., 2017), the Netherlands (Tsafarakis et al., 2017), France (Gromaire and te Heesen, 2015), UK/France (Leloux et al., 2015), France/Belgium (Leloux et al., 2011), India (Schacht et al., 2014; Pooppal and te Heesen, 2015), the Netherlands/Belgium/France/Germany/Italy (Nordmann et al., 2014; Kausika et al., 2018) and eleven countries worldwide (Killinger et al., 2018). Common to all publications is that they only take up to five years of data into account.
⁎
Due to their large potential, small roof-top PV systems are now moving into the focus of research on quality and performance (Moraitis et al., 2018). In Germany, more than 1.5 million roof-top PV systems with a capacity of up to 30 kWp have been installed (see Fig. 1). These systems have reached a total capacity of 15.5 GWp (Bundesnetzagentur, 2019; Netztransparenz.de, 2019). Thus, roof-top PV systems account for a significant share of German electricity production. A yield evaluation of 23,944 roof-top PV systems in Germany over a seven-year period from 2012 to 2018 is presented in this publication. This is the first time that long-term yield data from several thousand systems within a country have been analyzed. The focus of the data evaluation is on the spatial and temporal yield distribution, as well as on the variability of the monthly and annual yield data. First, the analytical methods used are presented in Section 2. Then, the results of the data analysis and evaluation are shown in Section 3. Finally, the results in Section 4 are critically questioned and evaluated. The conclusion is presented in Section 5. 2. Methods The yield data analyzed comes from the monitoring system provider
Corresponding author. E-mail address: [email protected] (H. te Heesen). URL: http://www.umwelt-campus.de (H. te Heesen).
https://doi.org/10.1016/j.solener.2019.10.019 Received 5 June 2019; Received in revised form 27 September 2019; Accepted 10 October 2019 0038-092X/ © 2019 International Solar Energy Society. Published by Elsevier Ltd. All rights reserved.
Solar Energy 194 (2019) 128–135
H. te Heesen, et al.
Fig. 1. Total capacity (bars) and total number (blue line) of roof-top installations in Germany with a capacity up to 30 kWp. Data sources: Bundesnetzagentur (2019), Netztransparenz.de (2019). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Electrotechnical Commission, 2016) is calculated using
Rp =
Solare Datensysteme GmbH (Solare Datensysteme GmbH, 2019). The company offers PV operators various types of monitoring solutions. The configuration of the PV system and the yield data are available on a web-based platform. Scraping scripts are used to collect information from PV systems by accessing public and freely available monitoring data. The following configuration data of each PV system is used for the evaluation: Year of grid connection, location (postcode, city), orientation(s) and inclination(s) of the PV modules, capacity, daily yield data. A higher temporal resolution is not used to reduce the amount of data. The regional distribution of PV systems with a capacity up to 30 kWp is shown in Fig. 2. The yield data shown are from 1 January 2012 to 31 December 2018. A total of 23,944 PV systems are included in the evaluation. The key indicator for evaluation is the specific yield Y (or full load hours) of a PV system. The specific yield is the quotient of the energy yield E within a time interval (day, month, year) and the capacity P of a PV system
The
E . P performance
3. Results 3.1. Data quality The regional distribution of the 23,944 PV systems is shown in Fig. 2 for each two-digit postcode region. Bavaria and Baden-Wurttemberg have a high density of PV systems, while the density in eastern Germany is smaller. This density correlates with the population density and
(1) ratio
Rp
as
a
key
indicator
(2)
where Yr is the reference yield. The hourly horizontal direct and diffuse irradiation data for calculation of the reference yield is taken from CAMS (ECMWF - COPERNICUS Atmosphere Monitoring Service, 2019). The reference location for the PV systems is set in the center of the 95 two-digit postcode regions. The reference yield for each PV system is computed by using pvlib (Holmgren et al., 2018) under consideration of the orientation and the inclination of the PV systems scraped from the website of Solare Datensysteme GmbH (Solare Datensysteme GmbH, 2019). This procedure for calculating the irradiation into the inclined module plane leads to sufficiently accurate irradiance data (Ruf et al., 2016). There is no consideration of effects like shading as the aim of this publication is the analysis and visualisation of the yield and performance of PV systems. The focus of this publication lies on the yield analysis, the performance ratio will only take a small part of the data evaluation and will be an issue for further analysis. The data analysis presented here only take into account PV systems that have provided more than 350 days of daily yield data in each reporting year as used by Kausika (Kausika et al., 2018). This means that a PV system must provide at least 350 days of operating data communication (which can also include a daily yield of 0 kWh). Systems with an annual specific yield of more than 1500 kWh/kWp are also neglected in the analysis, since such high yield values of PV systems in Germany cannot be technically achieved due to irradiation conditions. The PV systems are regionally clustered according to the two-digit postcode. In order to reduce the possible occurrence of regional yield hotspots, the yield data of PV systems are aggregated within a two-digit postcode region and its neighbouring postcode areas (te Heesen and Herbort, 2016).
Fig. 2. Regional distribution of PV systems included in data analysis. The number of PV systems is shown for each two-digit postcode in Germany.
Y=
Y , Yr
(International 129
Solar Energy 194 (2019) 128–135
H. te Heesen, et al.
coast. 2013 was the worst solar year in the considered period without the typical south-north gradient and with an average specific yield of 901 kWh/kWp. The yield distribution in 2014 varied between 1003 kWh/kWp in Baden-Wurttemberg and 896 kWh/kWp in North Rhine-Westphalia. 2015 showed a similar yield distribution to 2012 with high yield values in the south and southwest and small ones at the North Sea coast. In 2016, an even yield distribution was observed. The highest annual specific yield was 972 kWh/kWp in Baden-Wurttemberg and lowest yield was 875 kWh/kWp in Schleswig-Holstein. The smallest yield in the period 2012 to 2018 was found at the Baltic Sea coast in 2017. Only 816 kWh/kWp have been produced by PV systems in this region, although the annual yield in southern Germany reaches 1010 kWh/kWp. Finally, 2018 was a record year with a minimum yield of 942 kWh/kWp in northern Germany and 1049 kWh/kWp in the south. Overall, the typical southeast-northwest gradient of the annual yield is observed over the long term. The yield data in the maps correlate to the annual specific yield in Table 1 for the federal states in Germany. The procedure for annual yield data collection is described above. PV systems with an energy production of less than 350 days are neglected. The systems are clustered by federal states. The three city states (Bremen/Hamburg and Berlin) are assigned to the corresponding federal states. On average, an annual specific yield of 960 ± 141 kWh/kWp can be expected for rooftop PV systems in Germany. The years 2013, 2014, 2016, and 2017 were years that generated below the long-term average. 2012, 2015, and 2018 in contrast generated above the average yield. The southern federal states of Germany (Baden-Wurttemberg and Bavaria) have the highest yield values due to higher solar irradiance, the northwestern federal states the lowest yield (Schleswig-Holstein and Lower Saxony). A closer look at specific yield distribution can be found in Fig. 5. The boxplots represent annual yield density of roof-top PV systems in Germany with at least 350 days of yield data. The PV systems are not regionally clustered, the plots represent nationwide system data. In 2013, solar yield was the lowest. In comparison, and as mentioned above, 2018 shows the highest specific yield in the period considered. The interquartile range (difference between the first and the third quartile) is approximately 200 kWh/kWp. Although PV systems with more than 350 operating days have been taken into account, there are still PV systems with annual specific yield of 800 kWh/kWp or less due most likely to severe technical issues. The yield values from 2012 and 2013 correspond to the data of Nordmann (Nordmann et al., 2014) and from 2014 to 2016 to Kausika (Kausika et al., 2018). If PV systems with insufficient data quality are added to the evaluation, they will enlarge the length of the whiskers. The distribution of the performance ratio between 2012 and 2018 regarding the year of installation (between 2009 and 2017) is shown in Fig. 6. The PR values were calculated with the annual yield (aggregation of the daily yield values) and the annual reference yield (derived from the hourly irradiation values in module plane). The median PR of all considerable systems varies between 0.78 in 2012 and 0.72 in 2016. The shown performance ratio corresponds to PR data of Reich (Reich et al., 2012) and Kausika (Kausika et al., 2018). The authors reserve the analysis of the performance ratio on a higher spatial and temporal level for future work.
Fig. 3. Data quality of the PV systems. The annual specific yield values of each PV system are correlated with the number of days with data. Each point represents the annual specific yield and the number of data transmission days of a PV system.
the distribution of solar radiation in Germany. Fig. 3 takes a closer look at the quality of the yield data. The annual specific yield of each PV system in the period 2012 to 2018 is correlated to the number of days with data transmission. A data point of a system with a daily yield of 0 kWh/kWp is considered as a valid data transmission entry, although the system has not fed any energy into the grid due to a technical malfunction. The histogram of each variable is shown at the top (specific yield) and right (number of days) of the diagram. The specific yield shows a negative skew. The annual specific yield increases in accordance with the number of days of data transmission. 3.2. Annual yield analysis The regional distribution of the annual specific yield of roof-top PV systems in Germany from 2012 to 2018 and the long-term averages are shown in Fig. 4. The color bar is identical for all maps and ranges from 810 to 1060 kWh/kWp. The yield data of all PV systems with more than 350 days of data are clustered according to the two-digit postcode and its neighboring postcodes. Clustering reduces the occurrence of regional hotspots. The average yield for all 95 postcode regions in Germany is calculated and shown in Fig. 4. The individual years differ in the regional yield distribution. There are years with a large south-north yield gradient (2012, 2015, 2017, and 2018) or with a uniform yield pattern (2013, 2014, and 2016). The maximum and minimum annual specific yield also changes due to different irradiation conditions. 2013 was the worst year with an average yield of 901 ± 125 kWh/kWp in Germany. 2018 was the best year with an average specific yield of 1022 ± 151 kWh/kWp. On a long-term average, the smallest yield values can be observed in the northwest of Germany (North Rhine-Westphalia and Lower Saxony), the largest yield values in the south of the country (Baden-Wurttemberg and Bavaria). This corresponds to the typical climate conditions and weather patterns in Germany. 2012 is the first year with a sufficient number of PV systems for evaluation. The highest yield of more than 1000 kWh/kWp was recorded in Baden-Wurttemberg, Bavaria, Rhineland-Palatinate, Saarland, and Saxonia, lowest yield was recorded at the North Sea
3.3. Seasonal yield data The seasonal yield data provide more accurate insight into the variability of energy production over the period from 2012 to 2018. Fig. 7 shows the distribution of the monthly specific yield independent of the year. December and January can be clearly identified as winter months with a specific yield of less than 50 kWh/kWp. November can also be counted as a winter month, although it belongs to autumn. In February, the variability of energy production increases significantly with values between 0 (snow cover) and 85 kWh/kWp. In the spring months March, April, and May, the specific yield reaches values 130
Solar Energy 194 (2019) 128–135
H. te Heesen, et al.
Fig. 4. Distribution of the annual specific yield in Germany from 2012 to 2018 and for the long-term average (bottom center) based on the two-digit postcode. Green colors show a smaller specific yield, red colors show a larger one. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Table 1 Mean annual specific yield and standard deviation of the PV systems in the German federal states from 2012 to 2018. All values are given in kWh/kWp. BB: Brandenburg & Berlin, BW: Baden-Wurttemberg, BY: Bavaria: HE: Hesse, MV: Mecklenburg-Vorpommern, NI: Lower Saxony & Bremen & Hamburg, NW: North Rhine-Westphalia, RP: Rhineland-Palatinate, SH: Schleswig-Holstein, SL: Saarland, SN: Saxonia, ST: Saxony-Anhalt, TH: Thuringia, DE: Germany. State
2012
2013
2014
2015
BB BW BY HE MV NI NW RP SH SL SN ST TH
974 ± 130 1063 ± 132 1017 ± 138 964 ± 131 870 ± 205 892 ± 118 897 ± 129 1004 ± 157 881 ± 110 1000 ± 133 1022 ± 109 988 ± 102 972 ± 103
916 ± 127 932 ± 119 919 ± 119 886 ± 125 916 ± 179 872 ± 121 861 ± 116 911 ± 134 904 ± 108 908 ± 105 891 ± 134 932 ± 97 823 ± 174
963 ± 130 1003 ± 136 976 ± 126 922 ± 131 940 ± 173 916 ± 124 896 ± 133 964 ± 127 914 ± 142 961 ± 124 964 ± 151 945 ± 142 919 ± 154
990 ± 140 1041 ± 149 1020 ± 129 964 ± 131 929 ± 177 919 ± 121 934 ± 130 978 ± 140 891 ± 151 979 ± 130 1016 ± 191 989 ± 130 965 ± 160
954 972 967 893 901 892 900 923 875 907 936 947 917
DE
980 ± 149
901 ± 125
956 ± 137
985 ± 144
131
2016
2017
2018
116 136 125 132 162 124 130 118 129 130 164 128 126
897 ± 119 1010 ± 140 992 ± 127 908 ± 135 816 ± 165 833 ± 117 866 ± 113 966 ± 127 836 ± 137 956 ± 132 930 ± 152 884 ± 117 913 ± 121
1037 ± 161 1040 ± 149 1049 ± 141 999 ± 158 995 ± 179 981 ± 141 1001 ± 139 1022 ± 151 942 ± 171 1017 ± 151 1045 ± 141 1046 ± 134 978 ± 152
936 ± 135
939 ± 146
1022 ± 151
± ± ± ± ± ± ± ± ± ± ± ± ±
Solar Energy 194 (2019) 128–135
H. te Heesen, et al.
Fig. 5. Distribution of the annual specific yield from 2012 to 2018 of roof-top PV systems in Germany.
Fig. 7. Monthly specific yield of roof-top PV systems in the period 2012 to 2018.
between 30 and 190 kWh/kWp. This is due in particular to a large variability in May. The summer months June, July, and August show a constant yield level with a median yield of 125 kWh/kWp. The specific yield begins to decline in September and continues to decline in October. Fig. 8 takes a closer look at the seasonal variations. The boxplot shows the monthly specific yield from 2012 to 2018. The monthly yield data of all PV systems are used for the evaluation. Only systems with a daily specific yield that is less than 10 kWh/kWp are considered, since systems with a wrong configuration or an error in data acquisition are excluded. The installation year is also taken into account. The typical seasonal yield behavior in Germany can be clearly identified. The median of the specific yield varies in winter from 15 to 50 kWh/kWp and in summer from 110 to 150 kWh/kWp. The yield spread (interquartiles range) of the high-yield months (April to September) remains at a constant level of about 25 kWh/kWp. The months of the individual years can differ greatly from each other. For example, the monthly yield in July 2013 is the greatest, although the annual yield of 2013 is the least in the considered period. The other
months of 2013 are at the lower end of the monthly yield scale. On the other hand, 2018 is the year with the largest yield, but March and June 2018 are only average months. Thus, the yield data for one or more months do not allow for interpretation of the entire year. Typically, there are no general weather conditions over several weeks that lead to extraordinary small or large yield. The summer of 2018 (red color) is the first time to show such behavior with highest yield values from April to Oktober excluding June.
4. Discussion 4.1. Long-term yield data The evaluation of the monthly yield values of the PV systems under consideration (at least 350 days of yield data) in Germany compared to a reference yield is shown in Fig. 9. The blue boxplots represent the yield data of the PV systems for each month in the period from 2012 to 2018. PV systems with available daily yield data of more than 350 days in each year have been taken into account. There are no spatial
Fig. 6. Distribution of performance ratio between 2012 and 2018 for PV systems that have been installed from 2009 to 2017 (represented by the different colors). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) 132
Solar Energy 194 (2019) 128–135
H. te Heesen, et al.
Fig. 10. First, the average annual specific yield of PV systems with yield data of more than 350 days is derived for each two-digit postcode and the neighboring postcode regions. The average yield per postcode region is calculated for one, two, and up to six consecutive years. For example, using three consecutive years means that the mean annual yield for all PV systems in a postcode region is calculated for all possible three consecutive annual periods (2012–2014, 2013–2015, 2014–2016, 2015–2017, and 2016–2018). These mean annual yield values refer to the long-term average yield from 2012 to 2018. This ratio is calculated for all 95 two-digit postcodes in Germany. Finally, all relative deviation for each time period (one to six consecutive years) for all postcode regions is analyzed in a box-and-whisker diagram. If only one year of yield data is used for evaluation of a PV system, the deviation from the long-term yield can be between −10 percent (underestimation of the long-term yield) and +12 percent (overestimation of the long-term yield). If two consecutive years of yield data are available, the relative deviation decreases to ±5 percent. The more years available with annual yield information, the smaller the deviation from the long-term trend. Five consecutive years show a maximum deviation of ±2 percent from the long-term average. Therefore, at least five consecutive years of yield information are required for data evaluation to reduce evaluation artefacts by years with extraordinary large or small yield. A single year or two years cannot provide information about the quality of a PV system due to variability of solar irradiation and yield production within a region. A two-dimensional histogram of the annual specific yield and the capacity of roof-top systems is shown in Fig. 11. The frequency and the correlation of the annual specific yield of all PV systems with more than 350 days of sufficient yield information in relation to the installed capacity are analyzed. Brighter blue colors represent a larger amount of PV systems. Capacity is drawn on the vertical axis, the yield on the horizontal axis. The frequency of the two values is shown at the top and the right of the diagram. The histogram of the annual specific yield and the capacity shows no dependency between both variables. A larger capacity of roof-top systems does not lead to a higher annual yield. In particular, very small roof-top system (capacity smaller than 5 kWp), may be exposed to a higher shadow risk due to surrounding obstacles and may show a similar yield of roof-top systems with a capacity of more than 15 kWp (corresponds to a roof area of 90 to 180 m2).
Fig. 8. Boxplots of the monthly specific yield from 2012 to 2018 of all PV systems in Germany.
4.2. Economic and environmental impacts The impacts of the yield analysis of the roof-top PV systems can be distinguished in economic and environmental terms. First, the annual revenue of all PV systems in Germany smaller than 30 kWp and the range of the specific revenue in € per kWp is shown. Second, the annual reduction of greenhouse gas (GHG) emissions by PV systems is discussed. Table 2 shows several economic aspects of the roof-top PV systems in Germany. The average annual specific yield (see Table 1) multiplied by the average feed-in tariff of PV systems in Germany (Federal Ministry for Economic Affairs and Energy, 2018). The total annual feedin tariff of all PV systems from 2012 to 2018 amounts to approximately 4.1 billion€. The share of small rooftop systems is around 40 percent of the annual EEG compensation payments. The specific revenue reflects the expected range of payments per installed capacity in kWp. This values can be very useful for financial investigations of PV systems. The minimum or maximum specific value (fifth or seventh column in Table 2) is the product of the mean feed-in tariff and the minimum or maximum annual specific yield in Germany (Table 1). The range of the specific revenue reflects effects like minor technical issues, shading due to obstacles or spatial artefacts like fog. The calculation of the mean specific yield is based on the nationwide mean specific yield. The minimum specific revenue varies between 194 and 241 €/kWp, the mean revenue between 280 and 345 €/kWp, and
Fig. 9. Boxplot of the monthly yield in the period 2012 to 2018 of PV systems in Germany. The colored area represents the reference yield (te Heesen et al., 2018, 2017; te Heesen et al., 2013; te Heesen et al., 2014, 2019).
restrictions. The daily yield data are aggregated to monthly values. The reference yield is taken from Heesen et al. (te Heesen et al., 2017; te Heesen et al., 2013; te Heesen et al., 2014, 2019) and is a result of a two-step data cleansing algorithm and is divided into three areas the yield of very well maintained PV systems is green, of sufficient systems is yellow and of insufficiently maintained systems is red. The well maintained PV systems are represented by the upper fifty percent of the systems (monthly yield better than the upper quartile (light green) or better than the median (green color)). Sufficiently maintained systems (yellow color) belong to the group of systems with a monthly yield between the lower quartile and the median. All other systems (monthly yield worse than the lower quartile) are marked as insufficient systems (red color). The yield deviation from the long-term yield average is shown in 133
Solar Energy 194 (2019) 128–135
H. te Heesen, et al.
Fig. 10. Boxplot of the average yield deviation from the long-term yield average. The years indicate the number of consecutive years of yield data for the calculation of the average. Table 3 Environmental impact of roof-top PV systems in Germany. The substitution factor is taken from (Umweltbundesamt, 2018). The annual reduction of greenhouse gas (GHG) emissions remains constant due to an increase in the annual yield and a decrease in the substitution coefficient. Year 2012 2013 2014 2015 2016 2017 2018
Annual yield 11.6 11.6 12.9 13.7 13.4 13.8 15.6
TWh TWh TWh TWh TWh TWh TWh
Substitution factor 716 g 706 g 675 g 655 g 614 g 614 g 614 g
CO2 CO2 CO2 CO2 CO2 CO2 CO2
Annual savings of GHG emissions
eq/kWh eq/kWh eq/kWh eq/kWh eq/kWh eq/kWh eq/kWh
8.5 8.4 8.9 9.1 8.3 8.6 9.7
Mt Mt Mt Mt Mt Mt Mt
CO2 CO2 CO2 CO2 CO2 CO2 CO2
eq eq eq eq eq eq eq
the maximum revenue between 329 and 421 €/kWp. The environmental impacts of roof-top PV systems in Germany are shown in Table 3. Substitution factors show the savings of CO2 emissions by photovoltaics in the German electricity mix in the individual years (Umweltbundesamt, 2018). The annual yield of all roof-top PV systems multiplied by the substitution factor gives the total annual savings from roof-top PV in Megatons of CO2 equivalent. The annual savings have increased from 8.3 Mt CO2 eq in 2016 to 9.7Mt CO2 eq in 2018. Fig. 11. Histogram and correlation of the annual yield of PV systems with the capacity of the system. The colors white and light blue represent a large number of PV systems, the colours dark green and light green a small number of systems. There is no correlation between the yield and the capacity increase of rooftop systems. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
5. Conclusion Daily yield data of 23,944 roof-top photovoltaic (PV) systems in Germany from 2012 to 2018 have been taken from an online monitoring database and are used for evaluation purposes. The system configuration and location were also taken into account. The data have been aggregated spatially and temporally for analysis. The analysis
Table 2 Economic impacts of roof-top PV systems in Germany. The total installed capacity of PV systems in Germany smaller than 30 kWp is shown in Fig. 1. The average feed-in tariff is taken from Federal Ministry for Economic Affairs and Energy (2018). Year 2012 2013 2014 2015 2016 2017 2018
Installed capacity 12.1 13.1 13.7 14.1 14.4 15.0 15.5
GWp GWp GWp GWp GWp GWp GWp
Mean feed-in tariff 35.2 32.0 31.6 30.8 30.3 29.8 27.5
ct/kWh ct/kWh ct/kWh ct/kWh ct/kWh ct/kWh ct/kWh
Mean annual revenue € € € € € € €
4.1b 3.8b 4.1b 4.3b 4.1b 4.2b 4.4b
Min. spec. revenue 234 208 241 228 224 194 212
134
€/kWp €/kWp €/kWp €/kWp €/kWp €/kWp €/kWp
Mean spec. revenue 345 288 302 303 284 280 281
€/kWp €/kWp €/kWp €/kWp €/kWp €/kWp €/kWp
Max. spec. revenue 421 350 360 372 336 343 329
€/kWp €/kWp €/kWp €/kWp €/kWp €/kWp €/kWp
Solar Energy 194 (2019) 128–135
H. te Heesen, et al.
focuses on the specific yield of PV systems. The annual yield distribution in Germany typically shows a southnorth gradient. 2018 is the year with the highest yield in the considered period, 2013 is the year with the lowest. The yield data correspond to the results of other groups (Reich et al., 2012; Nordmann et al., 2014; Kausika et al., 2018; Killinger et al., 2018), which have also analyzed the yield of PV system in Germany in the given period. In order to identify long-term trends and interpret the annual yield data of PV systems, at least five years of data are required. The temporal and spatial variation of the energy yield of only one to two years does not allow any conclusions to be drawn about the technical quality of a PV system. If the yield of the PV systems are assumed to be representative for all roof-top PV systems smaller than 30 kWp in Germany, these systems have an annual revenue of approximately EUR 4.1 billion and an annual saving of greenhouse gases of 8.3 to 9.7 Megatons of CO2 equivalent.
176_h.teheesen/dokumente/Publikationen/Ertragsstudie_2012.pdf. Holmgren, W.F., Hansen, C.W., Mikofski, M.A., 2018. pvlib python: a python package for modeling solar energy systems. J. Open Source Softw. 3, 884–886. https://doi.org/ 10.21105/joss.00884. International Electrotechnical Commission, 2016. Photovoltaic system performance - Part 3: Energy evaluation method. RFC IEC TS 61724–3:2016. International Electrotechnical Commission. Geneva, Switzerland. International Renewable Energy Agency, 2019. Renewable Energy Sources - Solar Energy. URL: https://irena.org/solar. Jacobson, M.Z., Delucchi, M.A., Bauer, Z.A., Wang, J., Weiner, E., Yachanin, A.S., 2017. 100% clean and renewable wind, water, and sunlight all-sector energy roadmaps for 139 countries of the world. Joule 1, 1–14. https://doi.org/10.1016/j.joule.2017.07. 005. Kausika, B.B., Moraitis, P., van Sark, W.G.J.H.M., 2018. Visualization of operational performance of grid-connected PV systems in selected European countries. Energies 11, 1330–1339. https://doi.org/10.3390/en11061330. Killinger, S., Lingfors, D., Saint-Drenan, Y.M., Moraitis, P., van Sark, W.G.J.H.M., Taylor, J., Engerer, N.A., Bright, J.M., 2018. On the search for representative characteristics of PV systems: data collection and analysis of PV system azimuth, tilt, capacity, yield and shading. Sol. Energy 173, 1087–1106. https://doi.org/10.1016/j.solener.2018. 08.051. Leloux, J., Narvarte, L., Trebosc, D., 2011. Performance analysis of 10,000 residential PV systems in France and Belgium. In: 26th European Photovoltaic Solar Energy Conference and Exhibition, pp. 3939–3946. doi: https://doi.org/10.4229/ 26thEUPVSEC2011-5AO.6.4. Leloux, J., Taylor, J., Moretón, R., Narvarte, L., Trebosc, D., Desportes, A., 2015. Monitoring 30,000 PV systems in Europe: performance, faults, and state of the art. In: 31st European Photovoltaic Solar Energy Conference and Exhibition, pp. 1574–1582. doi: https://doi.org/10.4229/EUPVSEC20152015-5AO.8.1. Moraitis, P., Kausika, B., Nortier, N., van Sark, W., 2018. Urban environment and solar PV performance: the case of the Netherlands. Energies 11, 1333. https://doi.org/10. 3390/en11061333. Netztransparenz.de, 2019. Informationsplattform der deutschen Übertragungsnetzbetreiber. URL: https://www.netztransparenz.de/EEG/ Anlagenstammdaten. Nordmann, T., Clavadetscher, L., van Sark, W.G.J.H.M., Green, M., 2014. Analysis of Long-Term Performance of PV Systems. Utrecht University Repository, Utrecht. Pooppal, A., te Heesen, H., 2015. Survey on Specific Yield of Photovoltaic Systems in India 2014. In: 31st European Photovoltaic Solar Energy Conference and Exhibition, Hamburg. pp. 2307–2310. doi: https://doi.org/10.4229/EUPVSEC20152015-5BV. 2.45. Ram, M., Bogdanov, D., Aghahosseini, A., Oyewo, S., Gulagi, A., Child, M., Breyer, C., Fell, H.J., 2017. Global Energy System based on 100 percent Renewable Energy Power Sector. Technical Report. Lappeenranta University of Technology. URL: http://www.neocarbonenergy.fi/wp-content/uploads/2016/02/14_Ram.pdf. Reich, N.H., Müller, B., Armbruster, A., van Sark, W.G.J.H.M., Kiefer, K., Reise, C., 2012. Performance ratio revisited: is PR > 90% realistic? Prog. Photovoltaics Res. Appl. 20, 717–726. https://doi.org/10.1002/pip.1219. Ruf, H., Schroedter-Homscheidt, M., Heilscher, G., Beyer, H.G., 2016. Simulation of the load flow at the transformer in low voltage distribution grids with a significant number of pv systems using satellite-derived solar irradiance. Sol. Energy 135, 692–702. https://doi.org/10.1016/j.solener.2016.06.001. Schacht, V., Häberle, A., Warmuth, W., Mitra, I., Kumar, A., Srivastava, A., 2014. PV performance benchmarking in India. Results from the project solar mapping and monitoring - SolMap. In: 29th European Photovoltaic Solar Energy Conference and Exhibition, pp. 2642–2645. doi: https://doi.org/10.4229/EUPVSEC20142014-5BV. 1.21. Solare Datensysteme GmbH, 2019. Energy Management System Solar-Log. URL: https:// www.solar-log.com. Teske, S., Sawyer, S., Schäfer, O., Pregger, T., Simon, S., Naegler, T., Schmid, S., Özdemir, D., Pagenkopf, J., Kleiner, F., Rutovitz, J., Dominsh, E., Downes, J., Ackermann, T., Brown, T., Boxer, S., Baitelo, R., Rodriguez, L.A., 2015. energy revolution. A Sustainable World. Energy Outlook 2015. Technical Report 5. Greenpeace International. Tsafarakis, O., Moraitis, P., Kausika, B.B., van der Velde, H., ’t Hard, S., de Vries, A., de Rijk, P., de Jong, M.M., van Leeuwen, H.P., van Sark, W.G.J.H.M., 2017. Three years experience in a Dutch public awareness campaign on photovoltaic system performance. IET Renew. Power Gener. 11, 1229–1233. https://doi.org/10.3390/ en11040977. Umweltbundesamt, 2018. Emissionsbilanz erneuerbarer Energieträger. URL: https:// www.umweltbundesamt.de/publikationen/emissionsbilanz-erneuerbarerenergietraeger.
Declaration of Competing Interest The authors declare no conflicts of interest. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. References Breyer, C., Bogdanov, D., Aghahosseini, A., Gulagi, A., Child, M., Oyewo, S., Farfan, J., Sadovskaia, K., Vainikka, P., 2018. Solar photovoltaics demand for the global energy transition in the power sector. Prog. Photovoltaics Res. Appl. 26, 505–523. https:// doi.org/10.1002/pip.2950. Bundesnetzagentur, 2019. Photovoltaikanlagen: Datenmeldungen sowie EEGVergütungssätze. URL: http://www.bundesnetzagentur.de. ECMWF - COPERNICUS Atmosphere Monitoring Service, 2019. CAMS Radiation Service. URL: http://www.soda-pro.com/en/web-services/radiation/cams-radiation-service. Federal Ministry for Economic Affairs and Energy, 2018. EEG in Zahlen: Vergütungen, Differenzkosten und EEG-Umlage 2000 bis 2019. URL: https://www.erneuerbareenergien.de/EE/Redaktion/DE/Downloads/eeg-in-zahlen-pdf.pdf. Gromaire, M., te Heesen, H., 2015. Survey on Specific Yield of Photovoltaic Systems in France 2014. In: 31st European Photovoltaic Solar Energy Conference and Exhibition, Hamburg. pp. 2315–2317. doi: https://doi.org/10.4229/EUPVSEC20152015-5BV. 2.47. te Heesen, H., Herbort, V., 2016. Development of an algorithm to analyze the yield of photovoltaic systems. Renew. Energy 87, 1016–2022. te Heesen, H., Herbort, V., Pfatischer, R., 2014. Survey on yield of PV systems in Germany 2013. In: 29th European Photovoltaic Solar Energy Conference and Exhibition, Amsterdam. pp. 2198–2201. doi: https://doi.org/10.4229/EUPVSEC20142014-5AO. 7.3. te Heesen, H., Herbort, V., Rumpler, M., 2017. Survey on Yield of PV Systems in Germany 2014 to 2016. In: 33rd European Photovoltaic Solar Energy Conference and Exhibition, Amsterdam. pp. 1901–1903. doi: https://doi.org/10.4229/ EUPVSEC20172017-6BO.7.1. te Heesen, H., Herbort, V., Rumpler, M., 2018. Untersuchung des Ertrags von Photovoltaikdachanlagen bis 30 kWp in Deutschland im Zeitraum 2014 bis 2017. In: Czarnecki, C., Brockmann, C., Sultanow, E., Koschmider, A., Selzer, A. (Eds.), GIEdition Lecture Notes in Informatics. Workshops der INFORMATIK 2018 Architekturen, Prozesse, Sicherheit und Nachhaltigkeit, Berlin. pp. 177–188. te Heesen, H., Herbort, V., Rumpler, M., 2019. Studie zum Ertrag von Photovoltaikdachanlagen 2018 in Deutschland. Technical Report. Hochschule Trier. URL: https://www.umwelt-campus.de/ucb/index.php?id=ertragsstudie. te Heesen, H., Herbort, V., Pfatischer, R., 2013. Studie zum Ertrag von Photovoltaikanlagen in Deutschland 2012. Hochschule Trier und meteocontrol GmbH. Online verfügbar: https://www.umwelt-campus.de/ucb/fileadmin/users/
135
Contents lists available at ScienceDirect
Solar Energy journal homepage: www.elsevier.com/locate/solener
Performance of roof-top PV systems in Germany from 2012 to 2018 a,⁎
b
Henrik te Heesen , Volker Herbort , Martin Rumpler
T
c
a
Trier University of Applied Sciences, Environmental Campus Birkenfeld, Institute for Operations and Technology Management, Campusallee, 55768 HoppstädtenWeiersbach, Germany Technische Hochschule Ulm, University of Applied Sciences, Prittwitzstrasse 10, 89075 Ulm, Germany c Trier University of Applied Sciences, Environmental Campus Birkenfeld, Institute for Software Systems, Campusallee, 55768 Hoppstädten-Weiersbach, Germany b
A R T I C LE I N FO
A B S T R A C T
Keywords: PV system Performance Yield Roof-top
The photovoltaic (PV) sector is a central pillar for the global energy transition process which aims to reach the climate change mitigation goals. Roof-mounted systems in particular can make a significant contribution to greenhouse gas-free energy generation. By the end of 2018, more than 1.5 million rooftop systems have been installed in Germany. The yield data measured by monitoring systems and provided by web-based online systems from 2012 to 2018, as well as the configuration information of 23,944 PV systems, are evaluated in this publication. Long-term indicators are derived from the spatial and temporal distribution of the yield data. The yield analysis shows a typical south-north gradient in Germany. 2018 was the year with the highest yield, 2013 the year with the lowest. The annual specific yield varies between 816 kWh/kWp in 2017 at the Baltic Sea and 1049 kWh/kWp in 2018 in Bavaria. If analysis of data evaluation is narrowed to one to two years, a deviation of five percent and more from the long-term yield can be observed. In order to obtain detailed information on the quality of PV systems, at least five years of data are required. The yield data evaluation can be used by PV system operators and owners to identify an undersupply of their PV system and to initiate countermeasures.
1. Introduction Photovoltaic (PV) systems of various sizes and capacities have been installed globally in recent years. At the end of 2018, the total capacity worldwide reached 480.4 GWp with an average annual growth of about 30% (International Renewable Energy Agency, 2019). In the coming years, the PV sector will continue to play an important role in the global transition from a fossil-based energy system to a renewable and sustainable one (Teske et al., 2015; Ram et al., 2017; Jacobson et al., 2017; Breyer et al., 2018). There are several groups which have analyzed the performance and yield of PV systems with a systematic approach that focuses on the specific yield and performance ratio of these systems. Country specific evaluations have been published e. g. for Germany (te Heesen et al., 2017), the Netherlands (Tsafarakis et al., 2017), France (Gromaire and te Heesen, 2015), UK/France (Leloux et al., 2015), France/Belgium (Leloux et al., 2011), India (Schacht et al., 2014; Pooppal and te Heesen, 2015), the Netherlands/Belgium/France/Germany/Italy (Nordmann et al., 2014; Kausika et al., 2018) and eleven countries worldwide (Killinger et al., 2018). Common to all publications is that they only take up to five years of data into account.
⁎
Due to their large potential, small roof-top PV systems are now moving into the focus of research on quality and performance (Moraitis et al., 2018). In Germany, more than 1.5 million roof-top PV systems with a capacity of up to 30 kWp have been installed (see Fig. 1). These systems have reached a total capacity of 15.5 GWp (Bundesnetzagentur, 2019; Netztransparenz.de, 2019). Thus, roof-top PV systems account for a significant share of German electricity production. A yield evaluation of 23,944 roof-top PV systems in Germany over a seven-year period from 2012 to 2018 is presented in this publication. This is the first time that long-term yield data from several thousand systems within a country have been analyzed. The focus of the data evaluation is on the spatial and temporal yield distribution, as well as on the variability of the monthly and annual yield data. First, the analytical methods used are presented in Section 2. Then, the results of the data analysis and evaluation are shown in Section 3. Finally, the results in Section 4 are critically questioned and evaluated. The conclusion is presented in Section 5. 2. Methods The yield data analyzed comes from the monitoring system provider
Corresponding author. E-mail address: [email protected] (H. te Heesen). URL: http://www.umwelt-campus.de (H. te Heesen).
https://doi.org/10.1016/j.solener.2019.10.019 Received 5 June 2019; Received in revised form 27 September 2019; Accepted 10 October 2019 0038-092X/ © 2019 International Solar Energy Society. Published by Elsevier Ltd. All rights reserved.
Solar Energy 194 (2019) 128–135
H. te Heesen, et al.
Fig. 1. Total capacity (bars) and total number (blue line) of roof-top installations in Germany with a capacity up to 30 kWp. Data sources: Bundesnetzagentur (2019), Netztransparenz.de (2019). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Electrotechnical Commission, 2016) is calculated using
Rp =
Solare Datensysteme GmbH (Solare Datensysteme GmbH, 2019). The company offers PV operators various types of monitoring solutions. The configuration of the PV system and the yield data are available on a web-based platform. Scraping scripts are used to collect information from PV systems by accessing public and freely available monitoring data. The following configuration data of each PV system is used for the evaluation: Year of grid connection, location (postcode, city), orientation(s) and inclination(s) of the PV modules, capacity, daily yield data. A higher temporal resolution is not used to reduce the amount of data. The regional distribution of PV systems with a capacity up to 30 kWp is shown in Fig. 2. The yield data shown are from 1 January 2012 to 31 December 2018. A total of 23,944 PV systems are included in the evaluation. The key indicator for evaluation is the specific yield Y (or full load hours) of a PV system. The specific yield is the quotient of the energy yield E within a time interval (day, month, year) and the capacity P of a PV system
The
E . P performance
3. Results 3.1. Data quality The regional distribution of the 23,944 PV systems is shown in Fig. 2 for each two-digit postcode region. Bavaria and Baden-Wurttemberg have a high density of PV systems, while the density in eastern Germany is smaller. This density correlates with the population density and
(1) ratio
Rp
as
a
key
indicator
(2)
where Yr is the reference yield. The hourly horizontal direct and diffuse irradiation data for calculation of the reference yield is taken from CAMS (ECMWF - COPERNICUS Atmosphere Monitoring Service, 2019). The reference location for the PV systems is set in the center of the 95 two-digit postcode regions. The reference yield for each PV system is computed by using pvlib (Holmgren et al., 2018) under consideration of the orientation and the inclination of the PV systems scraped from the website of Solare Datensysteme GmbH (Solare Datensysteme GmbH, 2019). This procedure for calculating the irradiation into the inclined module plane leads to sufficiently accurate irradiance data (Ruf et al., 2016). There is no consideration of effects like shading as the aim of this publication is the analysis and visualisation of the yield and performance of PV systems. The focus of this publication lies on the yield analysis, the performance ratio will only take a small part of the data evaluation and will be an issue for further analysis. The data analysis presented here only take into account PV systems that have provided more than 350 days of daily yield data in each reporting year as used by Kausika (Kausika et al., 2018). This means that a PV system must provide at least 350 days of operating data communication (which can also include a daily yield of 0 kWh). Systems with an annual specific yield of more than 1500 kWh/kWp are also neglected in the analysis, since such high yield values of PV systems in Germany cannot be technically achieved due to irradiation conditions. The PV systems are regionally clustered according to the two-digit postcode. In order to reduce the possible occurrence of regional yield hotspots, the yield data of PV systems are aggregated within a two-digit postcode region and its neighbouring postcode areas (te Heesen and Herbort, 2016).
Fig. 2. Regional distribution of PV systems included in data analysis. The number of PV systems is shown for each two-digit postcode in Germany.
Y=
Y , Yr
(International 129
Solar Energy 194 (2019) 128–135
H. te Heesen, et al.
coast. 2013 was the worst solar year in the considered period without the typical south-north gradient and with an average specific yield of 901 kWh/kWp. The yield distribution in 2014 varied between 1003 kWh/kWp in Baden-Wurttemberg and 896 kWh/kWp in North Rhine-Westphalia. 2015 showed a similar yield distribution to 2012 with high yield values in the south and southwest and small ones at the North Sea coast. In 2016, an even yield distribution was observed. The highest annual specific yield was 972 kWh/kWp in Baden-Wurttemberg and lowest yield was 875 kWh/kWp in Schleswig-Holstein. The smallest yield in the period 2012 to 2018 was found at the Baltic Sea coast in 2017. Only 816 kWh/kWp have been produced by PV systems in this region, although the annual yield in southern Germany reaches 1010 kWh/kWp. Finally, 2018 was a record year with a minimum yield of 942 kWh/kWp in northern Germany and 1049 kWh/kWp in the south. Overall, the typical southeast-northwest gradient of the annual yield is observed over the long term. The yield data in the maps correlate to the annual specific yield in Table 1 for the federal states in Germany. The procedure for annual yield data collection is described above. PV systems with an energy production of less than 350 days are neglected. The systems are clustered by federal states. The three city states (Bremen/Hamburg and Berlin) are assigned to the corresponding federal states. On average, an annual specific yield of 960 ± 141 kWh/kWp can be expected for rooftop PV systems in Germany. The years 2013, 2014, 2016, and 2017 were years that generated below the long-term average. 2012, 2015, and 2018 in contrast generated above the average yield. The southern federal states of Germany (Baden-Wurttemberg and Bavaria) have the highest yield values due to higher solar irradiance, the northwestern federal states the lowest yield (Schleswig-Holstein and Lower Saxony). A closer look at specific yield distribution can be found in Fig. 5. The boxplots represent annual yield density of roof-top PV systems in Germany with at least 350 days of yield data. The PV systems are not regionally clustered, the plots represent nationwide system data. In 2013, solar yield was the lowest. In comparison, and as mentioned above, 2018 shows the highest specific yield in the period considered. The interquartile range (difference between the first and the third quartile) is approximately 200 kWh/kWp. Although PV systems with more than 350 operating days have been taken into account, there are still PV systems with annual specific yield of 800 kWh/kWp or less due most likely to severe technical issues. The yield values from 2012 and 2013 correspond to the data of Nordmann (Nordmann et al., 2014) and from 2014 to 2016 to Kausika (Kausika et al., 2018). If PV systems with insufficient data quality are added to the evaluation, they will enlarge the length of the whiskers. The distribution of the performance ratio between 2012 and 2018 regarding the year of installation (between 2009 and 2017) is shown in Fig. 6. The PR values were calculated with the annual yield (aggregation of the daily yield values) and the annual reference yield (derived from the hourly irradiation values in module plane). The median PR of all considerable systems varies between 0.78 in 2012 and 0.72 in 2016. The shown performance ratio corresponds to PR data of Reich (Reich et al., 2012) and Kausika (Kausika et al., 2018). The authors reserve the analysis of the performance ratio on a higher spatial and temporal level for future work.
Fig. 3. Data quality of the PV systems. The annual specific yield values of each PV system are correlated with the number of days with data. Each point represents the annual specific yield and the number of data transmission days of a PV system.
the distribution of solar radiation in Germany. Fig. 3 takes a closer look at the quality of the yield data. The annual specific yield of each PV system in the period 2012 to 2018 is correlated to the number of days with data transmission. A data point of a system with a daily yield of 0 kWh/kWp is considered as a valid data transmission entry, although the system has not fed any energy into the grid due to a technical malfunction. The histogram of each variable is shown at the top (specific yield) and right (number of days) of the diagram. The specific yield shows a negative skew. The annual specific yield increases in accordance with the number of days of data transmission. 3.2. Annual yield analysis The regional distribution of the annual specific yield of roof-top PV systems in Germany from 2012 to 2018 and the long-term averages are shown in Fig. 4. The color bar is identical for all maps and ranges from 810 to 1060 kWh/kWp. The yield data of all PV systems with more than 350 days of data are clustered according to the two-digit postcode and its neighboring postcodes. Clustering reduces the occurrence of regional hotspots. The average yield for all 95 postcode regions in Germany is calculated and shown in Fig. 4. The individual years differ in the regional yield distribution. There are years with a large south-north yield gradient (2012, 2015, 2017, and 2018) or with a uniform yield pattern (2013, 2014, and 2016). The maximum and minimum annual specific yield also changes due to different irradiation conditions. 2013 was the worst year with an average yield of 901 ± 125 kWh/kWp in Germany. 2018 was the best year with an average specific yield of 1022 ± 151 kWh/kWp. On a long-term average, the smallest yield values can be observed in the northwest of Germany (North Rhine-Westphalia and Lower Saxony), the largest yield values in the south of the country (Baden-Wurttemberg and Bavaria). This corresponds to the typical climate conditions and weather patterns in Germany. 2012 is the first year with a sufficient number of PV systems for evaluation. The highest yield of more than 1000 kWh/kWp was recorded in Baden-Wurttemberg, Bavaria, Rhineland-Palatinate, Saarland, and Saxonia, lowest yield was recorded at the North Sea
3.3. Seasonal yield data The seasonal yield data provide more accurate insight into the variability of energy production over the period from 2012 to 2018. Fig. 7 shows the distribution of the monthly specific yield independent of the year. December and January can be clearly identified as winter months with a specific yield of less than 50 kWh/kWp. November can also be counted as a winter month, although it belongs to autumn. In February, the variability of energy production increases significantly with values between 0 (snow cover) and 85 kWh/kWp. In the spring months March, April, and May, the specific yield reaches values 130
Solar Energy 194 (2019) 128–135
H. te Heesen, et al.
Fig. 4. Distribution of the annual specific yield in Germany from 2012 to 2018 and for the long-term average (bottom center) based on the two-digit postcode. Green colors show a smaller specific yield, red colors show a larger one. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Table 1 Mean annual specific yield and standard deviation of the PV systems in the German federal states from 2012 to 2018. All values are given in kWh/kWp. BB: Brandenburg & Berlin, BW: Baden-Wurttemberg, BY: Bavaria: HE: Hesse, MV: Mecklenburg-Vorpommern, NI: Lower Saxony & Bremen & Hamburg, NW: North Rhine-Westphalia, RP: Rhineland-Palatinate, SH: Schleswig-Holstein, SL: Saarland, SN: Saxonia, ST: Saxony-Anhalt, TH: Thuringia, DE: Germany. State
2012
2013
2014
2015
BB BW BY HE MV NI NW RP SH SL SN ST TH
974 ± 130 1063 ± 132 1017 ± 138 964 ± 131 870 ± 205 892 ± 118 897 ± 129 1004 ± 157 881 ± 110 1000 ± 133 1022 ± 109 988 ± 102 972 ± 103
916 ± 127 932 ± 119 919 ± 119 886 ± 125 916 ± 179 872 ± 121 861 ± 116 911 ± 134 904 ± 108 908 ± 105 891 ± 134 932 ± 97 823 ± 174
963 ± 130 1003 ± 136 976 ± 126 922 ± 131 940 ± 173 916 ± 124 896 ± 133 964 ± 127 914 ± 142 961 ± 124 964 ± 151 945 ± 142 919 ± 154
990 ± 140 1041 ± 149 1020 ± 129 964 ± 131 929 ± 177 919 ± 121 934 ± 130 978 ± 140 891 ± 151 979 ± 130 1016 ± 191 989 ± 130 965 ± 160
954 972 967 893 901 892 900 923 875 907 936 947 917
DE
980 ± 149
901 ± 125
956 ± 137
985 ± 144
131
2016
2017
2018
116 136 125 132 162 124 130 118 129 130 164 128 126
897 ± 119 1010 ± 140 992 ± 127 908 ± 135 816 ± 165 833 ± 117 866 ± 113 966 ± 127 836 ± 137 956 ± 132 930 ± 152 884 ± 117 913 ± 121
1037 ± 161 1040 ± 149 1049 ± 141 999 ± 158 995 ± 179 981 ± 141 1001 ± 139 1022 ± 151 942 ± 171 1017 ± 151 1045 ± 141 1046 ± 134 978 ± 152
936 ± 135
939 ± 146
1022 ± 151
± ± ± ± ± ± ± ± ± ± ± ± ±
Solar Energy 194 (2019) 128–135
H. te Heesen, et al.
Fig. 5. Distribution of the annual specific yield from 2012 to 2018 of roof-top PV systems in Germany.
Fig. 7. Monthly specific yield of roof-top PV systems in the period 2012 to 2018.
between 30 and 190 kWh/kWp. This is due in particular to a large variability in May. The summer months June, July, and August show a constant yield level with a median yield of 125 kWh/kWp. The specific yield begins to decline in September and continues to decline in October. Fig. 8 takes a closer look at the seasonal variations. The boxplot shows the monthly specific yield from 2012 to 2018. The monthly yield data of all PV systems are used for the evaluation. Only systems with a daily specific yield that is less than 10 kWh/kWp are considered, since systems with a wrong configuration or an error in data acquisition are excluded. The installation year is also taken into account. The typical seasonal yield behavior in Germany can be clearly identified. The median of the specific yield varies in winter from 15 to 50 kWh/kWp and in summer from 110 to 150 kWh/kWp. The yield spread (interquartiles range) of the high-yield months (April to September) remains at a constant level of about 25 kWh/kWp. The months of the individual years can differ greatly from each other. For example, the monthly yield in July 2013 is the greatest, although the annual yield of 2013 is the least in the considered period. The other
months of 2013 are at the lower end of the monthly yield scale. On the other hand, 2018 is the year with the largest yield, but March and June 2018 are only average months. Thus, the yield data for one or more months do not allow for interpretation of the entire year. Typically, there are no general weather conditions over several weeks that lead to extraordinary small or large yield. The summer of 2018 (red color) is the first time to show such behavior with highest yield values from April to Oktober excluding June.
4. Discussion 4.1. Long-term yield data The evaluation of the monthly yield values of the PV systems under consideration (at least 350 days of yield data) in Germany compared to a reference yield is shown in Fig. 9. The blue boxplots represent the yield data of the PV systems for each month in the period from 2012 to 2018. PV systems with available daily yield data of more than 350 days in each year have been taken into account. There are no spatial
Fig. 6. Distribution of performance ratio between 2012 and 2018 for PV systems that have been installed from 2009 to 2017 (represented by the different colors). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) 132
Solar Energy 194 (2019) 128–135
H. te Heesen, et al.
Fig. 10. First, the average annual specific yield of PV systems with yield data of more than 350 days is derived for each two-digit postcode and the neighboring postcode regions. The average yield per postcode region is calculated for one, two, and up to six consecutive years. For example, using three consecutive years means that the mean annual yield for all PV systems in a postcode region is calculated for all possible three consecutive annual periods (2012–2014, 2013–2015, 2014–2016, 2015–2017, and 2016–2018). These mean annual yield values refer to the long-term average yield from 2012 to 2018. This ratio is calculated for all 95 two-digit postcodes in Germany. Finally, all relative deviation for each time period (one to six consecutive years) for all postcode regions is analyzed in a box-and-whisker diagram. If only one year of yield data is used for evaluation of a PV system, the deviation from the long-term yield can be between −10 percent (underestimation of the long-term yield) and +12 percent (overestimation of the long-term yield). If two consecutive years of yield data are available, the relative deviation decreases to ±5 percent. The more years available with annual yield information, the smaller the deviation from the long-term trend. Five consecutive years show a maximum deviation of ±2 percent from the long-term average. Therefore, at least five consecutive years of yield information are required for data evaluation to reduce evaluation artefacts by years with extraordinary large or small yield. A single year or two years cannot provide information about the quality of a PV system due to variability of solar irradiation and yield production within a region. A two-dimensional histogram of the annual specific yield and the capacity of roof-top systems is shown in Fig. 11. The frequency and the correlation of the annual specific yield of all PV systems with more than 350 days of sufficient yield information in relation to the installed capacity are analyzed. Brighter blue colors represent a larger amount of PV systems. Capacity is drawn on the vertical axis, the yield on the horizontal axis. The frequency of the two values is shown at the top and the right of the diagram. The histogram of the annual specific yield and the capacity shows no dependency between both variables. A larger capacity of roof-top systems does not lead to a higher annual yield. In particular, very small roof-top system (capacity smaller than 5 kWp), may be exposed to a higher shadow risk due to surrounding obstacles and may show a similar yield of roof-top systems with a capacity of more than 15 kWp (corresponds to a roof area of 90 to 180 m2).
Fig. 8. Boxplots of the monthly specific yield from 2012 to 2018 of all PV systems in Germany.
4.2. Economic and environmental impacts The impacts of the yield analysis of the roof-top PV systems can be distinguished in economic and environmental terms. First, the annual revenue of all PV systems in Germany smaller than 30 kWp and the range of the specific revenue in € per kWp is shown. Second, the annual reduction of greenhouse gas (GHG) emissions by PV systems is discussed. Table 2 shows several economic aspects of the roof-top PV systems in Germany. The average annual specific yield (see Table 1) multiplied by the average feed-in tariff of PV systems in Germany (Federal Ministry for Economic Affairs and Energy, 2018). The total annual feedin tariff of all PV systems from 2012 to 2018 amounts to approximately 4.1 billion€. The share of small rooftop systems is around 40 percent of the annual EEG compensation payments. The specific revenue reflects the expected range of payments per installed capacity in kWp. This values can be very useful for financial investigations of PV systems. The minimum or maximum specific value (fifth or seventh column in Table 2) is the product of the mean feed-in tariff and the minimum or maximum annual specific yield in Germany (Table 1). The range of the specific revenue reflects effects like minor technical issues, shading due to obstacles or spatial artefacts like fog. The calculation of the mean specific yield is based on the nationwide mean specific yield. The minimum specific revenue varies between 194 and 241 €/kWp, the mean revenue between 280 and 345 €/kWp, and
Fig. 9. Boxplot of the monthly yield in the period 2012 to 2018 of PV systems in Germany. The colored area represents the reference yield (te Heesen et al., 2018, 2017; te Heesen et al., 2013; te Heesen et al., 2014, 2019).
restrictions. The daily yield data are aggregated to monthly values. The reference yield is taken from Heesen et al. (te Heesen et al., 2017; te Heesen et al., 2013; te Heesen et al., 2014, 2019) and is a result of a two-step data cleansing algorithm and is divided into three areas the yield of very well maintained PV systems is green, of sufficient systems is yellow and of insufficiently maintained systems is red. The well maintained PV systems are represented by the upper fifty percent of the systems (monthly yield better than the upper quartile (light green) or better than the median (green color)). Sufficiently maintained systems (yellow color) belong to the group of systems with a monthly yield between the lower quartile and the median. All other systems (monthly yield worse than the lower quartile) are marked as insufficient systems (red color). The yield deviation from the long-term yield average is shown in 133
Solar Energy 194 (2019) 128–135
H. te Heesen, et al.
Fig. 10. Boxplot of the average yield deviation from the long-term yield average. The years indicate the number of consecutive years of yield data for the calculation of the average. Table 3 Environmental impact of roof-top PV systems in Germany. The substitution factor is taken from (Umweltbundesamt, 2018). The annual reduction of greenhouse gas (GHG) emissions remains constant due to an increase in the annual yield and a decrease in the substitution coefficient. Year 2012 2013 2014 2015 2016 2017 2018
Annual yield 11.6 11.6 12.9 13.7 13.4 13.8 15.6
TWh TWh TWh TWh TWh TWh TWh
Substitution factor 716 g 706 g 675 g 655 g 614 g 614 g 614 g
CO2 CO2 CO2 CO2 CO2 CO2 CO2
Annual savings of GHG emissions
eq/kWh eq/kWh eq/kWh eq/kWh eq/kWh eq/kWh eq/kWh
8.5 8.4 8.9 9.1 8.3 8.6 9.7
Mt Mt Mt Mt Mt Mt Mt
CO2 CO2 CO2 CO2 CO2 CO2 CO2
eq eq eq eq eq eq eq
the maximum revenue between 329 and 421 €/kWp. The environmental impacts of roof-top PV systems in Germany are shown in Table 3. Substitution factors show the savings of CO2 emissions by photovoltaics in the German electricity mix in the individual years (Umweltbundesamt, 2018). The annual yield of all roof-top PV systems multiplied by the substitution factor gives the total annual savings from roof-top PV in Megatons of CO2 equivalent. The annual savings have increased from 8.3 Mt CO2 eq in 2016 to 9.7Mt CO2 eq in 2018. Fig. 11. Histogram and correlation of the annual yield of PV systems with the capacity of the system. The colors white and light blue represent a large number of PV systems, the colours dark green and light green a small number of systems. There is no correlation between the yield and the capacity increase of rooftop systems. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
5. Conclusion Daily yield data of 23,944 roof-top photovoltaic (PV) systems in Germany from 2012 to 2018 have been taken from an online monitoring database and are used for evaluation purposes. The system configuration and location were also taken into account. The data have been aggregated spatially and temporally for analysis. The analysis
Table 2 Economic impacts of roof-top PV systems in Germany. The total installed capacity of PV systems in Germany smaller than 30 kWp is shown in Fig. 1. The average feed-in tariff is taken from Federal Ministry for Economic Affairs and Energy (2018). Year 2012 2013 2014 2015 2016 2017 2018
Installed capacity 12.1 13.1 13.7 14.1 14.4 15.0 15.5
GWp GWp GWp GWp GWp GWp GWp
Mean feed-in tariff 35.2 32.0 31.6 30.8 30.3 29.8 27.5
ct/kWh ct/kWh ct/kWh ct/kWh ct/kWh ct/kWh ct/kWh
Mean annual revenue € € € € € € €
4.1b 3.8b 4.1b 4.3b 4.1b 4.2b 4.4b
Min. spec. revenue 234 208 241 228 224 194 212
134
€/kWp €/kWp €/kWp €/kWp €/kWp €/kWp €/kWp
Mean spec. revenue 345 288 302 303 284 280 281
€/kWp €/kWp €/kWp €/kWp €/kWp €/kWp €/kWp
Max. spec. revenue 421 350 360 372 336 343 329
€/kWp €/kWp €/kWp €/kWp €/kWp €/kWp €/kWp
Solar Energy 194 (2019) 128–135
H. te Heesen, et al.
focuses on the specific yield of PV systems. The annual yield distribution in Germany typically shows a southnorth gradient. 2018 is the year with the highest yield in the considered period, 2013 is the year with the lowest. The yield data correspond to the results of other groups (Reich et al., 2012; Nordmann et al., 2014; Kausika et al., 2018; Killinger et al., 2018), which have also analyzed the yield of PV system in Germany in the given period. In order to identify long-term trends and interpret the annual yield data of PV systems, at least five years of data are required. The temporal and spatial variation of the energy yield of only one to two years does not allow any conclusions to be drawn about the technical quality of a PV system. If the yield of the PV systems are assumed to be representative for all roof-top PV systems smaller than 30 kWp in Germany, these systems have an annual revenue of approximately EUR 4.1 billion and an annual saving of greenhouse gases of 8.3 to 9.7 Megatons of CO2 equivalent.
176_h.teheesen/dokumente/Publikationen/Ertragsstudie_2012.pdf. Holmgren, W.F., Hansen, C.W., Mikofski, M.A., 2018. pvlib python: a python package for modeling solar energy systems. J. Open Source Softw. 3, 884–886. https://doi.org/ 10.21105/joss.00884. International Electrotechnical Commission, 2016. Photovoltaic system performance - Part 3: Energy evaluation method. RFC IEC TS 61724–3:2016. International Electrotechnical Commission. Geneva, Switzerland. International Renewable Energy Agency, 2019. Renewable Energy Sources - Solar Energy. URL: https://irena.org/solar. Jacobson, M.Z., Delucchi, M.A., Bauer, Z.A., Wang, J., Weiner, E., Yachanin, A.S., 2017. 100% clean and renewable wind, water, and sunlight all-sector energy roadmaps for 139 countries of the world. Joule 1, 1–14. https://doi.org/10.1016/j.joule.2017.07. 005. Kausika, B.B., Moraitis, P., van Sark, W.G.J.H.M., 2018. Visualization of operational performance of grid-connected PV systems in selected European countries. Energies 11, 1330–1339. https://doi.org/10.3390/en11061330. Killinger, S., Lingfors, D., Saint-Drenan, Y.M., Moraitis, P., van Sark, W.G.J.H.M., Taylor, J., Engerer, N.A., Bright, J.M., 2018. On the search for representative characteristics of PV systems: data collection and analysis of PV system azimuth, tilt, capacity, yield and shading. Sol. Energy 173, 1087–1106. https://doi.org/10.1016/j.solener.2018. 08.051. Leloux, J., Narvarte, L., Trebosc, D., 2011. Performance analysis of 10,000 residential PV systems in France and Belgium. In: 26th European Photovoltaic Solar Energy Conference and Exhibition, pp. 3939–3946. doi: https://doi.org/10.4229/ 26thEUPVSEC2011-5AO.6.4. Leloux, J., Taylor, J., Moretón, R., Narvarte, L., Trebosc, D., Desportes, A., 2015. Monitoring 30,000 PV systems in Europe: performance, faults, and state of the art. In: 31st European Photovoltaic Solar Energy Conference and Exhibition, pp. 1574–1582. doi: https://doi.org/10.4229/EUPVSEC20152015-5AO.8.1. Moraitis, P., Kausika, B., Nortier, N., van Sark, W., 2018. Urban environment and solar PV performance: the case of the Netherlands. Energies 11, 1333. https://doi.org/10. 3390/en11061333. Netztransparenz.de, 2019. Informationsplattform der deutschen Übertragungsnetzbetreiber. URL: https://www.netztransparenz.de/EEG/ Anlagenstammdaten. Nordmann, T., Clavadetscher, L., van Sark, W.G.J.H.M., Green, M., 2014. Analysis of Long-Term Performance of PV Systems. Utrecht University Repository, Utrecht. Pooppal, A., te Heesen, H., 2015. Survey on Specific Yield of Photovoltaic Systems in India 2014. In: 31st European Photovoltaic Solar Energy Conference and Exhibition, Hamburg. pp. 2307–2310. doi: https://doi.org/10.4229/EUPVSEC20152015-5BV. 2.45. Ram, M., Bogdanov, D., Aghahosseini, A., Oyewo, S., Gulagi, A., Child, M., Breyer, C., Fell, H.J., 2017. Global Energy System based on 100 percent Renewable Energy Power Sector. Technical Report. Lappeenranta University of Technology. URL: http://www.neocarbonenergy.fi/wp-content/uploads/2016/02/14_Ram.pdf. Reich, N.H., Müller, B., Armbruster, A., van Sark, W.G.J.H.M., Kiefer, K., Reise, C., 2012. Performance ratio revisited: is PR > 90% realistic? Prog. Photovoltaics Res. Appl. 20, 717–726. https://doi.org/10.1002/pip.1219. Ruf, H., Schroedter-Homscheidt, M., Heilscher, G., Beyer, H.G., 2016. Simulation of the load flow at the transformer in low voltage distribution grids with a significant number of pv systems using satellite-derived solar irradiance. Sol. Energy 135, 692–702. https://doi.org/10.1016/j.solener.2016.06.001. Schacht, V., Häberle, A., Warmuth, W., Mitra, I., Kumar, A., Srivastava, A., 2014. PV performance benchmarking in India. Results from the project solar mapping and monitoring - SolMap. In: 29th European Photovoltaic Solar Energy Conference and Exhibition, pp. 2642–2645. doi: https://doi.org/10.4229/EUPVSEC20142014-5BV. 1.21. Solare Datensysteme GmbH, 2019. Energy Management System Solar-Log. URL: https:// www.solar-log.com. Teske, S., Sawyer, S., Schäfer, O., Pregger, T., Simon, S., Naegler, T., Schmid, S., Özdemir, D., Pagenkopf, J., Kleiner, F., Rutovitz, J., Dominsh, E., Downes, J., Ackermann, T., Brown, T., Boxer, S., Baitelo, R., Rodriguez, L.A., 2015. energy revolution. A Sustainable World. Energy Outlook 2015. Technical Report 5. Greenpeace International. Tsafarakis, O., Moraitis, P., Kausika, B.B., van der Velde, H., ’t Hard, S., de Vries, A., de Rijk, P., de Jong, M.M., van Leeuwen, H.P., van Sark, W.G.J.H.M., 2017. Three years experience in a Dutch public awareness campaign on photovoltaic system performance. IET Renew. Power Gener. 11, 1229–1233. https://doi.org/10.3390/ en11040977. Umweltbundesamt, 2018. Emissionsbilanz erneuerbarer Energieträger. URL: https:// www.umweltbundesamt.de/publikationen/emissionsbilanz-erneuerbarerenergietraeger.
Declaration of Competing Interest The authors declare no conflicts of interest. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. References Breyer, C., Bogdanov, D., Aghahosseini, A., Gulagi, A., Child, M., Oyewo, S., Farfan, J., Sadovskaia, K., Vainikka, P., 2018. Solar photovoltaics demand for the global energy transition in the power sector. Prog. Photovoltaics Res. Appl. 26, 505–523. https:// doi.org/10.1002/pip.2950. Bundesnetzagentur, 2019. Photovoltaikanlagen: Datenmeldungen sowie EEGVergütungssätze. URL: http://www.bundesnetzagentur.de. ECMWF - COPERNICUS Atmosphere Monitoring Service, 2019. CAMS Radiation Service. URL: http://www.soda-pro.com/en/web-services/radiation/cams-radiation-service. Federal Ministry for Economic Affairs and Energy, 2018. EEG in Zahlen: Vergütungen, Differenzkosten und EEG-Umlage 2000 bis 2019. URL: https://www.erneuerbareenergien.de/EE/Redaktion/DE/Downloads/eeg-in-zahlen-pdf.pdf. Gromaire, M., te Heesen, H., 2015. Survey on Specific Yield of Photovoltaic Systems in France 2014. In: 31st European Photovoltaic Solar Energy Conference and Exhibition, Hamburg. pp. 2315–2317. doi: https://doi.org/10.4229/EUPVSEC20152015-5BV. 2.47. te Heesen, H., Herbort, V., 2016. Development of an algorithm to analyze the yield of photovoltaic systems. Renew. Energy 87, 1016–2022. te Heesen, H., Herbort, V., Pfatischer, R., 2014. Survey on yield of PV systems in Germany 2013. In: 29th European Photovoltaic Solar Energy Conference and Exhibition, Amsterdam. pp. 2198–2201. doi: https://doi.org/10.4229/EUPVSEC20142014-5AO. 7.3. te Heesen, H., Herbort, V., Rumpler, M., 2017. Survey on Yield of PV Systems in Germany 2014 to 2016. In: 33rd European Photovoltaic Solar Energy Conference and Exhibition, Amsterdam. pp. 1901–1903. doi: https://doi.org/10.4229/ EUPVSEC20172017-6BO.7.1. te Heesen, H., Herbort, V., Rumpler, M., 2018. Untersuchung des Ertrags von Photovoltaikdachanlagen bis 30 kWp in Deutschland im Zeitraum 2014 bis 2017. In: Czarnecki, C., Brockmann, C., Sultanow, E., Koschmider, A., Selzer, A. (Eds.), GIEdition Lecture Notes in Informatics. Workshops der INFORMATIK 2018 Architekturen, Prozesse, Sicherheit und Nachhaltigkeit, Berlin. pp. 177–188. te Heesen, H., Herbort, V., Rumpler, M., 2019. Studie zum Ertrag von Photovoltaikdachanlagen 2018 in Deutschland. Technical Report. Hochschule Trier. URL: https://www.umwelt-campus.de/ucb/index.php?id=ertragsstudie. te Heesen, H., Herbort, V., Pfatischer, R., 2013. Studie zum Ertrag von Photovoltaikanlagen in Deutschland 2012. Hochschule Trier und meteocontrol GmbH. Online verfügbar: https://www.umwelt-campus.de/ucb/fileadmin/users/
135