Conference PaperPDF Available

Abstract and Figures

Performance statistics for an ensemble of distributed PV systems in the UK are rare, and there are few publications based on an ensemble representative of the UK PV fleet. This work presents the results of analysing the energy production and the performance of more than 2500 distributed PV systems in the UK monitored for up to 7 years, and provides an overview of the state of the art. We have mapped the annual energy yield of all the PV systems in the database according to the regions of the UK. The mean reported annual energy yield for PV in the UK in 2013 was 910 kWh/kWp, and we assessed the corresponding long-term value as 886 kWh/kWp. The mean value of the yearly-integrated PR for the years 2012-2014 was 84% (with 3% standard deviation). We have shown the presence of seasonal trends in mean monthly PR, with peaks in Spring/Autumn and troughs in Summer/Winter. We have found an increase in the overall performance of PV systems between 6% and 18% from 2004 to 2012, which represents a clear increasing trend with a mean gradient of 1.5%/year.
Content may be subject to copyright.
Monitoring thousands of distributed PV systems in the UK:
Energy production and performance
Jamie Taylor*, Jonathan Leloux, Aldous M. Everard, Julian Briggs, Dr Alastair Buckley
Sheffield Solar, University of Sheffield, Hicks Building, Hounsfield Road, Sheffield S3 7RH,
UK
Instituto de Energía Solar, Universidad Politécnica de Madrid, Spain
* Corresponding Author jamie.taylor@sheffield.ac.uk
Abstract
Performance statistics for an ensemble of distributed PV systems in the UK are rare, and there
are few publications based on an ensemble representative of the UK PV fleet. This work
presents the results of analysing the energy production and the performance of more than 2500
distributed PV systems in the UK monitored for up to 7 years, and provides an overview of the
state of the art. We have mapped the annual energy yield of all the PV systems in the database
according to the regions of the UK. The mean reported annual energy yield for PV in the UK in
2013 was 910 kWh/kWp, and we assessed the corresponding long-term value as 886
kWh/kWp. The mean value of the yearly-integrated PR for the years 2012-2014 was 84% (with
3% standard deviation). We have shown the presence of seasonal trends in mean monthly PR,
with peaks in Spring/Autumn and troughs in Summer/Winter. We have found an increase in the
overall performance of PV systems between 6% and 18% from 2004 to 2012, which represents
a clear increasing trend with a mean gradient of 1.5%/year.
Introduction
In 2014, the installed capacity of
photovoltaics (PV) in the UK reached 5
GW. The growth of the PV installation
sector in the UK can be mainly attributed to
the financial incentives offered by the
Government in the form of feed-in-tariffs
(FITs) [1]. In order to continue to encourage
growth in the PV sector and ensure PV
remains a viable investment under a
regime of decreasing FITs, stakeholders
are seeking to optimise the supply and
installation process whilst also enhancing
the performance of systems and improving
predictions of potential energy outputs.
Performance statistics for an ensemble of
distributed PV systems in the UK are rare,
and there are few publications [2] based on
an ensemble representative of the UK PV
fleet. This has led to a wide gap in the
knowledge of the real energy production of
the PV systems installed in the UK and the
main parameters explaining the observed
differences in performance between these
systems. This work presents the results of
analysing the energy production and the
performance of several thousands of
distributed PV systems in the UK monitored
up to 7 years, and provides an overview of
the state of the art. We have mapped the
annual energy yield of all the PV systems in
the database according to the regions
defined in the Micro-generation
Certification Scheme’s (MCS) Guide to the
Installation of Photovoltaic Systems [3]. We
have represented the yearly integrated PR
on a histogram, and have discussed its
general form and its implications. The
seasonal trends of PR have been studied
and represented on a graph of monthly PR.
We have also assessed the improvement
of the overall performance of the PV
systems over time.
Data and Method
Distributed PV generation data is collected
via the Microgen Database website [4], with
PV owners using the site as a portal to
upload readings and in return receiving free
monthly Performance Ratio (PR) analysis
and peer-to-peer performance checking in
the form of interactive maps. The majority
of data is measured by the energy meters
of the inverters and is collected from
commercial data donors who own/monitor
hundreds of systems using automated
transfers. The complete dataset of
Microgen Database comprises more than
7000 PV systems across the UK (see
Figure 1), at various temporal resolutions
(typically 10-min, 30-min, daily or monthly),
with historic data spanning up to seven
years [4], although most of the systems
were installed after 2011. The dataset
includes both domestic and commercial
scale installations between 0.7 and 69 kWp
with a wide range of orientation and tilt
angles. The data has been subjected to
several checks and validations in order to
isolate and remove as much erroneous
data as possible. In some cases system
details are investigated manually to verify
to a good degree of confidence that the
data should be removed, for example when
considering systems whose orientation or
tilt appears incorrect. After the reading
requirements and system validation has
been carried out there remains over 2500
systems which are analysed in this study.
Figure 1: Location of the PV systems analysed
from Microgen Database in the UK.
Global Horizontal Irradiation (GHI) has
been interpolated at each of the sites from
the UK Met Office (UKMO) ground based
pyranometers [5] as per the methodology
documented by Colantuono et al. [2].
Monthly GHI is transposed to the Global
Tilted Irradiation (GTI) using Klein &
Theilacker [6].
In order to provide an estimate of PV
energy yield, region by region, that would
be representative of the next decades, we
have used the values corresponding to the
year 2013 and translated them to a long-
term prospect by taking into account the
inter-annual variability of solar irradiation.
Recent works on solar resource
assessment have concluded that it is
advisable to consider the mean of the last
20-year period as representative of the next
decades [7]. We have therefore obtained
the energy yield values representative of
the next decades, by scaling the 2013
mean annual yield according to the ratio of
the 2013 mean annual irradiation and the
20 year mean annual irradiation (1995-
2014), calculated from the UKMO data
archives [5].
We have analysed the distribution of the
yearly-integrated PR on a histogram. Prior
to these analyses, in an attempt to isolate
outlier PR values caused by erroneous
data, we have filtered out all values of PR
below   and above  .
The statistic , defined in equation 1, is
not exactly the standard deviation of the
whole distribution around its mean value,
but is instead the standard deviation of the
values lying over the median. This allows
us to estimate the standard deviation of the
Gaussian part of the population only, thus
effectively filtering erroneous values. This
method has been developed in the context
of fault detection procedures [8]:

   (1)
where  is the median of the unfiltered
PR and  is the number of systems with

.
We have assessed the improvement of the
state of the art of the PV systems installed
in the UK through the comparison of their
yearly integrated PR for the year 2012. We
have represented these PR values as a
function of their year of installation, and we
have used a linear fit on the resulting graph.
Because the date of installation was not
available for all the PV systems of the
database, we have used the production
start date of the installed modules as a
proxy for installation date.
Results and Discussion
Table 1 and figure 2 show the mean energy
yield by region for the year 2013, and the
corresponding long-term values obtained
by scaling to the mean solar irradiation for
the last 20 years. In 2013 the mean solar
irradiation across the UK was 
, 3% higher than the 20-year mean of
, which gives a scaling
factor of 0.97.
Figure 2: Map of the energy yield by region
scaled according to the mean solar irradiation
over the last 20 years. Regions shaded in grey
did not have a statistically representative
number of PV systems.
Region
Mean Annual
Yield 2013
(kWh/kWp/yr)
Mean Annual
Yield Any Year
(kWh/kWp/yr)
3
1004
978
5E
1000
974
4
980
955
5W
977
952
13
951
926
1
945
921
12
930
906
2
930
906
6
907
883
9E
879
856
10
874
851
11
866
844
7E
857
834
14
848
826
15
829
807
9S
777
757
Mean
910
Table 1: Annual energy yield region by region
for the year 2013 and corrected for the 20 year
mean annual irradiation.
The mean value of the yearly-integrated PR
for all the PV systems in our database for
the years 2012-2014 was 83 ± 4%. After
filtering out the outliers, this value becomes
84 ± 3%. The distribution of filtered PR for
2530 systems for a total of 4427 years of
generation data suggests that 74% of
yearly-integrated PR values fall between
78% and 90%, whilst 95% fall between 72%
and 96% PR.
Figure 3 shows the distribution of these
yearly integrated PR for 2012-2014
inclusive. The distribution shows
tendencies of a Weibull distribution, which
is typical for situations where a stochastic
uncertainty is superimposed with an upper
limit i.e. PR is limited to around 100%.
For comparison, figure 3 also shows a fitted
normal distribution, which highlights the
skew to lower PR values. For the purposes
of drawing statistics and comparison, the
distribution is most accurately fitted by a
Johnson Su with the parameters:  
,   ,    and   .
Figure 4: Seasonal evolution of the monthly PR.
Points show the median PR for each month.
Red, green and blue dashed lines show the
mean monthly PR throughout 2012, 2013 and
2014 respectively.
Figure 4 shows the seasonal trends in
mean monthly PR. The monthly PR is
affected by a superposition of several
effects such as winter shading, ambient
temperature variations and snow cover
leading to monthly PR values with a range
of 22% in 2013.
Figure 5 shows a linear fit of yearly
integrated PR in 2012 for each system
against the corresponding production start
date of the installed modules. Performance
Ratio of PV systems is found to have
increased by between 6% and 18% from
2004 to 2012, which represents a clear
increasing trend with a mean gradient of
1.5% per year. This supports the
hypothesis that the state of the art of PV
installations in the UK has steadily
increased over the last decade, due to
improvements in both PV modules,
inverters and other components, and
installation standards.
Figure 3: Histogram of yearly-integrated PR
Figure 5: Linear fit of yearly integrated PR in
2012 against the corresponding production start
date for the specific panels used in each system.
Conclusion
The mean reported PV annual energy yield
in the UK in 2013 was . In
2013 the mean solar irradiation across the
UK was 3% higher than the 20-year mean,
and we therefore assessed the
corresponding long-term value of PV
energy yield as . The mean
value of the yearly-integrated PR for the
years 2012-2014 was 84%, with a standard
deviation of 3%. Nearly 95% of the yearly-
integrated PR values fall between 72% and
96% PR. These differences can be
attributed to several factors to be explored
in a future study. We have shown the
presence of seasonal trends in mean
monthly PR, with peaks in Spring/Autumn
and troughs in Summer/Winter. We have
found an increase in the overall
performance of PV systems, showing a
clear increasing trend with a mean gradient
of 1.5% per year. This increase in overall
performance is coherent with the results of
other studies that were recently carried out
in other countries [9] [10] [11] [12] [13] [14].
Acknowledgements
The Microgen Database is a public-
industry-academic collaboration providing
solar photovoltaic performance data for use
across the UK PV supply chain. The
Sheffield Solar project is funded by the
EPSRC (Solar Energy for Future Societies:
EP/I032541/1; Wise PV: EP/K022229/1)
and The University of Sheffield.
Thanks to Maria-Madalina Opincaru for her
role as database admin.
The work of Jonathan Leloux has been
partially financed by the European
Commission within the project PV CROPS
under the 7th Framework Program (Grant
Agreement nº 308468).
References
[1]
GOV.UK, “Feed-in Tariffs: get money for
generating your own electricity,”
Government Digital Service, 4 February
2015. [Online]. Available:
https://www.gov.uk/feed-in-tariffs/overview.
[2]
G. Colantuono, A. Everard, L. M. Hall and A.
R. Buckley, “Monitoring nationwide
ensembles of PV generators: Limitations
and uncertainties. The case of the UK,”
Solar Energy, 2014.
[3]
Microgeneration Certification Scheme
(‘MCS’), “Guide to the Installation of
Photovoltaic Systems,” 2012. [Online].
Available:
http://www.microgenerationcertification.org/
mcs-standards/installer-standards.
[Accessed 25 09 2014].
[4]
Sheffield Solar, “Microgen Database,”
Sheffield Solar - University of Sheffield,
[Online]. Available: http://www.microgen-
database.org.uk/.
[5]
Met Office, “Met Office Integrated Data
Archive System (MIDAS) Land and Marine
Surface Stations Data (1853-current),”
NCAS British Atmospheric Data Centre,
2012. [Online]. Available:
http://catalogue.ceda.ac.uk/uuid/220a65615
218d5c9cc9e4785a3234bd0. [Accessed 17
03 2015].
[6]
J. A. Klein and J. C. Theilacker, “An
Algorithm for Calculating Monthly-Average
Radiation on Inclined Surfaces,” Journal of
Solar Energy Engineering, 1981.
[7]
B. Müller et al., “Rethinking solar resource
assessments in the context of global
dimming and brightening,” Solar Energy,
2014.
[8]
J. Leloux et al., “Automatic fault detection on
BIPV systems without solar irradiation data,”
in 29th European PV Solar Energy
Conference and Exhibition, 2014.
[9]
J. Leloux, L. Navarte and D. Trebosc,
“Review of the performance of residential
PV systems in Belgium,” Renewable and
Sustainable Energy Reviews, 2012.
[10]
J. Leloux, L. Navarte and D. Trebosc,
“Review of the performance of residential
PV systems in France,” Renewable and
Sustainable Energy Reviews, 2012.
[11]
A. Woyte, “Monitoring of photovoltaic
systems: good practices and systematic
analyses,” 29th European PV Solar Energy
Conference and Exhibition, 2014.
[12]
D. C. Jordan and S. R. Kurtz, “Reliability
and Geographic Trends of 50,000
Photovoltaic Systems in the USA,” 29th
European PV Solar Energy Conference and
Exhibition, 2014.
[13]
T. OOzeki, “Performance trends in grid-
connected photovoltaic systems for public
and industrial use in Japan,” Progress in
Photovoltaics, 2010.
[14]
N. H. Reich, “Performance ratio revisited: is
PR > 90% realistic?,” Progress in
Photovoltaics, 2012.
... The PV generation data is uploaded to the Microgen database by PV system owners, and thus has various lengths and temporal resolutions across the different locations. The dataset includes both residential and commercial PV installations between 0.7 kilowattpeak (kWp) and 69 kWp with different orientations and tilt angles (Taylor et al., 2015). Few PV power generation datasets with such a large spatial coverage are publicly available (especially residential PV), primarily because of privacy concerns. ...
... We believe that the Microgen dataset provides valuable opportunities for data-driven research in both the solar energy and power system domains. The Microgen dataset has been subjected to several QC methods, and erroneous data has been isolated and corrected by Sheffield Solar (Taylor et al., 2015). ...
... The two advantages of Microgen are: (i) having a large spatial coverage and (ii) having behind-the-meter PV generation data, which have led to a wide range of publications on spatial solar energy analysis and distributed power system research. More specifically, the dataset's spatial coverage is beneficial to large-scale PV monitoring and characterization (Taylor et al., 2015;Colantuono et al., 2014), solar power spatial aggregation modeling (Lingfors and Widén, 2016), and spa-tial-temporal solar forecasting (Silva and Brito, 2018). Behind-themeter PV generation data has been used for distributed energy storage operation optimization (Hassan et al., 2017), distributed energy storage financial and environmental benefit assessment (Jones et al., 2017), PV investment analysis , and PV self-consumption evaluation . ...
Article
Observational solar data is the foundation of data-driven research in solar power grid integration and power system operations. Compared to other fields in data science, the openness and accessibility of solar data is lacking, which prevents solar data science from catching up with the emerging trends of data science (e.g., deep learning). In this paper, OpenSolar, a package with both R and Python versions, is developed to enhance the openness and accessibility of publicly available solar datasets. The OpenSolar package provides access to multiple types of solar data, primarily from four datasets: (1) the National Renewable Energy Laboratory (NREL) Solar Power Data for Integration Studies dataset, (2) the NREL Solar Radiation Research Laboratory dataset, (3) the Sheffield Solar-Microgen database, and (4) the Dataport database. Unlike other open solar datasets that only contain meteorological data, the four datasets in the OpenSolar package also contain behind-the-meter power data, sky images, and solar power data with satisfactory temporal and spatial resolution and coverage. The overview, quality-control methods, and potential usage of the datasets, in conjunction with sample code implementing the OpenSolar functions, are described in this paper. The package is expected to assist in bridging the gaps between the research fields of solar energy, power systems, and data science.
... When analysing the distribution of yearly-integrated PR, it is desirable to include the results for underperforming systems whilst excluding any outliers due to erroneous data and complete failures. This is complicated by the fact that the distribution takes the shape of a Weibull distribution [7] [8] which is non-symmetric and features a long tail at lower values. To achieve this, we employ a method developed specifically in the context of PV fault detection [9] whereby the upper limit is the median, í µí¼‡ 1/2 , plus 3í µí¼Ž 1/2 and the lower limit is í µí¼‡ 1/2 minus 6í µí¼Ž 1/2 (Equation (2)). ...
... 3.2 Monthly PR@STC As with PR, the PR@STC is more variable in winter than in summer, making it of limited use as a monitoring tool (Figure 6). With PR@STC the dip in summer due to increased temperatures is less pronounced [7] whilst a pronounced dip in winter indicates underperformance during these months. The main driving factor for this underperformance is thought to be increased shading as a result of lower solar elevation angles, but the drop in efficiency of inverters at lower generation levels may also contribute. ...
... We use the mean of these values weighted according to the number of systems each year in order to calculate the improvement in the state-of-the-art of UK PV to be 1.0% per year. This is lower than previously reported [7], but is more robust thanks to a thorough outlier removal process and the inclusion of more than one year's yearly-integrated PR data. The 95% confidence interval for the gradient is 0.68 to 1.4% per year. ...
Conference Paper
Full-text available
In June 2015, the UK fleet of solar photovoltaic (PV) systems reached 7.8 GWp of capacity, but there are wide gaps in our understanding of the performance of these systems, which has lead to the conservative limit of 10 GWp being imposed on UK PV capacity by the Department of Energy and Climate Change. Here we present the results of a statistical analysis of real world UK PV systems which donate data to the Microgen Database, of which there are over 7000. The mean yearly-integrated Performance Ratio (PR) of domestic scale UK PV is 83% with a standard deviation of 7%. By considering yearly-integrated PR, we have shown that 4.1 % of systems suffered long-term underperformance relative to their nominal efficiencies during 2013. The mean degradation rate for crystalline Silicon-based PV systems in the UK is-0.8 ± 0.1% per year. The state-of-the-art of UK PV, in terms of technology, manufacturing, and installation-standards, is found to have increased by 1% per year between 2002 and 2013.
... The PV monitoring data was taken from the Sheffield solar microgeneration database [69, 71,72]. The Sheffield microgeneration database records PV generation in the UK by collecting data from voluntary PV owners. ...
Article
Full-text available
Many efforts are recently being dedicated to developing models that seek to provide insights into the techno-economic benefits of battery storage coupled to photovoltaic (PV) generation system. However, not all models consider the operation of the PV – battery storage system with a feed-in tariff (FiT) incentive, different electricity rates and battery storage unit cost. An electricity customer whose electricity demand is supplied by a grid connected PV generation system benefiting from a FiT incentive is simulated in this paper. The system is simulated with the PV modelled as an existing system and the PV modelled as a new system. For a better understanding of the existing PV system with battery storage operation, an optimisation problem was formulated which resulted in a mixed integer linear programming (MILP) problem. The optimisation model was developed to solve the MILP problem and to analyse the benefits considering different electricity tariffs and battery storage in maximising FiT revenue streams for the existing PV generating system. Real data from a typical residential solar PV owner is used to study the benefit of the battery storage system using half-hourly dataset for a complete year. A sensitivity analysis of the MILP optimisation model was simulated to evaluate the impact of battery storage capacity (kWh) on the objective function. In the second case study, the electricity demand data, solar irradiance, tariff and battery unit cost were used to analyse the effect of battery storage unit cost on the adoption of electricity storage in maximising FiT revenue. In this case, the PV is simulated as a new system using Distributed Energy Resources Customer Adoption Model (DER-CAM) software tool while modifying the optimisation formulation to include the PV onsite generation and export tariff incentive. The results provide insights on the benefit of battery storage for existing and new PV system benefiting from FiT incentives and under time-varying electricity tariffs.
Conference Paper
Full-text available
BIPV systems are small PV generation units spread out over the territory, and whose characteristics are very diverse. This makes difficult a cost-effective procedure for monitoring, fault detection, performance analyses, operation and maintenance. As a result, many problems affecting BIPV systems go undetected. In order to carry out effective automatic fault detection procedures, we need a performance indicator that is reliable and that can be applied on many PV systems at a very low cost. The existing approaches for analyzing the performance of PV systems are often based on the Performance Ratio (PR), whose accuracy depends on good solar irradiation data, which in turn can be very difficult to obtain or cost-prohibitive for the BIPV owner. We present an alternative fault detection procedure based on a performance indicator that can be constructed on the sole basis of the energy production data measured at the BIPV systems. This procedure does not require the input of operating conditions data, such as solar irradiation, air temperature, or wind speed. The performance indicator, called Performance to Peers (P2P), is constructed from spatial and temporal correlations between the energy output of neighboring and similar PV systems. This method was developed from the analysis of the energy production data of approximately 10,000 BIPV systems located in Europe. The results of our procedure are illustrated on the hourly, daily and monthly data monitored during one year at one BIPV system located in the South of Belgium. Our results confirm that it is possible to carry out automatic fault detection procedures without solar irradiation data. P2P proves to be more stable than PR most of the time, and thus constitutes a more reliable performance indicator for fault detection procedures. We also discuss the main limitations of this novel methodology, and we suggest several future lines of research that seem promising to improve on these procedures.
Article
Full-text available
Solar resource assessments use solar radiation data from past observations to estimate the average annual solar radiation over the expected lifetime for a solar energy system. However, solar radiation at the Earth’s surface is not stable over time but undergoes significant long-term variations often referred to as “global dimming and brightening”. This study analyzes the effect of these long-term trends on solar resource assessments. Based on long-term measurement records in Germany, it is found that the additional uncertainty of solar resource assessments caused by long-term trends in solar radiation is about 3% on the horizontal plane and even higher for tilted or tracked planes. These additional uncertainties are not included in most uncertainty calculations for solar resource assessments up to now. Furthermore, for the measurement stations analyzed, the current irradiance level is about 5% above the long-term average of the years 1951–2010. Since the direction of future trends in solar radiation is not known, different possibilities to estimate the future solar resource are compared. In view of long-term trends that could extend beyond the period of past observations and beyond the projected lifetime of a solar energy application, a paradigm shift is proposed: instead of using the longest possible period to calculate an average value, only the 10 most recent years should be used as the estimator for future solar irradiance.
Article
Full-text available
In this study, we investigate the performance ratio (PR) of about 100 German photovoltaic system installations. Monitored PR is found to be systematically lower by ~2–4% when calculated with irradiation data obtained by pyranometers (henceforth denoted as PRPyr) as compared with irradiation amounts measured by reference cells (denoted as PRSi). Annual PRSi for the ~100 systems is found to be between ~70% and ~90% for the year 2010, with a median PR of ~84%. Next, simulations were performed to determine loss mechanisms of the top 10 performing systems, revealing a number of these loss mechanisms may still allow for some optimization. Despite the fact that we do not see such values from our monitoring data base up to now, we believe PRSi values above 90% are realistic even today, using today’s commercially available components, and should be expected more frequently in the future. This contribution may help in deepening our knowledge on both energy loss mechanisms and efficiency limits on the system level and standardization processes of system-related aspects.
Article
Full-text available
The main objective of this paper is to review the state of the art of residential PV systems in France. This is done analyzing the operational data of 6868 installations. Three main questions are posed. How much energy do they produce? What level of performance is associated to their production? Which are the key parameters that most influence their quality? During the year 2010, the PV systems in France have produced a mean annual energy of 1163kWh/kWp. As a whole, the orientation of PV generators causes energy productions to be some 7% inferior to optimally oriented PV systems. The mean performance ratio is 76% and the mean performance index is 85%. That is to say, the energy produced by a typical PV system in France is 15% inferior to the energy produced by a very high quality PV system. On average, the real power of the PV modules falls 4.9% below its corresponding nominal power announced on the manufacturer's datasheet. A brief analysis by PV modules technology has led to relevant observations about two technologies in particular. On the one hand, the PV systems equipped with heterojunction with intrinsic thin layer (HIT) modules show performances higher than average. On the other hand, the systems equipped with the copper indium (di)selenide (CIS) modules show a real power that is 16% lower than their nominal value.
Article
Full-text available
The main objective of this paper is to review the state of the art of residential PV systems in Belgium by the analysis of the operational data of 993 installations. For that, three main questions are posed: how much energy do they produce? What level of performance is associated to their production? Which are the key parameters that most influence their quality? This work brings answers to these questions. A middling commercial PV system, optimally oriented, produces a mean annual energy of 892kWh/kWp. As a whole, the orientation of PV generators causes energy productions to be some 6% inferior to optimally oriented PV systems. The mean performance ratio is 78% and the mean performance index is 85%. That is to say, the energy produced by a typical PV system in Belgium is 15% inferior to the energy produced by a very high quality PV system. Finally, on average, the real power of the PV modules falls 5% below its corresponding nominal power announced on the manufacturer's datasheet. Differences between real and nominal power of up to 16% have been detected.
Article
Sources of error in the performance of large ensembles of spatially distributed photovoltaic generators are investigated and reported. Errors are propagated to estimate uncertainty in modeled global tilted radiation and performance ratio (PR)(PR) for the typical UK generator. Uncertainties in generators’ azimuth and elevation lead to typical monthly errors of 4% and 1% on global tilted radiation and PRPR. Interpolation of global horizontal irradiance is affected by an average 5% monthly error and the conversion to the inclined plane leads to an estimated error from 7% to 8% on tilted radiation and PRPR. This prediction has been verified against a set of twenty pyranometers on the plane of the array deployed across the UK, which gauge a 6% monthly error. Mutual cancellations lower this value to 4% for annual periodicity. The estimated monthly error on interpolated global horizontal irradiance is half of the 10% error affecting widely-used Photovoltaic Geographical Information System (PVGIS), which experiences larger errors also on the inclined plane. The assessed uncertainties impact the net present value of the investment required for deploying a PV generator; such an impact has been quantified. The yearly PRPR for the typical UK microgenerator is 84%, a value 8% (6%) higher than recent studies in France (Belgium). In winter, the typical UK performance ratio drops to 75%, because of an increase in shading. Summer performance ratio remains greater than the yearly mean, possibly reflecting the relatively short intervals during which direct sunlight heats the PV modules and the windy conditions over the British Isles. The monthly/annual error affecting the typical individual generator virtually cancel out for the whole national ensemble.
Article
An analytical method is developed for estimating R, the ratio of the long-term monthly-average daily radiation on an inclined surface to that on a horizontal surface. This method differs from the Liu and Jordan [1] method in the manner in which the beam radiation component is determined. The method is applicable for surfaces of any orientation. R values calculated in this manner and by the Liu and Jordan method are compared with integrated hourly calculations for 23 years in Madison, WI, Albuquerque, NM and Miami, FL; the comparisons show that the method described in this paper agrees more closely with the integrated hourly calculations, especially for surfaces facing east or west of south.
Article
Photovoltaic (PV) systems have become popular globally as an important candidate in the establishment of low-carbon electrical and civil systems. Since the early 1990s, globally, many PV systems have been installed, and their performance parameters such as annual yield, performance ratio, and system losses have been monitored in order to understand their basic characteristics; these data also help in the development of these systems and in analyzing their reliability. In Japan, the performance of residential PV systems was evaluated and reported. However, these evaluations were carried out using older systems, and the current status and the performance trend for each installation year have not been sufficiently evaluated and discussed. Therefore, this report aims to provide a macroscopic evaluation of the performance trends of PV systems by using the monitoring data of systems that were installed between the year 2000 and the year 2007. As a result of the evaluation, we have concluded that the performance ratio of these systems improved to an average of 0.72 until fiscal year 2000 and to 0.78 after fiscal year 2001. For investigating the factors behind this increase in performance ratio, we have focused on characteristics of the actual system capacity (capacity of each PV module measured at the point of shipment) and inverter efficiency. Copyright © 2010 John Wiley & Sons, Ltd.
Feed-in Tariffs: get money for generating your own electricity Government Digital Service
  • Gov Uk
GOV.UK, " Feed-in Tariffs: get money for generating your own electricity, " Government Digital Service, 4 February 2015. [Online]. Available: https://www.gov.uk/feed-in-tariffs/overview.
Microgen Database Sheffield Solar -University of Sheffield
  • Sheffield Solar
Sheffield Solar, " Microgen Database, " Sheffield Solar -University of Sheffield, [Online]. Available: http://www.microgen-database.org.uk/.