Cian Ryan_project realisation_project no.35

The Feasibility of Solar Photovoltaics in Ireland’s Commercial
Sector
Cian Ryan, Cork Institute of Technology, Cork , Ireland
2014 Semester 2 Project Number (35)
__________________________________________________________________________________________
Student Number: R00070224
Degree Programme: Sustainable Energy Engineering (BEng Honours degree) CR510
Module Code: MANU8006 Project – realisation
Supervisor: Brian Quin
Assessor: Paul O’Sullivan
__________________________________________________________________________________________
Abstract:
The global capacity of Solar Photovoltaic (PV) has increased rapidly in recent years. However, this is not
the case in Ireland, where wind energy dominates in the renewable electricity generation capacity. The
main aim of this project is to first determine the viability of PV in Ireland and secondly discover why wind
is so dominant in the Irish electricity market.
Installing renewable energy devices in a building can be a large capital expense. The planning stage of the
process is key to the success of an investment. Mathematical modelling is used to represent the system in
an attempt to forecast energy yield. In this project a modelling software is created that will act as a tool for
predicting energy yield, allowing installers to determine the optimum operating conditions and size the
system accurately.
This software was used to determine the viability of Solar Photovoltaics in Ireland using existing case
studies. It was discovered that in certain cases, solar PV is a viable investment in Ireland. Viability mainly
depended on the price of each unit of electricity generated. Consuming energy on site rather than exporting
to the grid is crucial for the viability of a project.
It was concluded that the current renewable energy feed in tariff is not sufficient to support PV if all
electricity is exported. Germany remains a world leader in PV Despite similar solar PV resources, whereas
Ireland has just over 100kW installed capacity. This is mainly due to unattractive feed tariffs in Ireland. It
was also found that wind performed considerably better than PV when both resources were compared in a
case study.
__________________________________________________________________________________________
Declaration:
“This report is solely the work of (Cian Ryan) unless otherwise indicated and is submitted in partial fulfilment of
the degree of Bachelor of Engineering in Sustainable Energy. I understand that significant plagiarism, as
determined by the examiner, may result in the award of zero marks for the entire assignment. Anything taken from
or based upon the work of others has its source clearly and explicitly cited.”
Signature: Cian Ryan Date: 09/05/2014
__________________________________________________________________________________________

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Contents
Abstract...............................................................................................................................................................1
Declaration .........................................................................................................................................................1
1. INTRODUCTION.....................................................................................................................................3
2. METHODOLOGY....................................................................................................................................4
3. RESULTSANALYSIS.............................................................................................................................7
Energy Yield Forecast (Tramore)...................................................................................................................7
Energy Yield Forecast (CIT) ..........................................................................................................................8
Model Accuracy .............................................................................................................................................8
Model Interface and Applications.................................................................................................................10
Cell Efficiency..............................................................................................................................................11
Seasonal Optimisation ..................................................................................................................................12
PV Cell Type ................................................................................................................................................13
Comparing PV with Wind Energy................................................................................................................14
Viability of PV (Case Study)........................................................................................................................15
Potential Errors.............................................................................................................................................17
4. CONCLUSIONS .....................................................................................................................................17
5. REFERENCES........................................................................................................................................18
6. APPENDICES.........................................................................................................................................19
Table of Figures
Figure 1 Project objectives .....................................................................................................................................3
Figure 2 Renewable energy capacities
(http://www.eirgrid.com/media/EirGridAnnualRenewableReport2013.pdf) .........................................................3
Figure 3 PV and wind monthly output values ........................................................................................................7
Figure 4 Model output vs. PVGIS output .............................................................................................................10
Figure 5 Screenshot of model interface ................................................................................................................10
Figure 6 Cell efficiency over the course of a year ................................................................................................11
Figure 7 Efficiency and temperature over the course of a summer day................................................................11
Figure 8 Seasonal output, June, July and August are assumed to be 0. ................................................................12
Figure 9 PV cell prices (11)..................................................................................................................................13
Figure 10 Cell payback period..............................................................................................................................13
Figure 11 Economic payback period ....................................................................................................................16

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1. INTRODUCTION
The global and European capacity of Solar Photovoltaic (PV) has increased rapidly in recent years (see appendix
3). This is due to rapid advances in technology in recent years making PV more efficient and more affordable.
World leaders such as Germany have adopted aggressive REFIT tariffs and tax incentives to increase the number
of people investing in PV.
For an array less than 30 kwp, investors were getting between 9% and 10% return of investment in the year 2012.
(1) The return of investment in question is for a 20 year lifetime without borrowing the capital for the array.
The recent rapid increase in PV generating capacity cannot be seen in the same manner in Ireland. Wind energy
has the majority share of renewable energy generation capacity in Ireland. The aim of this project is to firstly
determine if solar PV is a viable source of renewable electricity in Ireland. Secondly if PV isn’t viable in Ireland,
determine the level of government support that would be required to make it viable. Third, determine why solar
PV isn’t used as extensively in Ireland as wind energy is. Mathematical modelling is used to determine the viability
of PV in Ireland, through the modelling of two existing PV arrays. Microsoft Excel software is used rather than
PVSOL so as to be able to tune the model to exactly what is desired. The Excel software gives more freedom and
allows the model to be built from scratch enhancing the learning process involved. The objectives of this report
can be seen in fig.1.
According to building regulation L3(b), all new buildings must have a minimum renewable energy generation
capacity of 4 kWh/m2
/yr (TGDL 1.2.1 see appendix 6). Due to the capital intensive nature of renewable electricity
generation it is paramount to ensure that the system design is viable. It is also important to be able to forecast the
energy yield in a given timeframe.
The mathematical model is a dual
purpose tool in this regard, it
determines viability and forecasts the
energy yield of the PV system at
hourly intervals.
The project investigates the viability
of two PV arrays recently installed.
Tramore School contains a 15.5 kWp
array in order to produce renewable
electricity as agreed in a private
Figure 1 Project objectives
Figure 2 Renewable energy capacities
(http://www.eirgrid.com/media/EirGridAnnualRenewableReport2013.pdf)

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public partnership contract with the government. Cork Institute of Technology contains a 20 kWp array to generate
electricity which will operate an air-to-water heat pump for the zero 2020 building. One mathematical model can
be used to obtain energy yields for both sites by changing the parameters. Both case studies will be key to
determining the viability of PV in Ireland as a whole.
Fig.2 illustrates the renewable generation installed capacities in MW in Ireland and highlights how much wind
energy dominates the market share. As of autumn 2013 solar PV had in an installed capacity of only 100 kW in
comparison to the 1879.3 MW of wind in the Republic of Ireland. Compared with Northern Ireland, the Republic
of Ireland has only a fraction of installed PV capacity with 5500kW of solar PV installed which is quite a large
market share and appears to break the logic of lower latitude countries have better solar resources (see appendix
1 for solar PV resource map). The overriding factor for Northern Ireland appears to be better government support
for PV.
2. METHODOLOGY
The project as a whole depends mainly on the mathematical model built in excel. It is through modelling that
almost all of the results were obtained. It was decided to use modelling methods rather than empirical methods so
that the software could be used as a tool in the future. Modelling is straight forward because the empirical data
that was being recorded at Tramore could be only accessed on site. The PV array also wasn’t commissioned until
March, meaning that only April’s data could be used to determine its viability. Clearly, at least one full year of
data is required to make an accurate assessment of the viability of a renewable energy source that varies seasonally.
The only solution to this problem is to build a model of the system as accurately as possible. Granted the model
won’t be as accurate as using empirical data because some variables can’t be accounted for. However, the lack of
data for an empirical method meant that modelling had to be used to gain results.
In order to account for a long time frame and make the data as accurate as possible, a TMY3 weather dataset was
used. The dataset was used in a previous module (Energy Systems Modelling) and is a representation of 30 years
of data at Cork Airport. The data was file was then edited to include only the desired variables. These included
Global Horizontal Irradiance (GHI), Direct Normal Irradiance (DNI), Diffuse Horizontal Irradiance (DHI), Wind
speed and ambient temperature. Each variable was plotted in excel at hourly intervals. The next step in the process
was to model the suns position, azimuth and elevation, at each hourly interval for the year. In order to ensure the
calculations were accurate, the University of Oregon sun chart diagram generator was used as a comparison. The
elevation for the winter solstice (21/12), spring equinox (20/03) and summer solstice (21/06) were plotted onto a
graph (elevation vs. time) and compared with the same dates on the sun chart diagram.
This process proved problematic at the beginning. At first the day number method was used to determine the
elevation angle of the sun, it was decided that this method didn’t have the required level of accuracy for the
project. The declination angle δ was determined for each day and used to find the elevation of the sun.
𝜹 = 𝟐𝟑. 𝟒𝟓°𝐬𝐢𝐧⁡[
𝟑𝟔𝟎
𝟑𝟔𝟓
(𝒅 − 𝟖𝟏)⁡(𝒆𝒒𝒏𝟏) ⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡
⁡𝜶 = 𝟗𝟎 − 𝝋 + 𝜹 (eqn2)
Having built the equations into the model, it became clear that the elevation of the sun remained constant for the
24 hour period. This method obviously had to be abandoned as it didn’t represent accurately the actual suns path.
An equation including hour angle (HRA) was employed in place of the previous formulae to better represent the
suns path at hourly intervals.
𝜶 = 𝒔𝒊𝒏−𝟏
[𝒔𝒊𝒏𝜹𝒔𝒊𝒏𝝋 + 𝒄𝒐𝒔𝜹𝒄𝒐𝒔𝝋 𝐜𝐨𝐬(𝑯𝑹𝑨)] (eqn3)
The hour angle is found through a series of formulae as follows,
HRA=15°(LST-12)(eqn4)
LST= LT+(TC/60) (eqn5)
TC= 4(Longitude-LSTM)+EoT (eqn6)
EoT= 9.87sin(2B)-7.53(B)-1.5sin(B) (eqn7)
B= (360/365)*(d-81) (eqn8)

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Where,
 δ= Declination angle
 d= day number
 φ= Latitude
 α= Solar Elevation Angle
 LST= Local Solar Time
 LT= Local Time
 TC= Time Correction Factor
 LSTM= Local Solar Time Meridian (time zone)
 EoT= Equation of time
First, the equation of time must be found. “The equation of time (EoT) (in minutes) is an empirical equation that
corrects for the eccentricity of the Earth's orbit and the Earth's axial tilt.” (2). EoT is then subbed in to find the
time correction factor, ie. how far ahead or behind Grenwich the location is. The site in question is approximately
35 minutes ahead of Grenwich. Local solar time is the actual time as seen by the sun. Time zones assume solar
time is constant across 15 degrees of latitude which is not the case. The time correction factor corrects this issue.
The hour angle is then found using local solar time. The hour angle is then subbed into equation 1 to find the suns
elevation at each hour. The hour angle can also be used to determine the azimuth angle of the sun for each hour.
The following formula is used to calculate azimuth,
𝑨𝒛𝒊𝒎𝒖𝒕𝒉 = 𝒄𝒐𝒔−𝟏
[
𝒔𝒊𝒏𝜹𝒄𝒐𝒔𝝋−𝒄𝒐𝒔𝜹𝒔𝒊𝒏𝝋𝐜𝐨𝐬⁡( 𝑯𝑹𝑨)
𝒄𝒐𝒔𝜶
] (eqn9)
In order to find the solar energy striking the plate, the diffuse and direct parts of the radiation had to be modelled
differently. Diffuse radiation can strike the collector at any angle because dust and water vapour in the atmosphere
causes it to scatter. Therefore the best way to model diffuse radiation is with the following formula,
D= DHI*(180-β/180) (W/m2
) (eqn10)
Where, β= array tilt angle.
Calculating the beam or direct irradiance is much more complex due to the sun changing azimuth and elevation
every hour and the earth changing declination angle. The following formula is how the direct irradiance was
calculated,
B=DNI*[(sinδsinφcosβ)+(sinδcosφsinβcosψ)+(cosδcosφcosβcosHRA)+(cosδsinφsinβcosψcosHRA)+
(cosδsinψsinHRAsinβ)] (W/m2
) (eqn11)
Where ψ= module azimuth.
It can be explained that the direct normal irradiance value (DNI) is multiplied by a ratio which depends on how
close the irradiance is to striking the plate perpendicularly. For example if the beam radiation was striking the
plate perpendicularly then the ratio would be 1 (max). The diffuse and beam parts of the irradiance are added
together to get the total irradiance striking the plate (I). This is the maximum amount of energy that the module
could convert to electricity not including efficiency or inverter losses.
The next objective of the project was to compare the PV array with another renewable resource. Wind was chosen
as a comparative because it appears to be used extensively in Ireland. The wind turbine selected for comparison
purposes was a CF15, a 15 kW rated turbine. The power curve was used to calculate the power output at each
wind speed. The COUNTIF function in excel was used to count the number of hours at each wind speed from
0m/s to 25m/s. The hours at each wind speed were multiplied by the power output at the specified wind speed to
get the annual energy yield. Monthly power output values were then plotted on an X/Y scatter graph.
Calculating how much electricity was produced by the array was less straight forward than calculating the energy
at the module. The efficiency was dependent on many variables, cell temperature, wind speed, cell type and fill
factor. The system got very complex quite quickly so it was decided to keep it simple until the model was proven
to be accurate. It would have been of no benefit to increase the models complexity without first determining how
accurate it was. The energy yield was simply calculated as,
(Energy at the plate)*(efficiency at STC)*(area of the collector).
The monthly output values were then plotted against the wind turbine output values in order to better understand
the generation profile of each system. A PVGIS survey was then conducted for Cork Airport in order to compare

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values with the model constructed. PVGIS is a software modelling tool that generates monthly and annual yield
values when the array specifications are entered. Satisfied with the comparison, the model was thought to be
accurate. However improvements were required as the energy conversion calculation wasn’t an accurate reflection
of how a PV system works.
Before increasing the complexity of the model, an interface had to be designed to allow a user to optimize a
proposed PV array before installing. Slider bars were added to allow the user to adjust the model parameters
easily. The parameters included were array tilt angle, array azimuth angle and peak generating capacity. Graphs
and tables of both the PV and wind output were included in the interface to allow the user to easily visualise
changes in output as the parameters are changed.
Having designed the interface it was decided to include economic payback period on the interface. Payback period
for the majority of firms is the determining factor when installing renewables, therefore it was of utmost
importance to include it in the interface. Three scenarios were graphed, German feed-in tariff 100% exported,
Irish feed-in tariff 100% exported and Irish consumption 100%. The economic forecast period was 20 years which
represented the manufacturers guarantee for the PV panels. The following formula was used to determine the net
present value for each year,
NPV= Σ Rt/(1+i)t
(eqn12)
Where,
 Rt= net cash flow
 i= discount rate
 t= cash flow period
Having completed the energy yield forecast model with results being satisfactory, attention was then turned to the
efficiency of the array at different temperatures and levels of solar irradiance. It is well documented that cell
temperature affects the efficiency. Given that the standard test conditions (STC) are at 25°C, the efficiency of the
cell could outperform the STC efficiency in Ireland. It was also decided that using the STC efficiency to determine
the energy yield was not accurate enough. However, there was a problem when calculating efficiency, the
temperature of the cells was needed. In order to overcome this problem, the following equation was employed,
Tc = Ta+(I*eWspd*(a+b)
) (eqn13)
The cell temperature was then subbed into the following formula,
η = ηSTC*{1-[βref*(Tc-TSTC)+(γ*log10I)]} (eqn14)
Where,
 Tc= cell temperature
 I= Solar Irradiance (W/m2
)
 Wspd= Wind Speed (m/s)
 (a+b)= coefficient for mount technique
 η(STC)= Standard Test Conditions Efficiency
 γ= Temperature Correction Factor (1.07 C-Si @ 15°C) (24)

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3. RESULTSANALYSIS
Energy Yield Forecast (Tramore)
Having constructed the model, the parameters for each site were entered into the software. The software is flexible,
allowing a number of variables to be changed in order to quickly obtain results. The Tramore School site
parameters were entered as follows,
 Tilt angle 15°
 Azimuth angle 151° East
 Peak generating capacity 15.5 kWp
 Cell efficiency (STC) 17.4%
 Latitude 52.164°N
 Longitude -7.15°W
Figure 3 PV and wind monthly output values
As expected the peak energy yield came in the month of June when there is the longest hours of sunlight. Due to
the low angle of tilt, the system is optimized for summer production. This is reflected in the results as there is an
eightfold increase in energy yield when
comparing January with June. The total
annual yield for Tramore School is
estimated at 12716 kWh or 820kWh/kWp.
According to PVGIS, 900 kWh/kWp is
achieved on the south coast. (6). This
gives an annual yield of 12214 kWh which
is similar to the model output. The SEAi
best practise guide states that the optimum
angle of tilt for a PV array is 30° and the
optimum azimuth angle is 0° from south.
As can be seen from table 1 the optimum
angle of tilt is 40° which is slightly
different to the figure quoted by SEAi. It
can clearly be seen from table 1 that low angles of tilt are optimum for the summer months (15° is optimum for
June). This is due to the sun having a large elevation angle in the summer. It can also be seen that large angles of
tilt are optimum for winter months (60° is optimum for January).
It is also clear from fig.3 that there is a mismatch between peak energy yield and peak demand when PV is used
in schools. The peak energy yield as expected is in the summer months, when the school is on holidays for the
months of June, July and August. The building load is at its maximum when the students are at school during the
remaining months where the energy yield is at its lowest. For these reasons it could be argued that the PV array is
unsuitable for a schools load profile. The blue plot on the graph is a wind turbines energy yield and can be seen
as a U-shaped curve with maximum yield in the winter months and lower yield in the summer months. The plot
Table 1
Energy yield Tramore School (Azimuth 0°)
Tilt Angle
(Deg from
horizontal)
Total Annual
Irradiance
(kWh)
January
Irradiance
(kWh)
June
Irradiance
(kWh)
0° 12007 222 2003
15° 12720 259 2045
30° 13112 291 2035
45° 13162 315 1975
60° 12859 331 1866
Optimum
(40°)
13184 308 2000

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in fact is directly opposing the N-shaped PV energy yield curve. The wind turbine fits the load profile much better
than the PV array. However, this is solely from an energy yield perspective.
Energy Yield Forecast (CIT)
The parameters for the PV arrays in CIT were entered into the model as follows,
 Tilt Angle 15°
 Azimuth Angle 190°
 Peak Generating Capacities 12kWp (mono), 5.5kWp (multi) and 3kWp (mono)
 Cell Efficiencies (multi c-Si 14.5% and mono c-Si 15.5%) at STC
 Latitude 51.85°N
 Longitude -8.51°W
Results obtained for CIT are similar to that of the Tramore site, with June giving the largest monthly output.
Unlike Tramore there are 3 different PV arrays. The total output from the system is 8788+3484+2197= 14469kWh
per annum or 723.45kWh/kWp. Despite having 4.5kWp more than Tramore School, CIT’s PV array doesn’t
perform as well (723kWh/kWp compared with 820kWh/kWp). This is attributed to the lower cell efficiencies of
the CIT modules.
Based on the amount of data available regarding cost, it was decided to continue the report based on the Tramore
site.
Model Accuracy
As mentioned previously the
model was tested for accuracy
against existing software. The
accuracy of the sun’s position was
tested against the University of
Oregon sun chart generator. The
axes in each graph are different, the
University of Oregon chart is
elevation vs. azimuth whereas the
model generated graph is elevation
vs. time. The model graph had to
be plotted as elevation vs. time
because the hour lines on the sun
chart diagram are not linear.
Despite the different axes, datum
points can still be compared from Figure 4 University of Oregon sun chart diagram

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each graph. As can be seen from
the sun chart diagram the
elevations at 12pm and 1pm on
the summer solstice (21st
June) are
both approximately 61°. The
elevation angles for the model are
60.88° and 61.15° respectively.
The correlation between both
graphs is clear for this particular
date. There is also a strong
correlation of results for the
winter solstice (January 21st
). The
sun chart diagram sun elevation
can be seen as approximately 15°
while the model shows the
elevation to be 14.4° at 12pm. The
accuracy of the results shown
gives confidence in the energy
yield results quoted in the previous section. The source for plotting the suns elevation is the same source for
calculating the plane of array (POA) irradiance which eventually gives the energy yield. Not only was the model
proven to be accurate, the elevation data could potentially be used as input data for an elevation axis tracker. Raw
cell data for every hour of the year is available, any specific day can also be illustrated graphically as it is in fig.3.
Data from the model could be used to actuate the axis tracker, tilting the panel to the correct angle so the beam
radiation strikes normally. Sensors usually are used to detect the suns elevation, however this method could also
be deployed.
The model was also tested against other PV modelling software. PVGIS was selected as comparison as it is the
software used in conjunction with the European Commission Joint Research Centre (7). Any location can be
selected in the model, the latitude and longitude of the weather data site was entered. The tilt angle and azimuth
angle were optimized for the location. The optimum angle of tilt for the PVGIS model was 39°, similarly the
optimum angle of tilt for the model built was 40°. The results of each of the models can be compared on the graph
fig.6. Both outputs are plotted and compared based on monthly output values at optimum operating conditions for
the locations. The annual outputs are similar with the model calculating 13257 kWh and the PVGIS model
calculating 15500 kWh for the same array. This is a large difference with the model calculating an output that is
86% of the PVGIS model output. Before the model complexity was increased to allow ambient temperature and
wind speed affect the efficiency of the cells, the standard test conditions efficiency was used. When this value for
efficiency was used the annual output was on a par with the PVGIS model, with 15306 kWh per year at optimum
conditions. Due to low values of irradiance, the efficiency is affected in a negative way. However, the efficiency
is increased in certain cases due to the low ambient temperature at the site (below 25°C STC). The average annual
efficiency for the PV cells is 15.83% which when compared to the STC efficiency of 17.4% accounts for the
decrease in output. In the PVGIS model the losses due to ambient temperature and low irradiance are estimated
at 7.6% which may be lower than the built model calculates and this would account for the difference in output.
The method of selecting the type of panel for the PVGIS model doesn’t have much resolution regarding efficiency.
The panel is just chosen by type, for example crystalline silicon was selected for the plot of monthly outputs for
fig.6. The type of panel was chosen, however the efficiency of this panel was unknown.
Figure 5 Model generated sun chart diagram

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Another difference between the model and the PVGIS model is the seasonal variation in output. The model has a
much more severe seasonal variation than the PVGIS model. For example, as can be seen in fig.6, the smallest
monthly output is 308 kWh and the largest is 2000 kWh for the model plot. The PVGIS model is less severe with
565 kWh the lowest and 1910 kWh the largest output. The mean total yield profile of the Ballymaloe House PV
array (see appendix 10) fits the model plot better than the PVGIS. This empirical data suggests that there is a large
difference in seasonal load which is reflected in the model plot.
The comparisons between the model and the PVGIS model are made under the same parameters, 15.5 kW peak
system operating at optimum conditions at the site location, 51.85°N -8.5°W. The accuracy of the model is
satisfactory based on these comparisons. However it isn’t perfect with a 15% deviation in annual output values.
The fact the plot from the model and Ballymaloe house are similar is encouraging also.
Model Interface and Applications
The model interface is constructed to allow the user to change PV system parameters easily and view the resulting
changes. A monthly PV output table and graph illustrate the energy yield in a method that is easy to view. Fig.7
is a screen shot of the model interface layout.
Figure 5 Screenshot of model interface
The slider bars on the top left of fig.7 allow system parameters to be changed to either determine optimum
conditions for a planned system or determine the yield of an existing system. The PV output table and both graphs
will update as a result. The economic performance graph is a basic payback model that can be updated on the
economics sheet. 4 scenarios are plotted, wind FIT, Irish PV FIT, Irish PV 100% consumption and German FIT
Figure 4 Model output vs. PVGIS output

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PV. The payback period is based on the cost of the Tramore School installation (29650 euro) which is the sub-
contractors fee. The cost of the wind turbine is based on the Irish Wind Energy Authorities estimates of 2000
euro/kW installed (8).
The other graph is PV output vs. wind output. It is based on a 15kW wind turbine and the 15.5 kWp Tramore
School PV array. The PV array plot updates as the system parameters are changed, while the wind turbine plot
remains constant.
This model is very flexible with a number of adjustable parameters. What sets it apart from other models such as
PVGIS is that the PV system is represented better. The efficiency that the array is operating at and the output
expected can be seen at hourly intervals. As well as temperature, it also takes into account the wind speed at the
site which is factored into the cooling of the cells. Each cell in the calculations are linked throughout the software,
allowing for quick and simple adjustments to the model. Any panel type or size can be changed from the current
panels in the model, which are the panels installed in Tramore. Location can easily be changed by simply changing
the longitude, latitude and weather data for the proposed site.
Besides being flexible, it allows users to better plan the installation of a PV array. For example if, like with
Tramore School, a better winter output is required the user can optimise the tilt angle for winter months. The
model built is a powerful tool for planning the installation of a PV array and will be crucial for the decision making
process.
Cell Efficiency
Cell efficiency is expressed as a
function of irradiance level, wind
speed and collector temperature. It
is calculated using (eqn14). The
average annual efficiency is 15.83%
which is 1.57% less than STC
efficiency of 17.4%. The loss in
efficiency is attributed to
temperature based on trends in fig.8.
As seen in fig.8 the efficiency stays
in the range of 14-18% apart from a
few outliers. Due to the high wind
speed and low ambient temperature
at the site, the performance of the
cells in terms of efficiency is very
good. The efficiency plot in fig.8 is the efficiency of the cell at 12pm for each day of the year. This smaller set
of data is better than an annual plot as the trends would be more difficult to see with such a large quantity of data.
The relationship between ambient temperature and efficiency can clearly be seen in fig.9. The graph is for a single
day in June and shows how the PV panel performs
over the course of the day. The panel begins at 17%
efficiency and drops to 14.3% when the temperature
peaks at 19°C. Hot operating conditions lead to a
loss in efficiency, in this case 2.7%. In certain cases,
the efficiency of a cell can exceed standard test
conditions efficiency. This is when the temperature
is less than 25°C and the irradiance is close to 1000
W/m2
. Taking the term Tc-TSTC from (eqn14), if the
cell temperature is lower than STC conditions it will
lead to a negative number. The negative number
will cause the efficiency to be larger than the STC
efficiency. However, the solar irradiance level is a
factor in the equation also. If the irradiance level is
low the efficiency will be reduced. Overall it can be
seen that temperature has the greatest effect on efficiency based on (eqn14) and fig.8 and fig.9. From fig.8 it can
clearly be seen during the summer months there is a dip in efficiency, this is despite the highest levels of solar
irradiance. The trend for the graph is a slight U-shape, with a few outliers. Temperature reduces the voltage output
in an I-V curve reducing the maximum power point and thus reducing the efficiency. The maximum power point
is reduced given that power = voltage*current and efficiency is derived by Pmp/Pin (2).
Figure 6 Cell efficiency over the course of a year
Figure 7 Efficiency and temperature over the course of a summer
day

Cian Ryan R00070224
12
Seasonal Optimisation
It is proposed that for a building with seasonal demand
such as a school, the system could be optimised for a
season rather than for the year. Tramore School will have
an occupancy schedule from September to May which
coincides with the academic year. Based on data in table 1
it seems that a large angle of tilt would be optimum for the
winter months. In order to optimise for this period, June,
July and August will be filtered out of the data. The data
will be filtered out but the system will still be operational
for the summer. Sacrificing some of the summer output by
optimising for the winter months may increase the amount
of money the system will make. The annual output will be
reduced because the system won’t be operating at the
optimum conditions for the year. Seasonal optimisation is
only viable if a proportion of the summer yield is being
exported to the grid due to the lower demand. This is
because exported electricity fetches only 9 cent per kWh
whereas consuming it directly from the PV could save up
to 18 cent depending on the price of electricity (9).
Fig.10 shows the PV array optimised for the winter
months. The total figure of 7682.45 kWh is for the period from September to May when the school is occupied.
The optimum tilt angle for September to May is 50°, as expected this is an increase in angle from the annual
optimum angle of 40°. This is due to the lower angles of solar elevation experienced in the winter period. For
lower angles of solar elevation, large angles of tilt are required so that the sun can strike the plate close to
perpendicularly. The optimisation for this period reduces the total output to 13170 kWh which is a reduction of
87 kWh from the annual yield. At annual optimum conditions the winter output would be 7635 kWh which is 47
kWh less than winter optimum. These differences are minimal and would make insignificant difference to the
annual income. However, at the tilt angle installed (15°) for Tramore, seasonal optimisation could make more
significant changes. At 15° the seasonal output for the winter months is 7140 kWh which is 542 kWh less than
seasonal optimum.
For the purpose of calculating income for the PV array, the feed in tariff is assumed to be 9 cent (9) and the current
electricity cost is 18 cent per kWh.
Table 2
Tilt angle Annual output Seasonal output Annual Income
15 12792 7140 7140*(0.18)+5652(0.09)=
1791.45
40 13257 7635 7635*(0.18)+5622(0.09)=
1880.28
50 13170 7682 7682*(0.18)+5488(0.09)=
1876.68
*assuming all electricity in the summer is exported.
The results from table 2 aren’t as expected, with the annual optimum tilt still having the greatest income. Between
40° and 50° there is little difference in outputs and this is the reason why there is little change in income when the
array is optimised for winter output. However, the difference in annual output between 15° and 50° is quite small
(378 kWh) but there is a relatively large difference in income (85.23 euro). Over the 20 year lifetime of the panels
that is a difference of 1704.60 euro for a difference in tilt of 35°. In some cases of PV installation, it isn’t quite as
simple as just increasing the tilt angle from 15° to 40° to get the increased income. Factors such as wind load on
the modules must be considered. Regarding the case study in Tramore School, the roof pitch angle is 15° and it
was decided to mount the panels flush with the roof rather than employing a support structure. Support structures
incur extra cost to installations and in the Tramore School case the total cost must be less than 1704.60 to make it
viable. Increased amounts of ballast are required when tilt angle is increased because a greater surface area of the
panel is exposed to wind. If the support structure is fixed to the roof it will need to be able to deal with extra load
(10) p231.
Photovolitaic Array (PV) kwh/month
January 321.72
February 500.27
March 1124.96
April 1592.30
May 1787.50
June 1951.14
July 1933.43
August 1564.45
September 942.45
October 665.08
November 424.05
December 324.12
Total 7682.45
Figure 8 Seasonal output, June, July and August are
assumed to be 0.

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PV Cell Type
Figure 9 PV cell prices (11)
Mono-crystalline silicon (C-Si) was used in the case study in Tramore School, the following section will explore
the results if other cell types had been used. For the purpose of consistency, all data is be taken from the same
source (11). Fig.11 shows the price of cells in euro/Wp, the most recent prices available (August 2013) are used.
The following calculations are based solely on module cost and doesn’t include labour, power conditioning
equipment etc. Payback period will be used as the determining factor as to which cell type gives the best return.
As with previous calculations the system in Tramore School will be modelled.
Table 3
Figure 10 Cell payback period
Cell Type Efficiency % Cost (Euro/Wp) Total Cost
Mono-crystalline
Silicon
18 0.75 11625
Cadmium Telluride
(CdTe)
12 0.58 8990
Amorphous Silicon
(a-Si)
9 0.37 5735

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14
Table 3 shows the efficiency of each cell and the total cost for 15.5 kWp of cells. Fig.12 shows the payback period
over the lifecycle of each type of PV cell. The least attractive payback period of each cell is Cadmium Telluride
(CdTe) which is 7.112 years. There is very little difference between amorphous silicon and crystalline silicon cells
based on payback period. Amorphous silicon performs marginally better with a payback period of 5.87 years.
Crystalline silicon cells have a payback period of 5.965 years. Crystalline silicon is preferred for the Tramore
School building because due to its high efficiency it occupies less space on the roof. The ventilation system in
Tramore School has the extractor fans mounted on the roof which limits the amount of space available for PV
modules. Based on payback figures in fig.10 almost double the amount of amorphous silicon panels are required
to produce the output of the crystalline silicon cells. The cost would be roughly the same but roof space would be
an issue which leads to the conclusion that crystalline silicon is the best cell type for this project.
The graphs were plotted using (eqn12) over a time period of 20 years. 20 years was selected as the time period
because this is the period that the subcontractor guaranteed the panels for. The discount or was set at 4% based
on the best Irish savings interest rate (19). The payback period was obtained from the graph at the x-intercept,
when the net present value is at 0. This is the breakeven point for a project, the INTERCEPT function in excel
was used to calculate it.
The analysis of cell payback period can’t be used to determine if a project is viable because other expenses such
as labour and inverters aren’t factored in. The expected payback period for the project would be greater as a result.
The results of the payback period analysis aren’t surprising, with crystalline silicon occupying 80-90% of the
market share of global PV cells (13). Amorphous silicon would be an attractive prospect to an investor who has
no roof space limitation. It also would appeal if a loan was needed to source the capital for purchasing the modules,
as it is significantly cheaper than mono-crystalline silicon.
Comparing PV with Wind Energy
There is a stark difference in annual energy yield between the wind turbine and the PV array. A wind turbine rated
15 kW, a similar output to the PV array, produces 46404 kWh per annum. The model appears to be quite accurate
when comparing results with the manufacturer’s data sheets. According to the manufacturer the CF15 wind turbine
will produce an annual output of 43071 kWh at an average wind speed of 6 m/s (14). The average wind speed for
the weather data is 5.68 m/s, meaning that the calculations are slightly above the manufacturer’s data. There is a
difference in the values because the method of calculating the outputs were different. Average wind speed wasn’t
used to calculate the annual energy yield in the model, instead the power curve was applied to hourly wind data.
Given the non-linear nature of a power curve using average wind speed to get the power output will get different
values than the method employed. Even with the manufacturer’s smaller energy yield figures, wind energy is still
at least 3 times the output of a PV system working at optimum conditions producing 13312 kWh.
Based on the IWEA estimate of 2000 euro/kWh (8) and a maintenance budget of 100 euro per annum, the wind
turbine would cost an estimated 32000 euro, 2350 euro more than the PV array. With similar costs of installation
it can clearly be seen that wind is a more attractive renewable energy source than PV for this site. The wind turbine
cost just 2350 euro more than the PV array yet it produces more than 3 times the energy. As discussed previously,
the generation profile of the wind turbine is a better fit than a PV array for a school.
These results shed some light on why wind energy is so dominant in the Irish renewables market. 3 times the
annual energy yield than PV with similar costs make it more attractive to invest in. Energy yield is income for
businesses which will make it far more likely for them to install wind energy rather than solar PV.
A major problem associated with wind energy is obtaining planning permission for the installation of a wind
turbine. The department of environment have planning exemptions for wind turbines less than 10 meters high and
6 meters rotor diameter. There is also planning exemptions for solar panels of 12m2
aperture area or systems
taking up less than 50% of the roof (15). There are difficulties in obtaining planning permission for wind turbine
due to problems such as visual impact, noise and shadow flicker. Planning permission for solar PV is much easier
to obtain as there are no such problems with the technology.

Cian Ryan R00070224
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Viability of PV (Case Study)
Three scenarios are considered when calculating the net present value (NPV) of the PV array in Tramore.
1. 100% of electricity exported to the grid at 9 cent/kWh feed in tariff.
2. 100% of electricity consumed at a saving of 16.6 cent/kWh (16)
3. 100% electricity exported at 21.7 cent/kWh German feed in tariff (17).
A fourth scenario, 4. 100% wind energy exported at Irish 9 cent/kWh feed in tariff, will serve as a comparison
with another renewable resource. The internal rate of return (IRR) will also be used to calculate the viability of
each scenario.
To increase the accuracy of the economic forecast further, 20 years of historic electricity prices were studied in
order to predict future Irish electricity prices. For the purposes of the calculation it is assumed that the increase in
electricity in the past 20 years will mimic the increase in prices in the following 20 years when the PV array is
operational. There is a risk with this assumption, the price increase trends may change with events such as
economic crisis or war. According to the ESRI the price of a unit of electricity in 1994 was 9 cent (18) which
increased to 16.6 cent in 2014 (16). Based on this data there is an increase of 7.6 cent in the 20 year period, an
increase of 0.38 cent per annum. This is added to the 2014 price (16.6 cent/kWh) for 20 years, giving 24.2
cent/kWh for 2034 when the project finishes. This rule only applies to scenario 2.
Different scenarios are used to highlight influence feed in tariffs have on a renewable project’s viability. The
current German feed in tariff is used to highlight the difference between the differing levels of government support
in Germany and Ireland. Scenario 4 can be directly compared with scenario 1 as both use the same Irish feed in
tariff. This direct comparison can be used to compare the viability of wind with the viability of PV. Scenario 2
highlights the importance of consuming electricity generated rather than exporting it to the Irish grid.
In order to calculate the NPV of the PV array after 20 years in operation, a discount rate or minimum attractive
rate of return must be considered. The discount rate is decided based on the level of risk of an investment, a high
discount rate is required for a high risk investment. Companies will have a minimum discount rate which needs
to be used in calculations if they are to invest. A risk free investment is an example of an investment where the
return is guaranteed at a set rate (long term savings account). The best risk free investment in Ireland is 2.66%
which is the state savings national solidarity bond (19). For a company to invest in the PV project the discount
rate must be higher than 2.66% as this is a risk free investment. Because companies have different minimum
attractive rate of return rates, 4.5% was selected (20). The discount rate depends on the risk, project scale and
investor so it was decided to select 4.5% as the rate. This rate could be changed based on an investor’s minimum
attractive rate of return. Investment rate of return was also calculated in the event the discount rate was inaccurate.
Again the payback period was calculated using the INTERCEPT function.
Table 4 Economic analysis for Tramore site
Scenario IRR NPV (4.5% discount)
1 0 (losing investment) -14,763
2 6% 2871
3 7% 6244
4 12% 22325
Table 3 highlights the importance of consuming electricity generated rather than exporting it to the grid. A
difference in NPV of 17,544 euro when comparing scenario 1 and scenario 2. Saving electricity is the best option
in Ireland, not only is the current price of electricity higher than the feed in tariff, the price of electricity could
increase further based on previous price trends. However, the feed in tariff could change if government support
for micro-generation increases by increasing the feed in tariff. Based on the data in table 3, it isn’t viable to export
all electricity generated to the grid. These figures are based on the current PV system in Tramore which isn’t
operating at optimum. If scenario 2 is employed, an IRR of 6% will be achieved for the project making it a viable
investment. However, it is challenging to consume 100% of the electricity generated as if the system is oversized
there will be an excess that must be exported to the grid. This highlights the importance of the model as the system
can be sized based on the output values. The model can be used to ensure nearly all of the electricity generated
will be consumed.
Comparing scenario 1 with scenario 3 illustrates the level of support each government gives to PV. There is a
difference of NPV of 21,007 euro which is enormous considering the project cost 29650 euro. It shows that the
German government adopts an aggressive policy of offering high feed in tariffs to investors. However, the German
feed in tariff has fallen recently. In January 2010 an array with a capacity less than 30 kWp was offered a feed in
tariff of 39.14 cent/kWh which fell to 21.66 cent/kWh in January 2014 (17).
Comparing scenario 1 with scenario 4 shows the drastic difference between the viability of PV and wind in Ireland.
In both cases the same feed in tariff of 9 cent/kWh is used. A difference in NPV of 37,088 euro highlights the

Cian Ryan R00070224
16
difference between Ireland’s solar resource and Ireland’s wind resource. This huge difference in NPV explains
why wind energy dominates the market in Ireland. In order for solar PV to become as attractive as wind as an
investment the government must provide greater support systems. The government could pursue the installation
of PV to further diversify the Irish grid.
The price of electricity has a large influence on whether scenario 2 is viable. The project returns an IRR of 4% if
electricity prices remain constant as of 2014 prices. The project would not be deemed viable if a discount rate of
4.5% is used, it returns an NPV of -2192 euro. However at a discount rate of 3.5% the project has an NPV of 351
euro. Given that 3.5% is greater than the risk free rate of 2.66% the project is deemed viable. Therefore even if
electricity prices don’t increase, scenario 2 is still viable.
Figure 11 Economic payback period
Another factor used for determining the viability of PV in Ireland is the payback period. Fig.13 shows the NPV
of each of the 4 scenarios over 20 years. If BAM Services Engineering had installed a CF15 wind turbine the
payback period is 10.4 years compared with 38.37 years for a 15.5kWp PV array. Both payback figures are based
on a 9 cent/kWh feed in tariff. It is clear from fig.11 that wind energy is the best option for this particular site. It
can be seen that both scenario 2 and 3 are viable in terms of payback period with 17.42 and 15.13 years
respectively. Fig.13 is based on the installed PV array at Tramore and not optimum operating conditions, 4.5%
discount is assumed also.
Optimising the tilt and azimuth angles reduces the payback period for each of the scenarios except scenario 4.
Table 5 shows economic performance for the PV array operating at optimum conditions.
Table 5 Values for PV operating at optimum conditions
Scenario IRR NPV (discount 4.5%) Payback period (yrs)
1 0 -14,148 36.80
2 5.69% 4215 16.70
3 7.01% 7727 14.48
4 11.60% 22325 10.40
Optimising the PV array causes a significant change in payback period for scenario 1, with a reduction of 1.57
years. The payback periods for scenario 2 and 3 are reduced by 0.72 and 0.65 years respectively. The larger the
payback period, the larger the reduction.
Overall the only scenario that is not viable is scenario 1 which is expected. This emphasises the problem of load
mismatch when using PV for a school. If it happens that the PV system is oversized for the required demand in
the school, the IRR of the project will be reduced significantly. Given that there is an enormous difference in

Cian Ryan R00070224
17
income between 100% electricity consumed and 100% electricity exported these issues must be studied carefully
before installing the system.
It wasn’t a surprise to see wind energy performing so well in the case study. Wind energy has a payback period
that is 26.40 years less than that of a similar sized PV array. A lower payback coupled with much better load
matching, high yield winter months and low yield summer months, makes wind a more attractive prospect for this
project. However, this isn’t taking into account the planning process which can be a prohibitive factor when trying
to install wind. Given that the CF15 turbine has a rotor diameter of 11.1 meters (14), it doesn’t qualify for a
planning exemption. However, given that the annual wind yield is more than 3 times that of solar PV, a smaller
turbine could be selected to match the PV energy yield and have a planning exemption if the rotor diameter is less
than 6 meters.
When studying wind atlas, it appears that Ireland has an excellent wind resource when compared to the rest of the
EU countries. The west coast of Ireland has an average of over 6 m/s at 50 meters above ground level. The south
coast, where the case studies are located has an average wind speed of 5-6 m/s according to the wind atlas. Apart
from coastal regions, Germany has an average wind speed of 3.5-4.5 m/s which is significantly less (21).
Conversely, it appears that Ireland has a poor solar resource relative to other EU countries, 900kWh/kWp
compared with greater than 1500kWh/kWp in Southern Spain (6). At a glance, it would appear that solar PV isn’t
viable in Ireland, fig.13 suggests otherwise. It can also be seen that the south coast in Ireland has a similar solar
resource as Ireland with both regions having 900 kWh/kWp. Despite this fact, Germany is a world leader in solar
PV (22) and Ireland barely has generating capacity (fig.2). The solar resource in Ireland doesn’t vary much either,
with approximately 900 kWh/kWp in the south and 750kWh/kWp in the north. Based on the model data, the wind
atlas and PVGIS it appears solar PV isn’t as prevalent in Ireland due to excellent wind energy resource and low
feed in tariffs.
Potential Errors
All results obtained are based on a mathematical model of the PV system in Tramore. The calculations are all
theoretical and may contain errors. Based on comparisons with other modelling software, the results appear to be
on a par.
Another issue is the weather data is sourced from Cork Airport which is over 100 km away from the case study.
However, when both locations were checked at optimum operating conditions on PVGIS there was a difference
of only 200kWh per annum (15700-15500).
As discussed previously, the wind energy yield may be over-estimated. This could also lead to errors in the
economic analysis.
4. CONCLUSIONS
Based on the data presented, it can be concluded that Solar PV is a viable energy source for generating electricity
in Ireland’s commercial sector. It can also be concluded that wind energy has a much greater annual energy yield
than PV. PV is viable in Ireland only if approximately 100% of the electricity produced is consumed on site. It is
highlighted throughout the results that a feed in tariff of 9 cent/kWh is not sufficient to support electricity
production by means of photovoltaic cells. Yet, 9 cent/kWh allows for an attractive IRR of 12% for wind energy
projects. It is argued that different feed in tariffs should be set for PV and wind energy. It would be unfair to
suggest the government invest in increasing the feed in tariff for wind energy when projects have attractive return
of investment already. Therefore the government should increase the feed in tariff for PV and keep it at 9 cent/kWh
for wind. PV can become a popular source of electricity in Ireland but first the feed in tariffs must be on a par
with Germany for investors to be attracted.
The planning process in installing a PV array is critical, with cell type, system size, azimuth angle and tilt angle
to be considered. The model constructed is a powerful tool for forecasting outputs and allows planners to
determine the best parameters for the system before installing.
Crystalline Silicon is the most popular cell type yet it doesn’t have the best payback period according to the model
with a payback period of 0.1 years longer than amorphous silicon. Due to the Staebler Wronski affect, degradation
occurs in amorphous silicon cells which leads to reduced efficiency after a few years of operation before the cells
are stabilised (23). For this reason it mono-crystalline silicon is the best choice for the project as there is very little
difference in payback period yet the cells won’t degrade as much. Roof space is also an issue with amorphous
silicon cells with roughly half the efficiency of c-Si cells.
Cells operate at high efficiency in Ireland due to low operating temperatures and high wind speeds that cool the
cells. Temperature is shown to be the main influence on efficiency based on evidence in fig.6. Granted low levels
of irradiance can affect cell efficiency however the relationship isn’t as strong as temperature.
Evidence based on the model and PVGIS suggests that the optimum angle of tilt for Ireland is approximately 40°
and the optimum azimuth angle is 0°. Large deviations from these parameters can cause significant loss in energy

Cian Ryan R00070224
18
yield. However, small deviations such as 5° cause only small losses in energy yield. It can also be concluded that
the cost of optimising the tilt angle of an array can outweigh the potential increase in energy yield. This is the case
when ballast systems are used to anchor module brackets with increased amounts of ballast required due to
increased surface area exposed to wind can incur costs.
Seasonal optimisation can only be viable when there are large deviations from optimum conditions. It can only be
used where there is high variations in seasonal load and can be used for example in summer cooling applications.
Low angles of tilt cause higher energy yield in the summer months based on data from the model. Conversely
large tilt angles cause higher energy yield in winter months.
The trends in Ireland’s renewable energy generating capacity are reflected in the results. It is shown that wind
energy is far more attractive from an investors point of view based on the data. For this reason wind energy
dominates in the market. For larger scale projects PV could be attractive because of planning exemptions
introduced by the department of the environment. It is argued that a wind turbine should have been installed in
the Tramore site as the generation profile is a perfect fit for a schools seasonal electricity demands. This coupled
with the better energy yield make a wind turbine a far more attractive prospect than the PV array installed.
However, PV is generating during the day when the school is occupied between the hours of 9am and 5pm. Wind
energy produced during the night would have to be exported to the grid. That said it can be concluded that wind
energy is the best renewable source for the Tramore site.
The model built is a success as it appears to be accurate. It must be validated by comparing theoretical output
values with actual data from the Tramore site. Unfortunately this could not be achieved as the system has only
been operational since April 2014. This amount of data would not give conclusive evidence as to how accurate
the model is. Regarding functionality it is flexible with almost all parameters adjustable.
Overall PV is a feasible renewable electricity source in Ireland but it has limitations. The size of the system must
be accurate as any excess electricity exported is considered a reduction in profit with such a low feed in tariff. PV
is viable but isn’t as attractive as wind energy based on IRR and payback period.
5. REFERENCES
(1) Photovoltaic renumeration policies in the European Union, Sarrassa-Maestro, Dufo-Lopez and Bernal-
Augustin (April 2013). (table 1)
(2) http://pveducation.org/pvcdrom/properties-of-sunlight (eqn1-9)
(3) http://www.mathsisfun.com/money/net-present-value.html (eqn12)
(4) http://pvpmc.org/modeling-steps/cell-temperature-2/module-temperature/sandia-module-temperature-
model/ (eqn 13+14)
(5) On the temperature dependence of photovoltaic module electrical performance: A review of
efficiency/power correlations, Skoplaki and Palyvos. (2009)
(6) http://re.jrc.ec.europa.eu/pvgis/cmaps/eu_cmsaf_opt/PVGIS-EuropeSolarPotential.pdf
(7) http://re.jrc.ec.europa.eu/pvgis/apps4/pvest.php#
(8) http://www.iwea.com/microgeneration
(9) http://www.kildarestreet.com/wrans/?id=2013-05-09a.367
(10) Planning and installing photovoltaic systems: a guide for installers, architects and engineers. (2008).
earthscan.
(11) http://www.pv-magazine.com/investors/module-price-index/#axzz30ec2tza3
(12) http://www.mortgages.ie/go/home/mortgage_rates
(13) http://www.solarbuzz.com/going-solar/understanding/technologies
(14) http://www.cfgreenenergy.com/sites/default/files/CF15-Power-Performance.pdf
(15)http://www.environ.ie/en/DevelopmentHousing/PlanningDevelopment/Planning/News/MainBody,3186
,en.htm
(16) http://www.bordgaisenergy.ie/publications/tariffs/?=hp
(17) Photovoltaic renumeration policies in the European Union, Sarrassa-Maestro, Dufo-Lopez and Bernal-
Augustin (April 2013). (Table 2)
(18) http://www.esri.ie/UserFiles/publications/WP452/WP452.pdf (figure 2)
(19) http://www.moneyguideireland.com/best-savings-rates/fixed-term-deposits-more-than-1-year
(20) http://blog.heatspring.com/renewable-energy-financing-101/
(21) http://www.windatlas.dk/Europe/landmap.html
(22)http://www.pv-magazine.com/news/details/beitrag/330-gw-of-global-pv-capacity-predicted-by-
2020_100010123/#axzz30xre71mb
(23)Kolodziej, A. (2004). opto-electronics review 12. Retrieved from
http://www.wat.edu.pl/review/optor/12(1)21.pdf
http://pveducation.org/pvcdrom/properties-of-sunlight/making-use-of-TM (eqn 10+11)

Cian Ryan R00070224
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6. APPENDICES
Solar PV resource map of Europe (appendix 1)
European wind atlas (appendix 2)

Cian Ryan R00070224
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Appendix 3
Rapid increase in PV capacity in Europe. Germany is the leader by far despite a similar resource to Ireland.
http://cleantechnica.com/2013/05/13/european-global-solar-pv-2012-2017-epia-report/
PV module specification (appendix 4)

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PV module specifications (appendix 5)
Regulation L3(b) (appendix 6) Technical guidance document part L 1.2.1

Cian Ryan R00070224
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Appendix 7 sketch of proposed solar PV array in Tramore School. (Source: BAM Services Engineering)
Appendix 8 CF15 power curve http://www.cfgreenenergy.com/product_uk/cf-15

Cian Ryan R00070224
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Appendix 9 CIT PV characteristics
Appendix 10 11kWp monitored solar PV array at Ballymaloe House
http://en.wikipedia.org/wiki/List_of_monitored_photovoltaic_power_stations

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