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Real Energy Payback time and Carbon Footprint of a GCPVS

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DOI: 10.3934/energy.2017.1.77

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 AIMS Energy, 5(1): 77-95.
DOI: 10.3934/energy.2017.1.77
Received: 22 October 2016
Accepted: 02 January 2017
http://www.aimspress.com/journal/energy Published: 09 January 2017

Research article

Real Energy Payback Time and Carbon Footprint of a GCPVS

Miguel de Simón-Martı́n1,2,∗ , Montserrat Dı́ez-Mediavilla2 , and Cristina Alonso-Tristán2

1
Energy Resources’ Smart Management (ERESMA) Research Group, Department of Electric,
Systems and Automatics Eng., University of León, Campus de Vegazana s/n, León, 24071, Spain.
2
Solar and Wind Feasibility Technologies (SWIFT) Research Group, Department of Electromecanics
Engineering, University of Burgos, Campus de Rı́o Vena s/n, Burgos 09001, Spain.

∗
Correspondence: Email: miguel.simon@unileon.es; Tel: +34-987-291-000-5391.

Abstract: Grid connected PV systems, or GCPVS, produce clean and renewable energy through the
photovoltaic effect in the operation stage of the power plant. However, this is the penultimate stage of
the facilities before its dismantlement. Before starting generating electricity with zero CO2 emissions,
a negative energy balance exists mainly because of the embodied energy costs of the PV components
manufacturing, transport and late dismantlement.
First, a review of existing studies about energy life cycle assessment (LCA) and Carbon Footprint
of PV systems has been carried out in this paper. Then, a new method to evaluate the Real Energy
Payback Time (REPBT), which includes power looses due to PV panels degradation is proposed and
differences with traditional Energy Payback Time are analysed. Finally, a typical PV grid connected
plant (100 kW nominal power) located in Northern Spain is studied in these sustainability terms. This
facility has been firstly completely modelled, including PV modules, inverters, structures and wiring.
It has been also considerated the energy involved in the replacement of those components with shorter
lifespan. The PV panels degradation has been analysed through the comparison of normalised flash
test reports on a significant sample of the installed modules before and 5 years after installation.
Results show that real PV degradation affect significantly to the Energy Payback Time of the instal-
lation increasing slightly a 4.2% more the EPBT value for the case study. However, along a lifespan
of 30 years, the GCPVS under analysis will return only 5.6 times the inverted energy on components
manufacturing, transport and installation, rather than the expected 9.1 times with the classical estima-
tion.

Keywords: Grid connected PV systems; Real Energy Payback Time; Life Cycle Assessment; PV
degradation; Carbon Footprint; clean energy
 78

1. Introduction

All manufactured products embody energy consumption because of the manufacturing process it-
self, transport and installation. In the case of energy generators, this energy can be recovered in the
operation stage. Moreover, the so-called Renewable Energy generators, such as the Grid Connected
Photovoltaic systems (GCPVS) generate back this energy in a sustainable way. To analyse this energy
balance and when this sort of energy generation plants are really renewable and sustainable systems
the Life Cycle Assessment (LCA) and the Energy Payback Time (EPBT) must be considered [1].
On the one hand, the LCA analyses the environmental impacts made by a manufactured product or
construction along its lifespan. On the other hand, the EPBT calculates the needed time to recover the
energy involved in the manufacturing, transport and installation processes.
In the case of GCPVS, three variables must be studied to evaluate the energy balance [1, 2, 3, 4, 5]:

• The global irradiation at the facilities location to estimate the energy production.
• The technical characteristics of the power plant parts.
• The Life Cycle Inventories (LCIs) of each component and involved operation.

In the related literature several approaches to evaluate the environmental impact of a PV system can
be found. Some authors, like [2, 6, 7], just focus on the photovoltaic module and consider the impact
of the other power plant components not significant. They consider that the manufacturing process
represents between the 70% to 85% of the whole plant embodied energy, independently of the tracking
devices (dual-axis, horizontal-axis or fixed systems) [1, 8]. Therefore, the PV panels’ LCA analysis
results mandatory. To calculate the LCA of a PV module the silicon purification process, the crystals
growth, the transformation of silicon wafers into solar cells and, finally, the module construction is
evaluated [4].
In [9, 10] the authors prefer to evaluate the impact of all the power plant components. The volume
of the materials involved in the power plant construction is calculated first and then, the energetic cost
of the manufacturing process, transport to the site location, start-up and replacement or dismantlement
is estimated. This deep analysis results far more complex and requires the collaboration with the
components’ manufacturers which, sometimes, is not affordable.
Finally, the generated energy must be estimated. For this purpose, hystorical data from the installa-
tion or similar facilites can be used. In case these data is not available, PV production can be estimated
through the measurement (or modelling) of the solar global radiation at the facilities’ site and tech-
nical parameters of the generators (peak power and efficiency). Solar global radiation estimation can
be conducted almost worldwide by data from private or open databases, such as HelioClim-3 [11] or
PVGIS from the European Commission [12].
Unfortunately, PV energy production does not remain constant along the whole lifespan of the
GCPVS. PV cells and encapsulation degradation and soiling effects on PV panels make modules’
efficiency decrease significantly. There exist several studies on PV modules degradation, but effects
under real operation conditions on field are still under study and thus, no general agreement has been
found in this issue. Moreover, they depend on the PV technology (mono-Si, amorphous Si, thin-
film...), installation conditions (on fixed structures or on sun-trackers), climatological conditions on
site, etc [13, 14]. Most manufacturers estimate power looses for degradation in a 10% of the nominal
power at the end of the PV modules’ lifespan. On the other hand, reference [15] estimates power

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looses for crystalline sylicon between 0.5%·yr−1 and 3.5%·yr−1 after reviewing several carried out
studies. Moreover, in this report, long-term measurements (more than 20 years exposition) show lower
degradation rates than short-term measurements. They also found larger degradation rates for entire
systems compared with modules degradation. The rapid initial degradation was attributed to oxygen
contamination in the bulk of the Si junction, whereas the slow long-term degradation correlated linearly
with ultraviolet exposure. It appeared unlikely that the slow loss was due to EVA browning [16]. High
degradation rates are directly correlated with decreasing values for the PV module’s fill factor (FF).
All electricity generation technologies generate carbon dioxide (CO2 ) and other greenhouse gas
emissions. To analyze the impact of a generating technology accurately, the total CO2 amounts emitted
throughout the system’s lifespan must be calculated both direct (arising during operation of the power
plant) and indirect (arising during other non-operational phases of the life cycle. Solar photovoltaics
technology is commonly referred to as “low carbon” or “carbon neutral” because it does not emit CO2
during its operation. However it is not really a carbon free generation system since CO2 emissions arise
in other phases of the life cycle such as construction, maintenance or dismantlement.
The carbon footprint is defined as the total amount of CO2 and other greenhouse gases emitted over
the full life cycle of a process or product and, in this case, is expressed as grams of CO2 equivalent per
kilowatt hour of generation which accounts for the different global warming effects of other greenhouse
gases [17]. It can be calculated using the LCA method in a “cradle-to-grave” approach.
In the case of PV generation systems, the most energy intensive phase (accounting for 60% of the
total energy requirement) is the extraction of silicon from quartz sand at high temperatures. Reductions
in the carbon footprint of PV technology are expected to be achieved in thin-film PV modules, which
use thinner layers of silicon, and with the development of new semi-conducting materials that would
be less energy intensive. Life cycle CO2 emissions are estimated in 35 gCO2 eq·kWh−1 for GCPVS
operating in Southern Europe [18]. Lower sunlinght hours increase significantly this value.
In this paper, a deep analysis by the LCA method has been applied to a real GCPVS with horizontal
axis tracking, which is a typical configuration in Southern Europe PV power plants. Moreover, for the
first time, the measurement and evaluation of the PV degradation have been introduced and, thus, the
real Energy Payback Time and carbon footprint have been calculated and presented as new parameters
to evaluate the evironmental impact of a GCPVS. Then, its influence on the recovered energy by the
PV generation along the GCPVS’s lifespan is evaluated accurately. Developed methodology can be
extended to other energy sources, resulting really worth of interest to compare objectively different
power generation systems.

2. Facilities’ Description

The Real Energy Payback Time and Carbon Footprint have been evaluated on a real fully-
operational GCPVS located at Torquemada, Palencia, at Northern region in Spain (42.308◦ N latitude,
4.308◦ W longitude and 740 m.a.s.l.). The facility stands on a gentle, south-facing slope that is con-
ductive to natural air circulation and has been used by the Solar and Wind Feasibility Technologies
(SWIFT) Research Group on many previous studies, such as [19], because it represents with high fi-
delity real operation conditions of GCPVS. The area benefits from very favourable atmospheric condi-
tions. Solar irradiation is estimated at approximately 1 450 kW·h·m−2 ·yr−1 and the ambient temperature
range between 4 ◦ C and 20 ◦ C. Moreover, the number of cloudy days is very low along the year. An

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aerial photograph of the facilities can be seen in figure 1.

In figure 1 it can be observed that few obstacles affect the power plant. The most significant one is a
two floors house located next to the south-east corner of the facilities but its shadow only affects some
PV panels from one string at the sunrise in winter. Thus, energy production is slightly affected from
November to January.

Figure 1. Aerial taken photograph of the facilities. Source [19].

The power plant has installed 101.01 kWp in 546 PV panels model BP-7185S manufactured by
BP. They can produce 185 Wp of peak power (2.5% tolerance), have an electric performance in the
range 14–15% (manufacturer specifications) and 36.5 V and 5.1 A for the maximum power point. PV
modules are arranged in groups of 14 panels in series to work with a voltage of 511 V (within the
voltage range of the inverter). The current for each group is 5.1 A. Panels are arranged in 12 rows with
three groups in each one and a further two rows with two groups and one group, respectively. This
means that the distance between the first and the last row is 60 m and the width is 42 m approx. [19].
A mobile structure was designed which adjusts the position of the panels according to the Sun’s
declination angle along the year, in order to optimize the energy production. The tracking system
consists of a hand-driven device that modifies the slope modules angle between 5◦ and 50◦ , allowing
an horizontal axis tracking. Figure 2 shows the optimal tilting angles according to [20, 21], both for
daily tracking (beta opt. daily) and monthly tracking (beta opt. monthly). As the angular regulation
has a 5 degrees step, the hand-driven system requires 20 days at least for changing positions of the
whole plant structure, and range is limited between 5◦ and 50◦ (due to structural and ground use), PV
modules slope must be modified approximately every 26 days in autumn and spring, while in summer
and winter their position is fixed at their extremes (beta practical). In figure 3 it can be observed the
PV panels in its lowest and highest extreme positions respectively. Finally, the structure was installed
in a way that needs no concrete foundations and it is directly driven into the ground.
PV panels generated energy is collected in one 100 kW nominal power inverter manufactured by
Ingeteam (model Ingecon Sun 100). It admits an input voltage range of 405–750 V and 187 A of input
current. Its harmonic distortion is less than 3% and its efficiency is higher than 96% at nominal oper-
ation conditions. In order to prevent unwanted disruptions due to any adverse effects of temperature,
a ventilation system to eliminates warm air in summer was installed alongside the inverter in a stall,

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75
70
65
60
55
50
45
40
35
Angle [deg.]

30
25
20
15
10
5
0
1
11
21
31
41
51
61
71
81
91
101
111
121
131
141
151
161
171
181
191
201
211
221
231
241
251
261
271
281
291
301
311
321
331
341
351
361
-5
-10
-15
-20
-25
Day of the year

Declina4on [º] Beta opt daily [º] Beta opt monthly [º] Beta rounded [º] Beta tracker [º] Beta prac4cal [º]

Figure 2. Horizontal axis tracking strategy for the PV power plant.

Figure 3. PV panels structures with hand-driven hor. axis regulation. Source [19].

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located 5 m from the front row.

Finally, the protection system is structured in rows. Each PV panels group has a 10 A fuse and a 10
A switch, and the output is added to the previous row. The output wiring section of each protection box
was designed to afford the maximum current from the previous rows groups. Thus, the output wiring
and the last row protection box closest to the inverter location supports a maximum current of 198.9
A, coming from the 39 groups of PV panels. This output wiring is attached to the DC input inverter,
located approximately 5 m from the enclosure of the first row.

3. Materials and Methods

3.1. Energy production estimation

The produced energy by the GCPVS case study has been estimated through real production data and
PVGIS estimations for the installation site. It can not be used only real production data because several
malfunctions and problems forced to stop and disconnect the power plant and made some production
data not representative of the real nominal operation conditions. On the other hand, only PVGIS
estimations may differ from the real behaviour of the power plant because of partial shading and real
power losses. Thus, a combination with estimations from PVGIS has been used. Real production data
has been weighted with a 0.8 factor, which has been considered appropriate for this case according to
the reliability of the data. The three final energy estimations can be compared in figure 4. An annual
production of 5 238 MJ·kWp−1 ·yr−1 is estimated on average.
20
x1000

18

16

14
Energy to Grid [kWh]

12

10

r
r
e

r
ry

ch

ril

ay
ry

be

be
be

be
l
n

Ju

gu
ua

ua

Ap

M
ar

Ju

em

m
to

m
Au
M
n

br

ce
ve
Oc
Ja

pt
Fe

De
No
Se

FIRST YEAR DATA [kWh] PVGIS [kWh] WEIGHTED ESTIMATION [kWh]

Figure 4. Energy injected to the Grid by the studied GCPVS.

Let’s notice that the hand-driven horizontal axis tracking must be considered to estimate the energy
production in PVGIS. This web utility does not allow this option by default. Thus, the power plant has
been simulated as several fixed GCPVS with different tilting angles.

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To compare generated energy and embodied energy, transformation of final electrical energy into
primary energy is mandatory. According to the latest report [22], where the National Mix for Spain is
considered, this conversion factor is 2.461 kWh p ·kWh−1f , where subindex f refers to final energy and
subindex p refers to primary energy. In this case, the contribution of renewable energy sources to the
primary energy is just the 13.25%. Thus, conversion efficiency is approx. 0.41, which differs from
other studies that consider just 0.35 [23].
In the same way, according to the National Energy Mix, PV generated energy would have associated
a negative carbon footprint of 0.399 kgCO2 eq·kWh−1 f [22]. This means that 1 kWh f generated on
a GCPVS is a kWh f that has not been generated by other sources and, thus, the associated carbon
emissions have been avoided. It also could be considered only the non renewable National Energy
Mix, with 0.521 kgCO2 eq·kWh−1 f associated emissions [22].

3.2. Degradation analysis

Photovoltaic generators are a relative new technology and they have not been widely tested cur-
rently. Although there exist several studies about PV modules performance, more tests along 5, 10, 15,
20 and 25 years are needed to characterize their durability and the properties of the energy production.
For the case study, a sample of 14 PV modules (2.6%) have been randomly selected and monitorized
along 5 years since their installation at the power plant. Then, a normalized flash-test for each panel
has been conducted and manufacturer specifications and flash-test results have been compared.
The flash test consists on the exposition of a PV module to the Standard Measurement Conditions
(1 000 W·m−2 incident irradiance, 25◦ C environmental temperature and 1.5 Air Mass) in a climatic
chamber. Through the exposition of a constant radiance flux, the PV module’s characteristic I-V curve
is obtained and thus, its electrical behaviour and performance. An example of an obtained flash-test
report is shown in figure 5. Results for the considered sample can be observed in tables 1, 2 and 3
respectively.

Figure 5. Example of a flash-test report for a PV module.

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Table 1. Flash-test results before installation.

Serial No. P [W] Voc [V] I sc [A] Vmp [V] Imp [A] FF [-] Cell [%] Mod. [%]
4461559 185 43.9 5.6 36.2 5.1 0.751 18.5 17.2
4461573 186 44.2 5.6 36.3 5.1 0.748 18.6 17.2
4462444 189 44.4 5.6 36.7 5.1 0.753 18.9 17.6
4463043 186 44.0 5.6 36.1 5.2 0.762 18.6 17.2
4463077 185 44.0 5.5 36.1 5.1 0.761 18.5 17.1
4463108 185 44.1 5.5 36.4 5.1 0.765 18.5 17.2
4463166 186 44.1 5.6 36.6 5.1 0.756 18.6 17.3
4463167 185 44.1 5.6 36.9 5.0 0.747 18.5 17.1
4465234 189 44.3 5.6 36.5 5.2 0.765 18.9 17.5
4465235 188 44.3 5.6 36.7 5.1 0.754 18.8 17.4
4465283 189 44.2 5.6 36.5 5.2 0.767 18.9 17.5
4465313 188 44.3 5.6 36.4 5.2 0.763 18.8 17.4
4465319 188 44.3 5.6 37.0 5.1 0.761 18.8 17.4
4465942 189 44.4 5.6 36.6 5.2 0.765 18.9 17.5
Mean 187 44.2 5.6 36.5 5.1 0.758 18.7 17.3
Std. dev. 38.0 0.3 0.0 1.0 0.0 0.001 0.4 0.3

Table 2. Flash-test results 5 years after installation.

Serial No. Pmax [W] Voc [V] I sc [A] Vmp [V] Imp [A] FF [-] Cell [%] Mod. [%]
4461559 178.0 43.96 5.4 35.37 5.0 0.749 15.5 14.2
4461573 180.0 44.33 5.4 35.85 5.0 0.752 15.7 14.3
4462444 177.8 44.35 5.2 36.22 4.9 0.776 15.5 14.2
4463043 177.6 44.24 5.3 35.88 5.0 0.765 15.5 14.1
4463077 177.3 44.37 5.3 35.93 4.9 0.760 15.5 14.1
4463108 175.6 43.97 5.3 35.69 4.9 0.755 15.3 14.0
4463166 176.4 43.81 5.4 35.68 4.9 0.745 15.4 14.1
4463167 177.3 44.48 5.3 36.42 4.9 0.756 15.5 14.1
4465234 184.8 44.92 5.4 36.52 5.1 0.760 16.1 14.7
4465235 184.1 44.93 5.3 36.56 5.0 0.767 16.1 14.7
4465283 184.5 44.91 5.4 36.64 5.0 0.765 16.1 14.7
4465313 184.0 44.78 5.4 36.44 5.1 0.764 16.1 14.7
4465319 185.2 44.98 5.4 36.70 5.1 0.760 16.2 14.8
4465942 186.5 45.24 5.4 36.96 5.1 0.769 16.3 14.9
Mean 180.7 44.52 5.3 36.20 5.0 0.760 15.8 14.4
Std. dev. 201.0 2.57 0.1 2.88 0.1 0.001 1.6 1.4

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Table 3. Annual variation of the PV parameters.

Serial No. Pmax Voc I sc Vmp Imp FF Cell Mod.
4461559 -0.76% 0.03% -0.68% -0.46% -0.27% -0.05% -3.24% -3.49%
4461573 -0.65% 0.06% -0.71% -0.25% -0.31% 0.11% -3.12% -3.37%
4462444 -1.19% -0.02% -1.54% -0.26% -0.75% 0.62% -3.60% -3.86%
4463043 -0.90% 0.11% -1.25% -0.12% -0.96% 0.08% -3.33% -3.60%
4463077 -0.83% 0.17% -0.87% -0.09% -0.67% -0.02% -3.24% -3.51%
4463108 -1.02% -0.06% -0.76% -0.39% -0.71% -0.27% -3.46% -3.72%
4463166 -1.03% -0.13% -0.71% -0.50% -0.63% -0.29% -3.44% -3.70%
4463167 -0.83% 0.17% -1.18% -0.26% -0.52% 0.24% -3.24% -3.51%
4465234 -0.44% 0.28% -0.64% 0.01% -0.54% -0.13% -2.96% -3.20%
4465235 -0.41% 0.28% -0.93% -0.08% -0.27% 0.33% -2.87% -3.10%
4465283 -0.48% 0.32% -0.82% 0.08% -0.62% -0.05% -2.96% -3.20%
4465313 -0.43% 0.22% -0.79% 0.02% -0.58% 0.03% -2.87% -3.10%
4465319 -0.30% 0.31% -0.64% -0.16% -0.20% -0.02% -2.77% -2.99%
4465942 -0.26% 0.38% -0.86% 0.20% -0.58% 0.09% -2.75% -2.97%
Mean -0.68% 0.15% -0.88% -0.16% -0.54% 0.05% -3.13% -3.38%
Std. dev. 0.01% 0.00% 0.01% 0.01% 0.01% 0.01% 0.01% 0.01%

Table 3 shows the annual variation of the PV modules parameters under the hypothesis of a linear
variation [24, 25, 26] between the initial conditions (table 1) and 5 years after installation (table 2).
Thus, it can be observed that the modules peak power decreases 0.68%·yr−1 on average, while the
modules fill factor (FF) increases 0.05%·yr−1 . On the other hand, the cell and module efficiencies
decreases more than 3.10%·yr−1 and 3.35%·yr−1 respectively.

3.3. Life Cycle Analysis

The LCA analysis requires, as first stage, the spatial and temporal limits definition about compo-
nents and processes [1, 27, 28, 29, 30]. In our study, we have evaluated the energy needed for the
manufacturing and transport of the PV modules, wiring, inverter and support structures. On the other
hand, we have not considered the impact of the transformation centres, electrical protections, man-
power, communication devices and documentation duties because of their impact can be considered
almost negligible in the global sum.
To estimate the energy needed by the manufacturing process of a PV module, the methodology
exposed in [4, 6, 31, 32, 33, 34] have been applied. The manufacturing process of a PV module
can be considered mainly electrical (the 80% of the input primary energy). Therefore, the primary
energy quantities depend strongly on the conversion efficiency of the energetic systems which feed the
different stages of the whole process. These efficiency values have been estimated through a energy
sources mix, which may differ between manufacturing countries and regions [1]. In the case study,
the primary energy involved in the manufacturing process of PV mono-crystalline framed modules is
5 200 MJ p ·m−2 . As the power plant has 546 panels of 1.593 m x 0.790 m (1.26 m2 ), the total amount
of energy embodied in the PV panels is 3.573 x 106 MJ p .

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On the other hand, the needed energy estimation for the manufacturing of the inverter has been
calculated according to Table II in [9] and results from [1]. The embodied energy of the inverter
system has been estimated under the hypothesis that the requirement of materials is similar for different
manufacturers and proportional to the total weight of the inverter. In [9], a rate of 46.645 MJ p ·kg−1
is estimated for 150 kW inverters including transformer. Moreover, in [1], the energy involved in an
inverter housing for six 100 kW inverters is 162 174 MJ p (40 m2 surface and 3 m height). A replacement
rate of 10% for the housing components along the life-cycle is also considered. Then, for the inverter in
the case study, which has been manufactured by Ingeteam (Spain) and weights 1 250 kg, an embodied
energy of 88 402.17 MJ p has been considered (70.72 MJ p ·kg−1 or 88.40 MJ p ·kW−1 ).
The tracking system and support structure are made of steel, with a weight of 130 kg·kWp−1 . As the
structure has no concrete foundations and the tracking system is hand-driven, no other energy needs
have been considered for this component. The energy involved in the manufacturing process of the
steel has been calculated from [35], which analyse the energy demands of a list of typical construction
materials. Thus, for the manufacturing of 1 kg of steel, 35 MJ p of primary energy have been estimated
on average. To consider the energy needed for the installation, a 3% of the manufacturing process
needed energy has been added in the estimation.
For the manufacturing of the wiring, which is mainly made of cooper, estimations from [35] have
also been considered. Thus, 70 MJ p ·kg−1 have been estimated as wiring embodied energy. The volume
of wiring materials depends on the Ground Cover Ratio (GCR) of the power plant and local technical
regulations. In the case study, the total volume of wiring has been estimated taking into account
the power plant configuration, described in detail in reference [19]. The electrical scheme of the
installation shows 39 groups of 14 PV modules each one (511 V), which are arranged in 12 rows with
three groups in each one and a further two rows with two groups and one group, respectively. Each
group is connected by a set of 2 wires (positive and negative) which have 4 mm2 section and total
length of 40 m. Groups from rows are connected through protection boxes, which include a 10 A fuse
and a 10 A switch per group in a way the section of the wires between protection boxes increases
downstream. Thus, power losses remain minimum as total current increases. Distances between rows
are 6 m and the wires’ section at the inverter input is 95 mm2 . Details can be seen in table 4.
Thus, 6.4 × 10−4 m3 Cu are involved in the connection of each PV modules group, and 6.48 × 10−3
m3 Cu link the groupings. Moreover, a 1.5 factor has been added to take into account the ground
connections and the communications wiring. Then, total wiring is estimated in 0.046 m3 Cu.
Finally, along the lifespan of a GCPVS some components shall be substituted in order to guarantee
power quality and the system availability. Thus, energetic replacement costs must be also included
in the LCA analysis. The failure rates of PV modules, wiring or support structures are often very
low -manufacturers must guarantee their products for at least 20 years- and, consequently, their ener-
getic impact may be considered almost negligible. However, inverter devices show higher failure rates,
mostly because of the complexity and fragility of their electronic parts. Therefore, according to [1, 36],
the energetic impact of the inverters maintenance have been modelled as a replacement of the 10% of
the inverter parts every 10 years. An additional 5% of the embodied energy has been added to all com-
ponents to consider the dismantlement energy costs. Although this energy is not consumed until the
end of the installation’s life cycle, it has been considered as another energy cost in the manufacturing
process.
For the estimation of the carbon footprint of every GCPVS’s component, manufacturing origin has

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Table 4. Power plant wiring characteristics.

Row Distance to the inverter [m] Groups Nominal current [A] Section [mm2 ]
1 6 3 198.9 95
2 12 3 183.6 70
3 18 3 168.3 70
4 24 3 153.0 70
5 30 3 137.7 70
6 36 3 122.4 35
7 42 3 107.1 35
8 48 3 91.8 35
9 54 3 76.5 16
10 60 3 61.2 16
11 66 3 45.9 16
12 72 3 30.6 4
13 78 2 15.3 4
14 84 1 5.1 4

been taken into account. PV modules and structure were manufactured in Germany, while the Inverter
and wiring come from Spain. Thus, primary energy manufacturing requirements have been expressed
in final electrical energy terms through the appropriate conversion efficiency. Then, carbon emissions
for the industrial sector for each country is applied (45 gCO2 eq·MJ−1 −1
p for Spain and 13 gCO2 eq·MJ p
for Germany) [22, 37].
The energy devoted to the transport of equipment and materials has been calculated from tables in
[38]. It has been considered that the transport has been carried out by heavy trucks, with an energetic
cost of 2.426 kJ p ·km−1 ·kg−1 . The displacements have been evaluated from the component factories to
the installation site, through the main transport routes. Results are sumarized in table 5.
For the transport carbon footprint estimation, heavy trucks transport emission values have been
considered. For a diesel 14 tn capacity heavy truck, carbon emissions are estimated in 791.44
gCO2 eq·km−1 maximum [39, 40, 41]. Thus, specific emissions are 0.0233 gCO2 eq·km−1 kg−1 .

3.4. Energy/Carbon Payback Time vs. Real Energy/Carbon Payback Time

The classical evaluation of the EPBT is exposed in [42]:

E LCA
EPBT = , (1)
Eac
where E LCA is the required energy for the system manufacturing, transport and installation studied in
the LCA and Eac stands for the energy produced by the GCPVS along one year on average. Then, the
result will be expressed in number of years needed to recover the initial energy consumption.
However, we found that equation 1 does not take into account significant parameters in the real
performance of the GCPVS, such as the PV degradation and thus, power looses. Therefore, it results
mandatory to rewrite the classical EPBT definition with a most reliable expression.

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Table 5. Life Cycle Inventories (LCI) for the components of the GCPVS.
Component Units Energy Spec. Energy Contrib. CO2 emissions
−1
[-] [MJ p ] [MJ p ·kWp ] [%] [kgCO2 eq·kWp−1 ]
PV modules 546 3 573 027 35 373 83.96 464.96
Inverter 1 89 293 884 2.10 39.78
Structure 13 131 kg 459 595 4 550 10.80 59.79
3
Wiring 0.046 m 28 687 284 0.67 12.78
Total manuf. - 4 150 602 41 091 97.53 577.31
Component Weight Distance Spec. Energy Contrib. CO2 emissions
−1 −1
[kg·kWp ] [km] [MJ p ·kWp ] [%] [kgCO2 eq·kWp−1 ]
PV modules 83.24 2 000 404 0.96 3.88
Inverter 12.50 200 6 0.01 0.06
Structure 130.00 2 000 631 1.50 6.05
Wiring 4.05 100 1 0.00 0.01
Total transp. 229.79 - 1 042 2.47 10.00
TOTAL - - 42 133 100.00 587.31

Under the hypothesis that the GCPVS degradation is linear through time, it can be considered that
the energy injected to grid decreases an annual rate r:

Eac i = Eac 0 · (1 − r)i , (2)

where i is the i-th year and Eac 0 is the initial energy production by the power plant.
As the EPBT occurs when the initial energy is back by the produced energy:
n
X
E LCA − Eac i = 0, (3)
i=1

replacing equation 2 on equation 3, a geometric series is obtained:

n
X
E LCA − Eac 0 · (1 − r)i = 0,
i=1

1 − (1 − r)n
E LCA = Eac 0 · ,
1 − (1 − r)
E LCA 1 − (1 − r)n
= ,
Eac 0 r
r · E LCA
(1 − r)n = 1 − .
Eac 0
Thus, the Real Energy Payback Time (REPBT) can be evaluated by:

ln(Eac 0 − r · E LCA ) − ln(Eac 0 )

REPBT = n = . (4)
ln(1 − r)

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In the same way, Carbon Emissions Payback Time can be expressed similarly to equation (1):
C LCA
CPBT = , (5)
Cac
where C LCA are the carbon emissions for the system manufacturing, transport and installation studied
in the LCA; and Cac stands for the carbon emissions savings produced by the GCPVS along one year
on average. Then, the result will be expressed in number of years needed to retrieve the initial carbon
footprint.
While RCPBT refers to the Real Carbon Payback Time and its expression can been deduced as:

ln(Cac 0 − r · C LCA ) − ln(Cac 0 )

RCPBT = n = . (6)
ln(1 − r)

4. Results and Discussion

Taking into account the LCA analysis sumarized in table 5, the classical Energy Payback Time for
the case study is:

42 133 MJ p ·kWp−1
EPBT = = 3.30 yr.
12 776 MJ p ·kWp−1 ·yr−1
However, the calculated Real Energy Payback Time for the same facilities and conditions, taking
into account the 3.38% degradation rate obtained as mean annual variation in table 3, is:

ln(12 776 MJ p ·kWp−1 ·yr−1 − 0.0338 · 42 133 MJ p ·kWp−1 ) − ln(12 776 MJ p ·kWp−1 ·yr−1 )
REPBT =
ln(1 − 0.0338)
= 3.44 yr.

Results for the Carbon Payback Time are:

587.31 kgCO2 eq·kWp−1

CPBT = = 1.00 yr.
585.97 kgCO2 eq·kWp−1 ·yr−1

ln(585.97 kg·kWp−1 ·yr−1 − 0.0338 · 587.31 kg·kWp−1 ) − ln(585.97 kg·kWp−1 ·yr−1 )

RCPBT =
ln(1 − 0.0338)
= 1.00 yr.

Results show a difference between the REPBT and the EPBT of 4.22 %, which may result not
environmentally significant. Figure 6 plots the energy balances between the embodied energy in the
GCPVS and the returned energy by PV generation. The classical analysis and the new proposed one
taking into account the GCPVS degradation can be easily compared in the graph. At the end of the
GCPVS’s lifespan, 3.5 times the needed energy for the GCPVS existance is consumed by the system
degradation. Long-term more conservative scenarios (0.5% and 1.00% degradation rates) show 3.32 yr
and 3.34 yr REPBT respectively, while at the end of the GCPVS’s lifespan about 7 times the embodied
energy has been generated back in both cases.

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 90

120
WITHOUT DEGRADATION
WITH DEGRADATION (MEASURED: 3.38%)
100 WITH DEGRADATION (0.5%)
WITH DEGRADATION (1.0%)
Energy [MWhp/kWp]

80

60

40

20

0
0 5 10 15 20 25 30
-20
Opera2on 2me [years]
Figure 6. Energetic contribution of a GCPVS for several degradation rates.

On the other hand, manufacturing CO2 eq. emissions are retrieved by the GCPVS clean energy
production in just one year under any degradation conditions, as it can be seen in figure 7. From that
moment on, the energy produced by the CCPVS is completely free of carbon emissions and thus, it can
be considered that every kWh f has a negative carbon footprint equivalent to the carbon emissions that it
would have been produced if that kWh f would have been generated by other sources (according to the
National Energy Mix in Spain: 0.399 kgCO2 eq·kWh−1 f [22]). Then, final carbon footprint for the case
study is estimated to be (for a 30 years lifespan) of −16 992 kgCO2 eq·kWp−1 under non degradation hy-
pothesis, but only −10 192 kgCO2 eq·kWp−1 , −15 693 kgCO2 eq·kWp−1 and −14 513 kgCO2 eq·kWp−1
under degradation rates of 3.38% (measured in this case study), 0.5% and 1.0% respectively. Never-
theless, in the worst case, the GCPVS is able to retrieve 18.35 times the carbon emissions needed for
its components manufacturing, transport, installation and dismantlement.
Table 6 summarizes results from the bibliography and compares them with the obtained values in
the current study (last row in the table). Yearly specific production from reference [1] installations have
been estimated by PVGIS results according to the location latitude [12], in absence of real data. None
of the other studies have considered the degradation effects on the EPBT and carbon footprint balance.
The estimated LCA (42 133 MJ·kWp−1 ) is in accordance with other Megawatt GCPVS, as it can
be seen in table 6, showing very close results for MJ·kWp−1 in Spain. Moreover, the obtained EPBT
value is in the range of those calculated in [1]. However, results differ significantly from those of [9]
and [29].
Let’s notice that the described PV plant from [29] has very small size, and it is a rooftop instal-
lation. This sort of PV plants reduces significantly the LCA value because of they often do not need
foundations, use lighter structures and require less wiring. Thus, results from this reference may not
be representative. Moreover, the associated CO2 eq. amount is extremely low because of the large rate
of nuclear energy in the Japanese Energy Mix.
In the case of the PV plant described in [9] and located in Springerville (USA), the LCA value
is significantly low because of optimal installation procedures were applied. Nevertheless, obtained

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 91

2
x 1000

0
Carbon emissions [kgCO2eq/kWp]

0 5 10 15 20 25 30
-2

-4

-6

-8

-10

-12
WITHOUT DEGRADATION
-14
WITH DEGRADATION (MEASURED: 3.38%)
WITH DEGRADATION (0.5%)
-16
WITH DEGRADATION (1.0%)

-18
Opera6on 6me [years]
Figure 7. Carbon emissions balance of a GCPVS for several degradation rates.

result values, undoubtely, are out of range in comparison with those from our carried out study. The
italian PV plant cited in [9], and described in [43, 44], shows a reduced value for the LCA but closer
to the results obtained for several installations in [1].
Alsema carries out an overall study in [23] where he estimates the LCA, EPBT and CO2 eq. emis-
sions both for central PV power plants and rooftop installed PV plants. In table 6 only the results for
central PV power plants are shown. For his present case in 1997, LCA values are in the range between
26 500 and 83 000 MJ p ·kWp−1 , which agrees with our results. Moreover, the EPBT is in accordance
with our data. However, Alsema’s predictions for 2007 are too low in comparison with our results.
Only CO2 reduction estimation agrees with our calculated value.
From the results seen in [1], a significant scale effect is not observed. Also the EPBT value seems
not to be influenced by the tracking system. On the other hand, the ratio EPBT/LCA remains almost
constant in all studied cases.

5. Conclusions

Firstly, it has been observed that the LCA of the PV modules contribute, approximately, with the
80% of the embodied energy involved in the GCPVS. The supporting structure is the second highest
value, with an 11% contribution.
It has been observed that approximately 3.3 years are needed to produce the energy embodied in
the manufacturing, transport, start-up and dismantlement of a GCPVS. Moreover, if real components
degradation is taken into account, a 4.5% more is needed. Therefore, under the hypothesis of a useful
lifespan of 30 years for a GCPVS, it is able to give back the energy required for its existence 8.1 times
at the end of its lifespan with the classical analysis, but only 4.6 times with the accurate study including
components degradation.
Finally, the carbon footprint analysis for the case study shows that carbon emissions for the manu-
facturing, transport, installation and dismantlement of the GCPVS are retrieved in just one year, while

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Table 6. Summary of results from the bibliography.

Ref. Loc. Traking Size Production (yr) LCA EPBT CO2 eq.
[kWp] [kWh·kWp−1 ] [MJ p ·kWp−1 ] [yr] [kg·kWp−1 ]
[29] Japan Fixed 3 1 314 - - 34.2-69.6
[9] USA Fixed 3 500 1 730 4 252-4 381 0.21-0.37 234.44
[9, 43, 44] Italy Fixed 2 943 1 160 17 902 1.30 -
[23] 1997 Fixed - 1 280 26 602-82 816 1.8-8.0 2 304-5 760
[23] 2007 Fixed - 1 280 16 062 1.0-2.0 768-1 152
[1] Spain Fixed 832 ≈ 1 700 51 005 3.0-4.8 -
[1] Spain Fixed 1 152 ≈ 1 620 51 005 3.0-4.8 -
[1] Spain Hor. 14 069 ≈ 2 820 53 157 2.6-4.9 -
[1] Spain 2-axes 6 020 ≈ 2 150 60 140 2.1-4.3 -
[1] Spain 2-axes 2 064 ≈ 2 220 60 140 2.1-4.3 -
- Spain Hor. 101 1 455 42 133 3.44 587.31

degradation of the power plant does not affect this value. At the end of the GCPVS’s lifespan, between
10 and 17 tnCO2 eq·kWp−1 emissions have been avoided. The better the PV components (with lower
degradation rates) and the closer to the installation site they are manufactured, the more environmen-
tally friendly would be the GCPVS. Manufacturing countries also affect significantly to the LCA of
the power plant. These aspects may be clearly considered in the promoting of clean energy policies.

Conflict of Interest

All authors declare no conflict of interest in this paper.

References

1. Perpiñán O, Lorenzo E, Castro M A, et al. (2009) Energy payback time of grid connected PV
systems: comparison between tracking and fixed systems. Prog Photovoltaics 17(2): 137–147.
2. Fthenakis V, Alsema E A, de Wild-Scholten M (2005) Life cycle assessment of photovoltaics:
perceptions, needs, and challenges, in: Conference Record of the Thirty-first IEEE Photovoltaic
Specialists Conference, 1655–1658.
3. Keoleian G A, Lewis G M (1997) Application of life-cycle energy analysis to photovoltaic module
design. Prog Photovoltaics 5(4): 287–300.
4. Sherwani A, Usmani J, Varun (2010) Life cycle assessment of solar PV based electricity generation
systems: A review. Renew Sust Energ Rev 14(1): 540–544.
5. Tahara K, Kojima T, Inaba A (1997) Estimation of power plants by LCA. Kagaku Kogaku Ronbun
23(1): 93–94.
6. de Wild-Scholten M, Alsema E (2004) Towards cleaner solar PV: Environmental and health im-
pacts of crystalline silicon photovoltaics. Refocus 5(5): 46–49.

AIMS Energy Volume 5, Issue 1, 77-95.

 93

7. Fthenakis V, Alsema E (2006) Photovoltaics energy payback times, greenhouse gas emissions and
external costs: 2004 – early 2005 status. Prog Photovoltaics 14(3): 275–280.
8. Bayod-Rújula A A, Lorente-Lafuente A M, Cirez-Oto F (2011) Environmental assessment of grid
connected photovoltaic plants with 2-axis tracking versus fixed modules systems. Energy 36(5):
3148–3158.
9. Mason J E, Fthenakis V M, Hansen T, et al. (2006) Energy payback and life-cycle CO2 emissions
of the BOS in an optimized 3.5 MW PV installation. Prog Photovoltaics 14(2): 179–190.
10. Nawaz I, Tiwari G (2006) Embodied energy analysis of photovoltaic (PV) system based on macro-
and micro-level. Energ Policy 34(17): 3144–3152.
11. SoDa Team SoDa: HelioClim-3, 2016, Available from: http://www.soda-pro.com/
web-services/radiation/helioclim-3-for-free.
12. Europan Commission: PVGIS- PV Potential Estimation Utility, 2016, Available from: http:
//re.jrc.ec.europa.eu/pvgis/apps4/pvest.php.
13. Hacke P, Smith R, Terwilliger K, et al. (2013) Testing and Analysis for Lifetime Prediction of
Crystalline Silicon PV Modules Undergoing Degradation by System Voltage Stress. IEEE J Pho-
tovoltaics 3(1): 246–253.
14. Muñoz M A, Alonso-Garcı́a M C, Vela N, et al. (2011) Early degradation of silicon PV modules
and guaranty conditions. Sol Energy 85(9): 2264–2274.
15. Jordan D C, Kurtz S R (2012) Photovoltaic Degradation Rates-An Analytical Review. NREL/JA-
5200-51664 1(1): 1–32.
16. Osterwald C R, Anderberg A, Rummel S, et al. (2002) Degradation analysis of weathered
crystalline-silicon PV modules, in: Conference Record of the Twenty-Ninth IEEE Photovoltaic
Specialists Conference, 1392–1395.
17. University of Manchester: Carbon calculations over the life cycle of industrial activities, 2016,
Available from: http://www.ccalc.org.uk/.
18. Postnote from the Parliamentary Office of Science and Technology: Carbon footprint of electricity
generation, 2006, 268:1–4.
19. Dı́ez-Mediavilla M, Alonso-Tristán C, Rodrı́guez-Amigo M, et al. (2012) Performance analysis of
PV plants: Optimization for improving profitability. Energ Convers Manage 54(1): 17–23.
20. Khasawneh Q A, Damra Q A, Salman O H B Determining the Optimum Tilt Angle for Solar
Applications in Northern Jordan 9(3): 187–193.
21. Skeiker K Optimum tilt angle and orientation for solar collectors in Syria 50(1): 2439–2448.
22. Government of Spain: Carbon emission factors and primary energy conversion coefficients for the
different electrical energy sources in the Building Sector in Spain, 2014, IDAE, 1–32.
23. Alsema E (1998) Energy Requirements and CO2 Mitigation Potential of PV Systems, in:
BNL/NREL Workshop PV and the Environment, 1–11.
24. Adelstein J, Sekulic B (2005) Performance and reliability of a 1-kW amorphous silicon photo-
voltaic roofing system, in: Conference Record of the Thirty-first IEEE Photovoltaic Specialists
Conference, 1627–1630.

AIMS Energy Volume 5, Issue 1, 77-95.

 94

25. Chamberlin C E, Rocheleau M A, Marshall M W, et al. (2011) Comparison of PV module per-

formance before and after 11 and 20 years of field exposure, in: 2011 37th IEEE Photovoltaic
Specialists Conference (PVSC), 101–105.
26. Lorenzo E, Zilles R, Moretón R, et al. (2013) Performance analysis of a 7-kW crystalline silicon
generator after 17 years of operation in Madrid. Prog Photovoltaics 22(12): 1273–1279.
27. Espinosa N, Garcı́a-Valverde R, Urbina A, et al. (2011) A life cycle analysis of polymer solar cell
modules prepared using roll-to-roll methods under ambient conditions. Sol Energ Mat Sol C 95(5):
1293–1302.
28. Fthenakis V M, Kim H C (2013) Life cycle assessment of high-concentration photovoltaic systems.
Prog Photovoltaics 21(3): 379–388.
29. Hondo H (2005) Life cycle GHG emission analysis of power generation systems: Japanese case.
Energy 30(11-12): 2042–2056.
30. Peng J, Lu L, Yang H (2013) Review on life cycle assessment of energy payback and greenhouse
gas emission of solar photovoltaic systems. Renew Sust Energ Rev 19: 255–274.
31. Jiao Y, Salce A, Ben W, et al. (2011) Siemens and siemens-like processes for producing photo-
voltaics: Energy payback time and lifetime carbon emissions. JOM 63(1): 28–31.
32. Mann S A, de Wild-Scholten M J, Fthenakis V M, et al. (2013) The energy payback time of
advanced crystalline silicon PV modules in 2020: A prospective study. Prog Photovoltaics 22(11):
1180–1194.
33. Mohr N J, Meijer A, Huijbregts M a J, et al. (2013) Environmental life cycle assessment of
roof-integrated flexible amorphous silicon/nanocrystalline silicon solar cell laminate. Prog Pho-
tovoltaics 21(4): 802–815.
34. Yamada K, Komiyama H, Kato K, et al. (1995) Evaluation of photovoltaic energy systems in terms
of economics, energy and CO2 emissions. Energ Convers Manage 36(6–9): 819–822.
35. Hammond G, Jones C (2008) Embodied energy and carbon in construction materials. P I Civil
Eng Energ 161(2): 87–98.
36. Pacca S, Sivaraman D, Keoleian G A (2007) Parameters affecting the life cycle performance of
PV technologies and systems. Energ Policy 35(6): 3316–3326.
37. European Comission: Eurostat database, 2016, Available from: http://ec.europa.eu/
eurostat/data/database.
38. Davis S C, Diegel S W, Boundy R G (2012) Transportation Energy Data Book, volume 1, 31st
edition, Oak Laboratory: U.S. Department of Energy.
39. European Environment Agency E U (2016) Explaining road transport emissions, volume 1, 1st
edition, Luxembourg: Publications Office of the European Union.
40. Oficina Catalana del Canvi Climátic E S (2011) Practical Guide for the Carbon Emissions Calcu-
lation. Oficina Catalana del Canvi Climátic 1(1): 1–32.
41. Joint Research Centre I E T (Ed.) (2013) Well-to-wheels analysis of future automotive fuels and
powertrains in the European context, volume 1, 1st edition, Luxembourg: Publications Office of
the European Union.

AIMS Energy Volume 5, Issue 1, 77-95.

 95

42. Pucker N, Schappacher W (1994) Installation of new energy systems: Energy balances and instal-
lation times; application to a photovoltaic system. Renew Energ 5(1–4): 212–214.
43. Previ A, Iliceto A, Belli G, et al. The 3.3 MW-peak photovoltaic power station at Serre, in: Pro-
ceedings of 1994 IEEE 1st World Conference on Photovoltaic Energy Conversion - WCPEC (A
Joint Conference of PVSC, PVSEC and PSEC), volume 1, 750–753.
44. Iliceto A, Vigotti R (1998) The largest PV installation in Europe: Perspectives of multimegawatt
PV. Renew Energ 15(1): 48–53.

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