27th European Photovoltaic Solar Energy ... - Q CELLS€¦ · ENERGY LEARNING CURVES OF PV SYSTEMS Marzella Görig1,2,3 and Christian Breyer1,4 1 Q -Cells SE, Sonnenallee 17 21, 06766

ENERGY LEARNING CURVES OF PV SYSTEMS

Marzella Görig1,2,3 and Christian Breyer1,4 1 Q-Cells SE, Sonnenallee 17 - 21, 06766 Bitterfeld-Wolfen OT Thalheim, Germany,

E-Mail: [email protected], 2 Hochschule Anhalt, Bernburger Str. 55, 06366 Köthen, Germany,

3 now with: Technische Universität Clausthal, Adolph-Roemer-Straße 2A, 38678 Clausthal-Zellerfeld, Germany,

E-Mail: [email protected], 4 now with: Reiner Lemoine Institut gGmbH, Ostendstraße 25, 12459 Berlin, Germany,

E-Mail: [email protected]

ABSTRACT: Energy demand of photovoltaic (PV) systems is an important part of energy sustainability of PV

systems. PV systems are considered sustainable energy systems when the produced energy is higher than needed.

This paper is using financial learning curve concepts to determine energy demand of main PV modules and systems.

General PV module and PV system energy learning curves are calculated by weighting energy demand of different

PV systems according to their share in PV market. Additionally the contribution of module efficiency into energy

reduction is considered. Energy payback time (EPBT) and energy return on investment (EROI) in 2010 and 2020 are

calculated via energy learning rate.

Keywords: Learning curve, LCA, EPBT, EROI, Silicon Solar Cell, Thin Film Solar Cell, System

1 INTRODUCTION

PV is the fastest growing power technology [1]. The

reasons are on the one hand the cost reductions of PV

systems and on the other hand the increasing need for

sustainable power supply. But how sustainable are PV

systems in detail? Sustainability of PV is essential for

justifying both high growth rates of PV in the present and

PV systems as an important part of energy supply in

future. The relation of cumulated energy demand (CED)

for establishing PV systems and generated energy of

these PV systems is essential for the sustainability of PV.

Energy-Pay-Back-Time (EPBT) is one possibility to

show this relation. Determining how EPBT can develop

in future, it is necessary to analyse the development of

CED. In many publications the CED of PV modules or

PV systems are investigated by Life Cycle Analysis

(LCA) for one technology status but not for a longer

period of time.

The purpose of the presented work is to analyse PV

systems development in terms of CED from the 1970s to

2010 by collecting LCA studies of different publications.

2 METHOD

2.1 General Data Handling

CED data of 73 publications published between 1976

and 2010 [2-74] are registered in a database with the aim

to collect only CED data of full considered LCA. This

means that the total energy demand from silica mining up

to system assembly and recycling with all direct and

indirect energy demand like embodied energy must be

considered. Furthermore year of technology status is

taken from the respective publications.

The following six PV technologies are analysed:

single crystalline silicon (sc-Si), multi-crystalline silicon

(mc-Si), ribbon-Si, thin film amorphous silicon (a-Si),

cadmium telluride (CdTe), copper indium (gallium)

diselenide (CI(G)S). The sc-Si group includes also CED

data of sc-Si/a-Si technology and the a-Si group CED

data of a-Si/µc-Si and µm-Si regarding their similarity

and the rareness of qualitative well analysed LCA.

Actually part of real sc-Si and a-Si data is considerable

higher than that of the respectively familiar technology.

All CED data are first returned to primary energy per

module area on a square metre basis (m²) as much as

possible. Additionally, the publications’ information of

module efficiency increase enables the analysis to

calculate suitable annual module efficiencies for every

year and every PV technology. In this way only average

industrial modules are considered and the data of CED

per power are not biased by high performance modules.

2.2 Framed Modules

Primary energy demand from silica mining up to

module assembly is considered thereby crystalline

modules are considered with and thin film modules

without aluminium frame. Using CED data of

publications which consider crystalline modules without

Al-frame and thin film modules with frame, analysis of

the energy demand of Al-frame is made. Figure 1 shows

all CED values of Al-frames from the years 1983 till

2020.

Figure 1: Primary energy demand of Al-frame from 1983 to 2020. Data are presented in thermal GJ per m² module area.

Considering the fact that many LCA analyses do not

include all direct and indirect energy types, this may

explain the fluctuations of the CED values, leads to the

27th European Photovoltaic Solar Energy Conference and Exhibition

4682

motivation of developing an annual average value which

considers the high CED values more than the low ones.

In details, firstly an annual arithmetic average was

calculated. CED values which are higher than the

arithmetic average of the respective year are then

weighted twice higher than the remaining CED values.

By this method low CED values, which may had been

generated from an incomplete LCA study, did not lead to

a too low annual CED. An exponential best-fit curve is

calculated via the weighted annual average CED values.

The exponential curve allows calculating an average

CED value for Al-frames to transform CED values for

modules without frame to modules with frame and vice

versa. This method is used when publications considered

crystalline modules without Al-frame or thin film

modules with frame but only when no LCA for Al-frame

was made. The method is also necessary to achieve

enough CED data for further analyses.

2.3 Silica in PV-industry

Two types of silica have been used in the PV -

industry: On one hand before 2000 the off-spec Si was

mostly mentioned in publications and on the other hand

after 2000 the off-spec Si has been displaced by the solar-

grade (sog) Si. Off-spec Si can be considered as a waste

product from the microelectronic industry. Thus many

authors do not consider the total energy demand of silica

production but only a part of it. The aim of this analysis

is to show the development of total energy demand of PV

systems. Considering the share of Si feedstock in total

EPBT it is shown that the energy demand of Si feedstock

for mc-Si modules is more than a third of the total energy

demand of module production (figure 2). That means

specifically for mc-Si an incomplete consideration of Si

production is not justifiable.

Figure 2: EPBT of different PV systems between 2007 and

2009. Diagram is taken from de Wild-Scholten [69].

Using only LCA studies which include total energy

demand of Si feedstock of mc-Si modules leads to the

fact that only LCA studies after 1995 could be used.

Before 1995 no complete LCA studies of mc-Si modules

could be found.

For the two other crystalline Si modules sc-Si and

ribbon-Si the share of Si feedstock in module’s EPBT is

less than that of mc-Si. In this way it is sufficient to

calculate the annual average of energy demand via the

weighted average method (section 2.2).

As a consequence and also to avoid an incomplete

LCA, the annual weighted average method is used for all

six major PV technologies.

3 ENERGY LEARNING CURVE

Actually the learning curve model is widely used for

cost projects of industrial goods. Here it describes the

fact that the cost of an industrial product decreases

constantly with every doubling of its historic cumulative

production [75]. Substituting the cost parameter of the

traditional learning curve concept for CED values results

in an energy learning curve describing how energy

demand decreases with the doubling of the cumulative

capacity (equation 1). This allows the energy demand of

PV modules to be extrapolated for the future.

2log

1log

0

0.

LR

xx

P

PEE

(1)

Equation 1: Law of energy learning curves. Abbreviations stand

for: cumulated energy demand at historically cumulated capacity

of Px (Ex), CED at initial capacity P0 (E0), historically cumulated capacity (Px), initial capacity (P0), learning rate (LR).

By plotting the annual weighted average CED values

per nameplate module power against their cumulative

capacity the energy learning rate can be derived. Figure 3

shows the results of the six major PV technologies on

basis of primary energy per module power.

For all modules except of CI(G)S an energy learning

rate is determined. For CI(G)S modules too less data are

available, partly due to the latest market introduction

among the six PV technologies taken into account. Hence

the first complete LCAs for CI(G)S were made only after

2000 [5].

Sc-Si modules show high fluctuations especially

before the year 2000. That is mainly a consequence of the

uncertainty in differentiation between incomplete and

complete Si feedstock LCA. LCA data of mc-Si are more

detailed. The high energy reduction between 2004 and

2005 is a result of the sog-Si. Otherwise no complete

LCA data for mc-Si can be found before 1995 [19]

although mc-Si has been produced since the late 1970s

[76]. Including also the incomplete Si feedstock LCA of

mc-Si in the analysis leads only to an energy learning rate

of 8% instead of 24%. This shows the high relevance of

Si production for the energy demand and reduction.

Therefore mc-Si and sc-Si modules show the highest

energy learning rates of all PV technologies and ribbon-

Si with less Si demand shows only a learning rate of

12%.

Thin film modules namely a-Si and CdTe have

needed a relatively low energy demand from the

beginning, hence show also a reduced learning rate.

Although PV modules show a decreasing energy

demand over the last decades, it must be stated that the

energy demand in figure 3 deals with the module power

but the problem is that the module power of all PV

technologies has been increased. Therefore, the influence

of the module efficiency on the energy learning rate has

to be investigated.


4683

Figure 3: Energy learning curves of six main PV technologies dealing with annual weighted average CED per module power. No constant

learning rate (LR) could be estimated for CI(G)S as a consequence of less data available for the latest technology introduced to the market. The other five PV modules technologies show learning rates between 24% (mc-Si) and 7% (CdTe). C-Si modules include Al-frame. Thin

film modules are considered without frame.


4684

Table 1 shows the difference between two learning

rates. LRp indicates that the CED deals with module

power and LRA deals with CED per module area. The

results demonstrate that mostly the CED reduction of thin

film modules strongly depends on the increase of the

module power whereas the CED per module area of

crystalline module has been reduced significantly in the

last years. This means that the production of crystalline

Si modules shows enough potential in savings of energy

demand.

Table 1: Energy learning rates of PV module technologies. LRp deals with CED per module power and LRA with CED per

module area. Mostly for thin film modules increasing module

efficiency is the major driving force for a decreasing energy demand per module power. High rate of energy reduction of c-Si

modules is significantly less dependent on module efficiency

than thin film modules.

4 GENERAL ENERGY LEARNING CURVE FOR

PV MODULES

The average energy demand for cumulated PV

capacity can now be derived via the determined energy

learning rates of the six major PV module technologies

and equation 1. If the energy demand of every PV

technology is weighted relative to the share of the

respective technology to the total PV market (figure 4),

general energy demand of the PV module market can be

determined. Data for respective market share are taken

from [77, 78].

Figure 4: Share of different module technologies to the total PV

market from 1970 to 2020. Data from 1980 to 2010 are taken from [77, 78]. Other data are own estimations.

A constant energy demand of 17 GJth/kWp is used for

CI(G)S modules considering the missing energy learning

curve (figure 3). Figure 5 shows the result. Each point

indicates one year from 1977 to 2020.

Figure 5 also demonstrates that the calculated energy

demand of sc-Si in the 1970s is too low. This explains the

irregularity before 1977. However, only one CED value

for the period before 1977 is available. This value is

derived from the first recorded LCA analysis by Hunt

[30] and is may comprise an incomplete LCA. The

calculated values in this period are related to this value.

Considering the period from 1974 to 2010, an energy

learning rate of 17% for the total PV module market was

gained. Considering the time period from 1977 to 2010, a

learning rate of 20% was obtained. The following

analysis deals with the 17% learning rate. Therefore a

determination of too high energy reduction in the future

is avoided. It should be mentioned that the financial

learning rate of c-Si PV modules is also 17% [79]. That

indicates a correlation between cost and energy

reduction.

Figure 5: Energy learning curve of total PV module production from 1974 to 2010. Energy demand is calculated by energy

learning curves of the single PV technologies weighted relative

to their share in PV module market. CI(G)S modules are included with a constant energy demand of 17 GJth/kWp. From

1974 to 2010 the energy learning curve is 17% and from 1977 to

2010 20%.

5 CED OF BOS COMPONENTS

For a complete LCA analysis of PV systems it is also

necessary to consider the energy demand of the balance-

of-system (BOS) components. Available LCA studies

allow to differentiate between rooftop and ground-

mounted BOS components. The energy demand of BOS

components does not include the energy demand for

frames or batteries. LCA studies about BOS components

are not available for every year. Hence, lines of best fit

for rooftop and ground-mounted BOS components were

determined. The lines of best fit are calculated from the

annual weighted average LCA values.

For ground-mounted BOS components the values of

the line of best fit are between 1,600 – 1,800 MJth/m² for

the years 1974 – 2010. They are highly dominated by

ground-mounted BOS including concrete foundation.

Only a small amount of LCA studies already consider

pile driving method. In general, ground-mounted BOS

components show an energy learning rate of only 0.3%.

PV-technology LRP [%] LRA [%] relative

difference [%]

sc-Si

(with Al-frame)

18 14 -4

mc-Si

(with Al-frame)

24 22 -2

ribbon-Si

(with Al-frame)

12 10 -2

a-Si

(without frame)

15 9 -6

CdTe

(without frame) 7 3 -4


4685

For rooftop BOS components the line of best fit

results in CED values which fluctuate slightly around 400

MJth/m² between 1974 and 2010. Thus the energy

learning rate is 0.4%.

The energy demand of BOS components has not been

changed significantly since the 1970s. Only pile driving

method could reduce energy demand of ground-mounted

systems but not enough LCA studies are performed

considering this issue. Therefore they could not be

considered suitable.

Adding the energy demand of PV modules to the

BOS components result in a lower energy learning curve

than that for only PV modules. Therefore the BOS

components slow down the energy reduction of PV

systems.

Table 3 and 4 (section 12) demonstrate the influence

of BOS components on the system energy learning curve.

The tables also include the total energy demand of

ground-mounted and rooftop PV systems relative to the

PV technology share of the PV market (figure 6). Data

for PV application market shares are taken from [80].

That is a simplified method because it does not include

the difference between the share of PV technologies in

the total PV market and the share of PV technologies in

the PV rooftop or PV ground-market. However, such

detailed data have not been available.

Figure 6: PV application market share from 1970 to 2010. Data

from 1992 to 2009 are taken from [80].

6 TOTAL PV ON-GRID SYSTEMS

After considering single PV technologies and PV

systems the energy demand for the total PV systems

market can now be calculated by energy learning rates.

The general energy learning rate of on-grid PV modules

is determined by weighting the energy demand of

different PV technologies and BOS components relative

to their share in the PV market. Figure 7 indicates the

energy learning curve for CED per module power. The

influence of the different PV technologies can be

determined. Between 1980 and 1988 the share of a-Si in

the PV market raised from 0.38% to 32% [77]. Thus the

a-Si technology has a small amount of energy demand.

Therefore the energy reduction in this period was very

high. After 1998 the PV market was mainly determined

by mc-Si and sc-Si. Hence, the energy reduction was

damped. 2008 the solar boom in Spain has lead to a high

installation of ground-mounted systems. That explained

the high peak at that year.

The energy learning rate for CED per module power

of total PV on-grid systems was 14% for the years 1974 -

2010. Also the financial learning rate of PV systems is

14% [79]. This points out again the correlation between

energy and cost reduction.

Figure 7: Energy learning curve of total PV systems from 1974 to 2010. Energy demand is calculated by energy learning curves

of single PV modules and BOS components weighted relative to

their share in PV module and system market. CI(G)S modules are calculated with a constant energy demand of 17 GJth/kWp.

From 1974 to 2010 the energy learning curve is 14%.

7 VERIFICATION

After determining learning curves of PV systems by

using the energy learning rates of modules and BOS

components, it must be verified if the energy learning

rates are defensible or not. That can be easily done by

using energy parameter as EPBT or EROI. The EPBT of

six rooftop PV systems were calculated and compared to

the results of de Wild-Scholten [69] which is indicated in

figure 2. The annual irradiation on optimally tilted

modules of 1700 kWh/m² and the technology status of

the single PV rooftop systems are assumed like those of

de Wild-Scholten. It must be mentioned that de Wild-

Scholten considered only frameless modules. So the

energy demand of Al-frame must be eliminated by using

best-fit curve (section 2). The assumptions for the

Performance Ratio and lifetime are listed in Table 2.

Table 2: Estimation of Performance Ratio and lifetime of rooftop and ground-mounted PV systems in 2010 and 2020.

Performance Ratio

[%]

Lifetime

[y]

rooftop 2010 80 25

rooftop 2020 84 30

ground-mounted

2010 78 25

ground-mounted

2020 82 30

The results are indicated in Figure 8. Only minor

differences are found between the EPBT of de Wild-


4686

Scholten and the calculated value from the energy

learning curves. This determined energy learning curves

could be used for further analyses.

Figure 8: Calculated EPBT of six PV modules by using determined energy learning curves. Irradiation and technology

status are chosen analogue to figure 2. Performance Ratios are

listed in Table 2.

8 EPBT AND EROI IN 2010 AND 2020

The catenation between EPBT and energy return on

investment (EROI) is the lifetime which is demonstrated

in equation 2.

EPBT

LEROI (2)

Equation 2: Calculating of Energy Return on Investment.

Abbreviations stand for: Energy Return on Investment (EROI),

lifetime of PV system (L) and Energy Pay Back Time (EPBT).

Thus the development of EPBT in time can be well

calculated using energy learning curves. EROI can be

calculated also using energy learning curve.

2010 is chosen as current technology status and

compared to 2020. EPBT and EROI are calculated using

energy learning rate and annual industry growth rates of

new installed PV capacity of 30% p.a. in 2010s. The

assumed growth rate is less than the enormous annual

growth rate of about 45% in the last years [81]. Economic

demand for PV systems is by 2020 in the order of 3,000 –

4,000 GWp [82]. Hence the assumed growth rate of 30%

p.a. should be a realistic expectation.

Parameters of Performance Ratio and lifetime are

listed in Table 2.

Furthermore annual irradiation on module surface of

optimally fixed tilted PV systems is used [83]. Thus

irradiation is given in a coordinate of a 1°x1° mesh

within 65°S and 65°N, EPBT and EROI could be

determined in same resolution.

Using energy learning curve of total PV systems

leads to EPBT of 1 to 2.5 years worldwide for the

technology status of the year 2010. Within the next ten

years it can be reduced on a level of 0.5 to 1.5 years.

These EPBT has lead to an EROI of about 10 to 25 in

2010 and will lead to an EROI of 20 to 60 in 2020. PV

systems will soon be able to produce in average over 50

times more energy during their lifetime than what they

require for their production. Detailed results are indicated

in figures 9-12.

Figure 9: EPBT of total on-grid modules including all system components for the year 2010. EPBT is calculated by energy

learning rate and with annual irradiation on optimally fixed tilted

module surface in a mesh of 1°x1° within 65°S and 65°N. Performance Ratio is listed in Table 2.

Figure 10: EPBT of total on-grid modules including all system

components for the year 2020. EPBT is calculated by energy

learning rate and with annual irradiation on optimally fixed tilted module surface in a mesh of 1°x1° within 65°S and 65°N.

Performance Ratio is listed in Table 2. Annual growth rate of PV

installations is assumed to be 30% p.a.

Figure 11: EROI of total on-grid modules including all system

components for the year 2010. EROI is calculated by energy

learning rate and with annual irradiation on optimally fixed tilted

module surface in a mesh of 1°x1° within 65°S and 65°N.

Performance Ratio is listed in Table 2. Performance Ratio is

listed in table 4. Lifetime is 25 years.

Figure 12: EROI of total on-grid modules including all system components for the year 2020. EROI is calculated by energy

learning rate and with annual irradiation on optimally fixed tilted module surface in a mesh of 1°x1° within 65°S and 65°N.

Performance Ratio is listed in Table 4. Performance Ratio is

listed in Table 2. Lifetime is 30 years. Annual growth rate of PV installations is assumed to be 30% p.a.

rooftop installation

1700 kWh/m²/y irradiation at optimal tilt angle

0.00

0.50

1.00

1.50

2.00

2.50

sc-Si 2008

14.73%

mc-Si 2007

13.9 %

ribbon-Si

2009 12.8%

CI(G)S 2009

10.65%

CdTe 2009

11.85%

a-Si 2008

7.74%

EP

BT

[y]

BOS

module without frame


4687

EPBT and EROI will show strong development in the

next ten years. Thus PV systems show high energy

production and therefore they will occupy their position

in the energy supply chain.

9 CONCLUSION

To the knowledge of the authors this publication

shows for the first time that the energy consumption in

PV manufacturing follows the log-linear learning curve

law similar to the evolution of production cost. BOS

components soften thereby the energy reduction. This is

mostly important for thin film modules because they

show the lowest energy reduction. Crystalline Si modules

have the highest energy learning rates, which is mainly

driven by improved silicon production.

General PV module and PV system energy learning

rates show correlation between energy and financial

reduction. Additionally, energy learning curves show a

high potential of PV systems in decreasing EPBT and

increasing EROI. The energy learning curve allows

determining these parameters time-dependent for the past

and future.

10 ACKNOWLEDGEMENTS

The authors would like to thank Friederike Kersten,

Chris Werner, Jörg Müller, Dominik Huljić, Ina von

Spieß, Mahmoud Sayed and the Solarvalley Sachsen-

Anhalt e.V. for their great support during different steps

of preparing this paper.

11 REFERENCES

[1] European Photovoltaic Industry Association (EPIA):

Global Market Outlook – For Photovoltaics until 2016.

Belgium, May 2012, www.epia.org

[2] Alsema E.A.: Life-cycle assessment of crystalline

silicon PV module production by REC. 24th EU-PVSEC,

Hamburg, 21th – 25th September 2009

[3] Alsema E.A. and Wild-Scholten de M.J.: Reduction

of the Environmental Impacts in Crystalline Silicon

Module Manufacturing. 22nd EU-PVSEC, Mailand, 3rd -

7th September 2007

[4] Alsema E.A., Wild-Scholten de M.J., Fthenakis V.M.:

Environmental impacts of PV electricity generation a

critical comparison of energy supply options. 21st EU-

PVSEC, Dresden, 4th -8th September 2006


Environmental impacts of Crystalline Silicon

Photovoltaic Module Production. 13th CIRP Intern. Conf.

on Life Cycle Engineering, Leuven, 31st May – 2nd June

2006

[6] Alsema E.A. and Wild-Scholten de M.J.:

Environmental impacts of crystalline silicon photovoltaic

module production. Materials Research Society Fall 2005

Meeting, Boston, November 2005

[7] Alsema E.A. and Wild-Scholten de M.J.: The Real

Environmental Impacts of Crystalline Silicon PV

Modules: an Analysis based on up-to-date manufacturers

data. 20th EU-PVSEC, Barcelona, 6th -10th June 2005


Environmental life cycle assessment of advanced silicon

solar cell technologies. 19th EU-PVSEC, Paris, 7th-11st

June 2004

[9] Alsema E.A.: Energy Pay-back Time and CO2

Emissions of PV Systems. In: Progress in Photovoltaics:

Research and Applications (2000) 8, p. 17-25

[10] Alsema E.A. and Nieuwlaar E.: Energy viability of

photovoltaic systems. In: Energy Policy (2000) 28, p.

999-1010

[11] Alsema E.A., Frankl P., Kato K.: Energy pay-back

time of photovoltaic energy systems: present status and

prospectus. 2nd World Conference on Photovoltaic Solar

Energy Conversion, Wien, 6th-10th July 1998

[12] Alsema E.A.: Energy requirements of thin-film solar

cell modules - a review. In: Renewable and Sustainable

Energy Reviews (1998) 2, p. 387-415

[13] Aulich H.A., Schulze F.W., Strake B.: Energy pay-

back time for crystalline silicon photovoltaic modules

using new technologies. 18th IEEE PVSC, Piscataway,

1985

[14] Bankier C. and Gale S.: Energy Payback of Roof

Mounted Photovoltaic Cells. Energy Bulletin, 2006

[15] Battisti R. and Corrado A.: Evaluation of technical

improvements of photovoltaic systems through life cycle

assessment methodology. In: Energy (2005) 30, p. 952–

967

[16] Denholm P. and Kulcinski G. L.: Life cycle energy

requirements and greenhouse gas emissions from large

scale energy storage systems. In: Energy Conversion and

Management (2004) 45, p. 2153-2172

[17] Denholm P. and Kulcinski G. L.: Net energy balance

and greenhouse gas emissions from renewable energy

storage systems. Energy Center of Wisconsin, ECW

Report Number 223-1, 2003

[18] Dones R. and Frischknecht R.: Life-cycle

Assessment of Photovoltaic Systems: Results of Swiss

Studies on Energy Chains. In: Progress in Photovoltaics:

Research and Applications (1998) 6, p. 117-125

[19] Frankl P., Masini A., Gamberale M., Toccaceli D.:

Simplified Life-cycle Analysis of PV Systems in

Buildings: Present Situation and Future Trends. In:

Progress in Photovoltaics: Research and Applications

(1998) 6, p. 137-146

[20] Fthenakis V.M. and Kim H.C.: Photovoltaics: Life-

cycle analyses. In: Solar Energy (2011), in press,

doi:10.1016/j.solener.2009.10.002


4688

[21] Fthenakis V., Kim H.C., Held M., Raugei M.,

Krones J.: Update of PV energy payback times and life-

cycle greenhouse gas emissions. 24th EU-PVSEC,

Hamburg, 21st-25th September 2009

[22] Fthenakis V., Kim H., Alsema E.: Emissions from

Photovoltaic Life Cycles. In: Environmental Science &

Technology (2008) 42, p. 2168–2174

[23] Fthenakis V., Kim H., Alsema E.: Tables for

Emissions from Photovoltaic Life Cycles. Supporting

Information for Environmental Science & Technology,

2008

[24] Fthenakis V. and Kim H.: Greenhouse-gas emissions

from solar electric- and nuclear power: A life-cycle

study. In: Energy Policy (2007) 35, p. 2549–2557

[25] García-Valverde R., Cherni J.A., Urbina A.: Life

cycle analysis of organic photovoltaic technologies. In:

Progress in Photovoltaics: Reasearch and Applications

(2010), in press, doi: 10.1002/pip.967, 2010

[26] García-Valverde R., Miguel C., Martínez-Béjar R.,

Urbina A.: Life cycle assessment study of a 4.2 kWp

stand-alone photovoltaic system. In: Solar Energy (2009)

83, p. 1434–1445

[27] Greijer H., Karlson L., Lindquist S.-E., Hagfeldt A.:

Environmental aspects of electricity generation from a

nanocrystalline dye sensitized solar cell system. In:

Renewable Energy (2001) 23, p. 27–39

[28] Hagedorn G.: Kumulierter Energieaufwand von

Photovoltaik- und Windkraftanlagen. IfE Schriftenreihe

(1992) 25

[29] Hagedorn G.: Kumulierter Energieverbrauch und

Erntefaktoren von Photovoltaik-Systemen. In:

Energiewirtschaftliche Tagesfragen (1989) 11, p. 712-

718

[30] Hunt L.P.: Total energy use in the production of

silicon solar cells from raw materials to finished product.

Proceedings of 12th IEEE PVSC, Baton Rouge, 1976

[31] Hynes K., Baumann A., Hill R.: An Assessment of

the Environmental Impacts of Thin Film

Cadmiumtelluride Modules based on Life Cycle

Analysis. 1st WCPEC, Hawaii, 5th -6th December 1994

[32] Hynes K., Pearsall N., Hill R.: The Sensitivity of

Energy Requirements to Process Parameters of CuInSe2

Module Production. 22nd IEEE PVSEC, Las Vegas, 1992

[33] Ito M., Komoto K., Kurokawa K.: Life-cycle

analyses of very-large scale PV systems using six types

of PV modules. In: Current Applied Physics (2010) 10, p.

271-273

[34] Ito M., Kudo M., Nagura M., Kurokawa K.: A

comparative study on life-cycle analysis of 20 different

PV modules installed at a Hokuto mega-solar plant.

Proceedings 5th World Conference on Photovoltaic

Energy Conversion, Valencia, 6th -10th September 2010

[35] Ito M., Kudo M., Nagura M., Kurokawa K.: A

Comparative Study on Cost and Life-cycle Analysis for

100 MW Very Large-scale PV (VLS-PV) Systems in

Deserts Using m-Si, a-Si, CdTe, and CIS Modules. In:


(2008) 16, p. 17–30

[36] Jourde P.: Battery energy pay-back time.

Proceedings 16th EU-PVSEC, Glasgow, 1st –5th May

2000

[37] Jungbluth N.: Life Cycle Assessment of Crystalline

Photovoltaics in the Swiss ecoinvent Database. In:


(2005) 13, p. 429–446

[38] Kato K., Hibino T., Komoto K., Ihara S., Yamamoto

S., Fujihara H.: A life-cycle analysis on thin-film

CdS/CdTe PV modules. In: Solar Energy Materials &

Solar Cells (2001) 67, p. 279-287

[39] Kato K., Murata A., Sakuta K.: Energy Pay-back

Time and Life-cycle CO2 Emissions of Residential PV

Power System with Silicon PV Module. In: Progress in

Photovoltaics: Research and Applications (1998) 6, p.

105-115

[40] Kato K., Murata A., Sakuta K.: An Evaluation on the

Life Cycle of Photovoltaic Energy System Considering

Production Energy of off-grade Silicon. In: Solar Energy

Materials and Solar Cells (1997) 47, p. 95-100

[41] Keoleian G.A. and Lewis G.McD.: Application of

Life-cycle Energy Analysis to Photovoltaic Module

Design. In: Progress in Photovoltaics: Research and

Applications (1997) 5, p. 287-300

[42] Knapp K. and Jester T.: Empirical Investigation of

the Energy Payback Time for Photovoltaic Modules. In:

Solar Energy (2001) 3, p. 165–172

[43] Knapp K. and Jester T.: PV Payback. In: Home

Power (December 2000 / January 2001) 80

[44] Knapp K.: Energy Balances for PV Modules Status

and Prospects. 28th IEEE Anchorage, 2000

[45] Komoto K., Uchida H., Ito M., Kurokawa K., Inaba

A.: Estimation of Energy Payback Time and CO2

Emission of Various kinds of PV systems. 23rd European

Photovoltaic Solar Energy Conference, Valencia, 1st -5th

September 2008

[46] Krauter S. and Rüther R.: Considerations for the

calculation of greenhouse gas reduction by photovoltaic

solar energy. In: Renewable Energy (2004) 29, p. 345–

355

[47] Kreith F., Norton P., Brown D.: A Comparison of

CO2 Emissions from Fossil and Solar Power Plants in the

United States. In: Energy (1990) Vol. 15, No. 12, p.

1181-1198

[48] Mason J.E., Fthenakis V. M., Hansen T., Kim H. C.:

Energy Payback and Life-cycle CO2 Emissions of the

BOS in an Optimized 3.5 MW PV Installation. In:


4689


(2006) 14, p. 179-190

[49] Meijer A., Huijbregts M. A. J., Schermer J. J.,

Reijnders L.: Life-cycle Assessment of Photovoltaic

Modules: Comparison of mc-Si, InGaP and InGaP/mc-Si

Solar Modules. In. Progress in Photovoltaics: Research

and Applications (2003) 11, p. 275–287

[50] Mohr N., Schermer J. J., Huijbregts M. A. J., Meijer

A., Reijnders L.: Life Cycle Assessment of Thinfilm

GaAs and GaInP/GaAs Solar Modules. In: Progress in

Photovoltaics: Research and Applications (2007) 15, p.

163–179

[51] Müller A., Wambach K., Alsema E.: Life Cycle

Analysis of Solar Module Recycling Process. Materials

Research Society Symp. Proc., Vol. 895, 2005

[52] Nieuwlaar E., Alsema E., van Engelenburg B.:

Using Life-Cycle Assessments for the Environmental

Evaluation of Greenhouse Gas Mitigation Options. In:

Energy Conversion Mgmt. (1996) Vol. 37, No 6-8, p.

831-836

[53] Pacca S., Sivaraman D., Keoleian G.: Parameters

Affecting the Life Cycle Performance of PV

Technologies and Systems. In: Energy Policy (2007) 35,

p. 3316-3326

[54] Palz W. and Zibetta H.: Energy pay-back time of

photovoltaic modules. In: International Journal of Solar

Energy (1991) 10, p. 211-216

[55] Peharz G. and Dimroth F.: Energy Payback Time of

the High-concentration PV System FLATCON. In:


(2005) 13, p. 627-634

[56] Pérez Días O.: Life Cycle Assessment of the

Production of Solar Cells. Diplomarbeit, Technical

University of Denmark, Department of Manufacturing

Engineering and Management, Denmark, September

2007

[57] Perpinan O., Lorenzo E., Castro M. A., Eyras R.:

Energy Payback Time of Grid Connected PV Systems:

Comparison Between Tracking and Fixed Systems. In:


(2009) 17, p. 137-147

[58] Prakash R. and Bansal N.: Energy Analysis of Solar

Photovoltaic Module Production in India. In: Energy

Sources (1995) 17, p. 605-613

[59] Raugei M., Bargigli S., Ulgiati S.: Life cycle

assessment and energy pay-back time of advanced

photovoltaic modules: CdTe and CIS compared to poly-

Si. In: Energy (2007) 32, p. 1310-1318,

[60] Schaefer H. and Hagedorn G.: Hidden energy and

correlated environmental characteristics of PV power

generation. In: Renewable Energy (1992) 2, p. 159-166

[61] Schaefer H.: Erntefaktoren von Kraftwerken –

Definitionsmöglichkeiten. In: Energiewirtschaftliche

Tagesfragen (1998) 38, Vol. 10, p. 812

[62] Schleisner L.: Life cycle assessment of a wind farm

and related externalities. In: Renewable Energy (2000)

20, p. 279-288

[63] Tahara K.,Kojima T., Inaba A.: Evaluation of CO2

Payback Time of Power Plants by LCA.In: Energy

Convers. Mgmt (1997) 38, p. 615-620

[64] Varun, Bhat I. and Prakash R.: LCA of renewable

energy for electricity generation systems - A review. In:

Renewable and Sustainable Energy Reviews (2009) 13,

p. 1067–1073

[65] Wild-Scholten de M.J.: Solar as an environmental

product. SRC/MIST Forum on Solar-Electrical Energy

Systems, Abu Dhabi, 27th March 2011

[66] Wild-Scholten de M.J.: Energy payback and carbon

footprint of PV technologies. 20th Workshop on

Crystalline Silicon Solar Cells & Modules: Materials and

Processes, Breckenridge, 3rd August 2010

[67] Wild-Scholten de M.J.: Environmental sustainability

of CPV systems: preliminary LCA results of the Apollon

project. 25th EU PVSEC / 5th WCPEC, Valencia, 6th-10th

September 2010

[68] Wild-Scholten de M.J., Sturm M., Butturi M. A.,

Noack M., Heasman K., Timò G.: Environmental

sustainability of Concentrator PV systems: preliminary

LCA results of the Apollon project. 25th EU PVSEC / 5th

WCPEC, Valencia, 6th-10th September 2010

[69] Wild-Scholten de M.J.: Energierücklaufzeiten für

PV-Module und Systeme. 6. Workshop Photovoltaik-

Modultechnik, Cologne, 26th-27th November 2009

[70] Wild-Scholten de M.J.: Sustainability: keeping the

Thin Film Industry green. 2nd EPIA International Thin

Film Conference, Munich, 12th November 2009

[71] Wild-Scholten de M.J., Schottler M.: Solar as an

environmental product: Thin-film modules – production

processes and their environmental assessment. Thin Film

Industry Forum, Berlin, 24th April 2009

[72] Wild-Scholten de M.J., Gløckner R., Odden J.-O.,

Halvorsen G., Tronstad R.: LCA comparison of the

Elkem solar metallurgical route and conventional gas

routes to solar silicon. 23rd EU-PVSEC, Valencia, 1st-5th

September 2008

[73] Wild-Scholten de M.J., Alsema E. A., ter Horst E.

W., Bächler M, Fthenakis V. M..: A cost and

environmental impact comparison of grid-connected

rooftop and ground-based PV systems. 21st EU-PVSEC,

Dresden, 4th-8th September 2006

[74] Wilson R. and Young A.: The Embodied Energy

Payback Period of Photovoltaic Installations Applied to

Buildings in the U.K.. In: Building and Environment

(1996) 31, Vol. 4, p. 299-305

[75] Nemet G.: Beyond the learning curve: factors

influencing cost reductions in photovoltaics. In: Energy

Policy (2006) 34, p. 3218–3232


4690

[76] Fischer H., and Pschunder W.: Low-Cost Solar Cells

Based on Large-Area Unconventional Silicon, In: IEEE

Transactions on Electron Devices (1977) 24, p. 438-442

[77] Mints P.: Organic Photovoltaic Summit – First

Applications and Markets for OPV. Boston, 15th-16th

October 2009

[78] Hering G.: Das Jahr des Tigers - 2010 wurden mehr

Solarzellen produziert als in den vier vorherigen Jahren

zusammen. In: Photon – Solarstrom (2011) 4, p. 38–71

[79] Kersten F., Doll R., Kux A., Huljić D. M., Görig M.,

Breyer Ch., Müller J., Wawer P.: PV-Learning Curves:

Past and Future Drivers of Cost Reduction. Hamburg,

26th EU PVSEC, 5th – 8th September 2011

[80] International Energy Agency (IEA): Trends in

photovoltaic application - Survey report of selected IEA

countries between 1992 and 2009. Photovoltaic power

systems programme (PVPS), St. Ursen, 2010, p.37,

Report IEA-PVPS T1-19:2010

[81] Breyer Ch. and Gerlach A.: Global Overview on

Grid-Parity, In: Progress in Photovoltaics: Research and

Applications, published online 17th February 2012, DOI:

10.1002/pip.1254

[82] Breyer Ch.: The Photovoltaic Reality Ahead:

Terawatt Scale Market Potential Powered by Pico to

Gigawatt PV Systems and Enabled by High Learning and

Growth Rates, Proceedings 26th EU PVSEC, Hamburg,

5th – 9th September 2011, DOI:

10.4229/26thEUPVSEC2011-6EP.1.2

[83] Breyer Ch. und Schmid J.: Global Distribution of

optimal Tilt Angles for fixed tilted PV Systems, 25th EU

PVSEC / 5th WCPEC, Valencia, 6th -10th September 2010


4691

12 TABLES

Table 3: Energy learning rates of different PV ground-mounted systems. LR are distinguished between CED per power (LRp) and CED per

module area (LRA). “all PV-Technologies” includes all five listed PV-technologies and a constant energy demand for CI(GS) modules of 17

GJth/kWp, whereas the energy demand of all six PV technologies is weighted relatively to its share in the PV market. Additionally the energy demand (Ein) of the respective PV ground-mounted system in 2010 is calculated by the energy learning curve. Thereby the energy demand of

the ground-mounted BOS components is 1.6 GJth/m² in 2010.

PV-Technology LRP [%] LRA [%] time period Ein (2010)

[GJth/kWp]

Ein (2010)

[GJth/m²]

sc-Si (with Al-frame) 11 9 1974 - 2010 30,1

4,8

mc-Si (with Al-frame) 13 12 1995 - 2010 27,1

3,8

ribbon-Si (with Al-frame) 7 5 1983 - 2009 25,4

3,3

a-Si (without frame) 5 1 1989 - 2010 28,6

2,3

CdTe (without frame) 4 1 1991 - 2010 19,6

2,4

all PV-Technologies 11 8 1974 - 2010 26,3

3,8

Table 4: Energy learning rates of different PV rooftop systems. LR are distinguished between CED per power (LRp) and CED per module area (LRA). “all PV-Technologies” includes all five listed PV-technologies and a constant energy demand for CI(GS) modules of 17

GJth/kWp, whereas the energy demand of all six PV technologies is weighted relatively to its share in the PV market. Additionally the energy

demand (Ein) of the respective PV ground-mounted system in 2010 is calculated by the energy learning curve. Thereby the energy demand of the rooftop BOS components is 0.4 GJth/m² in 2010.

PV-Technology LRP [%] LRA [%] time period Ein (2010)

[GJth/kWp]

Ein (2010)

[GJth/m²]

sc-Si (with Al-frame) 14 11 1974 - 2010 21,3

3,4

mc-Si (with Al-frame) 18 16 1995 - 2010 19,6

2,8

ribbon-Si (with Al-frame) 9 7 1983 - 2009 18,2

2,4

a-Si (without frame) 9 5 1989 - 2010 14,7

1,2

CdTe (without frame) 5 2 1991 - 2010 10,2

1,2

all PV-Technologies 14 11 1974 - 2010 18,6

2,7


4692

Documents

27th European Photovoltaic Solar Energy ... - Q CELLS€¦ · ENERGY LEARNING CURVES OF PV SYSTEMS Marzella Görig1,2,3 and Christian Breyer1,4 1 Q -Cells SE, Sonnenallee 17 21, 06766