Empirica (2012) 39:165–189
DOI 10.1007/s10663-012-9185-8
ORIGINAL PAPER
Investment and employment from large-scale
photovoltaics up to 2050
Wolf Grossmann • Karl W. Steininger
Christoph Schmid • Iris Grossmann
•
Published online: 1 March 2012
Ó Springer Science+Business Media New York 2012
Abstract Investments in renewable energy were at US$211 billion in 2010 and
developing economies overtook developed ones for the first time in terms of new
financial investments in renewable energy. Photovoltaics for generation of electricity from sunlight has the highest growth rate among the competing forms of
renewable energy and has now begun to achieve grid parity in some regions. If these
trends of investments continue, solar energy will play a major economic role. We
analyze these developments and assess the ensuing amounts of investment and
employment for a range of sizes of the sector of solar energy. We find that by 2050
electricity from photovoltaics could cover up to 90% of total global energy demand,
with a then global capital investment in our main scenario in photovoltaic manufacturing capacity at 500 billion US$2010 by around 2030 and 1,500 billion by 2050.
Employment in photovoltaic manufacturing is predicted to rise to 6 million by
2050. Sensitivity analysis with respect to the core parameters of assumptions is
supplied.
W. Grossmann K. W. Steininger (&) C. Schmid
Wegener Center for Climate and Global Change, University of Graz,
Leechgasse 25, 8010 Graz, Austria
e-mail: karl.steininger@uni-graz.at
W. Grossmann
International Center for Climate and Society, University of Hawaii at Manoa,
1680 East-West Road, Honolulu, HI 96822, USA
K. W. Steininger
Department of Economics, University of Graz, Universitaetsstr. 15,
8010 Graz, Austria
I. Grossmann
Center for Climate and Energy Decision Making, Carnegie Mellon University,
5000 Forbes Ave, Pittsburgh, PA 15213, USA
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Keywords Renewable energy Scenarios for solar energy
Investments in energy Labor in renewable energy Energy market development
JEL Classification
Q41 Q42 O13
1 Driving forces for a new energy system
Today’s economic system strongly depends on energy supply, which faces
significant challenges. The dominating supply worldwide is still fossil fuel
[85.1% in 2008 (IPCC 2012)]. Large oil price fluctuations pose financial difficulties
for the economy (Ayres 2008), as was evident with the sudden increases of crude oil
price in 2008 and again in 2011. Future availability of oil is becoming highly
uncertain, as is evident in the discussion of peak oil. Coal had been regarded as
almost inexhaustible, although this view is disputed by Patzek and Croft (2010).
Some view natural gas as a bridging energy to a sustainable energy future (MIT
2010). The word ‘‘bridging’’ indicates that fossil fuels will eventually be replaced
by renewable energy.
On the demand side, governed also by energy efficiency and global economic
growth, the recent high growth in China and India widens the range of reasonable
demand scenarios. The first IPCC scenarios in 2000 (SRES) assumed economic
growth between factors 4 and 32 by 2100 (Nakicenovic et al. 2001). Depending on
growth, energy use and fossil fuel emissions in these scenarios increase by between
factor 5 and 35. As the fossil fuel sector already has difficulties to meet current
energy needs, increased difficulties can be expected with economic growth, even at
low growth rates.
The only renewables with sufficient potential to meet a growing global energy
demand are solar and to a lesser degree wind; the potential of each of all other forms
of renewable energy is very limited (Lewis 2007a)—although they all will have to
play their appropriate role. IPCC (2012) reports the range of estimates of global
technical potential, with even the lowest estimate for direct solar at a multiple level
of the highest available estimates of all other sources (wind and geothermal being
the most relevant among them). Solar energy is used as heat for heating and
processes, and as electricity through photovoltaics (PV) and solar thermal (ST), in
particular as concentrated solar power (CSP). For overall demand, solar energy has
a practical potential of about 600 terawatt (TW) (Lewis 2007b), based on World
Energy Assessment 2000, within a range of 50–1,500 TW, depending on practical
factors such as land fraction used. IPCC (2012) estimates a practical potential
between 50–1,600 TW. This compares to a total global primary energy consumption of 16.5 TW in 2009. Of these, 6.5 TW were used to generate 2.1 TW of
electricity at an efficiency of 31.3% (IEA 2009). Supplying just these 2.1 TW of
electricity from solar would decrease the primary energy demand by 6.5 TW.
In 2008 renewables accounted for 12.9% of the global energy supply, but direct
solar for only 0.1% (IPCC 2012). The role of individual renewables will depend on
their respective growth rates. Recent growth has been high (REN 21 2010): Wind
turbines had the highest absolute growth with the addition of 38 GW in 2009 and an
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average annual growth of 27% between 2004 and 2009, bringing the installed total
to 159 GW. PV grew at 120% in 2010 with 19 GW of new capacity to a total of
39 GW; annual growth during 2005–2010 was close to 50% (REN 21 2011). Solar
thermal (ST) grew by 264 MW to a level of 940 MW of capacity in 2009 with
average growth of 12% between 2004 and 2009 and 41% in 2010.
In early 2010 more than 100 countries had enacted renewable energy policy
targets and/or policies, up from 55 countries in early 2005 (REN 21 2010). Global
renewable energy investments were at US$211 billion, a 32% increase over 2009,
and a 540% rise since 2004 (UNEP 2011). For the first time, developing economies
overtook developed ones in terms of new investments in utility-scale renewable
energy projects and provision of equity capital for renewable energy companies
(UNEP 2011). US$211 billion is a substantial amount. Zweibel et al. (2008)
calculated that the US would have to invest more than $400 billion to provide 69%
of the US electricity supply and 35% of its total energy (including transportation)
from solar power by 2050.
Increase of solar installations is triggered by a dramatic price decline. Costs of PV
modules per MW have declined by 60% in the period 2008–2011 (UNEP 2011). PV
has reached grid parity (electricity production costs equal to those from fossil fuels) in
parts of Italy in 2010 (Pew 2011), followed closely by parts of California, Hawaii and
Spain (Zaman and Lockman 2011; Breyer et al. 2009). Consequently, PV achieved the
highest growth of all renewables. As grid parity is achieved in further regions (Ahearn
et al. 2011), this growth rate could remain very high for several years.
Based on falling costs for solar energy, large-scale schemes for solar electricity
generation have been proposed. The Desertec organization plans to generate
electricity for the EUMENA region (Europe, Middle East, North Africa) from wind
and solar in the Sahara and Saudi deserts (Desertec Foundation 2010). Zweibel et al.
(2008) proposed the ambitious US Solar Grand Plan which would replace 90% of its
fossil fuels in 2100.
As the global energy system is a major economic sector with a share of 8% in global
GDP (IER 2010), the prospects for investment and employment in the PV sector
become an issue of major interest. This paper presents an analysis of the scope for this
development in quantitative terms. How fast can solar supply realistically be built up?
What investment levels are necessary for a dominant share of global energy supply?
Which size of labor force will be needed? At present, few people are trained in the field
of PV and CSP, but demand for skilled workers will increase. An assessment of the
volume of investment and employment in this growing sector needs learning curves on
the development of PV, in particular with respect to levelized costs of energy (the gross
energy cost as supplied to the system). The price leader for PV panels, First Solar,
projects total systems manufacturing costs of $1.40–1.60/Watt peak in 2015 and a
gross margin of 15–20%, corresponding to electricity costs of 7.25–8.8 ¢/kWh in the
US Southwest (in the past, First Solar has consistently outperformed its projections,
hence these numbers have credibility). As present solar electricity costs in the same
region are at about 15 ¢/kWh, panel manufacturing costs and capital expenditures will
have to decrease rapidly.
A global energy supply from PV and CSP for all energy needs, not just
electricity, would need about 1.5% of the global desert area (Zweibel 2006; Lewis
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2007a). Using this comparatively small desert area to supply all energy is far less
critical than using agricultural land for energy crops. PV has no moving parts, a life
expectancy between 30 and 40 years (EC 2009), maintenance costs of less than 1 ¢/
kWh (Zweibel 2006), and it does not need water for operation—a big advantage in
desert climates. The raw material for most PV panels (Wadia et al. 2009), silicon, is
the second most abundant mineral in the upper earth crust.
The main obstacle to large-scale use of PV, CSP and wind are their intermittency
due to the cycles of day and night, and attenuation from cloudiness or ice on panels.
Combining PV, CSP and wind decreases intermittency to some extent (Palmintier
et al. 2008). Significant reductions in intermittency can be achieved through the
combination of solar installations in different time zones and in the two
hemispheres, based on optimal site selection and optimized generation and storage
capacity (Grossmann et al. 2011). Global or almost global solar systems could
completely eliminate intermittency even without energy storage (Grossmann et al.
2011). The configuration with the minimum number of sites includes cells of
1° 9 1° each (about 111 km 9 90 km at latitude 35°) in the east and west of
Australia, the Saudi Desert, the westernmost Sahara, the Atacama and Mojave
Deserts, i.e. just six sites. This configuration could constantly meet a demand of
[2 TW.
With such prospects, significant growth rates are predicted. Bloomberg (2010)
foresees 42%/y growth for PV in the US during 2010–2020, whereas First Solar
foresees compound annual revenue growth rates of 5–10% after 2011, or, with the
projected decrease of manufacturing costs and margins an annual increase of
manufacturing by 14–20%. This shows the large uncertainty regarding the volume
of manufacturing and costs. Accordingly, we provide a range of plausible outcomes.
We begin with an analysis of possible growth rates in Sect. 2. In Sect. 3 we develop
a collection of learning curves for the PV sector. Learning curves give
manufacturing costs, which are used to derive estimates of employment and
investment on a global basis in Sect. 4. The final section provides conclusions and
recommendations.
2 Possible growth rates for solar energy
Due to the favorable development of costs and the increasing pressure to substitute
fossil fuels by renewable energy, it seems highly likely that some of these grand
schemes will be implemented. The eventual size of the solar sector depends on
several factors, in particular, the fraction of the total energy demand that has to be
met by renewable energy, the growth of energy demand, and the competitive
situation of solar. The goal of the European Union, for example, for renewable
energy is an almost total decarbonization of energy supply by 2050 (EU 2011b).
2.1 The share of solar in future energy supply
To investigate how high the share of solar energy could become, Table 1 shows the
average use of primary energy for OECD countries. Globally, electricity generation
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Table 1 Primary energy
demand by sector in per cent,
OECD average (IEA 2010; EIA
2011; EU 2011b)
169
Sector
Demand (%)
Transportation
25
Buildings
30
Households
15
Services, service sector
15
Goods production
15
consumes 39% of the primary energy (as was said before, 6.5 TW of 16.5 total).
These 39% could be replaced by solar energy. Electricity is the main form of energy
consumed by the sectors household/living and services as well as about half of the
energy consumed in production. Electric vehicles (EVs) use electricity instead of
fuels. Road transportation consumes about 80% of the energy used in the
transportation sector. Thus, to the degree that EVs develop well they would allow to
shift up to 80% of the 25%, i.e. up to 20% of primary energy consumed by the
transportation sector to renewables.
Architecture has manifold options to decrease energy consumption by buildings;
some of which go far beyond simple thermal insulation, as in the case of the highly
sophisticated architecture of the Pearl River Tower in Guangzhou/China (EU
2011a). The European Parliament has mandated that all new buildings in Europe
have to be ‘‘nearly zero-energy’’ beginning 2021 (EU 2010, EC 2011). There are
reports of problems with some types of such buildings, e.g. mildew on walls,
insufficient air ventilation with resulting indoor pollution etc. Either those problems
will be overcome and zero-energy buildings succeed, which would decrease primary
energy consumption in the very long run by another 30%, or—that is the other
extreme—such buildings fail so that renewable energy would have to provide
another 30% of present energy consumption. We include the range between these
extremes into our projections.
In total, 39% ? 20% = 59% of primary energy could be replaced in the
electricity sector and the transportation sector, respectively, and up to 30% in
buildings, in total—assuming that the global and OECD numbers converge in the
long run—a figure up to almost 90% of all primary energy.
The remaining 10% cannot be substituted by electricity, as it is fuels in the
transportation sector for aviation, shipping, pipelines and non-electric trains and
non-energy use in production, e.g. use of coal as a chemical agent in the production
of pig iron or crude oil used by the chemical industry as a basis for production of
manifold chemicals.
As 6.5 TW of primary energy give 2.1 TW of electricity, the efficiency here is
roughly 32%.
The efficiency of EVs is about 90% whereas vehicles with internal combustion
engine have an efficiency of about 30%. As road transportation consumes 20% of
the primary energy demand of 16.5 TW, i.e. 3.3, EVs would decrease this demand
to 1/3 or 1.1 TW.
The total electricity demand, current electricity plus using EVs, at the current
demand level for electricity and transport services thus would amount to 3.2 TW.
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2.2 Energy demand scenarios
Taking into account increased efficiencies from use of electricity, Fthenakis et al.
(2009) argue that the energy demand in the US will be about the same in 2050 as it
was in 2009, taking into account economic growth. This argument does not fully
carry over to the global situation as economic growth in developing countries might
be higher than in developed countries. Using a global economic growth of 3.3% per
year as was the long-term average (Maddison 2001), and an increase of energy
efficiency by 1% per year, which was also a long-term average, energy demand
would increase by 3.3% - 1% = 2.3% per year, which from 2009 to 2050 gives a
factor of 2.5. The demand for electricity as derived above at 3.2 TW multiplied by
factor 2.5 gives a demand in 2050 at 8 TW. For total electricity demand in 2050 we
have to add demand from switching the energy supply for buildings. Applying the
factor of 2.5 to the current primary energy demand for buildings (5 TW), and
adjusting for a share of one-third of buildings having turned to nearly zero-energy
buildings by 2050, gives another 8.3 TW electricity demand. In total we get roughly
16.5 TW of electricity demand in 2050.
Alternatively, the IPCC-SRES from 2001 projected an increase in energy
consumption between a factor of 5 (1.6%/a) and 35 (3.6%/a) until 2100, which
corresponds to a factor of 2–14 increase for renewable electricity if higher
efficiencies of electricity are taken into account. The electricity demand in 2050
would be between 8.25 and 18 TW for the lower bound and up to 26 TW if
buildings are to be included. Electricity demand in 2050—according to these
scenarios—could thus be between 8 and 26 TW.
2.3 Solar generation capacity
The solar generation capacity which is necessary to fulfill this demand depends on
the locations of the solar installations. If the solar insolation in a location is w kWh/
m2/year, then the necessary solar capacity to give 1 kW is 8,760/w in kW peak
(8,760 is the number of hours in 1 year; typical values of w are between 970 kWh/
m2/year in the northern parts of Europe, 1,150 kWh/m2/year in wine-growing
regions in Europe, 1,700 kWh/m2/year in one of the sunniest locations in Europe
(Guadix in Spain which is the location of three CSP power stations with 50 MW
each), and *2,300 kWh/m2/year in desert regions around the world at 35° latitude.
kWp is a standardized unit to specify the output of a solar panel.). Hence, for an
output of 1 kW the necessary capacity is *9 kWp in northern Europe, *7.5 kWp
in wine-growing regions in Europe, 5 kWp in Guadix and 3.8 kWp in typical desert
regions (Table 2).
Naturally, the fraction of energy that will be met with solar depends on many
factors, including social and political. We will here neglect social and political
factors; Grossmann et al. (2011) show that it is much cheaper to generate electricity
in the Sahara and transmit it to Europe than generating the electricity in Europe,
even taking into account transmission costs. As was said above, PV has by now
achieved grid parity in some regions, so that the further development of costs of PV
is crucial for the penetration rate it may achieve. However, the European population,
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Table 2 Examples of locations and loads and the necessary solar generation capacity in kWp (third
column) and TWp (fourth to sixth column) to meet this load
Location
Insolation
(kWh/m2/y)
Generat. cap
for 1 kW
Capac for
8.25 TW
Capac for
18 TW
Capac for
26 TW
Graz/Austria
1,500
5.8
48
104
151
Hamburg/Germany
1,360
6.4
53
115
166
Sahara desert
2,300
3.8
31
68
99
Mojave desert
2,300
3.8
31
68
99
Atacama desert
2,750
3.2
26
58
83
like populations elsewhere, may prefer domestic supply. The respective costs of
solar compared to other forms of energy supply is a most important indicator but
does not suffice to predict the eventual penetration rate.
Very different growth rates are possible for solar. Global manufacturing capacity
for PV grew by 140% in 2010 (Metha 2011) to 19 GWp. Still, this manufacturing
capacity is so low that it would need 3,300 years to fulfill the current global energy
demand of 12 TW (16.5 TW minus current primary energy demand for thermal
electricity production, i.e. 6.5 TW, plus thermal electricity output at 2.1 TW).
However, if manufacturing capacity continues to grow at the present rate, it would
take *8.2 years to manufacture the capacity to meet a demand of 12 TW. Meeting
a load of 12 TW needs very different installed capacities depending on the
positioning of the generating capacity, as was outlined above. These are ranging
from 50 TWp in an optimized global network that uses only desert areas on both
hemispheres, to 120 TWp if the US Southwest were to produce all of this electricity,
and up to [200 TWp if only the territory of the European Union would be used
(based on Grossmann et al. 2011).
The general judgment is that present growth rates of PV manufacturing will
considerably decline. We use two statements, one from Bloomberg, one from First
Solar (FS), on predicted or planned growth rates and extend these to 2050 for a
long-term projection of the possible size of the solar sector. The resulting
considerable variations of expected growth rates allow a sensitivity analysis on
employment and investment.
FS has announced to grow by 240% between the 2 years 2011 to the end of 2012,
but may have revised these plans due to global overcapacity. Bloomberg (2010)
projected constant annual growth rates of 42% for the US solar market between
2011 and 2020. We develop one scenario for global installed capacity up to 2050 as
given in Table 3 that is based on the 16.5 TW of electricity demand in 2050 as
derived in the previous section as main scenario (and representing also a medium
value of electricity demand in 2050 for the IPCC SRES scenarios as given above in
the range of 8–26 TW). The scenario for PV growth in Table 3 allows 90%
renewable (here PV) supply of energy demand in 2050. The remaining 10% of
energy demand need other forms of energy, in particular fuel for airplanes and ships
and coke for production of pig iron. The installed capacity of 66 TWp translates to a
supply of 16.5 TW when, for example, 90% of the capacity are located under solar
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Table 3 Scenario for
development of PV installed
capacity that could meet 90% of
the energy demand in 2050
Empirica (2012) 39:165–189
Year
Growth
(%/year)
Installed capacity,
global C(t) (TWp)
Number
doublings
2010
120
0.03
2015
33
0.3
5.0
157
2020
21
2
7.7
681
2025
17
7
9.5
1,754
2030
11
17
10.7
3,508
2035
7
30
11.6
5,517
2040
5
45
12.1
7,491
2045
3
56
12.5
9,009
2050
2
66
12.7
10,120
0.0
Manuf.
capacity,
global (GWp)
19
insolation of 2,300 kWh/m2/year (desert areas) and 10% are of building integrated
type at 1,300 kWh/m2/year (a middle to southern European location).
The growth rates underlying Table 3 are far below the numbers given by
Bloomberg (2010). There is nothing exceptional in these growth rates given the past
experience with PV as one of the fastest growing technologies in human history.
China seems poised to master the political will to secure its energy supply through
renewables, see e.g. the massive Chinese investment in renewable energy
technology with 80% of the global manufacturing of PV cells. Thus, a considerable
fraction of the 66 TWp could be constructed in the Asian/Australian region along
the lines of the Desertec Asia outline.
The rapid decrease in panel prices encourages a growing number of businesses,
utilities and other organizations to invest in PV installations. For example in the US,
non-residential solar projects rose by 41% within 2 months to 24 GW (Korosec
2011).
Although the need for implementing a new energy system seems to be pressing,
and although the technologies are improving fast and are achieving grid parity in
ever more countries, the growth rates could be very different, higher or lower, from
those specified in Table 3. For our estimate of employment and investment we will
use the growth rates in Table 3 as the maximum and calculate a range for
employments and investments also using markedly lower growth rates.
The development of installed capacity C(t) is limited by the maximum capacity
K for renewable energy. This situation is typically described by the logistic
differential equation dC/dt = aC(1 - C/K) with the constant growth rate a and the
maximum K of C; for C = K, growth becomes 0. The solution of this differential
equation is tedious but can be well approximated by an equation of the form
C(t) = u0 ? vtn/(wn ? tn) with the initial value at time t = 0 for capacity of u0
(i.e. 19 GW in 2010). The maximum value of C for t ? ? is given by v. At time
t = w this expression has the value u0 ? v/2. The function given in Eq. 1
approximates a solution of the logistic differential equation with initially high
growth and later market saturation:
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CðtÞ ¼ 19 þ 28000 ðt 2010Þ2:4 =ð452:4 þ ðt 2010Þ2:4 Þ; CðtÞ in GWp
ð1Þ
This function approximates the capacity numbers given above in Table 3 for the
period 2010–2050 by choosing v, n and w. In the years from 2011 to 2020 it gives
much lower capacities than those in Table 3, but after year 2020 its error is \7%,
after 2027 the error is \2%, when the development of employment and investment
is most pronounced.
3 Learning curves for PV
We need costs for assessing the investment necessary for meeting the electricity
demand in 2050 by PV. The investment also allows an estimate how employment in
this sector may evolve. Development of costs is typically anticipated with learning
curves, which in the case of PV have been quite reliable.
A typical learning curve for a new technology has a negative exponential shape.
This is because the progress of a new technology is often very fast initially but slows
down over time as the technology is approaching its maximum potential. If learning
curves have a footing they can show the minimal cost level due to resources and the
minimum amount of labor.
The present rapid decrease of costs for PV was brought about by the advent of
competitive second generation PV panels. Precursors of first generation PV panels
have been made practical for satellites in the early 1960s, and costs were of minor
importance for that application. First generation PV panels use comparatively thick
layers of silicon. Second generation PV panels use thin-film technology for the
photosensitive layer, e.g. the CdTe-panels by Fist Solar (FS). Whereas first
generation PV is an established technology with slow learning rates, thin-film is a
young technology and its learning curve is still rapidly sloping downward. Second
generation CdTe-panels became cheaper than first generation Si-panels at around
2004 and are now cheaper by more than 30%. As the market potential is so big, the
manufacturers that use silicon-technology have sharply increased their R&D to stay
competitive and could again speed up their learning curves. Accordingly, the PV
industry is developing fast with a high intensity of R&D and continuing significant
investments also in the exploration of thin-film materials and even ‘‘third
generation’’ technologies, e.g. very low cost low to medium efficiency organic
modules (Wang et al. 2010) or dye-sensitized cells (Goldstein et al. 2010), very high
efficiency quantum cell and nanotechnology modules (Unold and Schock 2011;
Kamat and Schatz 2009) and solar concentrator systems (Raugei and Frankl 2009).
3.1 Learning curves for electricity costs from PV
Electricity costs from PV depend on the costs of the PV panel and costs of
installation, connection to the grid and other systems related factors. These latter
factors together constitute the balance of system (BOS). The total systems costs are
the sum of the panel manufacturing costs times the margin of the manufacturer for
the panel, plus the costs for the BOS times the margin for the BOS.
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Costs of both, PV panels and BOS, are usually given as $/Watt peak. Panel costs
of 1 $/Wp correspond to levelized electricity costs (LCOE, i.e. the costs including
amortization and interest rate) of 4.4 ¢/kWh at a yearly insolation of 2,300 kWh/m2
(Zweibel 2006), an insolation level which is typical for areas in or adjacent to the
Mojave Desert in the US or for Phoenix, Arizona or for large parts of the Sahara and
Australian deserts. Levelized costs include amortization and interest rate but not
operation and maintenance. Here, the 4.4 ¢/kWh correspond to an interest rate of
7%, and 30 years lifetime of the equipment.
The development of the LCOE of PV panels depends on many issues, as we
discuss below. Hence it is amazing that a simple learning curve c1(t) describes quite
well the decrease of costs by 20% per doubling in the volume of manufacturing
(Trancik and Zweibel 2006):
c1 ðtÞ ¼ c0 0:8ðlnðCðtÞ=C0 Þ= lnð2ÞÞ
ð2Þ
where c0 = 1.23 is the cost in $/Wp in year 2007 (the first year for which the
number of doublings is available), C(t) (in GW) is the capacity in year t and
C0 = 0.1 is the capacity in year 2006.
c1(t) in Eq. 2 would ultimately decrease to 0 with increasing number of doublings
n. This is not possible due to the costs of resources used for manufacturing and the
minimum required amount of labor for production. Hence, Eq. 2 is restated with a
footing F such that the footing plus the value of the other terms of the equation
approximate minimum costs of panel over time. The parameters of the learning
curve are determined using minimization of the square of the difference between the
learning curve and the time series of historic panel costs plus the value for costs
from the roadmap of FS for 2015. With the number of doublings in manufacturing
capacity given by n(t) = ln(C(t)/C0)/ln(2) this gives the learning curve:
c2 ðtÞ ¼ 0:3 þ 1:2 0:78n
ð3Þ
The footing value 0.3 is still higher than the expected minimal costs. This
learning curve is fine for a lower number of doublings in manufacturing, but for the
longer term a cost function with a lower footing is necessary. This is calculated
through minimization using the additional cost factor $0.2/Wp in 2035. For this
case, Eq. 3 becomes
c~2 ðtÞ ¼ 0:05 þ 1:58 0:811n
ð4Þ
Manufacturing costs following these learning curves are given in Fig. 1. Both
learning curves descend too fast until 2010; afterwards learning curve c2 is above
the projected decrease, whereas c~2 is below. The best approximation to the historic
data is given by a learning curve that decreases costs per year by 12.9%, which was
the average historic decrease achieved by FS (not shown), independent from the
number of doublings, with costs in year n, c(n)=1.59(n - 2004) and initial costs of
$1.59/Wp in year 2005. This learning curve also has 0 as its lower limit value. This
simple curve is most appropriate for the first 5 years and has then to be replaced by
a curve with footing. These differences have little influence on the long-term pattern
of investment and employment, as large-scale use of solar energy depends on the
achievement of grid parity, which is achieved by all learning curves by 2015.
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175
2
US$/Wp
1.5
1
Historic costs FS until 2011
plus roadmap from FS for 2015
plus estimate from Zweibel
Equation 3, c2
0.5
Equation 4 (up to 2050), c2
0
2007
2010
2015
2020
Year
2025
Fig. 1 Manufacturing costs of PV panels with different footing and different learning from doubling
The profit rate of PV panels shows an interesting pattern of exactly the type that
has to be expected from economic theory. Several countries gave extremely high
subsidies for electricity from solar in the form of feed-in tariffs, e.g. Germany and
Spain. Demand grew, accordingly, much faster than supply, so that companies with
the lowest manufacturing costs had for years shares of profits in revenues above
50% (FS 2010b). High subsidies were initially in accordance with very high costs of
panels and BOS from companies that would not have been profitable without
subsidies. High subsidies caused a boom in rooftop PV, so that country after country
decreased its subsidies markedly. Several companies have now gone bankrupt, e.g.
Solar Millenium (a manufacturer of CSP), others are on the ‘‘death list’’ of several
analysts (e.g. Q-Cells), and the stock market value of the price leader, FS, decreased
by 80% within several weeks. As subsidies will be further decreased, the
expectation is that after 2015 PV has to be able to survive in the markets on its
own. The profit rate will decrease substantially; in its 2012 guidance (Ahearn et al.
2011) FS states that it will decrease its gross margin to 15–20%.
In 2005 the costs for BOS per Wp were about the same as the costs for a PV
panel, but decreased slower afterwards. In summer 2010, the costs for a panel from
FS were $0.75/Wp; the costs of BOS were at $1.29/Wp. In 2011, FS could lower the
costs for a panel in its most effective plant to $0.69/Wp, the costs of BOS to $0.98/
Wp, a decrease by 24%. The roadmap for costs of BOS in the 2012 guidance
(Ahearn et al. 2011) shows costs of $0.9/Wp already in 2012, which is below the
previous goal of FS of $0.91–0.98/Wp in 2014, and $0.70–0.75/Wp in 2014.
Thus in 2011 total systems costs were at $1.67/Wp, which according to Zweibel
(2006) gives LCOE of *7.7 ¢/kWh from manufacturing costs in regions with an
annual insolation of 2,300 kWh/m2. A share of profit in revenues of 50% increases
this number to *15 ¢/kWh; a 30% share would increase it to 11 ¢/kWh. 15 ¢/kWh
is still more expensive than electricity from coal which can be as low as *4 ¢/kWh
for baseload plants (PB Power and Royal Academy of Engineering 2004). In 2015
total system costs would decrease to $1.20–1.29/Wp, and the price with a share of
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profit of 15% to $1.41–1.52/Wp corresponding to electricity costs of 6.20–6.78 ¢/
kWh. Additionally, there will be development costs incurred by the system owner,
which according to FS are typically around $0.20/Wp. This would add 0.9 ¢/kWh.
Even very cautious projections for the price of electricity from coal anticipate an
increase such that at *7 ¢/kWh grid parity of PV will have been achieved also in
those regions which have very low costs of electricity (Grossmann et al. 2010).
As all aggregated learning curves are limited in their performance, we analyze
individual cost factors in more detail. Of major importance is the efficiency of
panels, as a panel with higher efficiency gives more output from the same BOS than
a panel with a lower efficiency. Other than that there is no direct relationship
between efficiency and costs of the panel per Wp. Increasing the efficiency of a
panel can increase manufacturing costs so much that the LCOE from a more
efficient panel is higher. First Solar is constantly increasing the efficiency of its
panels from an initial value of 7.5% in year 2002 to 11.7% (2011, Q2). The present
efficiency of the panels of FS at 11.7% is still comparatively low, as some Si-panels
have efficiencies at around 20%. Although Si-panels are more expensive per Wp (by
about 50%, i.e. $0.76/Wp compared to about $1.10–1.30/Wp) the BOS-costs for
these more expensive panels are lower. FS calculates that a 2% increase in
efficiency lowers BOS-costs by $0.1/Wp (Ahearn et al. 2011). FS has recently
developed PV panels of its CdTe-type which have a much higher efficiency of
17.3%. Moreover, FS could produce panels on its present manufacturing equipment
which have an efficiency of 13.4%. FS expects to achieve 14.5–15% in 2015.
Additional cost factors in electricity supply from PV can be the transmission
lines between solar locations in desert areas and the areas of consumption. Average
costs for transmission from the US southwest to consumers are about 2 ¢/kWh
(Fthenakis et al. 2009); the same cost is given by Desertec (2010) and DLR (2006)
for transmission from the Sahara and Saudi Desert to Europe, so that investments for
transmission lines could be in the range of 2 ¢/kWh or about 1/3 of the total system
costs of PV. Additionally, storage is necessary in most configurations to overcome
intermittency. Costs of long-term storage can add 3–4 ¢/kWh (Fthenakis et al.
2009); storage for up to 24 h can be thermal storage with costs of *1 ¢/kWh; but
thermal storage needs CSP (Biello 2009), which is now about 25% more expensive
than PV (Mahon 2011). Both, storage and transmission lines further increase
employment and investment in the sector of renewable energy. Values for amount
of storage, costs of transmission lines, costs of electricity generation and necessary
capacity are given for 12 regional, supra-regional and global configurations in
Grossmann et al. (2011).
3.2 Analyzing the elements in aggregated learning curves for panel production
and BOS from economies of scale
There are four causalities triggering economies of scale: line run-rate, decrease in
material costs, decrease in costs of labor, and decrease in system costs. We will
briefly review each of these factors.
Manufacturing at FS is organized in lines; the manufacturing of one panel is a
continuous, highly automated process of about 2.5 h. The line run rate gives the
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177
yearly amount of panels in megawatt (MW) produced by one line. The higher the
annual output per line the lower the manufacturing costs. In the past 4 years FS has
increased its line run rate by about 12%/year to 59.6 MW at the end of 2010
(Cheyney 2010). The company’s roadmap calls for hitting the 80 MW run-rate mark
in 2014, an increase by 34% in 3–4 years or about 10%/year. Faster run rates
increase labor productivity but do not decrease costs of materials.
Regarding decrease in material costs, Buller (2008) stated ‘‘Materials cost is
*50% of typical TF (thin film)-module cost. Current materials are still nonstandard’’ and ‘‘Volume drives the custom materials towards raw material cost’’.
50% of module manufacturing costs of $1.08/Wp (in 2008) would mean $0.54/Wp
for material costs. Green (2010) calculated material costs of $0.53–$0.66/Wp for FS
in Q4/2009. Past decrease of material costs, according to Green (2010), was due to
improved production yield (less waste), and thinner layers of photosensitive
material. Panel development will continuously lower the specific material demand
per Wp, use cheaper materials and through increasing volume will decrease the
price per unit of material.
The decrease in the costs of labor has two main components, faster line run-rates
so that the same amount of labor gives higher output, and increase in labor
productivity due to higher automation. FS does not expect significantly lower costs
for labor from manufacturing in countries with low wages due to the high degree of
automation. In 2010, FS had 3,690 employees per 1 GW of production capacity.
Many of these are not active in production, but work in acquisition, management or
research and development.
Zweibel (2005) assumes lowest possible costs of BOS of $0.20/Wp. This number
is reasonable for utility scale installations where most of the labor could be done by
robots, and where inverters are no longer needed if the direct current (DC) from the
panels is fed into DC transmission lines. None of the materials for BOS is scarce or
special; thus material costs can become low.
Singling out the footing ($0.2/Wp) from the early 2010 BOS costs of $ 1.29/Wp,
and using the observed BOS cost decrease from FS at 16% per year we get for the
costs of BOS over time cB(t):
cB ðtÞ ¼ 0:20 þ 1:09 expððt 2009Þ lnð0:8ÞÞ
ð5Þ
Equation 5 takes the value of $1.29 for 2009, $0.76 for 2012, $0.56 for 2014,
$0.21 for 2030 and after 2054 it stagnates at $0.20. The year 2014 is seen by many
analysts as the break-through year for PV; but for 2014 BOS costs from Eq. 5 are
still at $0.56/Wp, which is $0.7/Wp with 20% margin, i.e. the same number as in the
2012 roadmap from FS for 2014.
3.3 Reduction of profit margin
Reduction of profit margin is not a learning curve, but the profit margin has to be
taken into account to get prices. The cautious assumption here is that the profit
margin is already somewhat below the value reported for the past by FS (FS
2010b)—we start with a profit margin of 50% (corresponding to share of profits in
revenues of 33%)—and assume that it will linearly decrease to 30% until 2020 due
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to increased competition and stay at that level for the remaining period (Ahearn
et al. (2011) foresee an even stronger decline of the margin to 15–20%).
c4 ðtÞ ¼ 1:5ðð2020 tÞ þ ð1:3=1:5Þðt 2010ÞÞ=10
ð6Þ
In 2010, c4 has a value of 1.5, which declines to c4 = 1.30 by 2020. Equation 6
specifies the factors with which the manufacturing costs of panels have to be
multiplied to get the sales prices over time. This assumed decrease in profit margin
leads to a price reduction of 13.3% relative to the initial sales price of PV panels.
3.4 Overall ‘learning’ curve for price development
The learning curve from increased production [Eq. 2 and the variants (3) and (4)]
and the development of profit rates (Eq. 6), operate independently from each other.
One depends on the growth of production volume of PV panels, the other on the
increase of competition through diffusion of innovation. The sales price of PV
panels, Eq. 7, is the product of the manufacturing costs from Eq. 2 with the margin
from Eq. 6:
cðtÞ ¼ c1 ðtÞc4 ðtÞ
ð7Þ
The equivalent equations are needed for the costs of BOS and the ultimately
resulting total costs of electricity. The equations are almost identical to the
equations for panels, but with different data. Equation 5 starts with costs for the
BOS of $1.29 in 2009 and decreases by 16% per year; the rate of decrease at FS.
This is a very rapid decrease which gives BOS costs of $0.50/Wp in 2014, i.e. about
the same as the projected manufacturing costs for panels from FS, and stagnates at
$0.24/Wp after year 2035. If margins for BOS decrease from 30% to 20% at the
same speed as was given above for margins of PV panels, then the sales price of
BOS would decrease from $1.68 in 2010 to $0.70 in 2015 and $0.24/Wp after 2035.
Indeed, part of the development anticipated here took place during the review of this
paper (see Ahearn et al. 2011, p. 17).
Figure 2 illustrates the projected sales price of PV systems per Wp, which
includes panels and BOS (left axis) and the resulting costs of electricity in ¢/kWh
$/Wp
c
4
20
2
10
Systems sales
price (left axis)
Electricity price
(right axis)
0
2010
0
2020
Year
2030
Fig. 2 Sales price of systems (panel and BOS) and resulting price of electricity
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179
(right axis). Costs decline from 17.5 ¢ in 2010 (shown above) to 2.5 ¢ in 2050 for
areas with insolation of 2,300 kWh/m2/year.
Since this cost estimate uses assumptions with some degree of uncertainty,
sensitivity of resulting costs is tested for variations of key parameters as
summarized in Table 4. This analysis uses a wide range of variations. Some
parameters have remarkable low effects. For example, if energy growth rates up to
2050 vary between 1%/a up to 2.3%/a, the number of doublings in manufacturing is
between *12 and *13 and the resulting difference in manufacturing costs is 0.6%.
Figure 3 illustrates the results of the sensitivity analysis (cost estimates for areas
with insolation of 2,300 kWh/m2/year). For all parameter values the generation
costs per kWh of electricity will fall below 10 ¢ by 2012. In the most unfavorable
case, grid parity at 6 ¢/kWh is only reached in 2018; however, it will most likely
occur before 2018. (The line a50 in Fig. 3, the development without parameter
variations, is here the curve of lowest costs.) This analysis is valid for utility-scale
installations in regions with high annual insolation of ca 2,300 kWh/m2.
For a broad range of parameter variations costs fall below 5 ¢/kWh after 2020,
and below 2.8 ¢/kWh after 2030. The outcome is neither much affected by
considerable differences in global economic growth rates, nor by additional
considerable differences in energy efficiency, as most of the doubling in
Table 4 Parameters and their
values used for sensitivity
analysis
Parameter
Standard
value
Minimum
value
Maximum
value
Cost decrease of panels per
doubling*
0.71
0.65
0.90
Footing panel ($)
0.2
0.1
0.3
* These values take into account
a footing of $0.20/Wp
Margin panel (%)
30
10
40
Cost decrease BOS in %/year*
13
5
20
Source own calculations and
sources given in article main
text
Footing BOS ($)
0.2
0.1
0.3
Margin BOS (%)
20
10
40
a50
75%
95%
100%
50%
Electricity price from PV per kWh
20
15
10
5
0
2010
2020
2030
2040
2050
Time (Year)
Fig. 3 Sensitivity analysis of electricity price in c/kWh of electricity from PV
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manufacturing is necessary anyway to enable solar to contribute markedly to global
energy supply. As was said above, the only issue that might prevent the low prices
shown in the sensitivity analysis would be persistently high costs of the footings of
panels and BOS. High footing in PV panels is unlikely because PV is a
semiconductor technology with a very rapid cycle of development. For computer
chips this type of technology has for four decades seen ever lower manufacturing
costs. Also, there are no known limiting factors that could prevent low costs, such as
scarcity of resources (given the abundance of silicon and emerging new thin-film
materials) or lack of area. High footing of BOS is even more unlikely, as BOS is a
pretty straightforward issue which can be highly automated, uses standard materials
and is a good candidate for marked decrease of costs from large-scale manufacturing. This is illustrated by the recent very rapid decrease of costs of BOS from
$2.70/Wp to below $1/Wp.
It can be concluded that low costs for photovoltaic energy are highly likely.
4 Development of investment and employment in the PV sector
In this section we determine the evolution of investment expenditure and
employment based on the learning curves from the preceding section, using data
and figures from the roadmaps from FS. The results for FS are to be considered as
representative also for the overall market as the price leader, in this case FS,
determines the path that the other companies have to take in order to survive in a
strongly competitive market. It is most interesting that the financial size of the sector
increases with the volume of production, but simultaneously decreases with the
volume of production as increasing volume decreases manufacturing costs. The
product of Eq. 4 with the manufacturing capacity C(t) (Eq. 1) with initial value C0
(19 GW) and n(t) = ln(C(t)/C0)/ln(2) describes this development. Here,
s ¼ t 2010:
c~2 ðtÞ CðtÞ ¼ ð0:05 þ 1:58 0:811n Þ ð19 þ 28000 s2:4 =ð452:4 þ s2:4 ÞÞ
ð8Þ
4.1 Capital stock and manufacturing of First Solar
During 2009 FS increased its manufacturing capacity by 500 MW from 720 to
1,220 MW with investments of $472 million (FS 2010a), corresponding to
investments of $944 million/GWp. 10 TWp of manufacturing capacity in 2050
would thus need investments of $9,480 billion at the costs of 2010, if all companies
are as effective as FS, otherwise the amount would be higher.
Continued learning in this industry decreases the investment in the PV sector
which is necessary for this manufacturing capacity. For example, learning curve (4)
c~2 ðtÞ ¼ 0:05 þ 1:58 0:811n with n = 10 doublings gives panel costs of $0.21/Wp
in year 2050 instead of the panel manufacturing costs of $0.75/Wp at the end of
2010. Accordingly, the resulting market capitalization would be in the range of
$9480 billion 9 0.21/0.75 = $2,650 billion, globally. Both investment and
employment must be even lower than that number, as a footing in the panel
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manufacturing costs implies that the same fraction of the $0.21/Wp are needed for
materials, energy etc., not for manufacturing capacity and employment. We will
now provide a range of possible outcomes for investment and employment in the
solar sector.
4.2 Employment and investment
At the end of 2009, FS globally engaged 4,500 employees (USSCEP 2010) to
develop, manage, make acquisitions and run the manufacturing capacity of
1,220 MW. Hence for a capacity of 1 GW/y, 3,690 employees is the most current
labor demand.
Work force is additionally needed for the installation of PV panels. Currently, the
installation of panels is not as automated as their production (e.g. RMI 2010). For
the residential construction sector a fully automated installation seems difficult also
in the future; however, at the utility scale, achievement of a high degree of
automation is likely. As experts assume the same footing for manufacturing costs of
panels and costs for their BOS (as described above), the costs for both would again
become quite similar at the larger scale of the utility level.
How will significant technological progress change the relative relevance of the
factor inputs of labor and capital? In empirical macroeconomic studies most
technological progress is found to be Hicks neutral, i.e. leaving the marginal rate of
substitution between input factors unaffected. More generally, most technological
change is found not to change the relative demand of capital and labor in production
(the total factor productivity, the context is the so called ‘‘Solow residual’’, e.g. Hall
1988). Without contrary information for the PV sector, the labor-capital input ratio
is assumed to be constant over time, while technological progress raises the
productivity of both factors.
For an analytical description of this relationship we normalize Eq. 7 to the value
of 1 in 2009 and multiply the normalized function with the initial values of
employees and capital investment expenditures needed to produce 1 GWp/year of
photovoltaic panels. The resulting functions give time series for the specific labor
demand M1(t) and capital investment K1(t)
M1 ðtÞ ¼ c1
i cðtÞM0
ð9Þ
K1 ðtÞ ¼ c1
i cðtÞK0 ;
ð10Þ
c(2009)c-1
i
where c(t) is given by Eq. 7; ci normalizes
to the value of 1; M0
represents the employees needed and K0 the capital investment needed to produce
1 GW of panels in 2009. Figure 4 illustrates the development over time of the
specific labor demand.
4.3 Manufacturing capacity and investment
The analogue procedure gives the specific amount of invested capital over time for
1 GWp/year of manufacturing capacity, Fig. 5. The initial value is $944 million/
GWp in 2009 as calculated above.
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Employees per 1 GWp of PV
6,000
4,500
3,000
Employees per
1 GWp of PV
1,500
0
2010
2030
Year
2050
Fig. 4 Specific employment per 1 GWp/year manufacturing capacity over time
The numbers on manufacturing capacity given in Table 3 together with the
specific numbers for employment and investment per GWp as shown in Figs. 4 and
5, allow an estimate of the development of employment and investment.
4.4 Global invested capital and employment in the solar sector
The development of the globally invested capital is calculated as the product of the
global manufacturing capacity, one example is Table 3, which we use here, times
the costs per unit of manufacturing capacity (Fig. 5). The resulting Fig. 6 shows a
transition at around 2035 when global manufacturing capacity is approaching
demand and its growth curve levels off.
The invested capital shown in Fig. 6 is lower than could be expected given present
investment costs due to decreasing costs for PV manufacturing capacity resulting from
economies of scale and global competition. As was stated before, total systems cost
could decrease from their present value of about $2/Wp–$0.40/Wp, i.e. by a factor of 5,
and thus specific investment has to decrease by at least this factor.
Capital investment for 1 GWp of PV manufacturing capacity
$
1.5 B
750 M
0
2010
Capital investment for
1 GWp of capacity
2030
Year
2050
Fig. 5 Development of specific investment for 1 GWp/year of manufacturing capacity
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$
183
Capital investment in PV production global
2e+012
1.5e+012
Capital investment in
PV production global
1e+012
500 B
0
2010
2030
Year 2050
Fig. 6 Projected global capital investment values in photovoltaic manufacturing capacity
In addition to capital investment shown in Fig. 6, investment is needed for
manufacturing capacity and BOS as well as for extended transmission capacity. If
the costs of panel manufacturing and BOS are similar, this would about double the
amount of investment; if transmission costs are at 1/3 of electricity costs, this would
further increase investment by about 1/3.
Similarly, the estimate of global development of employees needed in the PV
production sector, Fig. 7, is derived as the product of the growth rate of global
manufacturing capacity times the specific employment needed per unit of
manufacturing capacity (Fig. 4). As in the case of investment, the number of jobs
is surprisingly low in comparison to what might initially be expected, and for the
reasons given above.
4.5 Sensitivity analysis of capital investment and employment
Whereas electricity costs do not change much if parameter values are changed
according to Table 4, the development of capital investment is quite sensitive to
changes of these parameter values, as is given in Fig. 8. The lower range of investment
is due to both, low total systems prices for solar energy and low increase in energy
demand. The outcomes could be different by a factor of 2. However, these extremes are
highly unlikely as we might expect either high prices for solar energy and, accordingly,
low energy demand, or low prices for solar and high energy demand. Hence, the
middle of the range of this sensitivity analysis shows the more likely outcomes.
Due to the tight connection between investment and employment, the uncertainty
in outcomes of employment is very similar to those in the subject of capital
investments.
4.6 Further development of manufacturing capacity
The present rush towards grid parity will not trigger an immediate boom for the PV
industry. The energy sector is a very mature sector with long time horizons. Mature
sectors are slow in adopting radical change. For example, *4 TW of electricity
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Employment in PV production global
8M
6M
Employment in
PV production
d i global
l b l
4M
2M
0
2010
2030
Year
2050
Fig. 7 Global employment in photovoltaic manufacturing
a50
100%
75%
50%
95%
Capital investment in PV production global
2e+012
1.5e+012
1e+012
500 B
0
2010
2020
2030
2040
2050
Time(Year)
Fig. 8 Sensitivity of investment to changes of crucial parameters
would be needed to meet 80% of the total energy demand of the US in 2050
(Fthenakis et al. 2009). The transmission lines with the highest capacity are the
High-Voltage DC (HVDC) transmission lines in China with a length of about
3,000 km and a capacity of 6 GW at a cost of $2.35 billion (ABB 2010). The
transmission of *4 TW from the US Southwest with an average line length of
2,000 km would need 670 lines with 6 GW, each with present total investment costs
of about $1 billion. Fthenakis et al. (2009) present reasons for the considerable
decrease of costs, as HVDC is a new generation of transmission technology. Such
huge investments in transmission lines in addition to high investments in new
generation capacity are one reason why decision-making in the energy sector has to
be considerate and is often slow.
This might change once installations of solar power stations and HVDC lines are
sufficiently proven. Then, many investors will want to put money into solar
generating capacity, storage and transmission lines. Breakthroughs with a high
potential—here the achievement of grid parity by PV and its practicability—often
cause a buildup of large overcapacity; as described in the famous ‘‘Hype Cycle’’
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from Gartner (2010). Companies basically want to expand as fast as possible so that
they, and not their competitors, can benefit from economies of scale, as this is
imperative for their survival. Thus, overcapacity is a common phenomenon to be
expected at some point in time. A second economic issue that facilitates an
extension of the PV sector is the growth in energy demand. New capacity, whether
based on fossil fuels or renewables, has to be built from scratch anyway; and once
solar is competitive, the new capacity could be solar in most cases. Additionally, all
types of power plants have a limited lifetime, e.g. 30–40? years for coal power
plants. If the levelized cost of electricity (LCOE) from PV becomes lower than
LCOE of other energies, then an increasing share of capital expenditures will be put
into PV, for both new capacity and replacement of existing capacity, as this would
be the best option with respect to return on investment. 2010 already saw an
unexpectedly high growth of investment in renewable energy, in both, developing
and developed countries (UNEP 2011).
5 Conclusions
The field of renewable energy, and here in particular photovoltaics, experiences an
intriguing development and expansion. This is due to the very rapid decrease of
costs of renewable energy as well as to manifold problems in the established energy
sector. PV has now become competitive in some regions and is projected to become
competitive soon between 35° South and 35° North, where 2/3 of humanity live. As
PV is a semiconductor technology, applied to the field of energy, the immediate
promise is that it might become as transforming as the information and
communication technology. Interestingly, the technology in the field of PV is
now enabling the implementation of a new energy system, almost completely based
on renewable energy. The energy sector currently accounts for about 8% of global
GDP, which points out the economic relevance of a transition of this sector.
This article addressed the quantitative scope for expansion of solar electricity to
answer questions such as: How fast can solar electricity supply be built up? What
investment levels are necessary for a dominant share of global energy supply?
Which size of labor force will be needed?
We started by showing that—with current knowledge—about 90% of total
energy demand can be covered by energy supplied in the form of electricity, and
thus can be generated from solar electricity. This includes shifting road transport to
electric vehicles, for example. Using standard global economic (3.3%/year) and
energy efficiency (1%/year) growth rates and acknowledging the shift to electricity
we derive a main scenario for total global electricity demand in 2050 at 16.5 TW,
with a range derived from the IPCC SRES scenarios at 8–26 TW. Applying solar
insolation this is translated to a solar capacity of 66 TWp in 2050, and one specific
scenario for building such a capacity is developed. This scenario is shown to be both
quite realistic and is based on lower capacity growth rates than those used in
standard literature.
The subsequent two main steps in this article are (1) the derivation and analysis
of the details of learning curves for PV module (panel) and system (balance of
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system, BOS) production to derive the development of production costs for both
over time, governed by number of output doublings, and (2) the translation of these
costs and related labor force demands to global demand figures, using the
development path of global capacity as derived above.
We find that the projected sales price of PV systems per Wp, which includes
panels and BOS, results in costs of electricity declining from 17.5 ¢/kWh in 2010 to
2.5 ¢ in 2050 (for areas with solar insolation of 2,300 kWh/m2/year).
Sensitivity analysis of these results, using a large range of values for the
underlying parameters, shows that some parameters have remarkable low effects.
For example, if energy growth rates up to 2050 vary between 1%/a up to 2.3%/a, the
number of doublings in manufacturing is between *12 and *13 and the resulting
difference in manufacturing costs is only 0.6% in 2050.
For all parameter values the generation costs per kWh of electricity will fall
below 10 ¢ by 2012. In the most unfavorable case, grid parity at 6 ¢/kWh is only
reached in 2018; however, it will most likely occur before 2018 (with all values
given here valid for utility-scale installations in regions with high annual insolation
of about 2,300 kWh/m2).
Concluding on overall investment requirements for building up the PV capacity
to cover 90% of total global energy demand by 2050 we find the bi-directed impact
of growing output. Directly it increases financial demands, but indirectly—by
economies of scale and other learning effects—it reduces unit costs. As a net effect
we derive a projected global capital investment value in photovoltaic manufacturing
capacity at 500 billion US$2010 by around 2030 and 1,500 billion by 2050.
Sensitivity analysis shows that this figure may be only at half of its value in 2050,
albeit only at the unlikely combination of lowest capacity expansion and
nevertheless highest learning advantages.
In addition to this capital investment, investment is needed for manufacturing
capacity and BOS as well as for extended transmission capacity. If the costs of panel
manufacturing and BOS are similar, this would about double the amount of
investment; if transmission costs are at one-third of electricity costs, this would
further increase investment by about one-third.
In terms of labor demand, employment in the production of panels is projected to
be at 2 million in 2030 and at 6 million in 2050 for our main scenario (6 million is a
share of 0.2% of the current global labor force of 3.2 billion). Sensitivity of these
numbers is the same as with capital investment.
While the 2012 IPCC Special Report on renewable energy resources (IPCC,
2012) gives a very thorough overview of direct solar technologies and their
technical potential (as referenced above), it limits itself to cover projections up to
2020 only for this very dynamic field. The present article gives a specifically
reasoned scenario and sensitivity analysis for PV development up to 2050,
complementing IPCC (2012) in this respect.
The field of solar electricity sees an interplay between competing technologies,
companies, and countries. For future research there is a unique opportunity to use
economic theories and experience to observe and assess this young field, to arrive at
conclusions, to monitor the actual development on the basis of earlier conclusions
and to use all insights for doing a good job in providing timely advice in a field that
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187
is very likely to substantially grow, will absorb large sums for investment and offers
manifold opportunities in employment.
Acknowledgments This publication arose from the research project Energ.Clim funded by the Austrian
Climate and Energy Fund and carried out within its program line ‘‘New Energy 2020’’. The fourth author
was supported by the Climate Decision Making Center created through a cooperative agreement between
the National Science Foundation (SES-0345798) and Carnegie Mellon University.
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