This course covers the cost development of renewable energy technologies, which includes the analysis of technological change, in particular with regard to technological learning, the assessment of learning rates of renewable energy technologies available in literature and forecasting studies. For many (energy) technologies, a log-linear relation was found between the accumulated experience and the technical (e.g. efficiency) and economic performance (e.g. investment costs). The rate at which cost decline for each doubling of cumulative production is expressed by the progress ratio (PR). A progress ratio of 90% results in a learning Rate (LR) of 10% and similar cost reduction per doubling of cumulative production (IEA 2000; Junginger, Sark et al. 2010). Learning curves for the renewable energy technologies as well as levelised cost of electricity will be presented. The latter also include the impact of resource conditions (e.g. wind and solar yield) at different locations as well as operation and maintenance costs and fuel expenditures in the case of biomass technologies.

1. © Fraunhofer ISI
C o m p r e h e n s i v e C o u r s e o n E u r o p e ‘ s S u s t a i n a b l e E n e r g y P o l i c y
P r o f . D r. M a r i o R a g w i t z , F r a u n h o f e r I S I <
COST DEVELOPMENT OF RENEWABLE
ENERGY TECHNOLOGIES

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 The theory of technology costs and system costs and it‘s
application
 Trends of technology costs
 Learning curves
 Cost trends of renewable energy conversion
technologies
 Impact of technological learning on future perspectives
 Estimates of future total system costs
 Scenarios on price development and technological
competition
Contents

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 The theory of technology costs and system costs and
it‘s application
 Trends of technology costs
 Learning curves
 Cost trends of renewable energy conversion
technologies
 Impact of technological learning on future perspectives
 Estimates of future total system costs
 Scenarios on price development and technological
competition
Contents

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 General characteristics
 High capital intensity
 Long lifetime of plant operation ranging from 15 to 40 years
 Uncertainty over time
(Costs: fuel prices and CO2-prices, revenues: Demand development and electricity market prices )
 Long lead times ranging from a few months for PV to 8-10 years for nuclear
 Environmental impacts: CO2 emissions, ashes, slags, flue gases, waste heat
 Strong role of regulatory framework (renewables policies, CO2 market, market design, etc.)
 Characteristics of the product electricity
 Uniform product
 Infrastructure requirements for electricity grids
 Match of demand and supply required
 Limited options to temporally store electricity (Pumped storage, CAES,…)
 Different degrees of flexibility (stochastic influences from weather)
Characteristics of energy projects

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Costs of RET deployment
• Costs accruing on plant level and for the plant owner
• Expressed using the levelised cost of electricity (LCOE)
metric
Generation
Cost
• Direct and indirect costs and benefits of RE-deployment
• Additional costs/benefits of a RE-based system in
comparison to a system based on conventional
technologies (source: Breitschopf, B. and Held, A., 2014)
System Cost
• Comprising gross and net effects in an economy
• Measured at a macro-economic level (source:
Breitschopf, B. and Held, A., 2014)
Macro-
economic cost

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Costs and benefits of RET deployment
Source: Breitschopf, B. and
Held, A., 2014.

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 Allows comparison of different power plants and power generation technologies
 Methodology:
1. Accumulating all costs occuring while the construction and operating period of
a plant
2. Accumulating the electricity produced over the lifetime of the plant
3. Dividing the 2 figures, thereby obtaining the LCOE [Euro/kWh]
 The accumulation of costs and power is done by the net present value (NPV)
method
 Metric does not allow the analysis of the cost efficiency of a specific plant
 The LCOE does not take into account, in which hour of the year the electricity is
produced
 gives no indication regarding the actual value of electricity produced by a specific
plant
Electricity Generation Cost (using LCOE)

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Electricity Generation Cost (using LCOE)
 Formula:
With:
 LCOE Levelised cost of electricity in Euro/kWh
 I0 Investment expenditures in Euro
 Mt,el Produced quantity of electricity in the respective year in kWh
 i Real interest rate in %
 n Economic operational lifetime in years
 t Year of lifetime (1, 2, …n)
 At Annual total costs in Euro in year t = Fixed costs + variable costs +
residual value/disposal
Source: Kost et al., 2014

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Short overview cost elements
Capital expenditure
(Capex)
• Investment
• Engineering cost
• Costs for permissions
(acquisition and
environmental approval
requirements)
• Construction cost
• Infrastructure cost
Operating expenditure
(Opex)
• Fixed O&M
• Operating labour
• Maintenance
• Taxes, Insurance
• Network use of system
charges
• Variable O&M
• Periodic inspection
• Repair and maintanance
• Fuel costs
• CO2 costs
• CCS transport and
storage costs
Expected output
• Capacity
• Availability
• Efficiency
• Utilisation
(load factor)
• Heat revenues

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 Annual fixed costs (Cf)
 Costs independent from generated amount / utilization rate.
 Capital costs, interest payments, repayment, annuity payments, insurances, fixed
personal costs
 Variable costs (Cv)
 Costs depends on the usage-bound rate of the power plant: fuel costs, CO2
certificate costs, transportation costs, efficiency
 Annual total costs (CT)
 CT = Cf + Cv
Cost elements

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Economics of wind power plants
Power generation costs
Annual mean wind speed and thus full load hours have a major
impact on power generation costs
o The higher the mean wind speed, the lower the generation
costs
o This drives also a trend to higher towers
Turbine capacities and converter types also have a strong impact
on power generation costs
Offshore plants are characterised by higher electricity generation,
but also higher generation costs.

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Calculation of capacity factor and full load hours of a
wind turbine
For the calculation of the capacity factor you “multiply” the distribution of wind
speeds and the power curve of the wind turbine (convolution of both functions).
To calculate the full load hours the capacity factor is multiplied by 8760 hours.
Source: http://www.windatlas.ca
Power curve
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
wind speed v [m/s]
relativeperformanceasshare
oftheinstalledcapacity[%]

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a b
Wind speed [m/s] Relative frequency of
wind speed
Relative power (based
on the mean wind speed
of the interval and read
out of the power curve)
The product
of a and b
00-03 15,31% 0% 0,0%
03-06 31,33% 11% 3,4%
06-09 27,93% 31% 8,7%
09-12 16,24% 77% 12,5%
12-20 9,37% 100% 9,4%
Sum: 34,0%
full load hours at
installation site multiplied by 8760 h 2976
Calculation of full load hours

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Economics of wind power plants
Full load hours and power generation costs for onshore plants
Sites with 5,5m/s or 6,5 m/s (near the
shore/ hills) are favoured over sites
with 4,5 m/s (low mountain ranges).
Onshore load factors are often too low
for turbines with very high power.
o Power generation costs become
too high.
Power generation costs decrease
significantly with increasing wind
speed.
* [for each MW class at given wind speed/ h/a]
Cf. Kaltschmitt, 2013 (Ch.7)
Powergenerationcosts*(in€/kWh)
Fullloadhours(inh/a)

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Economics of wind power plants
Full load hours and power generation costs for offshore plants
German offshore sites mainly with 9,5
to 11 m/s annual mean wind speed
Sites with 8,5 m/s wind speed are of
less importance – generation costs
especially for the biggest plants would
be quite high [about 0,14 €/kWh].
Fullloadhours(inh/a)
Powergenerationcosts*(in€/kWh)
* [for each MW class at given wind speed/ h/a]
Cf. Kaltschmitt, 2013 (Ch.7)

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Energy sector project risk factors
Source: IRENA, 2015

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Energy sector project risk factors: Effect of
discount rate on LCOE, example of PV
0.00
20.00
40.00
60.00
80.00
100.00
120.00
140.00
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
USDc(2014)/kWh
Levelised Cost and Discount Rate
PV RES MAX (7,5 % Discount Rate) PVRES MED (5% Discount Rate)
PVRES Min (2% Discount Rate)

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 System refers to the energy sector as a whole or a specific technology level
 System-related effects consider the costs of integrating RES into the system
 System costs consist of generating costs + additional system-related costs
(direct & indirect)
 Additional system costs:
 Balancing costs
 Profile costs
 Grid costs
 Transaction costs
System costs
LCOE System Costs
Source: Ueckerdt et al., 2013

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 Occur because supply from volatile RES is uncertain and subject to forecast
errors
 In case of deviations of the actual production from RES from forecast, reserve
capacity (positive/negative) and intraday adjustment are necessary, causing
costs
 Indirect costs, relevant for electricity
 Different compensation measures in different countries (speed of response,
based on capacity / generation)
Balancing Costs
Source: Breitschopf and Held,
2014

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 Indirect costs, relevant for electricity
 Economic costs caused by variability of RES:
1.Adequacy- / Backup- / Capacity costs, as the capacity factor of RET is low.
Additional capacity is therefore required (conventional plants, dispatchable
renewable energy sources, storage)
2.Full-load hour reduction: variable RES contribute energy but hardly reduce
need for total generation capacity: Average utilisation of conventional
power plants is reduced: Inefficient redundancy in the system + increase of
generation costs of conventional plants
3.Overproduction: At high shares of RES, electricity generation may exceed
load: Overproduction, that needs to be curtailed
-> effective capacity factor of variable RES decreases, specific generation
costs increase
 Higher overall profile costs for higher shares of variable RES
Profile Costs
Source: Ueckerdt et al., 2013;
Breitschopf, B. and Held, A., 2014.

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Profile costs
Source: Ueckerdt et
al., 2013

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 Indirect costs, relevant in particular for electricity
1. Costs for transmission grid investments, if supply is located for
from load centers
2. RET may enhance grid constraints: Costs for congestion
management (re-dispatch of power plants…) (Source: Ueckerdt et
al., 2013)
3. Costs for technological solutions such as remote control or
interconnectors in order to overcome stability issues in distribution
grids (particularly caused by small power plants such as rooftop
PV)
Grid Costs
Source: Breitschopf, B. and
Held, A., 2014.

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System costs - overview
Source: Ueckerdt et al., 2013

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Grid Integration costs of PV
Source: IRENA, 2015; Pudjinato et al., 2013

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 Indirect costs, relevant for electricity and heat
 Higher complexity with increased RET deployment: Inducing increase of all
transaction costs among market participants
 Forecasting
 Planning and monitoring electricity supply and demand
 Establishing markets
 Contracting
 …
 Policy implementation costs
 Monitoring measures and activities
 Reporting
 Requirement of standards
 …
Transaction Costs
Source: Breitschopf, B. and
Held, A., 2014.

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 The theory of technology costs and system costs and it‘s
application
 Trends of technology costs
 Learning curves
 Cost trends of renewable energy conversion
technologies
 Impact of technological learning on future perspectives
 Estimates of future total system costs
 Scenarios on price development and technological
competition
Contents

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 Important tool for modeling technological change
 Evaluates the cost effectiveness of a technology
 Supporting the formation of long-term policy decisions
 Predicting technological change
 Guiding firm strategy
 Dynamical illustration of technology costs
 Technological change as a function of learning derived from the accumulation
of experiences in production (source: Arrow, 1962):
 Regarding renewable energy technology: The more renewable energy
generating units are produced, the more efficiently the production works
 Cost reduction
 Quality improvement
The theory of learning curves
Source: Nemet, 2006

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Theoretical background:
Parameters:
b Learning coefficient
PR Progress Ratio
LR Learning Ratio
C Unit Cost
q Cumulative Output
The theory of learning curves
Source: Nemet, 2006

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 Reasons for technical improvements leading to a decreasing learning
curve
 Experience
 Economies of scale / Economies of scope
 Knowledge spillovers
 Organizational learning
 Employee learning
The theory of learning curves
Source: Nemet, 2006

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Learning curve expectations realised for
photovoltaics from 1979 - 2015
Quelle:
Folz, Fh-ISI

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Learning curve expectations realised for
wind power from 1981 - 2005
1FLC WT DK 1981-2005
y = 2231x
-0.1283
100
1000
10000
1 10 100 1000 10000
cum. capacity (MW)
price(€2005/kW)
Average list price of WT in DK (€2005/kW)
1FLC DK (LR=8.5%)
Quelle:
Folz, Fh-ISI

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Price & deployment of PV-Modules
Source: IRENA, 2015; pvXchange, 2014

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Trends of technology costs for RET
Source: IRENA, 2015

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 Locations in
Germany in
2013
 Values are
global horizontal
irradiation (GHI)
for PV and full
load hours
(FLH) for the
other
technologies.
 Specific
investments are
taken into
account with a
minimum and
maximum value
Trends of technology costs for renewable
and conventional technologies
Source: Kost et al., 2014

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 The theory of technology costs and system costs and it‘s
application
 Trends of technology costs
 Learning curves
 Cost trends of renewable energy conversion
technologies
 Impact of technological learning on future perspectives
 Estimates of future total system costs
 Scenarios on price development and technological
competition
Contents

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Source: Kost et al., 2014

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Source: Kost et al., 2014
Different regional LCOE for rooftop PV plants for Germany in 2012 (left) and 2020
(right)

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 Though prices for RET have decreased significantly in the long term
perspective, prices for PV or Wind turbines have stabilized or even
increased in recent years
 Reasons:
 increasing demand for raw material (steel, concrete, silicon,
plastics)
 higher production costs (increase of coal, oil and gas prices)
Impact of key parameter on the mid-term
cost development of RET
Source: Hoefnagels, 2011

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Development of PV
Long term

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Development of PV module prices
last 4 years
0
500
1,000
1,500
2,000
2,500
3,000
EUR/kWpModule
Crystalline Europe Crystalline China Crystalline Japan
Thin Film CdS/CdTe Thin Film a-Si Thin Film a-Si/µ-Si
Source: Own composition based on SolarServer (2013) and PVXchange (2013)

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Development of PV enduser prices
0
1,000
2,000
3,000
4,000
5,000
6,000
EUR/kWpModule
Crystalline Europe Crystalline China Crystalline Japan
Thin Film CdS/CdTe Thin Film a-Si Thin Film a-Si/µ-Si
Source: Own composition based on SolarServer (2013) and PVXchange (2013).
PV Free field System Cost
Breakdown Germany 2013 in €/kW

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 The theory of technology costs and system costs and it‘s
application
 Trends of technology costs
 Learning curves
 Cost trends of renewable energy conversion
technologies
 Impact of technological learning on future perspectives
 Estimates of future total system costs
 Scenarios on price development and technological
competition
Contents

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Example: Impact Assessment of: EC COM “A policy framework for climate and
energy in the period from 2020 to 2030”
The following main scenarios considered here:
1. Reference:
 achievement of 2020 targets
 EU ETS Directive with the annual linear reduction factor of 1.74% continuing
also post-2020
 Phase out of incentives for RES and EE after 2020
2. GHG40: – 40% GHG reduction; focus on carbon pricing
3. GHG40-30%EE: – 40% GHG reduction, energy efficiency target of 30%
4. GHG40-30%EE-30%RES: – 40% GHG reduction, ambitious energy efficiency &
RES target 30%
5. GHG45-34%EE-35%RES: – 45% GHG reduction, energy efficiency target 34% &
RES target 35%
Estimates of future total system costs
Source: Impact
assessment
of COM(2014) 15

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Estimates of future total system costs
 30% RES target does not increase total system costs
 Differences in total system costs until 2030 are low and increase until 2050 due to
costs of energy efficiency
11,5
12
12,5
13
13,5
14
14,5
15
15,5
Ref GHG40 GHG 40 EE GHG 40 EE
30%RES
GHG 45 EE
35%RES
TotalSystemCosts[%GDP]
Ave annual 2011-2030
Ave annual 2031-2050
Held, Ragwitz et al., 2014; based on data
from European Commission (2014, 2011)

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Estimates of future investment expenditures
 Investment expenditures increase in scenarios with ambitious EE and RES
 Differences in investment expenditures compared to reference are substantial
0
50
100
150
200
250
300
350
400
450
GHG40 GHG 40 EE GHG 40 EE 30%RES GHG 45 EE 35%RES
Diff.inInvestmentexpend.[bn€]
Ave annual 2011-2030
Ave annual 2031-2050
Held, Ragwitz et al., 2014; based on data
from European Commission (2014, 2011)

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 The theory of technology costs and system costs and it‘s
application
 Trends of technology costs
 Learning curves
 Cost trends of renewable energy conversion
technologies
 Impact of technological learning on future perspectives
 Estimates of future total system costs
 Scenarios on price development and technological
competition
Contents

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LCEO of RES and conventional electricity in
UK including integration costs

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Source: IRENA, 2015
LCOE range of RES electricity in 2014 and 2025

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LCOE of variable RES and conventional
electricity
Source: IRENA,
2015

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 The analysis of costs of renewables should follow a robust
methodology differentiating between system related costs,
distributional and macroeconomic effects
 A very strong reduction of LCOE could be reached for
RES in the last decades and is expected for the future
 System integration costs will become more relevant in the
future but are still small compared to total LCOE
 Scenario analysis show that ambitious RES targets have
only a small impact on total system costs for the EU
Conclusions

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Thank you for your attention!
ragwitz@isi.fraunhofer.de

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Sources
Arrow, Kenneth J. (1962): The economic implications of learning by doing. In The review of economic studies,
pp. 155–173.
Breitschopf, B.; Held, A. (2014): Guildelines for assessing costs and benefits of RET deployment. Available
online at http://www.diacore.eu/results/item/d4-1-guidelines-for-assessing-costs-and-benefits-of-ret-
deployment, checked on 9/19/2014.
European Commission (2011): Energy Roadmap 2050. COM(2011) 885.
European Commission (2014): Impact Assessment accompanying the Communication from the European
Commission: A policy framework for climate and energy in the period from 2020 to 2030. SWD(2014) 15 final.
Held, A.; Ragwitz, M.; Eichhammer, W.; Sensfuss, F.; Pudlik, M.; Pfluger, B. (2014): Estimating energy system
costs of sectoral RES and EE targets in the context of energy and climate targets for 2030.
Hoefnagels, E. T.A.; Junginger, H. M.; Panzer, Christian; Resch, Gustav; Held, Anne (2011): Long Term
Potentials and Costs of RES-Part I: Potentials, Diffusion and Technological learning.
IRENA (2015): Renewable Power Generation Costs in 2014. Available online at
http://www.irena.org/DocumentDownloads/Publications/IRENA_RE_Power_Costs_2014_report.pdf, checked
on 5/27/2015.
Kost, Christoph (Fraunhofer ISE) (2014): Stromgestehungskosten Erneuerbare Energien.
Nemet, Gregory F. (2006): Beyond the learning curve: factors influencing cost reductions in photovoltaics. In
Energy Policy 34 (17), pp. 3218–3232. DOI: 10.1016/j.enpol.2005.06.020.
Pudjianto, D.; Djapic, P.; Dragovic, J.; Strbac, G. (2013): Grid Integration Cost of PhotoVoltaic Power
Generation. In Energy Futures Lab, Imperial College, London, UK, September.
pvXchange (2014): pvXchange market price data. Cologne.
Ueckerdt, Falko; Hirth, Lion; Luderer, Gunnar; Edenhofer, Ottmar (2013): System LCOE: What are the costs of
variable renewables? In Energy 63 (0), pp. 61–75. DOI: 10.1016/j.energy.2013.10.072.