Projected
Costs of
Generating
Electricity
2020 Edition

Projected Costs ofGenerating Electricity
2020 Edition

INTERNATIONAL ENERGY AGENCY
NUCLEAR ENERGY AGENCY
ORGANISATION FOR ECONOMIC CO-OPERATION AND DEVELOPMENTINTERNATIONAL ENERGY AGENCY
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Foreword

At a moment when the COVID-19 pandemic has not yet subsided, creating the appropriate
frameworks for ensuring adequate investment in the power sectors of OECD and non‑OECD
countries alike is of paramount importance. This is especially relevant at a moment where many
governments are initiating targeted stimulus packages to support the economy. Many of thesepackages include measures to accelerate the countries’ clean energy transitions – a shift fromcarbon-intensive electricity generation to low-carbon technologies, setting all areas of the economy
and society on a more sustainable path.
Affordability will be a key consideration in designing those transitions. Decision makers inpolicy and industry, assisted by modellers and energy experts, will have to chart a way forward
in increasingly complex contexts. Doing so will require reliable and relevant information. This iswhere this joint publication by the International Energy Agency (IEA) and the OECD Nuclear Energy
Agency (NEA) comes in. Projected Costs of Generating Electricity – 2020 Edition is the ninth edition of
their joint report on electricity generating costs by the major power generation technologies in
24 different countries.
Any comparison of generation costs today must also consider our rapidly changing powersystems. As in previous editions, this edition uses the levelised cost of electricity (LCOE) as a wellestablished, uniquely transparent and intuitive metric, widely used in policy making, modellingand public discussion. As the shares of wind and solar PV in our power systems grow, the valueof the electricity generated by a particular technology in a particular power system also changes.
Thus, this report also presents a new complementary metric, the “value-adjusted” LCOE to accountfor these impacts. In addition, five “boundary chapters” take a step back to discuss different aspectsof the evolving electricity systems of today and tomorrow in a broad and dynamic long-term
perspective.
Altogether, the different elements of Projected Costs of Generating Electricity – 2020 Edition aim
at contributing to a sound informational infrastructure for forward-looking decision making in
decarbonising energy sectors. In this context, knowing the costs and system value of different
electricity generation options and, in particular, of low-carbon generation, is indispensable.

5

Acknowledgements
The lead authors and co-ordinators of this report were Dr Stefan Lorenczik, Electricity Markets
Analyst, IEA, and Professor Jan Horst Keppler, Senior Economic Advisor, NEA. Effective managerial
oversight was provided by Mr Peter Fraser, Head of the IEA Gas, Coal and Power Markets Division
and Dr Sama Bilbao y León, Head of the NEA Division on Nuclear Technology Development andEconomics (NTE). Mr Lucas Mir and Mr Gabriel Sousa, both of the NEA, and Ms Sunah Kim, IEA,
provided valuable help with the analysis and preparation of the LCOE country data. Mr Brent
Wanner, Lead of Power Generation Modelling and Analysis for the IEA World Energy Outlook,wrote Chapter 4 on “The value-adjusted levelised costs of electricity (VALCOE)”, with support fromMr Connor Donovan. Dr Lorenczik prepared the “Sensitivity analysis” in Chapter 5.
The IEA and NEA Secretariats would like to acknowledge the essential contribution of theExpert Group on Projected Costs of Generating Electricity (EGC Expert Group), which assisted in
the sourcing of data, provided advice on methodological issues and reviewed successive drafts of
the report. The EGC Expert Group was effectively chaired by Professor William D’haeseleer from
Belgium and co-chaired by Dr Yuhji Matsuo (Japan) and Mr Thomas J. Tarka (United States). The fulllist of the members of the EGC Expert Group can be found at the end of this publication.
Five “boundary chapters” in Part II of this publication provide context and perspective for the
LCOE data presented in Chapter 3. Professor D’haeseleer, Professor Erik Delarue and Mr Tim Mertens(KU Leuven) wrote Chapter 6 on “The levelised cost of storage (LCOS)”. Professor Keppler and
Dr Klaus Hammes (Swedish Environment Agency) authored Chapter 7 on “Carbon pricing”.
Dr Michel Berthélemy and Mr Antonio Vaya Soler (NEA) are responsible for Chapter 8,“A Perspective on the costs of existing and new nuclear power plants”. Mr Olivier Houvenagel,
Dr Cédric Léonard and Dr Thomas Veyrenc (all three of Réseau de Transport d’Electricité [RTE],
France) contributed Chapter 9 on “Sector coupling: Understanding how the electricity sector candrive the decarbonisation of the overall economy”. Finally, Dr Jose Miguel Bermudez Menendez,
Dr Uwe Remme and Mr Taku Hasegawa (IEA) contributed Chapter 10 on “Hydrogen: An opportunity
for the power sector.”
Mr Christopher Gully, Ms Astrid Dumond and Mr Jon Custer of the IEA Digital News andInformation Unit contributed to the preparation of the website for this report.
Mr Andrew Macintyre and Ms Elisabeth Villoutreix (NEA) edited the report. Ms Fabienne
Vuillaume (NEA) provided assistance with the final preparation of the report layout.

6

Table of contents

Foreword .............................................................................................................................................................................................

5

Acknowledgements ......................................................................................................................................................................

6

List of tables ......................................................................................................................................................................................

9

List of figures ....................................................................................................................................................................................

10

Executive summary .....................................................................................................................................................................

13

Part 1 METHODOLOGY AND DATA ON LEVELISED COSTS FOR GENERATING TECHNOLOGIES
Chapter 1

Introduction and context .............................................................................................................................

25

Chapter 2

Methodology, conventions and key assumptions .......................................................................

33

2.1 The levelised cost of electricity (LCOE).......................................................................................

33

2.2

Methodological conventions and key assumptions...........................................................

36

2.3

Conclusions.................................................................................................................................................

41

LCOE Values by technology and country............................................................................................

43

Chapter 3

3.1 Overview and summary statistics............................................................................................................

43

3.2 Technology-by-technology data on overnight and investment costs.....................

48

3.3 Technology-by-technology LCOE data at standard capacity factors........................

55

3.4 Technology-by-technology LCOE data for flexible technologies
at lower capacity factors.....................................................................................................................

68

Chapter 4	The value-adjusted levelised cost of electricity (VALCOE)......................................................

75

4.1

Motivations for looking beyond the LCOE................................................................................

75

4.2

Key elements of VALCOE.....................................................................................................................

77

4.3

Sample results...........................................................................................................................................

80

Chapter 5	Sensitivity analysis...........................................................................................................................................

83

5.1

Discount rate sensitivity.....................................................................................................................

84

5.2

Load factor sensitivity..........................................................................................................................

85

5.3

Lifetime sensitivity.................................................................................................................................

86

5.4

Fuel cost sensitivity...............................................................................................................................

88

5.5

Carbon price sensitivity.......................................................................................................................

897

PART 2 BOUNDARY CHAPTERS
Chapter 6	The levelised cost of storage (LCOS)......................................................................................................
Introduction................................................................................................................................................

93

6.2 A primer on electricity storage: applications, available technologies,
current and estimated future costs..............................................................................................

93

6.3 The levelised cost of storage (LCOS).............................................................................................

99

6.4 Arbitrage cost metrics for storage technologies: The normative approach.........

100

6.5 Arbitrage price-based metrics for storage technologies:
The empirical approach......................................................................................................................

105

6.6 Combining all normative and empirical metrics:
Storage costs and price arbitrage in practice.........................................................................

110

6.7 Beyond arbitrage storage services or applications ............................................................

112

6.8 Concluding remarks..............................................................................................................................

116

Chapter 7	Carbon pricing and zero emission credits..........................................................................................

119

7.1 Carbon pricing as a means of rendering explicit the differencesin the full cost of different generation technologies ........................................................

119

7.2 Principal instruments for differentiating the costs of technologies
according to their carbon emissions ..........................................................................................

120

7.3 Impacts on electricity prices............................................................................................................

133

7.4 Distributional impacts.........................................................................................................................

135

7.5 Policy conclusions...............................................................................................................................

141

Chapter 8	Perspectives on the costs of existing and new nuclear power plants..............................

145

8.1 Status of nuclear development and role in future energy systems.........................

145

8.2 Long-term operation (LTO) of existing nuclear assets:
The most cost-effective low-carbon solution........................................................................

147

8.3 Cost reductions in large Generation III reactors:
Significant cost reduction opportunities in the short and long terms...................

151

8.4 Economic perspectives on small modular reactors (SMRs)...........................................

154

8.5 Conclusions.................................................................................................................................................

158

Chapter 9	Sector coupling and electrification: Understanding how the electricity sector
can drive the decarbonisation of the overall economy..............................................................

161

9.1 The context in France: From a debate about the electricity sector toa coherent policy to decarbonise the overall economy....................................................

161

9.2 Benchmarking scenarios: The need for a method to analyse impacts
at the system level ................................................................................................................................

164

9.3 Evaluating the costs at system level: An appropriate approachto compare the true economic costs of different policy options...............................

165

9.4 Sector 1: Electric mobility – A no regret option for decarbonisation
at reasonable cost...................................................................................................................................

168

9.5 Sector 2: Power-to-hydrogen – Decarbonising industry and heavytransportation at more significant abatement costs ........................................................

174

9.6 Sector 3: Heating in buildings – The need to co-ordinate policies
to advance buildings renovation and decarbonisation ..................................................

179

9.7 Comparative analysis on the decarbonisation costs in the different sectors....

182

9.8 Energy system integration analysis and public policy making
for decarbonisation: Next steps......................................................................................................

183

9.9 Conclusion...................................................................................................................................................

184

6.1

8

93

Chapter 10	Hydrogen: An opportunity for the power sector............................................................................

187

10.1 The unprecedented momentum of hydrogen ....................................................................

187

10.2 Hydrogen demand and potential for the future................................................................

188

10.3 Hydrogen production technologies...........................................................................................

191

10.4 Opportunities for hydrogen and linkages to the electricity system......................

199

10.5 Conclusions and recommendations.........................................................................................

209

A1	Learning rates for adjusting data for commissioning year 2025...........................................

213

A2

List of abbreviations and acronyms........................................................................................................

215

List of Members of the EGC Expert Group and Experts who contributed to the reportwith data or comment (by country).....................................................................................................................................

217

ANNEXESLIST OF TABLES
Table 1.1

Summary of responses by country and technology......................................................................

26

Table 2.1

Fuel prices...............................................................................................................................................................

39

Table 2.2

National currency units per USD (2018 average).............................................................................

41

Table 3.1

Summary statistics for different generating technologies.......................................................

43

Table 3.2a

Combined-cycle gas turbine (CCGT) generating technology...................................................

48

Table 3.2bOpen-cycle gas turbine (OCGT) generating technology..............................................................

48

Table 3.3

Coal-fired generating technologies ........................................................................................................

49

Table 3.4a

Nuclear generating technologies – New build .................................................................................

49

Table 3.4b

Nuclear generating technologies – Long-Term Operation (LTO)............................................

49

Table 3.5

Solar generating technologies ...................................................................................................................

50

Table 3.6aWind generating technology – Onshore ..............................................................................................

51

Table 3.6bWind generating technology – Offshore...............................................................................................

52

Table 3.7a

Other renewable generating technologies – Hydropower..........................................................

53

Table 3.7b

Other renewable generating technologies – Biomass..................................................................

53

Table 3.7c

Other renewable generating technologies – Geothermal...........................................................

54

Table 3.8

Combined heat and power (CHP) technologies................................................................................

54

Table 3.9

Storage technologies........................................................................................................................................

54

Table 3.10

Fuel cells..................................................................................................................................................................

55

Table 3.11a	Levelised cost of electricity for combined-cycle gas turbine (CCGT)
at 85% capacity factor......................................................................................................................................

56

Table 3.11b	Levelised cost of electricity for open-cycle gas turbine (OCGT)
at 30% capacity factor......................................................................................................................................

56

Table 3.12

Levelised cost of electricity for coal plants at 85% capacity factor......................................

56

Table 3.13a	Levelised cost of electricity for nuclear plants at 85% capacity factor –New build................................................................................................................................................................

58

Table 3.13b1	Levelised cost of electricity for nuclear plants at 85% capacity factor –Long-Term Operation (LTO), 10 years.....................................................................................................

58

Table 3.13b2	Levelised cost of electricity for nuclear plants at 85% capacity factor –Long-Term Operation (LTO), 20 years.....................................................................................................

58

Table 3.14

Levelised cost of electricity for solar generators.............................................................................

60

Table 3.15a Levelised cost of electricity for wind generators – Onshore ...................................................

62

Table 3.15b Levelised cost of electricity for wind generators – Offshore....................................................

64

Table 3.16a Levelised cost of electricity for other renewable generation – Hydropower..................

64

Table 3.16b Levelised cost of electricity for other renewable generation – Biomass...........................

66

Table 3.16c Levelised cost of electricity for other renewable generation – Geothermal...................

66

9

Table 3.17

Levelised cost of electricity for combined heat and power (CHP) plants..................................

66

Table 3.18

Levelised cost of electricity for storage technologies...................................................................

68

Table 3.19

Levelised cost of electricity for fuel cells.............................................................................................

68

Table 3.20a	Levelised cost of electricity for combined-cycle gas turbine (CCGT)
at 50% capacity factor......................................................................................................................................

70

Table 3.20b	Levelised cost of electricity for open-cycle gas turbine (OCGT)
at 10% capacity factor ....................................................................................................................................

70

Table 3.21

Levelised cost of electricity for coal technologies at 50% capacity factor.......................

70

Table 3.22a	Levelised cost of electricity for nuclear technologies at 50% capacity factor –New Build................................................................................................................................................................

72

Table 3.22b1	Levelised cost of electricity for nuclear technologies at 50% capacity factor –Long-Term Operation (LTO), 10 years.....................................................................................................

72

Table 3.22b2	Levelised cost of electricity for nuclear technologies at 50% capacity factor –Long-Term Operation (LTO), 20 years.....................................................................................................

72

Table 6.1	Grouping of 12 core applications in which storage technologies can participate......

95

Table 6.2	Storage parameters used for the reference case.............................................................................

103

Table 6.3	Different cost metrics for a storage with parameters as provided in Table 6.2............

103

Table 7.1	The distributional impacts of different forms of carbon pricing..........................................

136

Table 8.1	LTO LCOE values for LWRs as a function of the lifetime extension period,
discount rate, overnight costs and capacity factor........................................................................

149

Table 8.2	Construction costs of recent FOAK Generation III/III+ projects.............................................

152

Table 9.1	Summary of technical, economic and environmental impacts of developing
new uses of electricity.....................................................................................................................................

183

Table 10.1	Techno-economic characteristics of different electrolyser technologies........................

194

Table 10.2	Potential uses of hydrogen and hydrogen-derived fuel in transport applications....

202

LIST OF FIGURES

10

Figure ES1LCOE by technology...........................................................................................................................................

14

Figure ES2Median technology costs by region.........................................................................................................

15

Figure ES3

Costs for nuclear new build and lifetime extension of existing plants............................

16

Figure ES4

LCOE with and without CCUS for various carbon prices...........................................................

17

Figure ES5	Sensitivity of LCOE of baseload plants to capacity factor (left)and discount rate (right)................................................................................................................................

18

Figure ES6	Energy value by technology and region relative to average wholesale electricity
price in the Stated Policy scenario...........................................................................................................

20

Figure 3.1

Range of capacity...............................................................................................................................................

44

Figure 3.2

Range of overnight and fixed O&M costs.............................................................................................

45

Figure 3.3

Range of assumed load factors and net efficiency........................................................................

45

Figure 3.4

Overview of LCOE values...............................................................................................................................

46

Figure 3.5

Median technology costs by region.........................................................................................................

47

Figure 4.1	Hourly generation mix and wholesale market price of electricity in India
in the WEO-2018 Stated Policies Scenario, 2020 and 2040.........................................................

76

Figure 4.2	Illustrative example of adjusting the LCOE to incorporate value streams......................

77

Figure 4.3	Energy value by technology and region relative to average wholesale electricity
price in the Stated Policies Scenario.......................................................................................................

78

Figure 4.4	Average capacity credit by technology and region in
the Stated Policies Scenario.........................................................................................................................

79

Figure 4.5	Simulated value adjustments by technology and region based on
WEO 2019 Stated Policies Scenario, 2025.............................................................................................

81

Figure 5.1	LCOE as a function of the discount rate (left: non-renewables, right: renewables)...

84

Figure 5.2	Ratio of fixed to total costs as a function of the discount rate...............................................

85

Figure 5.3	LCOE of coal, natural gas and nuclear as a function of the capacity factor...................

86

Figure 5.4	LCOE as a function of lifetime....................................................................................................................

87

Figure 5.5	LCOE as a function of fuel cost..................................................................................................................

88

Figure 5.6	LCOE as a function of carbon price [in USD/tCO2]..........................................................................

89

Figure 5.7	LCOE with and without CCUS for various carbon prices [in USD/t CO2]..........................

90

Figure 6.1	Experience curves for electrical energy storage (EES) technologies
per nominal discharge power capacity, in different uses..........................................................

96

Figure 6.2	Experience curves of electrical energy storage (EES) technologies
per nominal energy storage capacity, in different uses..............................................................

97

Figure 6.3	Expected future product price based on the experience curves (of Figure 6.2)
for electrical energy storage (EES) technologies per nominal stored energy
capacity, for their different uses...............................................................................................................

98

Figure 6.4	Expected future product price as a function of time, based on Figure 6.3
and an S-type market penetration model...........................................................................................

98

Figure 6.5	Summary of the different cost components covered by RADP, RAPS and RAOP.........

102

Figure 6.6	The R-metrics RADP, RAPS and RAOP as functions of the number ofdischarging hours assuming constant average charging cost and constantround-trip efficiency.........................................................................................................................................

104

Figure 6.7	Dependency of RADP, RAPS and RAOP as a function of the average charging
cost (ACC) for a storage device with characteristics as given in Table 6.3, withconstant round-trip efficiency and constant amount of discharged electricity..........

104

Figure 6.8	Summary of the difference between the A-metrics, AADP, AAPS and AAOP.................

107

Figure 6.9	Comparison between the required and available average discharge price metrics,
RADP and AADP, as a function of the number of discharge hours.......................................

107

Figure 6.10	The available average discharge price as a function of the number ofdischarge hours (NDH)....................................................................................................................................

109

Figure 6.11	The available average price spread as a function of the number ofdischarge hours (NDH) ...................................................................................................................................

109

Figure 6.12	The available average operational profit as a function of the number ofdischarge hours (NDH)....................................................................................................................................

110

Figure 6.13	Comparison of the three available and the three required metrics....................................

111

Figure 6.14	RAOP and AAOP in the Belgian day-ahead market (2010-2015)
for two storage units of different size and cost...............................................................................

112

Figure 6.15	Assigned couples (cycles per annum, discharge duration)
for concrete modeling for each listed application of Table 6.1...............................................

114

Figure 6.16	Projected levelised cost of the cheapest technologies displayed in a 2-D plot
(number of annual cycles Ncyc, discharge duration τd) for the years 2020 and 2040...

115

Figure 7.1	Carbon price, share of emissions covered and carbon pricing revenuesof implemented...................................................................................................................................................

122

Figure 7.2	The Swedish CO2 tax over time (USD/tonne)....................................................................................

123

Figure 7.3	GHG-emissions and GDP development in Sweden since the introductionof the carbon tax.................................................................................................................................................

123

Figure 7.4	Feed-in tariff..........................................................................................................................................................

127

Figure 7.5	Feed-in premium................................................................................................................................................

128

Figure 7.6	Illustration of sliding premium/CFD.......................................................................................................

129

Figure 7.7	The merit order with and without carbon pricing.........................................................................

133

Figure 7.8	The Pigouvian Framework of optimal static internalisation...................................................

136

11

Figures 7.9a and 7.9b

Inframarginal rents of electricity producers in France.........................................
Inframarginal rents of electricity producers in Germany..............................

139

Figure 8.1

Trend in the projected cost of new nuclear in OECD countries.............................................

146

Figure 8.2

Figures 7.10a and 7.10b

12

138

Distribution of LTO investments...............................................................................................................

148

Figure 8.3	Sensitivity of LCOE of new build and LTO of NPPs to capacity factor (left)and discount rate (right)................................................................................................................................

150

Figure 8.4	Evolution of LTO LCOE for different lifetime extension periods (left)
and cost profile of lifetime extensions versus new nuclear (right).....................................

151

Figure 8.5

Nuclear cost and risk reduction drivers...............................................................................................

153

Figure 8.6

SMR economic drivers that help compensate diseconomies of scale...............................

156

Figure 8.7

The market opportunities for SMRs.......................................................................................................

157

Figure 9.1	Projected changes in the numbers of light-duty electric vehicles in France,
including all technologies: 100% electric vehicles (BEV) and plug-in hybrid
electric vehicles (PHEV)...................................................................................................................................

168

Figure 9.2	Carbon footprint of a vehicle over its entire life cycle depending on
the type of engine, the electricity production mix and the distance travelled(reference year 2035).........................................................................................................................................

170

Figure 9.3	Total annualised costs for 15.6 million vehicles by 2035 by engine type.........................

172

Figure 9.4	Estimated costs of reducing greenhouse gas emissions by the electrificationof transport............................................................................................................................................................

173

Figure 9.5	Current consumption and production of hydrogen in France................................................

175

Figure 9.6	GHG emissions avoided (and their location) by the generation of 1 kWhof carbon-free electricity in France depending on its use.........................................................

176

Figure 9.7	Comparison of costs of producing hydrogen between different production
modes........................................................................................................................................................................

177

Figure 9.8	Projections on the evolution of heating systems in residential buildings
in France between 2018 and 2035, according to the directions of the National
Low Carbon Strategy.........................................................................................................................................

180

Figure 10.1	Evolution of global annual demand of hydrogen (pure and mixed) since 1975...........

189

Figure 10.2	Hydrogen supply chain...................................................................................................................................

192

Figure 10.3	Current levelised cost of hydrogen productions for different technologies..................

195

Figure 10.4	Current and projected levelised cost of hydrogen (USD/kg H2) production
from water PEM electrolysis........................................................................................................................

196

Figure 10.5	Hydrogen costs from electrolysis using grid electricity..............................................................

197

Figure 10.6	Hydrogen costs from hybrid solar PV-onshore wind systems in the long term..........

197

Figure 10.7	Current and long-term projected levelised costs of production ofhydrogen-derived fuels and influence of electricity price, full load hours
and CO2 feedstock costs on the projected levelised cost of synthetic kerosene.........

199

Figure 10.8	Cost comparison of low-carbon hydrogen and conventional routes for ammonia
and steel in the long term.............................................................................................................................

201

Figure 10.9	Total cost of ownership for passenger cars and long-haul trucks by powertrain,
range and fuel.......................................................................................................................................................

203

Figure 10.10	Hydrogen prices needed to become competitive against natural gas boilers
and electric heat pumps in selected markets by 2030.................................................................

205

Figure 10.11	Levelised costs of storage as a function of discharge durationand the electricity price..................................................................................................................................

206

Figure 10.12	Levelised cost of electricity generation from natural gas and hydrogen.........................

208

Executive summary

Projected Costs of Generating Electricity – 2020 Edition is the ninth report in the series on the levelised
costs of generating electricity (LCOE) produced jointly every five years by the International Energy(IEA) and the OECD Nuclear Energy Agency (NEA) under the oversight of the Expert Group on
Electricity Generating Costs (EGC Expert Group). It presents the plant-level costs of generating
electricity for both baseload electricity generated from fossil fuel and nuclear power stations, anda range of renewable generation – including variable sources such as wind and solar. For the firsttime, this edition also includes cost data on storage, fuel cells and the long-term operation (LTO)of nuclear power plants. It is a forward-looking study, based on the expected cost provided byparticipating countries of commissioning these plants in 2025, which assumes moderate carbon
costs of USD 30 per tonne of CO2.
Overall, the report provides in total data for 243 plants in 24 countries.1 Figure ES.1 provides asynthesis of the different technologies analysed and the range of their LCOEs at plant-level at a realcost of capital cost and a corresponding discount rate of 7%. Given the increasing importance ofsystem considerations for a comprehensive comparison of different technologies, the LCOE analysis
is complemented by examples of the IEA’s value adjusted levelised costs of generating electricity(VALCOE) measure for selected regions and technologies.

Low-carbon generation is becoming cost competitive
The key insight from this 2020 edition is that the levelised costs of electricity generation of lowcarbon generation technologies are falling and are increasingly below the costs of conventional
fossil fuel generation. Renewable energy costs have continued to decrease in recent years. With
the assumed moderate emission costs of USD 30/tCO2 their costs are now competitive, in LCOE
terms, with dispatchable fossil fuel-based electricity generation in many countries.2 In particular,
this report shows that onshore wind is expected to have, on average, the lowest levelised costs ofelectricity generation in 2025. Although costs vary strongly from country to country, this is true fora majority of countries (10 out of 14). Also solar PV, if deployed at large scales and under favourable
climatic conditions, can be very cost competitive. Offshore wind is experiencing a major costdecrease compared to the previous edition. Whereas five years ago, the median LCOE still exceeded
USD 150/MWh, it is now significantly below USD 100/MWh and therefore in a competitive range.
Both hydro technologies analyses (run of river and reservoir) can provide competitive alternatives
where suitable sites exist, but costs remain very site-specific. The result of IEA’s value adjusted LCOE(VALCOE) metric show however, that the system value of variable renewables such as wind andsolar decreases as their share in the power supply increases.

1.
Participating countries include five non-OECD countries: Brazil, the People’s Republic of China (hereafter China), India,
Romania, the Russian Federation (hereafter Russia) and South Africa. Romania and Russia are, however, member countries ofNEA. Brazil, China and India are association countries of the IEA and key partners of the NEA.
2.
The influence of carbon costs on the LCOE of fossil-fuel based generation is analysed in Chapter 5. However, the report
does not systematically compare all technology LCOEs for different carbon costs.

13

Electricity from new nuclear power plants has lower expected costs in the 2020 edition thanin 2015. Again, regional differences are considerable. However, on average, overnight construction
costs reflect cost reductions due to learning from first-of-a-kind (FOAK) projects in several OECD
countries. LCOE values for nuclear power plants are provided for nth-of-a- kind (NOAK) plants to becompleted by 2025 or thereafter.
Nuclear thus remains the dispatchable low-carbon technology with the lowest expected
costs in 2025. Only large hydro reservoirs can provide a similar contribution at comparable costs
but remain highly dependent on the natural endowments of individual countries. Compared tofossil fuel-based generation, nuclear plants are expected to be more affordable than coal-fired
plants. While gas-based combined-cycle gas turbines (CCGTs) are competitive in some regions,
their LCOE very much depend on the prices for natural gas and carbon emissions in individual
regions. Electricity produced from nuclear long-term operation (LTO) by lifetime extension is highly
competitive and remains not only the least cost option for low-carbon generation - when compared
to building new power plants - but for all power generation across the board.
Coal- and gas-fired units with carbon capture, utilisation and storage (CCUS), for which only
the United States and Australia submitted data, are, at a carbon price of USD 30 per tonne of CO2,
currently not competitive with unmitigated fossil fuel-plants, nuclear energy, and in most regions,variable renewable generation. CCUS-equipped plants would constitute a competitive complement
to the power mix only at considerably higher carbon costs.
The LCOE calculations are based on a levelised average lifetime cost approach, using thediscounted cash flow (DCF) method. Costs are calculated at the plant level (busbar), and therefore
do not include transmission and distribution costs. The LCOE calculations also do not capture othersystemic costs or externalities beyond plant-level CO2 emissions such as, for instance, methaneleakage during the extraction and transport of natural gas. This report does however recognise, inparticular in Chapter 4, the importance of the system effects of different technologies, most notably
the costs induced into the system by the variability of wind and solar PV at higher penetration rates.

Figure ES1: LCOE by technology
250

LCOE [USD/MWh]

200
150
100
50

Fossil fueltechnologies

Gas (CCGT, CCUS)

Coal (CCUS)

Lignite (CCUS)

Biomass

Geothermal

Hydro (run of river, >= 5MW)

Hydro (reservoir, >= 5 MW)

Solar thermal (CSP)

Solar PV (residential)

Solar PV (commercial)

Solar PV (utility scale)

Offshore wind

Onshore wind (>= 1 MW)

Nuclear (LTO)

NuclearGas (CCGT)

Coal

Lignite0

Low-carbon technologies

Note: Values at 7% discount rate. Box plots indicate maximum, median and minimum values. The boxes indicate the central 50% of values,
i.e. the second and the third quartile.

14

Competitiveness depends on national and local conditions
The aggregated data for the 24 countries that provided data for this report does not tell the wholestory of levelised generation costs. Due to more or less favourable sites for renewable generation,
varying fuel costs and technology maturity, costs for all technologies can vary significantly bycountry and region. In addition, the share of a technology in the total production of an electricity
system makes a difference to its value, load factor and average costs.
Whereas renewables are very competitive in most countries participating in this report, thedata provided for Projected Costs of Generating Electricity – 2020 Edition shows that they still havehigher costs than fossil fuel- or nuclear-based generation in some countries (in this report: Japan,
Korea and Russia). Also within countries, different locational conditions can lead to differences ingeneration costs at the subnational and local level. In Europe, both onshore and offshore wind as
well as utility scale solar installations are competitive to gas and new nuclear energy.
In the United States, gas-fired power plants benefit from the expected low fuel prices in theregion, although fuel price assumptions are, in general, uncertain. Nevertheless, in terms of the
LCOE of the median plant, onshore wind and utility scale solar PV are, assuming emission costs of
USD 30/tCO2, the least cost options. Natural gas CCGTs are followed by offshore wind, nuclear newbuild and, finally, coal.
In China and India, variable renewables are having the lowest expected levelised generation
costs: utility scale solar PV and onshore wind are the least-cost options in both countries. Nuclear
energy is also competitive, showing that both countries have promising options to transition out of
their currently still highly carbon-intensive electricity generation.

Figure ES2: Median technology costs by region

Median LCOE [USD/MWh]

250
200
150
100
50

India

United States

China

Solar PV (utility scale)

Offshore wind

Onshore wind (>= 1 MW)

NuclearCoal

Gas (CCGT)

Offshore wind

EuropeSolar PV (utility scale)

NuclearOnshore wind (>= 1 MW)

Gas (CCGT)

Offshore wind

Solar PV (utility scale)

NuclearOnshore wind (>= 1 MW)

Gas (CCGT)

Coal

Solar PV (utility scale)

Offshore wind

Onshore wind (>= 1 MW)

NuclearCoal

Gas (CCGT)

Solar PV (utility scale)

Onshore wind (>= 1 MW)

Nuclear

Coal

0

Japan

Note: Values at 7% discount rate.

15

Lifetime extensions of nuclear plants can be very cost effective
Beyond investments in new sites (greenfield projects), this report includes levelised cost estimates
for the long-term operation of nuclear plants (LTO) – representing extensive refurbishments toenable a secure operation beyond the originally intended lifetime. The report shows that thisbrownfield investment, i.e. making use of the existing facilities and infrastructure, significantly
reduces costs compared to building new greenfield plants. Even at lower utilisation rates, a potential
scenario for nuclear units in systems with high shares of variable renewables, costs are below thoseof new investments in other low-carbon technologies. Other low-carbon technologies with long
lifetimes, in particular hydroelectric plants, could be similarly attractive for such LTO investments
but no cost data was submitted.

LCOE [USD/MWh]Figure ES3: Costs for nuclear new build and lifetime extension of existing plants
200
180
160
140
120
100
80
60
40
20
0
0%

20%

40%

60%

80%

100%

Capacity factor
New build

10-year extension

20-year extension

Note: Values at 7% discount rate. Lines indicate median values, areas the 50% central region.

Carbon capture technologies could be a viable option with higher carbon costs
In the default case with emission costs of USD 30/tCO2, equipping coal and gas plants with a CCUS
is, due to the higher investment costs of CCUS equipment and the reduced thermal efficiency, more
expensive than unmitigated fossil fuel-based electricity.
With higher emission costs however, the picture could change. For coal power plants, due tothe fuel’s relatively high carbon content, CCUS units become competitive at around USD 50 to 60
per tCO2. For gas-fired CCGTs, only carbon prices above USD 100/tCO2 would make plants with
CCUS competitive. At such high carbon prices, renewables, hydroelectricity or nuclear are likely toconstitute the least-cost options to ensure low-carbon electricity.
Although the necessary carbon price levels required for triggering a cost advantage of CCUS
plants exceed the majority of today’s prices, they are still relatively low compared to existing
estimates of the social cost of carbon. Although the estimates carry great uncertainties, global
social costs could exceed USD 100 per tCO2 (Nordhaus, 2017). Thus, if flexible low-carbon generationis needed, competitive alternatives are lacking and affordable fossil resources are available, CCUS
may become an option. Depending on national circumstances, with sufficiently high carbon prices,
CCUS could be a possible complement in certain low-carbon power mixes.

16

200
180
160
140
120
100
80
60
40
20
0

LCOE [USD/MWh]

LCOE [USD/MWh]Figure ES4: LCOE with and without CCUS for various carbon prices

0

20

40

60

80

100

200
180
160
140
120
100
80
60
40
20
0

0

Carbon price
Coal

Coal (CCUS)

20

40

60

80

100

Carbon priceGas (CCGT)

Gas (CCGT, CCUS)

Note: Values at 7% discount rate. Lines indicate median values, areas the 50% central region.

Technologies have to fit into the market
To enhance the comparability of costs between regions and markets, it was necessary to harmonise
certain assumptions. Therefore, in the base cases of our analyses we assume an 85% capacity
factor for nuclear, coal and CCGT plants as well as a 7% discount rate. Depending on the individual
market, these parameters can differ significantly, based on the existing technology mix as well as
the market environment.
With increasing shares of renewable generation for example, baseload plants may lose market
share and have to content themselves with satisfying the residual demand. This is why this report
includes also estimates for 50% load factors for dispatchable baseload technologies such as gas, coaland nuclear. In practice, load factors are country and system specific, but capacity factors of thismagnitude are not uncommon, both in OECD and non-OECD countries.3 Depending on their positionin the merit order, technologies will be affected differently. In the United States, with its low gas
prices, for instance, coal units will typically be dispatched last, and will have lower capacity factors.
The results show that, due to their relatively low investment costs and in many regions moderatevariable costs, gas-fired CCGTs are well suited for handling different generation levels. Nuclear units
on the other hand, due to high investment costs, require high utilisation rates.
A key determinant of competitiveness is the discount rate, which corresponds in the LCOE
methodology to the cost of capital. In its central case, this report assumes a uniform discountrate of 7% for all technologies and countries. In practice, the discount rate reflects, among others,
opportunity costs of investment as well as different kinds of risk and uncertainty, for example
regarding political and regulatory developments, the market design, the system development andfuture investment and fuel costs. Furthermore, in the real world the question of who bears the risk
is important: Government support, in the form of price guarantees for example, would shift the risk
from the investor to the public. Long-term power purchasing agreements would allow sharing the3.
With very high shares of variable renewables, also for example wind and solar PV might have to be curtailed, dependingon the available flexibility options. Their load factors would then be below their theoretical maximum that would increase thereported LCOE values.

17risk between project developers and electricity buyers. Although the overall risk remains the same,the investment would thus become cheaper from an investor perspective. Such factors, which canbe important at the level of an individual project, do not appear explicitly in the LCOE numbers
provided in this report, which does not include considerations of contractual structure or market
intervention.
The more capital-intensive a technology, the more sensitive its LCOE are to changes in the
discount rate. Among baseload plants, this means that in particular the costs of nuclear new build
depend on the discount rate. With a low discount rate of 3%, reflecting a stable market environment
with high investment security, the LCOE of new nuclear plants is lower than for new coal and gas
plant. With higher discount rates at 7% or 10%, which would reflect riskier economic environments,
the costs of a newly built nuclear plant would exceed those of fossil fuel-based plants.

200
180
160
140
120
100
80
60
40
20
0

LCOE [USD/MWh]

LCOE [USD/MWh]Figure ES5: Sensitivity of LCOE of baseload plants to capacity factor (left)and discount rate (right)

0%

20%

40%

60%

80%

100%

200
180
160
140
120
100
80
60
40
20
0
0%

Coal

10%

15%

20%

Discount rate

Capacity factor
Nuclear5%

Gas (CCGT)

Note: Values at 7% discount rate. Lines indicate median values, areas the 50% central region.

System costs are important to show the full picture
The LCOE is a well known and, thanks to its relative simplicity and transparency, well understood
metric for comparing different generation technologies. The common assumptions made in thisreport – for example assuming identical capacity factors for gas, coal and nuclear plants acrossregions – ensures that cost differences can be clearly identified. However, this approach neglects
the differences in individual systems and markets that considerably influence the competitive
position of technologies. These system-specific characteristics interact with the technical andeconomic characteristics of different technologies, i.e. their variability, dispatchability, response
time, cost structure and place in the merit order. This also includes the fact that not all units aredispatched to the same extent across technologies and markets, or that revenues in many markets
are determined by fluctuating prices and not, as assumed in the LCOE analysis, by a stable priceover a technology’s lifetime.

18More importantly, the LCOE metric applies to the level of the individual plant and does notaddress the value that different generation technology options add to the electricity system at
different levels of penetration. The electricity generation of variable renewables of a particular type
is correlated and not reliably available at all times. The simultaneity of generation, which is notnecessarily correlated with demand, reduces the value of generation. The lack of reliability requires
either dispatchable back-up or, alternatively, flexibility options such as storage or demand response
to ensure security of supply at all times. Additionally, potentially rapid changes in variable renewablegeneration need to be balanced. To understand this impact and to ensure that a given demand issatisfied with low-carbon electricity at least cost, electricity system-level analysis is required (see
IEA, 2019 and NEA, 2019). Overall, this means that LCOE increasingly needs to be contextualised byother analyses in order to obtain a meaningful picture of the relative competitiveness of different
electricity generating technologies.
In order to complement the LCOE approach and enable a more system specific cost comparison,
the IEA has developed a methodology to adjust the costs by a system value component known as
the value adjusted LCOE (VALCOE). It modifies the LCOE of an individual technology in a particularelectricity system according to its contribution to enabling all aspects of securely operating thesystem. Crucially, the calculated results reflect the value in existing, i.e. brownfield systems andtheir possible future development.
The results illustrate that a technology’s plant-level generating costs can vary significantly fromits value to the system. The importance of taking this into account is especially striking when
considering variable renewables: solar PV units show a high correlation in the output of individual
plants resulting, in the analysed scenarios, in a significant reduction of the generation value withincreasing shares. Curtailment during hours of high production is an additional issue and may inpractice reduce load factors and increase LCOE compared to reported values. This would be taken
into account in the system analysis. By contrast, the output of wind plants is less correlated amongindividual units and thus its loss in value is less even as its share increases. At current levels of
capacity, the impact of correlation is still limited in many markets, but it may rise if ambitious
renewable targets are realised and relative shares increase. Technologies with high variable costs(such as high-flexibility open-cycle gas turbines), that produce only during a few hours with very
high prices, provide on average a higher value (per unit of generation) to the system. Baseload
plants, typically CCGTs (an exception is Europe, where they are mostly operating during hours withhigh residual load), coal and nuclear, that produce reliably over a high number of hours provide avalue similar to the system average.
The results reported in Figure ES6 provide sample results of IEA’s VALCOE analysis for theEuropean Union, China and the United States. While covering these large geographical regions, themodel does not take into account grid bottlenecks or cross-border flows but instead assumes full
integration across areas. Results thus potentially underestimate the flexibility constraints of futuresystems. The VALCOE measure provides an innovative approach to capture the complexities ofsystem analysis in a single metric. Values depend not only on the overall share of variable renewables,
but also on the costs of complementary resources such as energy storage or interconnections
and the costs of competing technologies. Contrary to many other system analysis that simulate
future system developments assuming long-run cost optimality, the scenario underlying the
VALCOE calculations tries to replicate real-world systems. Future work will systematise and refine
current results.
Assessing the system contribution of different generation technologies provides a more
complete picture of their economic costs. However, in order to obtain a measure of their full costs
to society, the impacts on human health (both through air pollution and through major accidents),
the environment, employment, the availability of natural resources and the security of supply need
to be included (see, for instance, NEA 2018).

19

Figure ES6: Energy value by technology and region relative to average wholesale electricity pricein the Stated Policy scenario
European Union

China

United States

150%

Energy value

125%
100%
75%
50%
15%

30%

45%

10%

20%

30%

10%

20%

30%

Share of variable renewables
Gas CCGT

Solar PV

Onshore wind

Offshore wind

Source: IEA (2019).

Storage is becoming more important
Increasing shares of variable renewables in the energy mix increase the volatility of electricity
prices and therefore improve the profitability of flexibility and balancing options. At the sametime, sinking investment costs, for example for battery units, are already making short-term
battery storage an economically attractive option in some niche applications (e.g. ancillary services
markets). As more volatile electricity prices make inter-temporal arbitrage more attractive, storage
could become an attractive alternative to peaking units such as open-cycle gas turbines, thusincreasing its importance in the coming years. For the first time, a report of the Projected Costs ofGenerating Electricity series thus includes cost data for storage provided by participating countries.
Storage could complement variable renewable generation to improve the alignment of, forexample, wind and solar PV generation with electricity demand. In future low-carbon systems, a
mix of multiple flexibility options, for example storage, demand flexibility and flexible low-carbon
output from, for instance, nuclear and hydro plants is likely to provide minimal cost solutions.
To better understand the future of storage, its role in energy systems is scrutinised repeatedly
throughout the report. Expected cost data for 2025 form the basis for further analysis, followed
by a thorough discussion about options for measuring the competitiveness of storage throughenhancing the LCOE methodology to come up with a levelised cost of storage (see Chapter 8). One
important insight is that storage refers to a continuum of technologies with different ratios ofenergy to capacity (E/P) as well as different costs, load factors and economic roles in the complex
system interactions of modern electricity systems.

20Additional perspectives
Five “boundary chapters”, free-standing articles contributed by experts in the respective areas,
complement the report - considering wider issues related to the costs of electricity generation andbroadening the scope of the core analysis.
The 2020 edition of the Projected Costs of Generating Electricity series is the first to include data on
the cost of storage based on the methodology of the levelised costs of storage (LCOS). Chapter 6, acontribution from researchers at the Department of Mechanical Engineering at KU Leuven, shows
how to calculate the LCOS according to transparent and robust protocols – accounting for thedifferences between storage technologies.
Chapter 7 constitutes a synthesis of the state of knowledge about the impacts of carbon pricing in
the electricity sector. The collaboration by researchers from the NEA and the Swedish Environmental
Agency provides an overview of current carbon pricing initiatives and their impacts on the economy,carbon emissions, electricity prices and distribution. It analyses potential advantages of allocating
carbon emission cost to taxpayers rather than to electricity consumers.
As identified in the 2019 IEA report Nuclear Power in a Clean Energy System and confirmed in thisreport, life extension of existing nuclear power plants can be a highly cost effective investment
opportunity for low-carbon generation. Chapter 8, authored by the NEA, presents an up-to-dateview of the potential role of nuclear energy in decarbonised electricity systems. It highlights
the cost advantages of lifetime extensions (LTO), potentially significant cost reductions for new
constructions after gaining experience with new designs and the potential of small modular
reactors (SMRs).
To reduce energy-related emissions, it is not sufficient to decarbonise the electricity sector, but
electricity also has to replace fossil fuels in other end-use sectors. Chapter 9 is a contribution by
the French electricity TSO (transmission system operator) RTE on the transformation of the overall
energy sector through electrification and sector coupling. It concentrates on the impacts of theincreasing penetration of electric vehicles, industrial hydrogen use and energy efficiency measuresin residential heating on electricity demand and supply in France and Europe until 2035 – stressing
the increasing need for comprehensive system analyses.
Chapter 10, a contribution by the IEA, presents a detailed discussion of hydrogen as a potential
key element in the transition towards a clean, secure and affordable future energy system, further
strengthening the need to adopt a system perspective. Based on the 2019 IEA report The Future of
Hydrogen: Seizing Today’s Opportunities, the chapter highlights the critical role of the power sector
in the realisation of the new emerging opportunity, but also potential barriers and necessary
next steps. It concludes the five boundary chapters taking a broad, forward-looking approach to achanging energy world.

21

Conclusions
This ninth edition of Projected Costs of Generating Electricity focuses on the cost of electricity generationfrom a wide set of technologies in a large range of countries. Inevitably, regional, national and local
conditions have their importance. Nevertheless, the increasing competitiveness of low-carbon
technologies for electricity generation remains the key insight of this report. This holds both forvariable renewables such as wind and solar PV, as well as flexible low-carbon generators such ashydro and nuclear energy (including LTO). Even at a modest carbon price of USD 30 per tonne of CO2,
unmitigated coal is no longer competitive. Gas-fired electricity generation remains competitive in somemarkets, especially OECD North America, due to very low gas prices. CCUS would require considerably
higher carbon prices than those seen in most markets today to become competitive.
The report also considers for the first time in some depth the costs of the system effects of different
generating options, most notably the variability of wind and solar PV. Such system analysis will
become increasingly important as their penetration in the electricity systems of OECD and non-OECD
countries increases. Logically, the costs of storage are also included for the first time. Lastly, this reportprovides a perspective on the coming electrification of sectors such as transport, hydrogen or heatproduction, which will integrate electricity generation with the wider economy in new and importantways. The chances are that these latter two aspects will play an even greater role in future editions of
the Projected Costs of Generating Electricity.

References
IEA (2019), World Energy Outlook 2019, IEA, Paris, www.iea.org/reports/world-energy-outlook-2019.
NEA (2019), The Costs of Decarbonisation: System Costs with High Shares of Nuclear and Renewables, OECD
Publishing, Paris, https://doi.org/10.1787/9789264312180-en.
Full Costs: NEA (2018), The Full Costs of Electricity Provision, OECD Publishing, Paris, https://doi.
org/10.1787/9789264303119-en.
Nordhaus, W.D. (2017), “Social Cost of Carbon in DICE Model”, Proceedings of the National Academy of
Sciences, February 2017, No. 114 (7), pp. 1518-1523; https://doi.org/10.1073/pnas.1609244114.

22

Part 1
Methodology and data on the levelised costsof electricity (LCOE)

Chapter 1

Introduction and context

Projected Costs of Generating Electricity – 2020 Edition is the ninth edition of the report on electricity
generating costs published jointly every five years by the International Energy Agency (IEA) andthe OECD Nuclear Energy Agency (NEA). It is also referred to at times as the report on Electricity
Generating Costs (EGC). As with previous editions, this report relies on the direct contributions
on generation cost data from the governments of both OECD and non-OECD countries, which inturn consulted widely with national experts and industry organisations. This data is then vetted
and processed according to a common methodology by the EGC Expert Group (EGC EG), composedof experts from government, academia and industry. The EGC Expert Group provides advice on
methodology and data as well as on the content and format of the final report. Over the years, theProjected Cost series of reports on electricity generating costs has become a widely used tool forpolicymakers, modellers and experts, as well as the interested public, to inform discussions about
the economics of power generation.
The core of this edition of this Projected Costs report are the detailed data tables in Chapter 3 thatindicate the levelised cost of electricity (LCOE) and its components for different power generation
technologies in different OECD countries. These LCOE figures have been computed on the basisof the inputs received from participating countries and a common methodology that is identicalfor all technologies and countries. Chapter 2 presents in detail the methodology, conventions andassumptions underlying the LCOE analysis as they have been validated by the EGC Expert Group.
The set of countries included in the 2020 edition is broadly consistent with that of the 2015 and2010 editions. As usual, the IEA and NEA jointly invited representatives of OECD countries, and aselect group of non-OECD countries, to submit data for inclusion in this report. Countries participate
on a voluntary basis, and while some countries elected not to submit data this time, a number of
additional countries that did not do so in previous editions participated in this edition. The resulting
dataset offers a diversity of both countries and technologies that is broadly representative of thecosts of electric power generation today. For the first time, a Projected Cost report also includes cost
data on fuel cells and storage technologies in form of the levelised cost of storage (LCOS). In total,
the EGC 2020 report contains data for 243 power plants with different generating technologies from24 countries to be commissioned in 2025 (see Table 1.1 below).
The overall structure of the data received for this 2020 edition is comparable with the previous
2015 edition. The relative shares of submissions for the different technologies remain roughly stable.
As in previous editions, there are more than 20 submissions for power plants based on natural gas,solar PV, onshore wind and hydroelectricity. Similarly, there are more than ten submissions forcoal-fired power plants and combined heat and power (CHP) plants.

25

1
However, the 2020 edition contains now 26 entries for offshore wind plants, compared to only
12 in 2015, which reflects its status as an important additional asset in the progress towards
carbon neutrality. Cost data for eight storage installations is included for the first time, reflecting
its contribution to the provision of flexibility and balancing services in the presence of increasing
supply from variable wind and solar PV plants. Lastly, cost estimates for four nuclear LTO projects,
i.e. the lifetime extension of existing plants that have reached the end of their originally scheduled
operating lifetimes, are included for the first time (see Chapter 10 for a detailed discussion of
the underlying data). In principle, comparable LTO projects exist also for other technologies, inparticular large hydroelectric installations. However, participating countries did not provide LTO
data for other technologies.

Table 1.1: Summary of responses by country and technology
Country
Australia
Austria
Belgium
CanadaDenmark
FinlandFranceGermany
Hungary
Italy
Japan
Korea
MexicoNetherlands
NorwayRomania
RussiaSlovakRepublic
SwedenSwitzerland
United States

NaturalCoal Nuclear LTO
gas
3
4
6
3

1

2
1
2
3

1
1

1

1
1

1
1

Solar Solar Onshore Offshore
Hydro CHP Storage Other TOTAL
PV thermal wind
wind
1
1
1
1
1
12
1
1
1
3
4
4
2
16
2
1
1
7
4
1
2
7
2
16
1
1
2
3
1
1
3
10
1
1
4
4
7
14
15
1
1
6
46
2
1
1
1
8
2
1
1
8
3
3
1
4
1
1
2
1
5
2
3
2
3

1

2

8

1

1
1
2
18

1
1
8

2
1
1
1

15

4

1
1
1
52

3

1
3

10

4

1
1
1
44

14

8

2

1

1

2
1
64

Non-OECD countries
Brazil
China
India
TOTAL

2
1
26

1
23

1
30

12

2
8

1
14

7
6
9
243

*D
 ata for China and India was drawn from various publicly available sources. Romania and Russia are members of the OECD Nuclear Energy
Agency only.

Beyond LCOE
The levelised costs of electricity (LCOE) constitute an important reference for policy making andmodelling. Historically, the LCOE concept was developed in order to help choose between different
dispatchable baseload technologies in regulated systems. The authors of this report and themembers of the EGC Expert Group are aware that LCOE, just as other metrics, has specific strengths
and limitations as an indicator of competitiveness in today’s electricity markets. For example, as
operators earn a complex mix of revenues in volatile energy and flexibility markets, the risk andhence the cost may differ for an investor even for technologies with identical LCOE. Developing a
cost indicator for combined heat and power (CHP) plants remains challenging for any metric, as it26

1
needs to allocate costs to two different value streams and thus the value of heat can have a large
influence on final LCOE figures. Nevertheless, the simplicity, transparency and intuitive appeal of
the LCOE concept ensure that it remains a widely used metric by a large range of stakeholders inpolicy discussions, for modelling purposes or for communicating with the wider public.
The driver for a new approach, such as the one outlined in Chapter 4, is the recognition of
the growing share of variable renewable energy (VRE) technologies such as wind and solar PV in
today’s power systems and the impact they will have on the value of the electricity produced. Mostof today’s generation investment globally is in solar PV and wind power and this is anticipated tocontinue. In the Stated Policies Scenario of the 2019 World Energy Outlook (IEA, 2019a), which reflects
the impacts of implemented and announced government policies and evolution of the costs ofenergy technologies, wind and solar PV together provide over half of the growth in electricity
supply, raising their share from 7% in 2018 to 24% in 2040. In the more environmentally ambitious
Sustainable Development Scenario, which is consistent with limiting the temperature rise to 2 100
to 1.8°C with a 66% probability or a 50% chance of a 1.65°C stabilisation, the gains by wind and solar
PV are even more striking: rising to 40% of the global electricity supply by 2040.
As the share of variable renewable energy generation (VRE) increases, their contribution to thesystem – and their costs – will go beyond the energy they produce. Their advent requires taking
into account the system contribution or system costs of different technology as an indispensable
complement for a full picture of the costs and value of any given technology. The integration of
VRE results in more complex interdependencies. The cost of each technology thus depends on thespecific system to which it is being added (see Box 1.1).

Box 1.1

System costs of VREs
When considering the costs of generation at the level of the electricity system, this reportpursues a three-pronged strategy to complement the basic LCOE figures: (1) system costs analysis,(2) sensitivity analyses and (3) boundary issues.
First, regarding system costs analysis, a number of studies including Hirth, Ueckerdt and
Edenhofer (2015), IEA (2011) and (2016), MIT (2018) or NEA (2012) and (2019) have assessed thecosts at the level of the system rather than at the level of the plant of different technologies. The
key parameter here is the variability of a resource in function of meteorological conditions or,conversely, its ability to provide power at will at any given moment according to demand. Quite
obviously, this concerns primarily variable renewable resources such as wind and solar PV.
Other things equal and, in particular, at identical levels of security of electricity supply, the morevariable a resource and the less correlated with demand its output, the higher are the additional
profile costs that it imposes on the system as a whole. In most reference systems, offshore wind
thus generates less system costs than onshore wind, whose system costs in return are somewhat
lower than those of solar PV. At the same time, if variable output correlates well with demand,e.g. if peak solar PV production at noon coincides with peak demand and its capacity is modest,
its output can decrease system costs.
Conversely, the size of the additional costs will also depend on the particular electricity
system. Systems with smaller shares of VRE, greater shares of flexible resources, and moreinterconnections are able to incorporate additional VRE very inexpensively whereas those withhigh shares and limited or no interconnection will find incremental system costs to be higher.
The key point is that the system costs depend on the existing system, and not just the plant-leveltechnology.

27

1(continued Box 1.1)
The central insight of the system costs approach can be summarised as follows: different
technologies with different abilities to provide power at any given moment over any desired
timeframe have different functions in different integrated electricity systems. LCOE, while itcontinues to provide uniquely relevant information, no longer allows assessing a technology’s
costs fully on its own. In particular, LCOE does not say anything about the value a technology
brings to the system. Indeed, a technology’s full system costs and system contribution dependon the surrounding system, in particular the structure of the overall technology mix, the amountof interconnections as well as the structure and flexibility of demand. Finally, while there still iscompetition between different technologies there is also an increasing recognition that different
technologies are complementary. This holds, in particular, for the low-carbon systems of the
future where variable renewables such as wind and solar PV will have to be complemented, in
addition to flexibility providers such as storage or demand response, by dispatchable generatorssuch as biofuels, nuclear energy, hydropower, coal- and gas-based generation equipped withCCUS.
The challenge is to translate such system cost considerations into a simple metric similar to
the LCOE. As part of its work on the World Energy Model (WEM), which prepares the projectionsof its annual World Energy Outlook, the IEA has developed since 2018 the VALCOE metric. It adjusts
LCOE values at the plant-level for the system contribution of a technology and thus provides
a more complete picture of its system cost per MWh depending on the technology mix of thesystems including the level of VRE penetration. The VALCOE necessarily depends on the system
in which a technology is deployed. Currently, estimates exist for China, Europe, India and North
America as reported in Chapter 4. With time, advances in system cost analysis, both at the IEA
and elsewhere, will increasingly include a broader set of countries and regions. Future editionsof Projected Costs will undoubtedly contain an even more systematic assessment of the system
value and system costs of different generation technologies. An overview of methodologicalissues in system analysis, including an attempt to unify the different approaches pursued in thecurrent literature, will be prepared jointly by IEA and NEA in collaboration with the EGC EG for
publication in 2021.

Second, this report provides in Chapter 5 a comprehensive sensitivity analysis of the LCOE
values presented in the data tables of Chapter 3. Such sensitivity analysis constitutes a necessary
complement to the reference values derived on the basis of the default assumptions. For reasons ofreadability, transparency and comparability, it is indispensable to treat the raw cost data received
from participating countries according to a common methodology with identical assumptions. The
solidity of the methodology and the plausibility of the assumptions both ensured by the IEA andNEA Secretariats and the EGC Expert Group largely constitutes the value added that this series ofreports brings to the international debate. Given their importance, already the common data tables
differentiate values according to the real discount rates of 3%, 7% and 10% and load factors of 85%
and 50% for coal, nuclear and CCGTs as well as 30% and 10% for open-cycle gas turbines (OCGTs).
However, this is not sufficient for fully understanding the economics of electricity generation.
Once reference values based on common assumptions are established, it remains necessary toundertake a broader analysis of how LCOE figures vary as soon as the underlying assumptions
such as the discount rate, load factors, or fuel costs change over a broad range of possible values.
A new graphic display of those variations used for the first time in this report allows a form ofrepresentation that is both intuitive and systematic. The web-based version of the Projected Costs ofGenerating Electricity – 2020 Edition includes a LCOE calculator that allows users to complement the
published version with results computed with the raw cost data and their own assumptions.
Third, in order to provide further context for the LCOE numbers, similarly to previous editionsof the Projected Costs series, this report includes in Part II five so-called “boundary chapters”. The
boundary chapters are five free-standing articles which consider issues relating to the costs of28

1electricity generation costs in a broad and dynamic long-term perspective. The date of commissioningof the plants for which the LCOE are reported in Chapter 3 is supposed to be 2025. This is by andlarge a realistic timeframe for a power generation project, for which an investment decision istaken today, even if the construction times for different technologies differ considerably and are, on
average, somewhat longer for new nuclear or hydroelectricity projects. However even today, energy
policymakers and utility executives need to set their sight on far longer time horizons. Investment
plans are formulated for a decade ahead or more and energy policy objectives in many OECD
countries are referenced with respect to a long-term objective of net carbon neutrality by 2050. The
boundary chapters on sector coupling, carbon pricing, the costs of storage, the hydrogen economy,or the evolution of nuclear energy, thus provide added information on trends that will have abearing on electricity generating costs over the medium- and long-term. They are best thoughtof as different spotlights on the low-carbon electricity sectors of the future, where changes intechnologies, economics, and regulatory frameworks already underway will interact in promising
but as of yet still unforeseeable ways.
As mentioned earlier, this 2020 edition of the Projected Cost series is the first to include data on
the cost of storage based on the methodology of the levelised costs of storage (LCOS). Chapter 6,
a contribution from the energy modelling team of the Department of Mechanical Engineering at
KU Leuven, shows how to calculate the LCOS according to transparent and robust protocols. The
challenge in cost accounting for storage is always that its output, electricity, is also its input, only
provided or sourced at different moments. Assumptions about the time profile of charging anddischarging electricity are thus decisive for calculating its unit costs per MWh. In addition, there
are vast differences between different storage technologies with respect to the trade-offs theypresent between capacity and energy. Batteries with high capacities for a few seconds or minutes
are already widely available on commercial terms. The greater challenge consists in developing andfinancing storage technologies capable of delivering a sizeable amount of electricity for hours or
days. Beyond that, only dispatchable generation will be able to provide the flexibility for variations
in variable renewable generation and demand. This chapter provides a widely applicable and easily
replicable methodology that is helpful to assess the economic feasibility of different available
technological options.
Chapter 7 constitutes a synthesis of the state of knowledge about the impacts of carbon pricing
in the electricity sector based both on recent conceptual and empirical research. The collaboration
by researchers from the NEA and the Swedish Environmental Agency provides an overview ofcurrent carbon pricing initiatives and their impacts on the economy, carbon emissions, electricity
prices and distribution. In particular, the article contains not only information on carbon tax
regimes and emission trading systems but also on initiatives to support low-carbon generation
through per unit subsidies such as the zero emissions credit (ZEC) scheme in the United States.
It underlines, in particular, the potential advantages in the political economy of carbon pricing toallocate the costs of carbon emission reductions to tax payers through subsidy schemes rather thanto electricity consumers through taxes and higher prices.
In Chapter 8, authors from the NEA report on the potential of nuclear energy to make a decisive
contribution to low-carbon electricity generation in the short and long run. As evidenced by the LCOE
figures reported in Chapter 3, the existing nuclear fleet continue to be a highly competitive sourceof dispatchable low-carbon electricity. In the short run, nuclear new build will be complemented
by the long-term operations (LTO) of existing plants that have reached the end of their original
operating lifetimes. While requiring extensive refurbishments and replacement of some key
components, LTO constitutes currently the least-cost option for low-carbon electricity generation.
Building on the lessons learnt from recent first-of-a-kind projects important efforts are taking place
to significantly reduce the investment costs of the current large-scale Generation III+ reactors inthe short run (NEA, 2020). Over the longer run, a new generation of small modular reactors (SMR),
with a significant share of factory-produced components and lower financing requirements, holdout further promise to extend, in a complementary way to large reactors, the contribution of nuclear
power to low-carbon electricity.

29

1
Chapter 9, a contribution from RTE, the French electricity transmission system operator,
examines the transformation of the overall energy sector through electrification and sector
coupling. Different generation technologies thus interact in order to provide overall least-cost
solutions for carbon abatement. It concentrates on the impacts of the increasing penetration ofelectric vehicles, industrial hydrogen use and energy efficiency measures in residential heating onelectricity demand and supply in France and Europe until 2035. Recent advances in detailed energy
and electricity sector modelling allow today a far better understanding of how these policy-driven
developments will affect the level, structure and flexibility of demand -- and hence the compositionand cost of electricity supply. All the aspects of the analysis in Chapter 6 strongly confirm thegeneral insight that drives the thinking behind this edition of the Projected Costs series: today’scomplex electricity systems with large shares of variable renewables require that the analysis ofgeneration costs adopts an ever broader systems approach.
Chapter 10 broadens and deepens the analysis of one of the issues included in the system analysis
of Chapter 9: the potential of hydrogen in the low-carbon electricity and energy systems of the
future. The advantages of hydrogen, light, of high energy density and producing no emissions otherthan water vapour during combustion, as a vector for storage and transport in integrated electricity
systems are obvious and it is already widely used for industrial purposes today. However, its wider
adoption in areas such as transport (in particular trucking and shipping), industrial processes,
heating or electricity storage has been held back by the high carbon intensity of the current, still
most cost-effective method of hydrogen production, steam methane reforming. Chapter 10, basedon the recent IEA publication The Future of Hydrogen (IEA, 2019b) explores the potential for hydrogenbased on alternative production methods such as electrolysis using low-carbon electricity fromrenewables, nuclear and hydroelectricity or biomass conversion. Enjoying strong government
support and benefiting from a potential fall in the costs of both low-carbon electricity in several
parts of the world and of electrolysers, hydrogen is today at a critical juncture at which it can provethat it is indeed able to make a sizeable contribution to the low-carbon electricity systems of the
future.
Overall, the authors and contributors of Projected Costs of Generating Electricity – 2020 Edition havemade every effort to ensure that also this edition remains the same readily accessible and usefulreference on electricity costs for policymakers, modellers and experts as it predecessors. As far
as plant-level average cost numbers are concerned, the most interesting new extensions concern
offshore wind, storage and nuclear LTO. However, it is also clear that in increasingly complex andtime-differentiated electricity systems, different technologies have increasingly different functions
and hence very different revenues per MWh. This will further reduce the relevance of direct LCOE
comparisons in the long run.
Projected Costs of Generating Electricity – 2020 Edition provides a gradual entry into systems
considerations while striving to preserve traditional benefits of transparency and comparability.
This process will continue in the same gradual manner based on and methodological conventions
and modelling capabilities that still need to be established. Future editions will offer further insightsfrom the fast evolving field of electricity system analysis. In the meantime, the 2020 edition provides
an overview of a sector in transition. Most technologies presented here will play their part in the
decarbonising and increasingly differentiated electricity systems of the future. Several boundary
chapters also indicate that this future will be increasingly electric. The Projected Costs of Generating
Electricity will continue to accompany the development towards an electric low-carbon future.

30

1References
ADEME (2018), Trajectories d’évolution du mix électrique 2020-2060, Agence de l’Environnement et de laMaîtrise de l’Énergie, Paris.
Hirth, L., F. Ueckerdt and O. Edenhofer (2015), “Integration costs revisited – An economic framework
for wind and solar variability”, Renewable Energy, Vol. 74, pp. 925-939.
IEA (2019a), World Energy Outlook 2019, IEA, Paris www.iea.org/reports/world-energy-outlook-2019.
IEA (2019b), The Future of Hydrogen, IEA, Paris www.iea.org/reports/the-future-of-hydrogen.
IEA (2016), Next Generation Wind and Solar Power: From cost to value, IEA, Paris, https://doi.
org/10.1787/9789264268715-en.
IEA (2011), Harnessing Variable Renewables: A Guide to the Balancing Challenge, IEA, Paris www.iea.org/
reports/harnessing-variable-renewables.
IEA/NEA (2015), Projected Costs of Generating Electricity 2015, OECD Publishing, Paris, https://doi.
org/10.1787/cost_electricity-2015-en.
MIT (2018), The Future of Nuclear Energy in a Carbon-Constrained World: An Interdisciplinary MIT Study,
Massachusetts Institute of Technology, Cambridge (MA).
NEA (2020), Unlocking Reductions in the Construction Costs of Nuclear: A Practical Guide for Stakeholders,OECD Publishing, Paris, https://doi.org/10.1787/33ba86e1-en.
NEA (2019), The Costs of Decarbonisation: System Costs with High Shares of Nuclear and Renewables, OECD
Publishing, Paris, https://doi.org/10.1787/9789264312180-en.
NEA (2018), The Full Costs of Electricity Provision, OECD Publishing, Paris, https://doi.
org/10.1787/9789264303119-en.
NEA (2012), Nuclear Energy and Renewables: System Effects in Low-Carbon Electricity Systems, OECD
Publishing, Paris, https://doi.org/10.1787/9789264188617-en.

31Chapter 2

Methodology, conventions
and key assumptions

This chapter presents the EGC model used to calculate lifetime (long-run) average levelised costs(LCOE), as well as the methodological conventions and key assumptions to ensure consistency among
cost estimates for different countries. The economics and methodology behind the calculation oflevelised average lifetime cost for each generating technology are discussed here. However, only afew parameters can be included in any general model, and many factors that have not been taken
into account influence costs. Methodological limitations of the LCOE approach are discussed in thecontext of its extension towards value adjusted levelised cost of electricity (VALCOE) in Chapter 4.

2.1

The levelised cost of electricity (LCOE)

The LCOE is the principal tool for comparing the plant-level unit costs of different baseload
technologies over their operating lifetimes. The LCOE indicates the economic costs of a generictechnology, not the financial costs of a specific projects in a specific market. Due to the equality
between discounted average costs and the stable remuneration over lifetime electricity production,
which is at its heart, LCOE is in spirit closer to the costs of electricity production in regulated
electricity markets with stable tariffs, for which it was developed, than to the variable prices inderegulated markets. By adjusting the discount rate for the implicit cost of price volatility, the LCOE
concept can, in principle, also be applied in the context of deregulated markets.
However, one can identify a number of developments in the operations of today’s electricity
systems that, to different degrees, limit its immediate applicability. In the regulated markets ofthe past, the technology with the lowest LCOE really was indeed also the best baseload investment
choice. This no longer holds true. First, structural changes in demand and supply, in particular theadvent of significant shares of variable renewables with zero short-run marginal costs, but also
storage, distributed generation or demand response, challenge the notion of baseload production
itself, i.e. technologies with comparable characteristics that predictably produce at high loadfactors. Second, the complexity of markets with a spectrum of flexibility needs leads to increased“revenue stacking” even for former baseload producers rather than dedicated production for one
single (forward) market. Third, in the context of these increased flexibility needs, portfolio effects
become increasingly important rather than the technical characteristics of individual units. Fourth,and most importantly, in systems with large shares of variable renewables, one must complement
cost considerations with value considerations, or, equivalently, account for costs at the systemrather than at the plant level.

33

2
As explained more comprehensively in Chapter 4 on VALCOE, a technology’s true economic cost
will now depend also on its overall share and technical characteristics as well as on the costs andtechnical characteristics of all other technologies in the system rather than only on the discounted
sum of its investment costs and variable costs at the plant-level.
Nevertheless, despite its limitations, well understood by the experts, LCOE has maintained
its appeal. It remains a uniquely straightforward, transparent, comparable, and well understood
metric. While there is an increasing need to complement it with information, such as provided by
VALCOE, on the system contribution of different technologies under different constellations, the
LCOE retains its fundamental usefulness as a widely used tool for modelling, policy making andpublic debate.

The question of discounting
The calculation of the LCOE is based on the equivalence of the present value of the sum of discounted
revenues and the present value of the sum of discounted costs. Another way of looking at LCOE is
considering it the electricity tariff at which an investor would precisely break even after paying therequired rates of return on capital, given the costs incurred over the lifetime of a technology. Thisequivalence of discounted lifetime revenues and LCOE is based on two important assumptions:(1)	The real discount rate r used for discounting costs and benefits is stable, the same for alltechnologies and does not vary during the lifetime of the project under consideration. The
EGC 2020 edition uses a 3% discount rate (corresponding approximately to the “social costof capital”), a 7% discount rate (corresponding approximately to the cost of capital of a large
utility in a deregulated or restructured market), and a 10% discount rate (corresponding
approximately to cost of capital in an environment with relatively higher risks). Nominal
discount rates would be higher, reflecting inflation. While in practice the cost of capital, andhence the relevant discount rate, may vary between different technologies, assuming an
identical costs of capital for all technologies allows comparing the costs across technologies
and across regions.
(2)	The electricity tariff, PMWh, is stable and does not to change during the lifetime of the project.
All output, at the assumed capacity factor, is sold at this tariff.
With annual discounting, the LCOE calculation begins with Equation (1) expressing the equality
between the present value of the sum of discounted revenues and the present value of the sum of
discounted costs, including payments to capital providers. The subscript t denotes the year in which
the sale of production or the cost disbursement takes place. The summation extends from the startof construction preparation to the end of dismantling, which includes the discounted value at thattime of future waste management costs. All variables are real, i.e. net of inflation. On the left-handside one finds the discounted sum of benefits and on the right-hand side the discounted sum of costs:∑PMWh * MWh * (1+r)-t = ∑(Capitalt + O&Mt + Fuelt + Carbont + Dt) * (1+r)-t

34

PMWh

=

The constant lifetime remuneration to the supplier for electricity;

MWh=

The amount of electricity produced annually in MWh;(1+r)-t=

The real discount rate corresponding to the cost of capital;

Capitalt=

Total capital construction costs in year t;

O&Mt

=

Operation and maintenance costs in year t;

Fuelt

=

Fuel costs in year t;

Carbont =Carbon costs in year t;

Dt

Decommissioning and waste management costs in year t.

=

(1)

2
Because PMWh is a constant over time, it can be brought out of the summation, and Equation (1)can be transformed into
LCOE = PMWh =∑(Capitalt + O&Mt + Fuelt + Carbont + Dt) * (1+r)-t∑ MWh (1+r)-t(2)

where this constant. PMWh. is defined as the levelised cost of electricity (LCOE).
Equation (2) is the formula used here to calculate average lifetime levelised costs on the basis of
the costs for investment, operation and maintenance, fuel, carbon emissions and decommissioning
and dismantling provided by OECD countries and selected non-member countries. (For CHP plants,
a heat credit is subtracted from total unit costs to establish the LCOE.) It is also the formula that has
been used in previous editions of the EGC series on the cost of generating electricity, and in mostother studies on the topic.
Some confusion could arise if Equation (2) were taken out of context. In that equation, it looks as
if MWhs are being discounted. Because PMWh is a constant, it can be taken out of the summation ofrevenues over the plant’s lifetime and both sides of Equation (1) can be divided by this summation.
It is not the MWhs that are being discounted; it is the revenue from those MWh that is being
discounted. Revenue today has more value to the investor/owner/operator than revenue tomorrow.
It is not output per se that is discounted, but its economic value. This is standard procedure in costbenefit accounting.

Calculating the costs of generating electricity
Before presenting the different methodological conventions and default assumptions employed
to harmonise the data received from different countries, one major underlying principle must
be discussed: this report on Projected Costs of Generating Electricity is concerned with the levelised
cost of producing baseload electricity at the plant level. While this seems straightforward, it has
implications that are frequently less evident.
First, the assumed capacity factors are reasonable consensus assumptions for comparing
baseload technologies. They do not reflect the technical capabilities of different technologies.
For example, nuclear power plants can operate more than 90% capacity factors in years without
refuelling outages, and combined-cycle natural gas turbines (CCGTs) can operate at less than 80%
when they are too expensive to compete in the baseload market. For nuclear, coal and CCGTs astandard capacity factor of 85% was chosen. This is higher than the average observed capacity
factors in practice, and particularly so for CCGT plants (this report does not consider steam cycle
natural gas plants). The reason is that operators may choose to shut them down during baseload
periods, when prices are low, owing to their higher marginal costs (for example owing to highnatural gas prices).
Second, plant level costs imply that for the LCOE calculation the overall system effects are nottaken into account, i.e. the impact of a power plant on the electricity system as a whole. The systemeffects, however, can potentially have a significant impact. In the case of variable renewable energies
for example, they might negatively impact expected revenues at higher shares of penetration.
Finally, this report considers social resource costs: the cost to society to build and operate
a given plant, independent of all taxes, subsidies and transfers. For example a tax credit or afaster amortisation schedule can boost the profitability of a given project. Hence, they affect thecompetitiveness of specific technologies, aside from their social resource cost. Keeping in mind
these caveats concerning the nature of the analysis, it is now possible to provide an overview of themore detailed methodological conventions and key numerical assumptions employed to calculate
the levelised cost of electricity (LCOE) for different technologies in different countries.

35

2
2.2

Methodological conventions and key assumptions

The purpose of these methodological conventions for calculating levelised average lifetime costswith the EGC model is to guarantee comparability of the data received while preserving the countryspecific information. Defining them in a satisfactory manner implies finding a careful balance
between too much and too little homogenisation.
As an example, different fuel cost assumptions inside a single region such as Europe would blurmost other information but reveal little about national conditions for electricity generation costs.
At the same time, the important differences in fuel costs between Europe, North America and Asia
have to be adequately reflected. Globally harmonised values have thus been used for:•discount rate;•technical lifetime;•load factors of flexible plants;•

carbon price (including carbon emission factors of fuel, except lignite);•construction duration and expense schedule;•

decommissioning costs and duration.

Regionally differentiated values instead have been used for:•f uel prices (lignite and bio-fuel costs are project specific; nuclear fuel cycle costs for Japan andRussia reflect national specifications);•

load factors of renewable technologies.

In the light of occasionally incomplete national data, methodological conventions and default
assumptions serve to complete and harmonise data. Decisions on methodology were prepared by
the IEA and NEA Secretariats and taken in the EGC Expert Group by consensus. What follows is an
overview of key assumptions and conventions.

Lifetimes
The EGC harmonised expected lifetimes for each technology across countries are as follows throughconsensus:
Battery storage:

10 years

Solar PV, onshore and offshore wind:

25 yearsGas-fired power plants:

30 years

Coal-fired power and geothermal plants:

40 years

Nuclear power plants:

60 yearsHydropower plants:

80 years

Additional lifetime after nuclear LTO:	10 or 20 years, depending on regulatory
framework36

2Discount rates
The levelised costs of electricity were calculated for all technologies at 3%, 7% and 10% real discountrates.

Capacity
Wherever the distinction was made in national submissions, net rather than gross capacity wasused for calculations. If only gross capacity was provided, net values were deducted using provided
or generic own consumption. While this report compares plants of very different sizes, it does nottake into account the economies of larger multiple-unit plants. It is estimated that new units built at
an existing site may be 10% to 15% cheaper than greenfield projects if they can use, at least partially,
existing buildings, auxiliary facilities and infrastructure. Regulatory approvals are also likely to be
more straightforward.

Capacity factors
A standard capacity factor of 85% was used for all combined-cycle gas turbines (CCGTs), coal-fired,fuel cells and nuclear plants under the assumption that they operate in baseload. It is clearly
understood that many CCGTs are frequently used in mid-load or even peak load rather than inbaseload, and that coal-fired power plants habitually only achieve 50-60% load factors in major
markets. Nevertheless, the 85% assumption is used as a generic assumption for CCGTs and coalfired power plants as it allows for the straightforward comparison of the costs of three technologies
that could be used as baseload. For OCGTs, a capacity factor of 30% is assumed. However, as capacity
factors vary significantly in different markets, we also present results for capacity factors of 50% forflexible coal, nuclear and CCGT gas plants and 10% for OCGTs to illustrate the impact of this changeon the LCOE.
For all other technologies, country-specific capacity factors were assumed as they are largely
site-specific.

Overnight costs
Overnight construction costs include:1.

direct construction costs plus pre-construction costs, such as site licensing, including theenvironmental testing;2.

the indirect costs such as engineering and administrative costs that cannot be associatedwith a specific direct construction cost category, as well as capitalised indirect costs;

3.

owners’ costs that include any additional expenses incurred by the owners associated withthe plant and plant site, but excluding offsite, “beyond the busbar”, transmission costs;1 and4.

contingency to account for changes in overnight cost during construction.

1.
Cost information on overnight costs and LCOE pertain explicitly only to costs before the busbar, i.e. before connection to
the grid. While in some countries, project developers are obliged to pay for connection costs themselves, the latter are not partof the figures reported in this report.

37

2Contingency payments
Contingencies, increased costs resulting from unforeseen technical or regulatory events, areincluded in the overnight costs. The following conventions have been adopted by the EGC Expert
Group if national data was not available:
Nuclear energy (except LTO):

15% of overnight costs

All other technologies:

5% of overnight costs

Construction cost profiles
We assume linear expense schedules for all technologies and the followings constructions lengths:
Non-hydro/-geothermal renewables, fuel cells:

1 yearOCGT power plants:

2 years

CCGT power plants:

3 years

Coal- and biomass-fired power plants:

4 years

Geothermal and hydro plants:

5 years

Nuclear power plants:

7 years (LTO: 2 years)

Investment costs
Investment costs consist of overnight costs plus financing costs (e.g. interest during construction),referred to in Equations (1) and (2) as total capital construction costs.

Annual efficiency losses
Some power plants experience lower electric conversion efficiencies and hence reduced output as
their operational lifetime progresses. These losses are usually small and can be disregarded. The
only technology where efficiency losses have a systematic effect is solar PV. The study therefore
assumes a default value of an annual efficiency loss of 0.5%.

Emission costs
No costs for emissions other than CO2 (see below) were included in the LCOE calculations of thisreport. Increasingly strict regulations in OECD and non-OECD countries for emissions such as
particulates, NOx and SOx, however, increase the cost of power production primarily from coaland biomass. The degree to which the costs of pollution control equipment is included in overnight
capital costs varies from country to country.

Treatment of fixed operations and maintenance costs
Fixed O&M costs were added to each year of operation. Where available, provided national data wasused, otherwise default values were used.

38

2
Fuel prices
Fuel price assumptions for hard (black) coal and natural gas are comparable with the assumptions
used in the STEPS scenario in the World Energy Outlook 2019 (IEA, 2019).2 For the heat content ofbrown coal, national assumptions were used. The prices used are provided in standard commercial
units for coal (tonnes, weighted averages adjusted to 6 000 kilocalories per kilogramme) and fornatural gas in million British thermal units (MBtu).

Table 2.1: Fuel prices
RegionHard coal [USD/tonne]

Natural gas [USD/MBtu]

83
51
88
75

10
3.2
9.1
8

Asia
Americas
China
EuropeFor Australia, an individual gas price of USD 7.8 per MBtu was selected.
Due to the global coronavirus appearance in the first half of 2020, fuel prices saw significant
declines globally early that year. The values assumed for this report were fixed before much of thesedeclines, and therefore may be high relative to expectations at the time of publication. Of course,
any view of fuel prices in 2025 will have significant underlying uncertainties. For a discussion on
the impact of changes in fuel prices on the LCOE calculation, see Chapter 5.

Costs of the “once-through” nuclear fuel cycle
Many countries provided cost data on different components of the fuel cycle, including the costs ofrecycling used fuel. However, to increase comparability, cost data was harmonised for all countries
except Japan and Russia who indicated specific national values.
For the LCOE calculations, the following fuel prices per generated MWh of electricity were used:
Front-end of nuclear fuel cycle:(mining, enrichment, conditioning)

USD 7 per MWh

Back-end of nuclear fuel cycle:(spent fuel removal, disposal and storage)

USD 2.33 per MWh

Japan provided costs of 8.6 and 5.3 USD/MWh for front-end and back-end cost respectively,
Russia 4.0 and 0.9 USD/MWh.

Carbon price
The EGC includes a harmonised carbon price of USD 30 per tonne of CO2 common to all countries
over the lifetime of all technologies. Many countries do not have an explicit carbon price. In thesecases, the price can be assumed to be the implicit shadow price of carbon.

2.
Recent events, notably a decrease in energy demand following the Covid-19 pandemic, have led to short-term declines
in fuel prices. However, given that this report assumes commissioning in 2025 and that plants will only consume fossil fuelsfrom that date onward, there is no need to adjust what is in fact a central estimate for necessarily uncertain long-term priceassumptions.

39

2
We calculate emission costs based on the carbon content per MWh. For globally traded fuel, data
was derived from IPCC (IPPC, 2006, Volume 2, Chapter 2, “Stationary Combustion”, p. 2.16; confirmed
by IPCC, 2019).

Decommissioning and residual values
At the end of a plant’s lifetime, decommissioning and waste management costs are linearly spreadover the decommissioning period. We assume the following durations:
Nuclear power plants:

10 years

All other technologies:

2 years

The cost of decommissioning is defined as the total cost of dismantling an existing plant andrelated activities minus any residual value. This report uses the following generic values for thesum of residual value and decommissioning costs:
Nuclear energy:

15% of overnight costs

All other technologies:

5% of overnight costs.

Because of the levelised cost methodology, decommissioning costs become small when discounted
over 60 or 80 years, the lifetime of a nuclear plant or hydroelectric plant. In practice, the operator
makes annual contributions to a Decommissioning Trust Fund during operations whose sum
plus accrued interest will eventually correspond to the estimated total costs of decommissioning.
While the amount per MWh figure is small (see LCOE tables in Chapter 3), the long lifetime andthe accumulated return on the fund ensure full coverage of the estimated decommissioning costs(see PNNL, 2011). Such generic assumptions inevitably abstract from unanticipated cost inflation orother uncertainties related to expenditures that will take place several decades later.

Heat credit
A CHP device produces two products, electricity and heat, simultaneously. The cost of the expended
primary fuel should be allocated partly to the electric energy and partly to the thermal energy.
There are several ways of doing this, depending on the individual point of view.
A frequently used approach is to compare the heat produced by a CHP plant to the costs of a
dedicated heat production technology. For comparability reasons, a uniform heat price of USD 37.1/
MWhth assuming 90% efficiency was used, which represents the heat production costs of a highly
efficient natural gas combustion engine in Europe, which was the only region for which CHP data
was received.

Transmission and grid connection costs
Transmission and grid connection costs were disregarded, as the LCOE figures presented in thisreport exclusively consider plant-level production costs.

40

2
Exchange rates
All costs are reported here in 2018 USD terms. Table 2.2 shows the exchange rate used for eachnational currency unit (NCU) for which a conversion was performed, based on the average 2018 rate
as reported by the OECD.

Table 2.2: National currency units per USD (2018 average)
Country
Australia
BrazilCanadaChina
Euro area
Hungary
India
Japan
Korea
MexicoNorwayRomania
RussiaExchange rate
1.34
3.65
1.30
6.62
0.85
270.21
68.39
110.42
1 101
19.24
8.13
3.94
62.67

Source: OECD (2020), http://data.oecd.org/conversion/exchange-rates.htm.

2.3

Conclusions

This overview described the conventions and key assumptions adopted for calculating the levelised
cost of electricity generation. While individual assumptions can be debated – and several of them
have been the subject of extensive discussion in the EGC Expert Group – one should not lose sightof their essential function, which is to render comparable large amounts of heterogeneous data. In
fact, only by rendering the data comparable can the specificity of each individual dataset be brought
out and assessed. Readers are invited to form their own views. The sensitivity analyses in Chapter 5
and the interactive tools in the web-based version of this report will help them in assessing theimpact of different assumptions for different parameters.

41

2
References
EC (2016), Mapping and analyses of the current and future (2020-2030) heating/cooling fuel deployment( fossil/renewables), European Commission, Brussels, https://ec.europa.eu/energy/sites/ener/files/
documents/Report%20WP2.pdf.
IEA (2019), World Energy Outlook 2019, IEA, Paris www.iea.org/reports/world-energy-outlook-2019.
IEA/NEA (2015), Projected Costs of Generating Electricity 2015, OECD Publishing, Paris, https://doi.
org/10.1787/cost_electricity-2015-en.
IPCC (2006), 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Prepared by the National
Greenhouse Gas Inventories me, Eggleston H.S., Buendia L., Miwa K., Ngara T., and Tanabe K. (eds),
IGES, Japan, www.ipcc-nggip.iges.or.jp/public/2006gl/index.html.
IPPC (2019), 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Calvo
Buendia, E., Tanabe, K., Kranjc, A., Baasansuren, J., Fukuda, M., Ngarize S., Osako, A., Pyrozhenko,
Y., Shermanau, P. and Federici, S. (eds). IPCC, Switzerland, www.ipcc-nggip.iges.or.jp/public/2019rf/
index.html.
OECD (2020), Exchange rates (indicator), 10.1787/037ed317-en (Accessed on 01 July 2020).
PNNL (2011), Assessment of the Adequacy of the 10 CFR 50.75(c) Minimum Decommissioning Fund
Formula, Office of Nuclear Material Reactor Regulation, Pacific Northwest National Laboratory for
the US Nuclear Regulatory Commission, Richland, WA, http://pbadupws.nrc.gov/docs/ML1306/
ML13063A190.pdf.

42Chapter 3

LCOE Values by technology
and country
This chapter presents an overview of the technologies included in this report. Section 3.1 includes asummary of overnight and investment costs, capacities, and other relevant technical specifications
for all 243 plants in the EGC 2020 report. Sections 3.2, 3.3 and 3.4 provide plant-by-plant cost
data including a breakdown of the LCOE cost components, for each plant, separated according totechnology. All cost figures are in USD 2018. The assumed commissioning date is 2025.

3.1

Overview and summary statistics

Table 3.1 presents the size and overnight cost statistics for the various technologies included in
Projected Costs of Generating Electricity – 2020 Edition. For the calculation of mean and median values,
to weigh the values for each country equally, median cases for each country were selected in casemultiple datasets for the same technology exist. Otherwise, no weighing was performed (all figuresin this report follow this approach). As not all countries provided data for each technology, results
should not be mistaken for (capacity-weighted) averages over all countries.

Table 3.1: Summary statistics for different generating technologies
Technology

Numberof plants /countries

ACAES
Lithium-ion battery
Biomass
Biomass (CHP)
Coal
Coal (CCUS)
Coal (CHP)
Fuel cellGas (CCGT)
Gas (CCGT, CCUS)
Gas (CCGT, CHP)
Gas (OCGT/int. comb.)
Gas (OCGT/int. comb., CHP)
Geothermal
Hydro (reservoir, >= 5 MW)
Hydro (reservoir, < 5 MW)
Hydro (run of river, >= 5 MW)
Hydro (run of river, < 5 MW)
LigniteLignite (CCUS)
Lignite (CHP)
Nuclear

1/1
4/4
4/3
5/2
11 / 6
4/2
1/1
4/2
16 / 11
2/2
2/2
8/5
3/3
6/2
4/4
1/1
7/4
18 / 4
2/2
1/1
1/1
8/8

Net capacity (MWe)
Min

Mean

MedianOvernight costs (USD/kWe)
Max

250
250
250
250
1.0
7.5
2.0
19
0.42
18
25
30
0.42
130
130
358
138
693
722
954
499
641
641
650
700
700
700
700
0.003
0.20
0.20
15
471
762
743
1 372
437
541
541
646
5.80
253
253
500
100
464
500
980
35.90
119
125
195
5
19
19
40
11.95
58
23
175
0.32
0.32
0.32
0.32
5
85
44
248
0.02
2.54
3.00
4.80
709
805
805
900
570
570
570
570
2 900
2 900
2 900
2 900
950
1 234
1 137
1 650

Min

Mean

Median

Max

1 695
452
833
2 538
800
4 490
2 240
2 361
254
2 412
1 024
325
330
3 851
1 899
3 966
2 326
956
2 189
6 891
1 015
2 157

1 695
932
2 501
4 689
1 897
4 572
2 240
4 715
823
2 619
1 092
630
707
6 647
3 319
3 966
3 557
3 507
2 973
6 891
1 015
3 606

1 695
655
1 095
4 689
1 785
4 572
2 240
4 715
955
2 619
1 092
668
684
6 647
2 778
3 966
3 026
2 801
2 973
6 891
1 015
3 370

1 695
1 967
6 545
6 545
4 382
5 991
2 240
6 492
1 109
2 826
1 160
1 141
1 107
10 959
5 819
3 966
6 681
7 484
3 756
6 891
1 015
6 920

43

3
(continued Table 3.1)
Technology

Numberof plants /countries

Nuclear (LTO)
Pumped storage
Solar PV (floating)Solar PV (utility scale)
Solar PV (commercial)
Solar PV (residential)
Solar thermal (CSP)
Offshore wind
Onshore wind (>= 1 MW)
Onshore wind (< 1 MW)

4/4
3/3
1/1
21 / 1415 / 815 / 8
4/2
23 / 833 / 18
11 / 1

Net capacity (MWe)
Min

Mean

MedianOvernight costs (USD/kWe)
Max

1 000
1 000
1 000
1 000
175
458
200
1 000
8
8
8
8
0.83
26
20
100
0.05
0.25
0.24
0.50
0.004
0.01
0.01
0.02
100
125
125
150
11.25
186
100
600
1
58
40
280
0.01
0.19
0.19
0.90

Min

Mean

Median

Max

391
563
860
534
846
719
5 238
1 721
877
1 782

504
1 962
860
995
1 065
1 583
5 857
2 876
1 391
2 852

497
897
860
923
1 085
1 653
5 857
2 740
1 439
2 852

629
4 426
860
2 006
1 357
2 597
6 475
4 039
3 022
5 539

Note: CHP = combined heat and power. CCUS = carbon capture. utilisation and storage. CCGT = Combined cycle gas turbine. OCGT = Open cycle
gas turbine. LTO = long-term operation. CSP = concentrated solar power. ACAES = Adiabatic compressed air energy storage.

The EGC 2020 dataset includes a wide range of generating technologies, including natural gas-fired
generation (both combined-cycle gas turbines [CCGTs], and open-cycle gas turbines [OCGTs]), coalfired generation, nuclear plants, solar photovoltaic (of varying sizes) and concentrated solar power(CSP), onshore and offshore wind, geothermal, biomass, and combined heat and power (based on avariety of fuel types).
The main drivers for the costs of generating electricity are typically the construction, fuel(including expenses for CO2 emissions) as well as operation and maintenance costs. All these factors
are influenced by the size of the individual units, with economies of scale typically reducing costs atlarger scale. Despite the very modular nature of solar PV and wind turbines, costs can typically be
reduced by building wind or and solar farms. In case of renewables, costs can typically be reducedby constructing wind or solar farms. The following figures illustrate to what extent some of thesekey parameters vary in the provided data (limited to technologies with data from at least two to
three countries).
Figure 3.1 illustrates capacity ranges grouped by size. The values illustrate that technologies
vary considerably with respect to capacity. Only nuclear new builds have been provided with atleast 1 GW of capacity. Especially wind installations come in very different sizes, ranging fromsingle turbines with below 1 MW to farms of 200 MW. Similarly, offshore installations range from 11
to 600 MW (especially for offshore wind, larger projects exist, for example Hornsea One with 1.2 GW
which is due to be inaugurated in 2020). For utility scale solar PV, data for farms with up to 100 MW
total capacity was provided.

Figure 3.1: Range of capacity
Net capacity (medium scale)

1 500

250

0.5

1 200

200

0.4

900
600

[MW]

0.6

150
100

0.3
0.2

300

50

0.1

0

0

0.0

)
)
)
)
8)
5)
2)
6)
5)
0)
2)
/1
3/
0/
6/
8/
2/
7/4 (4/4 1/14 3/18 (4/
r(
)(
)
)
e ( al (1 ) (15 b.) ( d (2
2
3
a
t
(
(
P
i
W
W
)
S
e)
cl e i g n
Co CGT com win
W(C
5M
5M calLM
Nualre(C int.
>= r, >= ity s = 1
m
o
s
,
r
r
i
e
sh
e
>
il
Ga GT/thut
d(r i v e r voCOff
V ( w in o lar
s
of
(O
P
e
s
n
r
S
r
re
la
ru
o(
Ga
ho
o ( yd r
So
dr
ns
H
y
O
H

Range

Mean

MedianNote: Values in brackets indicate total number of data points and number of countries.

44

Net capacity (small scale)

300

[MW]

[MW]

Net capacity (large scale)
1 800

So

lar

PV

(c

om

m

er

c

)
/8
(15
)
l
ia

S

ar
ol

PV

(r

id
es

en

t

)
/8
(15
)
l
ia

3
Overnight investment costs vary significantly (see Figure 3.2). Even relatively mature coal- andgas-fired technologies show large differences – depending on the technology specifics, unit size andthe individual country. The same is true for annual fixed O&M costs.
The load factors illustrate the range of use cases for different power plants (here the expectedvalues as they were submitted are shown, in the subsequent LCOE calculations the load factors
are normalised to enhance comparability). Whereas nuclear and coal plants are used for baseload
operation, gas-fired OCGTs are typically peak load plants – CCGTs cover a wider range from mid- tobaseload operation.

Figure 3.2: Range of overnight and fixed O&M costs
Total overnight costs

Fixed O&M costs

8 000

300

7 000

250

[USD/kW][USD/kW]

6 000
5 000
4 000
3 000

200
150
100

2 000
50

1 000

0

0

8) 6) 1) 5) 4) 4) 4) 4) 8) 8) 8) 8)
8/ 1/ /1 8/ 4/ 7/ 8/ /1 5/ 5/ /1 3/r ( al (1 ) (16 b.) ( ) ( W) ( ) (1 (21 l) (1 l) (1 (33 d (2
a
W
)
)
e
cl Co GT om 5M 5M MW ale rcia ntia W win
Nu
CC t. c >= >= < 5 y sc me ide 1 M re
(
o
s
s
=
in r,
r, r,
it m
Ga GT/ r voi rive rive (util (co V (re d (> ffsh
O
O C res e n of of PV r PV ar P w in
(
l
n
r
s
(
a
e
Ga dro o (ru o (ru Sola Sol So horrsn
H y yd yd r
O
H H

Range

Nu

Mean

3)
3)
6)
4)
4)
4)
9)
5)
7)
7)
7)
7/ (10/ (14/ ) (7/ (3/ ) (7/ 0/1 (13/ (14/ 8/1 (21/. W) Wl(2 al) al)
)
(2 d
b
a
)
)
T
Co CCG com 5M 5M cale erci nti MW wins ( nt . , >= , >= y s m i d e 1 o r e
Ga T/i oir iver tilit com (res (>= ffsh
G rv
(
r
d O
V
(u
O C s e of PV r PV ar P w inns ( (relra
S o h o re
Ga dro o (ru Sola Solrs
H y yd
On
H

a
cl e

Medianr(

Note: Values in brackets indicate total number of data points and number of countries.

The efficiency of power plants determines fuel costs and emission intensity of electricity
generation. Provided values are rather similar within each technology group. Fossil fuel plants
with CCUS are on average 10% (coal) and 14% (gas CCGT) less efficient than without capturing CO2.
CHP technologies, which additionally to electricity generation make use of the resulting heat, reachsignificantly higher total efficiency values than electricity only plants. Even OCGTs, in terms ofefficiency in a clear disadvantage compared to CCGTs, reach in conjunction with heat production
on average an 84% overall efficiency.

Figure 3.3: Range of assumed load factors and net efficiency
100%

80%

80%

60%

60%

40%

[%]

[%]

Assumed actual load factor
100%

40%

20%

20%

0%

0%

4) /3) /8) /3) /4) 7/4) 14) /8) /8) 18) /8)
1
4 )(
4/
5
1/ (15 (15 33/ (23r ( al ( ) (1 b.) (4 ) (
(
a
l)
d
W
W
) (2 l)T
cle Co CG om 5M 5M ale rcia ntia W) winuc
C t. ce d e 1 M re
=
N
(
=
s
>
m
s
>
i
y
Ga T/in oir, iver, tilit com (res (>= sho
CG ser v of r V (u PV ( r PV ind OffOnr P ar o la re ws ( (reS ho
Ga dro o (ru Sola Sol
r
s
y
d
H
On
Hy
Range

Lig

Mean

Net efficiency

n

i te

/
(2

Median

)
)
)
)
)
)
5)
/4
/3
/2
/2
/2
10
7/
(8
4/
) (3
) (4
) (2 b.) (
) (2
1
(
S
P
S
P
U
H
H
T)
C
CU
om
,C
,C
CC
(C
CG
b.
T,
GT
t. c
al
(C
m
G
C
n
o
s
i
o
C
C
T/
CC
Ga
s(
t. c
s(
CG
in
Ga
Ga
(O
T/
s
G
Ga
OC
s(
Ga

2)

l
oa

Note: Values in brackets indicate total number of data points and number of countries.

45

3
Due to the indicated differences in input values also the LCOE of different electricity generation
technologies vary significantly depending on the country, the technology and the properties
of individual plants. It is thus not meaningful to point out a single power plant type, which
would outcompete the others in terms of average costs. This is in particular true for renewable
technologies, especially wind turbines and photovoltaics, whose costs are very much location
dependent. Therefore, they are frequently tailored to individual requirements: not only are PV
installations differing in size, from household rooftop installation and floating panels to utility
scale open space PV installations, but also wind turbines range from being installed in small-scale
individual units to large wind farms. The same holds true for hydro units, being connected to small
streams or large rivers, in the form of run of river or reservoir. Figure 3.4 illustrates this range oftechnological options for generating electricity and associated costs.

Figure 3.4: Overview of LCOE values
250

LCOE [USD/MWh]

200
150
100
50

Fossil fueltechnologies

Low-carbon technologies

Biomass (CHP)

Gas (CCGT, CHP)

Gas (OCGT/int. comb., CHP)

Coal (CHP)

Lignite (CHP)

Coal (CCUS)

Gas (CCGT, CCUS)

Biomass

Lignite (CCUS)

Geothermal

Hydro (run of river, < 5MW)

Hydro (reservoir, >= 5 MW)

Hydro (run of river, >= 5MW)

Solar PV (floating)Solar thermal (CSP)

Solar PV (residential)

Solar PV (commercial)

Offshore wind

Solar PV (utility scale)

Onshore wind (< 1 MW)

Onshore wind (>= 1 MW)

Nuclear

Nuclear (LTO)

Fuel cellGas (OCGT/int. comb.)

Coal

Gas (CCGT)

Lignite0

CHP technologies

Note: Values at 7% discount rate. Box plots indicate maximum. median and minimum values. The boxes indicate the central 50% of values, i.e.
the second and the third quartile.

The multitude of categories also illustrates the diversity of options on the thermal technology
side: several fuel categories are paired with technology options like CHP, CCUS and different turbine
types. One of the most striking results is the large range of LCOE values for renewable technologies.
Although utility-size power plants often have plant-level costs that are comparable to conventional
power plants, smaller plants can still be several times more expensive.
The aggregated data for the 24 countries that provided data for this report does not tell the wholestory of levelised generation costs. Due to more or less favourable sites for renewable generation,
varying fuel costs and technology maturity, costs for all technologies can vary significantly bycountry and region. In addition, the share of a technology in the total production of an electricity
system makes a difference to its value, load factor and average costs.

46

3
Whereas renewables are very competitive in most countries participating in this report, thedata provided for this report shows that they still have higher costs than fossil fuel- or nuclearbased generation in some countries (in this report Japan, Korea and Russia). Also within countries,
different locational conditions can lead to differences in generation costs at the subnational andlocal level. In Europe, both onshore and offshore wind as well as utility scale solar installations arecompetitive to gas and nuclear energy.
In the United States, gas-fired power plants benefit from the expected low fuel prices in theregion, although fuel price assumptions are in general uncertain. Nevertheless, in terms of the
LCOE of the median plant, onshore wind and utility scale solar PV are, assuming emission costs of
USD 30/tCO2, the least cost options. Natural gas CCGTs are followed by offshore wind, nuclear newbuild and, finally, coal.

Figure 3.5: Median technology costs by region

Median LCOE [USD/MWh]

250
200
150
100
50

India

United States

China

Solar PV (utility scale)

Offshore wind

Onshore wind (>= 1 MW)

NuclearCoal

Gas (CCGT)

Offshore wind

EuropeSolar PV (utility scale)

Onshore wind (>= 1 MW)

NuclearGas (CCGT)

Offshore wind

Solar PV (utility scale)

NuclearOnshore wind (>= 1 MW)

Gas (CCGT)

Coal

Solar PV (utility scale)

Offshore wind

Onshore wind (>= 1 MW)

NuclearCoal

Gas (CCGT)

Solar PV (utility scale)

Onshore wind (>= 1 MW)

Nuclear

Coal

0

Japan

Note: Values at 7% cost of capital.

In China and India, variable renewables are having the lowest expected levelised generation
costs: utility scale solar PV and onshore wind are the least-cost options in both countries. Nuclear
energy is also competitive, showing that both countries have promising options to transition out of
their currently still highly carbon-intensive electricity generation.

47

3
3.2

Technology-by-technology data on overnight and investment costs

Tables 3.2 to 3.9 present data on overnight costs and investment costs at discount rate of 3%, 7%,
and 10%. Since some submissions displayed only very minor differences, only 231 of the 243 power
plants, for which data was submitted, were included in the following tables. Units showing therange of values were selected in case multiple similar datasets were provided by single countries.
All data is included in the accompanying downloadable files. The overnight cost includes preconstruction (owner’s), construction (engineering, procurement, and construction) and contingency
costs, but not interest during construction (IDC). The latter is instead included in the investment
costs. All values for solar PV (as for the rest) are expressed on an alternating current (AC) basis.

Table 3.2a: Combined-cycle gas turbine (CCGT) generating technology
Country

Australia
Belgium
Canada
Italy
Japan
Korea
MexicoRomania
United States

Technology
CCGT
CCGT (CCUS)
CCGT
CCGT
CCGT
CCGT
CCGT
CCGT
CCGT
CCGT
CCGT
CCGT
CCGT
CCGT
CCGT
CCGT (CCUS)

Net
capacity
(MWe)
506
437
500
500
500
471
790
1 372
491
982
503
785
835
750
727
646

Electrical
Overnight
conversion
costs
efficiency(USD/kWe)
(%)
48%
955
41%
2 826
60%
767
64%
1 009
58%
974
57%
1 058
60%
590
56%
1 109
56%
1 107
59%
838
51%
669
60%
667
58%
466
58%
254
59%
952
48%
2 412

Investment costs (USD/kWe)
3%

7%

10%

998
2 955
802
1 055
1 018
1 106
617
1 160
1 158
877
700
698
488
265
995
2 522

1 058
3 133
850
1 119
1 079
1 173
654
1 229
1 228
929
742
740
517
281
1 055
2 674

1 105
3 271
888
1 168
1 127
1 224
683
1 283
1 281
970
775
772
540
294
1 102
2 791

1 002
586

1 062
621

1 108
648

Non-OECD countries
CCGT
CCGT

Brazil
China

980
475

58%
58%

958
560

Table 3.2b: Open-cycle gas turbine (OCGT) generating technology
Country

Technology

OCGT
OCGT
OCGT
Belgium
OCGT
OCGT
Canada
OCGT
OCGT
Italy
Non-OECD countries
OCGT
BrazilAutralia

48

Net
capacity
(MWe)
537
350
500
500
100
243
130
980

Electrical
Overnight
conversion
costs
efficiency(USD/kWe)
(%)
31%
668
40%
531
41%
670
38%
590
41%
1 141
40%
518
37%
325
44%

739

Investment costs (USD/kWe)
3%

7%

10%

688
547
691
608
1 175
533
334

715
569
718
632
1 221
554
348

736
585
738
650
1 256
570
357

761

791

814

3
Table 3.3: Coal-fired generating technologies
Country

TechnologyNetcapacity(MWe)Supercritical pulverised
Supercritical pulverised(CCUS)
Australia
Supercritical pulverised(lignite)Supercritical pulverised(lignite. CCUS)
Ultra-supercriticalJapan
Ultra-supercritical
Korea
Pulverised
Pulverised
Pulverised
Pulverised (CCUS)
United States Supercritical pulverised
Supercritical pulverised(CCUS)
Ultra-supercritical
Other coal (CCUS)

722

Electrical
Overnight
conversion
costs
efficiency(USD/kWe)
(%)
40%
2 433

Investment costs (USD/kWe)
7%

10%

2 583

2 794

2 961

633

30%

4 490

4 766

5 155

5 463

709

32%

3 756

3 987

4 312

4 571

570

21%

6 891

7 315

7 912

8 386

749
954
138
140
650
650
650

41%
43%
36%
36%
40%
31%
42%

2 419
1 151
4 382
3 447
2 478
4 604
2 582

2 568
1 221
4 651
3 659
2 630
4 887
2 741

2 777
1 321
5 031
3 958
2 845
5 286
2 965

2 943
1 400
5 332
4 195
3 015
5 603
3 142

650

33%

4 654

4 940

5 344

5 663

641
499

43%
31%

4 157
5 991

4 413
6 359

4 773
6 879

5 059
7 290

900
347
400
400

34%
45%
45%
45%

2 189
800
1 148
1 111

2 324
849
1 218
1 179

2 514
919
1 318
1 276

2 664
974
1 397
1 352

Non-OECD countries
Other coal (lignite)
Ultra-supercritical
Ultra-supercritical
Ultra-supercritical

BrazilChina
India

Table 3.4a: Nuclear generating technologies – New build
Country

Technology

EPR
France
ALWR
Japan
ALWR
Korea
VVER
RussiaOther nuclear
Slovak Republic
LWR
United States
Non-OECD countries
LWR
China
LWR
India

Net
capacity(MWe)Overnight
costs(USD/kWe)

1 650
1 152
1 377
1 122
1 004
1 100
950
950

Investment costs (USD/kWe)
3%

7%

10%

4 013
3 963
2 157
2 271
6 920
4 250

4 459
4 402
2 396
2 523
7 688
4 721

5 132
5 068
2 759
2 904
8 850
5 435

5 705
5 633
3 066
3 228
9 837
6 041

2 500
2 778

2 777
3 086

3 197
3 552

3 554
3 949

Table 3.4b: Nuclear generating technologies – Long-Term Operation (LTO)
Country
Switzerland
FranceSwedenUnited States

Technology
LTO
LTO
LTO
LTO

Net
capacity(MWe)Overnight
costs(USD/kWe)

1 000
1 000
1 000
1 000

550
629
444
391

Investment costs (USD/kWe)
3%

7%

10%

567
648
457
403

589
673
475
419

606
693
489
431

49

3
Table 3.5: Solar generating technologies
Country

Solar PV (residential)
Solar PV (utility scale)
Australia
Solar thermal (CSP)
Solar PV (residential)
Solar PV (residential)
Belgium**
Solar PV (residential)
Solar PV (utility scale)
Solar PV (utility scale)
CanadaSolar PV (utility scale)
Solar PV (residential)
Solar PV (commercial)Denmark
Solar PV (utility scale)
Solar PV (utility scale)
Solar PV (residential)
Solar PV (commercial)
FranceSolar PV (utility scale)
Solar PV (residential)
Solar PV (commercial)
Hungary
Solar PV (commercial)
Solar PV (utility scale)
Solar PV (commercial)
Solar PV (commercial)
Solar PV (commercial)
Solar PV (residential)
Italy**
Solar PV (residential)
Solar PV (utility scale)
Solar PV (utility scale)
Solar PV (residential)
Japan
Solar PV (utility scale)
Solar PV (commercial)
Korea
Solar PV (utility scale)
Solar PV (floating)
Netherlands Solar PV (commercial)
Solar PV (utility scale)
Solar PV (commercial)
NorwaySolar PV (residential)
Solar PV (residential)
Solar PV (residential)***Solar PV (residential)
Solar PV (residential)
Solar PV (commercial)
Solar PV (commercial)
Solar PV (commercial)***Solar PV (commercial)
United States
Solar PV (commercial)
Solar PV (utility scale)
Solar PV (utility scale)
Solar PV (utility scale)***Solar PV (utility scale)
Solar PV (utility scale)
Solar thermal (CSP)
Solar thermal (CSP)
Solar thermal (CSP)***

Austria50

Technology

Net
Capacity Annual Overnight Investment costs (USD/kWe)
capacity*factor efficiency
costs
3%
7%
10%
(MWe)
(%)
loss (%) (USD/kWe)
0.02
100
150
0.01
0.01
0.01
1.00
20
20
0.01
0.10
8
8
0.01
0.50
25
0.004
0.05
0.50
20
0.08
0.21
0.42
0.004
0.01
0.83
0.83
0.004
2
0.10
2.97
8
0.20
8
0.30
0.01
0.01
0.01
0.01
0.01
0.30
0.30
0.30
0.30
0.30
100
100
100
100
100
100
100
100

11%
28%
47%
13%
12%
13%
13%
25%
19%
12%
13%
16%
18%
18%
18%
24%
15%
14%
14%
14%
20%
17%
20%
20%
17%
20%
27%
12%
14%
15%
15%
14%
14%
14%
10%
14%
16%
17%
19%
21%
13%
15%
16%
19%
20%
23%
26%
28%
33%
36%
50%
61%
64%

0.5%
0.5%
0.0%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.0%
0.0%
0.0%

719
882
5 238
1 841
1 346
1 009
992
1 354
1 264
1 176
847
534
623
1 700
850
708
1 901
1 297
1 075
964
1 306
907
1 210
1 846
1 368
827
836
2 333
2 006
1 240
1 226
860
846
807
984
1 884
1 884
1 884
2 597
2 597
1 357
1 357
1 357
1 357
1 357
1 072
1 072
1 072
1 072
1 072
6 475
6 475
6 475

730
895
5 316
1 869
1 366
1 024
1 006
1 375
1 283
1 194
860
542
632
1 725
863
719
1 929
1 316
1 091
978
1 325
920
1 228
1 873
1 389
840
849
2 367
2 036
1 259
1 244
873
858
819
998
1 913
1 913
1 913
2 635
2 635
1 377
1 377
1 377
1 377
1 377
1 088
1 088
1 088
1 088
1 088
6 571
6 571
6 571

744
912
5 419
1 905
1 392
1 043
1 026
1 401
1 308
1 217
876
553
645
1 758
879
733
1 966
1 341
1 112
997
1 350
938
1 252
1 909
1 415
856
865
2 413
2 075
1 283
1 268
890
875
835
1 018
1 949
1 949
1 949
2 686
2 686
1 403
1 403
1 403
1 403
1 403
1 109
1 109
1 109
1 109
1 109
6 698
6 698
6 698

754
925
5 494
1 931
1 411
1 058
1 040
1 421
1 326
1 234
889
561
654
1 783
891
743
1 994
1 360
1 127
1 011
1 369
951
1 269
1 936
1 435
868
877
2 447
2 104
1 301
1 286
902
887
847
1 032
1 976
1 976
1 976
2 723
2 723
1 423
1 423
1 423
1 423
1 423
1 124
1 124
1 124
1 124
1 124
6 791
6 791
6 791

3
(continued Table 3.5)
Country

TechnologyNetcapacity(MWe)

Capacity Annual Overnight Investment costs (USD/kWe)factor efficiency
costs
3%
7%
10%
(%)
loss (%) (USD/kWe)

Non-OECD countries
Solar PV (utility scale)
Solar PV (utility scale)
Solar PV (utility scale)Brazil
China
India

25
20
35

31%
18%
20%

0.5%
0.5%
0.5%

1 197
730
629

1 215
741
638

1 239
756
650

1 256
766
659

*** All values represent AC capacity.
*** A generic sizing factor of 1.2 was used to convert peak DC capacity into peak AC capacity.
*** Indicated as reference datasets by the providing country.

Table 3.6a: Wind generating technology – Onshore
Country
Austria
Australia
Belgium
CanadaDenmark
FinlandFrance

Italy

Japan
Korea
Netherlands
NorwayRussiaSwedenUnited States

TechnologyNetcapacity(MWe)

Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (< 1 MW)
Onshore wind (< 1 MW)
Onshore wind (< 1 MW)
Onshore wind (< 1 MW)
Onshore wind (< 1 MW)
Onshore wind (< 1 MW)
Onshore wind (< 1 MW)
Onshore wind (< 1 MW)
Onshore wind (< 1 MW)
Onshore wind (< 1 MW)
Onshore wind (< 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)*
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)

3.00
100
4.50
5.00
30
30
200
4.48
30
50
0.01
0.02
0.06
0.06
0.10
0.19
0.50
0.80
0.83
0.90
0.90
1.00
10
20
20
14.85
50
130
60
280
5
100
100
100
100
100
100
100
100
100
100

Capacity Overnight
factorcosts(%)(USD/kWe)
27%
42%
25%
30%
28%
28%
40%
40%
40%
38%
17%
32%
20%
33%
31%
18%
33%
32%
30%
48%
43%
39%
37%
30%
20%
23%
48%
44%
27%
27%
42%
53%
49%
47%
45%
43%
41%
37%
33%
28%
18%

1 458
1 432
1 534
1 475
1 416
1 326
1 361
918
1 534
1 475
5 317
5 539
4 075
4 616
3 323
2 852
2 659
1 782
2 269
2 727
2 786
3 022
1 429
1 499
2 282
1 982
1 250
932
1 598
1 472
1 098
1 430
1 338
1 323
1 319
1 342
1 523
1 615
1 968
2 180
2 329

Investment costs (USD/kWe)
3%

7%

10%

1 479
1 454
1 557
1 497
1 438
1 345
1 382
931
1 557
1 497
5 396
5 621
4 136
4 685
3 373
2 895
2 698
1 809
2 302
2 767
2 827
3 067
1 450
1 521
2 316
2 011
1 269
946
1 622
1 494
1 114
1 451
1 358
1 343
1 339
1 361
1 546
1 639
1 997
2 213
2 364

1 508
1 482
1 587
1 526
1 465
1 371
1 408
949
1 587
1 526
5 500
5 729
4 215
4 775
3 438
2 950
2 750
1 844
2 347
2 820
2 882
3 126
1 478
1 551
2 360
2 050
1 293
964
1 653
1 523
1 136
1 479
1 384
1 369
1 365
1 388
1 575
1 670
2 035
2 255
2 410

1 529
1 502
1 609
1 547
1 486
1 390
1 428
962
1 609
1 547
5 576
5 809
4 274
4 841
3 485
2 991
2 788
1 869
2 379
2 860
2 922
3 169
1 499
1 572
2 393
2 078
1 311
977
1 676
1 544
1 151
1 500
1 404
1 388
1 384
1 407
1 597
1 694
2 064
2 287
2 443

51

3
(continued Table 3.6a)TechnologyNetcapacity(MWe)

Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)

30
50
65

Country

Capacity Overnight
factorcosts(%)(USD/kWe)

Investment costs (USD/kWe)
3%

7%

10%

1 333
1 191
890

1 359
1 214
907

1 378
1 231
920

Non-OECD countries
Brazil
China
India

47%
26%
27%

1 314
1 174
877

* Indicated as reference datasets by the providing country.

Table 3.6b: Wind generating technology – Offshore
Country
Australia
Belgium
Denmark
FranceJapan
Korea

United States

Technology
Offshore wind
Offshore wind
Offshore wind
Offshore wind
Offshore wind
Offshore wind
Offshore wind
Offshore wind
Offshore wind
Offshore wind
Offshore wind*
Offshore wind
Offshore wind
Offshore wind
Offshore wind
Offshore wind
Offshore wind
Offshore wind
Offshore wind
Offshore wind
Offshore wind
Offshore windNet
capacity(MWe)

Capacity Overnight
factorcosts(%)(USD/kWe)

Investment costs (USD/kWe)
3%

7%

10%

100
12
50
11.25
11.5
500
100
99
600
600
600
600
600
600
600
600
600
600
600
600
600
600

51%
39%
39%
52%
52%
45%
30%
30%
47%
46%
46%
46%
45%
39%
30%
53%
51%
50%
50%
47%
37%
31%

3 474
2 361
2 361
2 012
1 721
3 069
4 039
3 520
1 952
1 958
2 060
2 151
2 189
2 209
2 411
2 410
2 420
2 486
2 577
2 604
2 607
2 864

3 526
2 396
2 396
2 042
1 746
3 115
4 099
3 572
1 981
1 987
2 091
2 183
2 222
2 242
2 447
2 446
2 456
2 523
2 615
2 643
2 646
2 907

3 594
2 442
2 442
2 081
1 780
3 175
4 178
3 641
2 019
2 025
2 131
2 225
2 264
2 285
2 494
2 493
2 503
2 572
2 666
2 694
2 697
2 963

3 644
2 476
2 476
2 110
1 805
3 219
4 236
3 692
2 047
2 054
2 161
2 256
2 296
2 317
2 529
2 528
2 538
2 607
2 703
2 732
2 735
3 004

50

35%

2 265

2 299

2 343

2 375

Non-OECD countries
China

Offshore wind

* Indicated as reference datasets by the providing country.

52

3
Table 3.7a: Other renewable generating technologies – Hydropower
TechnologyNetcapacity(MWe)

Run of river (< 5 MW)
Run of river (>= 5 MW)
Run of river (< 5 MW)
Run of river (< 5 MW)
Run of river (< 5 MW)
Run of river (< 5 MW)
Run of river (< 5 MW)
Run of river (< 5 MW)
Run of river (< 5 MW)
Run of river (< 5 MW)
Run of river (< 5 MW)
Run of river (< 5 MW)
Run of river (< 5 MW)
Run of river (< 5 MW)
Run of river (>= 5 MW)
Reservoir (< 5 MW)
Reservoir (>= 5 MW)
Reservoir (>= 5 MW)
Reservoir (>= 5 MW)
Run of river (< 5 MW)
Run of river (< 5 MW)*Run of river (>= 5 MW)*Run of river (< 5 MW)*Run of river (>= 5 MW)*,**Run of river (< 5 MW)
Run of river (>= 5 MW)
Run of river (< 5 MW)
Run of river (>= 5 MW)**

2.2
24.5
0.02
0.1
0.2
0.25
0.25
0.25
0.5
0.50
0.50
0.7
1
2.1
5
0.32
15
12
30
3.0
4.8
82
4.2
45
3.7
44
4.3
94

51%
65%
55%
34%
55%
15%
8%
19%
20%
15%
22%
55%
15%
50%
37%
65%
38%
45%
52%
47%
62%
64%
60%
60%
66%
66%
62%
66%

248
175

50%
60%

Country
Austria
Germany

Italy

Japan
NorwayUnited States

Capacity Overnight
factorcosts(%)(USD/kWe)

Investment costs (USD/kWe)
3%

7%

10%

2 361
3 000
6 510
3 013
5 503
3 647
3 266
1 592
1 070
3 782
2 398
4 411
956
3 216
3 052
3 966
3 108
5 819
1 899
1 964
6 383
5 810
4 275
4 031
7 484
6 681
6 544
5 891

2 544
3 233
7 015
3 247
5 930
3 930
3 520
1 715
1 153
4 075
2 584
4 753
1 030
3 466
3 289
4 274
3 349
6 271
2 047
2 117
6 879
6 261
4 607
4 344
8 065
7 200
7 052
6 349

2 809
3 569
7 745
3 585
6 547
4 339
3 886
1 894
1 273
4 499
2 853
5 248
1 137
3 826
3 631
4 719
3 697
6 923
2 260
2 337
7 594
6 912
5 086
4 795
8 904
7 949
7 786
7 009

3 023
3 842
8 336
3 859
7 047
4 670
4 183
2 039
1 370
4 843
3 071
5 649
1 224
4 119
3 908
5 079
3 980
7 452
2 432
2 515
8 175
7 441
5 475
5 162
9 585
8 556
8 380
7 544

2 326
2 449

2 507
2 639

2 768
2 914

2 979
3 136

Non-OECD countries
Run of river (>= 5 MW)
Reservoir (>= 5 MW)

BrazilIndia** These are projects for powering non-powered dams (NPD). They are thus brownfield projects, for which part of the investment costs havealready been amortised.
** Indicated as reference datasets by the providing country.

Table 3.7b: Other renewable generating technologies – Biomass
Country

Italy

Technology
Biomass
Biomass

Net
capacity(MWe)

Capacity Overnight
factorcosts(%)(USD/kWe)

Investment costs (USD/kWe)
3%

7%

10%

0.5
0.42

89%
89%

4 605
6 545

4 888
6 948

5 288
7 515

5 604
7 965

25
30

30%
85%

1 095
833

1 162
885

1 257
957

1 332
1 014

Non-OECD countries
BrazilIndia

Biomass
Biomass53

3
Table 3.7c: Other renewable generating technologies – GeothermalCountry

Italy

United States

Technology
Geothermal
Geothermal
Geothermal
Geothermal
Geothermal
GeothermalNetcapacity
(MWe)
40
15
10
5
30
25

Capacity Overnight
factorcosts(%)(USD/kWe)
76%
86%
86%
86%
90%
80%

3 851
7 132
9 799
10 9594 120
5 538

Investment costs (USD/kWe)
3%

7%

10%

4 150
7 686
10 56011 810
4 440
5 968

4 581
8 485
11 65913 038
4 901
6 588

4 932
9 134
12 54914 0345 276
7 092

Table 3.8: Combined heat and power (CHP) technologies
Country

Denmark

Italy
Romania
Slovak RepublicTechnologyNetcapacity(MWe)

Biomass
Biomass
Biomass
Biomass
Coal (Ultra-supercritical)
Gas (CCGT)
Gas (OCGT)
Biomass
Pulverised (Lignite)
Gas (OCGT)
Gas (CCGT)
Gas (OCGT)

177
261
258
358
700
500
125
0.4
2 900
195
6
36

Capacity Overnight
factorcosts(%)(USD/kWe)
46%
46%
57%
57%
57%
57%
46%
89%
50%
90%
70%
98%

4 013
2 715
2 951
2 538
2 240
1 024
684
6 545
1 015
330
1 160
1 107

Investment costs (USD/kWe)
3%

7%

10%

4 260
2 882
3 132
2 694
2 378
1 071
705
6 948
1 077
340
1 213
1 141

4 608
3 117
3 388
2 914
2 572
1 135
732
7 515
1 165
353
1 286
1 186

4 884
3 304
3 591
3 088
2 726
1 185
753
7 965
1 235
363
1 343
1 220

Table 3.9: Storage technologies
Country

Technology

Pumped storage
ACAES*
Lithium-ion battery
Denmark
Pumped storage
Lithium-ion battery
Finland
Lithium-ion battery
Italy
Non-OECD countries
Lithium-ion battery
India
Pumped storage
Australia
Canada* Adiabatic Compressed Air Energy Storage

54

Net
capacity(MWe)

Storage
capacity(h)Capacity Overnight
factorcosts(%)(USD/kWe)

200
250
19
1 000
1.14
2

na
4
0.33
na
5.26
0.5

15%
15%
15%
15%
15%
15%

1
175

4
na

15%
15%

Investment costs (USD/kWe)
3%

7%

10%

897
1 695
484
4 426
1 967
452

910
1 772
491
4 492
1 997
459

927
1 879
501
4 579
2 035
468

940
1 961
508
4 642
2 063
474

826
563

838
571

854
582

866
590

3
Table 3.10: Fuel cells
Country

FranceNorway3.3

Technology
Fuel cell
Fuel cell
Fuel cell
Fuel cellNetcapacity(MWe)

Capacityfactor(%)

Overnight
costs(USD/kWe)

0.003
0.2
15
1

85%
85%
85%
85%

6 492
5 902
2 361
3 529

Investment costs (USD/kWe)
3%

7%

10%

6 589
5 990
2 396
3 582

6 715
6 105
2 442
3 650

6 809
6 190
2 476
3 701

Technology-by-technology LCOE data at standard capacity factors

Tables 3.10 to 3.17 present the calculated LCOEs for the power plants included in the EGC 2020
dataset, along with the relevant cost components, calculated at each of the three discount rates(3%, 7% and 10%). The LCOEs for coal, nuclear and CCGT gas plants are all calculated at the same
assumed capacity factor of 85%, while the LCOEs for OCGT gas plants are calculated at a capacity
factor of 30%.1 Plant-specific capacity factors were used for all renewable power plants and CHP
plants. Calculations include a carbon price of USD 30/tCO2. This reflects the expected averagesocial costs of emissions over the technology’s lifetime. The actual emission price being charged togenerators varies from country to country. This changes the private costs of investment experienced
by investors.
For each power plant, the net capacity, overnight cost, refurbishment and decommissioning
costs, and O&M costs are presented. Construction, refurbishment and decommissioning costs for
all plants are dependent on the discount rate used, and so the costs presented are calculated ateach discount rate. For solar PV, which efficiency is assumed to decrease by 0.5% each year, O&M
costs vary depending on the discount rate. Also included are the fuel, waste and carbon costs, whenrelevant. For fossil fuel plants a column indicating the electrical conversion efficiency is included.

1.

Alternate LCOEs assuming a 50% load factor are presented below.

55

3
Table 3.11a: Levelised cost of electricity for combined-cycle gas turbine (CCGT) at 85% capacity factor
Country

Technology

CCGT
Australia
CCGT (CCUS)
CCGT
Belgium
CCGT
CCGT
Canada
CCGT
Italy
CCGT
Japan
CCGT
CCGT
Korea
CCGT
CCGT
Mexico
CCGT
CCGT
Romania
CCGT
CCGT
United States
CCGT (CCUS)
Non-OECD countries
Brazil
CCGT
China
CCGT

Net capacity Electrical conversion(MWe)efficiency (%)

Investment (USD/MWh)

506
437
500
500
500
471
790
1 372
491
982
503
785
835
750
727
646

48%
41%
60%
64%
58%
57%
60%
56%
56%
59%
51%
60%
58%
58%
59%
48%

3%
6.74
19.95
5.42
7.12
6.87
7.47
4.17
7.83
7.82
5.92
4.73
4.71
3.29
1.79
6.72
17.03

7%
11.07
32.78
8.90
11.71
11.29
12.27
6.85
12.86
12.84
9.72
7.76
7.74
5.41
2.94
11.04
27.97

10%
15.01
44.43
12.06
15.86
15.31
16.63
9.28
17.43
17.41
13.18
10.52
10.49
7.33
3.99
14.96
37.91

980
475

58%
58%

6.76
3.95

11.11
6.49

15.06
8.80

Table 3.11b: Levelised cost of electricity for open-cycle gas turbine (OCGT) at 30% capacity factor
Country
Autralia
Belgium
CanadaItaly

Technology
OCGT
OCGT
OCGT
OCGT
OCGT
OCGT
OCGT

Non-OECD countries
BrazilOCGT

Net capacity Electrical conversion(MWe)efficiency (%)

Investment (USD/MWh)

537
350
500
500
100
243
130

31%
40%
41%
38%
41%
40%
37%

3%
13.16
10.47
13.21
11.63
22.48
10.20
6.40

7%
21.20
16.86
21.28
18.73
36.20
16.44
10.30

10%
28.31
22.51
28.42
25.01
48.34
21.95
13.76

980

44%

14.56

23.45

31.32

Table 3.12: Levelised cost of electricity for coal plants at 85% capacity factor
Country

56

Technology

Net capacity Electrical conversion(MWe)efficiency (%)

Investment (USD/MWh)Supercritical pulverised
Supercritical pulverised (CCUS)
Australia
Supercritical pulverised (lignite)
Supercritical pulverised (lignite, CCUS)
Japan
Ultra-supercritical
Korea
Ultra-supercritical
Pulverised
Pulverised
Pulverised
Pulverised (CCUS)
United States
Supercritical pulverised
Supercritical pulverised (CCUS)
Ultra-supercritical
Other coal (CCUS)

722
633
709
570
749
954
138
140
650
650
650
650
641
499

40%
30%
32%
21%
41%
43%
36%
36%
40%
31%
42%
33%
43%
31%

3%
14.79
27.28
22.82
41.88
14.70
6.99
26.63
20.95
15.06
27.98
15.69
28.28
25.26
36.41

7%
27.21
50.20
42.00
77.05
27.05
12.87
49.00
38.54
27.71
51.48
28.87
52.04
46.48
66.99

10%
38.78
71.54
59.85
109.80
38.54
18.34
69.82
54.93
39.48
73.36
41.14
74.16
66.24
95.46

Non-OECD countries
BrazilOther coal (lignite)
China
Ultra-supercritical
Ultra-supercritical
India
Ultra-supercritical

900
347
400
400

34%
45%
45%
45%

13.31
4.86
6.97
6.75

24.48
8.95
12.83
12.42

34.89
12.75
18.29
17.71

3
Decommissioning (USD/
MWh)
3%
0.13
0.38
0.10
0.14
0.13
0.14
0.08
0.15
0.15
0.11
0.09
0.09
0.06
0.11
0.13
0.33
0.13
0.06

7%
0.06
0.18
0.05
0.06
0.06
0.07
0.04
0.07
0.07
0.05
0.04
0.04
0.03
0.07
0.06
0.16
0.06
0.03

10%
0.03
0.10
0.03
0.04
0.03
0.04
0.02
0.04
0.04
0.03
0.02
0.02
0.02
0.05
0.03
0.09
0.03
0.01

Decommissioning (USD/
MWh)
3%
0.26
0.20
0.26
0.23
0.44
0.20
0.12
0.28

7%
0.12
0.10
0.12
0.11
0.21
0.09
0.06
0.13

10%
0.07
0.05
0.07
0.06
0.11
0.05
0.03
0.07

Decommissioning (USD/
MWh)
3%
0.21
0.38
0.32
0.59
0.21
0.10
0.37
0.29
0.21
0.39
0.22
0.40
0.35
0.51

7%
0.07
0.14
0.11
0.21
0.07
0.03
0.13
0.10
0.08
0.14
0.08
0.14
0.13
0.18

10%
0.03
0.06
0.05
0.09
0.03
0.02
0.06
0.05
0.03
0.06
0.03
0.06
0.05
0.08

0.19
0.06
0.08
0.07

0.07
0.02
0.03
0.03

0.03
0.01
0.01
0.01

Fuel
(USD/MWh)

Carbon(USD/MWh)

O&M(USD/MWh)

56.04
64.96
45.88
42.65
47.06
19.19
45.50
61.05
60.82
58.23
21.62
18.29
18.89
47.06
18.38
22.91

12.75
1.48
10.18
9.47
10.45
10.65
10.10
10.84
10.80
10.34
12.00
10.15
10.48
10.45
10.20
5.09

6.59
12.87
6.00
4.80
6.67
6.96
6.99
7.70
11.36
8.41
5.28
5.73
5.51
46.27
5.30
14.22

18.83
53.54

10.45
10.45

11.21
13.49

Fuel
(USD/MWh)

Carbon(USD/MWh)

O&M(USD/MWh)

86.91
68.24
66.58
71.83
26.44
27.30
73.98

19.77
15.15
14.78
15.94
14.67
15.15
16.42

9.09
7.50
9.83
7.99
8.70
18.53
11.11

24.82

13.77

16.71

Fuel
(USD/MWh)

Carbon(USD/MWh)

O&M(USD/MWh)

29.65
39.58
5.13
7.89
28.73
27.47
20.19
20.36
18.23
23.50
17.51
22.38
17.16
23.32

25.47
3.40
34.36
5.28
24.68
23.60
28.22
28.46
25.48
13.14
24.47
12.51
23.99
13.04

31.76
28.02
26.43
26.43

32.03
22.70
22.70
22.70

LCOE (USD/MWh)
3%
82.24
99.64
67.58
64.18
71.19
44.41
66.83
87.56
90.95
83.01
43.72
38.97
38.24
105.68
40.73
59.57
47.37
81.48

10%
90.42
123.84
74.14
72.82
79.52
53.46
71.88
97.06
100.43
90.19
49.45
44.68
42.23
107.82
48.88
80.21

51.65
84.00

Non-OECD countries
55.57
Brazil86.29
China

LCOE (USD/MWh)3%
129.19101.56104.66
107.6372.72
71.37
108.0370.14

Country7%
86.51
112.27
71.00
68.69
75.54
49.13
69.47
92.52
95.89
86.76
46.71
41.95
40.32
106.7944.98
70.34

Australia
Belgium
Canada
Italy
Japan
Korea
MexicoRomania
United States

Country7%137.09107.85112.59114.6186.22
77.50
111.8710%
144.15113.46
119.67120.8498.27
82.97
115.3078.88

Non-OECD countries
86.69
BrazilLCOE (USD/MWh)Autralia
Belgium
CanadaItaly

Country

8.69
19.34
11.26
25.56
19.31
11.62
30.47
22.25
17.01
30.45
17.20
30.15
29.50
42.96

3%
78.81
89.98
73.90
81.19
87.62
69.78
105.89
92.31
75.98
95.46
75.09
93.73
96.27
116.247%
91.10
112.6692.86
115.9999.84
75.59
128.01109.7188.50
118.71
88.13
117.23117.27146.4910%102.63133.92110.65148.63111.2981.04
148.76126.04100.23140.52100.35139.27136.95174.86

8.60
14.97
8.53
38.65

85.88
70.62
64.72
94.61

96.94
74.66
70.53
100.24

Non-OECD countries
107.31
Brazil
78.45
China
75.97
India
105.51

Australia
Japan
Korea

United States

57

3
Table 3.13a: Levelised cost of electricity for nuclear plants at 85% capacity factor – New build
Country
FranceJapan
Korea
RussiaSlovak Republic
United States

Technology
EPR
ALWR
ALWR
VVER
Other nuclear
LWR

Non-OECD countries
China
LWR
India
LWR

Net capacity Electrical conversion(MWe)efficiency (%)

Investment (USD/MWh)
3%

7%

10%

1 650
1 152
1 377
1 122
1 004
1 100

33%
33%
36%
38%
32%
33%

21.32
21.05
11.46
12.06
36.76
22.58

47.46
46.87
25.51
26.86
81.84
50.26

73.29
72.37
39.39
41.47
126.37
77.61

950
950

33%
33%

13.28
14.76

29.57
32.85

45.65
50.73

Table 3.13b1: Levelised cost of electricity for nuclear plants at 85% capacity factor –Long-Term Operation (LTO), 10 years
Country
Switzerland
FranceSwedenUnited States

Technology
LTO
LTO
LTO
LTO

Net capacity Electrical conversion(MWe)efficiency (%)
1 000
1 000
1 000
1 000

33%
33%
33%
33%

Investment (USD/MWh)
3%

7%

10%

8.79
10.05
7.10
6.25

10.88
12.45
8.79
7.74

12.62
14.44
10.19
8.97

Table 3.13b2: Levelised cost of electricity for nuclear plants at 85% capacity factor –Long-Term Operation (LTO), 20 years
Country
Switzerland
FranceSwedenUnited States

Technology
LTO
LTO
LTO
LTO

Net capacity Electrical conversion(MWe)efficiency (%)
1 000
1 000
1 000
1 000

33%
33%
33%
33%

Investment (USD/MWh)
3%

7%

10%

5.04
5.76
4.07
3.58

7.22
8.25
5.83
5.13

9.11
10.42
7.35
6.48

*C
 onservatively, decommissioning costs are calculated on the basis of 15% LTO overnight costs, assuming that decommissioning starts 5 years
after shutdown and lasts for 10 years as for new build. However, depending on the individual project, some of the incremental decommissioning
costs due to LTO have already been included in the LTO overnight costs or are very small. In these cases, incremental decommissioning costs
due to LTO will be closer to zero. Provisions for final decommissioning costs have already been accounted for in the cost of electricity produced
during the original design lifetime of the plant.

58

3
Decommissioning(USD/MWh)

Fuel(USD/MWh)

O&M(USD/MWh)

3%

7%

10%

0.36
0.36
0.20
0.21
1.80
0.39

0.05
0.05
0.03
0.03
0.96
0.05

0.01
0.01
0.01
0.01
0.64
0.01

9.33
13.92
9.33
4.99
9.33
9.33

0.22
0.25

0.03
0.03

0.01
0.01

Decommissioning*(USD/MWh)
3%

7%

10%

0.71
0.81
0.57
0.51

0.40
0.46
0.32
0.28

0.27
0.30
0.21
0.19

Decommissioning*(USD/MWh)
3%

7%

10%

0.29
0.34
0.23
0.21

0.13
0.15
0.10
0.09

0.07
0.08
0.06
0.05

LCOE (USD/MWh)Country

3%

7%

10%

14.26
25.84
18.44
10.15
9.72
11.60

45.27
61.16
39.42
27.41
57.61
43.90

71.10
86.67
53.30
42.02
101.8471.25

96.89
112.1367.16
56.61
146.06
98.56

10.00
9.33

26.42
23.84

49.92
48.17

66.01
66.06

Non-OECD countries
82.08
China
83.91
India

Fuel
(USD/MWh)

O&M(USD/MWh)

9.33
9.33
9.33
9.33

12.92
12.92
12.92
18.69

Fuel
(USD/MWh)

O&M(USD/MWh)

9.33
9.33
9.33
9.33

12.92
12.92
12.92
18.69

LCOE (USD/MWh)
3%

7%

10%

31.74
33.11
29.91
34.78

33.53
35.15
31.35
36.04

35.13
36.98
32.65
37.18

LCOE (USD/MWh)
3%

7%

10%

27.57
28.35
26.54
31.81

29.59
30.65
28.17
33.24

31.43
32.74
29.66
34.55

FranceJapan
Korea
RussiaSlovak Republic
United States

Country
Switzerland
FranceSwedenUnited States

Country
Switzerland
FranceSwedenUnited States

59

3
Table 3.14: Levelised cost of electricity for solar generators
Country

Technology

Net capacity* Capacity factor Annual efficiency
(MWe)
(%)
loss (%)

Solar PV (residential)
Solar PV (utility scale)
Australia
Solar thermal (CSP)
Solar PV (residential)
Solar PV (residential)
Belgium**
Solar PV (residential)
Solar PV (utility scale)
Solar PV (utility scale)
CanadaSolar PV (utility scale)
Solar PV (residential)
Solar PV (commercial)Denmark
Solar PV (utility scale)
Solar PV (utility scale)
Solar PV (residential)
FranceSolar PV (commercial)
Solar PV (utility scale)
Solar PV (residential)
Solar PV (commercial)
Hungary
Solar PV (commercial)
Solar PV (utility scale)
Solar PV (residential)
Solar PV (residential)
Solar PV (commercial)
Solar PV (commercial)
Italy**
Solar PV (commercial)
Solar PV (utility scale)
Solar PV (utility scale)
Solar PV (residential)
Japan
Solar PV (utility scale)
Solar PV (commercial)
Korea
Solar PV (utility scale)
Solar PV (floating)
Netherlands Solar PV (residential)
Solar PV (utility scale)
NorwaySolar PV (commercial)
Solar PV (residential)
Solar PV (residential)
Solar PV (residential)***Solar PV (residential)
Solar PV (residential)
Solar PV (commercial)
Solar PV (commercial)
Solar PV (commercial)***Solar PV (commercial)
United States
Solar PV (commercial)
Solar PV (utility scale)
Solar PV (utility scale)
Solar PV (utility scale)***Solar PV (utility scale)
Solar PV (utility scale)
Solar thermal (CSP)
Solar thermal (CSP)
Solar thermal (CSP)***

0.02
100
150
0.01
0.01
0.01
1.00
20
20
0.01
0.10
8
8
0.01
0.50
25
0.004
0.05
0.50
20
0.004
0.01
0.08
0.21
0.42
0.83
0.83
0.004
2
0.10
2.97
8
0.20
8
0.30
0.01
0.01
0.01
0.01
0.01
0.30
0.30
0.30
0.30
0.30
100
100
100
100
100
100
100
100

11%
28%
47%
13%
12%
13%
13%
25%
19%
12%
13%
16%
18%
18%
18%
24%
15%
14%
14%
14%
20%
17%
20%
17%
20%
20%
27%
12%
14%
15%
15%
14%
14%
14%
10%
14%
16%
17%
19%
21%
13%
15%
16%
19%
20%
23%
26%
28%
33%
36%
50%
61%
64%

0.5%
0.5%
0.0%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.0%
0.0%
0.0%

3%
43.54
21.75
73.07
96.32
75.17
54.49
51.87
37.41
45.94
67.13
44.65
22.87
24.02
66.53
33.27
20.38
87.49
66.32
54.97
49.29
65.14
55.06
46.07
36.48
42.71
29.11
21.77
134.23
98.94
57.08
56.43
42.05
42.48
39.47
66.11
96.37
83.47
77.99
94.38
86.40
71.89
62.02
58.14
49.76
45.97
32.34
28.06
26.03
22.40
20.35
84.22
69.36
66.53

7%
64.47
32.20
109.18142.62111.30
80.68
76.79
55.39
68.02
99.39
66.11
33.86
35.57
98.51
49.26
30.17
129.55
98.20
81.39
72.98
96.45
81.52
68.22
54.01
63.23
43.10
32.23
198.74146.50
84.52
83.55
62.25
62.89
58.44
97.89
142.69123.59115.48139.74127.93106.44
91.83
86.09
73.67
68.07
47.88
41.55
38.55
33.16
30.13
125.84103.64
99.41

10%
82.28
41.10
140.17182.03142.06102.97
98.02
70.69
86.82
126.86
84.37
43.22
45.40
125.7362.87
38.51
165.35125.34103.8893.15
123.10104.05
87.07
68.93
80.71
55.02
41.13
253.66186.98107.88106.63
79.46
80.28
74.59
124.94182.12
157.74147.39178.36163.28135.86117.20109.88
94.03
86.88
61.11
53.03
49.20
42.32
38.46
161.57133.05
127.63

Non-OECD countries
BrazilSolar PV (utility scale)
China
Solar PV (utility scale)
India
Solar PV (utility scale)

25
20
35

31%
18%
20%

0.5%
0.5%
0.5%

26.46
28.64
21.37

39.17
42.40
31.65

50.00
54.12
40.39

Austria*** All values represent AC capacity.
*** A generic sizing factor of 1.2 was used to convert peak DC capacity into peak AC capacity.
*** Indicated as reference datasets by the providing country.

60

Investment (USD/MWh)3
Decommissioning (USD/MWh)O&M (USD/MWh)

LCOE (USD/MWh)Country

10%
0.99
0.50
1.67
2.20
1.72
1.24
1.18
0.85
1.05
1.53
1.02
0.52
0.55
1.52
0.76
0.47
10.35
6.46
3.33
2.67
10.26
1.26
9.26
0.83
9.19
0.67
2.51
3.07
2.26
1.30
1.29
1.94
1.98
1.88
1.51
1.06
0.90
0.82
1.29
1.17
0.58
0.48
0.43
0.35
0.31
0.20
0.16
0.14
0.11
0.09
1.60
1.32
1.26

3%
0.54
0.27
0.91
1.19
0.93
0.67
0.64
0.46
0.57
0.83
0.55
0.28
0.30
0.82
0.41
0.25
8.61
5.28
2.55
2.00
13.30
0.68
12.27
0.45
12.23
0.36
3.14
1.66
1.22
0.70
0.70
1.40
1.43
1.37
0.82
0.57
0.48
0.44
0.70
0.63
0.31
0.26
0.23
0.19
0.17
0.11
0.09
0.08
0.06
0.05
0.87
0.72
0.68

7%
0.33
0.16
0.56
0.73
0.57
0.41
0.39
0.28
0.35
0.51
0.34
0.17
0.18
0.50
0.25
0.15
7.38
4.48
2.08
1.62
16.01
0.42
14.88
0.28
14.85
0.22
3.72
1.02
0.75
0.43
0.43
1.11
1.13
1.09
0.50
0.35
0.30
0.27
0.43
0.39
0.19
0.16
0.14
0.12
0.10
0.07
0.05
0.05
0.04
0.03
0.54
0.44
0.42

10%
43.57
4.63
19.46
15.48
13.78
21.32
12.90
6.68
19.53
14.23
11.20
7.04
7.76
24.54
24.54
3.55
3.56
2.12
20.53
11.17
166.34
58.06
21.28
16.67
18.44
14.76
27.85
23.12
24.56
13.02
12.44
22.92
25.38
20.33
42.13
12.59
10.90
10.19
12.33
11.28
9.19
7.93
7.44
6.36
5.88
6.74
5.85
5.42
4.67
4.24
15.31
12.61
12.10

3%
43.18
4.59
19.46
15.34
13.65
21.13
12.78
6.62
19.36
14.10
11.10
6.97
7.69
24.31
24.31
3.52
3.53
2.10
20.34
11.07
164.82
57.53
21.09
16.52
18.28
14.62
27.59
22.91
24.34
12.91
12.32
22.73
25.17
20.16
41.75
12.47
10.80
10.09
12.21
11.18
9.11
7.86
7.37
6.31
5.83
6.68
5.79
5.38
4.62
4.20
15.31
12.61
12.10

7%
42.92
4.56
19.46
15.25
13.57
21.00
12.71
6.58
19.24
14.01
11.03
6.93
7.65
24.17
24.17
3.50
3.51
2.09
20.22
11.00
163.86
57.19
20.97
16.43
18.17
14.54
27.43
22.77
24.19
12.83
12.25
22.61
25.03
20.06
41.50
12.40
10.74
10.03
12.14
11.12
9.06
7.81
7.33
6.27
5.79
6.64
5.76
5.34
4.60
4.18
15.31
12.61
12.10

10%
88.11
26.88
94.19
114.00
90.67
77.05
65.95
44.94
66.52
82.89
56.87
30.43
32.34
92.59
58.56
24.39
101.41
74.91
78.83
63.13
241.73114.37
76.62
53.98
70.34
44.53
52.12
160.41125.76
71.41
70.15
66.91
69.84
61.68
109.75110.01
95.26
89.00
108.00
98.85
81.66
70.43
66.01
56.47
52.16
39.28
34.07
31.60
27.17
24.68
101.14
83.28
79.89

7%
108.1837.06
129.55159.15125.88102.48
90.22
62.47
87.94
114.32
77.75
41.11
43.56
123.6573.98
33.94
141.69105.58104.2886.05
274.57139.73101.58
70.98
93.74
58.08
62.95
223.30172.05
98.13
96.56
86.39
89.50
79.97
140.45155.73134.87126.01152.65139.74115.87
99.94
93.69
80.16
74.06
54.66
47.43
44.00
37.84
34.39
142.03116.96112.2010%125.5445.83
160.19198.01
156.20124.39111.12
77.56
106.41141.39
95.75
50.32
53.23
150.41
87.29
42.16
176.23131.91
126.19105.77302.97161.66122.91
85.64
113.73
69.77
72.29
277.45211.92121.14119.31103.18106.4495.73
166.94194.87168.77
157.70190.93174.78145.11
125.17117.35100.41
92.77
67.82
58.85
54.59
46.96
42.67
177.42
146.11140.15

0.60
0.48
0.29

0.33
0.26
0.15

0.20
0.16
0.09

6.58
8.02
3.72

6.52
8.02
3.69

6.48
8.02
3.67

33.64
37.13
25.38

46.02
50.68
35.49

Non-OECD countries
56.68
Brazil
62.30
China
44.15
India

Austria
Australia*Belgium
CanadaDenmark

FranceHungary*ItalyJapan
Korea
Netherlands
NorwayUnited States

61

3
Table 3.15a: Levelised cost of electricity for wind generators – Onshore
Country
Austria
Australia
Belgium
CanadaDenmark
FinlandFrance

Italy

Japan
Korea
Netherlands
NorwayRussiaSwedenUnited States

Net capacity(MWe)

Capacity factor(%)

Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (< 1 MW)
Onshore wind (< 1 MW)
Onshore wind (< 1 MW)
Onshore wind (< 1 MW)
Onshore wind (< 1 MW)
Onshore wind (< 1 MW)
Onshore wind (< 1 MW)
Onshore wind (< 1 MW)
Onshore wind (< 1 MW)
Onshore wind (< 1 MW)
Onshore wind (< 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)*
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)
Onshore wind (>= 1 MW)

3.00
100
5.00
30
4.50
30
200
4.48
30
50
0.01
0.19
0.02
0.06
0.06
0.10
0.50
0.80
0.83
0.90
0.90
1.00
10
20
20
14.85
50
130
60
280
5
100
100
100
100
100
100
100
100
100
100

27%
42%
30%
28%
25%
28%
40%
40%
40%
38%
17%
18%
32%
20%
33%
31%
33%
32%
30%
48%
43%
39%
37%
30%
20%
23%
48%
44%
27%
27%
42%
53%
49%
47%
45%
43%
41%
37%
33%
28%
18%

3%
35.01
22.36
32.24
33.77
40.24
31.60
22.31
15.18
25.18
25.45
201.93104.51112.98132.57
90.52
70.76
52.14
36.90
50.36
37.17
42.08
51.02
25.19
32.91
74.80
56.48
17.01
13.89
38.81
35.74
17.13
17.57
18.07
18.51
19.09
20.23
24.40
28.52
39.25
50.70
85.66

7%
52.31
33.41
48.18
50.46
60.13
47.22
33.34
22.69
37.62
38.04
301.74156.17168.82198.09135.26105.73
77.90
55.13
75.24
55.54
62.88
76.24
37.65
49.17
111.76
84.40
25.42
20.75
57.99
53.40
25.60
26.26
27.00
27.66
28.52
30.23
36.46
42.62
58.66
75.76
127.99

10%
67.15
42.89
61.85
64.78
77.19
60.62
42.80
29.13
48.30
48.83
387.39200.49216.74254.32173.66135.74100.02
70.78
96.60
71.30
80.72
97.88
48.33
63.13
143.48108.36
32.64
26.64
74.45
68.56
32.87
33.71
34.67
35.51
36.62
38.81
46.81
54.72
75.31
97.26
164.32

30
50
65

47%
26%
27%

18.32
30.03
21.36

27.38
44.87
31.92

35.15
57.61
40.98

Non-OECD countries
BrazilOnshore wind (>= 1 MW)
China
Onshore wind (>= 1 MW)
India
Onshore wind (>= 1 MW)* Indicated as reference datasets by the providing country.

62

Investment (USD/MWh)Technology

3
Decommissioning (USD/MWh)
3%
0.80
0.51
0.74
0.77
0.92
0.72
0.51
0.35
0.58
0.58
4.61
2.39
2.58
3.03
2.07
1.62
1.19
0.84
1.15
0.85
0.96
1.17
0.58
0.75
1.71
1.29
0.85
0.32
0.89
0.82
0.39
0.24
0.23
0.22
0.22
0.23
0.31
0.37
0.57
0.77
1.32

7%
0.44
0.28
0.40
0.42
0.50
0.39
0.28
0.19
0.31
0.32
2.51
1.30
1.41
1.65
1.13
0.88
0.65
0.46
0.63
0.46
0.52
0.63
0.31
0.41
0.93
0.70
0.51
0.17
0.48
0.44
0.21
0.13
0.12
0.12
0.12
0.13
0.17
0.20
0.31
0.42
0.72

10%
0.27
0.17
0.25
0.26
0.31
0.24
0.17
0.12
0.19
0.20
1.55
0.80
0.87
1.02
0.70
0.54
0.40
0.28
0.39
0.29
0.32
0.39
0.19
0.25
0.57
0.43
0.34
0.11
0.30
0.27
0.13
0.08
0.08
0.08
0.08
0.08
0.10
0.12
0.19
0.26
0.44

0.42
0.59
0.34

0.23
0.32
0.19

0.14
0.20
0.12

O&M
(USD/MWh)

LCOE (USD/MWh)

23.61
9.31
19.96
13.23
17.71
10.29
13.21
6.30
6.94
17.73
591.75
27.55
41.87
41.46
39.28
29.29
20.38
19.69
12.91
15.37
15.61
14.56
14.91
9.94
27.48
28.22
15.23
9.84
13.82
13.50
27.15
8.71
9.57
9.92
10.26
10.69
11.36
12.53
14.15
16.49
26.07

3%
59.41
32.18
52.94
47.77
58.86
42.61
36.04
21.84
32.69
43.77
798.29134.46
157.43177.06131.87101.66
73.71
57.43
64.42
53.38
58.65
66.75
40.68
43.60
103.98
86.00
33.10
24.04
53.52
50.06
44.67
26.53
27.87
28.66
29.57
31.16
36.07
41.42
53.97
67.96
113.05

7%
76.35
43.00
68.54
64.11
78.33
57.90
46.83
29.18
44.88
56.08
896.00185.02212.10241.20175.67135.89
98.93
75.28
88.78
71.37
79.01
91.44
52.87
59.52
140.17113.33
41.17
30.76
72.29
67.35
52.96
35.10
36.70
37.71
38.90
41.05
47.99
55.35
73.11
92.66
154.78

10%
91.03
52.37
82.06
78.27
95.21
71.15
56.19
35.55
55.44
66.76
980.68228.85259.48296.80213.63165.57120.8090.75
109.9086.96
96.65
112.8363.43
73.32
171.54137.02
48.21
36.58
88.57
82.34
60.15
42.51
44.32
45.51
46.95
49.58
58.27
67.37
89.64
114.01190.84

5.98
13.18
3.72

24.73
43.80
25.43

33.59
58.37
35.83

41.28
70.99
44.82

Country
Austria
Australia
Belgium
CanadaDenmark
FinlandFrance

Italy

Japan
Korea
Netherlands
NorwayRussiaSwedenUnited States

Non-OECD countries
Brazil
China
India

63

3
Table 3.15b: Levelised cost of electricity for wind generators – Offshore
Country
Australia
Belgium
Denmark
FranceJapan
Korea

United States

Technology
Offshore wind
Offshore wind
Offshore wind
Offshore wind
Offshore wind
Offshore wind
Offshore wind
Offshore wind
Offshore wind
Offshore wind
Offshore wind*
Offshore wind
Offshore wind
Offshore wind
Offshore wind
Offshore wind
Offshore wind
Offshore wind
Offshore wind
Offshore wind
Offshore wind
Offshore wind

Non-OECD countries
China
Offshore windInvestment (USD/MWh)

Net capacity(MWe)

Capacity factor
(%)

100
12
50
11.25
11.5
500
100
99
600
600
600
600
600
600
600
600
600
600
600
600
600
600

51%
39%
39%
52%
52%
45%
30%
30%
47%
46%
46%
46%
45%
39%
30%
53%
51%
50%
50%
47%
37%
31%

3%
44.66
39.68
39.68
25.46
21.74
44.71
88.26
76.92
27.15
27.95
29.18
30.61
31.66
37.22
52.58
30.04
30.91
32.33
33.97
36.44
45.75
61.15

7%
66.73
59.30
59.30
38.04
32.49
66.81
131.88114.93
40.57
41.76
43.61
45.74
47.31
55.62
78.57
44.89
46.19
48.31
50.76
54.46
68.36
91.37

10%
85.68
76.13
76.13
48.84
41.71
85.77
169.32147.55
52.09
53.61
55.98
58.72
60.74
71.40
100.87
57.63
59.30
62.03
65.17
69.91
87.77
117.30

50

35%

41.93

62.66

80.44

* Indicated as reference datasets by the providing country.

Table 3.16a: Levelised cost of electricity for other renewable generation – Hydropower
Country
Austria
Germany

Italy

Japan
NorwayUnited States

Technology

Net capacity(MWe)

Capacity factor(%)

Run of river (< 5 MW)
Run of river (>= 5 MW)
Run of river (< 5 MW)
Run of river (< 5 MW)
Run of river (< 5 MW)
Run of river (< 5 MW)
Run of river (< 5 MW)
Run of river (< 5 MW)
Run of river (< 5 MW)
Run of river (< 5 MW)
Run of river (< 5 MW)
Run of river (< 5 MW)
Run of river (< 5 MW)
Run of river (< 5 MW)
Run of river (>= 5 MW)
Reservoir (< 5 MW)
Reservoir (>= 5 MW)
Reservoir (>= 5 MW)
Reservoir (>= 5 MW)
Run of river (< 5 MW)
Run of river (< 5 MW)*Run of river (>= 5 MW)*Run of river (< 5 MW)*Run of river (>= 5 MW)*,**Run of river (< 5 MW)
Run of river (>= 5 MW)
Run of river (< 5 MW)
Run of river (>= 5 MW)**

2.2
24.5
0.02
0.1
0.2
0.3
0.25
0.25
0.5
0.50
0.50
0.7
1
2.1
5
0.32
15
12
30
3.0
4.8
82
4.2
45
3.7
44
4.3
94
247.5
175

Non-OECD countries
BrazilRun of river (>= 5 MW)
India
Reservoir (>= 5 MW)

64

Investment (USD/MWh)

51%
65%
55%
34%
55%
19%
15%
8%
20%
15%
22%
55%
15%
50%
37%
65%
38%
45%
52%
47%
62%
64%
60%
60%
66%
66%
62%
66%

3%
18.43
18.52
47.13
35.36
39.83
32.82
99.35
155.72
21.04
100.17
43.77
31.92
26.42
25.63
33.16
24.32
32.95
51.90
14.66
16.77
41.51
36.63
28.73
27.06
45.76
40.51
42.13
35.58

7%
42.40
42.61
108.41
81.33
91.61
75.49
228.52358.1848.39
230.40100.67
73.43
60.77
58.94
76.28
55.93
75.78
119.38
33.72
38.58
95.47
84.26
66.09
62.24
105.26
93.18
96.89
81.84

10%
64.05
64.36
163.77122.86138.38114.03345.19541.0673.10
348.04152.07110.9291.80
89.04
115.22
84.49
114.47180.3350.94
58.28
144.21
127.2899.83
94.02
159.00140.76146.37
123.62

50%
60%

18.67
16.38

42.95
37.68

64.88
56.92

** These are projects for powering non-powered dams (NPD). They are thus brownfield projects, for which part of the investment costs havealready been amortised.
** Indicated as reference datasets by the providing country.

3
Decommissioning (USD/MWh)
3%
1.02
0.91
0.91
0.58
0.50
1.02
2.02
1.76
0.53
0.53
0.56
0.58
0.60
0.70
0.99
0.56
0.57
0.60
0.63
0.67
0.83
1.12

7%
0.56
0.49
0.49
0.32
0.27
0.56
1.10
0.96
0.29
0.29
0.30
0.32
0.33
0.38
0.54
0.31
0.31
0.32
0.34
0.36
0.45
0.61

10%
0.34
0.30
0.30
0.20
0.17
0.34
0.68
0.59
0.18
0.18
0.19
0.20
0.20
0.23
0.33
0.19
0.19
0.20
0.21
0.22
0.28
0.38

0.88

0.48

0.30

Decommissioning (USD/MWh)
3%
0.08
5.65
0.20
0.15
0.17
0.14
0.42
0.66
0.09
0.42
0.19
0.14
0.11
0.11
0.14
0.10
0.14
0.22
0.06
0.07
0.14
0.12
0.09
0.08
0.16
0.14
0.14
0.12

7%
0.01
5.89
0.02
0.01
0.02
0.01
0.04
0.06
0.01
0.04
0.02
0.01
0.01
0.01
0.01
0.01
0.01
0.02
0.01
0.01
0.01
0.01
0.01
0.01
0.02
0.01
0.01
0.01

10%
0.001
5.55
0.003
0.002
0.002
0.002
0.006
0.009
0.001
0.006
0.003
0.002
0.002
0.002
0.002
0.001
0.002
0.003
0.001
0.001
0.002
0.002
0.001
0.001
0.002
0.002
0.002
0.002

0.08
0.06

0.01
0.01

0.001
0.001

O&M
(USD/MWh)

LCOE (USD/MWh)

18.07
35.41
27.64
13.58
12.33
22.46
67.20
45.10
20.44
21.43
21.65
21.95
22.92
26.32
32.56
14.11
14.62
15.49
16.99
19.18
24.66
27.08

3%
63.75
76.00
68.23
39.62
34.57
68.19
157.48123.77
48.12
49.91
51.39
53.14
55.18
64.24
86.13
44.71
46.10
48.42
51.59
56.30
71.24
89.35

7%
85.36
95.20
87.43
51.94
45.09
89.82
200.18160.98
61.30
63.48
65.56
68.01
70.56
82.32
111.67
59.30
61.12
64.13
68.10
74.00
93.47
119.0610%104.09111.84104.0762.61
54.20
108.57237.19193.24
72.70
75.22
77.82
80.87
83.86
97.96
133.76
71.92
74.11
77.72
82.38
89.32
112.70144.76

18.68

61.49

81.82

99.42

Fuel
(USD/MWh)

O&M(USD/MWh)

0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

LCOE (USD/MWh)

11.92
8.50
24.64
17.01
21.47
22.09
29.18
51.07
13.01
17.52
12.07
12.47
8.71
8.42
27.53
16.85
18.37
23.00
4.92
8.61
22.24
5.93
24.42
8.33
23.40
7.57
23.17
5.35

3%
30.43
32.67
71.97
52.52
61.46
55.04
128.95207.4534.14
118.12
56.03
44.53
35.24
34.16
60.83
41.27
51.45
75.12
19.64
25.45
63.89
42.69
53.24
35.47
69.32
48.22
65.44
41.05

7%
54.32
57.00
133.0798.36
113.0997.59
257.74409.3161.41
247.97112.76
85.91
69.49
67.38
103.82
72.79
94.16
142.4038.65
47.19
117.72
90.21
90.51
70.58
128.67100.76120.08
87.20

10%
75.97
78.41
188.41139.87159.85136.12374.38592.1486.11
365.57164.15123.39100.51
97.46
142.75101.34132.84203.3455.86
66.89
166.45133.22124.25102.35182.40148.33169.54128.97

3.16
11.65

21.91
28.09

46.12
49.33

68.04
68.57

Country
Australia
Belgium
Denmark
FranceJapan
Korea

United States

Non-OECD countries
China

Country
Austria
Germany

Italy

Japan
NorwayUnited States

Non-OECD countries
Brazil
India

65

3
Table 3.16b: Levelised cost of electricity for other renewable generation – Biomass
Investment (USD/MWh)

Net capacity(MWe)

Capacity factor(%)

Biomass
Biomass

0.45
0.42

89%
89%

3%
26.59
38.16

7%
48.93
70.22

10%
69.73
100.07

Non-OECD countries
Brazil*Biomass
India
Biomass

25.00
30.00

30%
85%

18.85
5.06

34.68
9.32

49.42
13.28

Country
Italy

Technology* Fuel cost are zero, since the feedstock is bagasse, an agricultural residue from sugar cane processing.

Table 3.16c: Levelised cost of electricity for other renewable generation – GeothermalCountry

Italy
United States

Technology

Net capacity(MWe)

Capacity factor
(%)

40
15
10
5
30
25

76%
86%
86%
86%
90%
80%

Geothermal
Geothermal
Geothermal
Geothermal
Geothermal
Geothermal

Investment (USD/MWh)
3%
26.65
43.69
60.03
67.14
24.00
36.30

7%
50.04
82.06
112.74126.09
45.08
68.17

10%
72.42
118.76163.17182.4865.25
98.67

Table 3.17: Levelised cost of electricity for combined heat and power (CHP) plants
Country

Denmark

Italy
Romania
Slovak RepublicTechnologyNetcapacity(MWe)

Capacityfactor(%)

Biomass
Biomass
Biomass
Biomass
Coal (Ultra-supercritical)
Gas (CCGT)
Gas (OCGT)
Biomass
Pulverised (Lignite)*Gas (OCGT)*
Gas (CCGT)*
Gas (OCGT)

177
261
258
358
700
500
125
0.4
2 900
195
6
36

46%
46%
57%
57%
57%
57%
46%
89%
50%
90%
70%
98%

Investment(USD/MWh)
3%
45.40
30.71
26.71
22.97
20.27
10.77
8.86
38.16
10.48
2.17
9.95
6.68

7%
83.54
56.51
49.14
42.26
37.31
17.69
14.26
70.22
19.29
3.49
16.34
10.76

10%
119.04
80.53
70.02
60.22
53.16
23.97
19.05
100.07
27.49
4.66
22.15
14.37

Decommissioning(USD/MWh)
3%
0.64
0.43
0.37
0.32
0.28
0.21
0.17
0.54
2.18
0.17
3.14
0.13

7%
0.23
0.15
0.13
0.11
0.10
0.10
0.08
0.19
2.34
0.16
3.46
0.06

10%
0.10
0.07
0.06
0.05
0.04
0.05
0.05
0.08
2.33
0.16
3.59
0.03

* The O&M costs for these plants include costs for refurbishment at regular intervals. Minor adjustments have been made to LCOE figures tocompensate for the differentiated impact of these costs at 3% and 10% discount rates.

66

3
Decommissioning (USD/MWh)
3%
0.37
0.54

7%
0.13
0.19

10%
0.06
0.08

0.20
0.05

0.07
0.02

0.03
0.01

Decommissioning (USD/MWh)
3%
0.37
0.60
0.83
0.93
0.24
0.40

7%
0.13
0.22
0.30
0.33
0.09
0.14

10%
0.06
0.09
0.13
0.14
0.04
0.06

Fuel
(USD/MWh)

O&M(USD/MWh)0159.41
0
106

LCOE (USD/MWh)

26.89
59.00

3%
53.86
257.127%
75.96
288.8310%96.68
318.57

18.75
2.24

37.79
113.5953.50
117.8268.20
121.77

Fuel(USD/MWh)

O&M(USD/MWh)

0
0
0
0
0
0

17.96
21.25
23.02
25.38
16.63
25.19

Country
Italy
Non-OECD countries*Brazil
India

LCOE (USD/MWh)
3%
44.97
65.55
83.88
93.45
40.87
61.88

Fuel
(USD/MWh)

Carbon(USD/MWh)

O&M(USD/MWh)

Heat credit(USD/MWh)111.18126.32
76.87
95.07
21.28
47.47
67.65
154.81
12.28
33.88
84.46
57.46

0
0
0
0
20.22
10.54
15.02
0
33.50
7.52
18.75
12.75

33.29
20.77
19.36
14.28
10.73
11.96
10.77
59.00
129.88
11.75
7.74
6.10

-102.95-72.67-50.25-39.01-29.69-14.82-38.05-114.62-1.63-4.34-68.78-26.137%
68.13
103.52136.06151.80
61.80
93.50

10%
90.44
140.10186.32208.01
81.91
123.91LCOE (USD/MWh)3%
87.55
105.57
73.05
93.63
43.10
66.11
64.41
137.89186.68
51.15
55.25
57.00

7%
125.28131.0995.25
112.71
59.95
72.92
69.73
169.61
195.65
52.47
61.97
61.01

10%
160.65155.02116.06130.61
75.74
79.16
74.48
199.35203.85
53.63
67.90
64.59

Country

Italy
United States

Country

Denmark

Italy
Romania
Slovak Republic

67

3
Table 3.18: Levelised cost of electricity for storage technologies
Country
Australia
CanadaDenmark
Finland
Italy

Technology
Pumped storage
ACAES***
Lithium-ion battery
Pumped storage
Lithium-ion battery
Lithium-ion battery

Non-OECD countries
Lithium-ion battery
India
Pumped storage

Capacityfactor
(%)
15%
15%
15%
15%
15%
15%

3%

7%

10%

200
250
19
1 000
1.14
2

Storage
capacity****
(h)
na
4
0.33
na
5.26
0.5

22.59
51.65
43.19
111.54175.5240.34

47.98
100.1752.46
236.86213.1748.99

68.27
143.5559.96
337.03243.66
56.00

1
175

na
4

15%
15%

73.70
14.18

89.51
30.12

102.31
42.86

Net capacity(MWe)

Investment (USD/MWh)* The required average operational profit (RAOP) is the required total operational profit (OP**) on a per unit of discharged energy basis.
** The total required operational profit (OP) is the total required revenue from discharging electricity minus the total cost from charging
electricity.
*** Adiabatic Compressed Air Energy storage.
**** Without specific data available, the storage capacity was set at 4 hours by default.

Table 3.19: Levelised cost of electricity for fuel cells
Country
FranceNorwayTechnology
Fuel cell
Fuel cell
Fuel cell
Fuel cell

Net capacity
(MWe)
0.003
0.2
15
1

Capacityfactor
(%)
85%
85%
85%
85%

Investment (USD/MWh)
3%

7%

10%

58.60
53.28
21.31
31.86

82.30
74.82
29.93
44.74

102.41
93.10
37.24
55.67

3.4 Technology-by-technology LCOE data for flexible technologies
at lower capacity factors
Tables 3.18 to 3.20 present again the calculated LCOEs for dispatchable power plants included in the
EGC 2020 dataset, along with the relevant cost components, calculated at each of the three discountrates (3%, 7% and 10%). This time, however, the LCOEs for coal, nuclear and CCGT gas plants are all
calculated at the same assumed lower capacity factor of 50%, while the LCOEs for OCGT gas plants
are calculated at a capacity factor of 10%.
Actual capacity factors will depend on many country-specific factors, including relative fuelcost and the penetration of variable renewable power. Many baseload plants will in fact operate atlower capacity factors under actual operating conditions. The sensitivity of a given technology tocapacity factor is determined by its ratio of capital to operating costs. Units with low variable costs
are generally run at higher capacity factors, because there are many hours where their marginal
costs are lower than other units. Conversely, units with high operating to capital costs are run atlower capacity factors, because there are relatively few hours where their marginal costs are lower
than other units.

68

3
Decommissioning (USD/MWh)
3%

7%

10%

Charging Costs(USD/MWh)

0.10
2.51
1.54
0.50
6.25
1.44

0.01
1.99
1.21
0.05
4.90
1.13

0.00
1.70
1.00
0.01
4.08
0.94

0
0
0
0
0
0

1.80
0.03

1.41
0.00

1.17
0.00

Decommissioning (USD/MWh)

O&M(USD/MWh)

LCOS (RAOP*) (USD/MWh)Country

3%

7%

10%

3.41
12.71
2.73
8.08
19.03
6.85

26.11
66.88
47.46
120.13200.7948.62

51.40
114.8756.39
245.00237.0956.97

71.68
157.9663.70
345.12266.76
63.79

0
0

12.57
10.71

88.07
24.93

103.4940.83

Non-OECD countries
116.06
India
53.57

O&M
(USD/MWh)
42.66
22.59
36.75
1.69

3%

7%

10%

Charging Costs(USD/MWh)

1.55
1.41
0.56
0.84

0.96
0.87
0.35
0.52

0.66
0.60
0.24
0.36

111.11111.11111.11111.11LCOS (RAOP*) (USD/MWh)
3%

7%

10%

213.92
188.39169.74145.51237.03209.39178.14158.07256.84227.40185.35168.83Australia
CanadaDenmark
Finland
Italy

Country
FranceNorwayIt is important to bear in mind that the 50% capacity factor for coal, nuclear and CCGT gas
plants and the 10% capacity factor for OCGT gas plants factor results do not represent the expected
LCOEs for these technologies in 2025. Their function in the context of this publication is merely toshow how sensitive these technologies are to changes in operating conditions. This topic is further
explored in the sensitivity analysis presented in Chapter 5.

69

3
Table 3.20a: Levelised cost of electricity for combined-cycle gas turbine (CCGT) at 50% capacity factor
Country
Australia
Belgium
Canada
Italy
Japan
Korea
MexicoRomania
United States

Technology
CCGT
CCGT (CCUS)
CCGT
CCGT
CCGT
CCGT
CCGT
CCGT
CCGT
CCGT
CCGT
CCGT
CCGT
CCGT
CCGT
CCGT (CCUS)

Non-OECD countries
Brazil
CCGT
China
CCGT

Net capacity(MWe)

Electricalconversion
efficiency (%)

506
437
500
500
500
471
790
1 372
491
982
503
785
835
750
727
646

48%
41%
60%
64%
58%
57%
60%
56%
56%
59%
51%
60%
58%
58%
59%
48%

3%
11.46
33.92
9.21
12.11
11.69
12.70
7.08
13.31
13.29
10.06
8.03
8.01
5.60
3.04
11.42
28.95

7%
18.83
55.73
15.13
19.90
19.20
20.86
11.64
21.86
21.83
16.53
13.20
13.16
9.19
5.00
18.77
47.56

10%
25.52
75.53
20.50
26.97
26.02
28.27
15.77
29.63
29.59
22.40
17.89
17.83
12.46
6.78
25.44
64.45

980
475

58%
58%

11.50
6.72

18.89
11.04

25.60
14.96

Investment (USD/MWh)

Table 3.20b: Levelised cost of electricity for open-cycle gas turbine (OCGT) at 10% capacity factor
Net capacity(MWe)

Electricalconversion
efficiency (%)

OCGT
OCGT
OCGT
OCGT
OCGT
OCGT
OCGT

537
350
500
500
100
243
130

31%
40%
41%
38%
41%
40%
37%

3%
39.49
31.40
39.63
34.89
67.43
30.61
19.19

7%
63.60
50.57
63.84
56.19
108.60
49.31
30.91

10%
84.93
67.54
85.25
75.04
145.03
65.84
41.27

Non-OECD countries
Brazil
OCGT

980

44%

43.68

70.36

93.96

Country
Autralia
Belgium
CanadaItaly

Technology

Investment (USD/MWh)

Table 3.21: Levelised cost of electricity for coal technologies at 50% capacity factor
Country

Australia
Japan
Korea

United States

Technology

Net capacity(MWe)

Electricalconversion
efficiency (%)

Supercritical pulverised
Supercritical pulverised (CCUS)
Supercritical pulverised (lignite)
Supercritical pulverised (lignite, CCUS)
Ultra-supercritical
Ultra-supercritical
Pulverised
Pulverised
Pulverised
Pulverised (CCUS)
Supercritical pulverised
Supercritical pulverised (CCUS)
Ultra-supercritical
Other coal (CCUS)

722
633
709
570
749
954
138
140
650
650
650
650
641
499

40%
30%
32%
21%
41%
43%
36%
36%
40%
31%
42%
33%
43%
31%

3%
25.14
46.38
38.80
71.19
24.99
11.89
45.27
35.61
25.60
47.56
26.67
48.08
42.94
61.89

7%
46.26
85.34
71.40
130.99
45.98
21.87
83.30
65.52
47.10
87.52
49.08
88.47
79.02
113.8810%
65.92
121.62101.74186.6765.52
31.17
118.7093.37
67.12
124.71
69.94
126.07112.61
162.29

900
347
400
400

34%
45%
45%
45%

22.62
8.26
11.86
11.48

41.62
15.21
21.82
21.12

59.31
21.67
31.09
30.10

Non-OECD countries
BrazilOther coal (lignite)
China
Ultra-supercritical
Ultra-supercritical
India
Ultra-supercritical70

Investment (USD/MWh)3
Decommissioning(USD/MWh)
3%
0.22
0.65
0.18
0.23
0.22
0.24
0.14
0.25
0.25
0.19
0.15
0.15
0.11
0.18
0.22
0.55

7%
0.10
0.31
0.08
0.11
0.11
0.12
0.06
0.12
0.12
0.09
0.07
0.07
0.05
0.12
0.10
0.26

10%
0.06
0.17
0.05
0.06
0.06
0.06
0.04
0.07
0.07
0.05
0.04
0.04
0.03
0.09
0.06
0.15

0.22
0.10

0.10
0.05

0.06
0.03

Decommissioning(USD/MWh)
3%
0.77
0.61
0.77
0.68
1.31
0.59
0.37
0.85

7%
0.36
0.29
0.37
0.32
0.62
0.28
0.18
0.40

10%
0.20
0.16
0.20
0.18
0.34
0.16
0.10
0.22

Decommissioning(USD/MWh)
3%
0.35
0.65
0.54
1.00
0.35
0.17
0.63
0.50
0.36
0.67
0.37
0.67
0.60
0.87

7%
0.13
0.23
0.19
0.36
0.13
0.06
0.23
0.18
0.13
0.24
0.13
0.24
0.21
0.31

10%
0.05
0.10
0.08
0.15
0.05
0.03
0.10
0.08
0.06
0.10
0.06
0.10
0.09
0.13

0.32
0.10
0.13
0.12

0.11
0.04
0.05
0.04

0.05
0.02
0.02
0.02

Fuel
CarbonO&M(USD/MWh) (USD/MWh) (USD/MWh)

LCOE (USD/MWh)

56.04
64.96
45.88
42.65
47.06
19.19
45.50
61.05
60.82
58.23
21.62
18.29
18.89
47.06
18.38
22.91

12.75
1.48
10.18
9.47
10.45
10.65
10.10
10.84
10.80
10.34
12.00
10.15
10.48
10.45
10.20
5.09

7.34
14.26
7.55
7.02
8.89
10.37
9.44
10.87
16.86
11.85
8.82
9.58
9.20
60.90
7.82
20.23

3%
87.81
115.27
72.99
71.48
78.31
53.14
72.25
96.32
102.02
90.67
50.63
46.18
44.28
121.64
48.04
77.72

18.83
53.54

10.45
10.45

13.31
15.94

54.30
86.73

Fuel
CarbonO&M(USD/MWh) (USD/MWh) (USD/MWh)
86.91
68.24
66.58
71.83
26.44
27.30
73.98
24.82

19.77
15.15
14.78
15.94
14.67
15.15
16.42
13.77

11.53
12.63
26.90
16.97
24.56
48.48
26.33
33.71

Fuel
CarbonO&M(USD/MWh) (USD/MWh) (USD/MWh)10%
101.70156.40
84.16
86.17
92.48
68.54
80.84
112.46118.14102.87
60.37
55.89
51.07
125.2861.89
112.82

61.57
91.00

Non-OECD countries
68.24
Brazil94.91
China

LCOE (USD/MWh)3%
158.46128.03148.66140.32134.41
122.13136.29116.83

29.65
39.58
5.13
7.89
28.73
27.47
20.19
20.36
18.23
23.50
17.51
22.38
17.16
23.32

25.47
3.40
34.36
5.28
24.68
23.60
28.22
28.46
25.48
13.14
24.47
12.51
23.99
13.04

12.57
25.75
16.39
34.95
28.35
16.26
43.58
31.84
23.41
41.64
23.83
41.45
41.36
59.83

31.76
28.02
26.43
26.43

32.03
22.70
22.70
22.70

11.74
18.45
11.01
41.13

98.47
77.54
72.13
101.86Australia
Belgium
Canada
Italy
Japan
Korea
MexicoRomania
United States

Country7%
182.18146.89172.46161.27174.89140.51147.81

10%203.34163.72
193.71179.97211.04156.92
158.10143.06

Non-OECD countries
166.48BrazilLCOE (USD/MWh)3%
93.19
115.7795.22
120.31
107.1079.38
137.90116.7793.07
126.51
92.86
125.10126.06158.95Country7%
95.06
136.74
78.82
79.15
85.71
61.18
76.73
104.74110.44
97.04
55.71
51.25
47.82
123.5355.27
96.04

Australia
Belgium
CanadaItaly

Country7%114.08154.31
127.47179.47127.8789.26
175.52
146.36114.35166.03115.03165.05161.75210.3810%133.67190.45
157.70234.94147.3498.52
210.79174.11134.29203.09135.81202.52
195.22258.61117.2784.42
82.01
111.43

Non-OECD countries
134.89Brazil
90.86
China
91.25
India
120.38Australia
Japan
Korea

United States

71

3
Table 3.22a: Levelised cost of electricity for nuclear technologies at 50% capacity factor – New Build
Country
FranceJapan
Korea
RussiaSlovak Republic
United States

Technology
EPR
ALWR
ALWR
VVER
Other nuclear
LWR

Non-OECD countries
China
LWR
India
LWR

Net capacity Electrical conversion(MWe)efficiency (%)

Investment (USD/MWh)

1 650
1 152
1 377
1 122
1 004
1 100

33%
33%
36%
38%
32%
33%

3%
36.24
35.79
19.48
20.51
62.49
38.38

7%
80.69
79.67
43.37
45.7
139.13
85.45

10%
124.6
123
66.97
70.5
214.8
131.9

950
950

33%
33%

22.58
25.08

50.26
55.85

77.6
86

Table 3.22b1: Levelised cost of electricity for nuclear technologies at 50% capacity factor –Long-Term Operation (LTO), 10 years
Country
Switzerland
FranceSwedenUnited States

Technology
LTO
LTO
LTO
LTO

Net capacity Electrical conversion(MWe)efficiency (%)
1 000
1 000
1 000
1 000

33%
33%
33%
33%

Investment (USD/MWh)
3%
14.94
17.09
12.06
10.62

7%
18.50
21.16
14.94
13.15

10%
21.5
25
17.3
15

Table 3.22b2: Levelised cost of electricity for nuclear technologies at 50% capacity factor –Long-Term Operation (LTO), 20 years
Country
Switzerland
FranceSwedenUnited States

Technology
LTO
LTO
LTO
LTO

Net capacity Electrical conversion(MWe)efficiency (%)
1 000
1 000
1 000
1 000

33%
33%
33%
33%

Investment (USD/MWh)
3%
8.57
9.80
6.92
6.09

7%
12.27
14.03
9.90
8.72

10%
15.5
18
12.5
11

*C
 onservatively, decommissioning costs are calculated on the basis of 15% LTO overnight costs, assuming that decommissioning starts
5 years after shutdown and lasts for 10 years as for new build. However, depending on the individual project, some of the incremental
decommissioning costs due to LTO have already been included in the LTO overnight costs or are very small. In these cases, incremental
decommissioning costs due to LTO will be closer to zero. Provisions for final decommissioning costs have already been accounted for in thecost of electricity produced during the original design lifetime of the plant.

72

3
Decommissioning(USD/MWh)
3%
0.62
0.61
0.33
0.35
3.07
0.66

7%
0.08
0.08
0.05
0.05
1.63
0.09

10%
0.02
0.02
0.01
0.01
1.09
0.02

0.38
0.42

0.05
0.06

0.01
0.01

Decommissioning*(USD/MWh)
3%
1.21
1.38
0.98
0.86

7%
0.68
0.78
0.55
0.49

10%
0.45
0.52
0.36
0.32

Decommissioning*(USD/MWh)
3%
0.49
0.58
0.40
0.35

7%
0.22
0.26
0.18
0.16

10%
0.12
0.14
0.10
0.09

Fuel
(USD/MWh)

O&M(USD/MWh)

9.33
13.92
9.33
4.99
9.33
9.33

LCOE (USD/MWh)Country

15.12
43.92
30.48
15.77
15.87
18.47

3%
61.31
94.24
59.63
41.61
90.76
66.83

7%
105.22
137.6
83.23
66
166.0
113.3310%149.06
180.9
106.8
91.3
241.1
159.76

10.00
9.33

34.41
31.83

67.36
66.67

94.72
97.1

Non-OECD countries
122.03
China
127.4
India

Fuel (USD/MWh)

O&M(USD/MWh)

9.33
9.33
9.33
9.33

20.91
20.91
20.91
30.72

Fuel (USD/MWh)

O&M(USD/MWh)

9.33
9.33
9.33
9.33

20.91
20.91
20.91
30.72

LCOE (USD/MWh)
3%
46.39
48.71
43.27
51.54

7%
49.42
52.18
45.73
53.69

10%
52.15
55.29
47.92
55.63

LCOE (USD/MWh)
3%
39.29
40.62
37.55
46.50

7%
42.72
44.53
40.32
48.93

10%
45.84
48.09
42.84
51.15

FranceJapan
Korea
RussiaSlovak Republic
United States

Country
Switzerland
FranceSwedenUnited States

Country
Switzerland
FranceSwedenUnited States73Chapter 4

The value-adjusted levelised costof electricity (VALCOE)

4.1

Motivations for looking beyond the LCOE

The levelised cost of electricity (LCOE) is the most commonly used metric to assess costcompetitiveness of power generation technologies. The main strength of the LCOE is thatit compresses all the direct technology costs into a single metric, including those related toconstruction, fuel, carbon prices, operations and maintenance, and can be applied equally well totechnologies with a wide range of technical lifetimes.
However, the LCOE is not a complete metric of competitiveness, as it lacks representation of
the value provided to the system. Power generation technologies primarily generate electricity(or energy) but also provide other services that are critical to the adequacy, reliability and qualityof electricity supply. Among technologies that have similar operational characteristics and play
similar roles in power systems – and therefore have similar value to the overall system – the LCOE
can provide a robust metric of competitiveness. But not all power technologies contribute equallyto these essential services.
A comparison of a utility-scale solar PV project with and without a battery storage system
highlights the need to look beyond the LCOE alone. In terms of costs, adding a battery storage
system to a solar PV project necessarily raises the total costs of the project and, when charged onlyby the solar PV array, reduces the total output due to the round-trip efficiency of storage. In thiscase, both factors indicate a higher LCOE for solar PV plus storage compared with solar PV alone. At
the same time, the addition of a battery storage system would increase the value proposition of the
project for the owner and system. Output could be aligned dynamically to system needs, providing
energy when it is most valuable. The battery could provide ancillary services to the grid, including
frequency control, and could displace the need for other types of dispatchable capacity. For a system
planner or an investor considering both the costs and value aspects, solar PV plus storage could be
the better choice despite having a higher LCOE.
The nature of electricity demand and supply is rapidly changing – demand patterns are
shifting, the share of variable renewables is rising and the power system flexibility is increasing– underscoring the need to look beyond the LCOE. Electricity demand is set to reach higher peaks
relative to the average in many regions and wind power and solar PV have become the mostcommonly built power technologies worldwide. Both trends are changing how power systems arebeing operated and providing a spotlight on the importance of system services. For example inIndia, solar PV is set to increase dramatically over the next two decades, changing the operationsof the existing coal-fired power plant fleet and adding to the call for more flexibility from a wide
variety of sources (see Figure 4.1). At the hourly level, the increased contribution of solar PV also
shifts the highest-value hours to the evening as solar PV output ramps down. Considering the new
operational realities and value propositions will be essential (and increasingly complex) for powersystem planners, developers and investors.

75

4
Figure 4.1: Hourly generation mix and wholesale market price of electricity in India
in the WEO-2018 Stated Policies Scenario, 2020 and 2040
2020

700

2040

600

GWh

500
400
300
200
100

USD/MWh (2017)

0
0

12

Coal

Gas

0
Oil

12
Nuclear0

120Hour of day

Bioenergy

Hydro

0
Wind

12

0

Solar

Other

0
Storage120Hour of day
Demand

2040

2020

150

12

100
50
0
0

12

0

12

0

12
0
Hour of day

0

12

0

12

0

12

0

Hour of day

Demand almost triples, its profile becomes peakier, and variable renewables account for one-quarter ofannual capacity, calling for more flexibility in the power system.
Source: IEA (2018).

Recognising the importance of going beyond the LCOE and to enable more robust cross-technology
comparisons in evolving power systems, a new metric for competitiveness was developed for the2018 World Energy Outlook (WEO), called the value-adjusted LCOE (VALCOE). Developed with theanalytical capabilities of the WEO hourly power supply model and real-world market structuresand experience, the VALCOE underpins the capacity additions mechanism within the World Energy
Model (WEM) (IEA, 2019a).
The VALCOE is part of a family of competitiveness metrics that go beyond the LCOE. The VALCOE
is most closely related to the System LCOE (Ueckerdt et al., 2013), which provides a comprehensive
theoretical framework for assessing system value beyond the LCOE. The VALCOE and System LCOE
are similar in scope, and re-arranging terms can align significant portions of the computations.
The VALCOE is also similar to the Levelised Avoided Cost of Electricity (LACE), a metric created
for the Annual Energy Outlook published by the US Energy Information Administration (EIA, 2020).
All of these metrics are related to standard profitability metrics, such as net present value andinternal rates of return, that consider the costs and revenues associated with different power
technologies. Analysis of total power system costs for various power generation mixes are also
closely related, though they do not necessarily provide a direct indicator of competitiveness forindividual technologies. There have been real-world applications of the concept, with a locational
value-adjustments in clean energy auctions in Mexico in 2017 (Hochberg and Poudineh, 2018). As
clean energy transitions progress around the world, experience with higher shares of wind andsolar PV in large systems will increase and provide opportunities to refine the VALCOE and othermetrics of competitiveness.

76

4
4.2

Key elements of VALCOE

For each technology, the VALCOE combines the LCOE for a given year with the value of the multiple
system services it can provide in the context of the regional power mix at that time. More specifically,
the VALCOE captures the value of three system services: energy value, flexibility value and capacity
value by technology. The WEM hourly power supply model assesses these value streams for eachtechnology and compares them against a region-specific system average (across all three-service
categories). The sum of these differences – that is, the differences between these technology-specificvalue streams and the system average values - form the basis of the “value-adjustments” to movefrom the LCOE to the VALCOE (see Figure 4.2). The metric takes the perspective of system planners
and is applicable in all regions, no matter the prevailing regulatory structure, as the same essential
services are needed to support electricity reliability in all cases whether or not services are priced ina market. VALCOE aligns with an investor’s perspective in fully developed and functioning markets,
representing multiple value streams (or revenue stacking).

Figure 4.2: Illustrative example of adjusting the LCOE to incorporate value streams
Adjusting the LCOE (illustrative)

Components determining the adjustment
Averagesystemvalue

Energy Capacity

Levelised cost
(LCOE)

Value-adjusted
LCOE

Average
specific
technology

USD/MWh

USD/MWh

Flexibility

Adjustment
relative toaverage

EnergyCapacity

Flexibility

Combining costs and value provides a more robust basis for evaluating competitiveness acrosstechnologies than costs alone.

Of the three value streams considered in the VALCOE, the energy value is generally the largest,
varies significantly by technology and changes as the share of variable renewables increases. It is
calculated as the average price received per unit of generation over the course of a year based onleast-cost merit order dispatch and simulated wholesale electricity prices. Modelling electricity
demand and supply on an hourly-basis in the WEM captures the effect of the changing power mix
on energy values by technology. The energy value of a given technology also varies by region andpenetration of variable renewables. It is sensitive to a variety of factors, including the profile ofelectricity demand, the installed power generation capacities, profile of wind and solar PV output,
available energy storage capacity, prevailing fuel and carbon prices. The simulated energy values
also capture the extent of cannibalisation effects as the share of variable renewables increase,
whereby the expansion of variable renewables reduces their own market value. The simulations in
the WEO show that the effect tends to be more significant for solar PV due to highly concentrated
output during certain times of day compared with wind power (Figure 4.3), consistent with previous
studies (Mills and Wiser, 2012; Hirth, 2013). Pairing solar PV with energy storage technologies canmitigate this effect.

77

4
Figure 4.3: Energy value by technology and region relative to average wholesale electricity pricein the Stated Policies Scenario
European Union

China

United States

150%

Energy value

125%
100%
75%
50%
15%

30%

45%

10%

20%

30%

10%

20%

30%

Share of variable renewables
Gas CCGT

Solar PV

Onshore wind

Offshore wind

As the share of variable renewables rises, wind power’s energy value tends to be more stable relative to wholesale
electricity prices compared with solar PV.
Note: Hourly simulations of electricity demand and supply are performed for each region as a whole (US, EU, China) without grid congestion torepresent structural changes. In practice, each region is composed of several balancing areas and regularly experience grid congestion, both ofwhich would have an effect on energy value depending on the location of the project.

Source: IEA (2018).

Capacity value reflects the ability of a technology to reliably meet demand, contributing to
the adequacy of the system. It is calculated as the capacity credit1 of a technology multiplied by
the estimated basis capacity value for the system in each region and year. The capacity credit
indicates how much a technology is relied on to meet peak demand from a planning perspective. For
dispatchable power plants, the capacity credit can be up to 95% of installed capacity, reflecting theplant’s availability at times of peak demand after accounting for planned and unplanned outages.
For renewables, capacity value is derived from technology-specific values with hourly modelling thatvary by region and depend on the coincidence of system needs with simulated production profiles.
Taking offshore wind as an example, rising performance levels (i.e. capacity factors reaching 60%
for individual turbines and lower hourly variability compared to onshore wind), and alignment withsystem needs help to give offshore wind power a much higher capacity credit than solar PV in key
markets (though still less than dispatchable power plants due its variability) (Figure 4.4). The basis
capacity value in the World Energy Model represents the simulated market value for a unit of capacity
credit and is related to the amount and type of existing power generation capacities by technology,
their estimated availability, the level of peak electricity demand and the minimum required capacity
margin. Representing system adequacy requirements to ensure electricity security, a minimum
capacity margin is required above and beyond the peak level of demand in each region in any
given year. The model simulates a market where power generation technologies compete to meetthe required capacity margin at the minimum cost. Each technology effectively “bids” its available
capacity credit at a price, which reflects their remaining revenue needs after accounting for energy
and flexibility values (i.e. their LCOE minus the combined value of energy and flexibility services).
The winning bid is that which secures a sufficient amount of capacity credit to meet the required

1.
Capacity credit reflects the portion of installed capacity that can be reliably expected to be available during times of peakdemand.

78

4capacity margin at the lowest cost.2 This approximates the approach in several real-world capacity
markets, which are present in parts of the United States and Europe, and is an element of theelectricity market reforms in Japan.

Figure 4.4: Average capacity credit by technology and region in the Stated Policies Scenario
Offshore wind

Onshore wind

Solar PV

40%
30%
20%
10%
0%
2010

2040
United States

2010

European Union

2040
China

2010

2040

India

Offshore wind makes a larger contribution to system adequacy in key markets than onshore wind and solar PV.
Source: IEA (2019b).

The last segment of system value is a technology’s flexibility value, which is set to become
more important as the share of variable renewables rises in regions around the world. Flexibility
values encompass non-energy ancillary services required in power systems, such as primary andsecondary reserves, frequency regulation and synchronous inertia. The ability to provide these
services varies by technology, and so the amount of flexibility value they capture also varies
significantly. In present-day power systems with relatively low shares of VRE, the flexibility value
is a relatively small part of the value proposition for power plants that produce bulk energy, but
can represent a much larger share for “peaking plants” – dispatchable power plants that focus
on servicing peak electricity demand. For example, open-cycle gas turbines are able to respondto system needs more quickly than coal-fired power plants and so have been able to earn higher
revenues per unit of electricity production in existing ancillary service markets. Battery storage
also tends to have a high flexibility value because it has high availability and is able to respondextremely rapidly and accurately to system needs. In the WEM, the average value of flexibility
increases in proportion to total generation and is related to the share of variable renewables,
informed by market experience. Initial technology-specific values are based on market experienceand rise in proportion to the average flexibility value. Where technologies’ roles evolve, such as
focusing on meeting system flexibility needs to a greater degree rather than providing bulk energy,
so too will the value that they capture for each service (though the overall impact on system value
is not obvious). Additional market experience in power systems with higher shares of variable
renewables will provide opportunities to refine the assumed relationships and technology roles.

2.
The maximum basis capacity value that can be set in any year is equal to the annualised capital repayment for the lowest
cost peaking plant available, which are open-cycle gas turbines in many cases.

79

4
By capturing technology-specific costs and a wide set of value streams, the VALCOE provides
a robust metric of competitiveness across technologies with different operational characteristics,
including solar PV with and without storage, and more generally between variable renewables anddispatchable thermal technologies. However, the VALCOE does not attempt to be all-encompassing,as it does not account for network integration and other indirect costs, such as those related topollution.

4.3

Sample results

The value adjustments by technology from the WEM hourly power supply model are based onelectricity demand levels and patterns, the installed power capacity mix and fuel prices in 2025
in the WEO 2019 Stated Policies Scenario (STEPS). This scenario is based on the latest data fortechnologies and energy markets around the world, and explores the implications of existing
policies and announced targets across all energy sectors. For this analysis, to be consistent withLCOE estimations, a carbon price of USD 30 per tonne of CO2 emissions was applied to all power
plants and included in the merit order dispatch. For each technology, value streams were comparedagainst the system average to derive the value adjustments. Positive value adjustments indicate atechnology is less competitive than the LCOE would suggest. After adjustments are applied to alltechnologies, the VALCOE then provides a basis for evaluating competitiveness.
In most cases, the value adjustments in 2025 are modest, and reflect the fact that dispatchable
thermal power plants provide the majority of electricity supply in most regions (see Figure 4.5).
The results represent the average across the fleet of each technology and can be grouped into four
categories, starting with the large value adjustment for open-cycle gas turbines (OCGTs). OCGTs are
generally operated for a small proportion of the year and therefore can have relatively high LCOEs.
However, the significant negative values indicate that OCGTs are far more competitive than their
LCOEs alone suggest. This reflects that OCGTs provide electricity to the system when it is mostvaluable (and generally only a small proportion of the year), that these plants actively participate inancillary service markets and contribute to the overall adequacy of power systems.
The second category of technologies generally has zero or minimal value adjustments in thisanalysis to 2025, tending to have values close to the system average. These include nuclear reactors
in all four regions, coal-fired power in the United States, China and India, and combined-cycle
gas turbines (CCGTs) in the United States and China. This point reflects the fact that these plants
operate in the majority of hours in the year, contribute to system adequacy, and are capable ofproviding some flexibility to the system.
A third category includes technologies with small positive value adjustments, meaning that they
are somewhat less competitive than LCOEs alone would suggest. Solar PV without storage has thelargest effect, though still relatively modest, reflecting the concentration of all solar PV output inthe same midday hours. The effect on wind power is less pronounced, as production profiles are
more varied.
Combined-cycle gas turbines in the European Union and India are in a fourth category. In
both cases, mid-merit operations in systems that highly value flexibility lead to significant value
adjustments that mean CCGTs are more competitive than their LCOEs otherwise suggest.

80

4
Figure 4.5: Simulated value adjustments by technology and region basedon WEO 2019 Stated Policies Scenario, 2025
United States

20

Onshore wind

Offshore wind

Solar PV w storage

More competitive

Offshore wind

Solar PV w storage

More competitive

NuclearGas GT

EU28

20

- 40

- 80

- 100

- 100
Capacity correction

NuclearGas GT- 60- 80

Energy correction

Gas CCGT

- 20

Coal

Solar PV w storage

Onshore wind

Offshore wind

- 60

Solar PV utility

NuclearGas GT

Coal

0
Gas CCGT- 40

Onshore wind

- 100

0

Solar PV utility

- 100

- 20

Gas CCGT

- 60
- 80

India

Coal

- 40

- 80

20

USD/MWh- 20

Solar PV utility- 60

Solar PV w storage

Offshore wind

Onshore wind

NuclearGas GT

Solar PV utility

USD/MWh- 40

Gas CCGT

0
Coal

0
- 20

China

20

Flexibility correction

By 2025, value adjustments are already important in evaluating the competitiveness of solar PV without storage,
and remain important to peaking plants like open-cycle gas turbines.

As the generation mixes continue to evolve over the long term, particularly with rising shares ofvariable renewables in all of these regions, the importance and scale of the value adjustments will
only increase. The competitiveness of power generation technologies will continue to be influenced
by their operational attributes. For instance, cannibalisation effects could be more pronounced forsolar PV without storage, possibly shifting the balance towards coupling battery storage systems
more often. At the same time, the falling costs of solar PV could outweigh these effects. Flexibility
will be the cornerstone of electricity security in future power systems and potentially more highly
valued, which would boost the value proposition for dispatchable technologies. However, sector
coupling could expand the pool of low-cost flexibility sources, including from the rapidly growing
fleet of electric vehicles. In addition, higher shares of variable renewables will depress averagewholesale electricity prices in general, which may raise value streams related to contributions tosystem adequacy. The net effect of these factors is not obvious for any technology, which highlights
the need to carefully quantify the various effects. Further refinements of the VALCOE and otherefforts to create a more complete metric of competitiveness for power generation technologies will
be invaluable to policymakers, system planners, developers, investors and consumers around theworld in support of the most cost-effective clean energy transitions.

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4
References
IEA (2019a), World Energy Model Documentation (2019 version), 2019, IEA, Paris https://iea.blob.core.
windows.net/assets/d496ff6a-d4ca-4f6a-9471-220adddf0efd/WEM_Documentation_WEO2019.pdf.
IEA (2019b), World Energy Outlook 2019, IEA, Paris, www.iea.org/reports/world-energy-outlook-2019.
IEA (2018), World Energy Outlook 2018, IEA, Paris, www.iea.org/reports/world-energy-outlook-2018.
Ueckerdt, F., L. Hirth, G. Luderer and O. Edenhofer (2013), “System LCOE: What are the costs of variable
renewables?”, Energy No. 63, pp. 61-75, https://neon.energy/Ueckerdt-Hirth-Luderer-Edenhofer-2013System-LCOE-Costs-Renewables.pdf.
US Energy Information Administration (2020a), Levelized Cost and Levelized Avoided Cost of New
Generation Resources in the Annual Energy Outlook 2020, February 2020, www.eia.gov/outlooks/aeo/pdf/
electricity_generation.pdf.
US Energy Information Administration (2020b), Annual Energy Outlook 2020, www.eia.gov/outlooks/
aeo/.
Hochberg, M. and R. Poudineh (2018), Renewable Auction Design in Theory and Practice: Lessons from
Experiences of Brazil and Mexico, Oxford Institute for Energy Studies, Oxford, www.oxfordenergy.org/
wpcms/wp-content/uploads/2018/04/Renewable-Auction-Design-in-Theory-and-Practice-Lessonsfrom-the-Experiences-of-Brazil-and-Mexico-EL-28.pdf.
Mills, A. and R. Wiser (2012), Changes in the Economic Value of Variable Generation at High Penetration
Levels: A Pilot Case Study of California, Ernest Orlando Lawrence Berkeley National Laboratory, June
2012, https://emp.lbl.gov/sites/all/files/lbnl-5445e.pdf.
Hirth, L. (2013), “The market value of variable renewables: The effect of solar wind power variabilityon their relative price”, Energy Economics, Volume 38, 2013, https://neon.energy/Hirth-2013-MarketValue-Renewables-Solar-Wind-Power-Variability-Price.pdf.

82

Chapter 5

Sensitivity analysis

Simplifying assumptions have to be made when performing the cost analysis of generation units toenable comparability of results – for example by assuming a constant capacity factor or harmonised
fuel price. In reality, the input parameters will vary across – or even within – countries. To better
understand the sensitivity of the levelised cost of electricity (LCOE) with respect to the mostimportant drivers, a sensitivity analysis is performed for a particular set of generation types andparameters, comparing the relative sensitivity of the individual technologies.
As technology costs are subject to region-specific influences and individual plant characteristics,
a single LCOE value would oversimplify the results: in many cases, there is not the one single
technology having lower costs than the rest. To account for this, the figures in this section show
ranges of technology costs dependent on the variation of individual input parameters. As extreme
values and outliers distort the illustration and aggravate the interpretation of the results, thefigures are restricted to the median 50% of cases for flexible plants. As a very wide range of windand solar PV technologies were received with similarly wide cost ranges, for these technologies theanalysis focuses on the median 20% of costs. Furthermore, solid lines for each technology category
indicate the median costs.
As the number of data points per country differ significantly for certain technologies, eachcountry is represented only once, with their average cost technology, per figure and technology.
If not stated otherwise, the sensitivities are performed with respect to the medium 7% discountrate case with 85% capacity factor for baseload plants (renewable load factors are country specific).
The focus is on standard and most widespread technologies for gas- and coal-fired generation, i.e.
excluding CCUS and CHP units.

83

5
5.1

Discount rate sensitivity

The discount rate affects several aspects of LCOE costs, such as construction, refurbishment
and decommissioning expenses. In general, the more capital-intensive a technology is, the moresensitive it is to changes in the discount rate. The discount rate itself might also be influenced by
how capital-intensive a technology is: a technology with relatively high upfront costs might be more
exposed to market risks, which increases financing costs.
The impact of the discount rate is illustrated in Figure 5.1, with the discount rate being varied
between 0% to 20%.

200
180
160
140
120
100
80
60
40
20
0

LCOE [USD/MWh]

LCOE [USD/MWh]Figure 5.1: LCOE as a function of the discount rate
(left: non-renewables, right: renewables)

0%

5%

10%

15%

200
180
160
140
120
100
80
60
40
20
0

20%

0%

Discount rate
NuclearCoal

Gas (CCGT)

5%

10%

15%

20%

Discount rate
Onshore wind (>= 1 MW)

Offshore wind

Solar PV (utility scale)Note: Lines indicate median values, areas the 50% (20% for renewables) central region.

Within the group of flexible baseload plants, i.e. nuclear, coal and natural gas-fired CCGTs, thelatter are the least susceptible to changes in the discount rate. The average costs of the median caseare increasing by around 40% when comparing the 0% and 20% discount rate cases. Nuclear plants,
due to their relatively high investment costs, are impacted the most: whereas they appear to havea cost advantage over coal- and gas-fired plants when assuming low (one digit) discount rates, they
become relatively more costly when financing becomes more expensive (with median costs morethan five times higher at 20% discount rate compared to the lower bound). Coal, typically ranging
between nuclear and gas-fired plants in terms of the ratio between fix and variable costs, ranks
between the two other technologies with respect to discount rate sensitivity. The fixed cost shareis illustrated in Figure 5.2, showing how the share of fixed costs on total costs is increasing with thediscount rate and illustrating the relative position of the individual technologies.
Another factor contributing to nuclear being more impacted by the discount rate is its relatively
long technology lifetime: the higher the discount rate, the less beneficial are revenues at the end of
the observation period.

84

5
The variable renewable energy technologies wind and solar exhibit relatively high upfront andlow operating costs – as no fuel costs occur, the latter consists only of O&M and refurbishment costs.
These properties make them especially susceptible to changes in the discount rate. Compared to
likewise capital-intensive nuclear plants, they have a shorter deployment period, which counteracts
its sensitivity. Nonetheless, the LCOE values increase similarly for all technology types by a factor of
about three between the lowest and highest discount rate – positioning variable renewables below
nuclear technologies, but above coal and natural gas CCGTs.

Figure 5.2: Ratio of fixed to total costs as a function of the discount rate

Ratio of fixed to total costs [%]

100
90
80
70
60
50
40
30
20
10
0

0%

5%

10%

15%

20%

Discount rate
NuclearCoal

Gas (CCGT)

Note: Lines indicate median values, areas the 50% central region.

5.2

Load factor sensitivity

The levelised costs of generating electricity can the interpreted as the average market price a
generation unit has to receive for its produced electricity over its lifetime to recover total costs. For
baseload technologies, reflected in the assumption of a capacity factor of 85%, the LCOE reflects
more or less average wholesale energy prices.
When deviating from the assumption of being operated in baseload, the timing and therefore thevalue of generation – reflected in the time-varying market prices – becomes more important: midand peak-load plants only operate when market prices are high and thus receive, on average, higher
revenues per produced unit of electricity. At the same time, average costs increase as investment
and fixed costs are distributed over less operating hours. The profitability thus depends on the
power plants costs structure as well as the prevalent market price structure in individual markets.
Figure 5.3 illustrates the impact of varying the capacity factor on average production costs.
Driven by its high share of fixed compared to variable costs, nuclear power plants are the mostaffected technology out of the three under consideration. Nuclear plants are, from an economical
point of view, less suited to be operated at lower capacity factors. In an environment in which they
can be operated at maximum capacity, they benefit however from their low variable costs.

85

5
Natural gas-fired CCGTs plants appear as the most versatile technology with respect to theeconomic impact of a varying number of operating hours: due to their relatively low investment
costs and – depending on the region – low to moderate variable costs, they appear competitive in awide range of use cases.

LCOE [USD/MWh]Figure 5.3: LCOE of coal, natural gas and nuclear as a function of the capacity factor
200
180
160
140
120
100
80
60
40
20
0

0%

20%

40%

60%

80%

100%

Capacity factor
NuclearCoal

Gas (CCGT)

Note: Lines indicate median values, areas the 50% central region.

Coal-fired power plants appear to be in a challenging economic position given the underlying fueland moderate CO2-price assumptions (in this report uniformly USD 30/t CO2) – they typically comealong with higher investment costs than gas-fired plants but lose their competitive advantage ofsignificantly lower variable costs due to low gas prices and more frequently occurring CO2-reduction
mechanisms. Nonetheless, in particular markets with access to low-cost coal (or expensive gas) or
lower emission control costs, coal-fired plants can still be the most competitive option.
Also variable renewables such as wind and solar PV are subject to system conditions and might
not be operated at their theoretical maximum. As their share in the generation mix increases, theymight have to be curtailed to some extent.

5.3

Lifetime sensitivity

To assess the impact of the technical lifetime assumption on average generation costs, we vary thisparameter in the range of 50% (i.e. cutting the lifetime in half) to 150% (i.e. increasing the lifetime by50%) of the original values. A decrease in lifetime is equivalent to an early retirement, an increase
is equivalent to a no-cost lifetime extension.

865Each technology is assumed to have a different expected lifetime. The technologies under
scrutiny in this section are expected to last from 60 years for nuclear plants to 40 and 30 years forcoal and gas plants respectively. Wind and solar PV installations have been allocated an expected
lifetime of 25 years.

Figure 5.4: LCOE as a function of lifetime
200

180

180

160

160

140

140

LCOE [USD/MWh]

LCOE [USD/MWh]

200

120
100
80
60

120
100
80
60

40

40

20

20

0
50%

0

70%

90%

110%

130%

150%

50%

Lifetime
NuclearCoal

Gas (CCGT)

70%

90%

110%

130%

150%

Lifetime
Onshore wind (>= 1 MW)

Offshore wind

Solar PV (utility scale)Note: Lines indicate median values, areas the 50% (20% for renewables) central region.

In general, decreasing the lifetime of an asset has a bigger impact on average costs thanincreasing it by the same amount. This is due to the discount rate, which renders costs and revenues
in future periods less important – thus taking away years with profits is more relevant than adding
the same number of years in the distant future.
For the same reason, changing the expected lifetime of an asset impacts those who have ashorter expected lifespan more: for a nuclear plant with assumed 60 years of operation, adding
another year has virtually no effect. For a relatively short-living renewable technology, this could
be different.
An additional driver is the ratio between investment and operating costs: if costs occur
predominantly at the beginning, shortening the lifespan means having to recover high costs overfewer years. With relatively low investment costs, reallocating these costs is less severe.
For all flexible technologies, the impact of reducing or increasing the lifespan in the given range
has little impact on average costs. For all three, the difference between the lower and upper value
ranges between 7% and 12%, showing that at 7% discount rate and 30 or more years of expected
lifetime – in combination with a lower ratio of fix to total costs for the shorter lived assets – changing
the latter by a few years hardly plays a role for average costs.
For wind and solar units, which are more susceptible to lifetime changes due to their lower life
expectancy and high share of investment costs, cutting the expected lifetime in half increases
average costs between 27% and 31%. Extending it by 50% on the other hand reduces costs by around
8% to 9%.

87

5
5.4

Fuel cost sensitivity

When analysing the fuel cost sensitivity of the average generation costs, it is important to keep
several aspects in mind.
Fuel costs can vary significantly depending on the region. This is especially the case for the priceof natural gas, the price assumption in this analysis being that in America only about one-third of
the price paid in the rest of the world.
Fuel prices are in most cases not stable but being negotiated in international markets andvarying depending on the global economy and short- and long-term outlook of supply and demand.
Additionally, prices may be subject to long-term supply contracts as well as seasonal fluctuations.
This is true to different extents for the different fuels: whereas coal and gas prices can varysignificantly even within a year, nuclear fuel costs can be considered as being relatively stable.
The share of fuel expenditures on total costs varies largely between technologies: whereas
nuclear plants are characterised by high investment but relatively low fuel costs, this ratio is
typically reversed in the case of natural gas plants.
Important for the interpretation of the results is also to keep in mind that the technologies are
to different extents exposed to competition (assuming the power plant dispatch is done according
to least cost considerations). Whereas the variable costs of nuclear technologies are relatively
low and even a significant increase would hardly affect its short-run competitive position, coaland natural gas plants – depending on the market – are frequently competing for market shares.
Thus, an increasing or decreasing price for one of them can also affect their capacity factor, which
additionally affects their average generation costs. For the sensitivity analysis, we assume aconstant capacity factor of 85%.

Figure 5.5: LCOE as a function of fuel cost
200
180

LCOE [USD/MWh]

160
140
120
100
80
60
40
20
0

50%

70%

90%

110%

130%

150%

Fuel costs
NuclearCoal

Gas (CCGT)

Note: Lines indicate median values, areas the 50% central region.

88

5
Figure 5.5 illustrates that nuclear plants are only slightly affected by increasing or decreasing fuelcosts by 50% in either direction – due to total nuclear costs being dominated by fixed costs. Average
median costs change by about 8% in either direction when reaching the end of the sensitivity range.
Natural gas CCGT plants are the most susceptible among the analysed technologies with LCOEs
changing by about 7% with every 10% change of the fuel price (4% for coal). Natural gas prices, inparticular, also vary widely from region to region. In this report natural gas varies from an assumed
USD 3.2 per MBtu in North America to USD 10 per MBtu in Japan. Figure 5.5 uses average LCOE values
over all countries.

5.5

Carbon price sensitivity

A uniform emission price of USD 30 per tonne of CO2 is assumed throughout this report. In 2020 only
a fraction of all markets had implemented a CO2 mechanism (World Bank, 2020). In the European
Emission Trading System (ETS), emission allowances have occasionally already exceeded USD 30,
and many countries implemented higher national prices. To analyse the impact of these different
prices on the competitiveness of baseload technologies, we varied the emission costs in the rangeof USD 0-100 per tonne CO2.
Figure 5.6 illustrates the effects of different emission prices. As nuclear plants do not emit CO2,
they are not affected (but included as a reference). Coal, which causes approximately twice thecarbon output per generated unit of electricity than natural gas, is the most sensitive to varying
carbon prices.

Figure 5.6: LCOE as a function of carbon price [in USD/tCO2]
200
180

LCOE [USD/MWh]

160
140
120
100
80
60
40
20
0

0

20

40

60

80

100

Carbon price
NuclearCoal

Gas (CCGT)

Note: Lines indicate median values, areas the 50% central region.

Increasing (decreasing) the carbon price by 10% changes the LCOE of the median coal technologies
by around 8% – and thus almost twice as much as the median costs of a CCGT. Under the given fuelprice assumptions, at emission costs of around USD 50/t CO2, average ordinary coal technologies
without carbon capture have a clear cost disadvantage towards gas-fired CCGTs.

89

5
In the default case with emission costs of USD 30/tCO2, equipping coal and gas plants with acarbon capture and storage unit (CCUS) is, due to the higher investment costs of CCUS equipment
and the reduced thermal efficiency, more expensive than unmitigated fossil fuel-based electricity.
With higher emission costs however, the picture could change. For coal power plants, due tothe fuel’s relatively high carbon content, CCUS units become competitive at around USD 50 to 60
per tCO2. For gas-fired CCGTs, only carbon prices above USD 100/tCO2 would make plants with
CCUS competitive. At such high carbon prices renewables, hydroelectricity or nuclear are likely toconstitute the least-cost options to ensure low-carbon electricity.
Although the necessary carbon price levels required for triggering a cost advantage of CCUS plants
exceed the majority of today’s prices, they are still relatively low compared to existing estimatesof the social cost of carbon. Although the estimates carry great uncertainties, global social costs
could exceed USD 100 per tCO2 (Nordhaus, 2017). Thus, if flexible low-carbon generation is needed,
competitive alternatives are lacking and affordable fossil resources are available. Depending on
national circumstances, with sufficiently high carbon prices, CCUS could be a possible complementin certain low-carbon power mixes.

200
180
160
140
120
100
80
60
40
20
0

LCOE [USD/MWh]

LCOE [USD/MWh]Figure 5.7: LCOE with and without CCUS for various carbon prices [in USD/t CO2]

0

20

40

60

80

100

200
180
160
140
120
100
80
60
40
20
0

0

Carbon price
Coal

Coal (CCUS)

20

40

60

80

100

Carbon priceGas (CCGT)

Gas (CCGT, CCUS)

Note: Values at 7% discount rate. Lines indicate median values, areas the 50% central region.

References
World Bank (2020), Carbon Pricing Dashboard, https://carbonpricingdashboard.worldbank.org/(accessed 23 September, 2020).
Nordhaus, W.D. (2017), “Social Cost of Carbon in DICE Model”, Proceedings of the National Academy of
Sciences, February 2017, No. 114 (7), pp. 1518-1523; https://doi.org/10.1073/pnas.1609244114.

90

Part 2
Boundary chapters

Chapter 6

The levelised cost of storage (LCOS)
6.1

Introduction

This boundary chapter on the levelised cost of storage (LCOS) aims at generalising the concept oflevelised cost of electricity (which is commonly applied to electrical power generation technologies)
for dedicated electrical-storage devices.1,2 The objective of this chapter is to clarify and unify
different approaches or interpretations of what is commonly referred to as LCOS. To this purpose,
we introduce specific metrics, which each focus on particular aspects of the cost of storage, in order
to compare and integrate them in a single coherent approach to LCOS.
The emphasis of this contribution is on inter-temporal energy arbitrage, i.e. buying electricalenergy when it is cheap, storing it for a carefully determined time-period and selling it at a later
moment at a higher price. The costs of doing so, composed of a fixed capital investment for thestore, and the fuel costs, i.e. the cost of the electricity used for charging, can be interpreted in amanner analogous to classic cost accounting for electricity generation in terms of the levelised
cost of generating electricity (LCOE). This is an area receiving increasing attention and a numberof interesting attempts have been made to clarify the cost and value of electricity storage. In
describing the different elements of value and cost of storage for arbitrage, we rely heavily on thework by Belberdos et al. (2017a, 2017b) and Belberdos (2019). Furthermore, some hints are offeredon how to estimate the value of storage for applications and services other than arbitrage, therebyreferring to recent published material by Schmidt et al. (2019). A list of references is provided at theend of the chapter and the reader is encouraged to consult the further literature referred to in theabovementioned publications.

6.2 A primer on electricity storage: Applications, available technologies,
current and estimated future costs
Due to the (desired or imposed) growing annual share of electrical energy originating from renewable
technologies subject to naturally-fluctuating power flows (like PV solar and wind), characterised
by relatively low load factors, the combined installed capacities of those technologies in the
future are expected to be much larger than typical/conventional electrical peak power demand.3

1.

“Dedicated electricity storage” refers to storage technologies and applications for electricity in and electricity out.

2.
In general, throughout this boundary chapter, the terms “storage device”, “storage unit”, “storage facility” and “store” areused as synonyms.
3.
In this chapter, the terms “power” and “energy” are used in their proper physical sense: power is the time rate of changeof energy; energy is the time-integral of power over a certain period. Power can also be interpreted as a flow or flux: amount ofenergy per second flowing from one point to another. “Installed power capacity” is the maximum power that a storage device
can discharge (and charge), often simply abbreviated as “capacity”. Note, however, that for storage devices also the installed
energy capacity is important. The regrettable habit in some circles to blindly use the word “power” as a synonym of “electricity”
must be avoided in the context of storage. “Power” is charged into or discharged from a storage device, but it is “energy” thatis stored. For further clarification on the relationship of power and energy in a storage context, see Belderbos et al. (2017b).

93

6
The weather-related character of their electrical power production entails a mismatch between
instantaneous electric power demand and supply; at some moments much more power will be
produced than needed; at other times insufficient supply may result. Future electric power systems
with high shares of naturally-fluctuating renewables (often referred to as VRE)4 can only function
properly if sufficient flexibility measures can be relied upon, such as flexible thermal generation,
sufficient high-voltage transmission, active demand response/participation, sector coupling,
spilling/curtailment of superfluous generation, and, of course, dedicated storage of electricalenergy.5 In ideal energy markets, all these flexibility options should be able to compete on cost andvalue in a level playing field and an optimal portfolio of flexibility options should emerge.6 Clearly,
the availability of significant amounts of cost-effective dedicated electrical storage would make aworld of difference in markets with large fractions of naturally-fluctuating renewables. Evidently,
cost-effectiveness is key here and it is therefore of utmost importance to clearly determine the value
and the costs, and hence the competitiveness of different electrical storage technologies in a variety
of applications and services.
Storage technologies can be used by different actors (e.g. generating companies of different sizes),
large and small consumers, system operators) in a range of applications, related to provision ofenergy services, grid services and installed power capacity services, on the transmission network,
distribution network or “behind-the-meter”. There are several ways to categorise these applications
and services (e.g. Belderbos, 2017b and 2019 and IRENA, 2020, among others) but it is advisable toutilise the classification by Schmidt et al. (2019), as it allows reference to their published results
illustrating the value of using particular storage technologies for providing certain services. Schmidt
et al. (2019) have indeed classified 25 unique-purpose applications into 12 so-called core applications.
The result of the consolidation exercise leads to the core applications as shown in Table 6.1.

4.
“Naturally-fluctuating renewable energy sources” are often referred to as VRE, for “variable renewable energy”. Whenusing that designation, however, it must be understood that the VRE are not only (deterministically) variable, but that they also
have a stochastic character.
5.
Ideally, curtailment should be limited to a minimum by relying on the other flexibility measures. However, system stability
and cost/benefit analyses steering overall investment decisions will unavoidably lead to some curtailment. (As simple examples,the capacity of the invertor for rooftop PV installations is almost always sized lower than the installed peak power of the solar
panels. Likewise, transmission lines will never be sized to the maximum peak that may occasionally occur.)6.
“Optimal” refers to short-term operational as well as long-term (planning) objectives, encompassing also reliability,
availability, resilience, etc.

94

6Table 6.1: Grouping of 12 core applications in which storage
technologies can participate
Role

Application
1. Energy arbitrage

SystemOperation

2. Primary response
3. Secondary response
4. Tertiary response
5. Peaker replacement
6. Black start
7. Seasonal storage

Network
Operation

8. T&D investment deferral
9. Congestion management

Consumption

10. Bill management
11. Power quality
12. Power reliability

Description
Purchase power in low-priceand sell in high-price periods onwholesale or retail market
Correct continuous and sudden
frequency and voltage changes
across the network
Correct anticipated andunexpected imbalances between
load and generation
Replace primary and secondary
response during prolonged
system stress
Ensure availability of sufficient
generation capacity during peakdemand periods
Restore power plant operations
after network outage without
external power supply
Compensate long-term supply
disruption or seasonal variability
in supply and demandDefer network infrastructure
upgrades caused by peak power
low exceeding existing capacity
Avoid re-dispatch and local
price differences due to risk ofoverloading existing infrastructure
Optimise power purchase,
minimise demand charges andmaximise PV self-consumption
Protect on-site load against shortduration power loss or variations
in voltage or frequency
Cover temporal lack of variablesupply and provide power during
blackouts

Source: Schmidt et al. (2019).

In contrast to the applications indicated above, there exists wide range of different storage
technologies, which can conveniently be categorised in five groups:
1.

mechanical storage,
a. such as pumped hydro storage, compressed air storage, flywheels;2.

chemical storage,
a. e.g. P-to-H2-to-P with crucial technological components electrolysers and fuel cells,b. and/or more extended ‘P-to-fuels-to-P’, with fuels possibly also CH4, NH3, liquid fuels;

3.

electro-chemical storage,
a. such as batteries, redox flow batteries;

4.

electric storage,
a. e.g. supercapacitors;

5.

thermal storage, e.g.
a. (high-temperature) molten salt thermal storage,
b. very-high-temperature firebricks.

95

6
Today, pumped hydro storage still by far dominates the global storage mix.7 According to IRENA(2017), it accounts for 96% of the total installed electric storage power capacity worldwide, followedby thermal storage (almost 2%), electro-chemical storage (a bit more than 1%) and other mechanicalstorage (somewhat less than 1%). In the future, the share of electro-chemical storage is expected to
grow due to efficiency improvements and decreasing costs.
In its supplementary information, Schmidt et al. (2019) provides characteristics of several
storage technologies for the year 2015. Although many characteristics have their relevance, themost important characteristics are the installed (discharge) power capacity P, the installed energy
capacity E (allowing thus the specification of the discharge duration E/P at nominal power per cycle),
the typical number of cycles over the lifetime, the round-trip efficiency, the specific investment
costs per unit energy stored (USD/kWh), the specific investment costs per unit discharge power(USD/kW) and the response time.8 For the investment costs, the online repository mentioned in
Schmidt et al. (2019) includes an updated version of the experience curves relating the overall
cumulative installed discharge power capacity (GWcap) to capital costs per kW installed, and theoverall cumulative installed stored energy capacity (GWhcap) to capital cost per kWh installed; bothare reproduced below in Figures 6.1 and 6.2, respectively.

Figure 6.1: Experience curves for electrical energy storage (EES) technologies per nominal
discharge power capacity, in different uses
200 000
100 000

System2004
1976

50 000ModuleBattery

Pumped hydro (Utility, -1±8%)
Lead-acid (Multiple, 4±6%)

20 000

Product price (USD2015/kWcap)

Pack

10 000

2008

5 000

2013

2015

2013

1995

1983

2016 2007

1 000

Lithium-ion (EV, 19±5%)
Lithium-ion (Residential, 15±4%)

2015
1956
2017

1990

500

1989

2014

2012

2015
2016

2010

200
100

Lithium-ion (Electronics, 30±2%)

2013

2017

2010

2 000

Lead-acid (Residential, 13±5%)
2017

Nickel-metal hydride (HEV, 11±1%)
Sodium-sulfur (Utility, -)Vanadium redox-flow (Utility, 13±3%)

2013

1997

Lithium-ion (Utility, 16±5%)Electrolysis (Utility, 18±6%)

50

2017
2014

20

Fuel Cells (Residential, 16±2%)
PV Modules (-, 23±2%)
PV Inverters (-, 19±1%)100,0001

0,001

0,01

0,1

1

10

100

1 000

Cumulative installed nominal capacity (GWcap)
Note: The percentage terms in brackets express the Experience Rate ER ± uncertainty.9 Fuel cells and electrolysers must be considered together.

Source: Schmidt et al. (2018).

7.

More precisely mid-2017.

8.
Note that E/P represents the charging and discharging duration at nominal power in the absence of losses, i.e. for an
efficiency of 100%.
9.

96

For the exact definition of the uncertainty bracket, see Schmidt (2017 and 2019).

6
Figure 6.2: Experience curves of electrical energy storage (EES) technologies per nominal energy
storage capacity, in different uses
20 000
System
10 000

Product price (USD2015/kWhcap)

ModuleBattery

Pumped hydro (Utility, -1±8%)

5 000

Lead-acid (Multiple, 4±6%)

2013

2 000
1 000

Pack

2004

2008

1995

Lead-acid (Residential, 13±5%)

2010
2013

Lithium-ion (Electronics, 30±2%)
2015

Lithium-ion (EV, 19±5%)

2017

2016

1997

2017

2017

Lithium-ion (Residential, 15±4%)

2010

500

Lithium-ion (Utility, 16±5%)
2007

2015

1983

2014
1956

200

2016 1989

2017

Nickel-metal hydride (HEV, 11±1%)

2013
2012

Vanadium redox-flow (Utility, 13±3%)

2014

100

Electrolysis (Utility, 18±6%)
Fuel Cells (Residential, 16±2%)

50
0,001

Sodium-sulfur (Utility, -)

0,01

0,1

1

10

100

1 000

10 000

Cumulative installed nominal capacity (GWhcap)
Note: The percentage terms in brackets express the Experience Rate ER ± uncertainty. Fuel cells and electrolysers must be considered together.

Source: Schmidt et al. (2018).

The analysis by Schmidt et al. (2019) also provides an extrapolation of future cost estimates basedon the above experience curves. Furthermore, based on the market penetration model explainedin Schmidt et al. (2017), their online repository also gives an update of the expected future costs
as a function of time. Both updated figures are given in Figure 6.3 (extrapolation of Figure 6.2 up to
1 000 GWh installed stored energy capacity) and Figure 6.4.

97

6Figure 6.3: Expected future product price based on the experience curves (of Figure 6.2)
for electrical energy storage (EES) technologies per nominal stored energy capacity,
for their different uses
20 000
SystemPack

ModuleBattery

10 000

Product price (USD2015/kWhcap)

Pumped hydro (Utility, -1±8%)
5 000

Lead-acid (Multiple, 4±6%)
Lead-acid (Residential, 13±5%)2 000

Lithium-ion (Electronics, 30±2%)
Lithium-ion (EV, 19±5%)

1 000

Lithium-ion (Residential, 15±4%)500

Lithium-ion (Utility, 16±5%)
Nickel-metal hydride (HEV, 11±1%)

200

Vanadium redox-flow (Utility, 13±3%)100Electrolysis (Utility, 18±6%)
Fuel Cells (Residential, 16±2%)

50
0,001

0,01

0,1

1

10

100

1 000

10 000

Cumulative installed nominal capacity (GWhcap)
Note: The percentage given between brackets in the legend express the Experience Rate ER ± uncertainty. Fuel cells and electrolysers must be
considered together.

Source: Schmidt et al. (2018).

Figure 6.4: Expected future product price as a function of time, based on Figure 6.3
and an S-type market penetration model

Product price (USD2015/kWhcap)

2 000

SystemPack

ModuleBattery

Pumped hydro (Utility, -1±8%)

1 000

Lead-acid (Multiple, 4±6%)
Lead-acid (Residential, 13±5%)500

Lithium-ion (Electronics, 30±2%)
Lithium-ion (EV, 19±5%)
Lithium-ion (Residential, 15±4%)200

Lithium-ion (Utility, 16±5%)
Nickel-metal hydride (HEV, 11±1%)

100
50
2015

Vanadium redox-flow (Utility, 13±3%)

2020

2025

2030

2035

2040

Time
Note: The S-type market penetration model is explained in Schmidt et al. (2017). The percentage given in brackets in the legend express theExperience Rate ER ± uncertainty. Fuel cells and electrolysers must be considered together.

Source: Schmidt et al. (2018).

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6
6.3 The levelised cost of storage (LCOS)
To develop the different metrics related to the levelised cost of storage (LCOS), it helps to recall theoriginal meaning of the LCOE. The levelised cost of electricity for an electric power generating unit isproperly defined as the average electricity price that is required to cover the full lifetime costs of theplant composed of operations (including fuel, carbon and O&M costs) and investment including therequired rate of return on the capital invested. Thus, the LCOE is the fictitious stable (i.e. average) electricity price needed to make the present value of the sum of all costs and all revenues over the entire
operational life of the unit equal to zero and is calculated by Equation 2 in Chapter 2 of this report.
To introduce a levelised cost of storage (LCOS), a 1-on-1 translation of the LCOE might beconsidered, thereby adapting its meaning in the sense that fuel cost becomes charging cost (i.e.
the price at which input electrical power is bought by the storage facility) and “MWh generated”
becomes the amount of MWh discharged and thus sold in the market. The meaning of LCOS would
therefore read as: the average electricity price during discharging required to cover the full lifetime
costs of the storage plant composed of operations (including charging cost, carbon and O&M costs)
and investment including the required rate of return on the capital invested.10
Equivalent definitions of the levelised cost of storage (LCOS) are indeed used by Pawel (2013),
Jülch (2016) and Schmidt et. al. ( 2019). Sometimes the expressions are formulated a bit differently,
but they effectively all mean the same; as an example, Schmidt et al. (2019) also introduces an extra
term to take into account an end-of-life value. Nevertheless, a drawback of the LCOS definition
given above is that the cost of charging depends on the wholesale electricity price profile for which
the cost metric is established. This makes it dependent on the surrounding electricity system
and its temporal price profile. The World Energy Council (WEC, 2016) has therefore proposed aformulation for the LCOS where the cost for input energy, or the charging cost, is deliberately
left out of the calculation to avoid obscuring the results with too many assumptions. In this way,indeed, a system-independent metric is obtained that is applicable to each technology regardless ofthe power system at hand, but with the caveat that it ignores the fact that the input electric power
represents a manifest operational cost, whereby also the operational losses (reflected by the roundtrip efficiency) are ignored.
A more complete metric does need to take into account the charging cost, which unpredictability
and system dependence are indeed methodologically inconvenient. The system dependency of theactual time-dependent charging cost complicates the universal application of the LCOS metric foreach technology and its value should always be considered in relation to the underlying electrical
power system. Having said that, however, we will introduce a tailor-made metric in which the
charging cost appears to be absent, but where it is implicitly taken into account: that metric will be
named the required operational profit which suffices to repay the investment and fixed operationalcosts. This is an important step forward in operationalising the LCOS metric while remaining
methodologically coherent.
This boundary chapter aims at giving a more comprehensive analysis of the levelised cost
metric applied to storage technologies. In so doing, we introduce three so-called required cost
metrics (further sometimes referred to as the R-metrics) closely related to the generic LCOS, but
including charging costs to a varying degree. In a next stage they will need to be compared to theavailable revenue-based metrics (sometimes referred to as the A-metrics) that are based on thewholesale price profiles of the electricity market where the storage is to be installed. Furthermore,
we reflect on the metrics’ practical convenience in the context of energy arbitrage. In this regard,
the perspective taken in this chapter is that of an actor who sees a varying electricity price profileon which he can act to arbitrage between moments with high prices and moments with low prices.
For this first part (Sections 6.4 to 6.6), Belderbos (2017a) is the inspiration. In a smaller second part(Section 6.7), a brief introduction to the possible use of the LCOS concept for other applications suchas system operations, network operations and end-use consumption – as delineated in Table 6.1 –will be given, as an introduction to the work by Schmidt et al. (2019).

10.

For a power-to-gas-to-power storage facility using synthetic methane, a carbon cost (CO2 price) remains relevant.

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6
6.4 Arbitrage cost metrics for storage technologies:
The normative approach
In contrast to the traditional terminology that stresses a cost viewpoint, the metrics in thischapter are derived from, and labeled according to, a logic that takes a price perspective, whichallows drawing clearer distinctions between the different metrics. It is nevertheless important toemphasise that in the end, they will be expressed as cost metrics.
As a reminder, for typical generation units (both of the conventional and naturally-fluctuating
renewable type), the LCOE can be interpreted as an average fictitious electricity price that allows
break even considering all costs. In the following, three storage cost metrics, which differ withrespect to charging costs, are considered:•

the required average discharge price (RADP);•

the required average price spread (RAPS);•

the required average operational profit (RAOP).

Each one of these metrics is based on the principle of strictly balancing revenues with costs. Ina cost-benefit analysis (CBA), a storage project would thus have a zero net present value. All three
are fully-fledged cost metrics.

6.4.1 Three metrics for LCOS and breaking even: RADP, RAPS and RAOPRADP
The required average discharge price (RADP) is essentially a literal translation of the traditional
LCOE concept applied to a storage device. For the sake of simplicity and transparency of theformulation, carbon costs and decommissioning costs or end-of-life value are omitted, as are any
variable O&M.11 The formulation for the required average discharge price is thus:) ∙ (1 + )
∑(
+
+
(1)
RADP
=
∑
ℎ (1 + )where:OCCt= Overnight Construction Costs in year t

FOMt= Fixed Operation and Maintenance costs in year t

TCCt= Total cost of electricity used for charging in year t

MWhdt= The amount of electricity discharged in MWh in year t(1 + r)-t= The discount factor for year t, with r being the discount rate

11. For full-cycle power-to-methane-to-power storage, CO2 might be emitted by the electricity-producing unit and should thus betaken into account. However, for most storage facilities, there would be no (operational) CO2 costs and thus that term can be omitted.

100

6Similar to the LCOE, which reflects the required average electricity price that a generator sees
during operation hours to break even all cost, the RADP reflects the required average electricity
price a storage unit needs to see during discharge hours to break even considering fixed costs andcharging costs. The charging cost is the sum of the actual electricity prices during charging periods
multiplied by the charged volume, or said differently, the relevant ACC is the weighted average cost
obtained by averaging only during the charging hours and accounting for the amount of charged
energy. The average charging cost is thus by definition equal to:

∑(
∑

) (1
)
ℎ (1 + )

∙ +
(2)
≜

The ACC is, however, conventionally expressed per unit of output, i.e. MWhdt . This relationshipcan be established by the fact that for any year t, MWhdt =RT MWhct, with ηRT being the round-trip
efficiency, ignoring self-discharging and exogenous charging (e.g. via rain or snow water in pumped
hydro reservoir systems). It therefore holds that the ACC can be expressed as:

1 ∑(∑) ∙ (1 + )ℎ (1 + )

=
(3)
RAPS

The second cost metric is the required average price spread (RAPS) and is defined as the required
average discharge price (RADP) minus the average charging cost (ACC). Taking into account that the
total charged quantity is equal to the total discharged quantity (which appears in the denominatorof the metrics) corrected for the round-trip efficiency
as explained above, the required average
price spread can be written as follows:

∑(

+

+ (1
)∙
∑
ℎ (1 + )) ∙ (1 + )(4)= RADP − ACC =Equation (4) shows that this price-based metric covers the investment costs, the fixed operationand maintenance costs as well as the costs of the efficiency losses during the round-trip charging
and discharging processes. It does not however include the charging costs as such. RAPS is thus
the difference, which is required to break even, between the average price obtained for discharged
electricity and the average cost of electricity used for charging.

RAOP
Finally, we define the required average operational profit (RAOP) as the required total operational
profit (OP) on a per unit of discharged energy basis. The total required operational profit is the totalrequired revenue from discharging electricity minus the total cost from charging electricity:

= ∑(

+

+

) ∙ (1 + )(5)Required total OP∑() ∙ (1 + )1016Since the charging cost TCCt cancels out, the “Required total OP” for breaking even must equal
the total costs expended for capital investment and fixed operation and maintenance costs. Hence,the expression for the required average operational profit reduces to Equation (6).

RAOP =∑(∑+) ∙ (1 + )ℎ (1 + )(6)In other words, RAOP can be thought of as the average price that would be required for breaking
even if charging costs were zero.

6.4.2 Comparison of the three required LCOS cost metrics for energy arbitrage
Although the required metrics (sometimes referred to as the R-metrics) have been derived from
imposing cost/benefit break even by looking at the revenues from the electricity prices, it isimportant to observe that their expressions exhibit the costs per unit discharged electric energy.
Figure 6.5 shows a summary of the cost components covered by the required average discharge
price (RADP), the required average price spread (RAPS) and the required average operational profit(RAOP). As can be seen, the three metrics differ in the extent to which they account for charging
costs:•t he RADP includes the total charging cost per unit of discharged energy which consists of theaverage charging cost and the cost associated with efficiency losses;•

the RAPS only accounts for the part of the charging cost associated with efficiency losses; and,•the RAOP does not make explicit reference to any charging costs.

Averageelectricity
price during
discharging

Average charging
cost per MWhd

Average charging
cost per MWhc
Efficiency losses

EUR/MWh

EUR/MWh

Figure 6.5: Summary of the different cost components covered
by RADP, RAPS and RAOP
Total cost
per MWhd
Average charging
cost per MWhc
Efficiency losses

Fixed OM

Fixed OM

OCC

OCC

Required average
discharge price(RADP)

Average charging
cost per MWhc

Average charging
cost per MWhc

Required average
price spread(RAPS)

Efficiency losses
Required average
operational profit(RAOP)A small numerical example, taken from Belderbos (2019a) illustrates the three required cost
metrics (i.e. the R-metrics). It consider only a single year of operation for simplicity; the capitalcosts and fixed operation and maintenance cost are therefore converted in an equivalent annual
fixed cost (basically representing the annuity). Table 6.2 shows the assumed fixed cost on an annual
basis, the round-trip efficiency, the total number of discharging hours and the average charging
cost for a chosen reference case. The parameter values used are not based on a specific technology
but are merely chosen for illustrative purposes. For all cases, the storage unit is assumed to charge
and discharge at nominal power,12 and assuming no binding restriction on the amount of energy(E) stored.

12. This is not necessarily the case in real situations but results can easily be generalised by expressing them in equivalent
full load hours.

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6
Table 6.2: Storage parameters used for the reference caseInstalled power capacity (P)1 MW

Equivalent Annual Fixed cost (OCC + FOM)

EUR 30 000

Round-trip efficiency (η RT)

80%

Number of discharging hours (NDH)

1 000 hAveraged charging cost (ACC)EUR 20/MWhc

The RADP, RAPS and RAOP are calculated for the reference case of Table 6.2. For 1 000 h ofdischarging at nominal capacity of 1 MW (and thus discharging 1 000 MWh), a total amount of
1 250 MWh (= 1,000 h x 1 MW/) needs to be charged, costing EUR 25 000. Using Equations (1), (4) and(6), this leads to the results shown in Table 6.3.

Table 6.3: Different cost metrics for a storage withparameters as provided in Table 6.2
RADP

EUR 55/MWhd

RAPS

EUR 35/MWhd

RAOP

EUR 30/MWhd

From this example, it should be clear that:• he required average discharge price (RADP) covers all fixed and variable costs. The sum of allTcosts for the installed 1 MW unit in this example equals EUR 30 000 fixed cost and EUR 25 000
charging cost, resulting in a total cost of EUR 55 000. Divided by 1 000 MWh of discharged
electricity results in a required discharge price of EUR 55/MWhd.

•

 he required average price spread (RAPS) is equal to the required average discharge price,
T
RADP = EUR 55 /MWhd , minus the average charging cost (ACC), EUR 20/MWhc , which is equal
to EUR 35 /MWhd. Putting it in a different perspective, the RAPS is also equal to the fixed costand cost due to efficiency losses. Indeed, applied to this example, the efficiency losses per
MW capacity for a 1 000h discharge amount to (1 250 MWhc – 1 000 MWhd) x EUR 20/ MWhc =EUR 5 000. Added to the fixed cost and divided by all discharged electric energy leads to arequired average price spread (RAPS) of EUR 35/MWhd.

•

 he required average operational profit (RAOP) only needs to cover the fixed costs as shownTand explained by Equation (6): dividing the capital cost of EUR 30 000 by all discharged electricalenergy (being 1 000 MWh) leads to an RAOP of EUR 30/MWhd. Note that the RAOP does not
explicitly account for any operational costs as this is implicitly captured in the definition of
the required average operational profit (RAOP).

Clearly, all three metrics change with the numbers of full load discharge hours as fixed costs are
spread over a greater output. The three required cost metrics of a storage unit with characteristics
as given in Table 6.2 are shown in Figure 6.6 for different numbers of discharge hours. The reference
case, with NDH= 1 000h, is indicated by the diamond symbol. Given their definitions, the R-metrics,
RADP, RAPS and RAOP evolve together. RADP and RAPS stay vertically parallel, as their differenceis the constant average charging cost (ACC). RAOP stays parallel as the difference to RAPS is theconstant efficiency loss.

103

6
Figure 6.6: The R-metrics RADP, RAPS and RAOP as functions of the number of discharging hours
assuming constant average charging cost and constant round-trip efficiency
100
RADP
RAPS
RAOP
Reference case

Euro/MWh

80
60
40
20
0
500

1 000

1 500

2 000

2 500

3 000

3 500

Number of discharging hours [h]Source: Belderbos et al. (2017a).

To further highlight the dependency of the R-metrics on the average charging cost ACC, it isinstructive to show one more parameter variation based on the example of Table 6.2 and Table 6.3.
Figure 6.7 displays this dependency, where again the reference value of Table 6.3 is indicated by the
diamond.

Figure 6.7: Dependency of RADP, RAPS and RAOP as a function of the average charging cost(ACC) for a storage device with characteristics as given in Table 6.3, with constant round-trip
efficiency and constant amount of discharged electricity
150
RADP
RAPS
RAOP
Reference case

125Euro/MWh

100
75
50
25
0
0

20

40

60

80

100

Average charging cost (ACC) [euro/MWh]
Source: Belderbos et al. (2017a).

The required average discharge price, RADP, fully depends on the average charging cost, ACC,
whereas the required average price spread, RAPS, only has a weak dependency, because of the losses(round-trip efficiency less than 100%). This weak dependency on RAPS is by and large negligible forhigh-efficiency storage units such as Li-ion batteries with ~ 85% efficiency, but may be considerable
for low-efficiency stores like P-to-methane-to-P with round-trip efficiencies of only ~ 30%. The
required average operational profit RAOP is independent of the average charging cost ACC. All thisis reflected by the slope of the three lines as given by Equations (7) and (9).

104

6
∂RADP
∂ACC

∑(1 + )

∂RAPS
∂ACC

(1

=
(7)
) ∑(1 + )

=
(8)
∂RAOP
∂ACC

=0
(9)
More parameter variation, such as the dependency of the R-metrics on the round-trip efficiency,with accompanying graph, can be found in Belderbos et al. (2017) or Belderbos (2019).
Before leaving this example it is important to indicate two important observations for theseR-metrics so far.13 First, in the example of Table 6.2 and Table 6.3 and the parameter-variation of
Figure 6.6, the charging cost was assumed to be constant for all charging activities. Second, theseresults were obtained without the need to explicitly specify the energy storage capacity E, nor the
charging and discharge duration times-which in the loss-free case would be equal and simply begiven by E/P, although it must be recognised, however, that it was assumed that E was sufficiently
large. Also, there was no need to specify the time duration during which no charging or discharging
takes place, although that limits the overall total amount of MWh that can be discharged (or thetime during which electric power can be discharged).

6.5 Arbitrage price-based metrics for storage technologies:
The empirical approach
The three different required average cost metrics express the conditions for the storage investor
to break even on the full investment costs, including the required rate of return on investment aswell as possible operational costs, depending on the metric, whereby revenues come from energy
arbitrage. To assess whether storage investors would indeed break even, these required average
cost metrics must be compared to available average prices, as faced by the storage unit. Suchavailable average prices could be accessed by the storage unit if and when it would be physicallyavailable at that time. Note that available prices are not the same as simply market prices. The
reason for the difference is that possibly occurring high prices may be unattainable (and hence notavailable) for a storage unit, as it may find itself in an empty, fully-discharged state. Similarly, low
prices, which would, in principle be attractive for charging, will be irrelevant if it is already in a
fully charged state.
Using the notion of available prices, the three following revenue-based A-metrics can beformulated:•

the available average discharge price (AADP);•

the available average price spread (AAPS);•

the available average operational profit (AAOP).

13. This limitation will be lifted later on in the discussion.

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6
6.5.1 Three A-metrics in search of profitability: AADP, AAPS and AAOPAADP
The AADP is equal to the average electricity price during actual discharge hours and is entirely
defined by a given price profile and storage operation, as expressed in Equation (10),∑∙ (1 + )ℎ (1 + )AADP =
(10)∑where:

Discharge revenue= Total revenue of discharged electricity in year t

MWhdt= The amount of electricity discharged in MWh in year t(1 + r)-t= The discount factor for year t

AAPS
Secondly, the AAPS is defined as the difference between the AADP and the ACC as expressed in
Equation (11):

AAPS

=

−

(11)
) ∙ (1 + )∑∑(∙ (1 + )

=

∑

AAOP

ℎ (1 + )−∑ℎ (1 + )Finally, the AAOP is expressed in Equation (12). It is equal to the total revenue from discharged
electricity minus the total cost of charged electricity, averaged over the total amount of discharged
electricity:∑(−ℎ (1 + )) ∙ (1 + )(12)AAOP =∑

It must be pointed out that the A-metrics can only be computed exactly after the chronologic priceprofile is known. Hence the precise amount of maximum revenue and therefore the profitability can
only be known “after the fact”; that is why this should be based on historical price profiles. How thiscan be dealt with in practice will be discussed in Section 6.6.
To summarise, Figure 6.8 shows the three available average revenue-based metrics and how
they relate to the total discharge revenue per discharged MWh.

106

6
EUR/MWh

Figure 6.8: Summary of the difference between the A-metrics, AADP, AAPS and AAOP

Average charging
cost per MWhc
Total discharge
revenue per
MWhd

Available average
discharge price(AADP)

Average charging
cost per MWhc
Efficiency losses

Available average
price spread(AAPS)

Available average
operational profit(AAOP)

Note: With a focus on the maximum obtainable (net) revenue stream.

It should be observed from Equations (10), (11) and (12) that the A-metrics reflect the maximum
obtainable revenues by a storage unit (for AAPS and AAOP corrected for charging costs and/or
efficiency losses) derived from a particular price profile by charging and discharging power. At thisstage, the A-metrics do not refer to any break even requirement with the costs. The break-evencondition only comes up when the A-metrics are compared to the R-metrics. That aspect is the
subject of the next section.

6.5.2 Similarities and differences between the required and the available metrics
The reader will undoubtedly have noticed the similarities between what we call the required metrics(the R-metrics) and the available metrics (the A-metrics). Nevertheless, there are fundamental
differences between the two types of metrics. It is therefore instructive to highlight the similarities
and differences in order to avoid confusion and to be armed with the right interpretation to follow
the discussion below.
As an example, Figure 6.9 depicts a comparison between the required average discharge price,
RADP, and the AADP as a function of the number of discharge hours (NDH).

Average discharge price [euro/MWh]Figure 6.9: Comparison between the required and available average discharge price metrics,
RADP and AADP, as a function of the number of discharge hours
250
AADP
RADP

200
150
100
50
0
0

500

1 000

1 500

2 000

2 500

Number of discharging hours [h]Note: For a storage unit with E/P= 10h and round-trip efficiency of 80%.

Source: Belderbos et al. (2017a).

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6
The revenue-based A-metric has been calculated from historical price profiles. In this discussion
and Figure 6.9, the Belgian day-ahead electricity prices in 2015 have been used (Belpex, 2016),
but any other price profile would do. The A-metrics are computed by means of a short computer
program which optimises the charging and discharging decisions of the storage operator, assuming
perfect foresight of the prices and taking into account the installed storage capacity in terms ofcharging power, discharging power and energy storage. The optimisation result is a charging
and discharging sequence for the entire year and allows calculating the total charging cost, total
discharging revenue, an available average charging price and the three A-metrics. In the following
examples, the A-metrics are calculated as a function of the number of discharging hours (NDH)
which is prescribed in a range from 250h to 3 000h. For each point on the curve, the NDH is imposedon the storage operator as the maximum number of hours he is allowed to discharge in a year.
Figure 6.9 thus shows the AADP for different amounts of total discharged electrical energy – andthus for a different number of discharge hours (NDH) at nominal discharge power – for Belgian dayahead electricity prices of 2015 (obtained via an optimisation programme). The AADP decreases
for increasing NDH, which is reasonable since for any a-priory given value of NDH the storage
operator is incentivised to discharge first during hours with very high electricity prices. When more
discharging hours are allowed or possible, the storage operator will also discharge during hours
with lower electricity price which thus decreases the average revenues that can be earned basedon the available prices. It is stressed that the AADP curve reflects the maximum possible revenue
obtained from the available price profile.
Besides the revenue based A-metric, Figure 6.9 also depicts the required cost metric for breakeven, RADP, computed from the expressions in Equations (1), (2) and (3) which contain the fixed costterms with and , the denominator with the discharged electrical energy effectively reflecting theNDH, but also the ACC, which now should be compatible with the electricity price profile. Indeed,
the RADP is now not calculated with a constant ACC, but with the prevailing average charging cost
obtained from the optimised charging sequence which was used to calculate the AADP. The RADP
in Figure 6.9 exhibits a decreasing trend for an increasing NDH, effectively similar to Figure 6.6 asdiscussed before, where a given constant charging price was applied. However, the downward trend
in Figure 6.9 should not be generalised. Indeed, an increasing RADP with increasing NDH could
occur because of an increasing average charging cost, ACC. As said, the average charging cost, ACC,
used in the RADP calculation is thus now based on the ACC obtained from the AADP computation
which increases for larger NDH. This increase in ACC counters to some extent the decrease in fixed
costs per unit of discharged electricity. Depending on which of both effects is strongest, the RADP
could show an increasing or decreasing trend for increasing NDH.
The intersection between the RADP and the AADP curves in Figure 6.9 indicates the exact
amount of electricity that needs to be discharged, expressed as a number of discharging hours atfull power capacity, for the storage owner to break even the full investment cost and the costs madefor charging. Clearly, the same conclusion cannot be drawn if the RADP curve would have been
calculated by using an a priori given, or actually estimated, ACC. In such case, the intersection point
would only give an indication of the break-even point. Therefore, a more accurate estimation of the
ACC, compatible with the optimisation procedure for charging and discharging, has to be made inorder to compare the RADP to the AADP.

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6
6.5.3 Impact of E/P-ratio on the available price-metrics for arbitrage
Figures 6.10, 6.11 and 6.12 show respectively the AADP, AAPS and the AAOP as a function of thenumber of discharging hours for different E/P ratios (and a constant round-trip efficiency of 80%).
As can be seen, for any given E/P ratio (which effectively reflects the ideal charging and discharging
durations), the available price metrics decrease for an increasing number of discharging hours(NDH). As said in the context of Figure 6.9, this is reasonable since a very limited number ofdischarging hours incentivises the storage operator to discharge only during hours with very highelectricity prices. When more discharging hours are allowed or possible, the storage operator will
also discharge during hours with lower electricity price which thus decreases the average revenues
that can be earned based on the available prices. Note that the decrease of the AAOP is stronger
than the decrease of the AAPS, which in turn is stronger than the decrease in the AADP. This isbecause both the AAPS and the AAOP depend on the average charging cost, which increases as afunction of the number of discharging hours NDH.14 As such, the AAPS and the AAOP decrease not
only due to a decreasing average available discharge price, but also due to an increasing averagecharging cost.
Furthermore, the figures show that smaller E/P ratios shift the curves downward and that the
number of hours for which arbitrage is profitable is reduced. Indeed, for small E/P ratios, a storage
operator might be unable to discharge during a substantial period of consecutive hours with highprices as he can only store a limited amount of energy for such small E/P ratios. This can limit the
profitable arbitrage hours for the storage operator and force the operator to discharge during hours
with lower prices, resulting in a lower AADP, AAPD, AAOP.

Figure 6.10: The available average discharge
price as a function of the number of discharge
hours (NDH)
1 MWh/MW
5 MWh/MW
10 MWh/MW
50 MWh/MW50

01 MWh/MW
5 MWh/MW
10 MWh/MW
50 MWh/MW

100

AAPS [euro/MWh]100

AADP [euro/MWh]Figure 6.11: The available average price spread
as a function of the number of discharge hours
(NDH)

50

0
0

1 000

2 000

Number of discharging hours

3 000

0

1 000

2 000

3 000

Number of discharging hours

Note: The different curves are for different E/P values as indicated in the legend and ηRT = 80%.

Source: Belderbos et al. (2017a).

14. For completeness, the AADP does not directly depend on the value of the ACC, but its computation does take into account an
optimal sequence of charging and discharging periods, with corresponding charging costs and discharging revenues.

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Figure 6.12: The available average operational profit as a function of the numberof discharge hours (NDH)
1 MWh/MW
5 MWh/MW
10 MWh/MW
50 MWh/MW

AAOP [euro/MWh]

100

50

0
0

1 000

2 000

3 000

Number of discharging hours
Note: The different curves are for different E/P values as indicated in the legend and ηRT = 80%.

Source: Belderbos et al. (2017a).

6.6 Combining all normative and empirical metrics:
Storage costs and price arbitrage in practice
By comparing a required cost metric to the corresponding available revenue-based metric, an
investor can assess the profitability of a certain storage unit. When the required cost metric is lower
than the available revenue metric, it is profitable for the storage owner to invest in the storage unit.
The amount of discharged electricity necessary to break even the investment, is provided by theintersection of the required and available metrics (see Figure 6.9).
Figure 6.13 presents a comparison between the three required cost metrics and theircorresponding available revenue-based metrics. The intersection between the required andavailable metrics indicates the exact amount of electricity that needs to be discharged, expressed
as a number of discharging hours at full power capacity, for the storage owner to break even all
costs considered in each of the three required metrics.
As can be seen, the break-even point is equal for all three pairs of metrics. This implies that the
number of discharge hours at which it becomes profitable to invest in a particular storage unit does
not depend on the chosen set of required/available price metrics.
However, recall that the required RADP and RAPS metrics both depend on the average charging
cost associated with the storage unit. As was explained in the context of Figure 6.9, the RADP
and RAPS values shown in Figure 6.13 have been calculated based on the actually available ACC,
resulting from the optimisation exercise for the A-metrics. Only if the computation for these two
R-metrics uses the actually available ACC will the three intersection points coincide. If a merely
guessed ACC had been used then the RADP and RAPS curves would be different (and actually notquite consistent with their A-metric counterpart) resulting in different intersection points.
Furthermore, the attentive reader will have observed that the R-cost-metric describing therequired operational profit, RAOP, of Equation (6), does not depend on the ACC. So, the intersectionof the RAOP and the AAOP curves give the precise intersection point, and we have just explicitly
proven that the RADP and RAPS intersections are only correct if the actual ACC, compatible withthe optimised AADP and AAPS are used.

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6
Average price [euro/MWh]Figure 6.13: Comparison of the three available and the three required metrics
AADP
RADP
AAPS
RAPS
AAOP
RAOP

200

100

0
0

1 000

2 000

Number of discharging hours [h]Note: For a storage unit with E/P = 10h and round-trip efficiency of 80%.

Source: Belderbos et al. (2017a).

Hence, it is not necessary to assume ex ante any particular charging cost before calculating the
RAOP as a function of the expected number of discharging hours. Nevertheless, the profitability ofa storage unit for the investor and the required number of discharge hours, can only be determined
after the revenue-based A-metric has been determined (which does make use of the optimised/minimised charging cost). In summary, when using the RAOP, there is no concern about the charging
cost for displaying the dependency of RAOP on the NDH. To find the required NDH for profitability
for a particular price profile, this RAOP must be compared with the AAOP curve, which maximises
the arbitrage revenues and obviously takes into account the charging costs.
There is, clearly, a caveat. Although the behaviour of the RAOP curve as a function of the NDH canbe computed without the need for price profiles, we have of course based our profitability analysis
on historical price profiles to compute the revenue-based A-metrics. In addition, for whatever
possible guessed future given price profile, the optimisation programme uses perfect foresight. The
perfect foresight aspect is difficult to deal with in a simple analysis. The historical aspect can be
dealt with satisfactorily (at least assuming that the future will not behave dramatically differently
from the past years) if one employs the historical profiles of several years and even perhaps applies
some kind of random perturbation of those past years’ price profiles.
Purely as an example, Figure 6.14 shows the results for the past years 2010-2015 (for the Belgian
day ahead market prices) for the AAOP for a storage unit with an E/P of 10 hours (LHS panel) and one
with unlimited E/P (RHS panel). The dotted line representing the RAOP is independent of the priceprofiles and independent of the E/P ratio; it is the same in both panels. It is therefore recommended
to use the RAOP. Although all AAOP curves differ slightly, they are of the same order of magnitude,especially for a higher number of discharging hours. The difference between the left and rightpanel shows that the E/P ratio has a considerable impact on the AAOP and thus the profitability of astorage unit. The number of discharge hours required for profitability seems to be smaller for large
energy capacities (more precisely for large E/P or thus discharge durations).

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6
E/P = 10h200

RAOP
AAOP 2010
AAOP 2011
AAOP 2012
AAOP 2013
AAOP 2014
AAOP 2015

100

0
0

1 000

2 000

Average operational profit [euro/MWh]

Average operational profit [euro/MWh]Figure 6.14: RAOP and AAOP in the Belgian day-ahead market (2010-2015) for two storage unitsof different size and cost
E/P = unlimited
200

RAOP
AAOP 2010
AAOP 2011
AAOP 2012
AAOP 2013
AAOP 2014
AAOP 2015

100

0
0

Number of discharging hours [h]

1 000

2 000

Number of discharging hours [h]Note: Left, the E/P = 10 MWh/MW; right, E/P is unlimited; the remaining parameters of Table 6.1 apply.

Source: Belderbos et al. (2017a).

6.7 Beyond arbitrage storage services or applications
6.7.1 Transition of NDH to cycles and cycle duration
The analysis in the previous subsections concentrated entirely on energy arbitrage, and most of
the analysis was performed based on the total number of discharge hours (NDH) per year. It is
instructive to split the overall discharging per year into the discharge duration per cycle and thenumber of charging-discharging cycles per year. This leads to an alternative way for depicting the
LCOS results, and it permits furthermore to expand the LCOS analysis towards applications otherthan energy arbitrage, as listed in Table 6.1. At the same time, this different viewpoint can serve as
an introduction to recent work by Schmidt et al. (2019) in which a first attempt to evaluate the (far)future profitability of several storage technologies for a multitude of applications and services has
been undertaken.
The number of full cycles is identical to the number of discharging cycles since every discharging
cycle is always preceded by a charging cycle. The relationship of the time duration for charging anddischarging – both at the nominal power capacity P – is as follows:. In the interest of= �transparency of these few introductory paragraphs of Section 7.1, we assume that the depth ofdischarge (DoD) is 100%.
It is appropriate to say a few words about the round-trip efficiency. Although it is not fundamentally necessary to exactly split up the round-trip efficiency between the charging and the discharging parts of the overall storage cycle, it is nevertheless convenient to split it up in equal parts.

112

6
If we define as the E/P ratio, being the charging or discharging durations in the absence of any
losses, thus forone has15

(13)

= /

(14)

=

(15)

clearly indicating that, compared to the optimal duration E/P, charging at nominal power takes
longer, whereas discharging goes faster, with the consequence that more energy must be pumped
into the store than E, and that less energy can be withdrawn from it than the stored amount E. We
can thus write

ℎ =
(16)
⁄
ℎ =
,
(17)

where we have defined the number of cycles per year by and assuming that the discharge time
duration is constant for each discharge, we can also write that16
(18)
� �
=
=
=
.

With all this information, one can also derive that

8760
,
1
�1 +
�

(19)

≤

with the equality referring to the case with zero response time and no maintenance. Equation (19)serves as a warning that for technologies with very low round-trip efficiencies (such as P-tomethane-to-P with
~ 0.3) ) the maximal number of discharging hours is limited to about 2 000h,
independent of whatever price profile.

6.7.2 The LCOS or the RADP in terms of # cycles and discharge duration
The expression for the LCOS used by Schmidt et al. (2019) is effectively the same as the RADP
used in this document, albeit with some extra add-ons relevant for practical implementation.
Their expression includes an end-of-life cost (which de-facto could be considered as a negative
decommission cost), an efficiency for self-discharge, corrections for cycle and time degradation,
for replacement, etc. They consider for each technology the optimal depth of discharge (DoD), use a
discount rate of 8%, postulate a fixed electricity price for charging (i.e. the ACC) equal to USD 50­/MWhfor most applications.17 For the investment cost of the technologies the information pictures in theexperience and time-evolution curves of Figures 6.1 to 6.4 are used.

15. This is more accurate than was written in Belderbos (2017a).
16.

Instead of Ncyc Belderbos et al. (2017a) would use the symbol χa, whereas Schmidt et al. (2019) uses the symbol Cycpa.

17. Except for the “behind-the-meter” end-customer applications bill management, power reliability and power quality, for which
USD 100/MWh is used.

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6
It is useful to stress three particular points in this context, when interpreting the work by
Schmidt et al. (2019):•

 irst, from the expression of the discharged amount of electrical energy in Schmidt et al. (2019),
F
it can be deduced that the round-trip efficiency is allocated entirely to the discharge side.

• econd, when referring to the postulated electricity price of ⁄= USD 50/MWh, which isS
appropriately adjusted to ⁄to be used in the LCOS expression with units currency perunit discharged electrical energy, USD/MWhd, it must be understood that this electricity priceis assumed to exist only as input to the storage during charging, i.e. it is the ACC defined
above.18 The LCOS as computed is the actual earning per MWh that the storage must obtain
per discharged MWh to break even. For energy arbitrage and seasonal storage the LCOSSchmidt isthus nothing else than the earlier defined RADP, which – when the storage discharges into thegrid – would thus be the required electricity price for break even. For other applications where
remuneration perhaps occurs via other revenue schemes such as ancillary service markets
or auctions, the LCOSSchmidt expresses the earnings that the storage unit should get to deliverthat particular ancillary service; in the light of broader applications, it is perhaps not unwise
to generalise the earlier defined designation RADP into RADE where the E stands for Earnings.

Schmidt et al. (2019) effectively fixes all parameters (such as the efficiencies, the charging cost,etc.) for the nine storage technologies considered19 in order to be able to compute a levelised storage
cost for energy arbitrage and other applications20 by assigning a priori (and thus actually to someextent arbitrarily and independent of the system considered) the number of annual cycles andthe discharge duration for each application. The results of their LCOS are plotted in a 2-D diagram(Ncyc, τd).

The by Schmidt et al. (2019) assigned couples (Ncyc, τd) for the applications of Table 6.1 are shownin Figure 6.15. Energy arbitrage has been highlighted by a red box.

Figure 6.15: Assigned couples (cycles per annum, discharge duration) for concrete modelingfor each listed application of Table 6.1
10700Discharge duration (hours)

9

Seasonal
storage8
7
5

Tertiary
Response

3

Peakerreplacement21Black Start

1

EnergyArbitrage

Bill
Management

Congestion
Management

Elec. price USD/MWh:
100

10

100

50

50

Infeasible(insufficient hours
per year)

Secondary
Response

Power
Quality-

100

Response time:
>10s
<10s

Power
Reliability

64Size in MW:
1
10

T&D deferral

1,000

Primary
Response

10,000

Annual cycles (#)
Note: The couple assigned to arbitrage is (Ncyc = 300 cpa, τd = 4 h).

Source: Schmidt et al. (2019).

18. Schmidt (2019) uses the symbol P for electricity price, which we avoid since we prefer P for installed power capacity;
hence our choice to use script ⁄to designate the electricity price.

19. The technologies considered in Schmidt (2019) are pumped hydro, compressed air, flywheel, four types of batteries (leadacid, lithium-ion battery, sodium-sulphur, redox-flow), hydrogen and supercapacitor.

114

20. The applications considered are those listed in Table 6.1 above.

6
Figure 6.16 then shows the results obtained by Schmidt (2019) for the LCOSSchmidt or thus effectively
the required average discharge earnings (RADE) in the 2-D diagram (Ncyc, τd) for the years 2020 and
2040.21 However, at every point of the 2-D grid, the figure only depicts the result for the cheapest
technology (simply chosen from the results for 9 storage technologies after having scanned the full2-D domain).

Figure 6.16: Projected levelised cost of the cheapest technologies displayed in a 2-D plot
(number of annual cycles Ncyc, discharge duration τd) for the years 2020 and 2040

Note: The circled numbers refer to the modeled values of (Ncyc, τd ) for the 12 applications listed in Table 1. The colour legend refers to the optimal
required values for the LCOS Schmidt or thus Required Average Discharge Earnings (RADE).

Source: Schmidt et al. (2019).

The circled numbers in the 2-D plot of Figure 6.16 refer to the assigned points (Ncyc, τd) modelled
for the applications mentioned in Table 6.1. The circled point labeled by ‘1’ refers to energy arbitrage.
The colors refer to the optimal values for the LCOSSchmidt or thus the required average discharge
earnings (RADE). One observes that the color variations are quite similar in both panels, but please
be aware that this always refers to the most optima technology; hence for different technologies
scattered over the panel.
To make the link with the earlier sections on the required average discharge price or required
average discharge earnings (in more general terms), it should be clear that the LCOSSchmidt must be
seen in line with Figure 6.6 shown before for RADP = ƒ(NDH), whereby the conversion of the numberof cycles and the discharge duration to NDH follows from Equations (13), (15), and (18) above, butslightly adjusted for the depth of discharge (DoD) as follows22:
(20)
� �
=
=

For all technologies the typical DoD thus must be accounted for. As an example, the DoD = 100%for pumped hydro and hydrogen, whereas the DoD for Li-ion batteries is 92% when used for energy
arbitrage and about 60% when used for secondary response (Schmidt et al., 2019).

21. Recall that the expected investment costs are based on the experience curves Figure 6.1-6.4 above.
22. Note that in the convention of Schmidt et al. (2019), the equation would read as follows:

=

=

� �

, since the losses are fully allocated to the discharge side.

=

=

� �

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6.8

Concluding remarks

The transition towards high shares of naturally-fluctuating renewable energy sources (VRE) has
increased the flexibility needs required for covering demand at all time and for a secure operation ofelectric power systems. Together with other flexibility options, certainly storage applications overdifferent timeframes will play a key role in ensuring sustainable electricity supplies. To facilitate
this transition in a cost-efficient manner, a correct assessment of all potential flexibility providers’costs and system value is indispensable. For comparing the generating costs of different electricity
generation technologies, metrics like the LCOE have traditionally been used (see Chapter 3). In
complement to LCOE and system costs analysis, this boundary chapter carefully investigates ananalogous summary cost metric that specifically applies to storage technologies, the LCOS.
In practice, a great variety of storage options with very different technical and economic
characteristics exist. This chapter has attempted to analyse them in a single unified conceptual
framework. Within the context of energy arbitrage, three LCOS-metrics have been introduced thatdiffer in the extent to which they account for the (system dependent) charging costs. The required
average discharge price (RADP) can be thought of as a literal translation of the LCOE applied tostorage technologies, which includes a charging cost instead of a fuel cost, and is expressed perunit of discharged energy. The other metrics, i.e. the required average price spread (RAPS) and therequired average operational profit (RAOP) do not take into account the full charging costs. The
RAPS only accounts for efficiency losses, whereas the RAOP does not entail any charging costs at all.
To determine whether an investment in a particular storage technology is worthwhile, theserequired cost metrics need to be compared to their available revenue-based counterparts, which
represent a measure for the revenues that can be exploited by the storage technology in the specificpower system. It was shown that the break-even point for all three pairs of required and available
metrics occur at the same number of discharge hours (NDH). This property makes the RAOP
particularly useful for determining this break-even point, as it does not require an estimation of
the charging cost and as such, is a system independent metric.
Of course, storage technologies can have other applications than energy arbitrage. These
applications range from the provision of ancillary services over peak-power plant replacement,
to facilitating seasonal storage. Each of these applications has distinct operational characteristics
that imply certain operational requirements if a storage technology were to be used for that specificapplication. The NDH can in this perspective be further decomposed as a function of the numberof annual cycles and discharge duration. In the electricity markets of the future, there will bedifferent storage technologies for different applications (e.g. seasonal storage, inter-temporal
arbitrage in day-ahead and reserves markets or system control). They will also be remunerated
differently‑market, call for tender, vertical integration of TSO. The competitiveness of different
storage, and in fact broader flexibility options, will depend on their costs and on the price dynamics,
i.e. the market designs and load curves, for the different applications. While LCOS calculations
need to be performed with the specific application in mind, this chapter nevertheless provides one
unified conceptual framework that should facilitate the economic analysis of storage in tomorrow’s
electricity markets.

116

6References
Belderbos, A., E. Delarue, K. Kessels and W. D’haeseleer (2017), “Levelized cost of storage – Introducing
new metrics”, Energy Economics, 67, 287-299, https://doi.org/10.1016/j.eneco.2017.08.022.
Belderbos, A., A. Virag, W. D’haeseleer and E. Delarue (2017), “Considerations on the need forelectricity storage requirements: Power versus energy”, Energy Conversion and Management, 143, 137149, https://doi.org/10.1016/j.enconman.2017.03.074.
Belderbos, A. (2019), “Storage via Power-to-Gas in Future Energy Systems: The Need for Synthetic
Fuel Storage in Systems with High Shares of Intermittent Renewables”, PhD, KU Leuven, Belgium,
www.mech.kuleuven.be/en/tme/research/energy_environment/Pdf/phd-andreas-belderbos.
Braff, W. A., J. M. Mueller and J. E. Trancik (2016), “Value of storage technologies for wind and solar
energy”, Nature Climate Change, 6, 964-770, https://doi.org/10.1038/nclimate3045.
Cole, W. and A. W. Frazier (2019), “Cost projection for Utility-Scale Battery Storage”, National
Renewable Energy Laboratory, Technical Report NREL/TP-6A20-73222, www.nrel.gov/docs/
fy20osti/75385.pdf.
Denhholm, P. et al. (2013), “The Value of Energy Storage for Grid Applications”, National Renewable
Energy Laboratory, Technical Report NREL/TP-6A20-58465, www.nrel.gov/docs/fy13osti/58465.pdf.
EASAC (2017), “Valuing dedicated storage in electricity grids”, European Academies Science Advisory
Council, EASAC Policy Report 33, https://easac.eu/fileadmin/PDF_s/reports_statements/Electricity_
Storage/EASAC_Electricity_Web_low_res_30_June.pdf.
IRENA (2020), “Electricity Storage Valuation Framework: Assessing system value and ensuring
project viability”, International Renewable Energy Agency, Abu Dhabi, www.irena.org/-/media/Files/
IRENA/Agency/Publication/2020/Mar/IRENA_Storage_valuation_2020.pdf.
IRENA (2017), “Storage and renewables: costs and markets to 2030”, International Renewable Energy
Agency, Abu Dhabi.
Jülch, V. (2016), “Comparison of electricity storage options using levelized cost of storage (LCOS)
method”, Applied Energy, 183, pp.1594–1606, https://doi.org/10.1016/j.apenergy.2016.08.165.
Mongrid K. et al. (2019), “Energy Storage Technology and Cost Characterization Report”,
Hydrowires-US Department of Energy, PNNL-28866, www.energy.gov/sites/prod/files/2019/07/f65/
Storage%20Cost%20and%20Performance%20Characterization%20Report_Final.pdf.
Pawel, I. (2013), “The cost of storage - How to calculate the levelized cost of stored energy (LCOE) andapplications to renewable energy generation”, 8th International Renewable Energy Storage Conference
and Exhibition, IRES 2013, Elsevier, pp. 68–77, http://dx.doi.org/10.1016/j.egypro.2014.01.159.
Schmidt, O., S. Melchior, A. Hawkes and I. Staffell (2019), “Projecting the future levelized cost ofelectricity storage technologies”, Joule, 3 (2019), 81-100; https://doi.org/10.1016/j.joule.2018.12.008[Supplementary information available on line : https://doi.org /10.1016/j.joule.2018.12.008­(extended pdf).]
Schmidt, O., S. Melchior, A. Hawkes and I. Staffell (2018), “Update 2018 – The future cost of electricalenergy storage based on experience rates”, Figshare, Dataset (2018), https://doi.org/10.6084/
m9.figshare.7012202.v1.
Schmidt, O., S. Hawkes, A. Gambhir and I. Staffell (2017), “The future cost of electrical energy storage
based on experience rates”, Nature Energy 2, 17110 (2017) 1-8, https://doi.org/10.1038/nenergy.2017.110.
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nenergy.2017.110/MediaObjects/41560_2017_BFnenergy2017110_MOESM1_ESM.pdf.]
WEC (2016), “World Energy Resources – E-Storage: Shifting from cost to value Wind and Solar
Applications”, World Energy Council, London, www.worldenergy.org/assets/downloads/ResourcesE-storage-report-2016.02.04.pdf.

117Chapter 7

Carbon pricing and zero emission
credits
7.1 Carbon pricing as a means of rendering explicit the differencesin the full cost of different generation technologies
Carbon pricing in the electricity sector drives a wedge between the costs of low-carbon and carbonintensive power generation, considering the impacts the latter has on the climate and hence onhuman health, economic development and the environment. In the context of the Projected Costs
of Electricity: 2020 Edition, carbon pricing can thus be interpreted as a manner of making visible thetrue costs of an electricity generation option including social costs that it generates. Projected Costs
2020 includes a carbon price of USD 30 per tonne of CO2 for power plants beginning operations in
2025. This is somewhat higher than the average price of USD 25 per tonne of CO2 in 2019 in the
EU Emission Trading System (EU ETS), yet lower than the price of a European emission allowance(EUA) in summer 2020. USD 30 per tonne of CO2 are lower than what is usually considered the fullsocial cost of carbon emissions (see, for instance, The Full Costs of Electricity Provision, NEA, 2018) or
the level of the carbon taxes in certain OECD countries (in Sweden, for instance, the carbon tax
currently stands at EUR 114 per tonne of CO2). It nevertheless remains a level with the potential tohave significant impacts on the relative competitiveness of different generation options. In manymarkets, a carbon price of USD 30 per tonne of CO2 increases the variable costs of coal-fired power
generation such that they are higher than those of gas-fired power generation. It thus enables fuelswitching between the two technologies.
Other means of creating this wedge are possible. Instead of carbon pricing, policymakers canalso remunerate low-carbon generation technologies for their contribution to reducing carbon
emissions. While neither the distributional implications nor the impacts on electricity prices are
the same as carbon pricing, they accomplish the same essential objective.
Carbon pricing internalises the negative externality of climate change inducing greenhouse gas
emissions by making carbon-intensive generation less competitive. Minimising overall economic
costs requires that carbon pricing, whether by making carbon-intensive generation more expensiveor whether by making low-carbon generation less expensive, is applied in an equitable manner
across all sectors and technologies.
In this chapter, we will focus on the main instruments used to price carbon or to promote lowcarbon electricity generation such as Zero Emissions Credits (ZECs) in cases where direct carbon
pricing is impossible or politically infeasible. Border taxation adjustments for carbon embedded in
traded goods, which are currently much discussed in connection with the proposed European New
Green Deal, are not part of this chapter. The latter are mainly an instrument of trade policy aiming
at creating a level playing field between imports and local production.
In 2019, the World Bank counted 57 carbon pricing initiatives around the globe, at the regional,
national, provincial and municipal level, up from 51 in 2018 (World Bank, 2019). They consistof 28 emission trading systems (ETSs) in regional, national and subnational jurisdictions and29 carbon taxes regimes, primarily applied on a national level. In total, these carbon-pricing
initiatives cover 11 gigatonnes of carbon dioxide equivalent (Gt CO2e), or about 20% of global
greenhouse gas (GHG) emissions.

119

7
Carbon pricing has many supporters among economists and international institutions that
range from the United Nations to the Carbon Pricing Leadership Coalition. In addition, the OECD has
consistently argued in favour of carbon pricing. Economists appreciate the fact that carbon pricing
achieves the targeted emission reductions at the lowest cost and has the benefit of treating carbonfree firms neutrally (Baran and Fankhauser, 2020). Programmes like the PMR (Program for Market
Readiness) and others assist countries in the design and implementation of carbon pricing regimes.
The commitments under the 2015 Paris Agreement are inducing a number of additional countries
to introduce carbon pricing. In both North America and Latin America, more than 20 countries
have considered the use of some type of carbon pricing in their NDC. The report of the high-level
Commission on carbon prices (CPLC, 2017) for instance states: ountries may choose different instruments to implement their climate policies, dependingCon national and local circumstances and on the support they receive. Based on industry andpolicy experience, and the literature reviewed, duly considering the respective strengths andlimitations of these information sources, this Commission concludes that the explicit carbonprice level consistent with achieving the Paris temperature target is at least USD 40–80/t CO2 by2020 and USD 50–100/t CO2 by 2030, provided a supportive policy environment is in place.

7.2 Principal instruments for differentiating the costs of technologies
according to their carbon emissions
Section 7.2 will introduce the main instruments for differentiating the costs of carbon-intensive andlow-carbon generation options. This will include instruments that straightforwardly price carbon.
It will also include instruments that remunerate the contribution of low-carbon emission options.
Each subsection of this section will begin with a conceptual introduction followed by recent
relevant examples from OECD countries. Other instruments that can indirectly reduce carbon
emissions are subsidies to low-carbon generation through feed-in tariffs, feed in premia, contractsfor-difference or green certificate schemes that lower the costs or increase the revenues of lowcarbon power production and thus improve its competitiveness. Some of these instruments are
discussed further below. Other instruments such as, for instance, priority access are not part ofthis section.
There are two main ways to establish a carbon price, the government can levy a carbon tax on
fossil fuels thus encouraging a switch to less carbon-intensive production or consumption. The
alternative is to introduce a cap-and-trade system.
Implementing a carbon tax does not take place in a vacuum but requires careful integration into
the existing policy context. The World Bank and the OECD have developed the FASTER principles for
Successful Carbon Pricing, a set of principles for the design of a carbon-pricing instrument (OECD,
2019; World Bank, 2019):

120•

Fairness. Effective initiatives embody the “polluter pays” principle and ensure that both costs
and benefits are shared.

•

 lignment of policies and objectives. Carbon pricing is not stand-alone mechanism. It is mostAeffective when it meshes with and promotes broader policy goals, both climate and nonclimate related.

• tability and predictability. Effective initiatives exist within a stable policy framework andS
send a clear, consistent, and (over time) increasingly strong signal to investors.

•

Transparency. Effective carbon pricing is designed and carried out transparently.

• fficiency and cost-effectiveness. Effective carbon pricing lowers the cost and increases theEeconomic efficiency of reducing emissions.

•

 eliability and environmental integrity. Effective carbon pricing measurably reduces practices
R
that harm the environment.

7
Carbon pricing has not only the direct effect of reducing GHG emissions but can also lead toco-benefits in terms of improvements in public health, energy efficiency and accelerated innovation.
It remains so far unclear what is better for innovation, a carbon tax or an ETS type. Experience fromthe Swedish tradable green certificate scheme shows that even quota-based instruments can leadto rapid innovation in highly competitive sectors.
Taxes and emissions trading systems overlap frequently. However, taxing emissions that are
subject to a trading system does not result in additional emission reductions if the cap is binding.
The overlap then may compromise the cost-effectiveness of abatement as it undoes the uniformityof the price signals sent by a trading system. While this does not necessarily mean that taxes
and trading systems should never be combined, the justification for levying taxes on ETS-covered
emissions should be to address other market failures, or raise revenue (see Effective Carbon Rates:
Pricing CO2 through Taxes and Emissions Trading Systems, OECD, 2016).
There is an ongoing debate whether carbon taxes can work by themselves or should be part ofbroader policy packages. Acemoglu et al. (2012), for instance, argue that carbon pricing alone is
not enough, but should be complemented with research support. OECD-IEA-ITF-NEA (2015) also
supports the importance of policy packages over pure carbon pricing, a view supported by Greaker
et al. (2018).
A final point of consideration is the time dimension of the effects of carbon pricing. In the short
run carbon pricing leads to fuel switching (technologies fixed and at least one productive input
fixed), in the medium run we see an adjustment of capital investment and energy demand and in
the long run technologies change.

Carbon taxes
The government directly applies a tax on the carbon content of fossil fuels. Their most remarkablefeature is that there is no cap on greenhouse gas emissions. While generally thought to be theeconomically most efficient instrument, the large distributional impacts to the detriment of carbon
emitters and associated stakeholders can create political resistance.
Taxing carbon relates to the idea of Pigou (1920) to tax an amenity by its marginal damage.
For example, put a tax on an environmental bad or on a health hazard such as cigarettes, sugar,
alcohol etc. The idea here is to reach an optimal consumption of the bad from a societal perspective,
balancing cost and benefits to society.
OECD (2006) distinguishes two different kinds of environmental taxation – taxes imposed directlyon pollution or emissions and taxes with indirect relationship between pollution and a subjectof taxation. The difference between these kinds of environmental taxes lies in the subsequent
reaction of a polluter; however, the distribution of tax revenues can be also different.
Usually, unless the tax is prohibitively high and a tax converts to a regulation prohibiting theuse of an environmentally bad, Pigouvian taxation will not remove the bad entirely by reducing its
use to zero from society but reduce its use through the pricing of a good causing an amenity. Since
the main purpose of a carbon tax is to increase the price of an activity detrimental to society anddoes not have the main purpose to generate income to the treasury the income could be used forredistributive purpose compensating for the effects of the tax without distorting the incentives fromthe tax. In principle, the income could also be used to support research or the market introductionof technologies addressing the pollutant. This is frequently a politically expedient option. However,from an economic point of view, the revenue from carbon taxes is part of the government budget
and its use is not necessarily connected to the residual damage.
Many countries around the globe have implemented carbon taxes, but as Figure 7.1 shows, theinstrument choice, the apparent levels, and the shares differ quite heavily. The highest level ofcarbon taxes is found in Sweden, while the highest revenues from taxation is generated in France,even though both countries participate in the European Union Emission Trading System (EU ETS).

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Carbon price (USD/tCO2e)

Figure 7.1: Carbon price, share of emissions covered and carbon pricing revenuesof implemented

Source: World Bank (2019).

Sweden was among the very first countries to introduce a tax on carbon in 1991. The carbon
tax was initially levied on all sectors with exemptions for the industry exposed to international
competition. With the introduction of the EU ETS, industries within the ETS were exempted fromcarbon taxation except for district heating that was included in the EU ETS but continued to be
subjected to the carbon tax.

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The carbon tax was introduced in 1991 at a rate corresponding to SEK 250 (EUR 23) per tonneof carbon dioxide (t CO2)1 emitted and has gradually been increased to SEK 1 190 (EUR 110) in 2020
(currency conversion based on an exchange rate of SEK 10.80 per EUR). The tax was part of a larger
tax reform where the taxation of labour was reduced, and the reduction was compensated for by anincrease in environmental taxation. Reductions for industries outside the EU ETS were abolished in
2018. Figure 7.2 shows the development of the Swedish carbon tax since its inception.

Figure 7.2: The Swedish CO2 tax over time (USD/tonne)
140

CO2-tax (euro/tonne)

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0
91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20
19 19 19 19 19 19 19 19 19 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20

Carbon tax general level (euro/tonne)

Carbon tax industry level (euro/tonne)

Today the Swedish carbon tax contributes considerably to the Swedish state budget generating
about SEK 23 billion (EUR 2.15 billion). Figure 7.3 shows that Sweden still experienced significant
economic growth in the period after the introduction of the carbon tax. However, this could be
related to the reduction of labour cost that was partly financed by the carbon tax. Other factors to
be structural change in the economy, moving from industrial production to the service sector.

Figure 7.3: GHG-emissions and GDP development in Sweden
since the introduction of the carbon tax
GDP and CO2 Emissions in Sweden 1990 - 2017
200
180
Index (1990 = 100)

160
140
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60
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0
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016
CO2 Emissions from Fuel Combustion

1.

GDP

Actually, the carbon tax is levied on the average emission from the use of each type of fossil fuel using emission factors.

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Beyond the internalisation of negative externalities, there is an ongoing discussion about
the additional dynamic benefits of strict environmental regulation. Such a win-win relationshipwas first postulated by Michael Porter when he formulated in his “Porter hypothesis” that such
regulation can improve both environmental quality and the competitiveness of enterprises (see,for instance, OECD, 2006).

Advantages•

In countries with a well-functioning tax system, a carbon tax is easy to implement above
existing excise taxes. This type of tax is not suitable from a state budget perspective since itsvery purpose is to erode its tax base.

•Low economic cost, does not distort economic efficiency

Disadvantages•

Goal achievement not guaranteed.

•Tax base not stable.

•

Not always based on true carbon content, requires correct emission factors.

•

Risk of carbon leakage when the outside world does not tax carbon emissions.

Emission trading
Carbon-emitting industries receive tradable allowances under an overall constraint. This way theprice of the carbon will be determined by the market. Different forms of quota allocation permit
modulating distributional impacts. The largest and best-known emissions trading system for GHG
emissions as of today is the EU ETS. According to ICAP (2020) there are 21 emissions trading systems
covering 29 jurisdictions in place across the globe. Another nine jurisdictions are expected to put
systems in place in the coming years, among them China and Columbia. A further 15 jurisdictions
are considering an ETS as part of their policy-mix.
Emissions trading was first proposed by Dales (1968) and Weitzman’s (1974) seminal paper on
prices vs. quantities showing that a quota system and a tax/subsidy scheme are equivalent given
certain assumptions. A quota system can be used for different purposes, e.g. to implement amarket-based support scheme for renewable support, as a cap-and-trade system to limit emissionsof a certain pollutant and so on. The earliest implementation of the idea stems from the 1990 US
Clean Air Act Amendments and the market for SO2-emissions under the Acid Rain Program (Benzand Truck, 2009) that initiated sulphur dioxide permit trading and largely eliminated the acid rain
problem. A quota system usually limits the amount of emissions of a pollutant each year thus
securing the previously set goal is achieved. If the goal is somewhere in the future the quota system
can be combined with a trajectory with an annual reduction of the total emissions. Some quota
systems such as the EU ETS allow for banking of emission rights thus allowing for inter-temporal
optimisation. If an emitter does not use all emission rights the polluter owns, the surplus emission
rights can be used at a later point of time.
Different allocation mechanisms have been or are in use as well as combinations of these.

124•ree allocation through grandfathering usually combined with a new entry reserve andFbenchmarking (EU ETS 1 and 2).

•

Auctioning of emission rights (e.g. EU ETS 3rd period).

• ombinations of both with a share of emission rights allocated through grandfathering andCa share that is auctioned.

7
From an economic point of view, the allocation of emission rights should not make a difference,even with free allocation. The important point is that there is an opportunity cost for polluters.
Opportunity costs mean in this case that even though the emitter receives the emission rights
for free they have a market value. The emitter can now either use the emissions rights to coveremissions or reduce emissions and sell excess emissions rights to maximise profits. The emitter
will use emission reducing technologies or reduce production to the point where the marginal costof reducing emissions is equal to the price of the emission right on the market. Even though themethod allocation does not matter for the outcome in terms of emissions, the distribution of incomechanges. With auctioning, the revenue from auctioning ends up in the state budget and can be used
for whatever purpose or the revenue can be directly used for redistributive purposes. The larger theshare of allowances auctioned in terms of the total number of allowances the closer to a carbon tax
emission trading becomes.
The main difference between a carbon tax and emissions trading is the role of the price. In thecase of a carbon tax, the government sets the price of carbon and the resulting carbon abatement is
unknown. In the case of a quota system, the abatement is known but the price is a result of supplyof and the demand for emission rights.
Other than the uncertain level of the carbon price, emission trading systems have thedisadvantage of being considerably more complicated to set up and to administer than a simple
carbon tax. Exchanges must be created, carbon traders must be employed, rules for the allocation ofemission allowances must be negotiated, implemented and adjusted, anxious small and mediumsized enterprises require expert advice and so forth. Outcomes, just as for carbon taxes, will dependon continuing political adjustments and adaptations of the rules as much as on demand, or new
technological developments.

Advantages of emissions trading•

Goal achievement almost guaranteed.

•

Off the state budget.

•

Can be linked to other compatible cap-and-trade systems such as the EU and Swiss ETS.

•

Low transaction costs if implemented correctly (Jaraitê et al., 2010).

•

 ell-administered cap-and-trade schemes may exist at large scale without incurringWsignificant losses in cost-effectiveness (Zaklan, 2020) and allocation can be used as a part ofthe policy bargaining process.

Disadvantages of emissions trading•

Costly monitoring, reporting and-verification (MRV) mechanism needed.

•

Heavy administrative burden (Coria and Juraite, 2019).

•

 llocation of emission rights leads to varying distributional effects, with occasionallyA
politically unwanted results such as windfall profits to producers, high costs to consumers.

•

Risk of carbon leakage when the outside world does not tax carbon emissions

Hybrid systems
Hybrid tax usually combines a carbon tax with emissions trading: in these systems, a carbon tax
creates a level of certainty for investors by creating a price floor. Such a floor can either be stable or
gradually increase through time. In addition, the quantitative constraint provides an opportunity
for carbon trading. In principle, the tax it becomes the carbon price when the equilibrium price inthe ETS is lower than this level.

1257Several countries have complemented carbon pricing with carbon price floors such as Canada,
where the provinces are free to choose the carbon pricing instrument, however, the carbon price
may not be below CAD 10 and is set to increase to CAD 50 by 2022 (Parry and Mylonas, 2018) . For
those jurisdictions that do not meet the benchmark, a federal backstop system will be applied which
has two components: a regulatory charge on fossil fuels and a regulatory trading system called
the Output-Based Pricing System (OBPS) that applies to power generation and certain industrial
facilities. In the provinces in which the backstop applies, the OBPS took effect on January 1, 2019,
and the fuel charge took effect in April 2019.
The United Kingdom introduced a carbon floor in order to speed up the transition to low-carbon
electricity generation in times of low EU ETS prices. The Carbon price floor (CPF) was introduced
in 2013. The CPF was initially set at GBP 16 (EUR 18.05) per tCOe and was set to increase to GBP 30(EUR 33.85) in 2030. In the United Kingdom the carbon floor (PF) was realised in the form of a tax on
the use of fossil fuels in the power sector.
In Finland (IEA, 2018), there is also a hybrid system in place. A carbon tax thus applies tofacilities under the EU ETS. Combined heat and power (CHP) is thus subject to both energy/ CO2 andelectricity taxation, which strongly increased since the 2011 tax reform. To avoid overlaps with the
EU ETS and to promote energy-efficient CHP, the CO2tax was halved for the latter. CHP and fuels
used for electricity generation are also exempt from the electricity tax. This has not done away withall inconsistencies, the CHP tax reduction, for instance, is however more important for coal thanfor natural gas.

Support for low-carbon power generation
Policy motivations for such measures are a complex mix of (1) technology-specific support,(2) recognition of the disadvantages of low-carbon generators with high capital-intensity
in competitive electricity markets in the absence of carbon pricing and (3) recognition of thecontribution of such technologies to the reduction of carbon emissions and other public policy
objectives.
Feed-in-tariffs (FITs), feed-in-premia (FIPs), contracts for difference (CFDs) and production tax
credits (PTC) are instruments that either isolate low-carbon generators from market prices by
guaranteeing a fixed revenue per unit of output, reduce the exposure to market risk or provide a
tax credit for every MWh produced. It has also resulted in variable renewables (VRE) with zeromarginal costs keeping producing even at negative prices (up to the level at which the per unit
support is exhausted). It depends on the precise formulation of the measure to which extent it
is a straightforward technology-specific subsidy, e.g. if support is provided without competitive
tenders and only for certain technologies, or whether it can indeed be considered as part of the
remuneration for the service to provide low-carbon electricity. A further question is whether
electricity customers (FITs, FIPs and FDDs) or taxpayers (PTCs) bear the costs of such measures.
Support schemes for low-carbon electricity generation can be divided into two groups, pricebased, and volume-based schemes. The choice of support scheme is independent of the allocation
mechanism, e.g. auctioning. Auctioning does not automatically imply the presence of a specificsupport scheme but usually auctioning is a measure to decide upon a needed support level. Thissection intends to give a short survey of the most common support schemes for renewable electricity
production, but intends in no way to be comprehensive, especially when it comes to legal evaluationof certain support schemes in the light of e.g. European state aid rules. There also exists a plethoraof support schemes not directly linked to production such as investment tax credits, property tax
abatement, accelerated depreciation and others. None of them will be treated in this section either.

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Feed-in tariffs
Feed-in-tariffs used to be a very common support scheme within in the European Union. A wellknown example is the German feed-in tariff that in 2012 made Germany the country with thelargest installed capacity of solar PV (EPIA, 2013). Today, Germany still is the country with thehighest installed capacity per capita according to IEA-PVPS 2020. Power generators are usuallyawarded an in advance fixed support per MWh produced for a predetermined number of years.
The investor is thus guaranteed a fixed remuneration, reducing the market risk to zero, which isusually seen as a great advantage by the industry. This support can be differentiated for different
technologies as in the German support scheme. The support scheme can and should be combinedwith annual revisions of the support levels for new entries to account for technological progress. The
support scheme can be run through the state budget and thus be allocated to the taxpayer or can befinanced by a direct surcharge on the electricity price allocating the cost of support to the electricity
consumers. The choice depends inter alia on the question whether the objective is to decarbonise
the electricity sector or, through electrification, the wider energy sector. The administration of the
support scheme is often left to the transmission system operator who pays out the support, takes
over the produced electricity and sells it on the market and charges the customers either directlyor through the DSOs. A feed-in tariff can be quite costly if the support level is set too high due toinformation asymmetry regarding the cost of production and the speed of technological progress
between the power generators and the government but can be very effective at increasing theshare of renewables quickly. The same information asymmetry can swing the outcome in the otherdirection, when the remuneration is too low, renewable electricity production will not happen.
Figure 7.4 shows the remuneration to the power producer in the ideal case of known generation cost
as well as the cost of the support scheme depending on the electricity price.

Figure 7.4: Feed-in tariff
100
Income for electricity generators(EUR per MWh)

90
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30
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10
0
2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
Electricity price

Support cost

Level of FIT

Source: Swedish Energy Agency (2015).

Feed-in-premium (FIP)
FIP is a support scheme that combines a market-based remuneration with a fixed premium that is
independent of market price. The electricity generator receives two income streams, the revenue
from the sale of electricity to the market and the premium. The thought behind this is to provide a
certain form of stability but to continue to expose power generators to some market and price risk.
The latter, in particular, provides incentives to choose and operate technologies in a manner suchthat their system contribution, i.e. the price they earn is relatively higher.

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Either the State decides upon the support level in advance or the premium is determined throughan auction. The balance of the revenues of power producers comes from selling electricity on themarket, which provides incentives for reducing the cost of generation. The state support can berealised in different ways, a fixed payment or a fixed percentage of the electricity price. This risks
however, that the support at times can be below the needed level and at other times be beyond the
support level required. A feed-in premium has the advantage of reducing the exposure to market
risk for the electricity producer but still leaving incentives to reduce cost and for technologicalprogress increasing profitability for the producer. From a producer’s point of view, since market risk
is not completely removed, a fee-in tariff would still be preferable. Figure 7.5 provides an illustration
how a FIP works in principle.

Figure 7.5: Feed-in premium
Income for electricity generators(EUR per MWh)

140
120
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60
40
20
0
2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
Electricity priceFeed-in premium

Generation cost

Source: Swedish Energy Agency (2015).

Sliding premia or contracts for difference
Sliding premia as illustrated in Figure 7.6 have the generation cost and the required support as
their starting point. Power generators thus have two income streams, the actual electricity priceand the premium. The reference electricity price is usually defined in function of the average costof a plant in a given technology category over a given period. If averaging periods are very short (i.e.
for instance one hour), a sliding FIP or CFD behaves almost like a FIT with regard to the risk profile
faced by the producer. The market electricity price is almost certainly equal to the average price. If
averaging periods are longer (i.e. one or several months), project operators face a higher exposure
to market risks as the risk of deviations of the actual electricity market price from the reference
price increases. A contract for difference (CFD), as for instance applied in the United Kingdom, is a
special case of sliding FIP in which the winning price also constitutes a remuneration ceiling. Lowcarbon installations are required to pay back to the regulator the income they received during times
in which the reference electricity price is higher than their bid price.

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Figure 7.6: Illustration of sliding premium/CFD
Income for electricity generators(EUR per MWh)

120
100
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2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
Electricity pricePremium

Generation cost

Source: Swedish Energy Agency (2015).

Tradable green certificates
TGC-systems were at times quite popular with some EU countries such as Denmark, the United
Kingdom, Sweden, Belgium and Poland as well as the EU Commission. As of today, we find TGCsystem at least in, Australia, Sweden, Belgium (Walloon Region) and Texas (Renewable energy
credits, RECs). The Swedish system for example is supposed to operate until 2045 with a current
goal of 18 TWh in new renewable electricity production in 2030 compared to 2020. Tradable green
certificate is a volume-based support scheme.2 Amundsen and Mortensen (2001) describe a green
certificate as follows:I n short, the Green Certificates market consists of sellers and buyers of certificates. The sellers are
the producers of electricity using renewable sources. These producers are each allowed to sell anamount of certificates corresponding to the electricity that they feed into the electricity network.
The buyers of certificates are distribution companies that are required by the government
to hold a certain percentage of certificates corresponding to total end-use consumption ofelectricity. The Green Certificates are thus seen as permits for consuming electricity. The system
implies that the producers using renewable energy sources receive both the wholesale price anda certificate price for each kWh fed into the electricity network.
Like in an ETS in a TGC-scheme a desired volume of renewable electricity production at a
certain point of time can and usually is (but not necessarily has to) determined in advance. Whatis determined is the desired share of renewable electricity consumption of total consumption.
Producers then receive one certificate per MWh for renewable electricity. Consumers, possibly
through their suppliers, are obliged to buy such certificates as a part of a predetermined fractionof their electricity consumption. In Sweden, electricity wholesalers acquire green certificates fortheir customers and include the cost in the electricity bill. This creates a market for certificates. The
quotas are determined in advance towards the long-term goal and reviewed regularly to control thatthe goal is achieved even if energy consumption changes. Green certificates provide technologyneutral support 3 to renewable electricity through the market. In a TGC-system, the producer ofrenewable electricity faces two risky income-streams, the income from selling electricity and the2.
In August 2020, the Swedish government put forward a proposition to end access for new production to the system by
the end of 2021 and to terminate the system as a whole by the end of 2030.
3.
Technology-neutral in this context means that each type of low-carbon generation, usually a renewable technology, receives
the same number of certificates per MWh produced.

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income from selling green certificates. The income streams are nowadays often secured througha Power Purchase Agreement (PPA) selling the production in advance at a fixed price to one buyer.
However, this way of reducing risk is not specific to a green certificate scheme but to all systems
where the producer is exposed to market risk. The price of the green certificates should, in theory,
be equal to the long run marginal cost for the pivotal power producer. Setting a binding long-termtarget for green power production greatly helps in establishing stable equilibrium prices.

Production tax credits
The Production Tax Credit (PTC) is a state incentive that provides financial support for thedevelopment of renewable energy facilities. In the US companies that generate electricity fromwind, geothermal, and “closed-loop” bioenergy (using dedicated energy crops) are eligible for afederal PTC, which provides a 2.3-cent per kilowatt-hour (kWh) incentive for the first ten years of arenewable investment. In the United States, also CCUS and nuclear energy can receive PTCs. In In
principle, a PTC could be used to subsidy any kind of (carbon-free) electricity generation.

Zero emission credits (ZECs)
Currently, this instrument is primarily employed by US state regulators to provide capacity support
to nuclear energy to value its contribution in terms of fuel diversity, security of supply, carbon
emission reductions and reduced environmental impacts. Frequently, the aim of the measure isto counteract the fall in electricity prices induced by the advent of VRE receiving PTCs. Currently,
ZECs still constitute technology-specific support rather than across-the-board remuneration forcontributing to carbon emission reductions and improved air quality, even though the level of the
ZEC is explicitly connected to the value of an avoided tonne of CO2. Their costs are so far borne byelectricity customers, which means the financing charge increases general electricity prices (seealso box below “Pricing Emission Avoidance: The US Experience with Zero Emission Credits (ZECs)”).

Pricing emission avoidance: The US Experience with zero emission credits (ZECs)*
Economists have traditionally focused on the pricing carbon emissions as the preferred policy toaddress climate change. This has regularly led to resistance by fossil-fuel based power producers
concerned about higher costs and electricity consumers concerned about higher prices. However,
the objective of reflecting the differential in social costs between low-carbon and fossil fuelbased electricity generation and furthering a structural shift towards low-carbon generation
can also be achieved by the alternative means of supporting the latter. While the economic costsof taxing emissions and remunerating emission reductions are comparable, their distributional
effects and hence their social acceptance might be very different.4 For many years, renewableenergy sources have benefited from support mechanisms, as those described in Section 7.2.
However, nuclear electricity, which produces neither carbon nor other emissions, had so far notbenefited from equivalent measures.

4.
When taxing emissions, in the long run the economic costs of reductions are always borne by electricity customers. In thesecond case, it depends on the source of financing for the premia that remunerate emission reductions. If government budgets
are the source, taxpayers will ultimately bear the cost and electricity prices will remain unchanged. If the source is a surcharge
on electricity prices, again electricity customers will pay. However, even in the latter case, impacts will not be completely thesame. In the tax case, carbon-intensive producers include their marginal costs of internalising emission into their price bid.
In the support case, all producers or distributors will include the average costs of support into their prices. While prices forelectricity producers will go up in both cases, ultimate effects will depend on the structure of the load curve, demand, relative
costs and billing procedures.
In terms of political acceptability, short-run impacts can also be important. With a carbon tax, a coal plant taxed at its
marginal cost might find itself excluded from the merit order for good and shut down immediately. With a subsidy for emission
reductions, the same coal plant absorbing the average costs or emission reductions might continue in the market for sometime with a reduced number of hours.

1307(continued Pricing emission avoidance)
In 2016, the US states of New York and Illinois introduced zero emission credits (ZECs) to
reward also nuclear power for its contribution to reducing carbon emissions. From an economic
point of view, it would be preferable if both sets of policy instruments (support for either
renewables or nuclear) were provided in a manner that was not technology specific. In this case,any low-carbon technology could claim support for a tonne of CO2 avoided. This is currently not
yet the case, neither in the United States nor elsewhere. However, the ZEC programme could
constitute a blueprint for remunerating emission avoidance rather than taxing emissions. In thisperspective, it contains lessons that go beyond nuclear power.
The immediate trigger for the introduction of ZECs was that many nuclear plants have been
facing the prospect of early closure, as low gas prices, an absence of carbon pricing and extensive
support for renewable power have challenged their economic viability. Nuclear plants in New
York, Illinois, Connecticut, New Jersey and Ohio were thus facing early closure. While there
were variations in approaches, the predominant policy was for states to create a zero emission
credit (ZEC) to ensure that the value of generation without air emissions would be factored into
decisions about the future of the plant. Due to these policies, 14 nuclear units with almost 14 GW
of low-carbon baseload power avoided closure.

What are zero emission credits?
Zero emission credits (ZECs) are payments that electricity generators receive to compensate
them for the valuable attribute of not emitting greenhouse gases in the production of electricity.
ZECs work similarly to renewable obligations that support renewable energy production in manystates. Like renewable energy credits (RECs) that are generated by wind and solar generators andsold to utilities, ZECs are sold to utilities that are required to purchase a certain number of these
credits from low-carbon generators. The utility then rolls the cost of the credits into electricity
customers’ bills. Just like in the case of a carbon tax or an emission trading system, electricity
consumers will ultimately support the cost of switching towards a low-carbon system.
From the point of view of a nuclear plant owner, ZECs provide a source of revenue for theservice of reducing climate change-inducing greenhouse gas emissions that had previously been
provided free. Before these programmes were in place, the markets only paid nuclear plants forthe electricity they produced.5 State air regulators and public health advocates did recognise theclean-air benefits, but nobody paid for them directly. ZECs at least internalise the service of lowcarbon power production.

Setting a ZEC Price
Since the idea of the ZEC is to provide an incentive to preserve non-emitting generation, the priceof the ZEC should relate to the value of the avoided emissions. The initial ZEC programmes in
New York and Illinois used the social cost of carbon (SCC) as the starting point for determining
credit value. The SCC indicates the long-term economic impact of emissions. A central estimateof the SCC established is USD 42 per t CO2.

5.
Nuclear plants may also qualify for capacity payments in some markets. Capacity markets ensure that there will be
sufficient generation at times when the electricity system is under stress. Since nuclear plants run over 90% of the time, thevast majority of the revenues for these plants are from wholesale electricity sales. When ZEC prices are calculated, capacity
payments are included on a per-megawatt-hour basis.

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7(continued Pricing emission avoidance)
On this basis, regulators convert the SCC into a dollars-per-megawatt-hour value by
multiplying the SCC by the average emissions generated by plants running on natural gas orcoal. If the fuel is natural gas, the emissions rate will likely be somewhere around 0.5 t CO2 /MWh. It will be roughly double that number for coal plants. Multiplying the SCC by an emissions
rate will yield a baseline ZEC measured in dollars per megawatt-hour. Current ZEC programmes
also include a phase-out provision so that the value of a ZEC decreases if the market prices
for electricity rise. This shows the extent to which current ZEC programmes are temporary
expedients to compensate for low electricity prices. Changes in electricity prices, of course, do
not imply changes in the SCC. An environmentally coherent policy framework would provide
ZECs corresponding to the SCC independent of the level of electricity prices.
The figures below summarise the basic framework. The first figure shows the one-to-one
relationship between the market price of electricity on the bottom axis and the final value
received by the nuclear generator on the vertical axis, as would happen in standard electricity
markets. The second figure shows the impact of a ZEC on revenues and how its decrease as
market prices increase. Once the wholesale power price increases to a pre-set level (notionally
depicted at USD 33/MWh in this simplified example), the ZEC, shown in red, is reduced dollarfor-dollar as market prices exceed this threshold. In other words, in current designs ZECs act asa price floor that is gradually removed market prices rise.

Market revenue plus ZEC(USD per MWh)

Revenues without ZEC programme

Revenues with ZEC programme

90

90

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0

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20 25 30 35 40 45 50 55 60 65 70 75 80

Wholesale electricity price(USD per MWh)

Wholesale electricity price(USD per MWh)

Challenges to ZEC Programmes
While ZECs are an innovative policy instrument to remunerate low-carbon producers for theservice of emission avoidance they provide, they also impose an added cost on utilities, and to
the extent that the latter can pass them on, to electricity consumers. ZEC programmes have thus
been challenged in federal courts. Fossil fuel-based competitors also turned to federal energy
regulators arguing that ZECs distorted wholesale markets. The legal challenges did not succeed;
the regulatory challenge was more successful.
The challengers argued to FERC that they had made investments in fossil capacity in the
expectation that nuclear plants, now receiving ZECs, would have been forced to close, which
would then have resulted in higher market prices. FERC sided with the challengers. The
Commission held that it was solely an economic regulator and therefore precluded from weighing
the environmental benefits of such a policy. FERC held that if generators are receiving paymentsfrom state programmes then their bids to provide services in the market should be changed toreflect the value of these payments. Logically, this decision also affected the deployment of new
wind and solar resources developed with the support of comparable state programmes.

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7(continued Pricing emission avoidance)
This decision has created tensions between state governments and regional wholesalemarkets. It is widely accepted that more widespread carbon pricing would be a more robust
solution to these state-by-state solutions, but the prospects currently seem remote in a US
context. State governments are now trying to decide how much they want to rely upon organised
electricity markets to shape the power sector. This would be an understandable but unfortunate
development. From an economic point of view, it would be preferable if the contribution toemission avoidance by nuclear power, renewables and hydro were correctly valued at the SCC in
a comprehensive carbon management policy framework.

* This box is based on a personal contribution by Matthew P. Crozat, Senior Director, Business Policy, Nuclear Energy Institute, United States.

7.3 Impacts on electricity prices
Different instruments can have different impacts on both wholesale and retail electricity prices. If
taxes and emission trading raise prices, support measures for low-carbon electricity generation tend
to decrease electricity prices net of the cost for the support scheme in the long run. Carbon pricing
schemes as opposed to low-carbon support schemes give incentives for fuel switching between
different carbon-intensive generators such as coal and gas. Fuel switching, i.e. the differentiatedimpact on individual technologies, thus needs to be carefully accounted for in both cases.
According to economic theory an emission trading scheme for carbon leads to a rise in marginal
costs equal to carbon opportunity cost regardless of whether allowances are allocated for free ornot. In an electricity market with marginal cost pricing carbon pricing works through the meritorder effect. Carbon pricing affects/increases the marginal cost of fossil technologies such as coaland gas power and thus affects the relative price between carbon-free and fossil technologies andbetween different fossil technologies. If the price change is large enough the order between carbonfree and fossil production is changed in favour of carbon-free technologies. That means that thetransmission into prices depends on whether fossil technologies are needed to satisfy demand or
not (which technology is on the margin). Looking at a power system with both renewable and fossil
power generation, if demand at a certain hour is satisfied by carbon-free technologies alone there
will be no price effect at all. If demand cannot be satisfied by carbon-free production, the marginal
cost including carbon pricing of the production technology that lies at the margin will determine
the market price. Low-carbon producers will then be able to reap inframarginal rents as shown in
Keppler and Cruciani (2010).

Figure 7.7: The merit order with and without carbon pricing

Source: Keppler and Paskoff (2020).

133

7
There are a couple of studies on the pass-through of cost increases on wholesale markets such as
Nakamura (2008). With the start of the EU ETS, several studies starting with Sijm (2006), Honkatukia(2006) and Fell (2010) studied the pass through of emission cost to the electricity price. Carbon passthrough, following Sijm et.al. (2006), is often defined as the ratio of the change in electricity priceover the increase in cost for the generator:

=

∆

∆ (
(

)

)

Correctly assessing pass-through rates requires an analysis hour by hour. Regarding the impact
of carbon prices on power prices the literature is ambiguous. Some authors argue for a full (oralmost) full pass through (Sijm et al., 2006; Honkatukia et al., 2006) while others only see a partial
pass through or none at all to wholesale prices (Levy, 2005). Typical pass-through in the empiricalliterature range from 30% to 150%. Pass-through rates depend on the use of method, but also on
relative variable costs, carbon content and market structure. Chernyavska and Gullì (2007) evaluate
to what extent firms in imperfectly competitive markets will pass-through into electricity prices
the increase in cost. They show that the pass-through rates/ increase in price can be higher or lower
than the marginal CO2 cost. The pass-through rate depends on several structural factors, the degreeof market concentration, the available capacity, the power plant mix in the market and the power
demand (peak vs. of-peak hours).
In conclusion pass through is usually around 100% but can be lower if (a) fuel switching occursor (b) renewable production alone satisfies demand and no fossil-based power generation is used on
the margin in addition the results also depend on the granularity of the analysis.6

Effect on the individual power producers
As is evident from the description above carbon pricing will increase the cost of electricity production
from fossil fuels and higher carbon intensity plants will experience a larger cost effect than lower
carbon intensity plants. Carbon pricing thus will have several effects on a power producer with aportfolio of plants, for one it will induce a shift from less efficient plants to more efficient plants andit will induce a change from coal to gas everything else unchanged. If the effect of carbon pricing on
the merit order is large enough it will in addition shift production from fossil fuel-based generation
technologies to renewable power generation technologies. In connection with this transition we see
the appearance of what is commonly called “sunk cost”, that is fossil plants that have not reached
the end of their technical lifetime and initially calculated economic lifetime before carbon pricing
was introduced will be phased out in favour of new plants. The effect on the individual producer
will depend on the extent to which the producer can shift the increased cost to the wholesale price.
Carbon pricing will also most likely speed up innovation both when it comes to fossil-based
power plants as well as providing a push towards low-carbon technologies.
A real live example for the effectiveness of carbon pricing is the development of emissions
within the EU ETS. In 2019, total CO2 emissions from fossil fuel combustion declined by 4.3% in theEuropean Union (not including the United Kingdom), mainly due to the substantial increase in the
carbon price on the EU Emission Trading Scheme (EU ETS) in 2019 (over EUR 25/t CO2), which incited
power producers to use more gas and renewables. Emissions covered by the EU ETS declined evenby 8.7% in 2019 and contracted by 15% in the EU power sector, as power producers substituted coalfired power generation with renewable and gas-fired generation (Enerdata, 2020).

6.
Two further theoretically interesting cases exist. First, market power would also reduce pass-through, as a monopolist
would take into account the impact of higher prices on demand and revenue foregone. Second, with certain iso-elastic demand
curves – unlikely in electricity markets – a pass-through higher than 100% would be the profit-maximising solution.

134

77.4 Distributional impacts
Other than the well-being of the general population due to reduced impacts from climate change,carbon pricing in whichever form influences the revenues of generators and the prices that theyquote to their customers. In the electricity sector, carbon prices will drive a wedge between thecosts and profits of low-carbon generators such as nuclear, hydro or renewables and those of fossilfuel generators.
When considering these distributional impacts, it is important to distinguish between short
run and long run impacts, in particular when considering the profits of operators. In pure theory,
assuming competitive electricity markets with free entry and exit, profits are always zero in thelong run. However, this is frequently not the most policy-relevant perspective. Barriers to entry andlong investment cycles in the electricity industry ensure that electricity generators keenly observe
the impact of carbon pricing on their profits.
Looking at the distributional impacts of carbon pricing one must first consider the distributionof the costs of reducing emissions between carbon emitters such as fossil fuel-based electricity
generators and their customers on the one hand and governments and taxpayers on the other.
Second, one must consider its differentiated impacts on different generating technologies,
especially in electricity markets. In both case, the distributional impacts will depend decisively
on the instrument chosen. The distributional impacts of a carbon tax, a carbon trading systemwith free allocation, a technical standard or a per unit subsidy for emission reductions will not be
the same.

Distribution between fossil- fuel-generators, government and the general population
The Pigouvian framework for internalising social costs such as climate change inducing carbon
emissions in order to maximise economic welfare is a useful first step for discussing the distributional
impacts of carbon pricing (see Pigou, 1920). Although it is limited to static optimisation and thus
does not consider dynamic effects, it allows developing a good intuition for the distributional
impacts between generators and their customers and the general population of different formsof carbon pricing. The magnitudes are considerable and amount to billions of USD (see below for
the results of a case study of the French and German electricity sector). The benefits and costs ofdifferent stakeholders change dramatically in function of different forms of carbon pricing. This,for instance, explains the fierce resistance that carbon pricing frequently encounters as it heavily
reduces the benefits of carbon emitters.
Figure 7.8 below provides a simple representation of the Pigouvian framework, whose mainbuilding blocks are the marginal social costs of carbon, i.e. the impacts of climate change, andthe marginal private costs of reducing carbon emissions. Since a dollar saved is a dollar earned,
the latter are also equivalent to the marginal private benefit of emitting carbon. The right to emit
carbon at no cost is thus economically valuable. In technical terms, it constitutes what one refers
to as an environmental rent. In the absence of carbon pricing, this rent arises in the form of higher
consumer welfare due to cheaper goods and services. In Figure 7.8, this private welfare corresponds,
in the absence of carbon pricing to the combined areas A, B, C and D. At the same time, of course,
carbon emissions create the concomitant social cost of climate change, corresponding to the areas
C, D and E. From the point of view of an economist such as Pigou, the welfare of consumers and thewelfare of the general population suffering from climate change have equal standing. The optimal
trade-off then intervenes logically at the intersection of the marginal private costs of abatement
and the marginal social costs of climate change. At this point, the combined welfare of both parties
will be maximised and the total carbon rent will be maximised. In order to reach the optimal point
policymakers have essentially four instruments at their disposal. These are (1) setting a carbon
price in the form of tax for every t CO2 emitted, (2) setting a quantitative cap on total emissions(which puts an implicit price on carbon), (3) creating a market for emissions with quotas either
allocated freely or through auctioning or (4) subsidising emitters for reducing their emissions.

135

7
Figure 7.8: The Pigouvian Framework of optimal static internalisation
USD

Social costs ofclimate change

Private costs of C02
abatement

A
Optimalcarbon tax
or price ofC02 quota

E
B

C

D

Economically optimallevel of C02 emissions

tCO2

Instruments in these four categories have vastly different distributional implications. For
reasons of clarity, it is useful to abstract for the moment from dynamic effects and uncertainty. Bothplay a role in the concrete design and implementation of carbon pricing. However, the fundamental
differences in distributional impacts between these four kinds of instruments are even more
important and structure the global debate on carbon pricing in fundamental ways. Table 7.1 below
summarises these distributional impacts. The first observation is, of course, that the status quoof no carbon pricing will have a considerably lower overall welfare than the four measures ofinternalisation. Among the latter, all will yield the same overall benefit.7 From a general economic
point of view, all four are thus equally legitimate.
Interestingly, from the viewpoint of the general population affected by climate change there
is also no difference in the welfare impacts of the different measures. The general population
will suffer residual damages C in each case, but will be relieved of the damages corresponding toareas D and E. This is the decisive benefit of all forms of carbon pricing. However, from the point ofview of carbon emitters and the customers as well as governments and taxpayers, there are starkdifferences between different measures.
As already mentioned carbon emitters will in principle pass on their private benefits and costsavings to their consumers as competitive firms will compete on price. Even if market imperfections
might leave part of the benefits to emitting firms and their shareholders, the rank ordering of thedifferent measures is obvious. Emitters prefer a per-unit subsidy for reducing emissions (where B+C
corresponds to the amount of the total subsidy) as the preferred instrument over doing nothing (nocarbon pricing), a quantitative cap or permit trading with free allocation. A carbon tax or permit
trading with auctioning follows last. This easily explains the resistance of carbon-emitting fossil
fuel-based producers to any form of straightforward carbon pricing.

Table 7.1: The distributional impacts of different forms of carbon pricing

Population affected byclimate change
Carbon emitters andtheir customers
Government andtaxpayers
Total carbon rent

No carbon
pricing

Carbontax

Quantitative
cap

Carbontrading (freeallocation)

Carbontrading(auctioning)

Subsidy-C-D-E

-C

-C

-C

-C

-C

A+B+C+D

A

A+B+C

A+B+C

A

A+B+C+B+C

0

B+C

0

0

B+C

-B-C

A+B-E

A+B

A+B

A+B

A+B

A+B

7.
The above statement is theoretically not quite correct. A pure quantitative cap putting only an implicit price on emitting
carbon will have somewhat lower welfare benefits than measures with explicit carbon pricing, which reduce carbon emissions
in the most efficient manner. However, the welfare losses are second-order, when compared with the overall welfare gains ofcarbon pricing.

136

7The preferences of governments and taxpayers are, or rather should be, ranked precisely in the
inverse order: carbon taxes or permit trading with auctioning should be preferred to a quantitative
cap or permit trading with free allocation. Subsidies should be the least palatable choice. However,social choices are not always determined according to disinterested rationality. Historical precedent,
transaction costs, deep-rooted perceptions and relative political power can arrive at or entrenchchoices very different from those suggested by economic theory. As well-established carbon
emitters ponder substantial welfare losses due to carbon pricing, the benefits of carbon pricing togovernments and taxpayers may well be larger but are spread over a greater number of people. The
former thus have relatively greater incentives and lower transaction costs to build coalitions andengage in effective lobbying. Given that already the status quo is very profitable for carbon emitters,
they can also limit themselves to impeding change rather than advancing any particular policy.
If historical precedent and considering transaction costs favour subsidies or the status quo,
dynamic considerations favour carbon pricing whether by measure of a tax or a permit trading
system. Why? Because only a price signal provides producers with an effective incentive overtime to search for more efficient and less emitting technologies. This “induced technologicalchange” is perhaps the most important long-term benefit of carbon pricing in the real world,
as the cost of having to pay for each tonne of carbon emitted concentrates minds and focusses
effort.8 Considerations of uncertainty, i.e. the likelihood of missing either the optimal amount ofemissions abatement or the optimal cost point, favour a straightforward carbon tax even over andabove the argument of dynamic effects. This is because the most welfare-relevant uncertainties
arise at the level of the economy. Providing economic actors with a stable long-term price signal
reduces investor uncertainty, the cost of capital and, ultimately, the cost of abatement. However, as
mentioned, these are second-order considerations. From an economic point of view, the only truly
unacceptable option is the absence of any form of carbon pricing

Distribution between different power generation technologies9
In order to understand the distributional impacts of carbon pricing on electricity generators
one must consider two issues: first, the interaction between carbon prices and electricity prices
indicated in Section 7.3; second, the changes in “inframarginal rents” introduced by carbon pricing.
Inframarginal rents are the principal form, in which the revenues and profits of operators in theelectricity sector arise. Such rents arise because electricity is not storable. Otherwise, one would
produce electricity with the technology that has the lowest average costs, stock it and use wheneverneeded. This however is not possible in the electricity sector where different technologies will
supply the electricity supply that will meet demand second by second.
A generator earns inframarginal rent each time when his variable costs are lower than themarket price. This temporary rent will go towards the financing of his fixed costs. The market priceis set by the technology with the highest variable cost that is still called upon at a given moment.
As explained in the previous section, the price itself as well as the difference between the variable
costs of different inframarginal technologies and the price will change due to carbon pricing. Recent
work by Keppler and Paskoff (2020) provides an overview of the distributional impacts of a carbon
price of EUR 30/t CO2 on the inframarginal rents of power producers in France and in Germany.
Modelling the changes in prices and the merit order and assessing the resulting changes in hourly
inframarginal rents, it finds that carbon pricing increases the overall inframarginal rent of power
producers due to carbon trading in France and Germany by EUR 22.5 billion with free allocation andof EUR 17.1 billion with auctioning.

8.
Subsidies do not perform well at all in a dynamic perspective. Here, a producer will receive a higher subsidy to the extent
that his emissions are larger and his costs of abatement higher. In a static framework without uncertainty or asymmetries of
information, this will not have an impact as both emissions and abatement costs are fixed. However, one can easily appreciate
the incentives at play in a dynamic context, even where producers do not deliberately exploit any asymmetries of information.
9.

This section borrows heavily from Keppler and Paskoff (2020).

137

7
The first observation is that carbon trading can be highly profitable for electricity producers evenin the case of full auctioning. This differs from the conclusions of the preceding subsection, which
presented the simple Pigouvian framework. This is because the simple Pigouvian framework does
not distinguish between electricity producers and electricity consumers. As shown in Section 7.3
above, carbon pricing immediately feeds through into electricity prices. Electricity consumers thus
pay for the increase in inframarginal rents. Long-term consumption will decline due to higher
prices and reduce consumer surplus further.
The second, quite intuitive, observation is that the gains from carbon pricing vary strongly withthe technology. Whether a utility employs low-carbon or fossil fuel-based technologies makes, inparticular, a difference between the rents under auctioning or free allocation. Under free allocation,
everybody gains, even coal- and gas-based producers, due to higher prices and constant costs.
Under auctioning, in particular coal-based producers will lose, since they will have to buy their
quotas, but are unable to pass on the cost increase to the extent that they are not price-setting
marginal fuel.

Inframarginal rents (EUR billion)

Figures 7.9a and 7.9b: Inframarginal rents of electricity producers in France
25
19.6 19.6

20
15

13.2

10
5
1

2.8

1.1

3.7

3.7

0
Nuclear

1 200
Inframarginal rents (EUR million)

2.1

FossilWithout carbon priceHydro and VREFree allocation

Auctioning
1 050

1 000
800

663
554

600
341

400
200

183

319

306
118

0
Coal
Without carbon price

483

147
44

36

Combustion turbines
Free allocation

CHP

CCGT

Auctioning

Source: Keppler and Paskoff (2020).

The third observation the authors make based on the results for France and Germany is that thestructure of the overall electricity mix plays a large role. In France, where nuclear energy provides
75% of electricity and hydroelectricity and renewables another 15%, the benefits from carbon
pricing are largely independent of whether quotas are auctioned or allocated at no cost. Only coalbased power producers would experience losses of roughly EUR 0.1 billion under auctioning. Overall,French electricity producers would gain an additional EUR 8.4 billion from carbon pricing under138

7free allocation and EUR 7.4 billion under auctioning (see Figures 9a and 9b above). Of these, nuclear
power plants would gain EUR 6.4 billion in both cases. Hydropower and variable renewables (VRE)such as wind and solar PV would gain an additional EUR 0.9 billion in both cases.
In Germany, the high share of fossil fuels in the electricity supply introduces a strong
difference in inframarginal rents between free allocation and auctioning (see Figures 7.10a and7.10b below). Under free allocation, the gains of German power producers from a carbon priceof EUR 30/t CO2 would thus amount to EUR 12.8 billion. Of these, EUR 2.7 billion would accrue tolignite, EUR 1.3 billion to coal, EUR 2.2 billion to gas, EUR 1.6 billion to nuclear and EUR 5.0 billion to
renewable energies (biomass, hydro, onshore ad offshore wind and solar PV). Onshore wind alone
would gain EUR 1.9 billion.
The story is very different under auctioning. While carbon-free technologies would experience
an identical increase in inframarginal rents, this is obviously not the case for fossil-fuel based
producers. Lignite-based producers would experience a loss of EUR 0.7 billion when switching froma system without carbon pricing to a system with auctioning, while coal would lose EUR 0.3 billion.
Gas-fired power producers due to fuel switching would instead still gain EUR 0.6 billion. Total
gains in inframarginal rents due to carbon pricing at EUR 30 per t CO2 would thus amount inGermany to just EUR 9.5 billion. Producers based on lignite and coal would be absolutely worse off
by EUR 1 billion with a EUR 30/t CO2 price in a carbon trading system with auctioning.

Inframarginal rents (EUR billion)

Figures 7.10a and 7.10b: Inframarginal rents of electricity producers in Germany
5
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0

4.4 4.4

2.6 2.6

2.4 2.4
1.4

0.5

Inframarginal rents (EUR billion)

Hydro

Without carbon price

1.1

0.9 0.9

Offshore windFree allocation

Onshore wind

Solar PV

Auctioning

5.1

5
4
3

2

2

1.6

Biomass
6

2.5

3.3
2.4

2

2.1

1.7

1.7

3.3

1.7

1.1

0.8

1

3.3

0.5

0
LigniteWithout carbon price

Coal
Free allocation

Gas

NuclearAuctioning

Source: Keppler and Paskoff (2020).

139

7Higher carbon prices would of course increase the inframarginal rents. Due to fuel switching,
this increase would not take place in precisely a linear fashion, however the orders of magnitude of
the relative increase would be comparable.
All increases in inframarginal rents ultimately result from higher electricity prices. In otherwords, consumer surplus will be transferred from electricity consumers to power producers in theform of higher inframarginal rents. The fact that electricity consumers lose consumer surplus due
to carbon pricing is in political economy terms a strong reason to consider subsidy solutions, i.e. toreducing the costs of low-carbon producers. However, one needs to keep in mind that such solutions
usually have a more limited impact on fuel switching in the short run and on changes in thegeneration mix in the long run. In addition, it weakens incentives to reduce electricity consumption
and to invest in energy efficiency.10 Finally, as mentioned, subsidies have unfavourable dynamic
qualities and cause economic efficiency losses due to the need to finance them through increased
taxes or charges.
If governments auction the quotas, they will earn the resulting revenues. In other words, underauctioning, electricity consumers wholly pay for the somewhat lower gains of producers. Under
free allocation, consumers and governments share the costs of their somewhat higher gains. The
allowances received at no cost will serve to enable fossil-fuel based electricity production and thustranslate into overall higher inframarginal rents.
It is instructive to compare the relative share of the gains that depend on the mode of allocationor on the increase in electricity prices. As electricity production in France is largely decarbonised,
only few quotas are actually issued. Giving them away free or auctioning them off does not make a
big difference, so the price effect dominates. In Germany, where the power sector in 2017 emittedroughly seven times as much as in France, switching from auctioning to free allocation morethan doubles the additional inframarginal rents of power. This share is paid for by government
through revenues foregone. The price effect would contribute only half. In principle, the priceeffect is supported by consumers. This is true for wholesale markets. However, at the level of retailconsumers, final tariffs for end users will result from a complex interplay of partly off-setting
forces. For instance, higher wholesale prices will reduce the support payments for renewables.
The most important effect of carbon pricing is the reduction of carbon emissions and the
incentive for further carbon-reducing technological progress. Beyond that, they differentiate withauctioning strongly between the profitability of low-carbon and fossil fuel producers. This will in
the long run, i.e. allowing for exit and entry, further spur the transformation of the electricity sector
towards low-carbon generation. All three effects together ensure that carbon pricing is a highly
effective means of reducing emissions.

10. In a framework of static optimisation, one can however replicate the impacts of first-best carbon pricing by way of unit
subsidies for each tonne of CO2 abated that is previously assessed against a firm baseline. In systems with few transaction
costs, competitive suppliers will also pass on the remuneration for such emission reductions to consumers willing to reduce
their consumption or to adopt energy efficiency measures, i.e. pay them for negawatts or rather negatonnes of CO2.

140

77.5 Policy conclusions
In a context of overwhelming evidence of manmade climate change due to the emissionof greenhouse gases, about 40% of which stem from electricity generation, pricing the carbon
emissions of electricity generators or supporting their emission avoidance is unambiguously welfare
improving. Carbon pricing, whether directly or indirectly, is the appropriate manner to integrate theobjective of reducing greenhouse gas emissions into the incentives of private agents and the policy
frameworks of governments. From an economic point of view, the most efficient instruments arethose that internalise the social costs, i.e. climate change, directly by putting an appropriate priceor surcharge on the vector that causes it, i.e. CO2 emissions. Ideally, such a surcharge would target
all carbon emitters in a uniform manner, independent of the industry or economic sector in which
they operate. Integrating external effects in this manner would make carbon-intensive generation
more expensive than low-carbon generation, lead to fuel switching and changes in the generation
mix and, in addition, provide incentives for demand reduction and energy efficiency as well as for
induced technological change.
However, carbon pricing also needs to account for the realities of everyday policy making.
While experts recognise the theoretic superiority of carbon pricing since at least four decades, it
has elicited fierce resistance by fossil fuel-producers, electricity consumers and their respective
advocates. As shown, carbon pricing has sizeable distributional effects. Welfare transfers between
different types of generators as well as between electricity consumers, generators and governments
amount to billions of USD annually. Even environmentalists occasionally voice reservations about“pricing the environment”. In such situations, where the first best solution is unattainable, it isimportant to realise that this does not mean that countries are condemned to the status quo of
unabated carbon emissions with electricity sectors based primarily fossil fuels.
The preceding sections have presented the wide array of instruments that policymakers haveat their disposal to create a wedge between the costs of fossil fuel-based electricity generation andlow-carbon generation based on renewables or nuclear energy and thus reduce carbon emissions.
These instruments go beyond the classic first-best instruments of carbon taxes or emission trading
systems. Indeed, carbon pricing does not necessarily require taxing the emissions from fossil fuelbased electricity generation and making it more expensive. Remunerating low-carbon electricity
generation such as nuclear energy, renewables and hydroelectricity in order to reduce carbon
emission can achieve very similar objectives at comparable overall costs.
Of course, OECD governments have long practised such policies primarily by providing
guaranteed returns to capital-intensive low-carbon producers in the form of feed-in tariffs (FITs),
feed-in premia (FIPs), contracts for difference (CFDs) and, most recently, zero emission credits(ZECs) or by reducing their tax burdens through production tax credits (PTCs). Clearly, this does
not make all forms of lowering the cost of low-carbon electricity production first best instruments.
Current solutions too often still rely on governments picking winning technologies. In addition,
as implemented currently, such solutions have limited impacts on fuel switching in the short
run and can weaken incentives to reduce consumption and increase efficiency. However, such
shortcomings are largely due to detailed design and implementation. At this point in the debate, it
is appropriate to think about support for emission reduction in a more systematic and economicallymore coherent manner. Generalised per unit support for each tonne of carbon abated, assessedagainst a firm baseline, can indeed replicate most results of a straightforward carbon tax. Even
where this is not possible, e.g. for a lack of an appropriate informational infrastructure regarding
baselines, generalised support for low-carbon generators is a viable alternative. The fact that
support for low-carbon generation does not differentiate between the emission performances ofcoal- and gas-based generation is secondary in the light of long-term carbon neutrality objectives.
The key point for effective climate policy in the electricity sector remains creating a cost wedge
between low-carbon and fossil fuel-based generators.
All forms of carbon pricing need to be mindful of distributional effects between generators,
but also between consumers, generators and governments. Due to the economic importance141

7of the electricity sector, carbon pricing heavily affects the welfare of different stakeholders. In
particular, straightforward carbon taxes, despite all their real economic benefits, reduce the surplus
of electricity customers. To the extent that the consumption of fossil fuel-based electricity is a key
driver of climate change this is justifiable. However, in many countries electricity constitutes alsoa merit good with important positive externalities. While the implementation of effective policies
to reduce carbon emission is no longer negotiable, these policies must remain socially acceptable.
An important option in this context consists in financing support for carbon emission avoidance
through general government funds. In this manner, taxpayers rather than electricity consumers
would carry the economic costs for ensuring the public good of climate change avoidance. Whiletaxing carbon emissions remains a straightforward first-best solution, there are indications in thatsubsidising emission avoidance is in a number of OECD countries a politically more sustainable
solution. The large economic recovery programmes following the economic crisis in 2020 in thewake of the COVID-19 pandemic could be the right moment to implement such policies at theappropriate scale.
Different countries, both inside and outside the OECD, will choose different policy packages anddifferent distributional arrangements. However, after years of sterile stand-offs between supporters
and detractors of carbon taxes, the discussion has advanced and there is now a more sophisticatedunderstanding of the available options for carbon pricing and their impacts. For all country serious
about their commitments under the Paris Agreement, there now exist appropriate options for
reducing electricity sector emissions that allow striking a balance between economic efficiency,
environmental integrity, social fairness and political sustainability.

142

7
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Chapter 8

Perspectives on the costs of existingand new nuclear power plants
8.1

Status of nuclear development and role in future energy systems

If countries are to meet their objectives under the 2015 Paris Agreement to reduce their greenhousegas emissions, a significant contribution of nuclear power is indispensable. According to the IEA
Sustainable Development Scenario (SDS) new nuclear capacity and ambitious lifetime extension
programmes for existing nuclear power plants are needed. This growing role for nuclear power
to meet decarbonisation objectives is also confirmed by the Intergovernmental Panel on Climate
Change (IPCC, 2018).
So far, however, nuclear power is not on track to reach its required share in global electricity
generation. In fact, the rate of annual capacity additions of currently 5 GW would need to at least
double between 2020 and 2040 to meet the SDS.
As outlined in the SDS scenario, without lifetime extensions beyond 40 years, the pressure on
the nuclear supply chain will increased dramatically requiring capacity additions of up to 20 GW
starting from 2021 (IEA, 2020). In addition, decarbonisation efforts will become more challenging
and more expensive at both the level of the plant and the system with the premature closure of
nuclear capacity (IEA, 2019). However, in several countries the existing nuclear fleet is impacted
by current electricity market designs that that do not appropriately value the attributes of capitalintensive low-carbon technologies dispatchable such as nuclear and hydro power plants. Policy
decisions are also leading to early closures. Otherwise, long-term operation (LTO) of existing nuclear
power plants (NPP) remains a highly competitive low-carbon investment in the electricity supply.
For nuclear new build, there are many reasons for the short fall of capacity additions comparedto the SDS scenario. The most impactful factors are related to the high cost of new nuclear projects,
particularly in countries that have not built nuclear plants in recent decades. For those countries
that have launched new projects, the experience has been difficult. These first-of-a-kind (FOAK)
Generation III projects, particularly in most OECD countries, have been affected by construction
delays and cost escalations (see Figure 8.1). Consequently, stakeholder and public confidence in theability of the nuclear industry to build new projects has been eroded. Additionally, the perceptionthat new nuclear plants carry high project risk dissuades investors and has further reduced theability of countries to attract financing for future projects.
These issues are not present in those countries that have been building plants continuously.
In those countries, with experienced project organisations and well-established supply chains, anumber of nuclear projects are delivered in a more predictable manner achieving significant cost
reductions. The results of the 2020 edition of Projected Costs of Generating Electricity suggest that
nuclear power in OECD countries could join this trend and enter in a phase of rapid learning overthe next decade (see Figure 8.1). With several projects near completion that have served to establish
industrial capabilities, future projects could take advantage of the experience gained and be morecompetitive.

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8
Figure 8.1: Trend in the projected cost of new nuclear in OECD countries

Overnight costs (USD2018/kWe)

6 000
5 000
4 000
3 000
2 000
1 000
0
2010

2015

2020

2025-30

Source: IEA/NEA (2005, 2010, 2015, 2020).

Lastly, the development of new nuclear technologies is gaining pace. Small modular reactors(SMRs) have been capturing the attention of policymakers as technology options to address part ofthe challenges observed in recent nuclear projects. With higher share of factory fabrication theirdelivery model could yield higher predictability and productivity rates. SMRs also offer opportunities
to expand the role of nuclear energy for the decarbonisation of the energy mix. This is especially thecase in non-electrical applications of hard-to-abate sectors where low-carbon technology options
are more limited. However, while significant progress has been made in the validation of the first
designs, many challenges remain. The completion of first prototypes during the 2020s will therefore
be essential in the demonstration of the expected benefits of SMRs.
Overall, countries willing to pursue the nuclear option have at their disposal three maintechnology solutions to reduce cost at the system and plant level with nuclear power:

146

1.

TO of existing nuclear assets. Most cost-effective solution but representing a limited poolLof projects according to the findings of the NEA Expert Group on Maintaining Low-Carbon
Generation Capacity through LTO of Nuclear Power Plants: Economic, Technical and Policy
Aspects (EGLTO).

2.

 ost reductions in large Generation III reactors. These types of projects are moving from FOAKC
conditions and could leverage a series of incremental strategies to improve their economic
performance as documented in the NEA study Unlocking Reductions in the Construction Costs of
Nuclear: A Practical Guide for Stakeholders (NEA, 2020).

3.

 echnology innovation through SMRs. Thanks to their technical features, these reactor conceptsT
may provide further cost reduction and extend the value proposition of nuclear power.
However, several challenges need to be overcome in order to reach commercial viability.

8
8.2 Long-term operation (LTO) of existing nuclear assets:
The most cost-effective low-carbon solution
8.2.1 Definition and technical considerations
The long-term operation (LTO) of NPPs can be defined as “operation beyond an established timeframe which has been justified by a comprehensive safety assessment” (IAEA, 2008). The period
usually considered is the so-called design lifetime in which the facility (or a component) is expected
to perform according to their technical specification. For light water reactors (LWRs), the dominant
technology around the world, the design life typically considered is 40 years.
This value is based on initial design assumptions related to the lifetime of some key componentsthat cannot be replaced such as the reactor pressure vessel. This, however, should not be confoundedwith the remaining useful life of a component during which it is expected to safely provided its
intended function1 and that is, in most cases, greater than 40 years.
Trends in the United States, where 90% of fleet has been granted lifetime extensions from 40
to 60 years, and an increasing number of units are envisaging 80 years of operation, confirm theabsence of any major technical barriers to LTO.

8.2.2 Economic considerations and evaluation of LTO costs
In 2018, the NEA Ad hoc Expert Group on Maintaining Low-Carbon Generation Capacity throughLTO of Nuclear Power Plants: Economic, Technical and Policy Aspects (EGLTO) was established,among other reasons, with the purpose to provide updated cost figures of extended operation of theexisting fleet. The present analysis builds on the preliminary findings and results of the EGLTO’s
final report (NEA, forthcoming a).
In line with the object of this report, the present economic analysis of LTO is focused on theevaluation of LTO investment and the LCOE metric. Nevertheless, it is important to highlight thata more comprehensive analysis of the economics of LTO should take into account other economic
criteria such as market conditions, the impact of LTO refurbishments on the availability of theplants and a detailed risk assessment (NEA, 2012).
Once a nuclear power plant has been built, costs are low and stable. However, as the end of
the design lifetime approaches, investment costs suddenly increase as operators perform major
refurbishments to safely extend the lifetime of the plant.2 These expenses correspond to the LTO
investment costs. In general, their evaluation is not a simple task. The first challenge is to isolate
the envelope of the LTO investments. By definition, LTO investments are those that contribute to theextension of the lifetime of a plant beyond 40 years. They are usually different from replacements
carried out as part of regular maintenance procedures (see Figure 8.2) as they involve heavier
components and more extended outages. Typical SSCs replaced during LTO refurbishments include:• echanical equipment: upper vessel heads, steam generators, turbines, pumps, motors, largeM
diameter piping, etc.

•

Electrical equipment: Alternators, transformers, high voltage posts, electrical boards, etc.

1.
The Western European Nuclear Regulators’ Association underlines that there may be no real cliff edge effect due toageing when a nuclear power plant is being operated longer than the initial design lifetime of some of its components. It alsosuggests that the initial design lifetime of the reactor vessel may no longer be relevant as being periodically re-evaluatedconsidering actual plant operation and condition as well as current knowledge about ageing phenomena (WENRA, 2011).
2.
These cost trends are quite specific to nuclear technology as they also aligned with regulatory decisions. For othertechnologies, for instance, LTO expenses are simply part of their fixed operation and maintenance costs.

147

8
•

Instrumentation and control: control room, cables, sensors, etc.

•

Civil works: including cooling towers•

Post Fukushima:3 filter vents, diesel generators and other special protections

Labour and indirect cost are also part of the scope of the LTO investment and they can accountfor significant share. Main indirect cost items involve project management, regulatory and licensing
interfaces and initial engineering efforts necessary to evaluate to extent of the LTO refurbishment.

Regular maintenance

2045

2044

2043

2042

2041

2040

2039

2038

2037

2036

2035

2034

2033

2032

2031

2030

2029

2028

2027

2026

2025

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

Figure 8.2: Distribution of LTO investments

LTO

Source: NEA (Forthcoming, a)

While this categorisation is clear, its application may face some limitations in practice. It is
important to underline that the scope of an LTO programme is not universal and varies from a
project to another depending on the initial conditions of the plant and the regulatory framework.
At the same time, LTO investments are usually performed alongside other plant enhancements (i.e.
power uprates, flexible operations, etc.) and safety upgrades. The accounting conventions adopted
by some utilities may not differentiate LTO expenses from the rest making these cost data difficult
to retrieve.
Another issue lies in the time distribution of LTO investments. In fact, depending on the context
and plant situation, the expenditure profile could be upfront at the end of the initial design lifetime,or spread over several years before the LTO decision (see Figure 8.2). As such, it is possible to identify
some NPPs for which the actual LTO investments at the end of the design lifetime are very low as
operators have been investing on a continuous basis in order to maximise the lifetime of the asset.
In this case, the evaluation of the LTO costs will require the analysis of the investment patterns of
the plant over time and the identification of those investments relevant to LTO for the applicationof the LCOE formula.

3.
One could argue that post-Fukushima is not an LTO cost per se as is not related to ageing. In fact, most of these upgrades
are not necessary to extend the life of the plant. However, under current regulatory framework, no lifetime extension would be
granted without complying with post-Fukushima standards.

148

8
Evidence from the Loviisa project in Finland indicates that an increase of the operational
lifetime of a NPP has no major impact on decommissioning costs (NEA, 2016). Costs associated withthe dismantling and handling of contaminated components generated during the refurbishment
process are generally directly included in LTO investments. As such, it can be considered that theinitial decommissioning costs have been largely provisioned during the initial lifetime period,
and that there are no additional costs during the LTO period. Furthermore, from a financial
perspective, the lifetime extension period allows for a reduction of the provisions allocated to thedecommissioning fund.4
Taking into account all these considerations and the evidence collected from several casestudies, the EGLTO concluded that an average overnight LTO investment could range from USD 450
per kWe to USD 950 per kWe. This variability could be largely explained by the differences in thescope of LTO investments due to the reasons indicated previously. Moreover, these numbers havebeen estimated in a conservative way as they consider all plant enhancements as LTO expenses.5
Table 8.1 shows the associated LTO LCOE for different real discount rates, capacity factors andlifetime extensions periods.6 The assumptions are summarised at the bottom of the table. The casewith a capacity factor of 75% is a sensitivity analysis that captures short-term economic outlook insome OECD countries.
Overall, these results confirm that LTO of NPPs remains one of the most competitive options of
low-carbon electricity generation, in line with recent IEA findings (IEA, 2019).

Table 8.1: LTO LCOE values for LWRs as a function of the lifetime extension period, discount rate,
overnight costs and capacity factor
LWR LTO LCOE
(USD/MWh)

Overnight LTO
investment costs(USD/kWe)

10 years

20 years

Discount rate
3%

7%

450
700
950

29.4
33.4
37.4

31.2
36.1
41.1

450
700
950
Min.
Max.

31.9
36.5
41.0

33.9
39.5
45.1
29.4
48.5

Discount rate
10%

3%

7%

10%

28.6
31.4
34.7

29.7
33.8
38.0

30.5
34.2
37.9
26.4
41.6

32.2
36.9
41.6

Capacity factor = 85%
32.6
38.3
44.1

26.4
28.7
31.0

Capacity factor = 75%
35.5
42.0
48.5

28.4
31.0
33.6

Note: These values have been computed assuming a refurbishment period of two years, fixed O&M costs of USD 85/kWe variable O&M of
USD 1.5/kWh, front-end fuel costs of USD 7/kWh and back-end fuel cost of USD 2.33/kWh. The overnight LTO investment costs include otherplant enhancements beyond LTO and 5% of contingencies. Decommissioning costs have been assumed as completely provisioned during theinitial design lifetime. The financial benefits of postponing decommissioning expenses (e.g. returns of the funds assets) are not included in the
LCOE calculations.

4.
In an audit on the evaluation of the decommissioning costs in France, government experts estimated that a lifetime
extension from 40 to 50 years could lead to a reduction of the required provisions for decommissioning of the existing fleet by
EUR 3.2 billion (DGEC, 2015). From a LCOE perspective, this would translate into a negative cost for the decommissioning of
nuclear plants that enter LTO.
5.
This means that power uprates, safety upgrades and other type of plant enhancements not meant to extend the life of
the plant are included in the overnight costs of LTO considered in the present study.
6.
The lifetime extension period presented in Table 8.1 (i.e. 10 and 20 years) are the typical values considered for LWRs
reactors in most countries. Other technologies such as PHWRs and VVERs take into account life extensions of 30 years.

149

8
200
180
160
140
120
100
80
60
40
20
0

LCOE [USD/MWh]

LCOE [USD/MWh]Figure 8.3: Sensitivity of LCOE of new build and LTO of NPPs to capacity factor (left)and discount rate (right)

0%

20%

40%

60%

80%

100%

200
180
160
140
120
100
80
60
40
20
0
0%

5%

10-year extension

15%

20%

Discount rate

Capacity factor
New build10%

20-year extension

Note: The band width gives an idea of the variability of the data collected in the present study. The bold line corresponds to the median of all
LCOE values.

The sensitivity analysis included in Figure 8.3 gives an appreciation of the first-order impactof the capacity factor (or electricity price, looking at the value side) on the economic performanceof LTO. This behaviour supports the early closures observed in the United States and in Sweden
owing to degraded market conditions. The impact can be attenuated with longer lifetime extension
periods. The dominance of parameters governing the revenue side over the costs also indicates that
operators have an incentive to perform power uprates (or any other plant enhancement increasing
the availability of the plant) during LTO projects as this will improve attractiveness of the businesscase (IAEA, 2018).
In contrast, LTO is slightly impacted by variations in the discount rate. This can be explainedby the cost structure of the existing nuclear fleet. While capital costs dominate in the generation
cost of new nuclear, operational expenditures represent 70% of the costs for the existing fleet (seeFigure 8.4, right). Also, this cost structure is somehow similar to the one of fossil fuel plants which
confers an economic advantage allowing to better withstand market shifts and improving the
economic rationale of flexible operations which, overall, increases the value option of extending
the life of the existing fleet.
Finally, with LTO bands being considerably thinner than for new nuclear, it is possible toconclude that LTO refurbishments are more predictable and face lower project risks. With around
100 reactors older than 40 years, the industry has benefited from a volume of projects sufficiently
large to ensure learning effects and sustain supply chain capabilities over time. A comparison withNEA (2012) values confirms these trends showing that LTO costs are lower and far better contained
today (see Figure 8.4, left).

150

8Figure 8.4: Evolution of LTO LCOE for different lifetime extension periods (left)
and cost profile of lifetime extensions versus new nuclear (right)
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%

90
LCOE (USD2018/MWh)

80
70
60
50
40
30
20
10
0
10 years

10 years

LTO 2012

LTO 2020

20 years

20 years

Note: LTO 2012 values from NEA (2012) have been computed for overnight
costs ranging from USD2010500 to USD20101 090 per kWe, discount rates of
3% and 8% and a capacity factor of 85%. LTO 2020 values cover the resultspresented in Table 8.1.

12%
30%

17%

41%
71%
29%
LTO

Invest.

New build

O&M

Fuel

Note: Overnight costs of USD 700 and USD 4 500
per kWe for LTO and new build, respectively. Realdiscount rate of 7% at a capacity factor of 85%. The
operational expenditures assumptions are detailedin Table 8.1.

8.3 Cost reductions in large Generation III reactors:
Significant cost reduction opportunities in the short and long terms
Nuclear new build in OECD countries is at a critical juncture with the completion of several
FOAK projects. These projects were initiated after a long hiatus for nuclear construction thatsignificantly eroded the nuclear supply chain and the industry’s capabilities. This is reinforced by ade-industrialisation trend in some OECD countries. In addition, initial budget estimates were heavily
influenced by the lack of design maturity and execution planning at the time of the construction
start, as well as the increasingly uncertain political context. The sums invested in these FOAK
projects have served to finance not only the construction of the reactors themselves, but also torebuild these capabilities. Understanding the prospects and drivers of nuclear construction costs
reduction is therefore key to addressing the risk perception of near-term nuclear new build projects.

8.3.1 Recent trends in nuclear new build
The trend in projected overnight construction costs is presented in Figure 8.1 above for OECD
countries and shows a significant increase in costs between 2010 and 2015. The same applies to theconstruction delays. The announced schedules for these projects were typically of 5-6 years. Thosealready in operation were built in around 10 years. Some of them are still under development andcould be tentatively delivered more than 15 years after their construction start.

151

8Some of these recent projects are presented in Table 8.2, illustrating a similar trend in projected
overnight construction costs for OECD countries. Differences between initial announced budgets
and ex-post construction costs reflect the cost escalations that have affected these projects, but the
gap should be analysed cautiously as the initial announced budgets are the result of very specificconditions.

Table 8.2: Construction costs of recent FOAK Generation III/III+ projects
InitialEx-postConstruction announced
construction
start
construction
time
time
China
Sanmen 1, 2
2009
5
9
AP 1000
United States Vogtle 3, 4
2013
4
8/9*
APR 1400 Korea
Shin Kori 3, 4
2008
5
8/10
Finland
Olkiluoto 3
2005
5
16*
EPR
FranceFlamanville 3
2007
5
15*
China
Taishan 1, 2
2009
4.5
9
NovovoroVVER 1200 Russia
2008
4
8/10
nezh II-1 & 2
TypeCountry

Unit

Power
(MWe)
2 x 1 000
2 x 1 117
2 x 1 340
1 x 1 630
1 x 1 600
2 x 1 660
2 x 1 114

InitialActualannounced construction
budgetcost(USD/kWe) (USD/kWe)
2 044
3 154
4 300
8 600
1 828
2 410
2 020
>5 723
1 886
8 620
1 960
3 222
2 244

**

* Estimate. ** No data available.
Notes: MWe = megawatt electrical capacity. kWe = kilowatt electrical capacity.

Source: NEA (2020).

As a result, stakeholder and public confidence in the capability of the nuclear industry to deliver
new build projects has been eroded. This situation has also raised the level of perceived investment
risk, intimidating investors and further reducing the chances of attracting financing for future
projects.
However, if the nuclear industry in western OECD countries takes advantage of the accumulated
experience and the lessons learnt from recent projects, nuclear plant construction can enter in amore rapid learning phase allowing it to deliver the next projects at lower cost and with significantly
less uncertainties.
In several countries, nuclear power is delivered today essentially on time and on budget. In
China and Korea, a significant number of projects have been executed in less than six years overthe last decade. These differences could be explained by alternative design features (in termsof constructability, for example). However, even for a same design, there are notable differences
depending on the country in which the reactor is being built. This gap cannot be explained solely
by site-specific conditions inducing slight design modifications. Thus, the challenges experienced
in western OECD countries in delivering new nuclear are not inherent to the technology itself but
rather depend on the conditions in which these projects are developed and executed, and on theinteractions between the different stakeholders.

15288.3.2 Key drivers for reducing the construction costs of nuclear new build
The NEA study Unlocking Reductions in the Construction Costs of Nuclear: A Practical Guide for Stakeholders(NEA, 2020) identifies eight drivers to unlock positive learning as well as continuous improvement
on large Generation III reactor projects. These drivers are summarised in Figure 8.5.

Overnight onstruction costs(USD/kWe)

Figure 8.5: Nuclear cost and risk reduction drivers

DesignmaturityFOAK
reactorTechnology

Regulation Multi unit andEffective
stability and series effects
projectmanagement predictability

What new nuclear should cost

Delivery

Regulation

Designoptimisation

Technology
and process
innovation

Revisiting Harmonisation:
licensing,regulatoryinteractions codes andstandards

post-FOAK
reactor

What new nuclear could cost

PolicySMRs and advanced reactors

NOAK
reactor

Note: kWe = kilowatt electrical capacity.

Source: NEA (2020).

Lessons learnt from historical and recent nuclear new build projects
Historical and recent evidence suggest that the lessons learnt are well understood and can beeasily implemented in future projects. Several non-OECD countries delivering competitive nuclear
projects today, are already taking advantage of them. As a result, the next nuclear project should bedelivered at lower costs after entering in a phase of more rapid learning. Key lessons learnt include:• esign maturity: The detailed design has to be completed and ready for construction. ThisD
implies early involvement with the supply chain during the design process in order tointegrate the necessary requirements to improve constructability.

• roject management: The design also requires a robust implementation strategy with a clearP
definition of responsibilities and identification of competences at all levels and stages of the
project. A strong and experienced project management team is essential to ensure its proper
execution and to deal effectively with all interfaces and unexpected risks.

•

 egulation: Predictability and stability of the regulatory regime is a precondition for theR
implementation of these measures.

• ulti-unit and series effect: Once sufficient level of design maturity has been achieved, thereM
is a strong opportunity in freezing the design configuration and systematically replicate it as
many times as possible building up supply chain capabilities.

In the near term (early 2020s), considering these lessons learnt, the most effective way to achieve
construction costs reduction is to develop a nuclear programme that takes advantage of serial
construction with multi-unit projects on the same site, and/or construction of the same reactor
design on several sites.

153

8
Cost reduction opportunities in the short term (up to 2030)
In the short term (up to 2030), with the previous drivers and conditions already in place, the cost of
nuclear projects could be further reduced.
In technology and process innovation, a range of cost reduction opportunities could be exploited
through the interplay between the reactor design and the associated delivery processes. These
drivers are not necessarily sequential and can be mobilised even during early planning stages inorder to accelerate learning.
There is evidence that countries in more advanced stages of learning are already benefiting fromthese opportunities and working on a continuous improvement basis similar to other industries.
In addition, in order to maximise the potential of cost reduction, the right balance between
improvement and replication needs to be found in order not to alter the positive learning dynamics.
Timely decision making has also to be acknowledged with the objective to ensure the right pace of
new construction and diminish the risk of over engineering.
At the reactor design level, the experience gathered in the first constructions can be used toreach higher levels of simplification, standardisation and modularisation as well as to integrate
the latest technical advances. Organisational efficiencies can also be unlocked through a new setof innovative processes.

Additional opportunities in the longer run (beyond 2030)
Longer-term (beyond 2030) cost reductions are also possible. There are indications that countries inmore advanced learning stages are moving in this direction.
Further cost reductions can be achieved by means of higher levels of harmonisation in codesand standards, and licensing regimes. Other highly regulated activities such as the aviation sector
have already undertaken significant efforts in this field with positive results. Without neglecting
the strong political dimension and the need to protect the sovereignty of national regulators,
international collaboration for regulatory harmonisation has demonstrated that it is possible toreach common positions in some areas.7

8.4 Economic perspectives on small modular reactors (SMRs)
Small Modular Reactors (SMRs) are defined as nuclear reactors with a power output between
10 MWe and 300 MWe. Designs with power outputs smaller than 10 MWe, often designed for semiautonomous operation, have been referred to as Micro Modular Reactors (MMRs).
SMRs are often designed for factory fabrication, taking advantage of the benefits of economies
of series, to be transported and assembled on site, resulting in shorter construction times. This is
one of the key elements that might prove to make SMRs cost competitive with other energy options.

7.
See Multinational Design Evaluation Programme, www.oecd-nea.org/mdep/common-positions/PUBLIC%20USE%20DCPEPR-01-%20EPR%20Instrumentation%20and%20Controls%20Design.pdf.

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8
The most mature SMR concepts are based on LWR technology. Other concepts are Generation IV
reactors that incorporate alternative coolants (i.e. liquid metal, gas or molten salts) and advanced
fuels. SMR deployment configuration can vary between single-unit installations, multi-module
plants, or mobile power sets such as floating (i.e. barge-mounted) units. In 2018, the International
Atomic Energy Agency (IAEA) identified more than 50 concepts under development with different
technology and licensing readiness levels, including four concepts that were under construction at
the time.
Due to smaller reactor cores, very large water inventories and lower power densities, LWR SMRs
may benefit from reduced shielding requirements and reduced or eliminated offsite Emergency
Planning Zones (EPZ) which, in turn, will result in added flexibility for the siting of these reactors.
SMR designs often include an integral nuclear steam supply system and take advantage of overallsystem simplification. Inherent passive safety systems provide SMRs with greater and, in somecases indefinite, coping times in case of a loss of offsite power. Many SMRs are designed to beinstalled below-grade resulting in higher physical protection and protection from external hazards.

8.4.1 Key economics drivers of SMRs
While smaller cores bring the benefits described above, they also have a negative effect on theeconomic competitiveness of the unit. Reactor designers have traditionally scaled reactors up tolarger sizes to take advantage of the economies of scale. In other words, because the fixed costs
associated with a nuclear reactor grow very slowly as the size of the reactor increases, it makes
sense to increase the output of the reactor to reduce as much as possible the cost per unit ofelectricity produced. To counterbalance the impact of diseconomies of scale, the business case of
SMRs is supported by economies of series production, which in turn relies on design simplification,
standardisation and modularisation.
The benefits of serial construction have been well documented in other industries, such as
the shipbuilding and aircraft industries, in which serial manufacturing have resulted in learning
rates between 10 and 20% (NNL, 2014). For the first SMR units, serial production may also allow toamortise non-recurrent costs, such as research, development and design certification costs.
To support serial construction and achieve learning rates of the same order of magnitude asthese other industries, several specific drivers have been identified as summarised in Figure 8.5
below:•

implification: Passive mechanism improvements and greater design integration would
S
reduce the number of components and result in containment building savings.

•

 tandardisation: The lower power output of SMRs reduces the need to adapt to local siteS
conditions, raising the level of design standardisation compared with large reactors.

• odularisation: Smaller SMR size means that transporting their modules would be easierM
than for large reactors. In fact, the degree of modularisation increases considerably for power
outputs of less than 500 megawatts of electrical capacity (MWe). This trend could be improved
with more aggressive modularisation techniques tailored to the logistical constrains andtransport standards of each country. It is estimated that 60-80% factory fabrication levels are
possible for SMRs (with power outputs below 300 MWe) (Lloyd, 2019). This would also facilitate
the implementation of advanced manufacturing techniques such as electron beam welding
and diode laser cladding by 2025 (EY, 2016). Others, such as powder-metallurgy hot isostaticpressing and additive manufacturing, are at lower technology readiness levels but significant
progress is being made (EPRI, 2018).

•

 armonisation: Having access to a global market is necessary to foster series-production
H
economies, but this is possible only with regulatory and industrial harmonisation.

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8
In that respect, as highlighted in Figure 8.4, it is important to stress that SMR construction costs
will be impacted by the key cost drivers identified for large Generation III nuclear reactors. However,some specific drivers will carry more weight. This is especially the case of the series effect, as wellas simplification, standardisation, modularisation and harmonisation.
Attaining these economic benefits will require a co-ordinated effort between the different
stakeholders, as well as a dedicated policy and regulatory framework. It is also imperative toappropriately estimate the size of the global market required to establish a robust supply chain andsustainable construction know-how that result in cost-competitive capital costs.
If SMRs can be serially manufactured in a manner similar to commercial aircraft, the economic
benefits are significant. This requires, however, the market for a single design to be relatively large,
which denotes the need for a global market. For this to be realised, regulators will need to consider
how they might co-operate to enable a true global market for nuclear technologies.

Figure 8.6: SMR economic drivers that help compensate diseconomies of scale
Construction costs(USD/kWe)
Modularisation& factory build

Standardisation

Light-water SMR
economic drivers

Designsimplificationec Laron ge
om re
ies actof orsscaleHarmonisation

Size
SMR

Large reactor

Source: NEA (2020).

8.4.2 Market potential for SMRs: Broadening the value propositionof nuclear power
The smaller size and the shorter delivery times predicted make the upfront investments needed
for SMRs smaller. As a result, customers and investors may face lower financial risk, which could
make SMRs a more affordable and attractive option. Given their smaller size, SMRs also offer more
flexibility to meet demand growth in smaller increments, which would also improve their overallbusiness case.
However, the anticipated advantages of series deployment will only be realised if SMRs cantake advantage of a global supply chain and a global customer basis, which require a streamlined
multinational licensing framework and co-ordinated international codes and standards for the
manufacture of systems and components.

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Although most SMR technologies are in the relatively early stages of development and significant
uncertainties remain for their market outlook, at least three potential applications have been
identified for SMRs, in addition to the traditional role of providing baseload electricity:• ecarbonising energy systems by replacing coal plants and providing power for districtD
heating and desalination applications. Most advanced designs such as non-LWR SMRs,
that are designed to have higher operational temperatures, could supply process heat forindustrial sectors where substituting carbon-intensive sources of energy would otherwise
not be possible.

• omplementing the penetration of variable renewable energy (VRE) by providing systemCbenefits based on the flexible operation of SMRs and the possibility to be part of an integrated
portfolio of solutions in “hybrid” energy systems.

•

 acilitating the expansion of the nuclear sector in regions where economic, geographicalF
and/or grid-related constraints do not allow the use of large nuclear power plants. For suchmarkets, SMRs may already be a cost-competitive way to replace diesel generators to produce
electricity, heat and fresh water.

These different market drivers are therefore expected to broaden the value proposition of nuclear
power as part of future integrated energy systems, supporting in particular the decarbonisation ofhard-to-abate sectors. SMRs should therefore be seen as complementary to large Generation III
nuclear power plants as they will address different market needs.

Figure 8.7: The market opportunities for SMRs

Extendingnuclear market
off-grid / remotelocations,
adaptation to site
constraints: seismic
conditions, cooling source,
access to nuclear
for new-comers

Fostering
decarbonisation
replacement ofageing coal power
plants, non-electric
applications(e.g. district heating)

Fostering
decarbonisation
replacement ofageing coal power
plants, non-electric
applications(e.g. district heating)Source: NEA (Forthcoming, b).

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88.5 Conclusions
The general cost projections and specific cost drivers identified in this chapter suggest that nuclear
power support cost-effective decarbonisation in several complementary ways.
First, LTO remains one of the most cost-competitive options to generate low-carbon dispatchable
electricity in many regions with a LCOE ranging USD 30-50/MWh. Thanks to lower CAPEX needs
and back up by a solid industrial infrastructure, LTO faces limited project risks and represents asignificant pool of “shovel-ready” projects to maintain SDS emissions targets within reach.
Second, thanks to the experience gained with recent FOAK projects, new nuclear can enter ina phase of rapid learning in western OECD countries with near-term overnight costs reductionsof 20% to 30% compared to today’s levels. Moreover, leveraging several factors that arise at thetechnical, organisation and regulatory level could unlock additional cost reductions in new projects
alongside with higher predictability in their delivery. More active government intervention in risk
allocation and mitigation strategies for new projects will also have significant impact on financing.
As result, financing costs, which can represent 80% of the total investment costs, could notably fall
further improving the economic performance of nuclear.
Third, SMRs propose cost and risk reductions with factory built construction and higher
affordability of the projects. Nevertheless, while some of these benefits have been documented
in other industries, they still need to be proven in the nuclear sector. The construction of first
prototypes may materialise some of the announce benefits of SMRs and thus accelerate theircommercial viability. Government support is also essential on this front.
Countries envisaging to pursue the nuclear option will benefit from the mobilisation of thesethree complementary solutions. In these countries, while LTO may be more economically attractive,
new nuclear projects are required to prepare the renewal of the fleets and ensure that nuclear
power is on track to meet its long term contribution to decarbonisation. Progress made withthe development of large Generation III reactors will also support the development of SMRs as
most of the cost reductions drivers are not technology-specific. Finally, SMRs have specific value
propositions and target specific markets and applications that could accelerate the decarbonisationof hard-to-abate sectors.

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Chapter 9

Sector coupling and electrification:
Understanding how the electricity
sector can drive the decarbonisationof the overall economy
Sector coupling, the use of electricity in sectors such as transport, residential heating and the useof hydrogen, is integrating the electricity sector in new ways into the wider economy.
This specific form of technological progress promises significant benefits in terms of economic
efficiency and reduction of greenhouse gas emissions. However, it also poses a considerable
challenge in analysing the new economic constellations in a manner that informs social preference
formation and political decision making in a pertinent manner. Those new constellations are nolonger amenable to conventional approaches of analysis based on the modelling of individual
sectors according to well-established methodologies such as sector-specific optimisation models.
Recently, however, a systematic effort to come to terms at the conceptual level with these newdevelopments and to prepare them for societal decision making has been undertaken by variousorganisations, and is yielding relevant new results (OECD, 2012 and 2019). It resulted in the new
scenarios being published recently at national and European level, with more attention to economic
analysis.
In France, this effort has been lead in particular by the Réseau de Transport d’Electricité (RTE),
the transmission system operator for electricity, which has invested heavily over the last decadein new capabilities for modelling and policy analysis around the generation, transmission andconsumption of electricity. The following chapter provides an overview of recent advances inanalysing and understanding the impacts, benefits and constraints of sector coupling with a focus
on the areas of transport, residential heating as well as the production, storage and use of hydrogen.
This chapter also examines how those analyses led to complex questions regarding the best way
to “allocate” the low-carbon electricity generated in a given European member state, while taking
into account the fact that the power system is more and more interconnected at European level
and where emissions are partly governed by a joint mechanism (the EU ETS) and partly by national
schemes.

9.1 The context in France: From a debate about the electricity sector
to a coherent policy to decarbonise the overall economy
Context in France
Important steps have been made recently in France regarding scenario modelling, cost-assessmentand economic-based policy making in the field of energy transition.

161

9Part of this work has been made through successive reports published by RTE, acting on behalfof a broad mandate provided for by law.
It initially responded to a precise need: (i) articulating public objectives for the evolution of theelectricity generation mix, regarding both the development of new renewables (wind, solar PV)
and the reduction of the share of nuclear power; and (ii) integrating those prospects in a coherent
picture that would include significant reductions in CO2 emissions at country level to meet the goalsof the 2015 Paris Agreement.
When this work was initiated in 2017, there was no reference scenario for the electricity sector
fit for a broad public discussion. On the one hand, the long-term scenarios previously elaborated
by RTE in 2014 were essentially adequacy studies and were not designed for forming the basis of apublic debate. On the other hand, other scenarios used in the debate often lacked precise modellingof the power sector, failed to take into account the results of existing adequacy studies, or did not
integrate correctly the European scale of power system operation. This is to say that the balance
between nuclear and renewables was essentially addressed under a political framework.
Secondly, studies including a detailed economic analysis were rare and no cost-assessment
methodology had been explicitly agreed upon among stakeholders. In the economic field, the pros
and cons of the different options for decarbonising energy were thus largely undocumented.
Lastly, the discussion mainly concentrated on electricity in an isolated manner – which is not
surprising given the political nature of the debate on nuclear and renewables – but failed to address
key challenges for decarbonising the overall economy, and consequently did not address properly
sectoral integration.

Building new scenarios for the electricity sector
It is in this context that RTE initiated a large work of scenario building for the electricity sector in2017, in consultation with stakeholders. Before moving towards the analysis of sector coupling as
such it was deemed essential to reconstruct a solid basis in traditional electricity sector analysis.
This resulted in November 2017 in the publication of five scenarios for the power sector on the2020-2035 period (RTE, 2017). The scenarios varied according to when the power mix would be“rebalanced”, that is to say when it would reach the politically defined objectives of 50% for theshare of nuclear power in the overall electricity generation, and 40% for the share of renewables.
This allowed very different policy options to be addressed, from a strategy based to a large extent on
continuing with the existing nuclear reactors fleet – complemented with the progressive addition ofrenewables (Volt scenario) - to a complete nuclear phase-out over the next 20 years (Watt scenario),
Each scenario was analysed on three dimensions: technical feasibility and performances regarding
security of supply, CO2 emissions for the power sector, and costs for the society.
This work formed the basis of the elaboration of the new multiannual energy plan (PPE) by the
French government. In November 2017, the government announced that the 50% target would bepostponed beyond 2025, as it would otherwise lead to maintaining coal-fired generation plants andbuilding new gas turbines; the reverse to a coherent policy to reduce CO2 emissions. In early 2018,
it selected two out of the five scenarios (named Ampère and Volt) for public debate. In mid-2018, thedraft provisions for the PPE were published, the final scenario for electricity being a combination
between Volt and Ampère. In April 2020, the PPE was published and came into force.
This new set of scenarios helped taking important decisions on electricity in a given framework,
the debate on the share of nuclear power versus renewables in electricity generation, which is very
specific to France. Yet in the meantime, the policy discussion had partly shifted towards carbon
neutrality, with many European countries (including France) committing themselves to achieve
net-zero emissions by 2050 and the European Union proposing a large policy change through theEuropean Green Deal project.

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From sector-specific objectives to the broader goal of carbon neutrality
Considering the broader perspective of achieving net zero emissions by 2050, the scenarios
established in 2017 presented obvious limitations.
First, they focus on the 2020-2035 timeframe. This is a key period for obtaining significant results
on CO2 emissions, but it obviously needs to be complemented, for example to integrate structuraltransformations on sector coupling that could materialise in the 2030s – but more probably in the2040s.
Second and more importantly, they do not differentiate well on the metric of CO2 emissions
for the electricity sector at national level. The reason is specific to France and the “initial” stateof decarbonisation in electricity generation, which was already 93% carbon-free. Rebalancing theelectricity mix (higher share of renewables, reduction of the share of nuclear power) does notprovide per se gains on CO2 emissions: only the decommissioning of the last coal-fired generation
units (scheduled for 2022), and, to a lesser degree, the anticipated reduction in the use of gaspowered CCGTs, will have a marked effect on CO2 emissions for electricity generation, bringing
them from around 25 Mt CO2 /y in the period 2010-2020 to around 10-15 Mt CO2 by around 20252030. Therefore, contrary to other countries such as Germany, Italy, Spain, or the United Kingdom
where developing renewables in the power mix directly leads to reduced CO2 emissions, there is no
substantial gain on CO2 emission to come from electricity generation.
Focusing on GHG emissions for electricity generation in France only may thus provide a bias in
the evaluation, by failing to take into accounts two important results:1.

 hanges in the electricity mix in France may have a limited effect on GHG emissions atCnational level, but this is no longer the case at European level. Developing wind and solar PV,
if it is not compensated by equivalent decommissioning of nuclear units, brings electricity
exports up and mainly helps reducing load factors for gas- and coal-fired generation units at
EU level. Accordingly, in some of the scenarios studied by RTE, a massive increase in electricity
exports from France to other countries would occur from 2025 onwards and could culminate
to levels well beyond 100 TWh/yr in 2035 (compared to around 60 TWh/yr presently).

2.

 he different policy options on electricity generation have impact on the ability to decarboniseTthe wider economy, by allowing more transfers towards electricity as a final energy (intransportation or buildings), a more ambitious sector coupling in the form of power-to-X (forexample with low-carbon hydrogen).

The different scenarios established in 2017 have thus different potentials and interests withregard to their contribution to reducing GHG emissions at the level of the overall European economy.
Taking into account this broader perspective is even more important now that the electricity sector
forms the basis of nearly all scenarios of carbon neutrality by 2050, with a development of directelectrification and power-to-X to a large extent (EC, 2018; IEA, 2019a; ENTSO-E & ENTSO-G, 2020).
In order to address this new nature of the debate and complement the work on scenario-building
presented in 2017, RTE has since launched three dedicated studies on new uses for electricity,
including power-to-hydrogen: the first on electric vehicles (EVs), the second on low-carbon hydrogen,and the third on buildings and heating. Those three studies have the objective of providing an
in-depth study of the technical, economic and environmental consequences of those transfers ofuse. They require (1) a methodology to provide a thorough assessment of CO2 emissions reduction
beyond the power sector (including life-cycles assessment) and (2) a methodology to identify the
associated costs for the power sector and beyond.
This work is a first step before an unprecedented (and ongoing) study of new scenarios for 2050,
with and without new nuclear units, to reach carbon neutrality.

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99.2 Benchmarking scenarios: The need for a method to analyse impacts
at the system level
In France, electricity is currently largely decarbonised. Renewables (wind and PV) and nuclear
energy are the two options considered for this performance to remain on the long run. Key policy
elements at stake are different depending on the period considered:•In the short to medium term (2020-2035), the public debate on the electricity mix is mostly
about new renewables and existing nuclear assets and the best way to diversify electricity
generation, which is based at 70% from nuclear power;•

 t longer horizons (2040, 2050, 2060), the debate focuses on the (possible) competition betweenA
renewables and new nuclear units to achieve net zero, considering that most of the existing
nuclear reactors will be decommissioned in any case at those horizons. Many position papers
and stakeholders’ expression aim to weight on this debate.

Two opposing approaches… which do not provide relevant results
The simplest approach – still widely used in the public debate and often advocated by supporters ofrenewables – consists in comparing the generation cost of 1 kWh of nuclear and 1 kWh of wind or
solar energy and is often referred to as the levelised cost of energy (LCOE).
At first sight, this indicator is interesting: it takes into account all costs (investments and capital,
operation and maintenance, fixed costs and variable/fuel costs) over the lifetime of assets. However,the LCOE approach to compare technologies suffers from major limitations as it does not take into
account the cost of integration in the power system (see also the analysis in Chapters 2 and 4 of thisreport for more details).
Accordingly, simulations conducted for the Bilan prévisionnel have shown that, past a certain
level, the massive development of renewables created a “cannibalisation effect”, meaning that thecontinuous increase in zero-marginal cost units of a given types undermines their own value (thecontinuous development of some units undermine their own value though a collapse in energy
prices). This effect has been identified in particular for solar PV during the summer though thecalculation of average hourly revenues of PV panels under a large variety of situations. Similarly,
other studies1 have concluded that non-dispatchable units lead to an increasing need for flexibilities
or grid reinforcement that come with a cost. As a result, it is widely admitted that even if the LCOE
of renewables is below that of new nuclear generator, this brings no indication on the best solution
on the economic point of view.
On the other hand and regardless of the academic state of the art, the public debate on energy
matters in France has seen some critics of renewables promote an alternative narrative, basedon the idea that a system based on renewables would need to be supported by back-up units
that would make it strictly equivalent to a 100% dispatchable generation mix. In this approach,each megawatt of variable renewable added has to be complemented by one megawatt of storage,
generation or demand response capacity, regardless of the initial situation of the power system andof existing flexibilities. The idea that a gas plant is “behind” every new wind farm is, for example,
very often expressed in local public debates in France by opponents to wind energy. This approach
has important limitations as it does not quantify precisely the need for capacities and does notconsider the existing flexibilities (e.g. hydro reservoirs, interconnectors making it possible to benefit
from larger scale optimisation of the power system).
The limitations of the LCOE approach has led economists to think about going beyond the LCOE
indicator and develop new methodologies to analyse the costs of the power system. In particular,
this is the idea of the method developed by the NEA and by the IEA with the VALCOE (Value-

1.

164

For studies related to France, see, ADEME (2018) and Villavicencio (2017).

9
Adjusted LCOE) approach which is presented in Chapter 4. This type of approach takes into accountthe differences in terms of services for the power system by including an estimation of additionalcosts for capacity, flexibility or even grid and balancing costs.
The VALCOE approach clearly provides a much relevant understanding of the economics ofpower system and an easy way to compare the average costs of different technologies, including
system costs. It helps to identify the cheapest technologies to develop in a marginal approach fromthe current system.
However, long-term planning of the electricity mix, with the integration of major changes
on generation and consumption of energy, still requires additional economic evaluation tools.
Using an indicator “by technology” has indeed intrinsic limitations as it actually depends onsome sort of allocation of the overall system costs between technologies, raising methodologicaldifficulties. Moreover, both LCOE and VALCOE approaches are not suitable for evaluating the overall
economic issues of sector coupling and electrification which are increasingly important for the
decarbonisation of the economy. For those reasons, RTE has chosen to use a different method toevaluate the costs and benefits of different transformations of the power system.

9.3 Evaluating the costs at system level: An appropriate approachto compare the true economic costs of different policy options
In order to guide public decision making about planning of the energy mix, RTE has developed, from2017 onwards, a comprehensive socio-economic approach in its different studies on the evolutionof the power system in France. This methodology has been described in documents and discussedwith stakeholders. It consists in assessing the overall cost of each scenario, that is to say of eachpolicy option, with a multiplicity of sensibility checks to account for the large uncertainty over someof the key parameters (especially at a distant horizon like 2050). This work allows a comparison ofa large set of scenarios, based on homogenous and fully transparent methodology and metrics.
The comparison between the costs of different power generation mix is relevant only if theboundary conditions are identical. This means in particular that the macroscopic conditions (GDPgrowth, demography, degree of electrification, and prospects on energy efficiency) must be common
to all scenarios – which is often not the case in the public debate. It is thus necessary to go throughanother step of scenario harmonisation and to develop a unique testing ground with homogeneous
hypotheses. Therefore, in order to ensure consistency of results, the comparison of costs provided
for in Bilan prévisionnel 2017 was applied to standardised hypotheses (on GDP, demographics, public
policies in other European countries, and electricity consumption).
The analysis performed under this framework provided important results:•

 aintaining a significant part of the existing nuclear capacity until 2035 appears to be a nonM
regret option from an economic point of view and CO2 point of view (regardless of key hypotheseson technology costs for renewables and nuclear, the scenarios with the larger part of existing
nuclear units were the most affordable ones for the society). It is interesting to note that this conclusion is robust to a change in the expected cost of expanding the lifetime of existing reactors.

•

 here is a strong rationale for developing renewables such as onshore wind in the comingTyears, in addition to the current low-carbon generation fleet, in the prospect of decarbonising
electricity generation at European level and other sector at national level. Most of the cost of
renewables corresponds to the initial take-off of the technology, for example for solar PV or
offshore wind. It is important to note that this does not mean that renewables can develop
without support schemes in the general case – on the contrary, computation of hourly revenues for low-carbon technologies show that public support remains necessary in many situations as long as carbon does not reach a price on the EU ETS corresponding to its society value.

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9
•

 he policy option of forbidding the building of any new power plant using fossil fuels has alsoTbeen found to be economically sound (it does not increase costs from the society perspective).

Comparing scenarios under a uniform testing ground with standardised hypotheses can alsopose difficulties, as the interest of developing scenarios is especially to build coherent perspectives
and storylines (one scenario may be fit for a given context, but clearly not for another one). For
example, RTE tested one scenario of nuclear phasing-out (Watt) in which all nuclear units would be
decommissioned when attaining 40 years of operation. Even with an ambitious policy to develop
renewable energy sources at a much faster rate than today, such a scenario brings the need forbuilding new gas-fired units and leads to increasing CO2 emissions in electricity generation. Yet thisneed for new fossil fuels generation units may be mitigated (but not cancelled) in case an intensive
energy efficiency programmes is implemented and together with a broad public engagement inenergy sufficiency. Of course, when analysed on a uniform testing ground with standard hypotheses
on individual behaviours, energy efficiency and electrification rates, this scenario performs poorly
on the cost and CO2 metrics, but this does not tell all the story about it. This actually shows that fora phasing-out of nuclear to be implemented and still allow a decrease in CO2 emissions at national
level in France (albeit less important than in other scenarios) very strict boundary conditions
need to be met, with strong societal choices including a very fast expansion of renewables and asignificant commitment to energy sufficiency with sharp reduction of energy use.
The comparison of policy scenarios in France regarding their cost, security of supply andCO2 emissions also depends on the policy of neighbouring countries regarding their energy mix:
sensibility checks on the energy mix in the neighbouring countries are therefore an important partof the analysis and required to ensure the robustness of conclusions.
The same will apply to the degree of electrification. All energy scenarios for 2050 (ENTSO-E
and G, European Commission, Eurelectric, NGOs) rely on a strong development of electricity use intransportation, buildings and the industry. This needs to be taken into account when comparing
scenarios for the power sector. Some scenarios could, for example, lead to maintaining a small
amount of CO2 emissions in electricity generation if it makes it easier to expand to a vast degree
renewables to cope with new uses for electricity. This means that the scope of GHG accounting
and of the economic analysis must be expanded beyond the power sector itself, and integrates thevarious form of sector coupling and of direct development of electricity uses.
The approach used here for cost estimation takes into account all costs on the energy system,
including the costs of appliances (e.g. electric vehicles, electrolysers and hydrogen storage facilities,
heating systems). It can provide CO2 abatement costs at the societal welfare perimeter, to be used
for public decision making. They are not comparable to CO2 abatement costs at the individual level,
presented, for example, by the IEA in its recent publication Sustainable Recovery (IEA, 2020).
CO2 emissions assessments do not integrate the possible compensation effects on other sectors
through the functioning of the EU ETS market, which can be discussed. The alternative approach
would consist in assuming that each tonne of CO2 emission avoided would lead to an emission
in another sector as the EU-ETS is a cap-and-trade mechanism. Such a reasoning means that the
only policy to reduce GHG emissions in sectors covered by EU ETS would be to adapt the cap of themechanism, and that sector-specific policies do not have any effect on global GHG. Yet the levelof the cap is in fact, subject to political discussions, and adapted to the real level of emission andthe price level (e.g. stability reserve). Moreover, many CO2 emissions are not considered in the EU
ETS (such as emissions from cars, buses and trucks, of battery manufacturing outside Europe, ofgas or domestic fuel heating systems): the loopback effect of CO2 reduction on EU ETS price and
GHG emissions of other sector may therefore heavily depend on the sector and on fiscal provisions
specific to each country.

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9
Box 9.1: Specifics of the cost-assessment methodology developed by RTE
This approach relies on the following points
Costs taken into account
The costs are considered from the society perspective (societal welfare), or alternatively fromconsumers only.
Societal welfare assessment does not include economic transfers between the energy sector
and the rest of the economy. In particular, taxes on certain activities (e.g. power generation) are
not factored in the calculation.
All costs of the energy system, differing from one scenario to another, have to be taken
into account: generation costs, networks costs for transmission and distribution (includinginterconnectors), demand-side management systems costs or willingness-to-accept, etc. It is
possible to limit the assessment only to the power system, under the condition that all scenarios
have the same interactions with the other part of the energy system (same level of electricity
in the end-use), but it is often not the case: this means that it is necessary to embark in theassessment all the consequences on other sectors.
For a given country, the value of the electricity balance with neighbouring countries has also
to be taken into account (for example in France, it can yield differences of several billions of euros
between scenarios).

Externalities
Energy generation and consumption have many externalities. The perimeter of externalities iswide and most of the externalities do not have a consensual value to consider. CO2 emissions
could be valued at the carbon market price (if the analyses is restricted to the perimeter of thepower system participant) or the public value for the climate / shadow price of carbon (which is
public in several European member states, but has not been settled at European level).

Taking into account the time horizon
The comparison of different energy systems leads to different repartition of costs over timeand different systems at the end of the period considered. A precise method would consist in asimulation over a very long period of time, in order to limit the distortion effect of comparing
different scenarios having different system at the end of the period.
A simple way to limit this effect (and in particular the effect of investment costs made at theend of the period) is to reason on annual costs.

Necessity of multi-scenario analyses and sensibility checks
Costs of technologies are subject to many uncertainties: technical progress, economies of scale inlarge-scale developments, regulatory evolution and their impact on costs, etc. A robust economic
assessment requires to consider different scenarios on all the key parameters.
The following sections are devoted to the results and what we learnt from the three specificanalyses performed by RTE on the options for developing electricity instead of fossil fuels:transportation, heating in buildings and hydrogen.

167

99.4 Sector 1: Electric mobility – A no regret option for decarbonisation
at reasonable costContext and rationale for public action
In France, the energy consumption of the transport sector accounts for almost 30% of the final
energy consumption and around 30% of GHG emissions, 95% of which is emitted by road transport.
Achieving net zero by 2050 require a major reduction in the emissions in this sector.

Figure 9.1: Projected changes in the numbers of light-duty electric vehicles in France, including
all technologies: 100% electric vehicles (BEV) and plug-in hybrid electric vehicles (PHEV)
18

EV fleet in France (millions)

16
14
12
10
8
6
4
2
0
2010

2015

2020

2025

2030

2035

RTE high
RTE intermediate
RTE low
PFA "French association of car manufacturers" scenario "Green constraint"
PFA scenario "Green growth"
PFA scenario "Stagnation"
PFA scenario "Liberal world"
French strategic contract for the Automobile sector
French Multiannual Energy Plan
National low carbon strategy (RTE estimate)
Source : RTE (2019).

While electric vehicles are not the only solution for decarbonising the transport sector, theirdevelopment is significantly more advanced than the alternative low-carbon technologies. Due totheir energy efficiency and the fast reduction in batteries manufacturing costs, EV and PHEV are
currently considered as the main solutions for reducing the GHG emissions of road transport, using
low-carbon electricity.
The various projections of manufacturers and authorities are based on developing one million
electric vehicles in France by 2022-2023, slightly less than 5 million by 2028, and possibly between 7
to 16 million by 2035 (private vehicles and light duty commercial vehicles included). EVs and PHEVs
would then amount to between 20% to over 40% of the total number of vehicles. The development
would initially concern mainly private vehicles and light-duty commercial vehicles, but would
ultimately include HGVs (electric buses and trucks).

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9The massive development of electric vehicles is a challenge and a pivotal change for the energy
and transport sectors. Several conditions must be met for this development to be successful:accessibility of charging points, industrial change in the automotive sector, security of the electricity
supply, and managing the environmental impacts and costs for both the community and the user
are key factors to facilitate the development and integration of e-mobility. All those factors were
weighted in and assessed, using both an electricity dispatch model and a mobility model based on
available data on people’s transportation habits. The main results have been published recently(RTE, 2019) in order to identify the key drivers for a successful transition to electric vehicles.

Technical results
According to different scenarios tested (between 11.7 and 15.6 million EVs on the road in 2035 in
France), electric consumption for the transport sector (including rail transport) is expected to reach
between 40 and 65 TWh/yr. This represents less than 15% of the total electricity consumption in
France by 2035, and is also less than the rise in electricity consumption in France between 2000
and 2010.
Importantly, even with the scheduled decommissioning of some existing nuclear reactors, therecent energy strategy adopted by French authorities provides for a significant increase in lowcarbon electricity generation (nuclear and renewables), which is expected to progress from lessthan 500 TWh today to more than 600 TWh in 2035). Current forecasts see electricity consumption
to be broadly stable on the coming years, with the possibility to a slight increase as from 2030). In
any case, assuming energy efficiency continues to develop in electricity uses (digital equipment,cooling and heating, household appliances, lighting), total consumption could be in the region of
500 TWh/yr even with an ambitious development of e-mobility and other new electricity uses. Thisis far enough to allow for an electrification of mobility. The key issue concerns the impact on the
consumption peaks and the security of supply of the power system.
Detailed analysis shows that the power system is able to “absorb” the heavy holiday traffic
periods of July and August in France, or the long public holiday weekends: this alone is not
significant to cause concerns for security of supply. In fact, long-distance journeys account for asmall proportion of the total distances travelled each year, and – based on detailed modelling ofmobility habits – they tend to concentrate at times when the power system has important margins
in terms of supply: summer, weekends, public holidays, etc.
It is actually the daily mobility patterns which represent the main challenge for the electricity
system. In the absence of any incentive for consumers to engage into some form of smart charging,even if not sophisticated (through simple time-of-use tariffs such as peak-off peak), power demand
would be partially concentrated between 7 and 9 pm on week days.
Nevertheless, an important development of smart charging is not a prerequisite for e-mobility
integration into the power system.
Even in the most stressful scenario (big cars with large batteries aiming at increasing cars’autonomy, little access to charging at work and a low development of smart charging), system
adequacy can be ensured with only a limited development of smart charging:• he diversity of vehicle use profiles (workers who may or may not return home at lunch time,T
non-workers, etc.) would lead to inherently varied charging patterns, which helps limiting
consumption peaks associated with EV charging: in much the same way as people today do
not all turn their ovens or electric hobs on at the same time, even if no specific incentive is put
on EV charging, there would still be a natural spread of charging over time.

• he projected electricity generation mix in the long-term energy plan offers comfortableT
capacity margins by 2035 (nuclear power would still provide 50% of the overall electricity
generation, renewables would have been largely developed, and cross-border interconnection
capacity would have been developed by a factor of 2).

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9Results for CO2 emissions
The different studies (FNH, 2018; Agora Verkehrswende, 2018; ECF, 2019) comparing the life cycles ofinternal combustion engine (ICE) vehicles and electric vehicles generally agree on the environmental
analysis of e-mobility in terms of GHG emissions. The 2019 study confirms these results, taking due
consideration of all sources of emissions (in the power sector, during the manufacturing phase ofbatteries, under a large variety of scenarios, etc.). In a country like France, with an electricity systemalready nearly fully decarbonised, EVs or PHEVs with smaller batteries are the best solution in termsof carbon footprint (including life cycle analysis) as soon as the vehicle travels more than 30 000 to
50 000 kilometres over its lifetime (which is almost always the case as the average for a vehicle inFrance is 200 000 km).
This is to say that, in standard conditions, carbon emissions reductions at national level are
not offset by additional emissions in other countries due to the production of battery, even when
the latter are manufactured in countries where coal remains the dominant fuel for electricity
generation (China, Poland,2 etc.). Depending on the size, production sites, recycling rate and thetimescale considered, electric vehicles have a carbon footprint two to four times lower than ICE
vehicles.

Figure 9.2: Carbon footprint of a vehicle over its entire life cycle depending on the typeof engine, the electricity production mix and the distance travelled (reference year 2035)
45
40
35

tCO2 eq

30
25
20
15
10
5
0
Gasoline
vehicleElectric
Electric
vehicle vehicle low-carbon
gas electricity
electricity mix
mix(France)Electric
vehicle coal electricity
mix

Vehicle travelling 50 000 km over lifetime

Gasoline
vehicleElectric
Electric
vehicle vehicle low-carbon
gas electricity
electricity mix
mix(France)Electric
vehicle coal electricity
mix

Vehicle travelling 200 000 km over lifetime

Vehicle lifecycle

Battery lifecycle (with a recycling rate of 50%)

Electricity production lifecycle

Petroleum-based fuel lifecycle (extraction, distribution and combustion)

Source : RTE (2019).

This study also shows that additional benefits could derive from relocating battery production
in countries where the generation mix is nearly decarbonised, like France, concentrating on smallercars with lower battery storage capacity and higher recycling rates, and developing smart charging.

2.
As Poland is part of the EU-ETS mechanism, impact on CO2 emissions of Poland could have a loopback effect throughthe cap on EU-ETS. See Section 9.3 for a further discussion of the EU ETS.

170

9With 15.6 million EVs,3 the reduction in the carbon footprint of the transportation sector can be
expanded from circa 25 Mt CO2 /yr to 40 Mt CO2 /yr.

Cost assessment
The comparison between electric and ICE vehicles is a recurring debate. Whereas this is often seenfrom the consumer side (through the TCO, total cost of ownership), the methodology detailed in
Section 9.3 of this chapter leads to consider complete system costs for the two kinds of mobility.
For electric as well as ICE vehicles, the main cost components are the manufacturing phaseitself (including batteries for EVs), followed by maintenance and insurance over the lifetime. For ICE
vehicles, fuels (oil imports, refining and distribution) represent an important component, whereas
the charging infrastructure (that is yet to roll out in our countries, for private as well as for public
actors) is the key parameter for EVs (the cost of electricity generating the electricity used in cars isin itself not significant).
Based on this analysis and when excluding externalities, the overall economic cost of electricvehicles appears clearly greater than that of ICE vehicles – due to the cost of the batteries and of
charging infrastructures. The better engine efficiency of the electric motor is not enough to balancethese costs, meaning that the additional cost represents around EUR 10 000 per vehicle over its
lifetime. It is therefore through subsidies (including taxes on fuel) and stricter regulations that makeEVs attractive enough for consumers and for them to enjoy a relative expansion in use.
A full socio-economic assessment requires to value externalities in addition to material costs.
The first step is to integrate the costs of CO2 emissions: there exists an abundant literature on thecollective cost of GHG emissions (and a market price through the EU ETS). Going further requires tointegrate other externalities, such as noise, local pollution, resource depletion, etc., for which there
are no clear consensus references.
All things considered, the complete system cost of ICE is still lower than that of EVs for the time
being: valuing CO2 to a very high price and other externalities would be necessary to inverse thebalance. The turning point seems to occur around 2025. When integrating the long lead times ofsuch policies and the fact that the new vehicles sold today will still be on the roads in 10 years fromnow, incentivising consumers to switch to EVs immediately makes sense.
By 2030-2035,the conclusion is that – in line with other studies (Bloomberg, 2020; ECF, 2019;
OPESCT, 2019) – that the overall cost of electric mobility should be equivalent to the cost of traditionalcars using oil (in a range of 10% lower and 10% higher) even without integrating externalities. The
driving force behind this profound change is the expected drop in the cost of batteries.

3.

It also includes ~150,000 electric trucks and buses.

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9
Figure 9.3: Total annualised costs for 15.6 million vehicles by 2035 by engine type70

EUR billion/year

60
50
40
30
20
10
0
ICE vehicles (gasoline)

Electric vehicles

Low cost range –Crescendo high scenario –low-powered charging points variant

ICE vehicles (gasoline)

Electric vehicles

High cost range –Crescendo high scenario – basic case

Fuels (oil imports, refining and distribution)
Electricity generation
Connection of charging infrastructures and strengthening of transmission and distribution networks
Charging infrastructures (inc. installation, exc. connection)
Vehicle maintenance and insurance
Vehicles
Source : RTE (2019).

This allows a determination of the CO2 abatement cost for this policy, at around EUR 250 to
350/t CO2 avoided over the whole lifetime of the vehicle – a level which must be compared with theshadow price of CO2 and of other external effects (electric vehicles emit fewer fine particles than
ICE vehicles and therefore reduce the associated public health problems), and also compared toalternative ways of reducing emissions.
This result falls in line with results from previous studies. With the expected reduction in
the cost of batteries, the additional cost of e-mobility will fall, to an extent which is subject toconsiderable uncertainty (dependence on assumptions on changing costs, battery capacity, theprice of oil, etc.).
When integrating the level of uncertainty over EVs costs, the policy of developing electricvehicles seems very favourable: from no additional costs – and even a reduction in costs without
taking into account CO2 – to an abatement costs of EUR 200/t CO2 maximum by 2035 when taking
unfavourable assumptions. This last value might seem high compared to the current value of CO2
allowances on markets but it is actually well below the shadow price of carbon which has been
estimated in France at EUR 375/t CO2 in 2035 (Centre d’analyse stratégique, 2019).
Indeed, reducing GHG emissions appears more costly in the future as the easiest and cheapest
actions should have already been implemented.

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9
Figure 9.4: Estimated costs of reducing greenhouse gas emissions
by the electrification of transport
500
Shadow price of carbon (2035)

400

EUR/tCO2 avoided

300
200
100
0
by 2017-2020-100

by 2030-2035

-200
-300
Carbone 4 - decarbonisation barometer (November 2018) - EV vs ICE
CGDD - cost benefit analyses of electric vehicles - 2017 - vehicles in densely populated areas[RTE calculations]
CGDD - contribution for the estimation of shadow pric of carbon - vehicles in densely
areas populated: in densely populated areas
McKinsey - Swiss GHG abatement cost curve 2018
RTE (Crescendo high scenario - favourable assumptions)
RTE (Crescendo high scenario - unfavourable assumptions)
Source : RTE (2019).

Beyond this inversion in the costs assessment, several conclusions can be derived from thisanalysis. First, it appears that the specific cost item associated with electricity generation representsa small part of the cost of electric mobility but also something limited compared to the total costsof adapting the electricity system, which is itself only a fraction of the overall cost of a mobility
scenario. The annual electricity generation cost for electric mobility is estimated to be EUR 1 to
2 billion by 2035 for 15.6 million EV. And this cost of generating electricity for e-mobility can belargely optimised through smart charging. Even with simple smart charging devices (allowing
to start the charge at home at a specific moment of the day) leads to significant savings for theelectricity system, up to 1 billion euros. This can put some sense of perspective to headlines anddeclarations branding “electric mobility” as a revolution for the electricity system: it is surely asignificant change, but it will take some time before transport represents a significant share ofelectric consumption.
Second, smart charging of electric vehicles – even through relatively unsophisticated forms
of incentive like time-of-use tariffs – can play a role in reducing those costs. As long as EVs canbe frequently connected to the system, optimising EVs charging on a weekly basis is technicallyfeasible given the current capacity of batteries and the main uses of vehicles. If this theoreticalanalysis is confirmed by practical experience from users, EVs could prove a powerful tool for
operating the power system (by allowing to cope with renewable energy sources’ variability) as well
as a means to reduce GHG emissions.

173

99.5 Sector 2: Power-to-hydrogen – Decarbonising industry
and heavy transportation at more significant abatement costs
Context and rationale for public action
The development of synthetic gases, and in particular hydrogen, should play a significant role insector coupling and in energy transition.
The European Union has recently announced that hydrogen would be a priority area of theEuropean Green Deal and a key instrument for reaching net-zero greenhouse gas emissions by 2050.
In Europe but also in China, Japan and the United States, governments are currently drawing up
plans to foster the development of low-carbon hydrogen.
In its report, The Future of Hydrogen, published in 2019, the IEA states that “Hydrogen can help
tackle various critical energy challenges” (IEA, 2019b). Hydrogen is indeed often presented as both asource of flexibility and a key factor in achieving GHG emission reductions. These different reasons,however, should be clearly distinguished in prospective analyses.
In France, a first government Hydrogen plan was launched in 2018. It required RTE to undertake astudy of the issues for the French power system. This study was submitted to the minister of Energy
and published in January 2020 (RTE, 2020a).
The study distinguished two key time horizons for hydrogen production.
In the medium term, hydrogen offers a solution for reducing emissions of the industrial sector byreplacing the hydrogen today produced from fossil fuels and used in oil refineries, fertiliser plants
or other industries (chemicals, aeronautics, etc.). It also creates opportunities to reduce emissions
in the transport sector (for heavy transport and trains, as a replacement for oil) or gas networks.
In the long term, developing production and storage of low-carbon hydrogen can offer an
additional system flexibility solution, particularly interesting in view of scenarios with a significant
share of renewable energies in the power mix.
In France, as in other countries, the first step is therefore to develop in the coming years
significant volumes of low-carbon hydrogen production, in order to help reduce the emissions of theindustrial sector. Indeed, almost 1 million tonnes of hydrogen is used today in France (and around70 million tonnes of pure hydrogen in the world) by industry, and its production, widely based on
fossil fuels, accounts for significant quantities of GHG emissions: around 10 million tonnes of CO2
in France, which represents 2% of national emissions.

174

9Figure 9.5: Current consumption and production of hydrogen in France
Hydrogen consumption
Metallurgy(1%)

Hydrogen production
Steam methane
reforming(40%)

Others(4%)

Chemicals(10%)
Oxidation ofhydrocarbons(40%)

Substitutableproduction

Ammoniac
and fertilizers(25%)
Oil refining(60%)

Electrolysis of brine(5%)

Coal gasification(15%)

Source : RTE (2019).

The law on energy and climate passed in 2019 and the multiannual energy plan published by thegovernment in April 2020 outlines the objective to reach between 20 to 40% of low-carbon hydrogenby 2030.
As one the main options to produce low-carbon hydrogen today is based on the electrolysis
process which uses electricity, the achievement of the French public objectives requires significant
amounts of carbon-free electricity to support the development of this new process (up to 30 TWh in
15 years, which is almost 6% of contemporary total electricity demand in France).
Similar to the analysis on the development of EVs, this evolution does not raise any technicalconcerns with regard to system adequacy. Energy efficiency and expansion of renewables are
expected to ensure sufficient carbon-free electricity generation to cover electricity supply needed
for hydrogen production planned for 2030-2035. Furthermore, the flexibility of electrolysers givethem the ability to stop during peak periods, thus avoiding creating any specific problem in termsof the security of supply.

Results for CO2 emissions
Hydrogen is often presented as a no-regret option in terms of decarbonisation. Yet, contrary to thetransition towards electric mobility, simulations tell a more nuanced story – at least in the medium
term and when considered in a European perspective.
At first sight, the question of hydrogen production seems simple, and does not prompt debates
like those around the carbon footprint of electric vehicles given the initial process of manufacturing
batteries. The following analysis therefore focuses on the carbon emissions associated with theprocess of producing hydrogen.
On the perimeter of France, the carbon balance of a transition towards hydrogen production
by electrolysis is broadly positive, as fossil-produced hydrogen would be replaced by hydrogenproduced with low-carbon electricity.

175

9
But the complete analysis should also take into account the impacts on electricity exchanges
with other European countries.4 The balance at European level then appears less clear. Indeed, theuse of low-carbon electricity in France for hydrogen production is in competition with exporting
electricity and avoiding fossil fuel electricity generation (in gas or even coal plants) by neighbouring
countries, which have a more important impact on GHG emissions than the current use of natural
gas to produce hydrogen in France.

Figure 9.6: GHG emissions avoided (and their location) by the generation of 1 kWhof carbon-free electricity in France depending on its use
900
800
700

gCO2

600
500
400
300
200
100
0
Replacementof an oil-fired
boiler by
an electricheat pump

Electricity
exports
helping
to reducecoal power
generationin EuropeUsefor electric
mobility

Replacementof a gas-fired
boiler by
an electricheat pump

Electricity Replacement Replacement Replacement Replacement
exportsof steamof an oil-fired of gas-firedof steam
helpingmethaneboiler bymethaneboiler by
to reduce
reforming
an electricreforming
an electricgas power by electrolysis resistanceand CCS
resistancegeneration
heatingby electrolysis
heating(CCGT)
in Europe

Source : adapted from RTE (2019).

As a consequence, to ensure a positive carbon balance at European level, the development ofhydrogen production should be complemented with the development of a corresponding volume ofcarbon-free electricity generation.
Adding all these effects, the development of low-carbon hydrogen, as planned by the Frenchgovernment, would reduce GHG emissions by at least 5 million tonnes per year in 2035, due to thereplacement of steam methane reforming. This number could be increased when considering thepossibility to use hydrogen for heavy transport or directly in the existing gas grid as a substitutefor the fossil fuel natural gas.

4.
See Section 9.3 where the way the ETS mechanism affects global emissions is discussed. The ETS mechanism indeed
ensures a global level of emissions and with an unchanged cap a reduction in one sector will be offset by emissions in anothersector.

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9
Cost assessment
The economic analysis of the transition towards low-carbon hydrogen also needs to take into
account all cost components. The main components are the costs of production equipment(electrolysers) but also the costs of electricity generation needed which could represent up to 60%
of total costs of producing low-carbon hydrogen.
More precisely, the distribution of costs largely depends on the operating mode of electrolysers.
If they are used only when electricity is totally carbon free and prices are low i.e. when margins on
nuclear and renewable generation are available (operating mode 1 in Figure 9.7 below), their run time
would be limited even in 2035 with almost 50% renewable energy sources in France, and the main
driver of hydrogen production costs corresponds to the fixed costs of electrolysers (amortisationof CAPEX + OPEX). In such cases, costs related to electricity generation are not significant as theflexibility of electrolysers contribute to the economic optimisation of the power dispatch. On the
opposite, if electrolysers are running in baseload mode (operating mode 2 in Figure 9.7 below),then the amortisation of investment costs of electrolysers is not really significant in the economic
assessment and the major part of the costs corresponds to electricity generation (fixed and variable).
From a social welfare perspective, the comparison of production costs between electrolysis andsteam reforming appears very dependent on the value of GHG emissions, but also on the operating
mode of electrolysers. For a hypothesis of low CO2 valuation (EUR 30/t, close to the current priceon the ETS market), the full cost of electrolysis appears far above that of steam reforming. This
explains why the hydrogen used today is still of fossil origin. On the other hand, when retaininga high value for environmental externality electrolysis generally appears cheaper. For example,
by considering the shadow value of CO2 which is considered to reflect the long term cost of CO2
emissions is estimated at around EUR 375 /t in 2035 in France (Centre d’analyse stratégique, 2019).
This shows that it is relevant, from a socio-economic point of view, to substitute electrolysis forsteam reforming in France in the next 15 years.

Figure 9.7: Comparison of costs of producing hydrogen between different production modes
8
6.7

7

EUR/kg H2

6
4.9

5
4

3.0

3
2

3.5

1.8

1
0
CO2 value at
EUR 30/t

CO2 value at
EUR 375/t(=shadow value of
CO2 in 2035)

Steam methane reforming
Hydrogen storage costs
Grid costs
Electricity generation net costs (fixed and variable)
Electrolysers fixed costs

Operating mode 1: Operating mode 2: Operating mode 3:
baseload except coupling with local
when nuclear andduring extreme
RES generation
PV production
peak situations
margins areavailable

Electrolysis

CO2 costs
Gas costs
Steam methane reforming fixed costs

Source : RTE (2020a).

177

9It should be noted however that these figures refer to production costs of hydrogen in termsof societal welfare (i.e. net costs for all stakeholders involved, including socio-environmental
externalities, as presented in Box 9.1) and not to the market price of producing hydrogen. Indeed,
the cost from the point of view of a hydrogen producer could differ from societal welfare due to theeffect of specific market arrangements, taxes or subsidies. In particular, the analysis shows thatparadoxically, the increase of the CO2 price on the ETS market would not necessarily encourage thedevelopment of low-carbon hydrogen in France as it would result in an increase of power prices andtherefore an increase of electrolysis costs, which may be higher than the increase of costs for steam
methane reforming. This effect comes from the fact that power prices are fixed mainly at European
level with the operation of the interconnected power system, and still largely depend on fossil fueland CO2 prices, even if the French power mix is almost totally low-carbon.
In the broader public debate, this result can lead to two opposing interpretations. On one
hand, some would support that it reflects a well-functioning market which allows an economic
optimisation and a co-ordinated effort to reduce emissions at the European level. In this respect,
it would be understandable that carbon-free electricity from nuclear and renewables in France isexported and used to reduce fossil-fuel power generation in neighbouring countries as a priority,
before developing low-carbon hydrogen in France. On the other hand, the distributional effects
appear to some as are too important not to be taken into account, and it is politically problematic
to consider that the well-functioning carbon markets will make it unprofitable to develop new uses
for low-carbon electricity in a country that has plenty of it. Accordingly, it is difficult to believe that
European member states would agree to develop low-carbon generation in their country just to
reduce emissions from the power sector in other member states. The economic assessment should
therefore include an analysis on redistributive effects, in particular between different member
states. For instance, member states that bear the financial support and environmental impactsof developing renewable generation would expect that it is useful for their energy mix, or at leastthat redistributive mechanisms would ensure a fair sharing of costs (which is not consistently
the case with the European energy market). It is interesting to note that such an issue did notoccur for electric vehicle: the efficiency of the electric vehicle makes it more efficient to use lowcarbon electricity to decarbonise transportation at national level rather than power generation inneighbouring countries.
Furthermore, the figures presented above relate solely to the hydrogen production costs anddo not include the costs associated with the conversion of energy uses to hydrogen. In fact, theeconomic assessment differs whether considering existing uses of hydrogen (in refineries or
fertiliser plants for instance) and new uses of hydrogen (for instance in heavy transport). For the
existing uses of hydrogen, there should be no additional costs of conversion to low-carbon hydrogenas the equipment used today is already designed to use hydrogen. This might also be true, to acertain extent, for the use of hydrogen in the gas grid: the existing gas grid can integrate small
quantities of hydrogen but higher volumes would induce investments for the adaptation of the grid.
In contrast, for new uses such as the conversion of trucks, trains or industry processes to hydrogen,additional equipment costs should be included in the economic analysis (in particular, the extra
cost of a hydrogen vehicle compared to a conventional vehicle). Current studies conducted by RTE
on long term scenarios will provide economic assessment on the development of these new uses
for hydrogen.

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99.6 Sector 3: Heating in buildings – The need to co-ordinate policies
to advance buildings renovation and decarbonisation
Reducing the carbon footprint of buildings is a cornerstone of any policy aiming at achievingcarbon neutrality. In Europe, in general, heating relies mainly on fossil fuels (coal, fuel, and natural
gas through direct use or district heating networks), and is responsible for a large part of carbon
emissions.
In France, buildings are responsible for 43% of total energy needs, and heating only amounts
to 25% of energy needs. Buildings are responsible for around 80 MtCo2eq per year (including circa
65 Mt CO2eq per year for heating), that is to say 15% of GHG emissions at national level.
Contrary to several European countries, electric heating has been developed in France, mainly
as from the 1980s. Yet natural gas remains the dominant fuel (it amounts to 40% of final energy
consumption in heating in the residential sector), above domestic wood (27%), electricity (15%) and
fuel oil (12%). Nowadays, electric heating tends to be more widely used in smaller apartments andhouses.
Basically, the question of buildings has been addressed though two different frameworks.
The first idea is to largely reduce energy needs. This led to very strict regulations for newbuildings in terms of primary energy consumption, and ambitious plans to renovate and insulate
existing buildings. Given that this sector is characterised by a strong inertia (most of the buildingsof 2050 already exist), it is for existing buildings that the ambition is the most challenging. The lawof 2015 provides for a very ambitious objective that all buildings would be equivalent on average,by 2050, to new ones (low-energy buildings).
The second idea is to decarbonise heating fuel. This requires a switch to low-carbon solutions,as heat pumps, domestic wood, biogas or district heating and cooling networks using renewables
or waste. In any case, this means more direct uses of electricity in buildings, or the development of
power-to-heat solutions. In France, the National Low Carbon Strategy plans to develop massively
the use of electrical heat pumps for building heating. Following this strategy, the share of electricity
in the final energy consumption for heating would increase sharply from 15% today to around 30%
in 2050 (the rest being heat from the environment, solid biomass fuels, biogas and district heating
fueled with renewables or waste).

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Figure 9.8: Projections on the evolution of heating systems in residential buildings in France
between 2018 and 2035, according to the directions of the National Low Carbon Strategy

Source : RTE, based on data from DGEC, National Low-Carbon Strategy, 2020.

This prospect of further electrification has prompted a policy debate, which is not concluded.
In the recent past, the development of electricity in buildings through electric heaters (convectors)
has prompted an increase in peak load and sensitivity to temperature (2 300 MW/°C in winter). This
led to complex and somewhat conflicting views about the ability of the power sector to integrate
more electric heating and about the true balance of electrification in terms of its carbon footprint.
In this context, RTE is currently carrying out a study with the French Agence de la Transitionécologique (ADEME) on the impacts of the different measures aimed at reducing GHG emissions ofbuildings. Similarly to the other studies mentioned in this chapter, this work is based on precise
simulations performed by RTE, taking into account the whole European power system and modellingthe supply-demand balance with an hourly resolution for many meteorological scenarios. Thismethodology is necessary to assess actual and potential impacts of the development of electricheating on the GHG emissions of the power sector and on peak load.

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The provisional results show that electrification and an ambitious renovation programmes areboth necessary to reduce emissions by 50% by 2030, and by nearly 100% by 2050. They also showthat despite the development of electricity-based solutions for heating, peak load would be broadly
stable France in the next 15 years, with a small increase or a small decrease depending on various
parameters (flexibility on EV charging, the pace of development of energy efficiency measures, etc.).
This result is explained by the fact that the impacts of new uses of electricity would be offset byenergy efficiency gains, not only in heating but also in other existing uses (household appliances,
lighting, etc.).
Regarding CO2 emissions, electrification of heating, per se, is a no regret option for reducing
emissions in France and in line with the current trajectory proposed in the National Low Carbon
Strategy. At national level, there is no situation in which developing electric heating would leadto increase CO2 emissions, through for example a higher use of gas-fired generation units. The
sharp increase in renewable electricity generation makes it possible to accommodate most of theadditional heating consumption with low-carbon production. In particular, ending the use of fuel oilin the residential sector (around 20 Mt CO2 /yr) provides benefits that largely outweigh an eventual
small increase in fossil fuels use for electricity generation.
When considered at the European level, as it was the case for power-to-hydrogen, there isan inherent conflict between decarbonising electricity generation through French exports, anddecarbonising heating in France. If carbon-free electricity generated in France is used to replaceoil-fired boilers, the environmental benefits appears clearly greater than if this power is exported
in neighbouring countries to reduce electricity generated from gas (same result as for the transition
towards electric mobility). But if it is used to replace gas boilers, the result at the European levelappears less clear and would depend on the energy efficiency of the buildings and the heating
systems considered. Development of carbon-free electricity in France, in support of the developmentof electricity-based heating, would however ensure a positive carbon balance at the European levelin all cases.
As for the economic assessment, provisional results of the forthcoming RTE-ADEME study showthat the reduction of CO2 emissions in the buildings sector will require massive investments forthe renovation of buildings and the conversion of heating systems, around EUR 500 billion over the
next 15 years (taking into account public and private investments). However, when annualising thebenefits over the entire life cycle and comparing to the costs of a business-as-usual scenario, theseinvestments appear economically relevant to reduce greenhouse gas emissions (CO2 abatement
costs are still to be estimated but provisional results reveal a value lower than the shadow price ofcarbon for 2035 in France).
In comparison with the previous studies on electric mobility and hydrogen, the economic
assessment is however complicated by various factors, which makes it difficult to calculate aprecise value for CO2 abatement costs.
First is the fact that consumers are likely to seek an improvement of their comfort after arenovation of their houses, for example by increasing their heating temperature (as the energy
necessary to obtain this temperature has been reduced with the renovation). This phenomenon,
also referred as the “rebound effect”, has been widely documented in recent years but its magnitude
still remains uncertain. When taking it into account, the emission reductions might appear lower
than expected, which results in an increased abatement costs.
Secondly and related to this effect, is the integration of the different externalities that areprovided by building renovation. For instance, a better building insulation or a more efficientheating system can bring a better comfort for occupants and even prevent some health problems,
which is valuable for society as a whole.

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9Another example is the difficulty to estimate the costs of renovation as it refers to a large varietyof operations (window replacement, insulation of walls or roof, replacement or conversion of theheating system, etc.) and as costs are often not proportional to the number of operations completed(it may be more profitable to go for a “deep renovation” with many operations combined but it also
requires a more important initial investment).
Eventually, a comprehensive analysis of the costs of benefits of transformations of the buildings
sector, taking into account all the implications mentioned and including numerous sensitivity
analyses, is therefore needed. This is the object of the forthcoming RTE study about heating inbuildings.

9.7 Comparative analysis on the decarbonisation costsin the different sectors
The three dedicated studies on new uses for electricity eventually provide a detailed overview on
the benefits of e-mobility, power-to-hydrogen and renovation of buildings/electric heating in termsof CO2 emissions reduction, their impacts on the power system and the costs associated with theseconversions for the next 15 years.
A preliminary comparative analysis is displayed in Table 9.1.
It shows that one of the most efficient ways to cut down emissions is to replace internal
combustion vehicles with electric ones. It leads to an amount of avoided emissions (at least -25 Mt
CO2 /yr) two to five times superior than the evolution of hydrogen and electric heating.
This result reflects the fact that the transport sector is today the main sector emitting GHG in
France and that there is an important potential to reduce emissions. The building sector is also oneof the main emitting sectors but it is characterised by slow changes (the renewal rate of buildings
is around 1%/year compared to around 6%/year for the light vehicle fleet).
As for the economic results, the analysis shows that the overall costs of transition differ greatly
between the three sectors. In the automotive sector, the conversion to e-mobility involves not
only the adaptation of the power sector but also the conversion of thermal cars to electric ones,
the construction of charging infrastructures and their connection to the grid. This leads to total
annualised costs of several tens of billion euros per year, with costs of the power sector representing
only a small fraction of these total costs. In the same vein, decarbonisation of the building sector
involves the evolution of heating systems and the insulation of walls, roofs and windows. On theother hand, conversion to low-carbon hydrogen for existing uses in the industry “only” requires thereplacement of steam methane reformers by electrolysers, thus leading to lower overall costs andto a higher share of costs of the power sector.
However, high overall costs do not necessarily keep electrification of these sectors from being
considered. These overall costs should indeed be compared to the ones that would have occurredin a business-as-usual scenario. Hence, the costs of electric mobility (including the costs of electricvehicles) should be analysed against the costs of internal combustion vehicles and associated fuels.
Likewise, the costs of electric heat pumps in the buildings sector should be compared to the costsof conventional fossil-fueled boilers.
This is the difference of these two values, which can be referred to as “net costs”, put in relation
to the impact on GHG emissions (in tonnes of CO2 equivalent), that constitutes the “abatement
costs” and helps identifying the priority actions in the fight against climate change.

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9For the transport and hydrogen sectors, studies show that abatement costs, while being largely
dependent on the evolution of costs of the different technologies (for instance evolution of costsof electric vehicles or electrolysers), will certainly be lower than the shadow price of carbon
recommended in France (EUR 375/t in 2035).

Table 9.1: S ummary of technical, economic and environmental impacts of developing new usesof electricity
E-mobility

Low-carbon hydrogen

Buildings/heating

Contemporary GHG
emissions in France~125 Mt CO2eq/yr(road transport)~10 Mt CO2eq/yr(hydrogen in industry)

Public objectives

1 million EVs by 2022
~15 million by 2035 i.e. ~40%of light vehicles

20 to 40% of low-carbonhydrogen in 2030,
corresponding to around 200
to 400 kilotonnes per year

~65 Mt CO2eq/yr(heating in buildings,
exclusive of electricity)
Ending the use of oil-fired
boilers by 2028
Reducing the emissions ofbuildings by 50% by 2030

Additional electricity
consumption↑~40 TWh in 2035↑~30 TWh in 2035↑~6 TWh in 2035 (effectAdditional peak-load↑~4 GW in the high scenario ↑~ 0 GW

GHG effect ofelectrification

CO2 abatement costs(+/- 10 TWh)(~0 → ~40 TWh)for 2035
Between ↓5GW
(contribution to capacity
needs via V2G) and ↑8 GW
depending on the flexibility of
EV charging
Between -25 Mt CO2 /yr and-40Mt CO2 /yr, depending on
batteries manufacturing, size,
recycling, etc.
Between EUR -200/t CO2
and EUR 200/t CO2 in 2035
depending on the evolutionof costs of EVs(+/- 15 TWh)(~0 → ~30 TWh)(as electrolysers are flexible)of electrification of new
and existing building only,
without effect of thermal
insulation)(51 TWh5 → 57 TWh)↑~4 GW in 2035

- 5 Mt CO2 /yr

Around -7 Mt CO2 /yr

Between EUR 144/t CO2
and EUR 230/t CO2

To be developed in nextreport9.8 Energy system integration analysis and public policy making
for decarbonisation: Next steps
This work should now be extended to a longer term perspective, to set the path to carbon neutrality.
As members of the European Union are now committed to reaching net-zero emissions by 2050 with
the European Green Deal, energy system integration and sector coupling will be further developed
and accelerated.

5.
Situation in 2035 considering the effort in thermal insulation defined in the Low-Carbon National Strategy but considering
no new house using electricity for heating

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In France, the National Low Carbon Strategy (SNBC) provides guidance to reach the objectiveof carbon neutrality. It specifies an emissions trajectory for the different sectors. One of the main
outcomes is that new electricity uses are to be developed further in the period 2030-2050, especiallyin the industry, transport and buildings, along with much energy efficiency. Biogas and biofuels
are also to be developed but their capacities are limited by the potential of national sustainablebiomass. As a consequence, even though some current uses of electricity will be reduced thanks
to energy efficiency, the development of new uses of electricity and sector coupling will drive
electricity consumption up in the long term, to an extent which will be determined by the successor the failure of energy efficiency actions and by consumers’ response to prices, regulations andsocial trends.
While the National Low Carbon Strategy provides detailed trajectories for the evolution of gas,hydrogen or power demand, it does not specify the power mix that should be implemented tocover electricity needs. Yet, the power system should evolve not only for the development of newelectricity uses but also for the replacement of current nuclear reactors. Most of these nuclear power
plants, which produce today around 70% of power in France, are supposed to be decommissioned
between 2030 and 2050.
Two main options are considered – replacing part of the current nuclear fleet by new nuclear
reactors in addition to renewables, or only developing renewable energies. Those two options stillneed to be assessed in detail (in terms of costs, technical and environmental aspects, etc.). Thisis being addressed in an extensive study on 2050 scenarios, steered by the RTE and carried out in
consultation with all stakeholders (RTE, 2020b).

9.9 Conclusion
Planning an effective decarbonisation requires to go from sector-specific to an economy-wide
assessment of GHG emissions and costs. This means taking into account first the implication of eachpolicy options regarding electricity generation (hence the need to adopt robust assessment methods,
such as going from LCOE to VALCOE, and from VALCOE to complete system cost comparison), andthen the dynamic change it can drive for other sectors. The studies led by RTE help to clarify theissues and to assess the economic and environmental costs and benefits of evolutions in different
sectors: electric mobility, hydrogen (in the industry), heating for the building sector, and pave theway for full evaluation of policy choices to attain net zero carbon emissions by 2050.
Evaluating the cost efficiency of different energy policies raise some methodological difficulties
but a broad-based effort from energy economists in France, Europe and the rest of the world
through the IEA and NEA, brings a common framework to compare the socio-environmental valueof climate actions.
As many countries are now targeting net zero emissions for the second half of the century as
required by the Paris Agreement, this work should be pursued to determine priority actions toaddress climate change in the most efficient and rapid way.

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References
Agora Verkehrswende (2018), “Technology Neutrality for Sustainable Transport”, https://static.
agora-verkehrswende.de/fileadmin/Projekte/2019/Technologieneutralitaet/Agora-Verkehrswende_Technology-Neutrality-for-Sustainable-Transport.pdf.
BNEF (2020), “Electric Vehicle Outlook 2020”, Bloomberg New Energy Finance, https://about.bnef.
com/electric-vehicle-outlook-table-of-contents/.
Centre d’analyse stratégique (2019), The value for climate action – A shadow price of carbon for evaluationof investments and public policies - Report by the Commission chaired by Alain Quinet, La Documentationfrançaise, Paris.
ECF (2018), Fuelling Europe’s Future: How the transition from oil strengthens the economy, https://
europeanclimate.org/resources/fuelling-europes-future-how-the-transition-from-oil-strengthensthe-economy/.
ENTSO-E, ENTSO-G (2020), “TYNDP 2020 Scenario Report”, European Network of Transmission
System Operators for Gas and European Network of Transmission System Operators for Electricity,www.entsos-tyndp2020-scenarios.eu/.
EC (2018), “In-depth analysis in support on the COM(2018) 773: A Clean Planet for all - A European strategic long-term vision for a prosperous, modern, competitive and climate neutral
economy”, European Commission, Brussels, https://ec.europa.eu/knowledge4policy/publication/
depth-analysis-support-com2018-773-clean-planet-all-european-strategic-long-term-vision_en.
FNH (2017), “Quelle contribution du véhicule électrique à la transition énergétique”,
Fondat ion
Nat ureHomme,
w w w. f o n d a t i o n - n a t u r e - h o m m e . o r g / m a g a z i n e /quelle-contribution-du-vehicule-electrique-la-transition-energetique.
IEA (2020), Sustainable Recovery, IEA, Paris, www.iea.org/reports/sustainable-recovery.
IEA (2019a), World Energy Outlook 2019, IEA, Paris, www.iea.org/reports/world-energy-outlook-2019.
IEA (2019b), The Future of Hydrogen, IEA, Paris, www.iea.org/reports/the-future-of-hydrogen.
NEA (2019), The Costs of Decarbonisation: System Costs with high Shares of Nuclear and Renewables, OECD
Publishing, Paris, https://doi.org/10.1787/9789264312180-en.
NEA (2012), Nuclear Energy and Renewables: System Costs in Low Carbon Energy Systems, OECD Publishing, Paris.
OPECST (2019), “Les scénarios technologiques permettant d’atteindre l’objectif d’un arrêt de lacommercialisation des véhicules thermiques en 2040”, Office parlementaire d’évaluation des choix
scientifiques et technologiques.
RTE (2020a), “La transition vers un hydrogène bas carbone – Atouts et enjeux pour le système électrique à l’horizon 2035”, Réseau de Transport d’Électricité, Paris, www.rte-france.com/sites/default/
files/rapport_hydrogene_vf_2.pdf.
RTE (2020b), “Groupe de travail n° 3 Cadrage et scénarisation – Les principes de construction des scénarios pour étudier le système électrique à l’horizon 2050”, Réseau de Transport d’Électricité,Paris,
www.concerte.fr/system/files/document_travail/GT%20Sc%C3%A9narios%202050%20-%20document%20de%20travail%20-%20v1.pdf.
RTE (2019), Integration of electric vehicles into the power system in France – Main results, Réseau deTransport d’Électricité, Paris, www.rte-france.com/sites/default/files/integration_of_electric_vehicles_into_the_power_system_report.pdf.
RTE (2017), “Generation Adequacy Report on the electricity supply-demand balance in France – 2017
edition”, (Bilan prévisionnel 2017), Réseau de Transport d’Électricité, Paris, www.rte-france.com/
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Villavicencio (2017) “A capacity expansion model dealing with balancing requirements, short-term
operations and long-run dynamics”, Université Paris-Dauphine Working Paper No. 25.

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Chapter 10

Hydrogen: An opportunity
for the power sector

10.1 The unprecedented momentum of hydrogen
The global energy sector is facing an unprecedented transformation in the pursuit of sustainability.
This brings considerable challenges, such as the integration of high shares of variable renewableelectricity generation or the decarbonisation of the industrial sector with limited technologicalalternatives to fossil fuels. Tackling these challenges will require the contribution from an ample
suite of solutions, among which is hydrogen. Hydrogen is a very versatile fuel that can play asignificant role in many sectors across the energy system, which is why it has attracted strong
interest from a diverse range of stakeholders. National and local governments, electricity suppliers,
manufacturing industries, oil and gas companies, electricity and gas utilities or automakers are
just some examples of the stakeholders that have expressed interest in implementing differenthydrogen technologies to contribute to their decarbonisation targets. The reasons for this interest
can be found in the potential that hydrogen presents to help overcoming several of the challenges
that the energy system is facing:• ydrogen can help integrating greater amounts of variable renewable energy, whoseHavailability is not always well matched with demand. The generation of hydrogen throughelectrolysis powered by renewable energy is a promising alternative for storing electricity
over long periods (days, weeks or even months). Hydrogen can be used for generating back-up
power in periods of high demand and low renewable generation.

•

 ydrogen offers ways to deliver emissions reductions in sectors that are proving difficult toHdecarbonise, such as long-haul transport or industry. The utilisation of low-carbon hydrogencan reduce the carbon footprint of these sectors. Moreover, the use of hydrogen produced byelectrolysis can contribute to the development of a more integrated energy system.

• ydrogen can also help improving air quality since its combustion does not emit certainH
air pollutants (such as particulate matter or sulphur dioxide) and its utilisation in fuel cells
avoids the generation of any air pollutants, including nitrogen oxides. This is particularly
important in urban environments, where air quality has become an important concern for
public health.

• ydrogen can help improving energy security due to the variety of sources that can be usedH
for its production and its flexibility of use. Hydrogen can be produced from all fuels: naturalgas, coal, oil, renewables, or nuclear. In addition, it can be directly used or converted into
other products with different potential applications such as ammonia, synthetic methaneand synthetic liquid fuels.

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10
However, this is not the first time that hydrogen has generated high expectations. Hydrogen
was in the spotlight in the 1970s due to the oil price shocks, and in the 1990s and early 2000s
when concerns about climate change took centre stage. Hydrogen did not manage to fulfil the
expectations created on these occasions due to the recovery of low oil prices and the lack of strong
climate drivers. However, in the current scenario, with more developed hydrogen technologies anda greater attention to deliver deep emission reductions, the outcome could be different. For thispossibility to be realised, hydrogen has several key challenges to overcome:• lmost all current hydrogen supplies come from natural gas and coal, with considerableA
associated carbon dioxide (CO2) emissions. The production of hydrogen emits more than 800
million tonnes of CO2 per year (Mt CO2/y) – equivalent to the CO2 emissions of Indonesia andthe United Kingdom combined. Hydrogen can become a strong decarbonisation actor only if
its production is based on low-carbon routes, such as fossil fuels coupled with carbon capture
and use/storage (CCUS) or using low-carbon electricity.

• roducing low-carbon hydrogen is still costly. The technologies for low-carbon productionP
cannot compete with conventional hydrogen production technologies yet. However, theprospects look promising thanks to lessons learnt from the development of other low-carbon
technologies such as solar panels, wind turbines or electric vehicles.

• he development of hydrogen infrastructure is slow and is holding back widespread adoption.
T
The final hydrogen price for consumers strongly depends on the costs of the delivery ofhydrogen which itself depends on the infrastructure available and its optimum utilisation.
The deployment of robust infrastructures to minimise these costs requires planning andco-ordination among governments at different levels (national and local), industry andinvestors.

•

 urrent regulations are limiting the development of a clean hydrogen industry. For example,
C
certain regulations on hydrogen limits in the natural gas grids are a barrier for scaling
up demand of low-carbon hydrogen. The adoption of international standards can help
harmonising regulation and facilitate trade.

In this chapter, we provide an overview of the current situation of the hydrogen sector and theopportunities that are arising in the short/medium term, and could potentially arise in the longterm, for the power sector to benefit from the big momentum that hydrogen is showing.

10.2 Hydrogen demand and potential for the future
Hydrogen has received the nickname of the “fuel of the future” but it has a long history as part ofthe energy system and it is already a big industry. The demand for pure hydrogen has increased
more than threefold since the 1970s to reach around 75 million tonnes of hydrogen per year today(IEA, 2020). Demand comes mainly from oil refining and ammonia production (accounting foraround 95% of the demand almost in equal shares) and other applications that require hydrogen inprocesses with very low tolerance to contaminants. In addition, certain industries also use hydrogenas a part of a mixture of gases typically called synthesis gas (syngas, composed by hydrogen,carbon monoxide, carbon dioxide and other light hydrocarbons). The demand of hydrogen in theseprocesses has also increased significantly since 1975, currently accounting for around 45 Mt H2 /y,mainly from chemical production and the iron and steel sector ( ).

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Figure 10.1: Evolution of global annual demand of hydrogen (pure and mixed) since 1975

Million tonnes of hydrogen

80

Pure
Refining
Ammonia
Other pure

70
60
50

Mixed
Methanol
DRI
Other mixed

40
30
20
10
0

1975

1980

1985

1990

1995

2000

2005

2010

2015

2019

Source: IEA (2020).

Hydrogen uses today
Oil refining is currently the main source of hydrogen demand (39 MtH2 /yr). Hydrogen is used inhydrotreatment and hydrocracking to remove impurities (especially sulphur) and upgrade heavy
fractions into final products. The demand for hydrogen for refining purposes has grown substantiallyover time due to a larger demand for oil-derived products and also due to stricter fuel specifications,which led to lower acceptable levels of sulphur content. This growing trend is expected to continue
in the future since oil-derived products demand is growing and the International Maritime
Organisation has introduced fuel regulations limiting sulphur content to 0.5% from 2020. Beyond
2030, the pace of hydrogen demand growth is expected to slow down as oil demand is impacted by
a combination of efficiency improvements and electrification (IEA, 2019). However, these trends forhydrogen demand in oil refining are quite uncertain due to the high impact of the COVID-19 crisisand how quickly and strongly the sector will recover from the economic downturn. Recent IEA
forecasts suggest declines for gasoline (9%), diesel (6%) and jet fuel (37%) consumption in 2020 (IEA,
2020a; IEA, 2020b) which would lead to a drop in H2 demand for oil refining to 36 Mt H2 in 2020 (close
to 7%). Most of the hydrogen consumed in oil refining is mainly produced on-site, as a by-productof catalytic naphtha reforming or by dedicated generation through steam methane reforming,with some contribution from coal gasification in areas with access to low-cost coal (like China).
The remainder, which accounts for around a quarter of the demand, is procured from merchantsuppliers, and it is often produced via steam methane reforming as well.
The chemical sector is another large consumer of hydrogen, with ammonia and methanol production being the second and third largest sources of hydrogen demand (33 Mt H2 /yr and 14 Mt H2 /yr
respectively). The synthesis of these chemicals requires large quantities of hydrogen since it is partof their molecular structure. Most of the ammonia currently produced (80% of global production) isused to manufacture fertilisers, with the remainder used in other manufacturing industrial applications like explosives or specialty materials. Around two-thirds of methanol production is usedin different chemical synthesis processes including formaldehyde, methyl methacrylate, solventsand larger organic chemicals with the reminder used as a fuel, blended in pure form or used forconversion into gasoline. Ammonia and methanol demands for these applications are also expected
to grow in the future. Projected demands reach 38 Mt H2 /yr for ammonia 19 Mt H2 /yr for methanolby 2030. However, the COVID-19 crisis has also affected the chemical sector, generating uncertainty
over short-term prospects of future demands. Methanol and ammonia producers estimated adecrease in demand of 7% and 5% respectively in 2020 compared to 2019. Similarly to the case of oil
refining, the chemical industry relies on on-site natural gas reforming to meet its hydrogen demand
due to its higher efficiency compared with coal gasification. Coal gasification is practically limited
to China due to the lower cost of coal compared with natural gas. Hydrogen is also generated as aby-product in chlor-alkali processes and in the production of some high-value chemicals.

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In steel manufacturing, the direct reduced iron steel production (DRI) is another large sourceof hydrogen demand, currently accounting for 6 Mt H2 /yr. DRI uses hydrogen mixed with othergases (like carbon monoxide), although some pilot projects are studying the feasibility of using
pure hydrogen (HYBRIT, 2020). Roughly three-quarters of the hydrogen demand for DRI is supplied
using natural gas reforming while the remainder is produced through coal gasification technologies.
Steel demand is expected to grow globally (around 6% by 2030) as a consequence of the increase
in infrastructure needs due to the growth in population in developing regions. This will certainly
affect the demand of hydrogen, although it is difficult to determine the extent of this impact due
to the influence of factors like the share of the different steel manufacturing technologies and theextent to which pure hydrogen can be used in DRI. Again, the COVID-19 crisis has a strong impact
in the steel sector. The World Steel Association forecasts that steel demand will decrease by 6.4%
in 2020 with an expected partial recovery in 2021 (WSA, 2020). In India, the largest DRI producing
country, the decrease will be particularly sharp, with an expected 18% drop in 2020.
The evolution of the COVID-19 crisis in coming months will shape the trends of hydrogen
demand in the near future in traditional hydrogen consuming sectors. Factors such as social
distancing or a potential second wave of the pandemic with further lockdowns will determine how
quickly these sectors can recover to pre-crisis levels of activity and hydrogen demand can resume
its previous trends.

Emerging and new uses of hydrogen
In addition to the traditional demand sectors, hydrogen has been identified as a clean fuel withthe potential to deliver greenhouse gas (GHG) emissions savings in several sectors. Some of these
sectors are already increasingly demanding hydrogen and can become considerable sources ofhydrogen demand in the near future. The new hydrogen applications with higher potential to
become sources of large hydrogen demand are:

190

1.

Road transport. Hydrogen has long being heralded as a potential clean transport fuel, although
the current uptake of fuel cell electric vehicles (FCEVs) is very low. There has been a significant
acceleration in the uptake of hydrogen in road transport since 2017 and new opportunities
are appearing in other sectors that are particularly difficult to decarbonise, such as shipping
and aviation. The use of both hydrogen itself (long-haul transport, maritime) and hydrogenderived fuels (ammonia in shipping and synthetic hydrocarbons in aviation) are promisingalternatives to fossil fuels.

2.

Power generation. Hydrogen plays a negligible role in the power sector today, accounting forless than 0.2% of electricity generation. However, the use of hydrogen for flexible power
generation and long-term energy storage or the co-firing ammonia in coal-fired power plants
to reduce their CO2 intensity appear as promising opportunities for hydrogen in the future.

3.

Domestic heat. Buildings accounts directly and indirectly for 30% of the world final energy
consumed. Direct emissions from fossil fuel combustion for space conditioning, water
heating, cooking and other service applications account for about 3 Gt CO2. Decarbonising theenergy demand in buildings is a very complex task and it is very likely that it will require thefuture coexistence of a variety of energy sources and technologies, among which hydrogenis included.

4.

Industrial high-temperature heat. The majority of the energy demand for high-temperature
heating in the industrial sector comes from steel and chemical industries. However, there is
still a large demand in other industries, such as cement. Direct electrification is extensively
used in many high-heating applications, but some large-scale processes (like cement kilns)
are particularly difficult to electrify and CCUS and hydrogen seem to be the only technologicalalternatives to enable decarbonisation.

10
These sectors have not avoided the impact from COVID-19 crisis. FCEV sales dropped in the first
four months of 2020, compared with the same period in 2019, by 7%, 12% and 65% in China, Japanand the United States, respectively. In addition, tapping into the maximum potential of hydrogenas a clean energy solution in some applications, such as industrial high-temperature heat or power
generation, relies on the progress of many demonstration projects currently under development.
These could be delayed or even cancelled because of the economic downturn caused by the crisis.
However, only minor delays due to slower activity caused by lockdowns have been announced andeven some technology suppliers have announced plans to accelerate the development of hydrogentechnologies. This is the case for Volvo and Daimler, which announced in April a joint venture towork on hydrogen fuel cell for trucks, or Bosch, which included the manufacture of fuel cells in theirplans to response to COVID-19 crisis (Bosch, 2020; Daimler, 2020). In addition, several governments(such as Germany and the European Commission) have adopted in the last few months ambition
national hydrogen strategies and included hydrogen technologies within the COVID-19 recovery
packages.
The need for decarbonising traditional hydrogen consuming sectors, the increase in hydrogen
demand of these sectors and the emerging demand from new sectors, offer a significant opportunity
for the adoption of low-carbon hydrogen technologies. Fossil fuel technologies coupled with
CCUS have a head start over electrolysis to deliver low-carbon hydrogen at scale. However, theirdeployment will depend on the availability of suitable geological CO2 storage and the competitivenessand demand of CCU technologies. This opens the scope for scaling up hydrogen production via
electrolysis.

10.3 Hydrogen production technologies
Hydrogen is a very versatile fuel that can be produced through many different technological routes
and from many different energy sources, including fossil fuels, biomass and electricity (Figure 10.2).
However, the economics of each technology have defined the current mix of technological options
for hydrogen production:•

 round three-quarters of the current hydrogen dedicated production uses natural gas
Areforming. The vast majority of this production is unabated with a very limited adoption ofCCS.

• oal gasification is the other major route of production, accounting for around 23% of globalC
dedicated hydrogen production due to the extensive utilisation of this route in China. Similarly
to natural gas reforming, most of the production of hydrogen from coal is unabated, with an
anecdotic share of the supply incorporating CCS technologies.

•

Partial oxidation of oil and electrolysis account for the remainder of the dedicated production.

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10
Biomass gasification has long been suggested as a potential route for the future productionof low-carbon hydrogen (or negative-carbon if the process is coupled with CCUS). However,technological barriers and feedstock limitations have so far prevented this technology fromreaching commercialisation.

Figure 10.2: Hydrogen supply chain
Production

End useTransport and storage

Refining
Fossil fuels

StorageOil
Natural gas
Coal

Overground storage
Geological storage

Traditional
Chemicals

Transmission
Bioenergy

HydrogenBiomass
Organic wastes
Carbon-freeenergyRenewables
NuclearHydrogenderived fuels
Ammonia
Synthetic HC

Pipelines(pure/blended)
Rail
TrucksShipping

Power

Buildings
Emerging

Distribution
Pipelines(pure/blended)
TrucksTransport
Other
Industry

Note: Synthetic HC = synthetic hydrocarbons.

Natural gas methane reforming
Steam methane reforming is the most widespread technology for hydrogen production. The
natural gas reacts with steam at high temperature in the presence of a catalyst to produce syngas.
Afterwards, a water gas shift process transforms the carbon monoxide in the syngas into carbon
dioxide and hydrogen. The hydrogen is separated from the carbon dioxide and purified.
The production of hydrogen from steam methane reforming (SMR) generates, depending on theconversion efficiency, around 9 kgCO2 per kgH2. Therefore, the hydrogen produced by conventional
SMR is far from being low-carbon. CCUS technologies can be used to minimise the carbon footprintof SMR, especially by retrofitting current production plants, although this brings about important
implementation challenges. In traditional SMR designs, capture rates exceeding 90% can be achieved
but this increases significantly the production costs. Alternative technologies, like autothermal
reforming and gas heated reformers, have been developed with the objective of increasing theefficiency of the reforming process and facilitating the capture process. These advanced designs
can deliver carbon reductions over 97% (Progressive Energy, 2020).

Coal gasification
Coal gasification is a mature technology for the production of hydrogen. It has been used formany decades, especially for the production of ammonia in chemical and fertiliser industries.
Currently, the majority of the global production of hydrogen from coal gasification is based in
China. In coal gasification, coal reacts at high temperatures in the presence of an oxidising agent(air, steam, carbon dioxide or a combination of them) in stoichiometric deficit to produce syngas.

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This syngas undergoes the same sequence of processes as in the case of SMR. CO2 emissions ofhydrogen production from coal gasification are considerably larger than in SMR, over 20 kgCO2 /kgH2. Therefore, coal gasification has to incorporate CCUS in order to produce low-carbon hydrogen.
However, the use of CCUS technologies in coal gasification is particularly challenging. Coal contains
more impurities than natural gas when it is processed and it requires considerably larger capture
capacities, thus increasing the cost and the energy penalty of the capture process.

Water electrolysis
Water electrolysis is an electro-chemical process that uses electricity to split water into hydrogenand oxygen. Nowadays, water electrolysis accounts for less than 0.1% of the global hydrogenproduction, limited to sectors requiring relatively pure (therefore, high cost) hydrogen. Additionally,
electrolytic hydrogen is also currently produced through chlor-alkali electrolysis in the productionof chlorine and caustic soda. This route accounts for around 2% of total global hydrogen production.
There has been an acceleration in the deployment of electrolysis technologies, both in thenumber of projects and their size. The average unit size of the electrolysers installed in the early
2000s was around 0.1 MWe (megawatts electrical) and has increased up to an average of 1.0 MWetoday. Numerous projects in the range of 1-6 MWe have been deployed since 2013 and a 10 MWe
Power-to-Hydrogen project started operation in Japan in April 2020 (Asahi Kasei Corporation, 2020).
Several projects for electrolysis plants of up to 20 MWe are under construction and projects withinstalled capacities in the hundreds of MWe have been announced for the early 2020s. Up to 2.8 GW
in large-scale projects have been announced to be deployed just in the next three years, what could
bring the installed capacity to 3 GW by 2023.
This could start creating economies of scale that will help to drive down capital costs and toscale up the supply chain of the electrolyser industry. Currently, there are three main types ofelectrolysers:•

Alkaline electrolysers. This is the most mature technology and has been in use since the1920s. It is widely used in fertiliser and chlorine industries. Actually, alkaline electrolysers
with capacities up to 165 MWe operated in the 20th century, but most of them were
decommissioned as steam methane reforming became the most widespread technology forhydrogen generation. Besides maturity, it offers low capital costs due to the avoidance of
precious materials. On the negative sides, alkaline electrolysers use a solution of potassiumhydroxide as electrolyte that has to be recovered and recycled and are less flexible than otherdesigns. With a minimum load of 10% to full design capacity, their ability to adapt to variable
electricity inputs is limited.

• roton exchange membrane (PEM) electrolysers. Although PEM were introduced in the 1960s by
P
General Electric, they are a less mature technology. PEM electrolysers use pure water as anelectrolyte solution and are relatively small, compared with alkaline, making them more
attractive in dense urban areas and for mobile or remote applications. They are particularly
well suited for decentralised production and storage at refuelling stations since they cangenerate hydrogen at high pressure (30-60 bar, compared to 1-30 bar for alkaline). In addition,
PEM can operate more flexibly, with operation potentially ranging from zero up to 160% of
design capacity, being possible to overload the electrolyser for some time. This makes PEM
very attractive for providing grid services and to operate with the variable electric inputs.
However, they use expensive electrode catalysts (platinum, iridium) and membrane materials,
and their lifetime is currently shorter than that of alkaline electrolysers, resulting in a lower
deployment. Significant R&D efforts are being made to find alternative materials and to
improve operative lifetime.

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•

 olid oxide electrolysis cells (SOEC). SOEC are the least developed electrolysis technology. SomeS
companies are aiming to bring them to market and there have been some announcements
of projects using SOEC electrolysis to produced synthetic hydrocarbons at relatively large
scale. The electrode and electrolyte in SOEC are made of ceramic materials with relatively low
costs. They operate at high temperatures and can reach significantly higher efficiencies thanalkaline and PEM. SOEC need a source of heat since they use steam in the electrolysis process.
This brings about challenges to ensure low-carbon production of hydrogen since the originof the heat source can also be a source of CO2 emissions. However, it also offers interesting
opportunities for process system integration. In the production of synthetic hydrocarbons,the synthesis processes, like Fischer-Tropsch synthesis and methanation, release excess heatthat can be utilised in the production of the steam needed for the operation of the SOEC
electrolyser.

Table 10.1: Techno-economic characteristics of different electrolyser technologies
Alkaline

PEM

SOEC

Today

Long term

Today

Long term

Today

Long term

Electricalefficiency(LHV, %)

63-70

70-80

56-60

67-74

74-81

77-90

Stack lifetime(1 000s
operating h)60-90

100-15030-90

100-15010-30

75-100

Load range(% nominal
load)

10-110CAPEX(USD/kWe)

500-1 400

0-160
200-700

1 100-1 800

20-100200-900

2 800-5 600

500-1 000

Notes: LHV = lower heating value. No projections made for load range. For SOEC, electrical efficiency does not include the energy for steam
generation. CAPEX represents system costs, including power electronics, gas conditioning and balance of plant; CAPEX ranges reflect different
system sizes and uncertainties in future estimates.

Source: Agora (2018); Butler (2018); Element Energy (2018); FCHJU (2014); NOW (2018); Schmidt (2017); and IEA
analysis.

Box 10.1: Nuclear power and hydrogen production
Nuclear power was considered an alternative for low-carbon hydrogen production since theearliest waves of interest in hydrogen in the 1970s, but this was never realised due to ups anddowns in the nuclear industry, the higher hydrogen production costs compared with natural
gas and the fact that hydrogen did not fulfil the expectations of expanding to new applications.
Today, the progress achieved in electrolysis and the development of new emerging routes forhydrogen production from nuclear energy are creating new hopes in this sector:

194• he electricity from nuclear power plants can be directly used by fuel electrolysers,T
providing flexibility to the nuclear plant while generating low-carbon hydrogen. In addition,
the oxygen generated in the electrolysers can also be used in the nuclear reactors insteadof just being vented. Some demonstration projects have already been announced in the
United Kingdom and the United States to assess the incorporation of electrolysers into
existing nuclear power stations (EDF Energy, 2020; Patel, 2019). .

• team from nuclear light water reactors can be used in steam methane reforming, reducingS
the natural gas use for heat generation and the carbon intensity of the hydrogen produced.
Depending on local conditions, nuclear steam could be cheaper than steam generated fromnatural gas, providing an additional revenue for nuclear power plants.

10(continued Box 10.1)•

 mall modular reactors could also play a role in SOEC electrolysis in the future. ExploringS
non-electric applications for small modular reactors, such as hydrogen, is part of the Joint
Use Modular Plant (JUMP) research programme in the United States.

•

 uclear power plants simultaneously generate electricity and heat (temperatures of 300°C
N
in commercial reactors). For this reason, nuclear plants can provide the heat needed for
SOEC electrolysis. Research is underway to develop materials for these types of electrolysis
cells that could be well suited to the temperature at which nuclear heat can be obtained.

• uclear power plants simultaneously generate electricity and heat (temperatures of 300°C
N
in commercial reactors). For this reason, nuclear plants can provide the heat needed for
SOEC electrolysis. Research is underway to develop materials for these types of electrolysis
cells that could be well suited to the temperature at which nuclear heat can be obtained.

Costs of hydrogen production
The production cost of hydrogen is influenced by several factors with different weight in the
final cost depending on the fuel used (natural gas, coal or electricity) and the technology (SMR
with and without CCUS, coal gasification with and without CCUS or the different types of waterelectrolysis). The cost of hydrogen produced from natural gas varies in the range of USD 0.7-1.6/kg H2
for unabated production (Figure 10.3). In the case of natural gas reforming with CCUS, the cost rises
up to USD 1.2-2.0/kg H2, although this price gap could be influenced in the future by the evolution of
CCUS technologies and climate policies like carbon pricing or clean fuel standards.

Figure 10.3: Current levelised cost of hydrogen productions for different technologies
9
8
LCOH (USD/kg H2)

7
6
5
4
3
2
1
0
SMR

SMR w CCSCG

CG w CCSElectrolysis

Notes:– LCOH, levelised cost of hydrogen; SMR, steam methane reforming; CG, coal gasification, CCS, carbon capture and storage. Assumptions: 8%
discount rate, 25 years system lifetime, natural gas price USD 1.6-6.7/MBTU, coal price USD 67-159/toe, electricity price USD 36-116/MWh.
– SMR CAPEX USD 910/kW H2, OPEX 4.7% of CAPEX, 76% efficiency, 95% load factor.
– SMR w CCS CAPEX USD 1583/kW H2, OPEX 3% of CAPEX, 69% efficiency, 95% load factor, 95% capture rate.
– CG CAPEX USD 2672/kW H2, OPEX 5.0% of CAPEX, 60% efficiency, 95% load factor.
– CG w CCS: CAPEX USD 2783/kW H2, OPEX 5.0% of CAPEX, 58% efficiency, 95% load factor, 90% capture rate.
– Electrolysis CAPEX USD 872/kWe, OPEX 2.2% of CAPEX, efficiency 64%, 3000-4000 full load hours. Electrolysis CAPEX calculated as theaverage of global alkaline and PEM CAPEX.

Source: IEA (2020).

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10
The capital expenditure (CAPEX) and, especially, the fuel prices are the most influential factors
for the cost of hydrogen production from natural gas. Natural gas accounts for 60-80% of the
production costs, depending on the availability and price of natural gas. Low production costs canbe realised in regions such as the Middle East, Russia and the United States, while in countries
relying on natural gas imports, such as China, Japan or Korea, hydrogen production costs using
natural gas are higher
Low-cost coal resources in China create an ideal situation for coal gasification instead of natural
gas reforming. Coal gasification presents lower efficiency and higher CAPEX requirements than
SMR, but this is compensated by the low coal prices. However, the high carbon intensity of thisprocess requires CCUS technologies for low-carbon hydrogen production, increasing the production
costs by 5-15%.
The production cost of hydrogen from water electrolysis is influenced by several factors,
including fuel costs, CAPEX requirements, conversion efficiency and annual operating hours.
Figure 10.4 shows the levelised cost of hydrogen (LCOH) production today, and the potential costsin the near term and in the long term for PEM electrolysers. Similarly to SMR, fuel costs (electricity)
are the main factor determining the cost of hydrogen production. At high electricity costs, theeffect of other factors is diluted, whereas at low electricity costs, their impact increases. CAPEX
requirements present a considerable margin for improvement, thus offering an opportunity for cost
reductions in the long term.

Figure 10.4: Current and projected levelised cost of hydrogen (USD/kg H2) production
from water PEM electrolysis

USD/kgCurrent
CAPEX: USD 1 1200/kWe
Efficiency: 60%
9
8
7
6
5
4
3
2
1
0

10 20 30 40 50 60 70 80 90 100

USD/MWh

Potential long term
CAPEX: USD 230/kWe
Efficiency: 70%

Potential in 2025
CAPEX: USD 380/kWe
Efficiency: 64%
9
8
7
6
5
4
3
2
1
0

10 20 30 40 50 60 70 80 90 100

USD/MWh

9
8
7
6
5
4
3
2
1
0

10 20 30 40 50 60 70 80 90 100

USD/MWh

Note: FLH, full load hours. Discount rate: 8%. OPEX (% of CAPEX): current 2%, potential in 2025 1.6%, potential long term 1.5%. Stack lifetime(hours): current and potential in 2025 95 000, potential long term 100 000. System lifetime (years): current 28, potential in 2025 and longterm 30.

The operating hours also have an important impact on the final cost of hydrogen production. As
the operating hours increase, the impact of CAPEX costs on the LCOH declines and the impact ofelectricity costs rises (Figure 10.5). This is particularly relevant in electrolysers that operate with gridelectricity in systems with increasing shares of variable renewables, where surplus electricity may
be available at low costs, but potentially only available for a very few hours within a year. Utilising
only low-cost electricity implies a low utilisation of the electrolyser and high hydrogen costs due tothe impact of CAPEX costs. With increasing utilisation hours, grid electricity costs increase, but thehigher utilisation of the electrolyser leads to a decline in the cost of producing a unit of hydrogen.
These opposite trends regarding the impact of CAPEX and electricity costs generate an optimum
level of operating hours that lies at around 3 000-6 000 equivalent full load hours.

196

10
20

800

15

600
Area of lowest
hydrogen costs

10

USD/MWh

USD/kgH2

Figure 10.5: Hydrogen costs from electrolysis using grid electricity

400
Hydrogen costs from200

5Electricity use
CAPEX+OPEX

0

0

1 000

2 000

3 000

4 000

5 000

6 000

7 000

8 000 8 760

0Electricity price(right axis)

Full load hours
Source: IEA (2019a).

The use of grid electricity does not guarantee the production of low-carbon hydrogen since,
depending on the grid carbon intensity, the hydrogen produced can have a higher carbon footprint
than fossil fuel-based hydrogen. The use of dedicated electricity generation from renewables or
nuclear power offers a low-carbon alternative to grid electricity. The costs of solar PV and wind
generation have shown a sharp decline in recent years and it is expected that these costs will decrease
even further in the near future. For this reason, building electrolysers at locations with excellent
renewable resources could become an attractive supply option for low-carbon hydrogen, evenafter taking into account the costs of transporting hydrogen from these locations to the end users.

Figure 10.6: Hydrogen costs from hybrid solar PV-onshore wind systems in the long term

Source: IEA (2019a).

Geospatial analysis considering the long-term potential for technology improvements and cost
reductions in renewable electricity generation reveals that electrolytic hydrogen could becomecompetitive against traditional fossil hydrogen production in certain regions (Figure 10.6). Somelocations could produce electrolytic hydrogen at costs under USD 2/kg H2. The high disparity on
costs across different regions offers an opportunity for international trade of hydrogen, which isa backbone of a secure energy system. Regions with abundant solar and wind resources, such as
Australia or Northern Africa, could export renewable energy in the form of hydrogen to regionswith high energy demands and more limited or less attractive renewable resources, such as Japanor parts of Europe.

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10
Hydrogen-derived fuels
Products like methanol, methane, liquid hydrocarbons or ammonia can be synthesised fromhydrogen. Some of these fuels can act as drop in substitutes of some fossil fuels and can bedecarbonisation options for the so-called hard-to-abate sectors, where other decarbonisation
alternative, such as direct electrification, are more difficult and costly. While the low energy densityof hydrogen is a challenge for its transport, storage and utilisation in certain sectors (like shippingtransport and aviation), hydrogen-derived fuels present higher energy density, facilitating transportand storage, and some of them can more easily make use of existing infrastructure for storage andtransport.
The transformation of hydrogen into these derivatives involves, however, additional processing
steps, leading to energy losses and increasing costs. Typically, 45-60% of the electricity used forthe production of hydrogen-derived-fuels is lost across the value chain. In addition, many of the
production routes of these fuels are at early development stages and require additional inputs(nitrogen for the production of ammonia and carbon for synthetic hydrocarbons), resulting in higher
production costs than conventional production routes. For synthetic hydrocarbons, the origin of the
carbon also affects the carbon footprint of the synthetic hydrocarbon. Only in the case of biogenic
CO2 (captured from at a bioenergy conversion plants, such as a biomass power plants) or CO2 fromdirect air capture (DAC; CO2 extracted from the atmosphere), the final fuel can be consideredcarbon-neutral:•

 ynthetic methane is produced by converting hydrogen and carbon dioxide through a processS
called methanation. The majority of the projects so far for hydrogen-based fuels and feedstock
have been aimed at producing synthetic methane, most of them located in Germany andother European countries.

•

 ynthetic liquid hydrocarbons (such as diesel and kerosene) are produced by convertingShydrogen and carbon monoxide into long chain hydrocarbons using the Fischer-Tropsch
synthesis process, followed by an upgrading process to produce usable liquid fuels.

•

 mmonia is produced using the well-established Haber-Bosch process from clean hydrogenAand nitrogen. SMR and coal gasification are the main routes for producing the hydrogen usedin the synthesis of ammonia today, though some small-scale demonstration projects basedon electrolytic hydrogen have been in operation for several years.

The economics of producing hydrogen-derived fuels using electrolysis depend on various factors,
in particular the cost of electricity and, in the case of synthetic hydrocarbons, the availability andcost of the carbon source (Figure 10.7). The overall energy efficiency of the supply chain has a very
high impact in the final cost of these fuels. Improving the efficiencies of the conversion processes, inparticular of the electrolysis stage, can help to reduce the production costs for these fuels. CO2 costs
currently range from USD 30/tonne from bioethanol plants to USD 135-340/tonne from DAC. The
competitiveness of these hydrogen-based fuels will also depend on the implementation of carbon
prices (or alternative policy instruments) that could close the price gap with incumbent fossil fuels.

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10
Figure 10.7: Current and long-term projected levelised costs of production of hydrogen-derived
fuels and influence of electricity price, full load hours and CO2 feedstock costs on the projectedlevelised cost of synthetic kerosene
500

1 125

300

450

1 000

400

875

350

750

300

625

250

500

200

375

150

50

250

100

125

0

50

200

USD/bbl

USD/MWh

250

150
100

Current Long term Current Long term Current Long term
Synthetic Methane Synthetic Kerosene
Ammonia
CO2 feedstock - highElectricity
CAPEX
Kerosene price - USD 50/bbl

CO2 feedstock - low
OPEX
Gas price - USD 7/Mbtu
Ammonia price - USD 300/tNH3

10

20

30

40

50

60

70

80

90

0
100

CO2 price to match kerosene priceat 50 USD/bbl of oil

Synthetic Kerosene (long term)
350

Electricity price (USD/MWh)
30 USD/t CO2
FLH 2000
FLH 4000
FLH 6000

100 USD/t CO2
FLH 2000
FLH 4000
FLH 6000

Notes:Left graph:– 3 500 h full load hours and a discount rate of 8%. Electricity price USD 70/MWh (current) and USD 25/MWh (long term). CO2 feedstock costs
lower range based on CO2 from bioethanol production at USD 30/t CO2 (near and long term); CO2 feedstock costs upper range based on DAC
at USD 300/t CO2 (near term) and USD 100/t CO2 (long term).
– Electrolysis: CAPEX (current, long term): 872, 270 USD/kWe; Annual OPEX (% of CAPEX): current 2% and long term 1.5%; Stack lifetime: current
95 000 h and long term 100 000 h; System lifetime: current 28 and long term 30 years.
– Ammonia synthesis: CAPEX 108 USD/t NH3; Annual OPEX 1.5%; Electricity consumption 4 GJ/t NH3; Hydrogen consumption 0.0056 t NH3/
kg H2;– Methanation: CAPEX (current, long term) 845, 565 USD/kWh fuel; Annual OPEX 4%; Electricity consumption 0.013 GJ/GJproduct; Efficiency(% LHV): 77%,
– Fischer-Tropsch: CAPEX (current, long term) 890, 565 USD/kWh fuel; Annual OPEX 4%; Electricity consumption0.018 GJ/GJproduct; Efficiency(% LHV): 73%.
Right graph: FLH = full load hours.

Source: IEA (2020).

10.4 Opportunities for hydrogen and linkages to the electricity system
Low-carbon hydrogen possess a strong potential for contributing to GHG emission reductions in awide range of sectors. Several opportunities can be materialised in the short term. The replacementof hydrogen from unabated fossil fuels by low-carbon hydrogen in existing industrial uses, theuse of hydrogen in road transport or the injection of hydrogen in the gas grid are some of theseshort-term opportunities. These are technology-ready alternatives that will require relatively low
investment compared with other low-carbon alternatives (when these are available) and that are
already occurring at small scale. Scaling them up can deliver significant cost reductions that will
improve their competitiveness and can help enabling other long-term opportunities for hydrogen.
The use of hydrogen in the power sector to facilitate the integration of variable renewables (VRE), forexample in flexible generation from hydrogen-fired gas turbines or the provision of large-scale andlong-term storage, is among these long-term opportunities. Hydrogen and hydrogen-derived fuels
can be options to decarbonise transport modes, such as shipping or aviation, where electrification
is more difficult.

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Alongside other low-carbon options for producing hydrogen, notably fossil fuels with CCUS,
hydrogen from clean electricity can contribute to the decarbonisation of other parts of the energy
system, in particular in areas, where direct electrification is difficult. This section provides an
overview of the most appealing opportunities available in the short- and long-term for low-carbonhydrogen with a special focus on the role of and linkages with the electricity system.

Industrial sector and oil refining
The largest potential for low-carbon hydrogen resides in current uses in the industrial sector andoil refining. These applications represent an easy and immediate penetration route that will require
no modification in the operation of these processes. This is reflected in the increase of projects
focused on the use of low-carbon hydrogen in these sectors observed in recent years, along with
the increasing number of announced projects to be deployed in early 2020s, with the vast majorityof projects focusing on oil refining, the chemical sector and steel production (IEA, 2020c).

Current status and future opportunities
Depending on the application, different low-carbon hydrogen technologies may be better suited. For
this reason, the opportunity and potential for the power sector and electrolytic hydrogen will differ
depending on the industrial process:•Oil refining: This sector presents a large on-site generation from natural gas, coal and naphtha,
which is highly integrated within refining operations. Retrofitting these processes with CCUS
seems to be the best alternative in the short term, as long as geological storage and/or markets
for CO2-derived products are available. An increase in demand for oil refining products will
likely occur in the near future, although there is enough refining capacity available to meetthis growth, limiting the need for new hydrogen capacity. However, the power sector could
have an opportunity in replacing merchant hydrogen purchases with electrolytic hydrogen.

•Chemical industry: Similarly to oil refining, the majority of hydrogen demand in chemical
manufacturing is produced on-site and CCUS technologies look like a promising alternative
to minimise its carbon footprint. However, a strong increase in the demand for products
like ammonia and methanol is expected due to economic and population growth. Moreproduction capacity will be needed, thus generating an opportunity for electrolytic hydrogen.
In the case of carbon-containing products like methanol, the need for a source of carbon may
pose additional barriers, but ammonia production is well suited to implement electrolytichydrogen in new plants.

•

Iron and steel: It is quite unclear whether CCUS or electrolytic hydrogen would be a better
option to substitute fossil-derived hydrogen in this sector. It is possible to retrofit DRI facilities
with CCUS, although around 30% of natural gas can be replaced with electrolytic hydrogenin the current DRI route. The complete conversion of DRI route to hydrogen is still under
demonstration.

•

 igh temperature industrial heat could be another source of hydrogen demand growth in theHfuture where direct electrification faces implementation challenges (such as steam crackers
or cement kilns). However, the different burning characteristics of hydrogen compared withincumbent fuels will require process and equipment modifications or adaptation.

Economics
The biggest barrier that low-carbon hydrogen faces to replace current fossil-based hydrogen inoil refining and the chemical sector is the cost penalty. Figure 10.8 presents a cost comparison
for ammonia production and steel production through DRI. Electrolytic hydrogen requires low
electricity prices in both cases to become competitive with fossil-based hydrogen or other lowcarbon technologies (CCUS). Ammonia presents better prospects for electrolytic hydrogen since it

200

10
can compete with production based on SMR with CCUS at electricity prices under USD 50/MWh,depending on natural gas prices. Moreover, with electricity prices below USD 20/MWh, it canbecome competitive against conventional technologies. Prices in the USD 20-50/MWh range are
already observed for renewable generation, showing the good opportunity for electrolytic hydrogenin ammonia production in the short term. Electrolytic hydrogen would find more difficulties to becompetitive in the DRI route for steel production. Only at very low electricity prices (below USD 10/
MWh) it could compete with conventional DRI or DRI incorporating CCUS at low natural gas prices,whereas electricity prices under USD 40/MWh would be required to reach competitiveness at highnatural prices.

Figure 10.8: Cost comparison of low-carbon hydrogen and conventional routes for ammonia
and steel in the long term
Ammonia

Levelised cost (USD/t)

1 200

Steel - DRI

800

900

600

600
400

300
0

200
0

10

20

30

40

50

60

70

80

Electricity cost (USD/MWh)NG unabated (high)NG w/CCUS (high)Electrolysis (high)NG unabated (low)NG w/CCUS (low)Electrolysis (low)

0

10

20

30

40

50

60

70

80

Electricity cost (USD/MWh)
NG (high)
NG w/CCUS (high)Hydrogen (high)NG (low)
NG w/CCUS (low)Hydrogen (low)Note: The levelised cost includes the cost of CAPEX on core process equipment, fixed OPEX, fuel and feedstock costs, and the cost of capturing,transporting and storing CO2. Best practice energy performance is assumed for natural gas-based routes. Electrolyser CAPEX range = USD 455–
894/kWe. Electrolyser efficiency range = 64–74% on an LHV basis. 95% DRI charge to the EAF is assumed in all cases.

Source: IEA (2019a).

Transport
Hydrogen has generated high expectations as a potential alternative to conventional transportation
fossil fuels. However, the high cost of hydrogen and the slow progress in the development of fuelcells have prevented hydrogen to meet these prospects and the current use of hydrogen in transport
is very low. This situation is starting to change since there has been a notable acceleration in thelast couple of years thanks to the ambitious plans being developed by California, China, Korea
and Japan. Energy demands in road transport are expected to grow strongly in the next decade,driven by demand for trucks and rising car ownership in emerging economies. Given that any roadtransport mode can be powered using hydrogen, the potential for hydrogen is very large. However,direct electrification also presents very strong prospects and has a considerable head start incertain road transport modes, such as passenger cars (IEA, 2019b). This suggest that hydrogen could
have a bigger role in areas like long-distance transport, trucks or bus fleets, which operate withhigh power demands and long usage cycles, where electric vehicles run into limitations in terms ofrange and refuelling times.

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Current status and future opportunities
In general, hydrogen and hydrogen-derived fuels appear as the only clean alternative for thetransport energy demand that could not be electrified or met by biofuels. Table 10.2 summarises thecurrent situation and the potential that hydrogen and hydrogen-derived fuels present in different
transport modes. Hydrogen can complement direct electrification in road transport and rail andhas a significant potential for being an alternative to conventional fuels in shipping. On the sideof hydrogen-based fuels, ammonia offers particular advantages for shipping whereas synthetic
jet fuel can play a substantial role in aviation. Due to the nature of these transport modes, they
have a very limited pool of technological alternatives to current fossil fuels. The role of directelectrification is very limited given practical difficulties that come with the energy and power
density requirements and drop-in fuels look like the most promising alternative. Biofuels are thefurthest developed drop-in fuels, but their sustainable production presents serious challenges that
limit their availability and they will not be able to completely replace conventional maritime andaviation fossil fuels. In addition, both ammonia and synthetic could take advantage of existing
infrastructure with limited modification although their productions suffers, from these fuels look
more like a long-term alternative.

Table 10.2: Potential uses of hydrogen and hydrogen-derived fuel in transport applications
Transport mode
Cars and vans

Heavy duty vehicles

Current deployment
Close to 19 000 vehicles inoperation at the end of 2019,
mainly in California, Japanand Korea.
More than 4 400 buses and
1 800 trucks in operation at
the end of 2019, mostly in
China. Several thousand to
be deployed in early 2020s in
Europe.

Challenges

Hydrogen: Short refuellingtime; less weight added forenergy stored; zero tail-pipe
emissions; lower material
footprint of fuel cells thanlithium batteries.
Captive vehicle fleets canimprove utilisation ofrefuelling stations; longdistance and heavy duty
attractive option.
Hydrogen and ammonia
are candidates for domestic
shipping and the IMO
Greenhouse Gas Reduction
Strategy.

Hydrogen: Initial low
utilisation of refuelling
stations raises fuel cost;
reductions in fuel cell andstorage costs needed;
efficiency losses on a well-towheels basis.
Synthetic fuels: Relatively
high costs.

Maritime

Limited to demonstration
projects for small ships andon-board power supply inlarger vessels.

Rail

Two hydrogen trains
operating in Germany in 2019.
Expected to expand to 41 by
2023. Trials in China, Japan,
Korea, the Netherlands, Italy
and the UK.

Hydrogen trains can bemost competitive in rail
freight (regional lines with
low network utilisation, andcross-border freight) andremote regions presenting
challenges to electrify.

Aviation

Limited to small
demonstration projects andfeasibility studies. Larger
demonstration projects underdevelopment.

Synthetic fuels: Limitedchanges needed to current
infrastructure.
Hydrogen: Together withbatteries, can supply
on-board energy supply at
ports and during taxiing.

Notes: adapted and updated from IEA (2019a).

202

Future deployment
Opportunities

Hydrogen: Storage costhigher than other fuels.
Hydrogen/ammonia: cargovolume lost due to storage(lower density than current
liquid fuels).
Rail is the most electrified
transport mode; hydrogenand battery electrictrains with partial line
electrification are options
to replace non-electrified
operations, which aresubstantial in many regions.
Synthetic fuels: Currently
4-6 times more expensive
than kerosene, with potential
to decrease to 1.5-2 times in
the long-term.

10
Economics
As an example, the use of hydrogen in road transport is considered here. The competitiveness of
FCEVs depends mainly on the fuel cell costs and the fuel delivery price, which in turn is affectedby the production costs and the building and utilisation of refuelling stations (Figure 10.9). For
passenger cars, the cost of fuel cells and on-board hydrogen storage have to decrease to make themcompetitive with battery electric vehicles at long driving ranges (400–500 km). This could attract
consumers that prioritise range. Regarding refuelling stations, currently the utilisation ratio tends
to be quite low due to the limited uptake of FCEVs. A potential opportunity at early deployment
stages is to build hydrogen stations that serve captive fleets of buses and trucks, securing highrefuelling station utilisation.

Figure 10.9: Total cost of ownership for passenger cars and long-haul trucks by powertrain,
range and fuelTotal cost of ownership (USD/km)

0.9

1.8

Passenger cars

Long - haul trucks

1.6

0.8

1.4

0.7

1.2
1.0

0.6

0.8

0.5

0.6
0.4
0.3

0.4
Base vehicle cost
FCEV
BEV
BEV
FCEV
BEV
400 km 400 km 250 km 400 km 400 km
Today

0.2

Base truck cost
FCEV

Today

Long term

Low utilisation of infrastructure

Refuelling, charging

BEVElectricity, fuel

FCEV

BEV

Long term

Operations and maintenanceBattery, fuel cell

Notes: ICE = internal combustion engine. The y-axis intercept of the figure corresponds to base vehicle “glider” plus minor component costs,
which are mostly invariant across powertrains.

Source: IEA (2019a).

Although the fuel cell and the utilisation of refuelling infrastructure are the main cost drivers,
both long range and large vehicles present a high energy consumption per kilometre. This meansthat fuel costs make up a greater share of the total cost of ownership for this type of vehicles. Scaling
up the production of electrolytic hydrogen will drive down the production cost and, therefore, thetotal cost of ownership of the vehicle. Recently, several projects for dedicated generation combining
refuelling stations and gas grid injection to optimise operation have been developed.

Buildings
Decarbonising heat in buildings is very challenging since decision making involves many factors
like building type, geographical location, equipment running and purchasing costs, energy prices
or consumer acceptance. Many different technological options will be required for adapting to theneeds of all types of building, infrastructures and users. Hydrogen can contribute to decarbonisingheat provision in the building sector, depending on local conditions, such as building types andexisting infrastructure. There are two options for utilising hydrogen in domestic heating: blendinghydrogen in the existing natural gas networks or direct use of pure hydrogen.

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10
Hydrogen blending into natural gas distribution grids
This option is particularly interesting in regions relying on natural gas for heating. Hydrogen blends
up to 20% on a volumetric basis can make use of an important fraction of the existing natural gas
distribution infrastructure and would require minimal infrastructure and end-use equipment
adaptation. The use of 20% blending shares (on a volumetric basis) has already been demonstrated,
although it represents only around 7% on an energy basis and, therefore, less than 7% decrease in
GHG emissions depending on the carbon footprint of the hydrogen blended. Still, blending small
fractions of hydrogen into the gas grid could represent a significant increase in hydrogen demandin the short term, thus contributing to the scale up and cost reduction of hydrogen generation
technologies. However, it could also add a significant cost to the end user. Blending just 3% byvolume into all natural gas use around the world would boost clean hydrogen demand by close to
12 Mt H2 /yr but would add around 3–15% to natural gas supply costs. Meeting this demand withelectrolytic hydrogen would represent around 70 GW of electrolysis capacity.

Higher blending shares or pure hydrogen use
There are already some networks operating with pure hydrogen in industrial hubs, but reachinghydrogen blending shares larger than 20% will require major modifications in infrastructureand end-use equipment. However, higher shares of hydrogen can be enabled by using synthetic
methane, which is practically the same as natural gas since methane is the main component of
natural gas (reaching more than 95% of its composition), and its injection in the gas grid can avoid
the replacement of existing equipment. This is an option that has attracted a lot of interest in
Europe, where several projects for the demonstration of power-to-methane for grid injection havebeen developed in the last decade. Yet, synthetic methane presents some challenges related to its
carbon footprint, which requires a non-fossil CO2 feedstock to be considered a low-carbon fuel. At
the same time, the feedstock costs and the synthesis process and related conversion losses increase
the costs of synthetic methane compared to hydrogen, thus leading to higher final consumer costs.
The case for 100% hydrogen presents the same challenges in terms of infrastructure and end-use
appliances as the case of blending shares larger than 20%. The prospects for 100% hydrogen in thelong term will strongly depend on the costs of the equipment and the hydrogen price. Consumers
often prioritise upfront purchase prices over overall lifetime costs, taking into consideration otheraspects such as safety, ease of installation or space requirements. Regarding the price of hydrogendelivered to consumers, it would likely need to be in the range of USD 1.5–3.0/kg H2 to competewith natural gas boilers and electric heat pumps (Figure 10.10). In countries like Canada, withparticularly low gas prices, the price of hydrogen would need to be below USD 1/kg H2 to becomecompetitive, whereas in other countries like Japan higher hydrogen prices (USD 3–4/kg H2) might
still be competitive.
Due to this complex interaction between hydrogen costs and consumer preferences, some
types of building will be better suited to the use of hydrogen than others. The most cost-effective
solution will depend on the type of building and geographical context. Hydrogen can have an
opportunity to complement the electrification of heat in those situations when the benefits ofadopting hydrogen can outweigh its lower efficiency. Low performing buildings with difficulties
for heat pump installations and existing gas infrastructure are an attractive opportunity for the
adoption of hydrogen use in buildings.

204

10
Figure 10.10: Hydrogen prices needed to become competitive against natural gas boilers
and electric heat pumps in selected markets by 2030

Hydrogen price (USD/kg H2)

4

3

2

1

0
CanadaGas boiler

UnitedStates

Japan

Korea

WesternEuropeRussiaChina

Electric heat pumpNotes: Prices are average retail prices, including taxes, in USD 2017. Competitiveness of electric heat pumps assumes a typical seasonal
efficiency of the heat pump in those countries. Price competitiveness does not include capital costs of the equipment.

Source: IEA (2019a).

System considerations
The use of blended or pure hydrogen in buildings presents synergies with the wider energy system,
resulting in lower overall system costs than other alternatives like biomethane or full electrificationof heat. Even in the most optimistic conditions (use of high-efficiency heat pumps and majorbuilding energy efficiency improvements), the full electrification of heat would require large-scale
peak capacity or energy storage capacity. Biomethane depends on local availability of suitablebiomass feedstock (e.g. animal manure). Global biomethane production would need to increase
20-fold to meet current heat demand, thus limiting the use of sustainable biomass resources
for other applications. Therefore, hydrogen help reducing the stress on electricity systems andbioenergy resources.

Power sector
The transition towards electricity systems with high shares of variable renewable electricity will
require a more flexible system and hydrogen in providing this flexibility to the electric grids,
thus facilitating the integration of renewable electricity generation. Electrolyser can absorb highgeneration of renewables at times of low demand and support baseload technologies to avoid
ramping. Electrolysers could increase system stability and reduce costs by flattening residual load
(thanks to less ramping and avoiding start-ups and shut-downs), providing balancing power andit might even be used to release grid constraints, for example in the case of renewable electricity
generation hubs with insufficient demand. The hydrogen generated under these conditions can beused for flexible generation at times of high demand and for long-term storage to provide electricity
during long periods with very little wind or sunshine. Moreover, in regions with marked seasonalpatterns of renewable supply or electricity demand, interseasonal storage may be required, andhydrogen looks as the most cost effective option. However, these opportunities will only be realised
if the resulting full load hours when surplus electricity is available are high enough to justify theinvestment of the electrolyser (see Figure 10.5).

205

10
Large-scale and long-term energy storage
Hydrogen can be stored over ground, compressed or liquefied in tanks. However, undergroundstorage in salt caverns, depleted natural gas or oil reservoirs and aquifers offers better prospects forlong-term and large-scale storage. Salt caverns, which have been in operation for many years in the
United States and the United Kingdom seem to be the best choice for underground storage. They aretight, thus presenting low risk of contamination, and operate at high pressures, which enable highdischarge rates. Salt rock formations, however, exist not everywhere. Depleted gas fields or porestorage are more common, but experience with hydrogen is limited and investigated in research
projects, like the H2STORE project (Energie Speicher, 2017). Hydrogen-derived fuels like synthetic
methane or ammonia can also be interesting option. Methane, as mentioned before, could takeadvantage of the existing transport and storage infrastructure for natural gas, whereas ammonia
can be stored in large steel tanks, which is already a common practice in the fertiliser industry.
The cost competitiveness of hydrogen and ammonia against other storage technologies is shownin Figure 10.11. Batteries are currently on the spotlight as the leading technology for electricity
storage. They have achieved substantial cost reductions in recent years and this trend is expected
to continue in years to come. Batteries are characterised by high storage cycle efficiency, therefore
being well-suited for short-term storage and many storage cycles. However, they are less well suitedfor long-term and large-scale storage, as they suffer from self-discharge and because their low
energy density an immense number of batteries would be needed for large-scale storage.
Pumped hydroelectric storage looks like a better alternative for electricity storage over longer
periods. It has been used for many decades, reaching a global capacity of 156 GW in 2019. However,with very long-storage periods and low-number of cycles within a year, the CAPEX costs for the
storage volume start to dominate the overall levelised storage costs.

Figure 10.11: Levelised costs of storage as a function of discharge durationand the electricity price
10 000

Pump hydro USD 0/MWh
Pump hydro USD 50/MWh
Li-Ion Bat USD 0/MWh
Li-Ion Bat USD 50/MWh

USD/MWh

1 000

H2 USD 0/MWh
H2 USD 50/MWh
NH3 USD 0/MWh
NH3 USD 50/MWh

100

CCGT with CCUS 3/Mbtu
CCGT with CCUS 11/Mbtu

10
0,1

1

10

100

1 000

Discharge duration (h)Note: Notes: Li-Ion = lithium-ion battery. Compressed hydrogen storage refers to compressed gaseous storage in salt caverns, ammonia storage
to storage in tanks. For this analysis, the number of storage cycles within a year has been determined by dividing 8760 hours of a year by the
charge and discharge duration, with charge and discharge durations assumed to be the same. Other operating regimes are possible, i.e. lower
number of storage cycles for a given discharge duration. The underlying storage volume is assumed to correspond to the power output capacity
times discharge duration.

Source: IEA (2019a).

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10
Hydrogen and ammonia have relative low cycle efficiencies (around 40%), resulting in very highcosts for short-term storage. However, the costs for the storage volume are very low, so that the costs
when moving to longer storage periods and lower cycles increase only slightly. As a consequence of
this, hydrogen and ammonia result in the lowest cost of storage when discharge duration reaches
20-80 hours or more, depending on the price of electricity. Moreover, it can become competitive
against flexible power generation from natural gas with CCUS under certain natural gas prices.

Hydrogen as fuel for electricity generation
Hydrogen can be used in gas turbines, combined cycle gas turbines and fuel cells for generating
power. The current share of hydrogen in power generation is negligible, accounting for less than
0.2% of electricity generation. Today, pure hydrogen is not used for power generation, with somesmall-scale exceptions, whereas the use of hydrogen-rich gases from industrial processes is morecommon.
Gas turbines: The existing gas turbines can handle hydrogen shares of 3-5%, with some of themreaching 30% or higher. Turbine manufacturers have developed small-scale turbines able to operatewith pure hydrogen and expect to provide large-scale turbines able to run on pure hydrogen by
2030.
Fuel cells represent another opportunity for power generation with more immediate deployment
for certain applications. There are currently four types of commercial fuel cell technologies:• olymer electrolyte membrane fuel cells (PEMFCs) operate at temperatures below 100°C andP
have a quick start-up time, but they require pure hydrogen or an external reformer to operate.
They are used as micro co-generation units, operating with natural gas or LPG in buildings.

•

 hosphoric acid fuel cells (PAFCs) operate today as stationary power generators with outputs
P
in the 100-400 kW range. In addition to electricity, they produce heat at around 180°C that canbe used for space and water heating.

• olten carbonate fuel cells (MCFCs) operate at 600°C, thus being able to run on differentMhydrocarbon fuels without the need for an external reformer. MCFCs are used in the MW scale
for power generation, while the produced heat can be used for heating or cooling purposes inbuildings and industrial applications.

• olid oxide fuel cells (SOFCs) can also run on different hydrocarbons without a previous
S
reforming step since they operate at 800-1 000°C. SOFCs have similar application areas to
MCFCs, but generally at kW scale (such as micro co-generation units or off-grid power supply).

The growing interest on stationary fuel cell is reflected on the rapid expansion of global installedcapacity over the last decade. In 2018 almost 1.6 GW of capacity had been installed, though mainly
running on natural gas and only around 70 MW with hydrogen as fuel. The global deployment offuel cells reached 363 000 units and is largely dominated by a few countries, namely Germany,
Japan, Korea and the United States. Japan accounts for the majority of the units, mostly microco-generation systems for residential use and representing around 12% of the global capacity. The
residential fuel cell market is also growing in Germany. Larger fuel cell systems have been deployedin the United States and South Korea to provide back-up power or produce heat and electricity forindustrial processes or distributed systems.
Stationary fuel cells are highly efficient in converting hydrogen into electricity and heat (electric
efficiency over 60%). Fuel cells are particularly attractive for flexible operation since they are able
to achieve higher efficiency in part load than full load. However, they still have short technicallifetime when compared with gas turbines and typically have small power outputs (the largest
fuel cell plants reach up to 50 MW compared with up to 600 MW for gas turbines). These factors
make them more suitable for distributed generation, the provision of back-up power and off-grid
electricity generation.

207

10
Co-firing of ammonia in coal power plants: Another opportunity for hydrogen to contributein the decarbonisation of the power sector would be co-firing ammonia and coal. This has beensuccessfully demonstrated in Japan with a share of 1% of ammonia in a medium size power plant(120 MW). The trials also demonstrated that NOx emissions can be kept within usual limits andammonia slip into exhaust gas can be avoided. Blending shares up to 20% might be feasible withminor adjustments to coal power plants and have already been demonstrated in smaller furnaces(10 MW thermal).
There is an important number of coal power plants worldwide in operation or under construction
which will still be in operation for several years. It is projected that by 2030 around 1 750 GW of coal
generation will be in operation and will still have a remaining lifetime of at least 20 years. Co-firing
a 20% share of ammonia could avoid around 1.4 Gt CO2 from the annual emissions generated bythese coal plants. This would result in a demand of 860 Mt of ammonia and, therefore, 155 Mt H2.
However, the economics of this technological application for ammonia will strongly depend on theavailability of low-cost low-carbon hydrogen.

Economics
The competitiveness of hydrogen technologies against natural gas for flexible power generation(load balancing and peak load generation) depends on the hydrogen and gas prices and the potential
application of carbon prices (or comparable policy measures penalising CO2 emissions). Figure 10.12
shows an example assuming a load factor of 15%. At low hydrogen price (USD 1.5/kg H2) and highnatural price (USD 11/MBtu), hydrogen technologies would require CO2 penalties in the range of
USD 25-50/t CO2. However, in a scenario with low natural gas prices (USD 3/MBtu) these penalties
will need to reach USD 175/t CO2 for hydrogen technologies to become competitive against naturalgas. At higher hydrogen prices, it will be very difficult for hydrogen to compete with natural gas.

Figure 10.12: Levelised cost of electricity generation from natural gas and hydrogen300

175 USD/t CO2
100 USD/t CO2

250

50 USD/t CO2
USD/MWh200

25 USD/t CO2
Fuel costs

150

OPEX
100

CAPEX

50

GT NG

CCGT NG

CCGT H2

FC H2

USD3/kg H2

USD1.5/kg H2

USD3/kg H2

USD1.5/kg H2

USD3/kg H2

USD1.5/kg H2

USD11/Mbtu

USD3/Mbtu

USD11/Mbtu

USD3/Mbtu

0

GT H2

Note: GT = gas turbine; CCGT = combined-cycle gas turbine; FC = fuel cell; NG = natural gas. CAPEX = USD 500/kW GT, USD 1 000/kW CCGT
without CCS and hydrogen-fired CCGT, USD 1 000/kW FC. Gross efficiencies (LHV) = 42% GT, 61% CCGT without CCS and hydrogen-fired CCGT,
55% FC. Economic lifetime = 25 years for GT and CCGT, 20 years for FC. Capacity factor = 15%.

Source: IEA (2019a).

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10
10.5 Conclusions and recommendations
Hydrogen is emerging again as a key player in the transition towards a clean, secure and affordable
future energy system. It is leaving an unprecedented momentum reflected in the rapid expansion
in the number of policies and projects around the world. Hydrogen had other previous false starts
but the progress in the development of hydrogen technologies, the lessons learnt from the successin other clean energy technologies and the stronger societal commitment to deliver deep emissions
reductions have created an unparalleled environment for the deployment of hydrogen technologies.
The power sector can play a critical role in the realisation of this new emerging opportunity.
Nowadays, electrolytic hydrogen represents a small fraction of global hydrogen production.
However, the production of hydrogen from low-carbon electricity has a strong potential to deliver
carbon savings in otherwise hard-to-decarbonise sectors, such the industry and transport sectors.
A substantial expansion in low-carbon electricity generation will be required – in addition to
the growing electricity demands directly used in buildings, industry and transport – to provide
the amounts of hydrogen needed in the hard-to-carbonate sectors. For example, if all aviationfuel demand in 2019 had been covered by synthetic kerosene from electrolytic hydrogen, thiswould have resulted in an electricity demand of 11 000 TWh, more than 40% of global electricity
generation today. However, hydrogen can also support the decarbonisation of the electricity systemand the integration of VRE by being one of the few technology options for long-term and large-scale
electricity storage. This will contribute to the creation of a more integrated and flexible energy
system.
Clean hydrogen has to overcome several barriers to enable this contribution to a clean energy
transition. The most significant barrier is the price gap with conventional high-carbon hydrogenproduction technologies. Fortunately, clean hydrogen technologies have achieved a development
stage in which they are ready to scale up and bring down costs. As a result, hydrogen could become
more widely used. Near-term opportunities, like the deployment of clean hydrogen production anduse in industrial ports and hubs supporting transport fleets and corridors to make fuel cell vehicles
more competitive or the use of existing gas infrastructure, can facilitate the scaling up of thesetechnologies. This can support the adoption of clean hydrogen in conventional hydrogen uses today
and, especially in new applications that can significantly stimulate clean hydrogen demand.

209

10
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Annexes

Annex A1

A1: Learning rates for adjusting data for commissioning year 2025
Country

Australia

Austria
Canada
Italy

Korea

Netherlands

RussiaTechnology
Pumped storage
Onshore wind
Offshore wind
Solar PV (utility scale)
Solar thermal (CSP)
Run of river
Solar PV (residential)
Onshore wind
Solar PV (utility scale)
Onshore wind
Biomass
Biomass (CHP)
Geothermal
Solar PV (commercial)
Solar PV (utility scale)
Onshore wind
Offshore wind
Solar PV (commercial)
Solar PV (utility scale)
Solar PV (floating)Onshore wind
Offshore wind
Offshore wind

Original
commissioning
year
2022
2020
2020
2019
2020
2020
2020
2020
2021
2022
2018
2018
2023
2018
2018
2018
2018
2022
2024
2024
2023
2022
2021

Factor to transform Cost reduction
costs to 2025
until 2025 (%)
1.00
0.95
0.96
0.75
1.00
1.00
0.87
0.95
0.90
0.98
1.00
1.00
1.00
0.68
0.70
0.88
0.75
0.91
0.96
0.89
0.95
0.95
0.93

0
5
4
25
0
0
13
5
10
2
0
0
0
32
30
12
25
9
4
11
5
5
7

* Cost reductions based on IEA estimations in co-ordination with the respective countries.

213

Annex A2

List of abbreviations and acronyms
AC

alternative current

ACAES

adiabatic compressed air storage

ALWR

Advanced Light Water Reactor

A-USC

advanced ultra-supercritical

BICGTbiomass internal combustion gas turbine

BIGCC

biomass internal gasification combined cycle

BOS

balance of system

BWRboiling water reactor

CAES

compressed air energy storage

CAPEX

capital expenditure

CCGT

combined-cycle gas turbine

CCUS

carbon capture and storage (or sequestration)

CFDs

contracts for differences

CHP

combined heat and power

CPV

concentrating photovoltaic

c-Si

crystalline silicon

CSP

concentrating solar powerDC

direct current

D&D

decommissioning and dismantlement

DNI

direct normal irradiance

EGC

electricity generating costs

EGS

enhanced geothermal systems

EOR

enhanced oil recovery

ETP

IEA Energy Technology Perspectives study

ETS

emissions trading schemeFCsfuel cells

FiTs

feed-in-tariffs

Gen IVgeneration IV

GJ

gigajoules

HLW

high-level waste

215

A2

216

HTR

high-temperature reactor

IDC

interest during construction

IGCC

integrated gasification combined cycle

IPCC

Intergovernmental Panel on Climate ChangekWekilowatt of electricity capacity

LCOE

levelised cost of electricity

LFR

lead-cooled fast reactor

LHV

lower heating value

LNG

liquefied natural gas

LWR

light water reactor

MMBtu

million British thermal unitsMWhmegawatt-hour

NCU

national currency unit

NGCC

natural gas combined cycle

OCGT

open-cycle gas turbine

O&M

operation and maintenancePVphotovoltaic

PWR

pressurised water reactor

RD&D

research, development and demonstrationSCsupercritical

SFR

sodium-cooled fast reactor

SMR

small modular reactor (Chapter 8)

SMR

steam methane reforming (Chapter 10)

SOFC

solid oxide fuel cell

USD

US dollars

VALCOEvalue adjusted levelised cost of electricity

VRE

variable renewable energy

2DS

2 degree scenario

2DS hi-Ren

high-renewable variant

List of Members of the EGC Expert Group and Experts
who contributed to the report with data or comment(by country)

Shamim Ahmad

Australia

Candice El Asmar

Australia

Nicole Thomas

Australia

Katharina Giesecke

Austria

Michael Sorger

Austria

Erik Delarue

Belgium

William D’haeseleer (Chair)

Belgium

Tim Mertens

Belgium

Mariana de Queiroz Andrade

BrazilGanesh Doluweera

CanadaIsabel Murray

CanadaSaba Zarghami

CanadaJan Krkoška

Czech Republic

Lubor Žežula

Czech Republic

Filip Gamborg

DenmarkThøger Kiørboe

Denmark

Mikkel Sørensen

Denmark

Christoph Wolter

Denmark

Jacob Hjerrild Zeuthen

Denmark

Marie-Ursula Vaks

Estonia

Timo Ritonummi

Finland

Jean-Jacques Coursol

FranceGilles MathonniereFranceEndre Börcsök

Hungary

Veronika Oláhné Groma

Hungary

Attila Hugyecz

Hungary

Péter Jelen

Hungary

Luigi Mazzocchi

Italy

217

218

Carmen Valli

Italy

Hiroyuki IshidaJapan

Kenta Kitamura

Japan

Yuhji Matsuo (Co-Chair)

Japan

Manabu Nabeshima

Japan

Seunghee Hong

Korea

Ahreum Kim

Korea

JoonHan Kim

Korea

Kwon Kim

Korea

SeungSu Kim

Korea

Myungduck Park

Korea

Ana Souza

MexicoLuuk Beurskens

Netherlands

Sebastiaan Hers

Netherlands

Smekens Koen

Netherlands

Binod Koirala

Netherlands

Jørgen Bækken

NorwayJarand Hole

NorwayJanusz Malesa

PolandManuela Fonseca

Portugal

Eugen Banches

Romania

Marius Danila

Romania

Cosmin Ghita

Romania

Nikita Bashkatov

RussiaTatiana Medyakova

RussiaEgor Mukhortov

RussiaSergey Scheglov

RussiaNikita Vasiliev

RussiaJan Petrovi č

Slovak Republic

Martin Vanek

Slovak Republic

Bronislava Žemberová

Slovak Republic

Maria Sicilia

Spain

Klaus Hammes

SwedenWarren Schenler

Switzerland

Ismail Aydil

TurkeyMichael Apicelli

United States

Russell Conklin

United States

Francesco Ganda

United States

Seungwook Ma

United States

Brian Shelbourn

United States

Thomas J. Tarka (Co-Chair)

United States

Expert representatives in a personal capacity
Marko Aunedi

Imperial College London

John Bistline

Electric Power Research Institute (EPRI)

Alain Burtin

EDF R&D

Matthew P. Crozat

Nuclear Energy Institute (NEI)

Brent Dixon

Idaho National Laboratory (INL)John Paffenbarger

Exelon Corporation

John Parsons

Massachusetts Institute of Technology (MIT)Dany Pudjianto

Imperial College London

Ulrik Stridbæk

OrstedChristian Stolzenberger

VGB PowerTech e.V.

Falko Ueckerdt

Potsdam Institute for Climate Impact Research (PIK)

Representatives of International Organisations
Marco ComettoInternational Atomic Energy Agency (IAEA)

Fernando De Sisternes

World Bank

Michael Taylor

International Renewable Energy Agency (IRENA)

219

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Photo credits pages 23, 91 and 211: High-voltage power lines (Shutterstock, Urbans); Hydro station (Shutterstock,stocksolutions); The EPR Nuclear Power Plant of Olkiluoto, Finland (Framatome) and solar panels and wind turbins(Shutterstock, Diyana Dimitrova).

Projected Costs of Generating Electricity
This joint report by the International Energy Agency and the OECD Nuclear Energy Agency is the ninth in aseries of studies on electricity generating costs. As countries work towards ensuring an electricity supply that isreliable, affordable and increasingly low carbon, it is crucial that policymakers, modellers and experts have attheir disposal reliable information on the cost of generation. This report includes cost data on power generationfrom natural gas, coal, nuclear, and a broad range of renewable technologies. For the first time, information on
the costs of storage technologies, the long-term operation of nuclear power plants and fuel cells is also included.
The detailed plant-level cost data for 243 power plants in 24 countries, both OECD and non-OECD, is based on
the contributions of participating governments and has been treated according to a common methodology inorder to provide transparent and comparable results.
Low-carbon electricity systems are characterised by increasingly complex interactions of different technologies
with different functions in order to ensure reliable supply at all times. The 2020 edition of Projected Costs ofGenerating Electricity thus puts into context the plain metric for plant-level cost, the levelised cost of electricity(LCOE). System effects and system costs are identified with the help of the broader value-adjusted LCOE,
or VALCOE metric. Extensive sensitivity analyses and five essays treating broader issues that are crucial inelectricity markets round out the complementary information required to make informed decisions. A key
insight is the importance of the role the electricity sector plays in decarbonising the wider energy sector throughelectrification and sector coupling.
The key insight of the 2020 edition of Projected Costs of Generating Electricity is that the levelised costs ofelectricity generation of low-carbon generation technologies are falling and are increasingly below the costsof conventional fossil fuel generation. Renewable energy costs have continued to decrease in recent years andtheir costs are now competitive, in LCOE terms, with dispatchable fossil fuel-based electricity generation in
many countries. The cost of electricity from new nuclear power plants remains stable, yet electricity from thelong-term operation of nuclear power plants constitutes the least cost option for low-carbon generation. At theassumed carbon price of USD 30 per tonne of CO2 and pending a breakthrough in carbon capture and storage,
coal-fired power generation is slipping out of the competitive range. The cost of gas-fired power generation has
decreased due to lower gas prices and confirms the latter’s role in the transition. Readers will find a wealthof details and analysis, supported by over 100 figures and tables, that establish the continuing value of the
Projected Costs of Generating Electricity as an indispensable tool for decision-makers, researchers and experts
interested in identifying and comparing the costs of different generating options in today’s electricity sector.

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