[orcid=0000-0002-3219-7587] [1] [] []

[cor1]Corresponding author: mailto:lgo<EMAIL_ADDRESS>
# Economics of nuclear power in decarbonized energy systems

Leonard Göke<EMAIL_ADDRESS>Alexander Wimmers  Christian von
Hirschhausen  Workgroup for Infrastructure Policy (WIP), Technische
Universität Berlin, Straße des 17. Juni 135, 10623 Berlin, Germany.  Energy,
Transportation, Environment Department, German Institute for Economic Research
(DIW Berlin), Mohrenstraße 58, 10117 Berlin, Germany.

###### Abstract

Many governments consider the construction of new nuclear power plants to
support the decarbonization of the energy system. On the one hand,
dispatchable nuclear plants can complement fluctuating generation from wind
and solar, but on the other hand, escalating construction costs and times
raise economic concerns. Using a detailed energy planning model, our analysis
finds that even if—despite the historic trend—overnight construction costs of
nuclear half to 4,000 US-$2018 per kW and construction times remain below 10
years, the cost efficient share of nuclear power in European electricity
generation is only around 10%. This analysis still omits social costs of
nuclear power, such as the risk of accidents or waste management. To recover
their investment costs, nuclear plants must operate inflexibly and at
utilization rates close to 90%. As a result, grid infrastructure, flexible
demand, and storage are more efficient options to integrate fluctuating wind
and PV generation.

###### keywords:

macro-energy systems nuclear power decarbonization integrated energy system

##  1 Introduction

In the current energy crisis, surging prices for fossil fuels add to the
debate on alternatives to gas, coal, and oil for energy generation. In
addition to ongoing and planned efforts to expand renewable energy sources,
many governments have recently declared or emphasized their intention to
invest in new nuclear power plants: Japan is considering to invest in new
reactors for the first time since the meltdown of the Fukushima Daiichi plant,
France recently pledged to construct up to 14 new plants, although specifics
remain unclear, and the United Kingdom announced funding for a Sizewell C
station to pursue their target of 24 GW nuclear capacity by 2050 (Financial
Times, 2022; New York Times, 2022; Bloomberg, 2022).

Currently surging prices for fossil fuels only encourage existing plans for
nuclear power that pre-date the crisis and originate from government
strategies to mitigate carbon emissions and combat climate change (Goldstein
et al., 2019). One the one hand, from a technical perspective, nuclear power
offers two advantages: First, nuclear plants are dispatchable. While today’s
plants do not operate flexibly and thus provide baseload supply, research
suggests new reactor concepts or even established light-water reactors are
capable of flexible operation (Jenkins et al., 2018; Lynch et al., 2022; MIT,
2018). As a result, nuclear power could complement fluctuating supply from
renewables and ensure security of supply in decarbonized energy systems.
Second, nuclear offers a great energy potential. Compared to renewables,
nuclear power is far less constrained by land availability or meteorological
conditions (Ritchie, 2022). At the same time, decarbonization in heating,
industry, and transport requires electricity as a primary source of energy,
either directly or indirectly using synthetic fuels produced from electricity
(Luderer et al., 2022; Bogdanov et al., 2021). Therefore, nuclear power could
substantially contribute to fulfilling the increasing demand for electricity.

On the other hand, there are economic arguments against investing into nuclear
power. New plants are capital-intensive; especially since actual construction
cost and time frequently exceed forecasts (Lovins, 2022; Rothwell, 2022). For
instance, in France, offical construction costs of Flamanville 3 so far
quadrupled to 12,600 €/kW while project completion is currently delayed by
more than a decade (EDF, 2022; Rothwell, 2022). Similarly, costs of Olkiluoto
3 in Finland tripled over a construction time of 17 years (Deutsche Welle,
2022). Costs of Hinkley Point C in the United Kingdom have doubled to date,
and the reactor is currently scheduled to start operation in 2027—11 years
after construction started (BBC, 2022). Whether this trend is reversible, is
subject to debate: Drawing on the historic expansion of pressurized water
reactors in France, Berthélemy and Escobar Rangel (2015) suggest reinforced
investments and standardization could lower construction costs and time.
Grubler (2010) however states that the expansion of pressurized water reactors
never achieved positive learning effects in the first place.

With the high costs of nuclear power, other technologies might be more
efficient to achieve decarbonization. Instead of potentially flexible nuclear
plants, imports from other regions, batteries, or seasonal storage using
synthetic fuels can cover demand when supply from wind and photovoltaic (PV)
is low. In addition, electrification in heating, industry, and transport adds
a substantial share of flexible demand. For example, electricity consumption
of electrolyzers can adjust to wind and solar generation, thereby supporting
the integration of renewables (Wang et al., 2018). In the same way, flexible
electric heating or charging of electric vehicles can adapt to renewable
supply (Schill and Zerrahn, 2020; Schuller et al., 2015). Beyond flexibility,
high investment costs can render nuclear power more expensive even compared to
renewables in unfavorable sites, like offshore wind turbines in deep waters or
PV in locations with little sunshine (McKenna et al., 2014).

Against this background, we investigate the economic efficiency of nuclear
power in two steps: First, we review projected and actual costs of new nuclear
capacity to compute the conceivable range of levelized costs of energy (LCOE),
discuss plausibility and compare the outcome to renewables. Second, we compute
the efficient share of nuclear power for previously obtained cost ranges in a
decarbonized energy system with a comprehensive energy planning model (Göke,
2021). Due to its extensive scope, the model can consider all alternatives to
nuclear power for the provision of flexibility, most importantly cross-border
exchange of energy, flexible demand, and storage technologies. Previous
research limited the scope to single regions omitting the exchange of energy
as a source of flexibility, neglected the demand-side flexibility induced by
electrification, or only featured short-term storage systems (Duan et al.,
2022; Baik et al., 2021). The cost review focuses on OECD countries due to
discrepancy of nuclear development between OECD and non-OECD countries
observed in Rothwell (2022); the techno-economic analysis covers the European
continent.

Inevitable simplifications of the techno-economic model are made in a way that
favors investment in nuclear power. Therefore, the computed share of nuclear
power in the energy mix is not an accurate estimate, but rather an upper bound
of what might be if significant nuclear cost reductions were to be achieved.
This applies in particular because our techno-economic analysis omits external
social costs of nuclear power associated with the risk of accidents, waste
management, and proliferation (Lévêque, 2014; Lordan-Perret et al., 2021;
Wheatley et al., 2016; Lehtveer and Hedenus, 2015). Although their robust
quantification is difficult, external costs for nuclear typically exceed
estimates for renewable energies (Stirling, 1997).

The remainder of this paper is structured as follows: The next section 2
describes the methodology to compute LCOEs and introduces the applied energy
planning model. Section 3 presents the results for LCOE analysis and the
planning model. In Section 4 these results are discussed and Section 5
concludes.

##  2 Methods

The first subsection 2.1 describes the computation of LCOEs for nuclear with
an emphasis on the review of construction costs and financing costs during
construction. Then, we introduce the energy planning model applied for a more
detailed evaluation of nuclear power. The applied model uses the open
AnyMOD.jl modeling framework and is described with greater detail in previous
publications (Göke et al., 2023; Göke, 2021).

###  2.1 Computation of LCOEs

Whether nuclear power is an economically efficient option for decarbonization
strongly depends on its costs. Operational costs for fuel or maintenance only
constitute a minor share while construction and financing account for 80% of
the total costs of nuclear power (Haas et al., 2019; MacKerron, 1992). At the
same time, costs of construction and financing are also subject to the
greatest uncertainty. Therefore, we review construction and financing costs in
greater depth and vary them across the observed range in the subsequent
analysis. For all other parameters, section A of the appendix provides a
review. Table 1 lists the assumptions used for the analysis. For these
parameters, we use optimistic assumptions that correspond to at least the 25th
percentile of the reviewed data. All monetary values in this paper are
inflation-adjusted and given in US-$2018. Section B of the appendix provides
the corresponding conversion factors.

To assess the range of overnight construction costs, we reviewed different
academic publications and industry reports providing 88 individual future
projections or reported costs for different reactor concepts and countries
(NREL, 2021; OECD and NEA, 2018; Shirvan, 2022; Rothwell, 2022; Barkatullah
and Ahmad, 2017; Lazard Ltd, 2021; Wealer et al., 2021; International Atomic
Energy Agency, 2020; Green, 2019; Rothwell, 2016; Duan et al., 2022; Grubler,
2010; Stein et al., 2022; MIT, 2018; Boldon and Sabharwall, 2014; Stewart and
Shirvan, 2021; NREL, 2021; IEA et al., 2020; Tolley et al., 2004; Dixon et
al., 2017). We put a focus on light-water reactors (LWR), the most common
reactor concept111Table 13 in the appendix shows that considering a broader
range of reactor types does not give lower cost projections., and OECD
countries since (Rothwell, 2022) observes non-OECD estimates are not
comparable. Cost projections are often provided in the means of n-th-of-a-kind
cost, presenting assumptions of sometimes significant cost reductions through
standardization and learning effects, e.g., (Shirvan, 2022). These projections
however are not given in relation to a specific year or cumulated investment,
complicating the comparison with cost projections for renewables.

The left boxplot in Fig. 1 summarizes the distribution of the 45 remaining
estimates. Overall, projections range from 1,914 US-$ per kW projected by OECD
and NEA (2018) to 12,600 US-$ per kW, the actual costs of the Flamanville-3
project (Rothwell, 2022). The median is at 5,430 US-$ per kW; 50% of estimates
are between 4,328 and 9,966 US-$ per kW. Future projections are generally much
lower than recent observations of actual costs for the European Pressurized
Reactor (EPR) and the Westinghouse AP1000. Observations of actual costs are
still preliminary since of the 4 relevant projects, Olkiluoto 3 has not yet
started commercial operation and Flamanville 3, Hinkley Point C and Voglte
Station are still under construction.222For the Shin Hanul 1 reactor that
recently started operation in South Korea, final costs were not publicly
disclosed. KEPCO’s financial statements suggest costs as low as 3,120 US-$ per
kW (KEPCO, 2022). This seems very low when taking a construction time of
approx. 12 years into account. Some literature suggests that historically,
learning rates of 5 to 10% were achieved in the nuclear industy which would
coincide with the mentioned projections (Berthélemy and Escobar Rangel, 2015;
Mignacca and Locatelli, 2020; Lloyd et al., 2020). However, the discrepancy
between projected and actual costs is in line with previous analyses on the
actual development of nuclear construction costs in Western countries since
the 1970s (Grubler, 2010; Davis, 2012; Koomey and Hultman, 2007; Escobar
Rangel and Leveque, 2015; Harding, 2007), placing doubt on supposed learning
rates.

Figure 1: Boxplots for overnight construction costs and time

The right boxplot in Fig. 1 presents a similar image for construction time.
Future projections range from 5 to 9 years, but recent and ongoing projects
are currently taking 10 to 17 years. These overruns drive up financing costs
and can also threaten energy security.

Table 1: Parameters for nuclear power Parameter |  Unit |  Value / Range
---|---|---
Overnight construction cost |  US-$/kW |  1,914 - 12,600
Annual fixed operational cost |  US-$/kW |  88.81
Variable operational cost (incl. fuel) |  US-$/MWh |  10.96
Capacity factor |  % |  95
Construction time |  years |  4 - 10
Depreciation period |  years |  40

Together, construction and financing costs constitute total construction costs
(TCC). Financing costs reflect the interest paid and are typically calculated
from the length of depreciation and the interest rate. Following the
methodology for nuclear power investments introduced in (Rothwell, 2016), our
analysis additionally accounts for costs during construction. We compute the
financing costs during construction $f$ using Equation 1 introduced by
Rothwell (2016). In the equation $i$ is the interest rate, $t$ the
construction time, and $c$ the overnight construction costs.

$f=(\frac{i}{2\cdot t}+\frac{i^{2}}{6\cdot t^{2}})*c$ (1)

For the comparison of LCOEs and analysis with the energy system model, the
annuity $a$ is computed for the sum of financing and overnight construction
costs according to Equation 2. In the formula, $d$ is the depreciation period.

$a=(f+c)\cdot\frac{(1+i)^{d}\cdot i}{(1+i)^{d}-1}$ (2)

Finally, the LCOE $l$ for a single year corresponds to the sum of annuity and
variable costs $v$ divided by the yearly generation. The yearly generation
corresponds to the product of the utilization rate $u$ and the numbers of
hours in a year.

$l=\frac{a+v\cdot u\cdot 8760}{u\cdot 8760}$ (3)

###  2.2 Energy system model

The deployed planning model decides on the resources and technologies to
supply an exogenous demand for energy services. Fig. 2 provides an overview of
the considered primary energy sources, secondary energy carriers, and the
categories for final demand. Besides nuclear power, supply of primary energy
is restricted to renewable sources, namely wind, hydro, photovoltaic, and
biomass. To capture intermediate steps between final demand and primary
supply, the model includes secondary carriers, like hydrogen or synthetic gas
and oil. Finally, the model must meet the demand for residential and
industrial heat, passenger and freight transport, synthetic fuels for aviation
and navigation, and electricity.

Figure 2: Overview of model structure

To cover final demand, the model decides on the expansion and operation of
predefined technologies. Technologies in the model generate, convert, store,
or transport energy carriers or provide an energy service. For instance, wind
turbines generate electricity that can either satisfy the final electricity
demand, be temporarily stored by a battery or be converted to hydrogen by an
electrolyzer or to heat by a heat pump to satisfy final demand. Overall, the
model includes 22 energy carriers and 125 distinct technologies. Except for
nuclear, the choice and parametrization of technologies is based on reports of
the Danish Energy Agency (2022) and on Robinius et al. (2020) for the
transport sector. The graph in Fig. 3 shows all technoloiges supplying
electricity. In the graph, vertices either represent energy carriers, depicted
as colored squares, or technologies, depicted as gray circles. Entering edges
of technologies refer to input carriers; outgoing edges refer to outputs. The
appendix C provides a comprehensive graphs with all considered technologies.

Figure 3: Technologies for electricity supply

The model’s objective is to minimize the total system costs which consist of
annualized expansion and operational costs for technologies and costs of
energy imports from outside the system. Accordingly, the model takes a
strictly techno-economic social planner approach and does not consider the
different agents within the system, such as producers, consumers, or
governments, and their behavior. The model is formulated as a linear
optimization problem without constraints on unit commitment. Therefore,
operational restrictions, e.g., ramping rates or start-up times, for nuclear
power, or any other technology, are neglected. Previous studies agree that
operational detail has little impact on results if models include options for
short-term flexibility, like batteries or demand-side response (Poncelet et
al., 2016, 2020). In addition, most technologies subject to considerable
operational restrictions are excluded from our analysis, for instance coal
power plants. As a result, the approach only creates a positive bias in favor
of nuclear power, which is in line with our intention to compute an upper
bound on its deployment.

The temporal scope of the model consists of a single year. Using a brownfield
approach, today’s transmission infrastructure is available without further
investment due to its long technical lifetime. In addition, the model can
decide to invest into additional infrastructure. The same applies to all kinds
of hydro power plants. Additional transmission capacities and all other
technologies require investment to be available. Technology and demand data is
based on projections for the year 2040. The model uses an hourly temporal
resolution for electricity to capture fluctuations of renewables and the
sensitivity of the power grid to imbalances. For other energy carriers, the
model uses a coarser but at least daily resolution to reduce computational
load and capture the inherent flexibility (Göke, 2021). For instance, hydrogen
uses a daily resolution to capture its physical inertia and the resulting
storage capabilities of pipelines. The resolutions used for other carriers are
discussed in the section below.

For timeseries of electricity demand, heat demand, and renewable supply, the
model uses historic data from 2008. Since the scope only includes a single
climatic year, planning does not consider extreme events with low renewable
supply but high demand. On the other hand, we neither consider the risk of
coincident nuclear outages threatening security of supply, for instance during
the Winter of 2022/2023 in France (New York Times, 2022).

####  2.2.1 Spatial resolution and starting grid

The map in Fig. 4 shows the spatial resolution of the model. The spatial scope
includes the European Union plus the United Kingdom, Switzerland, Norway, and
the entire Balkan region. The spatial resolution of the model divides this
scope into 96 clusters with distinct energy balances and capacities.

Figure 4: Pre-existing grid infrastructure (solid) and new potential
connections (dashed) in the model

Representation of the electricity grid distinguishes HVAC and HVDC grids and
aggregates clusters according to the balancing zones of the European power
market. On the one hand, this resolution neglects interzonal congestions, but
on the other hand, it allows to build on specific ENTSO-E data on limits and
costs for the expansion of net-transfer capacities in the European power grid
ENTSO-E (2020). More detailed resolutions must rely on unspecific cost
assumptions that range from 400 to 1,000 EUR per MW and km (Fürsch et al.,
2013; Gerbaulet and Lorenz, 2017). Neglecting interzonal congestions does not
impose a bias in favor of renewables and against nuclear power since nuclear
capacities are typically centralized in remote areas and require substantial
interzonal connection with demand centers. For renewables this is only
partially true: While onshore and especially offshore wind depend on
transmission grids, too, PV can be built very close to demand. In addition, we
use a transport instead of flow formulation for power grid operation since
previous research found this simplification to be sufficiently accurate
(Neumann et al., 2022).

To exchange hydrogen between clusters, the model can build transmission
infrastructure between neighboring clusters, or overseas in plausible cases.
These pipelines are subject to costs of 0.4 million EUR per GW and km and
energy losses of 2.44% per 1,000 km (Danish Energy Agency, 2022). The distance
between the geographic center of clusters serves as an estimate for pipeline
length.

####  2.2.2 Flexibility and electricity demand

Nuclear power has a key advantage over renewable wind or PV—it is
dispatchable. In our analysis, nuclear plants are considered to be fully
flexible and are not subject to operational restrictions. To what extent these
characteristic drives investment strongly depends on if and how a model
includes alternatives to nuclear for the provision of flexibility. One
flexibility option, the interconnection of regions, is covered by the large
spatial scope and consideration of transmission infrastructure in our model.
This section details the representation of two additional options: energy
storage and flexible demand.

First, the model includes several technologies for electricity storage. It can
invest in batteries or utilize the pre-existing pumped storage and reservoir
plants. Second, and more importantly, the model considers investment into
storage technologies for other carriers, too. Tanks and caverns for hydrogen
storage decouple electricity consumption by electrolyzers from hydrogen demand
adding substantial flexibility since the modeled system requires a great
amount of hydrogen for high temperature process heat and synthetic aviation
fuels. In addition, using stored hydrogen for electricity generation provides
an option for long-term storage that batteries or pumped storage cannot.
Finally, the model includes different technologies for heat storage. At
additional investment costs, electric heating systems on the consumer level
can be equipped with short-term heat storages to mitigate load peaks. In
heating networks storage options include water tanks for short and thermal
pits for long storage durations.

Beyond storage, the model captures to what extent final demand is elastic and
can adapt to supply. To represent this, it varies temporal resolution by
energy carrier. For the final demand for electricity, we assume no elasticity
and use an hourly resolution.

The demand for residential and industrial heat uses a 4-hour resolution, but
the generation profile of each individual heating system on the consumer level
is fixed to match demand. Technologies within heating networks can instead
operate flexible but must jointly meet the final demand of the connected
consumers, similar to the power grid. In residential heating, the 4-hour
resolution captures the thermal inertia of buildings (Heinen et al., 2017); in
heating networks the inertia of the network itself (Triebs and Tsatsaronis,
2022); and in industrial heating the possibility to reschedule processes. It
should be noted that despite the 4-hour resolution, industrial heating is
mostly inflexible in this approach, because demand is almost constant, and
systems must continuously operate close to full capacity.

Demand from BEVs is flexible within limits: First, an hourly profile restricts
charging and reflects when BEVs are connected to the grid. Second, for each
day the sum of charged electricity must match consumption. To implement the
latter, transport services use a daily resolution in the model. This approach
is a compromise regarding the flexibility of BEVs in future power systems
which is still subject to significant uncertainty. On the one hand, we assume
charging can be flexible to some degree and can adapt to supply (Strobel et
al., 2022). On the other, we do not consider bidirectional loading, also known
as vehicle-to-grid, where BEVs can operate like regular batteries and supply
energy back to the grid (Hannan et al., 2022). Apart from BEVs, other
electricity consumption in the transport sector, for instance for rail
transport, is inflexible and uses an hourly resolution.

####  2.2.3 Renewable supply

In the model, the level of investment into nuclear power depends on the
potential and costs of its alternative renewable energy. Therefore, this
section provides further details on the used assumptions regarding renewable
supply.

Assumptions on the technical potential of wind and solar can vary
substantially. In our study, we use data from Auer et al. (2020) for the
potential of wind and solar. To put these numbers into context, we compare
them against results of a meta-analysis on renewable potential in Germany in
the appendix D (Risch et al., 2022). The material demonstrates that the
assumed potential is in line with results of the meta-analysis, except for PV,
for which our assumptions are more conservative.

To reflect that sites for renewables differ in quality, wind and PV
technologies are further subdivided. Onshore wind and openspace PV each have
three sub-technologies; rooftop PV has two. Energy yield and capacity
potential for each sub-technology are computed for each of the 96 clusters
based on the respective distribution of full-load hours. Offshore wind is
subdivided into two technologies with identical full-load hours, but different
investment costs representing shallow and deep waters. Figures in section D of
the appendix sort the entire potential of different renewables by full-load
hours and demonstrate how increased deployment results in smaller yields.

The use of biomass in each country is subject to an upper energy limit that
sums to 1,081 TWh for the entire model (Institute for Energy and Transport
(2015), Joint Research Center). In addition to domestic production, the model
can import renewable hydrogen by ship at costs of 131.8 US-$2018 per MWh and
by pipeline from Marocco or Egypt at 90.7 and 86.8 US-$2018 per MWh,
respectively (Hampp et al., 2021).

##  3 Results

This section 3 first compares the computed range of LCOEs for nuclear power
with renewable sources. Afterwards, we present the results of the more
elaborate analysis applying the energy planning model.

###  3.1 Levelized cost comparison

Based on the review of construction projections, we compute a conceivable
range for LCOE for nuclear power using the formula presented in section 2.1.
Fig. 5 compares this range against historic LCOEs on the left side (a) and
against cost projections for renewable energy on the right (b). Historic LCOEs
for all energy sources build on the corresponding reports by Lazard Ltd from
2009 to 2021.333To exclude subsidies and harmonize interest rates, we re-
computed LCOEs based on the reported techno-economic assumptions instead of
directly copying the values in the Lazard reports (Lazard Ltd, 2009, 2010,
2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021). The
projection for nuclear results from the parameters in Table 1, projections for
renewable sources use 2040 data from Danish Energy Agency (2022) for cost
assumptions and the 10th to 90th percentile of respective energy yield in
Europe for full-load hours. For a full documentation of assumptions on costs
and renewable potential, see the respective sections in the appendix A and B.

Figure 5: Comparison of LCOEs for generation technologies

The comparison of historic LCOEs shows how, in the past, costs for nuclear
power increased but dropped for renewable energy, in particular for PV.
Current costs for nuclear power reported by Lazard are at the upper end of our
projections for the future. For wind, current costs are already within the
project range; for PV, current costs are above projections implying a
continuous degression of costs. Overall, the comparison of projected LCOEs
suggests that nuclear can only compete with renewables if the industry can
reverse the historic trend and achieve substantial learning effects and
economies of scale to reduce project costs.

###  3.2 Planning model analysis

However, comparing LCOEs for nuclear and renewables is of limited insight
because nuclear and renewables ultimately provide different goods: nuclear
plants are dispatchable and can, within limits, adapt supply to demand;
renewable energy sources like wind and PV cannot. Therefore, we elaborate our
comparison and deploy a techno-economic planning model of the European energy
system for further analysis. The model captures fluctuations of wind and PV
and assumes fully flexible operation of nuclear plants. Its objective is to
minimize the sum of investment and operational costs for meeting energy demand
in a fully decarbonized system.

Fig. 6 presents the key results of the energy system model analysis. On the
left axis, the graph shows the share of nuclear power in total electricity
generation depending on the overnight construction costs and time. The right
axis provides the corresponding distribution of cost projections in the
literature and actual costs reported by recent projects.

Figure 6: Generation share of nuclear and distribution of overnight cost
projections

The results confirm the tentative conclusion from the LCOE analysis. Only if
construction costs and time drop substantially, nuclear power provides a
significant share of electricity. At 7 years construction time, overnight
costs must drop to 4,120 US-$ per kW to achieve a 10% share in generation, at
4 years construction time this threshold increases to 4,450 US-$ per kW. At
the same time, only 20% of overnight construction cost projections are below
4,120 US-$ per kW and actual costs of recent projects lie above 7,500 US-$ per
kW. At the lowest construction costs, projected by OECD and NEA (2018),
nuclear power provides up to 52 % of total electricity generation. Non-nuclear
electricity generation largely corresponds to wind and PV. Other sources are
hydro which is kept at today’s level supplying 540 TWh. Biomass supplies at
most 13.6 TWh since the model predominantly deploys the limited potential to
cover the demand for synthetic fuels instead.

Despite its assumed flexibility, nuclear power rather substitutes than
complements fluctuating supply from wind and PV generation. In the most
extreme case with a 52 % generation share, nuclear power plants are operating
at a utilization rate of 94.4%; close to the 95% limit. This changes only
slightly at smaller nuclear and consequently higher renewable shares. At a
nuclear share of 7.9%, utilization still amounts to 88.7%, which is above the
average of European nuclear plants operating today (Wealer et al., 2021). This
implies that even if technically capable, nuclear power is economically not
well suited to provide system flexibility due to its high investment costs.

To highlight the system’s renewable integration, Fig. 7 exemplarily compares
the residual load curves of Germany for a system with the highest nuclear
share of 52% and no nuclear capacity at all. Residual load is the remaining
electricity demand after subtracting supply of fluctuating renewables from
total demand. Plotted in descending order, the curve shows the residual peak
load on the y-axis, the amount of residual demand as the area above the
x-axis, and excess energy as the area below the x-axis. Appendix F shows an
extract of the hourly model results from which the duration curves were
computed.

Figure 7: Residual load curve for Germany in a system with a nuclear share of
52% (left) and no nuclear at all (right)

In the graphs, there are two residual load curves: ’Flexible demand’ shows the
actual residual load in the model; ’inflexible demand’ is computed ex-post
assuming demand cannot adapt to supply. To what extent demand is flexible
depends on its use. The conventional final demand for electricity is
inflexible, but only constitutes around 39% of total demand, exact shares
differing from case to case. Additionally, the creation of synthetic fuels
takes up 24%, electrified transportation 20% and heating 17% of total
electricity demand. This demand can be flexible within specific restrictions
detailed in Section 2. Some of this flexibility comes without costs, like
flexible charging of battery electric vehicles, but most require the model to
invest, for instance into heat or hydrogen storage.

In the 52% nuclear system, wind and PV only account for 10% of generation in
Germany and the need for flexibility is small. The residual load is flat and
does not substantially adapt to supply. For the system without nuclear,
results are contrary. To reduce the amount of residual demand and excess
energy, demand substantially adapts to supply. For instance, electrolyzers
operate more flexibly, thereby causing their full-load hours to decrease to
3,343 compared to 5,979 in the 52% nuclear system while more than doubling
their capacity from 35.9 to 77.1 GW. Investments in heat and hydrogen storage
provide additional flexibility. Storage capacities for district heating
increase to 40.9 TWh compared to 18.2 Twh in the 52% nuclear system; for
hydrogen even to 12.4 from 1.7 TWh.

Coloured areas in the graph indicate how the system covers the residual load.
In the 52% nuclear system, 132 GW of nuclear operate at full capacity and
cover the predominant share of residual load. Additional imports of 86.9 TWh
from neighboring countries cover peak loads. Vice versa, Germany exports 78.5
TWh of excess supply. In the system without nuclear, the residual load is
smaller, but imports are higher and cover 244 TWh of demand—the predominant
share of residual load. Dispatchable thermal plants fueled by hydrogen and
biogas only provide 7.3 TWh of generation. In both cases, there is no
additional investment in electricity storage and both systems only deploy the
existing pumped storage and hydro reservoirs.

Although the integration of renewables requires additional investments,
nuclear power is only a cost-efficient alternative at very low construction
costs as shown previously. Otherwise, investment costs of nuclear
significantly outweigh the costs of renewables and corresponding flexibility
options. For instance, the system without nuclear shows 9.7 billion US-$ of
additional annualized investment into demand side flexibility compared to the
52% nuclear system. Adding 8.2 billion US-$ for additional grid infrastructure
and 3.0 billion US-$ for thermal plants, total costs of renewable integration
add up to 20.9 billion US-$. But on the other hand, even if overnight
construction costs of nuclear drop to 5,430 US-$ per kW, the median of
projections in Fig. 6, the 52% nuclear system is already 46.9 billion US-$
more expensive than the system without nuclear. This 46.9 billion US-$
difference is composed of a 67.8 billion US-$ increase in generation costs
only partially offset by 20.9 billion US-$ of additional integration costs in
the system without nuclear exclusively deploying renewables. For reference,
the total costs of renewable generation amount to 150.5 billion US-$ in this
case.

Fig. 8 further investigates the role of import and export for both systems. It
shows electricity generation and net exchange of hydrogen and electricity by
high-voltage alternating current (HVAC) or direct current (HVDC) grids for
each country. Exchange of hydrogen and electricity increases substantially in
the system without nuclear reflecting the 8.2 billion US-$ rise of costs for
grid infrastructure. The maps show that this infrastructure not only serves
renewable integration but reduces generation costs as well because it allows
to utilize renewables where full-load hours are highest, for instance in
southern Europe for PV or in the North Sea for offshore wind.

Figure 8: Electricity generation and exchange the 52% nuclear system (top) and
the system without nuclear (bottom)

For a comprehensive overview of energy flows and conversion processes in the
compared energy systems, see appendix H.

##  4 Discussion

Although building on an extensive review of cost projections and a detailed
planning model, our analysis faces some inevitable limitations. Overall, these
limitations impose a positive bias on nuclear power, so results are rather an
upper bound than reliable estimates for the cost-efficient share of nuclear.

First, there are simplifications of the techno-economic model itself. We
consider construction cost of nuclear (and all other technologies) as
exogenous, although costs can depend on the level of investment due to
learning effects. Therefore, cases in Fig. 6 with low nuclear shares, but
construction costs dropping below 5,000 US-$ per kW, are implausible. If
learning can in fact achieve such a drop in costs, it will require capacity
investments that will already raise the share of nuclear in power generation
above 10%. To achieve this share, 41 GW of nuclear would have to be built
which approximately corresponds to 25 EPR-sized reactors. A nuclear share of
50% requires 132.5 GW of capacity, i.e. 83 EPR-sized reactors. Given the
current state of ongoing projects in France, Finland and the UK, this seems
rather challenging (Rothwell, 2022). Considering learning effects endogenously
is not an option, due to the uncertainty of their existence and computational
limits (Berthélemy and Escobar Rangel, 2015; Grubler, 2010; Harding, 2007).

The model focuses on the single year 2040, but decarbonization of the energy
system is a process over several decades. Computational limits however require
to limit the analysis to one year. On the one hand, this year should be
sufficiently far in the future to allow for learning effects of nuclear. On
the other hand, years further into the future will increasingly favor
renewables since learning effects are expected to keep reducing their costs,
too (Danish Energy Agency, 2022). Against this background, we choose 2040 as
the earliest date until that substantial reductions of costs are conceivable,
an optimistic assumption considering the construction times of recent European
nuclear projects.

On the more technical side, we omit unit commitment constraints, as already
mentioned. Of all considered technologies, these operational restrictions are
most pronounced for nuclear power. The method section discusses further
technical limitations like the representation of extreme events and grid
constraints.

Second, some adverse characteristics of nuclear power escape the techno-
economic perspective of the analysis. For instance, costs of managing nuclear
waste and decommissioning old plants are substantial but not included in our
analysis due to unavailability of reliable data and the long time frame
(Lordan-Perret et al., 2021; Lovins, 2022). Furthermore, nuclear power carries
the risk of severe accidents, proliferation of nuclear material and
radioactive contamination (Lévêque, 2014; Wheatley et al., 2016; Lehtveer and
Hedenus, 2015). Although robust quantification of these externalities is
difficult and therefore not part of the analysis, their social costs typically
exceed estimates for renewable energy (Stirling, 1997).

##  5 Conclusion and Policy Implications

Our least-cost analysis indicates that nuclear power is only a cost efficient
option for full decarbonization of Europe’s energy system if construction
costs at least half and drop below 4,000 US-$2018 per kW. In contrast, costs
and construction time for nuclear power in the last ten years have been
increasing rather than decreasing. This assessment uses optimistic assumptions
for other characteristics of nuclear, like availability and operational costs,
and still omits social costs, like the risk of accidents or waste management,
since these externalities escape robust quantification.

Results show that nuclear power reduces investments into system flexibility.
Since nuclear plants are, in contrast to wind or PV, dispatchable, their
system integration induces lower costs for grid infrastructure, energy
storage, demand-side flexibility, or thermal backup plants. However, if
generation costs of nuclear do not drop substantially, they outweigh this
benefit. For instance, assuming overnight construction costs of 5,430 US-$ per
kW, the median of compiled projections, a 52% generation share of nuclear will
reduce investments into system flexibility by 20.9 bn US-$ per year but
increase generation costs by 67.8 bn US-$ or 44% compared to a fully renewable
system.

A crucial detail to understand these results is that apart from grid
infrastructure, most investments into system flexibility occur outside of the
power sector. Instead of battery storage or conventional demand-side
management, new electricity demand induced by the decarbonization of heating,
transport, and industry adds substantial flexibility. For instance, hydrogen
production, accounting for 25% of electricity demand, supports renewable
integration by adapting to renewable supply and using storage to match
production with demand.

Results do not indicate that fluctuating renewables and flexibly operated
nuclear plants are economic complements. Even when neglecting operational
restrictions, nuclear power runs at utilization rates close to 90%. From a
cost perspective, it is preferable to invest into system flexibility rather
than running capital-intensive nuclear plants at low utilization rates.

Our analysis puts previous least-cost analyses on nuclear power that were
limited to the power sector and a single country into perspective. As we
showed, this setup omits two key options for system integration of fluctuating
renewables: First, grid infrastructure connecting regions with different
generation profiles, and second, the potential flexibility of electricity
demand added to decarbonize the heating, transport, and industry sector. As a
result, previous analyses overestimated the value of dispatchable nuclear
power plants. Not considering other types of firm generation, like hydrogen or
biogas fueled plants, adds to this bias.

Our results suggest that efforts to advance nuclear technology—if undertaken
at all—should focus on reducing costs and construction time. If plans to
extend nuclear capacity are followed through, and renewable investments
consequently neglected, project construction delays might put European energy
security at risk. Concepts for more flexible operation are not promising
because even fully flexible plants almost exclusively operate at full capacity
for economic reasons. Since the share of nuclear also affects investments into
grid infrastructure and energy storage, path dependencies in system
development arise. The risk of nuclear power not achieving substantial cost
reductions should thus be accounted for in long-term planning. Potential non-
electrical applications for nuclear power, such as high temperature provision
for industrial processes, are not considered in this paper but should be
addressed in future research.

## Supplementary material

All data and running scripts used in this paper are openly available here:
https://github.com/leonardgoeke/EuSysMod/tree/greenfield_nu Due to their size,
model outputs are not part of the repository but are available upon request.

The applied model uses the open AnyMOD.jl modeling framework (Göke, 2021). The
applied version of the tool is openly available under this link.
https://github.com/leonardgoeke/AnyMOD.jl/releases/tag/flexibleElectrificationWorkingPaper

##  6 Acknowledgments

The research leading to these results has received funding from the Deutsche
Forschungsgemeinschaft (DFG) under project number 423336886.

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##  Appendix A Review of other parameters

Table 14 show how future projections for overnight construction costs are
substantially below recently reported costs. Table 13 compares the costs of
different reactor types. Table 15 provides the factors for inflation
adjustments normalized to 2018 following inflationtool.com.

Figure 9: Boxplot diagram for for capacity factors, construction time, fuel cost, operational lifetime, efficiency and combined O&M cost Table 2: Unfiltered overnight construction costs for nuclear plants. Reference |  Page |  Cost Description |  Reactor |  Reference Year |  Capacity in kW |  OCC |  Unit
---|---|---|---|---|---|---|---
Barkatullah and Ahmad (2017) |  132 |  Asssumed parameters |  n.a. |  2017 |  n.a. |  4646 |  US-$(2018) per kWe
Boldon and Sabharwall (2014) |  19 |  Input Water-Cooled SMR |  SMR |  2014 |  1260 |  3767 |  US-$(2011) per kW
Stein et al. (2022) |  30 |  Lower Cost, High Learning (Best Case) // FOAK CAPEX |  Traditional Nuclear |  2020 |  n.a. |  4783 |  US-$(2020) per kWe
Stein et al. (2022) |  30 |  Lower Cost, High Learning (Best Case) // FOAK CAPEX |  SMR |  2020 |  n.a. |  5108 |  US-$(2020) per kWe
Stein et al. (2022) |  30 |  Lower Cost, High Learning (Best Case) // FOAK CAPEX |  HTGR |  2020 |  n.a. |  5518 |  US-$(2020) per kWe
Stein et al. (2022) |  30 |  Lower Cost, High Learning (Best Case) // FOAK CAPEX |  Advanced reactor with thermal storage (ARTES) |  2020 |  n.a. |  4000 |  US-$(2020) per kWe
Stein et al. (2022) |  30 |  Upper Cost, Low Learning (Worst Case) // FOAK CAPEX |  Traditional Nuclear |  2020 |  n.a. |  6338 |  US-$(2020) per kWe
Stein et al. (2022) |  30 |  Upper Cost, Low Learning (Worst Case) // FOAK CAPEX |  SMR |  2020 |  n.a. |  6974 |  US-$(2020) per kWe
Stein et al. (2022) |  30 |  Upper Cost, Low Learning (Worst Case) // FOAK CAPEX |  HTGR |  2020 |  n.a. |  7500 |  US-$(2020) per kWe
Stein et al. (2022) |  30 |  Upper Cost, Low Learning (Worst Case) // FOAK CAPEX |  Advanced reactor with thermal storage (ARTES) |  2020 |  n.a. |  6220 |  US-$(2020) per kWe
Dixon et al. (2017) |  xii |  Mean Costs |  Thermal LWR Reactor |  2017 |  n.a. |  4300 |  US-$(2017) per kW
Dixon et al. (2017) |  xii |  Mean Costs |  Fast Reactors |  2017 |  n.a. |  4700 |  US-$(2017) per kW
Dixon et al. (2017) |  xii |  Mean Costs |  Gas-cooled reactors |  2017 |  n.a. |  5170 |  US-$(2017) per kW
Dixon et al. (2017) |  xii |  Mean Costs |  PHWR |  2017 |  n.a. |  4230 |  US-$(2017) per kW
Duan et al. (2022) |  2 |  Capital Costs |  US nuclear |  n.a. |  n.a. |  4000 |  US-$ per kW
Duan et al. (2022) |  2 |  Capital Costs |  US nuclear |  n.a. |  n.a. |  6317 |  US-$ per kW
Green (2019) |  18 |  HTGR |  SMR |  2016 |  250 |  5000 |  US-$ per kW
Green (2019) |  18 |  CAREM25 |  SMR |  2017 |  25 |  21900 |  US-$ per kW
Green (2019) |  21 |  Median (Low) |  SMR |  2013 |  45 |  4000 |  US-$ per kW
Green (2019) |  21 |  Median (High) |  SMR |  2013 |  45 |  16300 |  US-$ per kW
Green (2019) |  21 |  Median (Low) |  SMR |  2013 |  225 |  3200 |  US-$ per kW
Green (2019) |  21 |  Median (High) |  SMR |  2013 |  225 |  7100 |  US-$ per kW
Grubler (2010) |  5179 |  Capital Costs France with no cost increase since 1998 |  French PWR |  1998 |  n.a. |  2600 |  US-$(2008) per kWe
Grubler (2010) |  5179 |  Capital Costs France with no cost increase since 1998 |  French PWR |  1998 |  n.a. |  2100 |  US-$(2008) per kWe
International Atomic Energy Agency (2020) |  56 |  SMART (FOAK) |  SMR |  2020 |  30 |  10000 |  US-$ per kW
IEA et al. (2020) |  49 |  Nuclear New Build - France |  EPR |  2019 |  1650 |  4013 |  US-$ per kW
IEA et al. (2020) |  49 |  Nuclear New Build - Japan |  ALWR |  2019 |  1152 |  3963 |  US-$ per kW
IEA et al. (2020) |  49 |  Nuclear New Build - Korea |  ALWR |  2019 |  1377 |  2157 |  US-$ per kW
IEA et al. (2020) |  49 |  Nuclear New Build - Russia |  VVER |  2019 |  1122 |  2271 |  US-$ per kW
IEA et al. (2020) |  49 |  Nuclear New Build - Slovak Republic |  Other nuclear |  2019 |  1004 |  6920 |  US-$ per kW
IEA et al. (2020) |  49 |  Nuclear New Build - United States |  LWR |  2019 |  1100 |  4250 |  US-$ per kW
IEA et al. (2020) |  49 |  Nuclear New Build - China |  LWR |  2019 |  950 |  2500 |  US-$ per kW
IEA et al. (2020) |  49 |  Nuclear New Build - India |  LWR |  2019 |  950 |  2778 |  US-$ per kW
Lazard Ltd (2021) |  18 |  Nuclear (New Build, Low Case) |  n.a. |  2021 |  2200 |  7800 |  US-$(2022) per kWe
Lazard Ltd (2021) |  18 |  Nuclear (New Build, High Case) |  n.a. |  2021 |  2200 |  12800 |  US-$(2022) per kWe
MIT (2018) |  236 |  Overnight Costs (Standard LWR) |  LWR |  2018 |  n.a. |  5000 |  US-$(2017) per kWe
MIT (2018) |  70 |  Total Overnight Costs |  Molten Salt Reactor |  2018 |  2275 (MWth) |  5400 |  US-$(2017) per kW
MIT (2018) |  236 |  FOAK CAPEX |  HTGR |  2017 |  n.a. |  5200 |  US-$(2017) per kW
MIT (2018) |  159, 220 |  Benchmark Scenario Cost |  Nuclear |  2018 |  n.a. |  4900 |  US-$(2017) per kW
MIT (2018) |  220 |  Barakah OCC |  APR1400 |  2010 |  n.a. |  4000 |  US-$(2017) per kW
MIT (2018) |  149 |  US (high) |  Nuclear |  2018 |  1000 |  6880 |  US-$(2017) per kW
MIT (2018) |  149 |  US (nominal) |  Nuclear |  2018 |  1000 |  5500 |  US-$(2017) per kW
MIT (2018) |  149 |  US (low) |  Nuclear |  2018 |  1000 |  4100 |  US-$(2017) per kW
MIT (2018) |  149 |  US (very low) |  Nuclear |  2018 |  1000 |  2750 |  US-$(2017) per kW
MIT (2018) |  150 |  China (nominal) |  Nuclear |  2018 |  1000 |  2800 |  US-$(2017) per kW
MIT (2018) |  150 |  China (low) |  Nuclear |  2018 |  1000 |  2080 |  US-$(2017) per kW
MIT (2018) |  151 |  France (nominal) |  Nuclear |  2018 |  1000 |  6800 |  US-$(2017) per kW
MIT (2018) |  151 |  France (low) |  Nuclear |  2018 |  1000 |  5070 |  US-$(2017) per kW
MIT (2018) |  150 |  UK (nominal) |  Nuclear |  2018 |  1000 |  8140 |  US-$(2017) per kW
MIT (2018) |  150 |  UK (low) |  Nuclear |  2018 |  1000 |  6070 |  US-$(2017) per kW
Laboratory (2016) |  46 |  LCOE build-up for GWe LWR |  LWR |  n.a. |  n.a. |  26.99 |  US-$ per MWh
NREL (2021) |  Xls Sheet ”Nuclear” |  Nuclear-Moderate |  Nuclear |  2019 |  n.a. |  6297 |  US-$(2019) per kWe
OECD and NEA (2018) |  68 |  Nuclear (min) |  n.a. |  2020 |  535 |  1807 |  US-$(2015) per kWe
OECD and NEA (2018) |  68 |  Nuclear (mean) |  n.a. |  2020 |  1343 |  4249 |  US-$(2015) per kWe
OECD and NEA (2018) |  68 |  Nuclear (median) |  n.a. |  2020 |  1300 |  4896 |  US-$(2015) per kWe
OECD and NEA (2018) |  68 |  Nuclear (max) |  n.a. |  2020 |  3300 |  6215 |  US-$(2015) per kWe
Rothwell (2016) |  106 |  FOAK, no sec |  SMR |  2013 |  360 |  7343 |  US-$ per kW
Rothwell (2016) |  106 |  NOAK, no sec |  SMR |  2013 |  360 |  5357 |  US-$ per kW
Rothwell (2016) |  106 |  FOAK, sec = 0.1% |  SMR |  2013 |  360 |  6722 |  US-$ per kW
Rothwell (2016) |  106 |  NOAK, sec = 0.1% |  SMR |  2013 |  360 |  4885 |  US-$ per kW
Rothwell (2022) |  2 |  Taishin 1 & 2, China |  EPR |  2019 |  1600 |  3200 |  US-$(2018) per kWe
Rothwell (2022) |  2 |  Sanmen, China |  AP1000 |  2018 |  1150 |  3270 |  US-$(2018) per kWe
Rothwell (2022) |  6 |  NuScale NOAK |  SMR |  2022 |  n.a. |  3600 |  US-$(2018) per kWe
Rothwell (2022) |  4 |  Belgium |  EPR |  2015 |  1600 |  5515 |  US-$(2018) per kWe
Rothwell (2022) |  4 |  UK |  EPR |  2015 |  1600 |  6588 |  US-$(2018) per kWe
Rothwell (2022) |  4 |  Hungary |  VVER1200 |  2015 |  1200 |  6745 |  US-$(2018) per kWe
Rothwell (2022) |  2 |  Olkiluoto, Finland |  EPR |  2020 |  1600 |  7600 |  US-$(2018) per kWe
Rothwell (2022) |  6 |  NuScale FOAK |  SMR |  2022 |  n.a. |  8000 |  US-$(2018) per kWe
Rothwell (2022) |  2 |  Hinkley Point, Uk |  EPR |  2019 |  1600 |  9300 |  US-$(2018) per kWe
Rothwell (2022) |  2 |  Vogtle Station, US |  AP1000 |  2021 |  1150 |  12000 |  US-$(2018) per kWe
Rothwell (2022) |  6 |  TerraPower FOAK |  SMR |  2022 |  n.a. |  12000 |  US-$(2018) per kWe
Rothwell (2022) |  2 |  Flamanville, France |  EPR |  2020 |  1600 |  12600 |  US-$(2018) per kWe
Shirvan (2022) |  7 |  10th unit AP1000 (Should cost) |  AP1000 |  2019 |  1150 |  2900 |  US-$(2018) per kWe
Shirvan (2022) |  7 |  Next AP1000 (Should cost) |  AP1000 |  2019 |  1150 |  4300 |  US-$(2018) per kWe
Shirvan (2022) |  7 |  10th unit AP1000 (Post-COVID high estimate) |  AP1000 |  2022 |  1150 |  4500 |  US-$(2022) per kWe
Shirvan (2022) |  7 |  Historic (Pre-Three-Mile-Island) |  PWR |  1979 |  1200 |  4700 |  US-$(2018) per kWe
Shirvan (2022) |  7 |  Next AP1000 (Post-COVID high estimate) |  AP1000 |  2022 |  1150 |  6800 |  US-$(2022) per kWe
Shirvan (2022) |  7 |  Vogtle Station, US (Official) |  AP1000 |  2021 |  1150 |  7956 |  US-$(2018) per kWe
Shirvan (2022) |  7 |  Vogtle Station, US (Independent) |  AP1000 |  2022 |  1150 |  9200 |  US-$(2018) per kWe
Shirvan (2022) |  7 |  Historic (Post-Three-Mile-Island) |  PWR |  ¿1979 |  1200 |  9512 |  US-$(2018) per kWe
Stewart and Shirvan (2021) |  13 |  FOAK (US) |  Large Passive Safety PWR |  2018 |  n.a. |  4328 |  US-$(2018) per kWe
Stewart and Shirvan (2021) |  13 |  FOAK (China) (min) |  Large Passive Safety PWR |  2018 |  n.a. |  1700 |  US-$(2018) per kWe
Stewart and Shirvan (2021) |  13 |  FOAK (China) (max) |  Large Passive Safety PWR |  2018 |  n.a. |  2200 |  US-$(2018) per kWe
Stewart and Shirvan (2021) |  13 |  First-4 unit average |  Large Active Safety PWR |  2018 |  n.a. |  5337 |  US-$(2018) per kWe
Stewart and Shirvan (2021) |  13 |  APR1400 4-unit average - Barakah |  APR1400 |  2018 |  n.a. |  4358 |  US-$(2018) per kWe
Stewart and Shirvan (2021) |  13 |  10th of a kind |  SMR |  2018 |  n.a. |  3856 |  US-$(2018) per kWe
Tolley et al. (2004) |  7 |  No-Policy Nuclear LCOE Installations (Max OCC) |  Advanced Nuclear Reactor |  2003 |  1000 |  1800 |  US-$(2003) per kW
Wealer et al. (2021) |  5 |  Inputs for MC |  n.a. |  2018 |  1600 |  4000-9000 |  US-$(2018) per kWe
Table 2: (continued) Table 3: Overnight construction costs reported for LWR in OECD countries. Reference | Cost Description | Cost in US-$(2018) per kW
---|---|---
Lazard Ltd (2021) | Nuclear (New Build, High Case) | 10883.42828
Lazard Ltd (2021) | Nuclear (New Build, Low Case) | 6632.089108
Rothwell (2022) | Flamanville, France | 12600
Rothwell (2022) | Hinkley Point, Uk | 9300
Rothwell (2022) | Olkiluoto, Finland | 7600
Rothwell (2022) | Vogtle Station, US | 12000
Shirvan (2022) | Historic (Post-Three-Mile-Island) | 9512
Shirvan (2022) | Historic (Pre-Three-Mile-Island) | 4700
Shirvan (2022) | Vogtle Station, US (Independent) | 9200
Shirvan (2022) | Vogtle Station, US (Official) | 7956
Table 4: Overnight construction costs projected for LWR in OECD countries. Reference |  Cost Description |  Cost in US-$(2018) per kW
---|---|---
Barkatullah and Ahmad (2017) |  Asssumed parameters |  4646
Stein et al. (2022) |  Lower Cost, High Learning (Best Case) // FOAK CAPEX |  4640.535558
Stein et al. (2022) |  Upper Cost, Low Learning (Worst Case) // FOAK CAPEX |  6149.218977
Dixon et al. (2017) |  Mean Costs |  4404.92
Duan et al. (2022) |  Capital Costs 1 |  4000
Duan et al. (2022) |  Capital Costs 2 |  6317
IEA et al. (2020) |  Nuclear New Build - France |  3941.656026
IEA et al. (2020) |  Nuclear New Build - Japan |  3892.544937
IEA et al. (2020) |  Nuclear New Build - Korea |  2118.652392
IEA et al. (2020) |  Nuclear New Build - Slovak Republic |  6796.974757
IEA et al. (2020) |  Nuclear New Build - United States |  4174.442589
MIT (2018) |  Benchmark Scenario Cost |  5019.56
MIT (2018) |  France (low) |  5193.708
MIT (2018) |  France (nominal) |  6965.92
MIT (2018) |  Overnight Costs (Standard LWR) |  5122
MIT (2018) |  UK (low) |  6218.108
MIT (2018) |  UK (nominal) |  8338.616
MIT (2018) |  US (high) |  7047.872
MIT (2018) |  US (low) |  4200.04
MIT (2018) |  US (nominal) |  5634.2
MIT (2018) |  US (very low) |  2817.1
NREL (2021) |  Nuclear-Moderate |  6185.050584
OECD and NEA (2018) |  Nuclear (max) |  6584.171
OECD and NEA (2018) |  Nuclear (mean) |  4501.3906
OECD and NEA (2018) |  Nuclear (median) |  5186.8224
OECD and NEA (2018) |  Nuclear (min) |  1914.3358
Rothwell (2022) |  Belgium |  5515
Rothwell (2022) |  Hungary |  6745
Rothwell (2022) |  UK |  6588
Shirvan (2022) |  10th unit AP1000 (Post-COVID high estimate) |  3826.205255
Shirvan (2022) |  10th unit AP1000 (Should cost) |  2900
Shirvan (2022) |  Next AP1000 (Post-COVID high estimate) |  5781.821274
Shirvan (2022) |  Next AP1000 (Should cost) |  4300
Stewart and Shirvan (2021) |  First-4 unit average |  5337
Stewart and Shirvan (2021) |  FOAK (US) |  4328
Wealer et al. (2021) |  Inputs for MC MAX |  9000
Wealer et al. (2021) |  Inputs for MC MIN |  4000
Table 4: (continued)

In literature, operation & maintenance (O&M) costs are often provided as two
components. The first component, fixed O&M costs, does not depend on yearly
generation quantities, but the second component, variable O&M costs, does and
is given in US-$/MWh. Some references however only provide one component,
e.g., Budi et al. (2019); Ingersoll et al. (2020); International Atomic Energy
Agency (2020), for a detailed overview see Table LABEL:tab:opex_unfiltered.

To make literature O&M cost comparable, we combine fixed and variable costs to
a single value in US-$/kW. For this, we assume an optimistic 95% capacity
factor to determine nuclear full load hours and are thus able to determine
combined O&M costs for all references. Hereby, we also caclulate the average
ratio of fixed and variable costs to then determine the average fixed and
variable O&M costs from the 25%-quantile of the combined O&M cost value, see
Tables 6 and LABEL:tab:opex_combined. This results in the input values given
in Table 1.

Analysis of nuclear power in the paper builds on several techno-economic
assumptions but only the most pivotal ones, construction costs and time, are
discussed in depth in the paper. The following material provides a
comprehensive overview of the entire reviewed material. First, Fig. 9 shows
boxplots illustrating the distribution of values for different parameters.
Afterwards, several tables list the underlying data for all parameters. These
include overnight construction cost (see Tables LABEL:tab:occ_unfiltered, 3,
LABEL:tab:occ_assumed_lwr_oecd), O&M cost (Tables LABEL:tab:opex_unfiltered,
6, LABEL:tab:opex_combined), construction time (see Table 10), nuclear fuel
cost (see Table LABEL:tab:fuel_cost), capacity factor (see Table 9),
operational lifetime (see Table 11), and thermal efficiency (see Table 12.

Table 5: Unfiltered fixed and variable O&M cost data for nuclear plants. Reference |  Page |  Cost Description |  Reactor |  Reference Year |  Capacity in kW |  Fixed O&M Cost |  Unit Fixed Cost |  Variable O&M Cost |  Unit Var. Cost
---|---|---|---|---|---|---|---|---|---
Barkatullah and Ahmad (2017) |  132 |  Asssumed parameters |  n.a. |  2017 |  |  93.28 |  US-$(2018) per kW |  2.14 |  US-$ per MWh
Boldon and Sabharwall (2014) |  19 |  Input Water-Cooled SMR |  SMR |  2014 |  1260 |  n.a. |  n.a. |  18 |  US-$(2014) per MWh
Stein et al. (2022) |  30 |  Lower Cost, High Learning (Best Case) // FOAK CAPEX |  Traditional Nuclear |  2020 |  n.a. |  121 |  US-$(2020) per kW-yr |  2.43 |  US-$(2020) per MWh
Stein et al. (2022) |  30 |  Lower Cost, High Learning (Best Case) // FOAK CAPEX |  SMR |  2020 |  n.a. |  98 |  US-$(2020) per kW-yr |  3.08 |  US-$(2020) per MWh
Stein et al. (2022) |  30 |  Lower Cost, High Learning (Best Case) // FOAK CAPEX |  HTGR |  2020 |  n.a. |  39 |  US-$(2020) per kW-yr |  0.35 |  US-$(2020) per MWh
Stein et al. (2022) |  30 |  Lower Cost, High Learning (Best Case) // FOAK CAPEX |  Advanced reactor with thermal storage (ARTES) |  2020 |  n.a. |  121 |  US-$(2020) per kW-yr |  2.43 |  US-$(2020) per MWh
Stein et al. (2022) |  30 |  Upper Cost, Low Learning (Worst Case) // FOAK CAPEX |  Traditional Nuclear |  2020 |  n.a. |  94 |  US-$(2020) per kW-yr |  7.31 |  US-$(2020) per MWh
Stein et al. (2022) |  30 |  Upper Cost, Low Learning (Worst Case) // FOAK CAPEX |  SMR |  2020 |  n.a. |  98 |  US-$(2020) per kW-yr |  3.08 |  US-$(2020) per MWh
Stein et al. (2022) |  30 |  Upper Cost, Low Learning (Worst Case) // FOAK CAPEX |  HTGR |  2020 |  n.a. |  189 |  US-$(2020) per kW-yr |  0 |  US-$(2020) per MWh
Stein et al. (2022) |  30 |  Upper Cost, Low Learning (Worst Case) // FOAK CAPEX |  Advanced reactor with thermal storage (ARTES) |  2020 |  n.a. |  40 |  US-$(2020) per kW-yr |  5 |  US-$(2020) per MWh
Budi et al. (2019) |  3 |  Annual O&M costs |  HTGR |  n.a |  600 |  59780000 |  US-$(2012) |  n.a. |  n.a.
Budi et al. (2019) |  3 |  Annual O&M costs |  HTGR |  n.a. |  350 |  56630000 |  US-$(2012) |  n.a. |  n.a.
Dixon et al. (2017) |  xii |  Mean Costs |  Thermal LWR Reactor |  2017 |  n.a. |  72 |  US-$(2017) per kW |  2 |  US-$(2017) per kW
Dixon et al. (2017) |  xii |  Mean Costs |  Fast Reactors |  2017 |  n.a. |  78 |  US-$(2017) per kW |  2.1 |  US-$(2017) per kW
Dixon et al. (2017) |  xii |  Mean Costs |  Gas-cooled reactors |  2017 |  n.a. |  0 |  US-$(2017) per kW |  0 |  US-$(2017) per kW
Dixon et al. (2017) |  xii |  Mean Costs |  PHWR |  2017 |  n.a. |  73 |  US-$(2017) per kW |  1.8 |  US-$(2017) per kW
International Atomic Energy Agency (2020) |  56 |  SMART (FOAK) |  SMR |  2020 |  30 |  n.a. |  n.a. |  28 |  US-$ per MWh
IEA et al. (2020) |  49 |  Nuclear New Build - France |  EPR |  2019 |  1650 |  n.a. |  n.a. |  14.26 |  US-$ per MWh
IEA et al. (2020) |  49 |  Nuclear New Build - Japan |  ALWR |  2019 |  1152 |  n.a. |  n.a. |  25.84 |  US-$ per MWh
IEA et al. (2020) |  49 |  Nuclear New Build - Korea |  ALWR |  2019 |  1377 |  n.a. |  n.a. |  18.44 |  US-$ per MWh
IEA et al. (2020) |  49 |  Nuclear New Build - Russia |  VVER |  2019 |  1122 |  n.a. |  n.a. |  10.15 |  US-$ per MWh
IEA et al. (2020) |  49 |  Nuclear New Build - Slovak Republic |  Other nuclear |  2019 |  1004 |  n.a. |  n.a. |  9.72 |  US-$ per MWh
IEA et al. (2020) |  49 |  Nuclear New Build - United States |  LWR |  2019 |  1100 |  n.a. |  n.a. |  11.6 |  US-$ per MWh
IEA et al. (2020) |  49 |  Nuclear New Build - China |  LWR |  2019 |  950 |  n.a. |  n.a. |  26.42 |  US-$ per MWh
IEA et al. (2020) |  49 |  Nuclear New Build - India |  LWR |  2019 |  950 |  n.a. |  n.a. |  23.84 |  US-$ per MWh
Ingersoll et al. (2020) |  29 |  Model assumptions |  Advanced Nuclear Reactor |  2020 |  n.a. |  31 |  US-$(2019) per kWe-yr |  n.a. |  n.a.
Lazard Ltd (2021) |  18 |  Nuclear (New Build, Low Case) |  n.a. |  2021 |  2200 |  121 |  US-$(2021) per kW-yr |  4 |  US-$ per MWh
Lazard Ltd (2021) |  18 |  Nuclear (New Build, High Case) |  n.a. |  2021 |  2200 |  140.5 |  US-$(2021) per kW-yr |  4.5 |  US-$ per MWh
Lazard Ltd (2021) |  18 |  Nuclear (Operating, Low Case) |  n.a. |  2021 |  2200 |  83.5 |  US-$(2021) per kW-yr |  2.6 |  US-$ per MWh
Lazard Ltd (2021) |  18 |  Nuclear (Operating, High Case) |  n.a. |  2021 |  2200 |  119.3 |  US-$(2021) per kW-yr |  4.2 |  US-$ per MWh
MIT (2018) |  159, 220 |  Benchmark Scenario Cost |  Nuclear |  2018 |  n.a. |  95000 |  US-$(2017) per MW-yr |  6.89 |  US-$(2017) per MWh
MIT (2018) |  149 |  US (low) |  Nuclear |  2018 |  1000 |  95 |  US-$(2017) per MW-yr |  6.89 |  US-$(2017) per MWh
MIT (2018) |  150 |  China (nominal) |  Nuclear |  2018 |  1000 |  59.677 |  US-$(2017) per MW-yr |  4.33 |  US-$(2017) per MWh
MIT (2018) |  151 |  France (nominal) |  Nuclear |  2018 |  1000 |  115.123 |  US-$(2017) per MW-yr |  8.35 |  US-$(2017) per MWh
MIT (2018) |  150 |  UK (nominal) |  Nuclear |  2018 |  1000 |  180.759 |  US-$(2017) per MW-yr |  13.11 |  US-$(2017) per MWh
Laboratory (2016) |  46 |  LCOE build-up for GWe LWR |  LWR |  n.a. |  n.a. |  n.a. |  n.a. |  13.55 |  US-$ per MWh
Institute (2021) |  6 |  Operating Costs |  US Nuclear |  2020 |  n.a. |  n.a. |  n.a. |  18.27 |  US-$ per MWh
NREL (2021) |  Nuclear-Moderate |  Nuclear |  2019 |  n.a. |  144.97 |  US-$(2019) per kW-yr |  2.35 |  US-$(2019) per MWh |
Timilsina (2020) |  12 |  Nuclear |  n.a. |  2019 |  n.a. |  n.a. |  n.a. |  4.6 |  US-$(2019) per MWh
Tolley et al. (2004) |  7 |  No-Policy Nuclear LCOE Installations (Max OCC) |  Advanced Nuclear Reactor |  2003 |  1000 |  60 |  US-$(2003) per kW |  2.1 |  US-$(2003) per MWh
Wealer et al. (2021) |  5 |  Inputs for MC |  n.a. |  2018 |  1600 |  93280 |  US-$(2018) per MW |  2.14 |  US-$ per MWh
Table 5: (continued) Table 6: Parameters and Interim Calculation Steps to Determine O&M Input Parameters Parameter | Value | Unit
---|---|---
Hours per Year | 8760 | Hours
Capacity Factor | 0.95 | %
Full Load Hours | 8322 | Hours
Median Ratio Fix/Var | 2.5 |
25% Percentile of Combined O&M Cost | 97.43 | US-$(2018)/kW
25%-Quantile Median Ratio Fixed O&M Cost | 69.59 | US-$(2018)/kW
25%-Quantile Median Ratio Variable O&M Cost | 3.35 | US-$(2018)/MWh
Table 7: Fixed and variable O&M costs for nuclear plants in OECD countries. Reference |  Cost Description |  Fix O&M Cost |  Unit_Fix |  Var. O&M Cost |  Unit_Var |  Fix O&M Cost in US-$(2018) per kW |  Var. O&M Cost in US-$(2018) per MWh |  Comb. O&M Cost in US-$(2018) per kW |  Ratio Fix/Var
---|---|---|---|---|---|---|---|---|---
Barkatullah and Ahmad (2017) |  Asssumed parameters |  93.28 |  US-$(2018) per kW |  2.14 |  US-$ per MWh |  93.28 |  2.14 |  111.09 |  5
Budi et al. (2019) |  Annual O&M costs |  99.63333333 |  US-$(2012) |  n.a. |  n.a. |  108.97 |  0.00 |  108.97 |  0
Budi et al. (2019) |  Annual O&M costs |  94.38333333 |  US-$(2012) |  n.a. |  n.a. |  103.23 |  0.00 |  103.23 |  0
Dixon et al. (2017) |  Mean Costs |  78 |  US-$(2017) per kW |  2.1 |  US-$(2017) per kW |  79.90 |  2.15 |  97.81 |  4
Dixon et al. (2017) |  Mean Costs |  72 |  US-$(2017) per kW |  2 |  US-$(2017) per kW |  73.76 |  2.05 |  90.81 |  4
Dixon et al. (2017) |  Mean Costs |  73 |  US-$(2017) per kW |  1.8 |  US-$(2017) per kW |  74.78 |  1.84 |  90.13 |  5
IEA et al. (2020) |  Nuclear New Build - Japan |  n.a. |  n.a. |  25.84 |  US-$ per MWh |  0.00 |  25.38 |  211.22 |  0
IEA et al. (2020) |  Nuclear New Build - Korea |  n.a. |  n.a. |  18.44 |  US-$ per MWh |  0.00 |  18.11 |  150.73 |  0
IEA et al. (2020) |  Nuclear New Build - France |  n.a. |  n.a. |  14.26 |  US-$ per MWh |  0.00 |  14.01 |  116.56 |  0
IEA et al. (2020) |  Nuclear New Build - United States |  |  |  11.6 |  US-$ per MWh |  0.00 |  11.39 |  94.82 |  0
IEA et al. (2020) |  Nuclear New Build - Slovak Republic |  |  |  9.72 |  US-$ per MWh |  0.00 |  9.55 |  79.45 |  0
Ingersoll et al. (2020) |  Model assumptions |  31 |  US-$(2019) per kW |  n.a. |  n.a. |  30.45 |  0.00 |  30.45 |  0
Lazard Ltd (2021) |  Nuclear (New Build, High Case) |  140.5 |  US-$(2021) per kW-yr |  4.5 |  US-$ per MWh |  130.20 |  4.17 |  164.91 |  4
Lazard Ltd (2021) |  Nuclear (Operating, High Case) |  119.3 |  US-$(2021) per kW-yr |  4.2 |  US-$ per MWh |  110.56 |  3.89 |  142.95 |  3
Lazard Ltd (2021) |  Nuclear (New Build, Low Case) |  121 |  US-$(2021) per kW-yr |  4 |  US-$ per MWh |  112.13 |  3.71 |  142.98 |  4
Lazard Ltd (2021) |  Nuclear (Operating, Low Case) |  83.5 |  US-$(2021) per kW-yr |  2.6 |  US-$ per MWh |  77.38 |  2.41 |  97.43 |  4
MIT (2018) |  UK (nominal) |  180.759 |  US-$(2017) per kW-yr |  13.11 |  US-$(2017) per MWh |  185.17 |  13.43 |  296.93 |  2
MIT (2018) |  France (nominal) |  115.123 |  US-$(2017) per kW-yr |  8.35 |  US-$(2017) per MWh |  117.93 |  8.55 |  189.12 |  2
MIT (2018) |  Benchmark Scenario Cost |  95 |  US-$(2017) per MWh |  6.89 |  US-$(2017) per MWh |  97.32 |  7.06 |  156.06 |  2
MIT (2018) |  US (low) |  95 |  US-$(2017) per kW-yr |  6.89 |  US-$(2017) per MWh |  97.32 |  7.06 |  156.06 |  2
Laboratory (2016) |  LCOE build-up for GWe LWR |  212.79 |  US-$(2016) per kW |  13.55 |  US-$(2016) per MWh |  222.62 |  14.18 |  340.59 |  2
NREL (2021) |  Nuclear-Moderate |  144.97 |  US-$(2019) per kW-yr |  2.35 |  US-$(2019) per MWh |  142.39 |  2.31 |  161.6 |  7
Timilsina (2020) |  Nuclear |  n.a. |  n.a. |  4.6 |  US-$(2019) per MWh |  0.00 |  4.52 |  37.6 |  0
Tolley et al. (2004) |  No-Policy Nuclear LCOE Installations (Max OCC) |  60 |  US-$(2003) per kW |  2.1 |  US-$(2003) per MWh |  81.90 |  2.87 |  105.76 |  3
Wealer et al. (2021) |  Inputs for MC |  93.28 |  US-$(2018) per kW |  2.14 |  US-$ per MWh |  93.28 |  2.14 |  111.09 |  5
Table 7: (continued) Table 8: Fuel costs for nuclear plants. Reference |  Page |  Description |  Fuel Type |  Reactor |  Reference Year |  Value |  Unit |  US-$(2018) per MWh
---|---|---|---|---|---|---|---|---
Barkatullah and Ahmad (2017) |  132 |  Inputs for MC |  n.a. |  n.a. |  2018 |  10.11 |  US-$ per MWe*h |  10.11
Boldon and Sabharwall (2014) |  19 |  Fuel Cost |  n.a. |  n.a. |  2014 |  9.1 |  US-$ per MWe*h |  9.65
IEA et al. (2020) |  39 |  Assumptions for LCOE |  |  Nuclear |  2019 |  9.33 |  US-$ per MWe*h |  9.16
IEA et al. (2020) |  39 |  Assumptions for LCOE (Japan) |  |  Nuclear |  2019 |  13.9 |  US-$ per MWe*h |  13.65
IEA et al. (2020) |  59 |  Nuclear New Build - France |  |  EPR |  2019 |  9.33 |  US-$ per MWe*h |  9.16
IEA et al. (2020) |  59 |  Nuclear New Build - Japan |  |  ALWR |  2019 |  13.92 |  US-$ per MWe*h |  13.67
IEA et al. (2020) |  59 |  Nuclear New Build - Korea |  |  ALWR |  2019 |  9.33 |  US-$ per MWe*h |  9.16
IEA et al. (2020) |  59 |  Nuclear New Build - Slovak Republic |  |  Other nuclear |  2019 |  9.33 |  US-$ per MWe*h |  9.16
IEA et al. (2020) |  59 |  Nuclear New Build - United States |  |  LWR |  2019 |  9.33 |  US-$ per MWe*h |  9.16
Ingersoll et al. (2020) |  29 |  Model assumptions |  Advanced Nuclear Reactor |  |  2019 |  4.44 |  US-$ per MWe*h |  4.36
Lazard Ltd (2021) |  18 |  Nuclear (New Build) |  n.a. |  n.a. |  2021 |  8.88 |  US-$ per MWe*h |  8.23
Lazard Ltd (2021) |  18 |  Nuclear (Operational) |  n.a. |  n.a. |  2021 |  6.24 |  US-$ per MWe*h |  5.78
MIT (2018) |  240 |  Fuel Cycle Cost min |  n.a. |  n.a. |  2017 |  9 |  US-$ per MWe*h |  9.22
MIT (2018) |  240 |  Fuel Cycle Cost max |  n.a. |  n.a. |  2017 |  17.54 |  US-$ per MWe*h |  17.97
MIT (2018) |  159 |  Fuel Cost Benchmark |  n.a. |  n.a. |  2017 |  10.7 |  US-$ per MWe*h |  10.96
MIT (2018) |  151 |  US Fuel Cost |  Uranium |  n.a. |  2017 |  10.7 |  US-$ per MWe*h |  10.96
MIT (2018) |  153 |  UK Fuel Cost |  Uranium |  n.a. |  2017 |  10.7 |  US-$ per MWe*h |  10.96
MIT (2018) |  153 |  France Fuel Cost |  Uranium |  n.a. |  2017 |  10.7 |  US-$ per MWe*h |  10.96
Laboratory (2016) |  46 |  Fuel and waste cost |  Uranium |  LWR |  2015 |  10.25 |  US-$ per MWe*h |  10.86
NREL (2021) |  Nuclear-Moderate |  Nuclear |  n.a. |  n.a. |  2013 |  7.10 |  US-$ per MWe*h |  7.65
Pannier and Skoda (2014) |  4 |  NuScale |  Enriched Uranium |  SMR |  2014 |  20.57 |  US-$ per MWe*h |  21.82
Pannier and Skoda (2014) |  4 |  SMART |  Enriched Uranium |  SMR |  2014 |  18.07 |  US-$ per MWe*h |  19.17
Pannier and Skoda (2014) |  4 |  mPower |  Enriched Uranium |  SMR |  2014 |  21.29 |  US-$ per MWe*h |  22.58
Pannier and Skoda (2014) |  4 |  HI-SMUR |  Enriched Uranium |  SMR |  2014 |  24.10 |  US-$ per MWe*h |  25.56
Pannier and Skoda (2014) |  4 |  W-SMR |  Enriched Uranium |  SMR |  2014 |  13.67 |  US-$ per MWe*h |  14.50
Pannier and Skoda (2014) |  4 |  n.a. |  Uranium |  EC 6 |  2014 |  15.25 |  US-$ per MWe*h |  16.18
Pannier and Skoda (2014) |  4 |  n.a. |  Enriched Uranium |  VVER1000 |  2014 |  11.97 |  US-$ per MWe*h |  12.70
Pannier and Skoda (2014) |  4 |  n.a. |  Enriched Uranium |  AP1000 |  2014 |  11.97 |  US-$ per MWe*h |  12.70
Pannier and Skoda (2014) |  4 |  n.a. |  Enriched Uranium |  ABWR |  2014 |  11.11 |  US-$ per MWe*h |  11.79
Pannier and Skoda (2014) |  4 |  n.a. |  Enriched Uranium |  US-APWR |  2014 |  10.19 |  US-$ per MWe*h |  10.81
Pannier and Skoda (2014) |  4 |  n.a. |  Enriched Uranium |  US-EPR |  2014 |  10.67 |  US-$ per MWe*h |  11.31
Pannier and Skoda (2014) |  4 |  n.a. |  Enriched Uranium |  ESBWR |  2014 |  11.28 |  US-$ per MWe*h |  11.97
Timilsina (2020) |  12 |  Nuclear |  n.a. |  n.a. |  2019 |  8.8 |  US-$ per MWe*h |  8.64
Tolley et al. (2004) |  7 |  No-Policy Nuclear LCOE Installations (Max OCC) |  |  Advanced Nuclear Reactor |  2003 |  4.35 |  US-$ per MWe*h |  5.94
Wealer et al. (2021) |  5 |  Inputs for MC |  n.a. |  n.a. |  2018 |  10.11 |  US-$ per MWe*h |  10.11
Table 8: (continued) Table 9: Capacity factors for nuclear plants. Reference |  Page |  Description |  Capacity Factor
---|---|---|---
Barkatullah and Ahmad (2017) |  132 |  Asssumed parameters |  0.9
IEA et al. (2020) |  39 |  Assumptions for LCOE |  0.85
Ingersoll et al. (2020) |  26 |  Model assumptions |  0.92
Lazard Ltd (2021) |  18 |  Nuclear (New Build, Low Case) |  0.92
Lazard Ltd (2021) |  18 |  Nuclear (New Build, High Case) |  0.89
Lazard Ltd (2021) |  18 |  Nuclear (Operating, Low Case) |  0.95
Lazard Ltd (2021) |  18 |  Nuclear (Operating, High Case) |  0.88
Laboratory (2016) |  46 |  Nationally nominated factor for modelling |  0.9
NREL (2021) |  Nuclear-Moderate |  0.93 |
Timilsina (2020) |  12 |  Nuclear |  0.9
Tolley et al. (2004) |  7 |  No-Policy Nuclear LCOE Installations (Max OCC) |  0.85
Wealer et al. (2021) |  58 |  Historical Capacity Factor France |  0.697
Wealer et al. (2021) |  58 |  Historical Capacity Factor Germany |  0.71
Wealer et al. (2021) |  5 |  Inputs for MC |  0.85
Table 10: Construction times of nuclear plants. Reference |  Page |  Description |  Reactor |  Construction Time in Years
---|---|---|---|---
International Atomic Energy Agency (2020) |  308 |  HTR-PM |  SMR |  9
International Atomic Energy Agency (2020) |  308 |  CAREM25 |  SMR |  9.5
International Atomic Energy Agency (2020) |  308 |  ACP100 |  SMR |  4.5
International Atomic Energy Agency (2020) |  308 |  NuScale |  SMR |  5.5
International Atomic Energy Agency (2020) |  308 |  RITM-200M |  SMR |  4.5
International Atomic Energy Agency (2020) |  56 |  SMART (FOAK) |  SMR |  4.5
IEA et al. (2020) |  39 |  Assumptions for LCOE |  Nuclear New Build |  7
Lazard Ltd (2021) |  18 |  Nuclear (New Build, High Case) |  n.a. |  5.75
Linares and Conchado (2013) |  n.a. |  n.a. |  n.a. |  5
Linares and Conchado (2013) |  n.a. |  n.a. |  n.a. |  9
MIT (2018) |  149 |  US (nominal) |  Nuclear |  7
MIT (2018) |  149 |  UK (nominal) |  Nuclear |  7
MIT (2018) |  149 |  China (nominal) |  Nuclear |  7
MIT (2018) |  149 |  France (nominal) |  Nuclear |  7
NREL (2021) |  Nuclear-Moderate |  Nuclear |  6 |
Tolley et al. (2004) |  7 |  No-Policy Nuclear LCOE Installations (Max OCC) |  |  7
Wealer et al. (2021) |  5 |  Inputs for MC |  n.a. |  8.7
Schneider et al. (2021) |  n.a. |  n.a. |  n.a. |  7
Table 11: Operational lifetimes of nuclear plants. Reference |  Page |  Description |  Reactor |  Operational Lifetime
---|---|---|---|---
Barkatullah and Ahmad (2017) |  132 |  Asssumed parameters |  Nuclear |  60
IEA et al. (2020) |  39 |  Assumptions for LCOE |  Nuclear New Build |  60
Lazard Ltd (2021) |  18 |  Nuclear (New Build, Low Case) |  Nuclear |  40
Lazard Ltd (2021) |  18 |  Nuclear (New Build, High Case) |  Nuclear |  40
Lazard Ltd (2021) |  18 |  Nuclear (Operating, Low Case) |  Nuclear |  40
Lazard Ltd (2021) |  18 |  Nuclear (Operating, High Case) |  Nuclear |  40
MIT (2018) |  149 |  US (nominal) |  Nuclear |  40
MIT (2018) |  149 |  UK (nominal) |  Nuclear |  40
MIT (2018) |  149 |  China (nominal) |  Nuclear |  40
MIT (2018) |  149 |  France (nominal) |  Nuclear |  40
Timilsina (2020) |  12 |  Nuclear |  n.a. |  45
Tolley et al. (2004) |  7 |  No-Policy Nuclear LCOE Installations (Max OCC) |  |  40
Wealer et al. (2021) |  5 |  Inputs for MC |  Nuclear |  40
Table 12: Thermal efficiency of nuclear plants. Reference |  Page |  Description |  Reactor |  Efficiency
---|---|---|---|---
IEA et al. (2020) |  49 |  Nuclear New Build - France |  EPR |  0.33
IEA et al. (2020) |  49 |  Nuclear New Build - Japan |  ALWR |  0.33
IEA et al. (2020) |  49 |  Nuclear New Build - Korea |  ALWR |  0.36
IEA et al. (2020) |  49 |  Nuclear New Build - Russia |  VVER |  0.38
IEA et al. (2020) |  49 |  Nuclear New Build - Slovak Republic |  Other nuclear |  0.32
IEA et al. (2020) |  49 |  Nuclear New Build - United States |  LWR |  0.33
IEA et al. (2020) |  49 |  Nuclear New Build - China |  LWR |  0.33
IEA et al. (2020) |  49 |  Nuclear New Build - India |  LWR |  0.33
Ingersoll et al. (2020) |  29 |  Model assumptions |  Advanced Nuclear Reactor |  0.4
Pannier and Skoda (2014) |  4 |  NuScale |  SMR |  0.28
Pannier and Skoda (2014) |  4 |  SMART |  SMR |  0.27
Pannier and Skoda (2014) |  4 |  mPower |  SMR |  0.31
Pannier and Skoda (2014) |  4 |  HI-SMUR |  SMR |  0.31
Pannier and Skoda (2014) |  4 |  W-SMR |  SMR |  0.33
Pannier and Skoda (2014) |  4 |  n.a. |  EC 6 |  0.36
Pannier and Skoda (2014) |  4 |  n.a. |  VVER1000 |  0.33
Pannier and Skoda (2014) |  4 |  n.a. |  AP1000 |  0.32
Pannier and Skoda (2014) |  4 |  n.a. |  ABWR |  0.35
Pannier and Skoda (2014) |  4 |  n.a. |  US-APWR |  0.36
Pannier and Skoda (2014) |  4 |  n.a. |  US-EPR |  0.36
Pannier and Skoda (2014) |  4 |  n.a. |  ESBWR |  0.35
Table 13: Overnight construction costs by reactor type in US-$ per kW. Measure |  All types |  Standard LWR |  SMR |  Non-LWR
---|---|---|---|---
Mean |  6,279.41 |  6,008.85 |  7,774.17 |  5,030.55
Median |  5,353.64 |  5,515.00 |  6,270.29 |  5,311.51
25%-Quantile |  4,319.80 |  4,328.00 |  4,472.66 |  4,453.58
75%-Quantile |  7,146.76 |  6,965.92 |  7,978.75 |  5,487.23
Table 14: Comparison of future projections and recently reported costs for LWR costs in US-$ per kW. Measure |  Assumptions |  Real
---|---|---
Mean |  5,144.10 |  9108,35
Median |  5,122.00 |  9250.00
25%-Quantile |  4,174.44 |  7867,00
75%-Quantile |  6,218.11 |  10134,00
Table 15: Inflation taken from inflationtool.com. Year | Inflation until 2018
---|---
1995 | 64.79%
1996 | 60.09%
1997 | 56.44%
1998 | 54.05%
1999 | 50.75%
2000 | 45.82%
2001 | 41.81%
2002 | 39.60%
2003 | 36.50%
2004 | 32.94%
2005 | 28.58%
2006 | 24.56%
2007 | 21.11%
2008 | 16.63%
2009 | 17.05%
2010 | 15.16%
2011 | 11.63%
2012 | 9.37%
2013 | 7.79%
2014 | 6.07%
2015 | 5.94%
2016 | 4.62%
2017 | 2.44%
2018 | 0.00%
2019 | -1.81%
2020 | -3.07%
2021 | -7.91%
2022 | -17.61%

##  Appendix B Inputs for historic LCOEs

The tables here list the cost assumptions to compute the historic LCOEs in
Fig. 5. Full-load hours are an average from the European data also used as an
input for the energy system model. All other data is taken from the Lazard
reports for the respective years (Lazard Ltd, 2009, 2010, 2011, 2012, 2013,
2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021). In addition, we assume a
uniform interest rate of 5%.

Table 16: Inputs for historic LCOEs. technology |  parameter |  unit |  category |  2009 |  2010 |  2011 |  2012 |  2013 |  2014 |  2015 |  2016 |  2017 |  2018 |  2019 |  2020 |  2021
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---
Nuclear |  Total capital cost (incl. construction) |  US-$/kW |  Low |  6,325 |  5,385 |  5,385 |  5,385 |  5,385 |  5,385 |  5,400 |  5,400 |  6,500 |  6,500 |  6,900 |  7,675 |  7,800
|  |  |  Up |  8,375 |  8,199 |  8,199 |  8,199 |  8,199 |  8,199 |  8,200 |  8,200 |  11,800 |  12,250 |  12,200 |  12,500 |  12,800
|  Fixed O&M |  US-$/kW/a |  Low |  12.8 |  12.8 |  12.8 |  12.8 |  60.0 |  95.0 |  135.0 |  135.0 |  135.0 |  115.0 |  108.5 |  119.0 |  121.0
|  |  |  Up |  12.8 |  12.8 |  12.8 |  12.8 |  60.0 |  115.0 |  135.0 |  135.0 |  135.0 |  135.0 |  133.0 |  133.3 |  140.5
|  Variable O&M |  US-$/MWh |  Low |  11.0 |  |  |  |  |  0.3 |  0.5 |  0.5 |  0.8 |  0.8 |  3.4 |  3.8 |  4.0
|  |  |  Up |  11.0 |  |  |  |  |  0.8 |  0.8 |  0.8 |  0.8 |  0.8 |  4.3 |  4.3 |  4.5
|  Heat Rate |  BTU/kWh |  Low |  10,450 |  10,450 |  10,450 |  10,450 |  10,450 |  10,450 |  10,450 |  10,450 |  10,450 |  10,450 |  10,450 |  10,450 |  10,450
|  |  |  Up |  10,450 |  10,450 |  10,450 |  10,450 |  10,450 |  10,450 |  10,450 |  10,450 |  10,450 |  10,450 |  10,450 |  10,450 |  10,450
|  Capacity factor |  % |  Low |  90% |  90% |  90% |  90% |  90% |  90% |  90% |  90% |  90% |  90% |  91% |  92% |  92%
|  |  |  Up |  90% |  90% |  90% |  90% |  90% |  90% |  90% |  90% |  90% |  90% |  90% |  89% |  89%
|  Fuel Price |  US-$/MMBtu |  Low |  0.50 |  0.50 |  0.50 |  0.50 |  0.65 |  0.70 |  0.85 |  0.85 |  0.85 |  0.85 |  0.85 |  0.85 |  0.85
|  |  |  Up |  0.50 |  0.50 |  0.50 |  0.50 |  0.65 |  0.70 |  0.85 |  0.85 |  0.85 |  0.85 |  0.85 |  0.85 |  0.85
|  Lifetime |  a |  Low |  20 |  40 |  40 |  40 |  40 |  40 |  40 |  40 |  40 |  40 |  40 |  40 |  40
|  |  |  Up |  20 |  40 |  40 |  40 |  40 |  40 |  40 |  40 |  40 |  40 |  40 |  40 |  40
PV, openspace |  Total capital cost (incl. construction) |  US-$/kW |  Low |  3,250 |  3,500 |  2,500 |  2,000 |  1,750 |  1,500 |  1,400 |  1,300 |  1,100 |  950 |  900 |  825 |  800
|  |  |  Up |  4,000 |  4,000 |  4,000 |  2,750 |  2,000 |  1,750 |  1,600 |  1,450 |  1,375 |  1,250 |  1,100 |  975 |  950
|  Fixed O&M |  US-$/kW/a |  Low |  25.0 |  37.5 |  15.0 |  13.0 |  13.0 |  13.0 |  10.0 |  9.0 |  9.0 |  9.0 |  9.0 |  9.5 |  9.5
|  |  |  Up |  25.0 |  37.5 |  25.0 |  25.0 |  20.0 |  20.0 |  13.0 |  12.0 |  12.0 |  12.0 |  12.0 |  13.5 |  13.0
|  Capacity factor |  % |  Low |  15.4% |  15.4% |  15.4% |  15.4% |  15.4% |  15.4% |  15.4% |  15.4% |  15.4% |  15.4% |  15.4% |  15.4% |  15.4%
|  |  |  Up |  15.4% |  15.4% |  15.4% |  15.4% |  15.4% |  15.4% |  15.4% |  15.4% |  15.4% |  15.4% |  15.4% |  15.4% |  15.4%
|  Lifetime |  a |  Low |  20 |  20 |  20 |  20 |  20 |  20 |  30 |  30 |  30 |  30 |  30 |  30 |  30
|  |  |  Up |  20 |  20 |  20 |  20 |  20 |  20 |  30 |  30 |  30 |  30 |  30 |  30 |  30
PV, rooftop residential |  Total capital cost (incl. construction) |  US-$/kW |  Low |  |  |  |  |  |  3,500 |  4,100 |  2,000 |  3,125 |  2,950 |  2,800 |  2,525 |  2,475
|  |  |  Up |  |  |  |  |  |  4,500 |  5,300 |  2,800 |  3,560 |  3,250 |  2,950 |  2,825 |  2,850
|  Fixed O&M |  US-$/kW/a |  Low |  |  |  |  |  |  25.0 |  17.5 |  20.0 |  20.0 |  14.5 |  14.0 |  15.0 |  15.0
|  |  |  Up |  |  |  |  |  |  30.0 |  22.5 |  25.0 |  25.0 |  25.0 |  25.0 |  18.0 |  18.0
|  Capacity factor |  % |  Low |  |  |  |  |  |  15.1% |  15.1% |  15.1% |  15.1% |  15.1% |  15.1% |  15.1% |  15.1%
|  |  |  Up |  |  |  |  |  |  15.1% |  15.1% |  15.1% |  15.1% |  15.1% |  15.1% |  15.1% |  15.1%
|  Lifetime |  a |  Low |  |  |  |  |  |  20 |  20 |  20 |  20 |  25 |  25 |  25 |  25
|  |  |  Up |  |  |  |  |  |  20 |  20 |  20 |  20 |  25 |  25 |  25 |  25
PV, rooftop industry |  Total capital cost (incl. construction) |  US-$/kW |  Low |  |  4,000 |  3,750 |  3,000 |  3,000 |  2,500 |  2,600 |  2,100 |  2,000 |  1,900 |  1,750 |  1,600 |  1,400
|  |  |  Up |  |  4,500 |  4,500 |  3,500 |  3,500 |  3,000 |  3,750 |  3,750 |  3,750 |  3,250 |  2,950 |  2,825 |  2,850
|  Fixed O&M |  US-$/kW/a |  Low |  |  25.0 |  15.0 |  13.0 |  13.0 |  13.0 |  15.0 |  15.0 |  15.0 |  15.0 |  15.0 |  11.8 |  11.8
|  |  |  Up |  |  25.0 |  25.0 |  20.0 |  20.0 |  20.0 |  20.0 |  20.0 |  20.0 |  20.0 |  20.0 |  18.0 |  18.0
|  Capacity factor |  % |  Low |  |  15.1% |  15.1% |  15.1% |  15.1% |  15.1% |  15.1% |  15.1% |  15.1% |  15.1% |  15.1% |  15.1% |  15.1%
|  |  |  Up |  |  15.1% |  15.1% |  15.1% |  15.1% |  15.1% |  15.1% |  15.1% |  15.1% |  15.1% |  15.1% |  15.1% |  15.1%
|  Lifetime |  a |  Low |  |  20 |  20 |  20 |  20 |  20 |  25 |  25 |  25 |  25 |  25 |  25 |  25
|  |  |  Up |  |  20 |  20 |  20 |  20 |  20 |  25 |  25 |  25 |  25 |  25 |  25 |  25
Wind, onshore |  Total capital cost (incl. construction) |  US-$/kW |  Low |  1,900 |  2,250 |  1,300 |  1,500 |  1,500 |  1,400 |  1,250 |  1,250 |  1,200 |  1,150 |  1,100 |  1,050 |  1,025
|  |  |  Up |  2,500 |  2,600 |  1,900 |  2,000 |  2,000 |  1,800 |  1,700 |  1,700 |  1,650 |  1,550 |  1,500 |  1,450 |  1,350
|  Fixed O&M |  US-$/kW/a |  Low |  40.0 |  60.0 |  30.0 |  30.0 |  30.0 |  35.0 |  35.0 |  35.0 |  30.0 |  28.0 |  28.0 |  27.0 |  22.5
|  |  |  Up |  50.0 |  60.0 |  30.0 |  30.0 |  30.0 |  40.0 |  40.0 |  40.0 |  40.0 |  36.5 |  36.5 |  39.5 |  36.0
|  Variable O&M |  US-$/MWh |  Low |  |  |  |  6.0 |  6.0 |  |  |  |  |  |  |  |
|  |  |  Up |  |  |  |  10.0 |  10.0 |  |  |  |  |  |  |  |
|  Capacity factor |  % |  Low |  24.8% |  24.8% |  24.8% |  24.8% |  24.8% |  24.8% |  24.8% |  24.8% |  24.8% |  24.8% |  24.8% |  24.8% |  24.8%
|  |  |  Up |  24.8% |  24.8% |  24.8% |  24.8% |  24.8% |  24.8% |  24.8% |  24.8% |  24.8% |  24.8% |  24.8% |  24.8% |  24.8%
|  Lifetime |  a |  Low |  20 |  20 |  20 |  20 |  20 |  20 |  20 |  20 |  20 |  20 |  20 |  20 |  20
|  |  |  Up |  20 |  20 |  20 |  20 |  20 |  20 |  20 |  20 |  20 |  20 |  20 |  20 |  20
Wind, offshore |  Total capital cost (incl. construction) |  US-$/kW |  Low |  |  3,750 |  3,100 |  3,100 |  3,100 |  3,100 |  3,100 |  2,750 |  2,360 |  2,250 |  2,350 |  2,600 |  2,500
|  |  |  Up |  |  5,000 |  5,000 |  5,000 |  5,000 |  5,500 |  5,500 |  4,500 |  4,500 |  3,800 |  3,550 |  3,775 |  2,500
|  Fixed O&M |  US-$/kW/a |  Low |  |  60.0 |  60.0 |  60.0 |  60.0 |  60.0 |  60.0 |  80.0 |  80.0 |  80.0 |  80.0 |  67.3 |  65.8
|  |  |  Up |  |  100.0 |  100.0 |  100.0 |  100.0 |  100.0 |  100.0 |  110.0 |  110.0 |  110.0 |  110.0 |  81.8 |  79.5
|  Variable O&M |  US-$/MWh |  Low |  |  13.0 |  13.0 |  13.0 |  13.0 |  13.0 |  13.0 |  |  |  |  |  |
|  |  |  Up |  |  18.0 |  18.0 |  18.0 |  18.0 |  18.0 |  18.0 |  |  |  |  |  |
|  Capacity factor |  % |  Low |  |  37.9% |  37.9% |  37.9% |  37.9% |  37.9% |  37.9% |  37.9% |  37.9% |  37.9% |  37.9% |  37.9% |  37.9%
|  |  |  Up |  |  37.9% |  37.9% |  37.9% |  37.9% |  37.9% |  37.9% |  37.9% |  37.9% |  37.9% |  37.9% |  37.9% |  37.9%
|  Lifetime |  a |  Low |  |  20 |  20 |  20 |  20 |  20 |  20 |  20 |  20 |  20 |  20 |  20 |  20
|  |  |  Up |  |  20 |  20 |  20 |  20 |  20 |  20 |  20 |  20 |  20 |  20 |  20 |  20
Table 16: (continued)

##  Appendix C Structure of system model

This section provides a comprehensive overview of all technologies and energy
carriers considered in the system model based on graphs. In these graphs,
vertices either represent energy carriers, depicted as colored squares, or
technologies, depicted as gray circles. Entering edges of technologies refer
to input carriers; outgoing edges refer to outputs. For illustrative purposes,
Fig 10 shows the graph of all carriers and technologies. In the following,
subgraphs of this graph are used to go into further detail.

Figure 10: Full graph of model carriers and technologies Figure 11: Subgraph
for electricity supply Figure 12: Subgraph for supply of space and district
heating Figure 13: Subgraph for process heat Figure 14: Subgraph of the
transport sector Figure 15: Subgraph for conversion of synthetic fuels

##  Appendix D Renewable potential

Figs. 16 and 17 show how full-load hours vary across the total energy
potential of renewables. The intention of these figures is to illustrate how
utillizing a greater share of the renewable potential results in decreasing
full-load hours and consequentially increasing incremental costs.

Figure 16: Full-load hours over total energy potential for wind Figure 17: Full-load hours over total energy potential for solar Table 17: Exemplary comparison of renewable potential for Germany in GW. | Own assumption | Result for | Literature values in
---|---|---|---
| based on | current legislation | in Risch et al. (2022)
| on Auer et al. (2020) | in Risch et al. (2022) | Lowest | Highest
Wind, onshore | 385.7 | 385.0 | 68.0 | 1188.0
Wind, offshore | 83.6 | 79.1 | 34.1 | 99.6
PV, openspace | 301.0 | 456.1 | 90.0 | 1285.0
PV, rooftop | 177.1 | 492.0 | 43.0 | 746.0

The available energy potential for biomass amounts to 1,081 TWh building on
Institute for Energy and Transport (2015) (Joint Research Center) and assuming
206 TWh of the total potential are used for the decarbonization of feedstock
in the chemical industry instead of the modeled energy system Bazzanella and
Ausfelder (2017).

##  Appendix E Technology data for system model

The tables here list the technology data for electricity generation in the
system model from Danish Energy Agency (2022). For hydrogen fuelled plants, we
assume a 15% mark-up on the costs of the corresponding natural gas plant in
line with Öberg et al. (2022). This assumption neglects the cheaper
alternative of retrofitting pre-existing gas plants instead. For all
technologies we assume a uniform interest rate of 5%.

Table 18: Technology data for electricity generation in system model. technology |  investment costs [m€/GWin] |  operational costs [m€/GWin/a] |  lifetime [a] |  electrical efficiency |  availability
---|---|---|---|---|---
combined cycle gas turbine, backpressure turbine |  586.0 |  13.7 |  25 |  50.3% |  93.0%
combined cycle gas turbine, extraction turbine |  480.7 |  15.9 |  25 |  58.3% |  93.0%
combined cycle H2 turbine, backpressure turbine |  673.9 |  13.7 |  25 |  50.3% |  93.0%
combined cycle H2 turbine, extraction turbine |  552.8 |  15.9 |  25 |  58.3% |  93.0%
open cycle gas turbine |  177.8 |  3.2 |  25 |  41.5% |  97.2%
open cycle gas turbine CHP |  226.6 |  7.7 |  25 |  41.5% |  93.0%
open cycle H2 turbine |  204.4 |  3.2 |  25 |  41.5% |  97.2%
open cycle H2 turbine CHP |  260.6 |  7.7 |  25 |  41.5% |  93.0%
engine, biogas |  384.8 |  3.9 |  25 |  43.3% |  95.1%
engine, diesel |  118.8 |  2.9 |  25 |  35.0% |  96.8%
engine, gas |  221.5 |  2.9 |  25 |  47.6% |  98.7%
engine CHP, gas |  415.5 |  4.2 |  25 |  46.9% |  95.5%
non-solid biomass plant CHP |  272.4 |  9.7 |  25 |  28.1% |  91.2%
solid biomass plant CHP |  870.5 |  24.9 |  25 |  26.8% |  91.2%
polymer electrolyte fuel cell |  475.0 |  23.8 |  10 |  50.0% |  99.7%
solid oxide fuel cell |  840.0 |  42.0 |  20 |  59.6% |  100.0%
PV, openspace |  271.2 |  5.4 |  18 |  |
PV, rooftop residential |  693.8 |  9.5 |  40 |  |
PV, rooftop industry |  511.7 |  7.8 |  40 |  |
wind, onshore |  963.1 |  11.3 |  30 |  |
wind, offshore, shallow water |  1,577.6 |  32.5 |  30 |  |
wind, offshore, deep water |  1,777.3 |  32.5 |  30 |  |
|  |  |  |  |
Table 18: (continued)

The energy-to-power ratios for both battery technologies are constrained to be
between 0.1 and 10.

Table 19: Technology data for electricity storage in system model. |  |  | investment costs | operational costs
---|---|---|---|---
technology | cycle efficiency | lifetime | power capacity | energy capacity | power capacity | energy capacity
|  | [a] | [m€/GW] | [m€/GWh] | [m€/GW/a] | [m€/GWh/a]
lithium battery | 0.89 | 18 | 80.9 | 199.6 | 1.21 | 2.99
redox battery | 0.52 | 18 | 614 | 174.5 | 9.21 | 2.62
pumped storage | 0.81 | - | - | - | - | -

##  Appendix F Supply and demand time-series

Figs. 18 and 19 show hourly electricity supply and demand for an exemplary
February week in Germany. The intention of these figures is to retrace the
duration curves presetented in Fig. 7 of the paper. In contrast to the 52%
nuclear system, Fig. 19 shows how demand adapts to supply greatly reducing the
residual demand to be met by thermal plants.

Figure 18: Supply and demand in Germany for one week and the 52% nuclear
system Figure 19: Supply and demand in Germany for one week and the system
without nuclear

##  Appendix G Final demand

The table below lists the final demand for energy and transport services the
system model must meet in each case.

Table 20: Final demand for different energy carriers in the energy system model. |  |  | process heat [TWh] | passenger transport [Gpkm] | freight transport [Gpkm] |
---|---|---|---|---|---|---
country |  electricity [TWh] |  space heat [TWh] |  low (until 100°C) |  medium (100 to 500°C) |  high (above 500°C) |  rail |  road private |  road public |  rail |  road heavy |  road light |  jet fuel [TWh]
Albania |  4.6 |  4.2 |  2.7 |  3.8 |  4.9 |  0.1 |  8.6 |  3.0 |  2.5 |  3.8 |  0.0 |  0.0
Austria |  62.8 |  45.2 |  17.0 |  26.2 |  20.7 |  14.0 |  83.8 |  11.0 |  12.6 |  25.6 |  0.9 |  18.1
Bosnia and Herzegovina |  9.6 |  4.9 |  3.2 |  4.5 |  5.7 |  0.1 |  11.5 |  4.0 |  3.4 |  5.1 |  0.1 |  0.0
Belgium |  73.6 |  62.5 |  17.1 |  22.6 |  28.8 |  11.7 |  117.8 |  14.9 |  5.7 |  30.9 |  3.9 |  51.7
Bulgaria |  29.2 |  11.0 |  9.1 |  4.7 |  4.8 |  1.4 |  55.9 |  7.8 |  7.1 |  20.2 |  0.3 |  5.4
Switzerland |  51.8 |  43.7 |  7.4 |  30.8 |  6.6 |  20.8 |  80.6 |  6.4 |  6.4 |  11.6 |  0.3 |  0.5
Czech Republic |  57.8 |  38.9 |  17.5 |  16.7 |  19.0 |  10.0 |  76.5 |  17.7 |  19.2 |  46.6 |  3.8 |  5.0
Germany |  480.7 |  470.9 |  112.1 |  119.0 |  153.0 |  101.7 |  950.7 |  64.8 |  84.8 |  303.3 |  8.6 |  151.0
Denmark |  29.5 |  30.3 |  5.6 |  7.0 |  2.6 |  6.9 |  67.1 |  7.9 |  2.0 |  14.8 |  0.2 |  4.3
Estonia |  7.2 |  5.3 |  1.0 |  1.9 |  0.6 |  0.4 |  11.3 |  2.4 |  4.1 |  4.7 |  0.1 |  0.0
Spain |  241.9 |  62.8 |  25.5 |  57.2 |  54.1 |  31.0 |  370.9 |  35.0 |  11.4 |  213.9 |  3.1 |  6.1
Finland |  65.8 |  30.4 |  27.7 |  42.4 |  8.7 |  4.6 |  67.8 |  8.1 |  11.8 |  28.4 |  0.5 |  16.7
France |  390.3 |  257.8 |  40.4 |  57.1 |  64.6 |  95.3 |  770.4 |  59.2 |  19.5 |  169.3 |  4.8 |  114.2
Greece |  45.3 |  16.0 |  5.2 |  5.9 |  5.9 |  1.2 |  106.0 |  21.0 |  0.6 |  27.0 |  1.2 |  69.5
Croatia |  16.7 |  10.3 |  1.6 |  3.9 |  2.6 |  0.8 |  25.6 |  5.3 |  3.6 |  12.2 |  0.3 |  3.3
Hungary |  37.1 |  36.6 |  7.1 |  4.1 |  6.9 |  6.7 |  55.1 |  16.2 |  14.5 |  35.8 |  1.1 |  6.1
Ireland |  24.9 |  15.9 |  4.1 |  5.8 |  3.8 |  1.1 |  28.5 |  5.2 |  0.1 |  9.0 |  0.2 |  0.0
Italy |  260.6 |  215.9 |  57.1 |  48.5 |  72.7 |  59.0 |  768.2 |  89.9 |  17.0 |  96.3 |  16.4 |  66.9
Lithuania |  11.7 |  8.0 |  3.7 |  2.1 |  1.5 |  0.4 |  30.3 |  2.9 |  112.4 |  52.3 |  0.8 |  29.6
Luxemboourg |  4.7 |  3.5 |  0.8 |  1.1 |  2.1 |  0.4 |  7.2 |  1.1 |  0.7 |  7.3 |  0.1 |  0.0
Latvia |  6.2 |  7.6 |  1.7 |  3.9 |  1.5 |  0.6 |  15.3 |  2.6 |  46.9 |  14.8 |  0.2 |  0.0
Montenegro |  2.8 |  0.9 |  0.5 |  0.7 |  1.1 |  0.1 |  4.1 |  0.1 |  0.9 |  1.4 |  0.0 |  0.0
Macedonia |  5.2 |  3.1 |  1.9 |  2.6 |  3.4 |  0.1 |  7.5 |  2.2 |  2.1 |  3.2 |  0.0 |  0.0
Netherlands |  107.7 |  62.8 |  30.0 |  28.1 |  42.5 |  24.5 |  187.4 |  6.8 |  8.7 |  66.7 |  1.6 |  215.6
Norway |  106.6 |  34.0 |  4.8 |  30.7 |  2.6 |  3.6 |  64.6 |  4.2 |  3.9 |  20.7 |  0.6 |  17.0
Poland |  145.1 |  98.0 |  23.6 |  40.8 |  42.5 |  21.8 |  218.7 |  35.6 |  127.9 |  340.0 |  9.0 |  35.1
Portugal |  46.5 |  9.1 |  8.4 |  16.1 |  9.1 |  4.0 |  85.2 |  7.0 |  5.1 |  30.4 |  0.6 |  29.1
Romania |  55.0 |  31.2 |  11.1 |  14.1 |  25.2 |  4.4 |  81.4 |  15.6 |  40.1 |  60.3 |  0.8 |  11.6
Serbia |  32.7 |  11.2 |  6.8 |  9.6 |  12.0 |  0.3 |  28.8 |  10.0 |  8.5 |  12.7 |  0.2 |  0.0
Sweden |  104.7 |  55.4 |  12.1 |  51.0 |  10.0 |  11.7 |  100.0 |  8.7 |  19.2 |  42.0 |  0.6 |  7.7
Slovenia |  12.3 |  6.5 |  1.7 |  2.6 |  2.3 |  0.5 |  26.3 |  3.6 |  13.1 |  23.7 |  0.3 |  0.0
Slovakia |  26.2 |  15.1 |  11.8 |  5.6 |  15.2 |  3.7 |  27.9 |  6.1 |  17.2 |  32.5 |  1.5 |  2.5
United Kingdom |  256.4 |  271.8 |  46.9 |  68.6 |  42.1 |  69.1 |  670.3 |  36.5 |  16.7 |  153.7 |  7.1 |  143.9
Table 20: (continued)

##  Appendix H Energy flows

Figs. 20 and 21 provide a comprehensive overview of energy flows and
conversion processes in the solved model for the 52% nuclear system and the
system without nuclear. The resulting Sankey diagrams are the quantitative
counterpart to the qualitative graphs in supplementary material C. Some
technologies distinct in the model were merged into a single node for the
graph to facilitate interpretation, for instance different types of BEVS.

Figure 20: Sankey diagram for the 52% nuclear system, in TWh/Gpkm/Gtkm

Figure 21: Sankey diagram for the system without nuclear, in TWh/Gpkm/Gtkm