The challenges of the French electricity generation sector: an
analysis using ESDMA
Tristan de Wildt
Delft University of Technology, Delft, the Netherlands
Van Zeggelenlaan 344, 2524 BB Den Haag
0031-610226523
t.e.dewildt@ student.tudelft.nl
Abstract
Nuclear energy dependency, large vulnerability to socio-political factors, high wind
and solar energy targets and progressive liberalization of the energy sector; those are
some of the main challenges the French electricity sector is currently facing. This paper
uses the multi-method Exploratory System Dynamics Modeling and Analysis to explore
the future of the French electricity generation sector given its unique specificity and the
wide range of deep uncertainties that this market contains. This methodology then allows
the exploration of the complex interaction between socio-political parameters and the
support for new nuclear energy installed capacity. The model used for this research was
created by Pruyt, et al. (Energy Transitions towards Sustainability: A Staged Exploration
of Complexity and Deep Uncertainty, 2011) and has been adapted to reflect the specific
dynamics of the French electricity generation sector. The paper will end with a
presentation of the work that will be carried out in the future for the purpose of this
research.
Keywords. Exploratory Systems Dynamics Modeling and Analysis, French electricity generation,
Nuclear energy, Renewables, Deep uncertainty
1, Introduction
The French electricity sector is characterized by its high specificity when compared to those of its
neighboring countries. After the first oil shock of 1974, France decided to massively develop civil
nuclear industry in order to benefit from an energy source that was less dependent on social and
economic events in energy exporting countries. Currently, 40% of the energy consumed in France is
produced by nuclear power plants, representing over 75% of the electricity consumption (RTE EDF
Transport 2011). This politically driven investment has resulted in a high degree of specialization in
this technology, leaving little room for innovation in other means of electricity generation. As a result
of this specialization, nuclear energy has become an element of strength for the French economy for
both the stability of the electricity price in the country and for the technology it enables to export
(Teravainen, Lehtonen and Martiskainen 2011). An illustration of this fact is the high number of
nuclear power plants in Europe that were built by the French company Areva (Thomas 2009).
The current context however puts a threat on the dominancy of nuclear energy and challenges
the French government to reconsider its energy policy. First, the Fukushima nuclear accident raised
once again the question of the high risks associated with the production of nuclear electricity
(Srinivasan and Rethinaraj 2013). As a result, many countries like Germany decided to end the
utilization of nuclear energy and to progressively shut down their power plants (Evans 2011).
Secondly the European Union, through the ‘climate and energy package’, has imposed renewable
1
energy targets to be reached by 2020. This leaves France with a gap of 9.4% of the total electricity
consumption to be covered by renewable power plants in less than eight years (RTE, Bilan électrique
2012 2013). Wind and solar energy are considered the two most interesting green energy sources in
order to achieve this goal.
The future of nuclear electricity generation in France is hence subject to a great amount of
complexity and deep uncertainty. This research aims to explore the future evolution of nuclear
energies in France, given the specificities of the country’s energy market, social and economic
characteristics and the uncertainties contained by these parameters. Several scenarios will be inserted
in the model to test the effect of possible nuclear incidents on the dominancy of this electricity source.
This will enable the analysis of the impact of nuclear energy’s evolution on renewable energies,
keeping in mind the fact that France has installed capacity targets to achieve by 2020, and will
probably receive supplementary goals for 2050. To perform this analysis, the system dynamics model
created by Erik Pruyt, Jan Kwakkel, Caner Hamarat and Goneng Yucel, presented at the 29th
International Conference of the System Dynamics Society (Energy Transitions towards Sustainability:
A Staged Exploration of Complexity and Deep Uncertainty 2011) will be utilized and adapted to the
specificity of the French electricity generation sector. The methodology applied here is Exploratory
System Dynamics Modeling and Analysis (ESDMA), a multi-method that uses both System Dynamics
and the Exploratory Modeling Analysis methodology to explore the development of a system that is
characterized by high complexity and contains deep uncertainties.
This paper is structured as follows. Section 2 provides more information about ESDMA and
the model used for this research. Section 3 presents the dynamics that characterize the French
electricity generation sector and demonstrates how they are inserted and represented in the model. In
section 4 we analyze the behavior of the model and present the simulation outputs. In section 5, we
present the future work that will be carried out in this research and section 6 will provide conclusions
on the development of the different technologies in order to analyze the complexity of the evolution
given the systems deep uncertainties.
2. Methodology: Exploratory Systems Dynamics Modeling and Analysis
The French electricity generation sector can be seen as a system that displays complex and dynamic
behavior as a result of the wide range of variables that influence each other both positively and
negatively. The evolution of this system is also dependent on factors containing deep uncertainties. An
example is the economic development of the country, which greatly influences the amount of new
generation capacity to be installed. The multi-method Exploratory System Dynamics Modeling and
Analysis (ESDMA) is applied to deal with those characteristics. It offers interesting opportunities to
test the deep robustness of potential policies to change the system’s behavior (Kwakkel and Pruyt
2012) (Pruyt and Hamarat, The Concerned Run on the DSB Bank: An Exploratory System Dynamics
Approach 2010).
ESDMA uses a dual methodology: System Dynamics (SD) and the research methodology
Exploratory Modeling and Analysis (EMA). While the SD simulation is used to create a model which
enables the generation of scenarios for the development of the system, the use of EMA offers the
possibility to test and demonstrate the robustness of policy decisions. It will here generate a high
number of plausible transient scenarios while varying the value of model parameters containing
important uncertainties (Lempert, Popper and Bankes 2003). Thereby, this will give the possibility to
test the effectiveness and robustness of different policies by taking into account the entire multi-
dimensional uncertainty space bringing the outcomes into a very limited amount of output displays
(Pruyt, Kwakkel, et al. 2011).
The EMA methodology consists of six steps: (1) conceptualization of the policy problem, (2)
specification of the uncertainties and certainties that play a role for policy analysis, (3) development of
a system model, (4) generation of thousands to millions of scenarios, (5) exploration and simulation of
the outcomes of the computational experiments to analyze the system behavior and lastly (6) test and
display of policy recommendations(A gusdinata 2008).
2
The model used in this research is based on a model created by Pruyt, Erik, Jan Kwakkel,
Caner Hamarat and Goneng Y ucel, which has been presented at the 29th International Conference of
the System Dynamics Society (Energy Transitions towards Sustainability: A Staged Exploration of
Complexity and Deep Uncertainty 2011). This model shows the battle between old and new electricity
generation technologies in the context of the current energy transition. Several elements drive the
development of a certain technology. One of them is the progress ratio of this technology, which
shows the remaining potential available to improve the efficiency of a type of electricity generation
and thus how its marginal costs will decrease. The lower the progress ratio, the higher the future
expected decrease of costs as a result of the utilization of this technology. More explanations about the
use of this progress ratio will be given in section 3.b. To match the characteristics of the French
electricity generation sector, the model created by Pruyt, et al. has been adapted by changing the
values of certain variables, by making changes into the structure of certain dynamics and by adding
new ones. The changes made to the model can be found in the same section (3.b).
3. Structure and specificity of the French electricity generation:
application
a. Type of technologies
The model describes the evolution of several types of electricity generation in France: nuclear energy,
wind energy, solar energy, hydroelectricity, and grey energy (electricity produced by coal and natural
gas). The different types of electricity generation are here modeled by taking their specific functions in
the French electricity network into account. For example, grey electricity (by means of gas turbines)
and hydroelectricity are used to cover peak electricity demand; their cumulated share in the total
capacity installed can therefore never decrease below a certain percentage to ensure a minimum
reliability of the electricity supply. The following table shows an overview of each type of technology
and the way it is utilized in the French electricity sector. Annex A provides more detailed information
about the state of those technologies in France.
Type of Situation in the French market —_ Function in the model
technology
Nuclear - dominant source of electricity - relatively profitable (due to feed-in tariff)
energy (49% of the installed capacity - reliable production of electricity
in 2012) - inflexible electricity production
- vulnerable to discontinuous - relatively high progress ratio (rather little efficiency improvements
level of socio-political trust expected)
Wind - anupcoming electricity source - relatively profitable (due to feed-in tariff)
energy - quasi non-existing installed - unreliable electricity (production fluctuation)
capacity in 2000 - rather low progress ratio (rather highefficiency improvements
expected)
Solar - anupcoming electricity source - highly profitable (due to feed-in tariff)
energy - very small installed capacity in - unreliable electricity (production fluctuation)
- low progress ratio (huge efficiency improvement expected)
Grey - 21% of the installed capacity - not really profitable and low investment security (no feed-in tariff)
electricity - used to cover the daily - reliable production of electricity
intermediate and peak demand - _ flexible electricity production
- no feed-in tariff - high progress ratio (very little efficiency improvement expected)
Hydro- - 19.5% of the installed capacity - rather profitable (feed-in tariff), but nearly all possible construction
electricity - used to cover the daily place for dams already used
intermediate and peak demand - reliable production of electricity
- flexible electricity production
- high progress ratio (very little efficiency improvement expected)
Table 1: Characteristics and functions of the different
in the French electricity sector
b. Dynamics of the system
The following subsection provides an overview of the main dynamics of the model that have been
added to or changed in the model of Pruyt, et al. The overview given here will show the influence of
those dynamics on the system by means of causal relations and feedback loops. Also, because of its
importance in the model, a description of the effects of the technology’s progress ratios(already
present in the model of Pruyt, et al) will be provided. Lastly, this section shows a list of all the
dynamics present in the model.
New technology cost
A progress ratio has been assigned to each technology. The progress ratio “derives from historical data
inexperience curves (and) are used for forecasting development of many technologies as a means to
model endogenous technical change in for instance climate-economy models” (Sark 2008). It thus
represents the speed of learning of a technology’s exploitation. The progress ratio influences the new
cost of the technology through a negative logarithmic function (‘-log’) and then through a negative
exponent (‘a[-X]’). The change in the new capacity costs is measured by the following formula:
-6
%
C(x) = C¢xo)(¥)
Where C(X,) is the cost of the technology. C(X o) is the cost of the technology at the starting point. X_
and X are then respectively the cumulated production at time t and time 0. B is the learning parameter
(Ferioli, Schoots and Zwaan 2009). The new cost of the technology thus also depends on the installed
and decommissioned capacity.
A higher progress ratio means a lower experience curve parameter (which is calculated by the
negative logarithmic function) and therefore a slower reduction of the new technology costs. The
height of the new technology costs then increases the expected earnings for the new power plants and
thus the preference for the technology. An example of a technology with a high progress ratio is
hydroelectricity, which is a technology that has been exploited for a long time and which is not
expected to have significant technological progress.
The new cost of the technology is then used to calculate the expected earnings for the
electricity producer when using the specific technology to build a power plant. Other parameters are
used to calculate the expected earnings: electricity average market price, average production per year,
variable costs and the lifetime of the technology installed. The expected earning per MW shows the
total profit (during the total lifetime of the power plant) that the company will make by installing the
technology. This variable is then compared to the expected earnings of the other technologies, which
allows it to become more or less preferable in comparison to its ‘rivals’. The comparison is made by
computing the fraction of expected earnings of a specific technology on the sum of the expected
earnings of all technologies.
Dynamics leading a nuclear incident
Commonly, there is a tendency to think that an industrial incident is the result of one main factor that
is responsible for the sudden failure of the system. In the case of Chernobyl, the accident could
essentially be explained by an operational error made by technicians who had a lack of knowledge in
the operation of nuclear power plants.
However, when studying those types of accidents, more interactions can be found with factors
that are not immediately expected to have a significant impact on the system but still correlate
significantly with the event. In their article about air transport safety, Ale et al. investigate the
elements leading to the occurrence of an air crash incident by using a causal model (Ale, Bellamy, et
al., Towards a causal model for air transport safety - an ongoing research project 2006) (Ale, Bellamy,
et al., Further development of a Causal model for Air Transport Safety (CATS): building the
mathematical heart 2009). In their model, they show that the increased probability of an air crash
accident can be traced back to a more complex succession and combination of several minor events
that aren’t solely technical. On the same basis, the Chernobyl event can be linked to a higher accident
probability that correlates among others with socio-political factors like the declining performance of
the national economy, the decreasing degree of the country’s specialization in this technology (due to
a lack of investment in new nuclear power plants in the years preceding the incident) and the
diminution of public attention towards this sector (Qureshi 2007).
In this research, four factors that together could increase the possibility of the occurrence of a
nuclear incident have been identified:
- Factor 1 - the state of the national economy: an economy that is not performing well has less
capacity to invest in infrastructure maintenance.
- Factor 2 - the amount of new nuclear energy capacity installed in recent years: a low
investment level in new nuclear energy capacity leads to a reduction of the specialization of
the energy sector in this technology and therefore to a decrease of knowledge in how to
manage nuclear energy.
- Factor 3 - temporary excessive electricity demand: difficult electricity production conditions
during winters (due to a high heating demand) and during summers (due to a high cooling
demand and the difficulty to cool power plants down) increase the chance of operational
incidents.
- Factor 4 - public and political attention: high public and political attention is considered to
encourage investments in the operational safety of nuclear power plants.
By using lookup functions, each factor creates a nuclear incident probability parameter. All
four probability parameters are multiplied together and in case a certain threshold is reached, a nuclear
energy incident happens. The fact that the multiplication of these four probability parameters
determines the occurrence of an incident means that all four factors are needed (i.e. have to be
sufficiently high) in order to create a nuclear incident. Four thresholds have been created. If only the
lowest threshold is reached, an INES 4 level nuclear incident will occur (IAEA 2013). If the highest
threshold is surpassed, an INES 7 level nuclear incident is simulated.
Factor 1 is modeled as a stock that ‘accumulates’ the last five years monthly economic
development figures. A low average economic development score in the last 5 years will strongly
increase the nuclear incident probability parameter of factor 1.
Similar to factor 1, factor 2 is modeled as a stock that ‘registers’ how many new nuclear
energy capacity has been installed in the last 5 years. A low level of investments increases the nuclear
incident probability parameter of factor 2.
Factor 3 is modeled as a sinus function. The assumption has been made that a temporary
excessive electricity demand only happens twice a year: one time in the winter and one time in the
summer. The sinus function is written as follows:
Factor 3 incident probability parameter = ABS( Randomiser extent of the electricity
demand*SIN((3.1415*2)*(Time-2000)))
The sinus part of the function is multiplied by a random parameter that increases or reduces
the extent of the electricity demand. 2000 is the start time of the model.
Factor 4 is part of a negative feedback loop that is influenced by the level of trust in nuclear
energy within the country. In case a nuclear incident occurs, the public attention increases and the
chances that a second nuclear incident occurs strongly decrease.
Effect 1
Effect of last years state of the
‘economy on possible nuclear
Effect 4
Effect of attention of
Diurtion effect state
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Randomiserextent of
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Random level stress ~ Random level stress Random level stress Random level stress
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MIN SEED MEAN ‘MAX
Effect 2 Effect 3
Figure 1: Overview the modeling of the dynamics leading to a nuclear incident
Finally, when a nuclear incident occurs in France, two variables are influenced. First a share
of the nuclear energy capacity is removed depending on the extent of the incident. Secondly the level
of trust in nuclear energy decreases, which means that new investments in nuclear energy are partly
blocked.
As mentioned before, this study uses the INES scale used by the International Atomic Energy
Agency to create different size nuclear incidents. An article written by D.Smythe provides estimations
of the occurrence frequency of each INES scale incident (Smythe 2011). Although these frequencies
are not used as fixed numbers in the model and have been included in a uniformly distributed
uncertainty range, these figures contain a large amount of uncertainty which will thus have
consequences for the interpretation of the model results. Many other parameters included in the
variables (and among other thresholds) and stocks are not strongly based on proven data since no data
for that kind of simulation is available yet. For the purpose of this research, work is still needed to deal
with and interpret these uncertainties.
Dynamics leading to a decrease of trust in nuclear energy
The level of trust in nuclear energy is influenced by two factors: potential nuclear incidents in France
and potential nuclear incidents in foreign countries. Nuclear incidents in other countries are here
assumed not to lead to removals of nuclear energy capacity in France and have a lower impact on the
level of trust. The level of trust in nuclear energy is modeled as a stock that is constantly filled up by a
confidence return rate, and ‘emptied’ by successive nuclear incidents of different sizes (including very
low size incidents). Figure 2 provides an overview of the behavior of the level of trust in the nuclear
energy stock.
Level of trust in nuclear energy
1 [= ae ee —
°
2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
‘Time (Y EAR)
Level of trust in nuclear energy : V17 normal
Figure 2: overview of the behavior of the level of trust in nuclear energy stock
Influence of the European development of renewable energies
The dependency between European energy markets is high, and therefore the French energy system
cannot be considered to evolve fully independently. The fact that a specific technology is relatively
new in neighboring countries (and thus a low number of mature companies in this sector) decreases
the speed at which the same technology is developed within France. Because the incorporation of
other countries’ energy systems would add an even higher amount of uncertainty into the model, a
theoretical evolution of wind and solar energy on European scale has been created to simulate the
effect of this development on the French electricity market. The formula used is the following:
_ co
Level of technology specilisation in Europe = 1/(1 + Ht e7k(e-t0)
pe
Where C0 is the European installed capacity of the technology in the year 2000, pC is the theoretical
potential European installed capacity of the technology, k is the adoption rate, t is the current time and
tO is the start time of the model.
P Wind energy Solar energy
European capacity in 2000 12.887 MW 2.000 MW
European potential capacity 297.500 MW 988.000 MW
Adoption rate 0,258 0,219
Percentage when market considered 15% 15%
mature enough
Table 2: Parameters for European influence on French wind and solar energy market
y of the model dynamics
Dynamic Effect in the model
Industry specialization Preference for a certain technology due to the intensity of its utilization in the past
Expected progress performance Preference for a certain technology as a result the expected decrease of its costs
New capacity costs Decrease of the capacity costs due to its utilization
New technology cost Preference for a certain technology due to its costs compared to other technologies
Dynamics leading a nuclear incident Effect of the simultaneous occurrence of 4 factors that lead to the occurrence of a
nuclear energy incident
Dynamics leading to a decrease of trust Effect of previous nuclear energy incidents that decrease the preference of nuclear
in nuclear energy energy
Influence of the European development Preference for a certain (RE) technology resulting from the investments made on
of renewable energies European level
Demand for reliable electricity sources __ Effect of the need for a minimum level of reliable electricity source for the good
functioning of the electricity sector
Demand for peak load installed Effect of the need for a minimum level of flexible energy generation for the good
capacity ioning of the electricity sector
Table 3: summary of the dynamics present the model
c. Input data
Parameter Unit Nuclear Wind energy Solar energy Grey Hydro-
energy electricity electricity
Capacity in 2000 MW 63000 61 120 27000 25000
Cumulative MW 0 0 0 2000 10000
decommissioned capacity
in 2000
Lifetime yr [30 - 50] [20 - 30] [20 - 30] [15 - 40] [30 - 50]
Technology cost 10°€/“MW 18 15 7.6 1.2 0.5
Progress ratio [0.85-0.95 [0.85-0.95] [0.85-0.95] [0.85-0.95] [0.85-0.95]
]
Feed-in tariff €/MWe 42 82 300 f 60.7
Average production per MWe/MW 8000 2000 1250 3000 2770
year
Variable cost €/MWe 28.4 2 5 46 43
European technology fo / 0.219 0.258 / i
adoption rate
European potential capacity MW / 197500 988000 if /
European capacity in 2000 MW I 12887 2000 /
Average planning and yr [1-5] (1-5) [1-5] (1-5) [1-5]
periods
Table 4: Input data of the five technologies
Parameter Unit Value
Economic growth ‘lyear [-1; 43]
End value of electricity intensity of economic growth %olyear [40 ; 100]
Average electricity price €/MWe [40 ; 70]
Average selling price grey energy €/MWe [55 ; 100]
Table 5: Other input data of the model
Some of the parameters’ values are highly or even fully predictable. This is for example the case of the
installed capacity of each technology in the year 2000 and their average production per year. However,
other parameters are highly uncertain (in table4 and 5 specified by ranges). The progress ratio is for
example a theoretical calculation of the estimated progress that a technology could reach through
higher efficiency and price decrease. Another example is the forecasted economic growth of the
country. Those parameters will have a major impact on the outcomes of the model and are deeply
uncertain. It is then by using the EMA methodology that it will be made possible to include those
ranges in the model, and explore their effect on the overall system by running the model thousands of
times.
4. Results
This chapter will present three different types of results. The first part (4.a) shows the development of
the five installed capacities (nuclear energy, wind energy, solar energy, grey electricity and
hydroelectricity) without any nuclear incident being generated. The second part (4.b) shows the effect
of nuclear energy incidents on the installed capacity of nuclear energy, when the dynamics leading to a
nuclear incident are taken into account (see chapter 3.b.). The third part (4.c) presents a comparison of
the effects of a large nuclear incident in France in 2020 and 2060 using the five different technologies.
To generate these outputs, the model has been run 1000 times by using among others the uncertainties
mentioned in section 3.c. The envelopes hereunder give an overview of the technologies’ evolution for
a period of 100 years starting from the year 2000. In each envelope, 25 randomly chosen lines show
the installed capacity over time. An end state distribution of the installed capacity in 2100 is provided
on the right side of the envelope.
a. Development of the different installed capacities (without nuclear
incident)
Development of nuclear energy
ee installed capacity T1
300000
250000
150000
2366 B2e20 2040 | BOSo 32o86 Bice c ise-05
Figure 3: the development of nuclear energy
The envelope shows a rather wide possibility of outcomes. The installed capacity never decreases
below 50 GW and in some occasions, it even rises above 325 GW in 2100. The installed capacity
however seems to concentrate between 100 and 150 GW. It seems that the uncertainties inserted into
the model parameters play an important role in determining the evolution of nuclear energy capacity.
Observing the lines, it can be observed that in most cases the installed capacity of nuclear energy rises
between 2010 and 2040. This is a trend that can also be observed in the wind energy envelope. The
period between 2010 and 2040 is thus a moment where, according to the model, the demand for new
generation capacity is high, and where nuclear and wind energy are seen by the model as the two
preferred types of electricity source. The period between 2040 and 2100 however displays a
stabilization of the total nuclear energy capacity, except in some rare cases where the share of installed
capacity increases constantly.
Development of wind energy
installed capacity T2
50000
20360 2020 2040 2O6G 2080 Bice o 166-05
ume
Figure 4: the development of wind energy
The envelope describing the wind energy evolution displays a minimum installed capacity of 30 GW
and a maximum of 210 GW by 2100. The installed capacity concentrates around 80 GW but in each
case, wind energy succeeds in developing and grabbing a strong part of the total installed capacity.
Analyzing the outcomes, it seems that two different types of lines can be observed: lines that remain
constant after 2040 and those that continue to increase. The same observation can be made for the
development of solar energy. The second type of lines has the tendency to fluctuate more whereas the
first type of lines has a more steady development. The period between 2020 and 2040 plays an
important role. If wind or solar energy succeed in pursuing their development after 2020 (i.e. after the
feed-in tariff is removed), they will continue to compete strongly with the other technologies which
creates this fluctuating pattern. If not, they will enter an equilibrium state and keep their level of
installed capacity until 2100.
Development of solar energy
installed capacity T3
50000
200 S620 2040 L6e-05
ume
Figure 5: the development of solar energy
According to the simulation made by running the model, solar energy does not succeed in developing
to the same extent as wind energy, even if the technology has a greater expected progress. Whereas
wind energy uses the period between 2000 and 2020 to gain a large share of the installed capacity,
solar energy only begins its development later since the solar technology is less mature than wind
energy technology. Therefore solar energy only succeeds in becoming an important electricity source
in a few cases. Figure 6 shows that, whatever the winner between solar and wind energy, the end state
distribution of the renewable energies fraction has the tendency to concentrate around 40 %.
total fraction new technologies
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Figure 6: the of the fraction of energies
Development of grey electricity
eee installed capacity T4
200000 | |
= s0000 |
=
5
& sooo} L |
Bz
aces [ |
20000
BGe Boz0 2020 — BO6o 2050 ae-05
Figure 7: the development of grey electricity
According to the model grey electricity is not expected to become as important as nuclear energy. The
end state distribution displays a range from 12 GW to 100 GW but the final installed capacity
concentrates between 12 GW and 40 GW. An increase of the installed capacity between 2020 and
2040 can enable grey electricity to become an important energy source but in many cases the amount
of installed capacity in 2000 and 2100 does not differ widely.
Development of hydroelectricity
mstalled capacity TS
28000
Figure 8: the development of hydroelectricity
The physical boundary that constrains further development of hydroelectricity prevents this
technology to take over an important share of the total installed capacity, but its low cost and utility in
the electricity generation market ensure that in most cases the capacity is utilized at full potential.
b. Effect of incidents in France and foreign countries on the nuclear energy
installed capacity
Chapter 3.b explains how the dynamics leading to a nuclear incident have been modeled. Figure 9
provides an overview of its effect on the installed capacity of nuclear energy and compares it to the
case when no nuclear incidents are generated.
The second part of the graph clearly shows one case (blue line) when two nuclear energy accidents
happen (one just after 2040 and one just after 2080). These nuclear incidents both happen after a
period in which no (or very little) new investments in nuclear energy have been made (the blue line
was progressively decreasing) and the economic development was low or even negative. These are
two of the factors that increase the chance of a nuclear incident.
However the end state distributions are very similar which firstly means that, given the parameters that
were used in the model, the amount of times that nuclear energy incidents happen is low and secondly
that nuclear energy often succeeds in taking back the installed capacity ‘lost’ after a nuclear energy
incident.
As mentioned above, more work is needed to identify the factors (and their interdependencies) and
increase the certainty of the parameters used currently to determine whether a nuclear energy accident
is likely to occur.
11
Without effect With effect
installed capacity T1 installed capacity T1
300000
250000
100000
50000
300000
250000
100000
50000 4
a
0 a5 206 208 a0 0 Tie-05 foo ae aa ss 208 00 Te-05
time time
Figure 9: comparison of the installed capacity of nuclear energy without and with potential nuclear energy incidents
c. Comparison of the effects of a large nuclear incident in France in 2020
and 2060
To study the effect of a nuclear energy incident on the five electricity sources, two ‘large’ nuclear
energy incidents have been simulated in 2020 and in 2060. In each case the amount of nuclear energy
removed is randomly chosen between a 40 and 60 percent). Additionally, the preference for nuclear
energy is reduced by 50 to 90% and then slowly retums to its initial value according to the confidence
retum rate.
By looking at the results, the following conclusions can be drawn:
In many cases, nuclear energy succeeds in taking back a great amount of installed capacity
after an accident. This is strongly explained by the specialization of the industry in this
technology. The higher the amount of nuclear energy before the crisis (and thus the higher the
specialization of the industry in this technology), the better and faster is the recovery after the
accident (both in 2020 and 2060).
Secondly, solar energy is able to take over a greater share of the total installed capacity in
2060 than it does in 2020. This is explained by the fact that in 2060, the solar energy
technology is more mature. Unlike solar energy, wind energy is able to take a more important
share of the installed capacity left over by nuclear energy in 2020.
The nuclear accidents create disturbances and fluctuations in the installed capacity of each
technology, but in general each level of installed capacity of each technology is tending
towards an equilibrium state: none of the technologies disappear or become insignificant as a
result of their competition.
12
Incident in 2020 Incident in 2060
installed capacity T1
installed capacity T1
> “rere 200000]
oo ss0000
B 200000
3 150000 et
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§ s0000
3 0000
installed capacity 12 installed capacity +2
—— 200000
q a
8
Ci 00000
BR
3
installed capacity 13 installed capacity T3
~
ee ree
$ 00000
5 co00e
Ss ae 100000
Bow i
Instatled capacity 14 Instatled"éSpacity 14
100000
Grey
installed capacity 15 installed capacity 15
—— ee a
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: a
3 5 on oa
8
ES 0
g8 a
a las cc
Boa 2
Figure 10: Simulation of nuclear energy incidents in 2020 and 2060
13
5. Future work
Dynamics of socio-political mistrust in nuclear energy and impact on electricity generation
Nuclear energy is highly vulnerable to fluctuations in the level of socio-political trust towards this
technology. In the research that is currently conducted, the focus lies on the dynamics of public and
political support for nuclear energy as a result of different system factors and on the way it affects the
new installed capacity rate.
There is for example a correlation between the demand for electricity and the speed at which
society trust nuclear energy again after an accident (Pligt, Eiser en Spears 1984). In France, the
investments in nuclear energy stopped in the 80s due to the social impacts of the sequential incidents
of Three Mile Island, Saint-Laurent-des-Eaux (France) and Chernobyl. It took over twenty years
before the decision was made to make new investments in nuclear energy. While the argument of
social impact is surely valid, the reason why no investments have been made in the technology within
this period can also be explained by the low demand for new installed capacity. By the end of the 80s,
there was so much new nuclear energy capacity installed that no further investment was really needed.
In the case of Japan however, two years after Fukushima, the new government has already announced
its intention to review its initial plans of abandoning nuclear energy generation (BBC News Asia
2012).
The model will be adapted by introducing a population of convinced and unconvinced citizens
and politicians. Several types of variables will then influence the rate at which people move from one
side to another (Pligt, Eiser en Spears 1984):
¢ People’s beliefs about the economic advantages of nuclear power (influenced for example
by the energy demand);
e People’s beliefs about the safety of nuclear energy (influenced among others by the
duration between the last nuclear incident and the present time); and
¢ People’s beliefs about the socio-political implications of nuclear power (partly influenced
by the rate at which new capacity is installed in comparison to development of renewable
energy).
Development of different nuclear accident scenarios
Further research and modeling will be done by creating different scenarios and investigating their
impact on the French electricity generation sector. For example, different nuclear crises on different
scales will be simulated at different points in time to investigate the effect on the total amount of
installed capacity available and the evolution of the other technologies. The occurrence of a large
nuclear accident just before a period of high demand for new installed capacity could play a decisive
role in the dominancy of this technology in France, since other electricity sources will then be
prioritized. Nuclear energy will also be separated in the four different technologies that are currently
used for electricity generation in France: CPO-CPY, P4-P’4, N4 and EPR (World Nuclear Association
2013). This will allow exploring the effect of a sudden failure of one of those technologies (for
example as a result of a reactor incident which leads to the prohibition of the usage of this particular
technology) on the development of the entire French electricity generation diversity. The prohibition
of CPO-CPY nuclear power plants (mostly power plants installed between 1977 and 1988)would lead
to a high amount of installed capacity to be replaced, while forbidding the use of the EPR technology
(newest generation, no installed capacity yet but one power plant under construction) could put a
threat on the future usage of nuclear energy in France.
The study and incorporation of those three elements in the model will enable a better
understanding of the impact of a country’s high dependency towards one unique technology in a world
that sees a fast development of renewable energies and in which the risk of nuclear energy’s utilization
will probably never fully be eliminated.
14
6. Conclusions
This paper investigates the challenges of the French electricity generation sector and focuses
particularly on the dependency of the country towards nuclear energy and its effect on the evolution of
the other types of energies. By using the ESDMA multi-method, the development of the five types of
electricity sources has been explored by considering the most important system uncertainties. The
following observations about the model outcomes can be made.
According to the model and the uncertainty inserted, the period between 2020 and 2040 is a
decisive moment for each technology. Whether one types of electricity source has an important place
in 2100 depends upon its success during the second twenty years period of the model.
An important observation provided by the model’s outcomes is that nuclear energy is expected
to keep an important role in the French energy sector, given the uncertainties included in the
parameters. The scenarios created in future work will test the robustness of this dominancy on its
ability to survive the socio-political consequences resulting from a nuclear accident.
The outcomes of the model also shows that wind and solar act as competitors. The fact that
wind energy is a more mature technology plays a decisive role in the success of its development by
2100.
The research results presented here also show that more work is needed on three different
domains. First more work has to be done to understand and use the dynamics leading to the occurrence
of a nuclear incident, both qualitatively and quantitatively. Secondly, the confidence return rate, which
currently is a constant parameter, can be replaced by a variable in order to include new feedback loops
in the model (effect of the state of the economy, the electricity demand and the installed capacity of
renewable energies). Lastly, the nuclear energy capacity can be divided in 4 different technologies
which are currently used in France. This will allow investigating the effects of the failure of one
particular nuclear energy technology according to the age of the power plants.
15
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19
Annexes
Annex A — Sources used for the specification of model parameters
Nuclear energy
The biggest source of electricity in France is nuclear energy. In the year 2000, the installed capacity
was 63000 MW and has remained unchanged since then (RTE, Energie Electrique en France 2000
2001)(RTE, Bilan électrique 2012 2013). Nuclear energy is thus the dominant source of electricity and
is in this model challenged by the other upcoming means of production (wind and solar). In this
model, it has been considered that the average lifetime of a nuclear power plant was 45 years (Bataille
and Birreaux 2003). Even if the price a nuclear power plants have dropped a little in the past year, a
megawatt was in the year 2000 worth 1.8 M€ (Gupta and Thompson 1999). The progress ratio of the
technology was also considered by many scientific articles to lie between 0.96 and 1 (Lund University
2006). In order to protect the development of nuclear energy, the French has set up a feed-in tariff. For
each MWe produced, a fixed selling price of 42 € is afforded (Le Monde 2011). Other specificities of
nuclear power plant are the large average production per year, 8000 MWe/MW (DGEC 2008), and a
variable cost of 28.4 €/MWe (DGEC 2008).
Wind energy
Wind energy has developed very lately in France. In 2000, the capacity didn’t exceed 61 MW (RTE,
Statistiques de l'Energie Electrique en France 2002 2003) but has however reached 7.500 MW in 2012
(RTE, Bilan électrique 2012 2013). Still, the progress ratio was rather high (between 0.90 and 0.96)
since the technology was already been used intensively in Spain and Germany (Lako 2010).The price
for a megawatt wind energy was approximately 1.5 M€ (Lako 2010).
As it did in the case of nuclear energy, the French government also came with a feed-in tariff in order
to encourage the development of wind energy. Each wind energy producer is rewarded with a feed-in
tariff of 82 €/MWe during the ten first year of the power plant. According to the production of the
power plant during those ten first years, a new feed-in tariff between 28 and 82 €/MWe is then decided
for an extra five year (IEA 2010). In this model, it has been considered that the average new feed-in
tariff was 62 €/MWe (the mean between the maximum feed-in tariff, 82 €/MWe, and the average
market price, 42€/MWe).
In the model, a wind energy power plant is considered to have a lifetime of 25 years (Lako 2010), an
average production per year of 2000 MWe/MW (Blanco 2009) and a variable cost of 2 €/MWe (Lako
2010). In order to compute the theoretical development of the European wind energy market, the
European potential capacity had been set to 197500 MW (Lako 2010). The potential capacity is the
capacity that could be achieved in 2050. The European capacity in 2000 was 12887 MW (EWEA
2011) with an adoption rate of 0.258 (Lund 2006).
Solar energy
The solar energy capacity was in 2000 much higher than wind energy: 120 MW (RTE, Statistiques de
l'Energie Electrique en France 2002 2003). A megawatt could be bought for 7.6 M€ (Lako 2010). The
fact that solar energy didn’t developed as fast as wind energy is partly to be explained by the fact that
the solar energy European market wasn’t as advance as the one of wind energy. The European
capacity in 2000 was approximately 2000 MW (SETIC 2008). Still solar energy has a great potential
in Europe: 988000 MW by 2050 (Lako 2010). The adoption rate is 0.219 (Lund 2006).
Other specificities of solar energy power plants are an average lifetime of 30 years (Lako 2010), and
average production per year of 1250 MWe/MW (ADEME 2011) and variable costs of 5 €/MWe (Lako
2010). The progress ratio is estimated to be between 0.77 and 0.84 (Lako 2010).
The feed-in tariff offered for solar energy producers is 300 €/MWe during the first 20 production years
of the power plant (IEA 2010).
20
Grey electricity
In the 70’s, the French authorities decided to invest massively in nuclear energy in order to reduce the
energetic dependence of the country. Therefore, grey electricity didn’t develop so rapidly as in other
countries. Still, it always played a significant role in order to fulfill the deficiencies of nuclear energy
(mainly dealing with flexibility). In 2000, the total capacity of grey means of production was 27000
MW (RTE, Energie Electrique en France 2000 2001). The cost of a megawatt was 1.2 M€ (Croezen
and Slingerland 2005) and the variable costs were 46 €/MWe (DGEC 2008). Since the different
technologies are since long used, the progress ratio is very high: between 0.93 and 0.99 (Lund
University 2006). No feed-in tariff is offered for grey electricity in France. Since the technologies does
not function as base loads (a role that is fulfilled by nuclear energy), the electricity produced is only
sold whenever the selling price is higher than a certain point. In France, the average production per
year of grey means of production is 3000 MWe/MW (DGEC 2008). The lifetime of the power plants
is in average 30 years (Croezen and Slingerland 2005)
Hydroelectricity
As grey electricity, hydroelectricity is mainly used to fulfill the deficiencies of nuclear energy in
production flexibility. It thus plays an important role in the French electricity sector and has the
advantage not to produce any greenhouse gases. Therefore, the French state has set up a feed-in tariff
of 60.7€/MWe (IEA 2010).
Hydroelectricity represented in 2000 a capacity of 25000 MW (RTE, Energie Electrique en France
2000 2001). A turbine for hydroelectricity is considered to have a lifetime of 45 years (Lako 2010).
The cost of the technology is relatively low (0.5 M€/MW) since the dams are already built (no extra
dam can be added in France) (Lako 2010).A investment in hydroelectricity thus only means the
replacement of the turbines.
The progress ratio of hydroelectricity is estimated to lie between 0.95 and 0.99 (Lako 2010), the
average production per year is in France 2770 MWe/MW (RTE, Energie Electrique en France 2000
2001) and the variable costs lies around 43 €/MWe (Lako 2010).
21
Annex B — Model quantitative pattern behavior comparison
Output Um Us Uc| Utotal
Nuclear energy 0.88 0.12 0.00 |1.00
Wind energy 0.25 0.65 0.10 |1.00
Solar energy 0.18 0.08 0.69 {0.95
Grey electricity 0.23 0.06 0.37 _|0.67
Table6: Summary of the ‘Theil inequality statistics’ test
The quantitative pattern behavior comparison uses the ‘Theil Inequality Statistics’ (University at
Albany 2010). First the test shows the correlation coefficient between the curve produced by the
model and the curve observed in reality. The correlation is thus a measure of the relation between a
certain numbers of variables and determines to which extent the variables are proportional to each
other. A correlation test assumes there is a certain linear relation between the variables, which is not
always the case. Secondly, it does not reveal at which point the two curves differs (or not) from each
other. This is why the ‘Theil Inequality Statistics’ also shows the constant deviance towards the
average (Um), the deviance towards the amplitude (Us) and the deviance towards the phase (Uc).
Those three terms are called the ‘inequality proportions’. Theoretically, the sum of the Um, Us and Uc
should be equal to 1.
68000 Nuclear energy
66000
Model (y)
64000 :
Reality (a)
62000
60000 : : . ; ;
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Figure 11: Quantitative pattern behavior comparison of nuclear energy
6000 Wind energy
3000 re a —— Model (y)
2000 we —Reality (a)
—— —-s
¥ ' — T r r T T T 1
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Figure 12: Quantitative pattern behavior comparison of wind energy
22
lar ener;
re Solar energy
1200
1000
NN
Oo T T T T T T T T T T ]
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Figure 13: Figure 3: Quantitative pattern behavior comparison of solar energy
29000 Grey electricity
28000
26000
Model (y)
— —>S=—=—— Reality (a)
24000
23000 T T T T T T
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Figure 14: Figure 3: Quantitative pattern behavior comparison of grey electricity
23
