Issue 
Emergent Scientist
Volume 6, 2022



Article Number  1  
Number of page(s)  9  
Section  Physics  
DOI  https://doi.org/10.1051/emsci/2022003  
Published online  01 June 2022 
Research Article
Modelling of energy transition as an optimization problem: is it possible to achieve energy transition in France without renewable energy research?
École polytechnique, Institut Polytechnique de Paris,
91128
Palaiseau,
France
^{*} email: lucas.riondet@grenobleinp.fr
Received:
3
June
2021
Accepted:
23
February
2022
To illustrate the energy transition concept and its associated stakes, we used the mathematical optimization formalism and designed a toymodel applied to the French energy system. This approach is not predictive but lays on four pedagogical scenarios considering industrial capacity and resources limitation, and recycling process to characterise a field of possibilities. Thus, productivity limit and lifetime of solar panels emerge as crucial factors in the short term. Massive recycling process seems to delay material scarcity effect to long term. In contrast, increasing demand remains a significant burden to energy production sustainability.
Key words: Energy transition / mathematical optimization / toy model / photovoltaic / planetary boundaries
© L. Riondet et al., published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
1 Introduction
To present the issues related to energy transitions, we designed a simple modular model of the French energy system, implemented as a linear optimisation problem. It is not a predictive model, but a toy model to experiment with effects of assumptions on the success of a transition. It aims at outlining the field of possibilities. We explicitly set aside performances improvement, i.e. renewable energy research outcomes, to assess the practicability of a transition with current mature technology. To do so, mathematical optimization (MO) programming enable us to determine the feasibility of a sustainable energy transition based on an objective and given constraints [1], including limitation of industrial capacity, existence of recycling process and energy needs. Optimization, in our case, results in computing optimal energy mixes and material consumption over time.
2 Method
Tools and problem definition.
2.1 Numerical tools
We used AMPL IDE version 3.6.1 with solver CPLEX 12.10.0.0 and created figures with Excel 2016 and Python version 3.8.2.
2.2 Problem definition
We consider a factory that can produce a number m(t) of photovoltaic panels (noted PV panels) per year. Then, each variable is discrete and depends on time t. The year 2010 is considered as the time origin of the runs, which is justified in the Grid demand D(t) section. N(t) is the number of operational solar panels and N_{ooo}(t) the number of solar panels out of order after an operational period, at a time t. Initially we consider both as empty stocks, to simulate an energy transition from scratch. Q_{y}(t) and Q_{x}(t) represents the stocks of fossil energy and matter as discrete functions of time. The material consumption of the factory xc_{f} (t) is sustained by mining production xp_{e}(t).
The energy production yppv (t) resulting from the N(t) solar panels must meet the energy demand of the grid D(t), to which energy consumption of the extraction production yc_{e}(t) and the PV panels factory yc_{f} (t) is added. If photovoltaic energy production does not meet the demand, fossil energy ypff (t) completes it. In parallel of the PV factory, and to account for endlife treatment issues, a recycling factory with a productivity m_{r}(t) converts a part of the panels out of order into recycled raw material (expressed by the variable xp_{r}(t) in kg per year) in exchange of a supplementary energy consumption yc_{r}(t). The whole process is visible in Figure 1, where stocks and energy mix are illustrated with squares and flows, associated to variables, with arrows. Table 1 sums up the variables of the problem, by PV panel numbers variables (unitless), energy variables expressed in kilo availability) and on the photovoltaic technology (efficiency, durability). Consequently, we set the six following assumptions on the characteristics of a standard solar panel based on the literature, given an order of magnitude perspective.
Fig. 1 Modular diagram representing interactions between PV panels factory, matter and energy stocks, and energy mix. The recycling module is surrounded in a green frame, its implementation depends on the chosen scenario to run. Same for grid energy demand D(t). 
Discrete variables of the model depending on time , with t_{f} a positive integer as the time horizon of a run.
Hypothesis of the model:
 1)
A panel is monomaterial and monocristalin, made of silicon;
 2)
The surface of a panel is 1.7 m^{2}, its thickness is 200 µm and weighs 0.8 kg [2];
 3)
A panel presents in average 0.3 kWp of capacity [3];
 4)
A panel operates for 25 years [4];
 5)
The energycapacity ratio of a PV panel is constant over its lifetime and is the same as in the South of France; which is equal to 1.3kWh/kWc [5];
 6)
The yearly sunshine hours is 2000 h in South of France [6];
 7)
“We need about 6 tons of raw material to produce 1 ton of silicon” [7];
Then, recycling process implies the two following hypothesis:
 8)
Panel’s material is recycled up to 95% [8];
 9)
The energy savings of a panel recycling is 15% [9]. This is an optimistic hypothesis according to silicon properties.
Lastly, 10) photovoltaic energy conversion does not emit CO_{2}, and fossil energy consumption has a fixed conversion coefficient of 0.65 kg of equivalent CO_{2} per kWh, which corresponds to use oil combustion to generate power [10]. Concerning PV CO_{2} emissions, it is a strong assumption which allows to assimilate the energy transition objective of a run to a zero emissions objective.
Based on these assumptions, we computed three additional parameters:
: the energy consumption to manufacture one PV panel. It includes the following steps: crystallization and contouring, Czochralsky process, wafering, cell processing module assembly, frame, add of electronic devices and human labour. The maximum values of frameworks are chosen but corrected by a factor 0.8/2 because module weight is 2 kg in references [11] and [12]), but only 0.8kg in our case (see hypothesis 2)).
: the energy consumption to extract the raw material quantity equivalent to one PV panel. It includes extracting [13] and purification [12].
Note that mining and manufacturing account respectively for 33% and 66% of the overall embodied energy of a PV panel. Moreover, the Energy Returned On Energy Invested (ERoEI) is equal to 5.95. More broadly, the set of parameters (see synthesis Tab. 2) and by extension the model’s results hinge on the assumption of constant characteristics, i.e. the absence of improvement through research, all along the simulation. To account for the industrial component of the problem, flows and stocks hypotheses are added to the model:
 11)
Factory’s productivity is limited;
 12)
Fossil energy stock and raw material stock are finite;
 13)
Recycling intensity, when it is implemented, is limited only by the existing quantity of PV panel out of order;
 14)
Energy demand of the grid can increase due to the population growth.
These hypotheses will be discussed in the result section with four scenarios highlighting specific aspects.
To make hypothesis 11) and 12) explicit, we define a maximum yearly productivity M_{max}, finite initial stocks of fossil energy and raw material, respectively Qy_{init} and Qx_{init}. Likewise, the multiplicative coefficient α imposes a linear increasing trend on the energy demand of the grid.

M_{max}: It corresponds to the limitation of the productivity of the PV panels factory, assumed proportional to the maximum photovoltaic capacity installed between 2009 and 2019 [14]. Considering a standard PV panel with fixed performances (cf. Hypothesis 3), it leads to the following equation: (2)
This parameter embodies manufacturing and installation of panels. This calculation gives order of magnitude of the current industrial limits. We will apply this limitation to all the run time to discuss energy transition phenomenon.
Qx_{init} and Qy_{init} have no realistic order of magnitude. They will be defined relatively to the model behaviour to experiment scarcity effect or carbon budget.
α: increasing demand factor of the energy demand. It varies between 0 (constant demand) and 0.1% per year for the most pessimistic scenario [15].
In addition to these three parameters, fossil fuel consumption has to be compared with the quantity Qyx °C, based on B_{Fx} °C, the CO_{2} global budget to mitigate global warming below X °C. For instance, global budget B_{F2} °C was about 1010 Gt in 2011 [16], and has to be distributed to countries and industrial sectors according o an allocating strategy. In our study we selected a demographic strategy, attributing to France a CO_{2} equivalent budget about 0.9% of the global budget, because France population account for 0.9% of the global population in 2011 [17]. Moreover, power production accounts for 41% of total CO_{2} emission [18]. Thus: (3)
Similarly, for a 1.5° C budget: (4)
Technological parameters of the model. “/pnl” means “per panel”.
2.4 Grid demand D(t)
Grid energy demand is modelized based on two assumptions: France energy demand linearly depends on time (considered constant in a first time according to demand data [19], see Fig. 2). Moreover, French nuclear production follows the energy mix imposed by the French Energy Transition law which intends to reduce nuclear component from 75% to 50% before 2025 [20]. Thus, the complement of nuclear production is considered as lowcarbon energy demand associated to D(t).
Figure 2 presents then a transitory regime (before 2025), corresponding to the “Energy Transition law”and an permanent regime (after 2025) where the part of nuclear production in energy mix is assumed constant and equal to 50%. This construction of the demand, added to the fact that the maximum installed PV capacity since 2010 was in 2011, imposes 2010 as the time origin of our model. Table 3 synthesizes the industrial parameters and range values we will consider in our scenarios.
We will investigate each parameter’s effect on the result using several scenarios. We now introduce the mathematical optimization model, based on an objective function and 16 mathematical constraints.
Fig. 2 Representation of the energy demand of France (red dot), the associated linear model (gray line), the scenario of the nuclear French production according to the "energy transition law" of 2015 (area in light gray) and the resulting lowcarbon energy demand D(t) (area in gray). 
Industrial parameters of the model.
2.5 Objective function
Considering the problem, we define a “good energy transition”objective, which means to reach rapidly and definitely an energy mix of 100% renewable. It is equivalent to minimize the cumulative fossil energy consumption over all the duration of the simulation, and make the yearly consumption decreasing over the simulation (a). Thus, the objective function will be the following one for all the scenarios: (5)
where t_{f} is considered the time horizon. Results have to be compared with the fossil energy budgets Qyx °C
2.6 Constraints
The black constraints ((a) to (g) and (k) to (p)) are always operational, when the green constraints ((h) to (i)) are activated only when recycling process is effective (i.e. in scenario C and D). Some of the constraints are also applied before or from tl, the first panels lifespan.
Constraint (a) Fossil energy consumption has to decrease during all the run;
Constraint (b) Annual production of PV panels is limited by a constant parameter M_{max};
Constraint (c) Manufacturing consumes energy and material, proportionally to the productivity;
Constraint (d) Extracting consumes energy proportionally to extracted raw material quantity;
Constraint (e) Fossil energy feeds the total energy demand subtracted of the renewable energy production;
Constraint (f) Raw material extracting feeds the material demand subtracted of the matter recycling production;
Constraint (g) Fossil energy stock and raw material stock are depleted according to extraction and fossil fuel consumption;
Constraint (h) Recycling consumes energy and produces material, proportionally to its productivity;
Constraint (i) Annual recycling productivity is limited by the number of recyclable panels.
These constraints are implemented as follows: (a) (b) (c) (d) (e) (f) (g) (h) (i)
Initial conditions are imposed by Constraint (j) Stocks are initially full, and Constraint (k) number of panels (working or not) is 0, both equivalent to:
Furthermore, the number of panels has to be computed depending on time and relatively to the panel life time t_{L}: When t < t_{L}, number of functional panels increases proportionally to productivity (Constraint (l)) and there is no panels out of order (Constraint (m)). Thus,
When t > t_{L}, number of functional panels depends also on the out of order panels number (Constraint (n)) and number of panels out of order increases (Constraint (o)). (n) (o)
Finally, to mitigate the boundary effect of the optimization, we impose an increase of the PV energy production for the five latest years of the run (Constraint p)). (p)
3 Results
Four scenarios have been studied.
3.1 Scenario A: Influence of factory’s productivity limit
Without recycling and with infinite raw material stock. Figure 3 presents the energy mix resulting from scenario A, with M_{max} equal to 10 million panels per year (up) and equal to 15 million per year (down) and with t_{L} = 100 years. Both seem to follow the same pattern: a first period of linear increase of the PV production and when the life time of the first produced panels is overtaken, PV production reaches a plateau. However, a productivity of ten million panels per year is too limiting to ever reach a free carbon energy mix.
Parameter M_{max} is proportional to the slope of the PV production in the increasing period. Thus, it strongly affects the success of the energy transition. To synthesize the effect of M_{max} on the success of the energy transition, Figure 4 presents productivity limitation on xaxis and total fossil fuel consumption over the simulation on yaxis, equal to the orange surface on Figure 3. Each point corresponds to a run with t_{f} = 100 years. Thus, we find the runs of Figure 3 on xaxis respectively at 10 million per year (in gray, meaning the failure of the energy transition) and at 15 million per year (in black, meaning that final energy mix is 100% renewable).
When M_{max} =18 × 10^{7} pnls/year, an optimal strategy which minimizes the total fossil fuel consumption is to build huge quantity of panels the first year, reaching a 100% renewable energy mix in only one year.
Moreover, our model makes structurally achieving an energy transition impossible after exceeding the panel life time (25 years). Finally, it appears that when energy transition is achieved, resulting total fossil fuel consumption remains below the lower carbon budget. For the next scenarios, we will attribute to M_{max} the value of 15 million panels per year.
Fig. 3 Energy mix of France following the scenario A over 100 years, varying limitation of factory productivity M_{max}, from 1 × 10^{7} (upper) to 1.5 × 10^{7} (lower) panels per year. Fossil fuel production (in orange), PV production (in yellow) and grid energy demand (dashed black line). Transition date t_{transition} (in green) pinpoints when a 100% PV energy mix is reached. 
Fig. 4 Total fossil fuel consumption of runs with varying maximum productivity M_{max}, with tf = 100 years. Each dot corresponds to a run with a specific M_{max}. Gray and black dots correspond respectively to failed and successful energy transition along with its duration (in green). Dashed lines represent power generation carbon budget of France to maintain global warming below 1.5 °C (in purple) and 2 °C (in red). 
3.2 Scenario B: Raw material scarcity
This scenario aims at examining the effect of the limitation of raw material on the energy transition. To do so, we compute runs with initial stock of raw material S_{x}(0) lower than what was consumed in scenario A, that is S_{x}(0) =6 × 10^{6} kg.
Figure 5 shows the resulting optimized energy mix over 100 years. This figure illustrates a scarcity effect, implying the reincrease of fossil fuel consumption after a period of full free carbon energy mix, due to the lack of material to produce new PV panels. In other terms, the determined solution can no longer satisfy constraint (a) after time t_{scarcity effect}. The bottom part of Figure 6 shows that PV production requires a continuous consumption of material which in linear until 2090. It could be considered as a energy/material conversion phenomena.
Remark that this trend should deplete the initial stock S_{x}(0) at the horizon of 2100, leading to the collapse of PV panels number, then of the PV production and as a consequence the reincrease of fossil fuel consumption. However, instead of consuming the entire raw material stock around 2100, a plateau appears before finally declining until zero. It is a model default which account for the duration of the run in the optimization that we called boundary effect. Thus, scarcity leads to failure of the energy transition.
Figure 7, based on the structure of Figure 4, plots points as synthesis of energy mix runs while dissociating which one reaches a final full free carbon energy mix (black dots) and others (gray dots), considering varying initial raw material stock S_{x}(0) and the resulting total fossil fuel consumption. It shows that a minimum of 7 × 10^{9} kg as initial raw material stock Sx(0) is required to achieve and maintain a successful energy transition. Below, scarcity effect appears before 100 years.
Note that for set maximum productivity M_{max}, total fossil fuel consumption is also set while energy transition is successful.
Fig. 5 Energy mix of France following the scenario B with S_{x}(0) = 6 billion kilograms and t_{f} = 100 years. Highlighting the scarcity effect on the PV production after 75 years. 
Fig. 6 Operational panels (black dashed line) and out of order panels (gray dashed line) (up), and stock of raw material (blue line) as functions of time, following the scenario B with S_{x}(0) = 6 billion kilograms. and t_{f} = 100 years. 
Fig. 7 Total fossil fuel consumption for runs with varying S_{x}(0) with t_{f} = 100 years and without recycling. Successful transitions (black dots) and failed transitions (gray dots). Red and purple dashed lines respectively embody power generation carbon budget of France to maintain global warming below 1.5 and 2 °C. 
3.3 Scenario C: Recycling and raw material scarcity
This scenario investigate the scarcity effect in parallel with scenario B, by presenting on Figure 8 the total fossil fuel consumption for runs with varying initial stock of raw material available Sx(0), considering recycling (green dots) or not (black dots) over 100 years.
On the one hand this figure shows that runs with and without recycling draw the same pattern, an asymptotic form for high initial raw material stock. However, recycling appears to achieve energy transitions when respective linear manufacturing, with mining as the only source of raw material, failed.
On the other hand, the previous remarks have to be taken with caution because results depend on the runs duration t_{f}, imposed equal to 100 years in this case. By extending it up to 400 years we can observe, on Figure 9, a slow down of the PV production after a certain time (about 370 years), which can be considered as a delayed scarcity effect.
Figures 9 and 10 show that recycling delays scarcity effect instead of erasing it. First, we see on Figure 10 that raw material stock is depleted in 2056 (bottom part), which matches with the peak of out of order panels number (up part). From this point, these panels are recycled to produce new ones which slowly erodes its total quantity. This dissipative effect is the consequence of the hypothesis 8) which implies 5% of material loss at each cycle.
Once not enough material remains in this stock of out of order panels to maintain by recycling a constant number of functional PV panels, around 2370, then productivity slows down and consequently PV production too, leading to the return of fossil fuel consumption.
In this scenario, used panels are deemed equivalent to a second source of material, but equally limited due to dissipative effect. However its depletion rate is slower than the raw material stock’s one. Hence, scarcity effect happens in 2380 which is about 400 years after the norecycling scenario B.
Fig. 8 Total fossil fuel consumption for runs with varying initial stock S_{x}(0), without recycling (black dots) and with recycling (green dots) and with t_{f} = 100 years. Highlighting the reduction of raw material requirement to achieve successful energy transition enabled by recycling. 
Fig. 9 Energy mix of France following the scenario C with t_{f} = 400 years and S_{x}(0) = 3 billion kilograms. Highlighting of delayed scarcity effect. 
Fig. 10 Number of panels (up part), produced (dashed black line) and out of order panels (dashed gray line) following scenario C with t_{f} = 400 years) and S_{x}(0) = 3 billion kilograms. Raw material stock as a function of time (bottom part, blue line). Highlighting of the two steps leading to scarcity effect after 370 years. 
3.4 Scenario D: Influence of increasing demand D(t)
This latest scenario imposes a constant increasing rate α to the demand D(t) from 2025 (permanent regime of energy demand, see Fig. 2) to the end of the simulation. Other conditions are similar to scenario C. Thus, Figure 11 presents the energy mix of france with an absolute increasing factor demand of 0.1 percent.
In these very strict conditions (S_{x}(0) = 3 × 10^{9} kg), fossil fuel consumption reincrease linearly due to limitation of productivity and C02 budgets are depleted about respectively 300 and 200 years before scarcity effect happens. Then, recycling and limited productivity over 400 years delay the climate deadlines after 2100, without solving them. Thus, the optimized solution violates constraint (a) like in scenario B and with too strict conditions.
Fig. 11 French energy mix following the scenario D, with t_{f} = 400 years and with S_{x}(0) = 3 billion kilograms. Purple and red vertical dashed lines respectively define dates, 2110 and 2200, for which total fossil fuel consumption overtakes power generation carbon budget of France to maintain global warming below 1.5 and 2 ^{°}C. 
4 Discussion
First of all hypothesis 1) to 6) have been chosen with the simplification that the current mature technology and existing manufacturers stay unchanged in the next century. In that respect, the resulting Energy Returned On Energy Invested (ERoEI) of our fictive PV panel is equal to 5.95, which is similar to PV performances on IEA report of 2011 [21].
To criticize the model, many of our hypotheses rely on the invariance of parameters over time, in other words, for all our runs technical and industrial performances, and by extension photovoltaic research results and development, have stopped to be improved since 2010 and for decades. If the model aimed at being predictive, it would raise so many uncertainties that nothing could be concluded.
Then, constraint (h) energetically favors recycling as compared with mining. This might no be the case if energy storage devices would have been included in the analysis. Along the same lines, focus is made on silicon because of its active role on the photovoltaic effect but other material, like copper or aluminium, seem to be much more critical [7,22] at industrial scale. Constraint (e) raises a more subtle issue, applying the national energy mix to all industries (mining, manufacturing and recycling) without regard to the processes characteristics defined by an energy nature focus; mechanical, chemical or thermal. Also, a different strategy of downscaling carbon budget from global to power generation industry changes the Qy_{2} °C value and implied results [23].
In spite of these model limitations, our approach enables us to study key factors of a simplified energy transition, which are productivity limitation, panel lifetime, energy demand and planetary boundaries, expressed in our case with mineral resources and CO_{2} emissions budget. It is dedicated to study the space and time scale of phenomena such as dissipative effect through hypothesis h) implementing that a 5% of the matter to be recycled, instead of being reuse to produce new panels, simply vanishes, leading to depletion of natural resources.
Therefore scenarios A, B and C present minimum values of raw material stock and productivity to achieve an energy transition while global warming staying below 1.5 °C over 100 years. We propose to detail the resulting land use, which is a stake specific to renewable energy systems versus fossil fuels: the maximum installed capacity corresponds to 618 km^{2} (see Fig. 10), ignoring space between panels, which is about 12% of the “Region BouchesduRhône”, a department in Southern France and about 0.11% of size of France. This result could be burden by accounting for broken panels, nonrecycled matter or storage energy systems, requiring supplementary space and energy to be treated. Thus our model intends to give orders of magnitude and absolutely not to be predictive. However, improvements could be to consider progressive evolution of technological parameters such as panel lifetime, embodied energy and material or maximum productivity and introduce different strategy of end life treatments (reuse, remanufacturing, recycling) and an energy nature focus. In addition, increasing electric demand is driven by climate change context urging to electrify transportation sector. We could account for this phenomenon with an evolving CO_{2} budget over time, gradually absorbing the remaining budget dedicated to transport sector.
5 Dead end
Constraint (p) can be considered as a failed attempt to remove the border effect of the optimization leading to decrease consumption variable instead of keeping a steady state. The five last years of some runs could be deleted to this end. Moreover, material depletion conflicts with the constraint (a), leading to what we called a scarcity effect and thus to a partial optimized solution.
6 Conclusion
This toymodel illustrates the interactions between raw material stock, industrial productivity and recycling, energy demand and climate objective. Consequently, we could conclude that energy transition, defined as reaching an energy mix fully sourced by renewable energy, can be achieved in some decades without performance improvements of existing photovoltaic panels based on runs over 100 years.
One aspect that ought, however, to receive our full attention is that results also pinpoint to the limitation that the productivity affects the transition duration, at the same level as panel lifetime. Indeed, on the one hand, we demonstrate that time horizon of a run influences the upholding of the steady state after transition. On the other hand, scarcity phenomenon despite massive recycling, increasing energy demand and disregarded issues, including storage systems contributing to stability network, or environmental and landuse constraints, threat the sustainability of our modelled energy production industry.
Sustaining this steady state within planetary boundaries and on the long term implies significant improvements of industrial practices, such as increasing the maximum productivity and intensifying recycling, but also requires improvements of technical performances, especially the lifetime of panels but also for instance power density and ecological footprint, which depends mainly on the renewable energy research.
Lastly, this extrapolating model can also be used to compare technologies, for instance monocristalline and bifacial panel technologies, or other renewable energy systems with a focus on a common material and in the same context to investigate respective landuses and energy transitions resulting from their performances.
A very special thanks to Claudia d’Ambrosio (teacher at Ecole Polytechnique) and her PhD students for their help and consideration.
References
 J.A. Snyman, D.N. Wilke, Introduction, in Practical Mathematical Optimization. Springer Optimization and Its Applications (Springer, Cham, 2018), Vol. 133 [CrossRef] [Google Scholar]
 Terre solaire website: terresolaire.com [Google Scholar]
 Engie website: mypower.engie.fr [Google Scholar]
 Solar Reviews website: solarreviews.com [Google Scholar]
 Quelle energie website: quelleenergie.fr [Google Scholar]
 Solar hours exposition: currentresults.com [Google Scholar]
 Critical raw material alliance website: crmalliance.eu [Google Scholar]
 Green match website: greenmatch.co.uk [Google Scholar]
 American geosciences website: americangeosciences.org [Google Scholar]
 Rensmart website: rensmart.com [Google Scholar]
 C. Bankier, S. Gale, Energy payback of roof mounted photovoltaic cells (2006) [Google Scholar]
 E.A. Alsema, Energy requirements and CO_{2} mitigation potential of PV systems (1998) [Google Scholar]
 UNE Programme, Environmental challenges of anthropogenic metals flows and cycles (United Nations Environment Programme, 2013) [Google Scholar]
 RTE, SER, Enedis & ADEeF, Panorama of renewable electricity synthesis as at December 31, 2019 (2020). [Google Scholar]
 N. Berghmans, Electricity demand in France: what's at stake for the energy transition? IDDRI Study 6 (2017) [Google Scholar]
 MCC Berlin website: mccberlin.net [Google Scholar]
 G. Pison, The population of the world, Popul. Soc. 480 (2011) [Google Scholar]
 IEA, Key World Energy Statistics 2020. Int. Energy Agency 33(August) (2020) [Google Scholar]
 RTE website: bilanelectrique2019.rtefrance.com [Google Scholar]
 French government website: gouvernement.fr [Google Scholar]
 IEAPVPS T12, Methodology Guidelines on the Life Cycle Assessment of Photovoltaic Electricity (2011) [Google Scholar]
 European Commission, Critical Raw Materials Resilience: Charting a Path towards greater Security and Sustainability. 474 Final (2020) [Google Scholar]
 M.W. Ryberg et al, Downscaling the Planetary Boundaries in absolute environmental sustainability assessments — a review, J. Cleaner Prod. 2020, 123–287 (2020) [Google Scholar]
Cite this article as: L. Riondet, F. Kpanou, M. Sawadogo. Modelling of energy transition as an optimization problem, Emergent Scientist 6, 1 (2022)
All Tables
Discrete variables of the model depending on time , with t_{f} a positive integer as the time horizon of a run.
All Figures
Fig. 1 Modular diagram representing interactions between PV panels factory, matter and energy stocks, and energy mix. The recycling module is surrounded in a green frame, its implementation depends on the chosen scenario to run. Same for grid energy demand D(t). 

In the text 
Fig. 2 Representation of the energy demand of France (red dot), the associated linear model (gray line), the scenario of the nuclear French production according to the "energy transition law" of 2015 (area in light gray) and the resulting lowcarbon energy demand D(t) (area in gray). 

In the text 
Fig. 3 Energy mix of France following the scenario A over 100 years, varying limitation of factory productivity M_{max}, from 1 × 10^{7} (upper) to 1.5 × 10^{7} (lower) panels per year. Fossil fuel production (in orange), PV production (in yellow) and grid energy demand (dashed black line). Transition date t_{transition} (in green) pinpoints when a 100% PV energy mix is reached. 

In the text 
Fig. 4 Total fossil fuel consumption of runs with varying maximum productivity M_{max}, with tf = 100 years. Each dot corresponds to a run with a specific M_{max}. Gray and black dots correspond respectively to failed and successful energy transition along with its duration (in green). Dashed lines represent power generation carbon budget of France to maintain global warming below 1.5 °C (in purple) and 2 °C (in red). 

In the text 
Fig. 5 Energy mix of France following the scenario B with S_{x}(0) = 6 billion kilograms and t_{f} = 100 years. Highlighting the scarcity effect on the PV production after 75 years. 

In the text 
Fig. 6 Operational panels (black dashed line) and out of order panels (gray dashed line) (up), and stock of raw material (blue line) as functions of time, following the scenario B with S_{x}(0) = 6 billion kilograms. and t_{f} = 100 years. 

In the text 
Fig. 7 Total fossil fuel consumption for runs with varying S_{x}(0) with t_{f} = 100 years and without recycling. Successful transitions (black dots) and failed transitions (gray dots). Red and purple dashed lines respectively embody power generation carbon budget of France to maintain global warming below 1.5 and 2 °C. 

In the text 
Fig. 8 Total fossil fuel consumption for runs with varying initial stock S_{x}(0), without recycling (black dots) and with recycling (green dots) and with t_{f} = 100 years. Highlighting the reduction of raw material requirement to achieve successful energy transition enabled by recycling. 

In the text 
Fig. 9 Energy mix of France following the scenario C with t_{f} = 400 years and S_{x}(0) = 3 billion kilograms. Highlighting of delayed scarcity effect. 

In the text 
Fig. 10 Number of panels (up part), produced (dashed black line) and out of order panels (dashed gray line) following scenario C with t_{f} = 400 years) and S_{x}(0) = 3 billion kilograms. Raw material stock as a function of time (bottom part, blue line). Highlighting of the two steps leading to scarcity effect after 370 years. 

In the text 
Fig. 11 French energy mix following the scenario D, with t_{f} = 400 years and with S_{x}(0) = 3 billion kilograms. Purple and red vertical dashed lines respectively define dates, 2110 and 2200, for which total fossil fuel consumption overtakes power generation carbon budget of France to maintain global warming below 1.5 and 2 ^{°}C. 

In the text 
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