Research Article
Print
Research Article
The development options of nuclear power under carbon dioxide emissions constraints *
expand article infoOleg V. Marchenko, Sergei V. Solomin
‡ Melentiev Energy Systems Institute, Siberian Branch of the Russian Academy of Sciences (ISEM SB RAS), Irkutsk, Russia
Open Access

Abstract

The aim of the work is forecasting the development of nuclear power in Russia and the world for the period up to 2050 under various scenarios of constraints on carbon dioxide emissions. A brief comparative analysis of the main characteristics of the forecasts of the International Energy Agency (IEA) and the International Renewable Energy Agency (IRENA) has been carried out. Additionally, calculations were performed using the mathematical models of the world energy system GEM and GEM-Dyn developed at the ISEM SB RAS. The optimal ratio of nuclear and non-nuclear energy sources has been determined. It is shown that nuclear power, including nuclear power plants operating on a closed fuel cycle, along with renewable energy sources, is an effective technology that can solve the problem of reducing carbon dioxide emissions. Calculations have shown that in the sustainable development scenario, the capacity of nuclear power plants in Russia in the period from 2020 to 2050 can increase by 2.7 times, and their share in electricity generation can reach 21–25% in 2030 and 26–35% in 2050. The average annual growth rate (for 30 years) of the installed capacity of nuclear power plants in Russia in the sustainable development scenario is 3.1% compared to 2.7% for the world as a whole. In the GEM and GEM-Dyn calculations performed by the authors, the scale of nuclear energy use turned out to be about 30% higher than in the scenarios of the International Energy Agency due to more conservative estimates of the opportunities for improving the performance of renewable energy sources and taking into account the need to back-up their capacity.

Keywords

Nuclear industry, nuclear power plants, environmental restrictions, efficiency, energy model, forecast

Introduction

In recent years, many politicians and scientists have been talking about the need to combat global warming. They argue in this regard that measures to combat climate change are urgent, since the consequences of such a change may be worse than previously expected (Global Warming 2019).

The 2015 Paris Climate Accords have set a benchmark to limit global temperature rise to “well below 2 °C”, and ideally to 1.5 °C above pre-industrial levels. Achieving this goal will require a profound transformation of the global energy sector. Since the combustion of fossil fuels increases greenhouse gas emissions, their further use should be limited (Future of solar photovoltaic 2019, Gielen et al. 2019, Hansen et al. 2019).

One of the most effective means of reducing the greenhouse gas emissions, primarily carbon dioxide (CO2), is the further development of the nuclear industry (Belyaev et al. 2002, Murogov and Ponomarev-Stepnoy 2005, Zrodnikov 2010, Belyaev et al. 2011, Adamov et al. 2012, Kagramanyan et al. 2013, Marchenko and Solomin 2015, Adamov et al. 2021). However, its share in the world’s total electricity generation has declined from 17% in 2000 to about 10% in 2020, despite the increased power and efficiency of modern reactors (World Energy Oulook 2021). This trend is largely driven by political factors.

The purpose of this work is to predict the development of the nuclear industry in Russia and the world for the period up to 2050. The authors consider various scenarios for limiting carbon dioxide emissions, some characteristics of the forecasts of international organizations and the results of calculating the prospects for the development of the energy sector using static (Belyaev et al. 2002, 2011, Kagramanyan et al. 2013) and dynamic (Filippov and Lebedev 2003, Marchenko and Solomin 2015) models of the global energy system developed at the ISEM SB RAS.

Mathematical models of the global energy system

The mathematical description of the problem of determining the optimal technological structure of the global energy system in a static (quasi-dynamic) formulation is as follows: it is necessary to find the minimum of the objective function

Z=rjcrjxrj, (1)

where crj is the unit reduced cost, xrj is the installed capacity; indices r and j denote the sets of regions (model nodes) R={1,…, ru} and energy technologies J={1,…,ju}, respectively.

The minimum of the objective function can be found subject to the following constraints: meeting the specified energy needs and peak power as well as balancing the production and consumption of primary, secondary and final energy, financial, environmental and other restrictions. Among the electricity generation technologies, the model describes base and peak power plants using fossil fuels and hydrogen, NPPs with thermal and fast reactors, hydraulic power plants (HPPs), solar power plants (SPPs), wind power plants (WPPs) and geothermal power plants (GeoPPs).

The static model describes the nuclear industry on the assumption that at each time interval the energy structure changes completely (all the existing technologies will be decommissioned and replaced with new ones or completely reconstructed). The continuous development of the nuclear industry is described by the dynamic model, which, when moving to a new time interval, takes into account the existing structure of energy technologies in the regions, timing of the decommissioning of facilities, differences in the service life as well as dynamics of technical and economic indicators of the technologies. The objective function of the problem in the dynamic formulation is written as:

Z=trjctrjxtrjtT, ∀ rR, ∀ jJ. (2)

Here, as before, ctrj is the specific reduced cost,xtrj is the installed capacity, and the index t refers to the time intervals tu, into which the entire considered period T={1,…,tu} is divided. In addition to the constraints of the static model, the solution of the problem must satisfy the conditions of continuity at the boundaries of the time intervals. As the experience of applying these two modifications of the global energy system model has shown, when considering a time period of several decades (as in the case of this work, up to 2050), the calculated structures at the end of the period differ insignificantly.

The models in these formulations are described in most detail in (Belyaev et al. 2002) and (Filippov and Lebedev 2003). The model in the quasi-dynamic formulation (1) is called GEM (Global Energy Model), the model in the dynamic formulation (2) is GEM-Dyn.

Global and Russian nuclear industry development scenarios

Table 1 shows the values of global carbon dioxide emissions in the scenarios proposed by the International Energy Agency (IEA). In Scenario 1 (STEPS, or the declared policy scenario), the emissions are approximately constant and remain at the current level (34 Gt/year), in Scenario 2 (APS, or the announced promises scenario), the emissions gradually decrease to 21 Gt/year by 2050, in Scenario 3 (SDS, or sustainable development scenario), they decrease up to 8 Gt/year. Scenario 4 (NZE, or zero-emissions scenario) will completely stop the emissions by 2050 (World Energy Oulook 2021). The last scenario appears to have no chance of being implemented and is considered only for the sake of completeness.

Table 1.

Current and predicted carbon dioxide emissions, Gt CO2/year

Scenarios Years
2010 2020 2030 2040 2050
1 STEPS 32.3 34.2 36.3 35.3 33.9
2 APS 32.3 34.2 33.6 26.7 20.7
3 SDS 32.3 34.2 28.5 16.4 8.2
4 NZE 32.3 34.2 21.1 6.3 0.0

The International Renewable Energy Agency (IRENA) proposed the following two emission scenarios: (1) inertial (35 Gt in 2030 and 33.1 Gt in 2050) and (2) REMAp (24.9 Gt in 2030 and 9.8 Gt) (Future of solar photovoltaic 2019). The first scenario is similar to the STEPS scenario of the IEA; the second one is similar to the SDS sustainable development scenario (see Table 1).

Table 2 shows the specific capital investments for the main electricity generation technologies in the IEA scenarios (upper part of the table), and Table 3 for illustrative purposes presents the results of calculating the cost of electricity (discounted costs of electricity generation) and ranking technologies according to this criterion, taking into account the uncertainty of both technical and economic indicators and the location of the energy source.

Table 2.

Specific capital investments for key technologies, $/kW

2030 2050
Europe / USA India / China Russia Europe / USA India / China Russia
IEA Model (2021)
NPP 4800/5100 2800 4500 2500/2800
SPP (PVC) 510/640 340/380 370/440 260/330
WPP 1390/1280 990/1160 1391/1200 960/1090
Coal 2000/2100 1200/800 2000/2100 1200/800
Gas 1000 700/560 1000 700/560
GEM Model (2022)
NPP (thermal) 4800 3000 3200 4500 3200 3500
NPP (FR) 5200 5000 3600 5000 4700 3500
SPP (PVC) 850 750 1000 750 500 800
WPP 1300 1050 1100 1200 1000 1000
HPP 4100/3950 2500/2400 2500 4100/3950 2800/2700 2600
Coal 1500 1250 1400 1450 1200 1350
Gas 1000 900 1050 1000 900 1100
Table 3.

Ranking key technologies by electricity cost in 2050

Energy source Rank Discounted cost of electricity, cent/kW×h
2030 2050
SPP 1 2.0–4.5 1.5–4.0
WPP 2 3.0–5.5 3.0–4.5
NPP 3 6.5–12.0 6.0–11.5
HPP 4 6.0–12.0 7.0–12.0
Gas 5 6.0–12.0 12.5–13.5
Coal 6 6.5–14.0 12.5–14.0

Specific investments are the most important indicators that affect the economic efficiency of the energy source. In addition, the cost of fuel plays an important role and, for renewable energy sources, climatic and meteorological conditions determine the installed capacity utilization factor.

The cost of electricity accumulates all the main technical and economic indicators of the energy source and is generally used for preliminary estimates and pairwise comparisons. The next approximation for evaluating economic efficiency is mathematical modeling, which takes into account the relationship of energy sources with each other and with the environment. This is especially important when considering renewable energy sources operating in an unmanaged mode, since they must be duplicated by other energy sources in order to provide uninterrupted electricity supply to consumers. In particular, in the GEM models, variables describing the renewable energy sources are included not only in energy balances but also in power balances with coefficients that take into account the insecurity of their generation.

In the period up to 2050, a sharp reduction is expected in the cost of solar power plants based on photovoltaic converters (PVCs). In this regard, nuclear and hydraulic power plants are inferior in terms of the cost of electricity to solar and wind power plants (SPPs and WPPs) (see Table 3).

Given these prerequisites, the global energy structure predicted in the IEA and IRENA scenarios assumes a radical increase in the role of renewable energy sources (RESs). In the REMAp scenario, the share of RESs in global electricity production increases to 86% by 2050. In the IEA scenarios, the same indicator ranges from 42% to 88%, and the share of NPPs decreases from 10% to 8–9% (Table 4). The maximum volume of world electricity production at NPPs in the IEA scenarios is 5.5 thousand TW×h/year (an increase over the current level for 30 years by a little more than 2 times, or about 2.4% per year).

Table 4.

Electricity generation (thousand TW×h/year) and NPP share in electricity generation (%) in the IEA scenarios

2020 2030 2040 2050
World, total 26.7 33.5–37.3 40.5–56.5 46.7–71.1
Including NPP 2.7 3.1–3.8 3.5–4.9 3.8–5.5
NPP share, % 10.1 9.3–10.1 8.7–9.4 7.7–8.1
Russia, total 1.1 1.3 1.4 1.5
Including NPP 0.2 0.2–0.3 0.3 0.3–0.4
NPP share, % 20.3 17.5–20.2 18.0–23.5 18.5–27.2

In this work, for the first three IEA scenarios (see Table 1), calculations were performed on the GEM and GEM-Dyn models with technology indicators from Table 2 (lower part of the table). Their results in comparison with similar results of IEA (WEM model) (World Energy Oulook 2021) are presented in Tables 5, 6.

Table 5.

Electricity generation forecast in Russia, TW×h/year

Energy sources Years
2020 2030 2050
Sc. 1 Sc. 2 Sc. 3 Sc. 1 Sc. 2 Sc. 3
IEA scenarios (2021)
RES 195 236 236 332 432 432 867
Nuclear energy 216 219 219 254 275 275 409
Fossil fuel 646 798 798 666 780 780 232
Total 1057 1253 1253 1255 1488 1488 1508
Authors’ scenarios (2020)
RES 209 270 290 310 410 420 600
Nuclear energy 216 290 300 330 420 490 540
Hydrogen 0 0 0 20 0 50 130
Fossil fuel 639 850 760 540 820 640 290
Total 1064 1410 1350 1200 1650 1600 1560
Table 6.

Share in electricity generation, %

Energy sources Years
2020 2030 2050
Sc. 1 Sc. 2 Sc. 3 Sc. 1 Sc. 2 Sc. 3
IEA scenarios (2021)
RES 18.5 18.8 18.8 26.5 29.0 29.0 57.5
Nuclear energy 20.4 17.5 17.5 20.2 18.5 18.5 27.1
Fossil fuel 61.1 63.7 63.7 53.1 52.4 52.4 15.4
Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0
Authors’ scenarios (2020)
RES 19.6 19.1 21.5 25.8 24.8 26.3 38.5
Nuclear energy 20.3 20.6 22.2 27.5 25.5 30.6 34.6
Hydrogen 0.0 0.0 0.0 1.7 0.0 3.1 8.3
Fossil fuel 60.1 60.3 56.3 45.0 49.7 40.0 18.6
Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0

As the restrictions on carbon dioxide emissions tighten, the scale of development of the nuclear industry in general increases (in IEA Scenarios 1 and 2, it remains constant). According to the GEM and GEM-Dyn forecasts, the scale of use of nuclear energy in all the scenarios exceeds the IEA forecasts (by about 30% in the sustainable development scenario). This is due to the fact that the calculations include more conservative estimates of the reduction in specific capital investments in SPPs and WPPs, taking into account the need to duplicate their capacity with peak energy sources. At the same time, the calculations show the expediency of a partial transition of the nuclear industry to fast neutron reactors with a closed fuel cycle, which increases their resource base, the efficiency of nuclear fuel use, and makes it possible to solve some problems related to the disposal of radioactive waste. Nuclear power plants in Scenarios 2 and 3 prove to be useful for producing not only electricity but also hydrogen for peak power plants.

The share of NPPs in the total Russian electricity production predicted in the GEM models also significantly exceeds the estimates of the IEA (35% versus 27% in the scenario of sustainable development in 2050) and the forecasts in (Lagerev and Hanaeva 2021), according to which the share of NPPs will remain almost constant (slightly more than 20%) up to 2050.

The results of calculating the economic development of installed NPP capacities in the global and Russian energy sectors, which are optimal in terms of economic criteria, are shown in Figs 1, 2.

Figure 1. 

Installed NPP capacities (GW) in the world.

Figure 2. 

Installed NPP capacities (GW) in Russia.

The average annual growth rate (over 30 years) of installed NPP capacities in Russia, according to the sustainable development scenario, is 3.1% compared to 2.7% for the world as a whole. In the considered scenarios, it will be economically optimal to increase the installed NPP capacities up to 56–72 GW by 2050 (an increase of 1.9–2.5 times compared to 2020).

It should be noted that the high growth rates of nuclear energy in Russia obtained as a result of calculations can hardly be realized, taking into account financial, political and other restrictions, but they reveal a trend in accordance with which it is expedient to develop nuclear energy in the coming decades.

Conclusions

  1. The authors have conducted optimization calculations of the technological structure of the power industry in the world and Russia for different scenarios of restrictions on carbon dioxide emissions using the mathematical models GEM (Global Energy Model) and GEM-Dyn. The optimal ratio of nuclear and non-nuclear energy sources has been determined for the conditions of Russia.
  2. It has been shown that nuclear power, including NPPs operating in a closed fuel cycle, along with renewable energy sources, is an effective technology that can solve the problem of reducing carbon dioxide emissions. In Russia, the share of NPPs in electricity generation may be 21–25% in 2030 and 26–35% in 2050.
  3. In the GEM and GEM-Dyn calculations, the scale of nuclear energy use has turned out to be about 30% higher than in the scenarios of the International Energy Agency due to more conservative estimates of the opportunities for improving the performance of renewable energy sources and taking into account the need to duplicate their capacity.

Acknowledgements

The research was carried out under State Assignment Project (no. FWEU-2021-0001) of the Fundamental Research Program of Russian Federation 2021-2030.

References

  • Adamov EO, Dzhalavyan AV, Lopatkin AV, Molokanov NA, Muravyov EV, Orlov VV, Kalyakin SG, Rachkov VI, Troyanov VM, Avrorin EN, Ivanov VB, Aleksakhin RM (2012) Conceptual framework of a strategy for the development of nuclear power in Russia to 2100. Atomnaya Energiya [Atomic Energy] 112(6): 391–403. https://doi.org/10.1007/s10512-012-9574-x
  • Adamov EO, Rachkov VI, Kashirsky AA, Orlov AI (2021) Global outlook on large-scale nuclear power development strategies. Nuclear Energy and Technology 7(4): 263–270. https://doi.org/10.3897/nucet.7.74217
  • Belyaev LS, Marchenko OV, Filippov SP, Solomin SV, Stepanova TB, Kokorin AL (2002) World energy and transition to sustainable development. Dordrecht, Boston, London: Kluwer Academic Publishers, 264 pp. https://doi.org/10.1007/978-94-017-3705-0
  • Belyaev LS, Marchenko OV, Solomin SV (2011) Issledovanie dolgosrochnyh tendenciy razvitiya energetiki Rossii i mira [Investigation of long-term trends of Russia and world energy systems development]. Izvestiya RAN. Energetika [Proceedings of the Russian Academy of Sciences. Power Engineering] 2: 3–11. [in Russian]
  • Filippov SP, Lebedev AV (2003) Multiregionalnaya dinamicheskaya model mirovoy energeticheskoy sistemy. Irkutsk: ISEM SO RAN, 74 pp.
  • Future of solar photovoltaic (2019) Future of solar photovoltaic: Deployment, investment, technology, grid integration and socio-economic aspects. IRENA, Abu Dhabi, 73 pp.
  • Gielen D, Boshell F, Saygin D, Bazilian M, Wagner N, Gorini R (2019) The role of renewable energy in the global energy transformation. Energy Strategy Reviews 24: 38–54. https://doi.org/10.1016/j.esr.2019.01.006
  • Global Warming (2019) Global Warming of 1.5 °C. An IPCC Special Report on the impacts of global warming of 1. 5 °C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty. IPCC, 616 pp.
  • Kagramanyan VS, Korobeynikov VV, Poplavskaya EV, Belyaev LS, Marchenko OV, Solomin SV (2013) Assessment of the economic risk due to delayed startup of NPP with fast reactors. Atomnaya Energiya [Atomic Energy] 114(2): 83–93. https://doi.org/10.1007/s10512-013-9676-0
  • Marchenko OV, Solomin SV (2015) Investigation of ecological constraints influence on competitiveness of nuclear power plants. Izvestiya Wysshikh Uchebnykh Zawedeniy, Yadernaya Energetika [News of Higher Educational Institutions. Nuclear Power Engineering] 3: 20–30. https://doi.org/10.26583/npe.2015.3.02
  • Murogov VM, Ponomarev-Stepnoy NN (2005) Yadernaya tehnologiya – garant stabilnosti Rossii v XXI veke [Nuclear Technology – Guarantee of the Stable Development Russia in the 21th Century]. Izvestiya vuzov. Yadernaya Energetika [News of Higher Educational Institutions. Nuclear Power Engineering] 2: 3–8. [in Russian]
  • World Energy Oulook (2021) World Energy Outlook 2021. IEA, Paris, 386 pp.

* Russian text published: Izvestiya vuzov. Yadernaya Energetika (ISSN 0204-3327), 2022, n. 2, pp. 5–14.
login to comment