The importance of renewable gas in achieving carbon-neutrality: Insights from an energy system optimization model
Graphical abstract
Introduction
In order to meet the 1.5 °C global warming objective, the European commission's ‘European Climate Law’ proposal sets the target of achieving climate neutrality by 2050 [1]. Similarly, several European states have set ambitious greenhouse gas (GHG) emission reduction targets; for instance, the official target in the French ‘energy-climate law’ is to reach net zero GHG emissions by 2050 [2]. Global energy consumption in 2019 was 418 EJ (116.11 PWh), of which 40.4% is accounted for by oil, 16.4% by gas, 9.5% by coal, 10.4% by biofuels and waste and 19.7% by electricity.1 Focusing on French energy consumption, in 2020 France consumed 1,562 TWh of energy.2 The main energy carriers for the French energy sector were oil and refined petroleum products (41.6%), electricity (25%) and natural gas (20%). Although French electricity has a very low carbon footprint (34gCO2eq/kWhe in 20213), electricity accounts for only one quarter of French final energy consumption and achieving carbon-neutrality requires decarbonization of the whole energy sector, not only of the electricity supply.
Energy scenarios aiming at carbon-neutrality by 2050 vary with respect to the role of different energy carriers, particularly gas and electricity. For instance, the French Energy Transition Agency (ADEME)'s ‘Energy-Climate Scenario 2035–2050’ and the French Ministry of Ecological Transition and Solidarity's ‘National Low-Carbon Strategy’, feature relatively electrified heating and transport sectors in France with up to 60% of the primary energy supply being electrified by 2050 [3,4]. However, another scenario for France, published by the NGO négaWatt, suggests only 35% of electrification by 2050 for the primary energy supply, with the transport sector dominated by gas-fuelled internal combustion engines [5].
These national scenarios are based on top-down allocation of energy sources and carriers and do not result from explicit optimization. Considering the entire energy system as an integrated whole and optimizing it on a national scale is complicated and highly demanding in computational terms. However, ideally, a rigorous energy policy that fully considers the relative role of the different energy sources, carriers and storage options should be based on optimal allocation of those options. This optimization should include endogenous choice of energy carriers and low-carbon options (renewable electricity, biogas, carbon capture and storage and nuclear power) since these choices are interdependent. For instance, considering power-to-gas as a long-term storage option in the context of the electricity sector alone requires highly inefficient gas-to-power conversion technologies [6]. To avoid overestimation of storage needs, studies should focus on the entire energy system, not on a single sector [7]. Therefore, the endogenous technology choice should include a multi-sectorial approach to enable sector-coupling. Sector-coupling enables optimal allocation of different energy sources, carriers and storage options to satisfy the main end-use demands by allowing an endogenous choice of energy carrier and conversion options for different end-uses [8].
Correct dimensioning of short-term and long-term storage options requires high temporal resolution. A coarser-than-hourly temporal resolution lowers the model accuracy because it averages short-term variations in wind speed and solar radiation, leading to underestimation in the dimensioning of short-term storage options [9]. Similarly, long-term storage options (typically inter-seasonal storage) are among cost-optimal solutions due to annual cycles of wind, solar irradiation and temperature [10,11], and correct dimensioning of long-term storage options requires the modelling of a continuous, long period of time, rather than defining representative periods [12]. Therefore, modelling an optimal energy mix must consider at least one full year at an hourly resolution.
Achieving carbon-neutrality in the energy sector requires not only the penalization of positive emissions as a carbon tax, but also the promotion of negative emissions [13]. To sum up, identification of the relative role of different energy carriers requires an integrated optimization that (1) includes the main energy sectors, (2) is based on endogenous energy carrier and technology choices (simultaneous optimization of investment and operation), (3) includes the main low-carbon options, (4) has a high temporal resolution over at least a full year and (5) accounts for both positive and negative CO2 emissions.
Most energy system optimization models only represent the electricity sector [[13], [14], [15], [16], [17], etc]. Although multi-energy systems featuring sector-coupling have gained significant attention recently, as shown in the reviews by Mancarella [74] and Fodstad et al., they face “a trade-off between the level of technological detail and temporal resolution” [75]. Therefore, the existing energy system optimization studies that include sector-coupling either lack the required temporal resolution [18] or lack complete endogeneity in the interactions between energy carriers and end-use demands. They also suffer from limited representation of the main low-carbon options, especially negative emission technologies [[19], [20], [21], [22], [23]]. Moreover, none of these studies include internalization of both negative and positive CO2 emissions, which is a key element in studying the potential of different mitigation options. This gap in the literature is mentioned in the recent review by Fogstad et al. who mention the lack of analysis of “combined natural-gas and CCS value chains, together with H2” [75]. To include all the conditions mentioned above in an optimal decision-making process aiming at carbon-neutrality, we develop the EOLES_mv (Energy Optimization for Low Emission Systems, multi-vector) model, which meets all the conditions highlighted above. EOLES_mv simultaneously optimizes dispatch (providing an hourly supply-demand balance) and investment in production, storage, network and energy conversion capacities, in order to minimize the total cost of energy systems. Box 1 compares EOLES_mv with some of the recent and state-of-the-art energy and electricity system models,representing at least one example of each type of model in the literature.
Applying the EOLES_mv model to the French context, we study, for different social cost of carbon4 scenarios (from 0 to €500/tCO2), the relative role of the main energy carriers and the importance of the key low-carbon technologies in achieving carbon-neutrality in cost-optimal ways. Finally, accounting for the main uncertainties, we propose a robust social cost of carbon to ensure that the goal of deep decarbonization is achieved.
The remainder of this paper is organized as follows. Section 2 presents the methods: the EOLES_mv model and the input parameters. Section 3 presents the results, which are discussed in section 4. Section 5 highlights the main findings and concludes.
Section snippets
Material and methods
This section documents the EOLES_mv model and the input data used in it. Fig. 1 shows the research flowchart for this study.
Energy mix
Fig. 3 shows the primary energy production. With no SCC, about 73% of the primary energy comes from fossil gas. For an SCC of €100/tCO2, the proportion of fossil gas in primary energy production more than halves and for €200/tCO2 it is completely abandoned and replaced by increased electrification and bio-methane from methanization. Nuclear power becomes part of the energy mix for an SCC between €100 and €400/tCO2, but the proportion of this technology in primary energy never exceeds 8%.
The gas
Comparison with existing scenarios
The second French “National Low-Carbon Strategy” (SNBC) proposes very high electrification rates for the transport and heating sectors [4]. Great efficiency improvements in the residential and tertiary sectors and modal change strategies in the transport sector, as well as the elimination of coal from industry, are the main enablers of the French energy transition in this scenario. Similarly, ADEME's update of the “Energy-Climate Scenario 2035–2050” study shows an energy mix consisting of
Conclusion and policy implications
This article studies the cost-optimal low-carbon energy mix, relative role of energy carriers and different low-carbon options applied to the case of France for the year 2050. To that end, we have developed a first-of-its-kind integrated model that allows for optimization of the energy system (EOLES_mv). We allowed the end-use demand for each major energy-consuming sector to choose endogenously among four different energy carriers (electricity, heat, gas and hydrogen), we maintained high
Author statement
Behrang Shirizadeh: Conceptualization, Methodology, Resources, Software, Formal analysis, Writing – original draft, Revision of the manuscript, Visualization, Funding acquisition; Philippe Quirion: Conceptualization, Formal analysis, Writing – original draft, Supervision, Project administration, Revision of the manuscript, Funding acquisition.
Declaration of competing interest
The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: The first author of this study, Behrang Shirizadeh benefitted from a CIFRE PhD grant (N°2017/1218) funded by TotalEnergies during the initial phases of the preparation of this article. The same author is currently a fulltime employee of Deloitte France. The study was conducted independently by the authors and the views set out in this article are those of the
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