Hourly CO2 Emission Factors and Marginal Costs of Energy Carriers in Future Multi-Energy Systems

Abstract

Hourly emission factors and marginal costs of energy carriers are determined to enable a simplified assessment of decarbonization measures in energy systems. Since the sectors and energy carriers are increasingly coupled in the context of the energy transition, the complexity of balancing emissions increases. Methods of calculating emission factors and marginal energy carrier costs in a multi-energy carrier model were presented and applied. The model used and the input data from a trend scenario for Germany up to the year 2050 were described for this purpose. A linear optimization model representing electricity, district heating, hydrogen, and methane was used. All relevant constraints and modeling assumptions were documented. In this context, an emissions accounting method has been proposed, which allows for determining time-resolved emission factors for different energy carriers in multi-energy systems (MES) while considering the linkages between energy carriers. The results showed that the emissions accounting method had a strong influence on the level and the hourly profile of the emission factors. The comparison of marginal costs and emission factors provided insights into decarbonization potentials. This holds true in particular for the electrification of district heating since a strong correlation between low marginal costs and times with renewable excess was observed. The market values of renewables were determined as an illustrative application of the resulting time series of costs. The time series of marginal costs as well as the time series of emission factors are made freely available for further use.

Design of Renewable and System-Beneficial District Heating Systems Using a Dynamic Emission Factor for Grid-Sourced Electricity

“…With a dynamic emission factor, the best emission value is 9.9% lower in case of the national emission factor, and 53.6% lower in case of the local emission factor for the historical data of 2018. Figure 4 presents the Pareto-fronts of future scenarios of the German energy system of the years 2030 and 2050 based on scenario calculations of Böing and Regett [7]. In emission scenario 2030, the deviation between constant and dynamic emission factors at minimum emission is 21.3% and reaches 44.4% in 2050 (see Figure 4).…”

“…In the future, electricity is probably the most important commodity crossing the system boundary of local energy systems. The emission attributed to one energy unit is time-dependent due to the volatile share of renewable electricity within the total electricity generation [5][6][7]. Hence, the climate impact of electricity usage strongly fluctuates over time, requiring operational strategies of dispatchable distributed energy resources to be as important as the chosen components themselves.…”

“…The scenario "local emission factor-2018" is explained in detail in Section 2.3.3. The results of a multi-energy systems optimization based on a trend scenario for Germany applying the International Energy Agency (IEA) allocation method is used as future emission parameters for the years 2030 and 2050 [7]. Each emission scenario is compared with a design approach using the average value as a constant emission factor for grid-sourced electricity.…”

“…Scenarios for the emission factor of grid-sourced electricity. Data-sources: dynamic national emission factor 2018: [5], dynamic national emission factor 2030 and 2050:[7].…”

Small-Scale Modelling of Individual Greenhouse Gas Abatement Measures in Industry

“…In order to divide the changed energy consumption, resulting from the implementation of individual cross-sectional measures, in a top-down manner from industry to economic branch level the percentage share of economic branches in the total energy consumption of the EAM at industry level must first be computed (see Equation (59)). The resulting distribution of energy consumption from industry level to economic branch level is based on Equations (60) and 61: Once the cross-sectional effect of the measures is distributed across the economic branches, the changed parameters resulting from the measure implementation are available both for process measures and for cross-sectional measures at the economic branch level. Once the effect of the individual GHG abatement measure is applied, three function-based aggregation steps are carried out, for both cross-sectional and process measure categories:…”