Analysis of different sector coupling paths for CO2 mitigation in the German transport sector
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The document analyzes sector coupling paths for CO2 mitigation in the German transport sector using the TIMES-D model. It finds that the contribution of trolley trucks is highly dependent on the GHG reduction targets selected, with no use in the scenario with an 80% reduction by 2050 but full use in the 95% reduction scenario. E-mobility adoption also varies significantly between scenarios based on the long-term target, ranging from 21% to 75% of personal transport demand by 2050. Limiting simultaneous charging infrastructure use can regulate load but allowing flexibility could cause additional loads up to 40 GW.
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Analysis of different sector coupling paths for CO2 mitigation in the German transport sector
- 1. Felix Kattelmann, Markus Blesl Click to edit Master subtitle style Felix Kattelmann Markus Blesl Analysis of different sector coupling paths for CO2 mitigation in the German Transport sector Source: Forschungszentrum Jülich/Tricklabor for the Kopernikus project “Power-to-X”
- 2. Felix Kattelmann, Markus Blesl 1. Introduction 2. TIMES Model • General modelling approach • Implementation of trolley trucks • E-Mobility • Scenario Analyses 3. Results 4. Conclusion 74th ETSAP Meeting, Stuttgart 07.11.2018IER University of Stuttgart 2 Outline
- 3. Felix Kattelmann, Markus Blesl 2015 2020 2030 2040 2050 overall GHG emissions -27.2% -40% -55% -70% -80% to -95% 74th ETSAP Meeting, Stuttgart 07.11.2018IER University of Stuttgart 3 Motivation German national targets for reducing the Greenhouse Gas emissions compared to 1990 [1] • ambitious goals for GHG emission reductions • almost complete decarbonisation of the entire German energy system necessary • need of renewables in heat and transport sector • potentials of renewables mostly in electricity sector sector coupling
- 4. Felix Kattelmann, Markus Blesl TIMES-D model
- 5. Felix Kattelmann, Markus Blesl • depiction of the entire German energy system • derived from the TIMES-PanEU model • linear optimization: total system costs minimized • complete competition between different technologies assumed • GHG emissions of the system are recorded • division into 280 time segments of 3 hours each • model horizon: 2010-2050 • determination of the economically optimal energy supply structure for a given target 74th ETSAP Meeting, Stuttgart 07.11.2018IER University of Stuttgart 5 General modelling approach TIMES-D model
- 6. Felix Kattelmann, Markus Blesl technology characteristics • electrically powered trucks • energy supply via pantograph and overhead lines • construction of the necessary infrastructure over motorways (as discussed) cost-intensive 74th ETSAP Meeting, Stuttgart 07.11.2018IER University of Stuttgart 6 Implementation of Trolley Trucks TIMES-D model modelling • one of multiple technologies in transport sector • selection of technologies in model dependent on process-costs (amongst other things) e.g. investment or maintenance costs • contribution to meeting the freight traffic demand limited to 90% picture: dpa/picture-alliance
- 7. Felix Kattelmann, Markus Blesl • costs for complete electrification of motorways distributed over assumed max. number of vehicles • problem: clear differences in the utilisation of motorways in Germany • overhead lines over heavily used motorways can power more vehicles than over low-frequented ones • new implementation • infrastructure costs distributed unevenly over three stages • stage 1: 1/3 of vehicles but 1/6 of infrastructure • potential of one stage limited to 1/3 of the max. overall contribution from Trolley Trucks 74th ETSAP Meeting, Stuttgart 07.11.2018IER University of Stuttgart 7 Implementation of Trolley Trucks – more accurate modelling of infrastructure TIMES-D model stage 1 stage 2 stage 3 new implementation vehicle infra- structure previous implementation investment cost Trolley Trucks 1/6 2/6 3/6
- 8. Felix Kattelmann, Markus Blesl • e-mobility: all electrically operated vehicles in transport sector (except for heavy goods traffic and trains) • sufficient charging infrastructure has to be used to charge the batteries • Power-to-grid possible • one of multiple technology pathways in transport sector 74th ETSAP Meeting, Stuttgart 07.11.2018IER University of Stuttgart 8 Modelling of e-mobility TIMES-D model charging infrastructure battery Power-to-grid electric vehicle electricity grid electricity grid mobility demand
- 9. Felix Kattelmann, Markus Blesl • gradual reduction of the system‘s GHG emissions • three scenarios with great differences in the long- term reduction targets • Trolley Trucks: scenario S90 with and without disaggregated infrastructure • analysis of the charging infrastructure‘s influence • share of simultaneously usable infrastructure limited to 10%, 50% and 90% scenario 2020 2030 2050 Reduction of overall GHG emissions S80 -40% -55% -80% S90 -40% -55% -90% S95 -40% -58% -95% 74th ETSAP Meeting, Stuttgart 07.11.2018IER University of Stuttgart 9 scenario analyses TIMES-D model
- 10. Felix Kattelmann, Markus Blesl Results
- 11. Felix Kattelmann, Markus Blesl 74th ETSAP Meeting, Stuttgart 07.11.2018IER University of Stuttgart 11 Freight Traffic – potential of the Trolley Truck Results • huge discrepancy between the scenarios: • entire possible demand provided by Trolley Trucks in S95 • no usage at all in S80 • deployment of trolley trucks highly dependent on the selection of GHG reduction targets 0 100,000 200,000 300,000 400,000 500,000 600,000 S80 S90 S95 S80 S90 S95 S80 S90 S95 S80 S90 S95 2035 2040 2045 2050 Freightdemandintkm Years Meeting the demand of freight transport Trolley Truck Rest
- 12. Felix Kattelmann, Markus Blesl • share of electricity: • 30% (disaggregated infrastructure) • 21% (base) • usage of Trolley Trucks depends strongly on the modelling of infrastructure 74th ETSAP Meeting, Stuttgart 07.11.2018IER University of Stuttgart 12 Freight Traffic – influence of disaggregated infrastructure modelling Results 0 100 200 300 400 500 600 700 800 900 1000 base S90 base S90 base S90 base S90 base S90 2030 2035 2040 2045 2050 FinalenegryconsumptioninPJ Years Final energy consumption in heavy road freight traffic Hydrogen Electricity Natural Gas Diesel Biofuels
- 13. Felix Kattelmann, Markus Blesl • 13.3 GW additional load in S90 • 18 GW additional load in S95 • load directly dependent on driving behaviour 74th ETSAP Meeting, Stuttgart 07.11.2018IER University of Stuttgart 13 Freight Traffic Results 0 2 4 6 8 10 12 14 16 18 20 ElectricalloadinGW day of the week Overall electrical load from trolley trucks in 2050 S80 S90 S95 Monday Tuesday Wednesday Thursday Friday Saturday
- 14. Felix Kattelmann, Markus Blesl • no difference between the scenarios until 2035 • contribution of electric vehicles varies between 21% (scenario S80) and 75% (S95) in 2050 • choice of the long-term target has a major impact on the utilization of electric vehicles 74th ETSAP Meeting, Stuttgart 07.11.2018IER University of Stuttgart 14 E-Mobility Results 0 100,000 200,000 300,000 400,000 500,000 600,000 700,000 800,000 900,000 1,000,000 S80 S90 S95 S80 S90 S95 S80 S90 S95 S80 S90 S95 S80 S90 S95 S80 S90 S95 2025 2030 2035 2040 2045 2050 Demandofpersonaltransportinpkm Years Meeting the personal transport demand Rest Electric Vehicles
- 15. Felix Kattelmann, Markus Blesl • higher electricity consumption in 2050 due to electrification of all sectors • in the S95 scenario e-mobility accounts for 10% of the total power consumption in 2050 (280 PJ) • S95: 190 PJ more by e-mobility compared to S80 74th ETSAP Meeting, Stuttgart 07.11.2018IER University of Stuttgart 15 E-Mobility Results 0 500 1000 1500 2000 2500 S80 S90 S95 S80 S90 S95 S80 S90 S95 2030 2040 2050 ElectrictiyconsumptioninPJ Years Electricity Consumption by sector E-Mobility Remaining Transport Supply Residential Industry Comercial Agriculture
- 16. Felix Kattelmann, Markus Blesl 74th ETSAP Meeting, Stuttgart 07.11.2018IER University of Stuttgart 16 E-Mobility – electrical load caused by charging Results huge peaks for 50% availability
- 17. Felix Kattelmann, Markus Blesl 74th ETSAP Meeting, Stuttgart 07.11.2018IER University of Stuttgart 17 E-Mobility – residual load and load by charging Results
- 18. Felix Kattelmann, Markus Blesl Conclusion
- 19. Felix Kattelmann, Markus Blesl • contribution of the Trolley Truck heavily dependent on the choice of emission reduction targets with no usage at all in the S80 scenario • maximal electrical load of trolley trucks is 18 GW, likely to vary significantly between different regions • significant influence of detailed modelling of infrastructures on the results of Trolley Truck analyses • GHG emission reduction targets with major impact on the utilisation of e-mobility (varying between 21% and 75%) • the load caused by charging electric vehicles can be regulated by limiting the simultaneousness of the charging infrastructure • by not limiting the simultaneousness e-mobility can serve as a very useful flexibility option, causing large additional loads of around 40 GW however 74th ETSAP Meeting, Stuttgart 07.11.2018IER University of Stuttgart 19 Conclusion
- 20. Felix Kattelmann, Markus Blesl [1] BMWi, “Die Energie der Zukunft - Vierter Monitoring-Bericht zur Energiewende,” Bundesministerium für Wirtschaft und Energie (BMWi), Tech. Rep., 2015. [2] Bundesministerium für Wirtschaft und Energie, “Energieffizienz in Zahlen,” Tech. Rep., 2017. [3] M. Wietschel et al., “Machbarkeitsstudie zur Ermittlung der Potentiale des Hybrid-Oberleitungs-Lkw,” Fraunhofer ISI, Tech. Rep., 2017. [4] B. Lenz, “Shell Lkw-Studie - Fakten, Trends und Perspektiven im Straßengüterverkehr bis 2030,” Deutsches Zentrum für Luft- und Raumfahrt, Tech. Rep., 2010. 74th ETSAP Meeting, Stuttgart 07.11.2018IER University of Stuttgart 20 References
- 21. Felix Kattelmann, Markus Blesl e-mail phone +49 (0) 711 685- fax +49 (0) 711 685- Universität Stuttgart Thank you! IER Institute for Energy Economics and Rational Energy Use Felix Kattelmann 87845 87873 Institute for Energy Economics and Rational Energy Use felix.kattelmann@ier.uni-stuttgart.de Heßbrühlstr. 49a 70565 Stuttgart Germany This work is part of the ENavi project, financed by the Federal Ministry of Education and Research of Germany.