TraM – Transport-Model

The transport model TraM is used to model future scenarios of the German transport sector. Using a bottom-up approach, the fleets in the road, rail, air, and inland waterway transport are modeled to derive time-resolved energy consumption, costs, and emissions for the years up to 2050. Furthermore, it is possible to implement CO₂ reduction measures and evaluate their impact on the energy system in interaction with the energy system model ISAaR.

Input Data

Due to significant uncertainties in technical as well as economic data for future years, the model was designed with flexibility in mind, enabling simple adjustments to the input data as as updates arise.

The following data serve as the basis for the model:

• Traffic volume per mode of transport and energy source, further broken down by vehicle class and local and long-distance traffic when possible
• Fleet and technology development (specific energy consumption, capacity utilization, annual mileage, service life, etc.)
• Investment costs, fixed operating costs, and (hourly resolved) energy costs
• Time-resolved emission factors
• and much more.

These input data are taken from literature and databases, and in individual cases, such as vehicle costs for passenger cars, are modeled by ourselves. [1]

Methodology of the scenario calculation

The model follows a bottom-up approach. This means that hourly resolved energy consumption is constructed based on the vehicle stock and its development in combination with its use (annual mileage, capacity utilization, etc.).

The inventory modeling is a stock-and-flow model. Here, the development of the existing fleet (“Stock”) is described by the commissioning and decommissioning (“Flow”) of the respective vehicle type and class. Each vehicle type and classification is served by specific drive types (technologies). Each technology, in turn, is assigned technical and economic parameters that can be used to determine both energy consumption and costs. Energy consumption is modeled at different temporal resolutions. While electricity is generated in hourly resolution and gaseous energy carriers in daily resolution, liquid energy carriers are stored in annual resolution due to their good storability. The modeling of the load profiles of electric vehicles is described in detail in [2].

Finally, the direct emissions from the combustion of energy carriers such as diesel and gasoline are calculated. Furthermore, hourly emission factors for e.g., electricity and hydrogen are derived – when applicable – in order to allocate the emissions from their supply to the consumers. The results of the time-resolved energy consumption can also be used as input data for energy system modeling.

A more detailed description of the methodology of the model can be found in [3].

Evaluation of CO₂ Mitigation Measures

TraM was developed as part of the project Dynamis, whose main objective was to evaluate CO₂ mitigation measures, including their impact on the utility sector, in terms of cost-effectiveness. To meet this objective, the model was constructed to ensure that measures can be implemented in specific reference scenarios. The methodology for evaluating CO₂ mitigation measures is shown in Figure 1.

Based on the reference scenario, the user now defines a measure. This measure can be, for example, a given investment budget for a specific technology. In addition to the degree of implementation, which in this case is determined by the budget, the implementation period and the displacement logic to be used must also be defined. For example, in the case of a measure in the passenger car sector, one can freely choose whether to displace only diesel vehicles or all vehicles with internal combustion engines. In this case, only the commissioning is displaced. This ensures that the natural replacement rate, which is given by history, continues to exist. In addition to the reference scenario, this results in an action scenario that can be compared with the reference.

In a purely sectoral assessment, the emission factors from the reference scenario can be used to evaluate the CO₂ reduction measure in terms of costs and emissions. If one also wants to evaluate the repercussions of the measure implementation on the energy system, a power plant deployment and expansion planning is carried out with the ISAaR energy system model as a follow-up to the TraM simulation run. The result is a so-called system dynamic evaluation of CO₂ reduction measures.

Example Results of the Sector Dynamic Assessment

Figure 2 shows some example results of the sector dynamic assessment.

Literatur:

[1]    Bayer, Caspar et al.: Der Einfluss von Prognoseunsicherheiten auf CO2-Verminderungskosten im Pkw-Bereich. In: et – Energiewirtschaftliche Tagesfragen 11/2018. Berlin: EW Medien und Kongresse GmbH, 2018.
[2]    Fattler, Steffen et al.: Charge optimization of privately and commercially used electric vehicles and its influence on operational emissions. Munich: Research Center for Energy Economics, 2018.
[3]    Pichlmaier, Simon et al.: Modelling the Transport Sector in the Context of a Dynamic Energy System, 41st IAEE conference Groningen. Research Center for Energy Economics, Munich. 2018.

Further Information (English Translations in progress):

• Dynamis – Dynamische und intersektorale Maßnahmenbewertung zur kosteneffizienten Dekarbonisierung des Energiesystems
• Modellierung des Verkehrssektors im Kontext eines dynamischen Energiesystems
• Einfluss von verschiedenen Ladesteuerungsvarianten untersucht