1 Intelligent Well Technology: Status and Opportunities for Developing Marginal Reserves SPE

COMBINING BOTTOM-UP AND TOP-DOWN APPROACHES IN A ROAD TROANSPORT MODEL FOR AUSTRIA

[Maximilian Kloess, Vienna University of Technology, +43 1 5880137371,

[Andreas Müller, Vienna University of Technology, +43 1 5880137362,

Overview

The transport sector as a whole and road transport in particular are strongly dependent on oil based energy carriers. This oil dependency represents a severe ecological and political problem for industrialised countries and calls for new solution strategies. The main objective of this analysis is to investigate the potential role of transport policy and technological progress on the way toward a less oil-dependent road-transport in the next decades.
The analysis is based on a model of the Austrian road transport sector that combines both bottom-up and top down approaches. The transport demand is modelled on a top-down basis considering the impact of income and energy price levels. On the other hand the vehicle fleet is modelled bottom-up with a detailed coverarge of the fleet structure in termis of specifications, age, efficiency, technologies and user patterns. Special attention was turned to the potential impact of technologic developments in the automotive sector and potential mid- to long-term impact of the current technological trendsthat might affect both passenger and car transport (e.g. vehicle powertrain electrification). Those potential technological shift are of special interest from an energy economic perspective since they may strongly affect the structure of the energy supply of the transport sector and even have implications on other sectors (e.g. electric cars).

Methods

Figure 1: Structure of the model

Similar to other well established transport models (Fulton et al. 2009), (Ceuster et al. 2007)(Zachariadis 2005) the model combines bottom-up and top-down approaches. The basic methodology of the model is illustrated in Figure 1. It consists of three main parts. There is a so called “vehicle technology model” where the different vehicle powertrain options are modelled bottom-up to analyse the influence of technological progress on their costs.

In the second part the market shares of technologies are derived from their specific service costs considering different levels of willingness to pay. The heterogeneity in user preferences was modelled using a logit model approach with the specific service costs as crucial parameter. To consider the specific competitive disadvantages of alternative propulsion technologies that might arise from handicaps in performance characteristics or lack of availability, diffusion barriers were used (Kloess & Haas 2009a).

The third part is a top down demand model where the road transport demand for both passengers and freight is determined. The transport demand is determined by the specific service cost of the transport mode and the GDP and is expressed in the model by the fleet size, the vehicle characteristics (weight & power) and the user intensity (kilometres/year).

The fourth part represents a bottom up fleet model of the Austrian motor vehicle fleet. The fleet is modelled in detail considering age structure of the fleet as well as usage categories and the main specifications of the vehicles (e.g. engine power, curb weight, propulsion technology, specific fuel consumption and greenhouse gas emissions etc.). The data pool includes detailed data on the fleet today and time series of historic developments(Statistics Austria 2009).

The model was developed stepwise in the framework of three research projects done for the Austrian Ministry of Transport Innovation and Technology. The first two projects were focusing on the passenger car sector with a detailed coverage of powertrain technologies, fuels and policies on this field(Kloess & Haas 2009b)(Kloess et al. 2009). The model has now been extended to the entire road transport sector inclunding both passenger and freight transport, giving a complete view on the technology, policy and crude oil price implications on the road transport sector.

Results & Conclusions

The results of the model are scenarios for the time frame 2010-2050 with different road transport demands depending on the assumed economic and politic framework conditions. The transport demand in theses scenarios is represented by the fleet size, the user intensities and the characteristics of the vehicles. From those results thegreenhouse gas emissions andthe energy consumpotions are determined using detailed well-to-wheel balances.
Preliminary results indicate that increasing fuel prices, caused either by fossil fuel price increases or higher fuel taxation, will siginificantly affect road transport. However higer prices can also be a strong driver for effcicient vehicle technologies and alternative fuels. It shows that a high price environment together with technological progress can lead to a significant reduction in overall energy demand and greenhouse gas emissions and would also foster the introduction of low carbon fuels.

References

Ceuster, G.D. et al., 2007. TREMOVE Service contract for the further development and application
of the transport and environmental TREMOVE model
Lot 1, Brussels: EU Comission DG Environment/Transport & Mobility Leuven.

Fulton, L., Cazzola, P. & Cuenot, F., 2009. IEA Mobility Model (MoMo) and its use in the ETP 2008. Energy Policy, 37(10), 3758-3768.

Kloess, M. & Haas, R., 2009a. Potentials of hybrid- and electric vehicles for the passenger vehicle sector in Austria – A model-based analysis. In 10th IAEE European Conference, Vienna. Vienna.

Kloess, M. & Haas, R., 2009b. The road towards electric mobility - An energy economic view on hybrid- and electric vehicle technologies and the influence of policies on their diffusion. In 32nd IAEE International Conference. San Francisco.

Kloess, M. et al., 2009. ELEKTRA-Project final Report. Available at:

Statistics Austria, 2009. Austrian motor vehicle fleet & registration statistics. Available at:

Zachariadis, T., 2005. Assessing policies towards sustainable transport in Europe: an integrated model. Energy Policy, 33(12), 1509-1525.