Fuel Distribution Networks

Diplomarbeit von Adam Strozek

Introduction:

Transport is a key factor in modern economies. There are an estimated 31.5 million road goods vehicles running on Europe’s motorways each year, which are coping with a steadily increasing amount of goods transported. Although these vehicles are crucial to guarantee the ubiquitous goods availability we are used to, and to assure the flexibility of European industry, they are also part of mankind’s most pressing current problems.

For instance, the emission of greenhouse gases, i.e. carbon dioxide (CO2), methane (CH4), nitrous oxide (NO2), hydrofluorocarbons (HFC), perfluorocarbons (PFC) and sulphur hexafluoride (SF6), due to fuel combustion in goods transport constitutes about 20 per cent of overall greenhouse gas emission and is only outnumbered by emissions of the energy industry. Overall decrease of these contaminants shall be, of course, one of the main objectives in the long-term, but in particular within urban agglomerations it is also of great interest to decrease local emission levels as a first sep. Changing over to less carbon-intensive fuels can reduce local carbon dioxide and other emissions, even if the well-to-wheel emission level does not improve notably.

Apart from the emission problem, nearly 99% of the overall fuel consumption in transport is provided by fossil fuels and therefore competing for the finite crude oil resources with other industries, in particular the energy industry. There are many different forecasts of how long world’s oil reserves will last, but independently of these estimations it is undeniable that they will end sometime. Hence, it is the second vital transport-related challenge to make it independent from fossil energy resources by developing and introducing renewable fuels, complying technologies to run them and a reliable infrastructure to distribute them. Although there are still many problems to solve regarding technical issues, many viable solutions for running vehicles by other means than diesel and gasoline are already available. However, the biggest problem seems to be the distribution. Since vehicles and fueling infrastructure are complementarities, most customers do not use these vehicles because they can not refill them properly, and fuel companies do not introduce new fuel stations due to a lack of customers, that would use them. Consequently the main challenge currently is to break through this ‘chicken-egg’ problem and build up a fuel distribution network, which allows the use of alternative fuels-driven vehicles in the same safe, cheap and convenient way it works with fossil fuels. Pursuing this idea the focus of this paper will lie on long distance transportation, although many of the findings can be transferred both, to other road transport sectors as well as to passenger traffic.

There are basically two different possibilities to approach the ‘chicken-egg’ problem, mentioned above. One of them considers a comprehensive public refueling network. The first step of this approach is to establish a fairly big initial number of fuel stations, ideally in order to maximize the number of clients, which can be served. Next this small network is being expanded stepwise according to the evolutionary dynamics discovered by research on the dynamic characteristics of the vehicle-infrastructure system. The main problem of this approach is that private clients are not willing to plan their trips to depend upon the fuel station network, so that a quite big number of fuel stations is required both at the beginning and during the later development.

The other approach is to introduce alternative fuels for limited transport applications first and then develop this first set of facilities towards a broad public infrastructure with respect to the same system’s dynamic characteristics. Those applications can be city buses, single interurban bus fleets, taxi fleets, single truck fleets or other. The infrastructure should then aim to cover 100% of the fuel demand, however, it could incorporate the behavior of rational drivers, so that fewer fuel stations are needed. It will be shown that the latter approach is more likely to be successful and can lead the way for the setup of a comprehensive fueling station network, wherefore it will be explored in this work.

Following this idea the present work aims to answer three questions. First, it shall be shown for different types of alternative fuels, what their distribution infrastructure within representative long haul transportation networks should look like, i.e. how many refilling facilities are needed and where they should be located, in order to provide competitive coverage of a certain area for minimal costs. Second, these results will be interpreted together with the fuels’ specific characteristics, resulting in an assessment of the suitability of the fuels for long haul applications. Third, an outlook will be given on fundamental coherences of the long-term development of a sustainable introduction of an alternative fuel.

In a first step technical and methodological background for the work will be given. Characteristics of the several fuels considered and their implications for handling the fuels will be discussed. Then, with the intention to consider industrial experience, it will be shown, what different kinds of industrial approaches for fuel station buildup respectively new fuel introduction exist and it will be discussed why first fuel introduction in limited applications is both the most promising alternative and the one allowing to point out the fuels’ infrastructure relevant differences in the best way. It will be shown that concentrating on this demand focused approach mentioned above the problem’s complexity can be reduced to such an extent that adequate research means in terms of mathematical optimization models can be applied, what leads to optimum solutions and therefore outperforms intuitive or simulation-based foundings of decision making. Therefore the vital backdrop section will be finally completed with the presentation of an adequate mathematical optimization model to support this approach with a profound methodology.

In the next step, the implementation and functionality of the optimization model will be discussed on the basis of a sample transportation network. Following, the optimum solution for locating fuel stations subject to minimize their total number as well as the result’s validity will be discussed for a representative set of haulage companies, leading to the answer of the first question. Combining these results with other qualitative characteristics of the fuels considered their overall suitability for long-haul applications will be discussed.

Finally an outlook will be given on the further development of the ideal distribution network for the most suitable alternative fuel. Thereby primarily possible first expansion steps will be discussed, before findings of both empirical research and research on infrastructure-vehicle adoption system’s dynamics with respect to a sustainable long-term development of the alternative fuel’s introduction will be examined.

Table of Contents:

Text Sample:

Chapter 4.4, Validity of Results:

The main reference for comparing the examined companies with the average long distance transportation is distance, more precise the distance between origin and destination of each route. As transportation is tremendously diverse it is very difficult to find a representative magnitude of distance. The common threshold as of which it can be generally spoken about long haulage strongly differs among the variety of related publications with values between 150 km and 500 km. However, the minimum distance of long-haul transportation is not only not clearly defined, but also does not provide more than the lower distance boundary for long distance transport, without stating the real distances occurring within this category of transport. The factor needed in order to compare the structure of the companies examined with the commonality of European long haul is the average transport distance within this branch, i.e. the average distance between origin and destination for all transports classified as long-distance transport. As it was the case for the minimum distance which defines long haul transportation, related publications allow to specify the average transport distance as a bandwidth only, ranging from 351km to 650 km.

The average transport distances of the sample companies depicted in Table 6 and Table 7 reproduce the overall long-haul transport distance in a very good way, ranging from 394 km up to 674 km. Therefore the transportation structure of the companies, which the results are based on, can be assumed as representative for long-haul transportation in general.

A second question arises with respect to the validity of the calculation, i.e. if assumptions of the optimization model represent the reality in an adequate way. Here the critical assumption is the possibility of locating fuel stations at predefined and limited locations only. As it became obvious by the results illustrated in Table 6 and Table 7 the minimum number of fuel stations decreases remarkably by increasing the number of possible fuel station locations. Thus it can be stated that, assuming the basic transportation network of a haulage company with nodes only at loading and unloading locations, nodal locations of fuel stations are not optimal. This is not surprising if one recalls the various cases exemplified in Figure 7. In that example 5 different cases of travelling a certain distance for 5 different vehicle ranges have been discussed. It has been shown that in case 2, where the vehicle range has been 700 km towards a total path’s length of 500 km, two stations have been required as to refill the vehicle with fueling facilities at start and destination points only, whereas one single location close to the path’s midway has been able to do the same. Transferring this statement to the companies calculated with the SCFLM one arrives at the conclusion that in some cases the required number of fuel stations can be reduced by additional locations along the network’s paths and have dedicated some work to this problem with respect to their FRLM and have developed two algorithms for locating fuel stations along the network’s paths, too. Both the Mid-Path Segment Algorithm (MPSA) and the Added Node Dispersion Problem (ANDP) turned out to be able to improve the total results remarkably in case of a vehicle range notably smaller than the length of the network’s paths. Nevertheless there are three critical points when thinking about applying these methods to the SCFLM.

First of all, both the MPSA and ANDP have been verified on the basis of the FRLM, i.e. a continuous flow maximizing model. However, the SCFLM is a set covering model with integer variables, i.e. the performance of the MPSA respectively ANDP can not be transferred to this work without restrictions. Therefore the improvement of the SCFLM’s objective function by adding additional nodes can not be expected as to occur in the same continuous way as it could be observed for the FRLM. However, doing so and therewith changing the finite, defined set of locations towards an infinite number of potential locations makes the location problem much more complex.

Second, fuel dispensing facilities which are situated along the paths between two nodes, i.e. two cities, are less likely to be part of the fuel supplying approaches for long haul, which have been discussed in chapter 3.1. Fuel stations which are not located in the neighbourhood of cities usually aim to have a share in public traffic flows instead of supplying a finite number of companies with fuel, and therefore are not likely to be built up neither within the scope of a single station supply strategy, nor of a stepwise introduction and consequently not part of this work’s focus.

Third, as has been discussed above all calculations have been repeated for extended transport networks, in which big cities with a location close to the path’s midway have been added to the basic network as additional opportunities of locating fuel stations. As can be seen in Appendix A.2, hereby the distances between the network’s several nodes have been reduced to a magnitude beneath the vehicles driving distance, so that an improvement by adding even more possible locations, e.g. with the MPSA or ANDP, can not be expected to be significant.

Anyhow, the MPSA as well as the ANDP are promising algorithms to deal with huge transportation networks. Especially when dealing with public fuel distribution networks they are likely to arrive at a solution which represents the reality in a better way than locations at vertices only.