A comparison of the presented modelling approaches

The approach through "intelligent" bounds is the most straightforward one as bounds are part of each model. Working on bounds is the task of every modeller and this is a skill that can be acquired with practice, hence a low cost. However there is a risk that the model requires a "man+machine" setup and the model is unusable without the presence of its creator. There is the risk of subjectivity as the quality of results depends upon the erudition of the modeller. Another risk is that the model becomes remote-controlled through the bounds and the modeller, be it knowingly or not, is presenting biased results as the output of optimisation.

An important merit of the Social MARKAL and Social TIMES approach is its lin-earity. Unlike in an approach through the economic function, we do not introduce any non-linear elements, with all the consequences on the resulting equation system which remains easily solvable without added computational complexity.

The approach demonstrated on lighting bulbs can be extended to passenger cars, household appliances, warm water and space heating, as well as to other technologies that can be described in the same way with a system of equation for generations of equipment. It can also be extended to all models based on the concept of economic equilibria.

One of the main limitation of the Social MARKAL / Social TIMES approach is that unlike econometric models, it does not give a complete behavioural response that in-clude all the factors. The contribution of the approach is to add information-triggered behaviour to the "classical" MARKAL competition of technologies. That the only con-sidered trigger is "better information" is the limit of the model and the behavioural description stops there. Behavioural schemas like spontaneous actions, or behavioural changes triggered by other events or causes are still left unaddressed. Also, in this ap-proach, unlike in a Share of Choice model, the perceived value is not reflected.

Another limitation of the Social MARKAL / Social TIMES is that the results are valid only for the group of demand devices for which the survey has been done. A special-crafted survey has to be run for each separate technology we are interested in (bulbs, refrigerators, TV sets, cars, ...). The bigger the sample the better is the precision, so the sample size should be minimum 400.

Using the results in a different context (country, epoch, technology) requires a new sociological survey in that context. Sociological surveys are costly and may represent a limitation. Repeating a survey in a 2 year interval has shown that the results are robust

A comparison of the presented modelling approaches 155 in time but the time interval is too small to make significant conclusions. An interest-ing findinterest-ing is that there are only few differences in behaviour of people between Geneva region and Romania when it comes to lighting bulbs. It means that a rich and a poor country in Europe can share the results for bulbs.

TIMES-TTI was the first method to replace the default technology competition within transportation modes by technology competition across modes. To achieve that, in addi-tion to TTB, Travel Time Budget, that has already been previously used, a new variable, TTI Travel Time Investment, has been added to the model. Thanks to that, more ex-pensive but faster transportation modes appear in the optimal solution.

Modal choice MoCho-TIMES goes even further by incorporating topology and rev-enue classes. Both TIMES-TTI and MoCho-TIMES are endogenous (no model linking necessary) but very specific as they can be used only for the transportation sector.

More methods how to include consumer behaviour specifically in transportation mod-elling were resumed in table 4.2 on page 122.

Unlike Social MARKAL / Social TIMES approach, soft-linking results of a market share simulation model with TIMES (COCHIN-TIMES) is using revealed preferences, observed market share of all the modes. The disadvantage is that it depends on availabil-ity of a study on the market shares. It is available only in the USA and Ireland and only for the transportation sector thanks to presence of research institutes where such studies are performed and published. The choice model MA3T (Market Allocation of Advanced Automotive Technologies) has been developed at the Oak Ridge National Laboratory.

Transposing the approach to a different context requires an entire study which is more costly than a mere sociological survey.

The Share-of-Choice approach is reflecting the perceived value. The problem leads to a combinatorial optimisation problem that can be computationally intensive. Modelling perceived value for each respondent introduces 2 integer variables per person. Thus the survey sample has to remain small, 100 to 200 respondents. In order to save the com-putational complexity for lumped investments that require binary or integer variables, it is wise not to integrate the Share-of-Choice with the energy model but compute a few typical scenarios and soft-link them with energy model. Moreover the Share-Of-Choice method can be very well used for market shares of goods such as lighting bulbs, house-hold appliances or passenger cars but cannot be simply transposed to describe the users’

energy savings behaviour.

Inclusion of behaviour into the bounds is subjective, getting the information from external sources such as surveys or behavioural studies is objective. Social MARKAL retains the linearity of the problem (introduces only 2 mixed integer variables for the 2 campaigns since an information campaign is a lumped investment) and is fully compatible

with all developed tools supported by ETSAP while other approaches are no more linear.

Data from surveys or from simulation models coming from external studies can only be soft-linked while data from Share-of-Choice can be either hard-linked or soft-linked.

However, it is a good idea to detach the Share-of-Choice equations from the energy model in order to keep the modelling complexity for energy modelling. There is no merit to solve a hundred MIP equations on each run again and again.

All the mentioned approaches are retaining the rich technology database.

A comparison of MARKAL/TIMES global scale models has been drawn in Table 3.3 on page 83.

5 | Main Results and Findings from Sociologic