COCHIN-TIMES, a coupled simulation model for the transportation sector with TIMES

Integrating a market-share simulation model with a linear energy optimisation model is a way how to circumvent the "winner-takes-all" phenomenon occurring when using a least-cost optimisation linear model. Indeed, a frequent technique in market research is to model the customer choice using non-linear simulation approaches. Consumer choice

Figure 4.20: Transport technologies in intermodal competition in TIMES-TTI. All the transport technologies compete to satisfy the demand within short-range and long-distance traffic. Adapted from Daly et al. (2014).

theory states that arational consumer has a product preference in a way that max-imises his overall utility.

To capture the discrete preferences of customers, a Discrete Choice Model (DCM) is designed. The output of a DCM model is a probability at which a consumer is willing to make a given choice. A DCM model is a simulation model, thus non-linear. It is typically a logit or nested-logit model. Therefore, while it is able to capture qualitative parameters as part of decision-making process and include the heterogeneity of con-sumer preferences, it cannot be hard-linked with a linear-optimisation framework such as MARKAL or TIMES without touching the internal structure of the energy model.

Then the only way is soft-linking results from the simulation model with a linear energy optimisation model.

In the work of Ramea et al. (2013), Bunch et al. (2015), a special version of TIMES, called COCHIN³, that reduces the scope of household decision making to purchase and usage of vehicles to satisfy an exogenous demand, was soft-linked with MA³T (Market

3COCHIN, COnsumer CHoice INtegration

COCHIN-TIMES, a simulation model coupled with TIMES 141

Figure 4.21: Results of reference case (as percentage of vehicle sales) in "classical" TIMES with no consumer choice. On the X scale are years 2006 through 2035, the Y scale represents the market share in percents from 0 to 100%. Adapted from: Ramea et al. (2013).

Allocation of Advanced Automotive Technologies)⁴, a nested multinomial logit model developed by Oak Ridge National Laboratory, to create COCHIN-TIMES (COnsumer CHoice INtegration with TIMES) model. This is a non-linear simulation model that cannot be easily hard-linked with a linear optimisation model. In the MA³T model, a cost term "generalized cost" has direct and indirect cost components. Direct costs are vehicle prices and fuel costs. Among indirect costs, the following components are reckoned: range anxiety cost, refueling station availability, car model availability, new technology risk premium and towing capability. California was split into three regions:

rural, suburban and urban with three market segments and three VMT (Vehicle miles of travel) groups: early adopter, early majority, late majority for market segments and low, medium, high VMT.

In the figure 4.21, we can see a case of "classical" competition between technologies in a linear optimisation model where the winner takes all, in our case the Diesel car and gasoline 10-mile plug-in hybrid cars in their respective segments. The result of the optimisation of a "classical" TIMES model is that between 2006 and 2014, only Diesel vehicles are being sold. They are replaced in 2015 by gasoline plug-in cars with 10-mile range until 2034. The last year of the optimisation horizon, 2035, hydrogen cars with internal combustion engines (H2 ICE) are taking all the market.

4see the User Guide of the ORNL MA³T Model (V20130729) by Zhenhong Lin, David Greene & Jake Ward on URL http://cta.ornl.gov/ma3t/MA3T%20User%20Guide%20v20130729.pdf

This is a normal behaviour of an unconstrained linear model and the usual way to address the issue is to introduce bounds which are subjective and depend on the mod-eller’s erudition.

At first glance, it is obvious that this picture is charged with an immense systematic error. In the real life, people do not behave in a perfectly rational way and have their own preferences that do not follow the perfect economic rationality, one of assumptions of the optimisation model. There always are other criteria why to buy a different car, not an economically optimal one. These reasons are not depicted in the model. Considering the model inputs, the linear model is flawed. The best correction is to couple the model with a reality check in form of market share data. Integrating a non-linear simulation model with a linear energy model would bring computational difficulties, so the way to go is soft-linking data. And this is how the authors of this method have solved the problem.

The next figure 4.22 is showing the case with modal split as calculated by the Cochin-TIMES model, which consists of the MA³T model of market shares with results soft-linked with TIMES.

Figure 4.22: Results of reference case (as percentage of vehicle sales) in Cochin-TIMES.

Source: Ramea et al. (2013).

We see a realistic picture where all the technology diversity is represented in the

MoCho-TIMES, modal choice TIMES 143 solution. Instead of "winner takes it all", based on the least cost of the technology and perfect economic rationality that would people push to acquire only such cars, in the solution, all the types available on the market can be chosen and many types indeed are present in the solution. This is an improvement of the original linear model as it matches the market reality.

At the beginning of the optimisation, gasoline cars represent some 95% of the pas-senger car market with some 5% share of gasoline hybrid cars and a tiny proportion of Diesel cars. Then gasoline cars are gradually disappearing from the market until reaching a residual 25% share. They are never completely replaced by Diesel cars. Instead, there is a growing share of gasoline hybrid cars, then of gasoline plug-in hybrid cars with 10, 20 and 40 mile autonomy. There are short and limited appearances of other alternative technologies such as hydrogen cars with internal combustion engines (H2 ICE), fuel cell vehicles (FCV), electric range extended cars (EREVs), and others.

4.6 MoCho-TIMES, a modal choice endogenous TIMES model