A Share-of-Choice model integrated with OSeMOSYS

Social MARKAL was a first attempt to take into account consumer’s preferences through the modeling of a virtual technology directly in the energy model as a data set. One of possibilities to go a step further is to modify the structure of the energy model by including a Share of Choice model. The linear energy model is then transformed into a Mixed Integer Programming (MIP) model. The Share of Choice describes the con-sumers’ preferences of a given energy service (e.g. lighting) through the optimisation of salient attributes(i.e. elements of perceived value such as price, durability, ecological attributes). The data set or part-worths associated with the Share of Choice comes from a psychological statistical technique calledconjoint analysis. We propose a simple case study about lighting bulbs just for concept proven purposes.

From Social MARKAL to Share-of-Choice MARKAL

We learn from the field of Service Science that a service acquires value once the consumer perceives the benefits of it. Today most of these salient attributes are perceived during the service experience itself, when the process of service co-creation occurs between the client and the service provider. As a result, when designing a service experience, the full scope of cultural, operational, and experiential contexts from the perspectives of both the service provider and the client are needed.

Based on elements of Service Science (Fragnière, Nanchen and Sitten, 2012), we could argue that useful demands that are also energy services should be complemented with in-formation expressing value perception from the point of view of consumers. If we consider the RES (Reference Energy System), demand technologies that satisfy useful demands provide also the consumer with different kinds of benefits. For instance, the quality of light of new bulbs is problematic: dimming and spectre issues are forcing theatres and art galleries to seek using incandescence or halogen bulbs. The perception of the light color, flicker and poor color reproduction make the fluorescent light uncomfortable for many people. Age-related macular degeneration reduces vision and increases the importance of well-dimensioned lighting. Elderly people rather prefer halogen lights on fluorescent bulbs even if their technical characteristics were chosen to be as close as possible to halo-gen light (Holton et al., 2011).

A new approach of the Social MARKAL framework complements the quantitative useful demands for energy services with information related to consumer perceived value.

Consequently, the optimal allocation of demand technologies and even production tech-nologies are not solely related to associated energy efficiency and costs, but also to elements of consumer perceived value. The Share of Choice model is based on conjoint analysis and mathematical programming that combines energy consumers’ perception and market share (Fragnière, Lombardi and Moresino, 2012). To understand individu-als’ perceptions and behaviour related to energy consumption, a survey is conducted in the form of a card-system questionnaire. Then through Conjoint Analysis techniques,

A Share-of-Choice model integrated with OSeMOSYS 147 utilities per respondent are estimated to finally feed a Share of Choice.

The purpose of this new model is to explore individuals’ energy consumption be-haviour and willingness to turn to other kinds of technologies satisfying elements of per-ceived values. So we introduce in the model economic variables such as WTP (Willingness-To-Pay) and WTA (Willingness-To-Accept).

To illustrate this new methodology, a case study has been developed in collaboration with a Romanian research team participating in SNF research programme IZERZ0_142217.

The model extension is modelled in OSeMOSYS (Howells et al., 2011), an Open Source tool that offers the freedom to touch the model structure itself and has the ad-vantage to be a free and open source system. A short description of the OSeMOSYS is in the tools chapter on page 89.

Let’s note that such development would not be possible with ETSAP-supported MARKAL nor TIMES models because they have been developed in a way to give the user no access to the internal structure of model equations.

A literature review concerning the technique of Conjoint analysis and especially the Share of Choice is on page 60.

Share of Choice: Research setup

We investigate two kinds of policies regarding the purchase of either an incandescent or fluorescent light bulb:

• Information campaign

• Subside for low consumptions bulbs

The survey takes place in two stages. At the first stage, two bulbs are presented to the respondent: a low consumption bulb and an incandescent bulb. The characteristics are the price, the lifetime and the energy class according to the EU energy label. The respondent must tell which one he prefers. Then we announce that there will be a subside for low consumption bulbs. The respondent must tell the break-even level of the subside.

At the second stage concrete information about the cost is given (how much each bulb cost per year). Then the same questions are asked again.

Let’s recall that a linear optimisation problem (without added Share of Choice) can be expressed as:

minx c·x s.t.

A·x≥b.

Now, let’s add the equations that correspond to Share of Choice. The notation used is:

Respondent k = 1. . . K.

Period t = 1. . . T.

Campaign level j = 0,1. 1 if campaign is taking place and 0 otherwise.

Subsides level l ≥ 0.

For each respondent k, we obtain for both campaign levelj, the break even subsides levell_b(k, j). As the utility function can be calibrated as desired, we make the following choice. The utility function for the incandescent bulb is given by −l, whatever the level of the campaign. The part-worths for the low consumption bulb are given by:

u(k, j) =







1 if l_b(k, j) = 0

−l_b(k, j) otherwise.

The model structure of the energy model added with a Share of Choice can be written as:

d(t) forecasted demand for bulbs c_q cost of the information campaign λ cost of the subsides

and the following decision variables have to be added to the existing system of equations:

z1(t) installed capacity of incandescent bulbs z₂(t) installed capacity of low consumption bulbs

q campaign configuration: 1 if campaign and 0 otherwise.

l subside level - amount of subside per bulb

p(k) preference for respondentk: 1 if respondent becomes a new client 0 otherwise.

x= (y, z) the variable describing the activities in the classical model (i.e. investment in each technology, etc.)

We have

x,p,q,lmin c·x+c_q·q+λ·l s.t.

A·x≥b.

The term c·x is the usual objective function in MARKAL or TIMES consisting of costs multiplied by activities. c_q·q is the cost of the information campaign and the term λ·l the cost of the subsidies.

The Share of Choice model can thus be presented as follows. For each respondent k= 1. . . K, the following two inequalities must hold :

u(k,0)·(1−q) +u(k,1)·q+l≥(p(k)−1)·M,

A Share-of-Choice model integrated with OSeMOSYS 149 u(k,0)·(1−q) +u(k,1)·q+l≤p(k)·M,

whereq and p(k) are binary variables and M is a big number. The proportion P of all low consumption bulbs purchased by respondents k∈ {1, ..., K} is

P = 1

Finally, we must include in the model the following constraints, where capacity and demands are put in relation, after being multiplied by corresponding capacity factors and activity to capacity factors not shown here:

z₁(t) =d(t)·(1−P), z₂(t) =d(t)·P Share of Choice: Case study

The survey is based on a card system data collection that is typical in the context of conjoint analysis. The respondent is presented with a set of cards that are then ranked according to its preferences.

Show first set of cards (Fig. 4)

Show second set of cards (Fig. 5)

Figure 4.25: Share of Choice survey process Once all the data sample is collected,

we calculate the part-worths through conjoint analysis procedures. We start with a sampling strategy based on the main socio-economic pa-rameter to obtain a representative sample of the population under study. The survey pro-cess that we have designed is shown in Fig-ure 4.25.

We are currently applying it to full-scale data collection based on random sam-ple in Romania. There is still no ban for incandescent light bulbs in Romania and thus we are interested in using our methodology in a context where salient at-tributes such as a low purchasing price dominates the motivation of the purchasing act.

Step 1

We ask the respondent whether he/she ac-cepts to take part in the survey. The

respon-dent is first presented with two cards. The left card (see Figure 4.26) shows a light bulb

that corresponds to an incandescent light bulb with the EC label E, the lifetime 1000 hoursand the purchasing price 0.95 Frs.

Figure 4.26: Card system questionnaire unveiling the price, life and efficiency.

card, we then ask the respondent to answer a WTP (Willingness to Pay) question on scale 8, . . . ,1 Frs. In this case, we propose each time to the respondent a decreasing subsidised price until he/she changes his/her choice from an incandescent bulb light to a fluorescent one and accepts the covered price.

Step 2

Figure 4.27: Second card system questionnaire showing the yearly cost.

Data collected through this survey process is then used to calculate the part-worths.

Then we follow the steps of the full modelling process as presented in Figure 4.25. Once the part-worths are calculated, they are integrated in the data set of the energy model.

When the instance of the model is generated, the data set of the part-worths are mixed with the model structure of the Share of Choice. The solver employed uses integer op-timisation solution techniques because of the Share of Choice integrated in the linear programming modelling structure contains integer and binary variables. Finally, the re-sults obtained can be analysed for scenario planning purposes.

A Share-of-Choice model integrated with OSeMOSYS 151

The case study is implemented in OSeMOSYSUtopia model. Utopiamodels are demo models provided by the authors of MARKAL as well as OSeMOSYS to give a beginner a template to start with. We see in Figure 4.28 that two demand technologies RL1 and RL4, respectively representing the incandescent light bulb and the fluorescent light bulb, have been added to the originalUtopiaRES. RL corresponds to the demand for Residential Lighting.

Results of the instances optimisation solution processes are presented in Figure 4.29.

We see that the instance not containing the Share of Choice provides an optimal so-lution where the fluorescent bulb is solely selected because ultimately the overall cost is minimised. Then we have considered three instances including the Share of Choice.

First, the instance without subsidies and without campaign leads to a penetration of the incandescent bulbs of 80%. Second, the instance only with campaign leads to a penetra-tion of the incandescent bulbs of 40%. Third, the instance only with subsidies leads to a penetration of the incandescent bulbs of 90%.

We notice that when the model instance includes the Share of Choice and related part-worths the solution remains optimal, however, it takes into account the perception of the consumers. This result is interesting since it mimics true market behaviours. When the Share of Choice is integrated into the energy model, the incandescent light bulbs are regaining market shares, a pattern that is closer to reality.

SRE

Figure 4.28: RES of the model OSeMOSYS Utopia with fluorescent lighting RL4 added. Rectangular boxes represent technologies, arrows represent rules (energy flows), SRE is a refinery with two outputs GSL and DSL, and E51 is storage.

Based on this kind of re-sults, we can then for the price of 1), financing (i.e. leasing contract) salient attributes.

Figure 4.29: Bulbs penetration according to the different model instances

Share of Choice MARKAL:

Conclusion

We often observe a significant difference between the scenarios provided by long-term en-ergy and environmental models and the reality of enen-ergy consumption and related impact to the environment. The main reason is that these models do not take into account the fact that demand-side technologies such as lighting, transportation, heating systems are offering services rather than goods to consumers. Consequently, we should not forget that a service acquires value once the client perceives the benefit of it. So energy technologies are purchased, then used by clients, according to the kinds of benefits provided by the corresponding delivered services. For instance, some refrigerators do offer functionalities that other do not. Some lighting bulbs create a better appreciated colour than others.

Some cars allow the owner to display his social status that is perceived more important than the transportation service offered by the car, its acceleration or speed issues are again not related to an efficient use of the car. These service benefits are calledsalient attributes. The proper mix of these attributes will ultimately define the market shares of the different energy technologies as well as their related consumption patterns.

To address this problematic, we have developed a model structure that integrates a Share of Choice directly in the energy model. A Share of Choice model is an integer programming problem that optimises the salient attributes as perceived by clients. The Share of Choice is a conjoint analysis procedure based on a card system of data collection.

A Share-of-Choice model integrated with OSeMOSYS 153

We employ an MARKAL-like energy and environmental model, OSeMOSYS. This category of energy models are driven by useful demands. Integrating the Share of Choice directly in the model makes that the solution process takes simultaneously into account the useful demands and the consumer preferences.

For illustration and concept proven purposes, we have developed a simple case study based on the Utopia model available in OSeMOSYS. Utopia is a standard model pro-vided with modelling frameworks for testing purposes which is complete and ready to run. Modifications can be set up quickly. On the other side, setting up a "real" model takes a couple of weeks.

Originally, the lighting useful demand in Utopia model was supplied by a single light bulb technology. After the introduction of a competing fluorescent bulb, solely this new low-consumption bulb is appearing in the optimal solution since its lifetime, cost and energy efficiency over the long-term surpass the low purchasing price of the incandescent bulb. After including in the model structure of OSeMOSYS the Share of Choice, the optimal solution retains now both bulb technologies, which reflects more the reality of real-world consumer behaviour.

The exercise of conducting a survey based on a card system has been done for Geneva region (on fluorescent vs. LED bulbs) and in Romania where incandescent light bulbs can still be legally purchased. Thanks to the new model taking into account consumer preferences regarding energy services, it is possible to mimic more accurately the energy technology and consumption evolution in the future. Based on these scenarios the de-cision makers could more relevantly adapt marketing campaigns to improve the public perceptions on economic, energy efficiency as well as ecological criteria or better define technology subsidies.

The Share-of-Choice MARKAL can be used to assess the user preferences in household sector when it comes to lighting bulbs or household appliances. The method can also be transposed to the case of purchase of passenger cars and modal split of urban traffic.

Share of Choice MARKAL: Further research

As it is, Share of Choice MARKAL is a proof of concept. In order to make it useful to solve real problems, it will be necessary:

• replace Utopia by a country or a regional model to work with real data on a real case

• break the integrated model to energy part and Share of Choice part and soft-link them in order to avoid the computational complexity to be a burden on each single run; this will leave the Mixed Integer Programming complexity to energy issues such as lumped investments and indivisible technologies

• transpose the concept to other demand categories such as transportation and space heating where much energy is spent