Exploring domestic micro-cogeneration in the Netherlands: An agent-based demand model for technology diffusion

ARTICLE IN PRESS Energy Policy 38 (2010) 2763–2775

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Exploring domestic micro-cogeneration in the Netherlands: An agent-based demand model for technology diffusion Albert Faber a,n, Marco Valente b, Peter Janssen a a b

Netherlands Environmental Assessment Agency (PBL), Bilthoven, P.O. Box 303, 3720 AH Bilthoven, The Netherlands Faculty of Economics, University of L’Aquila, Roio Poggio, L’Aquila, Italy

a r t i c l e in fo

abstract

Article history: Received 6 October 2009 Accepted 6 January 2010 Available online 27 January 2010

Micro-cogeneration (micro-CHP) is a new technology at the household level, producing electricity in cogeneration with domestic heating, thereby increasing the overall efficiency of domestic energy production. We have developed a prototypical agent-based simulation model for energy technologies competing for demand at the consumer level. The model is specifically geared towards the competition between micro-CHP and incumbent condensing boilers. In the model, both technologies compete on purchase price and costs of usage, to which various (types of) consumers decide on the installation of either technology. Simulations with various gas and electricity prices show that micro-CHP diffusion could be seriously inhibited if demand for natural gas decreases, e.g. due to insulation measures. Further simulations explore various subsidy schemes. A subsidy for purchase is only found to be effective within a limited range of h1400–3250. A subsidy based on decreasing price difference between the competing technologies is much more cost effective than fixed purchase subsidies. Simulations of a subsidy scheme for usage show that a fast market penetration can be reached, but this does not yet take full advantage of technological progress in terms of decreasing CO2 emissions. Selection of the most effective scheme thus depends on the policy criteria assumed. & 2010 Elsevier Ltd. All rights reserved.

Keywords: Micro-cogeneration Agent-based modelling Technology diffusion

1. Introduction The introduction of distributed, de-centralised energy production systems could contribute considerably to diversify and secure energy supply, to increase system-scale efficiency and the presumed reduction in various polluting gas emissions. Users of micro-scale energy production systems may be particularly interested in security of energy supply and reduction of fuel charges, while grid operators may promote such systems because it could help defer infrastructure updates and improve operational flexibility (Bruckner et al., 2005). One such distributed energy production technology is micro-cogeneration (micro-CHP, for co-heating and power), which is capable of producing electricity in co-generation with domestic heating at the household level. Micro-CHP is fuelled by natural gas and replaces high efficiency heating units in domestic dwellings, co-producing electricity as an additional feature. This electricity can be used domestically or sold back to the grid, provided that some technical and institutional problems are tackled. A micro-CHP

n

Corresponding author. Tel.: + 31 30 2743683; fax: +31 30 274 4435. E-mail address: [email protected] (A. Faber).

0301-4215/$ - see front matter & 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.enpol.2010.01.008

system generally replaces and basically extents conventional natural gas-driven condensing boilers. Micro-CHP is now in its demonstration phase in a number of countries, on the brink of commercial market entry. In order for micro-CHP to become adopted, this novel and still expensive technology will have to compete with a mature and proven technology for domestic heating. Furthermore, the additional benefit of electricity production will have to exceed the higher cost associated with micro-CHP. Interestingly, many stakeholders in the micro-CHP innovation system pay little or no attention to the willingness of consumers to use micro-CHP (Feenstra, 2008). This willingness to adopt relates to many issues, ranging from economic competitiveness of the technology (Schneider, 2006; Faber et al., 2008), (lack of) knowledge and uncertainty (Meijer et al., 2007), typology and applicability of housing (Taanman et al., 2008), strive for offgrid independence (as discussed in Praetorius (2007) and integration in the socio-technical electricity system (Voß and Fischer, 2006). The attractiveness of micro-CHP depends largely upon the technical feature to co-produce electricity, which requires a moderate additional intake of natural gas (or another fuel). Moreover, micro-CHP has a considerably higher purchase price than conventional condensing boiler systems. In this paper we present a model addressing the diffusion of micro-CHP on the market in the Netherlands, following economic

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dynamics at the household level. In the model, micro-CHP enters the market to compete with the already existing condensing boiler domestic heating technology. We focus specifically on the role of demand to articulate the diffusion of micro-CHP in competition with condensing boilers, which offers the opportunity to grasp consumer preferences in a dynamic and realistic way. The agent-based character of the model provides added value in comparison to ‘regular’ systemic approaches that often reduce consumption to a fixed price-articulated utility (Faber and Frenken, 2009). With this modelling exercise, we aim to draw generic lessons on the market development of energy technologies in competition with a predecessor and to gain some understanding on specific issues related to the conditions and mechanisms underlying the adoption of micro-CHP. For obvious reasons of space we limit our analysis to consider only a few of the large number of tests that may be possible (or even relevant) with the presented model. While choosing what seems to us the most likely future patterns, we stress that it is not possible within the space and scope of this paper to explicitly report a detailed probabilistic analysis for all elements involved and for their interactions. However, from a methodological perspective, we want to stress the flexibility of the instrument proposed, that allows to easily modify any of the assumed element, directly providing the expected results. This flexibility would be particularly relevant, not only to design a policy initiative, but also to assess its deployment and, if necessary, study its modification if required by large departure from the expected results. The paper is structured as follows. The next section introduces environmental issues concerning the technology for household heating systems. Section 3 describes the elements of the model implementing the economic and technological element of the market. Before concluding in Section 5, Section 4 reports on various simulation results exploring different price scenarios and evaluating a few policy initiatives.

2. Micro-cogeneration: technology, context and environmental issues Micro-CHP is a technology co-generating heat and electricity at household level. Micro-CHP generally meets the need for heat first, with electricity production as the secondary product. MicroCHP is therefore usually designed to replace conventional domestic heating systems, with the additional feature of electricity production. Domestically produced electricity can be used within the house or business, or (if permitted by the grid management) sold back into the electric power grid. Various technologies can be used to produce electricity in a micro-CHP unit, most principally the Stirling engine and gas engine as first generation technologies and fuel cells as a next generation technology (see technical overviews in Pehnt et al., 2006; Kuhn et al., 2008). The application of fuel cell-based microCHP in the context of a hydrogen-based infrastructure is rather complex and requires the hybrid use of resources as a transition step to allow for large scale diffusion of the technology (Taanman et al., 2008). Our model does not explicitly focus on either of the technologies available, but does take the electricity production features of Stirling engines as a starting point for the simulations. Electric efficiency of the Stirling micro-CHP in most prototypes is about 10–18%, while thermal efficiency is about 90%.1 The relation between heat and electricity production is given by its heat-to1 Aggregate efficiency can be larger than 100% (as in present condensing boilers), because of additional heat recovery from condensation. Maximum aggregate efficiency is about 109%, so improved electric efficiency in the future will largely be at the cost of thermal efficiency.

power ratio (HPR), which describes the amount of heat produced for each unit of electricity (power) produced. First generation micro-CHP technologies are characterized by a relatively high HPR, or a low production of electricity in co-production with heat. Stirling engines typically have a HPR in the range of 4–9. Energy demand in an average Dutch household has a HPR of 4.4, based on average natural gas demand for heating purposes and average electricity demand. In most cases net production of electricity will thus be lower than demand and additional electricity intake from the grid will be needed. Environmental impact of micro-CHP application relates to overall improvement in efficiency of electricity production, and to a minor extent also to avoiding electricity transport losses. An application of still inefficient Stirling micro-CHP (HPR of 9) in 50% of Dutch houses could save 2.5  108 m3 of natural gas, accounting for 0.8 Mton CO2 per year, when compared with a reference of central gas-fuelled plants for electricity as well as condensing boilers for domestic heat production. This accounts for 0.4% of annual Dutch CO2 emissions, or 4.4% of annual household CO2 emissions. With a much more efficient Stirling micro-CHP (HPR of 4) this figure could raise to 13.8  108 m3 of natural gas, accounting for 4.6 Mton CO2 per year (Faber et al., 2008). These figures are significantly higher, up to 10 Mton of avoided CO2 emissions, when compared with the actual production plants, which mostly use coal rather than gas (Elzenga et al., 2006). Moreover, it should be noted that these calculations are illustrative only, as they depend strongly upon efficiency coefficients used. NOx-emissions of future Stirling-based microCHP systems are assumed to be comparable with conventional gas burners (Pehnt, 2008). On a national level, large-scale penetration of these systems could lead to a considerable decrease in NOx emissions, since estimated emissions for Stirling engine systems are much lower than the emission caps under the national NOx emission trading scheme for centralised electricity production units. In the Netherlands, many agents and institutions are involved in the development, marketing and application of micro-CHP systems (Colijn, 2006; Faber et al., 2008). A major argument for the energy producers and retailers to support micro-CHP relates to the increase in electricity supply, which allows the producers to postpone the investment in production plants for peak loads. This argument is conditional to the possibility that household producers feed electricity into the grid during peak hours, which requires additional incentives or smart technologies. This issue of technological embedding is not further explored in this paper. In our model we assume no limitations on the provision of microCHP units, neglecting some practical barriers that may have to be overcome on the supply side in the early stages of development, specifically with respect to reliability and system integration of the technology. Furthermore, many stakeholders in the innovation system consider micro-CHP to be a transition technology toward a truly renewable energy system in 2020–2030 (Feenstra, 2008). We do not take this notion into further account for our modelling exercise, but it could clearly be important for policy interpretations. Demand side dynamics of the micro-CHP market involves household customers and housing corporations, but also utilities such as office building. Our model only considers the purchase of single units, therefore neglecting the opportunity for larger customers to articulate demand through procurement of large numbers of a micro-CHP units, which can, for example, be applied in large housing complexes. Private house owners generally replace their domestic heating system at the end of its lifetime, usually about 15 years, taking into account issues such as price, performance, reliability, comfort and environmental effects. For micro-CHP households will have to consider whether excess

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produced electricity will be used domestically or be sold back to the retailer, provided that electricity feedback is technically, economically and institutionally possible. The cost-effectiveness of micro-CHP for households depends largely on the amount of heat used, a parameter destined to decline in importance in the future due to more efficient heating technologies as well as improved insulation, while trends in electricity demand rise due to increased use of electric appliances (Elzenga et al., 2006). Moreover, different housing types have different profiles for energy use, which renders micro-CHP attractive in older, less energy efficient houses, rather than in newly built, highly insulated buildings. Presently (2009), the market introduction price of micro-CHP is still uncertain, but prices of about h6500 have been mentioned, aiming eventually for a price reduction to about h1500–2000 higher than the price of a regular condensing boiler. We will adopt h6500 in the baseline settings of our model, but explore various other scenarios.

3. An agent-based demand model for micro-CHP diffusion 3.1. Modelling approach The general viability of technological diffusion on the market is assumed to depend on a large number of issues based on the interaction of technological, economic and social factors. This inserts analytical difficulties on the identification of aggregate dynamics such as the function of market shares for the new technology through time, because of the complexity of the interactions among the factors involved and the intrinsic uncertainty in technological, economical and social developments (Epstein and Axtell, 1996; Pyka et al., 2004; Windrum et al., 2007; Moss, 2008; Dawid and Fagiolo, 2008; Faber and Frenken, 2009; Verspagen, 2009). Aggregate analytical models have difficulty modelling discontinuities and non-linearities, specifically when these arise from a large number of interlinked micro-variables. This implies that their functional form can be only approximated, and in some cases with high error margins. In addition, the very compactness of aggregate models bears the risk that relevant micro-dynamics are hidden in aggregated functions in order to maintain analytical tractability, decreasing empirical validity at the micro-level. Phenomena of structural change or non-linearity, even when liable to be reproduced using analytical models, limit to provide the dynamic pattern of aggregate variable, but are intrinsically unable to shed light on the underlining micro events supporting those aggregate results. Analytical models, even when carefully calibrated to a specific period of time, may thus widely depart from observed patterns in another period, and users have no possibility to assess the reason for the deviation and correct it. Rather than estimating the functional form of aggregate dynamics, we have set out to build an agent-based model (ABM) on the basis of the available information on the various elements involved. In comparison to other approaches, agent-based modelling has the capability to show the emergence of collective phenomena from interactions between autonomous and heterogenous agents, replicating the behaviour through time of real entities interacting at different aggregation levels. The attractiveness of simulation tools like agent-based modelling is largely due to fact that they do not constrain the adoption of any functional form for the equations of the model, allowing, for example, nonlinear, non-derivable functions. For this reason it is possible to build models whose variables are not meant to capture only a few aggregate properties of a system, but that can obtain such properties as a result of the dynamics and interaction of a large number of micro-variables representing lower level behaviours.

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Simulation models may thus reproduce and replicate results from analytical models. The opposite, that is, the replication of results from simulation models using an analytical format, is, in general, not feasible. Moreover, simulation models are generally more flexible and more powerful in handling the dimensionality and complexity of the modelled situation. Generally, simulation models are useful to test different scenarios with potential intervention on micro-level, allowing to assess the effects of individual behaviour decisions with respect to various aggregate outcomes. Moreover, simulation models provide not only the final distribution of aggregate variables, but also the time patterns leading to those levels, both at macro- and micro-level. This means that in case of anomalies it is possible to track down and evaluate which variable(s) of the model are responsible for the unexpected event, which could be particularly relevant for the assessment of long-term policy initiatives. This requires the model to allow the possibility to intervene within its internal functioning. This latter feature, normally required for debugging (i.e. fixing errors) in computer programming, becomes also a valuable tool for the full exploitation of simulation models for two reasons. First, including a specific assumption into large, non-linear models is likely to affect, directly and indirectly, a large number of aspects of the model. Only at run-time it is therefore possible to appreciate the full implications of a given assumptions and, if necessary, adjust accordingly the model implementation. Second, a given result can be traced to the elements (numerical or functional) responsible for its generation, increasing the understanding of the system modelled and the consequent possibility of intervention. The wider applicability of simulation models is particularly useful in the cases in which assumptions on behaviour are crucial to the results of the model. Agent-based modelling is hence useful for exploring various paths of (energy) technology diffusion (Pyka et al., 2004). Given the central role of demand in the diffusion process, we devote comparatively greater importance to the representation of consumers and the articulation of demand for micro-CHP in competition for adopters with the incumbent condensing boiler technology.

3.2. General model outline and purpose The demand side of the market in our model is designed to represent individual consumers, where each is associated to a specific consumer class and to a specific cohort, that is, people purchasing at a specific point in time. Each class contains a number of individual consumers sharing the same need and preferences. Fig. 1 provides a graphical sketch of the demand structure as implemented in the model. A consumer is supposed

Demand

Class 1

Class 2

…

Class N

Cohort t

Cohort t+1

…

Cohort t+T

Consumer 1

Consumer 2

…

Consumer M

Fig. 1. The demand structure of the model is composed by N consumer classes, each containing several cohorts, which, in turn, contains several consumers. Only the consumers in a cohort purchase a new product at each time step. A consumer replaces the heating system every t + 1 periods.

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to buy a unit and hold unto it until the end of its technological lifetime. At that time the consumer enters the market and makes a comparison of available alternatives, choosing the most favourable one. Therefore, at any unit of time (representing one year), the number of buyers is made by all consumers (across the various classes) requiring a replacement of their heating unit. At the supply side the technologies available are defined by the technical features relevant to the consumers, as well by the price for purchase of a unit and costs of average usage over the unit lifetime. The supply side is composed of only two technologies – micro-CHP and condensing boilers – since we do not aim to study the intra-technology competition among firms. The unit price decreases as a function of cumulative sales, following a general learning curve. Furthermore, the efficiency of electricity production by micro-CHP increases over time due to technological and productive improvements. The estimation of the cost of use is individually computed for each technology depending on the consumers’ requirements (e.g. average dwelling dimension), which are defined by the consumers’ class. A time step of the model begins by computing the number of households in need of a new heating unit, and then proceeds, for each consumer, by executing a decisional algorithm that results in the choice of one of the two competing technologies. Finally, statistics, such as market shares, total subsidies paid, etc. are collected before the end of the simulation step. The central part of the model is therefore composed by the decisional algorithm determining consumer’s choice. The algorithm first considers whether the consumer is aware of the new technology, assuming that the chances of a consumer knowing of the existence of the new technology increases with its share of the market and the marketing efforts of its proponents. In case the consumer does actually consider both options, then the algorithm computes their total costs for producing the same amount of heating, as defined by the consumer class. These costs are divided in purchase costs, including installation, and usage costs over the expected working life of the unit. Usage costs vary for the consumer classes, depending on class characteristics, technical properties of the product and settings of the regulatory and economic regime such as resource prices and optional subsidies for usage. The computation includes subsidies, if any, and savings due to electricity production for the novel technology. Eventually, the consumer chooses the alternative with the lowest cost. The choice of the consumers’ decisional algorithm is central to the model results, and therefore deserves some further comments. We adopted a rather straightforward minimal-cost decision, even though there is ample evidence that standard consumers’ behaviour for general goods, such as cars, electronic devices, etc. is affected by a far larger number of factors. For example, imitation and bandwagon effects usually play a much larger role than usage costs in consumers’ decisions. However, the particular good we are considering and the goal of our paper justify, in our opinion, such simplistic choice because of two reasons concerning the nature of the product and the goal of our research. The first reason is that the home’s heating system is a rare purchase of a relatively sizeable cost, both features that decrease the likelihood of routinised behaviour in consumer decisions, therefore increasing the attention to the objective properties of the available choices. Moreover, heating systems are usually not ‘experience’ or ‘status’ goods, for which a consumer cares not only the personal functionality, but also the impact on his/her social and emotional state. Rather, it is a type of good most likely to be perceived as providing a specific function, and therefore the choice will be based only on the evaluation of the performance. The second reason for our choice of a simple minimal-cost decisional relates to the goal of our research. We are

primarily interested in the assessment of the market distribution between two different technologies, rather than in assessing the market shares for different producers of technologically similar products. A simple decisional procedure in choosing between technologies is likely to include an evaluation of the alternative purchase, sharing very similar technological features. An elaborate decisional procedure would also involve an wider evaluation, including the function to be addressed (e.g. heating), the infrastructural embedding (e.g. presence of gas infrastructure), and an evaluation of social motivations (e.g. appraisal of solar heating systems). Given the goal of our research, and given the functional status of heating systems, we assume a simple decisional algorithm in our modelling exercise, based on a minimal cost consideration between two available technologies. We use the model to study the possibility of the new technology to erode the dominance of the existing one, and therefore assume that the old technology initially dominates the market. We explore the effects of various assumptions concerning different technological, economic and policy scenarios. Before presenting the results, the next section describes in detail the main elements of the model. The model is implemented in Lsd, an object oriented C++ platform specifically geared for evolutionary modelling (Valente, 2008).2 3.3. Model specifications At each time step a consumer, representing a regular household, enters the market to consider the purchase of a new heating unit. We classify consumers in terms of housing type, which is the most important parameter determining the level of natural gas needed for domestic heating. Data is available for gas and electricity use for five distinct housing types, together accounting for 98.7% of all Dutch houses. Upon entering the market, the consumer’s considerations follow a decision algorithm in three basic steps. First, it is assessed whether a new heating unit is required, following an average breakdown of 15 years. Second, the consumer scans the market for heating units, which are ‘visible’ to the consumer following a function of ‘technology awareness’. Technology awareness is described by a ‘visibility’ function, which reflects the openness of the market to a specific technology and hence the probability that this technology j is considered as an available purchase. Visibility is a value between 0 and 1, which is compared to a random drawn value for each consumer defining the probability to pass the visibility condition. At any time t the visibility Vj(t) for product j is Vj ðtÞ ¼ MAX½Vj ðt1Þ; minð1; Aj þ ðmsj ðt1ÞÞsj Þ

ð1Þ

where Aj is the exogenously set level of advertising, reflecting a steady increase in visibility of the new technology over time. It is measured without dimension and set at 0.01 for micro-CHP throughout all simulations. msj ðt1Þsj is the market share of product j at t 1 raised to the power of sj, which is a technology-specific parameter reflecting the effect of market size or confidence in the market. This parameter sj is exogenously set and reflects a bandwagon effect, with consumers following the mass of previous decisions (Smallwood and Conlisk, 1979). In a third step of the decision algorithm, among all visible products, each consumer in class i assesses the total costs for the technology j and chooses the cheapest one. We assume that the total costs Ci,j(t) considered by the consumer principally concern the upfront cost and usage cost (i.e. cost of operation), since they 2 Lsd (Laboratory of simulation development) is open source available at http://www.business.aau.dk/  mv/Lsd/lsd.html. Our specific model can be obtained with the corresponding author.

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both are expected to be of a similar importance in the economical decision of what new heating system to buy: u ðtÞ Ci;j ðtÞ ¼ Cjf ðtÞ þCi;j

ð2Þ

Cjf ðtÞ

Upfront cost includes the price of purchase Pj of the new technology and exogenously set installation costs, and possibly a subsidy for purchase Sj. Usage cost Cju ðtÞ includes the cost of natural gas use. Moreover, for the micro-cogeneration technology, usage costs include the savings (negative costs) of electricity cogenerated as well as the profits from selling domestically produced electricity, which can be seen as an extra stimulus to consider purchasing this technology. Prices of both technologies will slowly drop as research and learning-by-doing progresses as a function of price at t =0 and cumulative installed capacity: Pj ðtÞ ¼ Pj ð0ÞðXj ðtÞÞaj

ð3Þ

where Pj(t) is price at time t, Pj(0) is price at time 0, Xj(t) is cumulated installed capacity and aj is a measure of responsiveness, determining the progress rate PR through: PR =2  a. Progress ratio thus gives the relative price decease with each doubling of cumulative installed capacity X(t). The size of the Dutch market determines maximum installed capacity; in our simulations we will assume a fixed value of 4.0 million for the size of the market where micro-CHP competes with condensing boilers. Settings for learning effects on condensing boilers follow from Weiss et al. (2008) and although these settings are in principle technologyspecific, for our simulations they are kept similar for both technologies. Installation costs for both technologies are not explicitly modelled to calculate upfront costs, but are assumed to be incorporated in the price of purchase. For some further reflections on the conceptual and methodological difficulties of dealing with learning in the early stages of technological development see Faber et al. (2008). Usage costs are discounted and include a time horizon, i.e. a limited amount of time to take into account. Usage costs are calculated as follows: u Ci;j ðtÞ ¼

Ti P G ðGTH þ GE ÞðP B E ð1s Þ þðP S þSF ÞE s þSU E Þþ CM Þ X i ij i j i i j j ij kW ij kW t¼1

ð1þ ri Þt

ð4Þ with the following class specific parameters and variables:

 Ti: user horizon. Time span considered by the consumer when evaluating usage costs;

 ri: discount rate;  PG: price of gas;  GTH i : gas consumption for heating per household (TH denoting ‘thermal’); B  PkW : price of electricity bought from the grid;  Ei,j: electricity produced in a household i by means of the

   

technology j, i.e. where Ei;j ¼ GEi 1/HPRj, where HPRj is set at 1 for condensing boilers (note that GEi is then 0 anyway); GEi : gas consumption for electricity production per household (set at 0 for condensing boilers); S PkW : price of electricity sold to the grid (feedback price); si: share of CHP-produced electricity that is sold back into the grid. The complementary share 1 si is used domestically; CMj: annual cost of maintenance for product j.

The cost of usage may be decreased by either one of two subsidy schemes: subsidy of usage SU j for each kWh of electricity produced or subsidy for feedback SFj for each kWh of electricity sold back to the retailer.

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Micro-CHP units are assumed to enter the market with a HPR of 9 at the start of the simulations, then slowly declining over time due to technological learning and improving the domestic production of electricity. The model does not explicitly distinguish between various types of engines and their specific HPR values, but includes a continuum of HPR improvements. We assume that HPR improvements follow a logistic curve to reflect ¨ progress rate for a technology in the long term (Pan and Kohler, 2007), falling along the S-shaped curve to an asymptote value in the long run:

HPRt ¼ HPRLO þ

HPRUP ¼ HPRLO 1þ ebðtTLFÞ

ð5Þ

This curve describes dynamic learning for the heat-to-power ratio of a micro-CHP unit, where HPR drops logistically from an exogenously set high asymptote (HPRUP) to an exogenously set low asymptote (HPRLO). HPR is a function of time, because it is considered to depend mainly upon research, rather than on learning by doing. Growth rate of the curve is reflected by b, set exogenously at 0.2. Time is reflected by t, while the point of inflection of the S-curve is determined by TLF, representing paradigmatic technological lifetime, i.e. a measure to determine ex ante the time it takes to reach technological maturity. A reasonable assumption for the technological lifetime is 15 years, i.e. the time of a new technological generation. The model calculates HPR for micro-CHP only, and not for condensing boilers, which clearly do not produce any electricity at all. All parameters and variables are set exogenously or initialized according to the settings in Appendix 1. These settings generally follow directly from empirical data or from empirically based estimates. For further elaboration on these estimates, on the sources underlying them and on the parameter dynamics during model runs see Faber et al. (2008). Parameter settings are generally fixed throughout the baseline simulations, but explored various scenario-specific simulations. Concerning un-observable data we have explored the impact of different choices and, where this was negligible within a reasonable range, we settled for a most likely value. Visibility, advertising and sigma are conceptually easy to grasp but it is very hard to include empirics here, which requires relatively simple settings. These are for now not further explored in our model. In other cases, where the assumption may have affected the results, we present and comment different results. A sensitivity analysis was performed on the settings of market size, progress rate, discount rate, technological life time, user horizon and share of electricity feedback. Specifically the settings of progress and discount rate are important to take into account as important for affecting final results. User horizon is important, but essentially shortens discount rates. For all other settings, empirical findings can be made. The results of an elaborate sensitivity analysis can be obtained from the corresponding author. A full dynamic analysis incorporating changes of gas and electricity prices over time will affect the usage costs directly, resulting in shifts in the installed market share. This issue has not been explored quantitatively, as it requires an explicit consideration of price scenarios. Gas and electricity prices have a constant level throughout the simulations, but variations have been tested in a sensitivity analysis, showing their impacts on the usage costs (see Section 4.1). Also other extensions of the decision algorithm like considering social interaction effects and imitation behaviour more explicitly in the consumer’s purchase decision could have important effects on the results. This would likewise require more extensive modelling using appropriate assumptions for which only limited empirical backing material is available.

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4. Simulations of prices, demand effects and policy scenarios

profitable. A scenario with double prices for both electricity and natural gas shows a 50% installed market share in about 30 years, which is significantly faster than the baseline, suggesting that the effect of electricity price considerably offsets the effect of gas price. This effect becomes more important in the longer term, as micro-CHP technology improves and electricity production becomes more efficient.

We have performed a series of simulations to explore the effects of resource prices and policy interventions on the market penetration of micro-CHP. The model is initialized with the parameter settings in Appendix 1, unless stated otherwise. All simulations involve 100 time steps (years). Micro-CHP technology enters the market at t = 0 with a niche market of 1% of the total market and a price of h6500.

4.2. Supply dynamics: feedback tariffs and introduction prices 4.1. Exogenous effects: natural gas price and electricity prices

Profits of micro-CHP usage and thus attractiveness of this new technology for the consumer increases with higher feedback prices. For our simulations we assume a fixed share of electricity that is sold back to the retailer (‘feedback’) at 20%, while the remaining 80% will be used domestically. A feedback share of 0.2 is close to the average HPR of 4.4 of Dutch households. This figure is in our calculations not class-dependent, although the model could be adapted to do so in future analyses. The opportunity for consumers to react to market prices or to trade electricity is excluded, which is in line with a micro-CHP deployment scenario of relatively strong market control by the grid operator and electricity retailer (see Sauter and Watson (2007) for some deployment scenarios for distributed energy technologies). Attractiveness of micro-CHP clearly increases with raised feedback prices: a feedback tariff of h0.10/kWh would ensure to reach the same level of installed capacity more than 20 years earlier than without a feedback tariff. An increased feedback tariff could reduce cost of usage considerably and make the new technology significantly more attractive. This effect may be enhanced with an (additional) subsidy for feedback, which will be explored below. For all further analysis we will maintain a feedback tariff of h0.08/kWh, which is in line with present Dutch market prices.

Attractiveness of micro-CHP becomes higher when electricity sales and feedback prices are high, but higher gas may inhibit attractiveness, since the micro-CHP requires a larger intake of gas in comparison to condensing boilers. Gas price is a constant parameter in the model, set at 2007 levels of h0.55/m3. With these settings, and with feedback tariff set at 0 to focus the simulation on resource price effects, a 50% market share of installed capacity can be expected after 84 years. If gas price increases and electricity price remains constant, costs of usage Cju ðtÞ will also increase and thus inhibit the diffusion of micro-CHP. Only with very low gas prices of less than lower than h0.25/m3 a 50% market share of installed capacity is conceivable within 60 years. On the other hand, an increase in electricity price (presently set at h0.23/kWh) could stimulate the diffusion of the new technology, since it becomes more attractive to produce electricity domestically rather than purchase it from a retailer. When electricity price exceeds h0.42/kWh a 50% installed market share can be reached within 30 years. For all electricity prices higher than about h0.47/kWh usage costs Cju ðtÞ drop below zero in the course of several decades, making the use of the micro-CHP highly

1.0 market share (installed) after 50 years 0.9

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introduction price P(0) Fig. 2. Market shares (installed capacity) at t =30 and t = 50 as a function of introduction price.

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1

0.75

0.5

setting: all demand 3 at 2559 m (free standing houses)

setting: all demand baseline setting

0.25

setting: all demand at 1108 m3 (apartments) 0 0

25

50

75

100

Fig. 3. Market shares of installed capacity for micro-CHP for three runs with varying gas demand levels (settings according to housing type).

A key variable for suppliers of micro-CHP is the introduction price, which largely determines the cost of purchase and therefore the attractiveness of the new technology on the market. Any supplier will try to set an introduction price P(0) such that sales take off and production costs will drop, which in turn will lead to lower prices again (assuming that lower production costs are reflected in the sale prices). Very high introduction prices will not affect the market and therefore not initiate sales to ensure a price decrease. Very low introduction prices will stimulate sales and further price drops, but this comes at an investment for the supplier. Simulations with introduction prices in the range h2500 (i.e. equal to condensing boilers at t= 0) up to h12,500 show significantly effects on installed market share (Fig. 2). The baseline introduction price of h6500 shows almost 12% market share of installed capacity after 50 years, but lower introduction prices have considerably more effect: an introduction price of h5500 shows a spectacularly higher market share of 74% after 50 years. A diffusion rate equivalent to that of condensing boilers from the 1980s onwards requires an introduction price for microCHP of about h3800–4000, or even less when assessed in terms of sales rather than installed capacity. A lower introduction price requires high investments for the suppliers, but may also be reached by means of a subsidy, which will be explored in the simulations of policy scenarios below. The increasing difference between the installed market shares after 30 and 50 years as a function of introduction price essentially reflects a horizontal shift of the underlying S-curve of technology diffusion.

4.3. Demand dynamics: variations in demand levels for gas and electricity A high natural gas demand for heating purposes domestically co-produces relatively large amounts of electricity when using a micro-CHP, thus providing a profit by considerably lowering demand for electricity from the grid. Therefore, micro-CHP is more attractive to consumers with relatively high gas demands,

such as households in older and generally less well insulated houses (Watson et al., 2006). From this perspective, the diffusion of micro-CHP competes with measures to decrease gas demands, e.g. insulation measures. To explore the effect of gas demand levels, we run one simulation assuming that all consumers have a high gas demand level (2559 m3) equivalent to those for free-standing houses, and another simulation assuming that all consumers have a low gas demand level (1108 m3) equivalent to those for apartments. The effect of these simulations is highly significant (Fig. 3): after 50 years, the market share of installed capacity for micro-CHP would only be 10% in the baseline setting, but in the high gas demand scenario a market share of installed capacity of over 88% would be reached. In 36 years a 50% market share of sales would be reached. On the other hand, in the scenario where all households would have an energy demand equal to apartment dwellers shows no market penetration at all, not even after 100 years. The effect of gas demand is further enhanced at higher electricity prices, which reinforce the opportunity to take advantage of high gas demand levels. This result shows that insulation measures to decrease heating and thus gas demand are in competition with the attractiveness of micro-CHP, which is operated following heat demand in households. Theoretically, this conclusion is no surprise (Van Den Bergh et al., 2006), and with considerable scope left for reducing gas demand, specifically for older homes, the implementation of such measures would make micro-CHP less attractive (Watson et al., 2006). This result is confirmed in tests with very high and with very low demands for all consumer classes, showing that application of micro-CHP thus not only competes with condensing boilers, but also with insulation or other energy saving measures. An option to promote both efficiency measures as well as micro-CHP diffusion could lie in the combination of micro-CHP use with two or more households. This option is in practice sometimes applied in block apartments (then generally referred to as mini-CHP), but not further explored in the context of our analysis.

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installed market share after 20 years 0.600

0.500

installed MS

0.400

0.300

0.200

0.100

0.000 0

1000

2000

3000

4000

5000

6000

subsidy level (€) Fig. 4. Installed market shares of micro-CHP after 20 years for various subsidy levels.

4.4. Policy dynamics: simulations of various subsidy schemes 4.4.1. Subsidy schemes for purchase A policy intervention will generally aim to reinforce market penetration of micro-CHP. First, we explore a fixed subsidy for purchase and assess the effect in terms of installed market share after 20 years. The subsidy is maintained during the whole simulation period. As the subsidy lowers the purchase costs for the consumer, the technology becomes more attractive and larger numbers will purchase a micro-CHP unit rather than a condensing boiler. As a consequence, the technology price drop will accelerate with larger installed capacity, following Eq. (3), thus becoming increasingly more attractive to consumers. In other words, an early subsidy may kickstart market dynamics based on a positive feedback cycle. Results show that relatively low subsidies, until about h1400, show no significant effect, while subsidies higher than about h3250 show no significant additional effects in terms of installed market share. Maximum installed market share after 20 years is about 54%, which relates to a saturation of the market (Fig. 4). In the range between h1400 and h3250 the new technology becomes competitive and the effect of higher subsidies is considerable, similar to the effect in relation to higher introduction prices. Clearly, higher subsidies account for higher cumulative subsidy budgets required: a subsidy of h1000 for each unit would cumulate up to only h20 million in 20 years, while a double subsidy of h2000 would cumulate to no less than h1655 million in that same period. Generally, we find that the cost-effectiveness of a subsidy (measured as subsidy/market penetration) decreases as the subsidy per unit is higher. However, rather than maintaining a fixed subsidy irrespective of its effect, a policy maker can balance the effect of a subsidy scheme with its costs by phasing out or abolishing a subsidy when its effect is reached. Our model allows to run simulations with a

fixed subsidy budget MaxSP; the subsidy drops to zero after the budget is exhausted. We perform a range of simulations with subsidies in the range of h[0–4000] and various subsidy budgets constraints. The simulations show that, once the subsidy budget is exhausted, upfront costs quickly elevate again to a level determined solely by price of purchase and installation costs, but this level is lower than what would have been without a subsidy at all, due to scale effects and associated learning and price drops. This finding inserts serious doubt that micro-CHP can ever be successfully incorporated in the market in a short period of time without significant and sustained subsidies. Subsidy budget constraints show a relatively moderate effect on the speed of market diffusion: a 50% installed market share is reached 13 years earlier when subsidy budget increases from h10 million to h100 million (Fig. 5). In 2008, Dutch authorities have introduced a considerable h4000 purchase subsidy, with a budget of h40 million.3 This would generate a 50% market share in terms of sales after 41 years (cf. 56 years without a subsidy), but the subsidy budget will be exhausted in about 3 years. Moreover, it can be argued that a subsidy level of h4000 has little additional effects in terms of market share, when compared to levels of about h3000, thus creating considerable windfall profits for the consumer. Of course, providing a subsidy irrespective of the level of market penetration of the new technology is not very costeffective. Our model therefore allows to explore two more subsidy-for-purchase schemes, taking market share into account. Both subsidy schemes can again be capped by a subsidy budget. First, we explore the case where a subsidy for purchase is proportional to the potential number of adopters, which decreases as more consumers have already adopted the new

3 This budget is allocated until 2011, but in our further calculations here we exclude such time constraints.

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Installed market share for a subsidy level of €1500 and various budget constraints 1.00

0.90

0.80

market share (installed)

0.70

10 20 30 40 50 60 70 80 90 100

0.60

0.50

0.40

0.30

0.20

0.10

0.00 0

10

20

30

40

50

60

70

80

90

100

time Fig. 5. Market shares for seven simulations with a subsidy of h1500 and subsidy budgets of h10–100 million, in steps of h10 million.

6500

no subsidy

4700

1500

2900

1100

5000

-700 1

25

50

75

100

Fig. 6. Upfront costs (h) for various initial subsidy levels and under the scheme where subsidy is inversely proportional to the number of adopters. Subsidy levels from h0– h5000, with the top graph representing zero subsidy and the lowest graph representing h5000 initial subsidy.

technology. We simulate a range of settings for the initial subsidy per unit, which start to decrease as its effect starts to accumulate. For all initial subsidy levels, the subsidy will decrease to (almost) zero after about 60 years. Cumulative subsidy level in the long run

is determined by the time needed for decrease to zero, as well as by the initial subsidy level per unit. Upfront costs, i.e. the sum of purchase price and subsidy, shows an optimum at the point where the aggregate of decreasing

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price and decreasing subsidy provides the best bargain: early adopter get a high subsidy, but also face a high purchase price for the new technology, while laggards face lower purchase prices, but also do not qualify for any substantial subsidy. For all subsidies higher than about h1500 there is such an optimum. The higher the subsidy, the earlier the optimum is reached (Fig. 6). Moreover, it can be noted that upfront costs for a micro-CHP unit arrive at a more or less stable level of about h2000, but a higher subsidy will reach this level earlier than a low subsidy, stimulating a stronger adoption of this technology. Note that in these simulations consumers are not attributed with the capability of foresight, so they will not postpone their purchase following an assessment of more attractive micro-CHP due to lower upfront costs in the future. Simulations with similar settings but with a capped subsidy budget show that upfront costs quickly rise again after the subsidy stops and that installed market share after some decades can be considerably lower. Second, we explore a subsidy scheme that is set proportional to a fraction of the price difference between the two competing technologies. Since the price difference will decrease as more consumers adopt micro-CHP as their technology, the subsidy for purchase of a unit will decrease as well. Note that the initial price difference is h4000. In a number of simulations with a range of fixed fractions of the price difference we find very significant effects: while no subsidy leads to 50% installed market share after 62 years, e.g. a subsidy of 40% of the price difference would lead to a 50% installed market share after 35 years (Table 1). The environmental cost-effectiveness in the long term (in euro per tonne of CO2 avoided) is in a similar range of recent high CO2 prices of about h50 in the context of the European emission trading scheme (which itself does not cover the built environment).

4.4.2. Subsidy for usage Another policy option is to provide a subsidy for the use of the new technology, rather than for purchase. Here, we explore a scheme of subsidy for domestic electricity production. Exploring a subsidy for usage Suj in the range h[0.00–0.30] for each kWh of electricity produced we find significant effect on installed market share: without any subsidy 50% market share would be reached in 62 years, but this would be 34 years with a subsidy of h0.10/kWh and about 25 years for a subsidy of h0.20/kWh. The effect of the subsidy is already very effective for small subsidies. For any subsidy higher than h0.18/kWh the cost of usage for the (average) consumer will drop below zero after a while, following technological improvements in electricity production in line with Eq. (5). While a subsidy for usage is thus very effective, it comes at a cost of high budgets requirements (Table 2). Moreover, while higher subsidies are of course more effective in reaching higher market shares, each euro invested becomes marginally less effective in terms of installed market shares. With respect to most parameters, effectiveness of this subsidy is largest in the middle ranges of about h0.05–0.20/kWh. Small subsidies have only a small effect, while higher subsidies have marginally decreasing effect, providing increasingly larger windfall gains at a (very) high cost. Another issue to take into account is that market penetration may run faster than technological improvements, which gives a lower cost effectiveness in terms of CO2 emissions avoided. In other words: this subsidy scheme is effective in enhancing a fast market penetration, but since the technology itself is technically less efficient in the early stages, the impact on environmental improvement is relatively moderate.

Table 1 Various subsidy levels (% of price difference) and their effect after 20 years on market penetration and cumulated amount of subsidy. Installed market Subsidy (fraction share after 20 years of price difference) (%) 0.0 0.2 0.4 0.6 0.8 1.0 a b

0.0 0.4 4.7 22.0 38.9 47.5

Environmental costCumulated amount of Annual subsidy in the Years to reach Annual CO2-savings effectiveness (h/ton) subsidy ( millions) 20 year period (h) 50% market share (Mton/yr) after 20 yearsb a after 20 years after 20 years 0.0 13.5 232.7 1014.3 1821.3 2477.4

0.0 0.7 11.6 50.7 91.1 123.9

62 45 35 28 23 21

0.0 0.0 0.2 0.9 1.5 1.9

– – 58 56 61 65

Undiscounted costs. CO2-reductions are compared with gas-fuelled electricity plants as a reference case.

Table 2 Effect for various levels of subsidy for usage. Subsidy for electricity production (h/kWh) 0.00 0.01 0.05 0.10 0.15 0.20 0.25 0.30 a b

Installed market share after 20 years (%)

Cumulated amount of subsidy (hmillions) after 20 years a

Environmental costAnnual subsidy in the Years to reach Annual CO2-savings effectiveness (h/ton) 20 year period (h) 50% market share (Mton/yr) after 20 years b after 20 years

0.0 0.1 0.5 4.5 16.4 29.1 38.4 44.6

0.0 0.3 13.8 205.8 1107.4 2966.5 5343.6 7998.2

0.0 0.0 0.7 10.3 55.4 148.3 267.2 399.9

Undiscounted costs. CO2-reductions are compared with gas-fuelled electricity plants as a reference case.

62 55 42 34 29 25 22 22

0.0 0.0 0.0 0.2 0.7 1.2 1.5 1.7

– – – 52 79 124 178 235

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Table 3 Overall results for various subsidy schemes after 25 years according to three assessment criteria. Subsidy scheme

CRITERIUM 1: 15% market share Subsidy for purchase Depends on market share Subsidy for price difference Subsidy for usage

Level required

(installed) after 25 years h1420/unit h1430/unit (at t = 0) 40% of price difference h0.10/kWh produced

Cumulative subsidy (million h)

Average annual subsidy (million h/year)

Other effects Price for unit of micro-CHP

Market share (sales) (%)

CO2 savings (Mton)

Env. effectiveness (h/ton)

965 809 565 955

38 32 22 38

3622 3709 3671 3600

49 44 43 50

0.7 0.7 0.8 0.8

56 47 30 46

2296 2155 1177 3631

91 86 47 145

3132 3057 3122 3042

66 65 63 66

1.3 1.5 1.4 1.6

2345 1984 2159 1853

CRITERIUM 3: Diffusion rate at least as fast as condensing boilers Subsidy for purchase h2400/unit 5895 Depends on market share h 2500/unit (at t = 0) 4423 Subsidy for price difference 80% of price difference 2310 Subsidy for usage h0.24/kWh produced 12,609

235 176 92 504

2696 2693 2663 2680

83 81 80 81

2.6 2.6 2.6 2.6

1036 1032 1007 1012

CRITERIUM 2: h 1000 price difference after 25 years Subsidy for purchase h1800/unit Depends on market share h1810/unit (at t = 0) Subsidy for price difference 53% of price difference Subsidy for usage h0.15/kWh produced

4.4.3. Comparing effectiveness and costs of various subsidy schemes We can compare various subsidy schemes in an assessment with respect to three (fictive) policy criteria, which are assumed for analytical purpose:

 a fixed policy goal of 15% installed market share after 25 years;  a price difference of h1000 within 25 years;  development of micro-CHP at least in line with the historic diffusion rate of condensing boilers: installed market shares of 30% after 20 years and 57% after 25 years; 50% market share in terms of sales after 16 years. These criteria are explored for all subsidy schemes and with unlimited budgets. Results are shown in Table 3. With respect to the first criterion, we find that the most cost effective subsidy scheme is a subsidy based on price difference, which reaches the same effect as the other schemes, but at about 2/3 of their costs. With respect to the second criterion, we find that subsidy for usage is especially expensive compared to subsidies for purchase. The subsidy scheme based on price difference is again most cost effective. With respect to the third criterion, costs for all subsidy schemes become very high, but again a subsidy for price difference is the most cost effective scheme. The cost differences among the various subsidy schemes becomes very large with this policy criterion, with e.g. a subsidy for usage being about five times as expensive as a purchase subsidy based on price difference between the technologies, while the effect is almost the same. This policy evaluation for three assessment criteria provides a general conclusion in favour of a smart subsidy scheme, with decreasing subsidy levels as the new technology diffuses on the market. Facing limited budgets, another option for a policy maker could be to set a cap for cumulative subsidies. In one example simulation we run a regular subsidy for purchase again, but now with a cap of h565 million, which equals the cumulative subsidy found for the scheme based on price difference and tested against the first policy criterion. Now we find that 15% installed market share after 25 years requires a subsidy for purchase of h1500, which is only slightly higher than h1420 in the scheme with unlimited subsidy. A similar analysis, again with the budget

constraint of h565 million, can be made with respect to subsidy for usage, where we find that a subsidy of h0.11/kWh is required to reach the same policy target of 15% installed market share after 25 years. Again, this is only slightly higher than the level required with unlimited budgets. These findings suggests that it is more effective to set a higher subsidy, which is limited by a subsidy budget, than to set a lower subsidy with infinite budget. Generally, our findings argue that it is most cost-effective to promote micro-CHP in the early stages in order to gain a niche market, and from there develop at a moderate pace without subsidies in the market. A final important assessment of policy effectiveness evaluates the environmental effects of various policy regimes. While the subsidies in early years are in the order of several thousand euros per tonne of CO2, the environmental cost-effectiveness increases over time, as subsidy schemes are phased out and the new technology diffuses in the market. With respect to the first criterion, environmental effects are relatively moderate, but at a similarly moderate environmental cost-effectiveness (see again Table 3). Higher environmental impacts following policies in the light of criteria 2 or 3 are associated with disproportionally lower cost-effectiveness. In other words: technology subsidy becomes very expensive per tonne of CO2 avoided. It can generally be concluded from the analysis that a fast diffusion on the market of the new technology is effective in terms of environmental improvement, but the technology itself may technically still be less efficient in the early stages. These findings argue that it is most cost-effective for policy makers to align with the goals associated with criterium 1. This implies a policy to promote micro-CHP in the early stages in order to gain a niche market, and from there develop at a moderate pace without subsidies in the market. The size of this niche market does not necessarily have to be 15% of the full market, but depends on the subsidy budget available. However, given the results in Table 1 after 20 years and the results in Table 2 after 25 years, a subsidy scheme based on price difference that is smaller than 40% of this difference is not very effective anymore. After reaching about 10–15%, further promotion of micro-CHP could accelerate technology diffusion somewhat, but not to a level that justifies large public investments.

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5. Conclusions This paper presents an agent-based model designed to investigate the pattern of evolution of a new technology that replaces an existing one. Consumer demand is explored as the key driver for diffusion of a new technology, in competition with an incumbent technology. The case study concerns the market for households heating systems, comparing the incumbent condensing boiler technology with the new micro-cogeneration technology, producing electricity as co-product. The model implementation allows to calibrate several aspects: market features, technological parameters and policy measures. Concerning the market, the model defines classes of consumers, defined by the average usage for heating, dwellings’ feature, etc. The technologies are defined by their technical values, and the implementation allows to include future technical development. Finally, the range of policy measures considered includes subsidies for purchase and/or usage, caps on the programmed costs and variations of subsidies as a function of applications. The simulations presented are calibrated on the Dutch market, using publicly available data from various sources. The results concern the likelihood and timing of the success of the new technology in replacing the old one. Different scenarios permit to analysed the outcome of different assumptions on a few core parameters of the model, including different policy initiatives. We find, not surprisingly, that the market diffusion of microCHP is affected significantly by fuel prices. Since the technology requires a higher intake of natural gas in comparison with condensing boilers, a higher gas price will severely inhibit diffusion of micro-CHP on the market. On the other hand, a higher price of electricity will render the micro-CHP technology more attractive, since it allows to replace expensive electricity with domestically produced electricity. On aggregate, our simulations show that the effect of electricity price considerably offsets the effect of gas price. Moreover, as electricity demand increases, it may surely be interesting to produce at least part of the amount required domestically in order to avoid buying electricity from a retailer. This development is, however, offset by developments in household natural gas demands. While high gas demand levels renders micro-CHP to be attractive, future gas demand is generally decreasing because of efficiency measures such as insulation. Such efficiency measures could quickly compete micro-CHP out of the market. At the baseline settings, with an introduction price of h6500 and a feedback tariff of h0.08/kWh, micro-CHP will enter the market as a small niche technology, slowly increasing market share along an S-shaped diffusion curve to 50% of installed market share after 62 years. Diffusion is primarily driven by the lower costs of usage for micro-CHP, provided that gas prices remain low and energy efficiency measures are not effective. A subsidy could considerably accelerate the diffusion of micro-CHP. Various subsidy schemes are explored in this paper, affecting either costs for purchase or costs for usage. All subsidy schemes can be constrained by budgets. A comparison of the various subsidy schemes with unlimited budget shows that a purchase subsidy that relates to the price difference of the technologies is most cost effective. However, if we limit the budgets of the other subsidy schemes, only slightly higher subsidies are required to reach the same effects in terms of market penetration. This shows that a limited subsidy budget can best be used to set a relatively high subsidy per unit for the consumer in order to kickstart diffusion of the new technology with early adopters. Generally, our findings argue that it is most cost-effective to promote micro-CHP in the early stages in order to gain a niche market, and from there develop at a moderate pace without subsidies in the market.

Finally, it can be concluded from the analysis that a fast diffusion on the market of the new technology is effective in terms of environmental improvement, but the technology itself may technically still be less efficient in the early stages. Therefore, policy support to promote micro-CHP requires an assessment that should be made relative not only to its predecessor technology, but also to other competing technologies such as e.g. solar technologies, as well as to energy saving measures. This argues to consider micro-CHP as a transition technology rather than a longterm energy solution, levering the deployment of more environmentally effective small-scale technologies in the context of a distributed electricity system. With our model, we do not claim to provide an estimation of actual future development, but rather to suggest a novel tool for policy analysis. We stress that our approach allows to disentangle the issue of market responses (on which we mostly focus) and external conditions when designing a policy initiative, so that each of these parts can be treated and adjusted individually. Our proposal concerns a scenario evaluation tool, which we believe to be superior in terms of flexibility and detail to other approaches. The actual deployment of such tool requires several specifications. Firstly, a definition of the policy goal(s), e.g. in terms of pollution, preference for a specific type of energy, constraints on the funds available for subsidies, etc. Secondly, one or more scenarios for the relevant future variables. We show that each of these elements can be easily plugged into the model or revised. At any moment the model would show the expected patterns allowing for (re-)evaluations and analysis of the effects of possible interventions. This work supports the use of agent-based models for policy assessments, specifically when analysing empirical issues. The advantage of such type of models is the possibility to carefully represent any element deemed relevant for the issue at hand, exploiting a wide range of information available. Moreover, the format of the results produced by simulation models are made of expected temporal patterns. This means that they can be used for planning, detailing both the eventual expected results and the patterns leading to those results. Finally, by its very nature, these models can be used not only for planning future events, but also for evaluation and real-time adjustments to new events. In fact, any deviation of the expected patterns from observed realities may be fed back into the model in order to adjust for un-expected events or modifying running programmes for new goals. These features provide, in our opinion, a compelling reason for the application of agent-based models in policy planning and deployment.

Acknowledgements We greatly value comments by various reviewers and discussants on various earlier versions of this paper. Specifically, valuable reflections and comments were provided by Koen Frenken, Johanna Montfoort, Jan Ros and two anonymous reviewers. Useful feedback was provided by the participants of the 7th International Summer Academy on Technology Studies in Deutschlandsberg (August 2007), the DIME Workshop on Empirical Analyses of Environmental Innovation in Karlsruhe (January 2008) and the DIME Conference on Innovation, sustainability and policy in Bordeaux (September 2008).

Appendix 1. Initial baseline parameter settings See Table A1

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Table A1 General parameters PG Price of gas B Price of electricity bought from the grid PkW S PkW MS

Price of electricity sold to the grid (feedback price) Market size

h0.55/m3 h0.23/kWh h0.08/kWh 40,000

Technology (j) specific parameters and variables (t 1) Pj(first unit) Pj(0) X(0) LT PR

a HPRj

b TLF TF SPj Suj Aj Vj

sj CMj

Price of first unit Price at t = 0 Initial cumulative sales (sum equals market size MS) Life time of technology (age of replacement) Progress rate Alpha Power-to-heat ratio at t= 0 Parameter beta (growth rate HPR learning) (Paradigmatic) technological lifetime Technology factor to describe effect of HPR Subsidy for purchasing technology j Subsidy for using technology j Advertising factor Visibility at t = 0 Parameter sigma (confidence in the market) Annual cost of maintenance

Condensing boiler h2500 h68,162 39,600 15 years 0.86 0.217 1.0  0.2 15 years 0.0 h0.00 h0.00 0.00 1.0 0.33 h80

Micro-CHP h6500 h65,205 400 15 years 0.86 0.217 9.0  0.2 15 years 1.0 h0.00 h0.00 0.01 0.0 0.33 h150

Free 2559 0.04 7.0 0.2 14.7 0.1

2-shared 1855 0.04 7.0 0.2 12.9 0.1

Class (i) specific parameters GTH i DRi UHi FBshare i CSi Vi

Gas consumption per household for heating purposes (m3) Discount rate User horizon (years) Share of electricity feedback to retailer Class size (share) Variance (error in computing usage costs)

Simulations are run at a market size of 40,000, which is 1% of the real market. The model includes corrections in the calculations where required due to this setting. References Bruckner, T., Morrison, R., Wittmann, T., 2005. Public policy modeling of distributed technologies: strategies, attributes and challenges. Ecological Economics 54, 328–345. Colijn, M., 2006. Micro cogeneration in the Netherlands. In: Pehnt et al. (Eds.), Micro Cogeneration, Towards Decentralized Energy Systems. Springer, Heidelberg. pp. 277–289. Dawid, H., Fagiolo, G., 2008. Agent-based models for economic policy design: introduction to the special issue (editorial). Journal of Economic Behavior & Organization 67, 351–354. Elzenga, H.E., Montfoort, J.A., Ros, J.P.M., 2006. Micro-warmtekracht en de virtuele centrale. MNP Report 500083003, Bilthoven (in Dutch). Epstein, J.M., Axtell, R., 1996. Growing Artificial Societies. Social Science from the Bottom Up. MIT Press, Cambridge, MA. Faber, A., Frenken, K., 2009. Models in evolutionary economics and environmental policy: towards an evolutionary environmental economics. Technological Forecasting and Social Change 76 (4), 449–452. Faber, A., Valente, M., Janssen, P., Frenken, K., 2008. Domestic micro-cogeneration in the Netherlands: an agent-based demand model for technology diffusion. DIME Working paper on Environmental Innovation, No. 8. Available on /http://www.dime-eu.org/working-papers/wp25/8S. Feenstra, C.F.J., 2008. The flexible future of micro combined heat and power, an analysis of the social embedding of micro CHP in Dutch households in 2030. ECN Report ECN-E-008-038, Petten. Kuhn, V., Klemeˇs, J., Bulatov, I., 2008. MicroCHP: overview of selected technologies, products and field test results. Applied Thermal Engineering 28, 2039–2048. Meijer, I.S.M., Hekkert, M.P., Koppenjan, J.F.M., 2007. How perceived uncertainties influence transitions; the case of micro-CHP in the Netherlands. Technological Forecasting and Social Change 74 (4), 519–537. Moss, S., 2008. Alternative approaches to the empirical validation of agent-based models. Journal of Artificial Societies and Social Simulation 11 (5), 1. ¨ Pan, H., Kohler, J., 2007. Technological change in energy systems: learning curves, logistic curves and input–output coefficients. Ecological Economics 63, 749–758. Pehnt, M., 2008. Environmental impacts of distributed energy systems— the case of micro-cogeneration. Environmental Science & Policy 11 (1), 25–37.

Corner 1771 0.04 7.0 0.2 12.6 0.1

Block 1495 0.04 7.0 0.2 28.4 0.1

Apartment 1108 0.04 7.0 0.2 31.5 0.1

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