This research has examined the potential for micro-generation in the domestic sector in the Netherlands, focussing on three technologies: micro-CHP, solar PV and micro-wind turbines. In order to gain insight into the restrictions and possibilities of micro-generation, firstly, a literature study is performed to identify the actors involved in the introduction of the technologies and to describe their role and interests in the subject. From the description, insight is gained in the possibilities and constraints for a large scale introduction of micro-generation from a socio-economic and technological macro-level. Secondly, in order to gain quantitative insight in the potential for micro-generation, a set-up was created for an agent-based computer model (ABM) that simulates the purchase decision of households in the category of owner occupied households for a specific micro-generation technology.
In the ABM set-up, the agents are households who are given the decision to purchase a micro-generation technology (the micro-level behavior). The level of diffusion of a technology (the emergent properties of the system) is determined from the number of households that decide to purchase that technology under a specific set of conditions. The model set-up exists of three parts: (1) A division of households into different agent categories based on their type of ownership, physical properties (type of house), energy demand and purchase decision making behaviour, (2) A description of the technologies (3) A decision making algorithm that simulates a high involvement, rational purchase decision of the agents.
The theory of planned behaviour is used as a framework to describe the decision making of the agents, assuming that the purchase of micro-generation technologies is preceded by a deliberate decision. According to the main principles of this theory, the purchase decision making of the agents is determined by the attitude regarding a product, the experienced perceived behavioural control and a subjective norm. These three determinants have been elaborated using existing consumer research concerning micro-generation technologies.
In the model set-up, the attitude of an agent is formed by the preferences it has regarding specific characteristics of a technology. Four attributes are selected to determine the attitude formation of an agent: the payback period, the environmental performance, independency from an external supplier and the performance (comfort level, noise production, visibility, predictability of production). The preferences for specific characteristics of the technology that determine an agents’ attitude formation towards a technology are dependent on its type of value pattern.
Technological attributes that are taken to influence the perceived behavioral-control, are the investment costs, the compatibility of a specific technology with the existing infrastructure and the bureaucratic barrier associated with micro-generation in general. Two types of agent characteristics are set to influence the perceived behavioral control: (1) the housing type and (2) the level of income. The first relates to the compatibility of the technologies with the existing infrastructure of a house. Given the physical properties of households in stacked dwellings, PV and micro-wind are not purchased individually by this group of households. The second relates to the investment cost of the technologies. Micro-generation technologies are associated with high investment costs, which are identified by households as an important barrier to purchase.
The subjective-norm reflects the communication between the agents and its effect on the diffusion dynamics in the model. In this study it is taken as a function of the market-share of a product, reflecting the popularity of a product which may change an agents’ perception of it. An agents’ sensitivity to a subjective norm (whether its purchase decision is strongly guided by the consumption behaviour of other agents) is dependent on its value orientation.
Following the TNS-NIPO WINTM model, the agents are classified based on eight value patterns. These value patterns are subdivided into three income categories (a high, a middle and low income category), resulting in 24 consumer types. From statistics of the residential housing stock of the Netherlands and energy use statistics, the Dutch households are differentiated into 900 household categories with the same type of house, equal heat and electricity usages and the same type of house ownership. By taking the income level as mediating variable, a distribution of the 24 consumer types over the 900 household categories is determined. As a result the households are differentiated into agent categories that are classified based on their consumer behaviour, type of ownership of a house, heat and electricity use, and housing type.
Together with a description of the technologies and a decision making algorithm, the agent categories form the basis for the ABM model set-up. To illustrate what dynamics may be expected from the agent-based conceptual model created, it has been used to calculate the diffusion of PV-systems in the owner occupied households in the Dutch domestic sector. In the calculation examples the agents are given the choice to purchase a PV-system of 1 kWp or to remain using only grid electricity. The modeling exercise was performed in excel, no agent-based simulation software was used.
The experiments have indicated that the model set-up is able to simulate findings form consumer research regarding PV-systems. Furthermore the experiments illustrate that different types of agents are motivated to purchase the technology under different conditions (e.g. grid electricity prices, investment costs, changing environmental concerns in society). In more general terms, the ABM model set-up is able to simulate the expected number of buyers of a specific micro-generation technology in the Netherlands under different conditions. Also, it is able to identify these buyers in terms of their purchase behaviour, housing characteristics and electricity and heat use. The sensitivity of the agents to the variables in the model (e.g. investment cost, market-share) gives insight in the factors that are likely to determine the potential for diffusion in the future. This may provide insight in what policy measures are effective to stimulate specific groups of households to purchase a micro-generation technology (e.g. targeting the group of households with the highest shares of electricity and heat use), or to increase the diffusion of a specific technology in the domestic sector as a whole.||