Incorporating human behaviour in an agent based model of technology adoption in the transition to a smart grid

The requirement for affordable, secure and sustainable energy production is a pressing global challenge and the production of electricity with low carbon emissions is crucial. This usually entails large quantities of renewable energy generation, which is intermittent and often highly distributed throughout the electricity supply system. One of the proposed schemes to manage such generation is the smart grid, the transition to which forms the context for this research. The aim is to investigate the effect of certain psychological and social influences on the adoption of technology necessary to enable smart grids, in order to understand the implications for effective energy policy. In particular, the case of photovoltaic (PV) system adoption in the UK is studied. Empirical data detailing PV installations registered for the Feed in Tariff is analysed in order to understand rates of adoption and how they vary across both time and space. This analysis is combined with a review of policy intervention and literature from psychology to understand drivers for adoption among householders. The results from this study are then used to inform the design of an Agent Based Model of technology adoption within the smart grid context. The decision making of householders is modelled using an algorithm based on Social Cognitive Theory. The model is used to simulate different conditions and generate adoption scenarios in order to understand the potential effects of different parameters on adoption rates. In order to combine the analysis resulting from these methods, the multi-level perspective on transition in socio-technical systems is used to understand how a transition to a smart grid could be described and how adoption of PV in the UK under the Feed in Tariff incentive fits into such a transition. The results show that whilst economic incentive policies have had success in some areas adoption is also dependent on many non-financial parameters. Simulations show that the observability of adoption and the perceived inconvenience or urgency of adoption can have dramatic effects on rates of adoption, in some cases outweighing the rational economic effects of financial incentives. The implication for smart grid related policy is that non-financial factors should be taken into account as well as the more typical financial considerations in efforts to encourage adoption of necessary enabling technology by householders. The models developed could be used in further work to examine in detail adoption of other technologies such as smart home energy management systems and the interaction between adoption rates of multiple smart technologies.