Chris RYNIKIEWICZ

Smart grid research has tended to be compart-mentalised, with notable contributions from economics, electrical engineering and science and technology studies. However, there is an acknowledged and growing need for an integrated systems approach to the evaluation of smart grid initiatives. The capacity to simulate and explore smart grid possibilities on various scales is key to such an integrated approach but existing models – even if multidisciplinary – tend to have a limited focus. This paper describes an innovative and flexible framework that has been developed to facilitate the simulation of various smart grid scenarios and the interconnected social, technical and economic networks from a complex systems perspective. The architecture is described and related to realised examples of its use, both to model the electricity system as it is today and to model futures that have been envisioned in the literature. Potential future applications of the framework are explored , along with its utility as an analytic and decision support tool for smart grid stakeholders.

Investigating the dynamics of consumption is crucial for understanding the wider socio-technical transitions needed to achieve carbon reduction goals in the energy sector.  Such insight is particularly necessary when considering Smart Grids and current debates about potential transition pathways (and contingent benefits) for the electricity system and coupled gas and transport systems. 
The electricity grid is a complex adaptive system comprising physical networks, economic markets and multiple, heterogeneous, interacting agents.  Fundamental to innovation studies is that social practices and technological artefacts shape and are shaped by one another.  Different trajectories of socio-technical systems’ transition are intrinsically linked to the behavioural and cognitive norms of individuals, businesses, communities, sectors, and governance institutions.  Therefore the transition to smart(er) grids inevitably requires a knowledge transition and behaviour change among such actor groups.  To date, these effects have not been modelled.
We present a prototype Agent Based Model (ABM) as a means to examine the effect of individual behaviour and social learning on energy use patterns, from the perspectives of adoption of energy saving behaviours, energy saving technologies and individual or community based energy use practices.  We draw on the Energy Cultures framework to understand real-world observations and incorporate representative energy use behaviours into the model and discuss the model’s relation to case studies, e.g. energy use in island communities. 
Such models enable examination of how far we can learn and scale up lessons from case studies to similar Socio-Technical Systems with bigger scale and greater interconnectivity such as the UK national grid.