Understanding energy behaviours and transitions through the lens of a smart grid agent based model

Understanding energy behaviours and transitions through the lens of a smart grid agent based model / J. Richard Snape, Katherine N. Irvine, Christophe Rynikiewicz. Energy efficiency first. The foundation of a low-carbon society : Proceedings of ECEEE 2011 Summer Study, Belambra Presqu’Ile de Giens, France, pp. 1919-1930, (2011), 978-91-633-4455-8

draft : http://dmu.academia.edu/JRichardSnape/Papers/620034/Understanding_energy_behaviours_and_transitions_through_the_lens_of_a_smart_grid_Agent_Based_Model

Abstract :

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 realworld 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. (© authors)

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