Computational models for Smart grids diffusion

Our society is facing the necessity of rethinking the electricity system. The traditional grid in which the electric energy is unidirectionally exchanged between a big producer and a collectiveness of consumer, is no longer efficient, mostly because of three reasons. First, the rapid growth of global population and its energy-consuming lifestyle is increasing the electricity demand. Secondly, the shortage of fossil fuels is driving the supply mix toward an escalation of energy production from alternative sources. Moreover, our society needs to cope with climate change and other environmental issues, requiring a decarbonization of the energy sector. The improvement and diffusion of Smart Grid technologies seem to be an effective solution to all these three situations. That is because, on the one hand, it can ensure a higher grade of flexibility and efficiency to the electric system and, on the other hand, it would ease the switching from a consuming to a prosuming behaviour. The active participation of prosumers in the electric market is seen as vital for renewable energy usage acceleration and thus for increasing the sustainability of the power system. This thesis investigates those factors that seem to affect the diffusion of Smart Grid technologies. With the support of the open-source software NetLogo, designed an Agent-based Model and run computational simulations of the diffusion of Smart Grids through a network. By changing the initial values of the agents' variables and the network structure, the model will attempt to identify the factors that can promote the adoption of Smart Grid among residential prosumers, focusing especially on socio-demographic and economic ones.