A Framework for Exploring Social Network and Personality-Based Predictors of Smart Grid Diffusion

Abstract

So-called smart technologies are at the forefront of modern energy research. Many existing works focus on the effects of smart technologies, with scales ranging from a single household to an entire city. One area that is less studied is the adoption of these technologies. In this paper, we extend our prior work to develop a more robust framework for exploring the diffusion of basic smart grid technologies using a social-network-based model to study demand response adoption. This network is based around considering an end-user as a node, and any relationship where mutual trust and communication exists as an edge. In addition, we have incorporated mathematical representations of user personality traits in the decision-making progress to better simulate real-world actions. We observe greater usage of demand response when conventional electricity is high, when conscientiousness is high, and when the network is densely connected; all of these are reasonable results given logical behavior of the individual agents. Our model includes many tunable parameters in the update stages, of which the effects of only a few are included in this paper. This quantity of potential parameters, as well as the broad nature of the model and algorithm, makes our model a candidate for future improvement and development based on including different parameters.