Multi-layer modelling of adoption dynamics in energy demand management

Multi-layer modelling of adoption dynamics in energy demand management

Abstract

Due to the emerging of new technologies, the whole electricity system is undergoing transformations on a scale and pace never observed before. In particular, the decentralisation of energy resources and the smart grid have changed the rules of the game and have forced utility services to rethink their relationships with customers. The so-called demand response (DR) seeks to adjust the demand for power instead of adjusting the supply. However, DR business models rely on customer participation and might only be effective if large numbers of customers in close geographic vicinity, e.g. connected to the same transformer, opt in. Here, we introduce a model for the dynamics of service adoption, in which the behaviour of a customer is influenced by its social contacts, in addition of also depending on the specific spatial configuration of other customers in close proximity within the power grid service area. In particular, we use a multiplex network with two layers coupled together, the social layer among customers and the power-grid layer connecting the households. While the adoption process, modelled as an epidemic spreading, runs on the social layer, the node- and time- dependent recovery rate of the nodes depends on the states of their neighbours on the power-grid layer, so that the dynamics tends to preserve clusters of infected individuals by making an infected node surrounded by nodes in the same state less keen to recover. Numerical simulations of the model on synthetic and real-world networks show that strong local influence of the costumers actions leads to a discontinuous transition where either no or all nodes in the network are infected, depending on the infection rate and the social pressure to adopt. We find that clusters of local early adopters act as points of high local pressure, helping maintaining adopters, and facilitating an eventual adoption of all nodes. This suggests direct marketing strategies on how to efficiently establish and maintain new technologies such as DR schemes.

Publication
arXiv
Date