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Multi-layer modelling of adoption dynamics in energy demand management

Due to the emerging of new technologies, the whole electricity system is undergoing transformations on ascale and pace never observed before. In particular, the decentralization of energy resources and the smartgrid have changed the rules of the game and have forced utilities to rethink therelationships with the customers. The so-called demand response (DR) seeks to adjust the demand for powerinstead of adjusting the supply. However, DR business models rely on customer participation and are effectiveonly if large numbers of customers in the same region of the power grid opt in.

Here, we introduce a model for the dynamics of service adoption, in which the behavior of a customer isinfluenced by its social contacts but also depends on the specific spatial configuration of the other customersin close proximity in the power grid. In particular, we use a multiplex networks with two layers, the sociallayer among customers and the power grid connecting the households, which are coupled together. While theadoption process runs on the social layer, the node- and time-dependent recovery rate of the nodes dependson the states of their neighbors on the power-grid layer, so that the dynamics tends to preserve clusters ofinfected individuals by making an infected node surrounded by nodes in the same state less keen to recover.

We find that strong local influence of the costumers actions leads to a discontinuous transition where eitherno or all nodes in the network are infected, depending on the infection rate, i.e. the advertisement force andsocial pressure to adopt. For high local pressure to keep the adoption, we find that clusters of local earlyadopters help that eventually all nodes adopt. This suggests direct marketing strategies on how to efficientlyestablish and maintain new technologies such as demand response schemes.


I. Iacopini, B. Schafer, E. Arcaute, C. Beck, V. LatoraMulti-layer modelling of adoption dynamics in energy demand management, Chaos 30 (2020) 013153

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