Multilayer modeling of adoption dynamics in energy demand management
arXiv:1907.13096 · doi:10.1063/1.5122313
Abstract
Due to the emergence of new technologies, the whole electricity system is undergoing transformations on a scale and pace never observed before. The decentralization of energy resources and the smart grid have forced utility services to rethink their relationships with customers. Demand response (DR) seeks to adjust the demand for power instead of adjusting the supply. However, DR business models rely on customer participation and can only be effective when 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 on a two-layer multiplex network: the layer of social interactions among customers and the power-grid layer connecting the households. While the adoption process - based on peer-to-peer communication - runs on the social layer, the time-dependent recovery rate of the nodes depends on the states of their neighbors on the power-grid layer, making an infected node surrounded by infectious ones less keen to recover. Numerical simulations of the model on synthetic and real-world networks show that a strong local influence of the customers' actions leads to a discontinuous transition where either none or all the nodes in the network are infected, depending on the infection rate and social pressure to adopt. We find that clusters of early adopters act as points of high local pressure, helping maintaining adopters, and facilitating the eventual adoption of all nodes. This suggests direct marketing strategies on how to efficiently establish and maintain new technologies such as DR schemes.
arXiv comment14 pages, 6 figures
References in corpus (35)
- Statistical mechanics of complex networks
- The structure and function of complex networks
- Epidemic processes in complex networks
- Synchronization in complex networks
- Identification of influential spreaders in complex networks
- Multilayer Networks
- The structure and dynamics of multilayer networks
- Spatial Networks
- Immunization of complex networks
- Influence maximization in complex networks through optimal percolation
- Dynamical interplay between awareness and epidemic spreading in multiplex networks
- Mathematical Formulation of Multi-Layer Networks
- Epidemic dynamics on an adaptive network
- Simplicial models of social contagion
- Random walks and diffusion on networks
- Discrete-time Markov chain approach to contact-based disease spreading in complex networks
- Evidence of Complex Contagion of Information in Social Media: An Experiment Using Twitter Bots
- The Simple Rules of Social Contagion
- Crossover from Scale-Free to Spatial Networks
- Network dynamics of innovation processes
- Modeling the adoption of innovations in the presence of geographic and media influences
- Risk perception in epidemic modeling
- The new challenges of multiplex networks: measures and models
- Cities and Regions in Britain through hierarchical percolation
- Complex contagion process in spreading of online innovation
- The limitations of discrete-time approaches to continuous-time contagion dynamics
- Critical behaviors in contagion dynamics
- Epidemic Threshold in Continuous-Time Evolving Networks
- The Impact of Heterogeneous Thresholds on Social Contagion with Multiple Initiators
- Competition of simple and complex adoption on interdependent networks
- Influence of trust in the spreading of information
- Impact of the distribution of recovery rates on disease spreading in complex networks
- Social Clustering in Epidemic Spread on Coevolving Networks
- Interplay between cost and benefits triggers nontrivial vaccination uptake
- From continuous to discontinuous transitions in social diffusion