Multi-layer diffusion model of photovoltaic installations

TL;DR

This paper introduces a multi-layer agent-based diffusion model for photovoltaic adoption, combining opinion dynamics and innovation spread, analyzed through simulations and mean-field approximation to understand sustainability impacts.

Contribution

It presents a novel multi-layer network model extending the q-voter model to study photovoltaic diffusion and opinion dynamics simultaneously.

Findings

Innovation always succeeds within certain parameter ranges.

Mean-field approximation aligns qualitatively with simulation results.

Model helps understand sustainability and stability in renewable energy adoption.

Abstract

Nowadays, harmful effects of climate change are becoming increasingly apparent. A vital issue that must be addressed is the generation of energy from non-renewable and often polluting sources. For this reason, the development of renewable energy sources is of great importance. Unfortunately, too rapid spread of renewables can disrupt stability of the power system and lead to energy blackouts. One should not simply support it, without ensuring sustainability and understanding of the diffusion process. In this research, we propose a new agent-based model of diffusion of photovoltaic panels. It is an extension of the q-voter model that utilizes a multi-layer network structure. The novelty is that both opinion dynamics and diffusion of innovation are studied simultaneously on a multidimensional structure. The model is analyzed using Monte Carlo simulations and the mean-field approximation. The impact of parameters and specifications on the basic properties of the model is discussed. Firstly, we show that for a certain range of parameters, innovation always succeeds, regardless of the initial conditions. Secondly, that the mean-field approximation gives qualitatively the same results as computer simulations, even though it does not utilize knowledge of the network structure.