An agent-based diffusion model for Residential Photovoltaic deployment in Singapore : Perspective of consumers’ behaviour

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Detail(s)

Original languageEnglish
Article number132793
Journal / PublicationJournal of Cleaner Production
Volume367
Online published23 Jun 2022
Publication statusPublished - 20 Sep 2022

Abstract

Residential Photovoltaic (RPV) is designed for residential buildings to generate electricity from solar energy. Despite various government regulations to boost PV deployment, RPV diffusion in Singapore has been slow, accounting for less than 4% of installed PV capacity by 2021. Investment volatility and uncertainty impede RPV deployment as it significantly affects consumers' adoption decisions. RPV diffusion analysis helps researchers develop deployment strategies by considering and modelling the RPV diffusion process and its major influencing factors. However, the existing works neglect heterogenous consumers' behaviours to address investment volatility in the network. Consumer behaviours, including spontaneous adoption decisions and interpersonal communications, are essential in determining RPV adoption and diffusion. This study constructed a Real Options Analysis supported Agent-Based Diffusion Model (ROA-ABDM) to investigate the RPV diffusion in Singapore by applying the “bottom-up” simulation modelling approach. ROA is used in simulation based RPV diffusion analysis to offer consumers flexible options to address investment volatility by calculating the option's value. Afterward, the Agent-based diffusion model is developed, calibrated, and validated with RPV historical diffusion data in Singapore. From the “what-if” analysis using the developed model, this study identifies that the optimal RPV subsidy intensity is 30–40% and reveals that RPV diffusion is more effective in a tight-knit network and sensitive to investment volatility. Based on the findings, this study proposes two policy suggestions, including stage-based dynamic policy and trusted-network oriented campaigns. This study contributes to the body of knowledge by integrating ROA analysis into agent-based modelling for the RPV diffusion analysis, and the proposed model can be extended to analyse other renewable energy technologies in different geographic regions.

Research Area(s)

  • Adoption decisions, Agent-based modelling, Consumer behaviours, Diffusion analysis, Real options analysis, Residential photovoltaic