title: Modelling Adoption Behaviour of Home Battery and Solar PV Systems
Creators: name: Alipour, Mohammad
Publisher: name: Griffith University
Year: 2022
Subjects: subject: Home battery
subject: Solar PV systems
subject: battery adoption behaviour

description: Residential solar photovoltaic (PV) systems are a key contributor to the ongoing transition to renewable energy supply chain systems for addressing the onslaught of climate challenges. With the costs of small-scale PV systems reaching grid parity, it is making up a significant proportion of supply in many countries. Generating electricity from PV arrays at the point of demand curbs the necessity to transfer power over long distances through an expensive grid infrastructure. Behind the rich spectrum of benefits rendered to all sides, however, is the rising socio-technical challenges that stem from the use of rooftop PV at scale. Household-level battery systems as a nascent innovation in the residential sector has been brought forward to counterbalance PV electricity’s disruptive effects on the grid. Accordingly, efforts have been initiated by governments towards the mass acceptance of home batteries. However, success or failure in deployment policies concerning the uptake of batteries hinges on the household’s decision. Households are the main actor in the uptake decision-making process and as such will ultimately decide whether and the extent to which any considered deployment policies will succeed. The critical role of this micro-behaviour in the uptake no-go decision calls for better understanding of their householder behaviours to develop effective policy measures. To this end, the present study strives to analyse and model small-scale battery adoption behaviour by households. However, modelling such behaviours is complex since the behaviour of bounded rational individuals is non-linear and does not completely align with traditional economic patterns in a socio-economic system. Interested households without PVs should decide whether they want to install only a PV system or will install PVs with a connected battery. At the same time, those with existing PVs should decide whether they want to install a battery or wait until the current system becomes inefficient and then choose an integrated PV and battery systems, or not adopt any system. Not only are there choices to make, but also the interactions between PV and battery systems further complicate to the existing problem. The simultaneous analysis of both systems appears indispensable, as households’ regarding whether to accept or reject PVs is interlinked with and closely tied to battery adoption behaviour. To accurately model the choice behaviour of households regarding battery, PV, and PV plus battery system, this research begins by identifying relevant predictors in the literature. The literature review is further extended to reveal the behavioural theories and methods employed in the domain to find the most suitable tool. Evaluation of the potential users leads to a novel household classification followed by a determination of the most significant, relevant factors. A comprehensive survey is then structured into three blocks that cover these significant constructs, ranging from sociodemographic, environmental, and behavioural factors to the choice set for each group designed through a D-efficient choice experiment. To fully understand these characteristics, an online questionnaire was distributed to residential householders located in South East Queensland, Australia, and a total of 604 responses were received. This research continues to propose a novel term in social science, namely ‘attitude change’, to underpin the hidden interrelations between PV and battery systems, and the way the acceptance or rejection of PVs impacts adoption behaviour regarding batteries. This term is introduced to highlight the past role of PV purchasing decisions for the acceptance of battery through two elements: ‘regret’ and ‘despair’. The significance of factors driving these two elements is investigated through 25 supervised machine learning algorithms. The outcome of the previous steps is used to model the choice decision using the discrete choice models (DCMs) that can adequately capture the complexities embedded in the problem. Two sets of DCMs were built: one for current PV adopters, and another for nonadopters. Both models are hybrid and capture the complexity of individuals’ preferences by incorporating sociodemographic factors into the latent variables that cannot be revealed from traditional approaches. Four latent groups, characterised by certain features, are proposed. The results of the case study indicate that differences exist between the preferences of PV adopters and those of non-adopters, and they highlight the effective latent variables behind the change in choices. By developing a hybrid DCMs, the research addresses the challenges of decision complexity, and distinguishes the structure of decision, which varies depending on PV adoption status. Understanding the factors influencing each adoption behaviour is vital for better identifying market acceptance and establishing practical recommendations that will lead to more balanced diffusion rates of batteries and PVs in residential households. Optimal take-up of solar PV and battery storage systems will ensure renewable energy transition targets can be achieved, without jeopardising grid voltage stability and quality requirements for customers. Overall, this research makes important contributions to the current body of knowledge, as it redefines how PV and battery systems should be analysed. The integrated study of PVs and batteries is followed by the new household category and the novel proposal of the role of solar systems as the past behaviour of battery uptake decision. The integration between these contributions and the outcome of the novel DCMs provides a platform to support more effective policy directions.