Insights in Environmental Attitudes: Economic and Psychological Methods for Engaging Electricity Utility Customers and Electric Vehicle Drivers
Student: Max Andrew Gamerman
Year: 2023
Supervisors: Randall Morgan Greene, Simon Einarsson
Abstract:
Iceland is a unique case study with respect to energy resources, but is no stranger to
climate change. Worsening environmental conditions and a lack of development of
new energy infrastructure has finally caught up with the island nation. Last winter, the
Þorisvatn Reservoir was at its second lowest levels in recorded history (Lansvirkjun,
2022). Still, despite a growing need to expand and improve upon existing power
infrastructure, there exists no significant movement for improvements (Talnaefni
Orkustofnunar, 2022). Iceland’s plan to ban the import and sale of new non-electric
vehicles by 2030 (Shafiei et. al., 2019) further exacerbates the complexity of the issue.
Multiple simulations have already shown the country’s power system can’t
accommodate the fast approaching EV fleet both in terms of energy supply or structural
capacity (Dillman et. al., 2021, & Ólafsson, 2021). Previous electro-mobility research
has covered many related subjects, but there exists two gaps in the literature we aim to
fill in with our research: dynamic pricing and behavioral economic methods to promote
the shift of flexible points of electricity consumption to non-peak hours. Standard
economic theory conflicts with respect to electricity in terms of utility maximization
and price elasticity of demand (Green, 2005 & Upadhyay, 2020), thus, we aim to study
how these rigid assumptions of utility maximization hold up in a real-world setting.
Additionally, we examine various behavioral economic concepts and outline them in a
“behavioral toolbox” for ease of application. We designed the Dynamic Pricing Project
and upcoming website and mobile application based on our findings from the literature,
however, due to budgetary constraints we were only able to implement the Dynamic
Pricing Project which offers an opt-in price discount to 34% to domestic Straumlind
customers between the hours of 2-6 AM. From this, we found statistically significant
relationships between hourly electricity consumption data of Straumlind customers and
the discounted time frame as well as duration with an active smart-meter and customer
age, however, many of the linear models posited changes in behavior that were only
marginal to saving energy. Future research should focus on more longitudinal data at a
great sample size, reducing the issue of selection bias in our sample, and implementing
the mobile application and website.