Load-shifting for cost, carbon, and grid benefits

A model-driven adaptive survey with German and Swiss households

Bibliographic Data

ID13064866
AuthorsMatteo Barsanti (Ecole polytechnique fédérale de Lausanne, corresponding author), Jan Sören Schwarz (0000-0003-0261-4412, Carl von Ossietzky Universität Oldenburg), F M A Ghali (Ecole polytechnique fédérale de Lausanne), Selin Yılmaz (0000-0001-7982-4587, University of Lausanne), Sebastian Lehnhoff (0000-0003-2340-6807, Carl von Ossietzky Universität Oldenburg), Claudia R Binder (0000-0002-2921-9896, Ecole polytechnique fédérale de Lausanne)
Year2025
Volume121
Pages103931-103931
Publication date2025-01-31
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEnergy Research & Social Science (JOURNAL)
Journal identifiersISSN: 2214-6296 • E-ISSN: 2214-6326
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.erss.2025.103931
OpenAlexW4407021326
LanguageEN
Citations received2
References cited60

Survey data helps understand user energy behaviour and inform policies supporting the transition to a renewable, user-centric electricity grid. To explore user responses to dynamic, hypothetical energy scenarios – such as time-varying electricity tariffs or fluctuations in renewable energy availability – surveys often rely on standardised fixed-choice questions. However, these methods frequently oversimplify the complexity, diversity, and temporal dynamics of user behaviour, resulting in generalised and incomplete insights for interventions. To address these challenges, we introduce a model-driven adaptive survey. By integrating a conventional survey design with a feedback loop between participant responses and an energy demand model, this method allows end-users to iteratively evaluate and adjust their choices through a set of indicator scores. We implemented this approach in a survey conducted across Germany and German-speaking Switzerland (N=803), investigating user willingness to time-shift dishwashing usage under four scenarios: time-of-use tariffs, congestion risks, renewable energy availability, and their combinations. Our findings highlight the value of integrating energy demand models into survey designs to assist respondents in making complex energy-related decisions in a tailored manner. Respondents exhibited significant variability in their load-shifting practices, with over 56% reporting a likelihood of time-shifting energy use even without financial incentives. Participants using the feedback mechanism achieved notable improvements: 19% reduction in energy costs, 80% reduction in peak energy demand, and 9% increase in renewable energy usage on average for running the dishwasher. Beyond its utility for data collection, we discuss how this approach could extend to real-world applications, enabling users to navigate decision-making in increasingly dynamic energy systems. • An energy model is coupled with a household survey via an adaptive survey design (ASD). • End-user load-shifting preferences are investigated through a model-driven ASD. • A model-driven ASD supports user decision-making with real-time tailored feedback. • Most of the respondents would engage in load-shifting even without financial rewards. • Energy models hold potential to assist end-users in navigating dynamic energy systems

Business · Carbon fibers · Economics · Environmental economics · Geodesy · Geography · German · Grid · Computer Science · demographic modeling and climate adaptation · Environmental Science · Smart Grid Energy Management · Urban Transport and Accessibility

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