Peer-reviewed paper – Intervening me softly – Modeling nudging interventions to change EV user preferences (2022)


The charging of an increasing number of electric vehicles (EVs) leads to load peaks in the distribution grid. Controlled charging can reduce these peaks, but could also impair the mobility needs of the EV owners. Financial incentives are a frequently discussed measure to stimulate grid-friendly consumption, but they are limited in their attractiveness for the consumers. A more intuitive approach is the so-called nudging interventions, which influence the decision-making of consumers through a change in their environment.

The design of nudging interventions, such as social comparison and normative feedback, is investigated in the literature but – so far – not simulated. A translation of nudging interventions, into a modelling environment would, however, capture effects beyond a theoretical setting. We address this research gap – for the case of EV charging – by setting up an agent-based simulation that models the decision-making of and interaction between EV users.

Our model displays the effect of nudging interventions on the preferred EV battery state of charge (SoC) for each agent. Based on social networks, we model how interventions spread within the agent population. The selected interventions, social comparison, and normative feedback aim to minimize the preferred SoC. The model captures different sensitivities of agents towards the interventions, different sizes, and structures of the networks, frequency of interventions, as well as the boomerang effect.

Our results show an overall reduction of the SoC for all interventions. The strongest impact can be allocated to the normative feedback. Our findings thus indicate that nudging interventions cause agents to accept a lower SoC. Correspondingly, a larger share of the flexibility potential provided by EVs would be made accessible for controlled charging. While our model is theoretical, it can be substantiated with empirical data on consumer preferences and combined with the modelling of controlled charging on the household, grid, and electricity system levels.

Courtesy of eceee Summer Study

Jonas Burkhardt, Fraunhofer Institute for Systems and Innovation Research ISI, Germany
Matthias Kühnbach, Fraunhofer Institute for Systems and Innovation Research ISI
Sabine Pelka, Fraunhofer Institute for Systems and Innovation Research ISI