The continuous growth of global electricity demand is an issue that becomes more and more urgent these days. Although the revolution of renewable sources of energy has surpassed all expectations over the past decade, the existing static power grid has not yet been able to cope with the new dynamic electricity production pattern. Thus, innovative ways to balance the load and shift the demand are imperative in order to facilitate a sustaining electric power grid. Electric Vehicles (EVs), which are rapidly gaining significant market shares across the globe, consist of a unique opportunity not only to move to a new low-carbon mobility era but also to balance the electricity grid. Although, many studies have focused on finding optimal pricing mechanisms and solutions to coordinate EV charging, they are based primarily on simulation results, which assume that EV drivers are represented by intelligent agents that are fully rational. This study bridges the gap between theoretical approaches and real-world behavior by taking into account the behavioral aspect of the users. Specifically, a 21 days experiment is conducted where 154 users are provided with a smartphone application, the TamagoCar app, which simulates the operation and charging of an EV. Through TamagoCar app, two smart-charging pricing mechanisms are tested; 1) Real-Time pricing (RTP), where the prices presented to the users are related to the electricity retail-prices and 2) Variable-rate pricing, where pricing is also correlated with the energy capacity desired by the users. Results reveal that Variable-rate pricing mechanism significantly improves the observed phenomenon of demand peaks when compared to the smart-pricing alternative of Real-Time Pricing and leads to an average Peak-to-Average ratio reduction of 80-87%. In addition, the study shows that users do not experience a significant difference in the cognitive load required to use the two different pricing mechanisms. This finding implies that variable-rate pricing could be a viable alternative to RTP when it comes to EV smart-charging coordination and thus further examination with real EV drivers is proposed. In addition, the study presents a user-friendly and already applicable interface that can be used in reality in future research projects.
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