Abstract
Electric vehicles (EVs) are crucial in achieving a low-carbon transportation sector and can inherently offer demand-side flexibility by responding to price signals and incentives, yet real-world strategies to influence charging behavior remain limited. This paper combines bilevel optimization and causal machine learning as complementary tools to design and evaluate dynamic incentive schemes as part of a pilot project using a supermarket's EV charging station network. The bilevel model determines discount levels, while double machine learning quantifies the causal impact of these incentives on charging demand. The results indicate a marginal increase of 1.16 kW in charging demand for each one-percentage-point increase in discount. User response varies by hour and weekday, revealing treatment effect heterogeneity, insights that can inform business decision-making. While the two methods are applied independently, their combined use provides a framework for connecting optimization-based incentive design with data-driven causal evaluation. By isolating the impact of incentives from other drivers, the study sheds light on the potential of incentives to enhance demand-side flexibility in the electric mobility ecosystem.