Optimal planning of fast-charging stations with spatiotemporal heterogeneous range anxiety

Driven by the global pursuit of energy transition and carbon neutrality, the electric vehicle (EV) market has experienced exponential expansion in recent years. Nevertheless, the widespread adoption of EVs continues to be impeded by range anxiety–a phenomenon characterised by substantial heterogeneity arising from individual driver behaviours and contextual variability across travel scenarios. To address these challenges, this study investigates the planning of fast-charging stations using distributionally robust optimisation (DRO) approach with Wasserstein distance, aiming to minimise the aggregate range anxiety across transportation network while jointly considering path coverage and planning costs. A chance-constrained programming model is first established to dynamically model EV market penetration. Subsequently, data-driven nonparametric distributions within Wasserstein ambiguity sets are derived, allowing the original DRO problem to be reformulated into a tractable mixed-integer linear program via duality theory, thereby facilitating large-scale planning. The effectiveness of the proposed approach is demonstrated through a case study based on real-world traffic data from nine central urban districts of Chongqing, China. The model offers stronger application capability by avoiding the distribution assumptions of SP and the excessive robustness of RO, while endogenously modelling market penetration dynamics.