  When deploying fast charging stations (FCSs) to support long-distance trips
of electric vehicles (EVs), there exist indirect network effects: while the
gradual diffusion of EVs directly influences the timing and capacities of FCS
allocation, the decisions for FCS allocations, in turn, impact the drivers'
willingness to adopt EVs. This interplay, if neglected, can result in uncovered
EVs and security issues on the grid side and even hinder the effective
diffusion of EVs. In this paper, we explicitly incorporate this interdependence
by quantifying EV adoption rates as decision-dependent uncertainties (DDUs)
using decision-dependent ambiguity sets (DDASs). Then, a two-stage
decision-dependent distributionally robust FCS planning (D$^3$R-FCSP) model is
developed for adaptively deploying FCSs with on-site sources and expanding the
coupled distribution network. A multi-period capacitated arc cover-path cover
(MCACPC) model is incorporated to capture the EVs' recharging patterns to
ensure the feasibility of FCS locations and capacities. To resolve the
nonlinearity and nonconvexity, the D$^3$R-FCSP model is equivalently
reformulated into a single-level mixed-integer linear programming by exploiting
its strong duality and applying the McCormick envelope. Finally, case studies
highlight the superior out-of-sample performances of our model in terms of
security and cost-efficiency. Furthermore, the byproduct of accelerated EV
adoption through an implicit positive feedback loop is highlighted.
