Robust optimization with decision dependent uncertainty for electric vehicle network expansion

Artikel i vetenskaplig tidskrift, 2027

The rapid growth of electric vehicles (EVs) is driving the need for charging infrastructure expansion, which in turn has significant implications for power system operations. Such expansion may also affect the EV-charging demand, making this uncertainty decision-dependent. This paper develops a two-stage robust optimization model that determines EV-charging station expansion decisions in the first stage and power system operational decisions in the second stage, while explicitly capturing decision-dependent uncertainty of charging demands to enhance planning robustness. To efficiently solve the resulting challenging optimization problem, we design a column-and-cut generation algorithm that obtains high-quality solutions quickly. Comprehensive numerical experiments demonstrate that generation availability and line congestion play critical roles in shaping optimal expansion decisions.