Vulnerability Assessment of Charging Stations in the Electrified Road Network

As the adoption of electric vehicles (EVs) within electrified road networks (ERNs) continues to grow, the criticality of fast-charging stations (FCSs) to support this growth and alleviate range anxiety for EV users is increasingly evident. Due to the complex operating environment of FCSs, their reliable, safe operation faces significant challenges, from exposure to natural disasters, deliberate attacks, and technical failures. This leads to the need to analyze the vulnerability of FCSs within ERNs. This paper provides a mathematical framework to assess the vulnerability of the ERN to intentional attacks and identify critical FCSs within ERNs. We propose a System Optimal Dynamic Mixed Traffic Flow Assignment Model (SODTA) game-theoretical for an ERN where electric and fuel vehicles coexist. Building upon this model, an attacker-defender (AD) framework is established for assessing the vulnerability of FCSs within the ERN. We transform this AD problem into a mixed integer linear programming (MILP) one to solve it efficiently. Two indices are proposed to evaluate the vulnerability and the reallocation of charging loads within the ERN. Finally, we apply the proposed framework to conduct a vulnerability assessment of the ERN in North Carolina, USA. The key findings are as follows: 1) As the EV penetration increases, the studied ERN becomes more vulnerable to attacks. 2) With limited attack resources, mitigating performance losses over time is possible through charging load redistribution. 3) Variation in overall vulnerability levels with increasing attack resources reveals distinct patterns, ranging from mitigable to unmitigable vulnerability.