Power systems are becoming increasingly vulnerable to cyberattacks due to extensive information exchange. This paper proposes a novel fuzzy inference system (FIS)-enhanced data integrity attack (DIA)-recovery framework and analyzes the combined effects of attacks simultaneously launched by groups with different motivations. These range from market participants seeking a competitive edge for personal economic benefits to outsider groups aiming to drive the system into an uneconomic operation state. A twostage optimization problem and a fuzzy-Bayesian approach are formulated to counteract the potential attacks on the Electric Vehicle Charging Management Center (EVCMC), where successful manipulation can result in substantial financial gains or disruptions to the power grid. According to the simulation results: 1) The proposed FIS approach provides a dynamic assessment of the attacker’s capability and the vulnerability of EVCMC to potential DIAs. 2) The combined effects of attack launched by market participants and outsider groups can cause more severe economic impacts compared to attack conducted separately. 3) The proposed attack-recovery scheme can identify the most vulnerable EVCMC to attackers and recover the optimal power dispatch under DIAs.

As fixed charging stations (FCSs) approach saturation, mobile charging stations (MCSs) have emerged as a flexible complement. This study proposes a bi-level optimization framework that integrates dynamic siting of MCSs with coordinated pricing for both MCSs and FCSs. The upper level maximizes the operator’s total net profit by jointly deciding MCS deployment and spatio-temporal prices, while the lower-level models EV users’ charging choices via cost minimization. The bi-level problem is reformulated as a single-level mathematical program with equilibrium constraints (MPECs) by replacing the lower-level with Karush–Kuhn–Tucker (KKT) optimality and complementarity conditions. The nonconvexities are addressed using the Big-M method, auxiliary variables, and piecewise linearization. This reformulation converts the problem into a mixed-integer linear program (MILP). The case studies show that the proposed coordinated strategy substantially improves the operator’s total net profit compared with the fixed sitting benchmark. This improvement is mainly achieved by allowing the MCSs to respond to spatiotemporal demand variations. Compared with the MCS-only optimization benchmark, the increase in total net profit is marginal under the tested scenario. This result suggests that coordinated MCS–FCS pricing mainly improves the investor’s portfolio-level outcome by reducing internal competition between MCSs and FCSs, rather than by increasing the standalone profit of the MCSs. The proposed framework provides an optimization approach for coordinated MCSs and FCSs operation in saturated charging networks.