In recent years, reinforcing the electric power system against natural disasters has emerged as a critical challenge and concern. Natural disasters, events, and cyber-attacks pose significant challenges to distribution networks, leading to widespread outages and blackouts. One effective approach to addressing such challenges is to implement a microgrid formation strategy in conjunction with mobile or stationary distributed energy resources. This paper addresses a significant research gap by analyzing load restoration during outages as a part of network resilience strategy, through two simultaneous approaches: (i) microgrid formation and graph theory, and (ii) mobile charging station with battery swapping technology. The proposed microgrid formation utilizes tie-line breaker switches (BS) and a mobile battery-swapping van (MBSV) in a coordinated manner to enhance resilience of system. The IEEE 33-bus network serves as a case study, incorporating both active and reactive powers into the nonlinear power flow equations. Mixed integer linear programming (MILP) is employed, effectively linearizing nonlinear equations for efficient computation. The results show that within 24 h, the objective function value (total power of restored loads) is approximately 687,421 kW, and load restoration is achieved at a rate of 76 %. According to the comparative study, the network without formation suffers from voltage collapse while the proposed plan properly deals with voltage fluctuations and fixes the voltage magnitude within bounds. Additionally, the present research shows load restoration rates that are 5.8 % higher compared to the formed network without a battery swapping station, and 2.3 % higher compared to the formed network with a fixed battery swapping station. During the outages, the MBSV is dispatched to the affected area to maximize the total power of restored loads, primarily due to the high priority of loads in this region. Simulations of network performance under long-term failure is conducted with limited and unlimited fuel. In both cases, batteries discharge after 8 h. With limited fuel, the network performance drops to 55 %, while with unlimited fuel, it drops to 40 %.
