الفهرس 
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Abstract
Electric vehicles (EV) usage is growing recently due to environmental concerns and lower running costs; however, their limited range and long charging time are two major problems compared to gasoline-based vehicles. Therefore, incorporating more DC fastcharging (DCFC) stations into modern electric grid is expected to overcome these limitations. Latest standards for DCFC stations enable up to 900 kW of charging power per EV. Unfortunately, for a limited-capacity isolated Microgrid (MG), this high amount of power may lead to MG instability. In this thesis, a fully distributed event-triggered dynamic consensus technique is proposed for controlling the DCFC process. The proposed control consists of three phases: estimation phase, determination phase, and tracking phase. The estimation phase estimates the MG sparse capacity (the difference between the MG rating and the total demand). The determination phase determines a proper charging rate considering the estimated real-time sparse capacity of the MG. Meanwhile, a safety factor is used to account for the system losses, demand growth, and sparse capacity estimation error. The safety factor sizing methodology is provided. Finally, the tracking phase charges the EV using the determined charging rate. Moreover, the admissible communication delay effect is evaluated. The control scheme is validated using a MATLAB/Simulink model. The model involves a limited-capacity isolated MG, the DCFC station, and multiple EVs. The physical layer of this MG consists of two grid-forming units, a single grid-following unit and a lumped load. Moreover, a ring communication network forms the MG cyber-layer, which is used for information exchange between the agents. Different case studies with different conditions are simulated to validate the control scheme. The case studies involve load perturbation, concurrent arrival, charging interruption, generation change, communication delay effect, and broken communication links effect. The load perturbation is used to evaluate the controller performance upon sudden load changes in the MG at different timestamps namely: at beginning of the estimation phase, near estimation phase convergence, and at bottleneck condition with all the EVs charging. The concurrent arrival case demonstrates the ability of the controller to handle multiple EVs arriving at the same instant without destabilizing the grid. The charging interruption case covers the effect of EV charging interrupting on other EVs estimation phase. The generation change case studies the controller robustness to alternation of the injected power and transients due to unit outage. The communication delay case embraces a case with different communication delays. It proves the controller durability at high communication delays. The broken communication link effect is evaluated using two broken communication links cases; the first case encompasses isolation of a generation unit from the cyber network, while the second case tackles isolation of the DCFC station agent. The simulated cases show success of the proposed control strategy to have successful fast vehicle charging under different operating conditions without loss of the MG stability.