Impact Assessment of Electric Vehicles Integration and Optimal Charging Schemes Under Uncertainty: A Case Study of Qatar

The integration of electric vehicles (EVs) is rapidly growing compared to conventional vehicles in Qatar. To assess how these electric vehicles will impact Qatar’s distribution network, it is necessary to accurately model EV loads. However, EV loads exhibit uncertainties due to driving behaviour in charging time, state of charge (SOC), number of trips, and distance travelled. This necessitates the development of a probabilistic model. The Monte-Carlo method is employed to predict EV charging profiles probabilistically. The generated EV load profiles are assigned to different sectors and compared with the base case voltage profile curve. The IEEE-33 bus system is utilized to evaluate EV impacts considering the load pattern of Qatar. EV load profile generation is performed using MATLAB software, and impact assessment is conducted in DIgSILENT software. The results indicate that following EV integration, the system’s voltage profile experiences drops in the early morning and afternoon. A proposed charging scheme (R2), coupled with the integration of solar PV into the system, can mitigate this voltage drop issue. The PV panels have a rating of 1503 kW and are connected to the 14th bus. In Qatar, the hot summer months span from June to September, so the average PV generation data for September is used. The implementation of the proposed reward charging scheme improves system performance in terms of the voltage profile, ensuring grid resilience.