电动汽车与光伏不确定性下的电力系统可靠性评估

In recent years, the adoption of electric vehicles (EVs) and variable energy resources such as photovoltaic (PV) has increased with the desire to reduce reliance on fossil fuels, decrease emissions, and promote sustainable energy. However, the increasing adoption of EVs and PVs has introduced unprecedented challenges to the reliability of power systems. The challenge lies in the inherent intermittency associated with solar generation and the uncertainty introduced by the charging load of EVs on the demand side of power grids. Therefore, it is indispensable from the perspective of power system operation and planning to consider the uncertainties associated with the output power of these resources in the reliability assessment framework. This paper develops an electric vehicle load model considering diverse charging station locations, EV types, and drivers' behavior. Also, the proposed method integrates the uncertainty of PV generation through interval prediction utilizing the K-Nearest Neighbors regressor. A sequential Monte Carlo simulation is used to analyze the impact of PV interval (forecasted lower and upper generation profile), EV load (hourly and peak), line failures, and demographic characteristics associated with EV on power system reliability. The reliability assessment is extended to sensitivity analysis and evaluation of the impact of EV loads and PV generation profiles on the capacity value of PV generators with different capacities, utilizing the Discrete Convolution approach. The proposed approach is demonstrated on the IEEE Reliability Test System and the results show the effectiveness of the proposed approach in determining the reliability of the power system by explicitly accommodating PV uncertainties and the intricacies of EVs.