In light of the growing challenges posed by the variable nature of wind and solar energy outputs alongside the inconsistent patterns of Electric Vehicle (EV) charging, this study introduces a groundbreaking approach to cooperative scheduling between wind-solar energy systems and EVs. Departing from traditional optimization models that primarily concentrate on operational efficiency, our research innovatively incorporates passenger travel preferences into the equation when determining EV charging demands, thereby optimizing renewable energy utilization.Our methodology involves employing a sophisticated optimization strategy that meticulously considers the stochastic behavior of wind and solar resources and unique EV characteristics. This process uses a state-of-the-art scenario construction technique to manage renewable energy uncertainties, complemented by an enhanced fast forward-backward elimination algorithm to efficiently condense the number of scenarios analyzed. We further refine this model by examining the travel behaviors and needs of EV users in a community setting to develop a user satisfaction metric that seamlessly aligns travel convenience with the financial implications of charging and discharging cycles.Significantly, our work has culminated in the establishment of a bi-level optimization framework that simultaneously reduces power system network losses and elevates overall user satisfaction concerning travel experiences. This novel model effectively orchestrates the integration of wind-solar energy outputs with EV charging necessities, thereby enhancing the economic robustness of the power system and ensuring a higher level of consumer satisfaction. Ultimately, this research contributes to the sustainable and efficient operation of future power systems where renewable energy and electric mobility play pivotal roles.