A day-ahead scheduling strategy is proposed for a fast charging station with battery energy storage and PV. Considering the uncertainty of PV output and fast charging demand, a scenario-based stochastic optimization method is applied to minimize the expected operation cost by determining the reference value of power exchange with the grid in the next day under limited battery life loss. The sequential Monte Carlo simulation method is used to generate fast charging demand scenarios under the assumption of non-homogeneous Poisson process. A piecewise linear calculation model of battery energy storage life loss is used to quantify the battery life loss related to the depth of discharge. The case study verifies the effectiveness of the proposed strategy in peak shaving according to electricity price signals by using energy storage systems.