As an important infrastructure of electric vehicles (EVs), EV fast charging station is of great significance in the popularization and development of EVs. Through the analysis of the characteristics of EV’s arriving time and charging duration in fast charging station, the stochastic service system was introduced and the queuing system model based on queuing theory was established. By calculating the indexes of the queuing system model, the desire model was used to optimize the number of EV chargers, which could save customers’ waiting time and reduce the investment of charging station. Finally, an example was simulated and calculated with MATLAB used as a simulation tool to verify the effectiveness of this approach.

A newly constrained multi-objective differential evolution optimization technique (CMODE) for security constrained economic/environmental dispatch (EED) was proposed. The proposed CMODE evolved a constrained multi-objective version of differential evolution (DE) by employing the traditional multi-objective differential evolution (DEMO) and constrain handle technique to balance the search between feasible region and infeasible region. The proposed CMODE method had been applied to solve the security constrained EED problem. Experiments had been carried on a standard test system. The results demonstrate the high efficiency of the proposed method to solve security constrained EED problem, and the necessary of taking security constrains into consideration.

Considering the randomness of wind speed and wind direction, the partial wake effect between wind turbines (WTs) in complex terrain was analyzed and a multiple wake model in complex terrain was established. Taking the power output characteristic of WT into consideration, a wind farm reliability model which considered the outages of connection cables was presented. The model is implemented in MATLAB using sequential Monte Carlo simulation and the results show that this model corrects the power output of wind farm, while improving the accuracy of wind farm reliability model.