Multi-Objective Optimization Scheduling for Electric Vehicle Charging and Discharging: Peak-Load Shifting Strategy Based on Monte Carlo Sampling

The uncoordinated charging behaviors of electric vehicles (EVs) challenge the stable operation of the grid, e.g., increasing the peak-to-valley ratio of the grid and diminishing power supply reliability. A Monte Carlo sampling method is employed to develop a charging behavior model for EVs to solve the problems raised by random charge mode. The probability densities of daily driving distance, initial charging time, charging power, and charging duration are incorporated and analyzed. The proposed model enables multiple random sample values for EVs, considering varying weather conditions and time-of-use electricity prices. For charge and discharge optimization, an EV charge and discharge scheduling model is constructed, aiming to balance multiple objective functions, including battery degradation costs, user charging costs, grid load fluctuations, and peak-to-valley differences. The weighting method is applied to transform the multi-objective framework into a single-objective comprehensive solution, facilitating the identification of optimal charge and discharge strategies. Results demonstrate that the Monte Carlo sampling can satisfactorily generate datasets with realistic characteristics on the driving range and charging initiation time of the EVs. Furthermore, the load results achieved through multi-objective optimization demonstrate that the proposed strategy effectively mitigates peak-to-valley disparities. The peak load reduction and trough load increment are 27.6% and 160.1%, respectively. Through post-peak load balancing, the average costs of each EV for daily charging and battery degradation are reduced to be 7.58 yuan and 15.68 yuan, respectively. This approach can significantly enhance the grid stability, simultaneously address the economic interests of users, and extend battery lifespan.