Monte Carlo Sampling of Electric Vehicle Charging Duration Distribution

Monte Carlo simulation for electric vehicle charging duration distribution is a probability-based numerical computation method used to predict the stochastic characteristics of charging behavior. Its core concept involves approximating real-world charging time patterns through extensive random sampling.

Key implementation aspects include: Probability Model Construction - Requires determining the probability distribution type (e.g., normal distribution, Weibull distribution) based on historical data or assumptions, along with setting corresponding distribution parameters Random Number Generation - Utilizes uniformly distributed random numbers as seeds, converting them into target distribution samples through methods like inverse transform sampling Batch Simulation - Generates tens of thousands of sampling results through iterative loops, forming statistically significant charging duration datasets

The method's value lies in its ability to reflect charging behavior randomness, making it suitable for applications like grid load forecasting and charging station utilization analysis. Practical implementation requires careful calibration of probability models to ensure distribution assumptions align with actual charging behavior characteristics.