Multi-Timescale Loss Reduction Optimization Study of Distribution Network Considering Flexible Charging of Electric Vehicles

Aiming at the problems of uncertainty of wind and photovoltaic power output and disorderly charging of a large number of electric vehicles accessing the distribution network leading to the superposition of distribution network load peaks and the increase of network loss, a multi-timescale loss-reduction operation strategy of the distribution network considering electric vehicles is proposed. The probability distribution of EV behavior is established, and the EV charging output model conforming to the user's travel habit is built using the Latin hypercubic sampling method. Considering the influence of the deviation of the wind and photovoltaic output forecasts on different time scales, a multi-timescale optimal dispatch strategy based on the day-ahead-intraday is established, and a mathematical model is constructed by introducing the dynamic time-of-day tariff and using the network loss and the operating cost as the objective function. Different scenarios are simulated in the improved IEEE 33-bus system, and the comparative results show that the proposed optimization strategy is effective in reducing losses and smoothing load fluctuations, and the flexibility of EVs participating in the demand response of the grid reduces the impact of the wind and photovoltaic power integration while compensating for the deviation of the forecast of the wind and photovoltaic power outputs, thus maintaining the system balance.