Multi objective optimization of grid connected photovoltaic and V2G operation based on the influence of schedulable capacity

The disorderly charging of large-scale electric vehicles (EVs) connected to the power grid will lead to excessive load variance in the distribution network system. The dual attributes of load and energy storage possessed by EVs can reduce the load variance of the power grid and achieve efficient utilization of green electricity, in which EV’s scheduling capacity is an important factor affecting the application of vehicle to grid interaction. First, the Manhattan distance in Niche technology was employed to calculate the crowding degree of multi-objective particle swarm optimization (MOPSO), in order to solve the ordered charging and discharging load of EVs at different scheduling capacity in the article. Secondly, the impact of time of use (TOU) electricity pricing mechanism and incentive pricing mechanism on V2G revenue was compared. Finally, the proposed model was simulated and validated using the typical daily basic electricity load and photovoltaic power generation load curves of a certain region. The results indicate that the photovoltaic consumption rate of EVs gradually decreases, and the load variance shows a trend of first decreasing and then increasing as the EV scheduling capacity increases in the workplace. Niche technology optimizes load variance and peak load at the same scheduling capacity, resulting in reduced charging costs or increased revenue for drivers. In addition, the revenue under V2G incentive pricing mechanism is much greater than that under TOU electricity pricing mechanism.