Research On Cooperative Scheduling Strategy Of Renewable Energy And Electric Vehicles

In recent years,with the problems of environmental pollution and energy shortage becoming more and more serious,renewable energy power generation and electric vehicles have spread rapidly and developed rapidly around the world.However,the randomness and fluctuation of renewable energy grid connection,and the randomness and intermittentness of electric vehicle charging have brought tremendous pressure to the safe and stable operation of the distribution network.First,the Monte Carlo simulation method is used to analyze the negative impact of electric vehicle disorderly charging on the power grid.The analysis results show that:electric vehicle disorderly charging will increase the system load peak and valley difference.With the expansion of the scale of electric vehicles,the impact on the system load peak and valley difference is increasing.In order to avoid this effect,a collaborative scheduling scheme for electric vehicles and renewable energy based on the concept of V2G is proposed.By implementing a reasonable time-sharing electricity price system at charging stations,electric vehicle users are attracted to go to charging stations during peak and valley electricity price periods.So as to concentrate electric vehicles,and the dispatch center will formulate electric vehicle charging and discharging plans according to certain strategies.Secondly,it introduces the collaborative optimization scheduling strategy of renewable energy and electric vehicles in detail.This strategy takes into account the reactive power of renewable energy grid-connected inverters and electric vehicle charging piles,fully considers the space-time distribution characteristics of electric vehicles,and establishes a two-layer optimization model.The upper model optimizes the charging and discharging power of the electric vehicle in the system from time to time,based on the updated renewable energy output and load demand forecast values,combined with the user's reservation information.The lower model performs rolling optimization of the power of each renewable energy source and each electric vehicle charging station in each period.In addition,in order to avoid the influence of various uncertain factors in the scheduling process as much as possible,a model predictive control method is adopted,and a feedback correction link is added.Finally,the solution of the optimization model is introduced in detail.The quadratic programming and the second-order cone programming are used to solve the upper and lower models respectively.The solution speed is fast and the accuracy is high.The improved IEEE33 node distribution network system is used for simulation.and the simulation results show that the strategy can effectively reduce the load peak and valley difference and reduce the active power loss of the system and improve the voltage level of the network.