Electrical Vehicle Charging Scheduling Strategy

As a new-type of transportation,electric vehicles(EVs)have enormous advantages over fuel vehicles in emission reduction and energy saving.Therefore,as environmental protection and oil resources are increasingly valued,many countries have introduced various policies to promote the development of EVs,which makes it a trend for EVs to replace fuel vehicles gradually.As the number of EVs is surging,their charging demands are also growing enormously.Charging stations(CSs)are the most common places to charge EVs.However,without proper management,the heavy charging load not only poses a serious threat to power grids due to sudden and uncertain spikes,but also causes tremendous charging cost and unsafety to CSs,which significantly degrades the efficiency of a CS.The disorderly charging behavior of EVs will not only affect the stability and efficiency of the power grid and CSs,but also transfer the charging cost to the owners of EVs,thus affecting the development of EVs.Therefore,it is imperative to implement effective coordinated scheduling strategies in CSs,considering that the charging demands of EVs are deferable,mobile and random.The main works of this dissertation are presented as follows:The Flask framework is used to build a data acquisition server to obtain real-time charging data from South China charging technology co.,ltd.According to the obtained charging data,the current charging habits of electric vehicle owners,the potential flexibility for charging scheduling and existing problems of the current charging mode are analyzed.This becomes a foundation for subsequent algorithm designs and simulations.In order to minimize the purchase cost of a CS,an offline charging scheduling algorithm is proposed,such that energy demands of all EVs are fulfilled before departure.However,as the offline algorithm requires global EV charging information,it is difficult to be applied in practice.Therefore,we proceed to design a simple,yet effective and implementable online algorithm that requires zero future information.Matlab simulations verify that the performance of the online scheduling algorithm optimization is similar to that of the offline scheduling algorithm.In order to further improve the performance of the online algorithm,a pricing mechanism and a second-stage scheduling strategy are proposed based on the online algorithm.There are three-fold benefits of the proposed pricing mechanism: providing discounts to users with high scheduling flexibility,ensuring fairness among EV users and the individually optimal strategies of EV users jointly minimize the cost of the CS.Numerical results based on real data further verify the effectiveness of the proposed online algorithm and the pricing mechanism.At the same time,the simulation results also show that the second-stage scheduling strategy can reduce the total charging time of EVs and improve the utilization rate of charging piles and user satisfaction.