Research On Electric Vehicle Charging Strategy Based On Two-stage Economic Dispatc

In recent years,with the development of industry,the problems of energy shortage and environmental pollution have become more and more serious.In the field of transportation,the emission of vehicle exhaust has become one of the factors that have become an increasingly serious environmental pollution problems.New energy electric vehicles due to their zero-emission nature are more popular.However,due to the increase in the number of electric vehicles,the problem of road congestion has followed,and “difficulty in charging” has become the biggest problem for many electric vehicle users.In addition,traffic congestion and improper charging strategies lead to excess energy dispatched to charging stations by the power generation side,resulting in loss of energy,thus causing serious energy waste.There are two main challenges in designing a charging strategy in an intelligent transportation system.Firstly,due to the time-varying road traffic network and the volatility of energy prices,it is difficult to devise a reasonable strategy to guide electric vehicles to choose the appropriate charging stations for charging.This strategy can not only improve the user’s charging satisfaction but also the remaining energy in the charging station can be consumed as much as possible.Secondly,it is also crucial to design a reasonable power economic dispatch strategy,which can not only meet the energy load demand of charging stations,but also accommodate the electricity generated by new energy generation as much as possible and reduce environmental pollution.To overcome the above challenges,this article proposes an intelligent charging model for electric vehicles based on two-stage economic dispatching.This model can not only provide a reasonable charging navigation strategy for electric vehicles,but also meet the economic needs of power economic dispatch,so as to achieve high-precision power dispatch.The main contributions of this article are as follows:Firstly,a charging model for electric vehicles considering power economic scheduling is proposed,which aims to maximize user charging satisfaction and energy consumption of charging stations while minimizing power generation costs and carbon emissions.Secondly,to effectively solve the proposed model,a three-step optimization framework is proposed for the first time,in which the first step provides the unit’s power generation plan through day-ahead dispatching,the second step improves the accuracy of power dispatching through intraday scheduling,and the third step selects the appropriate charging station for electric vehicles to realize charging dispatching.Thirdly,we respectively use the deep reinforcement learning algorithms based on the proximal policy optimization to obtain the optimal dispatching results in the three-step optimization process.From the experimental results,our proposed algorithm is superior to other deep reinforcement learning algorithms in terms of the convergence speed and actual dispatching results.