Research On Optimal Scheduling Strategy For Intelligent Charging Of Electric Vehicles

In order to reduce fossil energy consumption and greenhouse gas emissions,get rid of energy import dependence,and create a clean,harmonious,efficient and beautiful living environment,the Chinese government solemnly promises to achieve carbon peaks by 2030 and carbon neutrality by 2060.Under this vision,focusing on the development and promotion of electric vehicles is one of the key measures.However,the main factors restricting the development of my country’s electric vehicles are concentrated in the lack of core key technologies,insufficient cruising range(low battery energy density),and imperfect supporting facilities(charging pile construction).Therefore,through reasonable planning and construction of centralized public charging stations,and formulating effective electric vehicle intelligent charging optimization scheduling strategies,while ensuring the benefits of charging stations,it provides each electric vehicle user with an optimal charging scheduling strategy.Minimize the waiting time in the queue as much as possible.This strategy will be an important means to alleviate the anxiety of charging for a long time in the future.Existing research on optimal scheduling of electric vehicle smart charging services mainly focuses on the location planning of charging stations,the impact of large-scale electric vehicle charging on the grid,orderly charging technology,and static path planning for electric vehicle charging.But in fact,the charging of electric vehicle users(demand side)and the income of charging station operators(supply side)are naturally opposed,and there is a game relationship between them.That is to say,the supply side expects all charging piles to run at full load and maximize revenue,and does not consider the user’s queuing situation;while the demand side expects to be able to charge directly or only need to wait for a small amount of time under the limited cruising range.A charging station that can be charged.Therefore,in order to alleviate the anxiety of charging,it is of great significance to study the two aspects of reducing the cost of charging and traveling for users and ensuring the economic benefits of operators to the greatest extent.First,conduct a demand analysis of the current travel needs of electric vehicle users and the improvement goals expected by charging station operators,and determine the factors affecting both parties during the charging process.Three indicators are analyzed from the perspective of electric vehicle users: total distance traveled,total time spent,and total charging cost.Among them,the focus is on the time that electric vehicle users wait in line for charging;the same analysis is done from the perspective of charging station operators Three indicators are given: charging station revenue,charging station service level,and charging station congestion.Finally,the corresponding weight coefficients are calculated for each influencing factor according to the research preferences and expected optimization direction,which lays the foundation for the subsequent establishment of the charging service optimization model.Secondly,according to demand analysis and weight calculation,the intelligent charging user model,operator model,global optimal comprehensive model and comprehensive model of non-cooperative game are established respectively.The first three models use the fuzzy programming method in the multi-objective optimization method to transform the multiobjective model,and then solve it according to the genetic algorithm;the final non-cooperative game comprehensive model uses the game idea,and both parties complete the search for advantageous strategies to reach Nash equilibrium.Finally,the four scheduling optimization models established in this paper are simulated and analyzed.It can be seen from the research results that,first of all,the electric vehicle smart charging user model can reduce the user’s travel cost and give the user a better charging experience compared to the single-target model.At the same time,the electric vehicle smart charging operator model A single-objective model can also improve the overall benefits of charging station operators.Secondly,the globally optimized intelligent charging integrated model of electric vehicles is compared with the models of electric vehicle users and operators,and a compromise optimization result is obtained.The solution can reduce the user’s travel cost while increasing the overall revenue of the charging station.Finally,the non-cooperative game integrated model and the global optimization integrated model are compared and analyzed,and it is found that users and operators make corresponding decisions based on the information they have obtained,although the overall situation cannot be achieved.Optimal,but the two parties reach a consensus on the basis of the balance of interests,and obtain a scheduling result that is actually acceptable to both parties.