Research On Optimal Energy And Vehicle Scheduling Of Electric Vehicle Charging Station Aggregator On Temporal And Spatial Dimensions

Increasing shortages of traditional energy sources,increasing environmental problems,and the rapid increase in transportation energy demand have caused worldwide attention to electric vehicles(EVs).With the rapid expansion of the EV market,unreasonable charging energy management has seriously impacted the safe and stable operation of the power system and the problem of traffic jams affecting the transportation network,which has caused social problems and seriously hindered the development of electric vehicles.Therefore,reasonable pricing of charging stations and charging management of charging loads are important means to achieve collaboration between transportation systems and power systems.This article is based on the optimization of the time and space of EV charging stations to realize the interaction between power and transportation systems.The charging side of the charging station guides the EV’s traffic flow distribution and charging load distribution through a reasonable charging pricing strategy.At the same time,the charging load will affect the charging pricing;the supply side optimizes the combination of self-generation,distributed electrical energy and market power purchase to realize the benefits of the charging station Maximize and reduce the impact on power system congestion.Therefore,based on the three perspectives of energy management,coordinated scheduling and shared optimization,this paper proposes a space-time optimization management method for EV charging station clusters.The specific work is as follows:First of all,an energy management strategy for charging optimization is proposed for EV charging stations.The unique characteristics of EV charging loads provide flexibility for energy management of EV charging stations,while its self-generation,distributed renewable energy and power market procurement decisions provide a variety of power supply combinations to meet charging loads.In order to manage the related operational risks and obtain the maximum operating benefits of the charging station,operators need to optimize the scheduling of energy on both sides of supply and demand.This paper proposes a random energy management method for EV charging stations.The load side makes decisions about charging load scheduling and charging pricing,while the supply side optimizes the energy supply mix for energy procurement and internal power generation in a multi-time-scale market.In addition,the model takes into account the uncertainty of spot market prices and renewable energy generation.Secondly,from the perspective of time and space,the interaction and coordination between the EV charging station and the EV,that is,the power system and the transportation system are studied.This paper proposes a model for coordinating the interaction between two non-cooperative subjects of EV charging stations and EV clusters without sharing private information.In the EV charging station model,the energy management of the charging station cluster is performed according to the EV path selection and charging management situation;in the EV model,the best strategy for EV route selection is decided according to the charging pricing of the EV charging station.The effectiveness and advantages of the proposed model are verified through cases.Compared with three other traditional methods,the model used can reduce the total cost by 78.3%.Finally,shared electric vehicles provide high-quality services at a lower cost to replace personal transportation in large cities,which has a positive impact on safety,parking infrastructure and congestion,greatly reducing environmental pollution and improving the efficiency of SEV use.However,at present,there is a lack of good guiding measures for vehicle scheduling and charging management of shared electric vehicles.In view of the problems of high-cost charging stations and the inability to meet user needs caused by the unreasonable charging scheduling of shared electric vehicles and vehicles,this paper proposes an optimal scheduling model based on the power-transport system in the sharing mode.On the one hand,based on the network flow model considering traffic conditions,it can simultaneously manage vehicle scheduling,route selection,and charging optimization of shared electric vehicles on the premise of satisfying user needs.On the other hand,the cost/benefit balance of the two decision-making bodies of charging stations and shared electric vehicles is achieved to reach the Nash equilibrium point.Finally,in order to solve the problem of nonlinearity of equilibrium model,deep reinforcement learning algorithm is used to solve.The case verifies the feasibility of the proposed equilibrium model and the convergence of the algorithm.