Research On Optimization Charging Strategy Of Electric Vehicles In Distribution Network Based On Profit Chain

To cope with the dual pressure on energy and environment,the development of new energy vehicles(EVs)has become an important trend in the future of transportation.China's "New Energy Vehicle Industry Development Plan(2021-2035)" proposes that by 2035,pure electric vehicles will become the mainstream of new vehicles sold,and public sector vehicles will be fully electric.In the wave of "new infrastructure",the impact on the power distribution system which brought by the large-scale application of EVs,cannot be ignored.The charging load of EVs has strong spatial and temporal uncertainty,and the random access of large number of EV charging loads increases the difficulty of system operation control.While through reasonable control strategies and guidance measures for EV charging,it can not only improve the economy and stability of the distribution network,but also improve the ability of the distribution grid to accept renewable energy.The charging process of EVs involves multiple stakeholders,and the needs of each stakeholder are both overlapping and conflicting.Therefore,giving full play to the positive role of EVs in the distribution network and taking into account the needs of different stakeholders is important for the promotion of EVs.Based on the prediction of EV charging load,this paper studies the optimization strategy of EV charging from the perspective of different stakeholders,and the main contents are summarized as follows.(1)The EV charging load prediction and characteristic analysis are carried out.Taking household EVs electric private as the research object,the influencing factors such as battery characteristics,charging mode and user behavior are considered,and then based on residential travel statistics and travel chain theory,the model of EV charging load under one charge per day and multiple charges per day scenarios are established to lay the foundation for the subsequent charging optimization strategy research.(2)The impact of random access of EVs on the distribution network is analyzed,and from the perspective of the interests of the distribution network operators,the optimization strategy of EV charging considering peak shaving and valley filling is proposed.In the case of one charge per day,the charging optimization strategy is proposed to take into account the charging delay of EVs;in the case of multiple charges per day,the charging optimization strategy is proposed to take into account the spatial and temporal shifting of EVs.The charging optimization model is constructed with the objective function of reducing the peak-valley difference of load,and the feasibility and effectiveness of the proposed strategy are verified by the simulation case of IEEE33 distribution system.(3)An EV charging optimization strategy based on dynamic electricity price guidance in charging stations is proposed from the perspective of EV users' interests.In view of the rapid growth of EV charging demand,the increasing power supply pressure of transformer in charging stations,and new load spikes are easily generated under the existing time-of-use(TOU)pricing strategy,a dynamic spike period as well as a spike markup is introduced into the user charging price with reference to the existing critical peak pricing(CPP)mechanism,and the dynamic pricing scheme is formulated.With the objective of reducing the charging cost of users,an EV charging optimization model is established,and the feasibility and effectiveness of the proposed strategy is verified by simulation cases.(4)Considering the situation that there are conflicting interests between the power supply side and the user side in the profit chain of EVs accessed to the network,an EV charging optimization strategy based on stackelberg game is proposed.After the analysis of the respective interests of the power supply side and the user side,the stackelberg game model is established with the charging station operator as the dominant player and the EV users as the followers.The upper layer aims at maximizing the charging station revenue and taking into account the peak-valley difference penalty of the distribution network,while the lower layer aims at minimizing the charging cost of EV users.The two-layer optimization algorithm is adopted to solve the charging price and charging plan hierarchically.Simulation results show that the proposed strategy can achieve a win-win situation between the power supply side and the user side.(5)In order to reduce the impact of the uncertainty of renewable energy output on the system,a price-based demand response charging strategy is proposed for EVs and renewable energy cooptimization.The set pair analysis theory is introduced to deal with the uncertainty of renewable energy,the uncertain relation number model of wind power output and photovoltaic output is established.And the EVs are considered as demand-side loads to participate in charging optimization based on real-time electricity price.With the fluctuation index of renewable energy output and charging cost of users as the objective function,a benefit co-optimization model is established and solved by immune genetic algorithm,and the effectiveness of the proposed model and strategy is verified by comparison of arithmetic cases.This thesis systematically investigates the characteristics of EV charging load,the EV optimization strategy for different stakeholders and the synergistic optimization strategy of EV charging and renewable energy.The research results can provide guidance for EV charging load management in different scenarios,which is conducive to giving full play to the positive effect of EVs accessed to the grid and enhancing the promotion value of EVs.