| As an important strategic emerging industry in China,the scale of electric vehicles is showing an accelerated growth trend.However,as the large-scale electric vehicles are connected to the power grid,the disorderly charging behavior will cause a series of impacts on the operational stability of the power grid,such as reducing the power quality and the load exceeding the limited capacity.Therefore,this paper investigates the orderly charging behaviors of EVs,taking into account the user’s electricity demand,travel characteristics,as well as the charging mode and charging and discharging time period of EVs,and uses the Monte Carlo method to establish the EV disorderly charging load model,the time-sharing tariff charging load model and the dual-mode coordination orderly charging and discharging model.In order to find the optimal parameters in the models,this paper improves the Sparrow Search Algorithm with Adaptive Collaborative Updating(SSA-ACU).The main contents are as follows:(1)In this paper,we establish the charging load model of electric vehicles and use the great likelihood estimation to fit the travel law of electric vehicles,and use the Monte Carlo method to simulate the travel behavior and charging load of electric vehicles.By simulating and analyzing the charging load under EV disorderly charging and time-sharing tariff strategy,the results show that the time-sharing tariff strategy can effectively improve the deficiency of EV disorderly charging behaviors,but the time-sharing tariff strategy has the problem of wave shifting and the strategy cannot guarantee the success rate of trips.To address the above problems,this paper proposes a dual-mode coordination charging and discharging strategy based on the SSA-ACU algorithm.(2)The dual-mode charging and discharging strategy based on the SSA-ACU algorithm is divided into two modes,mode 1 is an on-demand charging mode based on user travel behavior,which calculates the State of Charge(SOC)required for travel by obtaining the destination transfer in the travel plan as the spatial variable of the SOC and the mileage of the next section of travel with the destination transfer probability.mode 2 adopts the V2 G approach,using EVs as a distributed energy source,realizing bi-directional transmission between EVs and the grid,and minimizing charging costs with successful trips by optimizing the charging and discharging time periods,which makes the charging load curve flatten.(3)In order to calculate the optimal starting time period for charging and discharging,the traditional sparrow algorithm is improved and the SSA-ACU algorithm is proposed.The SSA-ACU algorithm initializes the population through a chaotic search strategy to make the population distribution more uniform,then introduces adaptive parameters to update the position of the leader to provide a more reliable direction for the search of the particles.Finally,the position of the concomitant is updated through a proposed affine transformation mechanism.Then the positions of the concomitants are updated by the proposed affine transformation mechanism to enhance the population exploitation.After that,the SSA-ACU algorithm is tested and compared with other sparrow search algorithm variants and classical algorithms using an international standard test suite to verify the performance of the SSA-ACU algorithm.The performance of the proposed charging strategy is analyzed in terms of three indicators: load-to-peak ratio,cost saving rate and convenience.The multi-objective problem is transformed into a single-objective multi-benefit model for both supply and demand benefits.It is proved that the dual-mode charging and discharging strategy based on the SSA-ACU algorithm has the highest comprehensive evaluation index,and effectively solves the problem of “peak on peak” and “peak shift” caused by the large-scale electric vehicles connected to the grid,and satisfies the purpose of successful.Not only to meet the purpose of going out,but also reduce the charge. |
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