Operation Control Method of Charging Load on the Edge Side Considering the Dynamic Accommodation Capacity of the Distribution Network

Abstract

A method for controlling the operation of the edge-side charging load considering the dynamic acceptance capacity of the distribution network is proposed to solve the problem of limited acceptance capacity of electric vehicles in residential distribution networks. It proposes multiple types of evaluation indicators to evaluate the acceptance capacity of electric vehicles in residential areas from the perspective of time-slot operation and overall operation. At the same time, the charging costs of electric vehicle users are considered, and an edge-side charging load operation control model considering the dynamic acceptance capacity of the distribution network is established to optimize the charging power of electric vehicles on the edge of the residential distribution network. Further considering the user's willingness, the charging load operation control strategy on the edge of the residential distribution network is corrected to effectively improve the acceptance capacity of electric vehicles in residential areas and the safety of system operation.

Description

Technical Field

[0001] The present invention relates to a method for operating and controlling charging loads on the edge side of a distribution network. In particular, it relates to a method for operating and controlling charging loads on the edge side considering the dynamic acceptance capacity of the distribution network. Background Art

[0002] With the increasing number of electric vehicles and the gradual improvement of basic charging facilities for electric vehicles, the charging load of residential distribution networks will increase sharply. At the same time, the disorderly charging behavior of a large number of electric vehicles will increase the risk of overload of distribution transformers, making the distribution transformers prone to damage and challenging the operation safety of residential distribution networks. On the premise of ensuring the safe and stable operation of the system, due to the limitation of the rated capacity of distribution transformers, the existing residential distribution networks have limited acceptance capacity for electric vehicles, and the charging demands of a large number of electric vehicles cannot be met. Therefore, it is necessary to evaluate the capacity of current residential distribution networks to accept electric vehicles and formulate reasonable charging load operation control strategies to ensure the flexible and efficient operation of residential distribution networks.

[0003] At the same time, the access of large-scale charging loads in residential areas will lead to a sharp increase in communication data volume, posing huge challenges to traditional regulation methods and information processing modes. The emergence of edge computing has solved such problems. By deploying edge computing devices on the low-voltage side of distribution transformers in residential distribution networks, the operation of electric vehicles can be flexibly controlled to achieve effective management of charging loads in residential areas.

[0004] To solve the above problems, a method for operating and controlling charging loads on the edge side considering the dynamic acceptance capacity of the distribution network is proposed, which can achieve flexible control and effective management of the operation of electric vehicles on the edge side, improve the acceptance capacity of electric vehicles in residential areas, and ensure the flexible and efficient operation of residential distribution networks. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method for operating and controlling charging loads on the edge side considering the dynamic acceptance capacity of the distribution network, which can achieve flexible control and effective management of the operation of electric vehicles on the edge side.

[0006] The technical solution adopted by the present invention is as follows: A method for operating and controlling charging loads on the edge side considering the dynamic acceptance capacity of the distribution network includes the following steps:

[0007] 1) Based on the selected residential distribution network with electric vehicle charging loads that realizes data acquisition, storage, analysis, calculation, and optimization control by the distribution network edge computing device, input the basic parameter information of the residential distribution network and the operation parameters of electric vehicles, including the basic load of the residential area, time-of-use electricity price in the residential area, rated capacity of the distribution transformer, maximum charging power of the electric vehicle, battery capacity of the electric vehicle, arrival time, departure time, and initial state of charge of the electric vehicle;

[0008] 2) Based on the basic parameter information of the residential distribution network and the operation parameters of electric vehicles in step 1), establish an evaluation index formula for the acceptance capacity of electric vehicles on the edge side of the distribution network. The evaluation indexes for the acceptance capacity of electric vehicles on the edge side of the distribution network include the load rate of distribution transformers at each time period, the system load fluctuation margin, and the heavy-load duration of distribution transformers.

[0009] 3) Based on the evaluation indexes for the acceptance capacity of electric vehicles on the edge side of the distribution network in step 2), establish an operation control model for the charging load on the edge side considering the dynamic acceptance capacity of the distribution network, including setting the sum of the charging cost of electric vehicle users, the compensation cost for uncompleted user charging, and the penalty cost for the over-limit of the load rate of distribution transformers as the objective function, and considering the charging constraints of electric vehicles and the operation constraints of distribution transformers respectively.

[0010] 4) Based on the operation control model for the charging load on the edge side considering the dynamic acceptance capacity of the distribution network in step 3), optimize the charging power of electric vehicles to obtain the charging results when electric vehicle users participate in the operation control of the charging load on the edge side; at the same time, without controlling the electric vehicles in the residential area, obtain the charging results when electric vehicle users do not participate in the operation control of the charging load on the edge side.

[0011] 5) Based on the charging results when electric vehicle users participate in the operation control of the charging load on the edge side and the charging results when electric vehicle users do not participate in the operation control of the charging load on the edge side in step 4), establish an operation control correction strategy for the charging load on the edge side considering the willingness of electric vehicle users, correct the charging behavior of electric vehicles, and obtain the final charging results of the operation control of the charging load on the edge side.

[0012] 6) Based on the final charging results of the operation control of the charging load on the edge side in step 5), obtain the evaluation indexes for the acceptance capacity of electric vehicles on the edge side of the distribution network in step 2), and evaluate the acceptance capacity of the residential distribution network for electric vehicles.

[0013] The operation control method for the charging load on the edge side considering the dynamic acceptance capacity of the distribution network of the present invention aims to solve the problem of limited acceptance capacity of the residential distribution network for electric vehicles. It proposes evaluation indexes for the acceptance capacity of electric vehicles on the edge side, dynamically evaluates the acceptance capacity of electric vehicles in the residential area, and combines the dynamic evaluation indexes to establish an operation control model for the charging load on the edge side considering the dynamic acceptance capacity of the distribution network. In addition, further considering the willingness of users to participate, it establishes an operation control correction strategy for the charging load on the edge side considering the willingness of electric vehicle users, corrects the charging behavior of electric vehicles, effectively improves the acceptance capacity of electric vehicles in the residential area, and ensures the flexible and efficient operation of the residential distribution network.

[0014] The present invention can effectively optimize the charging power of the electric vehicle charging load on the edge side of the residential distribution network and manage it effectively, thereby improving the acceptance capacity of electric vehicles in the residential area and ensuring the flexible and efficient operation of the residential distribution network. Description of the Drawings

[0015] Figure 1 is a flowchart of the operation control method for the edge-side charging load considering the dynamic acceptance capacity of the distribution network according to the present invention;

[0016] Figure 2 is the basic load curve of the residential area;

[0017] Figure 3a is the probability distribution curve of the arrival time and departure time of electric vehicles;

[0018] Figure 3b is the probability distribution curve of the initial state of charge of electric vehicles;

[0019] Figure 4 is the time-of-use electricity price curve of the residential area;

[0020] Figure 5a is the membership function graph of the difference in the state of charge;

[0021] Figure 5b is the membership function graph of the difference in charging cost;

[0022] Figure 5c is the membership function graph of the charging probability for electric vehicle users to participate in the operation control of the edge-side charging load;

[0023] Figure 6 is the surface graph of the fuzzy inference system for the charging probability of electric vehicle users to participate in charging;

[0024] Figure 7 is the system power comparison graph of Scheme I and Scheme II. Detailed Embodiment

[0025] The following will make a detailed description of the operation control method for the edge-side charging load considering the dynamic acceptance capacity of the distribution network according to the present invention in conjunction with the embodiments and the drawings.

[0026] As Figure 1 shown, the operation control method for the edge-side charging load considering the dynamic acceptance capacity of the distribution network according to the present invention includes the following steps:

[0027] 1) According to the selected residential distribution network with electric vehicle charging load that realizes data acquisition, storage, analysis, calculation, and optimization control by the edge computing device of the distribution network, input the basic parameter information of the residential distribution network and the operating parameters of electric vehicles, including the basic load of the residential area, time-of-use electricity price of the residential area, rated capacity of the distribution transformer, maximum charging power of the electric vehicle, battery capacity of the electric vehicle, arrival time, departure time, and initial state of charge of the electric vehicle;

[0028] For the embodiments of the present invention, the rated capacity of the residential distribution transformer is 2500 kVA, the power factor is 0.95, and the basic load curve of the residential area is as Figure 2 shown. The maximum charging power of the electric vehicle is 7 kW, the battery capacity of the electric vehicle is 48 kWh, and the arrival time, departure time, and initial state of charge of the electric vehicle all follow a normal distribution, as shown in Figure 3a and 3b respectively; the time-of-use electricity price of the residential distribution network is shown in Figure 4 .

[0029] 2) Based on the basic parameter information of the residential distribution network and the operating parameters of electric vehicles in step 1), establish an evaluation index formula for the acceptance capacity of electric vehicles on the edge side of the distribution network. The evaluation indexes for the acceptance capacity of electric vehicles on the edge side of the distribution network include the load rate of the distribution transformer in each period, the system load fluctuation margin, and the heavy-load duration of the distribution transformer; among them,

[0030] The evaluation index of the load rate of the distribution transformer in each period is expressed as:

[0031]

[0032] The evaluation index of the system load fluctuation margin is expressed as:

[0033]

[0034] The evaluation index of the heavy-load duration of the distribution transformer is expressed as:

[0035]

[0036] In the formula, β t represents the load rate of the distribution transformer at time t; N EV represents the total number of electric vehicles; represents the charging power of the nth electric vehicle at time t; represents the basic load of the residential area at time t; represents the rated power of the distribution transformer; M RDN represents the system load fluctuation margin; N T represents the total optimization period; represents the heavy-load duration of the distribution transformer; Indicates the overload flag of the distribution transformer during period t; Δt is the length of the optimization period;

[0037] Among them, the rated power of the distribution transformer Is expressed as:

[0038]

[0039] In the formula, Indicates the rated capacity of the distribution transformer; Indicates the power factor;

[0040] The overload flag of the distribution transformer during period t Is expressed as:

[0041]

[0042] In the formula, β t Indicates the load rate of the distribution transformer during period t.

[0043] 3) Based on the evaluation index of the electric vehicle acceptance capacity on the edge side of the distribution network in step 2), establish an operation control model for the edge-side charging load considering the dynamic acceptance capacity of the distribution network, including setting the sum of the charging cost of electric vehicle users, the compensation cost for incomplete user charging, and the penalty cost for the overload of the distribution transformer load rate to be minimized as the objective function, and considering the charging constraints of electric vehicles and the operation constraints of the distribution transformer respectively; among them,

[0044] (1) The above-mentioned setting of the sum of the charging cost of electric vehicle users, the compensation cost for incomplete user charging, and the penalty cost for the overload of the distribution transformer load rate to be minimized as the objective function is expressed as:

[0045] minF = f1 + f2 + f3 (6)

[0046]

[0047]

[0048]

[0049]

[0050]

[0051] In the formula, F represents the objective function; f1 represents the charging cost of electric vehicle users; f2 represents the compensation cost for incomplete charging of electric vehicle users; f3 represents the penalty cost for the overload of the distribution transformer load rate; Represents the charging power of the nth electric vehicle during period t; N T Represents the total optimization period; N EVrepresents the total number of electric vehicles; c t is the time-of-use electricity price in the residential area during period t; Δt is the length of the optimization period; C EV (·) represents the compensation coefficient corresponding to different state of charge when the electric vehicle user leaves the residential area; E EV,exp represents the expected value of the electric vehicle battery capacity; represents the charging priority of the nth electric vehicle; c EV ,max represents the limit value of the compensation coefficient; represents the state of charge of the nth electric vehicle when leaving the residential area; s exp represents the expected value of the state of charge when the electric vehicle leaves the residential area; C TF (·) represents the penalty coefficient corresponding to different load rates of the distribution transformer; represents the basic load in the residential area during period t; β t represents the load rate of the distribution transformer during period t; β represents the load rate of the distribution transformer corresponding to the starting value of the penalty coefficient; c TF,max represents the limit value of the penalty coefficient;

[0052] Among them, the charging priority of the nth electric vehicle is expressed as:

[0053]

[0054]

[0055]

[0056]

[0057] In the formula, α1, α2, α3 represent the weight coefficients of each index, satisfying α1 + α2 + α3 = 1; When it is 0, it represents the lowest priority, and when it is 1, it represents the highest priority; represents the departure time index of the nth electric vehicle; represents the residence duration index of the nth electric vehicle; represents the charging demand index of the nth electric vehicle; represents the maximum value of the departure time of electric vehicles in the residential area; represents the minimum value of the departure time of electric vehicles in the residential area; represents the departure time of the nth electric vehicle in the residential area; represents the maximum value of the initial state of charge of electric vehicles in the residential area; represents the minimum value of the initial state of charge of electric vehicles in the residential area; represents the initial state of charge of the nth electric vehicle in the residential area; Represents the maximum residence time of electric vehicles in the residential area; Represents the minimum residence time of electric vehicles in the residential area; Represents the residence time of the nth electric vehicle in the residential area;

[0058] The state of charge of the nth electric vehicle when leaving the residential area Is expressed as:

[0059]

[0060] In the formula, Represents the initial state of charge of the nth electric vehicle in the residential area; Represents the arrival time of the nth electric vehicle in the residential area; Represents the departure time of the nth electric vehicle in the residential area; η EV Represents the charging efficiency of the electric vehicle; E EV,cap Represents the battery capacity of the electric vehicle; Represents the charging power of the nth electric vehicle in the t period;

[0061] The expected value E of the battery capacity of the electric vehicle EV,exp Is expressed as:

[0062] E EV,exp = s exp ·E EV,cap (17)

[0063] In the formula, s exp Represents the expected value of the state of charge of the electric vehicle when leaving the residential area;

[0064] Introduce auxiliary variables into formula (8) containing non - linear terms For linearization, and add constraints:

[0065]

[0066]

[0067]

[0068] Introduce auxiliary variables into formula (10) containing non - linear terms For linearization, and add constraints:

[0069]

[0070]

[0071]

[0072] In the formula, represents an auxiliary variable related to the compensation cost for the incomplete charging of the nth electric vehicle user; represents an auxiliary variable related to the penalty cost for the over-limit load rate of the distribution transformer in the t period.

[0073] (2) The charging constraint of the electric vehicle is expressed as:

[0074]

[0075]

[0076]

[0077]

[0078] In the formula, represents the maximum charging power of the electric vehicle; s n,t represents the state of charge of the nth electric vehicle in the t period; s n,t-1 represents the state of charge of the nth electric vehicle in the t-1 period; Δt is the length of the optimization period; s exp represents the expected value of the state of charge when the electric vehicle leaves the residential area; represents the upper limit of the state of charge of the electric vehicle; s represents the lower limit of the state of charge of the electric vehicle; represents the initial state of charge of the nth electric vehicle in the residential area; represents the residence time of the nth electric vehicle in the residential area; represents the arrival time of the nth electric vehicle in the residential area; represents the departure time of the nth electric vehicle in the residential area; η EV represents the charging efficiency of the electric vehicle; E EV,cap represents the battery capacity of the electric vehicle; represents the charging power of the nth electric vehicle in the t period.

[0079] (3) The operation constraint of the distribution transformer is expressed as:

[0080]

[0081] In the formula, represents the charging power of the nth electric vehicle in the t period; N EV represents the total number of electric vehicles; represents the basic load of the residential area in the t period; represents the rated power of the distribution transformer.

[0082] 4) Based on the edge - side charging load operation control model considering the dynamic acceptance capacity of the distribution network in step 3), optimize the charging power of electric vehicles to obtain the charging results when electric vehicle users participate in the edge - side charging load operation control; at the same time, without controlling the electric vehicles in residential areas, obtain the charging results when electric vehicle users do not participate in the edge - side charging load operation control;

[0083] 5) Based on the charging results when electric vehicle users participate in the edge - side charging load operation control and the charging results when electric vehicle users do not participate in the edge - side charging load operation control in step 4), establish an edge - side charging load operation control correction strategy considering the willingness of electric vehicle users, correct the charging behavior of electric vehicles, and obtain the final charging results of the edge - side charging load operation control;

[0084] (1) According to the charging results when electric vehicle users participate in the edge - side charging load operation control and the charging results when electric vehicle users do not participate in the edge - side charging load operation control in step 4), use the fuzzy inference method to obtain the charging probability of electric vehicle users participating in the edge - side charging load operation control, including:

[0085] Based on the charging results, select two main influencing factors, namely the ratio of the difference in the state of charge of electric vehicles under non - participation and participation in the edge - side charging load operation control and the ratio of charging costs, as the input variables of fuzzy inference, and take the charging probability of electric vehicle users participating in the edge - side charging load operation control as the output variable of fuzzy inference. Use a fuzzy logic controller to implement the inference process of electric vehicle users participating in the edge - side charging load operation control; the implementation of the inference process using the fuzzy logic controller is as follows:

[0086] First, fuzzify the variables. Specifically, convert the input and output variables of fuzzy inference into fuzzy variables through membership functions, and select Z - type functions, bilateral Gaussian functions, and S - type functions as membership functions; then set the fuzzy inference rules: that is, establish fuzzy inference rules based on the principle that the smaller the difference in the state of charge of the two electric vehicles and the larger the ratio of charging costs, the higher the willingness of electric vehicle users to participate; finally, obtain the precise output of the fuzzy logic controller through defuzzification to obtain the charging probability of electric vehicle users participating in the edge - side charging load operation control; where,

[0087] The Z - type membership function is expressed as:

[0088]

[0089] In the formula, z1 and z2 represent the shape parameters in the Z - type function, which are used to determine the curve shape.

[0090] The bilateral Gaussian membership function is expressed as:

[0091]

[0092] In the formula, σ and c represent the parameters in the bilateral Gaussian function, σ represents the standard deviation of the Gaussian function, and c represents the mean value of the Gaussian function.

[0093] The described S-type membership function is expressed as:

[0094]

[0095] In the formula, s1 and s2 represent the shape parameters in the S-type function, which are used to determine the curve shape.

[0096] (2) Correct the charging selection behavior of electric vehicle users according to the charging probability of electric vehicle users participating in the operation control of the edge-side charging load; charge according to the charging selection result of electric vehicle users, where

[0097] For electric vehicle users who do not participate in the operation control of the edge-side charging load: Charge them 1.5 times the charging electricity price for their charging behavior,; regard the electric vehicle load that does not participate in the operation control of the edge-side charging load as a new load and superimpose it on the residential area basic load to obtain a new basic load. If the new basic load exceeds the rated power of the distribution transformer, restrict the charging behavior of electric vehicle users who do not participate in the operation control of the edge-side charging load in chronological order according to the rule that the electric vehicles that arrive first are charged first until the new basic load does not exceed the rated power of the distribution transformer; if the new basic load does not exceed the rated power of the distribution transformer, then do not restrict the charging behavior of electric vehicle users who do not participate in the operation control of the edge-side charging load; obtain the charging result of electric vehicle users who do not participate in the operation control of the edge-side charging load; For electric vehicle users who participate in the operation control of the edge-side charging load: On the basis of the new basic load, use the edge-side charging load operation control model considering the dynamic acceptance capacity of the distribution network to obtain the charging result of electric vehicle users who participate in the operation control of the edge-side charging load;

[0098] (3) Combine the charging results of electric vehicle users who do not participate in the operation control of the edge-side charging load and the charging results of electric vehicle users who participate in the operation control of the edge-side charging load to obtain the final charging result of the edge-side charging load operation control.

[0099] 6) Based on the final charging result of the edge-side charging load operation control in step 5), obtain the evaluation index of the electric vehicle acceptance capacity on the edge side of the distribution network in step 2), and evaluate the acceptance capacity of the residential area distribution network for electric vehicles.

[0100] The following gives a specific example:

[0101] For this embodiment, the total optimization period N TSet to 24 hours, the optimization period length Δt is set to 15 minutes, and the expected value s of the state of charge of the electric vehicle leaving the residential area exp is set to 0.95, the starting load rate of the distribution transformer β is set to 0.8, the total number N of electric vehicles EV is set to 400, the charging efficiency η of the electric vehicle EV is set to 0.9;

[0102] In order to verify the feasibility and effectiveness of the edge - side charging load operation control method considering the dynamic acceptance capacity of the distribution network in the present invention, in this embodiment, the following two schemes are adopted for verification and analysis:

[0103] Scheme I: Do not control the electric vehicles in the residential area to obtain the initial operation state of the residential area distribution network.

[0104] Scheme II: Use the edge - side charging load operation control method considering the dynamic acceptance capacity of the distribution network and the participation willingness of electric vehicle users in the present invention to optimize the control of electric vehicles in the residential area, and improve the acceptance capacity of electric vehicles in the residential area and the operation safety of the residential area distribution network.

[0105] Among them, the process of obtaining the charging probability of electric vehicle users participating in the edge - side charging load operation control by using the fuzzy inference method is as follows:

[0106] Six membership functions are used to describe the levels of the difference in the state of charge, namely "small, relatively small, moderately small, moderately large, relatively large, large", the range of the difference in the state of charge is [- 100%, 100%], and the membership function graph of the difference in the state of charge is shown in Figure 5a ; Three membership functions are used to describe the levels of the ratio of charging costs, namely "small, relatively large, large", the range of the ratio of charging costs is [0, 8], and the membership function graph of the ratio of charging costs is shown in Figure 5b ; Five membership functions are used to describe the levels of the charging probability of electric vehicle users participating in the edge - side charging load operation control, namely "low, relatively low, medium, relatively high, high", the range of the charging probability of electric vehicle users participating is [0, 100%], and the membership function graph of the charging probability of electric vehicle users participating in the edge - side charging load operation control is shown in Figure 5c ; Then set the fuzzy inference rules, as shown in Table 1; Finally, input the difference in the state of charge and the ratio of charging costs into the fuzzy logic controller to obtain the charging probability of electric vehicle users participating in the edge - side charging load operation control, and the surface graph of the fuzzy inference system is shown in Figure 6 .

[0107] The computer hardware environment for performing test calculations is an Intel(R) Xeon(R) W-2102 CPU with a main frequency of 2.90 GHz and a memory of 64 GB; the software environment is the Windows 10 operating system.

[0108] When there are 400 electric vehicles in the residential area, the system power comparison diagram of Scheme I and Scheme II is as Figure 7 shown. It can be seen from the figure that Scheme I will cause the distribution transformer to be overloaded, while Scheme II plays the role of "peak shaving and valley filling", and the peak value of the load curve does not exceed the rated power of the distribution transformer, ensuring that the distribution transformer can operate normally.

[0109] The evaluation indicators and charging situations of Scheme I and Scheme II are compared as shown in Table 2. All indicators of Scheme II are better than those of Scheme I. From the perspective of evaluation indicators, Scheme II can significantly reduce the load rate of the distribution transformer and the heavy-load time of the distribution transformer, suppress the load fluctuation of the system, and ensure the flexible and efficient operation of the residential area distribution network; from the perspective of the charging completion situation, 292 electric vehicles can complete charging under Scheme I, accounting for 73%, and 398 electric vehicles can complete charging under Scheme II, accounting for 99.5%. It can be seen that the acceptance ability of users participating in Scheme II is much greater than that of Scheme I. At the same time, by comparing the charging costs of the two schemes, it can be known that the charging cost of Scheme II is lower, indicating that Scheme II can reduce the charging cost of electric vehicles.

[0110] From the comparison between Scheme I and II, it can be seen that by using the edge-side charging load operation control method considering the dynamic acceptance ability of the distribution network in the present invention, the charging power of the edge-side charging load of the residential area distribution network can be effectively optimized and controlled, the charging cost of electric vehicles can be reduced, the acceptance ability of electric vehicles in the residential area can be improved, and the flexible and efficient operation of the residential area distribution network can be ensured.

[0111] Table 1 Fuzzy Rule Table of User Participation in Charging Probability

[0112]

[0113] Table 2 Comparison Results of Evaluation Indicators and Charging Situations of Two Charging Schemes

[0114]

Claims

1. A method for operating and controlling the charging load on the edge side considering the dynamic acceptance capacity of the distribution network, characterized in that It includes the following steps: 1) Based on the selected residential distribution network with electric vehicle charging loads that realizes data acquisition, storage, analysis and calculation, and optimization control by the distribution network edge computing device, input the basic parameter information of the residential distribution network and the operating parameters of electric vehicles, including the basic load of the residential area, the time-of-use electricity price in the residential area, the rated capacity of the distribution transformer, the maximum charging power of the electric vehicle, the battery capacity of the electric vehicle, the arrival time, the departure time and the initial state of charge of the electric vehicle; 2) According to the basic parameter information of the residential distribution network and the operating parameters of electric vehicles in step 1), establish an evaluation index formula for the acceptance capacity of electric vehicles on the edge side of the distribution network. The evaluation indexes for the acceptance capacity of electric vehicles on the edge side of the distribution network include the load rate of the distribution transformer in each time period, the system load fluctuation margin, and the heavy load duration of the distribution transformer; 3) According to the evaluation indexes for the acceptance capacity of electric vehicles on the edge side of the distribution network in step 2), establish an operating control model for the charging load on the edge side considering the dynamic acceptance capacity of the distribution network, including setting the sum of the charging cost of electric vehicle users, the compensation cost for uncompleted user charging, and the penalty cost for the overload of the distribution transformer load rate to be the minimum as the objective function, and respectively considering the charging constraints of electric vehicles and the operating constraints of the distribution transformer; 4) According to the operating control model for the charging load on the edge side considering the dynamic acceptance capacity of the distribution network in step 3), optimize the charging power of electric vehicles to obtain the charging results when electric vehicle users participate in the operating control of the charging load on the edge side; at the same time, without controlling the electric vehicles in the residential area, obtain the charging results when electric vehicle users do not participate in the operating control of the charging load on the edge side; 5) According to the charging results when electric vehicle users participate in the operating control of the charging load on the edge side and the charging results when electric vehicle users do not participate in the operating control of the charging load on the edge side in step 4), establish an operating control correction strategy for the charging load on the edge side considering the willingness of electric vehicle users, correct the charging behavior of electric vehicles, and obtain the final charging results of the operating control of the charging load on the edge side; 6) Based on the final charging results of the operating control of the charging load on the edge side in step 5), obtain the evaluation indexes for the acceptance capacity of electric vehicles on the edge side of the distribution network in step 2), and evaluate the acceptance capacity of the residential distribution network for electric vehicles.

2. The edge-side charging load operation control method considering the dynamic acceptance capacity of the distribution network according to claim 1, wherein In step 2), The evaluation index of the load rate of the distribution transformer in each time period is expressed as: The evaluation index of the system load fluctuation margin is expressed as: The evaluation index of the heavy load duration of the distribution transformer is expressed as: where β t represents the load rate of the distribution transformer during period t; N EV represents the total number of electric vehicles; represents the charging power of the nth electric vehicle during period t; represents the basic load of the residential area during period t; represents the rated power of the distribution transformer; M RDN represents the system load fluctuation margin; N T represents the total optimization period; represents the heavy load duration of the distribution transformer; represents the heavy load flag of the distribution transformer during period t; Δt is the length of the optimization period; Among them, the rated power of the distribution transformer is expressed as: In the formula, represents the rated capacity of the distribution transformer; represents the power factor; Heavy load flag of the distribution transformer during period t It is expressed as: where β t represents the load rate of the distribution transformer during period t.

3. The edge-side charging load operation control method considering the dynamic acceptance capacity of the distribution network according to claim 1, characterized in that In step 3), setting the sum of the charging cost of electric vehicle users, the compensation cost for uncompleted user charging, and the penalty cost for the overload of the distribution transformer load rate to be the minimum as the objective function is expressed as: minF = f1 + f2 + f3 (6) In the formula, F represents the objective function; f1 represents the charging cost of electric vehicle users; f2 represents the compensation cost for incomplete charging of electric vehicle users; f3 represents the penalty cost for the overload of the distribution transformer load rate; represents the charging power of the nth electric vehicle in the t time period; N T represents the total optimization time period; N EV represents the total number of electric vehicles; c t is the time-of-use electricity price in the residential area at the t time period; Δt is the length of the optimization time period; C EV (·) represents the compensation coefficient corresponding to different state of charge when electric vehicle users leave the residential area; E EV,exp represents the expected value of the electric vehicle battery capacity; represents the charging priority of the nth electric vehicle; c EV,max represents the limit value of the compensation coefficient; represents the state of charge of the nth electric vehicle when leaving the residential area; s exp represents the expected value of the state of charge when the electric vehicle leaves the residential area; C TF (·) represents the penalty coefficient corresponding to different load rates of the distribution transformer; represents the basic load in the residential area at the t time period; β t represents the load rate of the distribution transformer at the t time period; β represents the load rate of the distribution transformer corresponding to the starting value of the penalty coefficient; c TF,max represents the limit value of the penalty coefficient; Among them, the charging priority of the nth electric vehicle is expressed as: Wherein, α1, α2, and α3 represent the weight coefficients of each index, satisfying α1 + α2 + α3 = 1; When it is 0, it indicates the lowest priority, and when it is 1, it indicates the highest priority; Represents the departure time index of the nth electric vehicle; Represents the residence duration index of the nth electric vehicle; Represents the charging demand index of the nth electric vehicle; Represents the maximum value of the departure time of electric vehicles in the residential area; Represents the minimum value of the departure time of electric vehicles in the residential area; Represents the departure time of the nth electric vehicle in the residential area; Represents the maximum value of the initial state of charge of electric vehicles in the residential area; Represents the minimum value of the initial state of charge of electric vehicles in the residential area; Represents the initial state of charge of the nth electric vehicle in the residential area; Represents the maximum value of the residence duration of electric vehicles in the residential area; Represents the minimum value of the residence duration of electric vehicles in the residential area; Represents the residence duration of the nth electric vehicle in the residential area; State of Charge of the nth electric vehicle when leaving the residential area Expressed as: Wherein, represents the initial state of charge of the nth electric vehicle in the residential area; represents the arrival time of the nth electric vehicle in the residential area; represents the departure time of the nth electric vehicle in the residential area; η EV represents the charging efficiency of the electric vehicle; E EV,cap represents the battery capacity of the electric vehicle; represents the charging power of the nth electric vehicle in the t period; Expected value E of the battery capacity of an electric vehicle EV,exp Expressed as: E EV,exp = s exp ·E EV,cap (17) where s exp represents the expected value of the state of charge when the electric vehicle leaves the residential area; Introduce auxiliary variables into formula (8) containing non-linear terms Linearize and add constraints: Introduce an auxiliary variable into the formula (10) containing a non-linear term Perform linearization and add constraints: In the formula, represents an auxiliary variable related to the compensation cost for the incomplete charging of the nth electric vehicle user; represents an auxiliary variable related to the penalty cost for the over-limit load rate of the distribution transformer in the t period.

4. The edge-side charging load operation control method considering the dynamic acceptance capacity of the distribution network according to claim 1, characterized in that The charging constraints of electric vehicles described in step 3) are expressed as: In the formula, represents the maximum charging power of the electric vehicle; s n,t represents the state of charge of the nth electric vehicle at time t; s n,t-1 represents the state of charge of the nth electric vehicle at time t - 1; Δt is the length of the optimization period; s exp represents the expected value of the state of charge when the electric vehicle leaves the residential area; represents the upper limit of the state of charge of the electric vehicle; s represents the lower limit of the state of charge of the electric vehicle; represents the initial state of charge of the nth electric vehicle in the residential area; represents the residence duration of the nth electric vehicle in the residential area; represents the arrival time of the nth electric vehicle in the residential area; represents the departure time of the nth electric vehicle in the residential area; η EV represents the charging efficiency of the electric vehicle; E EV,cap represents the battery capacity of the electric vehicle; represents the charging power of the nth electric vehicle in the t period.

5. The operating control method for the edge-side charging load considering the dynamic acceptance capacity of the distribution network according to claim 1, wherein The operating constraints of the distribution transformer described in step 3) are expressed as: In the formula, represents the charging power of the nth electric vehicle in the t time period; N EV represents the total number of electric vehicles; represents the basic load of the residential area in the t time period; represents the rated power of the distribution transformer.

6. The edge-side charging load operation control method considering the dynamic acceptance capacity of the distribution network according to claim 1, wherein In step 5), establishing an operating control correction strategy for the charging load on the edge side considering the willingness of electric vehicle users is expressed as: (1) Based on the charging results when electric vehicle users participate in the operation control of the edge-side charging load and the charging results when electric vehicle users do not participate in the operation control of the edge-side charging load, the charging probability of electric vehicle users participating in the operation control of the edge-side charging load is obtained by using the fuzzy inference method, including: According to the charging results, two main influencing factors, namely the ratio of the difference in the state of charge of electric vehicles under non-participation in the operation control of the edge-side charging load and participation in the operation control of the edge-side charging load and the charging cost, are selected as the input variables of fuzzy inference. The charging probability of electric vehicle users participating in the operation control of the edge-side charging load is used as the output variable of fuzzy inference. A fuzzy logic controller is used to realize the inference process of the willingness of electric vehicle users to participate in the operation control of the edge-side charging load. The realization of the inference process by using the fuzzy logic controller is as follows: First, the variables are fuzzified. Specifically, the input and output variables of fuzzy inference are respectively converted into fuzzy variables through membership functions. The Z-type function, bilateral Gaussian function, and S-type function are selected as membership functions. Then, fuzzy inference rules are set, that is, according to the principle that the smaller the difference in the state of charge of the two electric vehicles and the larger the ratio of the charging cost, the higher the participation willingness of electric vehicle users, fuzzy inference rules are established. Finally, the exact output of the fuzzy logic controller is obtained through defuzzification, and the charging probability of electric vehicle users participating in the operation control of the edge-side charging load is obtained. (2) Correct the charging selection behavior of electric vehicle users according to the charging probability of electric vehicle users participating in the operation control of the edge-side charging load; charge according to the charging selection results of electric vehicle users, where For electric vehicle users who do not participate in the operation control of the edge-side charging load: charge them 1.5 times the charging electricity price for their charging behavior; regard the electric vehicle load that does not participate in the operation control of the edge-side charging load as a new load and superimpose it on the residential area basic load to obtain a new basic load. If the new basic load exceeds the rated power of the distribution transformer, according to the rule that the electric vehicles that arrive first are charged first, restrict the charging behavior of electric vehicle users who do not participate in the operation control of the edge-side charging load in chronological order until the new basic load does not exceed the rated power of the distribution transformer; if the new basic load does not exceed the rated power of the distribution transformer, then do not restrict the charging behavior of electric vehicle users who do not participate in the operation control of the edge-side charging load; obtain the charging results of electric vehicle users who do not participate in the operation control of the edge-side charging load; for electric vehicle users who participate in the operation control of the edge-side charging load: on the basis of the new basic load, use the edge-side charging load operation control model considering the dynamic acceptance capacity of the distribution network to obtain the charging results of electric vehicle users who participate in the operation control of the edge-side charging load. (3) Combine the charging results of electric vehicle users who do not participate in the operation control of the edge-side charging load and the charging results of electric vehicle users who participate in the operation control of the edge-side charging load to obtain the final charging results of the edge-side charging load operation control.

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