A load balancing method for flexible distribution network feeder under large-scale electric vehicle access

Abstract

A load balancing method for flexible distribution network feeder under large-scale electric vehicle access is provided, and a load balancing model for flexible distribution network feeder under large-scale electric vehicle access is established for the load balancing operation optimization problem of the flexible distribution network feeder. The model comprehensively considers the characteristics of electric vehicle grid access and grid exit, the initial state of charge of electric vehicle, the charging power constraint of electric vehicle, the state of charge constraint of electric vehicle, the operation constraint of multi-terminal intelligent soft switch, the operation constraint of distributed power supply and the operation constraint of flexible distribution network. Finally, the output strategy of multi-terminal intelligent soft switch and the aggregated charging power of electric vehicle under large-scale electric vehicle access are determined, and the operation loss cost of flexible distribution network, the loss cost of multi-terminal intelligent soft switch, the electricity purchase cost of electric vehicle and the line load over-limit penalty cost are obtained. The method effectively alleviates the feeder load imbalance problem caused by large-scale electric vehicle access and improves the operation economy of distribution network.

Description

Technical Field

[0001] This invention relates to a load balancing method for feeders in flexible distribution networks. In particular, it relates to a load balancing method for feeders in flexible distribution networks with large-scale electric vehicle integration. Background Technology

[0002] As a new type of electrical load, electric vehicles (EVs) have developed rapidly due to their clean and low-carbon characteristics, gradually replacing traditional gasoline-powered vehicles. The large influx of EVs has led to more complex power flow distribution in power distribution networks. Unguided and disorderly charging poses new challenges to the safe and economical operation of power distribution systems, such as increased peak-to-valley differences, higher losses, voltage exceeding limits, and unbalanced feeder loads. Unbalanced feeder loads can lead to low operating efficiency of the power distribution network and even network congestion. To meet the electricity demand of large-scale EVs while ensuring that the feeder load rate of the power distribution network remains within the economic operating range, research on load balancing methods for flexible power distribution networks under the conditions of large-scale EV integration is urgently needed.

[0003] In recent years, with the rapid development of flexible power distribution technology, power electronic devices, represented by intelligent soft switches, have been widely used. As a new type of flexible power distribution equipment, intelligent soft switches can flexibly adjust power flow distribution, possessing high adjustment accuracy and fast response speed, greatly improving the controllability of the power distribution system and providing technical support for adapting to the large-scale grid connection of electric vehicles. At the same time, the controllable characteristics of electric vehicles also become an effective means of smoothing peak loads. Combining these two functions is of great significance for meeting the diverse needs of electric vehicles and the power distribution network.

[0004] Research on distribution network operation optimization considering large-scale electric vehicle (EV) integration has been conducted both domestically and internationally, primarily focusing on formulating orderly charging strategies based on EV demand-side response. However, shortcomings and challenges remain in the coordinated optimization and scheduling of distribution network-level equipment power flow control and EV charging power. It is necessary not only to consider guiding EV charging behavior but also to consider flexible power regulation based on intelligent soft switches. Therefore, there is an urgent need for a flexible distribution network feeder load balancing method under large-scale EV integration, utilizing the controllable characteristics of EVs and the flexible power regulation capabilities of intelligent soft switches to alleviate feeder load imbalance caused by large-scale EV integration and improve the economic operation of the distribution network. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method for balancing the feeder load of flexible distribution networks under the large-scale electric vehicle access, which can effectively alleviate the problem of feeder load imbalance caused by the large-scale electric vehicle access.

[0006] The technical solution adopted in this invention is: a method for load balancing of feeders in flexible distribution networks with large-scale electric vehicle access, comprising the following steps:

[0007] 1) Based on the selected flexible distribution network, input the flexible distribution network parameter information, including: distribution network topology and line parameter information, power load access location and power prediction curve information, distributed power source access location, type, capacity and output prediction curve information, economic operating range of load rate for each line, and maximum line current information; input electric vehicle parameter information, including: electric vehicle type, charging power limit, battery capacity, initial load, expected load, and charging station location; input multi-terminal intelligent soft switch parameter information, including: port converter capacity, port access location, and active power loss coefficient; input electricity price parameter information, including: system time-of-use electricity price, loss penalty electricity price, and line load over-limit penalty electricity price;

[0008] 2) Based on the electric vehicle parameter information provided in step 1), construct electric vehicle operation constraints, including: establishing electric vehicle charging power constraints and electric vehicle charge constraints based on the electric vehicle's grid connection and off-grid characteristics and the electric vehicle's initial charge characteristics;

[0009] 3) Based on the flexible distribution network parameter information provided in step 1) and the electric vehicle operation constraints provided in step 2), establish a flexible distribution network feeder load balance model under large-scale electric vehicle access, including: setting the minimum of the sum of flexible distribution network operation loss cost, multi-terminal intelligent soft switching loss cost, electric vehicle electricity purchase cost and line load over-limit penalty cost as the objective function, while considering electric vehicle operation constraints, multi-terminal intelligent soft switching operation constraints, distributed power source operation constraints and flexible distribution network operation constraints;

[0010] 4) Based on the load balancing model of the flexible distribution network feeder under large-scale electric vehicle access obtained in step 3), the unit cost of load over-limit penalty is linearized, the distribution network constraints are convexly relaxed, and the second-order cone programming method is used to solve the load balancing model of the flexible distribution network feeder under large-scale electric vehicle access. The solution results are output, including: multi-terminal intelligent soft switching operation strategy, electric vehicle aggregated charging power, distribution network operation loss cost, multi-terminal intelligent soft switching loss cost, electric vehicle electricity purchase cost, and line load over-limit penalty cost.

[0011] This invention presents a load balancing method for feeders in flexible distribution networks with large-scale electric vehicle (EV) integration. It aims to improve the operational economy of flexible distribution networks by fully considering the precise power flow regulation of multi-terminal intelligent soft switches and the charging characteristics of EVs. A load over-limit penalty method is proposed, introducing a load over-limit penalty term to economically penalize lines whose load rate exceeds a threshold, thereby reducing the over-limit range. The real-time and precise power flow control capability of multi-terminal intelligent soft switches is combined with the virtual energy storage function of EVs, leveraging the coordinated optimization potential between distribution network equipment and EVs. A feeder load balancing model for flexible distribution networks with large-scale EV integration is established, yielding the output strategy of multi-terminal intelligent soft switches and the optimized EV charging strategy. This effectively alleviates the feeder load imbalance problem caused by large-scale EV integration and improves the operational economy of the distribution network. Attached Figure Description

[0012] Figure 1 This is a flowchart of a method for load balancing of feeders in a flexible distribution network under large-scale electric vehicle access, according to the present invention.

[0013] Figure 2 It is an improved diagram of the actual power distribution network calculation example;

[0014] Figure 3 This is the daily operating curve of a distributed power source;

[0015] Figure 4 It is a load fluctuation curve;

[0016] Figure 5 It is the system time-of-use electricity price curve;

[0017] Figure 6 This represents the maximum line load factor for each line throughout the entire time period under the three schemes;

[0018] Figure 7 The load rate of the line with the most severe overload limit violation throughout the day;

[0019] Figure 8 The total power loss over the entire time period under the three schemes;

[0020] Figure 9 Active transmission for SOP in Scheme 3;

[0021] Figure 10 This is the reactive power compensation for SOP 3. Detailed Implementation

[0022] The following detailed description of a load balancing method for feeders in a flexible distribution network with large-scale electric vehicle access, in conjunction with embodiments and accompanying drawings, illustrates the present invention.

[0023] This invention provides a method for load balancing of feeders in flexible distribution networks with large-scale electric vehicle integration, such as... Figure 1 As shown, it includes the following steps:

[0024] 1) Based on the selected flexible distribution network, input the flexible distribution network parameter information, including: distribution network topology and line parameter information, power load access location and power prediction curve information, distributed power source access location, type, capacity and output prediction curve information, economic operating range of load rate for each line, and maximum line current information; input electric vehicle parameter information, including: electric vehicle type, charging power limit, battery capacity, initial load, expected load, and charging station location; input multi-terminal intelligent soft switch parameter information, including: port converter capacity, port access location, and active power loss coefficient; input electricity price parameter information, including: system time-of-use electricity price, loss penalty electricity price, and line load over-limit penalty electricity price.

[0025] For this embodiment, the improved actual flexible distribution network line parameters, node load parameters, and network topology connections are first input. The network includes three feeders and one three-terminal intelligent soft switch. It is set that a 3MVA distributed photovoltaic unit is connected at node C10 of the flexible distribution network, and one 3MVA wind turbine is connected at nodes C4 and C9 respectively. All distributed power sources operate at a constant power factor of 1.0. The connection locations are as follows... Figure 2 As shown, the daily operating curve of the distributed power source is as follows: Figure 3 As shown in the figure. The system rated voltage is 10.5kV, the base power is 1MVA, the total active load is 20.074MW, and the total reactive power demand is 6.587Mvar. Detailed parameters are shown in Tables 1 and 2. Assuming that the three feeders connect to the industrial load, commercial load, and residential load respectively, the load fluctuation curve is as follows. Figure 4 As shown. Nodes A10 and B14 each have a charging station. The stations are designed to serve 1000 electric vehicles per day. The ratio of electric vehicles with controllable charging power to those with uncontrollable charging power is 1:1. The maximum charging power for each electric vehicle is 16kW. Each electric vehicle has a battery capacity of 100kWh, an expected charge capacity of 0.9kWh, and a maximum charge capacity of 1.0kWh. The time-of-use pricing is set as follows: Figure 5 As shown in Table 3, the unit cost of network loss, the unit cost of multi-terminal intelligent soft-switching loss, and the unit cost of line load over-limit penalty are all listed. The capacity of the multi-terminal intelligent soft-switching converter is set to 6 MVA, the active power loss coefficient is set to 0.01, and the port converters are connected at nodes A7, B7, and C4. Figure 2 As shown.

[0026] Table 1 shows the load connection locations and power in the improved actual distribution network example.

[0027]

[0028]

[0029] Table 2. Line parameters in the improved actual distribution network example.

[0030]

[0031] Table 3 Price Parameters

[0032]

[0033]

[0034] 2) Based on the electric vehicle parameter information provided in step 1), construct electric vehicle operation constraints, including: establishing electric vehicle charging power constraints and electric vehicle charge constraints based on the electric vehicle's grid connection and off-grid characteristics and initial charge characteristics. Wherein:

[0035] (1) The electric vehicle's grid access and grid disconnection characteristics are expressed as follows:

[0036]

[0037] Among them, f IN The piecewise probability density function representing the time of electric vehicle registration; f OUT The piecewise probability density function representing the time an electric vehicle is disconnected from the grid; t a Indicates the time when an electric vehicle entered the network; μ a σ represents the average time it takes for electric vehicles to be registered in the network. a The standard deviation of the time it takes for electric vehicles to be registered in the network; t d Indicates the off-grid time of electric vehicles; μ d σ represents the average time that electric vehicles are off-grid; d This represents the standard deviation of the time electric vehicles are off-grid.

[0038] (2) The initial charge characteristics of the electric vehicle are expressed as follows:

[0039]

[0040] Among them, f SOC The probability density function representing the initial charge of an electric vehicle; s a μ represents the initial charge of the electric vehicle. SOC σ represents the average initial charge of the electric vehicle; SOC This represents the standard deviation of the initial charge of an electric vehicle.

[0041] (3) The electric vehicle charging power constraint mentioned above is expressed as:

[0042] (3.1) Category I—Uncontrollable Electric Vehicle Charging Power Constraints:

[0043]

[0044] in, This represents the charging completion time for the nth type I electric vehicle user; This represents the expected charge of the nth Class I electric vehicle; This represents the initial charge of the nth Class I electric vehicle; This represents the capacity of the nth type I electric vehicle; This represents the upper limit of the charging power for the nth type I electric vehicle; This represents the charging power of the nth type I electric vehicle during time period t; This indicates the registration time of the nth Class I electric vehicle; This represents the offline time of the nth type I electric vehicle user;

[0045] (3.2) Category II—Controllable Electric Vehicle Charging Power Constraints:

[0046]

[0047] in, This represents the charging power of the nth Class II electric vehicle during time period t; This represents the upper limit of the charging power for the nth Class II electric vehicle; This indicates the registration time of the nth Class II electric vehicle. This represents the off-grid time of the nth Class II electric vehicle.

[0048] (4) The electric vehicle charge constraint mentioned above is expressed as:

[0049] (4.1) Category I—Uncontrollable Electric Vehicle Charge Constraints:

[0050]

[0051] in, This represents the charge of the nth Class I electric vehicle during time period t; This represents the charge of the nth Class I electric vehicle during the time interval t-Δt; This represents the initial charge of the nth Class I electric vehicle; This represents the expected charge of the nth Class I electric vehicle; This represents the charging power of the nth type I electric vehicle during time period t; This represents the capacity of the nth type I electric vehicle; This indicates the registration time of the nth Class I electric vehicle; Δt represents the off-grid time of the nth Class I electric vehicle; Δt represents the duration of each scheduling period.

[0052] (4.2) Category II—Controllable Electric Vehicle Charge Constraints:

[0053]

[0054] in, This represents the charge of the nth Class II electric vehicle during time period t; This represents the charge of the nth Class II electric vehicle during the time interval t-Δt; This represents the initial charge of the nth Class II electric vehicle; This represents the expected charge capacity of the nth Class II electric vehicle; This represents the upper limit of the charge capacity of the nth Class II electric vehicle; This represents the charging power of the nth Class II electric vehicle during time period t; This represents the capacity of the nth Class II electric vehicle; This indicates the registration time of the nth Class II electric vehicle. This represents the off-grid time of the nth Class II electric vehicle.

[0055] 3) Based on the flexible distribution network parameter information provided in step 1) and the electric vehicle operation constraints provided in step 2), establish a flexible distribution network feeder load balance model under large-scale electric vehicle access, including: setting the minimum of the sum of flexible distribution network operation loss cost, multi-terminal intelligent soft switching loss cost, electric vehicle electricity purchase cost and line load over-limit penalty cost as the objective function, while considering electric vehicle operation constraints, multi-terminal intelligent soft switching operation constraints, distributed power source operation constraints and flexible distribution network operation constraints.

[0056] (3.1) The objective function for minimizing the sum of the operating loss cost of the flexible distribution network, the loss cost of multi-terminal intelligent soft switching, the electricity purchase cost of electric vehicles, and the penalty cost for line load exceeding the limit is expressed as:

[0057]

[0058]

[0059]

[0060]

[0061]

[0062]

[0063] Where f represents the overall objective function; fl Indicates the operating loss cost of flexible distribution networks; f s Indicates the cost of multi-terminal intelligent soft switching losses; f c Indicates the cost of electricity for electric vehicles; f b Indicates the penalty cost for exceeding the line load limit; N T Indicates the total number of time periods; C L Indicates the unit cost of loss; Ω b Represents the set of branches in a flexible distribution network; R ij Indicates the resistance value of branch ij; l t,ij Ω represents the square of the current in branch ij during time period t; Δt represents the duration of each scheduling period; SOP This represents the set of nodes connected to each converter in a multi-terminal intelligent soft switch. This represents the converter loss at node i of the multi-terminal intelligent soft-switching circuit during time period t. This represents the time-of-use electricity price for period t; This represents the active power absorbed from the distribution network at electric vehicle aggregation node k during time period t; This represents the unit cost of the penalty for branch ij exceeding its load limit during time period t; This represents the charging power of the nth type α electric vehicle during time period t; Ω represents the number of type α electric vehicles connected to the charging station during time period t; EV Ω represents the set of electric vehicle types. EV ={I,II}; Ω represents the total load at the risk node caused by branch ij exceeding its load limit during time period t; ij Let represent the set of downstream nodes of branch ij; This represents the active load of node k during time period t; This indicates the current threshold on branch ij during economic operation; ρ represents the maximum current in branch ij; t,ij This represents the penalty parameter for branch ij exceeding its load limit during time period t.

[0064] The multi-terminal intelligent soft switch operation constraints mentioned in (3.2) are expressed as follows:

[0065]

[0066] in, This represents the active power loss of the converter at node β during time period t (SOP). This represents the active power injected into node β by SOP during time period t; This represents the reactive power injected into node β by SOP during time period t; These represent the upper and lower limits of the active power injected into node β by SOP, respectively. These represent the upper and lower limits of reactive power injected into node β at SOP, respectively; Aβ SOP This represents the loss coefficient of SOP at node β; This represents the capacity of the converter at node β; β represents the node index connected to the SOP, β∈Ω SOP Ω SOP Ω represents the set of nodes that connect to the SOP. SOP ={i,j,m}.

[0067] The distributed power source operation constraints mentioned in (3.3) are expressed as follows:

[0068]

[0069] in, This represents the active power output of the distributed power source at node i during time period t; This represents the predicted active power output of the distributed power source at node i during time period t. This represents the reactive power output of the distributed power source at node i during time period t; These represent the upper and lower limits of the reactive power output of the distributed power source at node i during time period t, respectively. This represents the capacity of the distributed power source at node i.

[0070] The flexible distribution network operation constraints mentioned in (3.4) are expressed as follows:

[0071]

[0072] Among them, v t,i v represents the square of the voltage amplitude at node i during time period t; t,j The value represents the square of the voltage amplitude at node j during time period t; t,ij Ω represents the square of the current amplitude of branch ij during time period t; ij represents the upstream branch of node j, and jh represents the downstream branch of node j; b Represents the set of distribution network branches; R ij X ij P represents the resistance and reactance of branch ij, respectively; t,ij Q represents the active power flowing from node i to node j on branch ij during time period t; t,ij P represents the reactive power flowing from node i to node j on branch ij during time period t; t,i Q represents the active power injected into node i during time period t; t,i This represents the reactive power injected into node i during time period t; This represents the active power injected into node i by the distributed photovoltaic system connected to node i during time period t. This represents the reactive power injected into node i by the distributed photovoltaic system connected to node i during time period t. This represents the active power injected into node i by the SOP during time period t; This represents the reactive power injected into node i by the SOP during time period t; This represents the active power consumed by the load at node i during time period t; U represents the reactive power consumed by the load at node i during time period t; t,i I represents the voltage amplitude at node i during time period t; t,ij This represents the magnitude of the current flowing from node i to node j on branch ij during time period t; U These represent the upper and lower limits of the node voltage, respectively.

[0073] 4) Based on the load balancing model of the flexible distribution network feeder under large-scale electric vehicle access obtained in step 3), the unit cost of load over-limit penalty is linearized, the distribution network constraints are convexly relaxed, and the second-order cone programming method is used to solve the load balancing model of the flexible distribution network feeder under large-scale electric vehicle access. The solution results are output, including: multi-terminal intelligent soft switching operation strategy, electric vehicle aggregated charging power, distribution network operation loss cost, multi-terminal intelligent soft switching loss cost, electric vehicle electricity purchase cost, and line load over-limit penalty cost.

[0074] (4.1) The linearization of the unit cost of the overload penalty is expressed as follows:

[0075]

[0076] Among them, LB t,ij The auxiliary variable representing the load exceeding the limit of branch ij during time period t; l t,ij Let represent the square of the current in branch ij during time period t; This indicates the current threshold on branch ij during economic operation; This indicates the maximum current value on branch ij; ρ represents the unit cost of the penalty for exceeding the load limit on branch ij during time period t; t,ij This represents the penalty parameter for branch ij exceeding its load limit during time period t.

[0077] (4.2) The convex relaxation of the distribution network constraints described above can be expressed as:

[0078]

[0079] Among them, P t,ij Q represents the active power flowing from node i to node j on branch ij during time period t; t,ij This represents the reactive power flowing from node i to node j on branch ij during time period t; v t,i The value represents the square of the voltage amplitude at node i during time period t; t,ij This represents the square of the current amplitude of branch ij during time period t; This represents the active power loss of the converter at node β during time period t (SOP). This represents the active power injected into node β by SOP during time period t; A represents the reactive power injected into node β by SOP during time period t; β SOP This represents the loss coefficient of SOP at node β; This represents the capacity of the converter at node β; β represents the node index connected to the SOP, β∈Ω SOP Ω SOP Ω represents the set of nodes that connect to the SOP. SOP ={i,j,m}; This represents the active power output of the distributed power source at node i during time period t; This represents the reactive power output of the distributed power source at node i during time period t; Ω represents the capacity of the distributed power source at node i. DG This represents the set of nodes connected to the distributed power source.

[0080] To fully verify the advancement of the load balancing method for flexible distribution network feeders under large-scale electric vehicle access of the present invention, this embodiment adopts the following three schemes for comparative analysis:

[0081] Option 1: Without considering the regulation of multi-terminal intelligent soft switches and the controllability of electric vehicles, the initial operating state of the flexible distribution network is obtained;

[0082] Option 2: Without considering the regulation of multi-terminal intelligent soft switches, obtain the operating status of the flexible distribution network under the controllable condition of electric vehicles;

[0083] Option 3: Considering the regulation of multi-terminal intelligent soft switches and the controllability of electric vehicles, the operating state of the flexible distribution network under coordinated optimization is obtained.

[0084] The computer hardware environment for performing the optimized calculations was an Intel(R) Core(TM) i7-12700 with a clock speed of 2.10GHz and 16GB of memory; the software environment was a Windows 10 operating system.

[0085] The load rate levels of the three schemes are shown in Table 4, and the operation optimization results are shown in Table 5. Figure 6 The maximum line load factor for each line throughout the entire time period under the three schemes. Figure 7 This shows the load rate of the line with the most severe overload limit violation throughout the day. Figure 8 The total power loss for the next day is calculated for the three scenarios.

[0086] Table 4 Comparison of load rate levels for the three schemes

[0087]

[0088] Table 5 Comparison of the optimization results of the three schemes

[0089]

[0090] Comparing the system operation results under the three schemes, it can be seen that with the increase of control measures, both the maximum load rate and the degree of load exceeding the limit of the line show a downward trend. Initially, expensive penalties for exceeding load limits are incurred, and the operating costs of the flexible distribution network and the charging costs of electric vehicles are high, resulting in poor overall system economics. After considering the controllable characteristics of electric vehicles, the operating costs of the flexible distribution network, penalties for exceeding load limits, and charging costs of electric vehicles all decrease. When multi-terminal intelligent soft switches and electric vehicles are optimized through interaction, although the cost of losses from multi-terminal intelligent soft switches increases slightly, the operating costs of the flexible distribution network decrease significantly, there are no penalties for exceeding load limits, and the social benefits are significantly improved. Figure 9 Active transmission for SOP in Scheme 3; Figure 10 This is the reactive power compensation for SOP 3.

Claims

1. A method for load balancing of feeders in a flexible distribution network with large-scale electric vehicle integration, characterized in that, Includes the following steps: 1) Based on the selected flexible distribution network, input the flexible distribution network parameter information, including: distribution network topology and line parameter information, power load access location and power prediction curve information, distributed power source access location, type, capacity and output prediction curve information, economic operating range of load rate for each line, and maximum line current information; input electric vehicle parameter information, including: electric vehicle type, charging power limit, battery capacity, initial load, expected load, and charging station location; input multi-terminal intelligent soft switch parameter information, including: port converter capacity, port access location, and active power loss coefficient; input electricity price parameter information, including: system time-of-use electricity price, loss penalty electricity price, and line load over-limit penalty electricity price; 2) Based on the electric vehicle parameter information provided in step 1), construct electric vehicle operation constraints, including: establishing electric vehicle charging power constraints and electric vehicle charge constraints based on the electric vehicle's grid connection and off-grid characteristics and the electric vehicle's initial charge characteristics; 3) Based on the flexible distribution network parameter information provided in step 1) and the electric vehicle operation constraints provided in step 2), establish a flexible distribution network feeder load balancing model under large-scale electric vehicle access, including: setting the minimum of the sum of flexible distribution network operation loss cost, multi-terminal intelligent soft switching loss cost, electric vehicle electricity purchase cost and line load over-limit penalty cost as the objective function, while considering electric vehicle operation constraints, multi-terminal intelligent soft switching operation constraints, distributed power source operation constraints and flexible distribution network operation constraints; 4) Based on the load balancing model of the flexible distribution network feeder under large-scale electric vehicle access obtained in step 3), the unit cost of load over-limit penalty is linearized, the distribution network constraints are convexly relaxed, and the second-order cone programming method is used to solve the load balancing model of the flexible distribution network feeder under large-scale electric vehicle access. The solution results are output, including: multi-terminal intelligent soft switching operation strategy, electric vehicle aggregated charging power, distribution network operation loss cost, multi-terminal intelligent soft switching loss cost, electric vehicle electricity purchase cost, and line load over-limit penalty cost.

2. The method for load balancing of feeders in a flexible distribution network with large-scale electric vehicle access according to claim 1, characterized in that, The electric vehicle charging power constraint mentioned in step 2) is expressed as follows: (1) Category I - Uncontrollable electric vehicle charging power constraints: ； in, Indicates the first Charging completion time for Category I electric vehicle users; Indicates the first The expected charge capacity of Class I electric vehicles; Indicates the first The initial charge of a Class I electric vehicle; Indicates the first The capacity of Class I electric vehicles; Indicates the first The upper limit of charging power for Class I electric vehicles; express Time period Charging power of Class I electric vehicles; Indicates the first The registration time of Class I electric vehicles; Indicates the first Off-grid time for Category I electric vehicle users; (2) Category II - Controllable electric vehicle charging power constraints: ； in, express Time period Charging power of Class II electric vehicles; Indicates the first The upper limit of charging power for Category II electric vehicles; Indicates the first The registration time of Class II electric vehicles; Indicates the first Off-grid time for Class II electric vehicles.

3. The method for load balancing of feeders in a flexible distribution network with large-scale electric vehicle access according to claim 1, characterized in that, The electric vehicle charge constraint mentioned in step 2) is expressed as follows: (1) Category I - Uncontrollable electric vehicle charge constraints: ； in, express Time period The charge capacity of Class I electric vehicles; Indicates the first The initial charge of a Class I electric vehicle; Indicates the first The expected charge capacity of Class I electric vehicles; express Time period Charging power of Class I electric vehicles; Indicates the first The capacity of Class I electric vehicles; Indicates the first The registration time of Class I electric vehicles; Indicates the first Off-grid time for Class I electric vehicles; Indicates the duration of each scheduling period; (2) Category II - Charge Constraints for Controllable Electric Vehicles: ； in, express Time period The charge capacity of a Class II electric vehicle; express Time period The charge capacity of a Class II electric vehicle; Indicates the first The initial charge of a Class II electric vehicle; Indicates the first The expected charge capacity of each Category II electric vehicle; Indicates the first The upper limit of the charge capacity of Class II electric vehicles; express Time period Charging power of Class II electric vehicles; Indicates the first The capacity of Class II electric vehicles; Indicates the first The registration time of each Category II electric vehicle; Indicates the first Off-grid time for Class II electric vehicles.

4. The method for load balancing of feeders in a flexible distribution network with large-scale electric vehicle access according to claim 1, characterized in that, Step 3) describes setting the minimum sum of the operating loss cost of the flexible distribution network, the loss cost of multi-terminal intelligent soft switching, the electricity purchase cost of electric vehicles, and the penalty cost for line load exceeding limits as the objective function, which is expressed as: ； in, Overall objective function; This represents the operating loss cost of a flexible power distribution network; This indicates the cost of loss in multi-terminal intelligent soft switching; This indicates the cost of electricity for electric vehicles; This indicates the penalty cost for exceeding the line load limit; Indicates the total number of time periods; Represents a set of branches in a flexible distribution network; Indicates a branch The resistance value; express Time-of-day branch The square of the current in; Indicates the duration of each scheduling period; This represents the set of nodes connected to each converter in a multi-terminal intelligent soft switch. express Multi-terminal intelligent soft-switching node Converter losses at the location; express Time-of-use electricity pricing for different time periods; express Electric vehicle aggregation nodes during time periods Active power absorbed from the distribution network; express Time-of-day branch Overload exceeding limits incurs a penalty on unit cost. express Time period indivual Charging power of electric vehicles; express The time period connected to the charging station The number of electric vehicles; Represents a set of electric vehicle types. ; express Time-of-day branch Total load on risky nodes caused by exceeding load limits; Indicates a branch The set of downstream nodes; express Time period nodes Active load; Indicates the branch during economic operation Upper current threshold; Indicates a branch Maximum current; express Time-of-day branch Penalty parameters for exceeding load limits.

5. The method for load balancing of feeders in a flexible distribution network with large-scale electric vehicle access according to claim 1, characterized in that, Step 4) describes linearizing the unit cost of the overload penalty as follows: ； in, express Time-of-day branch Auxiliary variable for overload; express Time-of-day branch The square of the current; Indicates the branch during economic operation Upper current threshold; Indicates a branch Maximum current; express Time-of-day branch Overload exceeding limits incurs a penalty on unit cost. express Time-of-day branch Penalty parameters for exceeding load limits.

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