Method and system for evaluating charging load capacity of active flexible interconnected power distribution network

Abstract

This invention discloses a method and system for assessing the charging load capacity carrying capacity of an active flexible interconnected distribution network, belonging to the field of power system operation. The method includes: acquiring distribution network structure, operating parameters, flexible interconnection device parameters, typical charging scenario parameters, and distributed power source parameters; establishing a two-stage assessment model for the charging load capacity carrying capacity of the flexible interconnected distribution network. Stage one assesses the upper and lower limits of the charging load capacity that the distribution network can connect to; stage two verifies the impact of the worst-case access scenario within this capacity range on the safe operation of the distribution network; establishing an active flexible interconnected distribution network carrying capacity assessment model; and using a second-order cone relaxation, circumscribed polygon approximation, and improved column and constraint relaxation algorithms to design an iterative solution method to obtain the carrying capacity assessment results. This invention overcomes the shortcomings of traditional assessment methods that cannot simultaneously consider the safe operation of the distribution network and the level of new energy consumption.

Description

Technical Field

[0001] This invention belongs to the field of power system operation, specifically relating to a method and system for assessing the carrying capacity of charging loads in an active flexible interconnected distribution network. Background Technology

[0002] The widespread adoption of clean transportation tools such as electric vehicles is a crucial pathway to promoting energy transition. However, with the large-scale development of electric vehicles, the charging demand generated by them is connected to the power distribution network through charging infrastructure. This massive charging load can lead to a series of problems, including overload of power distribution equipment and voltage exceeding limits, seriously affecting the safe operation of the power grid. Therefore, assessing the permissible electric vehicle charging load capacity of the power distribution network, under the premise of meeting all economic and technical requirements—that is, the electric vehicle charging load carrying capacity of the power distribution network—helps guide users to connect to the grid rationally and orderly, ensure the safe and stable operation of the power distribution network, and promote the efficient and coordinated development of electric vehicles and the power distribution network.

[0003] However, current assessments of the charging load carrying capacity of distribution networks only focus on the maximum accessible charging load capacity that meets relevant economic and technical requirements. With the continuous integration of distributed power sources, such as distributed photovoltaic and decentralized wind power, the distribution network is gradually transforming from a traditional passive network to an active network. Power flow is changing from unidirectional to bidirectional. To ensure a certain level of renewable energy absorption, a certain scale of charging load needs to be connected to the distribution network. Therefore, the assessment of the charging load carrying capacity of the distribution network needs to shift from the traditional upper boundary to a range of access scale composed of upper and lower boundaries. The impact of the uncertainty of distributed power source output on carrying capacity also needs to be considered. Furthermore, with the emergence of new distribution equipment such as flexible interconnection devices, they can not only provide controllable power transmission channels between buses, enhancing operational flexibility, but also provide some reactive power support, helping to improve the charging load carrying capacity of the distribution network. Currently, research on the charging load carrying capacity assessment of active distribution networks that simultaneously considers the dual objectives of safe operation of the distribution network and renewable energy absorption has not been conducted. At the same time, further quantification of the uncertainty of distributed power source output and the impact of flexible interconnection devices on carrying capacity is also needed. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for assessing the charging load capacity of an active flexible interconnected distribution network, so as to make up for the shortcomings of traditional assessment methods that cannot simultaneously take into account the safe operation of the distribution network and the level of new energy consumption. At the same time, this method can analyze the impact of flexible interconnection devices on the carrying capacity of the distribution network.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: A method for assessing the charging load capacity carrying capacity of an active flexible interconnected distribution network includes the following steps: Step 1: Obtain the power distribution network structure, power distribution equipment operating parameters, flexible interconnection device parameters, typical charging scenario load parameters, and distributed power output parameters; Step 2: Based on the distribution network structure, distribution equipment operating parameters, flexible interconnection device parameters, and typical charging scenario load parameters, establish a two-stage evaluation model for the charging load capacity carrying capacity of the flexible interconnection distribution network. The first stage evaluates the range of charging load capacity that the distribution network can access, determined by the upper and lower limits of the carrying capacity. The second stage verifies whether the worst access scenario within this capacity range affects the safe operation of the distribution network. Step 3: Based on the two-stage evaluation model of the charging load capacity of the flexible interconnected distribution network, establish a distributed power source model according to the output parameters of the distributed power source, use a box-type uncertainty set to characterize its output uncertainty and add stage two to establish an evaluation model of the charging load capacity of the active flexible interconnected distribution network. Step 4: Based on the nonlinear and hierarchical characteristics of the active flexible interconnected distribution network charging load capacity carrying capacity assessment model, an iterative solution method is designed using second-order cone relaxation, circumscribed polygon approximation, and improved column and constraint relaxation algorithms to obtain the charging load capacity carrying capacity assessment results.

[0006] A further improvement of this invention is that, in step two, a two-stage evaluation model for the charging load capacity carrying capacity of the flexible interconnected distribution network is established. The first stage evaluates the range of charging load capacity that the distribution network can access, determined by the upper and lower limits of the carrying capacity. The objective function is shown in formula (1): (1) In the formula, t This is a time marker, with values ​​of 1, 2, ... T ; T This represents the total number of time periods; i This is the identifier for the distribution network node, with values ​​of 1, 2, ... N ; N This represents the total number of nodes. The spatiotemporal weighting factor of the distribution network node is set by the evaluator or calculated from the typical load curve according to the service scenario type of the charging infrastructure access point, as shown in formula (2). Indicates distribution network node i The maximum available charging load capacity at time t (in MW); Indicates distribution network node i exist t Lower limit of available charging load capacity at any time / MW; (2) In the formula: Represents a node i Charging scenarios in tTypical charging load curve load value at any given time; In terms of operation, the upper and lower limits of the access charging load capacity are limited by the rated capacity of the charging and discharging facilities already installed at the node. In terms of planning, the capacity limit of the charging and discharging facilities installed at the node is affected by both investment costs and available space. Therefore, the constraints of Phase 1 are as shown in Equation (3): (3) After obtaining the range of charging load capacity that can be accessed by the distribution network in Phase 1 assessment, Phase 2 verifies whether the worst access scenario within this capacity range affects the safe operation of the distribution network. Its objective function is shown in Formula (4): (4) In the formula, and This represents the non-negative relaxation variable introduced into the power balance constraints of the distribution network to ensure that the stage 2 verification model has a solution; Indicates the actual connected charging load capacity. The set of accessible charging load capacity ranges obtained from the Phase 1 assessment is shown in Equation (5): (5) Phase 2 feasibility verification considers operational constraints such as power distribution equipment and power balance, as well as safety limits on node voltage and equipment capacity; Formula (6) indicates that the active and reactive power output of the distribution network transformer should not exceed its capacity limit; Formula (7) indicates that the voltage of the distribution network node and the power transmitted by the line should not exceed the limit; The linearized Dist-flow model is used to calculate the power flow of the distribution network with radial characteristics, as shown in Formula (8); Formula (9) is the power balance and capacity limit constraint of the flexible interconnection device; Formula (10) is the active and reactive power balance constraint of the distribution network after introducing slack variables; (6) (7) (8) (9) (10) In the formula, p gi,t Indicates distribution transformer gi At any moment t Active power output / MW; p gi,max and p gi,min They represent distribution transformers gi Active power upper and lower limits / MW; q gi,t Indicates distribution transformer gi At any moment t No power output / MW; q gi,max and q gi,min They represent distribution transformers gi Reactive power upper and lower limits / MVar; tan φ gi,max and tan φ gi,min They represent distribution transformers gi Power factor upper and lower limits; v i,t Indicates distribution network node i At any moment t Voltage amplitude / kV; v i,max and v i,min Representing nodes respectively i Voltage amplitude upper and lower limits / kV; p ij,max Indicates the line ij Maximum active power / MW; R ij and X ij They represent the lines respectively. ij Resistance and reactance values ​​per kΩ; p ij,t and q ij,t They represent the lines respectively. ij Active power flowing through (MW) and reactive power (MVar); and Representing flexible interconnect devices i port n Active power input and power loss / MW; and These represent the reactive power / MVar and capacity / MVA of the flexible interconnect device port, respectively. and These represent the upper and lower limits of reactive power at the port / MVar, respectively. The power loss factor; p i,t and q i,t Representing nodes respectively i At any moment t Active and reactive loads / MW.

[0007] A further improvement of this invention is that, in step three, a distributed power source model is established, as shown in formula (11): (11) In the formula: , and These represent distributed power sources. drg At any moment t Active power output, available power, and power curtailment / MW; This represents the maximum permissible power abandonment factor for distributed generation; tan φ drg Indicates the power factor of a distributed power source; This represents the reactive power output of the distributed power source / MVar; The uncertainty of distributed photovoltaic and distributed wind power output is characterized by a box-type uncertainty set, as shown in formula (12): (12) In the formula: The predicted value of available output power of distributed generation in MW; and The upper and lower deviations between the predicted and actual values ​​are expressed in MW. Considering the impact of distributed generation on the charging load capacity of the distribution network, an evaluation model for the charging load capacity of the active flexible interconnected distribution network is established. The output of distributed generation is added to the power balance equation in formula (10), and it is modified to formula (13): (13).

[0008] A further improvement of this invention is that, in step four, for the uncertain sets shown in formulas (5) and (12), the uncertain parameters take values ​​at the boundary under the worst-case scenario, and by introducing auxiliary variables, they are modified as shown in formulas (14) and (15), respectively: (14) (15) In the formula: and These are 0-1 variables that indicate the upper or lower boundary of the set of charging load capacity access values; and These are 0-1 variables that indicate the upper or lower boundary of the set of available output power from distributed power sources; and These are the intermediate value and deviation value of the uncertain set, respectively, determined by formula (12); Since a certain uncertain parameter cannot simultaneously take the maximum and minimum values, it satisfies formulas (40) and (41). In addition, for the uncertainty of the output of distributed power sources, the “budget constraint” shown in formula (42) is used to limit the conservatism of the value of the uncertain parameter. (16) (17) (18) For the capacity constraint of the flexible interconnection device with a second-order cone shape in formula (9), an external connection is adopted. m The method for approximating polygons is linearized, as shown in formula (19): (19) In the formula: For the edge k The normal vector; For the constraint of the operating loss equation of the flexible interconnection device in formula (9), first use second-order cone relaxation to relax it to the formula shown, and then use the same method of circumscribed polygon approximation to linearize it. (20) Based on the above, the assessment model for the charging load capacity of the active flexible interconnected distribution network is summarized as a two-stage problem consisting of formulas (21) and (22): (twenty one) (twenty two) In the formula: Represents a unit vector; and To represent the decision variables of the distribution network operation status in the stage two feasibility verification model, the specific expression is shown in formula (23). and To run the constraint coefficient matrix, and The matrix corresponding to the constant terms; (twenty three) The active flexible interconnected distribution network charging load capacity carrying capacity assessment model is solved by an improved column and constraint generation algorithm. After the solution in stage two is fed back to stage one, it is a set of 0-1 variable markers indicating whether the upper or lower limit is taken. The corresponding generated constraints are shown in formula (24). The iterative processing of the uncertainty of distributed power output is still the traditional column and constraint generation algorithm. (twenty four) in, Indicates the first k Variables generated in the next iteration.

[0009] An active flexible interconnected distribution network charging load capacity assessment system includes: Parameter acquisition unit: Acquires power distribution network structure, power distribution equipment operating parameters, flexible interconnection device parameters, typical charging scenario load parameters, and distributed power output parameters; Two-stage evaluation model establishment unit: Based on the distribution network structure, distribution equipment operating parameters, flexible interconnection device parameters, and typical charging scenario load parameters, a two-stage evaluation model for the charging load capacity carrying capacity of the flexible interconnection distribution network is established. The first stage evaluates the range of charging load capacity that the distribution network can access, determined by the upper and lower limits of the carrying capacity. The second stage verifies whether the worst access scenario within this capacity range affects the safe operation of the distribution network. Distributed power generation model establishment unit: Based on the two-stage evaluation model of the charging load capacity of the flexible interconnected distribution network, a distributed power generation model is established according to the output parameters of the distributed power generation. The output uncertainty is represented by a box-type uncertainty set and a second stage is added to establish an evaluation model of the charging load capacity of the active flexible interconnected distribution network. Model Solving Unit: Based on the nonlinear and hierarchical characteristics of the active flexible interconnected distribution network charging load capacity carrying capacity assessment model, an iterative solution method is designed using second-order cone relaxation, circumscribed polygon approximation, and improved column and constraint relaxation algorithms to obtain the charging load capacity carrying capacity assessment results.

[0010] An electronic device includes: a processor and a memory coupled to the processor, the memory storing a computer program that, when executed by the processor, implements the steps of the active flexible interconnected distribution network charging load capacity carrying capacity assessment method.

[0011] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the active flexible interconnected distribution network charging load capacity carrying capacity assessment method.

[0012] Compared with the prior art, the present invention has at least the following beneficial technical effects: This invention provides a method and system for assessing the charging load capacity carrying capacity of an active flexible interconnected distribution network. First, a two-stage assessment model for the charging load capacity carrying capacity of the flexible interconnected distribution network is established. Stage one assesses the range of charging load capacity that the distribution network can access, determined by upper and lower limits of the carrying capacity. Stage two verifies whether the worst-case access scenario within this capacity range affects the safe operation of the distribution network. Based on this, a distributed generation model is established, and its output uncertainty is represented by a box-type uncertainty set and incorporated into Stage two to establish the charging load capacity carrying capacity assessment model for the active flexible interconnected distribution network. Finally, an iterative solution method is designed using second-order cone relaxation, circumscribed polygon approximation, and improved column and constraint relaxation algorithms to obtain the charging load capacity carrying capacity assessment result. This invention can obtain charging load capacity carrying capacity assessment results that balance the safe operation of the distribution network and the absorption of new energy sources. It also considers the uncertainty of distributed generation and the impact of flexible interconnection devices, making the distribution network carrying capacity more adaptable and flexible. Attached Figure Description

[0013] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0014] Figure 1 This is an overall flowchart of the method of the present invention.

[0015] Figure 2 The figures show the distributed photovoltaic load power curve and the charging load power curve for typical charging scenarios.

[0016] Figure 3 The charging load power curve is shown for a typical charging scenario.

[0017] Figure 4 This is a schematic diagram of the charging load capacity assessment results.

[0018] Figure 5 and Figure 6 A schematic diagram comparing the load-bearing capacity assessment results before and after the deployment of the flexible interconnect device.

[0019] Figure 7 This is a structural block diagram of the system of the present invention. Detailed Implementation

[0020] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0021] In the description of this invention, it should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0022] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0023] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0024] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0025] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0026] Example 1 like Figure 1 As shown, the method for assessing the charging load capacity of an active flexible interconnected distribution network provided by this invention includes the following steps: Step 1: Obtain the power distribution network structure, power distribution equipment operating parameters, flexible interconnection device parameters, typical charging scenario load parameters, and distributed power output parameters; Step 2: Based on the distribution network structure, distribution equipment operating parameters, flexible interconnection device parameters, and typical charging scenario load parameters, establish a two-stage evaluation model for the charging load capacity carrying capacity of the flexible interconnection distribution network. The first stage evaluates the range of charging load capacity that the distribution network can access, determined by the upper and lower limits of the carrying capacity. The second stage verifies whether the worst access scenario within this capacity range affects the safe operation of the distribution network. Step 3: Based on the two-stage evaluation model of the charging load capacity of the flexible interconnected distribution network, establish a distributed power source model according to the output parameters of the distributed power source, use a box-type uncertainty set to characterize its output uncertainty and add stage two to establish an evaluation model of the charging load capacity of the active flexible interconnected distribution network. Step 4: Based on the nonlinear and hierarchical characteristics of the active flexible interconnected distribution network charging load capacity carrying capacity assessment model, an iterative solution method is designed using second-order cone relaxation, circumscribed polygon approximation, and improved column and constraint relaxation algorithms to obtain the charging load capacity carrying capacity assessment results.

[0027] In step two of this embodiment, a two-stage evaluation model for the charging load capacity carrying capacity of the flexible interconnected distribution network is established. The first stage evaluates the range of charging load capacity that the distribution network can access, determined by the upper and lower limits of the carrying capacity. Its objective function is shown in formula (1): (1) In the formula, t This is a time marker, with values ​​of 1, 2, ... T ; T This represents the total number of time periods; i This is the identifier for the distribution network node, with values ​​of 1, 2, ... N ; N This represents the total number of nodes. The spatiotemporal weighting factor of the distribution network node is set by the evaluator or calculated from the typical load curve according to the service scenario type of the charging infrastructure access point, as shown in formula (2). Indicates distribution network node i The maximum available charging load capacity at time t (in MW); Indicates distribution network node i exist t Lower limit of available charging load capacity at any time / MW; (2) In the formula: Represents a node i Charging scenarios in t Typical charging load curve load value at any given time; In terms of operation, the upper and lower limits of the access charging load capacity are limited by the rated capacity of the charging and discharging facilities already installed at the node. In terms of planning, the capacity limit of the charging and discharging facilities installed at the node is affected by both investment costs and available space. Therefore, the constraints of Phase 1 are as shown in Equation (3): (3) After obtaining the range of charging load capacity that can be accessed by the distribution network in Phase 1 assessment, Phase 2 verifies whether the worst access scenario within this capacity range affects the safe operation of the distribution network. Its objective function is shown in Formula (4): (4) In the formula, and This represents the non-negative relaxation variable introduced into the power balance constraints of the distribution network to ensure that the stage 2 verification model has a solution; Indicates the actual connected charging load capacity. The set of accessible charging load capacity ranges obtained from the Phase 1 assessment is shown in Equation (5): (5) Phase 2 feasibility verification considers operational constraints such as power distribution equipment and power balance, as well as safety limits on node voltage and equipment capacity; Formula (6) indicates that the active and reactive power output of the distribution network transformer should not exceed its capacity limit; Formula (7) indicates that the voltage of the distribution network node and the power transmitted by the line should not exceed the limit; The linearized Dist-flow model is used to calculate the power flow of the distribution network with radial characteristics, as shown in Formula (8); Formula (9) is the power balance and capacity limit constraint of the flexible interconnection device; Formula (10) is the active and reactive power balance constraint of the distribution network after introducing slack variables; (6) (7) (8) (9) (10) In the formula, p gi,t Indicates distribution transformer gi At any moment t Active power output / MW; p gi,max and p gi,min They represent distribution transformers gi Active power upper and lower limits / MW; q gi,t Indicates distribution transformer gi At any moment tNo power output / MW; q gi,max and q gi,min They represent distribution transformers gi Reactive power upper and lower limits / MVar; tan φ gi,max and tan φ gi,min They represent distribution transformers gi Power factor upper and lower limits; v i,t Indicates distribution network node i At any moment t Voltage amplitude / kV; v i,max and v i,min Representing nodes respectively i Voltage amplitude upper and lower limits / kV; p ij,max Indicates the line ij Maximum active power / MW; R ij and X ij They represent the lines respectively. ij Resistance and reactance values ​​per kΩ; p ij,t and q ij,t They represent the lines respectively. ij Active power flowing through (MW) and reactive power (MVar); and Representing flexible interconnect devices i port n Active power input and power loss / MW; and These represent the reactive power / MVar and capacity / MVA of the flexible interconnect device port, respectively. and These represent the upper and lower limits of reactive power at the port / MVar, respectively. The power loss factor; p i,t and q i,t Representing nodes respectively i At any moment t Active and reactive loads / MW.

[0028] In step three of this embodiment, a distributed power source model is established, as shown in formula (11): (11) In the formula: , and These represent distributed power sources. drg At any moment t Active power output, available power, and power curtailment / MW; This represents the maximum permissible power abandonment factor for distributed generation; tan φ drg Indicates the power factor of a distributed power source; This represents the reactive power output of the distributed power source / MVar; In practice, the available output of distributed power sources, such as distributed photovoltaic and distributed wind power, is affected by weather conditions and has significant uncertainty. The uncertainty of distributed photovoltaic and distributed wind power output is characterized by a box uncertainty set, as shown in formula (12): (12) In the formula: The predicted value of available output power of distributed generation in MW; and The upper and lower deviations between the predicted and actual values ​​are expressed in MW. Considering the impact of distributed generation on the charging load capacity of the distribution network, an evaluation model for the charging load capacity of the active flexible interconnected distribution network is established. The output of distributed generation is added to the power balance equation in formula (10), and it is modified to formula (13): (13).

[0029] In step four of this embodiment, due to the hierarchical nature of the model, it cannot be solved directly, and a corresponding solution algorithm needs to be designed. For the uncertain set shown in formulas (5) and (12), the uncertain parameters take values ​​at the boundary in the worst-case scenario. By introducing auxiliary variables, they are modified as shown in formulas (14) and (15), respectively: (14) (15) In the formula: and These are 0-1 variables that indicate the upper or lower boundary of the set of charging load capacity access values; and These are 0-1 variables that indicate the upper or lower boundary of the set of available output power from distributed power sources; and These are the intermediate value and deviation value of the uncertain set, respectively, determined by formula (12). It can also be called uncertainty, which is used to represent the range of fluctuation of uncertain parameters. The larger the value, the more deviations the predicted value.

[0030] Since a certain uncertain parameter cannot simultaneously take the maximum and minimum values, it satisfies formulas (40) and (41). In addition, in response to the uncertainty of the output of distributed power sources, it is rare in actual operation for all distributed power sources to have the maximum / minimum output at all times. At the same time, the "budget constraint" shown in formula (42) is used to limit the conservatism of the value of the uncertain parameter. (16) (17) (18) For the capacity constraint of the flexible interconnection device with a second-order cone shape in formula (9), an external connection is adopted. m The method for approximating polygons is linearized, as shown in formula (19): (19) In the formula: For the edge k The normal vector; For the constraint of the operating loss equation of the flexible interconnection device in formula (9), first use second-order cone relaxation to relax it to the formula shown, and then use the same method of circumscribed polygon approximation to linearize it. (20) Based on the above, the assessment model for the charging load capacity of the active flexible interconnected distribution network is summarized as a two-stage problem consisting of formulas (21) and (22): (twenty one) (twenty two) In the formula: Represents a unit vector; and To represent the decision variables of the distribution network operation status in the stage two feasibility verification model, the specific expression is shown in formula (23). and To run the constraint coefficient matrix, and The matrix corresponding to the constant terms; (twenty three) The active flexible interconnected distribution network charging load capacity carrying capacity assessment model is solved by an improved column and constraint generation algorithm. Compared with the traditional column and constraint generation algorithm, the improvement is that the carrying capacity range used for the feasibility verification of stage two is determined by the assessment of stage one and changes continuously with the iteration process. Therefore, the worst access scenario fed back to stage one after the solution of stage two is not a specific upper or lower limit of carrying capacity, but a set of 0-1 variable markers indicating whether to take the upper or lower limit. The corresponding generated constraints are shown in formula (24). The iterative processing of the uncertainty of distributed power output is still the traditional column and constraint generation algorithm. (twenty four) in, Indicates the first k Variables generated in the next iteration.

[0031] Example 2 like Figure 1 As shown, the method for assessing the charging load capacity of an active flexible interconnected distribution network provided by this invention includes the following steps: Step 1: Obtain the power distribution network structure, power distribution equipment operating parameters, flexible interconnection device parameters, typical charging scenario load parameters, and distributed power output parameters.

[0032] This example is based on an improved IEEE-33 node distribution system, which includes 33 nodes, 32 distribution lines, a power base of 10MW, a voltage base of 12.66kV, and a total load of 3.715MW + j2.3MVar. It includes one main transformer with an active power capacity of 5.5MW and a reactive power capacity of 4MVar connected at node 1, and four distributed photovoltaic (PV) power sources with capacities of 2.5MW, 2MW, 2.5MW, and 3MW connected at nodes 3, 9, 20, and 22, respectively. The 24-hour distributed PV output and load power curves are shown below. Figure 2 As shown in the figure. Three-port flexible interconnection devices with a port capacity of 0.2MW are also deployed in the distribution network, connecting nodes 18, 22, and 33. Charging loads are planned to be connected to nodes 11, 18, 25, and 32, where node 11 represents an office area scenario, nodes 18 and 32 represent residential area scenarios, and node 25 represents a commercial area scenario. Typical load curves for different charging scenarios are shown in the figure. Figure 3 As shown.

[0033] Step 2: Based on the distribution network structure, power equipment operating parameters, flexible interconnection device parameters, and typical charging scenario load parameters in Step 1, establish a two-stage evaluation model for the charging load capacity carrying capacity of the flexible interconnection distribution network. The first stage evaluates the range of charging load capacity that the distribution network can access, determined by the upper and lower limits of the carrying capacity. The second stage verifies whether the worst access scenario within this capacity range affects the safe operation of the distribution network.

[0034] Step 3: Based on Step 2, and according to the distributed power output parameters from Step 1, establish a distributed power model, use a box-type uncertainty set to characterize its output uncertainty, and add Stage 2 to establish an active flexible interconnected distribution network charging load capacity assessment model. Here, the maximum allowable power curtailment factor for distributed photovoltaic power is set to 0, meaning that distributed photovoltaic power curtailment is not allowed. The uncertainty of photovoltaic output is set to 0.2. Considering that in actual operation it is rare for all distributed photovoltaic power to be at its maximum / minimum output at all times, the "budget constraint" is set to 20.

[0035] Step 4: Based on the nonlinear and hierarchical characteristics of the active flexible interconnected distribution network charging load capacity carrying capacity assessment model in Step 3, an iterative solution method is designed using second-order cone relaxation, circumscribed polygon approximation, and improved column and constraint relaxation algorithms to obtain the charging load capacity carrying capacity assessment results.

[0036] Firstly, to verify the invention's dual objectives of ensuring safe operation of the distribution network while balancing charging load access and guaranteeing the absorption of new energy sources, the evaluation results of the distribution network's charging load capacity carrying capacity are as follows: Figure 4 As shown, the carrying capacity result consists of an interval range defined by upper and lower boundaries. The upper boundary is limited by the safe operation of equipment such as transformers and lines in the distribution network and node voltage, and is the maximum value of the charging load capacity that can be connected. The lower boundary is affected by the output of new energy sources such as distributed photovoltaics, and is the minimum value of the charging load capacity that needs to be connected to ensure the consumption of new energy and the safe operation of the distribution network. In this sense, the lower boundary of the carrying capacity is significantly affected by the maximum allowable power abandonment of distributed power sources. By changing the maximum allowable power abandonment coefficient of distributed power sources, the carrying capacity assessment results are shown in Table 1.

[0037] Table 1 Comparison of the impact of the maximum permissible power abandonment factor of distributed generation on the evaluation results

[0038] Flexible interconnection devices not only provide additional controllable channels for power transmission in the distribution network, but also offer some reactive power support. The load-bearing capacity assessment results before and after the deployment of flexible interconnection devices are compared... Figure 5 and Figure 6 As shown in Table 2, it can be seen that introducing flexible interconnection devices into the distribution network can significantly improve the carrying capacity of charging loads. The positive effect of flexible interconnection devices is influenced by their capacity. Table 2 shows the impact of changes in the port capacity of flexible interconnection devices on the carrying capacity assessment results. It can be seen that as the capacity increases, the carrying range formed by the upper and lower boundaries continuously expands, indicating a continuous improvement in network carrying capacity. However, when the capacity increases to a certain scale, the carrying capacity no longer changes due to the limitation of the capacity of the distribution lines connected to the ports.

[0039] Table 2 Comparison of the impact of flexible interconnect device capacity changes on the assessment results

[0040] Example 3 like Figure 7 As shown, the active flexible interconnected distribution network charging load capacity assessment system provided by the present invention includes: Parameter acquisition unit: Acquires power distribution network structure, power distribution equipment operating parameters, flexible interconnection device parameters, typical charging scenario load parameters, and distributed power output parameters; Two-stage evaluation model establishment unit: Based on the distribution network structure, distribution equipment operating parameters, flexible interconnection device parameters, and typical charging scenario load parameters, a two-stage evaluation model for the charging load capacity carrying capacity of the flexible interconnection distribution network is established. The first stage evaluates the range of charging load capacity that the distribution network can access, determined by the upper and lower limits of the carrying capacity. The second stage verifies whether the worst access scenario within this capacity range affects the safe operation of the distribution network. Distributed power generation model establishment unit: Based on the two-stage evaluation model of the charging load capacity of the flexible interconnected distribution network, a distributed power generation model is established according to the output parameters of the distributed power generation. The output uncertainty is represented by a box-type uncertainty set and a second stage is added to establish an evaluation model of the charging load capacity of the active flexible interconnected distribution network. Model Solving Unit: Based on the nonlinear and hierarchical characteristics of the active flexible interconnected distribution network charging load capacity carrying capacity assessment model, an iterative solution method is designed using second-order cone relaxation, circumscribed polygon approximation, and improved column and constraint relaxation algorithms to obtain the charging load capacity carrying capacity assessment results.

[0041] In the two-stage evaluation model establishment unit of this embodiment, a two-stage evaluation model for the charging load capacity carrying capacity of the flexible interconnected distribution network is established. The first stage evaluates the range of charging load capacity that the distribution network can access, determined by the upper and lower limits of the carrying capacity. Its objective function is shown in formula (1): (1) In the formula, t This is a time marker, with values ​​of 1, 2, ... T ; T This represents the total number of time periods; i This is the identifier for the distribution network node, with values ​​of 1, 2, ... N ; N This represents the total number of nodes. The spatiotemporal weighting factor of the distribution network node is set by the evaluator or calculated from the typical load curve according to the service scenario type of the charging infrastructure access point, as shown in formula (2). Indicates distribution network node iThe maximum available charging load capacity at time t (in MW); Indicates distribution network node i exist t Lower limit of available charging load capacity at any time / MW; (2) In the formula: Represents a node i Charging scenarios in t Typical charging load curve load value at any given time; In terms of operation, the upper and lower limits of the access charging load capacity are limited by the rated capacity of the charging and discharging facilities already installed at the node. In terms of planning, the capacity limit of the charging and discharging facilities installed at the node is affected by both investment costs and available space. Therefore, the constraints of Phase 1 are as shown in Equation (3): (3) After obtaining the range of charging load capacity that can be accessed by the distribution network in Phase 1 assessment, Phase 2 verifies whether the worst access scenario within this capacity range affects the safe operation of the distribution network. Its objective function is shown in Formula (4): (4) In the formula, and This represents the non-negative relaxation variable introduced into the power balance constraints of the distribution network to ensure that the stage 2 verification model has a solution; Indicates the actual connected charging load capacity. The set of accessible charging load capacity ranges obtained from the Phase 1 assessment is shown in Equation (5): (5) Phase 2 feasibility verification considers operational constraints such as power distribution equipment and power balance, as well as safety limits on node voltage and equipment capacity; Formula (6) indicates that the active and reactive power output of the distribution network transformer should not exceed its capacity limit; Formula (7) indicates that the voltage of the distribution network node and the power transmitted by the line should not exceed the limit; The linearized Dist-flow model is used to calculate the power flow of the distribution network with radial characteristics, as shown in Formula (8); Formula (9) is the power balance and capacity limit constraint of the flexible interconnection device; Formula (10) is the active and reactive power balance constraint of the distribution network after introducing slack variables; (6) (7) (8) (9) (10) In the formula, pgi,t Indicates distribution transformer gi At any moment t Active power output / MW; p gi,max and p gi,min They represent distribution transformers gi Active power upper and lower limits / MW; q gi,t Indicates distribution transformer gi At any moment t No power output / MW; q gi,max and q gi,min They represent distribution transformers gi Reactive power upper and lower limits / MVar; tan φ gi,max and tan φ gi,min They represent distribution transformers gi Power factor upper and lower limits; v i,t Indicates distribution network node i At any moment t Voltage amplitude / kV; v i,max and v i,min Representing nodes respectively i Voltage amplitude upper and lower limits / kV; p ij,max Indicates the line ij Maximum active power / MW; R ij and X ij They represent the lines respectively. ij Resistance and reactance values ​​per kΩ; p ij,t and q ij,t They represent the lines respectively. ij Active power flowing through (MW) and reactive power (MVar); and Representing flexible interconnect devices i port n Active power input and power loss / MW; and These represent the reactive power / MVar and capacity / MVA of the flexible interconnect device port, respectively. and These represent the upper and lower limits of reactive power at the port / MVar, respectively. The power loss factor; p i,t andq i,t Representing nodes respectively i At any moment t Active and reactive loads / MW.

[0042] In the distributed power source model establishment unit of this embodiment, a distributed power source model is established as shown in formula (11): (11) In the formula: , and These represent distributed power sources. drg At any moment t Active power output, available power, and power curtailment / MW; This represents the maximum permissible power abandonment factor for distributed generation; tan φ drg Indicates the power factor of a distributed power source; This represents the reactive power output of the distributed power source / MVar; The uncertainty of distributed photovoltaic and distributed wind power output is characterized by a box-type uncertainty set, as shown in formula (12): (12) In the formula: The predicted value of available output power of distributed generation in MW; and The upper and lower deviations between the predicted and actual values ​​are expressed in MW. Considering the impact of distributed generation on the charging load capacity of the distribution network, an evaluation model for the charging load capacity of the active flexible interconnected distribution network is established. The output of distributed generation is added to the power balance equation in formula (10), and it is modified to formula (13): (13).

[0043] In the model solving unit of this embodiment, for the uncertain sets shown in formulas (5) and (12), the uncertain parameters take values ​​at the boundary under the worst scenario. By introducing auxiliary variables, they are modified as shown in formulas (14) and (15), respectively: (14) (15) In the formula: and These are 0-1 variables that indicate the upper or lower boundary of the set of charging load capacity access values; and These are 0-1 variables that indicate the upper or lower boundary of the set of available output power from distributed power sources; and These are the intermediate value and deviation value of the uncertain set, respectively, determined by formula (12); Since a certain uncertain parameter cannot simultaneously take the maximum and minimum values, it satisfies formulas (40) and (41). In addition, for the uncertainty of the output of distributed power sources, the “budget constraint” shown in formula (42) is used to limit the conservatism of the value of the uncertain parameter. (16) (17) (18) For the capacity constraint of the flexible interconnection device with a second-order cone shape in formula (9), an external connection is adopted. m The method for approximating polygons is linearized, as shown in formula (19): (19) In the formula: For the edge k The normal vector; For the constraint of the operating loss equation of the flexible interconnection device in formula (9), first use second-order cone relaxation to relax it to the formula shown, and then use the same method of circumscribed polygon approximation to linearize it. (20) Based on the above, the assessment model for the charging load capacity of the active flexible interconnected distribution network is summarized as a two-stage problem consisting of formulas (21) and (22): (twenty one) (twenty two) In the formula: Represents a unit vector; and To represent the decision variables of the distribution network operation status in the stage two feasibility verification model, the specific expression is shown in formula (23). and To run the constraint coefficient matrix, and The matrix corresponding to the constant terms; (twenty three) The active flexible interconnected distribution network charging load capacity carrying capacity assessment model is solved by an improved column and constraint generation algorithm. After the solution in stage two is fed back to stage one, it is a set of 0-1 variable markers indicating whether the upper or lower limit is taken. The corresponding generated constraints are shown in formula (24). The iterative processing of the uncertainty of distributed power output is still the traditional column and constraint generation algorithm. (twenty four) in, Indicates the first k Variables generated in the next iteration.

[0044] Example 4 The present invention provides an electronic device comprising: a processor and a memory coupled to the processor, the memory storing a computer program, wherein when the computer program is executed by the processor, the steps of the active flexible interconnected distribution network charging load capacity carrying capacity assessment method are implemented.

[0045] The electronic device may also include one or more of a multimedia component, an input / output (I / O) interface, and a communication component.

[0046] The processor controls the overall operation of the electronic device to complete all or part of the steps in the storage medium sharing method. The memory stores various types of data to support the operation of the electronic device. This data may include, for example, instructions for any application or method operating on the electronic device, and application-related data such as contact data, sent and received messages, pictures, audio, video, etc. The memory can be implemented using any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. Multimedia components may include a screen and audio components. The screen may be, for example, a touchscreen, and the audio components are used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in memory or transmitted via a communication component. The audio component also includes at least one speaker for outputting audio signals. The I / O interface provides an interface between the processor and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual or physical. The communication component is used for wired or wireless communication between the electronic device and other devices. Wireless communication includes Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof; therefore, the corresponding communication component may include a Wi-Fi module, a Bluetooth module, or an NFC module.

[0047] In an exemplary embodiment, the electronic device may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components for performing a storage medium sharing method.

[0048] Example 5 The present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the active flexible interconnected distribution network charging load capacity carrying capacity assessment method.

[0049] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0050] This application is described with reference to flowchart illustrations and / or block diagrams of methods, systems, and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A system that specifies functions in one or more boxes.

[0051] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0052] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0053] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the scope of the invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0054] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can be appropriately combined to form other embodiments that can be understood by those skilled in the art. The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A method for assessing the charging load capacity carrying capacity of an active flexible interconnected distribution network, characterized in that, Includes the following steps: Step 1: Obtain the power distribution network structure, power distribution equipment operating parameters, flexible interconnection device parameters, typical charging scenario load parameters, and distributed power output parameters; Step 2: Based on the distribution network structure, distribution equipment operating parameters, flexible interconnection device parameters, and typical charging scenario load parameters, establish a two-stage evaluation model for the charging load capacity carrying capacity of the flexible interconnection distribution network. The first stage evaluates the range of charging load capacity that the distribution network can access, determined by the upper and lower limits of the carrying capacity. The second stage verifies whether the worst access scenario within this capacity range affects the safe operation of the distribution network. Step 3: Based on the two-stage evaluation model of the charging load capacity of the flexible interconnected distribution network, establish a distributed power source model according to the output parameters of the distributed power source, use a box-type uncertainty set to characterize its output uncertainty and add stage two to establish an evaluation model of the charging load capacity of the active flexible interconnected distribution network. Step 4: Based on the nonlinear and hierarchical characteristics of the active flexible interconnected distribution network charging load capacity carrying capacity assessment model, an iterative solution method is designed using second-order cone relaxation, circumscribed polygon approximation, and improved column and constraint relaxation algorithms to obtain the charging load capacity carrying capacity assessment results.

2. The method for assessing the charging load capacity carrying capacity of an active flexible interconnected distribution network according to claim 1, characterized in that, In step two, a two-stage evaluation model for the charging load capacity carrying capacity of the flexible interconnected distribution network is established. The first stage evaluates the range of charging load capacity that the distribution network can access, determined by the upper and lower limits of the carrying capacity. Its objective function is shown in formula (1): (1) In the formula, t This is a time marker, with values ​​of 1, 2, ... T ; T This represents the total number of time periods; i This is the identifier for the distribution network node, with values ​​of 1, 2, ... N ; N This represents the total number of nodes. The spatiotemporal weighting factor of the distribution network node is set by the evaluator or calculated from the typical load curve according to the service scenario type of the charging infrastructure access point, as shown in formula (2). Indicates distribution network node i The maximum available charging load capacity at time t (in MW); Indicates distribution network node i exist t Lower limit of available charging load capacity at any time / MW; (2) In the formula: Represents a node i Charging scenarios in t Typical charging load curve load value at any given time; In terms of operation, the upper and lower limits of the access charging load capacity are limited by the rated capacity of the charging and discharging facilities already installed at the node. In terms of planning, the capacity limit of the charging and discharging facilities installed at the node is affected by both investment costs and available space. Therefore, the constraints of Phase 1 are as shown in Equation (3): (3) After obtaining the range of charging load capacity that can be accessed by the distribution network in Phase 1 assessment, Phase 2 verifies whether the worst access scenario within this capacity range affects the safe operation of the distribution network. Its objective function is shown in Formula (4): (4) In the formula, and This represents the non-negative relaxation variable introduced into the power balance constraints of the distribution network to ensure that the stage 2 verification model has a solution; Indicates the actual connected charging load capacity. The set of accessible charging load capacity ranges obtained from the Phase 1 assessment is shown in Equation (5): (5) Phase 2 feasibility verification considers operational constraints such as power distribution equipment and power balance, as well as safety limits on node voltage and equipment capacity; Formula (6) indicates that the active and reactive power output of the distribution network transformer should not exceed its capacity limit; Formula (7) indicates that the voltage of the distribution network node and the power transmitted by the line should not exceed the limit; The linearized Dist-flow model is used to calculate the power flow of the distribution network with radial characteristics, as shown in Formula (8); Formula (9) is the power balance and capacity limit constraint of the flexible interconnection device; Formula (10) is the active and reactive power balance constraint of the distribution network after introducing slack variables; (6) (7) (8) (9) (10) In the formula, p gi,t Indicates distribution transformer gi At any moment t Active power output / MW; p gi,max and p gi,min They represent distribution transformers gi Active power upper and lower limits / MW; q gi,t Indicates distribution transformer gi At any moment t No power output / MW; q gi,max and q gi,min They represent distribution transformers gi Reactive power upper and lower limits / MVar; tan φ gi,max and tan φ gi,min They represent distribution transformers gi Power factor upper and lower limits; v i,t Indicates distribution network node i At any moment t Voltage amplitude / kV; v i,max and v i,min Representing nodes respectively i Voltage amplitude upper and lower limits / kV; p ij,max Indicates the line ij Maximum active power / MW; R ij and X ij They represent the lines respectively. ij Resistance and reactance values ​​per kΩ; p ij,t and q ij,t They represent the lines respectively. ij Active power flowing through (MW) and reactive power (MVar); and Representing flexible interconnect devices i port n Active power input and power loss / MW; and These represent the reactive power / MVar and capacity / MVA of the flexible interconnect device port, respectively. and These represent the upper and lower limits of reactive power at the port / MVar, respectively. The power loss factor; p i,t and q i,t Representing nodes respectively i At any moment t Active and reactive loads / MW.

3. The method for assessing the charging load capacity carrying capacity of an active flexible interconnected distribution network according to claim 2, characterized in that, In step three, a distributed power source model is established, as shown in formula (11): (11) In the formula: , and These represent distributed power sources. drg At any moment t Active power output, available power, and power curtailment / MW; This represents the maximum permissible power abandonment factor for distributed generation; tan φ drg Indicates the power factor of a distributed power source; This represents the reactive power output of the distributed power source / MVar; The uncertainty of distributed photovoltaic and distributed wind power output is characterized by a box-type uncertainty set, as shown in formula (12): (12) In the formula: The predicted value of available output power of distributed generation in MW; and The upper and lower deviations between the predicted and actual values ​​are expressed in MW. Considering the impact of distributed generation on the charging load capacity of the distribution network, an evaluation model for the charging load capacity of the active flexible interconnected distribution network is established. The output of distributed generation is added to the power balance equation in formula (10), and it is modified to formula (13): (13)。 4. The method for assessing the charging load capacity carrying capacity of an active flexible interconnected distribution network according to claim 3, characterized in that, In step four, for the uncertain sets shown in formulas (5) and (12), the uncertain parameters take values ​​at the boundaries in the worst-case scenario. By introducing auxiliary variables, they are modified as shown in formulas (14) and (15), respectively: (14) (15) In the formula: and These are 0-1 variables that indicate the upper or lower boundary of the set of charging load capacity access values; and These are 0-1 variables that indicate the upper or lower boundary of the set of available output power from distributed power sources; and These are the intermediate value and deviation value of the uncertain set, respectively, determined by formula (12); Since a certain uncertain parameter cannot simultaneously take the maximum and minimum values, it satisfies formulas (40) and (41). In addition, for the uncertainty of the output of distributed power sources, the "budget constraint" shown in formula (42) is used to limit the conservatism of the value of the uncertain parameter. (16) (17) (18) For the capacity constraint of the flexible interconnection device with a second-order cone shape in formula (9), an external connection is adopted. m The method for approximating polygons is linearized, as shown in formula (19): (19) In the formula: For the edge k The normal vector; For the constraint of the operating loss equation of the flexible interconnection device in formula (9), first use second-order cone relaxation to relax it to the formula shown, and then use the same method of circumscribed polygon approximation to linearize it. (20) Based on the above, the assessment model for the charging load capacity of the active flexible interconnected distribution network is summarized as a two-stage problem consisting of formulas (21) and (22): (21) (22) In the formula: Represents a unit vector; and To represent the decision variables of the distribution network operation status in the stage two feasibility verification model, the specific expression is shown in formula (23). and To run the constraint coefficient matrix, and The matrix corresponding to the constant terms; (23) The active flexible interconnected distribution network charging load capacity carrying capacity assessment model is solved by an improved column and constraint generation algorithm. After the solution in stage two is fed back to stage one, it is a set of 0-1 variable markers indicating whether the upper or lower limit is taken. The corresponding generated constraints are shown in formula (24). The iterative processing of the uncertainty of distributed power output is still the traditional column and constraint generation algorithm. (24) in, Indicates the first k Variables generated in the next iteration.

5. An active flexible interconnected distribution network charging load capacity carrying capacity assessment system, characterized in that, include: Parameter acquisition unit: Acquires power distribution network structure, power distribution equipment operating parameters, flexible interconnection device parameters, typical charging scenario load parameters, and distributed power output parameters; Two-stage evaluation model establishment unit: Based on the distribution network structure, distribution equipment operating parameters, flexible interconnection device parameters, and typical charging scenario load parameters, a two-stage evaluation model for the charging load capacity carrying capacity of the flexible interconnection distribution network is established. The first stage evaluates the range of charging load capacity that the distribution network can access, determined by the upper and lower limits of the carrying capacity. The second stage verifies whether the worst access scenario within this capacity range affects the safe operation of the distribution network. Distributed power generation model establishment unit: Based on the two-stage evaluation model of the charging load capacity of the flexible interconnected distribution network, a distributed power generation model is established according to the output parameters of the distributed power generation. The output uncertainty is represented by a box-type uncertainty set and a second stage is added to establish an evaluation model of the charging load capacity of the active flexible interconnected distribution network. Model Solving Unit: Based on the nonlinear and hierarchical characteristics of the active flexible interconnected distribution network charging load capacity carrying capacity assessment model, an iterative solution method is designed using second-order cone relaxation, circumscribed polygon approximation, and improved column and constraint relaxation algorithms to obtain the charging load capacity carrying capacity assessment results.

6. The active flexible interconnected distribution network charging load capacity assessment system according to claim 5, characterized in that, In the two-stage evaluation model establishment unit, a two-stage evaluation model for the charging load capacity carrying capacity of the flexible interconnected distribution network is established. The first stage evaluates the range of charging load capacity that the distribution network can access, determined by the upper and lower limits of the carrying capacity. Its objective function is shown in formula (1): (1) In the formula, t This is a time marker, with values ​​of 1, 2, ... T ; T This represents the total number of time periods; i This is the identifier for the distribution network node, with values ​​of 1, 2, ... N ; N This represents the total number of nodes. The spatiotemporal weighting factor of the distribution network node is set by the evaluator or calculated from the typical load curve according to the service scenario type of the charging infrastructure access point, as shown in formula (2). Indicates distribution network node i The maximum available charging load capacity at time t (in MW); Indicates distribution network node i exist t Lower limit of available charging load capacity at any time / MW; (2) In the formula: Represents a node i Charging scenarios in t Typical charging load curve load value at any given time; In terms of operation, the upper and lower limits of the access charging load capacity are limited by the rated capacity of the charging and discharging facilities already installed at the node. In terms of planning, the capacity limit of the charging and discharging facilities installed at the node is affected by both investment costs and available space. Therefore, the constraints of Phase 1 are as shown in Equation (3): (3) After obtaining the range of charging load capacity that can be accessed by the distribution network in Phase 1 assessment, Phase 2 verifies whether the worst access scenario within this capacity range affects the safe operation of the distribution network. Its objective function is shown in Formula (4): (4) In the formula, and This represents the non-negative relaxation variable introduced into the power balance constraints of the distribution network to ensure that the stage 2 verification model has a solution; Indicates the actual connected charging load capacity. The set of accessible charging load capacity ranges obtained from the Phase 1 assessment is shown in Equation (5): (5) Phase 2 feasibility verification considers operational constraints such as power distribution equipment and power balance, as well as safety limits on node voltage and equipment capacity; Formula (6) indicates that the active and reactive power output of the distribution network transformer should not exceed its capacity limit; Formula (7) indicates that the voltage of the distribution network node and the power transmitted by the line should not exceed the limit; The linearized Dist-flow model is used to calculate the power flow of the distribution network with radial characteristics, as shown in Formula (8); Formula (9) is the power balance and capacity limit constraint of the flexible interconnection device; Formula (10) is the active and reactive power balance constraint of the distribution network after introducing slack variables; (6) (7) (8) (9) (10) In the formula, p gi,t Indicates distribution transformer gi At any moment t Active power output / MW; p gi,max and p gi,min They represent distribution transformers gi Active power upper and lower limits / MW; q gi,t Indicates distribution transformer gi At any moment t No power output / MW; q gi,max and q gi,min They represent distribution transformers gi Reactive power upper and lower limits / MVar; tan φ gi,max and tan φ gi,min They represent distribution transformers gi Power factor upper and lower limits; v i,t Indicates distribution network node i At any moment t Voltage amplitude / kV; v i,max and v i,min Representing nodes respectively i Voltage amplitude upper and lower limits / kV; p ij,max Indicates the line ij Maximum active power / MW; R ij and X ij They represent the lines respectively. ij Resistance and reactance values ​​per kΩ; p ij,t and q ij,t They represent the lines respectively. ij Active power flowing through (MW) and reactive power (MVar); and Representing flexible interconnect devices i port n Active power input and power loss / MW; and These represent the reactive power / MVar and capacity / MVA of the flexible interconnect device port, respectively. and These represent the upper and lower limits of reactive power at the port / MVar, respectively. The power loss factor; p i,t and q i,t Representing nodes respectively i At any moment t Active and reactive loads / MW.

7. The active flexible interconnected distribution network charging load capacity assessment system according to claim 6, characterized in that, In the distributed power source model establishment unit, a distributed power source model is established as shown in formula (11): (11) In the formula: , and These represent distributed power sources. drg At any moment t Active power output, available power, and power curtailment / MW; This represents the maximum permissible power abandonment factor for distributed generation; tan φ drg Indicates the power factor of a distributed power source; This represents the reactive power output of the distributed power source / MVar; The uncertainty of distributed photovoltaic and distributed wind power output is characterized by a box-type uncertainty set, as shown in formula (12): (12) In the formula: The predicted value of available output power of distributed generation in MW; and The upper and lower deviations between the predicted and actual values ​​are expressed in MW. Considering the impact of distributed generation on the charging load capacity of the distribution network, an evaluation model for the charging load capacity of the active flexible interconnected distribution network is established. The output of distributed generation is added to the power balance equation in formula (10), and it is modified to formula (13): (13)。 8. The active flexible interconnected distribution network charging load capacity assessment system according to claim 7, characterized in that, In the model solving unit, for the uncertain sets shown in formulas (5) and (12), the uncertain parameters take values ​​at the boundary under the worst scenario. By introducing auxiliary variables, they are modified as shown in formulas (14) and (15), respectively: (14) (15) In the formula: and These are 0-1 variables that indicate the upper or lower boundary of the set of charging load capacity access values; and These are 0-1 variables that indicate the upper or lower boundary of the set of available output power from distributed power sources; and These are the intermediate value and deviation value of the uncertain set, respectively, determined by formula (12); Since a certain uncertain parameter cannot simultaneously take the maximum and minimum values, it satisfies formulas (40) and (41). In addition, for the uncertainty of the output of distributed power sources, the "budget constraint" shown in formula (42) is used to limit the conservatism of the value of the uncertain parameter. (16) (17) (18) For the capacity constraint of the flexible interconnection device with a second-order cone shape in formula (9), an external connection is adopted. m The method for approximating polygons is linearized, as shown in formula (19): (19) In the formula: For the edge k The normal vector; For the constraint of the operating loss equation of the flexible interconnection device in formula (9), first use second-order cone relaxation to relax it to the formula shown, and then use the same method of circumscribed polygon approximation to linearize it. (20) Based on the above, the assessment model for the charging load capacity of the active flexible interconnected distribution network is summarized as a two-stage problem consisting of formulas (21) and (22): (21) (22) In the formula: Represents a unit vector; and To represent the decision variables of the distribution network operation status in the stage two feasibility verification model, the specific expression is shown in formula (23). and To run the constraint coefficient matrix, and The matrix corresponding to the constant terms; (23) The active flexible interconnected distribution network charging load capacity carrying capacity assessment model is solved by an improved column and constraint generation algorithm. After the solution in stage two is fed back to stage one, it is a set of 0-1 variable markers indicating whether the upper or lower limit is taken. The corresponding generated constraints are shown in formula (24). The iterative processing of the uncertainty of distributed power output is still the traditional column and constraint generation algorithm. (24) in, Indicates the first k Variables generated in the next iteration.

9. An electronic device, characterized in that, include: A processor and a memory coupled to the processor, the memory storing a computer program that, when executed by the processor, implements the steps of the active flexible interconnected distribution network charging load capacity assessment method according to any one of claims 1-4.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the active flexible interconnected distribution network charging load capacity assessment method according to any one of claims 1-4.