Method for improving toughness of power distribution network based on controllable flexible load and power distribution network reconstruction

Abstract

The invention belongs to the technical field of power distribution network toughness improvement, and provides a method for improving the toughness of a power distribution network based on a controllable flexible load and power distribution network reconstruction. Secondly, establishing a 5G base station load linear model based on data traffic and a schedulable capacity model considering a power-off standby demand; then, establishing a demand response coefficient model considering the coupling relation between the load at the current moment and the electricity price at other moments, and obtaining a linear relation between the charging power and the electricity price variation of the electric vehicle; then, establishing a single-period single-source radiation type branch power flow model, and linearly converting the single-period single-source radiation type branch power flow model into a second-order cone power flow model; and finally, establishing a power distribution network fault reconstruction model considering the limitation of the number of actions, and reconstructing the tie line of the power distribution network. The method can comprehensively consider the flexibility coincidence and line reconstruction to improve the toughness of the power distribution network.

Description

Technical Field

[0001] This invention relates to the field of distribution network resilience enhancement technology, and in particular to a method for enhancing distribution network resilience based on controllable flexible loads and distribution network reconfiguration. Background Technology

[0002] Typhoons can easily cause power line breaks and transformer failures, leading to load shedding accidents and affecting the distributed wind and solar power output of the distribution network. Therefore, it is necessary to improve the resilience of the distribution network during typhoons and proactively respond to disaster scenarios under typhoon weather. At the same time, the distribution network contains a large number of flexible loads, such as temperature-controlled loads, electric vehicles, and 5G base stations. Therefore, there is an urgent need for a solution that can comprehensively address flexible loads and distribution network reconfiguration to enhance the resilience of the distribution network. Summary of the Invention

[0003] The purpose of this invention is to provide a method for improving the resilience of distribution networks based on controllable flexible loads and distribution network reconfiguration, which can improve the resilience of distribution networks by combining flexible loads and distribution network reconfiguration.

[0004] The technical solution adopted by this invention to solve its technical problem is as follows: The method for improving the resilience of distribution networks based on controllable flexible loads and distribution network reconfiguration includes the following steps: Establish indicators for assessing the resilience of power distribution networks; Establish a linear load model for 5G base stations based on data traffic and a schedulable capacity model for power outage backup requirements; A demand response coefficient model considering the coupling relationship between the current load and the electricity price at other times is established to obtain the linear relationship between electric vehicle charging power and electricity price change. A single-time, single-source radial branch power flow model was established and linearly transformed into a second-order cone power flow model. Establish a distribution network fault reconfiguration model that considers the limitation on the number of actions, and reconfigure the tie lines of the distribution network.

[0005] As a further optimization, the distribution network resilience assessment indicators include the impact indicators of significant load power loss and the indicators of new system dispatch costs; The loads in the important load power loss impact index include loads of various importance levels, namely: Level 1 loads, Level 2 loads, and Level 3 loads; The load importance levels in the distribution network are assigned to each node, that is: , in, This is a set of time periods during the disaster. It is the set of the number of nodes in the power grid. The importance levels are respectively Load loss, , , These represent the load power failure penalty coefficients for importance levels one, two, and three, respectively, and require... , This represents the comprehensive value of the impact indicators of the power loss of critical loads in the distribution network under disaster conditions. This represents the comprehensive value of the impact indicators of the power loss of critical loads in the distribution network under disaster conditions. These are the load loss amounts for Level 1, Level 2, and Level 3 loads, respectively. The penalty coefficient for line repair. This refers to the total number of branches in the post-disaster power distribution network. For node sequence number, Branch number, The time period number during the disaster. The duration is the length of the time period. For branch current, For branch resistance, These are the sets of nodes in the distribution network with load importance levels of Level 1, Level 2, and Level 3, respectively. The load importance level is number 1 A collection of nodes for a class; The newly added scheduling cost indicator of the system is expressed as follows: , in, Add a comprehensive value to the system's scheduling cost indicator. This is an indicator of the economic losses caused by power outages during a disaster. The additional cost indicators resulting from demand response and emergency dispatch. For load time shift response electricity, The amount of electricity that the energy storage system participates in dispatching. For the first The power loss weighting factor for each branch. For the first in the disaster branch road during the time period The amount of power lost, These are the load shift response cost coefficient, load reduction response cost coefficient, emergency generation cost coefficient, and switch reconfiguration cost coefficient, respectively. This refers to the number of tie lines in the distribution network that perform reconfiguration actions. The scale of investment in energy storage and reinforcement lines based on post-disaster experience. For nodes During the period Load time-shift power, For nodes During the period Emergency power generation capacity.

[0006] As a further optimization, before establishing the 5G base station load linear model based on data traffic, the method further includes: obtaining the 5G base station structure, wherein the internal structure of the 5G base station includes communication equipment, air conditioning equipment and power supply equipment; The communication device includes a transmission device, an active antenna unit, and a baseband processing unit, used for transmitting, receiving, and processing wireless signals; The power supply equipment includes a switching power supply and an energy storage battery, which are used to provide DC power to the main equipment in the 5G base station. The air conditioning equipment is used to maintain the indoor ambient temperature of the 5G base station. When establishing the linear load model of 5G base stations based on data traffic, the power load of 5G base stations is decomposed into static load and dynamic load, as follows: , in, For the power load of 5G base stations, This refers to the static load in 5G base stations. For dynamic load in 5G base stations; The established linear load model for 5G base stations based on data traffic includes a static load model and a dynamic load model. The static load model is represented as follows: , in, For BBU power consumption, Baseline power consumption for AAU The power consumption of the AAU caused by downlink signaling is set to a fixed value. This refers to the power consumption of the switching power supply. The dynamic load model is expressed as follows: , in, Incremental load, This refers to the load on the air conditioning equipment.

[0007] As a further optimization, the steps for obtaining the AAU incremental load in the dynamic load model are as follows: The total data traffic in the 5G base station coverage area is represented as: , in, for Total data traffic in the region during the time period for The number of 5G user-activated terminals in this area during the specified time period. For the first 5G user-activated terminals Total 5G data traffic used during the time period; Based on the relationship between data traffic and downlink data rate, The total data traffic in the 5G base station coverage area during a given time period is used to obtain the data traffic of a single 5G base station. Equivalent downlink data rate at any given time : , in, The time granularity of the division; The communication load rate is defined as the ratio of the total downlink data rate of all users accessing the 5G base station within its coverage area to the maximum downlink rate of the 5G base station: , in, This marks the turning point for the downlink data rate carried by a 5G base station, indicating a change in the linear growth trend of power consumption after the 5G base station has been subjected to a certain communication load. This is the maximum downlink data rate that a 5G base station can support. The incremental load of AAU is expressed as the dynamic incremental load of the AAU equipment, as follows: , In the formula: For the dynamic incremental load of AAU equipment, This marks the power consumption inflection point for the AAU device. This represents the maximum power consumption of the AAU device.

[0008] As a further optimization, in the dynamic load model, the air conditioning equipment load Represented in a temperature-related form ,Right now: , in, This indicates the area of ​​the building's outer perimeter. Indicates the heat transfer coefficient. This indicates the temperature inside the computer room. Indicates the ambient temperature. This is the sum of the power consumption of the 5G base station AAU and BBU. The efficiency coefficient for converting energy consumption of 5G base station AAU and BBU into non-heat components. Indicates the energy efficiency ratio of air conditioning equipment. Energy utilization efficiency coefficient for 5G base station AAU and BBU; At this time, the dynamic load model Represented as ,Right now: .

[0009] As a further optimization, the establishment of a schedulable capacity model for power outage reserve demand includes the following steps: Establish energy storage power-capacity coupling constraints: , , , , in, and respectively 5G base station energy storage during the period The charging and discharging power, This is a charge / discharge indicator; 1 indicates charging, and 0 indicates discharging. and They are respectively 5G base station energy storage during the period The maximum charging and discharging power, and These represent the charging and discharging efficiency of energy storage for 5G base stations. For time period node The remaining power stored in the 5G base station For time period node The remaining power stored in the 5G base station For time period node The dispatchable capacity of energy storage for 5G base stations. For nodes The maximum available energy storage capacity for 5G base stations; The energy storage capacity of 5G base stations is divided into two parts: backup capacity and dispatchable capacity. The availability of dispatchable capacity depends on the energy storage battery configuration capacity, health status, and the size of the reserve capacity; , in, for 5G base station energy storage Scheduled capacity, For battery health status, , This refers to the rated capacity of the battery when it is initially connected. To take into account the minimum reserve capacity required by 5G base stations when considering communication load; Obtain the minimum reserve capacity required for 5G base stations when considering communication load. , is represented as: , in, 5G base station exist Power consumption during the period 5G base station Minimum backup power time; For distribution network nodes The feasible domain for backup energy storage aggregation of the connected 5G base station cluster is: , in, For nodes The number of 5G base stations connected. and They are nodes The maximum charging and discharging power of the 5G base station energy storage cluster, and They are nodes The charging and discharging power of the 5G base station energy storage cluster For nodes 5G base station energy storage clusters in Electricity consumption during the period For nodes The maximum schedulable capacity of the 5G base station energy storage cluster.

[0010] As a further optimization, the establishment of a demand response coefficient model that considers the coupling relationship between the current load and the electricity price at other times, to obtain the linear relationship between electric vehicle charging power and electricity price change, includes the following steps: definition Time period Electricity consumption changes within a time period The response coefficients to changes in electricity prices within the region are as follows: , ··in, and Separate the original electricity price and its change. For time period The initial electricity consumption of the electric vehicle. For time period The change in electricity consumption of electric vehicles after price changes; The scheduling duration is divided into Over a given time period, the linear relationship between electric vehicle charging power and electricity price change is obtained as follows: , in, For the first Changes in electric vehicle charging electricity consumption over a given time period For the first The original value of electric vehicle charging electricity consumption during each time period. For the first The change in electricity consumption during the first time period is relative to the first. The response coefficient of electricity price changes over a given period. For the first Electricity price changes over a period of time For the first The original electricity price for each time period, of which ; After adding the electricity price and power change to their initial values, the final power and electricity price of the electric vehicle after the electricity price response are obtained. At this point, a final electricity price ceiling and a shiftable load are set, where the final electricity price ceiling is expressed as: , in, The original electricity price for period 1. This represents the change in electricity price during period 1. This is the lower limit of the final electricity price. This is the upper limit of the final electricity price. It is the final electricity price after the load response of electric vehicles, which satisfies the minimum electricity price. and between; The load that can be moved is represented as: , in, , These are the electric vehicle charging loads before and after demand response. It is the load regulation amount.

[0011] As a further optimization, the objective function of the established single-time-period single-source radial branch power flow model is: , in, For the set of active power variables, For the set of reactive power variables, For the set of node voltage magnitude variables, For the set of branch current variables; The constraints of the established single-time single-source radial branch power flow model include line power flow constraints, voltage drop balance equations, node voltage constraints, branch current constraints, and node generator constraints. line exist The power constraints and voltage drop balance equations satisfied at time t are as follows: , , , , , , in, and branch road exist The active and reactive power flowing through at all times. and branch road exist The active and reactive power flowing through at all times. and Branch roads Resistance and reactance, branch road The current flowing through, and outflow node Active power and reactive power, and They are nodes The active and reactive power of electricity purchased from the main grid. For nodes reactive load demand, and They are nodes and voltage, For nodes During the period The active power load demand, For nodes The set of parent nodes, For nodes The set of child nodes; Node voltage constraints and branch current constraints are expressed as follows: , , in, and They are nodes exist The lower and upper voltage limits at time t, when node When it is a balanced node, we have , branch road exist The maximum current value that is allowed to flow at any given time; The node generator constraint is expressed as: , , in, and These are the minimum and maximum active power outputs of the generator, respectively. and These are the minimum and maximum values ​​of the generator's reactive power output, respectively.

[0012] As a further optimization, the linear transformation of the single-time-period single-source radial branch power flow model into a second-order cone power flow model includes the following steps: Construct new variables , Replace the square terms of voltage and current in the original constraints. , The nonlinear constraints containing the quadratic terms of active and reactive power flow of the lines are transformed, namely: , , , , , , , Pair Further relaxation yields: , Further equivalent transformation yields: In the formula: Denotes the 2-norm of a matrix; After the above steps, the nonlinear constraints in the branch power flow model can be transformed into linear constraints, or second-order cone constraints that conform to second-order cone programming. When considering the wind and solar power output at nodes, EV load, and 5G base station load, the equation is... Japanese style After correction, we get: , , in, for Time period nodes Electric vehicle load, for Time period nodes 5G base station load, and They are respectively Time period nodes The active and reactive power output of photovoltaic power. and They are respectively Time period nodes The active and reactive power output of wind power, for Time period nodes The shear load, for Time period nodes The charging and discharging power of 5G base station energy storage is positive during charging and negative during discharging. for Time period nodes The load shift of electric vehicles is positive when the load increases and negative when the load decreases.

[0013] As a further optimization, the establishment of a distribution network fault reconfiguration model that considers the limitation on the number of actions includes the following when reconfiguring the tie lines of the distribution network: Introducing the status of line switches A value of 1 indicates a closed circuit, and a value of 0 indicates an open circuit. Inequality constraints are introduced to relax the original branch power flow equations. When the circuit is open, the power flow is zero; when the circuit is closed, there are no constraints. The constraint conditions are then expressed as: , , , , , , in, For a sufficiently large positive number, that is The upper bound combination; A graph-based spanning tree method for describing the radial constraints of a distribution network is adopted, and the radial constraint conditions are expressed as follows: , , , , in, It is the set of all branches in the distribution network. This represents the number of nodes in the distribution network. Choose a binary variable for the directed edge to indicate whether it is a node in the spanning tree. Pointing to node 1 indicates selected, 0 indicates not selected. Choose a binary variable for the directed edge to indicate whether it is a node in the spanning tree. Pointing to node , branch road The on / off state, Indicates closure, Indicates disconnection. The total number of distribution network nodes. Point to node1 The directed edge selection of binary variables; When reconfiguring tie lines in a distribution network, a limit is set on the number of tie line reconfigurations, expressed as: , in, , They are time The indicator lights for opening and closing the connecting lines of the branch roads. The daily limit on the number of times the communication line can be reconstructed.

[0014] The beneficial effects of this invention are as follows: After establishing the distribution network resilience assessment index, on the one hand, this invention establishes a linear model of 5G base station load based on data traffic and a schedulable capacity model that calculates power outage reserve requirements, and establishes a demand response coefficient model that considers the coupling relationship between the current load and the electricity price at other times, thereby obtaining a linear relationship between electric vehicle charging power and electricity price changes, thus completing the consideration of the flexible load of 5G base stations and electric vehicles in how to improve distribution network resilience; on the other hand, this invention establishes a single-time-period single-source radial branch power flow model and linearly transforms it into a second-order cone power flow model, and establishes a distribution network fault reconfiguration model that considers the limitation of the number of actions, reconfiguring the tie lines of the distribution network, thereby completing the consideration of distribution network line reconfiguration in how to improve distribution network resilience. Attached Figure Description

[0015] Figure 1 This is a flowchart of a method for improving the resilience of a distribution network based on controllable flexible loads and distribution network reconfiguration in an embodiment of the present invention; Figure 2 This is a schematic diagram of a typical 5G base station structure in an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the relationship between communication load and AAU power consumption in an embodiment of the present invention; Figure 4 This is a schematic diagram illustrating the division of 5G base station energy storage capacity in an embodiment of the present invention; Figure 5 This is a schematic diagram of a radial distribution network in an embodiment of the present invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0017] Example This embodiment provides a method for improving the resilience of the distribution network based on controllable flexible loads and distribution network reconfiguration. See the flowchart below. Figure 1 The method may include the following steps: S1. Establish distribution network resilience assessment indicators; S2. Establish a linear load model for 5G base stations based on data traffic and a schedulable capacity model for power outage backup requirements. S3. Establish a demand response coefficient model that considers the coupling relationship between the current load and the electricity price at other times, and obtain the linear relationship between electric vehicle charging power and electricity price change. S4. Establish a single-time-period, single-source radial branch power flow model and linearly transform it into a second-order cone power flow model. S5. Establish a distribution network fault reconfiguration model that considers the limitation on the number of actions, and reconfigure the tie lines of the distribution network.

[0018] In this embodiment, to assess the urban power distribution network's ability to withstand typhoon disasters, quantify the impact of the disaster process, and evaluate the resilience enhancement effect of the proposed strategy, corresponding resilience indicators need to be established. The following two factors are typically considered when establishing resilience indicators: the impact of critical load power loss and the system's additional dispatch cost.

[0019] Regarding the impact indicators of critical load loss, China's power system primarily relies on the "Bilateral Agreement on Electricity Supply and Consumption" and relevant electricity market operation rules to classify loads into different importance levels. This guides power grid companies and electricity users in power dispatching and management during periods of tight power supply and demand. Generally, these levels can be categorized as follows: Level 1 loads (especially critical loads) refer to loads that are vital to national and public safety, as well as critical infrastructure for which power supply cannot be interrupted. Typical examples include emergency service facilities such as hospitals and emergency centers, fire stations, and police stations; key pumping stations for urban water supply and drainage systems; and traffic control systems such as airport control towers and railway dispatch centers.

[0020] Level 2 loads (critical loads) refer to loads that have a significant impact on economic and social life but can be temporarily interrupted in extreme circumstances. Typical examples include critical industrial production lines, government agencies, banking and financial service centers, data centers and communication base stations (including 5G base stations), large commercial centers, and important logistics centers.

[0021] Level 3 load (general load) refers to loads that can tolerate longer periods of interruption during times of energy shortage. Typical examples include general industrial enterprises, commercial shops, residential areas, and non-critical public services and entertainment facilities.

[0022] These classifications help the power grid make decisions during planned outages, emergency outages, or load dispatching, ensuring that the power supply to critical facilities and services is not affected.

[0023] Although the loads at various nodes in a distribution network often simultaneously include loads of multiple importance levels mentioned above, from the perspective of primary loads, the importance levels of loads in the distribution network can be assigned to each node rather than to each minor load type. This facilitates decision-making during distribution network dispatching. Therefore, in this embodiment, the importance levels of loads in the distribution network can be assigned to each node, i.e.: , in, This is a set of time periods during the disaster. It is the set of the number of nodes in the power grid. The importance levels are respectively Load loss, , , These represent the load power failure penalty coefficients for importance levels one, two, and three, respectively, and require... , This represents the comprehensive value of the impact indicators of the power loss of critical loads in the distribution network under disaster conditions. This represents the comprehensive value of the impact indicators of the power loss of critical loads in the distribution network under disaster conditions. These are the load loss amounts for Level 1, Level 2, and Level 3 loads, respectively. The penalty coefficient for line repair. This refers to the total number of branches in the post-disaster power distribution network. For node sequence number, Branch number, The time period number during the disaster. The duration is the length of the time period. For branch current, For branch resistance, These are the sets of nodes in the distribution network with load importance levels of Level 1, Level 2, and Level 3, respectively. The load importance level is number 1 A collection of nodes for a class.

[0024] The newly added scheduling cost metric in the system can be expressed as: , in, Add a comprehensive value to the system's scheduling cost indicator. This is an indicator of the economic losses caused by power outages during a disaster. The additional cost indicators resulting from demand response and emergency dispatch. For load time shift response electricity, The amount of electricity that the energy storage system participates in dispatching. For the first The power loss weighting factor for each branch. For the first in the disaster branch road during the time period The amount of power lost, These are the load shift response cost coefficient, load reduction response cost coefficient, emergency generation cost coefficient, and switch reconfiguration cost coefficient, respectively. This refers to the number of tie lines in the distribution network that perform reconfiguration actions. The scale of investment in energy storage and reinforcement lines based on post-disaster experience. For nodes During the period Load time-shift power, For nodes During the period Emergency power generation capacity.

[0025] Therefore, in this embodiment, the significance of setting distribution network resilience assessment indicators lies in quantifying and evaluating the distribution network's response capability and recovery speed to emergencies (such as natural disasters, technical failures, etc.). These indicators help identify vulnerabilities in the power grid, guide priority investments and improvement measures to enhance the grid's disaster resistance capabilities, ensure the continuity and reliability of power supply, promote the sustainable development and innovation of power grid infrastructure, and improve the overall resilience of cities to future challenges.

[0026] It should be noted that, see Figure 2In this embodiment, the 5G base station internally includes communication equipment, air conditioning equipment, and power supply equipment. The communication equipment includes transmission equipment, an active antenna unit (AAU), and a base band unit (BBU). The AAU is installed on a tower outside the equipment room. The communication equipment is mainly responsible for transmitting, receiving, and processing wireless signals, serving as the interface between the mobile terminal and the 5G network. The AAU uses advanced multiple-in multiple-out (MIMO) technology, effectively improving channel capacity and reliability, but also resulting in extremely high power consumption for the 5G base station, approximately 3 to 4 times that of a 4G base station. The BBU processes baseband digital signals, and its power consumption... Power consumption is relatively stable and related to baseband board configuration. AAU and BBU are the main forms of load in 5G base stations, with AAU accounting for approximately 90% of the main equipment's power consumption. Changes in AAU power consumption are highly correlated with the level of communication load it handles. Power supply equipment mainly includes switching power supplies and energy storage batteries, responsible for providing DC power to the main equipment within the 5G base station. The primary power source is external power, but the energy storage batteries will act as backup power when the power distribution network fails and power is lost. Air conditioning equipment is used to maintain the ambient temperature inside the 5G base station; in the case of an existing 5G base station equipment room, its performance is related to the ambient temperature.

[0027] In this embodiment, modeling the power consumption of 5G base stations as linearly related to data processing requirements is a simplified approach. In practical applications, this embodiment provides an intuitive and practical way to estimate and manage the energy consumption of 5G base stations. This model is based on the assumption that the power load model of 5G base stations is decomposed into static load and dynamic load, which is expressed as follows: , in, For the power load of 5G base stations, This refers to the static load in 5G base stations. This refers to the dynamic load in 5G base stations.

[0028] Therefore, the established linear load model for 5G base stations based on data traffic includes a static load model and a dynamic load model. The static load typically includes BBU load, AAU baseline load, and switching power supply load, primarily used to maintain the basic functions of the 5G base station itself. The static load model can be expressed as: , in, For BBU power consumption, Baseline power consumption for AAU The power consumption of the AAU caused by downlink signaling is set to a fixed value. This represents the power consumption of the switching power supply.

[0029] In this embodiment, the dynamic load includes the incremental load of the AAU and the load of the air conditioning equipment. Therefore, the dynamic load model can be expressed as: , in, For AAU incremental load, This refers to the load on the air conditioning equipment.

[0030] In practical applications, the incremental load of the AAU is sensitive to the communication load. When the communication load increases, the power consumption of the AAU in the master device increases. (See [link to relevant documentation]). Figure 3 The blue portion represents the static baseline load, and the orange portion represents the incremental load, the size of which is related to the communication load level.

[0031] Based on the above considerations, the step of obtaining the AAU incremental load in the dynamic load model of this embodiment is as follows: The total data traffic in the 5G base station coverage area (the number of active 5G user terminals and the amount of 5G data traffic used by users in the 5G base station coverage area are important factors affecting the communication load of 5G base stations) is represented as: , in, for Total data traffic in the region during the time period for The number of 5G user-activated terminals in this area during the specified time period. For the first 5G user-activated terminals Total 5G data traffic used during the time period; Based on the relationship between data traffic and downlink data rate, The total data traffic in the coverage area of ​​a 5G base station during a given time period can be used to obtain the data for a single 5G base station. Equivalent downlink data rate at any given time : , in, The time granularity of the division; The communication load rate is defined as the ratio of the total downlink data rate of all users accessing the 5G base station within its coverage area to the maximum downlink rate of the 5G base station: , in, This marks the turning point for the downlink data rate carried by a 5G base station, indicating a change in the linear growth trend of power consumption after the 5G base station has been subjected to a certain communication load. This represents the maximum downlink data rate that a 5G base station can support.

[0032] Therefore, the incremental load of AAU can be expressed as the dynamic incremental load of AAU equipment, as follows: , In the formula: For the dynamic incremental load of AAU equipment, This marks the power consumption inflection point for the AAU device. This represents the maximum power consumption of the AAU device.

[0033] In addition to AAU incremental load, the dynamic load of 5G base stations also includes air conditioning equipment load. The load on air conditioning equipment is mainly related to temperature. Therefore, in the dynamic load model described in this embodiment, the load on air conditioning equipment... Represented in a temperature-related form ,Right now: , in, This indicates the area of ​​the building's outer perimeter. Indicates the heat transfer coefficient. This indicates the temperature inside the computer room. Indicates the ambient temperature. This is the sum of the power consumption of the 5G base station AAU and BBU. The efficiency coefficient for converting energy consumption of 5G base station AAU and BBU into non-heat components. Indicates the energy efficiency ratio of air conditioning equipment. Energy efficiency coefficient for 5G base station main equipment (AAU and BBU); At this time, the dynamic load model Represented as ,Right now: .

[0034] It should be noted that, in this embodiment, establishing a schedulable capacity model for power outage reserve requirements may include the following steps: Establish energy storage power-capacity coupling constraints: , , , , in, and respectively 5G base station energy storage during the period The charging and discharging power, This is a charge / discharge indicator; 1 indicates charging, and 0 indicates discharging. and They are respectively 5G base station energy storage during the period The maximum charging and discharging power, and These represent the charging and discharging efficiency of energy storage for 5G base stations. For time period node The remaining power stored in the 5G base station For time period node The remaining power stored in the 5G base station For time period node The dispatchable capacity of energy storage for 5G base stations. For nodes The maximum available energy storage capacity for 5G base stations; 5G base station energy storage can act as an uninterruptible power supply (UPS) after an unexpected external power outage, ensuring the power supply for the 5G base station load. Surplus energy can be used for dispatching. Therefore, see [link to relevant documentation]. Figure 4 The energy storage capacity of 5G base stations can be divided into two parts: backup capacity and dispatchable capacity. and These are the minimum and maximum SOC thresholds set to prevent overcharging and over-discharging of the battery. The availability of dispatchable capacity depends on the energy storage battery configuration capacity, health status, and the size of the reserve capacity; , in, for 5G base station energy storage Scheduled capacity, For battery health status, , This refers to the rated capacity of the battery when it is initially connected. To take into account the minimum reserve capacity required by 5G base stations when considering communication load; Obtain the minimum reserve capacity required for 5G base stations when considering communication load. The size of the backup capacity of a 5G base station is affected by the minimum backup power time. When the 5G base station receives external power, it is expressed as: , in, 5G base station exist Power consumption during the period 5G base station Minimum backup power time; For distribution network nodes The feasible domain for backup energy storage aggregation of the connected 5G base station cluster is: , in, For nodes The number of 5G base stations connected. and They are nodes The maximum charging and discharging power of the 5G base station energy storage cluster, and They are nodes The charging and discharging power of the 5G base station energy storage cluster For nodes 5G base station energy storage clusters in Electricity consumption during the period For nodes The maximum schedulable capacity of the 5G base station energy storage cluster.

[0035] It should be noted that in order to improve the resilience of the power distribution network during typhoons, it is necessary to fully explore the load regulation capacity of electric vehicles. A load shifting control strategy for electric vehicles that takes into account the mutual influence of multi-period incentive pricing and response power can be formulated to enhance the grid's ability to guide electric vehicle charging load and reduce charging costs for electric vehicle users.

[0036] The conventional concept of how electricity prices affect load is: Electricity prices increase at any time. The load decreases over time. This concept ignores other factors. Electricity price at any given time is related to the current The effect of power at any given moment.

[0037] exist Electricity prices are constantly increasing. The load on electric vehicles at any given time will decrease due to profit motives. .exist If electricity prices are constantly increased, then the cost will increase. Load at any time The constant shift leads to The probability of an increase in the load on electric vehicles at any given time, i.e. This led to the development of a demand response coefficient model that considers the coupling relationship between the current load and the electricity price at other times.

[0038] Therefore, in this embodiment, establishing a demand response coefficient model that considers the coupling relationship between the current load and the electricity price at other times, and obtaining the linear relationship between electric vehicle charging power and electricity price change, may include the following steps: definition Time period Electricity consumption changes within a time period The response coefficients to changes in electricity prices within the region are as follows: , in, and Separate the original electricity price and its change. For time period The original electricity consumption of an electric vehicle (EV). For time period Changes in electricity consumption of electric vehicles (EVs) after price changes; The scheduling duration is divided into Over a given time period, the linear relationship between electric vehicle charging power and electricity price changes can be obtained as follows: , in, For the first Changes in electric vehicle charging electricity consumption (or equivalent electricity consumption) over a given period. For the first The original value of electric vehicle charging electricity consumption during each time period. For the first The change in electricity consumption during the first time period is relative to the first. The response coefficient of electricity price changes over a given period. For the first Electricity price changes over a period of time For the first The original electricity price for each time period, of which .

[0039] After adding the electricity price and power change to their initial values, the final power and electricity price of the electric vehicle after the electricity price response are obtained. To avoid excessively high resilient electricity prices that might alienate users, a final electricity price cap should be set. Since electric vehicle load affects daily travel, although the load can be adjusted by regulating the electricity price, the daily charging volume of electric vehicles must be kept constant. Essentially, this is equivalent to a shiftable load. Therefore, this embodiment requires setting a final electricity price cap and a shiftable load, where the final electricity price cap is expressed as: , in, The original electricity price for period 1. This represents the change in electricity price during period 1. This is the lower limit of the final electricity price (the minimum allowable electricity price). This is the upper limit of the final electricity price (the maximum allowed electricity price). It is the final electricity price after the load response of electric vehicles, which satisfies the minimum electricity price. and between; The load that can be moved is represented as: , in, , These are the electric vehicle charging loads before and after demand response. It is the load regulation amount.

[0040] In practical applications, with the high proportion of new energy access, power flow calculation has evolved through three stages: single-source radial unidirectional power flow, multi-source radial bidirectional power flow, and multi-source radial bidirectional power flow considering reconfiguration. Traditional power flow calculation methods such as the Newton-Lambert method and the Gauss-Seidel method, after using intelligent optimization algorithms to randomly select and optimize upper-level generators or demand response output variables, require multiple iterations at the lower level to converge and calculate the power flow under a set of power output schemes. This significantly affects computational efficiency, and when facing multi-period power flow optimization problems involving coupling before and after energy storage, the problem complexity is too high, the computation time is too long, and the optimality is too poor. Therefore, this embodiment gradually extends the single-period power flow that requires iterative optimization to a multi-period second-order conical convex programming model that can be quickly solved using commercial mixed integer linear programming software such as YALMIP+CPLEX. Furthermore, it realizes the dynamic reconfiguration function of tie lines that can consider the daily adjustment limit, providing strong support for improving the resilience of the distribution network under typhoon disasters.

[0041] The basic structure of a power distribution network is usually radial, and it can generally be described using a directed graph, such as... Figure 5 As shown, where, is recorded and These are nodes in the distribution network. The set of parent and child nodes, that is, the upstream and downstream nodes of the power flow in the branch. Figure 5 middle, , For complex power, , .

[0042] This embodiment takes the radial distribution network as the research object, and... A branch power flow model is established based on the current operating state. The objective function of the established single-time-period, single-source radial branch power flow model is: , in, For the set of active power variables, For the set of reactive power variables, For the set of node voltage magnitude variables, For the set of branch current variables; In practical applications, the constraints of the established single-time-period single-source radial branch power flow model include line power flow constraints, voltage drop balance equations, node voltage constraints, branch current constraints, and node generator constraints. line exist The power constraints and voltage drop balance equations satisfied at time t are as follows: , , , , , , in, and branch road exist The active and reactive power flowing through at all times. and branch road exist The active and reactive power flowing through at all times. and Branch roads Resistance and reactance, branch road The current flowing through, and outflow node Active power and reactive power, and They are nodes The active and reactive power of electricity purchased from the main grid. For nodes reactive load demand, and They are nodes and voltage, For nodes During the period The active power load demand, For nodes The set of parent nodes, For nodes The set of child nodes; Node voltage constraints and branch current constraints are expressed as follows: , , in, and They are nodes exist The lower and upper voltage limits at time t, when node When it is a balanced node, we have , branch road exist The maximum current value that is allowed to flow at any given time; The node generator constraint is expressed as: , , in, and These are the minimum and maximum active power outputs of the generator, respectively. and These are the minimum and maximum values ​​of the generator's reactive power output, respectively.

[0043] Since the branch power flow model is a nonlinear model, this embodiment can use the Second Order Conic Relaxation (SOCR) technique to transform the branch power flow model. Therefore, the linear transformation of the single-time single-source radial branch power flow model into the second order conic power flow model includes the following steps: Construct new variables , Replace the square terms of voltage and current in the original constraints. , The nonlinear constraints containing the quadratic terms of active and reactive power flow of the lines are transformed, namely: , , , , , , , Pair Further relaxation yields: , Further equivalent transformation yields: In the formula: Denotes the 2-norm of a matrix; After the above steps, the nonlinear constraints in the branch power flow model can be transformed into linear constraints, or second-order cone constraints that conform to second-order cone programming. When considering the wind and solar power output at nodes, EV load, and 5G base station load, the equation is... Japanese style After correction, we get: , , in, for Time period nodes Electric vehicle load, for Time period nodes 5G base station load, and They are respectively Time period nodes The active and reactive power output of photovoltaic power. and They are respectively Time period nodes The active and reactive power output of wind power, for Time period nodes The shear load, for Time period nodes The charging and discharging power of 5G base station energy storage is positive during charging and negative during discharging. for Time period nodes The load shift of electric vehicles is positive when the load increases and negative when the load decreases.

[0044] In this embodiment, the distribution network structure is optimized through the reconfiguration of the distribution network tie lines. The aim is to improve power supply reliability, reduce losses, or resolve overload issues by adjusting the connection methods of distribution lines. When a distribution network line fails due to a typhoon, a reconfiguration method can be used to transfer the load of the affected non-faulty lines to other lines via tie switches, thereby restoring as many affected loads as possible. Therefore, the traditional branch power flow model is improved. Thus, in this embodiment, the establishment of a distribution network fault reconfiguration model considering the number of operation limits, when reconfiguring the distribution network tie lines, includes: Introducing the status of line switches A value of 1 indicates a closed circuit, and a value of 0 indicates an open circuit. Inequality constraints are introduced to relax the original branch power flow equations. When the circuit is open, the power flow is zero; when the circuit is closed, there are no constraints. The constraint conditions are then expressed as: , , , , , , in, It should be a sufficiently large positive number, but not excessively large, otherwise it will worsen the condition number of the problem, thus affecting the solver's performance. Here, The upper bound combination; If the reconstructed distribution network forms a ring network, i.e., the power supply path forms a closed loop, it may bring the following potential hazards: Ring network operation may lead to uneven voltage distribution in the network, and some nodes may experience overvoltage or undervoltage, affecting the normal operation of electrical equipment; the ring network structure may make the selection and coordination of protection devices more complex. In a ring network system, the path and magnitude of fault current may change due to different network operating states, making traditional protection strategies difficult to adapt, and potentially leading to maloperation or failure of protection devices. To mitigate these potential hazards, the design and operation of the distribution network need to consider the adoption of advanced protection and control technologies, such as adaptive protection, rational planning and utilization of distributed energy resources, and intelligent distribution network technology, to improve the safety and reliability of ring network operation. Most power grids have not undergone sufficient upgrades and transformations, resulting in insufficient hardware and technology to support ring networks. Therefore, this embodiment adopts a graph-based spanning tree method for describing the radial constraints of the distribution network, representing the radial constraint conditions as follows: , , , , in, It is the set of all branches in the distribution network. This represents the number of nodes in the distribution network. Choose a binary variable for the directed edge to indicate whether it is a node in the spanning tree. Pointing to node (1 indicates selected, 0 indicates not selected). The definition is the same as above (by) point to ), branch road The on / off state, Indicates closure, Indicates disconnection. The total number of distribution network nodes. Point to node1 The directed edge selection of binary variables; Frequent switching of tie line switches can cause irreversible damage, and each switching action of the tie line has a significant impact, often requiring multi-level and multi-faceted discussions before a decision can be made. Therefore, considering the time-consuming decision-making process and the damage to equipment lifespan, it is often necessary to set a limit on the number of tie line reconfigurations. Thus, in this embodiment, when reconfiguring the tie lines of the distribution network, a limit on the number of tie line reconfigurations is set, expressed as follows: , in, , They are time The indicator lights for opening and closing the connecting lines of the branch roads. The daily limit on the number of times the communication line can be reconstructed.

[0045] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for enhancing the resilience of a power distribution network based on controllable flexible loads and power distribution network reconfiguration, characterized in that, The method comprises the following steps: establishing a power distribution network resilience evaluation index; establishing a 5G base station load linear model based on data flow and a dispatchable capacity model of standby power supply demand; establishing a demand response coefficient model considering the coupling relationship between the current time load and the electricity price at other times to obtain a linear relationship between the electric vehicle charging power and the electricity price variation; establishing a single-period single-source radial branch power flow model, and linearly converting it into a second-order cone power flow model; establishing a power distribution network fault reconstruction model considering the action frequency limit to reconstruct the tie line of the power distribution network.

2. The method for improving power grid resilience based on controllable flexible load and power grid reconfiguration according to claim 1, characterized in that, The power distribution network resilience evaluation index comprises an important load outage quantity influence index and a system newly added dispatching cost index; The load in the important load outage quantity influence index comprises loads of multiple important grades, namely, first-grade load, second-grade load and third-grade load; The important grade of the load in the power distribution network is divided into each node, namely: ， wherein, is a set of disaster duration, is a set of grid nodes, respectively, the importance level of the load is the amount of load loss, , , respectively represent the load loss penalty coefficient of the importance level of one two three, require , is the important load loss impact index comprehensive value of the distribution network under disaster, is the important load loss impact index comprehensive value of the distribution network under disaster, respectively, the first, second, third load loss, is the line repair penalty coefficient, is the total number of branches in the post-disaster distribution network, is the node number, is the branch number, is the disaster time period number, is the time period length, is the branch current, is the branch resistance, respectively, the node set of the load importance level in the distribution network is one, two, three, is the node set of the load importance level of the first class; The system newly added dispatching cost index is expressed as: ， wherein, is the comprehensive value of the new scheduling cost index of the system, is the economic loss index caused by the power failure of the load in the disaster, is the new cost index caused by demand response and emergency scheduling, is the load time shift response power, is the power of the energy storage system participating in scheduling, is the power failure weight coefficient of the th branch, is the power failure of the th branch in the disaster in the time period , are respectively the load time shift response cost coefficient, the load reduction response cost coefficient, the emergency power generation cost coefficient, and the switch reconstruction cost coefficient, is the number of tie lines in the distribution network that perform reconstruction actions, is the scale index of the post-disaster experience investment in the construction of energy storage and reinforcement lines, is the load time shift power of node in the time period , is the emergency power generation power of node in the time period .

3. The method for improving power grid resilience based on controllable flexible load and power grid reconfiguration of claim 1, wherein, Before the establishment of the 5G base station load linear model based on data flow, the 5G base station structure is obtained, and the 5G base station internal structure comprises communication equipment, air conditioning equipment and power supply equipment; The communication equipment comprises transmission equipment, active antenna unit and baseband processing unit, and is used for transmitting and processing wireless signals; The power supply equipment comprises switching power supply and energy storage battery, and is used for providing direct current for the main equipment in the 5G base station; The air conditioning equipment is used for maintaining the environment temperature in the 5G base station room; When the 5G base station load linear model based on data flow is established, the 5G base station power consumption load is divided into static load and dynamic load, and is expressed as: ， wherein, is the electrical load for a 5G base station, is the static load in a 5G base station, is the dynamic load in a 5G base station; The established 5G base station load linear model based on data flow comprises a static load model and a dynamic load model; The static load model is expressed as: ， wherein, PBBU is the BBU power consumption, PAAU is the AAU baseline power consumption PAAU is the AAU power consumption caused by downlink signaling, which is set as a fixed value, Pswitch is the switching power supply power consumption; The dynamic load model is expressed as: ， wherein, is the AAU incremental load, is the air conditioning equipment load.

4. The method for improving power grid resilience based on controllable flexible load and power grid reconfiguration of claim 3, wherein, In the dynamic load model, the step of obtaining the AAU incremental load is: The total data flow of the 5G base station coverage area is obtained, and is expressed as: ， wherein, is total data traffic of the region in the period, is number of 5G user active terminals of the region in the period, is total amount of 5G data traffic used by the i-th 5G user active terminal in the period, is Based on the relationship between data traffic and downlink data rate, based on the total data traffic of the time period 5G base station coverage area, the equivalent downlink data rate of a single 5G base station at the moment is obtained : ， wherein, is the time granularity of the division; The communication load rate is defined as the ratio of the total downlink data rate of all users accessing in the 5G base station coverage range to the maximum downlink rate of the 5G base station, and is expressed as: ， wherein, is a turning point of downlink data rate of the 5G base station, indicating a change in the linear growth trend of power consumption of the 5G base station under a certain communication load, is the maximum downlink data rate that the 5G base station can bear. The AAU incremental load is expressed as the dynamic incremental load of the AAU equipment, and is expressed as: ， wherein: is the dynamic delta load for the AAU device, is the power consumption break point for the AAU device, is the maximum power consumption for the AAU device.

5. The method for improving power grid resilience based on controllable flexible load and power grid reconfiguration of claim 3, wherein, In the dynamic load model, the air conditioning equipment load is expressed in a form related to temperature i.e.: ， wherein, represents the peripheral building structure area of the machine room, represents the heat transfer coefficient, represents the temperature in the machine room, represents the ambient temperature, is the sum of the power consumption of the 5G base station AAU and BBU, is the efficiency coefficient of the 5G base station AAU and BBU energy consumption converted into non-heat, represents the energy efficiency ratio of the air conditioning equipment, is the energy utilization efficiency coefficient of the 5G base station AAU and BBU; At this time, the dynamic load model is represented as i.e.: 。 6. The method for improving power grid resilience based on controllable flexible load and power grid reconfiguration of claim 3, wherein, The establishment of the dispatchable capacity model of standby power supply demand comprises the following steps: The energy storage power-capacity coupling constraint is established: ， ， ， ， wherein, and respectively the charging and discharging power of the 5G base station energy storage in the time period, is a charging and discharging flag, taking 1 to represent charging and 0 to represent discharging, and and respectively the maximum charging and discharging power of the 5G base station energy storage in the time period, and respectively the charging and discharging efficiency of the 5G base station energy storage, is the remaining power of the 5G base station energy storage at the node in the time period, is the remaining power of the 5G base station energy storage at the node in the time period, is the schedulable capacity of the 5G base station energy storage at the node in the time period, is the schedulable capacity of the 5G base station energy storage at the node in the time period, is the schedulable capacity of the 5G base station energy storage at the node in the time period, is the maximum available capacity of the 5G base station energy storage at the node . The 5G base station energy storage capacity is divided into standby capacity and dispatchable capacity; The dispatchable capacity depends on the energy storage battery configuration capacity, health state and the size of the standby capacity; ， wherein, is the schedulable capacity of the 5G base station, is the state of health of the battery, , is the rated capacity of the battery at initial investment, is the minimum reserve capacity required by the 5G base station taking into account the communication load;​ Obtaining minimum reserve capacity required for 5G base station taking into account communication load is expressed as: ， wherein, a 5G base station in power consumption of a time period, a 5G base station minimum backup power time; For a power distribution network node The feasible region of the backup energy storage aggregation of the connected 5G base station cluster is: ， wherein, is the number of 5G base stations connected to the node , and are the maximum charging and discharging power of the 5G base station energy storage cluster of the node , and are the charging and discharging power of the 5G base station energy storage cluster of the node , is the amount of electricity of the 5G base station energy storage cluster of the node in the time period , is the maximum schedulable capacity of the 5G base station energy storage cluster of the node .

7. The method for improving power grid resilience based on controllable flexible load and power grid reconfiguration of claim 1, wherein, The establishment of the demand response coefficient model considering the coupling relationship between the current time load and the electricity price at other times to obtain the linear relationship between the electric vehicle charging power and the electricity price variation comprises the following steps: Definition the amount of change in the electricity usage within the time period the amount of change in the electricity price within the time period the response coefficient of the amount of change in the electricity usage within the time period ， Wherein, And The original price and its change amount, respectively, The time period The original power consumption of the electric vehicle, The time period The power consumption change value of the electric vehicle after the price change; The scheduling duration is divided into The electric vehicle charging power-price change amount linear relationship is obtained as follows: ， wherein, is the change value of the power consumption of the electric vehicle charging in the first time period, is the original value of the power consumption of the electric vehicle charging in the first time period, is the change value of the power consumption in the first time period relative to the change of the electricity price in the first time period, is the change value of the electricity price in the first time period, is the original electricity price in the first time period, wherein ; After the electricity price and the power variation are added to the initial values respectively, the final power and the electricity price after the electric vehicle electricity price response are obtained, at this time, the final electricity price upper limit and the translatable load are set, wherein the final electricity price upper limit is expressed as: ， wherein, is the original electricity price for period 1, is the electricity price change amount for period 1, is the lower limit of the final electricity price, is the upper limit of the final electricity price, is the final electricity price after the electric vehicle load response, which satisfies the minimum electricity price and between and The translatable load is expressed as: ， wherein, , are the electric vehicle charging load before and after demand response, respectively, is the load adjustment amount.

8. The method for improving power grid resilience based on controllable flexible load and power grid reconfiguration of claim 1, wherein, The objective function of the established single-period single-source radial branch power flow model is: ， wherein, is a set of active power variables, is a set of reactive power variables, is a set of node voltage magnitude variables, is a set of branch current variables; The constraint conditions of the established single-period single-source radial branch power flow model include line power flow constraints, pressure drop balance equations, node voltage constraints, branch current constraints and node generator constraints; Line In The constraint condition that the power at the moment satisfies, and the pressure drop balance equation are: ， ， ， ， ， ， wherein, and are the active and reactive power flowing through branch at time, and are the active and reactive power flowing through branch at time, and are the resistance and reactance of branch respectively, is the current flowing through branch , and are the active and reactive power flowing out of node , and are the active and reactive power purchased from the main grid by node , is the reactive load demand of node , and are the voltage of node and respectively, is the active load demand of node at time period , is the parent node set of node , is the child node set of node . The node voltage constraints and the branch current constraints are expressed as: ， ， in, and They are nodes exist The lower and upper voltage limits at time t, when node When it is a balanced node, we have , branch road exist The maximum current value that is allowed to flow at any given time; The node generator constraints are expressed as: ， ， wherein, and Pmin and Pmax are the minimum and maximum values of the generator active power output, respectively, and Qmin and Qmax are the minimum and maximum values of the generator reactive power output, respectively.

9. The method for improving power grid resilience based on controllable flexible load and power grid reconfiguration of claim 8, wherein, The linear conversion of the single-period single-source radial branch power flow model into a second-order cone power flow model includes the following steps: Constructing new variables , Instead of the squared terms of voltage and current in the original constraints , Transforming the nonlinear constraints involving the squared terms of active and reactive power flow in the lines, i.e.: ， ， ， ， ， ， ， to the formula Further relaxation gives: ， Further equivalent deformation is performed to obtain: wherein: denotes the 2-norm of a matrix; Through the above steps, the nonlinear constraints in the branch power flow model can be converted into linear constraints or second-order cone constraints conforming to the second-order cone programming; When the node wind power output, EV load, and 5G base station load are considered, the equation of formula and formula is modified to obtain: ， ， in, for Time period nodes Electric vehicle load, for Time period nodes 5G base station load, and They are respectively Time period nodes The active and reactive power output of photovoltaic power. and They are respectively Time period nodes The active and reactive power output of wind power, for Time period nodes The shear load, for Time period nodes The charging and discharging power of 5G base station energy storage is positive during charging and negative during discharging. for Time period nodes The load shift of electric vehicles is positive when the load increases and negative when the load decreases.

10. The method for improving power grid resilience based on controllable flexible load and power grid reconfiguration of claim 9, wherein, The establishment of the distribution network fault reconfiguration model considering the action frequency limit includes: Switching state of transmission line 1 means the line is closed, 0 means the line is open, and inequality constraints are introduced to relax the original branch power flow equations. When the line is open, the power flow of the line is 0, and when the line is closed, there is no constraint. The constraint condition is represented as: ， ， ， ， ， ， wherein is a sufficiently large positive number, i.e. upper bound combination of The radial constraint description method of the distribution network based on the graph-based spanning tree is used to express the radial constraint conditions as: ， ， ， ， wherein, is the set of all branches in the distribution network, is the number of nodes in the distribution network, is a directed edge selection binary variable indicating whether the node is pointed to by the node 1 indicates selected, 0 indicates not selected, is a directed edge selection binary variable indicating whether the node is pointed to by the node , is the switch state of branch , indicates closed, indicates open, is the total number of nodes in the distribution network, is a directed edge selection binary variable for node 1 pointing to node . When the tie line of the distribution network is reconfigured, the tie line reconfiguration frequency limit is set, which is expressed as: ， wherein, , are respectively moment the branch line opening and closing action identification, is the daily line reconfiguration limit.

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