Power distribution network two-stage toughness improvement method and device, computer equipment and storage medium

Abstract

The invention discloses a two-stage toughness improvement method and device for a power distribution network, computer equipment and a storage medium. The method comprises the following steps: acquiring a pre-disaster fault scene and various known conditions required by calculation; establishing a power distribution network operation model according to various known conditions, and performing optimization solution by taking the minimum load reduction weighted value as an objective function to obtain a minimum load reduction weighted value; and taking the minimum load reduction weighted value as an upper limit of the load reduction weighted value, adding a new constraint in the power distribution network operation model, and using any fairness principle as a target function to carry out optimization solution to obtain an optimal strategy which satisfies the minimum coincidence reduction and the fairness reduction of general loads. The method has the advantages of guaranteeing priority recovery of important nodes and fairness of load reduction of non-important nodes on the premise of guaranteeing the minimum load reduction weighted value.

Description

Technical Field

[0001] This invention relates to a two-stage resilience enhancement method, device, computer equipment, and storage medium for power distribution networks, belonging to the field of power distribution network resilience enhancement technology. Background Technology

[0002] Global climate change has led to frequent extreme natural disasters, posing a severe challenge to infrastructure, especially the power distribution network system. Historical data shows that the power distribution network is less resilient to disasters than the transmission network. Therefore, it is crucial to improve the resilience of the power distribution network in the face of disasters. When a disaster causes a significant drop in the power supply capacity of the grid, planned load reduction is a key measure to quickly restore the balance between supply and demand, avoid cascading failures and large-scale power outages.

[0003] Current research on load shedding rarely considers fairness. Load shedding should not only focus on ensuring power supply to critical facilities such as hospitals, emergency service centers, and government offices to avoid severe political and economic losses, but also on the fairness of load shedding for general interruptible facilities. Insufficient fairness may lead to excessive load shedding in some areas, making it difficult to guarantee the basic living needs of vulnerable groups (such as the elderly and seriously ill patients), and even threatening their lives in emergencies. In the long run, obvious unfairness in load shedding may also trigger social discontent, affecting the post-disaster recovery process and social stability. Current research generally focuses on prioritizing critical loads by setting different weights for different types of loads to distinguish their importance, but it does not address the fairness of the proportion of ordinary load shedding. Therefore, exploring fair methods for load shedding has become an important issue that urgently needs to be addressed in distribution network resilience research. However, current research does not address the specific issues of the fairness of load shedding for ordinary users after extreme disasters, indicating a significant research gap. Summary of the Invention

[0004] In view of this, the present invention provides an iterative configuration method, apparatus, computer equipment and storage medium for a grid-type converter, which has the advantages of ensuring priority recovery of important nodes and fair load reduction of non-important nodes while ensuring the minimum load reduction weighting value.

[0005] The first objective of this invention is to provide a two-stage resilience enhancement method for power distribution networks.

[0006] The second objective of this invention is to provide a two-stage resilience enhancement device for power distribution networks.

[0007] A third objective of this invention is to provide a computer device.

[0008] A fourth objective of this invention is to provide a storage medium.

[0009] The first objective of this invention can be achieved by adopting the following technical solution:

[0010] A two-stage resilience enhancement method for power distribution networks, the method comprising:

[0011] Obtain the pre-disaster failure scenario and all known conditions required for calculation;

[0012] Based on various known conditions, a distribution network operation model is established, and the minimum load reduction weighted value is used as the objective function to perform optimization and obtain the minimum load reduction weighted value.

[0013] By using the minimum load reduction weighted value as the upper limit of the load reduction weighted value, a new constraint is added to the distribution network operation model. Then, any fairness principle is used as the objective function to perform optimization and obtain the optimal strategy that satisfies both the minimum load reduction and the fair reduction of general load.

[0014] The distribution network operation model imposes distribution network operation constraints, including constraints on mobile battery energy storage systems, distributed power output, load shedding, node voltage and transmission power, distribution network radiation, line power flow, line maintenance status, and maintenance team dispatch.

[0015] Furthermore, during the charging and discharging process, the charging and discharging power of the mobile battery energy storage system does not exceed the maximum value, and it can only perform charging or discharging behavior at the same time, as shown in the following formula:

[0016]

[0017] in, and This represents the charging power and discharging power of the m-th mobile battery energy storage system at time t; and This represents the maximum charging and discharging power of the m-th mobile battery energy storage system; and This represents the reactive power absorbed and reactive power released by the m-th mobile battery energy storage system at time t. and This represents the maximum value of the reactive charging power and reactive discharging power of the m-th mobile energy storage unit; This indicates whether the m-th mobile battery energy storage system is in a charging state at time t. This indicates whether the m-th mobile battery energy storage system is in a discharging state at time t; Ω M N represents a collection of mobile battery energy storage systems; CS This represents the set of nodes where charging stations exist. This represents the active power output by the mobile battery energy storage system at node i at time t. This represents the reactive power released by the mobile battery energy storage system at node i at time t;

[0018] The range of how the state of charge of the mobile battery energy storage system changes with charge and discharge behavior is as follows:

[0019]

[0020] in, This represents the state of charge of the m-th mobile battery energy storage system at time t; and Let represent the charging efficiency and discharging efficiency of the m-th mobile battery energy storage system; and These represent the minimum and maximum values ​​of the state of charge of the m-th mobile battery energy storage system.

[0021] The mobile battery energy storage system is subject to constraints on spatiotemporal flexibility, as shown in the following equation:

[0022]

[0023] in, Indicate whether the m-th mobile battery energy storage system is located at node i at time t; τ represents the time required for the m-th mobile battery energy storage system to move from node i to node j; τ represents less than or equal to Any time interval; Ω T This represents the set of moments within the duration of the disaster.

[0024] The spatial scheduling relationship between the actual vehicle speed and the equivalent travel distance of the mobile energy storage system is represented by a traffic fusion coefficient, as shown in the following formula:

[0025]

[0026] Among them, D ij This represents the road distance from node i to node j; It means that it can make The earliest moment; v0 represents the ideal speed of movement under normal weather conditions; This represents the actual moving speed of the mobile energy storage system at time t; This represents the equivalent moving speed of the mobile energy storage system at time t.

[0027] Furthermore, the output constraint of the distributed power source is as follows:

[0028]

[0029]

[0030] Where, N DG A set of nodes representing a distributed power source; and This represents the active and reactive power output of the distributed power source at node i at time t. and This indicates the upper limit of active and reactive power output of the distributed power source. and These are the upper and lower limits of the power factor for distributed generation, respectively.

[0031] Furthermore, the load reduction constraint is as follows:

[0032]

[0033] in, and These represent the rated active load and rated reactive load at node i at time t, respectively. and These represent the active load reduction and reactive load reduction at node i at time t, respectively.

[0034] The node voltage and transmission power constraints are as follows:

[0035] V i,min ≤U i,t ≤V i,max

[0036]

[0037] Among them, U i,t V represents the voltage at node i at time t. i,min and V i,max Let P be the lower and upper limits of the voltage at node i; ij,t and Q ij,t Let represent the active power and reactive power from node i to node j on line ij at time t, respectively. α represents the square of the allowed transmission capacity on line ij. ij,t This indicates the on / off state of the line at time t;

[0038] The radial constraint of the distribution network refers to satisfying the condition that the total number of closed loops equals the difference between the number of nodes and the number of substation nodes, as shown in the following formula:

[0039]

[0040] Among them, Ω L N represents the set of distribution network lines; B N represents the total number of nodes in the distribution network; station This represents the number of substation nodes.

[0041] The power flow constraint of the line is as follows:

[0042]

[0043]

[0044] Among them, Ω S (i) represents the set of child nodes of node i; Ω F (i) represents the set of parent nodes of node i; P ki,t and Q ki,t Let represent the active power and reactive power from node k to node i on line ki at time t, respectively. and R represents the squares of the voltages at node i and node j at time t, respectively. ij and X ij Let represent the resistance and reactance on line ij, respectively; M is a maximum value.

[0045] Furthermore, the line maintenance status constraint is as follows:

[0046]

[0047] Among them, L fault L represents the set of faulty lines; L represents the set of all lines; Ω R This indicates the assembly of the repair team; This indicates the required maintenance time for line ij. This indicates the maintenance status of the line at time t; This indicates whether maintenance team r is performing maintenance at line ij at time t.

[0048] Furthermore, the maintenance team scheduling constraints are as follows:

[0049]

[0050] Among them, L fault L represents the set of faulty lines; L represents the set of all lines; Ω R This indicates the assembly of the repair team; This indicates the required maintenance time for line ij. This indicates the maintenance status of the line at time t; This indicates whether maintenance team r is performing maintenance at line ij at time t; This indicates whether maintenance team r is performing maintenance at point xy on the line at time t+τ; This represents the time required for maintenance team r to move from line ij to line xy; Indicates whether maintenance team r is working at time t; n repairIndicates the number of repair teams; n line The number of lines is indicated by t; t represents the time when the disaster ends.

[0051] Furthermore, the objective function is to minimize the load reduction weighted value, as shown in the following equation:

[0052]

[0053] Among them, P * Ω represents the minimum weighted amount that load reduction can achieve. N Represents the set of nodes in the distribution network; w i Indicates the load weight of node i;

[0054] When the fairness principle is a global equilibrium principle, the global equilibrium principle is used as the objective function, as shown in the following equation:

[0055]

[0056] Among them, Ω C This indicates a node that generates load reduction among non-critical nodes; This indicates the reduction in active power load at node i at time t; This indicates the reduction in active power load at node j at time t; This represents the rated active load at node i at time t; This represents the rated active load at node j at time t;

[0057] The linearization process of the global equilibrium principle is as follows:

[0058]

[0059] Among them, Ω O Represents the set of non-significant nodes; y i,t Indicates whether node i experiences load shedding at time t; u ij,t Indicates whether node i and node j simultaneously experience load shedding at time t; d ij,t ε represents the absolute value of the load reduction ratio deviation between node i and node j at time t; ε is a minimum value.

[0060] When the fairness principle is the worst-case deviation criterion for nodes, the worst-case deviation criterion for nodes is used as the objective function, as shown in the following equation:

[0061]

[0062] The linearization process of the worst-case deviation criterion for the node is as follows:

[0063]

[0064] Where M is a local maximum; e i,t It is the absolute value of the maximum load reduction ratio deviation between node i and nodes with numbers greater than it at time t.

[0065] When the fairness principle is the principle of minimizing the system range, the objective function is the principle of minimizing the system range, as shown in the following equation:

[0066]

[0067] The linearization process of the system range minimization principle is as follows:

[0068]

[0069]

[0070] Among them, g t This represents the absolute value of the difference in load reduction ratio between the node pairs with the largest difference in load reduction ratio at time t.

[0071] The second objective of this invention can be achieved by adopting the following technical solution:

[0072] A two-stage resilience enhancement device for power distribution networks, the device comprising:

[0073] The acquisition module is used to acquire pre-disaster failure scenarios and various known conditions required for calculation.

[0074] The first solution module is used to establish a distribution network operation model based on various known conditions, and to perform optimization by taking the minimum load reduction weighted value as the objective function to obtain the minimum load reduction weighted value.

[0075] The second solution module is used to add new constraints to the distribution network operation model by taking the minimum load reduction weighted value as the upper limit of the load reduction weighted value, and using any fairness principle as the objective function to perform optimization solution, so as to obtain the optimal strategy that satisfies the minimum load reduction and the fair reduction of general load.

[0076] The distribution network operation model imposes distribution network operation constraints, including constraints on mobile battery energy storage systems, distributed power output, load shedding, node voltage and transmission power, distribution network radiation, line power flow, line maintenance status, and maintenance team dispatch.

[0077] The third objective of this invention can be achieved by adopting the following technical solution:

[0078] A computer device includes a processor and a memory for storing a processor-executable program, wherein when the processor executes the program stored in the memory, it implements the above-described two-stage resilience enhancement method for power distribution networks.

[0079] The fourth objective of this invention can be achieved by adopting the following technical solution:

[0080] A storage medium storing a program that, when executed by a processor, implements the aforementioned two-stage resilience enhancement method for power distribution networks.

[0081] The present invention has the following advantages over the prior art:

[0082] 1. The method of the present invention, while ensuring the minimum load reduction of the power system, can not only prioritize the power supply of important loads, but also ensure fair power supply of general loads; the method is scalable and can design fairness principles according to the needs of fairness to meet specific requirements.

[0083] 2. The method of this invention takes fairness into consideration and proposes three fairness principles. All three fairness principles can improve the fairness of load reduction. Each of the three fairness principles has its own emphasis, including emphasizing making the load reduction ratio of all nodes tend to be consistent, or emphasizing eliminating the gap between the most unfair node pairs. You can choose according to the needs of calculation time and accuracy. Attached Figure Description

[0084] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0085] Figure 1 This is a simplified flowchart of the two-stage resilience enhancement of the power distribution network according to Embodiment 1 of the present invention.

[0086] Figure 2 This is a flowchart illustrating the two-stage resilience enhancement of the power distribution network in Embodiment 1 of the present invention.

[0087] Figure 3 This is a topology diagram of the IEEE 33-node distribution network test system according to Embodiment 1 of the present invention.

[0088] Figure 4 This is a power variation curve of the load in Embodiment 1 of the present invention.

[0089] Figure 5 This is a graph showing the change in traffic congestion coefficient according to Embodiment 1 of the present invention.

[0090] Figure 6 This is a graph showing the output power variation of the photovoltaic unit in Embodiment 1 of the present invention.

[0091] Figures 7a to 7d This is a bar chart showing the load reduction percentage of each node in each time period in the four schemes of Embodiment 1 of the present invention.

[0092] Figure 8 This is a diagram showing the combination of load power and recovery ratio for each time period in the first and second schemes of Embodiment 1 of the present invention.

[0093] Figure 9 This is a radar diagram showing the state of charge change of the mobile battery energy storage system according to Embodiment 1 of the present invention.

[0094] Figure 10 This is a graph showing the change in active power of the diesel generator in Embodiment 1 of the present invention.

[0095] Figure 11 This is a stacked bar chart showing the line status of the interconnection lines in Embodiment 1 of the present invention.

[0096] Figure 12 This is a structural block diagram of the iterative configuration device for the grid-type converter in Embodiment 2 of the present invention.

[0097] Figure 13 This is a structural block diagram of the computer device according to Embodiment 3 of the present invention. Detailed Implementation

[0098] 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 some embodiments of the present invention, but not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0099] Example 1:

[0100] like Figure 1 and Figure 2 As shown in the figure, this embodiment provides a two-stage resilience enhancement method for distribution networks, which includes the following steps:

[0101] S201. Obtain the pre-disaster fault scenario and various known conditions required for calculation.

[0102] In this embodiment, various known conditions include the distribution network topology diagram, the traffic network topology diagram, the weights of important load nodes, the weights of general load nodes, the load change curve, the traffic congestion coefficient change curve, the photovoltaic unit output change curve, various equipment parameters, the repair time required for faulty lines, the ideal vehicle speed under zero traffic congestion conditions, the number of repair teams, and the photovoltaic power factor, etc.

[0103] This embodiment uses a modified IEEE 33-node distribution network test system as a simulation example, such as... Figure 3 As shown, the distribution network test system includes 32 normally closed lines, 5 tie lines, 4 charging stations (CS), 2 diesel generators (DG), and 3 photovoltaic units. The shaded area in the figure represents the traffic network topology. The distance between nodes is set to 2km. Nodes are classified into three categories: important load nodes, general load nodes, and slack nodes. Node 1 is a slack node; nodes 3, 6, 11, 15, 17, 19, 24, 28, and 33 are important load nodes with a reduction weight of 100 yuan / kW; the remaining nodes are general load nodes with a reduction weight of 10 yuan / kW. The line data comes from case33bw in MATPOWER, and the rated values ​​of the load data are the same as in case33bw. The load power variation curve is shown below. Figure 4 The voltage upper and lower limits for each node are 0.93 pu-1.07 pu; the parameters for the diesel generator are shown in Table 1, the parameters for the Mobile Energy Storage System (MESS) are shown in Table 2, the repair time required for the faulty line is shown in Table 3, the parameters required for the operation of the other systems are shown in Table 4, and the traffic congestion coefficient variation curve is shown in Table 5. Figure 5 The output power of the photovoltaic unit can be seen Figure 6 .

[0104] Table 1 Diesel Generator Parameters

[0105]

[0106] Table 2 Parameters of Mobile Battery Energy Storage System

[0107]

[0108] Table 3. Repair time required for faulty circuits

[0109]

[0110] Table 4 Parameters Required for Other System Operations

[0111]

[0112] S202. Based on various known conditions, establish a distribution network operation model, and use the minimum load reduction weighted value as the objective function to perform optimization and obtain the minimum load reduction weighted value.

[0113] In this embodiment, the distribution network operation model applies distribution network operation constraints, including constraints on mobile battery energy storage systems, distributed power generation output, load shedding, node voltage and transmission power, distribution network radial constraints, line power flow constraints, line maintenance status constraints, and maintenance team dispatch constraints, which are explained in detail below:

[0114] (1) Mobile battery energy storage system

[0115] Mobile battery energy storage systems can proactively move according to disaster scenarios and connect to the power distribution network through charging stations to improve grid resilience. During charging and discharging, the charging and discharging power of the mobile battery energy storage system cannot exceed the maximum value, and it can only perform charging or discharging activities at the same time. The specific formula is as follows:

[0116]

[0117] in, and This represents the charging power and discharging power of the m-th mobile battery energy storage system at time t; and This represents the maximum charging and discharging power of the m-th mobile battery energy storage system; and This represents the reactive power absorbed and reactive power released by the m-th mobile battery energy storage system at time t. and This represents the maximum value of the reactive charging power and reactive discharging power of the m-th mobile energy storage unit; This indicates whether the m-th mobile battery energy storage system is in a charging state at time t. It is a 0-1 variable; a value of 1 indicates that the m-th mobile energy storage system is in a charging state at time t, and a value of 0 indicates that it is not in a charging state. This indicates whether the m-th mobile battery energy storage system is in a discharging state at time t. It is a 0-1 variable; a value of 1 indicates that the m-th mobile energy storage system is in a discharging state at time t, and a value of 0 indicates that it is not in a discharging state. Ω M N represents a collection of mobile battery energy storage systems; CS This represents the set of nodes where charging stations exist. This represents the active power output by the mobile battery energy storage system at node i at time t. This represents the reactive power released by the mobile battery energy storage system at node i at time t.

[0118] The range of changes in the state of charge (SOC) of a mobile battery energy storage system with charge and discharge behavior is as follows:

[0119]

[0120] in, This represents the state of charge of the m-th mobile battery energy storage system at time t; and Let represent the charging efficiency and discharging efficiency of the m-th mobile battery energy storage system; and These represent the minimum and maximum values ​​of the state of charge of the m-th mobile battery energy storage system.

[0121] Mobile battery energy storage systems are also subject to spatiotemporal flexibility constraints during scheduling. These include the fact that each mobile battery energy storage system can only connect to one charging station node at a time; each charging station node can only connect to one mobile energy storage system at a time; and the mobility constraints of mobile energy storage systems, which require a certain amount of travel time to travel between charging station nodes, as follows:

[0122]

[0123] in, This indicates whether the m-th mobile battery energy storage system is located at node i at time t. It is a 0-1 variable, where a value of 1 indicates that the m-th mobile energy storage system is located at node i at time t, and a value of 0 indicates that the m-th mobile energy storage system is not located at node i at time t. τ represents the time required for the m-th mobile battery energy storage system to move from node i to node j; τ represents less than or equal to Any time interval; Ω T This represents the set of moments within the duration of a disaster.

[0124] Because transportation networks are susceptible to real-time road conditions and emergencies, the actual travel time of mobile energy storage on the same road may vary under different disaster periods and road conditions. Therefore, a traffic fusion coefficient is used to reflect the spatial scheduling relationship between the actual vehicle speed and the equivalent travel distance of mobile energy storage, thereby reflecting the impact of disaster severity on travel time, as follows:

[0125]

[0126]

[0127] Among them, D ij This represents the road distance from node i to node j; It means that it can make The earliest moment; v0 represents the ideal speed of movement under normal weather conditions; This represents the actual moving speed of the mobile energy storage system at time t; This represents the equivalent moving speed of the mobile energy storage system at time t; when When the time exceeds 1 hour, that is Since the equivalent movement speed per hour is different, it is necessary to calculate in segments.

[0128] (2) Output constraints of distributed power sources

[0129] There are two types of distributed generation output constraints: diesel generators and photovoltaic (PV). The output of each node should not exceed its maximum power limit. In addition, there are certain constraints on the active power, reactive power, and power factor of distributed generation. Diesel generators operate with a variable power factor, while PV power sources operate with a fixed power factor, as shown in the following formula:

[0130]

[0131] Where, N DG A set of nodes representing a distributed power source; and This represents the active and reactive power output of the distributed power source at node i at time t. and This indicates the upper limit of active and reactive power output of the distributed power source. and These are the upper and lower limits of the power factor for distributed generation, respectively.

[0132] (3) Distribution network operation constraints

[0133] The purpose of distribution network operation constraints is to build a simulation model of the distribution network, which includes a variety of constraints, namely load shedding constraints, node voltage and transmission power constraints, distribution network radial constraints, and line power flow constraints.

[0134] (3.1) Load reduction constraints

[0135] The load reduction value should meet a certain range, and the power factor of the load is assumed to be fixed, as shown in the following formula:

[0136]

[0137] in, and These represent the rated active load and rated reactive load at node i at time t, respectively. and These represent the active load reduction and reactive load reduction at node i at time t, respectively.

[0138] (3.2) Node voltage and transmission power constraints

[0139] The node voltage must be within a certain range, and the transmission power of the line must be within the allowable transmission capacity, as shown in the following formula:

[0140] V i,min ≤U i,t ≤V i,max

[0141]

[0142] Among them, U i,t V represents the voltage at node i at time t. i,min and V i,max Let P be the lower and upper limits of the voltage at node i; ij,t and Q ij,t Let represent the active power and reactive power from node i to node j on line ij at time t, respectively. α represents the square of the allowed transmission capacity on line ij. ij,t This represents the on / off state of the line at time t. It is a 0-1 variable; a value of 1 indicates that line ij is closed, and a value of 0 indicates that line ij is open. The line capacity constraint is a nonlinear constraint and needs to be converted into a linear constraint, as shown in the following formula:

[0143]

[0144] (3.3) Radiation constraints of distribution network

[0145] To ensure radial operation of the distribution network under normal operating conditions, the total number of closed circuits must equal the difference between the number of nodes and the number of substation nodes, as shown in the following formula:

[0146]

[0147] Among them, Ω L N represents the set of distribution network lines; B N represents the total number of nodes in the distribution network; station This represents the number of nodes in the substation.

[0148] (3.4) The model for line power flow constraints is the LinDistFlow model, as shown in the following equation:

[0149]

[0150] Among them, Ω S (i) represents the set of child nodes of node i; Ω F(i) represents the set of parent nodes of node i; P ki,t and Q ki,t Let represent the active power and reactive power from node k to node i on line ki at time t, respectively. and R represents the squares of the voltages at node i and node j at time t, respectively. ij and X ij Let represent the resistance and reactance on line ij, respectively; M is a maximum value.

[0151] (4) Line maintenance status constraints

[0152] After a disaster, some lines may fail, requiring repair teams to carry out maintenance. If a regular line fails, it must be in a closed operating state after repair; if a connecting line fails, it can be in an open or closed operating state after repair, as follows:

[0153]

[0154] Among them, L close and L inter These represent the set of ordinary lines and the set of connecting lines, respectively. This represents the maintenance status of the line at time t. It is a 0-1 variable, where a value of 1 indicates that the line can operate normally, and a value of 0 indicates that the line is in a fault state.

[0155] When the maintenance team's maintenance time reaches the required maintenance time for the line, the line's maintenance status changes from 0 to 1, i.e., the line maintenance constraint is as follows:

[0156]

[0157] Among them, L fault L represents the set of faulty lines; L represents the set of all lines; Ω R This indicates the assembly of the repair team; This indicates the required maintenance time for line ij. This indicates the maintenance status of the line at time t; This indicates whether maintenance team r is performing maintenance at line ij at time t. It is a 0-1 variable, where a value of 1 indicates that maintenance team r is performing maintenance at line ij at time t, and a value of 0 indicates that it is not performing maintenance at line ij.

[0158] (5) Maintenance team dispatch constraints

[0159] The same maintenance team can only maintain one line at a time, and each line can only be maintained by one maintenance team at a time, as shown in the following formula:

[0160]

[0161] Furthermore, once the maintenance team has repaired the faulty line, they will not perform any further repairs on it; and the maintenance team must complete the repair of the current line before leaving to repair the next line, as follows:

[0162]

[0163] The spatiotemporal movement constraints of the maintenance team are similar to those of a mobile battery energy storage system, as shown in the following formula:

[0164]

[0165] in, This represents the time required for maintenance team r to move from line ij to line xy, and its calculation formula and mobile battery energy storage system. Similarly, the midpoint of the line can be used to represent the position of the line.

[0166] During disaster recovery, the repair team's goal is to repair all faulty lines as quickly as possible. Therefore, constraints are needed to ensure that the repair team must repair any faulty lines and that all faulty lines are repaired within a specified timeframe, as follows:

[0167]

[0168] in, This indicates whether maintenance team r is working at time t. "Working" includes being on its moving path or repairing a faulty line. It is a 0-1 variable, with a value of 1 indicating it is working and a value of 0 indicating it is not working; n repair Indicates the number of repair teams; n line Indicates the number of lines; This represents the moment the disaster ended; the min expression above is a nonlinear constraint, and its linearization process is shown below:

[0169]

[0170] Where, χ 1,t and χ 2,t It is a 0-1 variable, and cannot be 0 at the same time. When χ 1,t When the value is 1, the equation is: When χ 2,t When the value is 1, the equation is:

[0171] To minimize the total power outage impact on all users and ensure priority power supply to critical loads and fairness in load reduction for general loads, an objective function needs to be established for optimization. This embodiment uses the minimum load reduction weighted value as the objective function for optimization; this is the first stage of the solution. This embodiment uses an optimization solver (such as GUROBI) to obtain the minimum load reduction weighted value, as shown in the following formula:

[0172]

[0173] Among them, P * Ω represents the minimum weighted amount that load reduction can achieve. N Represents the set of nodes in the distribution network; w i This represents the load weight of node i.

[0174] S203. Using the minimum load reduction weighted value as the upper limit of the load reduction weighted value, a new constraint is added to the distribution network operation model. Then, any fairness principle is used as the objective function to perform optimization and obtain the optimal strategy that satisfies both the minimum load reduction and the fair reduction of general load.

[0175] The minimum load reduction weighted value obtained in this embodiment is 236,700 yuan, and then new constraints are added. This embodiment uses any fairness principle as the objective function for optimization, which is the second stage of the solution. This embodiment proposes three fairness principles as objective functions:

[0176] (1) Using the principle of global equilibrium as the objective function

[0177] The global equilibrium principle (fairness principle 1) aims to comprehensively reflect the overall distribution among all nodes, ensuring that the load reduction ratios among all nodes tend to be consistent. For each time step, it extracts all non-critical node pairs participating in load reduction, calculates the absolute difference in load reduction ratios between these pairs, and then sums and minimizes the absolute differences across all node pairs, as shown in the following formula:

[0178]

[0179] Among them, Ω C This indicates a node that generates load reduction among non-critical nodes, i.e., a node that does not generate load reduction among all nodes excluding substation nodes, critical load nodes, and non-critical load nodes; i and j both represent the serial numbers of nodes in the distribution network, representing node i and node j. This indicates the reduction in active power load at node i at time t; This indicates the reduction in active power load at node j at time t; This represents the rated active load at node i at time t; Denote the rated active load at node j at time t.

[0180] The linearization process of the global equilibrium principle is as follows:

[0181]

[0182]

[0183] Among them, Ω O denotes the set of non-critical nodes; y i,t denotes whether load shedding occurs at node i at time t; u ij,t denotes whether load shedding occurs simultaneously at nodes i and j at time t. It is a 0-1 variable. When it is 1, it means that load shedding occurs simultaneously at nodes i and j at time t. When it is 0, it means that load shedding does not occur simultaneously at nodes i and j at time t; d ij,t denotes the absolute value of the deviation of the load shedding ratio between nodes i and j at time t; ε is a very small value; through the above transformation, the global equilibrium principle can be linearized.

[0184] (2) Use the node worst deviation criterion as the objective function

[0185] The node worst deviation criterion (fairness principle 2) eliminates the gap between each node and the most unfair node, preventing excessive load shedding at individual nodes. For each moment, it calculates the maximum deviation between each non-critical node participating in shedding and all other participating nodes, and then sums and minimizes these maximum deviations of all nodes. To avoid duplicate values, it is required that i < j, as shown in the following formula:

[0186]

[0187] The linearization process of the node worst deviation criterion is as follows:

[0188]

[0189] Among them, M is a very large value; e i,t is the absolute value of the maximum of the deviation of the load shedding ratio between node i and nodes with numbers greater than it at time t; through the above transformation, the node worst deviation criterion can be linearized. [[ID=!]] [[ID=!]]

[0190] (3) Use the principle of minimizing the system range as the objective function

[0191] The principle of minimizing the system range (fairness principle 3) aims to eliminate the most extreme gaps, thereby ensuring that the overall reduction level will not experience extreme unfair events. For each time step, it finds the maximum and minimum reduction proportions among the participating reduction nodes, calculates the extreme difference, and sums and minimizes the extreme differences over all time steps, as shown in the following formula:

[0192]

[0193] The linearization process of the system range minimization principle is as follows:

[0194]

[0195] Among them, g t This represents the absolute value of the difference in load reduction ratio between the node pairs with the largest difference in load reduction ratio at time t; after the above transformation, the principle of minimizing the system range can be linearized.

[0196] In this embodiment, schemes as shown in Table 5 were designed, namely, not using the fairness principle, using fairness principle 1, using fairness principle 2, and using fairness principle 3, respectively. Then, an optimization solver was used to solve the problem and the results were displayed.

[0197] Table 5 Comparison of Scheme Parameters

[0198]

[0199] Among them, Scheme 1 is to solve only the first-stage optimization model; Schemes 2-4 are complete two-stage resilience improvement strategies, the difference being that the objective function of the second-stage optimization model adopts different fairness principles.

[0200] Using three fairness principles and the time required for computation as indicators, we compare the advantages and disadvantages of traditional resilience enhancement strategies and resilience enhancement strategies that use these three fairness principles respectively. The different fairness principle indicator values ​​for different schemes are shown in Table 6.

[0201] Table 6 Comparison of four resilience enhancement strategies

[0202]

[0203] As shown in Table 6, the value of fairness index 1 in scheme 2 is 1.307, the value of fairness index 2 in scheme 3 is 1.078, and the value of fairness index 3 in scheme 4 is 0.063. These are all the lowest values ​​among the four schemes. In order to facilitate the comparison of the merits of various schemes, this embodiment uses the lowest value of these fairness indices as the benchmark to compare the effects of different schemes on different indices. Since scheme 3 takes the shortest time, the calculation time of 92.103s of scheme 3 is used as the benchmark.

[0204] Scheme 1 performed the worst in all three fairness metrics except for its shorter computation time. Scheme 2's fairness metrics 2 and 3 exceeded the benchmark by 12.2% and 27.0% respectively, showing a significant improvement over Scheme 1, but its solution time was excessively long, exceeding the benchmark by 175.7%. Scheme 3's fairness metrics 1 and 3 exceeded the benchmark by 167.4% and 20.6% respectively. Scheme 4's fairness metrics 1 and 2 exceeded the benchmark by 104.4% and 5.9% respectively, but its solution time exceeded the benchmark by 4.6%. The results show that Schemes 2-4 significantly outperformed Scheme 1 in all three fairness metrics, indicating a synergistic effect between different fairness principles. When the algorithm considers any fairness principle, not only is the target metric optimized, but the other two fairness principle metrics also improve simultaneously. Furthermore, Schemes 2-4 also exhibited differences in overall performance. Although Scheme 2 performs best in fairness index 1, its fairness indices 2 and 3 and solution time are the worst, making it the worst overall. Scheme 3 has the shortest solution time, but its fairness index 1 is the worst, so its overall evaluation is the second best. Scheme 4 shows near-optimal characteristics, with its fairness indices 1 and 2 being second only to the optimal value, and its solution time being only 4.6% less than the optimal value, so its overall evaluation is the best.

[0205] Based on the results analysis of the four resilience enhancement strategies, it can be seen that considering any one of the three fairness principles will naturally improve the other fairness indicators. This indicates that the effects of the three fairness indicators are similar and effective. Furthermore, through quantitative analysis and comparison, it can be concluded that fairness principle 3 has the best overall performance across various indicators.

[0206] Figures 7a to 7d The graph shows the load reduction percentages for each node at different times in four different scenarios. As can be seen from the graph, compared to Schemes 2-4, Scheme 1 prioritizes power supply to critical nodes. For example, in all four schemes, nodes 3 and 24 experience load reduction at 6:00 and 7:00, but are fully restored by 8:00 and thereafter. Nodes 6, 17, 28, and 33 do not experience any load reduction throughout the process. This demonstrates that considering the fairness of load reduction for general nodes does not have an additional impact on the recovery of critical load nodes. For non-critical load nodes, Schemes 2-4 provide a fairer load reduction compared to Scheme 1. Figure 6As shown, at 11:00, the load reduction ratios for nodes 4, 5, 7, 26, 27, and 32 in Scheme 2 were all 0.983, while those in Schemes 3 and 4 were all 1. In contrast, the load reduction ratios for Scheme 1 were 1, 1, 1, 0.929, and 1, respectively, with no power supplied to any load except node 27. At 14:00, the load reduction ratios for nodes 18 and 32 in Scheme 2 were both 0.980, those in Scheme 3 were 0.838, and those in Scheme 4 were 0.853, while those in Scheme 1 were 0.934 and 1, respectively. At 9:00, the load reduction ratios for nodes 4, 7, and 18 in Scheme 1 were 0.756, 0.720, and 0.826, respectively; in Scheme 2, the load reduction ratios were 1, 1, and 0; and in Schemes 3 and 4, they were 0, 1, and 0. This is because in the early stages of a disaster, there are too many isolated nodes, while the number of various distributed resources is limited. Priority must be given to ensuring power supply to critical load nodes, making it difficult to guarantee similar load reduction ratios for all non-critical nodes. Therefore, some non-critical nodes are planned to be fully powered. This demonstrates that the resilience enhancement strategy proposed in this embodiment, compared to traditional resilience enhancement strategies that only consider node importance, ensures fair load reduction for all nodes, ensuring equal load reduction ratios, when power supply can guarantee fair power supply to all non-critical nodes. In special circumstances, when it is impossible to guarantee equal load reduction ratios for all nodes, priority will be given to restoring full power supply to certain nodes.

[0207] To compare in detail the impact of considering fairness principles on resilience enhancement strategies, this embodiment selects Scheme 1 and Scheme 2 for comparison; wherein, the load power and recovery ratio at each time period are as follows: Figure 8 As shown in Table 7; the travel path and charging / discharging power of mobile energy storage are shown in Table 8; the changes in the state of charge of mobile energy storage are shown in Table 9. Figure 9 As shown; the maintenance routes of the repair team are shown in Table 8; the power output of the diesel generator is as shown in Table 8. Figure 10 As shown; the changes in the connecting lines are as follows Figure 11 As shown, Figure 11 The left side represents Option 1, and the right side represents Option 2.

[0208] Table 7. Travel routes and charging / discharging power of mobile energy storage

[0209]

[0210] Table 8. Maintenance Team Dispatch Results

[0211]

[0212] Depend on Figure 8It can be seen that during the entire disaster duration, the total power supply load of both schemes was 32.282MW. This indicates that Scheme 2, which incorporates the fairness principle, can still achieve the same minimum load reduction level as the traditional scheme, meaning that the fairness strategy will not negatively impact the overall power supply capacity. The main difference between the two lies in the difference in the load recovery pace during different time periods.

[0213] From Table 7 and Figure 9 It can be seen that the MESSs in both schemes are mainly located at nodes 5 and 33, and later at node 29. This is because CS5 can guarantee the power supply to nodes 6 and 28, and CS33 can guarantee the power supply to nodes 17 and 33. After 12:00, MESS1 no longer needs to guarantee the power supply to the island where node 5 is located, so it goes to node 29 to charge and then to node 33 to replace MESS2 in guaranteeing the power supply to critical load nodes. It can be seen that the location of critical load nodes determines the emergency resource scheduling strategy. Emergency resources will prioritize restoring the power supply to critical load nodes. The difference between the MESSs in the two schemes is that the power output of Scheme 1 is more irregular, with full power output in the early stages of the disaster, resulting in a rapid decline in the state of charge; while the power output of Scheme 2 is more planned, releasing power more evenly to ensure the fairness of load reduction.

[0214] From Table 8 and Figure 11 It can be seen that the maintenance strategies of the two schemes are consistent. Lines 1-2 and 23-24 were repaired at 8:00 AM, and power was restored to the isolated nodes 3 and 24 after they were reconnected to the main network. Repairs to lines 2-19 and 20-21 were completed by 10:00 AM, and power was restored to nodes 15 and 19. The load reduction on nodes 15 and 19 continued until after 10:00 AM before being fully lifted. Repairs to lines 3-4 and 12-22 were completed at 12:00 PM. Figure 11 The display shows that lines 12–22 were immediately activated, allowing node 11 to reconnect to the main network and restore power. Nodes 6, 17, 28, and 33, however, did not experience any load reduction throughout the process. This demonstrates that the location of critical load nodes determines the maintenance team's order of operations; the team prioritizes restoring power to critical load nodes whenever possible.

[0215] Depend on Figure 10It can be seen that under both scenarios, DG31 operated at full power continuously during the disaster period. This was to ensure the power supply to the critical load node 24 until 8:00 AM when node 24 was reconnected to the main grid. DG9, on the other hand, ensured the power supply to node 15. Before 10:00 AM, DG9 did not output at full power in either scenario because, although DG9 is a variable power factor generator, even operating at its highest power factor, the island could not absorb such a large amount of reactive power at this time. Therefore, it could only output power at 100kW to 110kW until the island was connected to the main grid after 10:00 AM, when the reactive power absorption could be met, and DG9 then operated at full power. As can be seen, the difference in DG output between the two schemes is very small, indicating that emergency resources will prioritize restoring power to important load nodes. In Scheme 1, DG9 suddenly experienced a power drop at 15:00 and 17:00, and DG31 suddenly experienced a power drop at 18:00. This is because after 12:00, all nodes in the entire power grid except for the island where CS33 is located have been connected to the main grid. The island where CS33 is located can meet its self-sufficiency, and the active power of the other nodes can meet the requirements because they are connected to the main grid. Therefore, whether DG outputs or not has no impact on the load reduction index that this paper focuses on.

[0216] It should be noted that although the method operations of the above embodiments are described in a specific order, this does not require or imply that these operations must be performed in that specific order, or that all of the operations shown must be performed to achieve the desired result. On the contrary, the described steps may be performed in a different order.

[0217] Example 2:

[0218] like Figure 12 As shown, this embodiment provides a two-stage resilience enhancement device for a power distribution network. The device includes an acquisition module 1201, a first solution module 1202, and a second solution module 1203. The specific descriptions of each module are as follows:

[0219] The acquisition module 1201 is used to acquire the pre-disaster fault scenario and various known conditions required for calculation.

[0220] The first solution module 1202 is used to establish a distribution network operation model based on various known conditions, and to perform optimization by taking the minimum load reduction weighted value as the objective function to obtain the minimum load reduction weighted value.

[0221] The second solution module 1203 is used to add new constraints to the distribution network operation model by taking the minimum load reduction weight value as the upper limit of the load reduction weight value, and using any fairness principle as the objective function to perform optimization solution to obtain the optimal strategy that satisfies the minimum load reduction and the fair reduction of general load.

[0222] The distribution network operation model imposes distribution network operation constraints, including constraints on mobile battery energy storage systems, distributed power output, load shedding, node voltage and transmission power, distribution network radiation, line power flow, line maintenance status, and maintenance team dispatch.

[0223] The specific implementation of each module in this embodiment can be found in Embodiment 1 above, and will not be repeated here. It should be noted that the device provided in this embodiment is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure can be divided into different functional modules to complete all or part of the functions described above.

[0224] Example 3:

[0225] This embodiment provides a computer device, such as... Figure 13 As shown, it includes a processor 1302, a memory, an input device 1303, a display device 1304, and a network interface 1305 connected via a system bus 1301. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium 1306 and an internal memory 1307. The non-volatile storage medium 1306 stores an operating system, computer programs, and a database. The internal memory 1307 provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. When the processor 1302 executes the computer programs stored in the memory, it implements the two-stage resilience enhancement method for the power distribution network described in Embodiment 1.

[0226] Example 4:

[0227] This embodiment provides a storage medium, which is a computer-readable storage medium, storing a computer program. When the computer program is executed by a processor, it implements the two-stage resilience enhancement method for the power distribution network described in Embodiment 1 above.

[0228] It should be noted that the computer-readable storage medium in this embodiment can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0229] In this embodiment, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this embodiment, the computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable program. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable storage medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable storage medium can be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0230] In summary, the method of this invention, while ensuring minimum load reduction in the power system, not only prioritizes the supply of power to critical loads but also guarantees fair power supply to general loads. This method is scalable, allowing for the design of fairness principles to meet specific needs. The method considers fairness and proposes three fairness principles, all of which can improve the fairness of load reduction. Each of the three fairness principles has its own emphasis, including focusing on making the load reduction ratio of all nodes more consistent, or focusing more on eliminating the gap between the most unfair node pairs. The appropriate principle can be selected based on the requirements of calculation time and accuracy.

[0231] The above description is merely a preferred embodiment of the present invention. It will be apparent to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects. The scope of the present invention is defined by the appended claims rather than the foregoing description, and thus all variations falling within the meaning and scope of the equivalents of the claims are intended to be included within the present invention.

Claims

1. A two-stage resilience enhancement method for a distribution network, characterized in that, The method includes: Obtain the pre-disaster failure scenario and all known conditions required for calculation; Based on various known conditions, a distribution network operation model is established, and the minimum load reduction weighted value is used as the objective function to perform optimization and obtain the minimum load reduction weighted value. By using the minimum load reduction weighted value as the upper limit of the load reduction weighted value, a new constraint is added to the distribution network operation model. Then, any fairness principle is used as the objective function to perform optimization and obtain the optimal strategy that satisfies both the minimum load reduction and the fair reduction of general load. The distribution network operation model imposes distribution network operation constraints, including constraints on mobile battery energy storage systems, distributed power output, load shedding, node voltage and transmission power, distribution network radiation, line power flow, line maintenance status, and maintenance team dispatch.

2. The two-stage resilience enhancement method for distribution networks according to claim 1, characterized in that, During the charging and discharging process, the charging and discharging power of the mobile battery energy storage system does not exceed the maximum value, and it can only perform charging or discharging behavior at the same time, as shown in the following formula: in, and This represents the charging power and discharging power of the m-th mobile battery energy storage system at time t; and This represents the maximum charging and discharging power of the m-th mobile battery energy storage system; and This represents the reactive power absorbed and reactive power released by the m-th mobile battery energy storage system at time t. and This represents the maximum value of the reactive charging power and reactive discharging power of the m-th mobile energy storage unit; This indicates whether the m-th mobile battery energy storage system is in a charging state at time t. This indicates whether the m-th mobile battery energy storage system is in a discharging state at time t; Ω M N represents a collection of mobile battery energy storage systems; CS This represents the set of nodes where charging stations exist. This represents the active power output by the mobile battery energy storage system at node i at time t. This represents the reactive power released by the mobile battery energy storage system at node i at time t; The range of how the state of charge of the mobile battery energy storage system changes with charge and discharge behavior is as follows: in, This represents the state of charge of the m-th mobile battery energy storage system at time t; and Let represent the charging efficiency and discharging efficiency of the m-th mobile battery energy storage system; and These represent the minimum and maximum values ​​of the state of charge of the m-th mobile battery energy storage system. The mobile battery energy storage system is subject to constraints on spatiotemporal flexibility, as shown in the following equation: in, Indicate whether the m-th mobile battery energy storage system is located at node i at time t; τ represents the time required for the m-th mobile battery energy storage system to move from node i to node j; τ represents less than or equal to Any time interval; Ω T This represents the set of moments within the duration of the disaster. The spatial scheduling relationship between the actual vehicle speed and the equivalent travel distance of the mobile energy storage system is represented by a traffic fusion coefficient, as shown in the following formula: Among them, D ij This represents the road distance from node i to node j; It means that it can make The earliest moment; v0 represents the ideal speed of movement under normal weather conditions; This represents the actual moving speed of the mobile energy storage system at time t; This represents the equivalent moving speed of the mobile energy storage system at time t.

3. The two-stage resilience enhancement method for distribution networks according to claim 1, characterized in that, The output constraint of the distributed power source is as follows: Where, N DG A set of nodes representing a distributed power source; and This represents the active and reactive power output of the distributed power source at node i at time t. and This indicates the upper limit of active and reactive power output of the distributed power source. and These are the upper and lower limits of the power factor for distributed generation, respectively.

4. The two-stage resilience enhancement method for distribution networks according to claim 1, characterized in that, The load reduction constraint is as follows: in, and These represent the rated active load and rated reactive load at node i at time t, respectively. and These represent the active load reduction and reactive load reduction at node i at time t, respectively. The node voltage and transmission power constraints are as follows: V i,min ≤U i,t ≤V i,max Among them, U i,t V represents the voltage at node i at time t. i,min and V i,max Let P be the lower and upper limits of the voltage at node i; ij,t and Q ij,t Let represent the active power and reactive power from node i to node j on line ij at time t, respectively. α represents the square of the allowed transmission capacity on line ij. ij,t This indicates the on / off state of the line at time t; The radial constraint of the distribution network refers to satisfying the condition that the total number of closed loops equals the difference between the number of nodes and the number of substation nodes, as shown in the following formula: where Ω L denotes the set of distribution network lines; N B denotes the total number of nodes in the distribution network; N station is the number of substation nodes; The power flow constraint of the line is as follows: Among them, Ω S (i) represents the set of child nodes of node i; Ω F (i) represents the set of parent nodes of node i; P ki,t and Q ki,t Let represent the active power and reactive power from node k to node i on line ki at time t, respectively. and R represents the squares of the voltages at node i and node j at time t, respectively. ij and X ij Let represent the resistance and reactance on line ij, respectively; M is a maximum value.

5. The two-stage resilience enhancement method for distribution networks according to claim 1, characterized in that, The line maintenance status constraint is as follows: Among them, L fault L represents the set of faulty lines; L represents the set of all lines; Ω R This indicates the assembly of the repair team; This indicates the required maintenance time for line ij. This indicates the maintenance status of the line at time t; This indicates whether maintenance team r is performing maintenance at line ij at time t.

6. The two-stage resilience enhancement method for distribution networks according to claim 1, characterized in that, The maintenance team scheduling constraint is as follows: Among them, L fault L represents the set of faulty lines; L represents the set of all lines; Ω R This indicates the assembly of the repair team; This indicates the required maintenance time for line ij. This indicates the maintenance status of the line at time t; This indicates whether maintenance team r is performing maintenance at line ij at time t; This indicates whether maintenance team r is performing maintenance at point xy on the line at time t+τ; This represents the time required for maintenance team r to move from line ij to line xy; Indicates whether maintenance team r is working at time t; n repair Indicates the number of repair teams; n line Indicates the number of lines; Indicates the moment when the disaster ends.

7. The two-stage resilience enhancement method for distribution networks according to any one of claims 1-6, characterized in that, The objective function, which minimizes the load reduction weighted value, is as follows: where P * represents the minimum weighted amount that load shedding can achieve; Ω N represents the set of distribution network nodes; w i represents the load weight of node i; When the fairness principle is a global equilibrium principle, the global equilibrium principle is used as the objective function, as shown in the following equation: Among them, Ω C This indicates a node that generates load reduction among non-critical nodes; This indicates the reduction in active power load at node i at time t; This indicates the reduction in active power load at node j at time t; This represents the rated active load at node i at time t; This represents the rated active load at node j at time t; The linearization process of the global equilibrium principle is as follows: where Ω O denotes the set of unimportant nodes; y i,t denotes whether node i generates load curtailment at time t; u ij,t denotes whether nodes i and j generate load curtailment at time t simultaneously; d ij,t denotes the absolute value of the load curtailment proportion deviation of nodes i and j at time t; ε is a minimum value; When the fairness principle is the worst-case deviation criterion for nodes, the worst-case deviation criterion for nodes is used as the objective function, as shown in the following equation: The linearization process of the worst-case deviation criterion for the node is as follows: Where M is a local maximum; e i,t It is the absolute value of the maximum load reduction ratio deviation between node i and nodes with numbers greater than it at time t; When the fairness principle is the principle of minimizing the system range, the objective function is the principle of minimizing the system range, as shown in the following equation: The linearization process of the system range minimization principle is as follows: Among them, g t This represents the absolute value of the difference in load reduction ratio between the node pairs with the largest difference in load reduction ratio at time t.

8. A two-stage resilience enhancement device for power distribution networks, characterized in that, The device includes: The acquisition module is used to acquire pre-disaster failure scenarios and various known conditions required for calculation. The first solution module is used to establish a distribution network operation model based on various known conditions, and to perform optimization by taking the minimum load reduction weighted value as the objective function to obtain the minimum load reduction weighted value. The second solution module is used to add new constraints to the distribution network operation model by taking the minimum load reduction weighted value as the upper limit of the load reduction weighted value, and using any fairness principle as the objective function to perform optimization solution, so as to obtain the optimal strategy that satisfies the minimum load reduction and the fair reduction of general load. The distribution network operation model imposes distribution network operation constraints, including constraints on mobile battery energy storage systems, distributed power output, load shedding, node voltage and transmission power, distribution network radiation, line power flow, line maintenance status, and maintenance team dispatch.

9. A computer device comprising a processor and a memory for storing a processor-executable program, characterized in that, When the processor executes the program stored in the memory, it implements the two-stage resilience enhancement method for power distribution networks as described in any one of claims 1-7.

10. A storage medium storing a program, characterized in that, When the program is executed by the processor, it implements the two-stage resilience enhancement method for power distribution networks as described in any one of claims 1-7.

Images