Method and system for improving toughness of power distribution network based on island dynamic division

Abstract

The invention discloses a power distribution network toughness improvement method and system for island dynamic division, and the method comprises the steps: determining a target function and constraint conditions with the maximum fault recovery power as an optimization target, and building a power distribution network toughness improvement model; acquiring a load node, a switch node and system parameters of the power distribution network, determining a fault line in the power distribution network, and determining a power-losing load node; taking the objective function as the fitness of each country, solving the power distribution network toughness improvement model based on an empire competition algorithm, and obtaining power distribution network island division of an optimal solution of the objective function; and performing switching operation of the corresponding switching node according to the power distribution network island division of the optimal solution. An empire competition algorithm is adopted to solve the optimization model to obtain a toughness improvement scheme including two measures of power distribution network fault reconstruction and island dynamic division, the solving efficiency of a power distribution network toughness improvement strategy is improved, and flexible island division has a positive influence on the toughness improvement of the power distribution network.

Description

Technical Field

[0001] The present invention relates to improving the resilience of a distribution network, and in particular to a method and system for improving the resilience of a distribution network with dynamic island division. Background Art

[0002] At present, the distribution network is facing more and more impacts of uncertain disturbances. In addition to the disturbance impacts caused by conventional equipment failures and human operation errors, low-frequency extreme events (such as major natural disasters, etc.) and high-frequency, progressively intensified small disturbance events (such as high proportion of distributed power sources and electric vehicles) will also have a major impact on the safe operation of the distribution network, and even cause catastrophic consequences such as large-scale destruction or loss of distribution network functions. To this end, it is necessary to accelerate the improvement of distribution network resilience to cope with the above-mentioned uncertain changes or disturbances. At present, the research conducted by research institutions in the establishment of distribution network resilience improvement models can be divided into three categories: "single source-single island", "island division", and "multi-source collaboration". The research conducted in the solution of distribution network resilience improvement models includes heuristic algorithms, multi-agent systems, meta-heuristic algorithms, mathematical programming methods, or a combination of two methods, which provides an effective means for improving distribution network resilience.

[0003] For example, the Chinese patent application with application number 202210410888.0 discloses a method for improving the resilience of distribution networks based on collaborative microgrids after natural disasters. This method is aimed at distribution network failure scenarios under natural disasters, analyzes the failure probability of each node in the distribution system to determine the faulty node, and combines the given distributed power source access location to form multiple microgrids that can continue to supply power by controlling the distribution network switch separation and the output size of the distributed power source. The distributed power source is used to participate in the power supply restoration of key loads in the distribution network, and the resilience improvement effect of the distribution network is evaluated based on the proposed resilience evaluation indicators.

[0004] However, the existing technology fails to take into account the sensitive loads of different user types in the optimization model for improving the resilience of distribution networks. The different requirements of sensitive loads on the voltage of distribution network nodes have an impact on the resilience improvement strategy formulated after extreme events. It also fails to take into account the improvement effect of the voltage support capacity of distributed power sources on the scenario where the post-event recovery power is reduced due to node voltage constraints. In terms of the solution algorithm for improving the resilience of distribution networks, it fails to adapt to the requirements for fast and high-precision solutions put forward in the actual dispatching and operation process of modern smart distribution networks with ever-expanding system scales and complex and changeable operating modes. Summary of the invention

[0005] Purpose of the invention: In view of the above shortcomings, the present invention provides a method and system for improving the resilience of a distribution network by dynamically dividing isolated islands to enhance the resilience of the distribution network.

[0006] Technical solution: To solve the above problems, the present invention adopts a distribution network resilience improvement method with dynamic island division, which includes the following steps:

[0007] Determine the objective function and constraints with the maximum fault recovery power as the optimization goal, and establish a distribution network resilience improvement model;

[0008] Obtain the load nodes, switch nodes and system parameters of the distribution network, determine the fault lines in the distribution network, and identify the power-off load nodes;

[0009] Taking the objective function as the fitness of each country, the distribution network resilience improvement model is solved based on the imperial competition algorithm, and the distribution network island division with the optimal solution of the objective function is obtained;

[0010] The switching operation of the corresponding switch nodes is performed according to the distribution network island division of the optimal solution.

[0011] Furthermore, the objective function OF is:

[0012]

[0013] Among them, P 0t is the total initial load of the distribution network at time t, N k is the number of islanded microgrids formed, is the load restored by the kth island microgrid at time t;

[0014]

[0015] Among them, w i,k is the weighting factor of the load at node i in the kth island microgrid, N i,k is the number of nodes in the kth island microgrid, is the load active power at node i in the kth island microgrid at time t;

[0016]

[0017] Among them, P 0i,k,t is the rated active power at node i in the kth island microgrid at time t, V i,k,t is the voltage at node i in the kth island microgrid at time t, and α is the voltage dependence coefficient of each type of load in the active power calculation.

[0018] Furthermore, the constraint condition includes a radial constraint:

[0019]

[0020] Among them, N bus is the total number of nodes in the distribution network, θ ij,tis the connection status of the branch ij between node i and node j, T is the total number of scheduling cycles in the fault recovery process, and Γ is the branch set in the distribution network;

[0021] Connectivity constraints:

[0022]

[0023]

[0024] Among them, α ij,t and α ji,t represents the subordinate relationship between node i and node j at time t; i,k represents the relationship between node i and the kth island microgrid, M is the set of nodes in the distribution network connected to the main distributed generation, Θ(i) is the set of nodes connected to node i, and Λ is the set of nodes in the distribution network;

[0025] Power balance constraints:

[0026]

[0027] in, are the active and reactive outputs of the main distributed generation g in the kth island microgrid at time t, is the active power output of the nth photovoltaic in the kth island microgrid at time t, are the active and reactive power consumed by load i in the kth island microgrid at time t, PFlow ij,k,t 、QFlow ij,k,t are the active and reactive power on the branch (i, j) in the kth isolated island microgrid at time t, respectively; N is the set of nodes connected to photovoltaic power in the kth isolated island microgrid at time t; Q 0i,k,t is the rated reactive power at node i in the kth island microgrid at time t, β is the voltage dependence coefficient that determines each type of load in the reactive power calculation;

[0028] Power flow constraints:

[0029]

[0030] Among them, BM is a positive number, V j,k,t is the voltage at node j in the kth island microgrid at time t, rij, xij are the resistance and reactance on branch (i, j), respectively, and V0 is the rated voltage;

[0031] Node voltage constraints:

[0032]

[0033] Among them, V max 、Vmin are the maximum and minimum voltage limits, δ i,k,t is the voltage phase angle in the kth island microgrid at time t, δ max is the voltage phase angle limit, is the voltage amplitude of the node connected to the main distributed power source in the kth island microgrid;

[0034] Branch capacity constraints:

[0035]

[0036] in, is the maximum capacity of branch (i, j) at time t;

[0037] Power balance constraints of isolated microgrid:

[0038]

[0039] in, are the active and reactive power losses at node i in the kth island microgrid at time t, is the reactive power at node i in the kth island microgrid at time t;

[0040] Distributed power generation output constraints:

[0041]

[0042]

[0043] in, They are respectively the minimum active output limit, minimum reactive output limit, maximum active output limit, and maximum reactive output limit of the distributed generation g in the kth island microgrid at time t.

[0044] Furthermore, the specific steps of solving the distribution network resilience improvement model based on the Empire Competition Algorithm are as follows:

[0045] (1) Input data to calculate the parameters required, including fault line, load power curve and type, number and location of main distributed generation, distributed generation output curve, initial empire number, and maximum number of iterations;

[0046] (2) Randomly form initialization countries according to the access location of the main distributed generation; each country represents a distribution network island;

[0047] (3) Determine whether the country satisfies the constraint conditions. If not, return to step (2);

[0048] (4) Calculate the fitness value of each country as power. Based on the initial number of empires, divide the countries into imperialist countries and colonies. Randomly assign colonies to corresponding imperialist countries to form an initialized empire.

[0049] (5) Colonial assimilation and revolution;

[0050] (6) Calculate the power of the imperialist countries and the colonies; when the colonies are stronger than the imperialist countries, exchange the positions of the colonies and the imperialist countries;

[0051] (7) Imperial competition: Calculate the total power of each empire. The more powerful empire controls and occupies the colonies of the less powerful empire. The less powerful empire continues to lose colonies. When the last colony is lost, the empire will perish.

[0052] (8) The algorithm ends when the maximum number of iterations is reached or only one empire remains, and the optimal solution for distribution network islanding is output.

[0053] The present invention also adopts a distribution network resilience improvement system with dynamic island division, comprising:

[0054] The model building module is used to determine the objective function and constraints with the maximum fault recovery power as the optimization goal, and to establish a distribution network resilience improvement model;

[0055] The data acquisition module is used to obtain the switch nodes and system parameters of the distribution network, determine the fault lines in the distribution network, and identify the power-off load nodes;

[0056] The solution module is used to solve the distribution network resilience improvement model based on the imperial competition algorithm by taking the objective function as the fitness of each country, and obtain the distribution network island division with the optimal solution of the objective function;

[0057] The execution module is used to perform switching operations of corresponding switch nodes according to the distribution network island division of the optimal solution.

[0058] The present invention also adopts a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0059] The present invention also adopts a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0060] Beneficial effects: Compared with the prior art, the significant advantage of the present invention is that it uses the imperial competition algorithm to solve the optimization model to obtain a resilience improvement solution that includes two types of measures: distribution network fault reconstruction and dynamic island division, which improves the efficiency of solving the distribution network resilience improvement strategy. Flexible island division has a positive impact on the improvement of distribution network resilience. The distribution network resilience improvement model takes into account the demand constraints of sensitive loads of different user types on node voltages and the supporting role of the main distributed power supply equipped with AVR on node voltages, reducing the restrictions on fault recovery power after extreme events due to the sensitivity of various load voltages, and generally improving the ability of the distribution network to actively respond to various disturbances. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a schematic diagram of the flow chart of the method for improving the resilience of the distribution network of the present invention.

[0062] Figure 2 This is a flow chart for solving the distribution network resilience improvement model based on ICA in the present invention.

[0063] Figure 3 It is a schematic diagram of the IEEE 33-node test system in the present invention.

[0064] Figure 4 It is a power demand curve diagram of different types of loads in the present invention. DETAILED DESCRIPTION

[0065] Example 1

[0066] like Figure 1 As shown, a method for improving the resilience of a distribution network with dynamic island division in this embodiment includes the following steps:

[0067] Step 1: Determine the objective function and constraints with the maximum fault recovery power as the optimization goal, and establish a distribution network resilience improvement model; the objective function OF is:

[0068]

[0069] Among them, P 0t is the total initial load of the distribution network at time t, N k is the number of islanded microgrids formed,

[0070] is the load restored by the kth island microgrid at time t. The calculation formula is as follows:

[0071]

[0072] Among them, w i,k is the weighting factor of the load at node i in the kth island microgrid, reflecting the power supply importance and restoration priority of each type of load, Ni,k is the number of nodes in the kth island microgrid, is the load active power at node i in the kth island microgrid at time t, which is determined by the load type and node voltage. The calculation formula is as follows:

[0073]

[0074] Among them, V i,k,t is the voltage at node i in the kth island microgrid at time t, P 0i,k,t is the rated active power at node i in the kth island microgrid at time t, and α is the voltage dependence coefficient that determines each type of load, as shown in Table 1.

[0075] In addition, when judging the constraint conditions, the load reactive power It is also affected by load type and node voltage. The calculation formula is as follows:

[0076]

[0077] Among them, Q 0i,k,t is the rated reactive power at node i in the kth island microgrid at time t, and β is the voltage dependence coefficient that determines each type of load, as shown in Table 1.

[0078] Table 1 Index values ​​for different load types

[0079] Load Type α β conventional 0 0 Industrial 0.18 6 Business 1.51 3.4 Community 0.92 4.04

[0080] The constraints include:

[0081] (1) Radial Constraints

[0082] The distribution network should always maintain a radial shape, and even after an island is formed, no loop can be formed within the island. Based on graph theory analysis, the graph G = (Λ, Γ) consists of nodes (Λ) and branches (Γ).

[0083]

[0084] Among them, N bus is the total number of nodes in the distribution network, θ ij,t is a binary variable, indicating the connection status of the branch ij between nodes i and j, θ ij,t =1 means the branch is connected, θ ij,t = 0 means the branch is disconnected, T is the total number of dispatch cycles in the fault recovery process, Γ is the set of branches in the distribution network, and Λ is the set of nodes in the distribution network. This is a necessary and sufficient condition to ensure the radial shape of the distribution network.

[0085] (2) Connectivity constraints

[0086] For each node to be connected to an island, at least one parent node should be connected to a node with a main distributed generation. The island is considered as a subtree of the distribution network, and the node with the main distributed generation is considered as the root node in the subtree.

[0087]

[0088]

[0089] Among them, α ij,t is a binary variable, α ij =1 means node i is the parent node of node j, α ij =0 means node j is the parent node of node i. If α ij =1 or α ji =1 holds true, then the branch (i, j) is connected, and except for the root node, each node has at least one parent node (except the main distributed power supply node), λ i,k is a binary variable, λ i,k =1 means that node i belongs to the kth island microgrid, λ i,k =0 means that node i does not belong to the kth island microgrid, M is the set of distribution network nodes connected to the main distributed power source (M∈Λ), and Θ(i) is the set of nodes connected to node i.

[0090] (3) Power balance constraints

[0091]

[0092] in, are the active and reactive outputs of the main distributed generation g in the kth island microgrid at time t, is the active power output of the nth photovoltaic in the kth island microgrid at time t, are the active and reactive power consumed by load i in the kth island microgrid at time t, PFlow ij,k,t 、QFlow ij,k,t are the active and reactive powers on the branch (i, j) in the kth isolated island microgrid at time t, respectively; N is the set of nodes connected to photovoltaic power in the kth isolated island microgrid at time t.

[0093] (4) Power flow constraints

[0094] Big-M is introduced to represent the relationship between power flow constraints and line voltage;

[0095]

[0096]

[0097] Among them, BM is a large positive number, V j,k,t 、V j,k,t are the voltages at nodes i and j in the kth island microgrid at time t, r ij 、x ij are the resistance and reactance on branch (i, j) respectively, and V0 is the rated voltage.

[0098] (5) Node voltage constraints

[0099]

[0100] Among them, V max 、V min are the maximum and minimum voltage limits, δ i,k,t is the voltage phase angle in the kth island microgrid at time t, δ max is the voltage phase angle limit, is the voltage amplitude of the node connected to the main distributed power supply in the kth isolated microgrid. This value is the controlled value set by the AVR, and the phase angle at the root node of the isolated microgrid is set to zero.

[0101] (6) Branch capacity constraints

[0102]

[0103] in, is the maximum capacity of branch (i, j) at time t.

[0104] (7) Power balance constraints of isolated microgrids

[0105]

[0106] in, are the active and reactive power losses at node i in the kth island microgrid at time t, is the reactive power at node i in the kth island microgrid at time t.

[0107] (8) Distributed power generation output constraints

[0108]

[0109] in, They are respectively the minimum active output limit, minimum reactive output limit, maximum active output limit, and maximum reactive output limit of the distributed generation g in the kth island microgrid at time t.

[0110] Step 2: Taking the objective function as the fitness of each country, solve the distribution network resilience improvement model based on the imperial competition algorithm:

[0111] The specific process is as follows Figure 2 The steps are detailed as follows:

[0112] Step (1): Input data to calculate the parameters required, including fault line, load power curve and type, number and location of main distributed generation, distributed generation output curve, initial empire number, and maximum number of iterations;

[0113] Step (2): randomly form initialization countries according to the access location of the main distributed generation; each country represents a distribution network island;

[0114] Step (3): Based on graph theory, determine whether the country meets the basic constraints. If so, proceed to step (4); otherwise, return to step (2);

[0115] Step (4): Calculate the fitness value of each country as power; based on the initial number of empires, distinguish between imperialist countries and colonies, and randomly assign colonies to corresponding imperialist countries to form an initialized empire;

[0116] Step (5): Colonial assimilation and revolution;

[0117] Step (6): Calculate the power of imperialist countries and colonies;

[0118] Step (7): Determine whether the current colony is stronger than the imperialist country. If so, proceed to step (8); otherwise, proceed to step (9);

[0119] Step (8): Exchange the status of colonies and imperialist countries;

[0120] Step (9): Reassessment of imperial power;

[0121] Step (10): Imperial competition: the more powerful empire controls and occupies the colonies of the less powerful empire, while the less powerful empire continues to lose colonies;

[0122] Step (11): Determine whether the empire has colonies. If yes, proceed to step (13); otherwise, proceed to step (12);

[0123] Step (12): Remove empires without colonies;

[0124] Step (13): Whether the maximum number of iterations has been reached or only one empire remains, if so, proceed to step (14), otherwise return to (6);

[0125] Step (14): Output the optimal strategy for distribution network islanding division.

[0126] Step 3: Perform switching operations on corresponding switch nodes according to the distribution network island division of the optimal solution.

[0127] In this embodiment, an improved IEEE 33-node test system is selected, which includes 37 lines, each line corresponds to a switch, including 32 normally closed switches (corresponding to the solid lines S1-S32 in the figure), 5 normally open switches (corresponding to the dotted lines S34-S37 in the figure), and three types of load types, such as Figure 3 As shown. Four distributed power sources are installed on nodes 13, 20, 23 and 30, each with a capacity of 500kVA, and are all equipped with AVR systems, which can serve as the main distributed power sources in the island. Distributed photovoltaics are installed on nodes 18, 15, 9, 27 and 33, each with a capacity of 150kVA.

[0128] Assume that an extreme event occurs at 13:00 and fault recovery takes 15 hours, that is, until 3:00 a.m. the next day. The event causes branches 1-2, 5-6, 6-26, and 30-31 to fail. The three types of loads have the same priority and are connected at the following locations: Figure 3 As shown, the load curve is Figure 4 As shown. The power factor of each distributed power source before the extreme event is 0.9. The power factor after the event will change according to the load power restored from the fault, while the power factor of distributed photovoltaic power before and after the event remains unchanged, both at 1. The node voltage limit is ±10%, i.e. 0.9 to 1.1 pu. The output limit of distributed power source is 50kW to 500kW.

[0129] At 19:00 when the load demand is the largest and the islands are fixedly divided into 4 distributed power sources, a comparative analysis is conducted on the impact of whether the load modeling is considered as a sensitive load and whether the main distributed power source is considered to be equipped with AVR on the resilience improvement strategy. The comparison results are shown in Table 2.

[0130] Table 2 Comparison of the impact of sensitive load models and AVR on resilience improvement strategies

[0131]

[0132] During island division, except for the switches in the open position after operation in Table 2, all other 37 switches (including normally open switches) of the system are closed. Table 2 shows that whether the load modeling is considered as a sensitive load and whether the main distributed power source is considered as equipped with AVR are two important factors that need to be considered in the modeling process of actual projects. Both factors have an impact on the network reconstruction strategy and the restoration load level in the resilience improvement strategy, and refined modeling will ultimately improve the resilience of the distribution network.

[0133] Furthermore, the effects of flexible island division on the improvement of distribution network resilience are compared and analyzed. The results of the fixed island division scenario are shown in Table 3, and the results of the flexible island division scenario are shown in Table 4.

[0134] Table 3 Results of fixed island partition scenario

[0135]

[0136] Table 4 Results of flexible island partitioning scenario

[0137]

[0138]

[0139] During island division, except for the switches in the open position after operation in Tables 3 and 4, all other 37 switches in the system (including normally open switches) are closed. By comparing Tables 3 and 4, it can be seen that flexible island division has a positive impact on improving the resilience of the distribution network.

[0140] Example 2

[0141] In this embodiment, a distribution network resilience improvement system for dynamically dividing isolated islands includes:

[0142] The model building module is used to determine the objective function and constraints with the maximum fault recovery power as the optimization goal, and to establish a distribution network resilience improvement model;

[0143] The data acquisition module is used to obtain the switch nodes and system parameters of the distribution network, determine the fault lines in the distribution network, and identify the power-off load nodes;

[0144] The solution module is used to solve the distribution network resilience improvement model based on the imperial competition algorithm by taking the objective function as the fitness of each country, and obtain the distribution network island division with the optimal solution of the objective function;

[0145] The execution module is used to perform switching operations of corresponding switch nodes according to the distribution network island division of the optimal solution.

[0146] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0147] The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0148] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0149] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0150] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which all fall within the protection of the present invention.

Claims

1. A method for improving the resilience of a distribution network by dynamically dividing isolated islands, characterized in that: The following steps are involved: Determine the objective function and constraints with the maximum fault recovery power as the optimization goal, and establish a distribution network resilience improvement model; Obtain the load nodes, switch nodes and system parameters of the distribution network, determine the fault lines in the distribution network, and identify the power-off load nodes; Taking the objective function as the fitness of each country, the distribution network resilience improvement model is solved based on the imperial competition algorithm, and the distribution network island division with the optimal solution of the objective function is obtained; The switching operation of the corresponding switch nodes is performed according to the distribution network island division of the optimal solution.

2. The method for improving the resilience of a distribution network according to claim 1, characterized in that: The objective function OF is: Among them, P 0t is the total initial load of the distribution network at time t, N k is the number of isolated microgrids formed, is the load restored by the kth island microgrid at time t; Among them, w i,k is the weighting factor of the load at node i in the kth island microgrid, N i,k is the number of nodes in the kth island microgrid, is the load active power at node i in the kth island microgrid at time t; Among them, P 0i,k,t is the rated active power at node i in the kth island microgrid at time t, V i,k,t is the voltage at node i in the kth island microgrid at time t, and α is the voltage dependence coefficient of each type of load in the active power calculation.

3. The method for improving the resilience of a distribution network according to claim 2, characterized in that: The constraints include radial constraints: Among them, N bus is the total number of nodes in the distribution network, θ ij,t is the connection status of branch ij between node i and node j, T is the total number of scheduling cycles in the fault recovery process, and Γ is the branch set in the distribution network; Connectivity constraints: Among them, α ij,t and α ji,t represents the subordinate relationship between node i and node j at time t; i,k represents the relationship between node i and the kth island microgrid, M is the set of nodes in the distribution network connected to the main distributed generation, Θ(i) is the set of nodes connected to node i, and Λ is the set of nodes in the distribution network; Power balance constraints: in, are the active and reactive outputs of the main distributed generation g in the kth island microgrid at time t, is the active power output of the nth photovoltaic in the kth island microgrid at time t, are the active and reactive power consumed by load i in the kth island microgrid at time t, PFlow ij,k,t 、QFlow ij,k,t are the active and reactive power on the branch (i, j) in the kth isolated island microgrid at time t, respectively; N is the set of nodes connected to photovoltaic power in the kth isolated island microgrid at time t; Q 0i,k,t is the rated reactive power at node i in the kth island microgrid at time t, β is the voltage dependence coefficient that determines each type of load in the reactive power calculation; Power flow constraints: Among them, BM is a positive number, V j,k,t is the voltage at node j in the kth island microgrid at time t, r ij 、x ij are the resistance and reactance on branch (i, j) respectively, V0 is the rated voltage; Node voltage constraints: Among them, V max 、V min are the maximum and minimum voltage limits, δ i,k,t is the voltage phase angle in the kth island microgrid at time t, δ max is the voltage phase angle limit, is the voltage amplitude of the node connected to the main distributed power source in the kth island microgrid; Branch capacity constraints: in, is the maximum capacity of branch (i, j) at time t; Power balance constraints of isolated microgrid: in, are the active and reactive power losses at node i in the kth island microgrid at time t, is the reactive power at node i in the kth island microgrid at time t; Distributed power generation output constraints: in, They are respectively the minimum active output limit, minimum reactive output limit, maximum active output limit, and maximum reactive output limit of the distributed generation g in the kth island microgrid at time t.

4. The method for improving the resilience of a distribution network according to claim 3, characterized in that: The specific steps of solving the distribution network resilience improvement model based on the imperial competition algorithm are: (1) Input data to calculate the parameters required, including fault line, load power curve and type, number and location of main distributed generation, distributed generation output curve, initial empire number, and maximum number of iterations; (2) Randomly form initialization countries according to the access location of the main distributed generation; each country represents a distribution network island; (3) Determine whether the country satisfies the constraint conditions. If not, return to step (2); (4) Calculate the fitness value of each country as power. Based on the initial number of empires, divide the countries into imperialist countries and colonies. Randomly assign colonies to corresponding imperialist countries to form an initialized empire. (5) Colonial assimilation and revolution; (6) Calculate the power of the imperialist countries and the colonies; when the colonies are stronger than the imperialist countries, exchange the positions of the colonies and the imperialist countries; (7) Imperial competition: Calculate the total power of each empire. The more powerful empire controls and occupies the colonies of the less powerful empire. The less powerful empire continues to lose colonies. When the last colony is lost, the empire will perish. (8) The algorithm ends when the maximum number of iterations is reached or only one empire remains, and the optimal solution for distribution network islanding is output.

5. A distribution network resilience improvement system with dynamic island division, characterized in that: include: The model building module is used to determine the objective function and constraints with the maximum fault recovery power as the optimization goal, and to establish a distribution network resilience improvement model; The data acquisition module is used to obtain the load nodes, switch nodes and system parameters of the distribution network, determine the fault lines in the distribution network, and identify the power-off load nodes; The solution module is used to solve the distribution network resilience improvement model based on the imperial competition algorithm by taking the objective function as the fitness of each country, and obtain the distribution network island division with the optimal solution of the objective function; The execution module is used to perform switching operations of corresponding switch nodes according to the distribution network island division of the optimal solution.

6. The distribution network resilience improvement system according to claim 5, characterized in that: The objective function OF is: Among them, P 0t is the total initial load of the distribution network at time t, N k is the number of isolated microgrids formed, is the load restored by the kth island microgrid at time t; Among them, w i,k is the weighting factor of the load at node i in the kth island microgrid, N i,k is the number of nodes in the kth island microgrid, is the load active power at node i in the kth island microgrid at time t; Among them, P 0i,k,t is the rated active power at node i in the kth island microgrid at time t, V i,k,t is the voltage at node i in the kth island microgrid at time t, and α is the voltage dependence coefficient that determines each type of load.

7. The distribution network resilience improvement system according to claim 6, characterized in that: The constraints include radial constraints: Among them, N bus is the total number of nodes in the distribution network, θ ij,t is the connection status of the branch ij between node i and node j, T is the total number of scheduling cycles in the fault recovery process, and Γ is the branch set in the distribution network; Connectivity constraints: Among them, α ij,t and α ji,t represents the subordinate relationship between node i and node j at time t; i,k represents the relationship between node i and the kth island microgrid, M is the set of nodes in the distribution network connected to the main distributed generation, Θ(i) is the set of nodes connected to node i, and Λ is the set of nodes in the distribution network; Power balance constraints: in, are the active and reactive outputs of the main distributed generation g in the kth island microgrid at time t, is the active power output of the nth photovoltaic in the kth island microgrid at time t, are the active and reactive power consumed by load i in the kth island microgrid at time t, PFlow ij,k,t 、QFlow ij,k,t are the active and reactive power on the branch (i, j) in the kth isolated island microgrid at time t, respectively; N is the set of nodes connected to photovoltaic power in the kth isolated island microgrid at time t; Power flow constraints: Among them, BM is a positive number, V j,k,t is the voltage at node j in the kth island microgrid at time t, r ij 、x ij are the resistance and reactance on branch (i, j) respectively, V0 is the rated voltage; Node voltage constraints: Among them, V max 、V min are the maximum and minimum voltage limits, δ i,k,t is the voltage phase angle in the kth island microgrid at time t, δ max is the voltage phase angle limit, is the voltage amplitude of the node connected to the main distributed power source in the kth island microgrid; Branch capacity constraints: in, is the maximum capacity of branch (i, j) at time t; Power balance constraints of isolated microgrid: in, are the active and reactive power losses at node i in the kth island microgrid at time t, is the reactive power at node i in the kth island microgrid at time t; Distributed power generation output constraints: in, They are respectively the minimum active output limit, minimum reactive output limit, maximum active output limit, and maximum reactive output limit of the distributed generation g in the kth island microgrid at time t.

8. The distribution network resilience improvement system according to claim 5, characterized in that: The specific steps of solving the distribution network resilience improvement model based on the imperial competition algorithm are: (1) Input data to calculate the parameters required, including fault line, load power curve and type, number and location of main distributed generation, distributed generation output curve, initial empire number, and maximum number of iterations; (2) Randomly form initialization countries according to the access location of the main distributed generation; each country represents a distribution network island; (3) Determine whether the country satisfies the constraint conditions. If not, return to step (2); (4) Calculate the fitness value of each country as power. Based on the initial number of empires, divide the countries into imperialist countries and colonies. Randomly assign colonies to corresponding imperialist countries to form an initialized empire. (5) Colonial assimilation and revolution; (6) Calculate the power of the imperialist countries and the colonies; when the colonies are stronger than the imperialist countries, exchange the positions of the colonies and the imperialist countries; (7) Imperial competition: Calculate the total power of each empire. The more powerful empire controls and occupies the colonies of the less powerful empire. The less powerful empire continues to lose colonies. When the last colony is lost, the empire will perish. (8) The algorithm ends when the maximum number of iterations is reached or only one empire remains, and the optimal solution for distribution network islanding is output.

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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