A power distribution network planned maintenance optimization method based on network reconfiguration interval optimization

Abstract

The application provides a power distribution network plan maintenance optimization method based on network reconstruction interval optimization, comprising: constructing an upper maintenance time optimization model with the lowest total maintenance cost as an objective function; constructing a lower power distribution network reconstruction interval optimization model with the minimum energy loss of the maintenance period as an objective function; performing cyclic solution iteration calculation on the upper maintenance time optimization model and the lower power distribution network reconstruction interval optimization model to determine the optimal maintenance period of the power distribution network equipment and the power distribution network reconstruction strategy in the corresponding period. The method solves the problems that the traditional maintenance optimization does not fully consider the uncertainty of the operation of the power distribution network and the maintenance scheme pays attention to the economy and to some extent ignores the power supply reliability, and obtains a more economic and reliable power distribution network maintenance plan optimization scheme.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power system optimization, in particular to a distribution network planned maintenance optimization method based on network reconfiguration interval optimization. BACKGROUND

[0002] In the daily operation and management of the distribution network, a reasonable maintenance plan can effectively reduce risks and ensure the safe operation of the distribution network. With the advancement of technology, high-proportion distributed power sources such as photovoltaic power are connected to the distribution network, and distributed power sources can bring more flexible power supply to the distribution network, but their uncertainty also has some impact on the daily operation of the distribution network. At the same time, with the economic development and adjustment of industrial structure, the types of loads connected to the distribution network increase, and the load presents fluctuation characteristics. When formulating the optimization scheme of the distribution network maintenance plan, these uncertain factors need to be considered to ensure the safe and stable operation of the distribution network during maintenance. Therefore, it is of great theoretical value and practical significance to study the optimization scheme of the distribution network maintenance plan considering uncertain factors.

[0003] The maintenance mode of the distribution network is divided into post-maintenance, planned maintenance and condition-based maintenance. Due to the limitations of related technologies, the maintenance mode of the distribution network in China is still mainly planned maintenance. The distribution network maintenance scheme has complex characteristics such as discreteness and nonlinearity, and its optimization is a combinatorial optimization problem. Most of the existing researches are based on deterministic factors for maintenance optimization, and do not consider the impact of distributed power sources and load uncertainty on the distribution network maintenance plan. When formulating the optimization scheme of the distribution network maintenance plan containing high-proportion distributed power sources, the uncertainty of distributed power sources and loads will cause the distribution network reconfiguration strategy based on deterministic factors to be unable to achieve the actual optimization. SUMMARY

[0004] In order to overcome the above technical defects, the present application provides a distribution network planned maintenance optimization method based on network reconfiguration interval optimization. In order to achieve the above purpose, the present application is implemented according to the following technical scheme:

[0005] The present application provides a distribution network planned maintenance optimization method based on network reconfiguration interval optimization, which comprises:

[0006] An upper maintenance time optimization model with the lowest total maintenance cost as the objective function is constructed;

[0007] A lower distribution network reconfiguration interval optimization model with the minimum energy loss in the maintenance period as the objective function is constructed;

[0008] The upper maintenance time optimization model and the lower distribution network reconfiguration interval optimization model are iteratively calculated by cyclic solution to determine the optimal maintenance period of the distribution network equipment and the distribution network reconfiguration strategy in the corresponding period.

[0009] Optionally, the constraint condition of the upper-layer maintenance time optimization model comprises the following:

[0010] The simultaneous maintenance constraint, the mutually exclusive maintenance constraint, the maintenance resource constraint, the maintenance start time constraint and the maintenance continuity constraint.

[0011] Optionally, the constraint condition of the lower-layer power distribution network reconstruction interval optimization model comprises the following:

[0012] The power constraint, the voltage constraint, the distributed power generation output constraint, the switch action frequency constraint and the power distribution network radiation constraint.

[0013] Optionally, the cyclic solving iteration calculation of the upper-layer maintenance time optimization model and the lower-layer power distribution network reconstruction interval optimization model to determine the optimal maintenance period of the power distribution network equipment and the power distribution network reconstruction strategy in the corresponding period comprises the following:

[0014] Step S401: substituting the power distribution network reconstruction strategy after the previous iteration optimization into the upper-layer maintenance time optimization model for solving to obtain the equipment maintenance time after the current iteration optimization;

[0015] Step S402: substituting the equipment maintenance time into the lower-layer power distribution network reconstruction interval optimization model for solving to obtain the power distribution network reconstruction strategy after the current iteration optimization;

[0016] Step S403: determining whether the iteration number is equal to the first preset iteration number;

[0017] If yes, the equipment maintenance time after the current iteration optimization output by the upper-layer maintenance time optimization model is taken as the optimal maintenance period of the power distribution network equipment, and the power distribution network reconstruction strategy after the current iteration optimization is taken as the power distribution network reconstruction strategy in the corresponding period of the optimal maintenance period of the power distribution network equipment;

[0018] If no, repeating steps S401-S403.

[0019] Optionally, the substituting the power distribution network reconstruction strategy after the previous iteration optimization into the upper-layer maintenance time optimization model for solving to obtain the equipment maintenance time after the current iteration optimization comprises the following:

[0020] After substituting the power distribution network reconstruction strategy after the previous iteration optimization into the upper-layer maintenance time optimization model, a particle swarm optimization algorithm is used for solving to obtain the equipment maintenance time after the current iteration optimization.

[0021] Optionally, the substituting the equipment maintenance time into the lower-layer power distribution network reconstruction interval optimization model for solving to obtain the power distribution network reconstruction strategy after the current iteration optimization comprises the following:

[0022] The equipment maintenance time is substituted into the lower distribution network reconstruction interval optimization model, and a clone selection algorithm is used to solve the model to obtain a distribution network reconstruction strategy after this iteration optimization.

[0023] Optionally, the particle swarm optimization algorithm is used to solve the model to obtain the equipment maintenance time after this iteration optimization, and the method specifically includes the following steps.

[0024] The maintenance start time of each equipment is coded, and the coded maintenance start time of each equipment is used as a particle of the particle swarm optimization algorithm.

[0025] The fitness function of each particle is calculated to obtain the equipment maintenance time after this iteration optimization.

[0026] Optionally, the clone selection algorithm is used to solve the model to obtain the distribution network reconstruction strategy after this iteration optimization, and the method specifically includes the following steps.

[0027] According to the equipment maintenance time, a candidate set of switch operations for distribution network reconstruction is generated, and the candidate set of switch operations includes a plurality of switch operations.

[0028] The fitness of the plurality of switch operations is calculated by interval power flow calculation and is evaluated to obtain the total number of switch operations after this iteration optimization.

[0029] The application has the following beneficial effects.

[0030] The method provided by the application solves the problem that the traditional maintenance optimization does not fully consider the uncertainty of the operation of the distribution network and the maintenance scheme pays more attention to economy and less attention to power supply reliability, and obtains a more economic and reliable distribution network maintenance plan optimization scheme.

[0031] In addition to the purposes, features and advantages described above, the application has other purposes, features and advantages. The application will be further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0032] The accompanying drawings, which form a part of the present application, are included to provide a further understanding of the present application, and are incorporated herein for purposes of explanation, and are not intended to limit the present application. In the drawings:

[0033] Figure 1 is a flowchart of a distribution network plan maintenance optimization method based on network reconstruction interval optimization provided by an embodiment of the application;

[0034] Figure 2 is a structural schematic diagram of an IEEE33 node test system provided in an experimental verification stage of an embodiment of the application. DETAILED DESCRIPTION

[0035] The embodiments of this application are described in detail below with reference to the accompanying drawings, but this application can be implemented in many different ways as defined and covered by the claims.

[0036] Currently, there are few studies on the impact of distributed generation and load uncertainty in distribution network planning optimization methods. Some studies only provide output constraints based on the predicted output values ​​of distributed generation and load, without considering the impact of distributed generation output and load fluctuations. The optimized maintenance plans have low economic efficiency and low power supply reliability, and cannot well reflect the actual maintenance situation.

[0037] Therefore, in order to solve the above problems, such as Figure 1 As shown, this application proposes a distribution network planned maintenance optimization method based on network reconfiguration interval optimization, including:

[0038] Step S101: Construct an upper-level maintenance time optimization model with the objective function of minimizing the total maintenance cost;

[0039] The purpose of upper-level optimization is to optimize maintenance time and obtain the lowest total maintenance cost. Therefore, an upper-level maintenance time optimization model is constructed with the objective function of minimizing the total maintenance cost, as shown in formula (1):

[0040] (1)

[0041] In the formula, Total maintenance cost; and These are maintenance costs and switch operation costs, respectively. This indicates the total number of equipment to be inspected; This represents the total number of maintenance periods. Indicates equipment exist Constant maintenance status. This indicates that the equipment has not been inspected. This indicates that the equipment is under maintenance. Indicates equipment exist The maintenance costs at any given time are labor costs that vary over time, excluding fixed costs such as equipment replacement. This indicates the cost of a single switch operation in a power distribution network. Indicates the total maintenance period Total number of internal switch operations.

[0042] After obtaining the upper-level maintenance time optimization model, it is necessary to constrain this upper-level maintenance time optimization model. The constraints that need to be satisfied include the following: simultaneous maintenance constraint, mutual exclusion maintenance constraint, maintenance resource constraint, maintenance start time constraint, and maintenance continuity constraint, and their expressions are (2)~(6):

[0043] (2)

[0044] (3)

[0045] (4)

[0046] (5)

[0047] (6)

[0048] In the formula, , They represent the first The and the first The start time for maintenance of each piece of equipment; Indicates the first Maintenance duration for each piece of equipment. Indicates the equipment The amount of human and material resources required for maintenance; Indicates equipment exist Constant maintenance status. This indicates that the equipment is operating normally. This indicates that the equipment is under maintenance. express The total amount of human and material resources that can be provided at any given moment; This indicates the total number of devices that can be repaired simultaneously. Indicates the first The set of maintenance times that each device is allowed to begin.

[0049] Step S102: Construct an optimization model for the reconfiguration interval of the lower-level distribution network with the objective function of minimizing energy loss during maintenance periods;

[0050] For distribution networks with a high proportion of distributed resources, the output of distributed resources is represented as intervals, and the node load is also represented as intervals. The purpose of lower-level optimization is to optimize the load transfer path and ensure the reliability of power supply during maintenance. The sum of the active power intervals injected into all network nodes equals the network loss interval. A lower-level distribution network reconfiguration interval optimization model is constructed with the minimum energy loss (network loss) during maintenance as the objective function, as shown in equation (7):

[0051] (7)

[0052] In the formula, This is due to network loss in the system. This represents the range of values ​​indicating system network loss. This represents the total number of nodes in the system. express Injecting nodes at all times The active power range value (i.e. (The sum of the power supply and load power of the node at that moment). express The range of network loss at any given time.

[0053] Here, the optimization model for the reconfiguration interval of the lower-level distribution network also needs to be constrained accordingly. The constraints include the following: power constraints, voltage constraints, distributed generation output constraints, switching action count constraints, and distribution network radial constraints, as shown in equations (8) to (13) below:

[0054] (8)

[0055] (9)

[0056] (10)

[0057] (11)

[0058] (12)

[0059] In the formula, Represents nodes A set of related nodes; and Indicates the mainnet injected node The active and reactive power ranges; and Represents distributed power injection node The active and reactive power ranges; and Represents a node The active and reactive power ranges of the load; Indicates a branch The on / off state, When the branch circuit is disconnected, The branch remains closed. and Indicates a branch The range of active and reactive power flowing out from the beginning. Indicates the voltage amplitude range. and Represents a node The lower and upper limits of the voltage amplitude range; and Represents a node Minimum and maximum permissible voltage values. and , and Representing nodes respectively The lower and upper limits of the active power output range and the lower and upper limits of the reactive power output range of distributed power sources; and , and Representing nodes respectively The minimum and maximum active power output, and the minimum and maximum reactive power output are set for the distributed power source. This indicates the total number of switch actions within the distribution network reconfiguration cycle; This indicates the upper limit of the total number of switching actions within the distribution network reconfiguration cycle. This indicates the network topology of the restored area; This represents a set of radial topologies of a power distribution network.

[0060] Step S103: Perform iterative calculations on the upper-level maintenance time optimization model and the lower-level distribution network reconfiguration interval optimization model to determine the optimal maintenance period of the distribution network equipment and the distribution network reconfiguration strategy in the corresponding period.

[0061] To address the problems described in the background art, this application constructs a two-layer model as previously mentioned. It requires iterative calculation and solution, as detailed below:

[0062] Step S401: Substitute the previously optimized distribution network reconfiguration strategy into the upper-level maintenance time optimization model for solution to obtain the equipment maintenance time after this iteration.

[0063] After substituting the previously optimized distribution network reconfiguration strategy into the upper-level maintenance time optimization model, the particle swarm optimization algorithm is used to solve the problem, and the equipment maintenance time after this iteration optimization is obtained.

[0064] The particle swarm optimization algorithm was used to solve the problem, and the equipment maintenance time after this iteration of optimization was obtained as follows:

[0065] First, the maintenance start time of each device is encoded. Then, the encoded maintenance start time of each device is used as a particle in the particle swarm optimization algorithm to initialize the particle swarm optimization population. Then, the fitness function of each particle is calculated. The fitness function is Equation (13):

[0066] (13)

[0067] In the formula, For the fitness function, In The time is for maintenance costs. The time is for switch operation fees. The penalty value. The weights are the corresponding objective function weights. The constraints shown in equations (2) to (6) are as follows: This represents the penalty coefficient for the corresponding constraint.

[0068] Based on the velocity and position update formulas of the particle swarm optimization algorithm, the positions of the particles are dynamically adjusted to explore better solutions, while ensuring that the particle positions meet the maintenance time window and network topology constraints. During the iteration process, the optimal positions of individual particles and the global optimal positions are recorded, and then the equipment maintenance time after this iteration is output.

[0069] Step S402: Substitute the equipment maintenance time into the optimization model of the lower-level distribution network reconfiguration interval for solution to obtain the distribution network reconfiguration strategy after this iteration optimization;

[0070] Since load transfer during maintenance requires network topology adjustment through switching operations, the distribution network reconfiguration strategy here specifically refers to the combination of opening and closing operations of tie switches and sectionalizing switches in the distribution network. By optimizing the switch state sequence, the load can be transferred and distributed between different power supply areas.

[0071] After substituting the equipment maintenance time into the optimization model of the lower-level distribution network reconfiguration interval, the clone selection algorithm is used to solve the problem, and the distribution network reconfiguration strategy after this iteration is obtained. The specific process is as follows:

[0072] After substituting the equipment maintenance time into the lower-level distribution network reconfiguration interval optimization model, a candidate set of switching operations (initial antibody population) for distribution network reconfiguration is generated based on the equipment maintenance time. This candidate set includes multiple different switching operations. Then, a fitness function is used to evaluate the fitness of these multiple switching operations, resulting in the total number of switching operations after local iterative optimization. The fitness function here... The reciprocal of equation (7) is shown in equation (14):

[0073] (14)

[0074] Because network losses are involved, calculating the fitness of multiple switching operations requires interval power flow calculation. The specific process for calculating fitness using interval power flow calculation is as follows:

[0075] The model expressions for the interval power flow in this application are (15)~(17):

[0076] (15)

[0077] (16)

[0078] (17)

[0079] In the formula, Represents a node The voltage amplitude range; and The unbalance matrix representing active and reactive power in interval form; and Correction matrices representing the voltage phase angle and magnitude in interval form; , , and Represents a Jacobian submatrix in interval form. and Indicates the injected node The range of active and reactive power imbalance values; and Indicates the injected node The range values ​​of active power and reactive power; and Indicates a branch Conductivity and susceptance; Represents a node Voltage range; Indicates a branch The range of voltage phase angle differences.

[0080] The system of interval equations represented by equations (16) to (17) can be rewritten as expression (18) by combining the equations:

[0081] (18)

[0082] In the formula, This represents an interval function. Represents a matrix of interval variables; and Represents the range of phase angle and voltage values; and , and This indicates the upper and lower limits of the phase angle range and the upper and lower limits of the voltage range.

[0083] The interval operators for the interval power flow equations are constructed as shown in expression (19):

[0084] (19)

[0085] In the formula, Represents interval operators; Represent a deterministic matrix; Represent a Non-singular matrices; Represents an interval function; Represent an identity matrix; Let represent an interval extended Jacobian matrix with monotonicity. and , and , and , and Let represent the lower and upper bound matrices of each Jacobian submatrix element, respectively.

[0086] For the interval power flow equation set given by equation (18), the initial iteration interval is first given. Then, iterative calculations are performed according to expression (20) until the convergence condition is met, thus obtaining the interval solution of equation (18), which is the solution of the interval power flow:

[0087] (20)

[0088] In the formula, and Representing interval variables No. The next iteration and The result of the next iteration; , They represent the first The next iteration and The interval operator for the next iteration. and Represent matrices respectively and No. The result of the next iteration; and They represent the first The interval function and the interval extended Jacobian matrix of the next iteration. and They represent and The midpoint value of the interval.

[0089] In interval power flow calculation, interval addition, subtraction, multiplication, and division operations are involved. Affine arithmetic can overcome the conservatism of interval addition and subtraction operations, and affine arithmetic and interval arithmetic can be converted to each other. Here, the interval number is expanded using second-order affine, as shown in equation (21):

[0090] (twenty one)

[0091] In the formula, This represents the midpoint value of the interval. ,in and These represent the upper and lower limits of the interval, respectively. Represents the radius of the interval. ; This represents the noise element interval, with values ​​in the range [-1, 1].

[0092] In other words, after calculating the power flow in the interval, the total number of switching operations after this iteration can be obtained. Then, in the next iteration, it is substituted into the upper-level maintenance time optimization model, i.e., formula (1). The particle swarm optimization algorithm is used to solve formula (1) to obtain the equipment maintenance time of this iteration. Then, the equipment maintenance time is substituted into formula (7) for calculation. This iterative calculation is repeated.

[0093] Step S403: Determine whether the number of iterations is equal to the first preset number of iterations;

[0094] If so, the equipment maintenance time optimized in this iteration output by the upper-level maintenance time optimization model shall be taken as the optimal maintenance period of the distribution network equipment, and the distribution network reconfiguration strategy optimized in this iteration shall be taken as the distribution network reconfiguration strategy in the corresponding period of the optimal maintenance period of the distribution network equipment.

[0095] If not, repeat steps S401-S403.

[0096] Before performing iterative calculations, in order to prevent the iterations from continuing indefinitely, a first preset number of iterations needs to be set in advance. When the number of iterations meets the first preset number of iterations, the equipment maintenance time output by the current iteration of the upper-level maintenance time optimization model is taken as the optimal maintenance period of the distribution network equipment, and the total number of switching operations output by the current iteration of the lower-level distribution network reconfiguration interval optimization model is taken as the distribution network reconfiguration strategy in the corresponding period with the optimal maintenance period of the distribution network equipment.

[0097] If the judgment condition is not met, repeat steps S401-S403 to perform the next round of iteration calculation until the iteration termination condition is met.

[0098] It should be noted that the first preset number of iterations can be set according to actual needs, and no specific requirements are made here.

[0099] Experimental verification

[0100] To verify the effectiveness and practicality of the proposed distribution network maintenance plan optimization model and algorithm, two numerical examples are used. 1) The proposed method is compared with a two-level distribution network maintenance plan optimization method based on fuzzy chance constraints, where uncertainties are represented by interval numbers and fuzzy numbers respectively, to verify the practicality of the proposed method in handling uncertainties. The IEEE 33-bus test system is used, such as... Figure 2As shown. The IEEE 33-node example used includes 32 branches, 5 tie switch branches, and 1 power network head. The base voltage is 12.66 kV and the base power is 10 MVA. The load node voltage range is 0.95~1.05 pu, and it is assumed that the load fluctuates within ±15% of the initial load. The distributed generation (DG) access nodes and their related parameters are shown in Table 1; the daily initial maintenance plan is shown in Table 2 (the required manpower and material resources are expressed as integers), the total manpower and material resources that can be provided at each moment are 10, the total number of devices that can be maintained at the same time is 2, and the maximum number of switching operations within the distribution network reconfiguration cycle is 30; the maintenance cost of the equipment is shown in Table 3, and the cost of a single switching operation is 20 yuan. 2) The lower-level cloning selection algorithm in this paper is compared with the traditional genetic algorithm and simulated annealing algorithm to verify the superiority of the lower-level solution algorithm in terms of convergence and optimization. The example used is the same as the IEEE 33-node example used in 1). The relevant parameter settings are the same as in 1).

[0101] Table 1 DG Access Nodes and Related Parameters

[0102] ;

[0103] Table 2 Initial Maintenance Plan

[0104] ;

[0105] Table 3 Equipment Maintenance Costs

[0106] ;

[0107] In summary, this application constructs a two-layer optimization model for distribution network maintenance plans that considers uncertainties. The purpose of the upper-layer optimization is to optimize the maintenance time, obtaining the optimized maintenance time for each device; the purpose of the lower-layer optimization is to reconfigure the distribution network within the maintenance period, given the optimized maintenance time for each device. During the reconfiguration process, the uncertainties of distributed power sources and loads are considered. Interval numbers are used to model the uncertainties of renewable energy output and loads, constructing a network reconfiguration interval optimization model to obtain the distribution network reconfiguration scheme within the maintenance period. This solves the problems of traditional maintenance optimization not fully considering the uncertainties of distribution network operation and the emphasis on economic efficiency in maintenance schemes, which to some extent neglects power supply reliability, resulting in a more economical and reliable distribution network maintenance plan optimization scheme.

[0108] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for optimizing planned maintenance of a distribution network based on network reconfiguration interval optimization, characterized in that, include: Construct a higher-level maintenance time optimization model with the objective function of minimizing total maintenance costs: ； In the formula, Total maintenance cost; and These are maintenance costs and switch operation costs, respectively. This indicates the total number of equipment awaiting repair. This represents the total number of maintenance periods. Indicates equipment exist Constant maintenance status. This indicates that the equipment has not been inspected. This indicates that the equipment is under maintenance. Indicates equipment exist The maintenance cost at any given time is the labor cost that varies over time, excluding the fixed cost of equipment replacement; This indicates the cost of a single switch operation in a power distribution network. Indicates the total maintenance period Total number of internal switch operations; A lower-level distribution network reconfiguration interval optimization model is constructed with the objective function of minimizing energy loss during maintenance periods. This model includes distributed resource output and node loads. ； In the formula, This is due to network loss in the system. This represents the range of values ​​indicating system network loss. This represents the total number of nodes in the system. express Injecting nodes at all times The active power range value, i.e. The sum of the power supply and load power of the node at any given moment; express The range of network loss at any given time; The upper-level maintenance time optimization model and the lower-level distribution network reconfiguration interval optimization model are iteratively solved to determine the optimal maintenance period of the distribution network equipment and the distribution network reconfiguration strategy in the corresponding period.

2. The method according to claim 1, characterized in that, The constraints of the upper-level maintenance time optimization model include the following: Simultaneous maintenance constraints, mutually exclusive maintenance constraints, maintenance resource constraints, maintenance start time constraints, and maintenance continuity constraints.

3. The method according to claim 2, characterized in that, The constraints of the lower-level distribution network reconfiguration interval optimization model include the following: Power constraints, voltage constraints, distributed generation output constraints, switching operation frequency constraints, and distribution network radial constraints.

4. The method according to claim 1, characterized in that, The process of iteratively solving the upper-level maintenance time optimization model and the lower-level distribution network reconfiguration interval optimization model to determine the optimal maintenance period for distribution network equipment and its corresponding distribution network reconfiguration strategy includes: Step S401: Substitute the previously optimized distribution network reconfiguration strategy into the upper-level maintenance time optimization model for solution to obtain the equipment maintenance time after this iteration. Step S402: Substitute the equipment maintenance time into the optimization model of the lower-level distribution network reconfiguration interval for solution to obtain the distribution network reconfiguration strategy after this iteration optimization; Step S403: Determine whether the number of iterations is equal to the first preset number of iterations; If so, the equipment maintenance time optimized in this iteration output by the upper-level maintenance time optimization model shall be taken as the optimal maintenance period of the distribution network equipment, and the distribution network reconfiguration strategy optimized in this iteration shall be taken as the distribution network reconfiguration strategy in the corresponding period of the optimal maintenance period of the distribution network equipment. If not, repeat steps S401-S403.

5. The method according to claim 4, characterized in that, The step of substituting the previously optimized distribution network reconfiguration strategy into the upper-level maintenance time optimization model for solution, to obtain the equipment maintenance time after the current iteration optimization, includes: Substituting the previously optimized distribution network reconfiguration strategy into the upper-level maintenance time optimization model, the particle swarm optimization algorithm is used to solve the problem, resulting in the optimized equipment maintenance time for this iteration.

6. The method according to claim 4, characterized in that, The step of substituting the equipment maintenance time into the optimization model of the lower-level distribution network reconfiguration interval for solution yields the distribution network reconfiguration strategy after this iteration, including: After substituting the equipment maintenance time into the optimization model of the lower-level distribution network reconfiguration interval, the clone selection algorithm is used to solve the problem, and the distribution network reconfiguration strategy after this iteration is obtained.

7. The method according to claim 5, characterized in that, The particle swarm optimization algorithm is used to solve the problem, and the equipment maintenance time after this iteration of optimization is obtained as follows: The maintenance start time of each device is encoded, and the encoded maintenance start time of each device is used as a particle in the particle swarm algorithm. Calculate the fitness function for each particle to obtain the equipment maintenance time after this iteration optimization.

8. The method according to claim 6, characterized in that, The clonal selection algorithm is used to solve the problem, resulting in the optimized distribution network reconfiguration strategy for this iteration, including: Based on the equipment maintenance time, a candidate set of switch operations for power distribution network reconfiguration is generated, and the candidate set of switch operations includes multiple switch operations. The fitness of the multiple switching operations is calculated and evaluated by interval power flow calculation to obtain the total number of switching operations after this iteration optimization.

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