Three-network coupled power distribution network maintenance decision-making method

Abstract

The invention relates to the technical field of power system maintenance, and discloses a three-network coupled power distribution network maintenance decision-making method, which comprises the following steps: constructing a power distribution network power flow model considering a distributed generator set, energy storage equipment, network reconstruction, a mobile power supply vehicle and a mobile signal vehicle; constructing constraint conditions generated by coupling of the information network and the power distribution network; constructing constraint conditions generated by coupling of the traffic network and the power distribution network; establishing an optimal path model of the traffic network, and solving an optimal path for the maintenance personnel to traverse all damaged lines and an optimal path for the mobile power supply vehicle and the mobile signal vehicle; constructing a power distribution network maintenance decision model of a mixed integer optimization problem; and solving the power distribution network maintenance decision model by using a Gurobi solver to obtain a maintenance decision scheme. The method has the advantages that reasonable distribution of power grid maintenance resources is promoted on the basis of considering actual information and traffic conditions by establishing the three-network coupling power distribution network maintenance decision model considering the power distribution network, the information network and the traffic network.

Description

Technical Field

[0001] The present invention relates to the technical field of power system maintenance, and particularly to a maintenance decision-making method for a distribution network with triple-network coupling. Background Art

[0002] In the development of modern cities, the deep integration of the distribution network, information network, and transportation network has become the key to improving the urban operation efficiency and the quality of residents' lives. However, in the face of the increasing energy demand and the complex and changeable network environment, the traditional maintenance decision-making methods for distribution networks are difficult to meet the current challenges. Summary of the Invention

[0003] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a maintenance decision-making method for a distribution network with triple-network coupling. The purpose of the present invention is achieved through the following technical solutions: A maintenance decision-making method for a distribution network with triple-network coupling, including:

[0004] Establishing datasets of the distribution network, information network, and transportation network before and after disasters, and constructing a distribution network power flow model considering distributed generation units, energy storage devices, network reconfiguration, mobile power supply vehicles, and mobile signal vehicles with the goal of minimizing the grid operation cost during maintenance;

[0005] Constructing the constraint conditions generated by the coupling of the information network and the distribution network;

[0006] Constructing the constraint conditions generated by the coupling of the transportation network and the distribution network;

[0007] Establishing an optimal path model for the transportation network, and solving the optimal paths for maintenance personnel to traverse all damaged lines and the optimal paths for mobile power supply vehicles and mobile signal vehicles based on the Floyd algorithm and the VPR algorithm;

[0008] Based on the optimal path of the transportation network, constructing the distribution network power flow model into a distribution network maintenance decision model of a mixed-integer optimization problem; using the Gurobi solver to solve the distribution network maintenance decision model to obtain a maintenance decision-making plan.

[0009] Specifically, the objective function of the distribution network power flow model is:

[0010]

[0011] In the formula, T is the number of power supply time periods, j is the distribution network node index; Ω j is the set of distribution network nodes; is the unit load shedding penalty cost; δ j,t is the load shedding power; W is the cost of purchasing electricity from the superior grid; C is the cost of distributed generation unit power generation.

[0012] Specifically, the constraint conditions of the power flow model of the distribution network include node power balance constraints, power flow constraints, distributed generator constraints, and load shedding constraints.

[0013] Specifically, the constraint conditions generated by the coupling of the information network and the distribution network, and the constraint conditions generated by the coupling of the transportation network and the distribution network include information network constraints, distributed generator coupling constraints, maintenance scheduling constraints, mobile power vehicle operation constraints, and mobile signal vehicle operation constraints.

[0014] Specifically, the objective function of the optimal path model of the transportation network is:

[0015] mind uv =(l uv ,l uw +l wv )

[0016] G=(N T ,ε T )

[0017] ε T ={d uv ∣u≠v; u,v∈{1,2,…,k}}

[0018]

[0019] In the formula, d uv is the path between nodes (u,v); l uv is the path length between traffic nodes u and v; l uw is the path length between traffic nodes u and w; l wv is the path length between traffic nodes w and v; G is the mathematical model of the transportation network; N T is the vertex set, that is, the set composed of the endpoints of the road segments in the transportation network or the intersection points of multiple road segments; ε T is the set composed of the roads in the transportation network; n u is the node u in the transportation network; k is the number of nodes in the transportation network; d uv is the connection relationship between nodes u and v in the transportation network; inf indicates that traffic nodes u and v are not adjacent, and the path length takes infinity; v uv (t) is the real-time driving speed considering the traffic flow between traffic nodes (u,v); v uv.0 is the zero-flow speed between adjacent traffic nodes (u,v); q uv (t) is the road flow of road (u,v) at time t; C uv is the traffic capacity of road segment (u,v); a, b, n are road coefficients under different road grades; ΔT uv is the total driving time between traffic nodes u and v; ΔTh is the driving time of the h-th direct connection road; d h is the length of the h-th direct connection road; V h (t) is the driving speed of the h-th direct connection section.

[0020] The present invention has the following advantages:

[0021] 1. Reduce maintenance costs: By simulating the actual working scenario of the distribution network, establishing a mixed-integer optimization model, and based on the principle of branch and bound method, the present invention can ensure the minimization of the power grid operation cost during maintenance in terms of mathematical principles, thus reducing maintenance costs.

[0022] 2. Comprehensively consider the influence of multi-network coupling: The three-network coupling network model constructed by the present invention can comprehensively consider the power supply stability, information transmission real-time performance, and traffic flow dynamics, providing a comprehensive perspective for maintenance decision-making.

[0023] 3. Optimize the power grid operation strategy: Based on the solution method combining the Floyd algorithm, VPR algorithm, and Gurobi solver, the present invention realizes the optimization of the maintenance decision-making for the distribution network and information network, ensuring the efficient operation of the power grid during maintenance.

[0024] 4. Enhance the practical value of the model: By establishing models for distribution network maintenance, information network maintenance, network reconstruction, information network coupling, traffic network coupling, and mobile power supply vehicles, the present invention enhances the practical value of the model in actual working scenarios.

[0025] 5. Enhance decision-making adaptability: The model of the present invention can adapt to different network conditions, coordinate the interests of each network, and solve the resource allocation problem during the maintenance process.

[0026] 6. Promote the intelligentization of the power grid: The method of the present invention promotes the development of the distribution network towards the intelligent direction. By replacing the elements in the parameter matrix with the corresponding sensor data, the optimal maintenance strategies for the distribution network and information network can be automatically solved based on real-time monitoring data, providing a new and efficient maintenance decision-making tool for power grid companies and assisting in the safe and stable operation of the power system.

[0027] 7. Improve the urban energy management level: The implementation of the present invention helps to improve the overall level of urban energy management, providing support for the improvement of urban operation efficiency and residents' living quality. Brief Description of the Drawings

[0028] Figure 1 is a schematic flow diagram of the decision-making method of the present invention;

[0029] Figure 2 is a topology diagram of the distribution network and signal network of the present invention;

[0030] Figure 3Line graph of the load shedding rate for each solution of the present invention;

[0031] Figure 4 Line graph of the output of the gas turbine unit for the example of the present invention;

[0032] Figure 5 Line graph of the output of the mobile power supply vehicle for the example of the present invention;

[0033] Figure 6 Line graph of the number of restored nodes of the information network for the example of the present invention;

[0034] Figure 7 Schematic diagram of the operation of the mobile signal vehicle for the example of the present invention;

[0035] Figure 8 Schematic diagram of the maintenance strategy for Solution 5 of the example of the present invention;

[0036] Figure 9 Schematic diagram of the maintenance strategies for each solution of the example of the present invention. Detailed implementation manners

[0037] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention, that is, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Usually, the components of the embodiments of the present invention described and illustrated herein can be arranged and designed in various different configurations.

[0038] Therefore, the detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed present invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0039] It should be noted that relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, the element defined by the statement "including one..." does not exclude the existence of another identical element in the process, method, article or device including the said element.

[0040] The present invention will be further described below in conjunction with the accompanying drawings, but the protection scope of the present invention is not limited to the following. As Figures 1 to 9 shown, a maintenance decision-making method for a three-network coupled distribution network includes:

[0041] Taking the minimization of the power grid operation cost during maintenance as the goal, a distribution network power flow model considering distributed generating units, energy storage devices, network reconfiguration, mobile power supply vehicles, and mobile signal vehicles is constructed; the actual working scenario of the distribution network is simulated.

[0042] The objective function of the distribution network power flow model is:

[0043]

[0044] In the formula, T is the number of power supply periods, j is the distribution network node index; Ω j is the set of distribution network nodes; is the unit load shedding penalty cost; δ j,t is the load shedding power; W is the cost of purchasing electricity from the superior power grid; C is the cost of distributed generating unit power generation.

[0045] The constraint conditions of the distribution network power flow model include node power balance constraints, power flow constraints, distributed unit constraints, and load shedding constraints;

[0046] Node power balance constraint:

[0047]

[0048] In the formula, s(j) represents the set of all child nodes with node j as the starting node; Ω L is the set of distribution network lines; P jk,t , Q jk,t respectively represent the active and reactive powers of the line with node j as the starting node; respectively represent the active power and reactive power transmitted from the superior power grid to the distribution network at time t; P ij,t , Q ij,t respectively represent the active power and reactive power of the power line ij; respectively represent the predicted magnitudes of the active and reactive loads at node j; δ j,t is the active load loss at node j; respectively represent the active and reactive power outputs of the gas turbine unit at node j; respectively represent the active and reactive power outputs of the wind turbine unit at node j; respectively represent the active and reactive power outputs of the mobile power supply vehicle at node j; (·) min / max is the minimum and maximum values of the variable; aij,t is a binary variable representing the state of power line ij at time t. If a ij,t = 1, the line ij is not damaged and is in the closed state; otherwise, the line ij is damaged or in the open state. Load shedding constraint:

[0049]

[0050] Power flow constraint:

[0051]

[0052] where M is a sufficiently large number; V i,t , V j,t are the voltage values of nodes i and j at time t; V0 is the reference node voltage; r ij is the line resistance; x ij is the line reactance.

[0053] Distributed gas turbine unit constraint:

[0054]

[0055] where are the minimum and maximum limits of the output of the gas turbine unit at node j respectively, are the minimum and maximum limits of the reactive power output of the gas turbine unit at node j respectively; I j,t is a binary variable representing the working state of the gas turbine unit at node j. If I j,t = 1, it means the unit starts at time t; otherwise, it stops; are the up and down ramp rates of the gas turbine unit respectively; are the minimum on and off times of the gas turbine unit at node j respectively; are the on and off counting times of the unit at time t respectively; are the heat consumptions when the unit starts and stops once respectively.

[0056] Establish an information network model coupled with the distribution network; control the start and stop of distributed generators, the charge and discharge states of energy storage devices, and the opening and closing of network reconfiguration switches through the information network lines; simulate the actual information network coupling scenario;

[0057] Establish a transportation network model coupled with the distribution network; affect the travel times of maintenance personnel, mobile power supply vehicles, and mobile signal vehicles through the hourly changes in the transportation network flow, and then affect the maintenance strategy; simulate the actual transportation network coupling scenario; achieve the three-network coupling modeling, simulate the actual working scenario, and improve the practical value of the model;

[0058] The constraint conditions generated by the coupling of the information network and the distribution network, as well as the constraint conditions generated by the coupling of the transportation network and the distribution network, include information network constraints, distributed unit coupling constraints, maintenance scheduling constraints, mobile power vehicle operation constraints, and mobile signal vehicle operation constraints;

[0059] Information network constraints:

[0060]

[0061] In the formula, Ω C is the node set of the information system; c is the communication node index; s is the source node index; l is the optical fiber communication link index; s(l) and r(l) respectively represent the sending end and receiving end of the information link l; L s,t is the information flow on the source node; L l,t is the information flow on the information link; indicates whether the communication node is connected to the control master station. If then the communication node is connected to the control master station at time t, otherwise it is not; the number 1 represents the demand of each node;

[0062] 0 ≤ L s,t ≤ N C

[0063] -N C ·z l,t ≤ L l,t ≤ N C ·z l,t

[0064]

[0065] In the formula, N c is the total number of communication nodes in the information system; z l,t indicates whether the communication link is available; when z l,t = 0, the communication link fails, otherwise the communication link is effective;

[0066] Distributed gas unit coupling constraints:

[0067]

[0068] A g,t + L c,t = 1

[0069] A w,t + L c,t = 1

[0070] In the formula, are the upper and lower ramp rates of the gas unit respectively; is the predicted output of the distributed wind turbine at node j at time t; are the minimum and maximum reactive power outputs of the distributed wind turbines, respectively; A g,t , A w,t respectively represent the control states of the corresponding communication nodes for the gas turbines and wind turbines in the distribution network; L c,t respectively represent the communication node information requirements for controlling the gas turbines and wind turbines. When L c,t = 0, it means that the communication node is connected to the control master station at time t, and the corresponding unit control state variable value is 1, indicating that the unit is in a controllable state, otherwise it is uncontrollable;

[0071] Maintenance scheduling constraint:

[0072]

[0073] In the formula, RL is the set of all power or communication lines that need to be repaired; y and z are the line indices that need to be repaired; d y,z is a binary variable for the dispatching of maintenance personnel. d y,z = 1 indicates that the maintenance personnel go from the fault location y to z, otherwise not; f y represents whether the maintenance personnel visit the fault location y; y 0 represents the location of the maintenance station, represents that the maintenance personnel start the maintenance operation from the maintenance station y 0 ;

[0074]

[0075] In the formula, is the time when the maintenance personnel arrive at the fault location y; is the time when the maintenance personnel arrive at the fault location z; is the time required for the repair of y; is the travel time for the maintenance personnel to go from y to z, approximately calculated considering the straight-line distance between each power load point and communication node; τ y,t is a binary variable. If τ y,t = 1, then the maintenance personnel complete the repair of the fault at y at time t, otherwise τ y,t = 0; ε is an extremely small number.

[0076]

[0077] q pl,t ≤q pl,t , pl ∈ Ω RPL

[0078] z cl,t =q cl,t , cl ∈ Ω RCL

[0079] In the formula, ΩRPL , Ω RCL is the set of all power and communication lines that need to be repaired, Ω RPL , Ω RCL ∈Ω RL ; q y,t represents the repair completion status of fault y at time t; a pl,t and z cl,t respectively represent the line status of the fault lines in the power system and the communication system at time t; q pl,t and q cl,t respectively represent the repair completion status of the fault lines in the power system and the communication system at time t.

[0080] Operating constraints of mobile power supply vehicles:

[0081]

[0082] In the formula, respectively represent the maximum limit of the output of the mobile power supply vehicle configured at node j; is a binary variable indicating whether the mth mobile power supply vehicle is configured at the distribution network node j in the t-th time period; N M is the number of mobile power supply vehicles in the system.

[0083] Operating constraints of mobile signal vehicles:

[0084]

[0085] In the formula, indicates whether there is an emergency wireless communication device configured at node c. If then there is a configuration, otherwise there is none; N WC is the total number of emergency wireless communication devices.

[0086] Establish an optimal path model for the transportation network. Based on the Floyd algorithm and the VPR algorithm, solve the optimal paths for the maintenance personnel to traverse all damaged lines and the optimal paths for mobile power supply vehicles and mobile signal vehicles;

[0087] The objective function of the optimal path model of the transportation network is:

[0088] mind uv =(l uv , l uv +l wv )

[0089] G=(N T , ε T )

[0090] ε T ={d uv ∣u≠v; u, v∈{1, 2, …, k}}

[0091]

[0092] Wherein, d uv is the path between nodes (u, v); l uv is the path length between traffic node u and node v; l uw is the path length between traffic node u and node w; l wv is the path length between traffic node w and node v; G is the mathematical model of the traffic network; N T is the vertex set, that is, the set composed of the endpoints of the road segments in the traffic network or the intersection points of multiple road segments; ε T is the set composed of the roads in the traffic network; n u is the node u in the traffic network; k is the number of nodes in the traffic network; d uv is the connection relationship between node u and node v in the traffic network; inf indicates that traffic node u and node v are not adjacent, and the path length takes infinity; v uv (r) is the real-time driving speed between traffic nodes (u, v) considering traffic flow; v uv.0 is the zero-flow speed between adjacent traffic nodes (u, v); q uv (t) is the road flow of road (u, v) in time period t; C uv is the traffic capacity of road segment (u, v); a, b, n are road coefficients under different road grades; ΔT uv is the total driving time between traffic nodes u and v; ΔT h is the driving time of the h-th direct-connected road; d h is the road length of the h-th direct-connected road; V h (t) is the driving speed of the h-th direct-connected road segment.

[0093] Based on the optimal path of the traffic network, the power flow model of the distribution network is constructed as a distribution network maintenance decision model of a mixed-integer optimization problem; the Gurobi solver is used to solve the distribution network maintenance decision model to obtain a maintenance decision plan. The specific solution process is as follows:

[0094] Step S1, establish data sets of the distribution network, information network, and traffic network before and after the disaster;

[0095] Step S2, set the relevant parameters of the Floyd algorithm and VPR algorithm, and the number of iterations of the solution; solve the optimal path for the maintenance personnel to traverse all damaged lines and the optimal paths of the mobile power vehicle and the mobile signal vehicle;

[0096] Step S3, based on the optimal path of the traffic network, model the above problems as a distribution network and information network maintenance decision model of a mixed-integer optimization problem;

[0097] Step S4: Solve the distribution network maintenance decision-making model using the Gurobi solver.

[0098] Step S5: Save the optimal solution of the iteration to obtain the maintenance decisions for the distribution network and information network and the system operation cost.

[0099] Figure 2 It is a schematic diagram of the overall topological structures of the distribution network and information network.

[0100] The selected instance has a time length of 24 hours, a time scale of 1 hour, a maximum useful power of 10 MW for each node, and the total useful work transmitted during normal operation throughout the day within the control range of the distribution network is 73.2700 MW·h. The maximum output of two gas distributed generation units is 1.2 MW, and the minimum start-stop times are 2 h and 1 h.

[0101] As shown in Table 1 are the parameters of the energy storage facilities in the distribution network.

[0102] Table 1. Parameters of energy storage equipment

[0103]

[0104] As shown in Table 2 is the comparison of the conditions of Examples 1 - 5

[0105] Table 2 Comparison of the conditions of Examples 1 - 5

[0106]

[0107] As shown in Table 3 is the comparison of the results of Examples 1 - 5

[0108] Table 3 Comparison of the results of Examples 1 - 5

[0109]

[0110] As shown in the column of the system operation cost in Table 3, the conditions adopted by the scheme can effectively reduce the operation cost of the distribution network during maintenance, and the effectiveness of this method in reducing the operation cost of the distribution network during maintenance is verified.

[0111] From Figure 3 By comparison, it can be seen that the five instances show an obvious increasing trend in the maintenance efficiency of the distribution network, and it can be considered that this method has a promoting effect on the maintenance efficiency of the distribution network.

[0112] When comparing Example 1 and Example 2, the load shedding rate and operation cost decrease significantly, indicating that the addition of power maintenance has an obvious effect on reducing the system operation cost.

[0113] In comparison between Example 2 and Example 3, an information maintenance team was added in Example 3. While the power maintenance team was repairing the power line, the information maintenance team cooperated in repairing the communication line. In Plan 2, when the joint maintenance of the information system was not added, due to the damage of the information line, the communication nodes where the signals were blocked could not regulate the corresponding distributed units, energy storage devices, and connection switches in the distribution network. With the successful repair of the communication link, the damaged communication nodes restored communication. The distributed units and energy storage devices in the distribution network corresponding to the communication nodes restored communication with the information network, could actively control the operating status of electrical equipment, and joined the system maintenance work, thus greatly reducing the system losses.

[0114] Figure 4 The output curves of the two distributed gas generator sets in Example 2 and Example 3 in the text corroborated this analysis.

[0115] Example 4 added network reconfiguration on the basis of Example 3. Example 4 showed that while the power-communication line was jointly maintained, the communication network could cooperate with network reconfiguration, actively adjust the load composition of each island, and could complete the post-disaster maintenance of the system with cost reduction and efficiency improvement. After the communication nodes restored the communication requirements, the corresponding connection switches joined the maintenance assistance. When the maintenance team did not reach the damaged power line, the information system controlled the connection switches to close, so that the corresponding nodes were no longer in the island state, effectively assisting in reducing the system's load shedding.

[0116] The above conclusions could be verified by the decrease in the load shedding rate and operating cost.

[0117] Example 5 added two mobile power supply vehicles and two mobile signal vehicles on the basis of Example 4. The mobile power supply vehicles were configured at power nodes 23 and 28, and the mobile signal vehicles were configured at information nodes 3 and 14 to reduce the system's load shedding. This measure further reduced the load shedding and operating cost of the distribution network during maintenance.

[0118] The output of the mobile power supply vehicle is as Figure 5 shown, the restoration diagram of the number of information nodes is as Figure 6 shown, the working schematic diagram of the mobile signal vehicle is as Figure 7 shown, the maintenance sequence schematic diagram of Plan 5 is as Figure 8 shown, which proved the above conclusions.

[0119] The maintenance strategy schematic diagrams of each plan are as Figure 9 shown.

[0120] In view of the problems of informatization and complexity in the new power system, relying on the virtual power flow framework of the distribution network, taking the maintenance model of the distribution network with triple-network coupling as an example, a maintenance decision-making method for the "triple-network coupling" distribution network considering the distribution network, information network, and transportation network is established. On the basis of considering the load shedding limit mode, it promotes the reasonable arrangement of maintenance strategies, and combines the Floyd algorithm and the mixed-integer optimization algorithm to enable the efficient interaction of relevant influencing factors of the distribution network, information network, and transportation network. Through the case analysis of the distribution network maintenance model under different conditions, it is found that the maintenance decision-making method for the "triple-network coupling" distribution network considering the distribution network, information network, and transportation network can reasonably formulate maintenance strategies during the distribution network maintenance decision-making process. By reasonably scheduling maintenance resources and coordinating the allocation of heterogeneous resources, it realizes the reduction of the operating cost during the distribution network maintenance period, promotes the stable and safe operation of the urban power grid, and verifies the effectiveness of the present invention.

[0121] The above are only the preferred embodiments of the present invention, and do not impose any form of limitation on the present invention. Any person skilled in the art can make many possible changes and modifications to the technical solution of the present invention by using the above-mentioned technical content within the scope of the technical solution of the present invention, or modify it into an equivalent embodiment with equivalent changes. Therefore, any changes, modifications, equivalent changes and modifications made to the above embodiments according to the technology of the present invention without departing from the content of the technical solution of the present invention all fall within the protection scope of this technical solution.

Claims

1. A maintenance decision-making method for a distribution network with triple-network coupling, characterized in that, Including: A distribution network power flow model considering distributed generating units, energy storage devices, network reconfiguration, mobile power supply vehicles, and mobile signal vehicles is constructed with the goal of minimizing the grid operation cost during maintenance. Constraint conditions generated by the coupling of the information network and the distribution network are constructed. Constraint conditions generated by the coupling of the transportation network and the distribution network are constructed. An optimal path model for the transportation network is established. Based on the Floyd algorithm and the VPR algorithm, the optimal paths for maintenance personnel to traverse all damaged lines and the optimal paths for mobile power supply vehicles and mobile signal vehicles are solved. Based on the optimal path of the transportation network, the distribution network power flow model is constructed as a distribution network maintenance decision model for a mixed-integer optimization problem; the Gurobi solver is used to solve the distribution network maintenance decision model to obtain a maintenance decision plan.

2. The method for making a maintenance decision for a three-network-coupled distribution network according to claim 1, wherein: The objective function of the distribution network power flow model is as follows: Where T is the number of power supply periods, j is the distribution network node index; Ω j is the set of distribution network nodes; is the unit cost of lost load penalty; δ j,t is the lost load power; W is the cost of purchasing electricity from the upper-level power grid; C is the cost of distributed generator power generation.

3. A maintenance decision-making method for a three-network-coupled distribution network according to claim 1, characterized in that: The constraint conditions of the distribution network power flow model include node power balance constraints, power flow constraints, distributed unit constraints, and load shedding constraints.

4. A maintenance decision-making method for a three-network-coupled distribution network according to claim 1, characterized in that: The constraint conditions generated by the coupling of the information network and the distribution network and the constraint conditions generated by the coupling of the transportation network and the distribution network include information network constraints, distributed unit coupling constraints, maintenance scheduling constraints, mobile power supply vehicle operation constraints, and mobile signal vehicle operation constraints.

5. A maintenance decision-making method for a three-network-coupled distribution network according to claim 1, characterized in that: The objective function of the transportation network optimal path model is as follows: mind uv =(l uv ,l uw +l wv ) G = (N T , ε T ) ε T = {d uv | u ≠ v; u, v ∈ {1, 2, …, k}} where d uv is the path between nodes (u, v); l uv is the path length between traffic node u and node v; l uw is the path length between traffic node u and node w; l wv is the path length between traffic node w and node v; G is the mathematical model of the traffic network; N T is the vertex set, that is, the set composed of the endpoints of the road segments in the traffic network or the intersection points of multiple road segments; ε T is the set composed of the roads in the traffic network; k is the number of nodes in the traffic network; d uv is the connection relationship between nodes u and v in the traffic network; inf indicates that traffic nodes u and v are not adjacent, and the path length takes infinity; v uv (t) is the real-time driving speed between traffic nodes (u, v) considering traffic flow; v uv.0 is the zero-flow speed between adjacent traffic nodes (u, v); q uv (t) is the road flow of road (u, v) at time t; C uv is the traffic capacity of road segment (u, v); a, b, n are road coefficients under different road grades; ΔT uv is the total driving time between traffic nodes u and v; ΔT h is the driving time of the h-th direct-connected road; d h is the road length of the h-th direct-connected road; V h (t) is the driving speed of the h-th direct-connected road segment.

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