Adaptive robust emergency maintenance decision-making method, system and equipment for urban power distribution network

Abstract

The invention discloses a self-adaptive robust emergency maintenance decision-making method, system and equipment for an urban power distribution network. The method comprises the following steps: acquiring network frame data information of the urban power distribution network; setting an attack budget and a maintenance budget; establishing a three-layer two-stage robust optimization model for emergency maintenance of the urban power distribution network based on a defense-attack-defense DAD architecture according to the network frame data information of the urban power distribution network, the attack budget and the maintenance budget; taking the minimum total load loss as an objective function, and generating Camp based on column and constraint under a constraint condition; solving the three-layer two-stage robust optimization model for emergency maintenance of the urban power distribution network through a CG algorithm; generating a Camp according to the column and constraint; and obtaining an emergency maintenance decision scheme for minimizing the influence of the extreme event on the power distribution network according to the solving result of the CG algorithm, and completing the urban power distribution network emergency maintenance plan arrangement. According to the method, the power grid maintenance plan is flexibly adjusted according to the actual situation, and quick response and system load recovery can be realized in the face of various uncertainties and emergencies.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a method, system and device for adaptive robust emergency maintenance decision-making of an urban distribution network. Background Art

[0002] With the acceleration of urbanization and the continued growth of electricity demand, the safe and stable operation of urban distribution networks is crucial to safeguarding urban life and economic development. However, faced with various uncertainties and emergencies such as natural disasters, equipment failures, and malicious attacks, the stability and reliability of urban distribution networks face severe challenges. Developing effective emergency maintenance plans is one of the keys to ensuring a safe and reliable power supply in urban power grids.

[0003] Currently, in the field of distribution equipment maintenance plan optimization, scholars at home and abroad have conducted in-depth research based on various analytical methods, such as particle swarm optimization, immune-taboo hybrid intelligent algorithms, and multi-objective optimization. While these methods have improved the efficiency of maintenance plan optimization based on historical statistical data, they cannot reflect the health status of equipment and the differences between similar equipment in real time, resulting in poor timeliness of analysis results and difficulty in effectively guiding condition-based maintenance and maintenance plan optimization. To address this issue, some scholars have proposed distribution equipment maintenance optimization schemes based on real-time equipment status information. Research on condition-based maintenance (CDM) at home and abroad has primarily focused on equipment condition assessment and maintenance decision-making. Equipment condition assessment primarily involves equipment monitoring, fault prediction, and condition evaluation, while maintenance decision-making involves considering the equipment itself, grid operation, and overall risk. Risk-based maintenance is another important maintenance decision-making approach that can rationally formulate maintenance decisions based on equipment and grid risk assessment to minimize risk. These studies provide important insights for CDM and maintenance decision-making for distribution equipment, but further improvements are needed to achieve more accurate and real-time maintenance optimization. In summary, traditional distribution network maintenance plans are mainly based on fixed periodic maintenance and overhaul strategies, and lack an effective response mechanism to emergencies and uncertainties. Summary of the Invention

[0004] The purpose of the present invention is to address the problems in the above-mentioned prior art and provide an urban distribution network adaptive robust emergency maintenance decision-making method, system and equipment, which can flexibly adjust the power grid maintenance plan according to actual conditions, and can quickly respond to various uncertainties and emergency situations and restore the system load.

[0005] In order to achieve the above object, the present invention has the following technical solutions:

[0006] In a first aspect, a method for adaptive robust emergency maintenance decision-making of an urban distribution network is provided, comprising:

[0007] Obtain grid data information of urban distribution network;

[0008] Set attack budget and overhaul budget;

[0009] Based on the grid data information of the urban distribution network, as well as the attack budget and maintenance budget, a three-layer two-stage robust optimization model for emergency maintenance of the urban distribution network is established based on the defense-attack-defense DAD architecture.

[0010] Taking the minimum total load loss as the objective function, a three-layer two-stage robust optimization model for emergency maintenance of urban distribution network is solved by using the column and constraint generation C&CG algorithm under the constraints.

[0011] Based on the solution results of the column-based and constraint-generated C&CG algorithm, an emergency maintenance decision-making plan that minimizes the impact of extreme events on the distribution network is obtained, and the emergency maintenance plan for the urban distribution network is completed.

[0012] As a preferred solution, the grid data information of the urban distribution network includes power supply, load, grid topology and branch flow limitation.

[0013] As a preferred solution, in the step of establishing a three-layer two-stage robust optimization model for emergency maintenance of the urban distribution network based on the defense-attack-defense DAD architecture according to the grid data information of the urban distribution network as well as the attack budget and the maintenance budget, the three-layer two-stage robust optimization model for emergency maintenance of the urban distribution network adopts the following formula as the objective function:

[0014]

[0015] The following formula is used as the constraint condition:

[0016]

[0017]

[0018] Where N is the set of nodes n, d n is the load loss of node n, p l is the active power capacity of transmission line l, g j is the active output of power source j, δ n is the phase angle of node n, z l is the maintenance decision variable of line l, that is, if line l is repaired, z l =1, otherwise 0; z j is the maintenance decision variable of power supply j, that is, if power supply j is repaired, z j =1, otherwise 0; v l is the attack decision variable of line l, that is, if line l is attacked, v l=0, otherwise 1; v j is the attack decision variable of power supply j, that is, if power supply j is attacked, v j =0, otherwise 1; is the attacker's attack decision set; L is the set of lines in the system, J is the set of power supplies in the system; R is the defender's maintenance decision budget, which represents the upper limit of the number of maintenance components; S is the attacker's attack budget, which represents the upper limit of the number of components that can be attacked; x l is the reactance of transmission line l, δ o(l) is the phase angle of the source node of transmission line l, δ d(l) is the phase angle of the target node of transmission line l, represents any transmission line, o(l) is the source node of transmission line l, d(l) is the target node of transmission line l; D n is the upper limit of active power demand of node n, J n is the set of power sources connected to node n, g j Output for power supply j, represents any node, δ n is the phase angle of node n, G j is the upper limit of active output of power source j, is the phase angle capacity of the connection node, Indicates any power supply.

[0019] As a preferred solution, in the step of solving the three-layer two-stage robust optimization model for emergency maintenance of urban distribution network by using the column and constraint generation C&CG algorithm under constraints with the objective function of minimizing total load loss:

[0020] Based on the columns and constraints, the C&CG algorithm solves the master problem MP under a given attack plan. The master problem MP uses the following formula as the objective function:

[0021] minα

[0022] The following formula is used as the constraint condition:

[0023]

[0024] Where α is the cutting plane of the subproblem, is the load loss of node n at the i-th iteration, is any number of iterations, k is the number of iterations required for the problem to converge, is the active power of transmission line l at the i-th iteration, is the phase angle of the source node of transmission line l at the i-th iteration, is the phase angle of the target node of transmission line l at the i-th iteration, is the output of power source j at the i-th iteration, is the attack strategy of power source j in the i-th iteration, is the phase angle of node n at the i-th iteration, For a given attack plan, Plan for a specific attack; is the attack scenario at the kth iteration, is the attack strategy for transmission line l at the kth iteration;

[0025] For nonlinear constraints The linearization is performed through the big M method and converted into the following constraints:

[0026]

[0027] Where, is the set of attacked lines, is the set of lines that have not been attacked, M is a sufficiently large real number; P l is the active power capacity of transmission line l;

[0028] Solve the above problem to get the optimal value MPobj and maintenance decision Update the lower bound LB = max{LB, MPobj}.

[0029] As a preferred solution, the column and constraint-based C&CG algorithm is generated according to the maintenance decision Solve the subproblem SP to obtain the optimal value SPobj and the optimal attack scenario The following formula is used as the objective function to solve the subproblem SP:

[0030]

[0031] The following formula is used as the constraint condition:

[0032]

[0033]

[0034] Where, is the maintenance strategy of transmission line l, For a given maintenance strategy;

[0035] For nonlinear constraints Use the Big M method to perform linearization and transform it into the following constraints:

[0036]

[0037] Where, L a For lines that have not been repaired, L bThe line being repaired.

[0038] As a preferred solution, the column and constraint generation C&CG algorithm obtains the corresponding dual problem for the minimization linear programming problem under the subproblem SP through strong duality. The dual problem uses the following formula as the objective function:

[0039]

[0040] The following formula is used as the constraint condition:

[0041]

[0042]

[0043] Where λ n yes The dual variable, γ j yes The dual variable, α n yes The dual variable, ξ n and χ n yes The dual variable, β l yes The dual variable, τ l yes The dual variable, θ l and ρ l yes The dual variable, μ l yes The dual variable, φ l and Yes, the dual variable is The dual variable of n|o (l) = n is The dual variable of the source node of the corresponding transmission line l, λ n|d(l) =n is The dual variable of the target node of the corresponding transmission line l, λ n|j∈Jn for The dual variable of the node where the corresponding power source j is located, λ n free means The dual variable of is unrestricted;

[0044] Solve the above problem to obtain the optimal value of the sub-problem SPobj and the optimal attack scenario Update the upper bound UB = min{UB, SPobj}.

[0045] As a preferred solution, the column and constraint-based C&CG algorithm generates the optimal attack scenario Add to a given attack plan and add the following constraints to the main problem MP:

[0046]

[0047] Where, is the load loss of node n at the kth iteration, is the active power of transmission line l at the kth iteration, is the phase angle of the source node of transmission line l at the kth iteration, is the phase angle of the target node of transmission line l at the kth iteration, is the line that has not been repaired at the kth iteration, is the line repaired at the kth iteration, is the output of power source j at the kth iteration, is the phase angle of node n at the kth iteration;

[0048] If (UB-LB) / LB≤ε, stop, where ε is the error bound; otherwise, set k=k+1 and return to the main problem to iterate again.

[0049] In a second aspect, an adaptive robust emergency maintenance decision-making system for an urban distribution network is provided, comprising:

[0050] Data acquisition module, used to obtain grid data information of urban distribution network;

[0051] Parameter setting module, used to set attack budget and maintenance budget;

[0052] An optimization model building module is used to build a three-layer, two-stage robust optimization model for emergency maintenance of urban distribution networks based on the defense-attack-defense DAD architecture, according to the grid data of the urban distribution network, as well as the attack budget and maintenance budget;

[0053] The optimization model solving module is used to solve the three-layer two-stage robust optimization model for emergency maintenance of urban distribution networks using the column-based and constraint-generated C&CG algorithm under constraints, with the objective function of minimizing total load loss.

[0054] The emergency maintenance decision module is used to generate the solution results of the C&CG algorithm based on columns and constraints, obtain the emergency maintenance decision plan that minimizes the impact of extreme events on the distribution network, and complete the emergency maintenance plan arrangement of the urban distribution network.

[0055] In a third aspect, an electronic device is provided, comprising a processor and a memory, wherein the processor is configured to execute a computer program stored in the memory to implement the adaptive robust emergency maintenance decision-making method for an urban distribution network.

[0056] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, the urban distribution network adaptive robust emergency maintenance decision-making method is implemented.

[0057] Compared with the prior art, the present invention has at least the following beneficial effects:

[0058] Based on the grid data information of the urban distribution network, as well as the attack budget and maintenance budget, a three-layer, two-stage robust optimization model for emergency maintenance of the urban distribution network is established based on the defense-attack-defense DAD architecture. Pre-disaster defense decision-makers, based on the limited maintenance budget and considering possible attack scenarios, perform maintenance on key components in the system. Attackers, considering the optimal operation strategies that the defenders can adopt, select system components to attack based on the limited attack budget. Operators, based on the defense and attack plans, utilize optimized control strategies to minimize system load loss. This invention uses the minimum total load loss as the objective function and, under constraints, employs a column-based and constraint-generated C&CG algorithm to solve the three-layer, two-stage robust optimization model for emergency maintenance of the urban distribution network, thereby improving solution efficiency. By rationally arranging emergency maintenance plans, the system load loss caused by equipment failures, natural disasters, and malicious attacks can be effectively reduced, the impact of extreme events on the safe and stable operation of the distribution network can be minimized, and the flexibility and adaptability of power supply requirements under extreme events can be improved, providing strong support for the safe and stable operation of the urban distribution network.

[0059] It can be understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0061] Figure 1 Flowchart of the adaptive robust emergency maintenance decision-making method for urban distribution networks according to an embodiment of the present invention;

[0062] Figure 2 Statistical chart of load loss changes under different maintenance and attack budget scenarios according to an embodiment of the present invention. DETAILED DESCRIPTION

[0063] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0064] See also Figure 1 The embodiment of the present invention proposes an adaptive robust emergency maintenance decision-making method for urban distribution networks, which can take various uncertainties into account to plan emergency maintenance plans for urban distribution networks and ensure reliable power supply in urban power grids.

[0065] Specifically, the adaptive robust emergency maintenance decision-making method for an urban distribution network according to an embodiment of the present invention includes the following steps:

[0066] S1. Obtaining grid data information of the urban distribution network;

[0067] S2. Set attack budget and maintenance budget;

[0068] S3. Based on the grid data information of the urban distribution network, as well as the attack budget and maintenance budget, a three-layer, two-stage robust optimization model for emergency maintenance of the urban distribution network is established based on the Defender-Attacker-Defender (DAD) architecture.

[0069] S4. Taking the minimum total load loss as the objective function, a three-layer two-stage robust optimization model for emergency maintenance of urban distribution network is solved using the column and constraint generation (C&CG) algorithm under constraints.

[0070] S5. Based on the solution results of the column-based and constraint-generated C&CG algorithm, an emergency maintenance decision-making plan that minimizes the impact of extreme events on the distribution network is obtained, and the emergency maintenance plan for the urban distribution network is completed.

[0071] In a possible implementation manner, the grid data information of the urban distribution network in step S1 includes power source, load, grid topology, branch flow limitation, etc.

[0072] In one possible implementation, the three-layer two-stage robust optimization model for emergency maintenance of the urban distribution network in step S3 uses the following formula as the objective function:

[0073]

[0074] The following formula is used as the constraint condition:

[0075]

[0076] Where N is the set of nodes n, d n is the load loss of node n (MW), p l is the active power capacity of transmission line l (MW), g j is the active power output of power source j (MW), δ n is the phase angle of node n (rad), z l is the maintenance decision variable of line l, that is, if line l is repaired, z l =1, otherwise 0; z j is the maintenance decision variable of power supply j, that is, if power supply j is repaired, z j =1, otherwise 0; v l is the attack decision variable of line l, that is, if line l is attacked, v l =0, otherwise 1; v j is the attack decision variable of power supply j, that is, if power supply j is attacked, v j =0, otherwise 1; is the attacker's attack decision set; L is the set of lines in the system, J is the set of power supplies in the system; R is the defender's maintenance decision budget, which represents the upper limit of the number of maintenance components; S is the attacker's attack budget, which represents the upper limit of the number of components that can be attacked; x l is the reactance of transmission line l, δ o(l) is the phase angle of the source node of transmission line l, δ d(l) is the phase angle of the target node of transmission line l, represents any transmission line, o(l) is the source node of transmission line l, d(l) is the target node of transmission line l; D n is the upper limit of active power demand of node n, J n is the set of power sources connected to node n, g j Output for power supply j, represents any node, δ n is the phase angle of node n, G j is the upper limit of active output of power source j, is the phase angle capacity of the connection node, Indicates any power supply.

[0077] In a possible implementation, step S4 is a step of solving a three-layer two-stage robust optimization model for emergency maintenance of an urban distribution network using a column-based and constraint-generated C&CG algorithm under constraints:

[0078] Initialize, set lower bound LB = -∞, upper bound UB = ∞, error limit ε, number of iterations k = 0, attack plan set

[0079] Based on the columns and constraints, the C&CG algorithm solves the master problem (MP) under a given attack plan. The master problem MP uses the following formula as the objective function:

[0080] minα

[0081] The following formula is used as the constraint condition:

[0082]

[0083]

[0084] Where α is the cutting plane of the subproblem, is the load loss of node n at the i-th iteration, is any number of iterations, k is the number of iterations required for the problem to converge, is the active power of transmission line l at the i-th iteration, is the phase angle of the source node of transmission line l at the i-th iteration, is the phase angle of the target node of transmission line l at the i-th iteration, is the output of power source j at the i-th iteration, is the attack strategy of power source j in the i-th iteration, is the phase angle of node n at the i-th iteration, For a given attack plan, Plan for a specific attack; is the attack scenario at the kth iteration, is the attack strategy for transmission line l at the kth iteration;

[0085] For nonlinear constraints The linearization is performed through the big M method and converted into the following constraints:

[0086]

[0087] Where, is the set of attacked lines, is the set of lines that have not been attacked, M is a sufficiently large real number; P l is the active power capacity of transmission line l;

[0088] Solve the above problem to get the optimal value MPobj and maintenance decision Update the lower bound LB = max{LB, MPobj}.

[0089] Furthermore, the column and constraint-based C&CG algorithm generates maintenance decisions based on Solve the sub-problem SP (Sub-Problem) and obtain the optimal value SPobj of the sub-problem and the optimal attack scenario

[0090] The following formula is used as the objective function to solve the subproblem SP:

[0091]

[0092] The following formula is used as the constraint condition:

[0093]

[0094] Where, is the maintenance strategy of transmission line l, For a given maintenance strategy;

[0095] For nonlinear constraints Use the Big M method to perform linearization and transform it into the following constraints:

[0096]

[0097]

[0098] Where, L a For lines that have not been repaired, L b The line being repaired.

[0099] Furthermore, the column and constraint generation C&CG algorithm obtains the corresponding dual problem for the minimization linear programming problem under the subproblem SP through strong duality. The dual problem uses the following formula as the objective function:

[0100]

[0101] The following formula is used as the constraint condition:

[0102]

[0103] Where λ n yes The dual variable, γ j yes The dual variable, α n yes The dual variable, ξ n and χ n yes The dual variable, β l yes The dual variable, τ l yes The dual variable, θ l and ρl yes The dual variable, μ l yes The dual variable, φ l and Yes, the dual variable is The dual variable of n|o(l) =n is The dual variable of the source node of the corresponding transmission line l, λ n|d(l) =n is The dual variable of the target node of the corresponding transmission line l, λ n|j∈Jn for The dual variable of the node where the corresponding power source j is located, λ n free means The dual variable of is unrestricted;

[0104] Solve the above problem to obtain the optimal value of the sub-problem SPobj and the optimal attack scenario Update the upper bound UB = min{UB, SPobj}. Add to a given attack plan and add the following constraints to the main problem MP:

[0105]

[0106] Where, is the load loss of node n at the kth iteration, is the active power of transmission line l at the kth iteration, is the phase angle of the source node of transmission line l at the kth iteration, is the phase angle of the target node of transmission line l at the kth iteration, is the line that has not been repaired at the kth iteration, is the line repaired at the kth iteration, is the output of power source j at the kth iteration, is the phase angle of node n at the kth iteration;

[0107] If (UB-LB) / LB≤ε, stop, where ε is the error bound; otherwise, set k=k+1 and return to the main problem to iterate again.

[0108] The IEEE RTS-96 high-voltage distribution network test system is used as an example to verify the feasibility of the adaptive robust emergency maintenance plan scheduling method for urban distribution networks proposed in the embodiment of the present invention.

[0109] This example consists of 24 nodes, 38 lines, 32 power sources, and 17 loads. The test system was calculated using different scenarios with attack budgets S = 1 to 12 and maintenance budgets R = 0 to 4. The DAD model was used to schedule maintenance plans for each scenario. The resulting minimum system load loss and calculation time are shown in Tables 1 and 2, respectively. As can be seen in each column of Table 1, the system's minimum load loss increases with increasing attack budget S. This result indicates that, given the same maintenance resource budget, the more powerful the attack, the greater the damage to the power grid. For each row (except for the scenario with an attack budget of 1, where the minimum load loss is zero), the minimum load loss generally decreases as the maintenance budget R increases. This indicates that, given the same attack intensity, increasing maintenance resources can improve the power grid's ability to respond to attacks.

[0110] Table 1 Load loss (MW)

[0111]

[0112]

[0113] Table 2 Calculation time (s)

[0114]

[0115] To verify the superiority of the proposed model, the present invention compared the maintenance plans of the Attacker-Defender (AD) model and the DAD model under the same maintenance budget. In the AD model, the maintenance budget R is first set to 0, which fixes all maintenance variables to 0, meaning no maintenance operations are performed. The attacker will attack the power source or line that causes the greatest grid load loss. In this model, the defender's optimal maintenance strategy is to repair these attacked components to prevent the worst-case scenario. After the defender repairs these critical components, the attacker makes a new attack decision, resulting in the final system load loss. Table 3 shows the load loss when the two models are used to arrange maintenance plans and no maintenance is performed, respectively, under scenarios with attack budgets S of 1 to 4. It can be seen that although both the AD model and the DAD model can reduce the load loss of the grid in emergencies and improve the grid's survivability, the AD model cannot produce the optimal maintenance plan, resulting in greater load loss than the DAD model.

[0116] Table 3 Comparison of load loss between AD model and DAD model

[0117]

[0118] In addition, in order to study the rationality of allocating maintenance resources on the power grid based on the DAD-based maintenance plan scheduling model, the embodiment of the present invention compares the load loss under different maintenance and attack budget scenarios. Figure 2 The load loss changes of the power grid under different maintenance and attack budgets are given. Figure 2 As can be seen from the figure, increasing the maintenance budget always reduces grid load losses and improves the grid's emergency response capabilities. When the attack budget S is large, the benefits of an increased maintenance budget are more significant. However, when the attack budget S is small, an increase in the maintenance budget has little effect on reducing grid load losses. For example, when the attack budget is 1, increasing the maintenance budget R from 0 to 2 only reduces load losses by 20%. This means that the costs incurred by increasing maintenance resources may exceed the benefits of increased maintenance resources. Therefore, maintenance planners need to select an appropriate maintenance budget based on the actual risk of attack faced by the grid.

[0119] The example analysis shows that the adaptive robust emergency maintenance decision-making method for urban distribution networks proposed in the embodiment of the present invention minimizes the impact of extreme events on the distribution network through pre-disaster defense decisions, attacker strategy selection, and optimized operation strategies of operators. It can improve the flexibility and adaptability of power supply requirements under extreme events and provide strong support for the safe and stable operation of urban distribution networks. By rationally arranging emergency maintenance plans, the system load loss under equipment failures, natural disasters, and human malicious attacks can be effectively reduced, minimizing the impact of extreme events on the safe and stable operation of the distribution network. The present invention establishes a distribution network component maintenance model based on the defense-attack-defense concept. By considering that pre-disaster defense decision-makers select important components in the distribution network for maintenance based on a limited maintenance budget; the attackers take into account the best operating strategies that the defenders may adopt and attack the system components based on the limited attack budget. Based on the defense and attack plans designed before the disaster, the dispatchers use optimal power flow calculations to optimize the system operation mode, minimize the system load loss, and reduce the impact of extreme scenarios on the system.

[0120] Another embodiment of the present invention further provides an urban distribution network adaptive robust emergency maintenance decision-making system, comprising:

[0121] Data acquisition module, used to obtain grid data information of urban distribution network;

[0122] Parameter setting module, used to set attack budget and maintenance budget;

[0123] An optimization model building module is used to build a three-layer, two-stage robust optimization model for emergency maintenance of urban distribution networks based on the defense-attack-defense DAD architecture, according to the grid data of the urban distribution network, as well as the attack budget and maintenance budget;

[0124] The optimization model solving module is used to solve the three-layer two-stage robust optimization model for emergency maintenance of urban distribution networks using the column-based and constraint-generated C&CG algorithm under constraints, with the objective function of minimizing total load loss.

[0125] The emergency maintenance decision module is used to generate the solution results of the C&CG algorithm based on columns and constraints, obtain the emergency maintenance decision plan that minimizes the impact of extreme events on the distribution network, and complete the emergency maintenance plan arrangement of the urban distribution network.

[0126] In response to the need for rapid formulation of maintenance plans for equipment in urban distribution networks, the present invention proposes a method for formulating equipment maintenance plans based on distribution network line flow matching. By encoding historical data and then matching it according to demand, the preliminary formulation of equipment maintenance plans is achieved, and fine-tuning is performed accordingly to complete the formulation of emergency maintenance plans for urban distribution networks.

[0127] Another embodiment of the present invention further provides an electronic device, including a processor and a memory, wherein the processor is configured to execute a computer program stored in the memory to implement the urban distribution network adaptive robust emergency maintenance decision-making method.

[0128] Another embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, the urban distribution network adaptive robust emergency maintenance decision-making method is implemented.

[0129] The computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form. The computer-readable storage medium may include: any entity or device, medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electric carrier signal, telecommunication signal and software distribution medium, etc. that can carry the computer program code. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals. For ease of explanation, the above content only shows the part related to the embodiment of the present invention. For specific technical details not disclosed, please refer to the method part of the embodiment of the present invention. The computer-readable storage medium is non-transitory and can be stored in a storage device formed by various electronic devices, and can implement the execution process recorded in the method of the embodiment of the present invention.

[0130] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0131] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, 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 produce 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 flowcharts and / or block diagrams. 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.

[0132] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work 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 The function specified in one or more boxes.

[0133] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0134] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. An adaptive robust emergency maintenance decision-making method for urban distribution network, characterized in that: include: Obtain grid data information of urban distribution network; Set attack budget and overhaul budget; According to the grid data information, attack budget and maintenance budget of the urban distribution network, a three-layer two-stage robust optimization model for emergency maintenance of the urban distribution network is established based on the defense-attack-defense DAD architecture; Taking the minimum total load loss as the objective function, the three-layer two-stage robust optimization model of emergency maintenance of urban distribution network is solved by using the column and constraint generation C&CG algorithm under constraint conditions. According to the solution results of the column-based and constraint-generated C&CG algorithm, an emergency maintenance decision-making plan that minimizes the impact of extreme events on the distribution network is obtained, and the emergency maintenance plan for the urban distribution network is completed.

2. The adaptive robust emergency maintenance decision-making method for urban distribution network according to claim 1 is characterized in that: The grid data information of the urban distribution network includes power source, load, grid topology and branch flow limitation.

3. The adaptive robust emergency maintenance decision-making method for urban distribution network according to claim 1 is characterized in that: In the step of establishing a three-layer two-stage robust optimization model for emergency maintenance of the urban distribution network based on the defense-attack-defense DAD architecture according to the grid data information of the urban distribution network and the attack budget and maintenance budget, the three-layer two-stage robust optimization model for emergency maintenance of the urban distribution network adopts the following formula as the objective function: The following formula is used as the constraint condition: Where N is the set of nodes n, d n is the load loss of node n, p l is the active power of transmission line l, g j is the active output of power source j, δ n is the phase angle of node n, z l is the maintenance decision variable of line l, that is, if line l is repaired, z l =1, otherwise 0; z j is the maintenance decision variable of power source j, that is, if power source j is repaired, z j =1, otherwise 0; v l is the attack decision variable of line l, that is, if line l is attacked, v l =0, otherwise 1; v j is the attack decision variable of power supply j, that is, if power supply j is attacked, v j =0, otherwise 1; is the attacker's attack decision set; L is the line set in the system, J is the power supply set in the system; R is the defender's maintenance decision budget, which represents the upper limit of the number of maintenance components; S is the attacker's attack budget, which represents the upper limit of the number of components that can be attacked; x l is the reactance of transmission line j, δ o(l) is the phase angle of the source node of the transmission line j, δ d(l) is the phase angle of the target node of transmission line j, represents any transmission line, o(l) is the source node of transmission line j, d(l) is the target node of transmission line j; D n is the upper limit of active demand of node n, J n is the set of power sources connected to node n, g j Output for power supply j, represents any node, δ n is the phase angle of node n, G j is the upper limit of active output of power source j, is the phase angle capacity of the connection node, Indicates any power supply.

4. The adaptive robust emergency maintenance decision-making method for urban distribution network according to claim 3 is characterized in that: In the step of solving the three-layer two-stage robust optimization model for emergency maintenance of urban distribution network by using the column and constraint generation C&CG algorithm under constraint conditions with the minimum total load loss as the objective function: Based on the columns and constraints, the C&CG algorithm solves the main problem MP under a given attack plan. The main problem MP uses the following formula as the objective function: minα The following formula is used as the constraint condition: In the formula, α is the cutting plane of the subproblem, is the load loss of node n at the i-th iteration, is any number of iterations, k is the number of iterations required for the problem to converge, is the active power of transmission line l at the i-th iteration, is the phase angle of the source node of transmission line l at the i-th iteration, is the phase angle of the target node of transmission line l at the i-th iteration, is the output of power source j at the i-th iteration, is the attack strategy of power source j in the i-th iteration, is the phase angle of node n at the i-th iteration, For a given attack plan, to plan for a specific attack; is the attack scenario at the kth iteration, is the attack strategy of transmission line l at the kth iteration; For nonlinear constraints The linearization is performed through the big M method and converted into the following constraints: In the formula, is the set of attacked lines, is the set of lines that have not been attacked, M is a sufficiently large real number; P l is the active power capacity of transmission line l; Solve the above problems to obtain the optimal value MPobj and maintenance decision Update the lower bound LB = max{LB, MPobj}.

5. The adaptive robust emergency maintenance decision-making method for urban distribution network according to claim 4 is characterized in that: The column-based and constraint-based C&CG algorithm generates maintenance decisions based on Solve the subproblem SP to obtain the optimal value SPobj and the optimal attack scenario The following formula is used as the objective function to solve the subproblem SP: The following formula is used as the constraint condition: In the formula, is the maintenance strategy of transmission line l, For a given maintenance strategy; For nonlinear constraints The big M method is used for linearization and converted into the following constraints: Where, L a For the line that has not been repaired, L b The line being repaired.

6. The adaptive robust emergency maintenance decision-making method for urban distribution network according to claim 5 is characterized in that: The column and constraint generation C&CG algorithm obtains the corresponding dual problem for the minimization linear programming problem at the lower level of the subproblem SP through strong duality. The dual problem uses the following formula as the objective function: The following formula is used as the constraint condition: In the formula, λ n yes The dual variable, γ j yes The dual variable, α n yes The dual variable, ξ n and χ n yes The dual variable, β l yes The dual variable, τ l yes The dual variable, θ l and ρ l yes The dual variable, μ l yes The dual variable, φ l and The dual variable is The dual variable of n|o(l)＝n for The dual variable of the source node of the corresponding transmission line l, λ n|d(l)＝n for The dual variable corresponding to the target node of transmission line l, λ n|j∈Jn for The dual variable of the node where the corresponding power source j is located, λ n free means The dual variable of is unrestricted; Solve the above problem to get the optimal value of the sub-problem SPobj and the optimal attack scenario Update the upper bound UB = min{UB, SPobj}.

7. The adaptive robust emergency maintenance decision-making method for urban distribution network according to claim 6 is characterized in that: The column-based and constraint-generated C&CG algorithm will generate the optimal attack scenario Add to a given attack plan and add the following constraints to the main problem MP: In the formula, is the load loss of node n at the kth iteration, is the active power of transmission line l at the kth iteration, is the phase angle of the source node of transmission line l at the kth iteration, is the phase angle of the target node of transmission line l at the kth iteration, is the line that has not been repaired in the kth iteration, is the line repaired at the kth iteration, is the output of power source j at the kth iteration, is the phase angle of node n at the kth iteration; If (UB-LB) / LB ≤ε, stop, where ε is the error limit; otherwise, set k=k+1 and return to the main problem and iterate again.

8. An adaptive robust emergency maintenance decision system for urban distribution networks, characterized in that: include: A data acquisition module is used to obtain grid data information of the urban distribution network; Parameter setting module, used to set attack budget and maintenance budget; The optimization model building module is used to build a three-layer two-stage robust optimization model for emergency maintenance of urban distribution network based on the defense-attack-defense DAD architecture according to the grid data information of urban distribution network as well as attack budget and maintenance budget; The optimization model solving module is used to solve the three-layer two-stage robust optimization model of emergency maintenance of urban distribution network by using the column-based and constraint-generated C&CG algorithm under constraints, taking the minimum total load loss as the objective function; The emergency maintenance decision module is used to obtain the emergency maintenance decision plan that minimizes the impact of extreme events on the distribution network based on the solution results of the C&CG algorithm generated based on columns and constraints, and complete the emergency maintenance plan arrangement of the urban distribution network.

9. An electronic device, characterized in that: It comprises a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the adaptive robust emergency maintenance decision-making method for an urban distribution network as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by the processor, the method for adaptive robust emergency maintenance decision-making of an urban distribution network as claimed in any one of claims 1 to 7 is implemented.

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