Elastic recovery method and system of power-communication coupling network

Abstract

The invention provides an elastic recovery method and system for an electric power-communication coupling network, and relates to the technical field of intelligent power distribution networks, in response to occurrence of a line fault, the elastic recovery method comprises the following steps: calculating a historical predicted power error and a predicted power error in a previous recovery period through a wind power output prediction model; obtaining a predicted power error set; based on the prediction power error set, constructing a prediction power error uncertainty set; optimizing the output power of each unit by using the prediction power error uncertainty set and taking the minimum load loss as a target to obtain an optimal scheduling strategy; according to the optimal scheduling strategy, the maintenance sequence of the fault lines is adjusted with the minimum economic and social loss and maintenance cost in the maintenance process as the target, and a maintenance plan is obtained and used for elastic recovery of the power-communication coupling network in the current recovery period. The method has the advantages in the aspects of optimizing the overhaul sequence, increasing the load recovery rate and reducing the load shedding amount of the system.

Description

Technical Field

[0001] The present disclosure relates to the technical field of smart distribution networks, and in particular to a method and system for elastic recovery of a power-communication coupling network. Background Art

[0002] With the increasing intelligence of power systems and the rapid expansion of network scale, the role of information systems has become increasingly prominent. Power systems have gradually evolved into power-communication coupled networks (PCCNs), deeply coupled between power grids and communication networks. While communication networks provide efficient and reliable deep perception and control for power networks, they also introduce new vulnerabilities. In a PCCN, a fault on one side of the network can propagate to the other side through the coupling relationship, or even cause cascading failures between coupled networks, leading to large-scale power outages. These large-scale power outages are often triggered or amplified by failures in the communication network.

[0003] Current research on PCCNs, both domestically and internationally, primarily focuses on the power side of coupled networks, with insufficient attention paid to the information side. As the role of information systems in PCCNs becomes increasingly prominent, their impact on coupled networks is also increasing, and the research focus is gradually shifting from the power side to the information side. If key nodes in the communication network fail or are attacked, faults can propagate across coupled networks, exposing the entire PCCN to significant security risks and severely impacting power system reliability.

[0004] The current modeling of the power-communication coupling network only models the business control between the communication system and the power system, but rarely involves the interaction mechanism of the physical layer and data link layer, as well as the impact of the communication side on the power side on the overall stability and reliability of the coupling network.

[0005] Existing uncertainty modeling for analyzing renewable energy output uncertainty is typically based on commonly used probability distributions (such as the Weibull distribution) or relies on long-term measurement data. However, renewable energy output under normal meteorological conditions cannot be directly applied to typhoon disasters. Few studies have considered the spatiotemporal correlation characteristics of renewable energy, and research on renewable energy output fluctuations caused by extreme weather conditions has rarely been applied to power system resilience assessments.

[0006] Research on power grid recovery from extreme disasters has divided the grid's transition process to disturbances into a pre-disaster prevention phase, a mid-disaster resistance phase, and a post-disaster recovery phase. Current research on grid resilience strategies often uses resilience improvement as an objective function, optimizing network structure, reinforcing power lines, and deploying distributed energy resources and energy storage. However, current research primarily focuses on specific disaster scenarios, ignoring the diversity of disasters, the uncertainty of renewable energy in the grid, and the impact of renewable energy output on the component maintenance sequence during the recovery process.

[0007] Therefore, existing research on resilient power grids under low-probability and high-risk events often ignores the impact of the uncertainty of renewable energy output, the structural coupling between electricity and communications, and the functional coupling relationship on resilient recovery, which affects the safe operation of the power system. Summary of the Invention

[0008] In order to solve the above problems, the present disclosure proposes a resilient recovery method and system for a power-communication coupling network, which has advantages in optimizing the maintenance sequence, accelerating the load recovery rate and reducing the system load shedding.

[0009] According to some embodiments, the present disclosure adopts the following technical solutions:

[0010] A method for elastic recovery of a power-communication coupling network, in response to a line fault, uses a preset time period as a recovery cycle and performs the following elastic recovery steps within the current recovery cycle:

[0011] The wind power output prediction model is used to calculate the historical prediction power error and the prediction power error in the previous recovery period to obtain the prediction power error set.

[0012] Based on the predicted power error set, a predicted power error uncertainty set is constructed;

[0013] By using the uncertainty set of predicted power errors and taking the minimum load loss as the goal, the output power of each unit is optimized to obtain the optimal dispatching strategy;

[0014] According to the optimal scheduling strategy, with the goal of minimizing economic and social losses and maintenance costs during the maintenance process, the maintenance order of the faulty lines is adjusted to obtain a maintenance plan for the resilient recovery of the power-communication coupling network within the current restoration period.

[0015] According to some embodiments, the present disclosure adopts the following technical solutions:

[0016] A resilient recovery system for a power-communication coupling network, in response to a line fault, performs resilient recovery within a current recovery period, with a preset time period as a recovery period, comprising:

[0017] The model building module is configured to: calculate the historical predicted power error and the predicted power error in the previous recovery period through the wind power output prediction model to obtain a predicted power error set;

[0018] The uncertainty set construction module is configured to: construct a predicted power error uncertainty set based on the predicted power error set;

[0019] The power optimization module is configured to: use the uncertainty set of predicted power errors to minimize load loss, optimize the output power of each unit, and obtain the optimal scheduling strategy;

[0020] The plan adjustment module is configured to: adjust the maintenance order of the faulty lines according to the optimal scheduling strategy, with the goal of minimizing economic and social losses and maintenance costs during the maintenance process, and obtain a maintenance plan for the resilient recovery of the power-communication coupling network within the current recovery period.

[0021] According to some embodiments, the present disclosure adopts the following technical solutions:

[0022] A computer program product includes a computer program, wherein when the computer program is executed by a processor, the computer program implements the elastic recovery method of the power-communication coupling network.

[0023] According to some embodiments, the present disclosure adopts the following technical solutions:

[0024] A non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the method for elastic recovery of a power-communication coupling network is implemented.

[0025] According to some embodiments, the present disclosure adopts the following technical solutions:

[0026] An electronic device includes: a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to implement the elastic recovery method of the power-communication coupling network.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] The present invention proposes a method and system for elastic recovery of a power-communication coupling network, which has advantages in optimizing maintenance sequence, accelerating load recovery rate, and reducing system load shedding, as shown in the following aspects:

[0029] (1) When analyzing the uncertainty of renewable energy output, DPGMM is used to cluster the output prediction errors, and the spatiotemporal correlation characteristics between wind farms are used to correct the uncertainty model parameters, thereby improving the power prediction accuracy of wind turbines.

[0030] (2) Establish a coupling relationship between power and communication, with the goal of minimizing the amount of load loss under extreme disasters, consider the impact of communication control service failure on the emergency dispatch of generator sets, establish a unit dispatch model, and optimize the output power of each unit.

[0031] (3) Carry out rolling optimization of new energy output forecast data and elastic recovery strategies, and dynamically adjust the maintenance sequence of faulty lines to minimize load shedding and improve the recovery rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The accompanying drawings, which constitute a part of the present disclosure, are used to provide a further understanding of the present disclosure. The exemplary embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation to the present disclosure.

[0033] Figure 1 This is a flow chart of the method of Example 1.

[0034] Figure 2 This is the improved Gibbs sampling flow chart of Example 1.

[0035] Figure 3 This is a diagram of the emergency dispatch strategy formulation process of Example 1. DETAILED DESCRIPTION

[0036] The present disclosure will be further described below with reference to the accompanying drawings and embodiments.

[0037] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present disclosure belongs.

[0038] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "comprising" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0039] Example 1

[0040] In one embodiment of the present disclosure, a method for elastic recovery of a power-communication coupling network is provided. In response to the occurrence of a line fault, elastic recovery is performed with a preset time period as a recovery cycle. The recovery is triggered by the occurrence of a line fault. When the line fault rate is detected to exceed a threshold, it is considered that a line fault has occurred. At this time, the iterative execution of steps S1-4 is started. Before the iteration, a wind power output prediction model for power prediction needs to be prepared. Figure 1 Provide detailed explanation.

[0041] Step S0: Obtain historical data of wind power output power and build a wind power output prediction model.

[0042] Based on the rated power, cut-in wind speed, cut-out wind speed, and rated wind speed of the wind turbine, the predicted wind speed V and output power P at the location of the wind farm are established by fitting historical data. W The relationship between the wind power output prediction model is obtained, which is expressed as follows:

[0043]

[0044] Where, P W is the predicted wind power output power, V is the predicted wind speed at the location of the wind farm, P wN is the rated power of the wind turbine; v ci 、v co 、v N They are the cut-in wind speed, cut-out wind speed and rated wind speed of the wind turbine respectively.

[0045] In order to ensure the prediction accuracy of the wind power output prediction model, the actual power of the previous recovery cycle is used to correct the v in the prediction model. ci 、v co .

[0046] Step S1: Calculate the historical predicted power error and the predicted power error in the last recovery period through the wind power output prediction model to obtain a predicted power error set.

[0047] The wind power output prediction model obtained in step S0 is used to predict the historical data and the power in the previous recovery cycle, and the predicted power error is calculated using the difference between the predicted value and the actual value, thereby obtaining a predicted power error set consisting of the historical predicted power error and the predicted power error in the previous recovery cycle.

[0048] Step S2: constructing a predicted power error uncertainty set based on the predicted power error set;

[0049] Based on the predicted power error set of wind power output, the rolling nonparametric Dirichlet process Gaussian mixture model is used to establish the uncertainty model of renewable energy output power error. The model parameters are corrected using the spatiotemporal correlation characteristics of wind farms, and finally the predicted power error uncertainty set is obtained.

[0050] Assume that the power prediction error of n wind power plants in time period t is δ t ∈R n , statistics N t The prediction error sample set obtained in each time period is The standard form of the Dirichlet process mixture model (DPMM) is as follows:

[0051]

[0052] Where, is the likelihood function; Categorical represents the multinomial distribution function; π k is the weight of the distribution; η k is the parameter of the distribution; DP represents the Dirichlet process; α is the clustering factor; G0 is the prior distribution; c i is the category to which the i-th sample belongs. The category here refers to which cluster the prediction error sample belongs to. The i-th error sample belongs to the i-th category. The data in this category can be described by a Gaussian distribution.

[0053] When the likelihood function When Gaussian distribution is adopted, the DPMM is called DPGMM.

[0054] The key to DPGMM is to calculate the posterior probability as well as And thus infer that the data set D δ The Gaussian distribution of the number of components (i.e., categories) can be composed and the parameters corresponding to each component. Since the above distribution cannot be directly sampled, this embodiment adopts the following method: Figure 2 The Gibbs sampling shown calculates the posterior probability. The specific steps are:

[0055] (1) Randomly initialize the sample category C and model parameter η; set i = 1;

[0056] (2) If c i Class sample point n ci =1, then remove c from C i , otherwise execute (3);

[0057] (3) For sample category c i Conduct sampling;

[0058] (4) If c i ≠c -i, execute (5), otherwise execute (2) after i=i+1;

[0059] (5) If i≤N t , execute (2) after i=i+1, otherwise execute (6);

[0060] (6) Sampling the model parameter η;

[0061] (7) Modify the model parameter η based on the spatiotemporal correlation characteristics of the wind farm;

[0062] (8) Value Mixing Weight

[0063] (9) If π c If the distribution is not stable, execute (2); otherwise, exit the loop and end.

[0064] The sampling formula is:

[0065]

[0066] p(η c |δ i )∝F(δ i ,η c )G0(η c )(4)

[0067]

[0068] Where c -i is the category to which all samples except the i-th sample belong; n c is the total number of sample points of category c; new is a newly created category; η represents the parameter set of all categories.

[0069] Since the geographical location relationship between wind turbines directly affects the power output of the wind farm, the output power of the downstream wind turbines is significantly reduced under the influence of the wake effect of the upstream wind farm, which in turn affects the overall power output of the wind farm. Therefore, this embodiment innovatively incorporates the uncertainty of the wind power output prediction error into the spatiotemporal correlation characteristics between wind farms.

[0070] The temporal-spatial correlation coefficient η is proposed by integrating the pre-set geospatial correlation coefficient of wind farms and the wind speed mutual information coefficient, and considering the dynamics of wind farm correlation at different time scales. ij :

[0071]

[0072]

[0073] Where τ is the sampling time scale, τmax is the maximum sampling time scale; is the mutual information coefficient of wind speed between wind farm i and wind farm j, is the geographic spatial correlation coefficient; δ[v i (t),v j (t)] is the probability density, δ[v i (t)]δ[v j (t)] is the marginal distribution; V i and V j are the wind speed sequences corresponding to wind farm i and wind farm j respectively.

[0074] Considering the wind farm as a node, the spatiotemporal correlation coefficient η ij As the edge information of the topological structure, the power-wind speed mutual information of the wind farm itself is taken into account while considering the wake effect of the large-scale wind farm. As the node information of the topological structure, the spatiotemporal correlation matrix M is constructed:

[0075]

[0076] Where n is the number of wind farms; ψ i is the power-wind speed mutual information.

[0077]

[0078] Where, P i is the power sequence of wind farm i.

[0079] Modified distribution parameter η c :

[0080] η′ c =η c ×M (11)

[0081] The following wind power prediction error fuzzy set can be obtained:

[0082] X(D δ )={δ~MNN(μ′ c ,σ′ c )} (12)

[0083] Where, k=1,2,…,K; K=|{π k}| is the total number of components.

[0084] Then construct the predicted power error uncertainty set:

[0085]

[0086] Where U can be regarded as the union of k ellipsoid uncertainty sets; u is the orthogonalized ellipsoid; Γ sis the space budget, used to control each subset U k The size of Γ s The larger the wind power prediction error is, the more it belongs to the set U k The higher the confidence level.

[0087] Step S3: Using the uncertainty set of predicted power errors, with the goal of minimizing load loss, optimize the output power of each unit and obtain the optimal scheduling strategy;

[0088] With the goal of minimizing load loss, the scheduling decision for the current time period t is obtained based on the coupled network topology and routing distribution.

[0089] Specifically, due to the large fluctuations in system load in extreme scenarios, if the generator set with communication failure maintains a high output, the system is very likely to become unstable. However, if the generator set is removed, the power outage will be expanded. Therefore, in this embodiment, the generator set with communication failure is treated as a restricted unit and participates in emergency dispatch, as shown in the formula:

[0090] P G,i ∈[0,v i P G,0,i ] if h i =1 (14)

[0091] Where, P G,i is the maximum output of the unit corresponding to the power node i where communication fails after the fault occurs; P G,0,i is the normal output of the unit corresponding to power node i before the fault occurs; v i Indicates the maximum output coefficient of the unit corresponding to power node i under the communication failure state; h i represents the communication status of power node i, h i ∈{0,1}, if h i =1, the output of the unit corresponding to node i is limited.

[0092] like Figure 3 As shown, the specific steps of scheduling are:

[0093] Step 1: An extreme scenario occurs, and power / communication network lines are damaged.

[0094] Step 2: The dispatch center receives the power network fault information u P and communication network fault information C .

[0095] Step 3: Calculate the service status of the communication node.

[0096] The dispatch center and the units achieve remote dispatching by transmitting communication information via the service route. The service route is carried by the communication line. Damage to any line on the path will cause the route to be interrupted. When both the primary and backup routes are interrupted, the service cannot be transmitted, the communication node service is lost, and the corresponding unit communication fails. Based on the discrete Markov process, the service status of the communication node after the communication network is damaged is calculated:

[0097]

[0098] In the formula, o u Indicates the communication node service u state variable, o u ∈{0,1}, if the communication node service u fails, then o u =1, otherwise o u =0.

[0099] Step 4: Based on the current coupling network fault information and considering the communication status of the units, the output power of each unit is optimized to obtain the optimal scheduling strategy with the goal of minimizing the load loss under the worst scenario of uncertain wind power output error. The objective function and constraints are as follows:

[0100]

[0101] Where w d is the unit power loss cost, in ten thousand yuan / (MW·h); s Indicates the power outage time in hours; ΔP D,i represents the load removed from power node i in an extreme scenario; n p Indicates the number of power nodes at the current moment.

[0102] Constraints:

[0103]

[0104]

[0105] Where, represents the state variable of the power line ij; is the power-communication node functional association matrix, which represents the “functional coupling” relationship between the communication site service and the power node monitoring and control. If the communication node service m is functionally coupled with the power node i, then P 1,ij represents the power flow of line ij after executing the optimized scheduling strategy; θ i is the voltage phase angle of node i; n p and N p Respectively represent the number of nodes and node set of the power system; P G,i,up Indicates that the power generation of node i increases its maximum output; P D,irepresents the load of node i; ΔP D,i,max Represents the maximum load shedding amount of node i.

[0106] Step S4: Based on the optimal scheduling strategy, with the goal of minimizing economic and social losses and maintenance costs during the maintenance process, the maintenance order of the faulty lines is adjusted to obtain a maintenance plan for the resilient recovery of the power-communication coupling network within the current recovery period.

[0107] 2. Generate a maintenance plan

[0108] Based on the dispatching strategy obtained by optimization, with the goal of minimizing economic and social losses and maintenance costs during the maintenance process, the maintenance sequence is optimized and adjusted according to the uncertainty of unit output and wind and solar unit output to obtain the maintenance plan for the current time step t, thereby achieving resilient recovery of the power-communication coupling network under fault scenarios.

[0109] The optimization and adjustment of the maintenance sequence may prioritize maintenance of a certain line to introduce another unit to provide support, assuming that the output of unit 1 at this moment is insufficient to support the load around it.

[0110] Among them, the objective function is:

[0111] min(λ1g1(X)+λ2g2(X))

[0112] Where, X={x1,x2,…,x n} represents the maintenance plan of n faulty lines; λ1 and λ2 are the weights of social and economic losses and repair costs; g1 and g2 are the social and economic losses and repair costs of the current repair strategy:

[0113]

[0114] In the formula, T(x i ) is the time when the fault line xi is repaired; w d is the unit power loss cost, in ten thousand yuan / (MW·h); The load removed from power node j when repairing fault line xi.

[0115]

[0116] Where mg is the fixed cost of repairing the faulty line; and is the time it takes for the power maintenance team and the communication maintenance team to complete the repair of the last fault respectively; T(x0) is the start time of maintenance; r, v and l are respectively the labor cost per unit time of each maintenance team, the driving speed of the maintenance vehicle and the unit driving cost.

[0117] The constraints are:

[0118] 1. Repair equipment restrictions: Power supply and communication faults can only be repaired by the corresponding type of maintenance team. Each maintenance team can only repair one faulty line at a time.

[0119]

[0120] Where, ne and nc are the number of power system faults and communication faults; Is the power maintenance team re and the communication maintenance team rc currently repairing the power equipment i and the communication equipment j. A value of 1 indicates that they are being repaired, while a value of 0 indicates that they have not been repaired yet.

[0121] 2. Repair time limit: Regardless of the type of fault, one repair is considered a full recovery and no further repair is required.

[0122]

[0123] Where D k (x i ) is whether the maintenance order of fault line k is the i-th, which is 1; Ω is the set of power and communication fault lines.

[0124] Example 2

[0125] In one embodiment of the present disclosure, a resilient recovery system for a power-communication coupling network is provided, which, in response to a line fault, includes:

[0126] The power acquisition module is configured to: acquire the actual output power of the wind turbine generator set in the power network;

[0127] The power prediction module is configured to: train a wind power output prediction model based on actual output power to obtain wind power output prediction power and error, and a prediction power error uncertainty set;

[0128] The power optimization module is configured to: utilize the uncertainty set of wind turbine power prediction errors, optimize the output power of each unit with the goal of minimizing load loss, and obtain the optimal scheduling strategy;

[0129] The plan adjustment module is configured to: adjust the maintenance order of the faulty lines according to the optimal scheduling strategy, with the goal of minimizing economic and social losses and maintenance costs during the maintenance process, and obtain a maintenance plan for the resilient recovery of the power-communication coupling network.

[0130] Example 3

[0131] In one embodiment of the present disclosure, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the computer program implements the method for resilient recovery of a power-communication coupling network.

[0132] Example 4

[0133] In one embodiment of the present disclosure, a non-transitory computer-readable storage medium is provided, wherein the non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the method for elastic recovery of a power-communication coupling network is implemented.

[0134] Example 5

[0135] In one embodiment of the present disclosure, an electronic device is provided, comprising: a processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes the method for resilient recovery of a power-communication coupling network.

[0136] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to 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 flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0137] 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.

[0138] Although the above describes the specific implementation methods of the present disclosure in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present disclosure. Those skilled in the art should understand that on the basis of the technical solution of the present disclosure, various modifications or variations that can be made by those skilled in the art without creative work are still within the scope of protection of the present disclosure.

Claims

1. A method for elastic recovery of a power-communication coupling network, characterized in that: In response to a line fault, a preset time period is defined as a recovery cycle, and the following elastic recovery steps are performed within the current recovery cycle: The wind power output prediction model is used to calculate the historical prediction power error and the prediction power error in the previous recovery period to obtain the prediction power error set. Based on the predicted power error set, a predicted power error uncertainty set is constructed; By using the uncertainty set of predicted power errors and taking the minimum load loss as the goal, the output power of each unit is optimized to obtain the optimal dispatching strategy; According to the optimal scheduling strategy, with the goal of minimizing economic and social losses and maintenance costs during the maintenance process, the maintenance order of the faulty lines is adjusted to obtain a maintenance plan for the resilient recovery of the power-communication coupling network within the current restoration period.

2. The method for elastic recovery of a power-communication coupling network according to claim 1, wherein: The response to the occurrence of a line fault is that when it is detected that the failure rate of the line exceeds a threshold, it is considered that a line fault has occurred.

3. The method for elastic recovery of a power-communication coupling network according to claim 1, wherein: The construction of the predicted power error uncertainty set adopts a Dirichlet process mixture model to calculate the posterior distribution of the wind turbine predicted power error, and uses the spatiotemporal correlation characteristics between wind farms to correct the distribution parameters of the posterior distribution.

4. The method for elastic restoration of a power-communication coupling network according to claim 3, wherein: The spatiotemporal correlation characteristics between wind fields are used to modify some parameters of the posterior distribution, specifically: The spatial-temporal correlation coefficient is calculated by integrating the geographic spatial correlation coefficient of wind farms and the mutual information coefficient of wind speed, and considering the dynamic nature of wind farm correlation at different time scales; The distribution parameters are corrected by multiplying them with the spatiotemporal correlation coefficient.

5. The method for elastic restoration of a power-communication coupling network according to claim 1, wherein: The goal is to minimize the load loss, and the generator set with communication failure caused by line failure is the power node to be removed. The load loss is the sum of the load losses caused by the removal during the power outage time, which can be expressed as follows: Where w d is the unit power loss cost; t s Indicates the power outage time caused by line fault; ΔP D,i represents the load removed from power node i; n p Indicates the number of power nodes that have been repaired at the current moment.

6. The method for elastic restoration of a power-communication coupling network according to claim 1, wherein: The goal is to minimize the economic and social losses and maintenance costs during the maintenance process, which can be expressed as follows: min(λ1g1(X)+λ2g2(X)) Where, X={x1,x2,…,x n } represents n repair plans; λ1 and λ2 are the weight values ​​of social and economic losses and repair costs; g1 and g2 are the social and economic losses and repair costs of the current repair plan.

7. A resilient recovery system for a power-communication coupling network, characterized in that: In response to a line fault, a preset time period is defined as a recovery period, and elastic recovery is performed within the current recovery period, including: The model building module is configured to: calculate the historical predicted power error and the predicted power error in the previous recovery period through the wind power output prediction model to obtain a predicted power error set; The uncertainty set construction module is configured to: construct a predicted power error uncertainty set based on the predicted power error set; The power optimization module is configured to: use the uncertainty set of predicted power errors to minimize load loss, optimize the output power of each unit, and obtain the optimal scheduling strategy; The plan adjustment module is configured to: adjust the maintenance order of the faulty lines according to the optimal scheduling strategy, with the goal of minimizing economic and social losses and maintenance costs during the maintenance process, and obtain a maintenance plan for the resilient recovery of the power-communication coupling network within the current recovery period.

8. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for elastic recovery of a power-communication coupling network according to any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium, characterized in that The non-transitory computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by a processor, the method for elastic recovery of a power-communication coupling network according to any one of claims 1 to 6 is implemented.

10. An electronic device, characterized in that: include: A processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to implement a method for elastic recovery of a power-communication coupling network as described in any one of claims 1 to 6.

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