A distributed robust method for multi-resource coordinated dispatching of power distribution network under earthquake disaster

Abstract

This invention discloses a multi-resource collaborative scheduling method for distribution networks under earthquake disasters, designed to address the challenges posed by the uncertainty of line faults and the interruption of power supply to critical loads caused by earthquakes to the operation of distribution systems. The method constructs a line fault probability model using a seismic attenuation model and vulnerability curves from the US Earthquake Disaster Loss Assessment System, and introduces line reinforcement decisions to form a decision-dependent uncertainty model, accurately reflecting the coupled impact of earthquake intensity and planning decisions on line faults. Simultaneously, a multi-dimensional flexible resource scheduling framework integrating mobile hydrogen energy resources, building thermal inertia, and demand response is adopted to enhance energy supply capacity and system resilience during the disaster phase. A 1-norm-based multi-resource collaborative scheduling optimization model is introduced to characterize the probability shift of earthquake scenarios, and a column constraint generation algorithm is designed to achieve efficient collaborative solution of planning and scheduling. This invention can significantly improve the operational reliability of distribution networks under earthquake disaster scenarios and has certain engineering application value.

Description

Technical Field

[0001] This invention relates to a distributed bar method for multi-resource collaborative scheduling of power distribution networks under earthquake disasters, belonging to the field of power system dispatching technology. Background Technology

[0002] With the increasing frequency of low-probability, high-impact disasters such as earthquakes, distribution networks face severe challenges including large-scale line damage, regional isolation, and critical load interruptions. Existing earthquake scenarios are mostly based on exogenous fixed failure rates, which fail to accurately reflect the differentiated risks of line segmentation caused by peak ground acceleration (PGA) decay and vulnerability to the HAZUS (Hazardous Earthquake Assessment System) in the United States, and also fail to consider the impact of line reinforcement on failure probability. Simultaneously, the coordinated scheduling of diverse and flexible resources such as mobile hydrogen energy, building thermal inertia, and demand response under disaster conditions has not been effectively utilized, and relying solely on fixed distributed power sources is insufficient to meet the post-earthquake resilience enhancement requirements. Furthermore, earthquake scenario probabilities exhibit significant shifts, making it difficult for traditional stochastic programming and robust optimization to simultaneously achieve both economic efficiency and reliability. There is a lack of effective methods to handle decision-dependent uncertainty while maintaining reliability. Therefore, there is an urgent need for a distribution network seismic resilience enhancement method that can achieve accurate earthquake scenario modeling, coordinated scheduling of diverse and flexible resources, and combines distributed robust optimization with efficient solution algorithms. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a multi-resource collaborative scheduling method for power distribution networks under earthquake disasters, so as to strengthen the coordination of scheduling of various resources and enhance the power supply and resilience of the power distribution system during disasters.

[0004] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: A distributed bar method for multi-resource collaborative scheduling of power distribution networks under earthquake disasters includes the following steps: Step 1: By changing the magnitude and source location, a non-sequential Monte Carlo method is used to generate an earthquake scenario sample set. The study area of ​​the power distribution network is divided into grids. Based on the ground motion attenuation relationship and HAZUS vulnerability curve, the basic fault probability of the power distribution lines under each earthquake scenario sample is calculated. On this basis, the decision-dependent uncertainty brought about by line reinforcement is considered to obtain the comprehensive line fault probability. The K-means++ clustering algorithm is used to cluster all earthquake scenario samples to obtain representative earthquake disaster fault scenarios and the probability of each earthquake disaster fault scenario appearing in the earthquake scenario sample set. Step 2: Taking into account mobile hydrogen energy resources, building heat load, and demand response, establish a two-stage coordinated constraint system for power distribution network planning and operation. Step 3: Based on collaborative constraints, establish a two-stage sub-Bruker optimization scheduling model under earthquake disasters, and use 1-norm fuzzy sets to characterize the probability uncertainty of each earthquake disaster fault scenario obtained from the clustering in Step 1 in the earthquake scenario sample set. Step 4: For the two-stage bibliometric optimization scheduling model, the column and constraint generation algorithm is used to decompose the model into the main problem and sub-problems and solve them alternately and iteratively. Finally, the results of distribution network planning and operation considering earthquake disasters are obtained by convergence.

[0005] Compared with the prior art, the present invention, employing the above technical solution, has the following technical effects: This invention coordinates and schedules distributed power sources, mobile hydrogen energy resources, and building thermal inertia during fault periods. The system can maintain controllable indoor temperature, reduce air conditioning power demand, and achieve reasonable allocation of distributed power output in various areas during fault periods. This demonstrates that the method of this invention can effectively improve the energy supply capacity and system resilience during disasters and has certain engineering application value. Attached Figure Description

[0006] Figure 1 This is a three-layer distributed bar framework diagram for improving the resilience of the power distribution system; Figure 2 This is a geographic grid map corresponding to the power distribution system; Figure 3 It is the probability of earthquake failure scenarios obtained by clustering; Figure 4 This refers to the deployment and operation of mobile hydrogen energy resources, including (a) the deployment plan during the planning period, (b) the operation and scheduling during disasters, and (c) the regional operation status during disasters. Figure 5 This is the output of distributed power sources within a day under a typical fault scenario; Figure 6 This is a comparison of indoor building temperatures before and after considering thermal inertia, where (a) is before considering thermal inertia and (b) is after considering thermal inertia. Figure 7 This is the power consumption of air conditioners in each building after taking thermal inertia into account. Detailed Implementation

[0007] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0008] To address the issues of insufficiently precise modeling of line fault rates and inadequate flexible resource utilization under earthquake conditions, this invention proposes a multi-resource collaborative scheduling method for distribution networks under earthquake disasters. The specific steps are as follows: Step 1: Divide the distribution network area into grids. Calculate the basic fault probability of the lines based on the seismic attenuation relationship and HAZUS vulnerability curves. Then, consider the decision-dependent uncertainty brought about by line reinforcement to obtain the comprehensive line fault probability, thus reflecting the impact of line reinforcement decisions on seismic scenarios. Representative seismic scenarios and reference probabilities are obtained through Monte Carlo simulation and clustering.

[0009] Step 101: Theoretical analysis methods are used to model the probability of line faults under seismic conditions. The degree of line damage is mainly affected by peak ground acceleration (PGA). Based on the attenuation relationship widely used in earthquake engineering, this invention uses the following empirical model to calculate the PGA at different locations: , In the formula, For PGA size, Indicates the earthquake magnitude. This indicates the distance of the calculation point from the epicenter of the earthquake.

[0010] After obtaining the PGA at each line location, the log-normal vulnerability function defined in the HAZUS technical manual is further used to evaluate the failure probability of the line under different damage levels. The line reaches the [missing information - likely a specific damage level]. The probabilities of different damage states (corresponding to minor, moderate, severe, and complete damage, respectively) can be expressed as: , In the formula, , The first The logarithmic mean and standard deviation of the vulnerability function under various damage states. This represents the current level of damage to the line.

[0011] To more accurately describe the differences in seismic intensity at different locations, the study area was divided into several... The latitude and longitude grid unit, with a certain line For example, if it spans multiple grids, it can be divided into multiple segments, each belonging to a corresponding grid cell. (Line) In the grid Earthquake vulnerability within the region can be calculated using the following weighted method: , In the formula, For the line In the grid The failure probability of the conductor segment in the middle. Indicates the first According to the HAZUS technical manual, the damage rates for distribution network lines under the four damage conditions are 4%, 12%, 50%, and 80%, respectively. Indicates the line In the grid Inner The probability of a damaged state. The total length of all lines in the distribution network. For the number of power distribution lines, For the line The number of grids involved For the line In the grid The length within.

[0012] Therefore, the power distribution line was obtained. Basic failure probability for: , In the formula, Indicates the line In the grid The probability of basic failures within the system; Next, considering decision-dependent uncertainty, the impact of reinforcement behavior is introduced into the earthquake-induced basic failure rate model. A single-level reinforcement strategy is considered for the railway line... The overall failure probability in an earthquake scenario is expressed as: , In the formula, Indicates the line Should reinforcement measures be taken? This represents the original failure rate calculated based on the seismic model when the line is not reinforced. This is the residual failure rate coefficient after reinforcement, reflecting the reinforcement effect.

[0013] Step 102: This invention simulates typical earthquake scenarios that may occur by changing the magnitude and source location. Because the construction of earthquake scenarios is highly random, the fault states of each line under different scenarios are also unstructured, non-sequential, and probability-driven. A non-sequential Monte Carlo method is used for earthquake sample generation and line fault rate modeling.

[0014] The comprehensive failure rate of the distribution network lines under a single earthquake disaster is obtained according to the method described in step 101. Then, in each simulated earthquake scenario, With Random numbers generated between By comparing, the route can be obtained. The fault state during this earthquake disaster is shown in the following formula: , In the formula, Indicates the line The on / off state.

[0015] The K-means++ clustering algorithm is then used to cluster the earthquake scene. This algorithm avoids the problem of traditional K-means being sensitive to initial values ​​by prioritizing the selection of sample points that are farther apart in the sample space as the initial cluster centers, thereby improving the stability of the clustering results and the global convergence performance.

[0016] In the stage of determining the number of clusters, a comprehensive evaluation strategy is adopted, using the elbow criterion and the silhouette coefficient to determine the optimal number of clusters. The former is used to identify the inflection point interval of the change in the sum of squared errors, while the latter is used to quantitatively verify the balance between intra-cluster cohesion and inter-cluster separation, thereby ensuring that the number of selected scenarios can represent the overall disaster distribution characteristics while avoiding sample redundancy.

[0017] The formula for calculating the profile coefficient is as follows: , In the formula, The value closest to 1 The corresponding number of clusters is the optimal number of clusters. The dissimilarity within a cluster reflects the degree of cohesion. This represents the dissimilarity between clusters, reflecting the degree of separation.

[0018] Step 2: Establish a coordinated operation constraint system for the power distribution network, taking into account various flexible resources such as mobile hydrogen energy resources, building heat load, and demand response.

[0019] Step 201: To describe the operational characteristics of mobile hydrogen energy resources (MHER) in earthquake disaster emergency energy supply, the following constraint set is established: (1) Deployment and connection logic constraints To ensure a reasonable number of MHER deployments and connection relationships, its connection status, transport status, and global deployment limits are defined, with the following constraint expressions: , , , , In the formula, Indicates that MHER is at time... Connecting nodes , Indicates the deployment status of MHER. and These are the connection and transportation state variables, respectively. This is the maximum number of MHERs allowed by the system.

[0020] (2) Dynamics of hydrogen reserves and energy balance constraints To characterize the change in hydrogen reserves over time during the operation of MHER, considering factors such as hydrogen input and output, leakage, and transportation energy consumption, its energy balance equation is established as follows: , , In the formula, For time step, Indicates the first Taiwan MHER at the moment hydrogen reserves, This represents the energy dissipation rate of MHER. and These represent the hydrogen input and output rates, respectively. and These represent the hydrogen charging and decharging efficiencies, respectively. For the first Fuel consumption per unit time during the MHER transportation process. , Due to hydrogen capacity limitations.

[0021] (3) Mutual exclusion constraint between hydrogen flow rate and operating state To avoid simultaneous hydrogen charging and discharging operations and to limit the flow rate within a safe range, the following constraints are established: , , In the formula, , It is a binary variable used for selecting the mutual exclusion state. , This represents the upper limit of the hydrogen inflow and outflow rates.

[0022] (4) Transportation and scheduling constraints To ensure that MHER adheres to the shortest transport time requirement during inter-node migration and to prevent spatial conflicts, its scheduling constraints are expressed as follows: , , In the formula, It is the first Taiwan MHER at the moment Connect to node binary variables, For MHER slave node Move to node The required time steps Represents a node arrive The shortest transportation time, This represents the total number of time steps in the scheduling cycle. For nodes To the node The set of transport segments corresponding to the shortest transport path, where each transport segment consists of a pair of adjacent nodes. This indicates that it is used to describe the mobile hydrogen energy resources from nodes. Transport to node The specific transportation routes taken during the process.

[0023] (5) Power generation and power factor constraints To characterize the power generation output capability and power factor characteristics of MHER, the following power relationship is established: , , , In the formula, This indicates the power generation efficiency of the fuel cell. , Indicates being at a node MHER at the moment The active and reactive power of power generation For the node The upper limit of MHER's power generation capacity, The power factor angle.

[0024] Step 202: Consider the impact of the heat storage characteristics of commercial buildings on the cooling load. According to the principle of energy conservation, the change in indoor heat in the building satisfies the following heat balance equation: , In the formula, air density, The specific heat capacity of air, The volume of air inside a building. This refers to the indoor temperature.

[0025] Taking into account factors such as indoor and outdoor temperature differences, solar radiation, and heat dissipation from people and equipment, the complete energy balance equation for the building is obtained: , In the formula, and These are the heat transfer coefficients of the exterior walls and exterior windows, respectively. , This refers to the area of ​​the exterior walls and windows. For a moment Outdoor and indoor temperatures, For a moment The intensity of solar radiation, Shading coefficient, Indicates time The heat generated by indoor personnel and equipment, For air conditioning at all times The cooling capacity.

[0026] Discretizing the above equation yields the dynamic update expression for the building's indoor temperature over adjacent time steps: , In the formula, For time step.

[0027] To ensure user comfort, the indoor temperature must be maintained within the permissible comfort range. The temperature constraints are as follows: , in, , These represent the acceptable lower and upper limits of indoor temperature, reflecting comfort requirements; For a moment Indoor temperature.

[0028] Air conditioner power must be limited by the rated value: , in, This is the maximum allowable cooling capacity of the air conditioner.

[0029] The total active and reactive loads of a building participating in dispatch at any given time can be expressed as: , in, Indicates buildings At any moment The active power of the air conditioner For the other electrical load power of the building, The power factor angle is used to determine the corresponding reactive load. and The total active and reactive loads of the building.

[0030] Therefore, the node load of the entire system can be expressed as follows: , in, and Representing nodes respectively At any moment Total active and reactive loads, and This refers to the original basic load of this node.

[0031] Step 203, Demand Response Constraints This invention addresses the challenge of supplying power to critical nodes under post-disaster power system constraints by reducing the operation of some loads. In this model, the demand response power must satisfy the following constraints: , in, Represents a node At any moment This can reduce active power; For nodes The original rated load power, this constraint is used to limit the reduction amount to not exceed the load rating.

[0032] The power factor of node i can be expressed as: , In the formula, For nodes The power factor, , These are the rated active power and reactive power of the node, respectively.

[0033] Power factor angle of the node Represented as: , Therefore, the reactive power of the node's demand response can be expressed as: , In the formula, For nodes At any moment The reactive power reduction.

[0034] Step 3: Based on the collaborative constraints, construct a two-stage sub-Bruker optimization framework for planning and operation. In the operation stage, introduce multiple flexible resources and establish a two-stage sub-Bruker optimization model that includes mobile hydrogen energy resources, building thermal inertia, and demand response. Use 1-norm fuzzy sets to characterize the probabilistic uncertainty of the earthquake scenario constructed in Step 1.

[0035] Step 301: To improve the resource allocation and emergency dispatch capabilities of the power distribution system under earthquake disaster scenarios, the model objective function comprehensively considers both investment costs and operating costs, as follows: , In the formula, the first stage is the planning layer, which minimizes the total investment cost of the system; the second stage is the emergency dispatch layer, which minimizes the expected operating cost under the failure probability distribution scenario in the case of earthquake disaster.

[0036] The total investment cost of the system includes investment in mobile hydrogen energy resources, distributed power generation, and line reinforcement costs, which is expressed as follows: , In the formula, Configuration costs for MHER For DG's investment costs, Cost of reinforcing the line; , , , In the formula, This represents the annualized coefficient of MHER investment. This represents the unit investment cost of a single MHER unit. Let be a binary variable, representing the first... Is MHER deployed in Taiwan? This is the annualized investment rate for DG. Represents a node Whether to configure DG The unit capacity investment cost For DG rated capacity, For DG's fixed investment costs; The annualized coefficient for line reinforcement, For the line length; Indicates the line Reinforced state, Cost per unit length for reinforcement.

[0037] The emergency operating cost of the system can be expressed as follows: , In the formula, The first phase of planning and decision-making vectors includes line reinforcement schemes, DG configuration, and MHER deployment; Earthquake disaster failure scenario Parameter set; This is the set of scheduling decision variables for the second phase of operation, including power purchases at each node, DG output, load reduction, and the operation and transport status of MHER. The number of scheduling days within a year. Main grid purchase cost represents the expenditure incurred by the system in purchasing electricity from the superior power grid during the post-disaster operation period; Characterizes the power generation cost of post-disaster DG; The cost of load reduction penalties is used to quantify the economic losses caused by insufficient power supply. For the operating costs of MHER; For MHER transportation costs; , , , , , In the formula, For time period The unit electricity purchase price, Earthquake disaster failure scenario Next moment Electricity purchased from the mainnet; The unit cost of generating electricity for DG, Earthquake disaster failure scenario Below, DG at node ,time The active power output; To reduce costs per unit load, Earthquake disaster failure scenario Next node ,time The reduced load; For MHER unit operating cost, and These are earthquake disaster failure scenarios. Next Taiwan MHER at the moment The amount of hydrogen flowing in and out; For the first The unit transportation cost of MHER Taiwan Indicates the first Taiwan MHER at the moment Is it in transit?

[0038] Step 302: Earthquakes and the aging state of various components cannot be accurately predicted, leading to a discrepancy between the theoretical and actual failure rates of the circuit. Therefore, the actual probability of an earthquake scenario is uncertain, and the following formula is used to characterize it: , In the formula, For the uncertain set of probability distribution of earthquake disaster failure scenarios, Earthquake disaster failure scenario The actual probability, It is Earthquake disaster fault scenarios after probability normalization of individual fault scenarios The relative theoretical probability, The number of earthquake disaster failure scenarios obtained by clustering, It is the total deviation between the probability distribution of actual and theoretical failure scenarios. , The coefficients used to limit the range of fluctuations allow for adjustment of the model's conservatism.

[0039] Introducing auxiliary variables As the absolute deviation between theoretical probability and actual probability: , In the formula, The fuzzy set represents the magnitude of the deviation between the actual probability and the reference probability. The absolute value expression in the equation can be replaced by the linear constraint in the following equation: , The following total deviation constraint is introduced: , The linearized fuzzy set is as follows: .

[0040] Step 4: For the two-stage sub-Bruker optimization model established in Step 3, design a customized column and constraint generation algorithm, decompose the model into main problem and sub-problems and iterate them alternately. Based on the line reinforcement decision in the first stage, dynamically update the earthquake failure scenario and its probability distribution to solve the two-stage sub-Bruker optimization model, and finally converge to obtain the distribution network planning and operation results considering earthquake disasters.

[0041] The main question is: , , In the formula, For Phase 1 constraints, For the number of iterations, For the slack variables of the subproblems, For the first Auxiliary variables introduced during the next iteration For the first The worst-case failure scenario probability is obtained by solving the subproblem in the next iteration. This is the second stage constraint.

[0042] The purpose of the subproblem is to find the probability distribution of the worst-case scenario that maximizes the minimum operating cost after the line breakage following a disaster, and to provide the optimal operating strategy. Once the variables for the first stage are determined, the subproblem can be expressed as: , , In the formula, The first-stage variables are determined after solving the main problem.

[0043] Since the minimization problems in each scenario within the subproblem are independent, the subproblem can be decomposed into two separate problems, which can then be solved independently in sequence. Solving the subproblems yields the probability distribution of the worst-case disconnection scenario, which can then be substituted into the main problem for iterative calculation, thereby achieving the solution of the two-stage DRO model.

[0044] The steps of the designed algorithm are as follows: 1) Initialization Set upper bound The lower realm Iteration counter Error threshold And given an initial set of failure scenarios .

[0045] 2) Solving the main problem Given the worst probability distribution of the l-th iteration Solving the main problem yields the current optimal pre-disaster decision. Auxiliary variables And update the lower bound: .

[0046] 3) Update the fault scenario Based on current pre-disaster decision-making Recalculate the line failure rate and update the earthquake disaster failure scenario set. .

[0047] 4) Solving subproblems Fixed first-stage decision For the updated fault scenario set Solve the subproblems and find the worst-case probability distribution. And calculate the worst-case operating cost target value. Update the upper bound: .

[0048] 5) Convergence determination like If the iteration stops, output the current optimal solution. Otherwise, proceed to the next step.

[0049] 6) Constraint and Variable Set Updates Update iteration count The newly obtained probability distribution The corresponding worst-case scenario and the variables corresponding to its operating cost Add it to the main problem, and update the set of constraints and variables in the main problem.

[0050] Figure 1 The paper demonstrates the relationship between the resilience evolution of the power distribution network under earthquake disasters and the three-layer bibliometric optimization framework proposed in this invention. The upper layer performs planning and decision-making, the middle layer searches for the most unfavorable probability distribution, and the lower layer performs operation scheduling during the disaster period. Figure 2 The topology of the distribution network in the latitude and longitude coordinate system is shown, which is used to calculate the regional PGA to obtain the line failure rate, and to provide basic input for subsequent generation of Monte Carlo-based earthquake scenarios. Figure 3 The probability distribution of 10 typical earthquake fault scenarios is shown in the bar chart. The probability of each scenario is obtained by normalization based on Monte Carlo sampling and clustering results, and the sum is 1.2% of the annual probability of earthquake disaster. Figure 4 (a) in the image shows the results of resource deployment during the pre-disaster planning phase, including line reinforcement, distributed power generation deployment, and initial deployment locations of mobile hydrogen energy resources; Figure 4 (b) shows the dispatch process after the earthquake, where mobile hydrogen energy resources quickly traveled to the disaster area according to the shortest path to provide emergency power to the isolated area; Figure 4 (c) shows three temporary power supply areas formed during the disaster, where distributed power sources and mobile hydrogen energy resources worked together to maintain local power supply capacity. Assume the earthquake fault occurred at 12:00 and all lines were repaired by 15:00. Figure 5 This paper demonstrates the daily output of distributed power sources obtained using the cooperative scheduling and distributed bar optimization model proposed in this invention. It can be seen that during a fault, the output of distributed power sources significantly increases to support the load in the damaged area, and gradually returns to normal levels after the fault is repaired. Figure 6 In the figure, (a) and (b) are the indoor temperature curves before and after using the collaborative scheduling method that takes into account the thermal inertia of buildings proposed in this invention. Figure 7 The simulation demonstrates the distribution of air conditioning power consumption in three buildings during and before an earthquake fault, under the collaborative scheduling method proposed in this invention. The results show that by incorporating the thermal inertia model of this invention, the room temperature can be maintained within the allowable range during the fault. By actively utilizing the buildings' own heat storage capacity to reduce the air conditioning load, the system's flexible energy supply capability and overall resilience during disaster periods are improved. These simulation results collectively verify the significant optimization effect of the method proposed in this invention under earthquake disaster scenarios.

[0051] Based on the same inventive concept, this application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the aforementioned method for acquiring point cloud data based on a simulation scanning BIM model.

[0052] Based on the same inventive concept, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the aforementioned method for acquiring point cloud data based on a simulated scanning BIM model.

[0053] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0054] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations 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, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0055] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0056] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0057] The above embodiments are merely illustrative of the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solutions based on the technical concept proposed in this invention shall fall within the scope of protection of this invention.

Claims

1. A distributed bar method for multi-resource collaborative scheduling of distribution networks under earthquake disasters, characterized in that, Includes the following steps: Step 1: By changing the magnitude and source location, a non-sequential Monte Carlo method is used to generate an earthquake scenario sample set. The study area of ​​the power distribution network is divided into grids. Based on the ground motion attenuation relationship and HAZUS vulnerability curve, the basic fault probability of the power distribution lines under each earthquake scenario sample is calculated. On this basis, the decision-dependent uncertainty brought about by line reinforcement is considered to obtain the comprehensive line fault probability. The K-means++ clustering algorithm is used to cluster all earthquake scenario samples to obtain representative earthquake disaster fault scenarios and the probability of each earthquake disaster fault scenario appearing in the earthquake scenario sample set. Step 2: Taking into account mobile hydrogen energy resources, building heat load, and demand response, establish a two-stage coordinated constraint system for power distribution network planning and operation. Step 3: Based on collaborative constraints, establish a two-stage sub-Bruker optimization scheduling model under earthquake disasters, and use 1-norm fuzzy sets to characterize the probability uncertainty of each earthquake disaster fault scenario obtained from the clustering in Step 1 in the earthquake scenario sample set. Step 4: For the two-stage bibliometric optimization scheduling model, the column and constraint generation algorithm is used to decompose the model into the main problem and sub-problems and solve them alternately and iteratively. Finally, the results of distribution network planning and operation considering earthquake disasters are obtained by convergence.

2. The distributed bar method for multi-resource collaborative scheduling of distribution networks under earthquake disasters according to claim 1, characterized in that, The specific process of step 1 is as follows: By changing the magnitude and focal location, a non-sequential Monte Carlo method is used to simulate earthquake scenarios and generate a set of earthquake scenario samples. Calculate the peak ground acceleration at different locations on the power distribution line based on the seismic attenuation relationship: ， In the formula, This represents the peak ground acceleration. The magnitude of the earthquake. This represents the distance from any point on the power distribution line to the epicenter of the earthquake. After obtaining the peak ground acceleration at various locations along the distribution line, and combining this with the log-normal vulnerability function defined in the HAZUS technical manual, the line is assessed to have reached the [missing information - likely a specific threshold or condition]. The probability of a damaged state , is represented as: ， In the formula, These correspond to mild, moderate, severe, and complete damage, respectively. and The first The logarithmic mean and standard deviation of the vulnerability function under various damaged states. The severity of the line damage condition; The research area of ​​the distribution network is divided into several Latitude and longitude grid units, if power distribution lines If it spans multiple grid cells, then it will be determined based on the grid cells it spans. The power distribution line is divided into multiple conductor segments, each belonging to a corresponding grid unit. In the The base failure rate within each grid is calculated using the following weighted method: ， In the formula, For power distribution lines In the The base failure rate within each grid, For power distribution lines In the The length within each grid cell, This refers to the total length of all lines in the distribution network. Indicates the first According to the HAZUS technical manual, the damage percentages for power distribution lines under the four damage conditions are 4%, 12%, 50%, and 80%, respectively. Indicates power distribution lines In the Within the first grid The probability of a damaged state. For the number of power distribution lines, For power distribution lines The number of grids involved; Then the power distribution line Basic failure probability for: ， Introducing decision-dependency uncertainty arising from line reinforcement behavior, distribution lines Overall line fault probability Represented as: ， In the formula, Indicates power distribution lines Should reinforcement measures be taken? This represents the residual failure rate coefficient after reinforcement. For each earthquake scenario sample, the power distribution lines are calculated based on their corresponding magnitude and focal location. The overall line fault probability will be calculated. With Random numbers generated between By comparing, the distribution lines can be obtained. Fault status in earthquake disasters As shown in the following formula: ， The K-means++ clustering algorithm was used to cluster all earthquake scene samples. In the stage of determining the number of clusters, the optimal number of clusters was determined jointly by the elbow criterion and the silhouette coefficient. Specifically, the elbow criterion was first used to determine the candidate range for the optimal number of clusters. Then, within the candidate range, the silhouette coefficient corresponding to each candidate optimal number of clusters was calculated. The candidate optimal number of clusters corresponding to the silhouette coefficient closest to 1 was the final optimal number of clusters. The formula for calculating the silhouette coefficient is as follows: ， In the formula, For the profile coefficient, , Intra-cluster dissimilarity The dissimilarity between clusters is represented by the center of each cluster obtained from clustering, which is the representative earthquake scene.

3. The multi-resource collaborative scheduling method for distribution networks under earthquake disasters according to claim 1, characterized in that, The specific process of step 2 is as follows: Step 201: To describe the operational characteristics of mobile hydrogen energy resources in earthquake disaster emergency power supply, the following constraint set is established: (1) Deployment and connection logic constraints ， ， ， ， In the formula, Indicates the first Taiwan's mobile hydrogen energy resources are always available Is it connected to the distribution network node? binary variables, Indicates the first The deployment status of Taiwan's mobile hydrogen energy resources. Indicates the first Taiwan's mobile hydrogen energy resources are always available Is it in a connected state? Indicates the first Taiwan's mobile hydrogen energy resources are always available Is it in transit? This indicates the maximum amount of mobile hydrogen energy resources allowed by the system; (2) Dynamics of hydrogen reserves and energy balance constraints ， ， In the formula, For time step, They represent the first Taiwan's mobile hydrogen energy resources are always available hydrogen reserves, Indicates the first Energy dissipation rate of Taiwan's mobile hydrogen energy resources and These represent the hydrogen input and output rates, respectively. and These represent the hydrogen charging and decharging efficiencies, respectively. For the first Fuel consumption per unit time during the transportation of mobile hydrogen energy resources in Taiwan. and These are the lower and upper limits for hydrogen capacity, respectively. (3) Mutual exclusion constraint between hydrogen flow rate and operating state ， ， In the formula, and These are binary variables used for selecting the mutual exclusion state. and These are the upper limits for hydrogen input and output rates, respectively. (4) Transportation and scheduling constraints ， ， In the formula, Indicates the first Taiwan's mobile hydrogen energy resources are always available Is it connected to the distribution network node? binary variables, For mobile hydrogen energy resources from nodes Move to node The required time steps Represents a node arrive The shortest transportation time, The total number of time steps in the scheduling cycle. Represents a node arrive The shortest transportation time; Represents a node arrive The set of transport segments corresponding to the shortest transport path, where each transport segment consists of a pair of adjacent nodes. express; (5) Power generation and power factor constraints ， ， ， In the formula, This indicates the power generation efficiency of the fuel cell. and These represent nodes respectively. The Taiwan's mobile hydrogen energy resources are always available The active and reactive power of power generation, For the node The The upper limit of power generation capacity of Taiwan's mobile hydrogen energy resources. The power factor angle; Step 202, considering the impact of the heat storage characteristics of commercial buildings on the cooling load, based on the principle of energy conservation, and taking into account indoor and outdoor temperature differences, solar radiation, and heat dissipation from people and equipment, the building's indoor energy balance equation is as follows: ， In the formula, and These are the heat transfer coefficients of the exterior walls and exterior windows, respectively. and These are the areas of the exterior walls and exterior windows, respectively. and They are time points Outdoor and indoor temperatures, For a moment The intensity of solar radiation, Shading coefficient, Indicates time The heat generated by indoor personnel and equipment, For air conditioning at all times cooling capacity, air density, The specific heat capacity of air, The volume of air inside a building; Discretizing the above equation, we obtain the dynamic update expression for the building's indoor temperature in adjacent time steps as follows: ， Indoor temperature constraints are: ， In the formula, , These represent the acceptable lower and upper limits for indoor temperature, respectively. The air conditioning cooling capacity constraint is: ， In the formula, This refers to the maximum allowable cooling capacity of the air conditioner. The total active and reactive loads of a building participating in dispatch at each time point are expressed as follows: ， In the formula, Indicates buildings At any moment Air conditioning cooling capacity, For buildings Other electrical load power, and These are the building's total active and reactive loads, respectively. The node load of the entire system is represented as follows: ， In the formula, and Representing nodes respectively At any moment Total active and reactive loads, and They are nodes The original foundation load; Step 203: Under the condition of power supply constraint after an earthquake disaster, the demand response power of the power system shall satisfy the following constraints: ， In the formula, For nodes At any moment This can reduce active power. For nodes The original rated load power; Power factor angle of the node Represented as: ， ， In the formula, For nodes The power factor, and They are nodes The rated active power and reactive power; The reactive power of the node's demand response is then expressed as: ， In the formula, For nodes At any moment The reactive power reduction.

4. The distributed bar method for multi-resource collaborative scheduling of distribution networks under earthquake disasters according to claim 1, characterized in that, The specific process of step 3 is as follows: Step 301: The objective function of the two-stage sub-Bruker optimization scheduling model under earthquake disaster comprehensively considers both investment cost and operating cost. ， In the formula, This represents the total system investment cost during the planning phase. This refers to the emergency operating cost of the system during the operational phase, i.e., the expected operating cost under the scenario of minimizing the failure probability distribution in the event of an earthquake disaster. These serve as decision vectors for the planning phase, including route reinforcement schemes, distributed power supply configurations, and mobile hydrogen energy resource deployments. The number of earthquake disaster failure scenarios obtained by clustering, Earthquake disaster failure scenario The actual probability, For all The probability distribution vector formed This represents the uncertain set of probability distributions for earthquake disaster failure scenarios. Earthquake disaster failure scenario Parameter set; This is a set of scheduling decision variables during the operation phase, including electricity purchases at each node, output of distributed power sources, load reduction, and the operation and transportation status of mobile hydrogen energy resources. Let be the decision vector at a given planning stage. and earthquake disaster failure scenarios Parameter set The set of feasible solutions for the next running phase; The total system investment cost includes investment in mobile hydrogen energy resources, distributed power generation, and line reinforcement costs, expressed as: ， ， ， ， In the formula, Cost of mobile hydrogen energy resource allocation For distributed power investment costs, Cost of reinforcing the line; This represents the annualized coefficient for investment in mobile hydrogen energy resources. This indicates the unit investment cost of a single mobile hydrogen energy resource. Indicates the first The deployment status of Taiwan's mobile hydrogen energy resources. Represents a collection of mobile hydrogen energy resources; This represents the annualized investment coefficient for distributed power sources. Represents a node Whether to configure distributed power supply The unit capacity investment cost For the rated capacity of distributed power sources, Fixed investment costs for distributed power sources; The annualized coefficient for line reinforcement, For power distribution lines length; Indicates power distribution lines Should reinforcement measures be taken? Cost per unit length for reinforcement; The emergency operation cost of the system is expressed as: ， ， ， ， ， ， In the formula, The number of scheduling days within a year; Main grid purchase cost represents the expenditure incurred by the system in purchasing electricity from the superior power grid during the post-disaster operation period; The cost of generating electricity from distributed power sources after a disaster; Reduce penalties for overload; The operating cost of mobile hydrogen energy resources; The transportation cost of mobile hydrogen energy resources; For a moment The unit electricity purchase price, Earthquake disaster failure scenario Next moment Electricity purchased from the mainnet; The unit generation cost of distributed power sources. Earthquake disaster failure scenario Below, distributed power sources are at the nodes ,time The active power output; To reduce costs per unit load, Earthquake disaster failure scenario Next node ,time The reduced load; The unit operating cost of mobile hydrogen energy resources, and These are earthquake disaster failure scenarios. Next Taiwan's mobile hydrogen energy resources are always available The amount of hydrogen flowing in and out; For the first The unit transportation cost of mobile hydrogen energy resources in Taiwan Indicates the first Taiwan's mobile hydrogen energy resources are always available Is it in transit status? Step 302: The 1-norm fuzzy set is used to characterize the probability uncertainty of each representative earthquake scene obtained from the clustering in Step 1 appearing in the earthquake scene sample set, resulting in the following uncertainty set: ， In the formula, As an auxiliary variable, This represents the total deviation between the probability distributions of actual and theoretical failure scenarios. Indicates will Earthquake disaster fault scenarios after probability normalization of individual fault scenarios The relative theoretical probability, and These are the lower and upper limits of the fluctuation range, respectively.

5. The multi-resource collaborative scheduling method for distribution networks under earthquake disasters according to claim 4, characterized in that, In step 4, the main problem is: ， ， In the formula, For the slack variables of the subproblems, As a constraint during the planning phase, For the number of iterations, For the first The worst-case failure scenario probability is obtained by solving the subproblem in the next iteration. For the first Auxiliary variables introduced during the next iteration Constraints for the operational phase; The subproblems are: ， ， In the formula, The decision vector for the planning stage, determined after solving the main problem; When using the column and constraint generation algorithm for iterative solution, for the ... In the next iteration, the main problem is solved to obtain the optimal planning stage decision vector for the current iteration. Based on the optimal planning stage decision vector for the current iteration, the line failure rate is recalculated and the earthquake disaster failure scenario set is updated. The optimal planning stage decision vector for the current iteration is fixed, and the sub-problems are solved using the updated scenario set. The worst-case scenario probability distribution that maximizes the minimum operating cost after the line breakage is found after the earthquake disaster is found, and the optimal operating strategy for the current iteration is given.

6. A computer device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the distributed bar method for multi-resource collaborative scheduling of distribution networks under earthquake disasters as described in any one of claims 1 to 5.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the distributed bar method for multi-resource collaborative scheduling of distribution networks under earthquake disasters as described in any one of claims 1 to 5.

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