Dc-ac distribution network distributed optimization operation method and device based on toughness improvement

Abstract

This application relates to a method and apparatus for distributed optimization operation of AC / DC distribution networks based on resilience enhancement, belonging to the field of distribution network optimization operation technology. The method includes: acquiring basic data of the AC / DC distribution network; constructing a target scenario based on the basic data; and optimizing the topology and resource scheduling of the AC / DC distribution network under the target scenario using the alternating direction multiplier method to obtain the optimal operating result of the AC / DC distribution network under the target scenario. This method can achieve the optimal operating result of the AC / DC distribution network.

Description

Technical Field

[0001] This application relates to the field of distribution network optimization operation technology, and in particular to a distributed optimization operation method and apparatus for AC / DC distribution networks based on resilience enhancement. Background Technology

[0002] As the proportion of new energy power generation in total installed capacity and power generation continues to increase, its status as the main power source of the new distribution network is becoming increasingly prominent. However, the inherent intermittency, volatility and randomness of new energy, coupled with the increased risk of equipment failure caused by frequent extreme natural disasters, have significantly exacerbated the challenges faced by the distribution network in extreme operating scenarios.

[0003] Currently, breakthroughs in power electronics technology are driving the rapid development of AC / DC hybrid distribution networks. Their zonal structure not only supports the autonomous operation of each AC / DC sub-region but also enables coordinated support between regions, making distributed optimized operation an inevitable trend.

[0004] However, the output uncertainty brought about by the large-scale access of distributed generation (DGs) has a superimposed effect with the multi-form characteristics of the AC / DC hybrid distribution network itself, which significantly increases the complexity of system optimization and operation. Summary of the Invention

[0005] This application provides a distributed optimization operation method and apparatus for AC / DC distribution networks based on resilience enhancement, which can obtain the optimal operation results of AC / DC distribution networks.

[0006] To achieve the above objectives, this application adopts the following technical solution:

[0007] Firstly, this application provides a distributed optimization operation method for AC / DC distribution networks based on resilience enhancement, including:

[0008] Acquire basic data of AC / DC distribution networks;

[0009] Based on the basic data, construct the target scenario;

[0010] Based on the alternating direction multiplier method, the topology and resource scheduling of the AC / DC distribution network under the target scenario are optimized to obtain the optimal operating results of the AC / DC distribution network under the target scenario.

[0011] In one embodiment, based on the underlying data, a target scenario is constructed, including:

[0012] Based on the type of meteorological disaster, a meteorological feature model is constructed; based on the Monte Carlo method and clustering algorithm, the meteorological feature model is selected to construct the target scenario.

[0013] In one embodiment, the topology and resource scheduling of the AC / DC distribution network under the target scenario are optimized based on the alternating direction multiplier method to obtain the optimal operating result of the AC / DC distribution network under the target scenario, including:

[0014] Based on the target scenario, the network architecture of the AC / DC distribution network is reconstructed, and the reconstructed AC / DC distribution network is adjusted to obtain the adjusted AC / DC distribution network. Based on the alternating direction multiplier method, the resource scheduling of each sub-region in the adjusted AC / DC distribution network is optimized to obtain the optimal operation result of the AC / DC distribution network under the target scenario.

[0015] In one embodiment, resource scheduling in each sub-region of the adjusted AC / DC distribution network is optimized based on the alternating direction multiplier method to obtain the optimal operating result of the AC / DC distribution network under the target scenario, including:

[0016] For each sub-region, the resource scheduling of the sub-region in the adjusted AC / DC distribution network is optimized based on the alternating direction multiplier method to obtain the optimal regional operation result of the sub-region; the optimal regional operation result of each sub-region is further optimized based on the alternating direction multiplier method to obtain the optimal operation result of the AC / DC distribution network under the target scenario.

[0017] In one embodiment, based on the alternating direction multiplier method, the optimal regional operation results for each region are further optimized to obtain the optimal operation results of the AC / DC distribution network under the target scenario, including:

[0018] Based on the alternating direction multiplier method, for each sub-region, the sub-region receives the optimal regional operation results of the adjacent sub-regions and updates the regional variables of the sub-region according to the optimal regional operation results of the adjacent sub-regions; based on the regional variables of each sub-region, the optimization accuracy of the AC / DC distribution network under the target scenario is calculated; when the optimization accuracy reaches the preset accuracy, the optimal operation result of the AC / DC distribution network under the target scenario is obtained.

[0019] In one embodiment, the optimal regional operating results of a sub-region include the sub-region's power purchase and sale and switching power.

[0020] In one embodiment, based on the alternating direction multiplier method, the optimal regional operation results of each sub-region are further optimized to obtain the optimal operation results of the AC / DC distribution network under the target scenario, including:

[0021] For each sub-region, the power loss and load penalty cost corresponding to the sub-region are calculated. Based on the power loss and load penalty cost corresponding to each sub-region, the optimal regional operation results of each sub-region are further optimized by the alternating direction multiplier method to obtain the optimal operation results of the AC / DC distribution network under the target scenario.

[0022] Secondly, this application provides a distributed optimized operation device for AC / DC distribution networks based on resilience enhancement, comprising:

[0023] The acquisition module is used to acquire basic data of the AC / DC distribution network;

[0024] The building module is used to construct the target scenario based on the basic data;

[0025] The optimization module is used to optimize the topology and resource scheduling of the AC / DC distribution network under the target scenario based on the alternating direction multiplier method, so as to obtain the optimal operating result of the AC / DC distribution network under the target scenario.

[0026] Thirdly, this application provides a computing device, including a memory and a processor;

[0027] The memory stores one or more computer programs, the one or more computer programs including instructions; when the instructions are executed by the processor, the computing device performs the method as described in any one of the first aspects.

[0028] Fourthly, this application provides a computer-readable storage medium for storing a computer program for performing the method as described in any one of the first aspects.

[0029] Fifthly, this application provides a computer program product comprising one or more computer instructions, wherein when the computer instructions are executed by a computer, the computer performs the method as described in any one of the first aspects.

[0030] As can be seen from the above technical solution, this application has at least the following beneficial effects:

[0031] This application provides data for optimizing the operation of AC / DC distribution networks by acquiring basic data. Furthermore, based on this basic data, target scenarios can be constructed to enrich the actual environment of AC / DC distribution networks. Then, based on the alternating direction multiplier method, the topology and resource scheduling of the AC / DC distribution network under the target scenario can be optimized to obtain the optimal operating results. This scheme enriches the practical application scenarios of AC / DC distribution networks by introducing target scenarios, laying the foundation for obtaining optimal operating results. Moreover, by introducing the alternating direction multiplier method, it provides a way to optimize the topology and resource scheduling of AC / DC distribution networks, ultimately achieving the optimal operating results.

[0032] It should be understood that the descriptions of technical features, technical solutions, beneficial effects, or similar language in this application do not imply that all features and advantages can be achieved in any single embodiment. Rather, it is understood that the description of a feature or beneficial effect means that a specific technical feature, technical solution, or beneficial effect is included in at least one embodiment. Therefore, the descriptions of technical features, technical solutions, or beneficial effects in this specification do not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions, and beneficial effects described in this embodiment can be combined in any suitable manner. Those skilled in the art will understand that embodiments can be implemented without one or more specific technical features, technical solutions, or beneficial effects of a particular embodiment. In other embodiments, additional technical features and beneficial effects may be identified in specific embodiments that do not embody all embodiments. Attached Figure Description

[0033] Figure 1 This is an application environment diagram of a distributed optimization operation method for AC / DC distribution networks based on resilience enhancement provided in the embodiments of this application;

[0034] Figure 2 This is a flowchart illustrating a distributed optimization operation method for AC / DC distribution networks based on resilience enhancement, provided in an embodiment of this application.

[0035] Figure 3 This is a schematic diagram of a process for constructing a target scene provided in an embodiment of this application;

[0036] Figure 4 This is a schematic diagram of an optimized AC / DC distribution network provided in an embodiment of this application;

[0037] Figure 5 This is a schematic diagram of the structure of a VSC converter station provided in the embodiments of this application;

[0038] Figure 6 This is a structural block diagram of a distributed optimized operation device for AC / DC distribution networks based on resilience enhancement, provided in the embodiments of this application.

[0039] Figure 7 This is an internal structural diagram of a computer device provided in the embodiments of the application. Detailed Implementation

[0040] The terms "first," "second," and "third," etc., used in this application specification and accompanying drawings are used to distinguish different objects, not to limit a specific order.

[0041] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0042] To ensure clarity and conciseness in the description of the following embodiments, a brief introduction to the related technologies is given first:

[0043] As the proportion of new energy power generation in total installed capacity and power generation continues to increase, its status as the main power source of the new distribution network is becoming increasingly prominent. However, the inherent intermittency, volatility and randomness of new energy, coupled with the increased risk of equipment failure caused by frequent extreme natural disasters, have significantly exacerbated the challenges faced by the distribution network in extreme operating scenarios.

[0044] Currently, breakthroughs in power electronics technology are driving the rapid development of AC / DC hybrid distribution networks. Their zonal structure not only supports the autonomous operation of each AC / DC sub-region but also enables coordinated support between regions, making distributed optimized operation an inevitable trend.

[0045] However, the output uncertainty brought about by the large-scale access of distributed generation (DGs) has a superimposed effect with the multi-form characteristics of the AC / DC hybrid distribution network itself, which significantly increases the complexity of system optimization and operation.

[0046] To make the technical solution of this application clearer and easier to understand, the application scenarios of the technical solution of this application are described below with reference to the accompanying drawings. Figure 1 As shown in the figure, this figure is a schematic diagram of an application scenario provided by an embodiment of this application.

[0047] In this application scenario, terminal 102 can collect basic data of AC / DC distribution network and send the collected basic data of AC / DC distribution network to server 104 through communication network. Server 104 calculates and analyzes the data to obtain the optimal operating result and sends the calculated basic data of AC / DC distribution network to terminal 102 through communication network. Terminal 102 then presents the optimal operating result for relevant technical personnel to view.

[0048] To make the technical solution of this application clearer and easier to understand, the following describes a distributed optimization operation method for AC / DC distribution networks based on resilience enhancement, using the above application scenarios as an example. Figure 2 As shown in the figure, this is a flowchart of a distributed optimization operation method for AC / DC distribution networks based on resilience enhancement provided in an embodiment of this application.

[0049] S201. Obtain basic data of AC / DC distribution network.

[0050] Among them, the AC / DC Hybrid Distribution Network is a distribution network structure that integrates AC and DC power transmission technologies; the basic data includes distribution network structure data, load time series data, and solar intensity time series data, etc.; optionally, the distribution network structure data includes the distribution network topology, equipment parameters, electrical connection relationships, etc.; the load time series data includes the load demand of each node at different time periods, etc.; the solar intensity time series data includes the output data of simulated photovoltaic power generation, etc.

[0051] For example, basic data of AC / DC distribution networks can be obtained through power grid companies, external data sources, measured data, and / or simulation data. For instance, DC-side specific data can be collected through DC measurement equipment and converter station dynamic recording. Furthermore, time stamping can be unified for SCADA (second-level), PMU (millisecond-level), and meteorological (minute-level) data through spatiotemporal alignment algorithms. Additionally, equipment parameters, topology relationships, and operating rules can be modeled as RDF triples by constructing knowledge graphs to ultimately achieve the fusion of multi-source data. Moreover, state estimation (SE) can be used to correct data errors to ensure the accuracy of basic data of AC / DC distribution networks.

[0052] S202. Based on the basic data, construct the target scenario.

[0053] The target scenario can represent a natural disaster scenario.

[0054] It should be noted that, considering the impact of natural disasters such as typhoons, ice storms, and heavy rainfall on the power distribution network, it is necessary to model the natural disaster scenarios. For example, common disasters such as typhoons, low-temperature icing, and heavy rainfall can be selected as research objects, and mathematical models can be performed on the main meteorological characteristics that have a significant impact on line components, such as typhoon wind speed, line icing quality, and the force of heavy rainfall. Furthermore, the strength and load of line conductors and towers, as well as the failure probability and output power of distributed generation such as wind turbines and photovoltaics, can be analyzed separately to establish failure probability models for lines and intermittent distributed generation resources, as well as output power models for distributed generation.

[0055] For example, the Monte Carlo method can be used to extract multiple scenarios of extreme natural disasters, including disaster intensity (affecting the failure probability of power lines and indirect distributed generation), occurrence time (affecting the output power of load and distributed generation), and duration. The extracted scenarios are then reduced using the K-means clustering algorithm, and one is selected as the typical extreme weather scenario for study. Furthermore, based on the failure probability model of power lines and distributed generation, the time-varying failure probability of power lines and distributed generation during the extreme natural disaster process under the typical extreme weather scenario can be calculated. Further, based on the time-varying failure probability of power lines and distributed generation, multiple failure scenarios can be extracted using the Monte Carlo method, and a typical failure scenario based on the maximum system information entropy can be selected as the research object. Finally, the failure state of each power line and each distributed generation device under this typical failure scenario can be determined.

[0056] It should be noted that the uncertainty of extreme natural disasters mainly lies in three aspects: the time of occurrence, the duration, and the intensity. Assuming that the time of occurrence, the duration, and the intensity all follow corresponding probability distribution models, the time of occurrence, the duration, and the intensity can be expressed as:

[0057] ① Occurrence time t0: follows a uniform distribution, as shown in the following formula:

[0058] (1)

[0059] Where t0 is the time of occurrence. and Both are two boundary values ​​that are uniformly distributed.

[0060] ② Duration T total : It follows a normal distribution, as shown in the following formula:

[0061] (2)

[0062] Among them, T total For duration, The expected value of the duration; This represents the standard deviation of the duration.

[0063] ③Intensity: Different extreme disasters have different typical intensity characteristics. Taking typhoon weather as an example, its intensity can be measured by the maximum wind speed v. Rmax It is characterized by three parameters: maximum wind speed radius and moving wind speed. Specifically, it can be expressed as:

[0064] Maximum wind speed follows a Weibull distribution:

[0065] (3)

[0066] Among them, v Rmax At maximum wind speed, The proportional parameters of the Weibull distribution model; These are the shape parameters of the Weibull distribution model.

[0067] Maximum wind speed radius InR max Follows a log-normal distribution:

[0068] (4)

[0069] Among them, InR max The radius of maximum wind speed. This is the expected value after taking the logarithm of the radius of maximum wind speed. The standard deviation is the logarithmic value of the radius of maximum wind speed.

[0070] Moving wind speed V move It can be represented as:

[0071] (5)

[0072] Among them, V move For moving wind speed, and Values ​​are derived from experience, such as 2.34 is acceptable. 0.7 is acceptable; For random numbers that conform to a standard normal distribution, i.e. .

[0073] S203. Based on the alternating direction multiplier method, the topology and resource scheduling of the AC / DC distribution network under the target scenario are optimized to obtain the optimal operation result of the AC / DC distribution network under the target scenario.

[0074] Among them, the Alternating Direction Method of Multipliers (ADMM) is a distributed optimization algorithm that combines the advantages of dual decomposition and augmented Lagrange multipliers.

[0075] For example, the process of optimizing the topology and resource scheduling of the AC / DC distribution network in the target scenario based on the alternating direction multiplier method is divided into two stages. The first stage of optimization mainly involves reconstructing the distribution network topology and optimizing the source-load status within the sub-region to ensure normal power supply and reduce the impact of faults. The second stage of optimization, based on the first stage optimization, further optimizes the power output and load loss to reduce network losses.

[0076] Optionally, for the AC sub-regional distribution network, the first AC cost function optimized in the first stage can be expressed as:

[0077] (6)

[0078] in, For communication sub-regions The first communication cost function, To reduce the cost of purchasing electricity from the higher-level power grid, Cost of purchasing and selling electricity to the DC sub-regional distribution network; for At the node Power purchased from the upper-level power grid during the specified time period for At the node The power exchanged between AC / DC sub-regional distribution networks during a specific time period; A positive value indicates that the AC power grid is selling electricity to the DC power grid. A negative value indicates that the AC power grid is purchasing electricity from the DC power grid; For the total time period set; for The cost coefficient for the power purchased from the upper-level power grid during a given time period is related to the time period. Related electricity prices, for The cost coefficient of AC / DC regional power grid switching power during a given time period represents the cost of converter loss or internal settlement price; sub-region The substation node set represents the location of nodes where electricity can be purchased. This is the VSC node set, representing AC / DC connection points.

[0079] For the DC sub-regional distribution network, the first DC cost function optimized in the first stage can be expressed as:

[0080] (7)

[0081] in, For communication sub-regions The first communication cost function, Cost of purchasing and selling electricity to the DC sub-regional distribution network; For the time period The unit power cost of buying and selling electricity can be a fixed value or a settlement price that varies over time; For nodes During the period Active switching power on the DC side of the converter, >0 indicates that electricity was purchased (from the AC side); <0 indicates selling (selling electricity to the AC side).

[0082] For the AC sub-regional distribution network, the second AC cost function for the second-stage optimization can be expressed as:

[0083] (8)

[0084] in, The cost of the second phase of communication for the communication sub-region. This is the unit power cost coefficient for load loss. Cost coefficient per unit power loss; The normalized weighted load loss is based on the sum of the power demands of the loads at each node in each time period. The normalized system operation network loss is based on the sum of the actual active power output of each power source in each time period.

[0085] For the DC sub-regional distribution network, the second DC cost function optimized in the second stage can be expressed as:

[0086] (9)

[0087] in, For AC sub-region, the second-stage AC cost is , for DC sub-region, the weighted load loss is , and for system operation network loss cost is . The unit power cost factor representing load loss. Cost coefficient per unit power loss; This represents the normalized weighted load loss, based on the sum of the power demands of the loads at each node within each time period. It represents the normalized system operating network loss, with the sum of the actual active power output of each power source in each time period as the benchmark value; These are the weighting coefficients.

[0088] It should be noted that, in order to ensure power supply to more loads, especially critical loads, during extreme scenarios, the second phase of optimization prioritizes weighted load loss as the primary objective, while minimizing system network losses is the secondary objective. The normalized weighted load loss is shown below. and the normalized system operation network loss The normalization process can be expressed as:

[0089] (10)

[0090] (11)

[0091] in, This represents the total duration of the extreme scenario. Quantity refers to the total number of study periods; Indicates node load During the period The active power lost internally; Node load under normal conditions During the period The active power demand within; Indicates the system during the time period Total network loss within; This indicates the total number of power sources within the distribution network, including distributed generation and energy storage. Indicates the first Each power node during the time period The actual contribution of the internal forces; This indicates the number of nodes in the distribution network that are connected to the upper-level power grid. For node load The upstream power supply connected at the location is in time period Injected active power; optionally, the system during time periods Total network loss within It can be represented as:

[0092] (12)

[0093] It should be noted that after normalization, the two functions can be combined into a unified optimization objective by setting weighting coefficients. Since the primary objective is to reduce load loss, weighting coefficients are only added to the system operation network loss term. Ultimately, this results in a total network loss. The multi-objective optimization function; where the weight coefficients The weights can be defined by the user based on their emphasis on the two objectives or their practical experience, or the optimal weights can be selected through further optimization and comparison. Different weight coefficient values ​​lead to different optimization strategy results; in this implementation, the weight coefficient can be set to 0.1.

[0094] The aforementioned distributed optimization operation method for AC / DC distribution networks based on resilience enhancement provides data for optimizing the operation of AC / DC distribution networks by acquiring basic data. Furthermore, based on this basic data, target scenarios can be constructed to enrich the actual environment of AC / DC distribution networks. Subsequently, the topology and resource scheduling of the AC / DC distribution network under the target scenario can be optimized using the alternating direction multiplier method to obtain the optimal operating results. This scheme enriches the practical application scenarios of AC / DC distribution networks by introducing target scenarios, laying the foundation for obtaining optimal operating results. Moreover, by introducing the alternating direction multiplier method, it provides a way to optimize the topology and resource scheduling of AC / DC distribution networks, ultimately achieving the optimal operating results for the AC / DC distribution network.

[0095] Based on the above embodiments, this application provides a detailed explanation of S202. Specifically, this application involves the process of constructing the target scene, as follows: Figure 3 As shown, the specific steps include:

[0096] S301. Construct a meteorological characteristic model based on the type of meteorological disaster.

[0097] Among them, meteorological disaster types can include, but are not limited to, typhoons, ice storms, heavy rainfall, high temperatures and droughts; meteorological characteristic models can quantify the conditions for disaster occurrence from the perspectives of the physical mechanisms of disaster formation, key meteorological parameters, spatiotemporal scales and correlations, through mathematical or data-driven methods.

[0098] It should be noted that natural disasters such as typhoons, icing, and heavy rainfall can exert strong forces on the physical structures of transmission lines, including conductors and towers, causing faults such as conductor breakage, tower collapse, and component damage. Therefore, a thorough understanding of the impact of various meteorological disasters on transmission lines in advance can reduce the probability of failures in transmission lines and distributed generation equipment.

[0099] For example, based on the characteristics of different types of meteorological disasters, a meteorological feature model can be constructed, which can be specifically represented as follows:

[0100] (1) Typhoon

[0101] The direct source of the force exerted by typhoons on components (such as conductors and towers in power distribution networks) is wind force. Therefore, it is necessary to model and calculate the wind speed at various locations within the typhoon's radiation area during a typhoon. Currently, the Batts model (Batts Wind Field Model) is generally used for simulation.

[0102] (13)

[0103] in, This represents the real-time wind speed at a location within the typhoon's radiation area. The wind speed at the radius of the typhoon's maximum wind speed; This refers to the distance of this location from the center of the typhoon. This is the radius of the typhoon's maximum wind speed, that is, the straight-line distance between the typhoon's center and the area with the maximum wind speed. It should be noted that... and Both are meteorological parameters, which can be calculated in practice using meteorological forecasts. In this embodiment, these two parameters can be generated by sampling from a probability distribution model.

[0104] Therefore, it can be seen that within the typhoon's interior, from the typhoon center to the area of ​​maximum wind speed, the wind speed increases with... The increase shows an upward trend; while outside the typhoon, the wind speed will increase with... The increase leads to a decrease.

[0105] (2) Ice accumulation (ice disaster)

[0106] It should be noted that when rain or snow falls in low temperatures, the snow or rainwater may turn into ice upon contact with the conductor, causing a significant increase in the conductor's weight and raising the risk of breakage. Therefore, it is necessary to analyze the potential icing mass on a conductor of a given length.

[0107] By simplifying the calculations, we assume that icing mass is only affected by wind speed, thus directly linking icing mass to typhoon wind speed. The specific model can be expressed as follows:

[0108] (14)

[0109] (15)

[0110] in, This represents the mass of ice formed per unit length of conductor after rain or snowfall collides with it. Indicates a length of The actual mass of ice formed on the conductor; The precipitation rate is 0.3 during typhoon weather. For rain and snow density, it is generally used ; This is a correction factor for wind speed being affected by terrain; The angle between the wind speed and the conductor; The outer diameter of the conductor.

[0111] (3) Heavy rainfall

[0112] When rainfall intensity is high, the free fall of rainwater in the vertical direction and its movement in the wind direction affected by wind speed will exert a significant force on the conductor, which can be calculated using the following formula:

[0113] (16)

[0114] (17)

[0115] (18)

[0116] in, The force exerted on the power line by heavy rainfall; The diameter of the raindrop. Generally, 1000 is used. ; For diameter is The number of tiny raindrops contained within a raindrop; The area exposed to rain by the conductor; This refers to the speed of rainfall.

[0117] S302. Based on the Monte Carlo method and clustering algorithm, meteorological feature models are selected to construct the target scene.

[0118] For example, after establishing a probability distribution model for the characteristic values ​​of extreme disasters, the Monte Carlo method can be used to sample each characteristic value multiple times, establishing a scenario set for each characteristic value. Furthermore, the K-means clustering algorithm can be used to reduce the scenario sets for each characteristic value, obtaining several typical values ​​for each characteristic value. The reduction steps are as follows:

[0119] Step 1: Determine the number of clusters Randomly selected One initial cluster center;

[0120] Step 2: Establish the iterative evaluation function and the cluster center selection formula;

[0121] Step 3: Perform iterative clustering until the clustering converges.

[0122] Wherein, the evaluation function of the k-th iteration and the selected new clusters It can be represented as:

[0123] (19)

[0124] (20)

[0125] in, This is the sum of the distances between each node and its respective cluster center at the k-th iteration; For the first The distance between each node and its cluster center; This represents the y-th cluster at the k-th iteration. for The Middle One node; Let y be the cluster center at the k-th iteration; for The number of nodes.

[0126] After the above clustering and reduction, the scenarios with reduced feature values ​​are combined to obtain several typical scenarios; finally, one of these typical scenarios is randomly selected as the typical extreme natural disaster scenario for the final study.

[0127] Furthermore, the initial fault time of each component can be treated as a random variable with multiple possible values. Combining this with component fault probability models (such as the strength probability distribution density function of conductors and towers, load effect calculation formulas, and the relationship between strength and load at the time of fault, which yields fault probability models for conductors and towers), the distribution of information entropy in the distribution network system can be calculated and analyzed. The fault scenario corresponding to the entropy value with the highest probability of occurrence can then be selected as the typical fault scenario. The specific steps are as follows:

[0128] Step 1: Using Monte Carlo sampling, based on the time-varying failure probabilities of power lines, wind turbines, and photovoltaic systems under typical extreme natural disasters, appropriate suggested probability distribution functions are selected, and samples are extracted respectively. One possible initial failure time , and The three together construct The possible failure scenarios, of which the first This scenario can be represented as:

[0129] (twenty one)

[0130] Step 2: Calculate the system information entropy for each possible failure scenario. The calculation formula for each scenario is:

[0131] (twenty two)

[0132] (twenty three)

[0133] in, Indicates the first System information entropy for each scenario; Indicates the number of lines in the system; Indicates the number of fans in the system; Indicates the number of photovoltaic cells in the system; Indicates the first The line is in The probability of failure at any given moment; Indicates the first Typhoon machine The probability of failure at any given moment; Indicates the first Taiwan Solar The probability of failure at any given moment; , , All are 0-1 variables. Indicates the first In the scenario, the first Lines Indicates the first In the scenario, the first Typhoon machine, Indicates the first In the scenario, the first Taiwan Solar's The fault state at a given time, where a value of 0 indicates that no fault has occurred at that time, and a value of 1 indicates that the time is the initial fault time of this component.

[0134] It should be noted that the above formula can be interpreted as follows: without considering repeated faults, , , At most one time, the value can be 1, thus ensuring the uniqueness of the initial fault time.

[0135] Step 3: Establish a system based on... A set consisting of the entropy values ​​of each system. The probability distribution is obtained by fitting the data. The system information entropy value with the highest probability of occurrence represents the system failure situation that is most likely to reach this severity under typical extreme disaster scenarios. Therefore, a certain extreme failure scenario corresponding to this value is selected as a typical extreme failure scenario.

[0136] Optionally, as can be seen from the above formula, the calculation of the system information entropy of the distribution network not only considers the fault state and probability of the lines, but also takes wind turbines and photovoltaics into account. This reflects the difference between the new and traditional distribution networks. Traditional distribution network resilience analysis typically only considers line faults when establishing fault scenarios. Even when considering the fault state of distributed generation, it often only considers the impact of changes in the output power of distributed generation on load supply under a given fault scenario. In the new distribution network, distributed generation, due to its high integration ratio, has itself become one of the main sources of extreme fault scenarios. Its fault state and probability also significantly affect the system information entropy of the distribution network. Therefore, the impact of distributed generation such as wind turbines and photovoltaics needs to be included in the calculation of information entropy so that the constructed typical fault scenarios can be more representative.

[0137] In this embodiment of the application, by constructing a meteorological feature model, a selection space is provided for determining the final target scenario, laying the foundation for obtaining a typical target scenario.

[0138] Based on the above embodiments, this application provides a detailed explanation of S203. Specifically, this application involves a process for optimizing AC / DC distribution networks, such as... Figure 4 As shown, the specific steps include:

[0139] S401. Based on the target scenario, the network architecture of the AC / DC distribution network is reconstructed, and the reconstructed AC / DC distribution network is adjusted to obtain the adjusted AC / DC distribution network.

[0140] For example, the strategy steps are executed sequentially for each target scenario i (1≤i≤n, where n is the total number of target scenarios) to obtain the optimal distributed scheduling strategy for that target scenario. Furthermore, the multi-source collaborative operation optimization strategy is performed within each sub-region to generate a new distribution network topology, which is ultimately reconstructed into a power-supplying, interconnected, and radial structure.

[0141] Optionally, in the process of generating a new distribution network topology, topological constraints and system operation constraints should be considered; among them, the topological constraints of the AC sub-regions can be constructed based on the maximum spanning tree algorithm to ensure that the network operates radially, as shown in the following formula:

[0142] (twenty four)

[0143] (25)

[0144] (26)

[0145] (27)

[0146] in, As a 0-1 variable representing the line connection status, when When =1, it indicates that the line is operating normally and can be used as a branch in the spanning tree; when When =0, it indicates that the line fault is in an open state and cannot be included in the maximum spanning tree consideration, so it needs to be excluded from the complete graph; and All are 0-1 variables, representing nodes. and nodes The parent-child relationship, when the node For nodes When the parent node, =1 and =0; otherwise, =0 and =1; if node and nodes If they are not connected, then = = =0; This represents the set of all nodes in a power distribution network. Represents a node The set of all nearby nodes, / 1 represents all nodes except node 1; therefore, the meaning of formula (25) can be: in the distribution network in operation, all nodes except root node 1 have one and only one parent node; formula (26) shows that root node 1 has no parent node.

[0147] System operation constraints in the AC sub-region may include VSC constraints (including AC side capacity constraints and consistency constraints of converter stations), power flow balance constraints, node load shedding constraints, node voltage upper and lower limit constraints, power supply constraints, and energy storage state constraints.

[0148] Optional, such as Figure 5 As shown, the energy conversion between AC and DC sub-regional distribution networks can be accomplished by the VSC station. In this embodiment, the VSC is equivalent to an impedance section and a converter valve section, and the internal impedance is equivalent to the AC side as follows: The converter valve is treated as a node and equivalent processing is performed. After the equivalent processing, a new node will be generated on the AC side. branch road The impedance is the aforementioned internal equivalent impedance; For time period node Active power input to the AC side of the converter station For time period node The reactive power input to the AC side of the converter station; for Time period The active power output from the node converter station to the DC side.

[0149] The topological constraints of the AC subregion are similar to those of the AC region. The system operation constraints of the AC subregion include VSC constraints (including DC side capacity constraints and consistency constraints of the converter station), DC power flow constraints, node unload constraints, node voltage upper and lower limit constraints, power supply constraints, and energy storage state constraints.

[0150] The strategy can be represented as follows: A typical extreme natural disaster scenario is received as input, including the disaster intensity, fault states of lines and distributed power sources, and their power output characteristics within a specific time period. Further, the entire AC / DC hybrid distribution network is divided into several AC and DC sub-regions. Each sub-region independently performs network structure reconfiguration and multi-source collaborative optimization based on equipment availability under the current disaster, ensuring maximum local power supply capacity. Then, after completing the optimization within each sub-region, considering energy exchange between regions via voltage source converters (VSCs), the system introduces power consistency constraints and employs the Alternating Directional Multiplier Method (ADMM) for distributed coordination, iterating boundary power and dual variables repeatedly until all sub-regions converge. Finally, based on the initial convergence of ADMM, a two-stage optimization is performed. The first stage focuses on power purchase and sale coordination, while the second stage introduces load loss and network loss penalties to refine the scheduling results. The final output is the optimal distributed scheduling scheme under this extreme disaster scenario, covering key aspects such as topology, power output, load reduction, power exchange, and operating costs.

[0151] Optionally, during the sub-region optimization phase, based on the typical extreme natural disaster scenarios selected in the previous phase, the equipment failure status caused by the disaster within the current sub-region can be identified, including information such as line interruptions and distributed generation unit failures. Further, a topology reconfiguration operation can be performed, aiming for maximum connectivity and power supply availability, by optimizing the states of open or closed line switches to construct a radial network structure that meets operational requirements. Then, after completing the network reconfiguration, the sub-region enters the second phase of source-load state optimization. Based on the current network topology and resource availability, various distributed energy sources (such as photovoltaics, wind power, and gas turbines), energy storage devices, and interruptible loads can be comprehensively scheduled to formulate power output plans and load reduction strategies, while also rationally allocating power purchased and sold with the upstream grid or other sub-regions. Throughout the optimization process, minimizing weighted load losses is the primary objective, while also considering system operation losses. Through linearized power flow modeling, VSC converter modeling, and multiple constraints such as voltage, current, and capacity, a distributed scheduling strategy that meets actual operating conditions is formed, laying a local optimization foundation for improving system resilience and power supply security under disaster scenarios.

[0152] S402. Based on the alternating direction multiplier method, the resource scheduling of each sub-region in the adjusted AC / DC distribution network is optimized to obtain the optimal operation result of the AC / DC distribution network under the target scenario.

[0153] The optimal operating result may include, but is not limited to, electrical operating parameters, resource scheduling schemes, economic indicators, reliability and safety, etc.

[0154] One possible approach is to optimize the resource scheduling of each sub-region in the adjusted AC / DC distribution network based on the alternating direction multiplier method to obtain the optimal regional operation result of the sub-region; and then further optimize the optimal regional operation result of each sub-region based on the alternating direction multiplier method to obtain the optimal operation result of the AC / DC distribution network under the target scenario.

[0155] The optimal regional operation results of a sub-region include the power purchase and sale and the power exchange of the sub-region.

[0156] Optionally, based on the alternating direction multiplier method, for each sub-region, the sub-region receives the optimal regional operation results of the adjacent sub-regions, and updates the regional variables of the sub-region according to the optimal regional operation results of the adjacent sub-regions; based on the regional variables of each sub-region, the optimization accuracy of the AC / DC distribution network under the target scenario is calculated; when the optimization accuracy reaches the preset accuracy, the optimal operation result of the AC / DC distribution network under the target scenario is obtained.

[0157] Optionally, for each sub-region, the power loss and load penalty cost corresponding to the sub-region are calculated; based on the power loss and load penalty cost corresponding to each sub-region, the optimal regional operation results of each sub-region are further optimized by the alternating direction multiplier method to obtain the optimal operation results of the AC / DC distribution network under the target scenario.

[0158] For example, the further optimization process involves optimizing the output power and load loss power of each power source. Specifically, this may include:

[0159] (1) Information exchange and update (i.e., each sub-region receives exchange variables from neighboring sub-regions) )

[0160] (2) Dual variable update (i.e., each sub-region is updated simultaneously)

[0161] The update process can be represented as:

[0162] (28)

[0163] in, Let be the dual variable of the (m+1)th iteration, representing the AC / DC sub-region. In the middle, the first Each node in the time period The Lagrange multipliers (dual variables) at the point reflect the tension of the power coupling constraint; they are used to guide how the local sub-region adjusts the next power exchange amount. Let be the dual variable of the m-th iteration, representing the value of the previous iteration, which serves as the baseline of the current dual variable; This is a penalty factor used to adjust the role of consistency error in updates, affecting convergence speed and stability; it is usually set empirically. For local variables on the AC side, representing AC / DC sub-regions On the communication side, in the Nodes and time periods The active power exchange (a local variable at the VSC converter point) is obtained through optimization within the current sub-region. This is a global variable, representing the average active power value of the system during the (m+1)th iteration at this node (calculated by averaging the power of the AC sub-region and the DC sub-region).

[0164] (3) Determine whether it converges

[0165] (29)

[0166] 30)

[0167] (31)

[0168] in, The maximum residual in the m-th iteration is the maximum value obtained by combining the original residual and the dual residual, and is used to determine the overall convergence. The infinite norm of the original residuals is used to measure the sub-regions. In the middle, the first Each node in the time period The maximum degree of inconsistency between local and global variables in the m-th iteration; The infinite norm of the dual residuals is used to measure subregions. In the middle, the first Each node in the time period The maximum degree of inconsistency between local and global variables in the m-th iteration.

[0169] It should be noted that the above formula is used as the termination criterion for the ADMM algorithm, determining whether the current iteration has converged. It only converges when the maximum values ​​of both the original residual and the dual residual are less than a set threshold. Only when the iteration stops, it is considered that global consistency has been achieved.

[0170] Optionally, the AC / DC distribution network consists of multiple AC sub-regions (such as AC0, AC1, AC2, ..., ACN) and one DC sub-region (DC); Indicates the first An AC sub-region or a DC sub-region; This indicates the node connected to the VSC, i.e., the location where the sub-region participates in coupling; This corresponds to each time slice in the scheduling cycle.

[0171] Therefore, the residual term actually iterates and calculates all (e, j, t) triples, including: boundary nodes in all AC subregions (connected to DC areas), boundary nodes in DC subregions (connected to multiple AC areas), and each time period t.

[0172] It should be noted that the above formula can be used to calculate the original residuals of each sub-region. and dual residual ,if If convergence is achieved, iteration stops, the optimal result is obtained, and the global variables are updated. If it does not meet the requirements Then let Continue iterating and calculating the optimal result.

[0173] Optional, global variables The calculation process can be represented as follows:

[0174] (32)

[0175] in, As a globally consistent variable, it represents the first... Within each sub-region, at the boundary node Scheduling period Within this, the power exchange value agreed upon by both AC and DC sides is the "average power" of the AC and DC regions at this node.

[0176] Optional, The local power variable on the DC side represents the power calculated by the local optimization model within the DC sub-region at the node. Time period The active power sent or received by the converter; Let be the local power variable of the AC side sub-region, representing the th Within each sub-region, at the boundary node Scheduling period The corresponding active power exchange value within; This represents the set of VSC boundary nodes in the e-th sub-region, which includes the set of all converter interface nodes j connected to the DC region; Sub-regions are numbered to represent different communication sub-regions, such as AC1, AC2, etc. This refers to the scheduling period, used to index all scheduling periods. This is used to number the boundary nodes, indexing the AC / DC boundary node numbers connected to the VSC.

[0177] In this embodiment of the application, the AC / DC distribution network is updated twice to achieve precise optimization of the AC / DC distribution network.

[0178] The above text combined Figures 1 to 5 The distributed optimization operation method for AC / DC distribution networks based on resilience enhancement provided in the embodiments of this application has been described in detail. The apparatus and equipment provided in the embodiments of this application will be described below with reference to the accompanying drawings.

[0179] like Figure 6 As shown in the figure, this is a schematic diagram of a distributed optimized operation device for AC / DC distribution networks based on resilience enhancement provided in an embodiment of this application. The device 400 includes: an acquisition module 401, a construction module 402, and an optimization module 403, wherein:

[0180] Module 401 is used to acquire basic data of AC / DC distribution networks;

[0181] Module 402 is used to build the target scene based on the basic data;

[0182] The optimization module 403 is used to optimize the topology and resource scheduling of the AC / DC distribution network under the target scenario based on the alternating direction multiplier method, so as to obtain the optimal operation result of the AC / DC distribution network under the target scenario.

[0183] In one embodiment, the construction module 402 is specifically used for:

[0184] Based on the type of meteorological disaster, a meteorological feature model is constructed; based on the Monte Carlo method and clustering algorithm, the meteorological feature model is selected to construct the target scenario.

[0185] In one embodiment, the optimization module 403 is specifically used for:

[0186] Based on the target scenario, the network architecture of the AC / DC distribution network is reconstructed, and the reconstructed AC / DC distribution network is adjusted to obtain the adjusted AC / DC distribution network. Based on the alternating direction multiplier method, the resource scheduling of each sub-region in the adjusted AC / DC distribution network is optimized to obtain the optimal operation result of the AC / DC distribution network under the target scenario.

[0187] In one embodiment, the optimization module 403 is specifically used for:

[0188] For each sub-region, the resource scheduling of the sub-region in the adjusted AC / DC distribution network is optimized based on the alternating direction multiplier method to obtain the optimal regional operation result of the sub-region; the optimal regional operation result of each sub-region is further optimized based on the alternating direction multiplier method to obtain the optimal operation result of the AC / DC distribution network under the target scenario.

[0189] In one embodiment, the optimization module 403 is specifically used for:

[0190] Based on the alternating direction multiplier method, for each sub-region, the sub-region receives the optimal regional operation results of the adjacent sub-regions and updates the regional variables of the sub-region according to the optimal regional operation results of the adjacent sub-regions; based on the regional variables of each sub-region, the optimization accuracy of the AC / DC distribution network under the target scenario is calculated; when the optimization accuracy reaches the preset accuracy, the optimal operation result of the AC / DC distribution network under the target scenario is obtained.

[0191] In one embodiment, the optimal regional operating results of a sub-region include the sub-region's power purchase and sale and switching power.

[0192] In one embodiment, the optimization module 403 is specifically used for:

[0193] For each sub-region, the power loss and load penalty cost corresponding to the sub-region are calculated. Based on the power loss and load penalty cost corresponding to each sub-region, the optimal regional operation results of each sub-region are further optimized by the alternating direction multiplier method to obtain the optimal operation results of the AC / DC distribution network under the target scenario.

[0194] The AC / DC distribution network distributed optimization operation device 400 based on resilience enhancement according to the embodiments of this application can correspondingly execute the method described in the embodiments of this application, and the other operations and / or functions of each module / unit of the AC / DC distribution network distributed optimization operation device 400 based on resilience enhancement are respectively for implementing Figures 2-4 For the sake of brevity, the corresponding processes of each method in the illustrated embodiments will not be described in detail here.

[0195] This application also provides a computing device. This computing device can be a local computing device or an application server.

[0196] like Figure 7 As shown in the figure, this is a schematic diagram of a computing device provided in an embodiment of this application. The computing device 700 includes a bus 701, a processor 702, a communication interface 703, and a memory 704. The processor 702, the memory 704, and the communication interface 703 communicate with each other via the bus 701.

[0197] The 701 bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 7 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0198] The processor 702 can be any one or more of the following processors: central processing unit (CPU), graphics processing unit (GPU), microprocessor (MP), or digital signal processor (DSP).

[0199] Communication interface 703 is used for external communication. For example, communication interface 703 can be used to communicate with terminal 102. Communication interface 703 is used to send optimal running results to terminal 102 so that terminal 102 can present the optimal running results.

[0200] Memory 704 may include volatile memory, such as random access memory (RAM). Memory 704 may also include non-volatile memory, such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid state drive (SSD).

[0201] The memory 704 stores executable code, and the processor 702 executes the executable code to perform the aforementioned distributed optimization operation method for AC / DC distribution networks based on resilience enhancement.

[0202] Specifically, in achieving Figure 6 In the case of the illustrated embodiment, and Figure 6 When the modules or units of the AC / DC distribution network distributed optimization operation device based on resilience enhancement described in the embodiments are implemented through software, the execution... Figure 6 The software or program code required for the functions of each module / unit can be partially or entirely stored in the memory 704. The processor 702 executes the program code corresponding to each unit stored in the memory 704, and executes the aforementioned distributed optimization operation method for AC / DC distribution networks based on resilience enhancement.

[0203] This application also provides a computer-readable storage medium. The computer-readable storage medium can be any available medium that a computing device can store, or a data storage device such as a data center containing one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive). The computer-readable storage medium includes instructions that instruct the computing device to execute the aforementioned distributed optimization operation method for AC / DC distribution networks based on resilience enhancement.

[0204] This application also provides a computer program product comprising one or more computer instructions. When the computer instructions are loaded and executed on a computing device, all or part of the processes or functions described in this application are generated.

[0205] The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, or data center to another website, computer, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means.

[0206] When the computer program product is executed by a computer, the computer executes any of the aforementioned methods of the resilient-enhanced distributed optimization operation method for AC / DC distribution networks. The computer program product can be a software installation package; when any of the aforementioned methods of the resilient-enhanced distributed optimization operation method for AC / DC distribution networks needs to be used, the computer program product can be downloaded and executed on the computer.

[0207] The descriptions of the processes or structures corresponding to the above figures each have their own emphasis. For parts of a process or structure that are not described in detail, please refer to the relevant descriptions of other processes or structures.

[0208] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be covered within the scope of protection of this application.

Claims

1. A distributed optimization operation method for AC / DC distribution networks based on resilience enhancement, characterized in that, The method includes: Acquire basic data of AC / DC distribution networks; Based on the aforementioned basic data, the target scenario is constructed; Based on the target scenario, the network architecture of the AC / DC distribution network is reconstructed, and the reconstructed AC / DC distribution network is adjusted to obtain the adjusted AC / DC distribution network. For each sub-region, the resource scheduling of the sub-region in the adjusted AC / DC distribution network is optimized based on the alternating direction multiplier method to obtain the optimal regional operation result of the sub-region; wherein, the optimal regional operation result of the sub-region includes the power purchase and sale and the power exchange of the sub-region; Based on the alternating direction multiplier method, the optimal regional operation results of each sub-region are further optimized to obtain the optimal operation results of the AC / DC distribution network under the target scenario. The further optimization includes information exchange and updating, dual variable updating, and determining whether convergence has occurred. A penalty factor is introduced in the dual variable updating to adjust the role of consistency error in the updating. The process of optimizing resource scheduling in the sub-regions of the adjusted AC / DC distribution network includes a first stage and a second stage. The first stage involves reconstructing the distribution network topology and optimizing the source-load status within the sub-region. The second stage involves optimizing power output and load loss. For the AC sub-regional distribution network, the second AC cost function optimized in the second stage is: in, The cost of the second phase of communication for the communication sub-region. This is the unit power cost coefficient for load loss. Cost coefficient per unit power loss; The normalized weighted load loss is based on the sum of the power demands of the loads at each node in each time period. The normalized system operation network loss is based on the sum of the actual active power output of each power source in each time period. These are the weighting coefficients; For the DC sub-regional distribution network, the second DC cost function optimized in the second stage is: in, Here, represents the AC cost of the second stage of the DC sub-region, represents the weighted load loss of the DC sub-region, and represents the network loss cost of system operation. Based on the alternating direction multiplier method, the optimal regional operation results of each sub-region are further optimized to obtain the optimal operation results of the AC / DC distribution network under the target scenario, including: For each sub-region, calculate the corresponding power loss and load penalty cost; Based on the power loss and load penalty cost corresponding to each sub-region, the optimal regional operation results of each sub-region are further optimized by the alternating direction multiplier method to obtain the optimal operation results of the AC / DC distribution network under the target scenario.

2. The method according to claim 1, characterized in that, The construction of the target scenario based on the aforementioned basic data includes: Based on the types of meteorological disasters, a meteorological characteristic model is constructed; Based on the Monte Carlo method and clustering algorithm, the meteorological feature model is selected to construct the target scenario.

3. The method according to claim 1, characterized in that, The method based on alternating direction multipliers further optimizes the optimal regional operation results for each region to obtain the optimal operation results of the AC / DC distribution network under the target scenario, including: Based on the alternating direction multiplier method, for each sub-region, the sub-region receives the optimal region operation results of the adjacent sub-regions, and updates the region variables of the sub-region according to the optimal region operation results of the adjacent sub-regions; Based on the regional variables of each sub-region, the optimization accuracy of the AC / DC distribution network under the target scenario is calculated; When the optimization accuracy reaches the preset accuracy, the optimal operating result of the AC / DC distribution network under the target scenario is obtained.

4. A distributed optimized operation device for AC / DC distribution networks based on resilience enhancement, characterized in that, The device includes: The acquisition module is used to acquire basic data of the AC / DC distribution network; The construction module is used to construct the target scene based on the basic data; The optimization module is used to optimize the topology and resource scheduling of the AC / DC distribution network under the target scenario based on the alternating direction multiplier method, so as to obtain the optimal operating result of the AC / DC distribution network under the target scenario. An optimization module is used to reconstruct the network architecture of the AC / DC distribution network based on the target scenario, and adjust the reconstructed AC / DC distribution network to obtain an adjusted AC / DC distribution network. For each sub-region, resource scheduling in the adjusted AC / DC distribution network is optimized based on the alternating direction multiplier method to obtain the optimal regional operation result of the sub-region. The optimal regional operation result of the sub-region includes the power purchase and sale and exchange power of the sub-region. Based on the alternating direction multiplier method, the optimal regional operation result of each sub-region is further optimized to obtain the optimal operation result of the AC / DC distribution network under the target scenario. The further optimization includes information exchange and updating, dual variable updating, and determining whether convergence has occurred. A penalty factor is introduced in the dual variable updating to adjust the role of consistency error in the updating. The optimization module is used to optimize resource scheduling in the sub-regions of the adjusted AC / DC distribution network. It includes a first stage and a second stage. The first stage involves reconstructing the distribution network topology and optimizing source-load states within the sub-region. The second stage optimizes power output and load losses. For the AC sub-region distribution network, the second AC cost function optimized in the second stage is: in, The cost of the second phase of communication for the communication sub-region. This is the unit power cost coefficient for load loss. Cost coefficient per unit power loss; The normalized weighted load loss is based on the sum of the power demands of the loads at each node in each time period. The normalized system operation network loss is based on the sum of the actual active power output of each power source in each time period. These are the weighting coefficients; For the DC sub-regional distribution network, the second DC cost function optimized in the second stage is: in, Here, represents the AC cost of the second stage of the DC sub-region, represents the weighted load loss of the DC sub-region, and represents the network loss cost of system operation. For each sub-region, the power loss and load penalty cost corresponding to the sub-region are calculated; based on the power loss and load penalty cost corresponding to each sub-region, the optimal regional operation results of each sub-region are further optimized by the alternating direction multiplier method to obtain the optimal operation results of the AC / DC distribution network under the target scenario.

5. A computing device, characterized in that, Including memory and processor; The memory stores one or more computer programs, the one or more computer programs including instructions; when the instructions are executed by the processor, the computing device performs the method as described in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store a computer program for performing the method as described in any one of claims 1 to 3.

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