Power distribution network restoration scheme generation method, device, medium and equipment for typhoon disaster

Abstract

The application relates to the technical field of power grid fault recovery, and discloses a power distribution network recovery scheme generation method, device, medium and equipment for typhoon disasters, which comprises the following steps: for a target period, a predetermined typhoon wind field model is used to calculate target wind speeds at each time; based on the target wind speeds at each time, fault simulation is performed on a target power distribution network to obtain target fault states of each node of the target power distribution network at a typhoon departure time; based on the target fault states of each node, a plurality of recoverable candidate power sources are determined from a plurality of power sources of the target power distribution network; a black start power source and a plurality of non-black start power sources are determined from the candidate power sources; based on the black start power source and the non-black start power sources, a target optimization model and model constraint conditions are constructed to determine a recovery sequence of the non-black start power sources, a load recovery amount and a new energy equipment output based on the target optimization model and the model constraint conditions, so that a target recovery scheme is generated.

Description

Technical Field

[0001] This application relates to the field of power grid fault recovery technology, and in particular to a method, apparatus, medium and equipment for generating a power distribution network recovery plan for typhoon disasters. Background Technology

[0002] In recent years, frequent extreme natural disasters such as typhoons have posed a severe challenge to the safe and stable operation of power distribution networks. As a crucial component of the power system, the reliability of power supply directly affects the normal order of social production and residents' lives. With the continuous development of smart grid technology and the widespread application of flexible resources such as distributed power sources and mobile energy storage, utilizing black-start technology to achieve emergency power supply after disasters has gradually become a research hotspot.

[0003] In the field of power distribution network fault recovery under typhoon disasters, scholars both domestically and internationally have conducted extensive research. Existing studies are mainly based on static fault scenarios, establishing optimization models to determine the startup sequence of distributed generation sources and load restoration schemes. Regarding equipment fault modeling, some studies use wind speed thresholds or empirical formulas to assess the impact of typhoons on power distribution equipment. In terms of black-start power source selection, existing methods typically evaluate based on single indicators such as power capacity and startup time. Regarding recovery strategy optimization, existing models mostly adopt a single-layer optimization structure, aiming to maximize load restoration or minimize power outage losses to achieve rapid reconfiguration and recovery of the power distribution network after a disaster.

[0004] Furthermore, some studies have introduced Monte Carlo simulation methods to generate disaster scenarios, improving the robustness of recovery strategies through multi-scenario analysis. These research findings provide important theoretical support and technical reference for enhancing the emergency power supply capacity of distribution networks under extreme disasters.

[0005] Existing technologies still have the following significant shortcomings in determining power distribution network fault recovery schemes under typhoon disasters: First, in terms of disaster modeling, most existing studies use static or quasi-static wind speed threshold models to assess the probability of equipment failure, failing to fully reflect the time-varying impact of the coupling effect of multiple factors such as wind speed, temperature, and humidity on equipment failure patterns during the dynamic evolution of typhoons. Although traditional Monte Carlo simulation methods can generate failure scenarios, they are difficult to accurately depict the dynamic transition process of node states over time, resulting in insufficient accuracy and timeliness of failure scenario simulation.

[0006] Second, in terms of selecting black-start power supplies, existing methods mostly evaluate based on single indicators such as capacity and startup speed, lacking a systematic multi-dimensional evaluation index system. Although the analytic hierarchy process (AHP) can quantify qualitative indicators, the selection of indicators is often not comprehensive enough, failing to comprehensively consider the synergistic effects of operational and safety characteristics, and making it difficult to objectively reflect the comprehensive performance differences of different distributed power supplies in emergency recovery.

[0007] Third, in terms of optimizing recovery strategies, existing models mostly adopt a single-layer optimization structure, which fails to effectively coordinate the coupling relationship between the unit recovery sequence and the fluctuation of renewable energy output and the priority of critical loads. The uncertainty of renewable energy output during the recovery process is not adequately considered, making it difficult to maximize renewable energy utilization while ensuring power supply to critical loads. Consequently, the overall adaptability and recovery efficiency of the recovery strategy in complex disaster scenarios need to be improved. Summary of the Invention

[0008] In view of this, this application provides a method, apparatus, medium and equipment for generating power distribution network restoration schemes for typhoon disasters, with the main purpose of solving the problem that it is currently impossible to reasonably and accurately determine power distribution network fault restoration schemes.

[0009] To address the aforementioned problems, this application provides a method for generating power distribution network restoration schemes for typhoon disasters, comprising: For the target time period, the target wind speed at each moment is calculated using a pre-defined typhoon wind field model; Based on the target wind speed at each time, fault simulation is performed on the target distribution network to obtain the target fault status of each node of the target distribution network at the time when the typhoon leaves the area. Based on the target fault state of each node, several recoverable candidate power sources are determined from several power sources in the target distribution network. Identify the black-start power source and several non-black-start power sources from among the candidate power sources; Based on black-start power sources and various non-black-start power sources, a target optimization model and model constraints are constructed. Based on the target optimization model and model constraints, the recovery sequence, load recovery amount, and output of new energy equipment for each non-black-start power source are determined to generate a target recovery scheme.

[0010] To address the aforementioned problems, this application provides a power distribution network restoration scheme generation device for typhoon disasters, comprising: The wind speed calculation module is used to calculate the target wind speed at each time point for a given typhoon wind field model. The fault simulation module is used to simulate faults in the target distribution network based on the target wind speed at each time, and to obtain the target fault status of each node in the target distribution network at the time when the typhoon leaves the area. The first determining module is used to determine several recoverable candidate power sources from several power sources in the target distribution network based on the target fault status of each node. The second determining module is used to determine the black-start power supply and a number of non-black-start power supplies from the candidate power supplies. The generation module is used to construct a target optimization model and model constraints based on the black-start power source and each non-black-start power source. Based on the target optimization model and model constraints, the recovery sequence, load recovery amount and output of new energy equipment of each non-black-start power source are determined to generate a target recovery plan.

[0011] To address the aforementioned problems, this application provides a storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method for generating a power distribution network restoration plan for typhoon disasters.

[0012] To address the aforementioned problems, this application provides an electronic device, comprising at least a memory and a processor. The memory stores a computer program, and the processor, when executing the computer program in the memory, implements the steps of the above-described method for generating a power distribution network restoration plan for typhoon disasters.

[0013] This application discloses a method, apparatus, medium, and equipment for generating distribution network restoration schemes for typhoon disasters. By utilizing a typhoon wind field model, it can accurately extrapolate wind speeds at various times, simulate distribution network fault conditions, and accurately predict the fault status of each node after the typhoon leaves the area. This allows for the pre-screening of candidate power sources and the pre-determination of black-start and non-black-start power sources, ensuring that the determination of black-start and non-black-start power sources more closely reflects the actual operation of the power grid. By constructing an optimization model and constraints, it can coordinate the restoration sequence of non-black-start power sources, the scale of load restoration, and the reasonable output of renewable energy equipment, thereby generating a reasonable and accurate target restoration scheme. This enables advance deployment and response to typhoon disasters, providing a guarantee for timely, reasonable, and accurate power grid fault restoration when actual faults occur. This application enhances the intelligence and stability of distribution network fault restoration under extreme typhoon disasters, strengthens the power grid's disaster prevention and mitigation capabilities, and ensures the reliability of distribution network power supply.

[0014] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0015] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 This is a flowchart illustrating a method for generating a power distribution network restoration plan in response to typhoon disasters, as described in an embodiment of this application. Figure 2 This is a structural block diagram of a power distribution network restoration scheme generation device for typhoon disasters, according to another embodiment of this application. Figure 3 This is a structural block diagram of an electronic device according to another embodiment of this application. Detailed Implementation

[0016] Various embodiments and features of this application are described herein with reference to the accompanying drawings.

[0017] It should be understood that various modifications can be made to the embodiments described herein. Therefore, the above description should not be considered as limiting, but merely as an example of embodiments. Other modifications within the scope and spirit of this application will be apparent to those skilled in the art.

[0018] The accompanying drawings, which are included in and form part of this specification, illustrate embodiments of the present application and, together with the general description of the present application given above and the detailed description of the embodiments given below, serve to explain the principles of the present application.

[0019] These and other features of this application will become apparent from the following description of preferred forms of embodiments given as non-limiting examples, with reference to the accompanying drawings.

[0020] It should also be understood that although this application has been described with reference to some specific examples, those skilled in the art can certainly implement many other equivalent forms of this application.

[0021] The above and other aspects, features and advantages of this application will become more apparent when taken in conjunction with the accompanying drawings and in view of the following detailed description.

[0022] Specific embodiments of this application are described thereafter with reference to the accompanying drawings; however, it should be understood that the claimed embodiments are merely examples of this application, which can be implemented in various ways. Well-known and / or repeated functions and structures are not described in detail to avoid unnecessary or redundant details that could obscure the application. Therefore, the specific structural and functional details claimed herein are not intended to be limiting, but merely to serve as a representative basis for teaching those skilled in the art to use this application in a variety of substantially any suitable detailed structures.

[0023] This specification may use the phrases “in one embodiment,” “in another embodiment,” “in yet another embodiment,” or “in other embodiments,” all of which may refer to one or more of the same or different embodiments according to this application.

[0024] This application provides a method for generating a power distribution network restoration plan for typhoon disasters, such as... Figure 1 As shown, it includes the following steps: Step S101: For the target time period, use a predetermined typhoon wind field model to calculate the target wind speed at each time. Step S102: Based on the target wind speed at each time, perform fault simulation on the target distribution network to obtain the target fault status of each node of the target distribution network at the time the typhoon leaves the area. Step S103: Based on the fault status of each node, determine several recoverable candidate power sources from several power sources in the target distribution network. Step S104: Determine the black start power supply and several non-black start power supplies from the candidate power supplies. In this step, a black-start power source refers to a power source that can start generating electricity on its own, without needing external grid power, when the power grid is completely shut down, or when the external power grid is completely cut off; a non-black-start power source refers to a power source that requires an external power source to start generating electricity.

[0025] Step S105: Based on the black-start power source and each non-black-start power source, construct a target optimization model and model constraints. Based on the target optimization model and model constraints, determine the recovery sequence, load recovery amount, and output of new energy equipment for each non-black-start power source to generate a target recovery plan.

[0026] By utilizing typhoon wind field models, wind speeds at various times can be accurately extrapolated, simulating distribution network fault conditions and precisely predicting the fault status of each node after the typhoon leaves the area. This allows for the pre-screening of candidate power sources and the pre-determination of black-start and non-black-start power sources, ensuring that the identification of these sources more closely aligns with actual grid operation. By constructing optimization models and constraints, the restoration sequence of non-black-start power sources, the scale of load restoration, and the reasonable output of renewable energy equipment can be coordinated, thereby generating a reasonable and accurate target restoration plan. This enables advance deployment and response to typhoon disasters, providing a guarantee for timely, reasonable, and accurate grid fault restoration when actual faults occur. This application enhances the intelligence and stability of distribution network fault restoration under extreme typhoon disasters, strengthens the grid's disaster prevention and mitigation capabilities, and ensures the reliability of distribution network power supply.

[0027] Another embodiment of this application provides a method for generating a power distribution network restoration plan for typhoon disasters, including the following steps: Step S201: Based on the typhoon center pressure, typhoon periphery pressure, typhoon maximum wind speed radius, radial distance from the target power grid to the typhoon center, and pressure distribution shape parameters, calculate the gradient wind at each moment using the Holland wind field model; In this step, the radius of the typhoon's maximum wind speed... The calculation formula is: (1) The Holland wind field model, i.e., the gradient wind calculation formula, is as follows: (2) (3) in, Indicates gradient wind; Indicates the central pressure of the typhoon; Indicates the outer air pressure of the typhoon; Indicates the radius of the typhoon's maximum wind speed; indicates the radial distance from the target power grid to the typhoon's center; This parameter represents the shape of the air pressure distribution and is used to characterize the strength of a typhoon and the concentration of its wind field. The larger the value, the more concentrated the wind field; the smaller the value, the more diffuse the wind field. air density; The Coriolis force coefficient varies with the latitude of the typhoon center. This refers to the latitude of the typhoon's center.

[0028] Step S202: Based on the velocity components of the typhoon center in each direction at each moment, calculate the typhoon's velocity at each moment using an asymmetric wind field model. Asymmetric wind field model, i.e., typhoon movement speed The calculation formula is: (4) in, and This represents the velocity components of the typhoon center in the east and north directions.

[0029] Step S203: Calculate the target wind speed at each time based on the gradient wind and the typhoon's moving speed at each time. Target wind speed The calculation formula is: (5) in, The azimuth angle between the observation point and the center of the typhoon; The angle between the gradient wind and the sea surface wind is typically 20° to 30°; here, we take... ; and As empirical correction factors, we use 0.9 and 0.8 respectively for wind speed simulation in this paper; This refers to the typhoon's movement speed; It is a gradient wind.

[0030] Step S204: Based on the target wind speed at each time, determine the target failure probability of each node at each time. In the specific implementation process, this step can determine the direct failure probability of each device at each time based on the target wind speed at each time, so as to obtain the direct failure probability of each node at each time; determine the indirect failure probability of each device at each time based on the attribute parameters and environmental parameters of each device, so as to obtain the indirect failure probability of each node at each time; and determine the target failure probability of each node at each time based on the direct failure probability and indirect failure probability of each node at each time.

[0031] In other words, in order to accurately assess the failure probability of equipment in a robust local power distribution system under extreme typhoon disasters, this application divides equipment failures into direct failures and indirect failures, constructs failure probability models for each, and then derives the target failure probability of each node in the power distribution system / target power distribution network.

[0032] Step S204-1: Calculate the probability of direct equipment failure; In this step, the direct failures of the power distribution equipment mainly stem from structural changes caused by excessive typhoon wind speeds. This application constructs a direct failure model based on typhoon wind speed calculations, specifically by fitting an exponential function to characterize the correlation between equipment failure rate and the time-varying characteristics of typhoon wind speed. Direct failure rate Expressed as a formula: (6) In the formula, For equipment of The target wind speed at any given time, i.e. ; The safe wind speed value for the equipment; This refers to the critical wind speed value of the equipment. The attenuation coefficient; is the damping coefficient. Power distribution equipment The internal failure probability is expressed by the formula: (7) when When the value is sufficiently small, the above formula can be simplified to: (8) The current time period corresponding to the device from the initial time to the current time. The cumulative failure probability within the series structure can be calculated using the following formula: (9) The direct failure probability of a node in a distribution network can be considered as an equivalent result of the direct failure probability of its connected equipment; therefore, the node... The direct failure probability is expressed by the formula: (10) That is, the direct failure probability of each node at each time can be calculated using the above formulas (6) and (10).

[0033] Step S204-2: Calculate the probability of indirect equipment failure; In this step, typhoons can cause indirect faults by leading to insulation degradation or protection tripping of electrical equipment due to environmental changes (such as increased temperature and humidity, harmonic pollution, etc.). This application uses a proportional risk model to construct an indirect fault model for power distribution equipment under the influence of typhoons. Time device Its own indirect failure rate The calculation formula is: (11) In the formula, The baseline time-varying failure rate of the equipment; Let be a covariate, representing the th External factors affecting the first The impact of indirect failure rate of individual devices; These are regression coefficients, reflecting the sensitivity of each factor to the failure rate; The total number of covariates represents the number of influencing factors considered.

[0034] For critical equipment such as transformers, the baseline failure rate is often described using the Weibull distribution, expressed by the formula: (12) In the formula, These are the shape parameters of the equipment, used to describe the characteristics of how the equipment failure rate changes over time; These are characteristic lifespan parameters of the equipment, determining the time scale corresponding to equipment failures. The larger the value, the longer the overall lifespan of the equipment.

[0035] Therefore, the following formula (13) is derived from the above formulas (11) and (12), and the Weibull proportional risk model of the main equipment can be expressed by the following formula (13).

[0036] (13) In the formula, For equipment Shape parameters, For equipment Characteristic lifetime parameters; For time conversion factor, , , These parameters represent the typical influencing factors on equipment under three typhoon disasters: air humidity, temperature, and rainfall. , , It reflects the degree of influence of different factors on equipment failure rate.

[0037] The current time period corresponding to the device from the initial time to the current time. The probability of indirect failure within the system is expressed by the formula: (14) Consider square magnification correction, Time Node The indirect failure probability is expressed by the formula: (15) In the formula, For nodes Total number of connected devices.

[0038] That is, the indirect failure probability of each node at each time can be calculated using the above formulas (13) and (15).

[0039] Step S204-3: Calculate the target failure probability; In this step, based on the direct and indirect fault models, the combined failure probability / target failure probability of the distribution network node can be obtained, expressed by the formula: (16) Step S205: Based on the target fault probability of each node at each time, the hybrid Monte Carlo simulation method is used to simulate the fault during the target period to determine the fault state of each node of the target distribution network at each time, so as to obtain the target fault state of each node at the time of typhoon departure. Assuming the distribution network includes The system has nodes, and each node is connected to at least one branch. At any given time, the system... The operating state can be represented by a node state vector, expressed by the formula: (17) Among them, nodes At any moment status The formula is expressed as: (18) In the formula, for A uniformly distributed random number; when A return value of 0 indicates a device malfunction. If the value 1 is returned, it indicates that the device is operating normally.

[0040] In this step, after a node fails, it is necessary to model its continuous state from failure to repair. To this end, this application uses a state duration sampling method to simulate fault scenarios in the power distribution system. The formula for calculating the repair duration is expressed as follows: (19) In the formula, Mean time to repair (MTR) of node devices. This refers to the time it takes for the typhoon to leave the country. For nodes Failure time.

[0041] Building upon this, this step further employs hybrid Monte Carlo simulation to characterize the dynamic changes in failure probability, and the process is as follows: Step 1: Estimate the total simulation duration Divided into several simulation periods and initialize the sampling rounds. ; Step 2: Set the current evaluation time ; Step 3: Obtain Comprehensive failure probability of each node at any given time ; Step 4: Generate random numbers ,like This indicates a node failure and calculates the device repair time; if This indicates that the node is running normally; Step 5: Update the system's state vector at the current moment. Record the overall network operation status; Step Six: Determine if the time progression has reached the total simulation duration. If not, then... (Return to step three) to continue the calculation; Step 7: Determine if the number of iterations has reached the set upper limit. If it is not achieved, then... A new round of sampling will be conducted.

[0042] Step 8: Repeat the above process until the number of sampling rounds reaches the set upper limit. The simulation ends, and the results are output.

[0043] In this step, the hybrid Monte Carlo simulation described above is used to obtain the distribution network nodes under the influence of the typhoon throughout the entire period. The dynamic fault state sequence within. Among them, the time of typhoon departure... Node state vector This vector serves as the initial failure scenario for subsequent black boot recovery strategies. It clarifies which nodes still possess normal operational capabilities at the time of recovery startup.

[0044] Step S206: Based on the target fault status of each node, determine several recoverable candidate power sources from several power sources in the target distribution network. In this step, after determining the target fault state of each node at the moment the typhoon departs, it becomes clear which nodes will still be capable of normal operation at the time of recovery. Then, based on the target fault state, the distributed power sources connected to the faulty nodes are identified as candidate power sources to participate in subsequent recovery. This allows for the selection of the set of distributed power sources that are still operational at the current moment. That is, to obtain several candidate power sources .

[0045] Step S207: Determine the black start power supply and several non-black start power supplies from the candidate power supplies; In the specific implementation process of this step, various evaluation indicators for each candidate power source can be calculated, and a target score for each candidate power source can be determined based on these indicators. Based on the target scores of each candidate power source, a black-start power source is determined from the candidate power sources, and the remaining candidate power sources are designated as non-black-start power sources. The evaluation indicators include any one or more of the following: ramp-up load rate, distributed power source recovery time, startup power, load importance, frequency and voltage regulation capability, anti-interference capability, and harmonic distortion. In this embodiment, in order to make the calculation of the target scores more reasonable and accurate, the weight of each evaluation index can be determined in advance using the analytic hierarchy process; then, based on the evaluation indexes corresponding to the same candidate power source and the weight of each evaluation index, the score of each candidate power source is determined, and the black start power source and several non-black start power sources are determined based on the scores of each candidate power source.

[0046] In this embodiment, the specific process of determining the weights of each evaluation index using the analytic hierarchy process is as follows: Step 1: Pre-set the relative importance between any two evaluation indicators; For example, taking the following seven evaluation indicators as an example: ramp-up load rate, distributed power source recovery time, startup power, load importance, frequency and voltage regulation capability, anti-interference capability, and harmonic distortion.

[0047] Based on the actual situation, the importance of the ramp-up load rate indicator relative to the distributed power source recovery time indicator can be set to 3, then the importance of the distributed power source recovery time indicator relative to the ramp-up load rate indicator is 1 / 3; similarly, the relative importance of the other indicators can be determined.

[0048] Step 2: Construct a judgment matrix based on the relative importance between any two evaluation indicators; The judgment matrix is: (20) In the formula, Evaluation indicators Evaluation indicators Importance, with values ​​of 1, 3, 5, 7, and 9, represents the evaluation index. With evaluation indicators In contrast, evaluation indicators range from equally important to extremely important; conversely, they differ in degree. Evaluation indicators Importance .

[0049] Step 3: Calculate the product of the elements in each row of the judgment matrix to obtain the product corresponding to each evaluation index. .

[0050] (twenty one) Step 4: Calculation of Root That is, based on the product of each evaluation index. Calculate the initial weights of each evaluation indicator. .

[0051] (twenty two) Step 5, for Normalization is performed; that is, the initial weights of each evaluation indicator are adjusted. Normalization is performed to obtain the weights of each evaluation indicator. .

[0052] (twenty three) The weight / relative weight of each evaluation indicator can then be obtained. .

[0053] In this embodiment, after determining the weights of each evaluation indicator, the indicator value of each indicator can be calculated. Then, based on the corresponding weights, the indicator values ​​are weighted to obtain the target score for each evaluation indicator. The weights of each evaluation indicator are calculated using the Analytic Hierarchy Process (AHP), and based on each evaluation indicator and its corresponding weight, the target score for each candidate distributed power source is further calculated. The scores are then sorted from highest to lowest, and the top scores are selected. Each power supply serves as a collection of black start power supplies. The remaining available power supplies are used as a collection of non-black start units. .

[0054] In this implementation, the evaluation indicators are divided into two categories: operational characteristic indicators and safety characteristic indicators.

[0055] I. Operational characteristic indicators include: 1) Rate of increase in load during ramp-up; Distributed power generation output ramp-up speed from zero to maximum output, and load ramp-up rate The larger the value, the greater the output of the distributed power source, and its expression is: (twenty four) In the formula, This represents the maximum active power of the power supply. For the active power of the power supply from 0 to Time required.

[0056] 2) Distributed power source recovery time metrics; When evaluating the recovery time of distributed power sources, factors such as unit characteristics and external recovery conditions must be considered. If the ramp-up rate of the unit during startup is high, the waiting time for the power source to be started will be relatively short. Given that unit recovery time itself is difficult to quantify precisely, the unit's load regulation capability and the number of recovery steps can be introduced to jointly construct the evaluation criteria for recovery time. Power recovery time indicators for: (25) In the formula, , These are the weights of the two calculation stages, respectively; Power has been restored The load adjustment speed; and These are the baseline values ​​for the two calculation stages, respectively taken as the minimum load adjustment speed and 1; To start the power supply and restore it to power. The number of steps taken.

[0057] 3) Starting power specifications; The starting power directly affects the minimum energy storage capacity required for power supply startup. The lower the starting power, the faster the system starts up, and the simpler the overall startup process. Based on actual needs, it can typically be categorized into five levels: "high," "relatively high," "moderate," "relatively low," and "low." Specifically, corresponding indicator values ​​can be pre-configured for different levels.

[0058] 4) Load importance index; During black start, distributed power sources closer to critical loads are prioritized for startup, ensuring power supply to key loads. (Load importance index) The expression is: (26) In the formula, This refers to the set of all loads near the distributed power source. For the first Active power of each node load; For the first The importance of each node's load; For the first The distance from the load of each node to the distributed power source.

[0059] II. Safety characteristics indicators: 1) Frequency and voltage regulation capability indicators; Distributed power sources maintain the stability of voltage and frequency in the distribution network. Based on their real-time operating status, they are categorized into five levels: "Good," "Fairly Good," "Average," "Poor," and "Very Poor." Specifically, corresponding indicator values ​​can be pre-configured for each level.

[0060] 2) Anti-interference capability indicators; In the initial stage of a black start, the system structure is relatively fragile, and any minor disturbance may cause the system to become unstable again. Based on its anti-interference capability, it can be divided into five levels: "very urgent, relatively urgent, moderate, not very urgent, and not urgent." Specifically, corresponding indicator values ​​can be pre-configured for different levels.

[0061] 3) Harmonic distortion index; During black start, the harmonic voltage at the point of connection of distributed power sources is prone to distortion. To ensure power quality during black start, the total distortion rate of the harmonic voltage at the power connection point is used as a reference. The harmonic distortion index is expressed as follows: (27) In the formula, For harmonic order; for The effective value of the voltage of the subharmonic; This represents the effective value of the sinusoidal voltage.

[0062] Step S208: Based on the black-start power source and each non-black-start power source, construct the first objective function with the available power generation as the objective to obtain the upper-level optimization model; based on the active power of the non-black-start power source, the reactive power of the non-black-start power source, the active power of the black-start power source, the reactive power of the black-start power source, the load demand, the voltage amplitude, the voltage phase angle, and the frequency deviation, construct the unit output constraint conditions, the power balance constraint conditions, the node voltage amplitude condition, the phase angle constraint conditions, and the frequency constraint conditions. In this step, the classification results of black-start power sources and non-black-start power sources in step S207 can be used as the mandatory constraint input for the upper-level optimization model: that is... The units in the recovery sequence must be started first. The recovery order of the units in the system is determined by the upper-level optimization model. That is, the upper-level optimization model uses... To force priority start-up of generating units and maximize available power generation, the recovery sequence of all generating units is optimized and determined. Under the recovery sequence determined by the upper level, the dispatching scheme is optimized with the goals of maximizing load recovery and maximizing the output of new energy sources.

[0063] Step S208-1: Construct the first objective function; In this step, during grid reconfiguration, the restoration sequence of each unit is determined with the objective of maximizing the restored available power generation of the distribution network. Available power generation is obtained by subtracting the power consumed by the restored non-black start units from the sum of the output of black start units and the output of restored non-black start units. The first objective function expression is: (28) In the formula, , These represent the number of black starter units and the number of non-black starter units, respectively. For the first Taiwan Black starter unit reaches the first The power generation output has been restored by now; For the first Taiwan's non-black starter unit reached the first The power generation output has been restored by now; For the first Taiwan's non-black starter unit reached the first The power consumed has been restored during the time step.

[0064] Step S208-2: Construct constraints for the upper-level optimization model; 1) Construct unit output constraints; The unit's output must be strictly controlled within its rated operating range; it must not exceed its upper limit threshold, nor is it allowed to fall below its lower limit. (29) In the formula, , The units In the Active and reactive power at each time step; , , , The units Minimum and maximum values ​​of active and reactive power.

[0065] 2) Construct power balance constraints; During a black start, the power should satisfy the following formula: (30) In the formula, For the first The load requirements of each time step.

[0066] 3) Construct node voltage amplitude constraints and phase angle constraints; During black start, the node voltage amplitude and phase angle must not exceed their maximum allowable value, nor be less than their minimum allowable value. (31) In the formula, Nodes for black startup In the The voltage amplitude at each time step; , These are the nodes during black boot. The minimum and maximum voltage amplitudes; Nodes for black startup In the Voltage phase angle at time step; , These are the nodes during black boot. The minimum and maximum voltage phase angles.

[0067] 4) Construct system frequency constraints; The system frequency deviation must not exceed or fall below its maximum permissible frequency deviation: (32) In the formula, For the system in the first Frequency deviation of time step; The maximum frequency deviation required for safe system operation.

[0068] Step S209: Based on the initial startup sequence of each non-black start power source, construct a second objective function with the sum of load recovery and new energy output as the objective to obtain the lower-level optimization model; based on distributed photovoltaic output, wind power output, power unit ramp rate, node conductance, node susceptance, node voltage range, and line apparent power range, construct new energy operation constraints, new energy grid connection timing constraints, power flow constraints, node voltage constraints, and line transmission capacity constraints. Step S209-1: Construct the second objective function; After determining the generator unit restoration sequence, given the uncertainty of renewable energy output and severe power outages at critical loads under extreme disasters, prioritizing the supply of power to critical loads and the stability of renewable energy output is paramount. With the objectives of maximizing load restoration and renewable energy output, the stability of power supply to critical loads and renewable energy output is ensured by adjusting the grid topology and generator power transmission paths. The objective function expression is as follows: (33) In the formula, , These are the weighting coefficients, and ; For nodes Importance weights; For nodes In the Time-step recovery load; For new energy in the first The effort of the time step, That is The parameters to be solved.

[0069] Step S209-2: Construct the constraints for the lower-level optimization model; 1) Constructing operational constraints for new energy sources; Distributed photovoltaic and wind power output cannot exceed their maximum output value: (34) In the formula, , Distributed photovoltaic and wind power were respectively in the first The effort exerted by the time step; , These are the maximum output values ​​for distributed photovoltaic and wind power, respectively.

[0070] 2) Constructing constraints on the timing of new energy grid connection; When new energy generating units are connected to the grid, the following formula should be met: (35) In the formula, For the unit Maximum gradeability; To restore the duration of the time step; For new energy in the first 0-1 variables representing the grid connection status at each time step; The maximum fluctuating power predicted for new energy sources.

[0071] 3) Construct power flow constraints; The active and reactive power of the power distribution system during operation should satisfy the following formula: (36) In the formula, , They are nodes The active and reactive power injected; , They are nodes , The voltage; , They are nodes and The electrical conductivity and susceptance between them; for and The phase angle between them; This represents the number of nodes.

[0072] 4) Construct node voltage constraints; The node voltage must not exceed its maximum allowable value, nor fall below its minimum allowable value. (37) In the formula, For nodes In the The voltage value at each time step; , They are nodes The minimum and maximum voltage values.

[0073] 5) Constructing line transmission capacity constraints; The transmission capacity of a line cannot exceed its maximum allowable value, nor can it be less than its minimum allowable value. (38) In the formula, For nodes and Apparent power transmitted over inter-line connections; , They are nodes and Minimum and maximum apparent power of inter-line transmission.

[0074] Step S210: Based on the target optimization model and model constraints, determine the recovery sequence, load recovery amount, and output of new energy equipment for each non-black start power source to generate the target recovery plan.

[0075] In this step, after constructing and obtaining the upper-level optimization model, the lower-level optimization model, and various constraints, the solution can be performed to obtain the recovery sequence, load recovery amount, and output of each non-black start power source.

[0076] In this embodiment, during the specific solution process, the upper-level optimization model can employ an improved chaotic gray wolf optimization algorithm, while the lower-level optimization model can use a CPLEX-based mixed-integer linear programming solution method. Alternatively, a nested two-level model solution method can be used to solve both the upper-level and lower-level optimization models simultaneously.

[0077] In terms of disaster modeling, this application proposes a typhoon dynamic wind field simulation method that integrates the Holland wind field model with asymmetric moving wind field correction, which can accurately characterize the spatiotemporal distribution characteristics of wind speed during typhoon evolution. Based on this, a time-varying fault probability model combining direct and indirect equipment faults is constructed. An improved hybrid Monte Carlo simulation method is used to dynamically simulate the evolution of distribution network node states over time, effectively improving the accuracy and timeliness of fault scenario simulation.

[0078] Regarding the selection of black start power supplies, this application constructs a multi-dimensional evaluation system covering operational and safety performance indicators, including indicators such as ramp-up load rate, recovery time, starting power, load importance, frequency and voltage regulation capability, anti-interference capability, and harmonic distortion. Combined with the analytic hierarchy process (AHP), it achieves quantitative optimization of black start power supplies, overcoming the limitations of traditional methods that rely on a single indicator or subjective experience.

[0079] Regarding the optimization of recovery strategies, this application establishes a two-layer optimization model for grid reconfiguration with multi-source coordinated output. The upper-layer model determines the unit recovery sequence with the goal of maximizing available power generation, while the lower-layer model performs coordinated scheduling with the goals of maximizing renewable energy output and maximizing the recovery of important loads, thus achieving coordinated optimization of unit recovery sequence, renewable energy output, and load recovery.

[0080] In terms of solution methods, this application proposes a double-nested solution strategy that combines the improved chaotic gray wolf optimization algorithm with mixed integer programming. By using chaotic mapping initialization, nonlinear convergence factor and elite preservation strategy, the global search capability is enhanced, effectively avoiding getting trapped in local optima and balancing solution accuracy and computational efficiency.

[0081] Another embodiment of this application provides a power distribution network restoration scheme generation device for typhoon disasters, such as... Figure 2 As shown, it includes: The wind speed calculation module 11 is used to calculate the target wind speed at each time point for the target period using a predetermined typhoon wind field model. The fault simulation module 12 is used to simulate faults in the target distribution network based on the target wind speed at each time, and to obtain the target fault status of each node in the target distribution network at the time when the typhoon leaves the area. The first determining module 13 is used to determine several recoverable candidate power sources from several power sources in the target distribution network based on the target fault status of each node. The second determining module 14 is used to determine the black-start power supply and a number of non-black-start power supplies from each candidate power supply. The generation module 15 is used to construct a target optimization model and model constraints based on the black-start power supply and each non-black-start power supply, so as to determine the recovery sequence, load recovery amount and output of new energy equipment for each non-black-start power supply based on the target optimization model and model constraints, and generate a target recovery scheme.

[0082] In this embodiment, the wind speed calculation module is specifically used to: calculate the gradient wind at each moment using the Holland wind field model based on the typhoon's central pressure, the typhoon's peripheral pressure, the typhoon's maximum wind speed radius, the radial distance from the target power grid to the typhoon's center, and the shape parameters of the pressure distribution; calculate the typhoon's moving speed at each moment using the asymmetric wind field model based on the moving speed components of the typhoon's center in each direction at each moment; and calculate the target wind speed at each moment based on the gradient wind and the typhoon's moving speed.

[0083] In this embodiment, the fault simulation module is specifically used to: determine the target fault probability of each node at each time based on the target wind speed at each time; and, based on the target fault probability of each node at each time, use a hybrid Monte Carlo simulation method to simulate faults during the target period to determine the fault state of each node in the target distribution network at each time, so as to obtain the target fault state of each node at the time of typhoon departure.

[0084] In this embodiment, the fault simulation module is specifically used to: determine the direct failure probability of each device at each time based on the target wind speed at each time, so as to obtain the direct failure probability of each node at each time; determine the indirect failure probability of each device at each time based on the attribute parameters and environmental parameters of each device, so as to obtain the indirect failure probability of each node at each time; and determine the target failure probability of each node at each time based on the direct failure probability and indirect failure probability of each node at each time.

[0085] In this embodiment, the second determining module is specifically used to: calculate each evaluation index of each candidate power source, and determine the target score of each candidate power source based on each evaluation index; determine the black start power source from each candidate power source based on the target score of each candidate power source, and regard the remaining candidate power sources as non-black start power sources.

[0086] In the specific implementation process of this embodiment, the evaluation indicators include any one or more of the following: ramp-up load rate indicator, distributed power source recovery time indicator, startup power indicator, load importance indicator, frequency and voltage regulation capability indicator, anti-interference capability indicator, and harmonic distortion degree indicator. The device further includes a weight calculation module, which is used to determine the weight of each evaluation index using the analytic hierarchy process. The second determining module is specifically used to: determine the score of each candidate power source based on the evaluation indicators corresponding to the same candidate power source and the weight of each evaluation indicator.

[0087] In this embodiment, the generation module is specifically used for: constructing a first objective function based on the black-start power source and each non-black-start power source, with the available power generation as the objective, to obtain an upper-level optimization model; constructing unit output constraints, power balance constraints, node voltage amplitude constraints, phase angle constraints, and frequency constraints based on the active power of non-black-start power sources, reactive power of non-black-start power sources, active power of black-start power sources, reactive power of black-start power sources, load demand, voltage amplitude, voltage phase angle, and frequency deviation; constructing a second objective function based on the initial startup sequence of each non-black-start power source, with the sum of load recovery and renewable energy output as the objective, to obtain a lower-level optimization model; and constructing renewable energy operation constraints, renewable energy grid connection timing constraints, power flow constraints, node voltage constraints, and line transmission capacity constraints based on distributed photovoltaic output, wind power output, power unit ramp rate, node conductance, node susceptance, node voltage range, and line apparent power range.

[0088] The device in this embodiment, by utilizing a typhoon wind field model, can accurately extrapolate wind speeds at various times, simulate power distribution network fault conditions, and accurately predict the fault status of each node after the typhoon leaves. This allows for the pre-screening of candidate power sources and the pre-determination of black-start and non-black-start power sources, ensuring that the determination of black-start and non-black-start power sources more closely reflects the actual operation of the power grid. By constructing an optimization model and constraints, the device can coordinate the restoration order of non-black-start power sources, the scale of load restoration, and the reasonable output of new energy equipment, thereby generating a reasonable and accurate target restoration plan. This enables advance deployment and response to typhoon disasters, providing a guarantee for timely, reasonable, and accurate power grid fault restoration when actual faults occur. This application enhances the intelligence and stability of power distribution network fault restoration under extreme typhoon disasters, strengthens the power grid's disaster prevention and mitigation capabilities, and ensures the reliability of power supply from the distribution network.

[0089] Another embodiment of this application provides a storage medium storing a computer program, which, when executed by a processor, implements the following method steps: Step 1: For the target time period, use the pre-defined typhoon wind field model to calculate the target wind speed at each time. Step 2: Simulate faults in the target distribution network based on the target wind speed at each time point to obtain the target fault status of each node in the target distribution network at the time the typhoon leaves the area. Step 3: Based on the target fault status of each node, determine several recoverable candidate power sources from several power sources in the target distribution network; Step 4: Identify the black-start power source and several non-black-start power sources from the candidate power sources; Step 5: Based on the black-start power source and each non-black-start power source, construct a target optimization model and model constraints. Based on the target optimization model and model constraints, determine the recovery sequence, load recovery amount, and output of new energy equipment for each non-black-start power source to generate a target recovery plan.

[0090] The specific implementation process of the above method steps can be found in any of the above embodiments of the method for generating power distribution network restoration schemes for typhoon disasters, and will not be repeated here.

[0091] The storage medium in this application, by utilizing a typhoon wind field model, can accurately extrapolate wind speeds at various times, simulate distribution network fault conditions, and accurately predict the fault status of each node after the typhoon leaves. This allows for the pre-screening of candidate power sources and the pre-determination of black-start and non-black-start power sources, ensuring that the determination of black-start and non-black-start power sources more closely reflects the actual operation of the power grid. By constructing an optimization model and constraints, the restoration sequence of non-black-start power sources, the scale of load restoration, and the reasonable output of new energy equipment can be coordinated, thereby generating a reasonable and accurate target restoration plan. This enables advance deployment and response to typhoon disasters, providing a guarantee for timely, reasonable, and accurate power grid fault restoration when actual faults occur. This application enhances the intelligence and stability of distribution network fault restoration under extreme typhoon disasters, strengthens the power grid's disaster prevention and mitigation capabilities, and ensures the reliability of distribution network power supply.

[0092] Another embodiment of this application provides an electronic device, such as... Figure 3 As shown, it includes at least a memory 1 and a processor 2. The memory 1 stores a computer program, and the processor 2 performs the following method steps when executing the computer program in the memory 1: Step 1: For the target time period, use the pre-defined typhoon wind field model to calculate the target wind speed at each time. Step 2: Simulate faults in the target distribution network based on the target wind speed at each time point to obtain the target fault status of each node in the target distribution network at the time the typhoon leaves the area. Step 3: Based on the target fault status of each node, determine several recoverable candidate power sources from several power sources in the target distribution network; Step 4: Identify the black-start power source and several non-black-start power sources from the candidate power sources; Step 5: Based on the black-start power source and each non-black-start power source, construct a target optimization model and model constraints. Based on the target optimization model and model constraints, determine the recovery sequence, load recovery amount, and output of new energy equipment for each non-black-start power source to generate a target recovery plan.

[0093] The specific implementation process of the above method steps can be found in any of the above embodiments of the method for generating power distribution network restoration schemes for typhoon disasters, and will not be repeated here.

[0094] The electronic equipment in this application, by utilizing a typhoon wind field model, can accurately extrapolate wind speeds at various times, simulate distribution network fault conditions, and accurately predict the fault status of each node after the typhoon leaves. This allows for the pre-screening of candidate power sources and the pre-determination of black-start and non-black-start power sources, ensuring that the determination of black-start and non-black-start power sources more closely reflects the actual operation of the power grid. By constructing an optimization model and constraints, the restoration sequence of non-black-start power sources, the scale of load restoration, and the reasonable output of new energy equipment can be coordinated, thereby generating a reasonable and accurate target restoration plan. This enables advance deployment and response to typhoon disasters, providing a guarantee for timely, reasonable, and accurate power grid fault restoration when actual faults occur. This application enhances the intelligence and stability of distribution network fault restoration under extreme typhoon disasters, strengthens the power grid's disaster prevention and mitigation capabilities, and ensures the reliability of distribution network power supply.

[0095] The above embodiments are merely exemplary embodiments of this application and are not intended to limit this application. Those skilled in the art can make various modifications or equivalent substitutions to this application within the scope and nature of this application, and such modifications or equivalent substitutions should also be considered to fall within the scope of protection of this application.

Claims

1. A power distribution network restoration scheme generation method for typhoon disaster, characterized by, include: For the target time period, the target wind speed at each moment is calculated using a pre-defined typhoon wind field model; Based on the target wind speed at each time, fault simulation is performed on the target distribution network to obtain the target fault status of each node of the target distribution network at the time when the typhoon leaves the area. Based on the target fault state of each node, several recoverable candidate power sources are determined from several power sources in the target distribution network. Identify the black-start power source and several non-black-start power sources from among the candidate power sources; Based on black-start power sources and various non-black-start power sources, a target optimization model and model constraints are constructed. Based on the target optimization model and model constraints, the recovery sequence, load recovery amount, and output of new energy equipment for each non-black-start power source are determined to generate a target recovery scheme.

2. The method of claim 1, wherein, The calculation of the target wind speed at each time point during the target period, using a predetermined typhoon wind field model, specifically includes: Based on the typhoon's central pressure, outer pressure, maximum wind speed radius, radial distance from the target power grid to the typhoon's center, and pressure distribution shape parameters, the gradient wind at each moment is calculated using the Holland wind field model. Based on the velocity components of the typhoon center in each direction at each moment, the typhoon's velocity at each moment is calculated using an asymmetric wind field model. The target wind speed at each moment is calculated based on the gradient wind and the typhoon's moving speed.

3. The method as described in claim 1, characterized in that, The fault simulation of the target distribution network based on the target wind speed at each time moment obtains the target fault state of each node of the target distribution network at the time of typhoon departure, specifically including: Based on the target wind speed at each time, determine the target failure probability of each node at each time. Based on the target fault probability of each node at each time point, a hybrid Monte Carlo simulation method is used to simulate faults during the target time period, determine the fault state of each node in the target distribution network at each time point, and obtain the target fault state of each node at the time of typhoon departure.

4. The method as described in claim 3, characterized in that, The determination of the target failure probability of each node at each time point based on the target wind speed at each time point specifically includes: Based on the target wind speed at each time, determine the direct failure probability of each device at each time to obtain the direct failure probability of each node at each time. Based on the attribute parameters and environmental parameters of each device, the indirect failure probability of each device at each time is determined, so as to obtain the indirect failure probability of each node at each time. Based on the direct and indirect failure probabilities of each node at each time point, the target failure probability of each node at each time point is determined.

5. The method as described in claim 1, characterized in that, The process of determining the black-start power source and several non-black-start power sources from the candidate power sources specifically includes: Calculate the evaluation indexes for each candidate power source, and determine the target score for each candidate power source based on the evaluation indexes. Based on the target score of each candidate power source, a black-start power source is determined from the candidate power sources, and the remaining candidate power sources are designated as non-black-start power sources.

6. The method as described in claim 5, characterized in that, The evaluation indicators include any one or more of the following: ramp-up load rate, distributed power source recovery time, startup power, load importance, frequency and voltage regulation capability, anti-interference capability, and harmonic distortion. The method also includes, The weights of each evaluation indicator were determined using the analytic hierarchy process (AHP). The determination of the target score for each candidate power source based on various evaluation indicators specifically includes: The score of each candidate power source is determined based on the evaluation indicators corresponding to the same candidate power source and the weight of each evaluation indicator.

7. The method as described in claim 1, characterized in that, The construction of the target optimization model and model constraints based on the black-start power source and each non-black-start power source specifically includes: Based on black-start power sources and each non-black-start power source with available power generation as the objective, a first objective function is constructed to obtain an upper-level optimization model. Based on the active power, reactive power, active power, reactive power, load demand, voltage amplitude, voltage phase angle, and frequency deviation of the non-black-start power source, the following constraints are constructed: unit output constraints, power balance constraints, node voltage amplitude constraints, phase angle constraints, and frequency constraints. Based on the initial startup sequence of each non-black start power source, and with the sum of load recovery and new energy output as the objective, a second objective function is constructed to obtain the lower-level optimization model; Based on distributed photovoltaic power output, wind power output, power unit ramp rate, node conductance, node susceptance, node voltage range, and line apparent power range, constraints for new energy operation, new energy grid connection timing, power flow, node voltage, and line transmission capacity are constructed.

8. A device for generating power distribution network restoration plans for typhoon disasters, characterized in that, include: The wind speed calculation module is used to calculate the target wind speed at each time point for a given typhoon wind field model. The fault simulation module is used to simulate faults in the target distribution network based on the target wind speed at each time, and to obtain the target fault status of each node in the target distribution network at the time when the typhoon leaves the area. The first determining module is used to determine several recoverable candidate power sources from several power sources in the target distribution network based on the target fault status of each node. The second determining module is used to determine the black-start power supply and a number of non-black-start power supplies from the candidate power supplies. The generation module is used to construct a target optimization model and model constraints based on the black-start power source and each non-black-start power source. Based on the target optimization model and model constraints, the recovery sequence, load recovery amount and output of new energy equipment of each non-black-start power source are determined to generate a target recovery plan.

9. A storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for generating a power distribution network restoration plan for typhoon disasters as described in any one of claims 1-7.

10. An electronic device, characterized in that, It includes at least a memory and a processor, wherein the memory stores a computer program, and the processor, when executing the computer program in the memory, implements the steps of the method for generating a power distribution network restoration plan for typhoon disasters as described in any one of claims 1-7.

Images