Power distribution network capacity planning method based on toughness improvement

Abstract

The invention discloses a power distribution network capacity planning method based on toughness improvement. The method comprises the following steps: collecting extreme weather feature scene data; power distribution network parameters are monitored in real time, power distribution network system diagnosis is carried out, and problems diagnosed by the power distribution network system are subjected to priority arrangement; calculating the fault rate of the power distribution network equipment according to the diagnosis result of the power distribution network system; constructing a fault scene according to the extreme weather feature scene data and the calculated fault rate; and making a capacity plan through the constructed fault scene and energy storage configuration. By constructing the fault scene based on the extreme weather scene characteristics, the influence degree and the influence range of the extreme weather on the power distribution network are analyzed more comprehensively, more accurate decision support is provided for capacity planning and toughness improvement of the power distribution network, a more optimized energy storage configuration scheme can be formulated based on the more comprehensive fault scene and toughness evaluation result, and the power distribution network capacity planning and toughness improvement method is suitable for the power distribution network capacity planning and toughness improvement. The utilization efficiency of energy storage equipment and the toughness of the power distribution network are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system capacity planning, and particularly to a distribution network capacity planning method based on resilience improvement. Background Art

[0002] Under the background of global climate change today, the frequent occurrence of extreme weather events has become a realistic challenge that cannot be ignored, posing a severe test to all fields of social economy, especially the power system. As the "last mile" of power supply, the distribution network is directly related to the power consumption safety and stability at the user end, and its resilience in the face of extreme weather is particularly crucial. Therefore, deeply analyzing the impact of extreme weather on the distribution network, constructing a resilience distribution network index system, and exploring effective resilience improvement measures are of great significance for ensuring the safety and stability of power supply in Liaoning and even the whole country.

[0003] For example, Chinese Patent Publication No. CN115994631B discloses a distribution network capacity planning method and system based on resilience improvement, belonging to the technical field of power system capacity planning. The method of the present invention includes: obtaining the basic information of the target distribution network and establishing typical fault scenarios of the distribution network and constructing distribution network resilience indicators based on the basic information of the target distribution network; considering the different operating states of the distribution network before and after power transfer based on the typical fault scenarios of the distribution network, taking the optimal resilience indicator as the goal and taking into account the minimization of the energy storage configuration capacity, establishing a two-stage energy storage optimization configuration model; solving to obtain the optimal access points and access capacities of the energy storage.

[0004] Based on the existing patented technologies, the situation of the impact of extreme weather on the operation of the distribution network is lacking, resulting in insufficient resilience of the distribution network in extreme weather. Summary of the Invention

[0005] The present application provides a distribution network capacity planning method based on resilience improvement, which more comprehensively analyzes the impact degree and scope of extreme weather on the distribution network by constructing fault scenarios based on the characteristics of extreme weather scenarios.

[0006] The present application provides a distribution network capacity planning method based on resilience improvement, including: S101, collecting extreme weather characteristic scenario data; S102, real-time monitoring of distribution network parameters, performing distribution network system diagnosis, and ranking the problems diagnosed by the distribution network system according to priority; S103, calculating the failure rate of distribution network equipment according to the distribution network system diagnosis result; S104, constructing a fault scenario according to the extreme weather characteristic scenario data and the calculated failure rate; S105, formulating a capacity plan through the constructed fault scenario and energy storage configuration.

[0007] Preferably, prioritize the diagnosed problems into three levels: urgent problems, important problems, and general problems. Classify the situations of transformer explosion, main transmission line break, and line burnout caused by overload as urgent problems, the situation of increased resistance due to line aging as an important problem, and equipment wear, loose line joints, and incomplete maintenance records as general problems. Arrange the priorities of urgent problems, important problems, and general problems as: urgent problems > important problems > general problems.

[0008] Preferably, calculate the failure rate through the Weibull distribution. The formula for the Weibull distribution is: , where h(t) is the failure rate function, representing the instantaneous failure rate at time t, η is the scale parameter, which determines the time scale of the distribution, i.e., the average value of the equipment life; β is the shape parameter, which determines the shape of the Weibull distribution and the change trend of the failure rate over time. When β < 1, the failure rate decreases over time; when β = 1, the failure rate is constant; when β > 1, the failure rate increases over time.

[0009] Preferably, by constructing a fault scenario, when a fault occurs in the distribution network, transfer the power supply, and the specific methods for quantifying and correcting the transfer efficiency are as follows: S201, collect fault data and restoration data, and measure the power loss; S202, calculate the transfer restoration load ratio and the power loss ratio based on the fault data and restoration data collected in step S201, and calculate the transfer efficiency based on the transfer restoration load ratio and the power loss ratio; S203, correct the transfer efficiency using a correction factor based on the calculated transfer efficiency; S204, optimize the corrected transfer efficiency.

[0010] Preferably, the formula for calculating the transfer restoration load ratio is: , according to the measured power loss data, , and the formula for calculating the transfer efficiency based on the transfer restoration load ratio and the power loss ratio is: transfer efficiency = transfer restoration load ratio × (1 - power loss ratio).

[0011] Preferably, the correction factor is a value between 0 and 1, which is used to reflect the influence degree of other factors on the transfer efficiency except the transfer restoration load ratio and the power loss ratio.

[0012] Preferably, the steps for constructing a distribution network resilience evaluation system and an uncertainty processing module based on energy storage configuration are as follows: S301, construct a distribution network resilience evaluation system, which includes supply capacity, network reliability, equipment performance and safety, and restoration capacity; S302. Identify the problems of the distribution network according to the constructed distribution network resilience evaluation system; S303. Integrate an uncertainty processing module into the energy storage configuration model; S304. Optimize the energy storage configuration model according to the optimization direction of the resilience evaluation system and the uncertainty processing module.

[0013] Preferably, the supply capacity is calculated by comparing the load forecast with the power supply capacity, the network reliability is calculated by counting the number of faults, power outage time, and restoration time of the power system, and the equipment performance and safety indicators are calculated by analyzing the maintenance records, fault reports, and regular inspection data of the equipment.

[0014] Preferably, the uncertainty processing module is used as a sub-module of the energy storage configuration model, the output of the uncertainty processing module is used as the input of the energy storage configuration model, the uncertainty ranges of load and generation are used as model parameters, and the parameters of the energy storage configuration model are adjusted according to the output of the uncertainty processing module.

[0015] Preferably, based on the supply capacity of the resilience evaluation system, the capacity and quantity of energy storage devices are increased for peak load periods or situations of insufficient distributed generation.

[0016] One or more technical solutions provided in this application have at least the following technical effects or advantages: By constructing a fault scenario based on the characteristics of extreme weather scenarios, the impact degree and scope of extreme weather on the distribution network are analyzed more comprehensively, providing more accurate decision-making support for the capacity planning and resilience improvement of the distribution network. Based on the more comprehensive fault scenarios and resilience evaluation results, a more optimized energy storage configuration plan can be formulated, improving the utilization efficiency of energy storage devices and the resilience of the distribution network; By quantifying the transfer efficiency and introducing a correction coefficient, the transfer strategy is comprehensively and accurately evaluated, improving the resilience and economic operation efficiency of the distribution network. At the same time, considering the restoration time, restoration load ratio, power loss, and correction coefficient, the evaluation is more comprehensive and can reflect the influence of more actual factors on the transfer efficiency. By optimizing the transfer strategy, the restoration time can be shortened, the restoration load ratio can be increased, and the power loss can be reduced. Considering the correction coefficient at the same time, the transfer efficiency and economy are comprehensively improved. The optimized transfer strategy can restore power supply faster, reduce the impact of faults on the distribution network, and improve the resilience and reliability of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic flowchart of a method for distribution network capacity planning based on resilience improvement according to the present invention; Figure 2 It is a schematic flowchart of quantifying the transfer efficiency and correcting the transfer efficiency in an embodiment of the present invention; Figure 3It is a schematic flowchart of constructing a resilient evaluation system and an uncertainty processing module for embodiments of the present invention. Detailed implementation manners

[0018] To facilitate the understanding of the present invention, the present application will be described more comprehensively with reference to the relevant drawings. The preferred embodiments of the present invention are shown in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, these embodiments are provided to make the disclosure of the present invention more thorough and comprehensive.

[0019] It should be noted that the terms "vertical", "horizontal", "upper", "lower", "left", "right" and similar expressions used herein are for illustrative purposes only and do not represent the only embodiments.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs. The terms used in the description of the present invention in this specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0021] Embodiment 1: Figure 1 It is a schematic flowchart of a method for planning the capacity of a distribution network based on resilience improvement according to an embodiment of the present invention, including the following steps: S101, collect data on extreme weather characteristic scenarios; Specifically, obtain temperature data in recent years from channels such as meteorological departments and statistical bureaus, including daily maximum temperature, daily minimum temperature, monthly average temperature, etc., and obtain data on extreme weather events from meteorological departments, including the time, location, intensity, duration, etc. of the events. The types of extreme weather events include extremely cold weather (such as low-temperature freezing), rain-snow-ice disasters (such as heavy snow, hail), high-temperature heatwaves, etc. Sort and classify the collected data, classify the data according to dimensions such as extreme weather event type, time, location, etc., and store the classification results in a database.

[0022] S102, monitor the parameters of the distribution network in real time and perform diagnosis on the distribution network system; Furthermore, the distribution network monitoring system (SCADA system) is used to monitor key parameters such as voltage, current, and load in the distribution network in real time, monitor the voltage level, current magnitude, load distribution, and power factor, set a fixed monitoring frequency to monitor voltage, current, and load, plot the changes in monitored voltage and current magnitude into a trend chart, identify potential problems in the distribution network based on the voltage and current trend charts and the load distribution, where potential problems include equipment aging, line overload, insulation performance degradation, etc. According to the nature of potential problems, they are classified into equipment - related problems, line - related problems, and management - related problems. Equipment - related problems mainly involve various types of equipment in the distribution network, such as faults or abnormalities in transformers, switches, cables, etc.; line - related problems focus on the operating status of transmission lines and distribution lines, such as line aging, overload, short - circuit, etc.; management - related problems involve the management and operation and maintenance of the distribution network, such as imperfect systems and untimely maintenance.

[0023] Arrange the potential problems in order of priority. The priority of potential problems is divided into three levels: urgent problems, important problems, and general problems. The priority is sorted according to the principles of severity and urgency. Evaluate the impact of potential problems on the safe and stable operation of the distribution network. Classify transformer explosion, main transmission line break, and severe overload situations that cause the line to be about to burn out as urgent problems; classify equipment performance degradation, line aging resulting in increased resistance, and loopholes in management systems as important problems; classify minor equipment wear, loose line joints, and incomplete maintenance records as general problems. The priority sequence is urgent problems > important problems > general problems, and store the diagnosis results of the distribution network system.

[0024] S103, calculate the failure rate of distribution network equipment according to the diagnosis results of the distribution network system; Specifically, based on the distribution network system diagnosis, extract information such as the number of faults, fault types, and fault times of various types of equipment during extreme weather in the past few years from historical fault data and equipment operation data in the operation and maintenance records. Equipment operation data includes operating parameters such as the normal working time, load condition, ambient temperature, and humidity of the equipment, as well as the performance changes before and after equipment maintenance. According to the data collected above, use the Weibull distribution to calculate the equipment failure rate. The formula for the Weibull distribution is: , where h(t) is the failure rate function, representing the instantaneous failure rate at time t, and t is the time variable, representing the time the device has been in operation; η is the scale parameter, also known as the characteristic life or mean life parameter, which determines the time scale of the distribution, i.e., the average or median value of the device life (depending on the value of β). In the Weibull distribution, η is usually used for specific life values related to the distribution shape and β parameter; β is the shape parameter, which determines the shape of the Weibull distribution and the trend of the failure rate over time. When β < 1, the failure rate decreases over time (indicating the early failure stage); when β = 1, the failure rate is constant (indicating the accidental failure stage, at which point the Weibull distribution degenerates into an exponential distribution); when β > 1, the failure rate increases over time (indicating the wear-out failure stage). Based on the above Weibull distribution calculation formula, the values of the scale parameter η and the shape parameter β are determined according to experimental data and historical data. Substitute the known β and η into the formula to calculate the failure rate at a given time t.

[0025] S104. Construct a fault scenario based on the extreme weather characteristic scenario data and the calculated failure rate. Furthermore, define the fault types according to the collected extreme weather characteristic scenario data and the calculated failure rate. The fault types include single faults, double faults, and cascading faults. Build a fault scenario framework through the time dimension, space dimension, and fault degree. The time dimension is to determine the time point or time period when the fault occurs, combined with the time distribution characteristics of extreme weather events. The space dimension determines the geographical location where the fault may occur according to the geographical impact range of extreme weather, including specific line segments, substations, and distribution rooms. Based on the fault types, combine the fault scenarios. When constructing a single fault scenario, for each device, combine the specific fault types that may occur under extreme weather conditions (such as strong wind, heavy rain, lightning, high temperature, etc.), such as line breakage, overheating caused by too high transformer oil level, tripping caused by poor contact of switchgear, and equipment damage caused by water ingress in the distribution room. For each single fault scenario, describe the specific manifestations of the fault, evaluate the impact range of the fault, and analyze the possible consequences of the fault. When constructing a double fault scenario, based on the analysis results of the single fault scenario, consider the possibility of two or more faults occurring simultaneously or successively. These faults may occur on the same type of device (such as two adjacent lines breaking at the same time), or on different types of devices (such as a line breakage causing a transformer to overload and be damaged). For the double fault scenario, describe the interaction and influence between the two faults or the symptoms where one fault may mask the other fault. When constructing a cascading fault scenario, first determine the initial fault point, which is the fault in any single fault scenario. Then, describe how the initial fault triggers subsequent faults. By gradually expanding the fault range, a cascading fault scenario containing multiple interrelated faults can be constructed.

[0026] S105. Develop a capacity plan based on the constructed fault scenarios and energy storage optimization configuration; Specifically, establish a resilience assessment model for the distribution network based on equipment failure rates, power supply gaps, and recovery times. Using the resilience assessment model, calculate the resilience indicators of the distribution network under different fault scenarios. Based on the resilience assessment results and power supply gaps, set the capacity plan for the distribution network. The energy storage configuration model is a two-stage energy storage configuration model. The two-stage energy storage configuration model divides the energy storage optimization configuration process into two stages, respectively considering the operation states of the distribution network before and after power transfer, in order to obtain the optimal energy storage connection points and connection capacities. Based on the previously constructed fault scenarios and resilience assessment results, identify the key nodes and regions in the distribution network that require enhanced energy storage support, determine the connection points of the energy storage devices, prioritize areas with frequent failures and critical load points, and determine the connection capacities of the energy storage devices to ensure that the power supply requirements of critical loads can be met under extreme conditions.

[0027] The technical solutions in the embodiments of the present application at least have the following technical effects or advantages: By constructing fault scenarios based on the characteristics of extreme weather scenarios, more comprehensively analyze the impact degree and scope of extreme weather on the distribution network, provide more accurate decision-making support for the capacity plan and resilience improvement of the distribution network. Based on more comprehensive fault scenarios and resilience assessment results, a more optimized energy storage configuration plan can be formulated, improving the utilization efficiency of energy storage devices and the resilience of the distribution network.

[0028] Embodiment 2: Based on the fault scenarios constructed in Embodiment 1, when a fault occurs or maintenance is required in the distribution network, transfer the electricity, change the power supply path, and ensure continuous power supply to the load. By quantifying indicators to evaluate the transfer efficiency and losses during the transfer process, the impact of faults on the operation of the power grid can be effectively reduced, improving the reliability and stability of power supply.

[0029] As Figure 2 shown, the specific method for quantifying the transfer efficiency and correcting the transfer efficiency: S201. Collect fault and recovery data and measure the power loss; Furthermore, record the time points of fault occurrence, record the time points of power supply recovery through the transfer strategy, calculate the transfer recovery time, measure and record the total load before the fault and the load after recovery through the transfer strategy. During the transfer process, use power meters or monitoring devices to measure the power input and output on the transfer path and calculate the power loss, which is the difference between the input power and the output power.

[0030] S202. According to the fault and recovery data collected in step S201, calculate the transfer recovery load ratio and power loss ratio, and calculate the transfer efficiency based on the transfer recovery load ratio and power loss ratio; Specifically, through the formula: , substitute the collected data into the formula to calculate the resupply recovery load ratio; calculate the power loss ratio based on the measured power loss data. The formula is: ; calculate the resupply efficiency based on the resupply recovery load ratio and the power loss ratio. The formula is: resupply efficiency = resupply recovery load ratio × (1 - power loss ratio). Calculate the resupply efficiency based on the above formula.

[0031] S203, according to the calculated resupply efficiency, use the correction factor to correct the resupply efficiency; Furthermore, the correction factor is a value between 0 and 1, which is used to reflect the influence degree of other factors on the resupply efficiency except the resupply recovery load ratio and the power loss ratio. The closer the correction factor is to 1, the smaller the influence of other factors on the resupply efficiency; the closer the correction factor is to 0, the greater the influence of other factors on the resupply efficiency. The correction factor is determined according to expert suggestions and historical data. According to the suggestions of experts in the field of distribution network and historical resupply data, compare the difference between the resupply efficiency and the actual recovery situation, and determine the correction factor through statistical methods. Correct the resupply efficiency through the determined correction factor. The formula is: corrected resupply efficiency = correction factor × resupply recovery load ratio × (1 - power loss ratio). Calculate the corrected resupply efficiency through the correction factor.

[0032] S204, optimize the corrected resupply efficiency; Specifically, preset the resupply efficiency threshold according to the historical data and actual demand of the distribution network. Compare the actual resupply efficiency obtained by the corrected resupply strategy with the set resupply efficiency threshold and parameter range. Through comparison, identify the deficiencies or inefficient links in the resupply strategy, such as unreasonable transmission paths, low efficiency caused by aging equipment, and inflexible control strategies. Based on network topology analysis, re-plan the power transmission path, reduce unnecessary power losses, upgrade or replace old or inefficient equipment, improve the energy efficiency of the equipment itself, introduce advanced automatic control systems, dynamically adjust the resupply strategy according to the real-time load situation, and achieve more refined energy management. Based on the evaluation results, continuously adjust and optimize the specific parameters and execution logic of the resupply strategy to form a continuous improvement closed loop.

[0033] The technical solutions in the embodiments of the present application at least have the following technical effects or advantages: By quantifying the transfer supply efficiency and introducing a correction coefficient, the transfer supply strategy can be comprehensively and accurately evaluated, improving the resilience and economic operation efficiency of the distribution network. At the same time, considering the restoration time, restoration load ratio, power loss, and correction coefficient, the evaluation is more comprehensive and can reflect the influence of more actual factors on the transfer supply efficiency. By optimizing the transfer supply strategy, the restoration time can be shortened, the restoration load ratio can be increased, and the power loss can be reduced. Considering the correction coefficient at the same time, the transfer supply efficiency and economy can be comprehensively improved. The optimized transfer supply strategy can restore power supply faster, reduce the impact of faults on the distribution network, and improve the resilience and reliability of the distribution network.

[0034] Embodiment 3: Based on the two-stage energy storage configuration model in Embodiment 1, in an actual distribution network, the load and distributed generation are uncertain, which may affect the effect of the two-stage model in practical applications. In this embodiment, an uncertainty processing module is added, and at the same time, a distribution network resilience evaluation system is constructed, which can improve the resilience level of the distribution network.

[0035] As Figure 3 shown, the steps of constructing a distribution network resilience evaluation system and an uncertainty processing module are as follows: S301. Construct a distribution network resilience evaluation system. The distribution network resilience evaluation system includes supply capacity, network reliability, equipment performance and safety, and restoration capacity. Specifically, the supply capacity is calculated by comparing the load forecast with the power supply capacity. The load forecast predicts the power demand within a certain future period based on factors such as historical electricity consumption data, economic development trends, and weather changes. The power supply capacity refers to the maximum power output that the power system can provide under current conditions. By comparing these two, the supply-demand ratio can be calculated, which is the ratio of the power supply capacity to the load demand. The network reliability is calculated by statistically analyzing data such as the number of faults, power outage time, and restoration time of the power system. The number of faults reflects the frequency of faults occurring in the power system within a certain period. The power outage time refers to the duration of the power supply interruption caused by faults. The restoration time refers to the time required for the power supply to resume from the occurrence of a fault. Based on the above data, indicators such as the failure rate (the ratio of the number of faults to the total operating time), the average power outage time (the sum of all power outage times divided by the number of power outages), and the average restoration time (the sum of all restoration times divided by the number of faults) can be calculated. The equipment performance and safety indicators are evaluated by analyzing data such as the equipment maintenance records, fault reports, and regular inspections. The maintenance records reflect the equipment maintenance situation and the timeliness of repairs. The fault reports provide information such as the type, cause, and frequency of equipment faults. The regular inspections are a comprehensive inspection of the equipment performance and safety performance. Based on the above data, the equipment failure rate (the ratio of the number of faults to the equipment operating time), the maintenance cycle (the time interval between two maintenance), and the safety performance evaluation indicators (such as insulation resistance, withstand voltage level, etc.) are calculated. The restoration capacity indicators are calculated by simulating fault situations. Specifically, it is assumed that a certain part of the power system fails, and then the fault location time (the time required to determine the fault location from the occurrence of the fault), the isolation time (the time required to isolate the faulty part from the power system after the fault is located), the repair time (the time required to repair the fault after isolating the fault), and the power supply restoration time (the time required for the power supply to fully resume from the occurrence of the fault) are calculated.

[0036] S302. Identify the problems of the distribution network according to the constructed distribution network resilience evaluation system; Furthermore, identify the problems of the distribution network in aspects such as supply capacity, network reliability, equipment performance and safety, and restoration capacity according to the distribution network resilience evaluation system, and optimize and improve the distribution network for the identified problems.

[0037] S303. Integrate the uncertainty processing module into the energy storage configuration model; Specifically, weather changes have a direct impact on loads (such as air conditioning and heating needs) and distributed generation (such as solar energy and wind energy). Historical data are collected, including load, power generation and related uncertainty factor data. The collected data presents a symmetrical bell-shaped distribution, and most of the data are concentrated near the mean. The normal distribution is selected as the probability distribution model, and the probability distribution model is fitted using the collected historical data. The fitting process enables the model to be as close as possible to the distribution of actual data. The uncertainty processing module is used as a submodule of the energy storage configuration model to ensure that the output of the uncertainty processing module can be used as the input of the energy storage configuration model. The uncertainty range or probability distribution of load and power generation is used as the model parameter. According to the output of the uncertainty processing module, the parameters of the energy storage configuration model are adjusted, and the capacity of the energy storage equipment, the charging and discharging strategy, etc. are adjusted to ensure that the model can handle uncertainty.

[0038] S304, optimizing the energy storage configuration model according to the optimization direction of the resilience assessment system and the uncertainty processing module; Furthermore, based on the supply capacity of the resilience assessment system, the capacity and number of energy storage devices should be increased to ensure the stability of energy supply during peak load periods or when distributed power generation is insufficient. For power outages caused by network failures or equipment failures, energy storage devices should be deployed at key nodes to improve the redundancy and reliability of the network. For the recovery process after a failure, consider configuring energy storage devices that can respond quickly and restore power supply to shorten the recovery time and reduce losses. According to the optimization direction and objectives, select the appropriate type of energy storage device (such as lithium-ion batteries, sodium-sulfur batteries, supercapacitors, etc.), determine the capacity of the energy storage device based on the analysis results of load demand, distributed power generation capacity and uncertainty factors, and plan the layout of the energy storage device based on the network structure and the location of key nodes to ensure its reasonable distribution in the network.

[0039] The technical solutions in the above-mentioned embodiments of the present application have at least the following technical effects or advantages: by comparing the energy storage configuration schemes before and after optimization, the improvement of indicators such as power supply capacity and energy storage efficiency under load peak and distributed power generation fluctuations is demonstrated, and the distribution network resilience indicators before and after optimization, such as supply capacity indicators, network reliability indicators, etc., are compared to demonstrate significant improvement results. By calculating the investment cost, operation and maintenance cost and reduced power outage losses before and after optimization, the economic benefits of the optimization scheme are evaluated, emphasizing the higher economic benefits and less power outage losses it brings. By dealing with the uncertainty in the distribution network, the actual application effect of the energy storage configuration model is improved, and the resilience level of the distribution network is comprehensively improved.

[0040] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A distribution network capacity planning method based on resilience improvement, characterized in that, Including: S101, collecting extreme weather characteristic scenario data; S102, real-time monitoring of distribution network parameters, diagnosing the distribution network system, and prioritizing the problems diagnosed in the distribution network system; S103, calculating the failure rate of distribution network equipment according to the distribution network system diagnosis result; S104, constructing a fault scenario based on the extreme weather characteristic scenario data and the calculated failure rate; S105, formulating a capacity plan through the constructed fault scenario and energy storage configuration.

2. The method for planning the capacity of a distribution network based on toughness improvement according to claim 1, wherein, The diagnosed problems are prioritized into three levels: urgent problems, important problems, and general problems. The situations of transformer explosion, main transmission line break, and line burnout caused by overload are classified as urgent problems, the situation of increased resistance caused by line aging is classified as an important problem, and equipment wear, loose line joints, and incomplete maintenance records are classified as general problems. The urgent problems, important problems, and general problems are prioritized as: urgent problems > important problems > general problems.

3. A method for capacity planning of a distribution network based on toughness improvement as described in claim 1, wherein Calculate the failure rate through the Weibull distribution. The formula for the Weibull distribution is: , where h(t) is the failure rate function, representing the instantaneous failure rate at time t, η is the scale parameter, which determines the time scale of the distribution, that is, the average value of the equipment life; β is the shape parameter, which determines the shape of the Weibull distribution and the change trend of the failure rate over time. When β < 1, the failure rate decreases over time; when β = 1, the failure rate is constant; when β > 1, the failure rate increases over time.

4. A method for planning the capacity of a distribution network based on resilience improvement as claimed in claim 1, characterized in that, The specific method of quantifying and correcting the transfer efficiency by transferring electricity when the distribution network fails through constructing a fault scenario: S201, collecting fault data and restoration data, and measuring power loss; S202, calculating the transfer restoration load ratio and power loss ratio according to the fault data and restoration data collected in step S201, and calculating the transfer efficiency according to the transfer restoration load ratio and power loss ratio; S203, correcting the transfer efficiency using a correction factor according to the calculated transfer efficiency; S204, optimizing the corrected transfer efficiency.

5. A method for planning the capacity of a distribution network based on toughness improvement, as claimed in claim 4, wherein The formula for calculating the resupplied restored load ratio is as follows: , according to the measured power loss data, , the formula for calculating the resupply efficiency based on the resupplied restored load ratio and the power loss ratio is: resupply efficiency = resupplied restored load ratio × (1 - power loss ratio).

6. A method for planning the capacity of a distribution network based on resilience improvement as claimed in claim 4, wherein, The correction factor is a value between 0 and 1, which is used to reflect the influence degree of other factors on the transfer efficiency except the transfer restoration load ratio and power loss ratio.

7. A method for planning the capacity of a distribution network based on resilience improvement as claimed in claim 1, characterized in that, Steps for constructing a distribution network resilience evaluation system and an uncertainty processing module based on energy storage configuration: S301, constructing a distribution network resilience evaluation system, which includes supply capacity, network reliability, equipment performance and safety, and restoration ability; S302, identifying the problems of the distribution network according to the constructed distribution network resilience evaluation system; S303, integrating an uncertainty processing module into the energy storage configuration model; S304, optimizing the energy storage configuration model according to the optimization direction of the resilience evaluation system and the uncertainty processing module.

8. A method for planning the capacity of a distribution network based on resilience improvement, as described in claim 7, characterized in that, The supply capacity is calculated by comparing the load forecast with the power supply capacity. The network reliability is calculated by statistically counting the number of faults, power outage time, and restoration time of the power system. The equipment performance and safety indicators are calculated by analyzing the equipment maintenance records, fault reports, and regular inspection data.

9. The method for planning the capacity of a distribution network based on toughness improvement according to claim 7, wherein, The uncertainty processing module is used as a sub-module of the energy storage configuration model, so that the output of the uncertainty processing module is used as the input of the energy storage configuration model. The uncertainty ranges of load and power generation are used as model parameters, and the parameters of the energy storage configuration model are adjusted according to the output of the uncertainty processing module.

10. A method for planning the capacity of a distribution network based on toughness improvement as claimed in claim 7, wherein, Based on the supply capacity of the resilience evaluation system, increase the capacity and quantity of energy storage devices during peak load periods or when distributed generation is insufficient.

Images