Multi-element power resource planning method, system and equipment for improving toughness of power system and medium

Abstract

The invention discloses a multi-element power resource planning method and system for improving the toughness of a power system, equipment and a medium, and the method comprises the following steps: obtaining historical climate data of a place where a power grid is located and a multi-source data set of a corresponding time period, carrying out the statistics of a correction coefficient under an extreme climate condition, and generating joint probability distribution; generating an extreme climate scene according to the joint probability distribution, and evaluating a toughness index of the power system to obtain an initial toughness index; determining a disaster prevention key node, evaluating the toughness improvement effect of the disaster prevention key node, and determining a multi-element power resource scale according to the initial toughness index and the improved toughness index; the cost of different toughness improvement schemes is compared, and the scheme with low cost is used as a means for improving the toughness of the power system. By establishing the joint probability distribution of the meteorological factors, the new energy output and the power load, the extreme climate scene close to the reality is generated, and the problem of insufficient prediction precision of the extreme climate scene in the prior art is solved.

Description

Technical Field

[0001] This invention relates to the field of power system resilience enhancement technology, and in particular to a method, system, equipment and medium for multi-source power resource planning to enhance power system resilience. Background Technology

[0002] Against the backdrop of global warming, extreme weather events are impacting human society with unprecedented frequency and intensity. New energy power generation is affected by meteorological conditions, exhibiting intermittent and fluctuating characteristics. As the scale of new energy construction continues to expand, how to enhance system resilience and ensure the safe and stable operation of the power system under extreme weather conditions has become a new challenge. By analyzing the correlation between meteorological factors and wind, solar, and load, extreme weather power system operation scenarios are generated. Resilience capabilities are calculated using power system performance curves, key disaster prevention nodes in the power grid are identified, and reasonable allocation of power resources at these key nodes can effectively enhance the resilience of the power system and ensure its safe and stable operation under extreme weather conditions.

[0003] However, in existing methods for enhancing the resilience of power systems, the probabilistic relationship between renewable energy generation, load status, and meteorological factors under extreme weather events has not been fully revealed, resulting in significant deviations between the generated extreme climate scenarios and reality, making it difficult to accurately assess system resilience. Furthermore, a scientifically optimized plan for resource allocation required to enhance system resilience has not been developed, failing to fully leverage the synergistic effect of different types of power resources in resisting extreme weather conditions, leading to irrational resource allocation. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a method, system, device, and medium for planning multiple power resources to enhance the resilience of power systems, addressing the problems of insufficient accuracy in generating extreme weather scenarios and unreasonable allocation of multiple power resources in existing technologies.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, this invention provides a multi-source power resource planning method to enhance the resilience of power systems, comprising the following steps: acquiring historical climate data and corresponding multi-source datasets for the power grid location, analyzing the correlation between meteorological factors and wind power output, photovoltaic power output, and power load, determining core influencing factors, statistically calculating correction coefficients under extreme climate conditions, and generating a joint probability distribution; generating extreme climate scenarios based on the joint probability distribution, correcting the new energy output and power load data using the correction coefficients, simulating the extreme climate operation scenarios using power system simulation software, evaluating power system resilience indicators, and obtaining initial resilience indicators; identifying key disaster prevention nodes, evaluating the resilience enhancement effect of the key disaster prevention nodes, determining the scale of multi-source power resources based on the initial resilience indicators and the enhanced resilience indicators, and optimizing the scale of multi-source power resources; comparing the costs of different resilience enhancement schemes, and using the low-cost scheme as a means to enhance the resilience of the power system.

[0007] As a preferred embodiment of the multi-source power resource planning method for enhancing power system resilience described in this invention, the step of determining the core influencing factor includes: the multi-source dataset includes power output data and installed capacity of photovoltaic and wind power, and power load data; analyzing the correlation between wind power output, photovoltaic power output, power load and various meteorological parameters to obtain correlation coefficients; selecting meteorological parameters whose absolute value of the correlation coefficient is greater than a preset threshold as influencing items of wind power output, photovoltaic power output, and power load; statistically analyzing the meteorological parameters of the influencing items of wind power output, photovoltaic power output, and power load, and selecting the meteorological parameter with the highest cumulative frequency of influencing items as the core influencing factor.

[0008] As a preferred embodiment of the diversified power resource planning method for enhancing the resilience of the power system described in this invention, the step of statistically analyzing the correction coefficients under extreme weather conditions includes: statistically analyzing meteorological data separately for winter and summer; defining the values ​​above the high percentile of the core influencing factors in summer as extreme high temperatures, and defining the values ​​below the low percentile of the core influencing factors in winter as extreme low temperatures; statistically analyzing the wind power output coefficients and photovoltaic output coefficients under extreme high and low temperatures; statistically analyzing the average wind power output coefficients and average photovoltaic output coefficients in summer and winter during a preset time period; obtaining the wind power correction coefficient by the ratio of the wind power output coefficient under extreme weather conditions to the average wind power output coefficient; and obtaining the photovoltaic correction coefficient by the ratio of the photovoltaic output coefficient under extreme weather conditions to the average photovoltaic output coefficient.

[0009] The beneficial effects of this preferred technical solution are as follows: by statistically comparing the output coefficient under extreme climate conditions with that under normal climate conditions to obtain the correction coefficient, the impact of extreme climate on new energy output can be quantified, thereby improving the accuracy of extreme climate scenario generation.

[0010] As a preferred embodiment of the multi-source power resource planning method for enhancing the resilience of the power system described in this invention, the step of generating and simulating extreme climate scenarios includes: generating the extreme climate scenarios using the Monte Carlo method based on the joint probability distribution; correcting the wind power output, photovoltaic power output, and power load data in the power grid data using the correction coefficient to obtain the power system operation scenario under extreme climate; statistically analyzing the load rates of power grid transformers and lines under the extreme climate operation scenario; selecting the equipment with the highest load rate as the fault element, and performing transient fault simulation on the fault element.

[0011] As a preferred embodiment of the multi-source power resource planning method for enhancing power system resilience described in this invention, the step of evaluating the power system resilience index includes: calculating the system resistance capability, which is obtained by calculating the integral ratio of the actual system performance curve and the target system performance curve over a preset time period; calculating the system dynamic response capability, which is obtained by calculating the minimum system performance, the system performance recovery rate, the system performance degradation rate, and the duration of the system performance degradation; and combining the system resistance capability and the system dynamic response capability to obtain the power system resilience index, which is used as the initial resilience index.

[0012] The beneficial effects of this preferred technical solution are: by comprehensively evaluating the system's resistance capability and dynamic response capability, the resilience index can reflect the system's static resistance capability and dynamic recovery capability under extreme climate disturbances, making the resilience assessment more scientific and comprehensive.

[0013] As a preferred embodiment of the diversified power resource planning method for enhancing power system resilience described in this invention, the step of determining the scale of diversified power resources based on the initial resilience index and the improved resilience index includes: adding ideal power sources to adjacent-level grid nodes of the faulty component; re-evaluating the power system resilience index of each node and selecting the node with the maximum value of the power system resilience index as the disaster prevention key node; after removing the ideal power source, gradually increasing the capacity of the ideal power source at the disaster prevention key node; repeatedly evaluating the power system resilience index after each increase in the capacity of the ideal power source to obtain the improved resilience index; when the improvement of the improved resilience index and the initial resilience index reaches a preset threshold, the total capacity of the added ideal power source at this time is taken as the scale of diversified power resources.

[0014] The beneficial effects of this preferred technical solution are as follows: by tentatively adding ideal power sources to adjacent nodes and evaluating the resilience improvement effect to determine the critical disaster prevention nodes, it is possible to identify the node locations that contribute the most to the system resilience improvement, thereby improving the pertinence and effectiveness of resource allocation.

[0015] As a preferred embodiment of the diversified power resource planning method for enhancing power system resilience described in this invention, the step of optimizing the scale of the diversified power resources includes: dividing the scale of the diversified power resources into fundamental frequency power resource capacity and high frequency power resource capacity according to the system's dynamic response capability, wherein the fundamental frequency power resource capacity includes hydropower capacity, pumped storage capacity, and nuclear power capacity, and the high frequency power resource capacity includes energy storage capacity and gas-fired power capacity; calculating the fundamental frequency power resource capacity by integrating the difference between the actual performance curve of the system during the performance recovery phase and the target performance curve of the system; and the high frequency power resource capacity is the difference between the scale of the diversified power resources and the fundamental frequency power resource capacity.

[0016] The beneficial effects of this preferred technical solution are as follows: by dividing resources into two categories, fundamental frequency and high frequency, according to the dynamic response process of the system and calculating their respective capacities, the synergistic effect of different types of power resources at different stages of system recovery can be fully utilized, thereby achieving optimal resource allocation.

[0017] Secondly, the present invention provides a multi-source power resource planning system to enhance the resilience of the power system, including: a data acquisition and analysis module, used to acquire historical climate data and corresponding multi-source datasets of the power grid location, analyze the correlation between meteorological factors and wind and solar power output and power load, determine core influencing factors, statistically analyze correction coefficients under extreme climate conditions, and generate joint probability distribution; The scenario generation and evaluation module is used to generate extreme climate scenarios based on the joint probability distribution, correct the new energy output and power load data using the correction coefficient, simulate the extreme climate operation scenario through power system simulation software, evaluate the power system resilience index, and obtain the initial resilience index. The resource planning and optimization module is used to identify key disaster prevention nodes, evaluate the resilience improvement effect of the key disaster prevention nodes, determine the scale of diversified power resources based on the initial resilience index and the improved resilience index, and optimize the scale of diversified power resources. The solution decision module is used to compare the costs of different resilience enhancement solutions and select the low-cost solution as the means to improve the resilience of the power system.

[0018] Thirdly, the present invention provides an electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of a multi-source power resource planning method to enhance the resilience of the power system.

[0019] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the multi-source power resource planning method for improving the resilience of power systems.

[0020] Compared with existing technologies, the beneficial effects of this invention are as follows: by establishing a joint probability distribution of meteorological factors, new energy output, and power load, and by statistically analyzing correction coefficients under extreme climate conditions, a more realistic extreme climate scenario is generated, solving the problem of insufficient prediction accuracy of extreme climate scenarios in existing technologies; by comprehensively evaluating the system's resistance and dynamic response capabilities, a comprehensive resilience assessment index system is established, which can accurately reflect the resilience level of the power system under extreme climate conditions.

[0021] Furthermore, this invention identifies critical disaster prevention nodes by tentatively adding ideal power sources and optimizes the allocation of diverse power resources into two categories: fundamental frequency and high frequency resources based on the system's dynamic response process. This fully leverages the synergistic effect of different types of power resources, solves the problem of unreasonable resource allocation in existing technologies, effectively enhances the resilience of the power system under extreme weather scenarios, and ensures the safe and stable operation of the power system. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a schematic diagram of the overall process of a multi-source power resource planning method for improving the resilience of power systems according to an embodiment of the present invention.

[0024] Figure 2 This is a perturbation-based system performance curve of the multi-source power resource planning method for improving power system resilience, as described in an embodiment of the present invention. Detailed Implementation

[0025] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0026] Example 1, referring to Figure 1As an embodiment of the present invention, a multi-source power resource planning method for improving the resilience of power systems is provided, comprising the following steps S100: S100. Obtain historical climate data and corresponding multi-source datasets for the power grid location, analyze the correlation between meteorological factors and wind power output, photovoltaic power output, and power load, determine the core influencing factors, calculate the correction coefficients under extreme climate conditions, and generate a joint probability distribution.

[0027] S200. Generate extreme climate scenarios based on the joint probability distribution, correct the new energy output and power load data using the correction coefficient, simulate the extreme climate operation scenarios using power system simulation software, evaluate the power system resilience index, and obtain the initial resilience index.

[0028] S300. Identify key disaster prevention nodes, assess the resilience enhancement effect of the key disaster prevention nodes, determine the scale of diversified power resources based on the initial resilience index and the enhanced resilience index, and optimize the scale of diversified power resources.

[0029] S400 compares the costs of different resilience enhancement schemes and selects the low-cost scheme as the means to improve the resilience of the power system.

[0030] It should be noted that, against the backdrop of global climate change, extreme weather events are becoming increasingly frequent, posing a serious threat to the safe and stable operation of power systems. New energy power generation is significantly affected by meteorological conditions; under extreme weather conditions, its output volatility and uncertainty increase dramatically, while power load also experiences abnormal fluctuations, making it difficult to accurately predict the system's operating status. Existing methods for generating extreme weather scenarios fail to fully consider the coupling relationship between meteorological factors and new energy output and load, resulting in insufficient scenario prediction accuracy. Furthermore, there is a lack of scientific optimization methods for the allocation of diverse power resources needed to enhance system resilience, and the synergistic effects of different types of resources are not fully utilized, leading to low resource allocation efficiency. Therefore, resilience assessment and resource planning of power systems under extreme weather conditions are crucial.

[0031] Therefore, to address the aforementioned issues of scenario prediction and resource allocation, the S100-S400 steps establish a joint probability distribution of meteorological factors, renewable energy output, and load. Correction coefficients are used to generate realistic extreme climate scenarios, enabling accurate simulation of system operation. A comprehensive assessment of system resilience and dynamic response capabilities yields robust resilience indicators, enabling a scientific evaluation of system resilience levels. Key disaster prevention nodes are precisely identified, and the allocation of diverse power resources is optimized based on the system's dynamic response process, fully leveraging the synergistic effects of different resource types. Simultaneously, cost comparison analysis is used to evaluate the economic viability of resilience enhancement schemes, providing scientific decision support for the safe and stable operation of the power system under extreme weather conditions.

[0032] Example 2, refer to Figure 1 and Figure 2 This is one embodiment of the present invention. Based on the above embodiment, a multi-source power resource planning method for improving the resilience of power systems is provided.

[0033] In this embodiment of the application, the step of determining the core influence factor in step S100 includes A1~A3: The multi-source dataset includes power output data and installed capacity data of photovoltaic and wind power, as well as power load data.

[0034] Specifically, historical meteorological data, including parameters such as temperature, humidity, wind speed, precipitation, and radiation intensity, is obtained from the meteorological department where the power grid is located. Actual power output and installed capacity data of wind farms and photovoltaic power stations corresponding to the meteorological data time period, as well as power load data for the same period, are obtained from the power grid dispatch system. To ensure consistent data temporal resolution, this implementation uses an hourly or 15-minute time granularity to guarantee the accuracy of subsequent correlation analysis.

[0035] A1. Analyze the correlation between wind power output, photovoltaic power output, power load and various meteorological parameters to obtain the correlation coefficients.

[0036] In this embodiment, the Pearson correlation coefficient method is used to calculate the correlation between wind power output, photovoltaic power output, and electricity load and various meteorological parameters such as temperature, humidity, wind speed, precipitation, and radiation intensity. The calculation formula is as follows: ; In the formula, X i These represent wind power output, solar power output, and electricity load data, respectively. i Here are the meteorological parameter data, and r is the correlation coefficient, which ranges from -1 to 1. The closer the absolute value of the correlation coefficient is to 1, the stronger the correlation; the closer it is to 0, the weaker the correlation.

[0037] A2. Select meteorological parameters whose absolute value of the correlation coefficient is greater than a preset threshold as the influencing factors of wind power output, photovoltaic power output, and power load.

[0038] In this embodiment, the preset threshold is set to 0.7. For wind power output, if the absolute value of its correlation coefficient with wind speed is greater than 0.7, wind speed is considered as an influencing factor. For photovoltaic power output, if the absolute value of its correlation coefficient with radiation intensity and temperature is greater than 0.7, radiation intensity and temperature are considered as influencing factors. For electricity load, if the absolute value of its correlation coefficient with temperature is greater than 0.7, temperature is considered as an influencing factor. When two or more correlation coefficients have an absolute value greater than 0.7, the meteorological parameter with the larger absolute value is taken as the influencing factor; when all correlation coefficients have an absolute value less than 0.7, the meteorological parameter with the highest absolute value is taken as the influencing factor.

[0039] A3. Statistically analyze the meteorological parameters affecting wind power output, photovoltaic power output, and electricity load, and select the meteorological parameter with the highest cumulative frequency of each affecting item as the core influencing factor.

[0040] The influencing factors obtained in step A3 are statistically analyzed. For example, if temperature appears twice as an influencing factor for photovoltaic output and electricity load, and wind speed appears once as an influencing factor for wind power output, then the cumulative count for temperature is 2, and the cumulative count for wind speed is 1. The meteorological parameter with the highest cumulative count is selected as the core influencing factor. If there are influencing factors with the same highest count, the absolute values ​​of their correlation coefficients are compared, and the meteorological parameter with the larger value is selected as the core influencing factor. The core influencing factor serves as the key basis for subsequent determination of extreme climate conditions and the statistical analysis of correction coefficients.

[0041] In this embodiment of the application, the step of calculating the correction coefficient under extreme climate conditions in step S100 includes B1 to B5: B1. Compile meteorological data separately for winter and summer.

[0042] Specifically, historical meteorological data are divided according to the seasons based on the climate characteristics of the power grid's location. December to February of the following year is typically defined as winter, and June to August as summer. Statistical analyses are performed on the core influencing factors for winter and summer respectively to reflect the differentiated impacts of meteorological conditions on renewable energy output and power load under different seasons.

[0043] B2. The values ​​above the high percentile of the core influencing factors in summer are defined as extreme high temperatures, and the values ​​below the low percentile of the core influencing factors in winter are defined as extreme low temperatures.

[0044] In this embodiment, the percentile method is used to determine the threshold for extreme climate conditions. Summer core impact factor data are sorted in ascending order, and values ​​above the 95th percentile are defined as extreme high-temperature conditions; winter core impact factor data are sorted in ascending order, and values ​​below the 5th percentile are defined as extreme low-temperature conditions. For example, if the core impact factor is temperature, and the 95th percentile of summer temperature data is 35°C, then periods with temperatures above 35°C are considered extreme high-temperature conditions; and the 5th percentile of winter temperature data is -10°C, then periods with temperatures below -10°C are considered extreme low-temperature conditions.

[0045] B3. Statistical analysis of wind power output coefficient and photovoltaic power output coefficient under extreme high temperature and extreme low temperature conditions.

[0046] During periods of extreme high and low temperatures, the ratio of actual wind power output to installed wind power capacity was calculated to obtain the wind power output coefficient under extreme weather conditions; similarly, the ratio of actual photovoltaic (PV) output to installed PV capacity was calculated to obtain the PV output coefficient under extreme weather conditions. These output coefficients reflect the actual impact of extreme weather conditions on the power generation capacity of new energy sources.

[0047] B4. Calculate the average wind power output coefficient in summer and winter during the preset time period, and the average photovoltaic power output coefficient in summer and winter.

[0048] In this embodiment, the preset time period is set to the past 3 years. The average power output coefficient of wind power during the summer and winter periods over the past 3 years is statistically analyzed; similarly, the average power output coefficient of photovoltaic power during the summer and winter periods over the past 3 years is also statistically analyzed. These average power output coefficients serve as benchmark values ​​for the power output level of new energy sources under normal climatic conditions, and are used for subsequent calculation of correction coefficients.

[0049] B5. Obtain the correction coefficient for wind power by comparing the wind power output coefficient under extreme weather conditions with the average wind power output coefficient; obtain the correction coefficient for photovoltaic power by comparing the photovoltaic power output coefficient under extreme weather conditions with the average photovoltaic power output coefficient.

[0050] Correction coefficients were calculated for wind power under extreme high-temperature conditions, wind power under extreme low-temperature conditions, and photovoltaic (PV) power under extreme high-temperature conditions. These correction coefficients quantify the degree to which extreme weather conditions correct for the output of new energy sources. A correction coefficient greater than 1 indicates that extreme weather conditions enhance output capacity; a correction coefficient less than 1 indicates that extreme weather conditions weaken output capacity. The correction coefficients, along with core influencing factors, output data, and load data, were used to construct a joint probability distribution, providing a data foundation for the subsequent generation of extreme weather scenarios.

[0051] In an optional implementation, when analyzing correlation in step S100, nonparametric methods such as Spearman's rank correlation coefficient or Kendall's rank correlation coefficient can also be used. These methods have lower requirements for the data distribution and are particularly suitable for situations where there are nonlinear relationships or outliers in the data. By comprehensively applying multiple correlation analysis methods, the complex relationship between meteorological factors and new energy output and power load can be revealed more comprehensively.

[0052] In another optional implementation, when determining the threshold for extreme weather conditions in step S100, cluster analysis or machine learning methods can also be used. For example, the K-means clustering algorithm can be used to divide meteorological data into multiple categories such as normal, above average, extreme high temperature, below average, and extreme low temperature, or a classification algorithm such as support vector machine can be used to train an extreme weather identification model. This approach can more accurately identify extreme weather conditions and is particularly suitable for areas with complex meteorological data distribution and unclear characteristics of extreme events.

[0053] In this embodiment of the application, the step of generating and simulating extreme climate scenarios in step S200 includes C1 to C4: C1. The extreme climate scenario is generated using the Monte Carlo method based on the joint probability distribution.

[0054] Specifically, by using the joint probability distribution of the core influencing factors established in step S100 and the output of wind and solar power and electricity load, multiple extreme climate scenarios are generated using the Monte Carlo random sampling method. In this embodiment, the number of scenarios generated is set to 1000 to ensure the representativeness and statistical significance of the scenarios.

[0055] The Monte Carlo sampling process is as follows: First, based on the probability distribution function of the core influencing factors in the joint probability distribution, a pseudo-random number generator is used to generate random samples that conform to the distribution; then, based on the extracted core influencing factor values, the corresponding extreme climate type (extreme high temperature or extreme low temperature) and its probability of occurrence are determined in conjunction with the joint probability distribution; finally, meteorological parameters, correction coefficients, time series and other information are combined to form a complete description of the extreme climate scenario.

[0056] For example, when the core influencing factor obtained from sampling is temperature and its value is 37℃, the scenario is determined to be an extreme high-temperature scenario, the corresponding meteorological factor level is Level I, and the probability is [missing value]. At this point, a correction factor for extreme high-temperature conditions should be applied. , , By repeating the sampling process, a set of scenarios covering different extreme weather conditions and varying probabilities of occurrence can be obtained, providing input data for subsequent power system simulations.

[0057] C2. Use the correction coefficients to correct the wind power output, photovoltaic power output, and power load data in the power grid data to obtain the power system operation scenario under extreme climate conditions.

[0058] In this embodiment, grid operation data for a typical day is obtained from the grid dispatch system, including the predicted output curves of each wind farm, the predicted output curves of each photovoltaic power station, the load curves of each load node, and basic data such as the grid topology, line parameters, and transformer parameters.

[0059] For each extreme weather scenario generated in step C1, the power grid data is corrected according to its corresponding correction factor. The correction method is as follows: Wind power output correction: ; Photovoltaic output correction: ; Power load correction: ; In the formula, , , These are the predicted wind power output, predicted photovoltaic power output, and predicted electricity load under normal climatic conditions, respectively. , , The correction coefficient for the corresponding extreme climate scenario s obtained in step S100; , , These are the corrected values ​​for wind power output, solar power output, and electricity load.

[0060] For example, in extreme high-temperature scenarios, if the correction factor for wind power... If the predicted output of a wind farm is 150MW, then the corrected output is 150 × 0.85 = 127.5MW. By correcting the output and load data of all new energy sources, a power system operation scenario reflecting the impact of extreme weather is obtained, which is closer to the actual operating state under extreme conditions.

[0061] C3. Calculate the load rate of power grid transformers and lines under the extreme climate operating scenarios.

[0062] In this embodiment, the corrected power grid operation scenario data is imported into power system simulation software (such as PSASP, PSD-BPA, PSS / E, etc.) for power flow calculation. Power flow calculation can obtain the operating status of all equipment in the power grid, including the active power and reactive power transmission of each line, and the load power of each transformer.

[0063] For each line, calculate its load rate; for each transformer, calculate its load rate.

[0064] The load rates of all lines and transformers are statistically analyzed to create a load rate distribution table. Under extreme weather conditions, due to changes in renewable energy output and load, the load rate of some equipment may rise, even exceeding the rated value. These devices become weak links and potential points of failure in the power grid.

[0065] C4. Select the device with the highest load rate as the faulty component and perform transient fault simulation on the faulty component.

[0066] Specifically, the load rates of all devices obtained in step C3 are sorted, and the device with the highest load rate is selected as the faulty component. In this embodiment, assuming that the load rate of a certain 500kV transmission line L1 reaches 98% under extreme high temperature conditions, which is the highest value among all devices, then line L1 is identified as the faulty component.

[0067] For the faulty component L1, the fault type is set as a three-phase short-circuit fault, and the fault occurs after the system has stabilized. At any given time, the fault duration is 0.1 seconds (typical relay protection operating time), and the line is permanently taken out of service after the fault is cleared. A transient simulation model is established in the power system simulation software, with the simulation duration set to 30 seconds and the time step to 0.01 seconds.

[0068] Run transient simulations to monitor key system performance indicators, including system frequency, critical node voltages, generator power angle, and load shedding. Record performance indicator values ​​at various points in time: before the fault occurs, during the fault occurs, after the fault is cleared, and during system recovery. Plot system performance curves. This curve reflects the dynamic response process of the power system after being subjected to fault disturbances under extreme weather conditions, providing basic data for subsequent resilience index calculations.

[0069] In an alternative implementation, multiple fault scenarios can be considered, where two or more devices from the top load factors are selected as faulty components to simulate a cascading failure scenario. For example, a fault in line L1 may cause the load factor of the adjacent line L2 to rise further and trigger protection actions, creating a chain reaction. By simulating multiple fault scenarios, the vulnerability of the power system under extreme weather conditions can be more comprehensively assessed.

[0070] In another optional implementation, the fault type can be set to different types such as single-phase ground fault, two-phase short-circuit fault, and open-circuit fault, to examine the impact of different fault modes on system resilience. Simultaneously, the time and location of the fault can be changed to generate multiple sets of fault simulation results, and the most unfavorable operating scenario of the system can be determined through comprehensive analysis.

[0071] In this embodiment of the application, the step of evaluating the power system resilience index in step S200 includes D1 to D3: D1. Calculate the system resistance capability, which is obtained by the integral ratio of the actual system performance curve and the target system performance curve over a preset time period.

[0072] Specifically, the system resilience F reflects the power system's ability to maintain its performance level after being subjected to disturbances, and its calculation is based on the actual system performance curves. With respect to the system target performance curve The comparison.

[0073] The system target performance curve represents the performance level that the system should maintain under ideal conditions, and is usually set to the performance value when the system is running normally. The actual system performance curve R(t) is obtained from the transient simulation results in step C4, reflecting the actual performance change process of the system under fault disturbance.

[0074] In this embodiment, the system performance index is defined as the system power supply capacity, and the calculation formula is: ; In the formula, Let t be the total power supply of the system at time t. Let be the load reduction amount at time t. Let be the total load demand at time t. When the system is operating normally, When the system suffers a failure that causes part of the load to be cut off, R(t) decreases.

[0075] The preset time period is set from the time the fault occurred. Until the system performance recovery ends The entire process. The formula for calculating the system's resistance is: ; This formula represents the ratio of the area under the actual performance curve of the system to the area under the target performance curve. The closer the F-value is to 1, the closer the actual performance of the system is to the target performance, and the stronger the system's resistance; the smaller the F-value, the greater the performance loss of the system, and the weaker its resistance.

[0076] For example, in a certain simulation, the fault occurred At the second, the system is The time interval is fully restored, and the result is obtained through numerical integration. ,but This indicates that the system maintained 88.3% of the target performance level.

[0077] D2. Calculate the dynamic response capability of the system, which is obtained by calculating the minimum system performance, the system performance recovery rate, the system performance degradation rate, and the duration of the system performance degradation.

[0078] In this embodiment, the system dynamic response capability D reflects the dynamic characteristics of the power system during disturbances, including the severity of performance degradation, the speed of performance recovery, and the duration of performance degradation. The following key parameters are defined: (1) Minimum system performance λ: Represents the lowest point of system performance degradation, calculated using the following formula: ; In the formula, This represents the system's normal performance, typically 100%. The system's performance under extreme emergency conditions, i.e., the lowest value of the system performance curve, is obtained by iterating through R(t) over the simulation time period. λ is negative, and the larger its absolute value, the greater the performance degradation and the more severe the impact of disturbances on the system.

[0079] (2) System performance degradation rate α: represents the speed at which the system performance degrades from its normal state to its lowest point, and is calculated using the following formula: ; In the formula, This is the moment when system performance begins to decline, i.e., the moment the fault occurs; The moment when system performance begins to remain constant after a period of decline, i.e., the moment when performance reaches its minimum, is determined by... Confirmed. The larger the α value, the faster the system performance deteriorates, and the higher the system's sensitivity to disturbances.

[0080] (3) System performance recovery rate β: represents the speed at which the system performance recovers from the lowest point to a steady state, and is calculated using the following formula: ; In the formula, The system performance after all recovery measures have been exhausted, i.e., the performance level of the system when it finally reaches a stable running state; The moment when system performance begins to recover, i.e., the moment when the system performance curve begins to rise, is determined by... Sure; The system performance recovery ends at the time of judgment. (where ε is the tolerance threshold, taken as 0.5%). The larger the β value, the stronger the system's recovery capability and the better its resilience.

[0081] (4) Duration E after system performance decline: This represents the length of time the system performance remains in a sluggish state. The calculation formula is as follows: ; The smaller the E value, the shorter the time the system remains in a low-performance state, the faster it can initiate recovery measures, and the better its resilience.

[0082] Combining the above four parameters, the formula for calculating the system's dynamic response capability is as follows: ; This formula takes into account the depth of system performance degradation (λ), the speed of degradation and recovery (α and β), and the duration of low performance (E). Since λ is negative, the first term of D is negative, and E is positive, the smaller the value of D (the larger the absolute value of the negative value), the stronger the dynamic response capability of the system.

[0083] D3. Combining the system's resistance capability and dynamic response capability, a power system resilience index is obtained, and this power system resilience index is used as the initial resilience index.

[0084] In this embodiment, to comprehensively assess the resilience of the power system, it is necessary to consider two dimensions: system resistance capability (F) and system dynamic response capability (D). System resistance capability focuses on the overall performance maintenance level, reflecting the cumulative performance loss of the system during disturbances; system dynamic response capability focuses on the dynamic characteristics during disturbances, reflecting the speed and duration of system performance degradation and recovery.

[0085] Since F takes values ​​in the range [0,1], while D has a larger range and may be negative, D needs to be normalized to unify their dimensions. The normalization method is as follows: ; In the formula, and These represent the minimum and maximum values ​​of D across multiple simulation scenarios. In practical applications, these two boundary values ​​are typically obtained statistically based on historical extreme climate scenario simulation results. This represents the normalized dynamic response capability, with a value range of [0,1]. A larger value indicates a better dynamic response capability.

[0086] The formula for calculating the comprehensive resilience index R of a power system is: ; In the formula, 0.7 and 0.3 are weighting coefficients, representing 70% weight for system resilience and 30% weight for system dynamic response capability in the resilience assessment. This weighting is based on engineering practice experience in power system resilience assessment, emphasizing the importance of maintaining overall system performance while also considering the dynamic response process. The selection of weighting coefficients is based on the following: as a critical infrastructure, the primary goal of a power system is to maintain power supply capacity as much as possible during disturbances; therefore, system resilience F is given a higher weight. At the same time, the dynamic response characteristics of the system cannot be ignored, especially for systems with a high proportion of renewable energy integration, where rapid dynamic response capability is crucial for preventing cascading failures.

[0087] Record this resilience index as the initial resilience index. This serves as a benchmark value for subsequent evaluation of the resilience enhancement effect at key disaster prevention nodes. The higher the value, the stronger the resilience of the power system under extreme weather scenarios, and the better its ability to withstand disturbances and recover quickly.

[0088] For example, continuing from the previous example, F=0.883, D=6.973, assuming that the results are obtained based on multiple simulations and statistics... , ,but: ; ; The initial toughness index This indicates that the system's overall resilience level under current extreme climate scenarios and fault conditions is 80%. This value serves as a benchmark and is used in subsequent step S300 to evaluate the resilience improvement effect after configuring diverse power resources at key disaster prevention nodes.

[0089] In one alternative implementation, the weighting coefficient can be adjusted according to the characteristics and operational requirements of different power grids. For urban core power grids with high load density and strict power supply reliability requirements, the weight of F can be increased to 0.8, and the weight of F can be reduced. The weight is set to 0.2 to better emphasize maintaining overall system performance; for power grids with high renewable energy penetration and complex dynamic characteristics, the weight can be appropriately increased. The weight is set to 0.4 or 0.5 to give more attention to the system’s dynamic response process and rapid recovery capability.

[0090] In another alternative implementation, the system performance index R(t) can be defined in other forms. For example, the system frequency deviation can be used as the performance index, defined as follows: ,in, For frequency deviation, The frequency safety threshold; or the critical node voltage deviation as a performance indicator, defined as... ,in Let be the voltage deviation at node i. This refers to the voltage safety threshold. The selection of different performance indicators should be based on the specific characteristics and priorities of the power grid. For microgrids operating in islanded mode, frequency stability may be a more critical performance indicator; for long-distance transmission systems, the power transmission capacity of the transmission section may be a more critical performance indicator.

[0091] In this embodiment of the application, step S300, which involves determining the scale of diversified power resources based on the initial resilience index and the improved resilience index, includes steps E1 to E5: E1. Add ideal power sources to the adjacent grid nodes of the faulty component.

[0092] Specifically, to identify the most effective key nodes in the power grid for improving resilience, ideal power sources need to be added to multiple candidate nodes near the faulty component for testing. In this embodiment, an ideal power source refers to a power source model with ideal characteristics, capable of instantaneously responding to system demands and providing active and reactive power support, without considering specific power source types and technical constraints.

[0093] The method for determining adjacent-level grid nodes is as follows: First, identify the voltage level and network location of the faulty element determined in step S200; then, starting from the faulty element, expand outward by two electrical levels and select all 500kV voltage level nodes (if there is no 500kV voltage level in the system, select the node with the highest voltage level in the system) as candidate nodes.

[0094] The electrical hierarchy is defined as follows: nodes directly connected to faulty components are level 1 nodes; nodes connected to level 1 nodes but not faulty components are level 2 nodes, and so on. Nodes at each level can be quickly identified by traversing the power grid topology using graph search algorithms (such as breadth-first search, BFS).

[0095] For example, suppose the faulty component is a 500kV line L1, connecting node A and node B. The first-level nodes include node A and node B; the second-level nodes include nodes C and D connected to node A, and nodes E and F connected to node B (excluding nodes A and B). Therefore, the candidate node set is {A, B, C, D, E, F}.

[0096] Add a capacity of [capacity] to each candidate node. The ideal power source is set in this embodiment. The ideal power source model is set as a PV node (voltage-active power control node) in the simulation software, with a voltage setpoint of 1.0 per unit, an active power output upper limit of 500MW, and a reactive power output range of [−200,200]Mvar. After the ideal power source is connected to the node, the extreme climate scenario simulation and resilience index assessment process in step S200 is rerun.

[0097] E2. Reassess the power system resilience index of each node, and select the node with the maximum power system resilience index as the critical disaster prevention node.

[0098] In this embodiment, for each candidate node with an added ideal power supply, the simulation process of C1C4 and the toughness index calculation process of D1D3 in step S200 are repeated to obtain the system toughness index after the node is configured with an ideal power supply. , where the subscript i represents the i-th candidate node.

[0099] E3. After removing the ideal power source, gradually increase the capacity of the ideal power source at the critical disaster prevention node.

[0100] Specifically, in determining the key points for disaster prevention Then, remove the test ideal power sources from all candidate nodes to restore the original topology and parameters of the power grid. Then, at the disaster prevention critical nodes... The system reconnects to the ideal power source and uses an iterative method of gradually increasing capacity to determine the optimal power resource allocation scale.

[0101] The initial capacity of the iterative process is set to This means that without an ideal power supply, the system resilience index is the initial resilience index. The capacity increase step size is set to... That is, each iteration adds 100MW of ideal power capacity at critical disaster prevention nodes.

[0102] In the k-th iteration, the ideal power capacity of the disaster prevention critical node is: ; Where k = 1, 2, 3, ... represents the iteration number. In power system simulation software, the ideal power supply parameters of the critical disaster prevention node are modified, and its active power output upper limit is set to... The reactive power output range is adjusted proportionally to the capacity. (Assume the power factor is 0.9).

[0103] E4. After each increase in ideal power supply capacity, the power system resilience index is re-evaluated to obtain an improved resilience index.

[0104] In this embodiment, after each increase in the ideal power supply capacity, the simulation process of C1C4 and the toughness index calculation process of D1D3 in step S200 are repeated to obtain the system toughness index under this capacity configuration, which is denoted as the improved toughness index.

[0105] E5. When the improvement in the improved resilience index and the initial resilience index reach a preset threshold, the total ideal power capacity increased at this time shall be taken as the scale of the diversified power resources.

[0106] In this embodiment, the preset threshold is set to 15%. When the improvement in resilience index reaches or exceeds 15%, the iterative calculation stops. The ideal power capacity at this point is the total scale of diverse power resources required for critical disaster prevention nodes, denoted as [missing information]. .

[0107] The judgment condition is: ; When the above conditions are met .

[0108] For example, continuing the previous example, the iteration continues: in the fourth iteration, a 400MW ideal power source is configured, and the calculation is as follows: The increase is The fifth iteration configured a 500MW ideal power source, and the calculations yielded... The increase is The sixth iteration configured a 600MW ideal power source and calculated the following: The increase is .

[0109] at this time The stopping conditions are met, therefore the total scale of diversified power resources is determined to be... .

[0110] After determining the overall scale, the internal composition of the diverse power resources is further determined based on the system's dynamic response process.

[0111] In one alternative implementation, the capacity increment step size can be adjusted according to the system size and computational accuracy requirements. For large power grid systems, a larger step size (such as 200MW or 500MW) can be used to reduce computational load; when approaching the target capacity, a smaller step size (such as 50MW or 10MW) can be used to improve configuration accuracy. Furthermore, optimization algorithms such as the bisection method or the golden section method can be used to accelerate convergence.

[0112] In another alternative implementation, the preset threshold can be adjusted according to the grid resilience improvement target. For critical grids with extremely high resilience requirements, a higher threshold (such as 20% or 25%) can be set; for general grids, a lower threshold (such as 10% or 12%) can be used to balance the resilience improvement effect with investment costs. Meanwhile, in practical engineering applications, factors such as land resources, grid connection conditions, and environmental constraints also need to be considered, and the theoretically calculated resource scale needs to be adjusted accordingly.

[0113] In this embodiment of the application, step S300, the step of optimizing the scale of the diverse power resources, includes F1 to F3: F1. Based on the system's dynamic response capability, the scale of the diverse power resources is divided into basic frequency power resource capacity and high frequency power resource capacity according to their supporting capabilities. The basic frequency power resource capacity includes hydropower capacity, pumped storage capacity, and nuclear power capacity. The high frequency power resource capacity includes energy storage capacity and gas power capacity.

[0114] Specifically, the total scale of diversified power resources needed to be allocated at key disaster prevention junctures. Subsequently, in order to fully leverage the technical characteristics of different types of power resources and achieve complementary advantages, it is necessary to rationally allocate the total scale into two major categories: fundamental frequency power resources and high frequency power resources, based on the characteristics of the system's dynamic response process.

[0115] Reference Figure 2 The system performance curves shown indicate that the dynamic response process of a power system under extreme weather conditions can be divided into three stages: the performance degradation stage (time...). to ), performance slump phase (moment) to ), performance recovery phase (moment) to The performance degradation phase is short in duration but rapid in rate, requiring fast-responding power resources to provide instantaneous power support; the performance recovery phase is long in duration, requiring continuous and stable power resources to support the system's gradual recovery.

[0116] Fundamental frequency power resources primarily undertake the task of providing long-term, continuous power support during the performance recovery phase. These resources have relatively slow response times (seconds to minutes), but strong continuous power supply capabilities and stable output power, including hydropower, pumped storage power plants, and nuclear power plants. Among them, pumped storage power plants are the most widely used in practical engineering due to their higher operational flexibility, capable of generating power during high-load periods and pumping water for energy storage during low-load periods. High-frequency power resources primarily undertake the task of rapid response and flexible adjustment during performance degradation and low-performance phases. These resources have extremely fast response times (milliseconds to seconds), flexible adjustment, and can quickly suppress system frequency and voltage fluctuations, including energy storage systems (battery storage, flywheel storage), gas turbines, etc. Among them, battery storage is the most widely used in improving power system resilience due to its advantages such as fast response speed, high control precision, and short construction period. By classifying and allocating diverse power resources according to their support capabilities, a synergistic effect of "rapid response + continuous support" can be achieved: in the early stages of a fault, high-frequency resources act quickly to suppress a sharp decline in system performance; in the recovery phase, base-frequency resources provide continuous and stable power support to ensure a smooth system recovery.

[0117] F2. Calculate the fundamental frequency power resource capacity by integrating the difference between the actual performance curve of the system during the performance recovery phase and the target performance curve of the system.

[0118] In this embodiment, the capacity of the fundamental frequency power resource The determination is based on the system's power support requirements during the performance recovery phase. System target performance curve. R(t) represents the ideal performance level the system should maintain, while the actual performance curve R(t) represents the true performance level of the system during the recovery process. The difference between the two is... This reflects the performance gap of the system during the recovery process. By integrating it over time during the performance recovery phase, the cumulative performance gap during the recovery phase can be obtained.

[0119] The formula for calculating the capacity of fundamental frequency power resources is: ; The numerator of this formula represents the cumulative performance gap during the performance recovery phase; the denominator represents the weighted total performance gap throughout the entire disturbance process. The second term is an additional consideration for the impact during the performance degradation phase, and the coefficient 2 reflects the impact intensity of the rapid degradation phase on the system. The proportion of fundamental frequency resource capacity to total capacity is equal to the proportion of the performance gap during the recovery phase to the total performance gap.

[0120] For example, suppose the system performance parameters are , The time parameter is Total scale of diversified power resources The integral for the performance recovery phase, calculated through numerical integration, is approximately... The integral during the performance degradation phase is approximately The performance degradation impact item is The capacity of the fundamental frequency power resources is: ; Therefore, approximately 560MW of baseband power resources are needed at critical disaster prevention points. In actual projects, pumped storage power stations or conventional hydropower stations can be selected.

[0121] F3. The high-frequency power resource capacity is the difference between the scale of the diversified power resources and the capacity of the fundamental frequency power resources.

[0122] In this embodiment, high-frequency power resource capacity The result is obtained by subtracting the fundamental frequency resource capacity from the total size: ; Therefore, 40MW of high-frequency power resources are needed at critical disaster prevention junctures. In practical engineering applications, it is recommended to configure a lithium-ion battery energy storage system, which has a response time down to the millisecond level and can achieve rapid power charging and discharging switching. For the 40MW capacity requirement, a 40MW / 40MWh battery energy storage system can be configured (assuming a 1-hour discharge time). This system can meet the rapid power support needs in the early stages of a fault and provide flexible adjustment during periods of low performance. Together with 560MW of base-frequency power resources, it forms a diversified power resource configuration scheme of 600MW.

[0123] This completes the optimized allocation of diverse power resources at key disaster prevention nodes, resulting in a configuration scheme of "560MW pumped storage + 40MW battery storage". This scheme ensures both rapid power response during faults and continuous power support during recovery, significantly improving the resilience of the power system under extreme weather conditions.

[0124] In one alternative implementation, for special scenarios such as island power grids or independent power grids in remote areas, the allocation ratio of high-frequency power resources can be appropriately increased to enhance the system's rapid response capability and frequency stability.

[0125] Step S400: Compare the costs of different resilience enhancement schemes and use the low-cost scheme as a means to enhance the resilience of the power system.

[0126] In this embodiment, there are two main approaches to enhancing the resilience of the power system: Approach one is to construct diversified power resources at the key disaster prevention nodes identified in step S300; Approach two is to conduct differentiated construction on the faulty components identified in step S200 to improve their load-bearing capacity and disturbance resistance under extreme weather conditions. To determine the most economical resilience enhancement method, a comparative analysis of the full life-cycle costs of the two approaches is necessary.

[0127] In summary, by establishing a joint probability distribution of meteorological factors, renewable energy output, and power load, and by statistically analyzing correction coefficients under extreme weather conditions, a more realistic extreme weather scenario was generated, solving the problem of insufficient prediction accuracy for extreme weather scenarios in existing technologies. Furthermore, by comprehensively evaluating the system's resilience and dynamic response capabilities, a comprehensive resilience assessment index system was established, which can accurately reflect the resilience level of the power system under extreme weather conditions.

[0128] Furthermore, this invention identifies critical disaster prevention nodes by tentatively adding ideal power sources and optimizes the allocation of diverse power resources into two categories: fundamental frequency and high frequency resources based on the system's dynamic response process. This fully leverages the synergistic effect of different types of power resources, solves the problem of unreasonable resource allocation in existing technologies, effectively enhances the resilience of the power system under extreme weather scenarios, and ensures the safe and stable operation of the power system.

[0129] Example 3 illustrates a schematic scheme for a multi-source power resource planning method to enhance power system resilience. It should be noted that the technical solution of this multi-source power resource planning system for enhancing power system resilience is based on the same concept as the technical solution of the aforementioned multi-source power resource planning method for enhancing power system resilience. Details not described in detail in this embodiment can be found in the description of the aforementioned multi-source power resource planning method for enhancing power system resilience.

[0130] This embodiment also provides a multi-source power resource planning system to enhance the resilience of power systems, including: The data acquisition and analysis module is used to acquire historical climate data and corresponding multi-source datasets for the power grid location, analyze the correlation between meteorological factors and wind and solar power output and power load, determine core influencing factors, calculate correction coefficients under extreme climate conditions, and generate joint probability distributions. The scenario generation and evaluation module is used to generate extreme climate scenarios based on the joint probability distribution, correct the new energy output and power load data using the correction coefficient, simulate the extreme climate operation scenario through power system simulation software, evaluate the power system resilience index, and obtain the initial resilience index. The resource planning and optimization module is used to identify key disaster prevention nodes, evaluate the resilience improvement effect of the key disaster prevention nodes, determine the scale of diversified power resources based on the initial resilience index and the improved resilience index, and optimize the scale of diversified power resources. The solution decision module is used to compare the costs of different resilience enhancement solutions and select the low-cost solution as the means to improve the resilience of the power system.

[0131] This embodiment also provides an electronic device suitable for the planning of diversified power resources to enhance the resilience of power systems, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the diversified power resource planning method for enhancing the resilience of power systems as proposed in the above embodiment.

[0132] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the multi-source power resource planning method for improving the resilience of the power system as proposed in the above embodiments.

[0133] The storage medium proposed in this embodiment and the multi-source power resource planning method for improving the resilience of the power system proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0134] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0135] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A multi-source power resource planning method for enhancing the resilience of power systems, characterized in that, Includes the following steps: Historical climate data and corresponding multi-source datasets for the power grid location are obtained. The correlation between meteorological factors and wind power output, photovoltaic power output, and power load is analyzed to determine the core influencing factors, calculate the correction coefficients under extreme climate conditions, and generate a joint probability distribution. Extreme climate scenarios are generated based on the joint probability distribution. The new energy output and power load data are corrected using the correction coefficient. The extreme climate operation scenarios are simulated using power system simulation software to evaluate the power system resilience index and obtain the initial resilience index. Identify key disaster prevention nodes, assess the resilience enhancement effect of the key disaster prevention nodes, determine the scale of diversified power resources based on the initial resilience index and the enhanced resilience index, and optimize the scale of diversified power resources. By comparing the costs of different resilience enhancement schemes, the lowest-cost scheme will be used as a means to improve the resilience of the power system.

2. The multi-source power resource planning method for enhancing power system resilience as described in claim 1, characterized in that, The steps for determining the core influencing factors include: The multi-source dataset includes power output data and installed capacity data of photovoltaic and wind power, as well as power load data; The correlation coefficients between wind power output, photovoltaic power output, power load and various meteorological parameters were obtained. Meteorological parameters whose absolute values ​​of the correlation coefficients are greater than a preset threshold are selected as the influencing factors of wind power output, photovoltaic power output, and power load. Meteorological parameters affecting wind power output, photovoltaic power output, and electricity load were statistically analyzed, and the meteorological parameter with the highest cumulative frequency of each affecting item was selected as the core influencing factor.

3. The multi-source power resource planning method for enhancing power system resilience as described in claim 2, characterized in that, The steps for calculating the correction coefficient under extreme climate conditions include: Meteorological data are statistically analyzed separately for winter and summer. The values ​​above the high percentile of the core influencing factor in summer are defined as extreme high temperatures, and the values ​​below the low percentile of the core influencing factor in winter are defined as extreme low temperatures. Statistics on wind power output coefficient and photovoltaic power output coefficient under extreme high and low temperature conditions; The average wind power output coefficient and the average photovoltaic power output coefficient during summer and winter were statistically analyzed during the preset time period in the past. The correction factor for wind power is obtained by comparing the wind power output factor under extreme weather conditions with the average wind power output factor; the correction factor for photovoltaic power is obtained by comparing the photovoltaic power output factor under extreme weather conditions with the average photovoltaic power output factor.

4. The multi-source power resource planning method for enhancing power system resilience as described in claim 3, characterized in that, The steps involved in generating and simulating extreme climate scenarios include: The extreme climate scenario is generated using the Monte Carlo method based on the joint probability distribution. By using the aforementioned correction coefficients to correct the wind power output, photovoltaic power output, and power load data in the power grid data, the power system operation scenario under extreme weather conditions can be obtained. The load rates of power grid transformers and lines under the aforementioned extreme climate operating scenarios are statistically analyzed. The device with the highest load rate is selected as the faulty component, and transient fault simulation is performed on the faulty component.

5. The multi-source power resource planning method for enhancing power system resilience as described in claim 4, characterized in that, The steps for assessing the power system resilience indicators include: The system resistance capability is calculated by the integral ratio of the actual system performance curve and the target system performance curve over a preset time period. The dynamic response capability of the system is calculated by measuring the minimum system performance, the system performance recovery rate, the system performance degradation rate, and the duration of the system performance degradation. By combining the system's resistance capability and its dynamic response capability, a power system resilience index is obtained, which is then used as the initial resilience index.

6. The multi-source power resource planning method for enhancing power system resilience as described in claim 5, characterized in that, The steps for determining the scale of diversified power resources based on the initial resilience index and the improved resilience index include: Ideal power sources are added to the adjacent grid nodes of the faulty component; The power system resilience index of each node is reassessed, and the node with the maximum power system resilience index is selected as the critical disaster prevention node. After removing the ideal power source, gradually increase the capacity of the ideal power source at the critical disaster prevention nodes; The power system resilience index is repeatedly evaluated after each increase in ideal power capacity to obtain an improved resilience index. When the improvement in the improved resilience index and the initial resilience index reach a preset threshold, the total ideal power capacity increased at this time is taken as the scale of the diversified power resources.

7. The multi-source power resource planning method for enhancing power system resilience as described in claim 6, characterized in that, The steps for optimizing the scale of the diverse power resources include: Based on the system's dynamic response capability, the scale of the diverse power resources is divided into basic frequency power resource capacity and high frequency power resource capacity according to their supporting capabilities. The basic frequency power resource capacity includes hydropower capacity, pumped storage capacity, and nuclear power capacity, while the high frequency power resource capacity includes energy storage capacity and gas power capacity. The capacity of the fundamental frequency power resources is calculated by integrating the difference between the actual performance curve of the system during the performance recovery phase and the target performance curve of the system. The high-frequency power resource capacity is the difference between the scale of the diversified power resources and the capacity of the fundamental frequency power resources.

8. A multi-source power resource planning system for enhancing power system resilience, employing the method described in any one of claims 1-7, characterized in that, include: The data acquisition and analysis module is used to acquire historical climate data and corresponding multi-source datasets for the power grid location, analyze the correlation between meteorological factors and wind and solar power output and power load, determine core influencing factors, calculate correction coefficients under extreme climate conditions, and generate joint probability distributions. The scenario generation and evaluation module is used to generate extreme climate scenarios based on the joint probability distribution, correct the new energy output and power load data using the correction coefficient, simulate the extreme climate operation scenario through power system simulation software, evaluate the power system resilience index, and obtain the initial resilience index. The resource planning and optimization module is used to identify key disaster prevention nodes, evaluate the resilience improvement effect of the key disaster prevention nodes, determine the scale of diversified power resources based on the initial resilience index and the improved resilience index, and optimize the scale of diversified power resources. The solution decision module is used to compare the costs of different resilience enhancement solutions and select the low-cost solution as the means to improve the resilience of the power system.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the multi-source power resource planning method for improving the resilience of the power system as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the multi-source power resource planning method for enhancing the resilience of a power system as described in any one of claims 1 to 7.

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