A power grid resource planning method and system based on voltage weak areas

Abstract

The application relates to the technical field of power grid resource planning, and discloses a power grid resource planning method and system based on a voltage weak area, which comprises the following steps: obtaining branch flow calculation results according to topological structure data and real-time load data of a power grid, calculating a current overload coefficient of a branch, and determining a heavy-load branch; determining potential weak points according to voltage amplitude deviations of load nodes of the heavy-load branch, aggregating, obtaining a weak area, and calculating a voltage stability margin of the weak area; obtaining risk indexes of the potential weak points, calculating risk levels of the potential weak points according to the voltage stability margin and the risk indexes, and screening out a first weak point; calculating a resource matching degree between a regulation and control resource and the first weak point; establishing a resource planning model according to the risk levels and the resource matching degree, and obtaining a resource planning scheme. Through multi-dimensional risk identification, accurate resource matching and multi-scenario strategy optimization, the application improves the accuracy and dynamic adaptability of resource planning.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power grid resource planning, in particular to a power grid resource planning method and system based on voltage weak areas. BACKGROUND

[0002] In the field of power system operation and planning, ensuring the stability and reliability of the power grid is the core task. With the continuous growth of power demand, the increasing penetration of new energy and the complexity of load characteristics, the operating environment of the power grid is becoming increasingly complex, and higher requirements are placed on the accuracy and dynamic adaptability of resource allocation.

[0003] The limitations of the current resource allocation method are that, on the one hand, traditional methods are mostly based on historical data or typical operating modes for static planning, ignoring the dynamic changes in the structure of the power grid, and cannot match real-time reactive power demand; on the other hand, the spatial structure of the power grid has a decisive influence on the support effect of reactive power resources, and existing methods mostly use uniform allocation strategies, without considering the voltage sensitivity and reactive power demand priority of different areas, resulting in insufficient support in key areas and redundant resources in non-key areas. Therefore, there is an urgent need for a power grid resource planning method that can solve the above-mentioned defects and improve the effectiveness of resource allocation. SUMMARY

[0004] To solve the above technical problems, the present application provides a power grid resource planning method and system based on voltage weak areas, which accurately identifies weak points in the power grid and uses differentiated scheduling planning schemes to improve the accuracy and dynamic adaptability of resource scheduling planning.

[0005] In a first aspect, the present application provides a power grid resource planning method based on voltage weak areas, which comprises:

[0006] According to the topological structure data and real-time load data of the power grid, branch flow calculation is performed to obtain branch flow calculation results, the current overload coefficient of each branch is calculated according to the branch flow calculation results, and the overloaded branch is determined according to the current overload coefficient;

[0007] According to the voltage amplitude deviation of the load nodes at both ends of the overloaded branch, a potential weak point is determined, the potential weak points are aggregated to obtain a plurality of weak areas, and the voltage stability margin of each weak area is calculated;

[0008] The risk indicators of each potential weak point in the weak area are obtained, the risk level of each potential weak point is calculated according to the voltage stability margin and the risk indicators, and the first weak point is selected from each potential weak point according to the risk level;

[0009] obtain the regulation resource data corresponding to the weak area, and calculate the resource matching degree between each regulation resource and the first weak point according to the regulation resource data;

[0010] According to the risk level and the resource matching degree, a resource planning model is established and solved to obtain a resource planning scheme.

[0011] Further, the step of performing branch flow calculation according to the topology structure data and the real-time load data of the power grid to obtain the branch flow calculation result comprises:

[0012] According to the topology structure data of the power grid, the substation position is determined, the power supply area is determined according to the substation position, and the area load density is calculated according to the area area of the power supply area and the real-time load data of each load node;

[0013] The power supply area with the area load density exceeding the density threshold is regarded as a high load density area, and the load correlation degree between the load nodes and adjacent nodes in the high load density area is calculated according to the real-time load data and the topology structure data by using Pearson correlation coefficient;

[0014] According to the comparison result of the load correlation degree and the correlation degree threshold, each load node in the high load density area is clustered and divided to obtain a plurality of load clusters;

[0015] According to the load nodes of each load cluster and the topology structure data, the directly associated branches of each load node are extracted, and the flow calculation of the directly associated branches is performed to obtain the branch flow calculation result.

[0016] Further, the step of determining potential weak points according to the voltage amplitude deviation of the load nodes at both ends of the heavy load branch, and aggregating the potential weak points to obtain a plurality of weak areas comprises:

[0017] According to the real-time load data, the voltage amplitude deviation of the load nodes at both ends of each heavy load branch is calculated;

[0018] Determine whether the voltage amplitude deviation exceeds the amplitude deviation threshold, and regard the load node exceeding the amplitude deviation threshold as a potential weak point;

[0019] According to the electrical distance, each potential weak point is aggregated to obtain a plurality of weak areas.

[0020] Further, the step of calculating the voltage stability margin of each weak area comprises:

[0021] A regional equivalent model of the weak area is constructed, and the current active power and the critical active power of the weak area are obtained by flow calculation according to the regional equivalent model.

[0022] According to the current active power and the critical active power, a voltage stability margin of the weak area is calculated.

[0023] Further, the step of obtaining a risk indicator of each potential weak point in the weak area, calculating a risk level of the each potential weak point according to the voltage stability margin and the risk indicator, and screening a first weak point from the each potential weak point according to the risk level comprises:

[0024] Obtaining a voltage amplitude deviation, an associated overload branch proportion and a load growth rate of each potential weak point in the weak area, and calculating a voltage stability margin deviation value according to the voltage stability margin and a reference margin;

[0025] Normalizing and weightedly summing the voltage stability margin deviation value and the voltage amplitude deviation, the associated overload branch proportion and the load growth rate of each potential weak point to obtain a risk score of each potential weak point;

[0026] According to a comparison result of the risk score and a preset score threshold, determining a risk level of each potential weak point, and taking a potential weak point with a risk level higher than a preset level as the first weak point.

[0027] Further, the step of calculating a resource matching degree between each control resource and the first weak point according to the control resource data comprises:

[0028] According to the voltage stability margin deviation value, determining an adjustment accuracy requirement of the first weak point, according to the voltage amplitude deviation and the associated overload branch proportion, determining a capacity requirement of the first weak point, and according to the load growth rate, determining a response time requirement of the first weak point;

[0029] Extracting available capacity, response time, adjustment accuracy and reliability of each control resource from the control resource data;

[0030] According to the capacity requirement and the available capacity, calculating a capacity matching degree, according to the response time requirement and the response time, calculating a time matching degree, according to the adjustment accuracy requirement and the adjustment accuracy, calculating an accuracy matching degree, and according to the reliability and the risk level, calculating a reliability matching degree;

[0031] Weightedly summing the capacity matching degree, the time matching degree, the accuracy matching degree and the reliability matching degree to obtain the resource matching degree between each control resource and the first weak point.

[0032] Further, the step of establishing a resource planning model according to the risk level and the resource matching degree and solving the resource planning model to obtain a resource planning scheme comprises:

[0033] A resource planning model based on multi-objective optimization is established, with a risk level reduction, a resource scheduling cost minimization and a resource matching degree maximization as objective functions, and with resource constraints, power grid safety constraints and response time constraints as constraint conditions;

[0034] The resource planning model is solved by using a non-dominated sorting genetic algorithm to obtain a Pareto solution set;

[0035] An optimal solution is selected from the Pareto solution set according to the current risk level of the first weak point, and a resource planning scheme is generated according to the optimal solution.

[0036] Further, the step of taking the resource constraints, the power grid safety constraints and the response time constraints as constraint conditions comprises:

[0037] The resource constraints are that the deployment capacity of each control resource is less than or equal to the upper limit of the resource capacity;

[0038] The power grid safety constraints are that the node voltage deviation is within a deviation threshold range and the line power flow is less than or equal to a power flow threshold;

[0039] The response time constraints are that the response time of different risk levels is less than or equal to the corresponding risk response time threshold.

[0040] Further, the step of selecting an optimal solution from the Pareto solution set according to the current risk level of the first weak point comprises:

[0041] According to the current risk level of the first weak point, a scene type is determined, and the scene type comprises an emergency scene and a regular scene;

[0042] In response to the emergency scene, an optimal solution is selected from the Pareto solution set based on the lowest risk level;

[0043] In response to the regular scene, an optimal solution is selected from the Pareto solution set based on the lowest scheduling cost.

[0044] In a second aspect, the present application provides a power grid resource planning system based on a voltage weak area, which comprises:

[0045] A weak area division module is configured to perform branch power flow calculation according to topology structure data and real-time load data of a power grid to obtain a branch power flow calculation result, calculate a current overload coefficient of each branch according to the branch power flow calculation result, and determine an overload branch according to the current overload coefficient;

[0046] Based on the voltage amplitude deviation of the load nodes at both ends of the heavy-load branch, potential weak points are identified, and these potential weak points are aggregated to obtain several weak regions. The voltage stability margin of each weak region is then calculated.

[0047] The weak point screening module is used to obtain the risk index of each potential weak point in the weak area, calculate the risk level of each potential weak point based on the voltage stability margin and the risk index, and screen out the first weak point from each potential weak point based on the risk level.

[0048] The resource matching calculation module is used to obtain the control resource data corresponding to the weak area, and calculate the resource matching degree between each control resource and the first weak point based on the control resource data.

[0049] The scheme generation module is used to establish a resource planning model based on the risk level and the resource matching degree, and solve it to obtain a resource planning scheme.

[0050] This invention provides a method and system for power grid resource planning based on voltage-weak areas. By establishing a multi-parameter comprehensive scoring system, this invention improves the accuracy of weak point identification. Through constructing a four-dimensional matching degree model, it accurately calculates the resource matching degree between nodes and resources, thereby effectively shortening the time for control decisions and improving decision-making efficiency. Furthermore, through a multi-objective resource planning model, it achieves adaptability and security of control strategies across multiple scenarios. This invention, through a closed-loop design of multi-dimensional risk identification, precise resource matching, and multi-scenario strategy optimization, achieves a systematic improvement in the risk management of power system weak points, thereby ensuring the safety and stability of power grid operation. Attached Figure Description

[0051] Figure 1 This is a flowchart illustrating the power grid resource planning method based on voltage-weak areas in an embodiment of the present invention.

[0052] Figure 2 This is a schematic diagram of the power grid resource planning system based on voltage-weak areas in an embodiment of the present invention;

[0053] Figure label:

[0054] 10. Vulnerable Area Delineation Module; 20. Vulnerability Point Screening Module; 30. Resource Matching Calculation Module; 40. Solution Generation Module. Detailed Implementation

[0055] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0056] Referring to Figure 1 The first embodiment of the present application provides a power grid resource planning method based on voltage weak areas, including steps S10-S50.

[0057] In step S10, branch power flow calculation is performed according to the topological structure data and real-time load data of the power grid to obtain a branch power flow calculation result, the current overload coefficient of each branch is calculated according to the branch power flow calculation result, and the overload branch is determined according to the current overload coefficient.

[0058] In step S20, the voltage amplitude deviation of the load nodes at both ends of the overload branch is used to determine potential weak points, the potential weak points are aggregated to obtain a plurality of weak areas, and the voltage stability margin of each weak area is calculated.

[0059] In step S30, the risk indicators of each potential weak point in the weak area are obtained, the risk level of each potential weak point is calculated according to the voltage stability margin and the risk indicators, and the first weak point is selected from each potential weak point according to the risk level.

[0060] In step S40, the control resource data corresponding to the weak area is obtained, and the resource matching degree between each control resource and the first weak point is calculated according to the control resource data.

[0061] In step S50, a resource planning model is established according to the risk level and the resource matching degree, and is solved to obtain a resource planning scheme.

[0062] The application provides a method for weak area analysis based on a topology of a power grid and resource scheduling planning, which comprises the following steps: obtaining topology data of the power grid and real-time load data of each load node, wherein the topology data can be obtained from a Supervisory Control And Data Acquisition (SCADA) or a Geographic Information System (GIS) of the power grid, the topology data comprises node connection relationships and electrical parameters, the electrical parameters comprise line impedance, transformer ratio and device parameters, and the real-time load data is obtained by a substation monitoring device or a Phasor Measurement Unit (PMU), and the real-time load data comprises active power, reactive power, voltage amplitude and phase angle difference of each node; and performing preprocessing on the data, wherein the preprocessing comprises data cleaning and standardization, the data cleaning comprises removing outliers and filling in missing data, and the standardization is normalization of voltage amplitude and power, so as to eliminate dimensional influence.

[0063] Through data preprocessing, a standardized data set is obtained, and branch power flow calculation is performed on the standardized data set to obtain a branch power flow calculation result, and the specific steps comprise the following steps:

[0064] According to the topology data of the power grid, the positions of the substations are determined, the power supply areas are determined according to the positions of the substations, and the area load density is calculated according to the area area of the power supply area and the real-time load data of each load node;

[0065] The power supply area with the area load density exceeding a density threshold value is regarded as a high load density area, the load correlation degree between the load node and the adjacent node of the high load density area is calculated according to the real-time load data and the topology data by using a Pearson correlation coefficient;

[0066] According to a comparison result of the load correlation degree and a correlation degree threshold value, each load node of the high load density area is divided into clusters to obtain a plurality of load clusters;

[0067] According to the load node of each load cluster and the topology data, a directly associated branch of each load node is extracted, and the directly associated branch is subjected to power flow calculation to obtain a branch power flow calculation result.

[0068] In the embodiment, first, the power grid is divided according to the topology data, the positions of the substations are extracted from the topology data, the power supply radius of the substations is taken as the area radius, the power supply areas of each substation are determined, and then the boundaries of the power supply areas are corrected. The correction rule is that if there is load intersection between adjacent substation power supply areas, for example, the same industrial park is powered by two substations, the two substations can be merged according to a preset rule. The preset rule is to calculate the electrical distance of the nodes in the intersection area from the two substations, and attribute it to the substation area with shorter distance. If the load proportion of the intersection area exceeds 10% of the total load of any area, the two substations are merged into a “joint power supply area”.

[0069] For the power supply area, the load characteristics of the area are analyzed from two dimensions of spatial distribution and time correlation. The spatial distribution is characterized by the area load density, which refers to the total load in a unit power supply area, reflecting the intensity of the area. The time correlation is characterized by the load correlation degree, which refers to the synchronization of the load of adjacent nodes over time.

[0070] Specifically, the area load density is equal to the sum of the real-time active power of all load nodes in the area divided by the area power supply area. For example, a power supply area contains three load nodes with active power of 100 MW, 150 MW and 200 MW, and the power supply area is 30 km 2 , then the area load density is (100+150+200) / 30=15 MW / km 2 . The area load density reflects the intensity of the area. Through the area load density, the geographical area can be preliminarily screened, such as retaining the area with a load density greater than the density threshold, and quickly excluding the area with a low load density, thereby narrowing the subsequent calculation range.

[0071] Although the area with high load intensity can be screened out by the area load density, these areas are divided by geographical area, which does not take into account the electrical correlation between nodes. For example, a region contains a steel plant (three-shift continuous high load) and a residential area (night peak load), which are geographically in the same power supply area, but the load characteristics are completely opposite. If they are combined in one area, the independent characteristics of the two will be hidden. For example, the boundary nodes of two adjacent areas (belonging to different substation power supply) have high power fluctuation synchronization due to close line interconnection, but geographical division will separate them, but they should be attributed to the same cluster in terms of electricity.

[0072] In order to overcome the limitation of geographical zoning, after the high load density area is screened out in the regional zoning, the load correlation degree between each load node of the high load density area and the adjacent load node is calculated, the geographical boundary is broken through by the load correlation degree, and the electrical cluster is identified from the perspective of power fluctuation synchronization. Specifically, the high load density area is taken as a candidate pool, the load correlation degree of the nodes in the area is calculated, the full power grid traversal is avoided, and the calculation amount is reduced. Preferably, the load correlation degree is calculated by using the sliding window Pearson correlation coefficient, and the formula is represented as:

[0073]

[0074] In the formula, r xy represents the load correlation degree between the load node x and the load node y, P x,t represents the active power of the load node x at the time t, represents the average power of the load node x in the window, P y,t represents the active power of the load node y at the time t, represents the average power of the load node y in the window, and n represents the number of window sampling points.

[0075] Then, the hierarchical clustering method is used to aggregate the load nodes with the correlation degree greater than the correlation degree threshold value into the same electrical cluster, whether they belong to the same geographical area or not, so as to obtain a plurality of load clusters. The division mode of the load cluster is based on the initial geographical boundary, the high load density and the strong electrical correlation are used for accurate positioning, and through the progressive logic of the geographical zoning coarse screening and the electrical correlation fine screening, the calculation efficiency is ensured, and the electrical characteristic consistency of the area is ensured, which lays a foundation for subsequent accurate analysis and calculation.

[0076] According to the topology data of the power grid, all nodes in the load cluster are extracted, including the upstream power supply branch and the downstream load branch, and then only the extracted associated branch and the nodes at both ends are subjected to power flow calculation. At this time, the branch list to be calculated and the corresponding node parameters are obtained, including node voltage amplitude, phase angle difference, line impedance and transformer ratio, etc. According to the branch list and the corresponding node parameters, the branch power flow calculation can be carried out. In the power flow calculation, the conventional power flow calculation method can be used, such as Newton-Raphson method, direct current power flow method and fast decoupling method, etc. Preferably, the fast decoupling power flow method is used to calculate the branch power flow in the embodiment. The fast decoupling power flow method is based on the following two key assumptions: the active power in the power system is mainly related to the voltage phase angle, and the reactive power is mainly related to the voltage amplitude, which makes it possible to decouple the complete power flow equation into two independent sub-problems, namely the active-phase angle sub-problem and the reactive-voltage sub-problem. Through parameter initialization, the two sub-problems are iteratively solved until the convergence condition is reached, so as to obtain the branch power flow calculation results, including the active power, reactive power and current value of each branch. The specific power flow calculation steps can refer to the conventional solving steps of the fast decoupling power flow method, which will not be repeated here.

[0077] For the branch power flow calculation results, the current overload coefficient of each branch is calculated by the branch current value in the embodiment, and according to the comparison result of the current overload coefficient and the overload threshold, it is judged whether the branch is an overload branch. The current overload coefficient is the ratio of the branch current value to the rated current value. Assuming that the overload threshold is 0.95, the branch with a ratio greater than the overload threshold is marked as an overload branch. It can be seen that the overload branch is a branch with large load current. Such branch is prone to line overheating, voltage reduction and other situations, so it belongs to the branch with potential risks.

[0078] In fact, according to the topological structure data and real-time load data of the power grid, the heavily loaded branch in the power grid is determined, in addition to the above-mentioned mode, the power flow calculation can be directly performed on the power grid, so as to obtain the current value of each branch in the power grid, thereby determining the heavily loaded branch; in addition, the current value of each branch can also be obtained by monitoring the current value of the power grid; but the former mode needs to traverse all nodes of the power grid, and the calculation efficiency is low, and the latter mode completely depends on the monitoring data, but not all branches are equipped with monitoring devices, in the actual power grid, only the key nodes (such as hub substations and important load centers) are equipped with high-precision current transformers and voltage transformers, and a large number of distribution network branches are not equipped with real-time monitoring devices, and the acquisition of monitoring data is also directly related to the stability of the transmission network, sensor failure, communication delay or interference may cause data loss, which directly affects the availability of current data. Therefore, the embodiment identifies the electrical cluster by the regional load density and the load correlation degree, and judges the heavily loaded branch by using the power flow calculation, which can not only effectively reduce the calculation amount, but also improve the accuracy of the judgment.

[0079] The heavily loaded branch can be understood as a branch with potential risk, by analyzing the voltage amplitude of the nodes at both ends of the heavily loaded branch, the potential weak point is further determined from the potential risk branch, so as to determine the weak area, the specific steps include:

[0080] According to the real-time load data, the voltage amplitude deviation of the load nodes at both ends of each heavily loaded branch is calculated;

[0081] It is judged whether the voltage amplitude deviation exceeds the amplitude deviation threshold, and the load node exceeding the amplitude deviation threshold is taken as a potential weak point;

[0082] According to the electrical distance, each potential weak point is aggregated to obtain a plurality of weak areas.

[0083] In the embodiment, the voltage amplitude deviation is obtained by calculating the voltage amplitude of the load nodes at both ends of each heavily loaded branch, wherein the calculation steps of the voltage amplitude deviation are to calculate the difference value between the voltage amplitude and the rated voltage first, then divide the difference value by the rated voltage to obtain a ratio, take the absolute value of the ratio and multiply it by a percentage, thereby obtaining the voltage amplitude deviation.

[0084] Then, the load node with a voltage amplitude deviation exceeding a voltage amplitude deviation threshold value is taken as a potential weak point. For example, the voltage amplitude deviation threshold value is set to 5% according to a power grid safety specification. If the threshold value is exceeded, it indicates that the node has a safety risk, and thus the load node is marked as a potential weak point. Finally, the potential weak points are aggregated by electrical distance, that is, the potential weak points are aggregated on the condition that the electrical distance between nodes is less than a distance threshold value, so as to obtain a plurality of weak areas. The weak area is an area with a potential risk.

[0085] Although the weak area contains a plurality of potential weak points, not every potential weak point has the same potential risk. In order to screen out nodes with a high risk level from the potential weak points, the embodiment determines the risk level of the potential weak points by performing voltage stability margin analysis on the weak area and combining the parameters of the potential weak points. First, the voltage stability margin of the weak area is calculated, and the calculation steps include:

[0086] An area equivalent model of the weak area is constructed. According to the area equivalent model, the current active power and the critical active power of the weak area are obtained by power flow calculation.

[0087] The voltage stability margin of the weak area is calculated according to the current active power and the critical active power.

[0088] In the embodiment, the voltage stability margin refers to the maximum disturbance that the power system can withstand without losing voltage stability under given operating conditions, and is an important indicator for measuring the stability of the power system. The voltage stability margin in the embodiment adopts a static voltage stability margin. The mathematical model is the basis for analyzing the voltage stability margin. In order to facilitate calculation, the equivalent model of the area can be constructed by a model. The equivalent model is a simplified model of the area to be calculated. Conventional simplified models include single-machine infinite system model, Thevenin equivalent model, etc. The area equivalent model is constructed by these simplified models. The simplified model can be represented as a nonlinear equation system, and then the nonlinear equation is solved by traditional power flow calculation methods such as Newton-Raphson method or fast decoupled power flow method. The continuous power flow method can also be used for calculation. The continuous power flow method is an advanced technology for calculating the voltage stability margin. By introducing a continuity parameter, the smooth transition of the system operating point is realized, so that the change path of the power flow solution when the parameter changes can be effectively tracked. The specific solving algorithm can be flexibly selected according to the actual situation, and is not specifically limited here.

[0089] By solving the regional equivalent model, the current active power and the critical active power of the weak region can be obtained, wherein the current active power refers to the sum of the active power of all load nodes in the region, and the critical active power refers to the critical power value at which voltage instability occurs in the region under the current network structure. The voltage stability margin is obtained by subtracting the current active power from the critical active power and then dividing by the current active power. The physical meaning of the voltage stability margin is the safety margin ratio of the current load to the critical instability point. Under normal circumstances, the voltage stability margin is a value greater than zero, and the larger the value, the more stable the regional system, and the smaller the value, the higher the risk of the regional system. It can be understood that the voltage stability margin of the region is used as a risk index representing the risk situation of the weak region.

[0090] After obtaining the risk representation of the weak region, the risk index of each load node in the region needs to be calculated, and then the risk index of the node is combined with the risk index of the region to divide the risk level of each load node. The specific steps include:

[0091] Obtain the voltage amplitude deviation, associated overload branch proportion and load growth rate of each potential weak point in the weak region, and calculate the voltage stability margin deviation value according to the voltage stability margin and the reference margin;

[0092] The voltage stability margin deviation value and the voltage amplitude deviation, the associated overload branch proportion and the load growth rate of each potential weak point are normalized and weighted summed to obtain the risk score of each potential weak point;

[0093] According to the comparison result of the risk score and the preset score threshold, the risk level of each potential weak point is determined, and the potential weak point with a risk level higher than the preset level is taken as the first weak point.

[0094] In this embodiment, the voltage amplitude deviation, the associated overload branch proportion and the load growth rate of each potential weak point in the weak region are taken as the risk index of the node, wherein the voltage amplitude deviation can be directly extracted from the voltage amplitude deviation calculation result of the overload branch, or it can be recalculated. The associated overload branch proportion is the ratio of the number of overload branches associated with the node to the number of all branches associated with the node, and the load growth rate is the ratio of the load growth of the node within a preset time period. Among them, the associated overload branch proportion reflects the severity of the node affected by the overload branch, the more the associated overload branches, the more complex the risk propagation path, and the higher the risk degree. The load growth rate represents the load fluctuation of the node, and the higher the growth rate, the stronger the load fluctuation, and the higher the risk degree.

[0095] In order to more accurately characterize the risk situation of the region, according to the power grid safety regulations, a reference margin such as 15% is set, and the difference between the voltage stability margin and the reference margin is taken as a voltage stability margin deviation value, the larger the deviation value, the more stable the regional system, and the smaller the deviation value, the less the margin.

[0096] Then the voltage stability margin deviation value and the voltage amplitude deviation, the associated overload branch proportion and the load growth rate of each potential weak point are normalized and weighted summed to obtain the risk score of each potential weak point. It should be noted that the weight value used in the weighted sum in the embodiment is a pre-set weight value, which can be pre-set by experts or by using weight calculation methods such as analytic hierarchy process. Then the risk score is compared with the pre-set score threshold value, and the risk level of the node is determined according to the threshold range in which the risk score is located. Preferably, four risk levels are set, which are extremely high risk, high risk, medium risk and low risk. For example, let S represent the risk score, and assume that the score threshold values are 0.3, 0.5 and 0.8. When S<0.3, it is a low risk, indicating that the current node stability margin is sufficient, and routine inspection is required. When 0.3≤S<0.5, it is a medium risk, indicating that there is no urgent risk, and monitoring needs to be strengthened. When 0.5≤S<0.8, it is a high risk, indicating that the risk will increase in the short term (such as 2-4 hours). When 0.8≤S, it is an extremely high risk, indicating that voltage instability may occur in a short time (such as 1 hour). Taking the above four levels as an example, the medium risk level can be pre-set, and nodes higher than this level are taken as the first weak point, that is, nodes with higher risk levels are selected for resource regulation according to the risk level.

[0097] The embodiment combines the risk indicators of the region where the node is located while comprehensively judging various risk indicators of the node, so that the risk score of the node is more reasonable and accurate, thereby providing support for subsequent differentiated regulation.

[0098] After the first weak point is screened out, the regulation resource data corresponding to the weak area is obtained, and the resource matching degree between each regulation resource and the first weak point is calculated according to the regulation resource data. The specific steps include:

[0099] According to the voltage stability margin deviation value, the adjustment accuracy requirement of the first weak point is determined, according to the voltage amplitude deviation and the associated overload branch proportion, the capacity requirement of the first weak point is determined, and according to the load growth rate, the response time requirement of the first weak point is determined;

[0100] The available capacity, response time, adjustment accuracy and reliability of each regulation resource are extracted from the regulation resource data;

[0101] Based on the capacity requirement and the available capacity, calculate the capacity matching degree; based on the response time requirement and the response time, calculate the time matching degree; based on the adjustment accuracy requirement and the adjustment accuracy, calculate the accuracy matching degree; and based on the reliability and the risk level, calculate the reliability matching degree.

[0102] The resource matching degree between each control resource and the first weak point is obtained by weighted summation of the capacity matching degree, the time matching degree, the accuracy matching degree and the reliability matching degree.

[0103] In this embodiment, the first step is to acquire the control resource data corresponding to the vulnerable area. This control resource data, based on grid control methods, can be categorized into reactive power compensation resources, active power control resources, network reconfiguration resources, and emergency support resources. Reactive power compensation resources are devices that can quickly adjust reactive power, such as static var generators and capacitor banks. Active power control resources are active loads / power sources that can be reduced or transferred, such as interruptible industrial loads and energy storage power stations. Network reconfiguration resources are switching equipment that can change the topology, such as tie switches and sectionalizing switches. Emergency support resources are temporarily deployed mobile equipment or personnel, such as mobile energy storage vehicles and emergency repair teams. The data collection range extends outward from the vulnerable area, for example, within a geographical area of ​​5km, to ensure rapid resource response, or covers the entire power grid area of ​​the vulnerable area, such as a county-level power grid. Key parameters collected include device ID, rated capacity, response time, current available capacity, reduceable capacity, and maximum cut-off time.

[0104] The available capacity, response time, regulation accuracy, and reliability of each regulation resource are extracted from the acquired regulation resource data. Available capacity refers to the maximum capacity that the resource can call upon. Response time refers to the time from resource startup to reaching the target output. Regulation accuracy refers to the deviation rate between the resource output and the target value, such as SVG voltage regulation deviation ≤ ±1%. Reliability refers to the probability of resource availability within the regulation cycle, such as the percentage of historical fault-free operation time.

[0105] Simultaneously, based on the risk level assessment parameters of the first weak point, its core resource requirements are determined. Specifically, based on the voltage stability margin deviation, the precision regulation requirement is determined. Assuming the voltage stability margin deviation is ΔK, the precision regulation requirement A ≥ (ΔK / α) / 100, where α is the precision coefficient, which can be taken as 0.1. Assuming ΔK = 0.2 and α = 0.1, then A ≥ 2%. Then, based on the voltage amplitude deviation ΔV, the reactive power capacity requirement C1 is determined, where C1 = ΔV * Ss / 100, and Ss represents the node apparent power. Generally, voltage deviation is mainly caused by reactive power imbalance. According to power system circuit theory, the relationship between node voltage amplitude V and reactive power Q can be derived through the Thevenin equivalent circuit, resulting in Q ≈ ΔV * V.N X, V N is the rated voltage, X represents the reactance, the formula indicates that the reactive power Q and the voltage amplitude deviation AV are approximately proportional, so adjusting the reactive power is the core means to improve the voltage deviation, in the reactive power capacity demand formula, the apparent power Ss is used instead of the reactive power Q, on the one hand, in the heavy load node, the proportion of the reactive power in the apparent power is high, the Ss is used for simplified calculation, in addition, in the actual engineering, the total apparent power of the node can be directly read from the SCADA system, and the active / reactive power needs to be additionally calculated (or relies on the power factor table with low accuracy), therefore, the apparent power is used instead of the reactive power in the embodiment, so as to avoid the complexity of data splitting and improve the engineering practicability.

[0106] According to the correlation overload branch ratio R, the active capacity demand C2 is determined, and the calculation formula is: C2=R*P*β, wherein P represents the current active power sum of the correlation overload branch, and β represents the reduction ratio, and the physical meaning of P value is to directly measure the "absolute overload level" of the overload branch, based on the "N-1 safety criterion" of the power system and the overload treatment experience, β can be set to 20%, for a single branch, the active power Pi of the single branch, that is, the first branch, is reduced by 20%, so that it can be controlled within the safety range, and the reduction amount is equal to Pi*0.2, the sum of all correlation overload branches of the node is weighted according to the correlation overload branch ratio, so as to obtain the final active capacity demand C2. It should be noted that the calculation formula of the reactive capacity demand and the active capacity demand in the embodiment is an optimal way, and the reactive capacity demand and the active capacity demand of the node can also be obtained by the power flow calculation and other calculation methods, which are not limited here.

[0107] According to the load growth rate, the response time demand is determined, for example, when the load growth rate is greater than 10%, the response time demand should be less than or equal to 1 hour, otherwise the response time demand should be less than or equal to 3 hours.

[0108] Then, the matching degree is calculated according to the core demand of the first weak point and the resource characteristic parameters of each regulation resource, and the matching degree includes capacity matching degree, time matching degree, accuracy matching degree and reliability matching degree. Specifically, the capacity matching degree M C represents whether the resource available capacity C meets the capacity demand of the weak point, and the formula is: M C =min (C / C1, C / C2), if M C ≥1, it means that the capacity is completely met, and M C =1 is taken, if M C <0.5, it means that the capacity is seriously insufficient, and M C =0 is taken. The time matching degree M TThis indicates whether the resource response time meets the critical time requirement corresponding to the load growth rate. If the resource response time is less than or equal to the response time requirement, then M... T =1, if the resource's response time is greater than the response time requirement but less than or equal to twice the response time requirement, then M T The value is the ratio of the response time requirement to the resource's response time. If the resource's response time is greater than twice the response time requirement, then M... T =0. Precision matching degree M A This indicates whether the resource regulation accuracy meets the accuracy requirements of the voltage stability margin deviation. If the resource regulation accuracy is less than or equal to the regulation accuracy requirement, then M... A =1, if the adjustment precision of the resource is greater than the adjustment precision requirement but less than or equal to twice the adjustment precision requirement, then M A The value is the ratio of the required adjustment accuracy to the resource's adjustment accuracy. If the resource's adjustment accuracy is greater than twice the required adjustment accuracy, then M... A =0. Reliability matching degree M R The matching degree indicates whether the resource reliability meets the requirements of the risk level for continuous regulation. Its value is the resource reliability multiplied by a weight, which is determined based on the node's risk level; that is, the higher the risk level, the higher the reliability requirement, and the larger the weight value. Finally, the various matching degrees calculated above are normalized and weighted summed to obtain the resource matching degree between the node and each regulation resource. It should be noted that the weight values ​​of the matching degree in this embodiment can be flexibly set according to the actual needs of the power grid. For example, each matching degree can have the same weight, or the capacity matching degree can be the primary matching degree, with other matching degrees as auxiliary matching degrees. No specific limitation is made here. Preferably, the capacity matching degree, time matching degree, accuracy matching degree, and reliability matching degree can be set to 0.6, 0.2, 0.1, and 0.1, respectively.

[0109] After determining the risk level of each node and the resource matching degree between each node and the control resources through the above steps, a resource planning scheme is generated based on the node's risk level and resource matching degree. For example, a resource planning scheme is generated with reducing the node's risk level as the control objective and the resource matching degree as the scheduling order. To optimize the control strategy, in a preferred embodiment, the present invention uses a multi-objective optimization model to construct the resource planning model and solve it to obtain the optimal resource planning scheme. The specific steps include:

[0110] With the objective functions of reducing risk level, minimizing resource scheduling cost, and maximizing resource matching degree, and with resource constraints, power grid security constraints, and response time constraints as constraints, a resource planning model based on multi-objective optimization is established.

[0111] The non-dominated sorting genetic algorithm is used to solve the resource planning model to obtain a Pareto solution set;

[0112] According to the current risk level of the first weak point, an optimal solution is selected from the Pareto solution set, and a resource planning scheme is generated according to the optimal solution.

[0113] In this embodiment, the optimization objectives of the resource planning model include reducing the risk level, minimizing the resource scheduling cost, and maximizing the resource matching degree, wherein the optimization direction of reducing the risk level is to reduce the target value by at least one level from the current level; the resource scheduling cost includes the resource calling cost, the resource transmission loss cost, and the standby resource reservation cost, and the optimization direction of minimizing the resource scheduling cost is to minimize the sum of the costs; and the optimization objective of maximizing the resource matching degree is to maximize the sum of the resource matching degrees of the called control resources and the nodes.

[0114] The constraint conditions of the resource planning model include resource constraints, power grid safety constraints, and response time constraints, wherein the resource constraints refer to that the deployment capacity of each control resource is less than or equal to the upper limit of the resource capacity; the power grid safety constraints refer to that the node voltage deviation is within the deviation threshold range and the line flow is less than or equal to the flow threshold; and the response time constraints refer to that the response time of different risk levels is less than or equal to the corresponding risk response time threshold, for example, the response time of the extremely high risk level should be less than 30 minutes, the response time of the high risk level should be less than 2 hours, and the like.

[0115] Based on the above optimization objectives, a multi-objective function is constructed, and in combination with the above constraint conditions, the resource planning model is obtained. It can be seen that the model is a multi-objective optimization model, and therefore a conventional multi-objective optimization model solving algorithm such as a genetic algorithm, a multi-objective particle swarm optimization algorithm, or a multi-objective ant colony algorithm can be used for solving. In this embodiment, a non-dominated sorting genetic algorithm is preferably used to solve the resource planning model, and a Pareto solution set is obtained through Pareto front screening, and the screening criteria include risk level compliance: ensuring that all schemes in the solution set reduce the risk level by at least one level; cost-matching degree trade-off: in the compliance schemes, selecting solutions with a cost lower than the average value and a matching degree higher than 0.85; and robustness verification: simulating N-1 fault scenarios and eliminating schemes with a voltage stability margin decrease of >0.1.

[0116] Finally, according to the risk level of the node, an optimal solution is selected from the Pareto solution set, thereby obtaining an optimal resource planning scheme, and the steps include:

[0117] According to the current risk level of the first weak point, a scene type is determined, and the scene type includes an emergency scene and a regular scene;

[0118] In response to the emergency scene, an optimal solution is selected from the Pareto solution set based on the lowest risk level.

[0119] In response to a regular scenario, an optimal solution is selected from the Pareto solution set based on the lowest scheduling cost.

[0120] In this embodiment, the scenario type is determined according to the risk level, and the scenario type includes an emergency scenario and a regular scenario. Assuming that nodes of an extremely high risk level and a high risk level are selected in the preset level in the early stage, the extremely high risk level is taken as the emergency scenario, and the high risk level is taken as the regular scenario. If nodes of the extremely high risk level, the high risk level and a medium risk level are selected in the early stage, the extremely high risk level is taken as the emergency scenario, and the remaining levels are taken as the regular scenario. For the emergency scenario, a solution with the lowest risk level is selected from the Pareto solution set, that is, a node with the largest risk level reduction is selected. Assuming that there are two scheduling planning solutions in the Pareto solution set, and the risk levels of the two scheduling planning solutions after scheduling are a high risk level and a medium risk level respectively, the solution with the medium risk level is selected. For the regular scenario, a planning solution with the lowest scheduling cost is selected from the Pareto solution set. In this embodiment, the relationship between risk, cost and matching degree is balanced through the resource planning model, and finally a resource regulation strategy with safety and economy is generated, so that differentiated regulation of the nodes of the power grid is realized, and the dynamic adaptability of the regulation strategy is ensured.

[0121] The power grid resource planning method for a voltage weak area provided in this embodiment improves the accuracy of state perception of the power grid system through the cooperative analysis of power flow calculation and monitoring data, improves the identification accuracy of weak points through the establishment of a comprehensive scoring system of multiple parameters, and accurately calculates the resource matching degree between nodes and resources through the construction of a four-dimensional matching degree model, thereby effectively shortening the time of regulation and decision and improving the decision efficiency. At the same time, through the multi-objective resource planning model, the adaptability and safety of the multi-scenario regulation strategy are realized. Through the closed-loop design of multi-dimensional risk identification, accurate resource matching and multi-scenario strategy optimization, the systematic improvement of the risk control of the weak points of the power system is realized, thereby ensuring the safety and stability of the operation of the power grid.

[0122] Referring to Figure 2 , based on the same inventive concept, the power grid resource planning system for a voltage weak area provided in the second embodiment of the present application comprises:

[0123] The weak area division module 10 is configured to perform branch power flow calculation according to the topological structure data and real-time load data of the power grid to obtain a branch power flow calculation result, calculate a current overload coefficient of each branch according to the branch power flow calculation result, and determine an overload branch according to the current overload coefficient.

[0124] According to the voltage amplitude deviation of the load nodes at both ends of the heavy load branch, a potential weak point is determined, the potential weak points are aggregated to obtain several weak areas, and voltage stability margins of the weak areas are calculated;

[0125] The weak point screening module 20 is configured to obtain a risk indicator of each potential weak point in the weak area, calculate a risk level of the each potential weak point according to the voltage stability margin and the risk indicator, and screen a first weak point from the each potential weak point according to the risk level.

[0126] The resource matching calculation module 30 is configured to obtain control resource data corresponding to the weak area, and calculate a resource matching degree between each control resource and the first weak point according to the control resource data.

[0127] The scheme generation module 40 is configured to establish a resource planning model according to the risk level and the resource matching degree, and solve the resource planning model to obtain a resource planning scheme.

[0128] The technical features and technical effects of the power grid resource planning system for voltage weak areas according to the embodiments of the present application are the same as those of the method according to the embodiments of the present application, and are not repeated here. The above-mentioned modules in the power grid resource planning system for voltage weak areas can be realized by software, hardware, or a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to the above-mentioned modules.

[0129] In summary, the power grid resource planning method and system based on voltage weak areas proposed in the embodiments of the present application, the method performs branch power flow calculation according to the topological structure data and real-time load data of the power grid, obtains the branch power flow calculation result, calculates the current overload coefficient of each branch according to the branch power flow calculation result, determines the overloaded branch according to the current overload coefficient, determines the potential weak point according to the voltage amplitude deviation of the load nodes at both ends of the overloaded branch, aggregates the potential weak points to obtain a plurality of weak areas, and calculates the voltage stability margin of each weak area; obtain the risk index of each potential weak point in the weak area, calculate the risk level of each potential weak point according to the voltage stability margin and the risk index, and select a first weak point from each potential weak point according to the risk level; obtain the corresponding regulation and control resource data of the weak area, calculate the resource matching degree between each regulation and control resource and the first weak point according to the regulation and control resource data; according to the risk level and the resource matching degree, a resource planning model is established and solved to obtain a resource planning scheme. The present application improves the accuracy of power grid system state perception through the collaborative analysis of power flow calculation and monitoring data, improves the identification accuracy of weak points by establishing a comprehensive scoring system with multiple parameters, and accurately calculates the resource matching degree between nodes and resources by constructing a four-dimensional matching degree model, thereby effectively shortening the time of regulation and control decision and improving the decision efficiency. At the same time, through the multi-objective resource planning model, the adaptability and safety of the multi-scenario regulation and control strategy are realized. The present application realizes the systematic improvement of the weak point risk control of the power system through the closed-loop design of multi-dimensional risk identification, accurate resource matching and multi-scenario strategy optimization, thereby ensuring the safety and stability of the power grid operation.

[0130] Each of the embodiments in the specification is described in a progressive manner, and the same or similar parts of each embodiment can be referred to each other, and each embodiment mainly describes the difference from other embodiments. Especially, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the related parts can be referred to the part of the method embodiment. It should be noted that, each technical feature of the above-mentioned embodiments can be combined arbitrarily, in order to make the description simple, not all possible combinations of each technical feature in the above-mentioned embodiments are described, however, as long as the combination of these technical features does not exist contradictory, it should be considered as the scope of the present application.

[0131] The above embodiments only express several preferred embodiments of the present application, which are described in a more specific and detailed manner, but cannot be understood as a limitation to the patent scope of the application. It should be pointed out that, for ordinary skilled in the art, several improvements and replacements can be made without departing from the technical principles of the present application, and these improvements and replacements should also be considered as the protection scope of the present application. Therefore, the protection scope of the present application patent should be subject to the protection scope of the claims.

Claims

1. A method for voltage weak area based grid resource planning, the method comprising: The method comprises the following steps: According to the topology data and real-time load data of the power grid, branch power flow calculation is performed to obtain branch power flow calculation results, the current overload coefficient of each branch is calculated according to the branch power flow calculation results, and the overload branch is determined according to the current overload coefficient; According to the voltage amplitude deviation of the load nodes at both ends of the overload branch, a potential weak point is determined, the potential weak points are aggregated to obtain a plurality of weak areas, and the voltage stability margin of each weak area is calculated; The risk indicators of each potential weak point in the weak area are obtained, the risk level of each potential weak point is calculated according to the voltage stability margin and the risk indicator, and a first weak point is screened out from each potential weak point according to the risk level; The control resource data corresponding to the weak area is obtained, and the resource matching degree between each control resource and the first weak point is calculated according to the control resource data; According to the risk level and the resource matching degree, a resource planning model is established and solved to obtain a resource planning scheme; The step of obtaining the risk indicators of each potential weak point in the weak area, calculating the risk level of each potential weak point according to the voltage stability margin and the risk indicator, and screening out a first weak point from each potential weak point according to the risk level comprises: The voltage amplitude deviation, associated overload branch proportion and load growth rate of each potential weak point in the weak area are obtained, and the voltage stability margin deviation value is calculated according to the voltage stability margin and the reference margin; The voltage stability margin deviation value, the voltage amplitude deviation, the associated overload branch proportion and the load growth rate of each potential weak point are normalized and weighted summed to obtain the risk score of each potential weak point; According to the comparison result of the risk score and the preset score threshold, the risk level of each potential weak point is determined, and the potential weak point with a risk level higher than the preset level is taken as the first weak point.

2. The voltage-weak-area-based grid resource planning method of claim 1, wherein, The step of performing branch power flow calculation according to the topology data and real-time load data of the power grid to obtain branch power flow calculation results comprises: According to the topology data of the power grid, the substation position is determined, the power supply area is determined according to the substation position, and the area load density is calculated according to the area area of the power supply area and the real-time load data of each load node; The power supply area with the area load density exceeding the density threshold value is taken as a high load density area, the load correlation degree between the load nodes and the adjacent nodes in the high load density area is calculated by using the Pearson correlation coefficient according to the real-time load data and the topology data; According to the comparison result of the load correlation degree and the correlation degree threshold value, each load node in the high load density area is clustered and divided to obtain a plurality of load clusters; According to the load nodes of each load cluster and the topology data, the directly associated branches of each load node are extracted, and the branch power flow calculation is performed on the directly associated branches to obtain the branch power flow calculation results.

3. The voltage-weak-area-based grid resource planning method of claim 2, wherein, The step of determining potential weak points according to the voltage amplitude deviation of the load nodes at both ends of the heavy load branch, aggregating the potential weak points, and obtaining several weak areas comprises: According to the real-time load data, the voltage amplitude deviation of the load nodes at both ends of each heavy load branch is calculated; It is judged whether the voltage amplitude deviation exceeds the amplitude deviation threshold value, and the load nodes exceeding the amplitude deviation threshold value are taken as potential weak points; According to the electrical distance, each potential weak point is aggregated to obtain several weak areas.

4. The voltage-weak-area-based grid resource planning method of claim 1, wherein, The step of calculating the voltage stability margin of each weak area comprises: A regional equivalent model of the weak area is constructed, and the current active power and critical active power of the weak area are obtained through power flow calculation according to the regional equivalent model; According to the current active power and the critical active power, the voltage stability margin of the weak area is calculated.

5. The voltage-weak-area-based grid resource planning method of claim 1, wherein, The step of calculating the resource matching degree between each control resource and the first weak point according to the control resource data comprises: According to the voltage stability margin deviation value, the adjustment accuracy requirement of the first weak point is determined, the capacity requirement of the first weak point is determined according to the voltage amplitude deviation and the proportion of the associated heavy load branch, and the response time requirement of the first weak point is determined according to the load growth rate; The available capacity, response time, adjustment accuracy and reliability of each control resource are extracted from the control resource data; According to the capacity requirement and the available capacity, the capacity matching degree is calculated, according to the response time requirement and the response time, the time matching degree is calculated, according to the adjustment accuracy requirement and the adjustment accuracy, the accuracy matching degree is calculated, and according to the reliability and the risk level, the reliability matching degree is calculated; The capacity matching degree, the time matching degree, the accuracy matching degree and the reliability matching degree are weighted and summed to obtain the resource matching degree between each control resource and the first weak point.

6. The voltage-weak-area-based grid resource planning method of claim 1, wherein, The step of establishing a resource planning model according to the risk level and the resource matching degree, and solving the resource planning model to obtain a resource planning scheme comprises: A resource planning model based on multi-objective optimization is established, taking the reduction of risk level, the minimization of resource scheduling cost and the maximization of resource matching degree as the objective function, and taking resource constraints, power grid safety constraints and response time constraints as the constraint conditions; A non-dominated sorting genetic algorithm is used to solve the resource planning model to obtain a Pareto solution set; According to the current risk level of the first weak point, an optimal solution is selected from the Pareto solution set, and a resource planning scheme is generated according to the optimal solution.

7. The voltage-weak-area-based grid resource planning method of claim 6, wherein, The step of taking resource constraints, power grid safety constraints and response time constraints as constraint conditions comprises: The deployment capacity of each control resource is less than or equal to the upper limit of the resource capacity as the resource constraint; The node voltage deviation meets the deviation threshold value, and the line flow is less than or equal to the flow threshold value as the power grid safety constraint; The response time of different risk levels is less than or equal to the corresponding risk response time threshold value as the response time constraint.

8. The voltage-weak-area-based grid resource planning method of claim 6, wherein, The step of selecting an optimal solution from the Pareto solution set according to the current risk level of the first weak point comprises: determining a scenario type according to the current risk level of the first weak point, the scenario type including an emergency scenario and a normal scenario; selecting an optimal solution from the Pareto solution set based on the lowest risk level in response to the emergency scenario; selecting an optimal solution from the Pareto solution set based on the lowest dispatching cost in response to the normal scenario.

9. A voltage weak area based grid resource planning system, characterized in that, The system is applied to the method of any one of claims 1 to 8, comprising: a weak area division module configured to perform branch flow calculation according to topology data and real-time load data of the power grid to obtain a branch flow calculation result, calculate a current overload coefficient of each branch according to the branch flow calculation result, and determine an overload branch according to the current overload coefficient; determine a potential weak point according to a voltage amplitude deviation of load nodes at two ends of the overload branch, aggregate the potential weak points to obtain a plurality of weak areas, and calculate a voltage stability margin of each weak area; a weak point screening module configured to obtain a risk indicator of each potential weak point in the weak area, calculate a risk level of the each potential weak point according to the voltage stability margin and the risk indicator, and screen a first weak point from the each potential weak point according to the risk level; a resource matching calculation module configured to obtain dispatching resource data corresponding to the weak area, and calculate a resource matching degree between each dispatching resource and the first weak point according to the dispatching resource data; a scheme generation module configured to establish a resource planning model according to the risk level and the resource matching degree, and perform solving to obtain a resource planning scheme.

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