Power grid resource planning method and system based on weak voltage region

Abstract

The invention 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 region, and the method comprises the steps: obtaining a branch load flow calculation result according to the topological structure data and real-time load data of a power grid, calculating the current overload coefficient of a branch, and determining a heavy load branch; according to the voltage amplitude deviation of the load node of the heavy load branch, potential weak points are determined and aggregated to obtain a weak area, and the voltage stability margin of the weak area is calculated; obtaining a risk index of each potential weak point, calculating a risk level of the potential weak point according to the voltage stability margin and the risk index, and screening out a first weak point; calculating a resource matching degree between the regulation and control resource and the first weak point; and according to the risk level and the resource matching degree, establishing a resource planning model to obtain a resource planning scheme. According to the invention, through multi-dimensional risk identification, accurate resource matching and multi-scene strategy optimization, the accuracy and dynamic adaptability of resource planning are improved.

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 characteristics of the power grid have 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: 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; 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; 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 a first weak point is selected from each potential weak point according to the risk level; The corresponding control resource data of 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.

[0006] Further, the step of performing branch power flow calculation according to the topology data and the real-time load data of the power grid to obtain a branch power flow calculation result comprises: According to the topology data of the power grid, a substation position is determined, a power supply area is determined according to the substation position, and a regional load density is calculated according to the area of the power supply area and the real-time load data of each load node; The power supply area with the regional load density exceeding a density threshold is taken as a high load density area, and a 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 data by using a Pearson correlation coefficient; According to the comparison result of the load correlation degree and a correlation degree threshold, 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, a directly associated branch of each load node is extracted, and a power flow calculation is performed on the directly associated branch to obtain a branch power flow calculation result.

[0007] Further, the step of determining a potential weak point according to the voltage amplitude deviation of the load nodes at both ends of the heavy load branch, and aggregating the potential weak point to obtain a plurality of 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 an amplitude deviation threshold, and the load node exceeding the amplitude deviation threshold is taken as a potential weak point; According to the electrical distance, each potential weak point is aggregated to obtain a plurality of weak areas.

[0008] Further, 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 the critical active power of the weak area are obtained by 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.

[0009] Further, the step of obtaining the risk index 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 index, and screening a first weak point from each potential weak point according to the risk level comprises: obtain voltage amplitude deviation, associated overload branch proportion and load growth rate of each potential weak point in the weak area, and calculate voltage stability margin deviation value according to the voltage stability margin and the reference margin; normalize and weightedly sum 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; determine a risk level of each potential weak point according to a comparison result of the risk score and a preset score threshold, and take the potential weak point with a risk level higher than a preset level as the first weak point.

[0010] Further, the step of calculating resource matching degree between each control resource and the first weak point according to the control resource data comprises: determining adjustment accuracy requirement of the first weak point according to the voltage stability margin deviation value, determining capacity requirement of the first weak point according to the voltage amplitude deviation and the associated overload branch proportion, and determining response time requirement of the first weak point according to the load growth rate; extract available capacity, response time, adjustment accuracy and reliability of each control resource from the control resource data; calculate capacity matching degree according to the capacity requirement and the available capacity, calculate time matching degree according to the response time requirement and the response time, calculate accuracy matching degree according to the adjustment accuracy requirement and the adjustment accuracy, and calculate reliability matching degree according to the reliability and the risk level; weightedly sum 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.

[0011] Further, the step of establishing a resource planning model and solving it according to the risk level and the resource matching degree to obtain a resource planning scheme comprises: establish a resource planning model based on multi-objective optimization with reduction of risk level, minimization of resource scheduling cost and maximization of resource matching degree as objective functions, and with resource constraint, power grid safety constraint and response time constraint as constraint conditions; solve the resource planning model by using a non-dominated sorting genetic algorithm to obtain a Pareto solution set; select an optimal solution from the Pareto solution set according to the current risk level of the first weak point, and generate a resource planning scheme according to the optimal solution.

[0012] Further, the step of taking resource constraint, power grid safety constraint and response time constraint as constraint conditions comprises: The resource constraint is that the deployment capacity of each regulation resource is less than or equal to the upper limit of the resource capacity; The power grid safety constraint is that the node voltage deviation is within the deviation threshold range and the line power flow is less than or equal to the power flow threshold; The response time constraint is that the response time of different risk levels is less than or equal to the corresponding risk response time threshold.

[0013] Further, the step of selecting the optimal solution from the Pareto solution set according to the current risk level of the first weak point comprises: According to the current risk level of the first weak point, the scene type is determined, and the scene type includes an emergency scene and a regular scene; In response to the emergency scene, the optimal solution is selected from the Pareto solution set based on the lowest risk level; In response to the regular scene, the optimal solution is selected from the Pareto solution set based on the lowest scheduling cost.

[0014] In a second aspect, the present application provides a power grid resource planning system based on a voltage weak area, which comprises: A weak area division module 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 the current overload coefficient of each branch according to the branch power flow calculation result, and determine the overload branch 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. A weak point screening module is configured to obtain the risk indicators 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 indicators, and screen a first weak point from the potential weak points according to the risk level. A resource matching calculation module is configured to 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. A scheme generation module is configured to establish a resource planning model according to the risk level and the resource matching degree, and solve the model to obtain a resource planning scheme.

[0015] The application provides a power grid resource planning method and system based on a voltage weak area. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 is a flowchart of the power grid resource planning method based on a voltage weak area in the embodiment of the application; Figure 2 is a structural diagram of the power grid resource planning system based on a voltage weak area in the embodiment of the application; Reference signs: 10, weak area division module; 20, weak point screening module; 30, resource matching calculation module; 40, scheme generation module. DETAILED DESCRIPTION

[0017] To make the objectives, technical solutions and advantages of the embodiments of the application clearer, the technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are some but not all of the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the application.

[0018] Please refer to Figure 1 The power grid resource planning method based on a voltage weak area provided by the first embodiment of the application comprises steps S10-S50. Step S10: According to the topological structure data and real-time load data of the power grid, branch current flow calculation is performed to obtain a branch current flow calculation result. According to the branch current flow calculation result, the current overload coefficient of each branch is calculated. According to the current overload coefficient, the overload branch is determined. Step S20: According to the voltage amplitude deviation of the load nodes at both ends of the overload branch, the 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. In step S30, a risk index of each potential weak point in the weak area is obtained, a risk level of each potential weak point is calculated according to the voltage stability margin and the risk index, and a first weak point is screened from each potential weak point according to the risk level; In step S40, regulation resource data corresponding to the weak area is obtained, and a resource matching degree between each regulation resource and the first weak point is calculated according to the regulation resource data. 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.

[0019] The present application provides a method for weak area analysis and resource scheduling planning based on the topology of a power grid. First, topology data of the power grid and real-time load data of each load node are obtained. The topology data can be collected from a Supervisory Control And Data Acquisition (SCADA) system or a Geographic Information System (GIS) of the power grid. The topology data includes node connection relationships and electrical parameters, such as line impedance, transformer ratio, and device parameters. The real-time load data is collected by a substation monitoring device or a Phasor Measurement Unit (PMU). The real-time load data includes active power, reactive power, voltage amplitude, and phase angle difference of each node. Then, the data is preprocessed, including data cleaning and standardization. Data cleaning includes removing outliers and filling in missing data. Standardization normalizes voltage amplitude and power indicators to eliminate dimensional effects.

[0020] Through data preprocessing, a standardized data set is obtained. Then, branch flow calculation is performed on the standardized data set to obtain branch flow calculation results. The specific steps include: According to the topology data of the power grid, the location of the substation is determined. According to the location of the substation, the power supply area is determined. According to the area of the power supply area and the real-time load data of each load node, the regional load density is calculated. The power supply area with a regional load density exceeding a density threshold is regarded as a high load density area. According to the real-time load data and the topology data, the Pearson correlation coefficient is used to calculate the load correlation between the load nodes and adjacent nodes in the high load density area. According to the comparison result of the load correlation and the correlation threshold, each load node in the high load density area is clustered and divided to obtain multiple load clusters. According to the load nodes of each load cluster and the topology structure data, a directly associated branch of each load node is extracted, and power flow calculation is performed on the directly associated branch to obtain a branch power flow calculation result.

[0021] In the embodiment, first, the power grid is divided according to the topology structure data, the positions of the substations are extracted from the topology structure data, the power supply radius of the substations is taken as the area radius, and each power supply area is determined. Then, the boundary of the power supply area is corrected. The correction rule is that if there is load intersection between adjacent substation power supply areas, for example, the same industrial park is supplied by two substations, the two can be merged according to a preset rule. The preset rule is to calculate the electrical distance between the nodes in the intersection area and 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 substation power supply areas are merged into a “joint power supply area”.

[0022] 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 synchronism of the load of adjacent nodes changing over time.

[0023] 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, for example, the area with a load density greater than the density threshold is retained, and the low load density area is quickly excluded, thereby reducing the subsequent calculation range.

[0024] Although the area with high intensity of electricity can be screened out by the area load density, these areas are divided in geographical areas, which does not take into account the electrical correlation between nodes. For example, a steel plant (three-shift continuous high load) and a residential area (night peak load) are included in a power supply area, 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 synchronism due to close interconnection of the lines, but the geographical division will separate them, but they should be in the same cluster in terms of electricity.

[0025] 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, and the load correlation degree of the nodes in the area is calculated, so as to avoid full grid traversal and reduce the calculation amount. Preferably, the load correlation degree is calculated by using the sliding window Pearson correlation coefficient, and the formula is as follows: 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.

[0026] 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 the progressive logic of the geographical zoning coarse screening and the electrical correlation fine screening ensures the calculation efficiency and the consistency of the electrical characteristics of the area, and lays a foundation for the subsequent accurate analysis and calculation.

[0027] 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.

[0028] 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.

[0029] In fact, according to the topology 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 manner, 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; however, the former method needs to traverse all nodes of the power grid, and the calculation efficiency is low, and the latter method 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 and transmission network stability of the monitoring data are directly related, sensor failure, communication delay or interference may cause data loss, which directly affects the availability of current data. Therefore, the electrical cluster is identified by the regional load density and load correlation degree, and the power flow calculation is adopted to determine the heavily loaded branch, which can not only effectively reduce the calculation amount, but also improve the accuracy of the determination.

[0030] 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, thereby determining the weak area, and the specific steps include: 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; It is judged whether the voltage amplitude deviation exceeds the amplitude deviation threshold value, and the load node exceeding the amplitude deviation threshold value is taken as a potential weak point; According to the electrical distance, the potential weak points are aggregated to obtain a plurality of weak areas.

[0031] In this 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.

[0032] Then, the load node with the voltage amplitude deviation exceeding the amplitude deviation threshold value is taken as a potential weak point, for example, according to the power grid safety specification, the amplitude deviation threshold value is set to 5%, if the threshold value is exceeded, it means that the node has a safety hazard, which can be understood as the load node has a potential safety risk, therefore it is marked as a potential weak point. Finally, the potential weak points are aggregated by the electrical distance, that is, the aggregation condition is that the electrical distance between the nodes is less than the distance threshold value, the potential weak points are aggregated, thereby obtaining a plurality of weak areas, and the weak area is the area with potential risk.

[0033] Although the weak area contains multiple potential weak points, not every potential weak point has the same potential risk. In order to screen out the nodes with high risk levels from the potential weak points, the embodiment determines the risk levels 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: constructing a regional equivalent model of the weak area, and obtaining the current active power and the critical active power of the weak area through power flow calculation according to the regional equivalent model; calculating the voltage stability margin of the weak area according to the current active power and the critical active power.

[0034] 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 region can be constructed through the model. The equivalent model is a simplified model of the region to be calculated. Conventional simplified models include single-machine infinite system model, Thevenin equivalent model, etc. The regional equivalent model is constructed through 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 decoupling 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.

[0035] By solving the regional equivalent model, the current active power and the critical active power of the weak area can be obtained. 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 the voltage instability occurs in the region under the current network structure. The critical active power is subtracted from the current active power, and then divided by the current active power, to obtain the voltage stability margin. 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 greater than zero, and the larger the value is, the more stable the regional system is. The smaller the value is, the higher the risk of the regional system is. It can be understood that the voltage stability margin of the region is used as a risk indicator representing the risk situation of the weak area in the embodiment.

[0036] After the risk characterization of the weak area is obtained, the risk indicators of each load node in the area are calculated, and then the risk indicators of the nodes are combined with the risk indicators of the area to divide the risk levels of each load node, and the specific steps include: 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 and the voltage amplitude deviation, associated overload branch proportion and 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.

[0037] In this embodiment, the voltage amplitude deviation, associated overload branch proportion and load growth rate of each potential weak point in the weak area are taken as the risk indicators of the nodes, wherein the voltage amplitude deviation can be directly extracted from the calculation result of the voltage amplitude deviation of the overload branch, or 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 length. Among them, the associated overload branch proportion reflects the severity of the influence of the node by the overload branch, the more the associated overload branches, the more complex the risk propagation path, and the higher the risk degree, and the load growth rate represents the load fluctuation of the node, the higher the growth rate, the stronger the load fluctuation, and the higher the risk degree.

[0038] In order to more accurately represent the risk situation of the area, according to the power grid safety regulations, the reference margin is set, such as 15%, and the difference between the voltage stability margin and the reference margin is taken as the voltage stability margin deviation value, the larger the deviation value, the more stable the area system, and the deviation value less than zero indicates that the margin is insufficient.

[0039] 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 a 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 using weight calculation methods such as analytic hierarchy process. Then, the risk score is compared with pre-set score thresholds, and according to the threshold range in which the risk score is located, the risk level of the node is determined. 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 thresholds are 0.3, 0.5 and 0.8. When S < 0.3, it is low risk, indicating that the current node stability margin is sufficient, and regular inspection is sufficient. When 0.3≤S < 0.5, it is medium risk, indicating that there is no urgent risk, and monitoring needs to be strengthened. When 0.5≤S < 0.8, it is high risk, indicating that the risk will increase in the short term (such as 2-4 hours). When 0.8≤S, it is 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 regarded as the first weak point, that is, nodes with higher risk levels are selected for resource regulation according to the risk level.

[0040] The embodiment combines the risk indicators of the node and the area where the node is located when 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.

[0041] 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: The adjustment accuracy requirement of the first weak point is determined according to the voltage stability margin deviation value, the capacity requirement of the first weak point is determined according to the voltage amplitude deviation and the associated overload branch proportion, 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 regulation resource are extracted from the regulation resource data; The capacity matching degree is calculated according to the capacity requirement and the available capacity, the time matching degree is calculated according to the response time requirement and the response time, the accuracy matching degree is calculated according to the adjustment accuracy requirement and the adjustment accuracy, and the reliability matching degree is calculated according to the reliability and the risk level; The capacity matching degree, the time matching degree, the accuracy matching degree and the reliability matching degree are weighted summed to obtain the resource matching degree between each regulation resource and the first weak point.

[0042] In the embodiment, first, the control resource data corresponding to the weak area is acquired, wherein the control resource data can be divided into reactive power compensation resource, active power control resource, network reconfiguration resource and emergency support resource based on power grid control means. The reactive power compensation resource is a device capable of quickly adjusting reactive power, such as static var generator, capacitor bank, etc. The active power control resource is active load / power that can be cut or transferred, such as interruptible industrial load, energy storage power station, etc. The network reconfiguration resource is a switching device capable of changing the topology structure, such as tie switch, sectionalizing switch, etc. The emergency support resource is a temporary deployment of mobile equipment or personnel, such as mobile energy storage vehicle, emergency repair team, etc. The data collection range is centered on the weak area and extends outward to a geographical range of, for example, 5 km, to ensure the rapid response of the resource or cover the power grid area of the weak area, such as the district-level power grid, etc. The key parameters collected include device ID, rated capacity, response time, current available capacity, cuttable capacity, maximum trip time, etc.

[0043] The available capacity, response time, adjustment accuracy and reliability of each control resource are extracted from the acquired control resource data, wherein the available capacity refers to the maximum capacity that can be adjusted by the resource, the response time refers to the time from starting to reaching the target output of the resource, the adjustment accuracy refers to the deviation rate of the resource output from the target value, such as SVG voltage regulation deviation ≤ ± 1%, and the reliability refers to the availability probability of the resource within the control period, such as the historical fault-free operation time ratio.

[0044] Meanwhile, according to the risk level evaluation parameters of the first weak point, the core demand for resources is determined. Specifically, according to the voltage stability margin deviation value, the precision adjustment demand A is determined, assuming that the voltage stability margin deviation value is △K, then the precision adjustment demand A ≥ (△K / α) / 100, α is the precision coefficient, which can be taken as 0.1, assuming that ΔK = 0.2, α = 0.1, then A ≥ 2%. Then according to the voltage amplitude deviation △V, the reactive power capacity demand C1 is determined, wherein C1 = △V*Ss / 100, wherein Ss represents the node apparent power. Generally speaking, the voltage deviation is mainly caused by the imbalance of reactive power. According to the circuit theory of power system, the relationship between node voltage amplitude V and reactive power Q can be derived through Thevenin equivalent circuit, and Q ≈ △V*V N / X, V NFor the rated voltage, X represents the reactance, this 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 higher, by using Ss 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 splitting of the active power / reactive power needs additional calculation (or relies on the power factor table with lower accuracy), therefore, the apparent power is used instead of the reactive power in this embodiment, so as to avoid the complexity of data splitting and improve the engineering practicability.

[0045] According to the correlation overload branch ratio R, the active power capacity demand C2 is determined, and the calculation formula is: C2=R*P*β, wherein P represents the current total active power 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, i.e. 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 power capacity demand C2. It should be noted that the calculation formula of the reactive power capacity demand and the active power capacity demand in this embodiment is the preferred mode, and the reactive power capacity demand and the active power capacity demand of the node can also be obtained by the power flow calculation and other calculation modes, which are not limited here.

[0046] 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.

[0047] 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 TIndicates 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 response time is greater than the response time requirement and less than or equal to 2 times the response time requirement, then M T The value is the ratio of the response time requirement to the resource response time. If the resource response time is greater than 2 times the response time requirement, then M T =0. Accuracy matching degree M A Indicates whether the resource regulation accuracy meets the voltage stability margin deviation accuracy requirement. If the resource regulation accuracy is less than or equal to the regulation accuracy requirement, then M A =1, if the resource adjustment accuracy is greater than the adjustment accuracy requirement and less than or equal to 2 times the adjustment accuracy requirement, then M A The value is the ratio of the adjustment accuracy requirement to the adjustment accuracy of the resource. If the adjustment accuracy of the resource is greater than 2 times the adjustment accuracy requirement, then M A =0. Reliability matching degree M R Indicates whether the resource reliability meets the risk level's requirement for continuous regulation. Its value is the resource reliability multiplied by a weight. The weight is determined based on the node's risk level, i.e., the higher the risk level, the higher the reliability requirement, and the greater the weight value. Finally, each matching degree obtained by the above calculation is normalized and weighted summed to obtain the resource matching degree between the node and each regulated resource. It should be noted that the weight value of the matching degree in this embodiment can be flexibly set according to the actual needs of the power grid, such as the weight of each matching degree is the same, or the capacity matching degree is used as the main matching degree, and other matching degrees are used as auxiliary matching degrees for weight setting. 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.

[0048] After determining the risk level of each node and the resource matching degree between each node and the control resource through the above steps, a resource planning scheme is generated based on the risk level and resource matching degree of the node. For example, a resource planning scheme is generated with reducing the risk level of the node as the control goal and resource matching degree as the scheduling order. In order to optimize the control strategy, in a preferred embodiment, the present invention adopts a multi-objective optimization model to construct a resource planning model and solves it to obtain the optimal resource planning scheme. The specific steps include: A resource planning model based on multi-objective optimization is established with the objective functions of reducing risk level, minimizing resource scheduling cost and maximizing resource matching, and with resource constraints, grid security constraints and response time constraints as constraints. 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.

[0049] In this embodiment, the optimization target of the resource planning model includes 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 resource calling cost, resource transmission loss cost, and 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 target of maximizing the resource matching degree is to maximize the sum of the resource matching degrees of the called regulation resources and nodes.

[0050] 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 regulation 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.

[0051] Based on the above optimization target, a multi-objective function is constructed, and in combination with the above constraint conditions, a 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.

[0052] Finally, according to the risk level of the node, an optimal solution is selected from the Pareto solution set, so as to obtain an optimal resource planning scheme, and the steps include: According to the current risk level of the first weak point, a scenario type is determined, and the scenario type includes an emergency scenario and a regular scenario; In response to the emergency scenario, an optimal solution is selected from the Pareto solution set based on the lowest risk level; In response to the regular scenario, an optimal solution is selected from the Pareto solution set based on the lowest scheduling cost.

[0053] In this embodiment, the scene type is determined according to the risk level, the scene type includes an emergency scene and a regular scene, 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 scene, and the high risk level is taken as the regular scene, 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 scene, and the remaining levels are taken as the regular scene, for the emergency scene, a scheme 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 schemes in the Pareto solution set, and the risk levels of the two scheduling planning schemes are a high risk level and a medium risk level respectively, the scheme with the medium risk level is selected, for the regular scene, a planning scheme with the lowest scheduling cost is selected from the Pareto solution set. The embodiment balances the relationship among the risk, the cost and the matching degree through the resource planning model, finally generates a resource regulation strategy with safety and economy, so that the differentiated regulation of the power grid nodes is realized, and the dynamic adaptability of the regulation strategy is ensured.

[0054] The power grid resource planning method for a voltage weak area provided in this embodiment improves the accuracy of power grid system state perception through the coordinated analysis of power flow calculation and monitoring data, improves the identification accuracy of weak points through the establishment of a multi-parameter comprehensive scoring system, 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 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-scene regulation and control strategy are realized. Through the closed-loop design of multi-dimensional risk identification, accurate resource matching and multi-scene 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 power grid operation.

[0055] Please refer 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 application comprises: A 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, obtain a branch power flow calculation result, calculate the current overload coefficient of each branch according to the branch power flow calculation result, and determine the overload branch according to the current overload coefficient. According to the voltage amplitude deviation of the load nodes at both ends of the overload branch, determine the potential weak point, aggregate the potential weak point to obtain a plurality of weak areas, and calculate the voltage stability margin of each weak area. The weak point screening module 20 is configured to acquire a risk index 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 index, and screen a first weak point from the each potential weak point according to the risk level. The resource matching calculation module 30 is configured to acquire 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. 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.

[0056] The technical features and technical effects of the power grid resource planning system for the voltage weak area proposed in the embodiments of the present application are the same as those of the method proposed in the embodiments of the present application, and are not repeated here. Each module in the power grid resource planning system for the voltage weak area can be realized by software, hardware, or a combination thereof, in whole or in part. Each module can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a memory in a computer device in software form, so as to be called and executed by a processor to perform the operations corresponding to each module.

[0057] 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.

[0058] 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 explains 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.

[0059] The above-described embodiments are merely illustrative of several preferred embodiments of the present application, which are described in more detail and in a more specific and detailed manner, but should not be construed as limiting the scope of the patent. It should be noted that for those 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 patent of the present application 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, the 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 index of each potential weak point in the weak area is obtained, the risk level of each potential weak point is calculated according to the voltage stability margin and the risk index, and the first weak point is screened out from each potential weak point according to the risk level; The corresponding regulation resource data of 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; According to the risk level and the resource matching degree, a resource planning model is established and solved to obtain a resource planning scheme.

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 the following steps: 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 is taken as a high load density area, the load correlation degree between the load nodes and 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, 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 the potential weak point according to the voltage amplitude deviation of the load nodes at both ends of the overload branch, aggregating the potential weak points, and obtaining a plurality of weak areas comprises the following steps: According to the real-time load data, the voltage amplitude deviation of the load nodes at both ends of each overload branch is calculated; 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; According to the electrical distance, the potential weak points are aggregated to obtain a plurality of 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 the following steps: 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 by performing 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 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 indicators, and screening a first weak point from the potential weak points according to the risk level comprises: obtaining the voltage amplitude deviation, the associated overload branch proportion and the 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; 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; determining the risk level of each potential weak point according to the comparison result of the risk score and a preset score threshold, and taking the potential weak point with a risk level higher than a preset level as the first weak point.

6. The voltage-weak-area-based grid resource planning method of claim 5, 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: determining the adjustment accuracy requirement of the first weak point according to the voltage stability margin deviation value, determining the capacity requirement of the first weak point according to the voltage amplitude deviation and the associated overload branch proportion, and determining the response time requirement of the first weak point according to the load growth rate; extracting the available capacity, response time, adjustment accuracy and reliability of each control resource from the control resource data; calculating the capacity matching degree according to the capacity requirement and the available capacity, calculating the time matching degree according to the response time requirement and the response time, calculating the accuracy matching degree according to the adjustment accuracy requirement and the adjustment accuracy, and calculating the reliability matching degree according to the reliability and the risk level; 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.

7. The voltage-weak-area-based grid resource planning method of claim 1, wherein, The step of establishing a resource planning model and solving the resource planning model according to the risk level and the resource matching degree to obtain a resource planning scheme comprises: establishing a resource planning model based on multi-objective optimization with the risk level reduction, the resource scheduling cost minimization and the resource matching degree maximization as objective functions, and the resource constraint, the power grid safety constraint and the response time constraint as constraint conditions; solving the resource planning model by using a non-dominated sorting genetic algorithm to obtain a Pareto solution set; selecting an optimal solution from the Pareto solution set according to the current risk level of the first weak point, and generating a resource planning scheme according to the optimal solution.

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

9. The voltage-weak-area-based grid resource planning method of claim 7, wherein, The step of selecting the 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 comprising an emergency scenario and a regular scenario; selecting the optimal solution from the Pareto solution set based on the lowest risk level in response to the emergency scenario; selecting the optimal solution from the Pareto solution set based on the lowest scheduling cost in response to the regular scenario.

10. A voltage weak area based grid resource planning system, characterized in that, Comprise: a weak area division module configured to perform branch flow calculation according to topology data and real-time load data of a 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 point 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 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; a scheme generation module configured to establish a resource planning model according to the risk level and the resource matching degree, and perform solution to obtain a resource planning scheme.

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