Methods and systems for determining the optimal renewable energy integration capacity in a power grid system

Abstract

This invention discloses a method and system for determining the optimal access capacity of renewable energy in a power grid system. The invention relates to the field of power grid security technology and includes the following steps: Data processing of network topology parameters of all renewable energy connection points in the power grid is performed to calculate multi-feed fault correction values ​​and network point inertia coefficients, constructing a two-dimensional feature vector; a clustering algorithm is used to divide the connection points into primary, secondary, and tertiary load-bearing network points; conventional power system operation constraints are applied to primary and secondary load-bearing network points, and additional instantaneous stability constraints are applied to tertiary load-bearing network points to ensure that, under anticipated fault conditions, the instantaneous energy does not exceed the difference between the critical energy and the preset stability margin; based on these constraints, an optimization algorithm is used to determine the optimal access capacity of each renewable energy connection point, achieving effective integration of power grid stability and renewable energy.

Description

Technical Field

[0001] This invention relates to the field of power grid security technology, specifically to a method and system for determining the optimal access capacity of renewable energy in a power grid system. Background Technology

[0002] Against the backdrop of the rapid global power industry's transition to renewable energy, ensuring the safety and stability of the power grid system has become increasingly important. Renewable energy sources such as wind and solar power pose challenges to power grid management and dispatch due to their unpredictability and intermittency. Therefore, developing an effective method to determine the optimal capacity for renewable energy integration can improve the load-carrying capacity of the power grid and ensure safety under various operating conditions, which is an important direction for the development of power system technology. Existing technologies typically rely on statistical analysis based on historical data and simple dynamic models to assess the impact of renewable energy integration on grid stability. While these technologies can provide some reference, they often lack in-depth analysis of multiple infeed faults and their dynamic responses, making it difficult to accurately assess renewable energy integration capacity in complex grid environments. Furthermore, many existing methods fail to fully consider the grid inertia and safety mode characteristics of each connection point, often relying solely on empirical rules or fixed safety margins for capacity assessment, resulting in certain blind spots and limitations in practical applications. Therefore, the shortcomings of existing technologies are mainly reflected in the inability to comprehensively consider the dynamic characteristics and security modes of different types of nodes, and insufficient assessment of the transient stability problems that may occur in the power grid after renewable energy is connected. This leads to potential safety hazards in actual operation because the power grid may be unable to support renewable energy beyond its dynamic capacity.

[0003] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for determining the optimal access capacity of renewable energy in a power grid system, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A method for determining the optimal renewable energy integration capacity in a power grid system, comprising the following steps: Collect the current network topology parameter values ​​of all renewable energy grid connection points in the target power grid, and perform data identification preprocessing and intermediate value correction preprocessing on them; Based on the preprocessed network topology parameter values, calculate the multi-feed fault correction value for each renewable energy grid connection point, and combine it with the equivalent inertia coefficient at each renewable energy grid connection point to calculate the grid inertia coefficient for each renewable energy grid connection point. Based on the feed-in fault correction value and the grid inertia coefficient, a two-dimensional feature vector is constructed for each renewable energy grid connection point. A clustering algorithm is used to cluster all renewable energy grid connection points based on the two-dimensional feature vectors, and the grid connection point type of each renewable energy grid connection point is determined by combining the cluster centers. These include primary grid connection points, secondary grid connection points, and tertiary grid connection points. The constraints are as follows: conventional power system operation constraints are applied to primary and secondary load-bearing points, and additional instantaneous stability constraints are applied to tertiary load-bearing points. The instantaneous stability constraint is that for each anticipated fault, after the tertiary load-bearing point connects to the renewable energy capacity to be decided, the instantaneous energy accumulated at the point when the grid fails due to the anticipated fault must not be greater than the difference between the critical energy of the point and the preset stability margin. The critical energy is determined by the multi-feed fault correction value of the renewable energy grid connection point and the equivalent inertia of the point. Under the constraints of each renewable energy grid connection point, an optimization algorithm is used to output the optimal access capacity of each renewable energy grid connection point.

[0006] Furthermore, the network topology parameters include the three-phase short-circuit capacity of the renewable energy grid connection point, the rated capacity of the new energy source, the self-impedance, the mutual impedance of the grid connection point, the inertia time constant, and the reference capacity; The specific method for data identification and preprocessing is as follows: For any renewable energy grid connection point, analyze the historical data of its various network topology parameters based on the Raida criterion to determine the normal range of its various network topology parameters. For each network topology parameter value currently collected for the renewable energy grid connection point, determine whether it exceeds the corresponding normal range. Network topology parameter values ​​that exceed the normal range are regarded as abnormal data points and removed. The specific method for intermediate value correction preprocessing is as follows: extract the intermediate value of the interval from the normal range corresponding to the abnormal data point to fill in the removed abnormal data point.

[0007] Furthermore, the logic underlying the calculation of the multi-infeed fault correction value for each renewable energy grid-connected point is as follows: For any renewable energy grid-connected point, the self-impedance of that renewable energy grid-connected point is extracted, and the mutual impedance between that renewable energy grid-connected point and the remaining renewable energy grid-connected points is analyzed. Combined with the self-impedance and three-phase short-circuit capacity of that renewable energy grid-connected point, the multi-infeed fault correction value for that renewable energy grid-connected point is comprehensively calculated. The specific method is as follows: Obtain the three-phase short-circuit capacity, self-impedance, new energy rated capacity, and inter-grid mutual impedance of each renewable energy grid connection point. Calculate the ratio of the new energy rated capacity of the renewable energy grid connection point to the magnitude of the inter-grid mutual impedance between the grid connection points. Based on the number of renewable energy grid connection points, accumulate this ratio to obtain the accumulated value. For any renewable energy grid connection point, calculate the product of this accumulated value and the self-impedance magnitude of the renewable energy grid connection point as the first feed-in correction value. The ratio of the three-phase short-circuit capacity of the renewable energy grid connection point to the first feed-in correction value is used as the multi-feed-in fault correction value for the renewable energy grid connection point.

[0008] Furthermore, the logic for calculating the grid inertia coefficient of each renewable energy grid connection point is as follows: For any renewable energy grid connection point, determine the synchronous generators connected to that grid point, forming a corresponding set of synchronous generators. Determine the inertia time constant and rated capacity of each synchronous generator in the set. Calculate the grid inertia coefficient based on the inertia time constant and rated capacity of each synchronous generator, specifically including: For any renewable energy grid connection point, determine the number of synchronous generators in its synchronous generator set, as well as the inertial time constant and rated capacity of each synchronous generator; For any synchronous generator within a renewable energy grid connection point, calculate the product of its inertial time constant and rated capacity. Based on the number of synchronous generators within the renewable energy grid connection point, accumulate the product to obtain the accumulated value. The ratio of the accumulated value to the reference capacity of the renewable energy grid connection point is used as the first inertial term. Calculate the product of the equivalent inertia coefficient of the virtual inertia control device at the renewable energy grid connection point and the rated power of the virtual inertia device, and use the ratio of this product to the reference capacity of the renewable energy grid connection point as the second inertia term; The sum of the first inertia term and the second inertia term is used as the grid inertia coefficient of the renewable energy grid connection point.

[0009] Furthermore, for each renewable energy grid connection point, a two-dimensional feature vector is formed based on the grid inertia coefficient and multi-feed fault correction value. The optimal number of clusters is determined by the elbow method. Then, the K-means clustering algorithm is used to analyze the two-dimensional feature vectors of each renewable energy grid connection point, and all renewable energy grid connection points are clustered. The cluster centers are optimized by minimizing the objective function. The objective function is specifically set based on the Euclidean distance between the feature vectors of each renewable energy grid connection point and the cluster centers. Specifically, the objective function is set with the minimum sum of the Euclidean distances between the two-dimensional feature vectors of all renewable energy grid connection points and the cluster centers as the optimization objective. Using each cluster center as the optimization variable, the objective function value under different cluster centers is calculated, and iterative optimization is performed to minimize the objective function value. The grid type of each renewable energy grid connection point is determined based on the cluster center corresponding to the minimum objective function value. The specific steps of iterative optimization include: calculating the objective function value based on the selected cluster center, adjusting it based on the current cluster center to obtain a new cluster center, and using the preset number of iterations as the termination condition. Iterative optimization is then performed to determine the minimum objective function value in the iterative process.

[0010] Furthermore, the specific method for determining the grid connection type of each renewable energy grid connection point is as follows: if the inertia coefficient of the grid connection points in the cluster center of a cluster is greater than the set inertia threshold, and the multi-feed fault correction value is greater than the set correction threshold, then all renewable energy grid connection points in that cluster are recorded as first-level carrying grid connection points. If the inertia coefficient and multi-feed fault correction value of the network points in the cluster center of a cluster are not greater than the corresponding threshold, then all renewable energy grid connection points in the cluster are recorded as third-level bearing network points. If the inertia coefficient and multi-feed fault correction value of a cluster center are both not greater than the corresponding threshold, then all renewable energy grid connection points in that cluster are recorded as secondary carrier grid points.

[0011] Furthermore, the conventional power system operation constraints include power balance constraints, unit output upper and lower limit constraints, line transmission capacity constraints, and node voltage constraints; The anticipated faults specifically include three-phase short circuits on the line and tripping of large-capacity generating units. The instantaneous stability constraint is specifically expressed as follows: the difference between the critical energy of the third-level bearing network point and the instantaneous energy injected into the power grid due to the access capacity under the expected fault is not less than the preset stability margin. The anticipated fault variables include three-phase short circuits on the line or tripping of large-capacity generating units. Under anticipated fault conditions, the specific method for obtaining the instantaneous energy injected into the power grid at any tertiary bearer point due to access capacity is as follows: Calculate the ratio of the access capacity of the third-level bearer network point to the multi-feed fault correction value, and multiply the ratio by the corresponding first expected fault adjustment coefficient as the first energy part; Calculate the ratio of the access capacity of the third-level bearer network point to the network point inertia coefficient, and multiply the ratio by the corresponding second expected fault adjustment coefficient as the second energy part; The sum of the first energy portion and the second energy portion is taken as the instantaneous energy injected into the power grid at the third-level bearer network point due to the access capacity.

[0012] Furthermore, the method for outputting the optimal grid connection capacity for each renewable energy grid connection point is as follows: An optimization model is constructed with the objective of minimizing the total grid cost and satisfying the constraints of each renewable energy grid connection point. Specifically, the optimization model sets an optimization function based on investment and operating costs, with minimizing the optimization function as the optimization objective. The renewable energy installed capacity of each renewable energy grid connection point is used as the optimization variable. The optimization function specifically includes: Within a set time period, the predicted power generation of renewable energy grid-connected points under any new energy installed capacity is analyzed. All renewable energy grid-connected points are traversed to obtain the total predicted power generation. The total predicted power generation is multiplied by the revenue coefficient of renewable energy power generation to obtain the first revenue item. The investment cost per unit capacity of the renewable energy grid connection point is obtained. The product of the investment cost per unit capacity and the installed capacity of any new energy source is taken as the capacity investment cost of the renewable energy grid connection point. The capacity investment cost of all renewable energy grid connection points is obtained by iterating through all renewable energy grid connection points and accumulating the capacity investment costs. This is recorded as the second revenue item. The total operating cost of the power grid during the set time period will be included as the third revenue item; The difference between the negative of the first benefit term and the second and third benefit terms is used as the value of the optimization function. A genetic algorithm is used as the optimization algorithm, with minimization of the optimization function as the optimization objective, and the new energy installed capacity of each renewable energy grid connection point as the optimization variable, in order to determine the optimal grid connection capacity of each renewable energy grid connection point.

[0013] The present invention also provides a system for determining the optimal renewable energy access capacity in a power grid system. The system for determining the optimal renewable energy access capacity in a power grid system is used to perform the above-described method for determining the optimal renewable energy access capacity in a power grid system, comprising: The data acquisition module is used to collect the current network topology parameter values ​​of all renewable energy grid connection points in the target power grid, and to perform data identification preprocessing and intermediate value correction preprocessing on them. The feature parameter analysis module is used to calculate the multi-feed fault correction value of each renewable energy grid connection point based on the preprocessed network topology parameter values, and to calculate the grid inertia coefficient of each renewable energy grid connection point in combination with the equivalent inertia coefficient at each renewable energy grid connection point. The grid connection point classification module is used to construct a two-dimensional feature vector for each renewable energy grid connection point based on the feed-in fault correction value and the grid connection point inertia coefficient. A clustering algorithm is used to cluster all renewable energy grid connection points based on the two-dimensional feature vector, and the grid connection point type of each renewable energy grid connection point is determined by combining the cluster centers. These include primary grid connection points, secondary grid connection points, and tertiary grid connection points. The constraint analysis module is used to apply conventional power system operation constraints to primary and secondary load-bearing points, and additional instantaneous stability constraints to tertiary load-bearing points. The instantaneous stability constraint is that for each anticipated fault, after the tertiary load-bearing point connects to the renewable energy capacity to be decided, the instantaneous energy accumulated at the point when the grid fails due to the anticipated fault must not be greater than the difference between the critical energy of the point and the preset stability margin. The critical energy is determined by the multi-feed fault correction value of the renewable energy grid connection point and the equivalent inertia of the point. The optimal access capacity module is used to output the optimal access capacity of each renewable energy grid connection point under the constraints of each renewable energy grid connection point by employing an optimization algorithm.

[0014] Compared with the prior art, the beneficial effects of the present invention are: This solution ensures data accuracy and reliability by collecting and preprocessing the network topology parameters of all renewable energy grid connection points in the target power grid. It employs a clustering algorithm to cluster the feature vectors of each grid connection point, classifying them into different types. By clearly classifying each renewable energy grid connection point into primary, secondary, and tertiary load-bearing grid points, the solution can accurately identify the differences and dynamic characteristics of the grid's carrying capacity after renewable energy integration. Based on the classification results, more detailed management and scheduling strategies can be implemented, thereby improving the grid's operational efficiency and security. In addition, the instantaneous stability constraints applied to the three-level load-bearing network points ensure that the power grid can maintain stable operation when experiencing certain faults after the access of renewable energy, preventing potential safety hazards. This constraint condition based on dynamic characteristics provides an effective guarantee for the safety of power grid operation. Under the constraints of each renewable energy grid connection point, the optimal access capacity is output by an optimization algorithm, providing a scientific basis for the decision-making process of grid access to renewable energy. By comprehensively considering the multi-feed fault correction value, grid inertia and dynamic response capability, the reasonable renewable energy access capacity of each grid connection point can be efficiently determined, so as to achieve optimal resource allocation and utilization and maximize the access effect of renewable energy. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the overall method flow of the present invention; Figure 2 A fitted curve of three-phase short-circuit capacity versus multi-infeed fault correction value; Figure 3 A curve showing the fitting of multi-feed fault correction value and grid point inertia coefficient. Figure 4 This is a schematic diagram of the overall system structure of the present invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0017] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0018] Example: Please see Figures 1-3 The present invention provides a technical solution: A method for determining the optimal renewable energy integration capacity in a power grid system, comprising the following steps: Step 1: Collect the current network topology parameter values ​​of all renewable energy grid connection points in the target power grid, and perform data identification preprocessing and intermediate value correction preprocessing.

[0019] The network topology parameters include the three-phase short-circuit capacity, rated capacity of new energy sources, self-impedance, mutual impedance of grid points, inertia time constant, and reference capacity of each renewable energy grid connection point; specifically, the operation data of the power grid, including the operation status of renewable energy grid connection points, can be collected in real time through a monitoring and data acquisition system (SCADA). The specific data identification and preprocessing method is as follows: For any renewable energy grid-connected point, historical data of its various network topology parameters are analyzed based on the Raida criterion. Specifically, the historical data is set to data from the year preceding the current moment to determine the normal range of its various network topology parameters. For each network topology parameter value currently collected for the renewable energy grid-connected point, it is determined whether it exceeds the corresponding normal range. Network topology parameter values ​​exceeding the normal range are considered abnormal data points and removed. Specific steps include: collecting and organizing all relevant network topology parameter data, including three-phase short-circuit capacity, renewable energy rated capacity, self-impedance, mutual impedance, inertial time constant, and reference capacity. Data completeness must be ensured, and all parameters are placed in a table, with each parameter occupying a column and each observation value occupying a row. For each network topology parameter, its mean and standard deviation are calculated. The mean reflects the central tendency of the data, while the standard deviation reflects the dispersion of the data. These two statistics help determine the normal range of the data. Based on the calculated mean and standard deviation, a normal range is set, typically around the upper and lower limits of the mean, usually defined as a multiple of the mean plus or minus a certain factor, generally 2-5 times the standard deviation. Each observation is examined to determine if it falls within the normal range. If an observation is below the lower limit or above the upper limit, it is considered an outlier and is removed from the dataset.

[0020] The specific method for the intermediate value correction preprocessing is as follows: extract the intermediate value of the interval from the normal range corresponding to the abnormal data point to fill in the removed abnormal data point; The median of an interval can well represent the central position of a normal dataset and reflect the overall characteristics of the dataset. Under the influence of outliers, extreme data may distort the overall characteristics of the data, while the median is generally not affected by extreme values ​​and can more accurately reflect the trend of normal data. Therefore, the median of the interval is selected to fill in the outlier data points that have been removed.

[0021] Step 2: Based on the preprocessed network topology parameter values, calculate the multi-feed fault correction value for each renewable energy grid connection point, and combine it with the equivalent inertia coefficient at each renewable energy grid connection point to calculate the grid inertia coefficient for each renewable energy grid connection point.

[0022] The logic underlying the calculation of the multi-infeed fault correction value for each renewable energy grid-connected point is as follows: For any renewable energy grid-connected point, the self-impedance of that renewable energy grid-connected point is extracted, and the mutual impedance between that renewable energy grid-connected point and the remaining renewable energy grid-connected points is analyzed. Combined with the self-impedance and three-phase short-circuit capacity of the renewable energy grid-connected point, the multi-infeed fault correction value for that renewable energy grid-connected point is comprehensively calculated. The specific method is as follows: Obtain the three-phase short-circuit capacity, self-impedance, new energy rated capacity, and inter-grid mutual impedance of each renewable energy grid connection point. Calculate the ratio of the new energy rated capacity of the renewable energy grid connection point to the magnitude of the inter-grid mutual impedance between the grid connection points. Based on the number of renewable energy grid connection points, accumulate this ratio to obtain the accumulated value. For any renewable energy grid connection point, calculate the product of this accumulated value and the self-impedance magnitude of the renewable energy grid connection point as the first feed-in correction value. The ratio of the three-phase short-circuit capacity of the renewable energy grid connection point to the first feed-in correction value is used as the multi-feed-in fault correction value for that renewable energy grid connection point. The specific formula used is as follows: In the formula, Let be the multi-feed fault correction value for the i-th renewable energy grid connection point. Let i be the three-phase short-circuit capacity of the i-th renewable energy grid connection point. Let i be the self-impedance of the i-th renewable energy grid connection point. Let the grid connection impedance be the mutual impedance between the i-th renewable energy grid connection point and the j-th renewable energy grid connection point. Let i be the rated capacity of renewable energy at the j-th renewable energy grid connection point, i and j be the indices of the renewable energy grid connection points, and N be the total number of renewable energy grid connection points. This represents the first feed correction value; It should be noted that the multi-feed fault correction value for the i-th renewable energy grid connection point This is used to measure the response capability and stability of the grid connection point when multiple feeder faults occur. A larger value means that the grid connection point can withstand a stronger short-circuit current, making it less susceptible to impact during a fault. This may also mean that the self-impedance of the grid connection point is small, indicating that the point responds quickly to the current and can effectively adjust its transmission performance, thereby enhancing the stability of the system. Among them, the three-phase short-circuit capacity of the i-th renewable energy grid connection point This reflects the maximum short-circuit current that the point can withstand during a short-circuit fault. A larger short-circuit capacity means a stronger fault current that the grid connection point can withstand, thus resulting in higher stability. and Proportional; The self-impedance of the i-th renewable energy grid connection point This indicates the point's ability to impede current. A higher self-impedance means a slower response to current, potentially leading to more frequent current overloads and stability issues during multi-infeed faults. It is inversely proportional to the multi-feed fault correction value; The mutual impedance, combined with the rated capacity of other grid-connected points, reflects the coupling characteristics of the overall power grid. When multiple renewable energy grid-connected points operate simultaneously, the magnitude of their mutual impedance affects the distribution of short-circuit current, thus impacting the stability of each grid-connected point; specifically through... This represents the contribution of each grid connection point, node The installed capacity of new energy sources is very large, and the power injected into the system is substantial. Once fluctuations occur, they will affect the nodes through mutual impedance. This causes a large voltage change. The smaller the value, the greater the influence between the two nodes. The larger the value, the more likely it is that the node... New energy power stations for nodes The more severe the impact on voltage stability, therefore and Inversely proportional; By comprehensively considering self-impedance, short-circuit capacity, and mutual impedance, the fault correction value of the grid connection point under multiple feeder fault conditions can be evaluated, which can more comprehensively reflect the safety and stability of each grid connection point in grid operation.

[0023] The logic underlying the calculation of the grid inertia coefficient for each renewable energy grid-connected point is as follows: For any renewable energy grid-connected point, the synchronous generators connected to that point are identified, forming a corresponding set of synchronous generators. The inertial time constant and rated capacity of each synchronous generator in the set are determined. The grid inertia coefficient is then calculated based on the inertial time constant and rated capacity of each synchronous generator, specifically including: For any renewable energy grid connection point, determine the number of synchronous generators in its synchronous generator set, as well as the inertial time constant and rated capacity of each synchronous generator; For any synchronous generator within a renewable energy grid connection point, calculate the product of its inertial time constant and rated capacity. Based on the number of synchronous generators within the renewable energy grid connection point, accumulate the product to obtain the accumulated value. The ratio of the accumulated value to the reference capacity of the renewable energy grid connection point is used as the first inertial term. Calculate the product of the equivalent inertia coefficient of the virtual inertia control device at the renewable energy grid connection point and the rated power of the virtual inertia device, and use the ratio of this product to the reference capacity of the renewable energy grid connection point as the second inertia term; The sum of the first inertia term and the second inertia term is taken as the grid inertia coefficient of the renewable energy grid connection point. The specific formula used is as follows: In the formula, Let be the grid inertia coefficient of the i-th renewable energy grid connection point. To connect the set of synchronous generators to the i-th renewable energy grid connection point, the i-th The inertial time constant of a synchronous generator, To connect the set of synchronous generators to the i-th renewable energy grid connection point, the i-th The rated capacity of the synchronous generator, Let be the equivalent inertia coefficient of the virtual inertia control device at the i-th renewable energy grid connection point. Let the rated power of the virtual inertia device at the i-th renewable energy grid connection point be . Let k be the baseline capacity of the i-th renewable energy grid connection point, and k be the synchronous generator index. This represents the total number of synchronous generators connected to the i-th renewable energy grid connection point; This is the first inertia term. This represents the second inertia term.

[0024] It should be noted that the grid inertia coefficient of the i-th renewable energy grid connection point The larger the value, the better the grid connection point can resist frequency shift and reduce the rate of frequency change when the power system frequency changes rapidly, such as when the load fluctuates or there is a fault. Grid connection points with a large inertia coefficient can provide more dynamic support during fault recovery and help the system recover to a stable state more quickly. In the operation of the power grid, the greater the inertia of the grid point, the lower the sensitivity of the system to frequency fluctuations and the higher the frequency stability. The formula can be decomposed into two parts: the synchronous generator inertia part and the virtual inertia part. This part integrates access to the first The inertial time constant of all synchronous generators at each grid connection point and rated capacity This is normalized to the baseline capacity of the network points, reflecting the dominant role of traditional synchronous generators in contributing inertia. This is because the rotor of a synchronous generator possesses inherent mechanical inertia, which can directly resist rapid changes in system frequency. The larger the value, the greater the kinetic energy stored in the generator relative to its capacity, and the stronger its frequency inertia. and Proportional to the grid connection point, multiple synchronous generators may be connected at one grid connection point. The generators rotate simultaneously, and their total kinetic energy is the sum of the kinetic energies of all generators. The kinetic energy of any generator is represented by the product of the grid connection point's inertia coefficient and its rated capacity. This is achieved by summing... This represents the total kinetic energy of the generators rotating simultaneously; the total kinetic energy is divided by the reference capacity of that node. In order to normalize the inertial time constant; Virtual inertia part The inertia contribution provided by virtual inertia control devices is becoming an important means of providing system inertia as the penetration rate of renewable energy in modern power systems increases. This is to compensate for the inertia shortage caused by the reduction of synchronous generators. Virtual inertia control devices can quickly respond to changes in system frequency and provide dynamic inertia support. This part supplements the lack of synchronous generator inertia at renewable energy grid connection points in modern power systems, ensuring the stability of system frequency.

[0025] The equivalent inertia coefficient of the virtual inertia control device at the i-th renewable energy grid connection point The larger the value, the stronger the virtual inertia response. As virtual kinetic energy, then divided by the baseline capacity The equivalent inertia coefficient is obtained; Synchronous generators and virtual inertia devices respond simultaneously to frequency disturbances. The synchronous generator releases rotor kinetic energy, while the virtual inertia device rapidly outputs power through the converter. The suppression effects of both on the rate of frequency change are additive. Therefore, the total equivalent inertia coefficient equals the sum of their contributions. Adding the inertia contribution of the synchronous generator to the virtual inertia contribution constitutes the equivalent inertia coefficient of the network point, which comprehensively reflects the network point's inertial support capability.

[0026] Step 3: Based on the feed-in fault correction value and the grid inertia coefficient, construct a two-dimensional feature vector for each renewable energy grid connection point. Use a clustering algorithm to cluster all renewable energy grid connection points based on the two-dimensional feature vectors, and combine the cluster centers to determine the grid connection point type of each renewable energy grid connection point, which includes primary grid connection points, secondary grid connection points and tertiary grid connection points.

[0027] A two-dimensional feature vector is formed for the grid inertia coefficient and multi-infeed fault correction value of each renewable energy grid connection point. The optimal number of clusters is determined using the elbow method. Then, the K-means clustering algorithm is used to analyze the two-dimensional feature vectors of each renewable energy grid-connected point, clustering all renewable energy grid-connected points. The cluster centers are optimized by minimizing an objective function. This objective function is specifically set based on the Euclidean distance between the feature vectors of each renewable energy grid-connected point and the cluster centers, with the optimization objective being the minimization of the sum of the Euclidean distances between the two-dimensional feature vectors of all renewable energy grid-connected points and the cluster centers. The formula used is: In the formula, The objective function value, Let q be the index of the q-th cluster center. The optimal number of clusters determined by the elbow method; Let Euclidean distance be the two-dimensional feature vector of the i-th renewable energy grid connection point and the cluster center of the q-th point. One method for determining the optimal number of clusters is the elbow method, a common technique, particularly suitable for the K-means clustering algorithm. Its main principle is to find the optimal number of clusters by observing the trend of the sum of squared errors under different numbers of clusters. In K-means clustering, as the number of clusters increases, the model's fit to the data usually improves, leading to a gradual decrease in the sum of squared errors. A series of cluster numbers are selected, and the corresponding values ​​are calculated for each cluster number and plotted on a graph to form a curve of the number of clusters versus the sum of squared errors. In the graph, the change in the sum of squared errors with the number of clusters is observed. Typically, after a certain number of clusters, the rate of decrease in the sum of squared errors slows significantly; this point is called the "elbow." The number of clusters corresponding to the elbow is usually considered the optimal number of clusters. After this point, increasing the number of clusters no longer significantly reduces the sum of squared errors.

[0028] It should be noted that minimizing the objective function value is essentially about finding an optimal set of cluster centers that minimizes the sum of the weighted distances from all nodes to their cluster centers. Using each cluster center as the optimization variable, the objective function value under different cluster centers is calculated, and iterative optimization is performed to minimize the objective function value. The grid type of each renewable energy grid connection point is determined based on the cluster center corresponding to the minimum objective function value. The specific steps of iterative optimization include: calculating the objective function value based on the selected cluster center, and adjusting it based on the current cluster center to obtain a new cluster center. The termination condition is to reach a preset number of iterations. Iterative optimization is performed in this way to determine the minimum objective function value in the iteration process. The specific method for determining the grid connection type of each renewable energy grid connection point is as follows: if the inertia coefficient of the grid connection points in the cluster center of a cluster is greater than the set inertia threshold, and the multi-feed fault correction value is greater than the set correction threshold, then all renewable energy grid connection points in that cluster are recorded as first-level grid connection points. If the inertia coefficient and multi-feed fault correction value of the network points in the cluster center of a cluster are not greater than the corresponding threshold, then all renewable energy grid connection points in the cluster are recorded as third-level bearing network points. If the inertia coefficient and multi-feed fault correction value of a cluster center are both not greater than the corresponding threshold, then all renewable energy grid connection points in that cluster are recorded as secondary carrier grid points.

[0029] The specific methods for setting the inertia threshold and correction threshold are as follows: The inertia threshold is used to determine the classification criteria for the inertia coefficient of the grid-connected points. First, descriptive statistics are performed on the inertia coefficients of all grid-connected points to calculate the mean, standard deviation, maximum value, and minimum value. Using the quantiles of the data, the 25th percentile can be selected as the inertia threshold; or it can be set based on historical experience. The correction threshold is used to distinguish the classification criteria for the multi-feed fault correction values ​​of renewable energy grid-connected points. The setting method can also be achieved using quantiles.

[0030] Step 4: Apply conventional power system operation constraints to the primary and secondary load-bearing points, and apply additional instantaneous stability constraints to the tertiary load-bearing points. The instantaneous stability constraint is that for each anticipated fault, after the tertiary load-bearing point connects to the renewable energy capacity to be decided, the instantaneous energy accumulated at the point when the grid fails due to the anticipated fault must not be greater than the difference between the critical energy of the point and the preset stability margin. The critical energy is determined by the multi-feed fault correction value of the renewable energy grid connection point and the equivalent inertia of the point.

[0031] The conventional power system operation constraints include power balance constraints, upper and lower limits of unit output constraints, line transmission capacity constraints, and node voltage constraints. Power balance constraints require that the input and output power of each node in the power system be equal. Generator output limits mean that the generating capacity of each generator must operate within its designed minimum and maximum output range. Line transmission capacity constraints require that each line in the power grid cannot exceed its rated transmission capacity when transmitting power. Node voltage constraints ensure that the voltage at each node in the power system remains within a safe and reasonable range; the voltage at each node must operate between its set minimum and maximum values ​​to guarantee normal equipment operation and power quality. Excessively high or low voltage can affect the normal operation of user equipment and may even cause damage. The specific range is determined based on the power grid's operating conditions.

[0032] The anticipated faults specifically include three-phase short circuits on the line and tripping of large-capacity generating units. The instantaneous stability constraint is specifically expressed as follows: the difference between the critical energy of the third-level bearing network point and the instantaneous energy injected into the power grid due to the access capacity under the expected fault is not less than the preset stability margin. The instantaneous stability constraint is specifically expressed as follows: In the formula, Let e ​​be the critical energy of the e-th level 3 bearing network point. In order to anticipate failures Below, at the e-th level 3 bearer network point, due to access capacity... The instantaneous energy injected into the power grid, To pre-determine a stability margin, The anticipated fault variables are a three-phase short circuit on the line or a tripping of a large-capacity generating unit. Let e ​​be the access capacity of the e-th tertiary bearer network point, where e is the index of the tertiary bearer network point; It should be noted that the critical energy of the e-th tertiary bearing network point This indicates the maximum energy value that the network point can maintain under specific conditions, reflecting the network point's ability to support system stability under normal operating conditions; Instantaneous energy The calculation of this parameter takes into account the instantaneous response capability and power output of the network point under fault conditions, reflecting the vulnerability of the network point under fault conditions; The preset stability margin is a safety margin designed to ensure that the system remains stable even in the worst-case scenario, such as when the instantaneous energy is large or the critical energy is small.

[0033] By setting this margin, an additional buffer can be provided for the system to avoid instability caused by excessive instantaneous energy. Specifically, based on historical experience data, combined with fault history records and system risk assessment, the weak links of the system under different conditions can be identified, thereby determining a reasonable margin.

[0034] For Level 3 load-bearing points, the additional instantaneous stability constraints are mainly aimed at the ability to cope with anticipated faults. When a fault occurs, the instantaneous energy accumulation of these points must not exceed the difference between their critical energy and the preset stability margin. This constraint ensures that the points can remain stable, respond quickly and resume normal operation after a fault occurs, which is crucial for the overall safety of the power system. Access capacity The capacity directly affects the instantaneous energy level. The larger the access capacity, the more energy the network point can inject during a fault, thus affecting system stability. Appropriate access capacity can enhance the network point's support capability in fault conditions, but it must also be reasonably matched with critical energy to ensure safety in actual operation.

[0035] Under anticipated fault conditions, the specific method for obtaining the instantaneous energy injected into the power grid at any tertiary bearer point due to access capacity is as follows: Calculate the ratio of the access capacity of the third-level bearer network point to the multi-feed fault correction value, and multiply the ratio by the corresponding first expected fault adjustment coefficient as the first energy part; Calculate the ratio of the access capacity of the third-level bearer network point to the network point inertia coefficient, and multiply the ratio by the corresponding second expected fault adjustment coefficient as the second energy part; The sum of the first and second energy components is taken as the instantaneous energy injected into the power grid at the third-level load-bearing point due to the access capacity; the specific calculation is based on the following formula: In the formula, This is the multi-feed fault correction value for the e-th level 3 bearer network point. Let e ​​be the inertia coefficient of the third-level load-bearing network point. This represents the change in disturbance capacity. and The adjustment coefficient is specifically obtained based on the anticipated fault type.

[0036] It should be noted that the formula design allows the system to dynamically evaluate the performance of each Tier 3 bearer network point under different fault conditions; by combining fault response with access capacity and inertia, the instantaneous energy changes of the system can be predicted more accurately by adjusting the coefficients. and The system can adapt to the characteristics of different types of faults and changes in fault duration, thereby adjusting the calculation of instantaneous energy accordingly. The multi-infeed fault correction value is used to measure the response capability and stability of the renewable energy grid connection point in the event of a multi-infeed fault. A higher value means the grid connection point can withstand a stronger short-circuit current, thus making it less susceptible to impact during a fault. Therefore, under the same injected capacity... The smaller the voltage transient energy caused, the more efficient the voltage transient response. This reflects the equivalent electrical load of the renewable energy grid connection point after considering voltage support capacity; When a fault occurs, the voltage at the renewable energy grid connection point will drop due to the short-circuit current; the connected capacity The larger the voltage, the greater the fault current it provides, and the deeper the voltage drop; however, the renewable energy grid connection point itself... This will inhibit the fall; The larger, the same The smaller the voltage drop; The larger the inertia coefficient of a third-level grid connection point, the better it can resist frequency shifts and reduce the rate of frequency change when the power system frequency changes rapidly, such as during load fluctuations or faults. Below this, the smaller the inertia coefficient, the higher the sensitivity of the third-level bearing network points to frequency changes. Therefore, through... It reflects the transient energy contributed by the frequency drop effect; Different faults (such as line short circuits and large-capacity unit trips) have different impacts on the power system. The regulation coefficient needs to be adjusted according to the fault type to reflect its varying degree of impact on instantaneous energy. The longer the fault duration, the greater the impact on instantaneous energy. Analysis of historical fault data can reveal the system's response characteristics at the time of the fault, thus providing... and The settings provide data support. and For different fault types, the value is generally set between 0.01 and 2.

[0037] Step 5: Under the constraints of each renewable energy grid connection point, an optimization algorithm is used to output the optimal access capacity of each renewable energy grid connection point.

[0038] The method for outputting the optimal grid connection capacity for each renewable energy grid connection point is as follows: An optimization model is constructed with the objective of minimizing the total grid cost and satisfying the constraints of each renewable energy grid connection point. Specifically, the optimization model sets an optimization function based on investment and operating costs, with minimizing the optimization function as the optimization objective. The renewable energy installed capacity of each renewable energy grid connection point is used as the optimization variable. The optimization function specifically includes: Within a set time period, the predicted power generation of renewable energy grid-connected points under any new energy installed capacity is analyzed. All renewable energy grid-connected points are traversed to obtain the total predicted power generation. The total predicted power generation is multiplied by the revenue coefficient of renewable energy power generation to obtain the first revenue item. The investment cost per unit capacity of the renewable energy grid connection point is obtained. The product of the investment cost per unit capacity and the installed capacity of any new energy source is taken as the capacity investment cost of the renewable energy grid connection point. The capacity investment cost of all renewable energy grid connection points is obtained by iterating through all renewable energy grid connection points and accumulating the capacity investment costs. This is recorded as the second revenue item. The total operating cost of the power grid during the set time period will be included as the third revenue item; The negative of the first payoff term is used as the difference between the second and third payoff terms, which is then used as the optimization function value. The specific formula used for the calculation is as follows: In the formula, To optimize the function value, Let i be the investment cost per unit capacity of the i-th renewable energy grid connection point. Let i be the renewable energy installed capacity to be decided at the i-th renewable energy grid connection point within a set time period. The total operating cost of the power grid within a set time period. To set the time period length, For the i-th renewable energy grid connection point with an installed capacity of The predicted power generation within the set time period. The profitability factor for renewable energy generation; This is the second source of income. This is the first benefit item; It should be noted that the core objective of optimization is to minimize the total cost of the power grid, an objective that comprehensively considers both investment and operating costs. It reflects the benefits obtained through renewable energy power generation. The optimization model encourages increased power generation to reduce total costs. The revenue coefficient of renewable energy power generation reflects the economic benefits brought by power generation. By simultaneously considering investment costs, operating costs, and renewable energy power generation revenue, it is possible to more comprehensively evaluate the economic benefits of each renewable energy grid connection point and ensure the rationality of decision-making.

[0039] Among them, the predicted power generation The specific acquisition method is as follows: collect historical power generation data of relevant renewable energy grid-connected points, such as wind power and solar power, at different time periods, and acquire them through sensors and monitoring equipment installed on the power generation facilities; acquire meteorological data related to power generation, such as wind speed, solar radiation, temperature, humidity, etc. Using historical power generation data and meteorological data, modeling is carried out through regression models, time series analysis models, and machine learning models (such as linear regression, support vector machines, neural networks, etc.). The input of the model is meteorological data, and the output is power generation. When building the model, the impact of different factors on power generation should be considered, such as equipment efficiency, environmental conditions, maintenance cycle, etc. Using the established model and future meteorological forecast data, the predicted power generation under the set installed capacity is calculated.

[0040] Profitability of renewable energy generation The specific determination method is as follows: analyze the fluctuations in local electricity market prices, especially the feed-in tariff for renewable energy; calculate the expected total revenue based on the feed-in tariff for renewable energy; and use the ratio of the expected total revenue to the expected power generation as the revenue coefficient for renewable energy power generation. .

[0041] The total operating cost of a power grid typically includes the following aspects: generation cost; this part of the cost refers to the expenses incurred in generating electricity to meet the grid's demand; it can be calculated by analyzing the generation costs of different power generation methods, such as traditional thermal power and renewable energy; generation cost is usually based on the following factors: fuel costs (coal, natural gas, etc.) and operation and maintenance costs; fixed costs; including equipment depreciation, fixed operating expenses, etc., which are usually fixed expenditures related to grid operation. Specific details can be obtained from historical grid system expenditure manuals or by analyzing historical grid operation data to obtain past operating costs; The specific methods for obtaining the investment cost per unit capacity of renewable energy grid connection points are as follows: conduct market research on renewable energy projects in the local or national scope to obtain current equipment and installation costs, or use industry reports and research literature to understand the average investment cost of various renewable energy projects, and then analyze the investment cost per unit capacity.

[0042] A genetic algorithm is used as the optimization algorithm, with minimization of the optimization function as the optimization objective and the new energy installed capacity of each renewable energy grid connection point as the optimization variable, in order to determine the optimal grid connection capacity of each renewable energy grid connection point. The genetic algorithm is a conventional existing technology and will not be elaborated here.

[0043] Please see Figure 4 The present invention also provides a system for determining the optimal renewable energy access capacity in a power grid system, for performing the above-described method for determining the optimal renewable energy access capacity in a power grid system, comprising: The data acquisition module is used to collect the current network topology parameter values ​​of all renewable energy grid connection points in the target power grid, and to perform data identification preprocessing and intermediate value correction preprocessing on them. The feature parameter analysis module is used to calculate the multi-feed fault correction value of each renewable energy grid connection point based on the preprocessed network topology parameter values, and to calculate the grid inertia coefficient of each renewable energy grid connection point in combination with the equivalent inertia coefficient at each renewable energy grid connection point. The grid connection point classification module is used to construct a two-dimensional feature vector for each renewable energy grid connection point based on the feed-in fault correction value and the grid connection point inertia coefficient. A clustering algorithm is used to cluster all renewable energy grid connection points based on the two-dimensional feature vector, and the grid connection point type of each renewable energy grid connection point is determined by combining the cluster centers. These include primary grid connection points, secondary grid connection points, and tertiary grid connection points. The constraint analysis module is used to apply conventional power system operation constraints to primary and secondary load-bearing points, and additional instantaneous stability constraints to tertiary load-bearing points. The instantaneous stability constraint is that for each anticipated fault, after the tertiary load-bearing point connects to the renewable energy capacity to be decided, the instantaneous energy accumulated at the point when the grid fails due to the anticipated fault must not be greater than the difference between the critical energy of the point and the preset stability margin. The critical energy is determined by the multi-feed fault correction value of the renewable energy grid connection point and the equivalent inertia of the point. The optimal access capacity module is used to output the optimal access capacity of each renewable energy grid connection point under the constraints of each renewable energy grid connection point by employing an optimization algorithm.

[0044] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0045] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0046] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0047] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for determining the optimal renewable energy access capacity in a power grid system, characterized in that, The specific steps include: Collect the current network topology parameter values ​​of all renewable energy grid connection points in the target power grid, and perform data identification preprocessing and intermediate value correction preprocessing on them; Based on the preprocessed network topology parameter values, calculate the multi-feed fault correction value for each renewable energy grid connection point, and combine it with the equivalent inertia coefficient at each renewable energy grid connection point to calculate the grid inertia coefficient for each renewable energy grid connection point. Based on the feed-in fault correction value and the grid inertia coefficient, a two-dimensional feature vector is constructed for each renewable energy grid connection point. A clustering algorithm is used to cluster all renewable energy grid connection points based on the two-dimensional feature vectors, and the grid connection point type of each renewable energy grid connection point is determined by combining the cluster centers. These include primary grid connection points, secondary grid connection points, and tertiary grid connection points. The constraints are as follows: conventional power system operation constraints are applied to primary and secondary load-bearing points, and additional instantaneous stability constraints are applied to tertiary load-bearing points. The instantaneous stability constraint is that for each anticipated fault, after the tertiary load-bearing point connects to the renewable energy capacity to be decided, the instantaneous energy accumulated at the point when the grid fails due to the anticipated fault must not be greater than the difference between the critical energy of the point and the preset stability margin. The critical energy is determined by the multi-feed fault correction value of the renewable energy grid connection point and the equivalent inertia of the point. Under the constraints of each renewable energy grid connection point, an optimization algorithm is used to output the optimal access capacity of each renewable energy grid connection point.

2. The method for determining the optimal renewable energy access capacity in a power grid system according to claim 1, characterized in that: The network topology parameters include the three-phase short-circuit capacity of the renewable energy grid connection point, the rated capacity of the new energy source, the self-impedance, the mutual impedance of the grid connection point, the inertia time constant, and the reference capacity. The specific method for data identification and preprocessing is as follows: For any renewable energy grid connection point, analyze the historical data of its various network topology parameters based on the Raida criterion to determine the normal range of its various network topology parameters. For each network topology parameter value currently collected for the renewable energy grid connection point, determine whether it exceeds the corresponding normal range. Network topology parameter values ​​that exceed the normal range are regarded as abnormal data points and removed. The specific method for intermediate value correction preprocessing is as follows: extract the intermediate value of the interval from the normal range corresponding to the abnormal data point to fill in the removed abnormal data point.

3. The method for determining the optimal renewable energy access capacity in a power grid system according to claim 2, characterized in that, The logic underlying the calculation of the multi-infeed fault correction value for each renewable energy grid-connected point is as follows: For any renewable energy grid-connected point, the self-impedance of that renewable energy grid-connected point is extracted, and the mutual impedance between that renewable energy grid-connected point and the remaining renewable energy grid-connected points is analyzed. Combined with the self-impedance and three-phase short-circuit capacity of the renewable energy grid-connected point, the multi-infeed fault correction value for that renewable energy grid-connected point is comprehensively calculated. The specific method is as follows: Obtain the three-phase short-circuit capacity, self-impedance, new energy rated capacity, and inter-grid mutual impedance of each renewable energy grid connection point. Calculate the ratio of the new energy rated capacity of the renewable energy grid connection point to the magnitude of the inter-grid mutual impedance between the grid connection points. Based on the number of renewable energy grid connection points, accumulate this ratio to obtain the accumulated value. For any renewable energy grid connection point, calculate the product of this accumulated value and the self-impedance magnitude of the renewable energy grid connection point as the first feed-in correction value. The ratio of the three-phase short-circuit capacity of the renewable energy grid connection point to the first feed-in correction value is used as the multi-feed-in fault correction value for the renewable energy grid connection point.

4. The method for determining the optimal renewable energy access capacity in a power grid system according to claim 3, characterized in that, The logic underlying the calculation of the grid inertia coefficient for each renewable energy grid-connected point is as follows: For any renewable energy grid-connected point, the synchronous generators connected to that point are identified, forming a corresponding set of synchronous generators. The inertial time constant and rated capacity of each synchronous generator in the set are determined. The grid inertia coefficient is then calculated based on the inertial time constant and rated capacity of each synchronous generator, specifically including: For any renewable energy grid connection point, determine the number of synchronous generators in its synchronous generator set, as well as the inertial time constant and rated capacity of each synchronous generator; For any synchronous generator within a renewable energy grid connection point, calculate the product of its inertial time constant and rated capacity. Based on the number of synchronous generators within the renewable energy grid connection point, accumulate the product to obtain the accumulated value. The ratio of the accumulated value to the reference capacity of the renewable energy grid connection point is used as the first inertial term. Calculate the product of the equivalent inertia coefficient of the virtual inertia control device at the renewable energy grid connection point and the rated power of the virtual inertia device, and use the ratio of this product to the reference capacity of the renewable energy grid connection point as the second inertia term; The sum of the first inertia term and the second inertia term is used as the grid inertia coefficient of the renewable energy grid connection point.

5. The method for determining the optimal renewable energy access capacity in a power grid system according to claim 4, characterized in that: For each renewable energy grid connection point, a two-dimensional feature vector is formed based on the grid inertia coefficient and multi-feed fault correction value. The optimal number of clusters is determined by the elbow method. Then, the K-means clustering algorithm is used to analyze the two-dimensional feature vectors of each renewable energy grid connection point and cluster all renewable energy grid connection points. The cluster centers are optimized by minimizing the objective function. The objective function is specifically set based on the Euclidean distance between the feature vectors of each renewable energy grid connection point and the cluster centers. Specifically, the objective function is set with the minimum sum of the Euclidean distances between the two-dimensional feature vectors of all renewable energy grid connection points and the cluster centers as the optimization objective. Using each cluster center as the optimization variable, the objective function value under different cluster centers is calculated, and iterative optimization is performed to minimize the objective function value. The grid type of each renewable energy grid connection point is determined based on the cluster center corresponding to the minimum objective function value. The specific steps of iterative optimization include: calculating the objective function value based on the selected cluster center, adjusting it based on the current cluster center to obtain a new cluster center, and using the preset number of iterations as the termination condition. Iterative optimization is then performed to determine the minimum objective function value in the iterative process.

6. The method for determining the optimal renewable energy access capacity in a power grid system according to claim 5, characterized in that, The specific method for determining the grid connection type of each renewable energy grid connection point is as follows: if the inertia coefficient of the grid connection points in the cluster center of a cluster is greater than the set inertia threshold, and the multi-feed fault correction value is greater than the set correction threshold, then all renewable energy grid connection points in that cluster are recorded as first-level grid connection points. If the inertia coefficient and multi-feed fault correction value of the network points in the cluster center of a cluster are not greater than the corresponding threshold, then all renewable energy grid connection points in the cluster are recorded as third-level bearing network points. If the inertia coefficient and multi-feed fault correction value of a cluster center are both not greater than the corresponding threshold, then all renewable energy grid connection points in that cluster are recorded as secondary carrier grid points.

7. The method for determining the optimal renewable energy access capacity in a power grid system according to claim 6, characterized in that: The conventional power system operation constraints include power balance constraints, upper and lower limits of unit output constraints, line transmission capacity constraints, and node voltage constraints. The anticipated faults specifically include three-phase short circuits on the line and tripping of large-capacity generating units. The instantaneous stability constraint is specifically expressed as follows: the difference between the critical energy of the third-level bearing network point and the instantaneous energy injected into the power grid due to the access capacity under the expected fault is not less than the preset stability margin. The anticipated fault variables include three-phase short circuits on the line or tripping of large-capacity generating units. Under anticipated fault conditions, the specific method for obtaining the instantaneous energy injected into the power grid at any tertiary bearer point due to access capacity is as follows: Calculate the ratio of the access capacity of the third-level bearer network point to the multi-feed fault correction value, and multiply the ratio by the corresponding first expected fault adjustment coefficient as the first energy part; Calculate the ratio of the access capacity of the third-level bearer network point to the network point inertia coefficient, and multiply the ratio by the corresponding second expected fault adjustment coefficient as the second energy part; The sum of the first energy portion and the second energy portion is taken as the instantaneous energy injected into the power grid at the third-level bearer network point due to the access capacity.

8. The method for determining the optimal renewable energy access capacity in a power grid system according to claim 7, characterized in that, The method for outputting the optimal grid connection capacity for each renewable energy grid connection point is as follows: An optimization model is constructed with the objective of minimizing the total grid cost and satisfying the constraints of each renewable energy grid connection point. Specifically, the optimization model sets an optimization function based on investment and operating costs, with minimizing the optimization function as the optimization objective. The renewable energy installed capacity of each renewable energy grid connection point is used as the optimization variable. The optimization function specifically includes: Within a set time period, the predicted power generation of renewable energy grid-connected points under any new energy installed capacity is analyzed. All renewable energy grid-connected points are traversed to obtain the total predicted power generation. The total predicted power generation is multiplied by the revenue coefficient of renewable energy power generation to obtain the first revenue item. The investment cost per unit capacity of the renewable energy grid connection point is obtained. The product of the investment cost per unit capacity and the installed capacity of any new energy source is taken as the capacity investment cost of the renewable energy grid connection point. The capacity investment cost of all renewable energy grid connection points is obtained by iterating through all renewable energy grid connection points and accumulating the capacity investment costs. This is recorded as the second revenue item. The total operating cost of the power grid during the set time period will be included as the third revenue item; The difference between the negative of the first benefit term and the second and third benefit terms is used as the value of the optimization function. A genetic algorithm is used as the optimization algorithm, with minimization of the optimization function as the optimization objective, and the new energy installed capacity of each renewable energy grid connection point as the optimization variable, in order to determine the optimal grid connection capacity of each renewable energy grid connection point.

9. A system for determining the optimal renewable energy access capacity in a power grid system, used to perform the method for determining the optimal renewable energy access capacity in a power grid system as described in any one of claims 1-8, characterized in that: include: The data acquisition module is used to collect the current network topology parameter values ​​of all renewable energy grid connection points in the target power grid, and to perform data identification preprocessing and intermediate value correction preprocessing on them. The feature parameter analysis module is used to calculate the multi-feed fault correction value of each renewable energy grid connection point based on the preprocessed network topology parameter values, and to calculate the grid inertia coefficient of each renewable energy grid connection point in combination with the equivalent inertia coefficient at each renewable energy grid connection point. The grid connection point classification module is used to construct a two-dimensional feature vector for each renewable energy grid connection point based on the feed-in fault correction value and the grid connection point inertia coefficient. A clustering algorithm is used to cluster all renewable energy grid connection points based on the two-dimensional feature vector, and the grid connection point type of each renewable energy grid connection point is determined by combining the cluster centers. These include primary grid connection points, secondary grid connection points, and tertiary grid connection points. The constraint analysis module is used to apply conventional power system operation constraints to primary and secondary load-bearing points, and additional instantaneous stability constraints to tertiary load-bearing points. The instantaneous stability constraint is that for each anticipated fault, after the tertiary load-bearing point connects to the renewable energy capacity to be decided, the instantaneous energy accumulated at the point when the grid fails due to the anticipated fault must not be greater than the difference between the critical energy of the point and the preset stability margin. The critical energy is determined by the multi-feed fault correction value of the renewable energy grid connection point and the equivalent inertia of the point. The optimal access capacity module is used to output the optimal access capacity of each renewable energy grid connection point under the constraints of each renewable energy grid connection point by employing an optimization algorithm.

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