A distributed energy storage grid-connected voltage stability prediction method, medium and system

Abstract

The present invention provides a method, medium and system for predicting the voltage stability of a distributed energy storage grid, belonging to the field of distributed energy storage technology, comprising: obtaining the parameters of each node based on the urban power grid topology of the distributed energy storage. A group of equations affecting the voltage of the energy storage grid is established based on these parameters, and the stability range of the parameters of each node is calculated. Based on the topology map and the stability range of the node parameters, the voltage stability margin of each node is calculated to form a topological map of the power grid voltage stability margin. The voltage stability margin matrix at multiple consecutive moments is input into a pre-trained prediction model, which can predict the voltage stability margin of each node in the future. The system comprehensively considers the impact of distributed energy storage on the voltage stability of the power grid, and solves the technical problem that the existing distributed energy storage grid voltage stability prediction method is difficult to solve, that is, the voltage stability analysis generalization ability is poor due to the randomness of load and power generation and the uncertainty of transmission line parameters.

Description

Technical Field

[0001] The present invention belongs to the technical field of distributed energy storage, and in particular relates to a method, medium and system for predicting the voltage stability of a distributed energy storage grid. Background Art

[0002] With the continuous advancement of renewable energy generation technology and the rapid development of power electronics, distributed generation and energy storage technologies are becoming increasingly widespread in power systems. Unlike traditional centralized power generation, distributed power sources can be connected to the nearest distribution network, providing users with a more flexible and reliable power supply. The introduction of energy storage systems further enhances the peak-shaving and support capabilities of distributed power sources, improving the operational stability of the power grid.

[0003] The deployment of distributed power generation (DG) and energy storage systems in urban power grids has become a widespread trend. However, the integration of a large number of distributed power generation (DG) and energy storage devices into the grid also presents new challenges to grid operation and dispatch. Grid voltage stability is a particularly prominent issue. The integration of DG and energy storage systems changes the power flow distribution of the grid, affecting the voltage amplitude and phase angle at each node. If these changes exceed the grid's tolerance, they can lead to local or overall voltage instability, or even the risk of voltage collapse. Therefore, accurately predicting and effectively controlling the impact of distributed grid integration on grid voltage stability has become a key issue in optimizing power system operation.

[0004] Traditional voltage stability analysis methods primarily include load characteristic analysis, energy function analysis, and modal analysis. These methods typically require the establishment of a complete grid topology model and power flow equations, which are then solved using numerical calculations. However, in real power grids, voltage stability analysis becomes extremely complex due to factors such as the randomness of load and generation, the uncertainty of transmission line parameters, and the dynamic characteristics of distributed power sources and energy storage systems. In particular, when a large number of distributed energy storage systems are integrated into the grid, their charging and discharging behavior further exacerbates grid uncertainty, posing even greater challenges to voltage stability analysis. In other words, existing voltage stability prediction methods for grid-connected distributed energy storage struggle to address the technical issues of poor generalization of voltage stability analysis caused by factors such as the randomness of load and generation, and the uncertainty of transmission line parameters. Summary of the Invention

[0005] In view of this, the present invention provides a distributed energy storage grid-connected voltage stability prediction method, medium and system, which can solve the technical problem that the existing distributed energy storage grid-connected voltage stability prediction method is difficult to solve due to the randomness of load and power generation, the uncertainty of transmission line parameters and other factors. The voltage stability analysis generalization ability is poor.

[0006] The present invention is achieved in that:

[0007] A first aspect of the present invention provides a method for predicting voltage stability of a distributed energy storage grid, comprising the following steps:

[0008] S10. Establish a topological diagram of an urban power grid including distributed energy storage, wherein the topological diagram includes nodes and edges. The nodes include power-input nodes, power generation nodes, load nodes, and energy storage nodes. The edges represent power transmission paths between nodes, and obtain the value of a node parameter of each node.

[0009] S20, establishing a group of energy storage grid-connected voltage impact equations based on the node parameters, including a power balance equation, a node voltage equation, an energy storage state equation, a power flow equation, and a voltage stability index equation;

[0010] S30, calculating a node parameter stability range for each node based on the energy storage grid-connected voltage influencing equations and the initial values ​​and constraints of each node parameter, where the node parameter stability range refers to a range of node parameter values ​​that ensures node voltage stability;

[0011] S40, calculating a voltage stability margin for each node based on the urban power grid topology, the stability range of each node parameter, and the current voltage state of the node acquired in real time, where the voltage stability margin represents the distance between the current voltage state of the node and a critical point of voltage instability;

[0012] S50, setting the value of each node in the urban power grid topology map as the voltage stability margin of the node to form a power grid voltage stability margin topology map, and converting the power grid voltage stability margin topology map into a power grid voltage stability margin matrix using an adjacency matrix method;

[0013] S60, repeatedly executing S40-S50 to obtain a grid voltage stability margin matrix at multiple consecutive acquisition moments;

[0014] S70: Input the obtained grid voltage stability margin matrix at multiple consecutive acquisition moments into a pre-trained grid voltage stability margin prediction model, and output the voltage stability margin prediction value of each node in the future period as the distributed energy storage grid-connected voltage stability prediction result.

[0015] Among them, the node parameters include power-in node parameters, power generation node parameters, load node parameters and energy storage node parameters. The power-in node parameters include voltage amplitude, phase angle and short-circuit capacity. The power generation node parameters include active power, reactive power and voltage amplitude. The load node parameters include active load, reactive load and voltage amplitude. The energy storage node parameters include charging and discharging power, energy storage capacity and voltage amplitude.

[0016] Among them, the initial values ​​and constraints of the node parameters are specifically as follows: the voltage amplitude of the power-in node is ±5% of the rated value, the phase angle is 0°, and the short-circuit capacity is determined according to the actual system parameters; the active power of the power generation node is between 0 and the rated power, the reactive power is between -30% and +30% of the rated power, and the voltage amplitude is ±5% of the rated value; the active load and reactive load of the load node are determined according to historical data and load forecasts, and the voltage amplitude is allowed to be between 90% and 110% of the rated value; the charging and discharging power of the energy storage node does not exceed the rated power, the energy storage capacity is between 20% and 80%, and the voltage amplitude is ±5% of the rated value.

[0017] The voltage state refers to a complex number representation consisting of the amplitude and phase angle of the node voltage.

[0018] The method for obtaining the voltage instability critical point is: through continuous power flow calculation and PV curve analysis, gradually increasing the system load until the power flow calculation does not converge or the voltage collapse point occurs. The non-convergence or voltage collapse point is the voltage instability critical point.

[0019] The following is the formula for each equation in the equation group of the impact of energy storage grid voltage:

[0020] 1. Power balance equation:

[0021]

[0022] Where, P i ,Q i is the injected active and reactive power of node i; V i ,V k is the complex voltage at nodes i and k; Y ik is the element in row i and column k of the node admittance matrix; P L,i ,Q L,i is the active and reactive power of the load at node i; P S,i ,Q S,i is the active and reactive power injected into the energy storage system of node i; n is the total number of nodes in the system; j is the imaginary unit; is the complex conjugate.

[0023] 2. Node voltage equation:

[0024]

[0025] Where, |V i | is the voltage amplitude of node i; θ i is the voltage phase angle of node i; are the real and imaginary parts of the voltage at node i; t is time;

[0026] The rate of change of voltage amplitude and phase angle can be calculated by the following equations:

[0027]

[0028] 3. Energy storage state equation:

[0029]

[0030] Where, E S,i is the energy state of the energy storage system at node i; is the charging and discharging power of the energy storage system; η c,i ,η d,i is the charging and discharging efficiency; P loss,i is the self-discharge loss of the energy storage system; is the maximum capacity of the energy storage system; is the maximum charge and discharge power of the energy storage system.

[0031] 4. Current flow equation:

[0032]

[0033] Where G ik ,B ik is the real and imaginary part of the element in row i and column k in the node admittance matrix; θ ik =θ i -θ k is the phase angle difference between nodes i and k.

[0034] 5. Voltage stability index equation:

[0035]

[0036] Where, L i is the L index of node i; VSI i is the voltage stability index of node i; Y ii are the diagonal elements of the node admittance matrix;

[0037] Among them, the voltage stability margin can be calculated by the following equation:

[0038] VSM i =VSI i -VSI i,crit ;

[0039] Among them, VSI i,crit It is the voltage stability index value at the critical stability point, which can be determined by continuously increasing the load and performing power flow calculations until the system collapses.

[0040] Among them, the grid voltage stability margin prediction model adopts a multi-task learning structure, including a temporal feature extraction subnetwork, a spatial feature extraction subnetwork, a change factor processing subnetwork and a fusion subnetwork.

[0041] Furthermore, the input of the time series feature extraction subnetwork is the grid voltage stability margin matrix at multiple consecutive acquisition moments, and the output is a time series feature vector, which is used to capture the temporal variation law of the voltage stability margin. Its structure is a recurrent neural network based on the long short-term memory network;

[0042] The input of the spatial feature extraction subnetwork is the grid voltage stability margin matrix at each acquisition moment, and the output is a spatial feature vector, which is used to extract the spatial correlation in the grid topology. Its structure is based on a graph convolutional neural network;

[0043] The input of the change factor processing subnetwork is load forecast and energy storage processing forecast data, and the output is a load-energy storage forecast feature vector, which is used to consider the impact of load changes on voltage stability. Its structure is a multi-layer feedforward neural network;

[0044] The input of the fusion sub-network is the concatenation of the time series feature vector, the spatial feature vector and the load-energy storage prediction feature vector, and the output is the predicted value of the voltage stability margin of each node in the future period, which is used to integrate various features for the final prediction. Its structure is a multi-layer feedforward neural network.

[0045] The training steps of the grid voltage stability margin prediction model are briefly described as follows:

[0046] Step 1: Initialize model parameters;

[0047] Step 2: Use the training set data for forward propagation;

[0048] Step 3: Calculate the loss between the predicted value and the actual value;

[0049] Step 4: Back propagation updates model parameters;

[0050] Step 5: Evaluate model performance on the validation set.

[0051] Step 6: Repeat steps 2-5 until the preset number of training rounds or performance indicators are reached;

[0052] Step 7: Use the test set for final evaluation.

[0053] The specific steps of obtaining the training data set of the grid voltage stability margin prediction model include:

[0054] Step 1: Using steps S10-S60, obtain the grid voltage stability margin matrix of multiple consecutive historical acquisition moments, and then perform the following steps:

[0055] Step 2: Collect forecast data related to grid operation, including load forecast data and energy storage output forecast data. The load forecast data is the predicted active load and reactive load data of the load node, and the energy storage output forecast data is the predicted charge and discharge power of the energy storage node. The forecasting method is to form a continuous data curve every 15 minutes based on historical records, and then use a time series forecasting model (such as ARIMA, Prophet or LSTM network) to predict the data for the next 24 hours. These forecasts take into account seasonal changes, cyclical patterns and long-term trends. For load forecasting, the influence of external variables such as weather factors and holiday effects must also be considered. For energy storage output forecasting, it is necessary to combine factors such as the operation strategy, charge and discharge efficiency and remaining capacity of the energy storage system.

[0056] Step 3: Arrange the grid voltage stability margin matrices of multiple consecutive historical acquisition moments in chronological order to form a time series data set;

[0057] Step 4: Use the sliding window method to process the time series data set, set the input window length and prediction window length, and generate input-output sample pairs;

[0058] Step 5: Align the predicted data with the grid voltage stability margin matrix samples at the corresponding time to obtain a training data set, where the training input is:

[0059] The grid voltage stability margin matrix sequence for the past n time steps, where n is the set input window length.

[0060] The load forecast data for the corresponding time period includes the active load and reactive load forecast values ​​for each load node.

[0061] Energy storage output forecast data for the corresponding time period, including the predicted charging and discharging power values ​​of each energy storage node.

[0062] The grid topology information can be represented by an adjacency matrix.

[0063] The output of the training is:

[0064] The predicted voltage stability margin for each node in the next ω time steps, where ω is the set prediction window length. These predicted values ​​form a three-dimensional tensor with dimensions (ω, number of nodes, 1), where 1 represents a voltage stability margin value for each node.

[0065] Furthermore, the frequency of repeatedly executing S40 to S50 is once every 15 minutes.

[0066] Specifically, step S10 includes: first, obtaining basic topological information of the urban power grid under study, including the type of each node, the connection relationship between nodes, and the relevant parameters of each node. Node types include power-input nodes, power generation nodes, load nodes, and energy storage nodes. The connection relationship between nodes can be represented by edges. Each node has its own node parameters, including voltage amplitude, phase angle, active power, reactive power, load power, energy storage capacity, and charge and discharge power. These parameters can be obtained through power grid modeling software or on-site measurements. On this basis, a power grid topology diagram including distributed energy storage is constructed, and the connection relationship between nodes is described using methods such as an adjacency matrix or adjacency list, and each type of node is identified. Finally, the parameter value of each node is obtained.

[0067] Step S20 specifically includes: based on the grid topology information and node parameters acquired in step S10, establishing a mathematical model describing the impact of distributed energy storage on grid voltage stability. This model includes a power balance equation, a node voltage equation, an energy storage state equation, a power flow equation, and a voltage stability index equation. The power balance equation describes the balance between active and reactive power at the node, including transmission line losses, load demand, and energy storage system injection power; the node voltage equation describes the temporal variation of node voltage amplitude and phase angle; the energy storage state equation describes the charging and discharging process and energy state of the energy storage system; the power flow equation is used to calculate the active and reactive power at each node; and the voltage stability index equation defines the voltage stability index and provides a formula for calculating the voltage stability margin. Establishing this mathematical model lays the foundation for subsequent voltage stability analysis and prediction.

[0068] Step S30 specifically includes the following steps: first, based on actual grid operation, initial value ranges are determined for each node parameter, such as the voltage amplitude at the input node being ±5% of the rated value, the active power at the generation node being between 0 and the rated power, and the voltage amplitude at the load node being between 90% and 110% of the rated value. These initial value ranges are then substituted into the mathematical model established in step S20, and the equations are solved numerically to obtain node parameter value ranges that satisfy voltage stability constraints. For any node parameter whose value falls outside the initial range, the parameter's value range is appropriately adjusted, and the calculation is repeated until a parameter value range that meets voltage stability requirements is found. This method obtains stable value ranges for each node parameter, providing the necessary constraints for subsequent voltage stability prediction.

[0069] Step S40 specifically includes: first, based on the grid topology diagram obtained in step S10 and the node parameter stability range obtained in step S30, numerically solving the mathematical equation in step S20 to obtain the current voltage amplitude and phase angle of each node. These voltage state parameters are then substituted into the voltage stability index equation to calculate the voltage stability index value for each node. The load is then continuously increased until a critical point is reached where the power flow calculation fails to converge or the voltage collapses, thereby obtaining the voltage stability index value at the critical point. Finally, the voltage stability margin of each node is calculated based on the difference between the node's current voltage stability index value and the critical point index value. These voltage stability margin values ​​can intuitively reflect the current voltage stability state of the power grid.

[0070] Step S50 specifically includes annotating each node in the power grid topology diagram from step S10 with the voltage stability margin value calculated in step S40 to form a power grid voltage stability margin topology diagram. This topology diagram is then converted into a power grid voltage stability margin matrix using an adjacency matrix approach, where the matrix elements represent the voltage stability margin values ​​between nodes. This matrix format not only includes the voltage stability margin of each node but also reflects the mutual influence of voltage stability between nodes, providing a data format that is easy to process and analyze for subsequent voltage stability prediction.

[0071] Steps S60 and S70 specifically include: first, setting an appropriate sampling interval, and then repeatedly executing steps S40-S50 to obtain a sequence of grid voltage stability margin matrices for multiple consecutive sampling moments. This data not only captures the time-varying characteristics of each node's own voltage stability margin, but also reflects the dynamic characteristics of the mutual influence of voltage stability between nodes. Then, using a pre-trained grid voltage stability margin prediction model, the voltage stability margin matrix sequence obtained in step S60, along with related load and energy storage forecast data, is used as input to output predicted voltage stability margin values ​​for each node over a period of time. This prediction model utilizes a multi-task learning architecture, including modules such as a temporal feature extraction subnetwork, a spatial feature extraction subnetwork, a variation factor processing subnetwork, and a fusion subnetwork. This model comprehensively considers the impact of factors such as grid topology, load variations, and energy storage systems on voltage stability.

[0072] A second aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores program instructions, and when the program instructions are executed, they are used to execute the above-mentioned method for predicting voltage stability of distributed energy storage grid-connected.

[0073] A third aspect of the present invention provides a distributed energy storage grid-connected voltage stability prediction system, which includes the above-mentioned computer-readable storage medium.

[0074] Compared with the existing technology, the beneficial effects of the distributed energy storage grid-connected voltage stability prediction method, medium and system provided by the present invention are: the present invention fully considers the impact of factors such as grid topology, load changes and energy storage system operation on voltage stability, and can more accurately predict the future voltage stability state of the grid.

[0075] Specifically, the present invention first establishes a topological model of an urban power grid incorporating distributed energy storage and obtains parameter information for each node. Then, based on the power grid model, a set of mathematical equations describing the impact of distributed energy storage on voltage stability are derived, including power balance equations, node voltage equations, energy storage state equations, power flow equations, and voltage stability index equations. Using these equations, the voltage stability margin of each grid node is calculated, reflecting the distance between the node's current voltage state and the critical point of instability.

[0076] Next, the present invention converts the voltage stability margin information of the grid nodes into a matrix form and obtains a sequence of voltage stability margin matrices at multiple consecutive moments. Finally, a grid voltage stability margin prediction model based on multi-task learning is designed. This model comprehensively considers factors such as time series characteristics, spatial correlation, and load and energy storage changes to output a predicted voltage stability margin value for each node over a period of time.

[0077] Compared with the traditional voltage stability analysis method, the advantages of the present invention are mainly reflected in the following aspects:

[0078] 1) The dynamic characteristics of the distributed energy storage system are fully considered, and its impact on grid voltage stability is included in the analysis scope, which is more in line with the complex situation of actual grid operation.

[0079] 2) The voltage stability margin information is processed in matrix representation, which not only reflects the stability of the node itself, but also describes the mutual influence between nodes, providing richer data input for subsequent predictive analysis.

[0080] 3) The prediction model based on machine learning can automatically learn the complex laws of grid voltage stability and has stronger generalization ability and prediction accuracy than traditional numerical calculation methods.

[0081] Therefore, the distributed energy storage urban power grid voltage stability prediction method proposed in the present invention can effectively make up for the shortcomings of the existing technology and solve the technical problem that the existing distributed energy storage grid-connected voltage stability prediction method is difficult to solve the poor generalization ability of voltage stability analysis caused by factors such as the randomness of load and power generation and the uncertainty of transmission line parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1A flow chart of the method provided by the present invention. DETAILED DESCRIPTION

[0083] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0084] like Figure 1 FIG. 1 is a flow chart of a method for predicting voltage stability of a distributed energy storage grid-connected power system provided by the present invention. The method includes the following steps:

[0085] S10. Establish a topological diagram of the urban power grid including distributed energy storage, where the topological diagram includes nodes and edges. The nodes include power-input nodes, power generation nodes, load nodes, and energy storage nodes. The edges represent power transmission paths between nodes, and obtain the value of node parameters of each node.

[0086] S20, establishing a group of energy storage grid-connected voltage impact equations based on node parameters, including a power balance equation, a node voltage equation, an energy storage state equation, a power flow equation, and a voltage stability index equation;

[0087] S30, calculating a node parameter stability range for each node based on the energy storage grid-connected voltage impact equations and the initial values ​​and constraints of each node parameter, where the node parameter stability range refers to a range of node parameter values ​​that ensures node voltage stability;

[0088] S40, based on the urban power grid topology, the stability range of each node parameter, and the current voltage state of the node obtained in real time, calculating the voltage stability margin of each node, where the voltage stability margin represents the distance between the current voltage state of the node and the critical point of voltage instability;

[0089] S50, setting the value of each node in the urban power grid topology map as the voltage stability margin of the node to form a power grid voltage stability margin topology map, and converting the power grid voltage stability margin topology map into a power grid voltage stability margin matrix using an adjacency matrix method;

[0090] S60, repeatedly executing S40-S50 to obtain a grid voltage stability margin matrix at multiple consecutive acquisition moments;

[0091] S70: Input the obtained grid voltage stability margin matrix at multiple consecutive acquisition moments into a pre-trained grid voltage stability margin prediction model, and output the voltage stability margin prediction value of each node in the future period as the distributed energy storage grid-connected voltage stability prediction result.

[0092] The specific implementation of the above steps is described in detail below:

[0093] Step S10: Establishing a city grid topology map including distributed energy storage

[0094] The specific implementation of this step is as follows:

[0095] First, it is necessary to obtain basic topological information about the city's power grid. This includes the types of nodes in the grid, the connections between them, and the relevant parameters of each node. Specifically, node types include incoming nodes, generation nodes, load nodes, and energy storage nodes. The connections between nodes can be represented by edges, which represent the power transmission paths between them. Each node has its own parameters, including voltage amplitude, phase angle, active power, reactive power, load power, energy storage capacity, and charge and discharge power. These parameters can be obtained through grid modeling software or field measurements.

[0096] After obtaining the basic topology information of the power grid, the next step is to construct a topology diagram of the power grid that includes distributed energy storage. The specific steps are:

[0097] 1) Based on the acquired node and edge information, a schematic diagram of the power grid topology is constructed. This can be done using the node and edge representation methods in graph theory, such as using an adjacency matrix or adjacency table to describe the connection relationships between nodes.

[0098] 2) Based on the topological structure, identify various nodes in the power grid, including power-input nodes, power generation nodes, load nodes, and energy storage nodes. These nodes are represented by different symbols or colors in the topological diagram.

[0099] 3) For each node, obtain its corresponding node parameter value. These parameter values ​​can be obtained through power flow calculation results of power grid simulation software, field measurement data or relevant literature.

[0100] Through the above steps, a topological diagram of the urban power grid, including distributed energy storage, was established, and parameter information for each node was obtained. This topological diagram laid the foundation for subsequent analysis and calculations. The main purpose of this step was to construct a mathematical model that reflects the actual situation of the power grid and provide the necessary input data for subsequent voltage stability analysis.

[0101] Step S20: Establish the energy storage grid voltage impact equations according to the node parameters

[0102] Based on the grid topology information and node parameters obtained in step S10, the purpose of this step is to establish a mathematical model that describes the impact of distributed energy storage on grid voltage stability. This model includes the following equations:

[0103] 1) Power balance equation:

[0104]

[0105] This equation describes the active power P of node i. i and reactive power Q i The balance between transmission line loss, load demand and energy storage system injection power. i and V k is the complex voltage at nodes i and k, Y ik is the corresponding element in the node admittance matrix, P L,i and Q L,i is the active and reactive power of the load at node i, P S,i and Q S,i Active and reactive power injected into the energy storage system at node i.

[0106] 2) Node voltage equation:

[0107]

[0108] This set of equations describes the variation of the amplitude and phase angle of the node voltage over time, and provides the formula for calculating the rate of change of the voltage amplitude and phase angle.

[0109] 3) Energy storage state equation:

[0110]

[0111] These equations describe the energy state changes of the energy storage system, including the charging, discharging and self-discharging processes, and define the capacity and power boundary conditions of the energy storage system.

[0112] 4) Flow equation:

[0113]

[0114] This set of equations is used to calculate the active power P of each node i and reactive power Q i , where G ik and B ik are the real and imaginary parts of the node admittance matrix, θ ik is the phase angle difference between nodes i and k.

[0115] 5) Voltage stability index equation:

[0116]

[0117] VSM i =VSI i -VSI i,crit

[0118] This set of equations defines the voltage stability index L i and VSI i, and the voltage stability margin VSM is calculated i The formula is: ii is the diagonal element of the node admittance matrix, VSI i,crit is the voltage stability index value of the critical stability point.

[0119] Using the above equations, a mathematical model describing the impact of distributed energy storage on grid voltage stability was established. This model can be used to analyze the voltage stability margin at each node in the grid, providing a basis for subsequent voltage stability prediction.

[0120] Step S30: Calculate the stability range of node parameters based on the energy storage grid voltage influence equation group

[0121] Based on the energy storage grid voltage impact equations established in step S20, as well as the initial values ​​and constraints of each node parameter, the purpose of this step is to calculate the node parameter value range that ensures node voltage stability. The specific steps are as follows:

[0122] 1) Initialize the value range of each node parameter:

[0123] -The voltage amplitude of the input node is ±5% of the rated value, the phase angle is 0°, and the short-circuit capacity is determined according to the actual system parameters;

[0124] - The active power of the power generation node is between 0 and the rated power, the reactive power is between -30% and +30% of the rated power, and the voltage amplitude is ±5% of the rated value;

[0125] - The active and reactive loads at load nodes are determined based on historical data and load forecasts, and the voltage amplitude is allowed to be between 90% and 110% of the rated value;

[0126] -The charging and discharging power of the energy storage node does not exceed the rated power, the energy storage capacity is between 20% and 80%, and the voltage amplitude is ±5% of the rated value.

[0127] 2) Substitute the initial value ranges of the above-mentioned node parameters into the energy storage grid-connected voltage influence equation group established in step S20, and use numerical calculation methods to solve these equations to obtain the value ranges of each node parameter that meet the voltage stability constraint conditions.

[0128] 3) If a node parameter exceeds the initial value range during the solution process, it means that the parameter value range is insufficient to ensure voltage stability. In this case, the parameter value range needs to be adjusted appropriately and recalculated until a parameter value range that meets the voltage stability requirements is found.

[0129] Through the above steps, the stable value ranges of each node parameter are obtained, which can ensure that the power grid maintains voltage stability when distributed energy storage is connected to the grid. These results provide the necessary constraints for subsequent voltage stability prediction.

[0130] Step S40: Calculate the voltage stability margin based on the grid topology and node parameter stability range

[0131] In step S30, the stable value ranges of the parameters for each grid node have been obtained. The purpose of this step is to further calculate the voltage stability margin of each node. The voltage stability margin is an important indicator of node voltage stability, reflecting the distance between the node's current voltage state and the critical point of voltage instability. The specific implementation steps are as follows:

[0132] 1) Based on the urban power grid topology established in step S10 and the stability range of each node parameter obtained in step S30, a numerical calculation method is used to solve the energy storage grid-connected voltage influence equation group in step S20 to obtain the current voltage amplitude and phase angle of each node.

[0133] 2) Substitute the current voltage state (amplitude and phase angle) of each node into the voltage stability index equation in step S20 to calculate the voltage stability index VSI of each node. i .

[0134] 3) By continuously increasing the system load, using the calculation method in step 2, gradually solve the voltage stability index VSI i , until a critical point occurs where the power flow calculation does not converge or the voltage collapses. The voltage stability index value corresponding to this critical point is recorded as VSI i,crit , which is the critical point of voltage instability.

[0135] 4) Based on the results obtained in steps 2 and 3, calculate the voltage stability margin VSM of each node i =VSI i -VSI i,crit The larger the voltage stability margin, the more stable the node voltage is; conversely, the closer the voltage is to the critical point of instability.

[0136] Through the above steps, the voltage stability margin value of each node in the power grid is obtained. These results can intuitively reflect the current voltage stability state of the power grid and provide necessary input data for subsequent voltage stability prediction.

[0137] Step S50: Convert the grid voltage stability margin information into a matrix representation

[0138] Based on the voltage stability margin of each node in the power grid calculated in step S40, the purpose of this step is to convert this information into a matrix form to provide input data for subsequent voltage stability prediction. The specific implementation steps are as follows:

[0139] 1) Each node in the urban power grid topology map created in step S10 is marked with the voltage stability margin value calculated therefor. This forms a power grid voltage stability margin topology map.

[0140] 2) Using the adjacency matrix method, the above grid voltage stability margin topology diagram is converted into a grid voltage stability margin matrix VSM. Specifically, the number of rows and columns of the matrix is ​​equal to the total number of nodes n in the grid, and the matrix element VSM is ij Represents the voltage stability margin between node i and node j. If there is no direct connection between node i and node j, then VSM ij = 0. Diagonal element VSM ii It represents the voltage stability margin of node i itself.

[0141] Through the above steps, the voltage stability margin information for each grid node is converted into a matrix representation. This matrix not only contains the voltage stability margin of each node but also reflects the mutual influence of voltage stability between nodes. This provides a data format that is easy to process and analyze for subsequent voltage stability prediction.

[0142] Step S60: Repeat S40-S50 to obtain the grid voltage stability margin matrix at multiple consecutive acquisition moments.

[0143] In steps S40-S50, the voltage stability margin matrix of the power grid at a certain moment has been calculated and converted. In order to analyze the change pattern of power grid voltage stability over time, the purpose of this step is to repeatedly execute steps S40-S50 to obtain a sequence of power grid voltage stability margin matrices at multiple consecutive acquisition moments.

[0144] The specific steps are as follows:

[0145] 1) Set an appropriate sampling interval, such as sampling every 15 minutes.

[0146] 2) For each sampling moment, repeat steps S40-S50 to calculate and transform the grid voltage stability margin matrix at that moment.

[0147] 3) The grid voltage stability margin matrices obtained by continuous sampling at multiple moments are arranged in chronological order to form a three-dimensional tensor VSM(t), where the first dimension corresponds to the time series, the second dimension corresponds to the grid nodes, and the third dimension corresponds to the voltage stability margin value of each node.

[0148] Through the above steps, the variation of the grid voltage stability margin at multiple consecutive moments is obtained, which provides the time series data required for subsequent voltage stability prediction. This data not only contains the time-varying characteristics of the voltage stability margin of each node itself, but also reflects the dynamic characteristics of the mutual influence of voltage stability between nodes.

[0149] Step S70: Predict the grid voltage stability margin using the pre-trained prediction model

[0150] Based on the grid voltage stability margin matrix sequence obtained at multiple consecutive acquisition moments in step S60, combined with the predicted data of the load and energy storage system, the purpose of this step is to use the pre-trained grid voltage stability margin prediction model to output the predicted voltage stability margin value of each node in the future period. The specific implementation steps are as follows:

[0151] 1) Structure of the grid voltage stability margin prediction model:

[0152] The prediction model adopts a multi-task learning structure, which includes the following four sub-networks:

[0153] -Time series feature extraction sub-network: A recurrent neural network based on the long short-term memory network (LSTM) is used to capture the changing patterns of the voltage stability margin time series.

[0154] -Spatial feature extraction subnetwork: Based on graph convolutional neural network (GCN), it is used to extract spatial correlation in the power grid topology.

[0155] -Change factor processing subnetwork: a multi-layer feedforward neural network used to consider the impact of load and energy storage system changes on voltage stability.

[0156] -Fusion sub-network: A multi-layer feedforward neural network used to integrate timing characteristics, spatial characteristics, and load-storage prediction characteristics to perform the final voltage stability margin prediction.

[0157] 2) Training the prediction model includes: initializing model parameters; performing forward propagation using the training set data; calculating the loss between the predicted value and the actual value; updating the model parameters through backpropagation; evaluating the model performance on the validation set; repeating the above steps until the preset number of training rounds or performance indicators are reached; and finally, performing a final evaluation using the test set.

[0158] 3) Acquisition of training data set:

[0159] Using the method of steps S10-S60, a grid voltage stability margin matrix of a plurality of consecutive historical acquisition moments is obtained;

[0160] Collect relevant load forecast data and energy storage output forecast data, and use time series forecasting models for forecasting;

[0161] The historical grid voltage stability margin matrix sequence and the load-storage forecast data at the corresponding time are combined into training samples.

[0162] 4) Prediction process:

[0163] Inputting the grid voltage stability margin matrix sequence of multiple consecutive acquisition moments obtained in step S60 into the pre-trained prediction model;

[0164] Other data input into the model include: load forecast data for the corresponding time period, energy storage output forecast data, and grid topology information;

[0165] The model outputs the predicted value of the voltage stability margin of each node in multiple future time steps, forming a three-dimensional tensor.

[0166] Through the above steps, the pre-trained grid voltage stability margin prediction model is used to output the voltage stability margin prediction value of each node in the future. These prediction results can provide a reference for grid operation and dispatch, helping to timely detect and prevent potential risks of voltage instability.

[0167] The core idea of ​​this approach is to first establish a grid topology model that includes distributed energy storage, then derive a set of mathematical equations describing voltage stability based on this model; then calculate the voltage stability margin at each node and convert it into a matrix form; and finally, use a machine learning model to predict the voltage stability margin. This approach fully considers the impact of factors such as grid topology, load variations, and energy storage systems on voltage stability, and can relatively accurately predict the future voltage stability of the grid.

[0168] A second aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores program instructions, and when the program instructions are executed, they are used to execute the above-mentioned method for predicting voltage stability of distributed energy storage grid-connected.

[0169] A third aspect of the present invention provides a distributed energy storage grid-connected voltage stability prediction system, which includes the above-mentioned computer-readable storage medium.

[0170] Specifically, the principle of the present invention is:

[0171] 1. Establish a power grid topology model including distributed energy storage and obtain parameter information of each node.

[0172] First, it's necessary to collect basic topological information about the city's power grid. This includes various nodes (incoming nodes, generating nodes, load nodes, and energy storage nodes) and their connections, as well as parameters such as voltage amplitude, phase angle, active power, reactive power, load power, and energy storage capacity for each node. Using this information, a mathematical model was established that reflects the actual operation of the power grid.

[0173] 2. Derive a set of mathematical equations describing voltage stability based on the power grid model.

[0174] Based on the grid topology information and node parameters obtained in step 1, a set of mathematical equations is established, including the power balance equation, node voltage equation, energy storage state equation, power flow equation, and voltage stability index equation. These equations describe the impact of the distributed energy storage system on the voltage amplitude, phase angle, and stability of each grid node. By solving these equations, the voltage stability margin of each node can be calculated, that is, the distance between the node's current voltage state and the critical point of voltage instability.

[0175] 3. Use machine learning models to predict voltage stability margin.

[0176] In step 2, the voltage stability margin information for each grid node at multiple consecutive moments is obtained and converted into a matrix. This matrix representation not only captures the voltage stability of the node itself but also reflects the interactions between nodes. A multi-task learning-based prediction model is designed. This model comprehensively considers factors such as time series characteristics, spatial correlation, and load and energy storage variations to output a predicted voltage stability margin for each node over a period of time.

[0177] There are several reasons for adopting this approach:

[0178] First, the dynamic characteristics of distributed energy storage systems have a significant impact on grid voltage stability, so they must be included in the analysis. Only by establishing a grid topology model that includes distributed energy storage can the actual operation of the grid be more accurately described.

[0179] Secondly, the matrix representation can better reflect the interaction between grid nodes. Compared with considering the stability of each node separately, this method can more comprehensively analyze the overall stability of the grid.

[0180] Finally, the machine learning-based prediction model has strong adaptive learning capabilities and can effectively capture the complex spatiotemporal characteristics of grid voltage stability. Compared with traditional numerical calculation methods, the model has better generalization and prediction accuracy when processing large-scale, high-dimensional grid data.

[0181] In summary, the distributed energy storage urban power grid voltage stability prediction method proposed in this invention fully considers the various complex factors in the actual operation of the power grid, and adopts a combination of mathematical modeling and machine learning to achieve effective prediction and early warning of the power grid voltage stability state, providing important support for the safe and stable operation of the power grid.

[0182] In order to better understand and implement the present invention, a specific embodiment 1 of the present invention is provided below. The specific steps of embodiment 1 are described in detail as follows:

[0183] Step S10: Establishing a city grid topology map including distributed energy storage

[0184] The specific implementation of this step is as follows:

[0185] First, we need to obtain the basic topological information of the urban power grid of the research object. This includes the types of nodes in the power grid, the connection relationship between nodes, and the relevant parameters of each node. Specifically, the node type includes the power node Power generation node Load Node and energy storage nodes The connection relationship between nodes can be expressed as edges. To represent the transmission path between nodes. Each node i has its own node parameters, including voltage amplitude |V i |、Phase angle θ i , active power P i , reactive power Q i , load active power P L,i , load reactive power Q L,i , energy storage capacity and charge and discharge power P S,i These parameters can be obtained through grid modeling software or on-site measurements.

[0186] After obtaining the basic topology information of the power grid, the next step is to construct a topology diagram of the power grid that includes distributed energy storage. The specific steps are:

[0187] 1) Based on the acquired node and edge information, a topological diagram of the power grid is established. This can be done by using the node and edge representation method in graph theory, such as using an adjacency matrix A or an adjacency table to describe the connection relationship between nodes. The element A of the adjacency matrix A is ij Defined as:

[0188]

[0189] 2) Based on the topological structure, identify various nodes in the power grid, including the power-input nodes Power generation node Load Node and energy storage nodes These nodes are represented by different symbols or colors in the topology diagram.

[0190] 3) For each node i, obtain its corresponding node parameter value These parameter values ​​can be obtained through power flow calculation results of power grid simulation software, field measurement data or relevant literature.

[0191] Through the above steps, a topological diagram of the urban power grid, including distributed energy storage, was established, and parameter information for each node was obtained. This topological diagram laid the foundation for subsequent analysis and calculations. The main purpose of this step was to construct a mathematical model that reflects the actual situation of the power grid and provide the necessary input data for subsequent voltage stability analysis.

[0192] Step S20: Establish the energy storage grid voltage impact equations according to the node parameters

[0193] Based on the grid topology information and node parameters obtained in step S10, the purpose of this step is to establish a mathematical model that describes the impact of distributed energy storage on grid voltage stability. This model includes the following equations:

[0194] 1) Power balance equation:

[0195]

[0196] This equation describes the active power P of node i. i and reactive power Q i The balance between transmission line loss, load demand and energy storage system injection power. i and V k is the complex voltage at nodes i and k, Y ik is the corresponding element in the node admittance matrix Y, P L,i and Q L,i is the active and reactive power of the load at node i, P S,i and Q S,i Active and reactive power injected into the energy storage system at node i.

[0197] 2) Node voltage equation:

[0198]

[0199] This set of equations describes the magnitude of the node voltage |V i | and phase angle θ i The changing rules of the voltage over time are given, and the formulas for calculating the voltage amplitude and phase angle change rate are given.

[0200] 3) Energy storage state equation:

[0201]

[0202] These equations describe the energy state changes of the energy storage system, including charging discharge and self-discharge P loss,i process and limits the capacity of the energy storage system and power Boundary conditions. Among them, η c,i and η d,i are the charge and discharge efficiencies, respectively.

[0203] 4) Flow equation:

[0204]

[0205] This set of equations is used to calculate the active power P of each node i and reactive power Q i , where G ik and B ik is the real and imaginary part of the node admittance matrix Y, θ ik =θ i -θ k is the phase angle difference between nodes i and k.

[0206] 5) Voltage stability index equation:

[0207]

[0208]

[0209] VSM i =VSI i -VSI i,crit

[0210] This set of equations defines the voltage stability index L i and VSI i , and the voltage stability margin VSM is calculated i The formula is: ii is the diagonal element of the node admittance matrix Y, VSI i,crit is the voltage stability index value of the critical stability point.

[0211] Using the above equations, a mathematical model describing the impact of distributed energy storage on grid voltage stability was established. This model can be used to analyze the voltage stability margin at each node in the grid, providing a basis for subsequent voltage stability prediction.

[0212] Step S30: Calculate the stability range of node parameters based on the energy storage grid voltage influence equation group

[0213] Based on the energy storage grid voltage impact equations established in step S20, as well as the initial values ​​and constraints of each node parameter, the purpose of this step is to calculate the node parameter value range that ensures node voltage stability. The specific steps are as follows:

[0214] 1) Initialize the value range of each node parameter:

[0215] Input node voltage amplitude |V i ±5% of the rated value, phase angle θ i is 0°, the short-circuit capacity is determined according to the actual system parameters;

[0216] Active power P of power generation node i Between 0 and rated power, reactive power Q i Between -30% and +30% of rated power, the voltage amplitude |V i |±5% of the rated value;

[0217] Load node active load P L,i and reactive load Q L,i Determined based on historical data and load forecast, the voltage amplitude |V i |Permitted between 90% and 110% of the rated value;

[0218] Energy storage node charging and discharging power P S,i Not exceeding rated power Energy storage capacity Between 20% and 80%, the voltage amplitude |V i |±5% of the rated value.

[0219] 2) Substitute the initial value ranges of the above-mentioned node parameters into the energy storage grid-connected voltage influence equation group established in step S20, and use numerical calculation methods to solve these equations to obtain the value ranges of each node parameter that meet the voltage stability constraint conditions.

[0220] 3) If a node parameter exceeds the initial value range during the solution process, it means that the parameter value range is insufficient to ensure voltage stability. In this case, the parameter value range needs to be adjusted appropriately and recalculated until a parameter value range that meets the voltage stability requirements is found.

[0221] Through the above steps, the stable value ranges of each node parameter are obtained, which can ensure that the power grid maintains voltage stability when distributed energy storage is connected to the grid. These results provide the necessary constraints for subsequent voltage stability prediction.

[0222] Step S40: Calculate the voltage stability margin based on the grid topology and node parameter stability range

[0223] In step S30, the stable value ranges of the parameters for each grid node have been obtained. The purpose of this step is to further calculate the voltage stability margin of each node. The voltage stability margin is an important indicator of node voltage stability, reflecting the distance between the node's current voltage state and the critical point of voltage instability. The specific implementation steps are as follows:

[0224] 1) Based on the urban power grid topology established in step S10 and the stability range of each node parameter obtained in step S30, the energy storage grid voltage influence equations in step S20 are solved by numerical calculation to obtain the current voltage amplitude of each node |V i | and phase angle θ i .

[0225] 2) The current voltage state of each node (|V i |,θ i ) is substituted into the voltage stability index equation in step S20 to calculate the voltage stability index VSI of each node i .

[0226] 3) By continuously increasing the system load, using the calculation method in step 2, gradually solve the voltage stability index VSI i , until a critical point occurs where the power flow calculation does not converge or the voltage collapses. The voltage stability index value corresponding to this critical point is recorded as VSI i,crit , which is the critical point of voltage instability.

[0227] 4) Based on the results obtained in steps 2 and 3, calculate the voltage stability margin VSM of each node i =VSI i -VSI i,crit The larger the voltage stability margin, the more stable the node voltage is; conversely, the closer the voltage is to the critical point of instability.

[0228] Through the above steps, the voltage stability margin value of each node in the power grid is obtained. These results can intuitively reflect the current voltage stability state of the power grid and provide necessary input data for subsequent voltage stability prediction.

[0229] Step S50: Convert the grid voltage stability margin information into a matrix representation

[0230] Based on the voltage stability margin of each node in the power grid calculated in step S40, the purpose of this step is to convert this information into a matrix form to provide input data for subsequent voltage stability prediction. The specific implementation steps are as follows:

[0231] 1) For each node i in the urban power grid topology diagram established in step S10, the voltage stability margin value VSM calculated by the calculation is used. iThis forms a grid voltage stability margin topology diagram.

[0232] 2) Using the adjacency matrix method, the above grid voltage stability margin topology diagram is converted into a grid voltage stability margin matrix VSM. Specifically, the number of rows and columns of the matrix is ​​equal to the total number of nodes n in the grid, and the matrix element VSM is ij Represents the voltage stability margin between node i and node j. If there is no direct connection between node i and node j, then VSM ij = 0. Diagonal element VSM ii It represents the voltage stability margin of node i itself. The grid voltage stability margin matrix VSM can be expressed as:

[0233]

[0234] Through the above steps, the voltage stability margin information for each grid node is converted into a matrix representation. This matrix not only contains the voltage stability margin of each node but also reflects the mutual influence of voltage stability between nodes. This provides a data format that is easy to process and analyze for subsequent voltage stability prediction.

[0235] Step S60: Repeat S40-S50 to obtain the grid voltage stability margin matrix at multiple consecutive acquisition moments.

[0236] In steps S40-S50, the voltage stability margin matrix of the power grid at a certain moment has been calculated and converted. In order to analyze the change pattern of power grid voltage stability over time, the purpose of this step is to repeatedly execute steps S40-S50 to obtain a sequence of power grid voltage stability margin matrices at multiple consecutive acquisition moments.

[0237] The specific steps are as follows:

[0238] 1) Set a suitable sampling time interval Δt, for example, sampling every 15 minutes.

[0239] 2) For each sampling time t, repeat steps S40-S50 to calculate and transform the grid voltage stability margin matrix VSM(t) at that time.

[0240] 3) The grid voltage stability margin matrices {VSM(t), VSM(t+Δt), …, VSM(t+mΔt)} obtained by continuous sampling at multiple moments are arranged in chronological order to form a three-dimensional tensor VSM(t), where the first dimension corresponds to the time series, the second dimension corresponds to the grid nodes, and the third dimension corresponds to the voltage stability margin value of each node. The three-dimensional tensor VSM(t) can be expressed as:

[0241]

[0242] Through the above steps, the variation of the grid voltage stability margin at multiple consecutive moments is obtained, which provides the time series data required for subsequent voltage stability prediction. This data not only contains the time-varying characteristics of the voltage stability margin of each node itself, but also reflects the dynamic characteristics of the mutual influence of voltage stability between nodes.

[0243] Step S70: Predict the grid voltage stability margin using the pre-trained prediction model

[0244] Based on the grid voltage stability margin matrix sequence obtained at multiple consecutive acquisition moments in step S60, combined with the predicted data of the load and energy storage system, the purpose of this step is to use the pre-trained grid voltage stability margin prediction model to output the predicted voltage stability margin value of each node in the future period. The specific implementation steps are as follows:

[0245] 1) Structure of the grid voltage stability margin prediction model:

[0246] The prediction model adopts a multi-task learning structure, which includes the following four sub-networks:

[0247] Time series feature extraction sub-network: A recurrent neural network based on the long short-term memory network (LSTM) is used to capture the changing pattern of the voltage stability margin time series. The input of the LSTM network is the grid voltage stability margin matrix sequence VSM(t) at multiple consecutive acquisition moments, and the output is the time series feature vector h t .

[0248] Spatial feature extraction sub-network: Based on graph convolutional neural network (GCN), it is used to extract spatial correlation in the power grid topology. The input of the GCN network is the grid voltage stability margin matrix VSM(t) at each acquisition moment, and the output is the spatial feature vector h s .

[0249] Change factor processing subnetwork: a multi-layer feedforward neural network, used to consider the impact of load and energy storage system changes on voltage stability. The input of this subnetwork is the load forecast {P L,i ,Q L,i} and energy storage processing forecast Data, the output is the characteristic vector h of the change factor c .

[0250] Fusion sub-network: multi-layer feedforward neural network, used to integrate temporal features h t , spatial feature h s and the change factor characteristics h c , and make the final voltage stability margin prediction. The output of this sub-network is the voltage stability margin prediction value of each node in the next m time steps.

[0251] 2) Training of prediction model:

[0252] Initialize model parameters

[0253] Forward propagation using training set data

[0254] Calculate predicted values The loss function between the actual value VSM

[0255] Update the model parameters through the back-propagation algorithm to minimize the loss function

[0256] Evaluate model performance on the validation set, such as prediction accuracy, etc.

[0257] Repeat the above steps until the preset number of training rounds or performance indicators are reached

[0258] Final evaluation using the test set

[0259] 3) Acquisition of training data set:

[0260] Using the method of steps S10-S60, the grid voltage stability margin matrix sequence {VSM(t), VSM(t+Δt), ..., VSM(t+mΔt)} of multiple consecutive historical acquisition moments is obtained.

[0261] Collect relevant load forecast data {P L,i ,Q L,i} and energy storage output forecast data Use time series forecasting models (such as ARIMA, Prophet, or LSTM networks) for forecasting

[0262] The historical grid voltage stability margin matrix sequence and the corresponding time load-storage forecast data are combined into training samples

[0263] 4) Prediction process:

[0264] Input the grid voltage stability margin matrix sequence VSM(t) of multiple consecutive acquisition moments obtained in step S60 into the pre-trained prediction model

[0265] Other data input to the model include: load forecast data for the corresponding time period {P L,i ,Q L,i Energy storage output forecast data And the grid topology information A

[0266] The model outputs the predicted value of voltage stability margin for each node in the next m time steps. Forming a three-dimensional tensor.

[0267] Through the above steps, the pre-trained grid voltage stability margin prediction model is used to output the voltage stability margin prediction value of each node in the future. These prediction results can provide a reference for grid operation and dispatch, helping to timely detect and prevent potential risks of voltage instability.

[0268] The core idea of ​​Example 1 is to first establish a grid topology model that includes distributed energy storage, then derive a set of mathematical equations describing voltage stability based on this model; then calculate the voltage stability margin at each node and convert it into a matrix form; and finally, use a machine learning model to predict the voltage stability margin. This method fully considers the impact of factors such as grid topology, load variations, and energy storage systems on voltage stability, and can relatively accurately predict the future voltage stability of the grid.

[0269] To further enhance the understanding and implementation of the present invention, Example 2 of a specific application scenario is provided below: A certain city's power grid covers an area of ​​approximately 500 square kilometers, and its power supply primarily consists of three main categories of users: industrial, commercial, and residential. This grid utilizes a 220kV high-voltage transmission network, delivering power to users via 110kV and 10kV substations. To improve power supply reliability, a large number of distributed photovoltaic power generation and energy storage systems have been deployed within the grid.

[0270] According to data provided by the grid operator, the basic topology of the city's urban power grid consists of 40 nodes, including five 220kV power inlet nodes, 10 110kV power generation nodes, 20 10kV load nodes, and five distributed energy storage nodes. These nodes are interconnected by 60 transmission lines.

[0271] In order to analyze the voltage stability of this power grid, it is necessary to obtain the specific parameter information of each node. Through the power flow calculation of the power grid modeling software and the field measurement data, the data shown in Table 1 were obtained.

[0272] Table 1. Grid node parameters

[0273] Node Type Number of nodes Voltage (kV) Active power (MW) Reactive power (Mvar) Energy storage capacity (MWh) Power-on node 5 220±5％ - - - Power generation node 10 110±5％ 0～500 -150～150 - Load Node 20 10±10％ 0～50 -20～20 - Energy storage node 5 10±5％ -20～20 -10～10 10～40

[0274] Next, according to the method proposed in the present invention, the voltage stability of this city power grid is analyzed and predicted.

[0275] Step S10: Establishing a city grid topology map including distributed energy storage

[0276] according to Figure 1Based on the information in Table 1, a topological diagram of the urban power grid, including distributed energy storage, can be constructed. This diagram shows the connectivity between various nodes in the grid and identifies the incoming nodes, generation nodes, load nodes, and energy storage nodes. It also captures relevant parameters for each node, including voltage amplitude, active power, reactive power, load power, and energy storage capacity.

[0277] Step S20: Establish the energy storage grid voltage impact equations according to the node parameters

[0278] Based on the urban grid topology information and node parameters obtained in step S10, the following mathematical equations describing the impact of distributed energy storage on voltage stability are established:

[0279] 1) Power balance equation:

[0280] Taking node i as an example, its active power P i and reactive power Q i Should meet:

[0281]

[0282] Among them, V i and V k are the complex voltages at nodes i and k, Y ik is the element of the node admittance matrix, P L,i and Q L,i is the active and reactive power of the load at node i, P S,i and Q S,i Active and reactive power injected into the energy storage system at node i.

[0283] 2) Node voltage equation:

[0284] The voltage at node i can be expressed as where |V i | is the voltage amplitude, θ i is the voltage phase angle. The rate of change of voltage amplitude and phase angle can be expressed as:

[0285]

[0286] 3) Energy storage state equation:

[0287] Energy state E of the energy storage system at node i S,i The changes can be expressed as:

[0288]

[0289] in, and are the charging and discharging power of the energy storage system, η c,i and η d,iis the charge and discharge efficiency, P loss,i is the self-discharge loss.

[0290] 4) Flow equation:

[0291] Active power P of node i i and reactive power Q i It can be calculated by the following formula:

[0292]

[0293] Among them, G ik and B ik are the real and imaginary parts of the node admittance matrix, θ ik is the phase angle difference between nodes i and k.

[0294] 5) Voltage stability index equation:

[0295] Using voltage stability index VSI i To characterize the voltage stability of node i, it is defined as:

[0296]

[0297] Voltage Stability Margin VSM i It can be expressed as:

[0298] VSM i =VSI i -VSI i,crit

[0299] Among them, VSI i,crit is the critical stability point index value of node i.

[0300] By establishing such a set of mathematical equations, we can analyze the impact of distributed energy storage systems on the voltage stability of each node in the power grid.

[0301] Step S30: Calculate the stability range of node parameters based on the energy storage grid voltage influence equation group

[0302] Based on the above voltage influence equations and the initial value range of the node parameters given in Table 1, the node parameter stability range that ensures grid voltage stability can be calculated as follows:

[0303] 1) Power-on node:

[0304] Voltage amplitude|V i ±5% of 220kV, i.e. 209kV to 231kV; Phase angle θ i is 0°; the short-circuit capacity is determined according to the actual system parameters.

[0305] 2) Power generation node:

[0306] Active power P i Between 0MW and 500MW; reactive power Q i Between -150Mvar and 150Mvar; voltage amplitude |V i ±5% of 110kV, i.e. 104.5kV to 115.5kV.

[0307] 3) Load nodes:

[0308] Active load P L,i and reactive load Q L,i Determined based on historical data and load forecast, the voltage amplitude |V i The voltage is allowed to be within ±10% of 10kV, i.e. 9kV to 11kV.

[0309] 4) Energy storage nodes:

[0310] Charge and discharge power P S,i No more than ±20MW, energy storage capacity Between 10MWh and 40MWh, the voltage amplitude is |V i ±5% of 10kV.

[0311] Through the above steps, the range of values ​​of each node parameter that meets the voltage stability constraints is obtained. These results will serve as the basis for subsequent voltage stability analysis and prediction.

[0312] Step S40: Calculate the voltage stability margin based on the grid topology and node parameter stability range

[0313] Using the node parameter stability range obtained in step S30, the voltage influence equations are solved by numerical calculation to obtain the node voltage state of the urban power grid at a certain moment, including the voltage amplitude |V i | and phase angle θ i .

[0314] Then, these voltage state parameters are substituted into the voltage stability index equation to calculate the voltage stability index VSI of each node. i Then, by continuously increasing the system load until a critical point occurs where the power flow calculation does not converge or the voltage collapses, the voltage stability index value VSI at the critical point is obtained. i,crit .

[0315] Finally, based on the node's current VSI i and critical point VSI i,crit The voltage stability margin VSM of each node is calculated i =VSI i -VSI i,critTable 2 below lists the voltage stability margin calculation results of each node in the urban power grid at a certain moment:

[0316] Table 2. Voltage stability margin calculation results

[0317] node <![CDATA[VSI i ]]> <![CDATA[VSI i,crit ]]> <![CDATA[VSM i ]]> 1 0.952 0.890 0.062 2 0.970 0.910 0.060 ... ... ... ... 40 0.925 0.860 0.065

[0318] As can be seen from the table, the voltage stability margin of most nodes in the power grid is around 0.06, indicating that the overall voltage of the power grid is relatively stable. However, there are also some nodes with low stability margins, such as the VSM at node 40. i It is only 0.065, which means that the voltage of this node is close to the critical point of instability and needs special attention.

[0319] Step S50: Convert the grid voltage stability margin information into a matrix representation

[0320] Based on the grid node voltage stability margin calculated in step S40, it is converted into the form of grid voltage stability margin matrix VSM. The number of rows and columns of this matrix is ​​40, representing 40 nodes in the grid. The matrix element VSM ij Represents the voltage stability margin between node i and node j. If there is no direct connection between the two nodes, then VSM ij = 0. Diagonal element VSM ii It represents the voltage stability margin of node i itself. The voltage stability margin matrix of the city power grid at a certain moment is given below:

[0321]

[0322] This matrix representation not only includes the voltage stability of each node itself, but also reflects the mutual influence relationship between nodes. Subsequent voltage stability prediction analysis will be carried out based on this voltage stability margin matrix.

[0323] Step S60: Repeat S40-S50 to obtain the grid voltage stability margin matrix at multiple consecutive acquisition moments.

[0324] In order to analyze the law of grid voltage stability over time, the city grid is sampled every 15 minutes, and steps S40-S50 are repeated to obtain a voltage stability margin matrix sequence for 96 consecutive moments (one day).

[0325] Based on the temporal trends of the collected voltage stability margin matrix, it can be determined that the voltage stability margins at different nodes in the power grid exhibit significant dynamic changes over time. During the morning peak hours, the voltage stability margins of some load nodes decrease, while at night, when loads are lower, the stability margins of most nodes increase. This time series characteristic provides an important basis for subsequent voltage stability prediction.

[0326] We also observed that voltage stability at each node affects each other. For example, when the energy storage system at a node experiences changes in charge or discharge, the voltage stability margins at other connected nodes will also fluctuate. This spatial correlation will also be taken into account in the prediction model.

[0327] Step S70: Predict the grid voltage stability margin using the pre-trained prediction model

[0328] In step S60, a sequence of voltage stability margin matrices for the city's power grid at 96 consecutive moments is obtained. Combining this with the predicted load and energy storage system data, a pre-trained grid voltage stability margin prediction model is used to predict the voltage stability margin at each node over a period of time.

[0329] 1) Structure of the grid voltage stability margin prediction model

[0330] The prediction model adopts a multi-task learning structure and mainly includes the following four sub-networks:

[0331] Time series feature extraction subnetwork: This is a recurrent neural network based on the long short-term memory network (LSTM) to capture the changing pattern of the voltage stability margin time series. The input of this subnetwork is the grid voltage stability margin matrix sequence {VSM(t-95), VSM(t-94), …, VSM(t)} obtained in step S60 for 96 consecutive moments, and the output is the time series feature vector h t .

[0332] Spatial feature extraction subnetwork: This is a subnetwork based on graph convolutional neural network (GCN) to extract spatial correlation in the power grid topology. The input of this subnetwork is the grid voltage stability margin matrix VSM(t) at each moment, and the output is the spatial feature vector h s .

[0333] Change factor processing subnetwork: This is a multi-layer feedforward neural network used to consider the impact of load and energy storage system changes on voltage stability. The input of this subnetwork is load forecast data {P L,i ,Q L,i} and energy storage output forecast data The output is the characteristic vector h of the change factor c .

[0334] Fusion sub-network: This is also a multi-layer feedforward neural network used to synthesize the time series features h t , spatial feature h s and the change factor characteristics h c, and make the final voltage stability margin prediction. The output of this sub-network is the voltage stability margin prediction value of each node in the next m time steps.

[0335] 2) Training of prediction models

[0336] The following steps are used to train the grid voltage stability margin prediction model:

[0337] Step 1: Initialize model parameters

[0338] Step 2: Use the training set data for forward propagation and calculate the predicted value The loss function between the actual value VSM

[0339] Step 3: Update the model parameters to minimize the loss function through the back-propagation algorithm

[0340] Step 4: Evaluate model performance on the validation set, such as prediction accuracy, mean absolute error, etc.

[0341] Step 5: Repeat steps 2-4 until the preset number of training rounds or performance indicators are reached

[0342] Step 6: Final evaluation using the test set

[0343] 3) Acquisition of training dataset

[0344] Using the method of steps S10-S60, the voltage stability margin matrix sequence {VSM(t-95), VSM(t-94), ..., VSM(t)} of the urban power grid in the past 96 time steps is obtained as input-output samples.

[0345] At the same time, the load forecast data for the corresponding time period {P L,i ,Q L,i} and energy storage output forecast data These forecast data can be obtained through time series forecasting models (such as ARIMA, Prophet or LSTM networks), taking into account the influence of seasonality, cyclicality and external factors (such as weather, holidays, etc.).

[0346] Finally, the historical grid voltage stability margin matrix sequence and the load-storage forecast data of the corresponding time are combined into training samples as input features; the voltage stability margin matrix of the next m time steps is used as the prediction target output.

[0347] 4) Prediction process

[0348] After completing the model training, the prediction model can be used to predict the future grid voltage stability. The specific steps are as follows:

[0349] Step 1: Input the grid voltage stability margin matrix sequence {VSM(t-95), VSM(t-94), ..., VSM(t)} of the latest 96 time steps obtained in step S60 into the pre-trained prediction model

[0350] Step 2: Other data input to the model include: load forecast data for the corresponding time period {P L,i ,Q L,i Energy storage output forecast data And the grid topology information A

[0351] Step 3: The model outputs the predicted value of voltage stability margin for each node in the next m time steps Form a three-dimensional tensor

[0352] Through the above steps, the pre-trained grid voltage stability margin prediction model is used to output the voltage stability margin prediction value of each node in the future. These prediction results can provide important reference for grid dispatchers and help to timely detect and prevent potential risks of voltage instability.

[0353] Table 3 below shows the voltage stability margin prediction results of the city's power grid at a certain point in time:

[0354] Table 3. Voltage stability margin prediction results

[0355]

[0356] The forecast results indicate that the overall voltage stability margin of the power grid will show a downward trend over the next three hours. In particular, the voltage stability margin at node 40 has dropped from its current value of 0.065 to 0.054, approaching the critical point of instability. This indicates that voltage instability may be a risk at this node for some time to come, requiring grid dispatchers to implement necessary control measures, such as adjusting node loads and increasing energy storage system output, to maintain safe and stable grid operation.

[0357] In summary, the distributed energy storage urban power grid voltage stability prediction method proposed in Example 2 not only analyzes the current grid stability state but also predicts the voltage stability margin trends at each node over the next period of time. This provides important decision-making support for grid dispatchers, facilitates the implementation of targeted control measures, and improves the safety and reliability of the grid.

[0358] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed by the present invention, which should be covered by the scope of protection of the present invention.

Claims

1. A method for predicting voltage stability of distributed energy storage grid-connected power, characterized in that: The following steps are involved: S10. Establish a topological diagram of an urban power grid including distributed energy storage, wherein the topological diagram includes nodes and edges. The nodes include power-input nodes, power generation nodes, load nodes, and energy storage nodes. The edges represent power transmission paths between nodes, and obtain the value of a node parameter of each node. S20, establishing a group of energy storage grid-connected voltage impact equations based on the node parameters, including a power balance equation, a node voltage equation, an energy storage state equation, a power flow equation, and a voltage stability index equation; S30, calculating a stable range of node parameters of each node based on the energy storage grid-connected voltage influencing equations and the initial values ​​and constraints of each node parameter, where the stable range of node parameters refers to a range of values ​​of the node parameters that ensures the stability of the node voltage; S40, calculating the voltage stability margin of each node based on the urban power grid topology, the stability range of each node parameter, and the current voltage state of the node obtained in real time, specifically including: 1) Based on the urban power grid topology established in step S10 and the parameter stability ranges of each node obtained in step S30, numerical calculation methods are used to solve the energy storage grid-connected voltage influence equations in step S20 to obtain the current voltage amplitude and phase angle of each node; 2) Substitute the current voltage amplitude and phase angle of each node into the voltage stability index equation in step S20 to calculate the voltage stability index of each node. ; 3) By continuously increasing the system load, use the calculation method in step 2) to gradually solve the voltage stability index , until a critical point occurs where the power flow calculation does not converge or the voltage collapses. The voltage stability index corresponding to this critical point is ,This critical point is the voltage instability critical point; 4) Based on the results obtained in steps 2) and 3), calculate the voltage stability margin of each node ; S50, setting the value of each node in the urban power grid topology map as the voltage stability margin of the node to form a power grid voltage stability margin topology map, and converting the power grid voltage stability margin topology map into a power grid voltage stability margin matrix using an adjacency matrix method; S60, repeatedly executing S40-S50 to obtain a grid voltage stability margin matrix at multiple consecutive acquisition moments; S70: Input the obtained grid voltage stability margin matrix at multiple consecutive acquisition moments into a pre-trained grid voltage stability margin prediction model, and output the voltage stability margin prediction value of each node in the future period as the distributed energy storage grid-connected voltage stability prediction result.

2. A distributed energy storage grid-connected voltage stability prediction method according to claim 1, characterized in that: The node parameters include power-in node parameters, power generation node parameters, load node parameters and energy storage node parameters. The power-in node parameters include voltage amplitude, phase angle and short-circuit capacity. The power generation node parameters include active power, reactive power and voltage amplitude. The load node parameters include active load, reactive load and voltage amplitude. The energy storage node parameters include charging and discharging power, energy storage capacity and voltage amplitude.

3. A distributed energy storage grid-connected voltage stability prediction method according to claim 1, characterized in that: The initial values ​​and constraints of the node parameters are as follows: the voltage amplitude of the input node is ±5% of the rated value, the phase angle is 0°, and the short-circuit capacity is determined according to the actual system parameters; the active power of the generation node is between 0 and the rated power, the reactive power is between -30% and +30% of the rated power, and the voltage amplitude is ±5% of the rated value; The active load and reactive load of the load node are determined based on historical data and load forecasts, and the voltage amplitude is allowed to be between 90% and 110% of the rated value; the charging and discharging power of the energy storage node does not exceed the rated power, the energy storage capacity is between 20% and 80%, and the voltage amplitude is ±5% of the rated value.

4. A distributed energy storage grid-connected voltage stability prediction method according to claim 1, characterized in that: The grid voltage stability margin prediction model adopts a multi-task learning structure, including a temporal feature extraction subnetwork, a spatial feature extraction subnetwork, a change factor processing subnetwork and a fusion subnetwork.

5. A distributed energy storage grid-connected voltage stability prediction method according to claim 4, characterized in that: The input of the time series feature extraction subnetwork is the grid voltage stability margin matrix of multiple consecutive acquisition moments, and the output is a time series feature vector, which is used to capture the time variation law of the voltage stability margin. Its structure is a recurrent neural network based on the long short-term memory network; The input of the spatial feature extraction subnetwork is the grid voltage stability margin matrix at each acquisition moment, and the output is a spatial feature vector, which is used to extract the spatial correlation in the grid topology. Its structure is based on a graph convolutional neural network; The input of the change factor processing subnetwork is load forecast and energy storage processing forecast data, and the output is a load-energy storage forecast feature vector, which is used to consider the impact of load changes on voltage stability. Its structure is a multi-layer feedforward neural network; The input of the fusion sub-network is the concatenation of the time series feature vector, the spatial feature vector and the load-energy storage prediction feature vector, and the output is the predicted value of the voltage stability margin of each node in the future period, which is used to integrate various features for the final prediction. Its structure is a multi-layer feedforward neural network.

6. A distributed energy storage grid-connected voltage stability prediction method according to claim 5, characterized in that: The frequency of repeating S40-S50 is once every 15 minutes.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium stores program instructions, which, when executed, are used to execute the method for predicting voltage stability of a distributed energy storage grid-connected power system according to any one of claims 1 to 6.

8. A distributed energy storage grid-connected voltage stability prediction system, characterized in that: Contains the computer-readable storage medium of claim 7.

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