A Method for Analyzing the Random Operation Characteristics of New Energy Power Systems Based on Power Flow Models

Abstract

This invention discloses a method for analyzing the stochastic operating characteristics of a new energy power system based on power flow modalities. The method includes: converting the current state of the power grid at the initial moment into a graph and labeling the graph; using the Monte Carlo method to sample massive amounts of actual generator output and load values, and performing power flow calculations on all scenarios; recording node voltages, line power flows, and power flow directions; for each power flow calculation result, drawing a directed graph by marking the edges in the graph with power flow directions, counting the number of various four-node subgraphs in the directed graph, and counting directed four-node modalities; calculating the voltage stability of the current sample using voltage and line power flows; calculating the correlation between all directed four-node modalities and voltage stability, and statistically analyzing the correlation data to derive the stochastic operating characteristics of the power grid; and inferring the stability risk of the power grid at the moment the stability risk is to be assessed based on the stochastic operating characteristics and the power flow direction at the moment the stability risk is to be assessed.

Description

Technical Field

[0001] This invention relates to the field of electrical engineering technology, and in particular to a method for analyzing the stochastic operating characteristics of a new energy power system based on a power flow model. Background Technology

[0002] The power system has evolved into one of the most complex man-made industrial networks in the world. Theoretically, the inherent self-organizing criticality of complex networks can potentially trigger a series of complex, unpredictable, and disordered chaotic states. In recent years, the widespread integration of new energy sources has further increased the randomness of power grid operation, making the data required for grid stability calculations increasingly complex. This data includes power flow data, voltage data, and line parameters. In areas with weak distribution networks, this data is difficult to obtain, and its accuracy is questionable. Therefore, a method with lower data requirements is needed to supplement traditional methods, enabling more reliable stability analysis and timely responses to unstable conditions. Summary of the Invention

[0003] To address the problems existing in the prior art, this invention proposes a method for analyzing the stochastic operating characteristics of a new energy power system based on power flow models, using complex network technology.

[0004] To achieve the above objectives, the present invention adopts the following technical solution:

[0005] A first aspect of this invention provides a method for analyzing the stochastic operating characteristics of a new energy power system based on a power flow model, characterized in that the method specifically includes the following steps:

[0006] (1) Convert the current state of the power grid at the start time into a graph and label the graph;

[0007] (2) The Monte Carlo method is used to sample a large number of scenarios of the actual output of the generator and the actual load, and power flow calculation is performed on all scenarios; node voltage, line power flow and power flow direction are recorded.

[0008] (3) For each power flow calculation result, the power flow direction is used as the direction of the edge in the graph marked in step (1), a directed graph is drawn, the number of various four-node subgraphs in the directed graph is counted, and the directed four-node module is counted. The voltage stability of the current sample is calculated using voltage and line power flow.

[0009] (4) Calculate the correlation between all directed four-node modules and voltage stability, and statistically analyze the correlation data to obtain the random operating characteristics of the power grid;

[0010] (5) Based on the random operating characteristics obtained in step (4) and the power flow direction at the moment when the stability risk is to be evaluated, the stability risk of the power grid at the moment when the stability risk is to be evaluated is inferred.

[0011] Furthermore, the process of transforming the current state of the power grid at the initial moment into a graph includes: collecting the location information of each device in the power grid, including substations, generators, and loads, and transforming loads, power plants, and substations into nodes, and transmission lines and transformer branches into edges in the network. Nodes are connected to edges to form a graph; in the graph, the location of each node corresponds to its location in reality.

[0012] Furthermore, the process of annotating the map includes: collecting the rated power of substations, lines, and generators in the current power grid, as well as the historical peak load, to refer to whether the area of ​​the power grid can be used to build power plants, substations, and other facilities. The map locations corresponding to places where power plants and substations cannot be built, as well as locations unsuitable for new energy sources including photovoltaic power plants, wind power plants, and tidal power plants, are marked.

[0013] Furthermore, step (3) specifically includes the following sub-steps:

[0014] (3.1) Based on the graph marked in step (1), determine the direction of all edges in the graph according to the direction of active power flow in the corresponding line to obtain a directed graph;

[0015] (3.2) Find all four-node subgraphs with connectivity in the directed graph, classify the subgraph types according to the connectivity of the considered directions, and count the proportion of each type of subgraph in the total number of subgraphs. This proportion is the directed four-node module.

[0016] (3.3) Calculate the voltage stability parameters at each node using the per-unit voltage amplitude and the active power flowing through it.

[0017] Furthermore, the voltage stability parameters at each node The calculation formula is as follows:

[0018]

[0019] b = 2P·r + 2Q·xU^2

[0020] c = (P^2 + Q^2) * (R^2 + x^2)

[0021] X = b / 2

[0022] Among them U max and U minThese are the sum and difference of the rated voltage and the voltage safety margin, respectively; U is the per-unit value of the current voltage; P and Q are the active power and reactive power flowing through the node, respectively; and r and x are the voltage and reactance of the adjacent line, respectively.

[0023] Furthermore, when the stability parameter When the value is less than 0, the corresponding node is considered to be in an unstable state.

[0024] Furthermore, step (4) specifically includes the following sub-steps:

[0025] (4.1) Calculate the number of unstable nodes in all scenarios, and the various types of modalities in all scenarios;

[0026] (4.2) Use the entropy weight method to determine the correlation coefficient between all directed four-node modules and voltage stability, sort the correlation coefficients from high to low, set the correlation sorting threshold q, and take the first q directed four-node modules with higher correlation coefficients.

[0027] (4.3) For all types of directed four-node phantoms, set a granularity threshold and divide the phantom concentration range into p intervals, and calculate the confidence level of power grid instability under different proportion intervals;

[0028] (4.4) All the confidence scores obtained in step (4.3) are combined into a q×p-dimensional confidence matrix, which is used as the random operating characteristics of the power grid.

[0029] Furthermore, the stability risk of the power grid at the moment when the stability risk is to be assessed is the sum of all elements in the confidence matrix.

[0030] A second aspect of the present invention provides an electronic device, including a memory and a processor, wherein the memory is coupled to the processor; wherein the memory is used to store program data, and the processor is used to execute the program data to implement the above-described method for analyzing the stochastic operating characteristics of a new energy power system based on a power flow model.

[0031] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the above-described method for analyzing the stochastic operating characteristics of a new energy power system based on a power flow model.

[0032] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention proposes a method for analyzing the stochastic operating characteristics of new energy power systems based on power flow models. Based on existing grid topology and power flow direction data, this invention uses power flow models as a tool to analyze the operating status of distribution networks containing distributed new energy sources. The analysis results can provide a reference for the risk level of the operating status of new power systems and assist in risk analysis based on voltage stability. Simultaneously, it fully considers the situation where grid parameters are unknown and measurement data is limited in new energy sources, allowing for a rough judgment of the grid's stability even without knowing node voltage and other data, thus enabling more reliable stability analysis of the grid's stochastic operation. Attached Figure Description

[0033] Figure 1 This is a flowchart of the method;

[0034] Figure 2 This is a schematic diagram of the transformation from an actual power grid to a graph.

[0035] Figure 3 This is a schematic diagram of the subgraph type;

[0036] Figure 4 A heat map used to verify the correlation between the phantom and power grid stability;

[0037] Figure 5 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0038] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of methods consistent with some aspects of the invention as detailed in the appended claims.

[0039] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The singular forms “a,” “the,” and “the” used in this invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0040] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments. Unless otherwise specified, the features of the following embodiments and implementation methods can be combined with each other.

[0041] This invention proposes a hierarchical power grid growth and evolution model based on the concept of system evolution and complex network technology, such as... Figure 1 As shown, the specific steps include:

[0042] (1) Convert the current state of the power grid at the start time into a graph and label the graph.

[0043] Among them, such as Figure 2 As shown, transforming the initial state of the power grid into a graph involves: collecting the location information of various devices in the power grid (mainly including substations, generators, and loads), converting loads, power plants, and substations into nodes, and transmission lines and transformer branches into edges in the network. Nodes are connected to edges to form a graph; in the graph, the location of each node corresponds to its location in reality.

[0044] During the process of annotating the map, the rated power of substations, lines, and generators in the current power grid, as well as the historical peak load, are collected. This information is used to determine whether the area of ​​the power grid can be used to build power plants, substations, and other facilities. Locations on the map corresponding to places where power generation and distribution facilities (power plants and substations) cannot be built, as well as locations unsuitable for specific types of new energy sources, including photovoltaic power plants, wind power plants, and tidal power plants, are marked.

[0045] (2) The Monte Carlo method was used to sample a large number of scenarios of actual generator output and actual load, and power flow calculations were performed on all scenarios. Node voltage, line power flow, and power flow direction were recorded.

[0046] (3) For each power flow calculation result, the power flow direction is used as the direction of the edge in the graph labeled in step (1), a directed graph is drawn, the number of various four-node subgraphs in the directed graph is counted, and the directed four-node phantoms are counted. The voltage stability of the current sample is calculated using voltage and line power flow. The specific process is as follows:

[0047] (3.1) Based on the graph marked in step (1), determine the direction of all edges in the graph according to the direction of active power flow in the corresponding line to obtain a directed graph;

[0048] (3.2) Find all four-node subgraphs with connectivity in the directed graph, where different subgraphs may have repeated edges and nodes. Subgraphs are categorized into 16 types based on connectivity in the considered directions, with typical subgraph types listed in the appendix. Figure 3 As shown, the proportion of each type of subgraph in the total number of subgraphs is calculated, and this proportion is the directed four-node module. The proportion is then represented as a 16-dimensional vector.

[0049] (3.3) Calculate the voltage stability parameters at each node using the per-unit voltage magnitude and the active power flowing through it. The specific formula is as follows:

[0050]

[0051] b = 2P·r + 2Q·xU^2

[0052] c = (P^2 + Q^2) * (R^2 + x^2)

[0053] X = b / 2

[0054] Among them U max and U min Let be the sum and difference between the rated voltage and the voltage safety margin, respectively; U be the per-unit value of the current voltage; P and Q be the active power and reactive power flowing through the node, respectively; and r and x be the voltage and reactance of the adjacent line, respectively. b, c, and X are two process quantities and one discrimination quantity, respectively.

[0055] When the stability parameter is less than 0, the corresponding node is determined to be in an unstable state.

[0056] (4) Calculate the correlation between all directed four-node modules and voltage stability, and derive the confidence matrix based on statistical data as the stochastic operating characteristics of the power grid. The specific process is as follows:

[0057] (4.1) Calculate the number of unstable nodes in all scenarios and the various types of modalities in all scenarios.

[0058] (4.2) The correlation coefficients between all directed four-node modules and voltage stability are determined using the entropy weight method. The correlation coefficients are sorted from high to low, and a correlation sorting threshold q is set. The top q directed four-node modules with higher correlation coefficients are selected and labeled as m1, m2, ..., m q .

[0059] (4.3) For all types of directed four-node phantoms, set a granularity threshold and divide the phantom concentration range into p intervals, then calculate the confidence level of grid instability under different proportion intervals. The calculation formula is:

[0060]

[0061] In the formula, S corresponds to the scene, and m corresponds to the directed four-node phantom type. Let D be the value of the directed four-node motif m in scene S. s The value represents whether there are unstable nodes in scenario S; it is 1 if they exist and 0 otherwise. i is the lower bound of a proportion interval.

[0062] (4.4) Finally, a q×p-dimensional confidence matrix is ​​formed, where one row represents a type of directed four-node module and one column represents a type of interval proportion. This confidence matrix represents the stochastic operating characteristics of the power grid, that is, the correlation between power flow direction and stability under the stochastic operation of the new energy system.

[0063] (5) Based on the random operating characteristics obtained in step (4), and according to the power flow direction at the moment when the stability risk to be assessed is to be determined, the stability risk of the power grid at that moment is inferred by summing all elements in the confidence matrix. In this example, the calculation formula is:

[0064]

[0065] In the formula, ρ U Let be the stability risk coefficient of the power grid at the moment when the stability risk is to be assessed. For the phantom m i for The confidence level of node voltage instability exists in the power grid.

[0066] Figure 4 This is a heatmap used to verify the correlation between the module and grid stability. In each subplot, the horizontal axis represents the installation strategy of distributed generation, and the vertical axis represents the total installed capacity of distributed generation. The meanings of the horizontal and vertical axes are consistent across all subplots. The first subplot represents the number of unstable nodes in the grid. In the remaining 16 subplots, each subplot represents the numerical values ​​of the module corresponding to its subplot title under different scenarios. The graph shows whether the hierarchical phenomena in subplots M1, M2, and M3 are correlated with unstable nodes. The method of this invention is based on this heatmap.

[0067] Accordingly, this application also provides an electronic device, including: one or more processors; a memory for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the above-described method for analyzing the stochastic operating characteristics of a new energy power system based on a power flow model. Figure 5 The diagram shown is a hardware structure diagram of any device with data processing capabilities for the analysis method of random operation characteristics of a new energy power system based on a power flow model provided in an embodiment of the present invention, except for... Figure 5 In addition to the processor, memory, and network interface shown, any data processing device in the embodiment may also include other hardware depending on the actual function of the data processing device, which will not be described in detail here.

[0068] Accordingly, this application also provides a computer-readable storage medium storing computer instructions, which, when executed by a processor, implement the above-described method for analyzing the stochastic operating characteristics of a new energy power system based on a power flow model. The computer-readable storage medium can be an internal storage unit of any data-processing device as described in any of the foregoing embodiments, such as a hard disk or memory. The computer-readable storage medium can also be an external storage device, such as a plug-in hard disk, smart media card (SMC), SD card, flash card, etc., equipped on the device. Furthermore, the computer-readable storage medium can include both internal storage units of any data-processing device and external storage devices. The computer-readable storage medium is used to store the computer program and other programs and data required by the data-processing device, and can also be used to temporarily store data that has been output or will be output.

[0069] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only.

[0070] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. A method for analyzing the stochastic operating characteristics of a new energy power system based on a power flow model, characterized in that, The method specifically includes the following steps: (1) Convert the current state of the power grid at the initial moment into a graph and label the graph; (2) The Monte Carlo method is used to sample a large number of scenarios of the actual output of the generator and the actual value of the load, and power flow calculation is performed on all scenarios; node voltage, line power flow and power flow direction are recorded. (3) For each power flow calculation result, the power flow direction is used as the direction of the edge in the graph marked in step (1), a directed graph is drawn, the number of various four-node subgraphs in the directed graph is counted, and the directed four-node modules are counted. The voltage stability of the current sample is calculated using voltage and line power flow. Step (3) specifically includes the following sub-steps: (3.1) Based on the graph marked in step (1), determine the direction of all edges in the graph according to the direction of active power flow in the corresponding line to obtain a directed graph; (3.2) Find all four-node subgraphs with connectivity in the directed graph, classify the subgraph types according to the connectivity of the considered directions, and count the proportion of each type of subgraph in the total number of subgraphs. This proportion is the directed four-node module. (3.3) Calculate the voltage stability parameters at each node using the per-unit voltage amplitude and the active power flowing through it; Among them, the voltage stability parameters at each node The calculation formula is as follows: ； in and These are the sum of the rated voltage and the voltage safety margin, and the difference thereof. This is the per-unit value of the current voltage. and Let r and x be the active power and reactive power flowing through the node, respectively, and let r and x be the voltage and reactance of the adjacent line, respectively. (4) Calculate the correlation between all directed four-node modules and voltage stability, and statistically analyze the correlation data to obtain the random operating characteristics of the power grid; (5) Based on the random operating characteristics obtained in step (4) and the power flow direction at the moment when the stability risk is to be evaluated, the stability risk of the power grid at the moment when the stability risk is to be evaluated is inferred.

2. The method for analyzing the stochastic operating characteristics of a new energy power system based on a power flow model according to claim 1, characterized in that, The process of transforming the current state of the power grid at the initial moment into a graph includes: collecting the location information of each device in the power grid, including substations, generators, and loads; transforming loads, power plants, and substations into nodes; and transforming transmission lines and transformer branches into edges in the network. Nodes are connected to edges to form a graph; in the graph, the location of each node corresponds to its location in reality.

3. The method for analyzing the stochastic operating characteristics of a new energy power system based on a power flow model according to claim 2, characterized in that, The process of annotating the map includes: collecting the rated power of substations, lines, and generators in the current power grid, as well as the historical peak load, to refer to whether the area of ​​the power grid can be used to build power plants, substations, and other facilities. The map will mark the locations on the map where power plants and substations cannot be built, as well as locations that are not suitable for new energy sources, including photovoltaic power plants, wind power plants, and tidal power plants.

4. The method for analyzing the stochastic operating characteristics of a new energy power system based on a power flow model according to claim 1, characterized in that, When stability parameter When the value is less than 0, the corresponding node is considered to be in an unstable state.

5. The method for analyzing the stochastic operating characteristics of a new energy power system based on a power flow model according to claim 1, characterized in that, Step (4) specifically includes the following sub-steps: (4.1) Calculate the number of unstable nodes in all scenarios, and the number of various types of modalities in all scenarios; (4.2) Use the entropy weight method to determine the correlation coefficient between all directed four-node modules and voltage stability, sort the correlation coefficients from high to low, and set a correlation sorting threshold. Take the front A directed four-node motif with a high correlation coefficient; (4.3) For all types of directed four-node phantoms, set a granularity threshold and divide the range of phantom concentration values ​​into three categories. For each interval, calculate the confidence level of power grid instability under different proportion intervals; (4.4) All the confidence scores obtained in step (4.3) are combined into a q×p-dimensional confidence matrix, which is used as the random operation characteristics of the power grid.

6. The method for analyzing the stochastic operating characteristics of a new energy power system based on a power flow model according to claim 5, characterized in that, The stability risk of the power grid at the moment when the stability risk is to be assessed is the sum of all elements in the confidence matrix.

7. An electronic device comprising a memory and a processor, characterized in that, The memory is coupled to the processor; wherein the memory is used to store program data, and the processor is used to execute the program data to implement the method for analyzing the stochastic operating characteristics of a new energy power system based on a power flow model as described in any one of claims 1-6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method for analyzing the stochastic operating characteristics of a new energy power system based on a power flow model as described in any one of claims 1-6.

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