New energy transmission and distribution collaborative consumption intelligent control method and system

Abstract

The application discloses a new energy transmission and distribution collaborative consumption intelligent control method and system, relates to the technical field of new energy, and comprises the following steps: collecting real-time monitoring data of each node in a power system and performing pretreatment; constructing a power grid complex network model according to an adjacency matrix to calculate the power weight of each power transmission line; and storing all data to a relational database and performing management. The application accurately models the nodes and edges of the power system, constructs an adjacency matrix, comprehensively reflects the power grid topology structure and the power flow of the power transmission line, accurately assesses the power grid load and the operation state, avoids the error in the traditional method, calculates the degree, clustering coefficient and extended number of the nodes, combines a propagation dynamics model, simulates the propagation process of the fault in the power grid in real time, predicts the rate and range of the fault spread, and thus realizes multi-level early warning, improves the accuracy of the power grid vulnerability assessment, and enhances the stability and emergency response capability of the power grid.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of new energy technology, in particular to a new energy transmission and distribution collaborative consumption intelligent control method and system. BACKGROUND

[0002] With the transformation of global energy structure, the wide application of new energy has become an important trend of the development of power system. In order to adapt to the rapid development of new energy, the transmission and distribution collaborative consumption ability of power grid has become a key challenge for the stable operation of power system. The traditional power grid control method mainly depends on fixed dispatching strategy and device configuration, which obviously has many deficiencies in the face of large-scale renewable energy access, fluctuation and uncertainty of power load change. Therefore, how to improve the dynamic adaptability of power grid and enhance the new energy consumption ability has become one of the hotspots of current power system research. In recent years, with the continuous progress of computer technology, artificial intelligence, big data analysis and other technologies, the power grid control method gradually develops towards intelligence and collaboration. The power grid dispatching model based on intelligent control and big data analysis, especially the multi-time scale regulation and adaptive optimization method, has begun to be widely applied. These methods can significantly improve the adaptability of power grid to new energy consumption through real-time monitoring and dispatching, and to a certain extent, reduce the impact of new energy instability on power grid.

[0003] Although the existing intelligent control method of power grid has made significant progress in improving the stability and reliability of the system, there are still certain limitations in the face of complex power grid system, especially in the multi-region, multi-level and diversified new energy access scene. The traditional power grid dispatching method mainly focuses on static analysis and lacks deep mining of dynamic changes of power grid state. Especially in the power system with high new energy penetration rate, the nonlinearity of power flow and large-scale system effect are difficult to be fully considered. Although the existing power grid vulnerability assessment and fault warning system can identify potential risk nodes, it still has deficiencies in risk propagation analysis and multi-dimensional emergency response mechanism, which leads to response lag and decision-making errors of power grid in the face of sudden situations. SUMMARY

[0004] In view of the problems existing in the existing new energy transmission and distribution collaborative consumption intelligent control method and system, the present application is proposed.

[0005] Therefore, the present application provides a new energy transmission and distribution collaborative consumption intelligent control method and system to solve the problems that the existing power grid control system cannot provide optimal decision support in the process of new energy access and consumption, which affects the overall operation efficiency and stability of the power grid.

[0006] To solve the above technical problems, the present application provides the following technical solutions:

[0007] In a first aspect, the embodiments of the present application provide a new energy transmission and distribution collaborative consumption intelligent control method, which comprises collecting real-time monitoring data of each node in a power system and preprocessing the data;

[0008] Each node and edge of the power system is modeled to construct an adjacency matrix, and a power grid complex network model is constructed according to the adjacency matrix to calculate the power weight of each transmission line;

[0009] The measurement index of each node is calculated, a propagation dynamics model is constructed to calculate the propagation probability of each node and perform hierarchical early warning, and all data are stored in a relational database and managed.

[0010] As a preferred scheme of the new energy transmission and distribution collaborative consumption intelligent control method, the collection of real-time monitoring data of each node in the power system and the preprocessing of the data include identifying main components of the power system, defining each main component as a node, obtaining real-time monitoring data of the node through a sensor and preprocessing the data, the main components include power plants, substations and transmission line terminals, the real-time monitoring data include input and output power and load of each node, the preprocessing includes checking all real-time monitoring data and excluding duplicate records, using linear interpolation to fill in missing data, using Z-score method to detect and correct abnormal values, and standardizing all real-time monitoring data.

[0011] As a preferred scheme of the new energy transmission and distribution collaborative consumption intelligent control method, the modeling of each node and edge of the power system to construct an adjacency matrix includes the following steps,

[0012] Each node in the power system is modeled, the transmission line between nodes is defined as an edge, the topological structure of the power system is represented by constructing an adjacency matrix, the element A ij in the adjacency matrix represents the connection relationship between node i and node j, the maximum carrying capacity of each edge is set as the weight, if there is a direct transmission line connection between node i and node j, then A ij = ω ij , otherwise, A ij = 0, wherein ω ij represents the weight of the edge between node i and node j;

[0013] The modeling of each node in the power system includes assigning a unique node number to each node and establishing the load demand of each node;

[0014] The maximum carrying capacity includes introducing a dynamic heat dissipation function, correcting wind speed and temperature difference, calculating the influence value of wind speed on heat dissipation, and the formula is:

[0015]

[0016] wherein f(V wind , ΔT) represents the influence value of the wind speed on the heat dissipation, β represents the forced convection coefficient, V wind represents the wind speed, α represents the parameter, T s represents the surface temperature of the conductor, T env represents the ambient temperature;

[0017] The heat conduction model composed of the thermal conductivity, the density and the conductor length factor is added to calculate the carrying capacity of the line;

[0018] The maximum carrying capacity formula is constructed to calculate the maximum carrying capacity G max of the power transmission line, and the formula is:

[0019]

[0020] wherein ∈ represents the emissivity of the power transmission line, σ represents the Stefan-Boltzmann constant, D represents the surface area of the power transmission line, k env represents the environmental correction coefficient, dT represents the small variable of the temperature T, R represents the resistance of the power transmission line, μ represents the thermal conductivity of the power transmission line, ρ represents the density of the power transmission line, and L represents the length of the power transmission line.

[0021] As a preferred scheme of the new energy transmission and distribution collaborative consumption intelligent control method, wherein: the power weight of each power transmission line is calculated according to the complex network model of the power grid constructed according to the adjacency matrix, including setting the power flow direction of each power transmission line according to the actual operation of the power grid;

[0022] The power of each node is integrated into the adjacency matrix as a weighted coefficient, and the power flow of each power transmission line is calculated by a formula, and the formula is:

[0023]

[0024] wherein P ij (t) represents the power flow of the power transmission line between node i and node j, w u and w l are power state coefficients, represents the power flow output by node i to branch u at time point t, represents the power flow input by node j to branch l at time point t, h and v respectively represent the output and the input, and branch u and branch l constitute the power transmission line from node i to node j, and U and L respectively represent the branch set related to node i and node j.

[0025] As a preferred scheme of the new energy transmission and distribution collaborative consumption intelligent control method, the calculation of the measurement index of each node includes calculating the degree, clustering coefficient and extended betweenness of each node and performing data standardization, and the first M nodes with large extended betweenness are selected as important nodes by comparing the extended betweenness of each node.

[0026] As a preferred scheme of the new energy transmission and distribution collaborative consumption intelligent control method, the calculation of the measurement index of each node includes calculating the degree, clustering coefficient and extended betweenness of each node and performing data standardization, and the first M nodes with large extended betweenness are selected as important nodes by comparing the extended betweenness of each node.

[0027] The two end nodes of the power transmission line with the overload hidden danger are marked as a fault state and used as a starting point of the propagation.

[0028] The SIR model is used to construct a propagation dynamics model to simulate the propagation process of the fault in the power grid.

[0029] The construction of the propagation dynamics model includes defining the infection state of all nodes.

[0030] The infection state includes a susceptible state, in which the node is in a normal state and has not been affected by the fault.

[0031] The infection state, in which the node is marked as a fault state and is in a risk state.

[0032] The recovery state, in which the node is restored to a normal state through repair.

[0033] Based on the power flow of each power transmission line, the propagation function is used to calculate the propagation probability of each node, and the formula is:

[0034]

[0035] where H ij represents the propagation probability from node i to node j, N represents all nodes in the power system, P in (t) represents the power flow of the power transmission line between node i and node n.

[0036] A propagation threshold is set, and at each time point, all nodes in the susceptible state judge the infection condition according to the propagation probability. If the propagation probability of each node is greater than the propagation threshold, the node corresponding to the node is marked as a fault state, and after each node is marked as a fault state, it is gradually restored to a normal state.

[0037] The number of nodes affected by the fault within a certain time step is calculated, the time step required for the fault influence to spread from the source node to other nodes is measured, a propagation speed threshold is set, if the propagation speed exceeds the propagation speed threshold, it is determined that a cascading failure occurs in the power grid, and a level three early warning is determined, the load condition and new energy generation of the power grid are continuously monitored, the intelligent dispatching system is used for conventional load dispatching, the proportion of traditional power and new energy is balanced, and a level two early warning is determined, otherwise, no early warning is performed.

[0038] The importance of the nodes affected by the fault is determined, if the important nodes are contained, a level two early warning is determined, according to the current consumption capacity of the power grid, the renewable energy is preferentially consumed, the energy storage system is started in time to ensure the stability of the power grid, the power grid load dispatching is dynamically optimized, the high fluctuation risk is reduced, the power output of each power source is balanced, and a level one early warning is determined, otherwise, the fault is output as a level three early warning.

[0039] The vulnerability C of the power system is evaluated by calculating the proportion of the change of the clustering coefficient, and the formula is:

[0040]

[0041] Wherein, C1 represents the average clustering coefficient of all nodes of the power system after the fault occurs, and C0 represents the average clustering coefficient of the power system not affected by the fault.

[0042] A vulnerability threshold is set to determine the vulnerability of the power system after the fault occurs, if the vulnerability of the power system exceeds the vulnerability threshold, the fault is determined as a level three early warning, the emergency plan is started immediately, the standby power supply is dispatched, the load is distributed, the local power supply recovery is started, and the power grid is isolated, the connection between the fault area and the normal area is cut off, otherwise, the fault is output as a level two early warning.

[0043] As a preferred scheme of the new energy transmission and distribution collaborative consumption intelligent control method, wherein: the all data is stored in a relational database and managed, including selecting a relational database to store and manage data and analysis results thereof, designing a database table structure to store different types of data, setting a regular backup task to backup all data in the database, managing the rights of database users and encrypting the storage of data.

[0044] In a second aspect, the embodiment of the present application provides a new energy transmission and distribution collaborative consumption intelligent control system, which comprises: a data acquisition module, configured to acquire real-time monitoring data of each node in a power system and perform preprocessing; a matrix construction module, configured to model each node and edge of the power system to construct an adjacency matrix, and calculate power weights of each power transmission line according to the adjacency matrix to construct a complex network model of the power grid; a fault early warning module, configured to calculate a measurement index of each node, construct a propagation dynamics model to calculate a propagation probability of each node and perform hierarchical early warning; and a data storage module, configured to store all data to a relational database and perform management.

[0045] In a third aspect, the embodiment of the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, any step of the new energy transmission and distribution collaborative consumption intelligent control method described above is implemented.

[0046] In a fourth aspect, the embodiment of the present application provides a computer readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, any step of the new energy transmission and distribution collaborative consumption intelligent control method described above is implemented.

[0047] The present application has the following beneficial effects: by accurately modeling the nodes and edges of the power system and constructing an adjacency matrix, the power grid topology and the power flow of the power transmission line are comprehensively reflected, the power grid load and operating state are accurately evaluated, the errors in the traditional method are avoided, the degree, clustering coefficient and expansion order of the nodes are calculated, the propagation dynamics model is combined, the propagation process of the fault in the power grid is simulated in real time, the rate and range of the fault propagation are predicted, and then multi-level early warning is realized, the accuracy of the power grid vulnerability evaluation is improved, and the stability and emergency response capability of the power grid are enhanced. BRIEF DESCRIPTION OF DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor. Among them:

[0049] Figure 1 The flowchart of the new energy transmission and distribution collaborative consumption intelligent control method in embodiment 1.

[0050] Figure 2 The schematic diagram of the new energy transmission and distribution collaborative consumption intelligent control system in embodiment 1. DETAILED DESCRIPTION

[0051] In order to make the above objectives, characteristics and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application are described in detail below with reference to the drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work should fall within the protection scope of the present application.

[0052] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. The present application, however, can be practiced in a variety of ways beyond the specific details set forth herein, having regard to the content of the present application, without departing from the spirit of the present application. Accordingly, the present application is not limited to the specific embodiments described below.

[0053] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. "In one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an embodiment that is independent of or selected from other embodiments.

[0054] The present application is described in detail in conjunction with the schematic drawings. In the detailed description of the embodiments of the present application, the cross-sectional view of the device structure is locally enlarged without regard to the general proportion for the convenience of description, and the schematic drawings are only examples which should not limit the scope of protection of the present application. In addition, the three-dimensional spatial dimensions of length, width and depth should be included in actual manufacture.

[0055] Meanwhile, in the description of the present application, it should be noted that the terms "upper, lower, inner and outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the indicated device or element must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application. In addition, the terms "first, second or third" are only for the purpose of description, and cannot be understood as indicating or implying relative importance.

[0056] In the present application, unless otherwise explicitly specified and limited, the terms "mounting, connecting, connection" should be understood broadly, for example: it can be fixed connection, detachable connection or integral connection; it can also be mechanical connection, electrical connection or direct connection, it can also be indirectly connected through an intermediate medium, or it can be the communication inside two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0057] Embodiment 1

[0058] Reference Figure 1 and Figure 2For the first embodiment of the present application, the embodiment provides a new energy transmission and distribution collaborative consumption intelligent control method, comprising:

[0059] Specifically, collecting real-time monitoring data in the power system and preprocessing includes,

[0060] Identify the main components of the power system, define each main component as a node, obtain the real-time monitoring data of the node through the sensor and preprocess;

[0061] The main components include power plants, substations and transmission line terminals;

[0062] The real-time monitoring data includes the input and output power and load of each node;

[0063] The preprocessing includes checking all real-time monitoring data and excluding duplicate records, using linear interpolation to fill in missing data, using Z-score method to detect and correct outliers, and standardizing all real-time monitoring data.

[0064] By collecting real-time monitoring data of each main component in the power system and preprocessing, the accuracy and consistency of the data can be effectively improved. By defining power plants, substations and transmission line terminals as nodes and collecting relevant data, the operation state of the power grid can be fully mastered. The preprocessing steps, such as excluding duplicate records, filling in missing data, correcting outliers and data standardization, ensure the data quality and provide reliable basis for subsequent power grid analysis and decision-making. These measures can improve the monitoring accuracy of the system, reduce the misjudgment caused by data problems, and thus enhance the stability and reliability of the power system, support more accurate risk assessment and optimization control.

[0065] S2, model each node and edge of the power system to build an adjacency matrix, and build a power grid complex network model according to the adjacency matrix to calculate the power weight of each transmission line;

[0066] Specifically, modeling each node and edge of the power system to build an adjacency matrix includes,

[0067] Model each node in the power system, define the transmission line between nodes as an edge, build an adjacency matrix to represent the topological structure of the power system, and the element A ij in the adjacency matrix represents the connection relationship between node i and node j, set the maximum carrying capacity of each edge as the weight, if node i and node j are directly connected by a transmission line, then A ij = ω ij , otherwise, A ij = 0, where ω ij represents the weight of the edge between node i and node j;

[0068] Modeling each node in the power system includes assigning a unique node number to each node, establishing the load demand of each node;

[0069] The maximum carrying capacity includes introducing a heat balance equation to calculate the heat Q generated by the current absorbed by the conductor in the transmission line w , the formula is:

[0070] Q w = Q o

[0071] Where Q o represents the heat dissipated by the conductor;

[0072] The transmission line will absorb a certain amount of electrical energy (heat generated by current) and dissipate heat through the conductor surface during operation. If the temperature of the conductor is too high, it may cause overload, insulation damage or line breakage;

[0073] The heat dissipation of the conductor is mainly through natural convection Q conv , forced convection Q rad and radiation Q forced , the formula is:

[0074] Q conv = h conv ·D·(T s -T env )

[0075]

[0076] Q forced = h forced ·D·(T s -T env )

[0077] Where h conv represents the natural convection transfer coefficient, obtained through experimental data, D represents the surface area of the transmission line, T s represents the surface temperature of the transmission line, T env represents the ambient temperature, h forced represents the forced convection heat transfer coefficient, obtained through the formula , F and α represent parameters adjusted according to different conductor types and climate conditions, V wind represents the wind speed, ∈ represents the emissivity of the transmission line, and σ represents the Stefan-Boltzmann constant;

[0078] In the prior art, the conventional heat balance equation mainly considers temperature difference, heat dissipation coefficient and other factors. However, in actual application, factors such as wind speed and environmental humidity have important influence on heat dissipation of the line. A dynamic heat dissipation function is introduced to correct the wind speed and temperature difference, and to calculate the influence value of the wind speed on heat dissipation. The formula is:

[0079]

[0080] wherein f(V wind , ΔT) represents the influence value of the wind speed on heat dissipation, β represents a forced convection coefficient, the specific value of which is set according to the wind speed and environmental conditions, V wind represents the wind speed, α represents a parameter, which is adjusted according to different conductor types and climate conditions, T s represents the surface temperature of the conductor, and T env represents the environmental temperature.

[0081] The existing formula generally directly describes radiation heat dissipation, natural convection and forced convection heat dissipation, and a heat conduction model is composed by adding factors such as thermal conductivity, density and conductor length to calculate the carrying capacity of the line. The formula is: wherein Z represents the heat conduction model, μ represents the thermal conductivity of the power transmission line, ρ represents the density of the power transmission line, and L represents the length of the power transmission line.

[0082] A maximum carrying capacity formula is constructed to calculate the maximum carrying capacity G max of the power transmission line. The formula is:

[0083]

[0084] wherein ∈ represents the emissivity of the power transmission line, σ represents the Stefan-Boltzmann constant, D represents the surface area of the power transmission line, k env represents an environmental correction coefficient, which is derived through experimental data and environmental characteristics, dT represents a small variable of the temperature T, R represents the resistance of the power transmission line, μ represents the thermal conductivity of the power transmission line, ρ represents the density of the power transmission line, and L represents the length of the power transmission line.

[0085] By modeling the power system nodes and transmission lines, constructing the critical matrix and introducing the maximum carrying capacity calculation, the application can accurately reflect the topological structure of the power system and the power flow characteristics of each transmission line, introduce the heat balance equation and dynamic heat dissipation function, consider the influence of environmental factors such as wind speed and temperature difference, and more accurately calculate the maximum carrying capacity of each transmission line, avoid power grid failure or equipment damage caused by overload and overheating, based on the introduction of the heat conduction model, considering the thermal conductivity, density and length of the conductor and other factors, the evaluation of the power grid load carrying capacity is more accurate, thereby improving the safety, reliability and intelligent scheduling ability of the power system, especially in the face of high load or complex environmental conditions, which can effectively avoid potential risks.

[0086] Further, according to the adjacency matrix, the power grid complex network model is constructed to calculate the power weight of each transmission line, including,

[0087] According to the actual operation of the power grid, the power flow direction of each transmission line is set;

[0088] The power of each node is integrated into the adjacency matrix as a weighted coefficient, and the power flow of each transmission line is calculated by the formula:

[0089]

[0090] Where, P ij (t) represents the power flow between node i and node j, w u and w l are power state coefficients, used to mark the transmission power of the corresponding branch, when branch u and branch l represent a transmission line, if the transmission line exists power flow, then the corresponding w u or w l is 1, otherwise, the corresponding w u or w l is 0, represents the power flow output by node i to branch u at time point t, represents the power flow input by node j to branch l at time point t, h and v represent output and input respectively, and branch u and branch l form the transmission line from node i to node j, U and L represent the branch set related to node i and node j respectively.

[0091] By setting the power flow direction of each transmission line according to the actual operation of the power grid and integrating the power of each node as a weighted coefficient into the adjacency matrix, the power flow of each transmission line can be accurately calculated. This method can dynamically reflect the power flow state, improve the calculation accuracy and reliability of the power flow of the power grid, and help optimize the load distribution and operation scheduling of the power grid. Through the introduction of the power state coefficient, the power flow of the transmission line can be flexibly marked to ensure real-time operation monitoring and adjustment of the power grid under different loads and environmental conditions, and to improve the response capability of the power grid to sudden failures and load fluctuations.

[0092] S3, calculate the measurement index of each node, build a propagation dynamics model to calculate the propagation probability of each node and perform hierarchical early warning;

[0093] Specifically, the measurement index of each node is calculated, including,

[0094] The degree, clustering coefficient and extended betweenness of each node are calculated and standardized, and the first M nodes with large extended betweenness are selected as important nodes by comparing the extended betweenness of each node, and the number of M is determined according to the actual situation;

[0095] The degree is the number of edges directly connected to the node, which is calculated by the in-degree formula and out-degree formula. The degree reflects the network connectivity of the node. The larger the degree of the node, the greater the influence of the node on the overall stability of the network.

[0096] The clustering coefficient measures the degree of connection between other nodes. By obtaining the neighbor node set of node i and calculating the actual connection of different neighbor node pairs in the set, the number of edges of all different neighbor node pairs is calculated, which is represented as the number of edges between neighbor nodes. The clustering coefficient is calculated using the formula:

[0097]

[0098] Where C i represents the clustering coefficient of node i, E i represents the number of edges between neighbor nodes of node i, and k i represents the degree of node i.

[0099] The extended betweenness reflects the importance value of node i as an intermediate node in all shortest paths. By calculating the number of shortest paths σ jk from node j to node k and the number of paths σ jk (i) passing through node i in the shortest path, the extended betweenness B i is calculated using the formula:

[0100]

[0101] where k i denotes the degree of node i.

[0102] By calculating the degree, clustering coefficient and extended degree of each node in the power system, and performing data standardization processing, the application can comprehensively quantify the structural properties and functional characteristics of the nodes in the power grid. The degree reflects the network connectivity of the node, which can intuitively evaluate the influence of the node on the overall stability of the network. The clustering coefficient measures the connection tightness between the neighbors of the node, which helps to identify the local structural characteristics. The extended degree accurately evaluates the importance of the node in the global network by analyzing the intermediary role of the node in the shortest path. By comprehensively considering these indicators, key nodes can be effectively selected and the vulnerability of the power grid can be accurately analyzed, which provides important support for optimizing the operation of the power grid, improving the stability of the power grid and the emergency response capability.

[0103] Furthermore, constructing a propagation dynamics model to calculate the propagation probability of each node and performing hierarchical early warning includes,

[0104] According to the real-time monitoring data of the power grid, the maximum power of each power transmission line is calculated in real time. If the maximum power exceeds the maximum carrying capacity of each power transmission line, it is judged that there is an overload risk for the power transmission line, otherwise no operation is performed.

[0105] The two end nodes of the power transmission line with an overload risk are marked as "fault state" and serve as the starting point of the propagation.

[0106] The SIR model is used to construct a propagation dynamics model to simulate the propagation process of the fault in the power grid.

[0107] The construction of the propagation dynamics model includes defining the infection state of all nodes.

[0108] The infection state includes the susceptible state, in which the node is in a normal state and has not been affected by the fault.

[0109] The infection state, in which the node is marked as "fault state" and is in a risk state.

[0110] The recovery state, in which the node is restored to a normal state through repair.

[0111] Based on the power flow of each power transmission line, a propagation function is used to calculate the propagation probability of each node, and the formula is:

[0112]

[0113] where H ij denotes the propagation probability from node i to node j, N denotes all nodes in the power system, P in (t) denotes the power flow of the power transmission line between node i and node n.

[0114] A propagation threshold is set, at each time point, all nodes in the susceptible state are judged according to the propagation probability, if the propagation probability of each node is greater than the propagation threshold, the node corresponding to the node is marked as "failure state", after each node is marked as "failure state", gradually restore to normal state, the time of the recovery process is dynamically adjusted according to the importance of the node;

[0115] The number of nodes affected by the fault is calculated within a certain time step, the time step required for the fault influence to spread from the source node to other nodes is measured, a propagation speed threshold is set, if the propagation speed exceeds the propagation speed threshold, it is determined that a cascading failure occurs in the power grid, and a three-level early warning is determined, the load condition and new energy power generation of the power grid are continuously monitored to ensure the balance between power generation and demand, the intelligent scheduling system is used for conventional load scheduling, the proportion of traditional power and new energy is balanced, and a secondary early warning is determined, otherwise, no early warning is performed;

[0116] The importance of the nodes affected by the fault is judged, if there is an important node, it is determined as a secondary early warning, according to the current consumption capacity of the power grid, renewable energy is preferentially consumed, energy storage system is started in time to ensure the stability of the power grid, dynamic optimization of power grid load dispatching is carried out to reduce the risk of high fluctuation, balance the output of each power source, and a primary early warning is determined, otherwise, the fault is output as a three-level early warning;

[0117] The vulnerability C of the power system is evaluated by calculating the proportion of the change of the clustering coefficient, the formula is:

[0118]

[0119] Wherein, C1 represents the average clustering coefficient of all nodes of the power system after the fault occurs, C0 represents the average clustering coefficient of the power system not affected by the fault;

[0120] A vulnerability threshold is set to judge the vulnerability of the power system after the fault occurs, if the vulnerability of the power system exceeds the vulnerability threshold, the fault is determined as a three-level early warning, the emergency plan is started immediately, the standby power supply is dispatched, the load is distributed, the local power supply recovery is started, and the power grid is isolated, the connection between the fault area and the normal area is cut off, otherwise, the fault is output as a secondary early warning.

[0121] By constructing a propagation dynamics model and calculating the propagation probability of each node, the application can accurately simulate the expansion process of faults in the power system, identify the transmission lines and nodes that may cause cascading faults in time, and then realize a multi-level early warning mechanism. By setting the propagation threshold and the vulnerability threshold, the risk of the power grid can be effectively evaluated to ensure that appropriate emergency response measures such as load scheduling, energy storage system activation, and power grid isolation can be quickly started when a fault occurs, thereby maximizing the stability and reliability of the power grid. This method combines real-time adjustment of new energy consumption capacity, optimizes the coordinated consumption of traditional power grids and new energy, and helps to improve the adaptability and emergency handling capacity of the power grid under high penetration of new energy.

[0122] S4, store all data to a relational database and manage them;

[0123] Specifically, storing all data to a relational database and managing them includes,

[0124] The relational database is selected to store and manage the data and its analysis results, the database table structure is designed to store different types of data, a regular backup task is set to backup all data in the database, the database users are managed in terms of authority, and the data are stored in an encrypted manner.

[0125] By storing all data to a relational database and managing them, efficient storage, systematic management and security protection of the data are realized. The relational database provides structured storage for the data, ensures standardized management and efficient query of different types of data, guarantees the security and disaster recovery capability of the data through the regular backup task, effectively reduces the risk of data loss, and further improves the security of the data through authority management and encrypted storage, so that only authorized users can access sensitive data, thereby ensuring the reliability of the system and the confidentiality of the data.

[0126] The embodiment also provides a new energy transmission and distribution collaborative consumption intelligent control system, which includes:

[0127] A data acquisition module is configured to acquire real-time monitoring data of each node in the power system and perform preprocessing.

[0128] A matrix construction module is configured to model each node and edge of the power system to construct an adjacency matrix, and calculate the power weight of each transmission line based on the adjacency matrix.

[0129] A fault early warning module is configured to calculate a measurement index of each node, construct a propagation dynamics model to calculate the propagation probability of each node, and perform hierarchical early warning.

[0130] A data storage module is configured to store all data to a relational database and manage them.

[0131] The embodiment also provides a computer device suitable for the new energy transmission and distribution collaborative consumption intelligent control method, including a memory and a processor; the memory is used for storing computer executable instructions, and the processor is used for executing the computer executable instructions to realize the new energy transmission and distribution collaborative consumption intelligent control method proposed in the above embodiment.

[0132] In a preferred embodiment, the new energy transmission and distribution collaborative consumption intelligent control system includes a data acquisition module for acquiring real-time monitoring data of each node in the power system and performing preprocessing;

[0133] A matrix construction module is configured to model each node and edge of the power system to construct an adjacency matrix, and calculate the power weight of each transmission line according to the adjacency matrix to construct a complex network model of the power grid;

[0134] A fault early warning module is configured to calculate the measurement index of each node, construct a propagation dynamics model to calculate the propagation probability of each node and perform hierarchical early warning;

[0135] A data storage module is configured to store all data to a relational database and perform management.

[0136] The computer device can be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is configured to perform wired or wireless communication with external terminals. The wireless communication can be achieved through WIFI, operator network, NFC (near field communication) or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the computer device. The input device can also be an external keyboard, touchpad or mouse, etc.

[0137] In summary, the present application accurately models the nodes and edges of the power system, constructs an adjacency matrix, comprehensively reflects the power grid topology and the power flow of the transmission line, accurately evaluates the power grid load and operating state, avoids the errors in the traditional method, calculates the degree, clustering coefficient and expansion order of the nodes, combines the propagation dynamics model, simulates the propagation process of the fault in the power grid in real time, predicts the rate and range of the fault spread, and then realizes multi-level early warning, improves the accuracy of the power grid vulnerability evaluation, and enhances the stability and emergency response capability of the power grid.

[0138] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present application, and they should be covered in the scope of the claims of the present application.

[0139] Embodiment 2

[0140] Referring to Figure 1 and Figure 2 , the second embodiment of the present application provides a new energy transmission and distribution collaborative consumption intelligent control method. In order to verify the beneficial effects of the present application, scientific demonstration is carried out through simulation experiment.

[0141] In order to verify the effectiveness and practicability of the new energy transmission and distribution collaborative consumption intelligent control method of the present application, comprehensive experiments were carried out in a power system containing 150 nodes and 200 transmission lines. During the experiment, through the sensors distributed in each power plant, transformer substation and transmission line terminal, the input power, output power and load data of each node were collected in real time, and more than 1 million real-time monitoring records were collected in total. In the preprocessing stage, about 3% of the missing data were filled using linear interpolation, and about 2% of the abnormal values were detected and corrected by Z-score method, ensuring the accuracy and consistency of the data.

[0142] Each node and edge of the power system was modeled, and an adjacency matrix was constructed to represent the topological structure of the power system. The maximum carrying capacity of each transmission line was dynamically adjusted according to actual operation data and environmental factors, for example, the carrying capacity of a transmission line decreased by about 10% in high temperature environment, and increased by 5% in high wind speed. Through these adjustments, we ensure the safe operation of the transmission line.

[0143] Based on the adjacency matrix, a complex network model of the power grid was constructed, and the power weight of each transmission line was calculated. The experimental results show that the power flow of some key nodes is significantly higher than that of other nodes, among which the average power flow of the top 10 important nodes reaches 40% of the total power. The degree, clustering coefficient and extended betweenness of each node were calculated and standardized, and finally the top 20 nodes with the largest extended base were selected as important nodes for key monitoring.

[0144] In order to evaluate the fault propagation risk of the power grid, the SIR model is used to simulate the propagation process of the fault in the power grid. In the experiment, when the maximum power of a certain transmission line exceeds its maximum carrying capacity, the system immediately marks the nodes at both ends of the line as fault state and starts the propagation warning. In a simulated fault, the time step required for the fault to spread from the source node to other nodes is 5 minutes, and the propagation speed exceeds the set threshold, triggering a three-level warning. The system quickly dispatches backup power, allocates load, and starts local power supply recovery, effectively preventing the occurrence of cascading failures.

[0145] All data is stored in a relational database, and a special table structure is designed to store different types of data, including real-time monitoring data, model parameters of nodes and edges, fault warning information, etc. A daily backup task is set to ensure data security and recoverability. The database users are strictly managed in terms of permissions, and sensitive data is encrypted to ensure data security and privacy. The experimental data is shown in Table 1 below:

[0146] Table 1 Experimental data table

[0147]

[0148] Table 1 shows that the new energy transmission and distribution collaborative consumption intelligent control method of the present application demonstrates high efficiency, intelligent management and strong security, fully proving its feasibility and advantages in practical application. The comparison between the present application and the prior art is shown in Table 2 below:

[0149] Table 2 Comparison table of the present application and the prior art

[0150]

[0151] Through these comparisons, the present application demonstrates its advantages in data acquisition, carrying capacity calculation, power grid modeling, key node identification, fault propagation simulation, consumption capacity optimization and data management, significantly improving the intelligent level and security of new energy transmission and distribution collaborative consumption.

[0152] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit it. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the present application, and they should be covered in the scope of the claims of the present application.

Claims

1. A smart control method for coordinated transmission, distribution, and consumption of new energy sources, characterized in that: include, Collect real-time monitoring data from each node in the power system and perform preprocessing; Model each node and edge of the power system to construct an adjacency matrix, and construct a complex network model of the power grid based on the adjacency matrix to calculate the power weight of each transmission line; Calculate the metrics for each node, construct a propagation dynamics model to calculate the propagation probability of each node and provide graded early warnings, and store all data in a relational database for management. The process of modeling and constructing an adjacency matrix for each node and edge of the power system includes the following steps. Each node in the power system is modeled, and the transmission lines between nodes are defined as edges. The topology of the power system is represented by an adjacency matrix, where the element A in the adjacency matrix is... ij This represents the connection relationship from node i to node j. The maximum carrying capacity of each edge is set as its weight. If there is a direct power transmission line connection between node i and node j, then A... ij =ω ij Otherwise, A ij =0, where ω ij This represents the weight of the edge from node i to node j; The modeling of each node in the power system includes assigning a unique node number to each node and establishing the load demand of each node. The maximum load-bearing capacity includes the introduction of a dynamic heat dissipation function to correct for wind speed and temperature difference, and the calculation of the impact of wind speed on heat dissipation, as shown in the formula: Where, f(V) wind ΔT) represents the effect of wind speed on heat dissipation, β represents the forced convection coefficient, and V wind Let T represent wind speed, α represent a parameter, and T represent wind speed. s T represents the surface temperature of the conductor. env Indicates ambient temperature; The load-bearing capacity of the line is calculated by incorporating thermal conductivity, density, and conductor length into a heat conduction model. Construct the maximum carrying capacity formula and calculate the maximum carrying capacity G of the transmission line. max The formula is: Where ∈ represents the emissivity of the transmission line, σ represents the Stefan-Boltzmann constant, D represents the surface area of ​​the transmission line, and k env dT represents the environmental correction factor, R represents the resistance of the transmission line, μ represents the thermal conductivity of the transmission line, ρ represents the density of the transmission line, and L represents the length of the transmission line. The calculation of the power weight of each transmission line based on the adjacency matrix to construct a complex power grid network model includes setting the power flow direction of each transmission line according to the actual operation of the power grid; The power of each node is incorporated into the adjacency matrix as a weighting factor, and the power flow of each transmission line is calculated using the following formula: Among them, P ij (t) represents the power flow of the transmission line between node i and node j, w u and w l P is the power state coefficient. i u (t) h This represents the power flow output from node i to branch u at time point t. Let represent the power flow input from node j to branch i at time t, h and v represent the output and input respectively, branch u and branch l form the transmission line from node i to node j, and U and L represent the sets of branches related to node i and node j respectively. The metrics for calculating each node include calculating the degree, clustering coefficient, and extended betweenness of each node and standardizing the data. By comparing the extended betweenness of each node, the top M nodes with the largest extended betweenness are selected as important nodes.

2. The intelligent control method for coordinated transmission and distribution of new energy sources as described in claim 1, characterized in that: The process of collecting and preprocessing real-time monitoring data from each node in the power system includes identifying the main components of the power system, defining each main component as a node, acquiring real-time monitoring data from nodes via sensors and preprocessing the data. The main components include power plants, substations, and transmission line terminals. The real-time monitoring data includes the input, output power, and load of each node. The preprocessing process includes checking all real-time monitoring data and eliminating duplicate records, using linear interpolation to fill in missing data, using the Z-score method to detect and correct outliers, and standardizing all real-time monitoring data.

3. The intelligent control method for coordinated transmission and distribution of new energy sources as described in claim 1, characterized in that: The construction of the propagation dynamics model to calculate the propagation probability of each node and to perform graded early warning includes calculating the maximum power of each transmission line in real time based on the real-time monitoring data of the power grid. If the maximum power exceeds the maximum carrying capacity of each transmission line, it is determined that the transmission line has an overload risk; otherwise, no operation is performed. The two ends of the transmission line with overload potential are marked as fault states and used as the starting point for propagation. A propagation dynamics model was constructed using the SIR model to simulate the propagation process of a fault in the power grid. The construction of the propagation dynamics model includes defining the infection status of all nodes; The infection status includes a susceptible state, where the node is in a normal state and has not yet been affected by the fault. In an infected state, the node is marked as faulty and is in a risky state. The node has been restored to a normal state through repair. Based on the power flow of each transmission line, the propagation probability of each node is calculated using the propagation function, as shown in the formula: Among them, H ij P represents the propagation probability from node i to node j, N represents all nodes in the power system, and P represents the propagation probability from node i to node j. in (t) represents the power flow of the transmission line between node i and node n; A transmission threshold is set. At each time point, all nodes in a susceptible state are judged to be infected based on the transmission probability. If the transmission probability of each node is greater than the transmission threshold, the node corresponding to that node is marked as faulty. After each node is marked as faulty, it is gradually restored to the normal state. Calculate the number of nodes affected by the fault within a certain time step, measure the time step required for the fault impact to spread from the source node to other nodes, set a propagation speed threshold, and if the propagation speed exceeds the propagation speed threshold, it is determined that a cascading fault has occurred in the power grid and is judged as a level three warning. Continuously monitor the load status of the power grid and the power generation of new energy sources, use the intelligent dispatch system to carry out routine load dispatch, balance the ratio of traditional power sources and new energy sources, and make a level two warning judgment; otherwise, no warning is issued. The importance of the nodes affected by the fault is determined. If important nodes are involved, it is classified as a Level 2 warning. Based on the current absorption capacity of the power grid, renewable energy is prioritized for absorption. Energy storage systems are activated in a timely manner to ensure grid stability. The grid load dispatch is dynamically optimized to reduce the risk of high fluctuations and balance the output of each power source. A Level 1 warning is then issued. Otherwise, the fault is output as a Level 3 warning. The vulnerability C of a power system is assessed by calculating the proportion of change in the clustering coefficient, using the following formula: Where C1 represents the average clustering coefficient of all nodes in the power system after a fault occurs, and C0 represents the average clustering coefficient of the power system before the fault occurs; A vulnerability threshold is set to determine the vulnerability of the power system after a fault occurs. If the vulnerability of the power system exceeds the vulnerability threshold, the fault is classified as a Level 3 warning, and the emergency plan is immediately activated. Backup power sources are quickly dispatched, loads are allocated, local power supply restoration is initiated, and grid isolation is carried out to cut off the connection between the fault area and the normal area. Otherwise, the fault is output as a Level 2 warning.

4. The intelligent control method for coordinated transmission and distribution of new energy sources as described in claim 3, characterized in that: The process of storing and managing all data in a relational database includes selecting a relational database to store and manage the data and its analysis results, designing database table structures to store different types of data, setting up regular backup tasks to back up all data in the database, managing database user permissions, and encrypting and storing the data.

5. A smart control system for the coordinated transmission and consumption of new energy, based on the smart control method for the coordinated transmission and consumption of new energy as described in any one of claims 1 to 4, characterized in that: include, The data acquisition module is used to collect real-time monitoring data from each node in the power system and perform preprocessing. The matrix construction module is used to model each node and edge of the power system and construct an adjacency matrix. Based on the adjacency matrix, a complex network model of the power grid is constructed, and the power weight of each transmission line is calculated. The fault early warning module is used to calculate the metrics for each node, construct a propagation dynamics model to calculate the propagation probability of each node, and provide graded early warnings. The data storage module is used to store all data in a relational database and manage it.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the intelligent control method for coordinated consumption of new energy transmission and distribution as described in any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the intelligent control method for coordinated consumption of new energy transmission and distribution as described in any one of claims 1 to 4.

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