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

Abstract

The invention discloses a new energy transmission and distribution collaborative consumption intelligent control method and system, and relates to the technical field of new energy, and the method comprises the steps: collecting real-time monitoring data of each node in a power system, and carrying out the preprocessing; constructing a power grid complex network model according to the adjacent matrix to calculate the power weight of each power transmission line; and storing all data into a relational database and managing the data. According to the method, the nodes and the edges of the power system are accurately modeled, the adjacent matrix is constructed, the power flow of a power grid topological structure and a power transmission line is comprehensively reflected, the load and the operation state of the power grid are accurately evaluated, and errors in a traditional method are avoided; the propagation process of the fault in the power grid is simulated in real time, and the fault propagation rate and range are predicted, so that multi-stage early warning is realized, the accuracy of power grid vulnerability assessment is improved, and the stability and emergency response capability of the power grid are enhanced.

Description

Technical Field

[0001] The present invention relates to the field of new energy technologies, and particularly to an intelligent control method and system for coordinated consumption of new energy in power transmission and distribution. Background Art

[0002] With the transformation of the global energy structure, the wide application of new energy has become an important trend in the development of power systems. To adapt to the rapid development of new energy, the coordinated consumption ability of power grids in power transmission and distribution has become a key challenge for the stable operation of power systems. Traditional power grid control methods mainly rely on fixed scheduling strategies and equipment configurations, which obviously have many deficiencies in the face of large-scale renewable energy access, volatile and uncertain power load changes. Therefore, how to improve the dynamic adaptability of power grids and enhance the new energy consumption ability has become one of the hot topics in current power system research. In recent years, with the continuous progress of technologies such as computer technology, artificial intelligence, and big data analysis, power grid control methods have gradually developed towards intelligence and coordination. Power grid scheduling models based on intelligent control and big data analysis, especially multi-time scale regulation and adaptive optimization methods, have begun to be widely used. These methods can significantly improve the adaptability of power grids to new energy consumption through real-time monitoring and scheduling, and to a certain extent, reduce the impact of the instability of new energy on power grids.

[0003] Although the existing power grid intelligent control methods have made remarkable progress in improving system stability and reliability, there are still certain limitations when facing complex power grid systems, especially multi-region, multi-level, and diversified new energy access scenarios. Traditional power grid scheduling methods mainly focus on static analysis and lack in-depth exploration of the dynamic changes in power grid states. Especially in power systems with a high new energy penetration rate, the non-linearity of power flow and large-scale system effects are difficult to be fully considered. The existing power grid vulnerability assessment and fault warning systems, although able to identify potential risk nodes, are still insufficient in risk propagation analysis and multi-dimensional emergency response mechanisms, resulting in problems such as delayed response and decision-making errors when power grids face emergencies. Summary of the Invention

[0004] In view of the problems existing in the existing intelligent control methods and systems for coordinated consumption of new energy in power transmission and distribution, the present invention is proposed.

[0005] Therefore, the present invention provides an intelligent control method and system for coordinated consumption of new energy in power transmission and distribution to solve the problems that the existing power grid control system cannot provide optimal decision support during the process of handling 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 invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a new energy transmission and distribution collaborative consumption intelligent control method, which includes collecting real-time monitoring data of each node in the power system and preprocessing it;

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

[0009] 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, and store all data in a relational database for management.

[0010] As a preferred solution of the new energy transmission and distribution collaborative consumption intelligent control method of the present invention, wherein: the step of collecting real-time monitoring data of each node in the power system and preprocessing it includes identifying the 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 it. 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 includes checking all real-time monitoring data and excluding duplicate records, filling missing data using linear interpolation, detecting and correcting outliers using the Z-score method, and normalizing all real-time monitoring data.

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

[0012] Model each node in the power system, define the transmission line between nodes as an edge, and represent the topological structure of the power system by constructing an adjacency matrix. The element A in the adjacency matrix ij represents the connection relationship from node i to node j, and set the maximum carrying capacity of each edge 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, where ω ij represents the weight of the edge from node i to node j;

[0013] The step of modeling 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 the wind speed and temperature difference, and calculating the influence value of the wind speed on heat dissipation. The formula is:

[0015]

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

[0017] By adding factors such as thermal conductivity, density, and wire length to form a heat conduction model, the carrying capacity of the line is calculated;

[0018] Construct a maximum carrying capacity formula to calculate the maximum carrying capacity G max of the transmission line. The formula is:

[0019]

[0020] Among them, ∈ represents the emissivity of the transmission line, σ represents the Stefan-Boltzmann constant, D represents the surface area of the transmission line, k env represents the environmental correction coefficient, dT represents the infinitesimal variable of temperature T, 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.

[0021] As a preferred solution of the new energy transmission and distribution collaborative consumption intelligent control method described in the present invention, wherein: the constructing a power grid complex network model according to the adjacency matrix to calculate the power weight of each transmission line includes setting the power flow direction of each transmission line according to the actual operation of the power grid;

[0022] Incorporate the power of each node as a weighting coefficient into the adjacency matrix, and calculate the power flow of each transmission line through the formula. The formula is:

[0023]

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

[0025] As a preferred solution of the intelligent control method for coordinated consumption of new energy in power transmission and distribution according to the present invention, wherein: calculating the measurement indexes of each node includes calculating the degree, clustering coefficient and extended betweenness of each node and performing data standardization, and by comparing the extended betweenness of each node, the top M nodes with large extended betweenness are selected as important nodes.

[0026] As a preferred solution of the intelligent control method for coordinated consumption of new energy in power transmission and distribution according to the present invention, wherein: constructing a propagation dynamics model to calculate the propagation probability of each node and performing hierarchical early warning includes, according to the real-time monitoring data of the power grid, calculating the maximum power of each transmission line in real time. If the maximum power exceeds the maximum carrying capacity of each transmission line, it is determined that there is a potential overload hazard for this transmission line; otherwise, no operation is performed.

[0027] Mark the two end nodes of the transmission line with potential overload hazard as the fault state and use them as the starting point of propagation.

[0028] Use the SIR model to construct a propagation dynamics model to simulate the propagation process of faults in the power grid.

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

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

[0031] The infected state, where the node is marked as the fault state and is in the risk state.

[0032] The recovered state, where the node is restored to the normal state through repair.

[0033] Based on the power flow of each transmission line, use the propagation function to calculate the propagation probability of each node. 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, and P in (t) represents the power flow of the transmission line between node i and node n.

[0036] Set the propagation threshold. At each time point, all nodes in the susceptible state judge the infection situation according to the propagation probability. If the propagation probability of each node is greater than the propagation threshold, mark the corresponding node as the fault state. After each node is marked as the fault state, it gradually returns to the normal state.

[0037] Calculate the number of nodes affected by a 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. If the propagation speed exceeds the propagation speed threshold, it is determined that a cascading fault has occurred in the power grid, and a third-level early warning is issued. Continuously monitor the load status and new energy power generation of the power grid, use the intelligent dispatching system for conventional load dispatching, balance the ratio of traditional power sources and new energy, and conduct a second-level early warning determination. Otherwise, no early warning is issued;

[0038] Judge the importance of the nodes affected by the fault. If important nodes are included, a second-level early warning is determined. According to the current consumption capacity of the power grid, preferentially consume renewable energy, start the energy storage system in a timely manner to ensure the stability of the power grid, dynamically optimize the power grid load dispatching, reduce the high volatility risk, balance the output of each power source, and conduct a first-level early warning determination. Otherwise, output this fault as a third-level early warning;

[0039] Evaluate the vulnerability C of the power system by calculating the proportion of the change in the clustering coefficient. The formula is:

[0040]

[0041] Among them, C 1 represents the average clustering coefficient of all nodes in the power system after the fault occurs, and C 0 represents the average clustering coefficient of the power system without being affected by the fault;

[0042] Set a vulnerability threshold to judge the vulnerability of the power system after the fault occurs. If the vulnerability of the power system exceeds the vulnerability threshold, determine this fault as a third-level early warning, immediately start the emergency plan, quickly dispatch standby power sources, distribute loads, start local power supply restoration, and conduct power grid isolation to cut off the connection between the fault area and the normal area. Otherwise, output this fault as a second-level early warning.

[0043] As a preferred solution of the new energy transmission and distribution collaborative consumption intelligent control method described in the present invention, among them: storing all data in a relational database and managing it includes selecting a relational database to store and manage data and its analysis results, designing the database table structure to store different types of data, setting regular backup tasks to back up all data in the database, performing permission management on database users, and encrypting and storing data.

[0044] In a second aspect, an embodiment of the present invention provides a new energy transmission and distribution collaborative consumption intelligent control system, which includes: a data acquisition module for collecting real-time monitoring data of each node in the power system and performing preprocessing; a matrix construction module for modeling each node and edge of the power system to construct an adjacency matrix, and constructing a power grid complex network model based on the adjacency matrix to calculate the power weight of each transmission line; a fault warning module for calculating a measurement index of each node, constructing a propagation dynamics model to calculate the propagation probability of each node and performing hierarchical warning; and a data storage module for storing all data in a relational database and managing it.

[0045] In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and: when the processor executes the computer program, any step of the above-mentioned new energy transmission and distribution collaborative consumption intelligent control method is implemented.

[0046] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and: when the computer program is executed by a processor, any step of the above-mentioned new energy transmission and distribution collaborative consumption intelligent control method is implemented.

[0047] The beneficial effects of the present invention are as follows: By accurately modeling the nodes and edges of the power system and constructing an adjacency matrix, the power grid topology structure and the power flow of transmission lines are comprehensively reflected, the power grid load and operating status are accurately evaluated, and the errors in traditional methods are avoided. By calculating the degree, clustering coefficient, and expansion series of nodes, combined with the propagation dynamics model, the propagation process of faults in the power grid is simulated in real time, the rate and range of fault spread are predicted, and then multi-level warning is realized, improving the accuracy of power grid vulnerability assessment and enhancing the stability and emergency response ability of the power grid. Description of the Drawings

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for description in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts. Among them:

[0049] Figure 1 It is a flowchart of the new energy transmission and distribution collaborative consumption intelligent control method in Embodiment 1.

[0050] Figure 2 It is a schematic diagram of the new energy transmission and distribution collaborative consumption intelligent control system in Embodiment 1. Detailed Embodiments

[0051] To make the above objects, features, and advantages of the present invention more apparent and understandable, the following provides a detailed description of the specific embodiments of the present invention in conjunction with the accompanying drawings of the specification. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present invention.

[0052] In the following description, many specific details are set forth to facilitate a thorough understanding of the present invention. However, the present invention may be practiced in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the spirit of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0053] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation manner of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it an embodiment that is separate or selectively exclusive of other embodiments.

[0054] The present invention is described in detail in conjunction with schematic diagrams. When detailing the embodiments of the present invention, for ease of explanation, the cross-sectional views showing the device structure are enlarged locally not in accordance with the general scale, and the schematic diagrams are only examples and should not limit the scope of protection of the present invention herein. In addition, in actual production, three-dimensional spatial dimensions including length, width, and depth should be included.

[0055] At the same time, in the description of the present invention, it should be noted that the orientation or positional relationships indicated by terms such as "upper, lower, inner, and outer" are based on the orientation or positional relationships shown in the drawings. This is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation to the present invention. In addition, the terms "first, second, or third" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance.

[0056] Unless otherwise clearly defined and limited in the present invention, the terms "mounted, connected, and coupled" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can also be a mechanical connection, an electrical connection, or a direct connection, or can be indirectly connected through an intermediate medium, or can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0057] Embodiment 1

[0058] Refer to Figure 1 and Figure 2, which is the first embodiment of the present invention. This embodiment provides an intelligent control method for coordinated consumption of new energy in power transmission and distribution, including:

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

[0060] Identifying the main components of the power system, defining each main component as a node, and obtaining and preprocessing the real-time monitoring data of the node through sensors;

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

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

[0063] Performing preprocessing includes checking all real-time monitoring data and excluding duplicate records, filling in missing data using linear interpolation, detecting and correcting outliers using the Z-score method, and normalizing all real-time monitoring data.

[0064] By collecting and preprocessing the real-time monitoring data of each main component in the power system, 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 operating status of the power grid can be comprehensively grasped. Preprocessing steps such as excluding duplicate records, filling in missing data, correcting outliers, and data normalization ensure the data quality, providing a reliable basis for subsequent power grid analysis and decision-making. These measures can improve the monitoring accuracy of the system, reduce misjudgments caused by data problems, thereby enhancing the stability and reliability of the power system, and supporting more accurate risk assessment and optimal control.

[0065] S2. Modeling each node and edge of the power system to construct an adjacency matrix, and constructing a complex network model of the power grid based on the adjacency matrix to calculate the power weight of each transmission line;

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

[0067] Modeling each node in the power system, defining the transmission line between nodes as an edge, and representing the topological structure of the power system through constructing an adjacency matrix. The element A in the adjacency matrix ij represents the connection relationship from node i to node j, setting the maximum carrying capacity of each edge 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, where ω ij represents the weight of the edge from node i to node j;

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

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

[0070] Q w = Q o

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

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

[0073] The heat dissipation of the conductor mainly occurs through natural convection Q conv , forced convection Q rad and radiation Q forced , and 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 from 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 from 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, conventional heat balance equations mainly consider factors such as temperature difference and heat dissipation coefficient. However, in practical applications, factors such as wind speed and environmental humidity have an important impact on the heat dissipation of the circuit. By introducing a dynamic heat dissipation function, the wind speed and temperature difference are corrected, and the influence value of wind speed on heat dissipation is calculated. The formula is as follows:

[0079]

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

[0081] Existing formulas usually directly describe radiative heat dissipation, natural convection, and forced convection heat dissipation. By adding factors such as thermal conductivity, density, and wire length, a heat conduction model is formed to calculate the load-carrying capacity of the circuit. The formula is as follows: Among them, Z represents the heat conduction model, 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;

[0082] Construct a maximum load-carrying capacity formula to calculate the maximum load-carrying capacity G max of the transmission line. The formula is as follows:

[0083]

[0084] Among them, ∈ represents the emissivity of the transmission line, σ represents the Stefan-Boltzmann constant, D represents the surface area of the transmission line, k env represents the environmental correction coefficient, which is derived from experimental data and environmental characteristics. dT represents a small variable of temperature T, 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.

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

[0086] Furthermore, according to the adjacency matrix, a complex network model of the power grid 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 incorporated into the adjacency matrix as a weighting coefficient, and the power flow of each transmission line is calculated through the formula. The formula is:

[0089]

[0090] where P ij (t) represents the power flow of the transmission line between node i and node j, w u and w l are power state coefficients used to mark the transmission power situation of the corresponding branch. When branch u and branch l represent a certain transmission line respectively, if there is power flow in this transmission line, the corresponding w u or w l is 1, otherwise, the corresponding w u or w l is 0. represents the power flow output from node i to branch u at time point t, represents the power flow input from node j to branch l at time point t. h and v represent output and input respectively. Branch u and branch l form the transmission line from node i to node j. U and L represent the branch sets 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 incorporating the power of each node as a weighting 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 grid power flow, and help optimize the load distribution and operation scheduling of the power grid. By introducing 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 load and environmental conditions, and enhance the power grid's ability to respond to sudden faults and load fluctuations.

[0092] S3. Calculate the measurement index of each node, construct a propagation dynamics model to calculate the propagation probability of each node and conduct hierarchical early warning;

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

[0094] Calculate the degree, clustering coefficient and extended betweenness of each node and conduct data standardization. By comparing the extended betweenness of each node, select the top M nodes with large extended betweenness as important nodes, and the number of M is determined according to the actual situation;

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

[0096] The clustering coefficient measures the degree of mutual connection between other nodes. By obtaining the neighbor node set of node i and calculating the actual connection situation of different neighbor node pairs within the set, calculate the number of edges of all different neighbor node pairs, which is expressed as the number of edges between neighbor nodes, and use the formula to calculate the clustering coefficient. The formula is:

[0097]

[0098] Among them, C i represents the clustering coefficient of node i, E i represents the number of edges between the neighbor nodes of node i, 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 this shortest path number, use the formula to calculate the extended betweenness B i , and the formula is:

[0100]

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

[0102] By calculating the degree, clustering coefficient, and extended node number of each node in the power system and performing data normalization, the present invention can comprehensively quantify the structural attributes and functional characteristics of nodes in the power grid. The degree reflects the network connectivity of nodes and can intuitively evaluate the impact of nodes on the overall stability of the network; the clustering coefficient measures the tightness of connections between node neighbors and helps identify local structural characteristics; the extended betweenness accurately evaluates the importance of nodes in the global network by analyzing the mediating role of nodes in the shortest paths. By synthesizing these indicators, key nodes can be effectively selected and the vulnerability of the power grid can be accurately analyzed, providing important support for optimizing the operation of the power grid, improving the stability of the power grid, and enhancing the emergency response ability.

[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 transmission line is calculated in real time. If the maximum power exceeds the maximum carrying capacity of each transmission line, it is determined that there is a risk of overload for that transmission line; otherwise, no operation is performed.

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

[0106] Use the SIR model to construct a propagation dynamics model to simulate the propagation process of faults in the power grid.

[0107] Constructing a propagation dynamics model includes defining the infection status of all nodes.

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

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

[0110] The recovered state, where the node returns to the normal state through repair.

[0111] Based on the power flow of each transmission line, use the propagation function to calculate the propagation probability of each node. The formula is:

[0112]

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

[0114] Set the propagation threshold. At each time point, all nodes in the susceptible state judge the infection situation according to the propagation probability. If the propagation probability of each node is greater than the propagation threshold, the node corresponding to this node is marked as the "fault state". After each node is marked as the "fault state", it gradually returns to the normal state, and the time of the recovery process is dynamically adjusted according to the importance of the node;

[0115] 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 the propagation speed threshold. If the propagation speed exceeds the propagation speed threshold, it is determined that a cascading fault has occurred in the power grid, and it is determined as a third-level early warning. Continuously monitor the load status and new energy power generation of the power grid to ensure the balance between power generation and demand. Use the intelligent dispatching system to conduct conventional load dispatching, balance the ratio of traditional power sources and new energy, and conduct a second-level early warning determination. Otherwise, no early warning is issued;

[0116] Judge the importance of the nodes affected by the fault. If important nodes are included, it is determined as a second-level early warning. According to the current power grid accommodation capacity, give priority to the consumption of renewable energy, start the energy storage system in a timely manner to ensure the stability of the power grid, dynamically optimize the power grid load dispatching, reduce the high volatility risk, balance the output of each power source, and conduct a first-level early warning determination. Otherwise, output this fault as a third-level early warning;

[0117] Evaluate the vulnerability C of the power system by calculating the ratio of the change in the clustering coefficient. The formula is:

[0118]

[0119] where C 1 represents the average clustering coefficient of all nodes in the power system after the fault occurs, and C 0 represents the average clustering coefficient of the power system without being affected by the fault;

[0120] Set the vulnerability threshold to judge the vulnerability of the power system after the fault occurs. If the vulnerability of the power system exceeds the vulnerability threshold, this fault is determined as a third-level early warning. Immediately start the emergency plan, quickly dispatch the standby power supply, distribute the load, start the local power supply restoration, and conduct power grid isolation to cut off the connection between the fault area and the normal area. Otherwise, output this fault as a second-level early warning.

[0121] By constructing a propagation dynamics model and calculating the propagation probability of each node, the present invention can accurately simulate the expansion process of faults in the power system, timely identify the transmission lines and nodes that may lead to cascading faults, and then implement a multi-level early warning mechanism. By setting the propagation threshold and vulnerability threshold, the risk of the power grid can be effectively evaluated, ensuring that when a fault occurs, corresponding emergency response measures can be quickly activated, such as load dispatching, energy storage system activation, and power grid isolation, to maximize the stability and reliability of the power grid. This method combines the 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 ability of the power grid under the condition of high-penetration new energy.

[0122] S4. Store all data in a relational database and manage it;

[0123] Specifically, storing all data in a relational database and managing it includes

[0124] Select a relational database to store and manage data and its analysis results, design the database table structure to store different types of data, set up a regular backup task to back up all data in the database, manage the permissions of database users, and encrypt the storage of data.

[0125] By storing all data in a relational database and managing it, efficient storage, systematic management, and security protection of data are achieved. The relational database provides structured storage for data, ensuring the standardized management and efficient query of different types of data. The regular backup task guarantees the security and disaster recovery ability of data, effectively reducing the risk of data loss. Permission management and encrypted storage further improve the security of data, ensuring that sensitive data is only accessible to authorized users, and guaranteeing the reliability of the system and the confidentiality of data.

[0126] This embodiment also provides a new energy transmission and distribution coordinated consumption intelligent control system, including:

[0127] A data acquisition module, used to collect real-time monitoring data of each node in the power system and perform preprocessing;

[0128] A matrix construction module, used to model each node and edge of the power system to construct an adjacency matrix, and construct a power grid complex network model based on the adjacency matrix to calculate the power weight of each transmission line;

[0129] A fault early warning module, used 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;

[0130] A data storage module, used to store all data in a relational database and manage it.

[0131] This embodiment also provides a computer device, which is applicable to the case of the new energy transmission and distribution collaborative consumption intelligent control method, and includes: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the new energy transmission and distribution collaborative consumption intelligent control method proposed in the above embodiment.

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

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

[0134] A fault warning module for calculating a measurement index of each node, constructing a propagation dynamics model to calculate the propagation probability of each node and performing hierarchical warning;

[0135] A data storage module for storing all data in a relational database and managing it.

[0136] This computer device can be a terminal, which includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of this computer device is used to provide computing and control capabilities. The memory of this 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 operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of this computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a carrier network, NFC (Near Field Communication) or other technologies. The display screen of this computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of this computer device can be a touch layer covered on the display screen, or a button, a trackball or a touchpad set on the shell of the computer device, or an external keyboard, touchpad or mouse, etc.

[0137] In summary, the present invention 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 transmission lines, accurately evaluates the power grid load and operating status, avoids errors in traditional methods, calculates the degree, clustering coefficient and expansion series of nodes, combines the propagation dynamics model, and real-time simulates the propagation process of faults in the power grid, predicts the rate and scope of fault spread, and then realizes multi-level warning, improves the accuracy of power grid vulnerability assessment, and enhances the stability and emergency response ability of the power grid.

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

[0139] Embodiment 2

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

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

[0142] Each node and edge of the power system were 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 the actual operation data and environmental factors. For example, the carrying capacity of a certain transmission line decreased by about 10% in a high-temperature environment and increased by 5% in a high-wind speed situation. Through these adjustments, we ensured the safe operation of the transmission lines.

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

[0144] To evaluate the fault propagation risk of the power grid, the SIR model was used to simulate the fault propagation process in the power grid. In the experiment, when the maximum power of a certain transmission line exceeded its maximum carrying capacity, the system immediately marked the nodes at both ends of the line as fault states and initiated the propagation warning. In a simulated fault, the time step required for the fault impact to spread from the source node to other nodes was 5 minutes, and the propagation speed exceeded the set threshold, triggering a level-three warning. The system quickly dispatched backup power supplies, distributed loads, and initiated local power supply restoration, effectively preventing the occurrence of cascading failures.

[0145] All data was stored in a relational database. A special table structure was designed to store different types of data, including real-time monitoring data, model parameters of nodes and edges, fault warning information, etc. A regular backup task was set up once a day to ensure the security and recoverability of the data. Strict permission management was carried out for database users, and sensitive data was encrypted for storage to ensure the security and privacy of the data. 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 invention demonstrates high efficiency, intelligent management, and strong security, fully proving its feasibility and advantages in practical applications. The comparison between the present invention and the prior art is shown in Table 2 below:

[0149] Table 2 Comparison Table between the Present Invention and the Prior Art

[0150]

[0151] Through these comparisons in Table 2, the present invention demonstrates its advantages in aspects such as data collection, 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 invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. An intelligent control method for coordinated consumption of new energy transmission and distribution, characterized by: include, Collect real-time monitoring data of each node in the power system and pre-process it; Model each node and edge of the power system to construct an adjacency matrix, and build a complex network model of the power grid based on the adjacency matrix to calculate the power weight of each transmission line; Calculate the measurement indicators of each node, build a propagation dynamics model to calculate the propagation probability of each node and conduct graded warnings, and store and manage all data in a relational database.

2. The intelligent control method for coordinated consumption of new energy transmission and distribution according to claim 1, characterized in that: The collecting of real-time monitoring data of each node in the power system and preprocessing includes identifying the main components of the power system, defining each main component as a node, obtaining the real-time monitoring data of the node through sensors and preprocessing, the main components include power plants, substations and transmission line terminals, the real-time monitoring data include the 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 the Z-score method to detect and correct outliers, and standardizing all real-time monitoring data.

3. The intelligent control method for coordinated consumption of new energy transmission and distribution as claimed in claim 2 is characterized by: The method of modeling each node and edge of the power system and constructing an adjacency matrix includes the following steps: Model each node in the power system, define the transmission lines between nodes as edges, and construct an adjacency matrix to represent the topological structure of the power system. The element A in the adjacency matrix is ij 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, where ω ij 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 a load demand for each node; The maximum load capacity includes introducing a dynamic heat dissipation function, correcting the wind speed and temperature difference, and calculating the influence of wind speed on heat dissipation. The formula is: Among them, f(V wind ,ΔT) represents the effect of wind speed on heat dissipation, β represents the forced convection coefficient, V wind represents wind speed, α represents parameter, T s Indicates the surface temperature of the conductor, T env Indicates the ambient temperature; The load-bearing capacity of the line is calculated by adding thermal conductivity, density and wire length factors to form a heat conduction model; Construct the maximum carrying capacity formula to 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 represents the environmental correction factor, dT represents the small variable of temperature T, 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.

4. The intelligent control method for coordinated consumption of new energy transmission and distribution as claimed in claim 3 is characterized by: The calculation of the power weight of each transmission line by constructing a complex network model of the power grid according to the adjacency matrix 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 weighted coefficient, and the power flow of each transmission line is calculated by the 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 is the power state coefficient, represents the power flow output from node i to branch u at time point t, represents the power flow from node j to branch l at time point t, h and v represent output and input respectively, branch u and branch l constitute the transmission line from node i to node j, Y and L represent the branch sets related to node i and node j respectively.

5. The intelligent control method for coordinated consumption of new energy transmission and distribution as claimed in claim 4 is characterized by: 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 by comparing the extended betweenness of each node, selecting the first M nodes with large extended betweenness as important nodes.

6. The intelligent control method for coordinated consumption of new energy transmission and distribution as claimed in claim 5 is characterized by: The construction of the propagation dynamics model to calculate the propagation probability of each node and to perform graded warning includes calculating the maximum power of each transmission line in real time according to 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 a hidden danger of overload, otherwise, no operation is performed; The nodes at both ends of the transmission line with potential overload are marked as faulty and serve as the starting point for propagation; Use the SIR model to build a propagation dynamics model to simulate the propagation process of faults in the power grid; The constructing of the propagation dynamics model includes defining the infection status of all nodes; The infection state includes a susceptible state, where the node is in a normal state and has not been affected by the fault; Infected state, the node is marked as faulty and at risk; Recovery state: the node is restored to 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, which is: Among them, H ij represents the transmission probability from node i to node j, N represents all nodes in the power system, P in (t) represents the power flow of the transmission line between node i and node n; Set a propagation threshold. At each time point, all nodes in the susceptible state judge the infection status according to the propagation probability. If the propagation probability of each node is greater than the propagation threshold, the node corresponding to the node will be marked as a fault state. After each node is marked as a fault state, it will gradually return 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 the propagation speed threshold, and if the propagation speed exceeds the propagation speed threshold, it is determined that the power grid has a cascading fault and is judged as a third-level warning. Continuously monitor the load status of the power grid and the power generation of new energy, use the intelligent dispatching system to perform conventional load dispatch, balance the ratio of traditional power sources and new energy, and make a second-level warning judgment. Otherwise, no warning will be issued; Determine the importance of the nodes affected by the fault. If there are important nodes, it will be judged as a second-level warning. According to the current absorption capacity of the power grid, renewable energy will be absorbed first, and the energy storage system will be started in time to ensure the stability of the power grid, dynamically optimize the load dispatch of the power grid, reduce the risk of high fluctuations, balance the output of each power source, and make a first-level warning judgment. Otherwise, the fault will be output as a third-level warning. The vulnerability C of the power system is evaluated by calculating the proportion of the clustering coefficient change. The formula is: Among them, C1 represents the average clustering coefficient of all nodes in the power system after the fault occurs, and C0 represents the average clustering coefficient of the power system before it is affected by the fault; Set a vulnerability threshold 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 will be judged as a third-level warning, and the emergency plan will be immediately activated to quickly dispatch backup power supplies, distribute loads, start local power restoration, isolate the power grid, and cut off the connection between the faulty area and the normal area. Otherwise, the fault will be output as a second-level warning.

7. The intelligent control method for coordinated consumption of new energy transmission and distribution as claimed in claim 6 is characterized by: The storing and managing of all data in a relational database includes 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 regular backup tasks, backing up all data in the database, managing permissions for database users and encrypting and storing data.

8. An intelligent control system for coordinated consumption of new energy transmission and distribution, based on the intelligent control method for coordinated consumption of new energy transmission and distribution according to any one of claims 1 to 7, characterized in that: include, Data acquisition module, used to collect real-time monitoring data of 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. The complex network model of the power grid is constructed based on the adjacency matrix to calculate the power weight of each transmission line. The fault warning module is used to calculate the measurement indicators of each node, build a propagation dynamics model to calculate the propagation probability of each node and perform graded warnings; The data storage module is used to store and manage all data in a relational database.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the intelligent control method for coordinated consumption of new energy transmission and distribution are implemented as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, 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 7 are implemented.

Images