Power grid architecture value optimization method based on power grid online equipment multi-dimensional technology upgrading

Abstract

The invention provides a value optimization method of a power grid architecture based on multi-dimensional technical upgrade of power grid online equipment, which belongs to the technical field of power grid architectures and comprises the following steps: firstly, establishing a power grid topological graph by collecting power grid data in a transformer area; then, calculating power flow among nodes in the power grid topological graph according to a preset time period, and dividing the power flow into two logic layers, namely low-power flow and high-power flow, according to a power threshold value; next, respectively calculating active power and reactive power of each node, and establishing a power balance constraint equation; based on the double-layer logic diagram, the method further calculates the stability index of each node, and normalizes the stability index to obtain a node stability coefficient. And according to the node stability coefficient, judging a power grid operation state and formulating a corresponding optimization strategy. And finally, constructing a power grid architecture optimization matrix as a value optimization target of the power grid architecture, thereby solving the technical problem that it is difficult to comprehensively and accurately evaluate the operation state of the power grid for optimization in the prior art.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid architecture, and specifically relates to a value optimization method for a power grid architecture based on multi-dimensional technical upgrades of power grid online equipment. Background Art

[0002] As the infrastructure for national economic development, the safe and stable operation of the power system is crucial to social and economic development. In recent years, with the continuous optimization of energy structure and the rapid development of new energy technologies, the power system has become more complex and changeable. On the one hand, the access of large-scale renewable energy generation has made the operation of the power grid more dynamic, exacerbating the uncertainty of power grid operation; on the other hand, with the widespread application of smart grid technology, the monitoring and control capabilities of online power grid equipment have been continuously enhanced, providing new technical means for power grid optimization. How to use these monitoring data and control methods to analyze the operation status of the power grid and provide targeted suggestions for the optimization of the power grid architecture has become the focus and difficulty of power system operation and management.

[0003] The existing power system operation status analysis and optimization strategy formulation are mainly based on the following methods: 1) Optimization method based on power flow calculation and voltage stability analysis. This method can analyze the power flow and voltage status of the power grid and provide some basic suggestions for power grid optimization, but it cannot fully describe the dynamic characteristics of the power grid. 2) Optimization method based on control theory and intelligent optimization algorithm. This method can adjust the control parameters in real time according to the changes in the power grid state, but it requires a large amount of historical data training, and its applicability and robustness need to be further improved. 3) Power grid analysis method based on complex network theory. This method can describe the overall characteristics of the power grid from the perspective of topology, but it is difficult to provide guidance for specific optimization schemes. In general, although the existing power grid operation status analysis and optimization methods have made progress in some aspects, it is still difficult to comprehensively and accurately evaluate the operation status of the power grid and provide targeted optimization suggestions. Summary of the invention

[0004] In view of this, the present invention provides a value optimization method for a power grid architecture based on multi-dimensional technical upgrades of online power grid equipment, which can solve the technical problem that it is difficult to comprehensively and accurately evaluate the operating status of the power grid for optimization in the prior art.

[0005] The present invention is achieved in that: The present invention provides a value optimization method for a power grid architecture based on multi-dimensional technical upgrades of online power grid equipment, which includes the following steps: S10, obtaining the power grid data in the substation area and establishing a power grid topology map of the substation area; S20, calculating the power flow between nodes in the power grid topology diagram based on the collected data according to a continuous period of preset duration, and setting a power threshold for distinguishing high and low power flows; S30, dividing the power grid topology diagram into a two-layer logic diagram according to the set power threshold; the first layer is used to represent the flow of electric energy below the preset power threshold, and the second layer is used to represent the flow of electric energy exceeding the power threshold; S40, calculating the power flow of each node in the double-layer logic diagram, including active power and reactive power, and establishing a power balance constraint equation; S50, based on the double-layer logic graph, calculate the stability index of each node; S60, normalizing the calculated stability index to obtain a node stability coefficient; S70, judging the operation status of the power grid according to the node stability coefficient and formulating corresponding optimization strategies; S80. Based on the judgment results and optimization strategies, a power grid architecture optimization matrix is ​​constructed as the value optimization target of the power grid architecture.

[0006] On the basis of the above technical solution, the value optimization method of the power grid architecture based on multi-dimensional technical upgrade of power grid online equipment of the present invention can also be improved as follows: The power grid data includes bus voltage, line impedance, load power, and generator output.

[0007] Furthermore, in the power grid topology diagram, nodes represent substations, distribution transformers, and users, and edges represent lines and transformers.

[0008] Furthermore, the power grid topology diagram uses a graph theory method to represent the power grid topology, and the bus voltage is in complex form.

[0009] Furthermore, the power flow calculation involves complex power, admittance matrix and voltage conjugate.

[0010] Furthermore, the power threshold is set based on statistical analysis of historical data, taking into account the power mean and standard deviation.

[0011] Furthermore, the two-layer logic graph retains the original nodes, but divides the edges into different levels according to the power size.

[0012] Furthermore, the stability index of each node is calculated by using the grayscale-gradient co-occurrence matrix to describe the node characteristics and calculating the two indicators of energy and grayscale entropy.

[0013] Furthermore, the normalization process considers the load power and transforms the energy and gray entropy indicators into comparable stability coefficients.

[0014] Furthermore, the matrix elements of the power grid architecture optimization matrix represent optimization suggestions from one node to another, including at least maintaining the status quo, increasing or decreasing the consumption of new energy, increasing or decreasing the output of the energy storage system, and strengthening the line capacity.

[0015] Specifically, the step S10 includes: obtaining grid data from the substation, including bus voltage, line impedance, load power, generator output, etc. The grid topology is represented by graph theory, where nodes represent key equipment such as substations, distribution transformers, and users, and edges represent the connection relationships between these equipment such as lines and transformers. The bus voltage is represented in complex form, including real and imaginary parts; the line impedance is also represented in complex form, including resistance and reactance. By establishing such a detailed grid topology diagram ,in Represents a node collection, Representing the edge set, it can comprehensively reflect the physical structure and electrical parameters of the power grid in the substation area, laying the foundation for subsequent power flow analysis and power grid status assessment.

[0016] Specifically, the step S20 includes: based on the obtained grid data, performing power flow calculation for each continuous preset time period. The calculation formula is: ,in Represents a slave node To Node The complex power, and are active power and reactive power respectively, and is the node voltage, is the element of the admittance matrix. Through this calculation, the actual power flow between nodes in the power grid can be obtained. Then, based on the statistical analysis of historical data, a power threshold is set to distinguish between high and low power flows. ,in is the mean power, is the standard deviation of power, is the adjustment coefficient, usually 2-3. As a dividing line, it helps in the subsequent division of the power grid topology and judgment of the power grid status.

[0017] Specifically, the step S30 includes: according to the power threshold set in step S20 , the original power grid topology Divide into two subgraphs and .in Indicates that the power flow is below the threshold The sub-graph, Indicates that the power flow is above the threshold The basis for division is: if an edge Power flow , then divide it into In the set; otherwise, if , then divide it into Through the establishment of this double-layer logic diagram, the power flow characteristics under different load levels in the power grid can be better identified and described, laying the foundation for subsequent power grid status assessment and optimization strategy formulation.

[0018] Specifically, the step S40 includes: the double-layer logic diagram constructed in step S30 and , calculate the active power and reactive power flow of each node respectively, and establish the corresponding power balance constraint equation. The active power balance equation is , the reactive power balance equation is ,in and Node The injected active and reactive power, and Node The load active and reactive power, and Line The active and reactive power losses of the line are calculated as follows: and ,in and are the real and imaginary parts of the line current, respectively. By establishing these power balance constraint equations, the power balance relationship between nodes in the power grid can be better described, providing support for subsequent power grid stability assessment.

[0019] Specifically, the step S50 includes: based on the double-layer logic diagram constructed in step S30 and , the grayscale-gradient co-occurrence matrix method is used to calculate the stability index of each node. The specific calculation process is as follows: First, the complex power value of each node Perform grayscale and gradient calculation to obtain grayscale value and the gradient value Then construct the grayscale-gradient co-occurrence matrix ,in To have gray value and the gradient value The number of pixels, is the number of gray levels, the default value is 16. Next, calculate the energy index and grayscale entropy index These two indicators can effectively describe the stability characteristics of the node and provide a basis for subsequent grid status evaluation.

[0020] Specifically, the step S60 includes: calculating the energy index obtained in step S50 and grayscale entropy index Normalization is performed to obtain the node stability coefficient ,in Take 1 or 2, corresponding to and ; and They are respectively the active and reactive power of the total load of the node. This normalization process can transform indicators of different dimensions into comparable stability coefficients, which can better reflect the actual operating status of the node.

[0021] Specifically, the step S70 includes: calculating the node stability coefficient obtained in step S60 and , judge the operation status of the power grid and formulate corresponding optimization strategies. The details are as follows: 1) If , it is determined that the power grid is in normal operation, and the optimization strategy is to increase the consumption of new energy and reduce operating costs. 2) If , it is determined that the power grid is in a regulated operation state, and further judgment is required: if , indicating that the consumption of new energy is insufficient, and the optimization strategy is to increase the output of the energy storage system; if , indicating that the energy storage system output is too much, and the optimization strategy is to reduce the energy storage system output. The thresholds 0.3 and 2.4 here are empirical values ​​obtained through analysis of a large amount of measured data and can be used as a reference. The entire optimization strategy can be used with the function To describe, 1 means increasing the consumption of new energy, 2 means increasing the output of the energy storage system, and -2 means reducing the output of the energy storage system.

[0022] Specifically, the step S80 includes: constructing a power grid architecture optimization matrix based on the judgment result and optimization strategy in step S70 .in is the number of nodes, Represents a slave node To Node The optimization suggestions are as follows: ; Optimization Matrix The calculation process is ,in is the decision function, according to the node and The stability state of the To determine the specific optimization recommendations. This decision function can be implemented based on fuzzy logic or expert systems. By constructing this optimization matrix ,It can intuitively represent the optimization direction of the grid architecture, provide intuitive decision support for grid operators, and help to formulate targeted grid optimization strategies.

[0023] Compared with the prior art, the value optimization method of the power grid architecture based on the multi-dimensional technology upgrade of the online power grid equipment provided by the present invention makes full use of multiple factors such as power grid topology information, power flow characteristics and node stability, and establishes a complete power grid state evaluation and optimization framework, which can more comprehensively and accurately diagnose the operation state of the power grid and provide specific and feasible suggestions for power grid optimization. Compared with the prior art, the main effects of the method of the present invention include: 1. A two-layer logic diagram is used to describe the power grid topology in detail. The method of the present invention divides the power grid topology into two sub-graphs, a low-power area and a high-power area, according to the size of the power flow. This detailed description can not only better reflect the actual operating characteristics of the power grid, but also provide a more reliable basis for subsequent power grid status evaluation and optimization.

[0024] 2. Analyze node stability based on grayscale-gradient co-occurrence matrix. The method of the present invention uses the grayscale and gradient calculation of power values ​​to construct a node stability evaluation index, which can more comprehensively and accurately describe the actual operating status of power grid nodes and provide a more reliable decision-making basis for power grid optimization.

[0025] 3. Construct a grid architecture optimization matrix to guide optimization decisions. The method of the present invention establishes a grid architecture optimization matrix based on the node stability analysis results and power flow characteristics, which intuitively represents the optimization suggestions from one node to another. This optimization matrix provides intuitive and operational decision support for grid operators, which is conducive to the formulation of targeted grid optimization strategies.

[0026] In general, the grid architecture value optimization method proposed in the present invention makes full use of the online equipment monitoring data of the grid, conducts a comprehensive analysis from multiple dimensions such as topology structure, power flow characteristics and node stability, and can more accurately diagnose the operating status of the grid and provide targeted suggestions for grid optimization, solving the technical problem that the existing technology is difficult to comprehensively and accurately evaluate the operating status of the grid for optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 A flow chart of the method provided by the present invention; Figure 2 is a suggested graph of multiple nodes in an embodiment. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0029] like Figure 1 As shown, it is a flow chart of a value optimization method of a power grid architecture based on multi-dimensional technology upgrade of power grid online equipment provided by the present invention. The method comprises the following steps: S10, obtaining the power grid data in the substation area and establishing a power grid topology map of the substation area; S20, calculating the power flow between nodes in the power grid topology diagram based on the collected data according to a continuous period of preset duration, and setting a power threshold for distinguishing high and low power flows; S30, dividing the power grid topology diagram into a two-layer logic diagram according to the set power threshold; the first layer is used to represent the flow of electric energy below the preset power threshold, and the second layer is used to represent the flow of electric energy exceeding the power threshold; S40, calculating the power flow of each node in the double-layer logic diagram, including active power and reactive power, and establishing a power balance constraint equation; S50, based on the double-layer logic graph, calculate the stability index of each node; S60, normalizing the calculated stability index to obtain a node stability coefficient; S70, judging the operation status of the power grid according to the node stability coefficient and formulating corresponding optimization strategies; S80. Based on the judgment results and optimization strategies, a power grid architecture optimization matrix is ​​constructed as the value optimization target of the power grid architecture.

[0030] The specific implementation methods of the above steps are described in detail below: The specific implementation of step S10 is as follows: First, obtain the bus voltage from the power grid area , Line impedance , Load power The topology of the power grid is represented by graph theory. , where the node Represents key equipment such as substations, distribution transformers, and users. Represents the connection relationship between these devices such as lines and transformers. Expressed in plural form , including the real part and the imaginary part ; Line impedance Also in plural form , including resistors and reactance By building a detailed grid topology diagram , can comprehensively reflect the physical structure and electrical parameters of the power grid in the substation area, laying the foundation for subsequent power flow analysis and grid status assessment.

[0031] The specific implementation of step S20 is as follows: Based on the grid data obtained in step S10, power flow calculation is performed for each continuous preset period. The calculation formula is: ,in Represents a slave node To Node The complex power, and are active power and reactive power respectively, and Node and The complex conjugate of the voltage, is the element of the admittance matrix. Through this calculation, the actual power flow between nodes in the power grid can be obtained. Then, based on the statistical analysis of historical data, a power threshold is set to distinguish between high and low power flows. ,in is the mean power, is the standard deviation of power, is the adjustment coefficient, usually 2-3. As a dividing line, it helps in the subsequent division of the power grid topology and judgment of the power grid status.

[0032] The specific implementation of step S30 is as follows: According to the power threshold set in step S20 , the original power grid topology Divide into two subgraphs and .in Indicates that the power flow is below the threshold The sub-graph, Indicates that the power flow is above the threshold The basis for division is: if an edge Power flow , then divide it into In the set; otherwise, if , then divide it into Through the establishment of this double-layer logic diagram, the power flow characteristics under different load levels in the power grid can be better identified and described, laying the foundation for subsequent power grid status assessment and optimization strategy formulation.

[0033] The specific implementation of step S40 is as follows: The two-layer logic diagram constructed for step S30 and , calculate the active power and reactive power flow of each node respectively, and establish the corresponding power balance constraint equation. The active power balance equation is ,in For Node The injected active power, For Node The load active power, For line The reactive power balance equation is ,in For Node The injected reactive power, For Node The load reactive power, For line The reactive power loss of the line is calculated as follows: and ,in and are the real and imaginary parts of the line current, respectively. By establishing these power balance constraint equations, the power balance relationship between nodes in the power grid can be better described, providing support for subsequent power grid stability assessment.

[0034] The specific implementation of step S50 is as follows: The double-layer logic diagram constructed based on step S30 and , the grayscale-gradient co-occurrence matrix method is used to calculate the stability index of each node. The specific calculation process is as follows: First, the complex power value of each node Perform grayscale and gradient calculation to obtain grayscale value and the gradient value ,in is the maximum power in the network, is the maximum power gradient. Then construct the grayscale-gradient co-occurrence matrix ,in To have gray value and the gradient value Next, we calculate the energy index and grayscale entropy index These two indicators can effectively describe the stability characteristics of the node and provide a basis for subsequent grid status evaluation.

[0035] The specific implementation of step S60 is as follows: The energy index calculated in step S50 and grayscale entropy index Normalization is performed to obtain the node stability coefficient ,in Take 1 or 2, corresponding to and ; and They are respectively the active and reactive power of the total load of the node. This normalization process can transform indicators of different dimensions into comparable stability coefficients, which can better reflect the actual operating status of the node.

[0036] The specific implementation of step S70 is as follows: The node stability coefficient calculated in step S60 and , judge the operating status of the power grid and formulate corresponding optimization strategies. The details are as follows: 1) If , it is determined that the power grid is in normal operation, and the optimization strategy is to increase the consumption of new energy and reduce operating costs.

[0037] 2) If , it is determined that the power grid is in a regulated operation state, and further judgment is needed: if , indicating that the consumption of new energy is insufficient, and the optimization strategy is to increase the output of the energy storage system; if , indicating that the energy storage system output is too much, and the optimization strategy is to reduce the output of the energy storage system.

[0038] The thresholds 0.3 and 2.4 here are empirical values ​​obtained through analysis of a large amount of measured data and can be used as a reference. The entire optimization strategy can be used with the function To describe, 1 means increasing the consumption of new energy, 2 means increasing the output of the energy storage system, and -2 means reducing the output of the energy storage system.

[0039] The specific implementation of step S80 is as follows: Based on the judgment results and optimization strategies in step S70, a power grid architecture optimization matrix is ​​constructed. .in is the number of nodes, Represents a slave node To Node The optimization suggestions are as follows: ; Optimization Matrix The calculation process is ,in is the decision function, according to the node and The stability state of the To determine the specific optimization recommendations. This decision function can be implemented based on fuzzy logic or expert systems. By constructing this optimization matrix ,It can intuitively represent the optimization direction of the grid architecture, provide intuitive decision support for grid operators, and help to formulate targeted grid optimization strategies.

[0040] Specifically, the principle of the present invention is to use the online equipment monitoring data of the power grid to analyze the operation status of the power grid from multiple dimensions, find out the key links that need to be optimized, and put forward targeted optimization suggestions. Specifically, the method includes the following key steps: 1. Establish a grid topology model. First, obtain the grid data in the substation area, including bus voltage, line impedance, load power, and generator output, and use graph theory to describe the physical topology of the grid. This detailed topology model lays the foundation for subsequent power flow analysis and grid status assessment.

[0041] 2. Analyze the power flow characteristics. Based on the power grid topology model, calculate the actual power flow between nodes, set a power threshold based on historical statistical data, and divide the power grid topology into two sub-graphs: low-power area and high-power area. The establishment of this two-layer logic graph can better describe the characteristics of the dynamic operation of the power grid and provide support for subsequent power grid status assessment.

[0042] 3. Evaluate node stability. For the above two-layer logic diagram, the grayscale-gradient co-occurrence matrix method is used to calculate the stability index of each node, including energy and grayscale entropy. These indicators can fully and accurately reflect the actual operating status of the node and provide a basis for power grid optimization decision-making.

[0043] 4. Formulate optimization strategies. Finally, according to the node stability analysis results, determine whether the power grid is in normal operation or regulation operation, and propose corresponding optimization strategies, such as increasing the consumption of new energy and adjusting the output of energy storage systems. At the same time, the method of the present invention also constructs a power grid architecture optimization matrix, which intuitively describes the optimization suggestions from one node to another, and provides decision support for power grid operators.

[0044] Through the above steps, the method of the present invention can fully grasp the operating characteristics of the power grid, accurately diagnose the state of the power grid, and provide targeted suggestions for the optimization of the power grid architecture. Compared with the existing technology, this method has significant advantages in the following aspects: first, the use of a double-layer logic diagram can better describe the dynamic characteristics of the power grid and improve the accuracy of the power grid state analysis; second, the use of gray-gradient co-occurrence matrix to analyze node stability can more comprehensively evaluate the operating state of the power grid and provide a reliable basis for optimization decisions; third, the constructed power grid architecture optimization matrix provides intuitive and operational decision support for operators, which is conducive to the formulation of targeted optimization strategies.

[0045] In order to better understand and implement the present invention, the following provides an embodiment 1 of a specific application scenario of the present invention: the power supply range of a distribution network system includes industrial parks, commercial centers and residential areas, among which industrial loads and new energy power generation account for a large proportion. In order to improve the operating efficiency and economy of the distribution network, the power company decided to use the grid architecture value optimization method proposed in the present invention to optimize and transform it.

[0046] First, the power company collected relevant monitoring data of the distribution network system, including bus voltage, line impedance, load power and renewable energy power at each node. By sorting and analyzing these data, a topological model of the distribution network was established. In this topology, there are 45 nodes , representing 11 main substations, 18 distribution transformers and 16 main users. There are 110 lines between these nodes. Interconnection, line impedance The average value of Table 1 shows the voltage and load conditions of some nodes in the distribution network.

[0047] Table 1 Voltage and load conditions of a distribution network node

[0048] After establishing the topological model, the power company then analyzed the power flow characteristics of the distribution network. Specifically, they first calculated the complex power flow between each node. ,The results show that the power flow of most lines is below 10MW, but there are also individual lines with power flow exceeding 30MW. In order to further subdivide the grid topology, they set the power flow threshold based on historical data statistical analysis. The distribution network is divided into two sub-graphs: and ,in Indicates the area where the power flow is less than 20MW, Indicates the area where the power flow is higher than 20MW. The result of this division is shown in Table 2.

[0049] Table 2 Power flow division of a distribution network

[0050] Next, the power company established a two-layer logic diagram and , the active and reactive power balance constraint equations of each node are calculated. Taking node 15 as an example, its active power balance equation is , the reactive power balance equation is .in, and are the active and reactive loads of node 15, and is the active and reactive power loss flowing through the connected lines. By performing such power balance calculations on all nodes, the power company has mastered the actual power flow of each node in the distribution network.

[0051] After analyzing the power flow characteristics of the distribution network, the power company then focused on the operational stability of each node. They used the grayscale-gradient co-occurrence matrix-based method proposed in this invention to evaluate the stability of each node. Taking node 15 as an example, the calculation process of its grayscale value and gradient value is as follows: First, according to the complex power of node 15 , calculate its gray value and gradient value: ; ; Then construct the grayscale-gradient co-occurrence matrix of node 15 , and calculate the energy index and grayscale entropy index : ; ; ; Finally, substitute the above calculation results into the formula to obtain the stability coefficient of node 15: ; ; By performing similar calculations on all nodes, the power company has mastered the stability indicators of each node in the distribution network. The results show that the stability of most nodes is The value is above 0.3, indicating that the power grid is in normal operation as a whole; however, there are also individual nodes Less than 0.3, If it exceeds 2.4, it indicates that these nodes have insufficient absorption of new energy.

[0052] Based on the above analysis results, the power company has formulated the following optimization strategies: 1) For The optimization focus is to increase the consumption of new energy and reduce operating costs. Specific measures include: increasing the configuration of energy storage systems nearby and optimizing the scheduling of new energy power generation.

[0053] 2) For and At the node, the consumption of new energy is insufficient, and the optimization strategy is to increase the output of the energy storage system. This can be achieved by increasing the energy storage power and adjusting the charging and discharging strategy.

[0054] 3) For and At the node, the energy storage output is too much, and the optimization strategy is to reduce the output of the energy storage system. Measures such as reducing the energy storage power and optimizing the charging and discharging sequence can be taken.

[0055] Finally, the power company constructed a grid architecture optimization matrix based on the above optimization strategy. , as shown in Table 3.

[0056] Table 3 Numerical table of grid architecture optimization matrix

[0057] In this matrix: 0: No optimization required 1: Slightly increase new energy consumption 2: Significantly increase new energy consumption -1: Slightly reduce energy storage system output -2: Significantly reduce energy storage system output.

[0058] The matrix intuitively describes the optimization suggestions between nodes, providing intuitive decision support for power grid operators. Taking node 15 as an example, the optimization suggestions between it and nodes 7 and 27 are: , indicating that the output of the energy storage system at node 7 needs to be increased; , indicating that the energy storage system output of node 27 needs to be reduced.

[0059] Give a spider web graph for multiple nodes, such as Figure 2 As shown in the figure, it provides an intuitive view for grid operators to help them quickly identify which nodes need to be focused on and what types of optimization measures should be taken, as follows: 1. New energy consumption (blue): The value range is 0-5, the higher the value, the more new energy consumption needs to be increased; nodes 7 and 38 show the highest values, which means that these two nodes need to increase new energy consumption the most; the value of node 27 is 0, indicating that there is no need to increase new energy consumption; 2. Energy storage system adjustment (green): The value range is 0-5. The higher the value, the more the energy storage system needs to be adjusted (which may be to reduce the output). Node 27 shows the highest value, which means that this node needs to adjust the energy storage system the most, which may be to reduce the output. The value of node 7 is 0, which means that the energy storage system does not need to be adjusted. 3. Node comparison: Node 1: Moderate demand for new energy consumption, slight demand for energy storage adjustment; Node 7: High demand for new energy consumption, no demand for energy storage adjustment; Node 15: Moderate demand for new energy consumption and energy storage adjustment; Node 27: No demand for new energy consumption, but the highest demand for energy storage adjustment; Node 38: High demand for new energy consumption, slight demand for energy storage adjustment. Through this optimization matrix, power companies can formulate grid architecture optimization measures more targetedly to improve the safe, stable operation and economic operation efficiency of distribution networks.

[0060] The figure reflects the optimization suggestions between the nodes of the distribution network. As can be seen from the figure, the optimization suggestions between most nodes are to increase the consumption of new energy, while a few nodes need to adjust the output of the energy storage system. Based on this optimization matrix, the power company can take targeted measures to improve the overall operating efficiency of the distribution network.

[0061] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A value optimization method for power grid architecture based on multi-dimensional technical upgrade of power grid online equipment, characterized in that: The following steps are involved: S10, obtaining the power grid data in the substation area and establishing a power grid topology map of the substation area; S20, calculating the power flow between nodes in the power grid topology diagram based on the collected data according to a continuous period of preset duration, and setting a power threshold for distinguishing high and low power flows; S30, dividing the power grid topology diagram into a two-layer logic diagram according to the set power threshold; the first layer is used to represent the flow of electric energy below the preset power threshold, and the second layer is used to represent the flow of electric energy exceeding the power threshold; S40, calculating the power flow of each node in the double-layer logic diagram, including active power and reactive power, and establishing a power balance constraint equation; S50, based on the double-layer logic graph, calculate the stability index of each node; S60, normalizing the calculated stability index to obtain a node stability coefficient; S70, judging the operation status of the power grid according to the node stability coefficient and formulating corresponding optimization strategies; S80. Based on the judgment results and optimization strategies, a power grid architecture optimization matrix is ​​constructed as the value optimization target of the power grid architecture.

2. A value optimization method for a power grid architecture based on multi-dimensional technical upgrades of online power grid equipment according to claim 1, characterized in that: The power grid data includes bus voltage, line impedance, load power, and generator output.

3. A value optimization method for a power grid architecture based on multi-dimensional technical upgrades of online power grid equipment according to claim 2, characterized in that: In the power grid topology diagram, nodes represent substations, distribution transformers, and users, and edges represent lines and transformers.

4. A value optimization method for a power grid architecture based on multi-dimensional technical upgrades of online power grid equipment according to claim 3, characterized in that: The power grid topology diagram uses a graph theory method to represent the power grid topology, and the bus voltage is in complex form.

5. A value optimization method for a power grid architecture based on multi-dimensional technical upgrades of online power grid equipment according to claim 4, characterized in that: The power flow calculations involve complex power, admittance matrix, and voltage conjugate.

6. A method for optimizing the value of a power grid architecture based on multi-dimensional technical upgrades of online power grid equipment according to claim 5, characterized in that: The power threshold is set based on statistical analysis of historical data, taking into account the power mean and standard deviation.

7. A method for optimizing the value of a power grid architecture based on multi-dimensional technical upgrades of online power grid equipment according to claim 6, characterized in that: The two-layer logic graph retains the original nodes, but divides the edges into different levels according to the power size.

8. A method for optimizing the value of a power grid architecture based on multi-dimensional technical upgrades of online power grid equipment according to claim 7, characterized in that: The stability index of each node is calculated by using the grayscale-gradient co-occurrence matrix to describe the node characteristics and calculating the two indicators of energy and grayscale entropy.

9. A method for optimizing the value of a power grid architecture based on multi-dimensional technical upgrades of online power grid equipment according to claim 8, characterized in that: The normalization process considers the load power and transforms the energy and gray entropy indicators into comparable stability coefficients.

10. A method for optimizing the value of a power grid architecture based on multi-dimensional technical upgrades of online power grid equipment according to claim 9, characterized in that: The matrix elements of the grid architecture optimization matrix represent optimization suggestions from one node to another, including at least maintaining the status quo, increasing or decreasing the consumption of new energy, increasing or decreasing the output of the energy storage system, or strengthening the line capacity.

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