An intelligent power grid planning coordination evaluation method

Abstract

An intelligent power grid planning coordination evaluation method provided by the present invention relates to the technical field of intelligent power grids and includes: collecting power grid structure data, power operation data, and external environment data related to the power grid; forming a power grid structure database based on the power grid structure data, power operation data, and external environment data; extracting the characteristics of any data in the power grid structure database; constructing a coordination evaluation model based on the topological characteristic information, operation characteristic information, and environmental characteristic information of the power grid; inputting the data collected through data acquisition into the coordination evaluation model to obtain an evaluation result; and generating an evaluation decision suggestion according to the evaluation result. The present invention comprehensively collects power grid structure, operation, and external environment data, constructs a model by integrating multi-agent, complex network, and fuzzy logic theories, accurately extracts topological, operation, and environmental characteristics, accurately identifies key nodes, optimizes load distribution, provides a scientific basis for planning and operation, and effectively improves the reliability and overall performance of the power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of smart grids, and particularly to a method for evaluating the coordination of smart grid planning. Background Art

[0002] With the advancement of smart grid construction, the coordination of its planning is crucial for ensuring the stable and efficient operation of the power grid.

[0003] However, traditional evaluation methods often have limitations. Some evaluation methods only focus on a single factor. For example, they only pay attention to the grid structure and ignore the impact of operation data and external environment, or only analyze the power operation status without considering the actual physical structure and external conditions of the grid. This one-sidedness lacks full consideration of the complex relationship among the grid structure, operation state, and external environment, and it is difficult to evaluate the coordination of smart grid planning. Therefore, there is an urgent need for an evaluation method that comprehensively considers multiple factors to improve the quality and effect of smart grid planning.

[0004] Therefore, it is necessary to provide a method for evaluating the coordination of smart grid planning to solve the above technical problems. Summary of the Invention

[0005] The present invention provides a method for evaluating the coordination of smart grid planning, which solves the problems raised in the background art.

[0006] To solve the above technical problems, a method for evaluating the coordination of smart grid planning provided by the present invention includes the following steps:

[0007] S1, collect grid structure data, power operation data, and external environment data related to the power grid; form a grid structure database based on the grid structure data, power operation data, and external environment data;

[0008] S2, extract the features of any data in the grid structure database:

[0009] S21, extract topological feature information based on the grid structure data;

[0010] S22, extract operation feature information based on the power operation data;

[0011] S23, extract environmental feature information based on the external environment data;

[0012] S3, construct a coordination evaluation model based on the topological feature information, operation feature information, and environmental feature information of the power grid;

[0013] S4, input the data collected in step S1 into the coordination evaluation model to obtain an evaluation result; generate an evaluation decision recommendation according to the evaluation result.

[0014] Preferably, based on step S1, power grid structure data, power operation data, and external environment data related to the power grid are collected, specifically:

[0015] The power grid structure data specifically refers to the connection relationships of each node in the power grid, including the topological structure information of substations, transmission lines, and distribution lines; the power operation data specifically refers to the voltage, current, and power of each node in the power grid; the external environment data specifically refers to meteorological data, geographical data, and social and economic data related to the operation of the power grid.

[0016] Preferably, based on step S3, the specific steps for constructing the coordination evaluation model are as follows:

[0017] S31, Initialize the evaluation model using a multi-agent system. Consider each node in the power grid as an agent, and each agent has its own matching state information and behavior rules; where the state information includes voltage, active power, and reactive power, and the behavior rules include power regulation strategies, fault response mechanisms, and cooperation rules.

[0018] S32, Design a communication protocol based on a message queue between agents to achieve information interaction, and use a distributed cooperative control algorithm, specifically a consensus algorithm: Perform interactions between agents. Among them, xi(t) represents the state variable of the agent numbered i at time t, Ni represents the neighbor set of the agent numbered i, and aij represents the connection weight between the agent numbered i and the agent numbered j.

[0019] S33, Introduce complex network theory, consider the power grid as a complex network, and construct a complex network model of the power grid. Specifically, consider each node in the power grid as a node in the complex network, and consider the transmission lines and distribution lines connecting the nodes as edges.

[0020] S34, Use the node indicators in the complex network model to calculate the structural characteristic indicators of the power grid; the structural characteristic indicators include the node degree value, clustering coefficient, node betweenness centrality, edge betweenness centrality, critical nodes, or critical lines of the nodes.

[0021] S35, Conduct robustness and vulnerability analysis on the complex network model of the power grid to obtain coordination evaluation information; the coordination evaluation information includes connectivity indicators, power transmission capacity indicators, connectivity differences and power transmission differences, and the corresponding time integrals and coordination evaluation values.

[0022] S36 incorporates fuzzy logic to process the fuzzy information in the evaluation process; a fuzzy rule base is established, and the extracted feature data is used as fuzzy inputs; for each fuzzy input and output variable, appropriate fuzzy sets and membership functions are determined, and the membership functions are used to describe the boundaries and degrees of fuzziness of each fuzzy set; the topologic features, operation features, and environmental impact feature data after fuzzy processing are input into the fuzzy rule base, and the fuzzy evaluation result of the power grid planning coordination is obtained through inference calculation by the fuzzy inference engine.

[0023] Preferably, when constructing the evaluation model by integrating fuzzy logic, the quantum fuzzy logic algorithm is adopted to process the fuzzy information by using the superposition state and entanglement characteristics of quantum bits; the quantum fuzzy logic algorithm specifically performs quantization processing on the fuzzy inputs through quantum gate operations, and then performs inference operations according to the quantum fuzzy rules to finally obtain the fuzzy evaluation result of the power grid planning coordination.

[0024] Preferably, the acquisition logic of the connection weight aij is as follows:

[0025] Identify the electrical distance and line impedance between any group of adjacent agents;

[0026] Set the monitoring time zone, and calculate the mean value and standard deviation of the line impedance in the monitoring time zone to obtain the impedance mean value and impedance fluctuation value;

[0027] Perform weighted calculation on the line impedance, impedance mean value, and impedance fluctuation value to obtain the line impedance influence value;

[0028] Perform weighted calculation on the electrical distance and line impedance influence value to obtain the connection weight aij.

[0029] Preferably, the acquisition time interval of the power operation data is dynamically adjusted according to the power grid load fluctuation situation, specifically as follows:

[0030] Set the real-time load monitoring period, and calculate the change rate of the load in each real-time load monitoring period of the power grid and mark it as the load change value;

[0031] Set the acquisition time interval frequency group, including the normal time interval frequency and the first-level time interval frequency;

[0032] If the load change value is within its preset reasonable load change range, it is determined that the load change is within the reasonable range, and the power operation data is collected using the normal time interval frequency;

[0033] If the load change value is not within its preset reasonable load change range, it is determined that it is a period of large load change, and the power operation data is collected using the first-level time interval frequency.

[0034] Preferably, based on step S33, the structural characteristic indexes of the power grid are calculated using the node indexes in the complex network model, specifically as follows:

[0035] Identify the number of edges connected to the node and mark it as the node degree value;

[0036] Count the frequencies of nodes with different degree values to obtain the node degree distribution;

[0037] Calculate the clustering coefficient of each node, and the formula is expressed as: ; where j represents the index of the node, Cj represents the clustering coefficient of node j, ej represents the actual number of edges existing between the neighbor nodes of node i, and kj represents the node degree value of node j;

[0038] Calculate the node betweenness centrality and edge betweenness centrality of any node and line respectively; among them, the calculation formula of the node betweenness centrality is: ; the calculation formula of the edge betweenness centrality is: ;

[0039] Set the betweenness centrality threshold, and compare the node betweenness centrality and edge betweenness centrality of the node and line with their corresponding betweenness centrality thresholds. If the node betweenness centrality and edge betweenness centrality are greater than their corresponding betweenness centrality thresholds, then mark the node or line as a key node or key line.

[0040] Preferably, based on step S34, perform robustness and vulnerability analysis on the complex network model of the power grid, specifically as follows:

[0041] Identify the current simulated fault scenario and disturbance situation;

[0042] Calculate the number of nodes included in the largest connected subgraph in the power grid after the fault or disturbance in the complex network model And the total number of nodes The ratio is marked as the connectivity index H, and the formula is expressed as: ;

[0043] Calculate the actual transmission power of the power grid after the fault or disturbance in the complex network model And the transmission power during normal operation The ratio is marked as the power transmission capacity index G, and the formula is expressed as: ;

[0044] Set the expected values corresponding to the connectivity index and the power transmission capacity index, and calculate the connectivity difference LH and the power transmission difference LG by subtracting the power transmission capacity index and the power transmission capacity index from their corresponding expected values respectively;

[0045] Introduce the concept of time integration to evaluate the changes in the connectivity difference LH and the power transfer difference LG, specifically as follows:

[0046] Let be the function of the connectivity difference changing with time, be the function of the power transfer capacity difference changing with time, t0 is the start time of the fault or disturbance, and t1 is the end time of observation; calculate the time integral of the connectivity difference, and the formula is expressed as: ; calculate the time integral of the power transfer difference, and the formula is expressed as: ;

[0047] Perform weighted calculation on the time integrals of the connectivity difference and the power transfer difference to obtain the coordination evaluation value E, and the formula is expressed as ; where, w1 and w2 respectively represent the weights corresponding to the time integrals of the connectivity difference and the power transfer difference; mark the connectivity index, the power transfer capacity index, the connectivity difference and the power transfer difference, and the corresponding time integrals and the coordination evaluation value as coordination evaluation information;

[0048] Judge the coordination of the power grid in the simulated scenario or under the disturbance according to the coordination evaluation information, specifically as follows:

[0049] Set the coordination evaluation threshold. If the coordination evaluation value is less than this coordination evaluation threshold, it means that the power grid has good coordination in this simulated scenario or under the disturbance; if the coordination evaluation value is greater than or equal to this coordination evaluation threshold, it means that the power grid has weak coordination, then identify the redundant lines corresponding to the key nodes and key lines in the power grid and enable them in turn;

[0050] Evaluate the coordination of the power grid after enabling the redundant lines in turn to obtain the comprehensive coordination evaluation value; when the comprehensive coordination evaluation value is greater than its preset comprehensive coordination evaluation threshold, it means that the enabled redundant lines are sufficient to supplement the weak coordination of the power grid.

[0051] Preferably, evaluate the coordination of the power grid after enabling the redundant lines in turn, specifically as follows:

[0052] Calculate the difference between the coordination evaluation value of the power grid after enabling the redundant lines and the coordination evaluation value before enabling to obtain the evaluation change value; calculate the difference between the coordination evaluation value of the power grid after enabling the redundant lines and the coordination evaluation threshold to obtain the coordination evaluation difference, and calculate the difference between the coordination evaluation value of the power grid after enabling the redundant lines and the coordination evaluation threshold to obtain the coordination threshold difference; perform weighted calculation on the coordination evaluation difference and the coordination threshold difference to obtain the coordination evaluation value;

[0053] Traverse the cooperation evaluation values after enabling the redundant lines of different key nodes or key lines in sequence, calculate the mean value and standard deviation of the cooperation evaluation values after the redundant lines of different key nodes or key lines to obtain the cooperation evaluation mean value and the cooperation evaluation fluctuation value; perform weighted calculation on the cooperation evaluation value, the cooperation evaluation mean value, and the cooperation evaluation fluctuation value to obtain the comprehensive cooperation evaluation value.

[0054] Compared with the related technologies, an intelligent power grid planning coordination evaluation method provided by the present invention has the following beneficial effects:

[0055] 1. The present invention comprehensively collects power grid structure, operation, and external environment data, constructs a model by integrating multi-agent, complex network, and fuzzy logic theories, accurately extracts topological, operation, and environmental features, accurately identifies key nodes, optimizes load distribution, provides a scientific basis for planning and operation, and effectively improves the reliability and overall performance of the power grid.

[0056] 2. By adjusting the operation data collection interval according to load fluctuations, updating power grid information in a timely manner, and regularly re-evaluating coordination, the present invention can effectively respond to changes in power grid structure, operation status, and external environment, ensure that the power grid is always in the optimal coordination state, and enhance the flexibility and adaptability of power grid operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a method flow chart of an intelligent power grid planning coordination evaluation method provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0058] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0059] The terms used in this disclosure are only for the purpose of describing specific embodiments and are not intended to limit this disclosure. The singular forms of "class", "group", and "the" used in this disclosure and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0060] It should be understood that although the terms first, second, third, etc. may be used in this disclosure to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of this disclosure, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "when" or "while" or "in response to a determination".

[0061] Please refer to Figure 1 . An intelligent power grid planning coordination evaluation method includes the following steps:

[0062] S1. Collect power grid structure data, power operation data, and external environment data related to the power grid; form a power grid structure database based on the power grid structure data, power operation data, and external environment data;

[0063] S2. Extract the characteristics of any data in the power grid structure database:

[0064] S21. Extract topological feature information based on the power grid structure data;

[0065] S22. Extract operation feature information based on the power operation data;

[0066] S23. Extract environmental feature information based on the external environment data;

[0067] It should be noted that the feature extraction in steps S21 - S23 is a mature existing technology, so it is not elaborated in this application;

[0068] S3. Build a coordination evaluation model based on the topological feature information, operation feature information, and environmental feature information of the power grid;

[0069] S4. Input the data collected in the data acquisition into the coordination evaluation model to obtain an evaluation result; generate an evaluation decision recommendation according to the evaluation result.

[0070] In this application, based on step S1, the power grid structure data, power operation data, and external environment data related to the power grid are collected, specifically:

[0071] The power grid structure data is specifically the connection relationship of each node in the power grid, including the topological structure information of substations, transmission lines, and distribution lines; the power operation data is specifically the voltage, current, and power of each node in the power grid; the external environment data is specifically meteorological data, geographical data, and social and economic data related to the power grid operation.

[0072] In this application, based on step S3, the specific steps for building the coordination evaluation model are:

[0073] S31. Initialize the evaluation model using a multi-agent system. Consider each node in the power grid as an agent, and each agent has its own matching state information and behavior rules. The state information includes voltage, active power, and reactive power, and the behavior rules include power regulation strategies, fault response mechanisms, and cooperation rules.

[0074] The specific process of setting the behavior rules is as follows:

[0075] Power regulation strategy: Obtain the load change value of the power grid , compare the load change value with its change threshold. If the load change value is greater than its change threshold, generate a power regulation strategy. The power regulation strategy is used to control the output power of the agent according to the dynamic regulation coefficient . The specific calculation formula of the dynamic regulation coefficient is: ; where represents the power regulation factor (determined according to the power grid characteristics and equipment response capabilities), represents the rated power of the agent numbered i;

[0076] Fault response mechanism: Collect the state information of the agent, calculate the difference value between any parameter in the state information and its preset reference value to obtain the state difference value;

[0077] Set the state monitoring time zone, calculate the average value and standard deviation of the state difference values of the parameters in the state information of the agent to obtain the state difference average value and state difference discrete value;

[0078] Perform weighted calculation on the state difference value, state difference average value, and state difference discrete value of the agent to obtain the state parameter influence value; perform weighted calculation on the state parameter influence values of all parameters in the state information of the agent to obtain the fault evaluation value; set the fault evaluation threshold. If the fault evaluation value is greater than its fault evaluation threshold, generate a fault response mechanism. The fault response mechanism is used to trigger the sending of a fault signal to the adjacent agents of the agent and cut off the line between the adjacent agents;

[0079] Cooperation rule: Obtain the load change value of the agent; set the overload change threshold, compare the load change value of the agent with its overload change threshold. If the load change value is greater than or equal to its overload change threshold, generate the cooperation rule of the agent. The cooperation rule is used to trigger the cooperation evaluation and analysis of the adjacent agents, specifically:

[0080] Obtain the electrical distance, line impedance, and load change value between the adjacent agents corresponding to the cooperation rule of the agent; perform weighted calculation on the electrical distance, line impedance, and load change value to obtain the cooperation value;

[0081] Arrange the adjacent agents corresponding to the cooperation rules of the generated agents in the order of the cooperation values to obtain an adjacent agent cooperation list;

[0082] Select a set number of adjacent agents from the adjacent agent cooperation list in the order of size as cooperation load agents;

[0083] Analyze the load transfer power of the cooperation load agents, specifically:

[0084] Obtain the load change value corresponding to the cooperation rule of the generated agent, and calculate the difference between the load change value and the preset normal load change value of the agent to obtain the load excess difference;

[0085] Sum the cooperation values of the set number of cooperation load agents to obtain the total cooperation value; Mark the ratio between the cooperation value of each cooperation load agent and the total cooperation value as the cooperation distribution coefficient; Allocate the excess of the agent to each cooperation load agent according to the cooperation distribution coefficient;

[0086] S32. Design a communication protocol based on message queues between agents to achieve information interaction, and adopt a distributed cooperative control algorithm, specifically the consensus algorithm: Conduct interactions between agents. Among them, xi(t) represents the state variable of the agent numbered i at time t, Ni represents the neighbor set of the agent numbered i, and aij represents the connection weight between the agents numbered i and j;

[0087] S33. Introduce complex network theory, regard the power grid as a complex network, and construct a complex network model of the power grid. Specifically, regard each node in the power grid as a node in the complex network, and regard the transmission lines and distribution lines connecting the nodes as edges;

[0088] S34. Use the node indicators in the complex network model to calculate the structural characteristic indicators of the power grid; The structural characteristic indicators include the node degree value, clustering coefficient, node betweenness centrality, edge betweenness centrality, key nodes or key lines of the node;

[0089] S35. Conduct robustness and vulnerability analysis on the complex network model of the power grid to obtain coordination evaluation information; The coordination evaluation information includes the connectivity index, power transmission capacity index, connectivity difference and power transmission difference and their corresponding time integrals and coordination evaluation values;

[0090] S36 incorporates fuzzy logic to process the fuzzy information in the evaluation process; a fuzzy rule base is established, and the extracted feature data is used as fuzzy inputs; for each fuzzy input and output variable, appropriate fuzzy sets and membership functions are determined. The division of fuzzy sets should be reasonably determined according to the actual value range and characteristics of the variables, and the membership functions are used to describe the boundaries and degrees of fuzziness of each fuzzy set; the topologic features, operation features, and environmental impact feature data after fuzzy processing are input into the fuzzy rule base, and a fuzzy evaluation result of the power grid planning coordination is obtained through inference calculation by a fuzzy inference engine; the Mamdani inference method or the T-S inference method is adopted for the fuzzy inference engine.

[0091] In this application, when constructing an evaluation model by integrating fuzzy logic, a quantum fuzzy logic algorithm is adopted to process fuzzy information by using the superposition state and entanglement characteristics of quantum bits; specifically, the quantum fuzzy logic algorithm performs quantization processing on fuzzy inputs through quantum gate operations, and then performs inference operations according to quantum fuzzy rules to finally obtain a fuzzy evaluation result of the power grid planning coordination.

[0092] In this application, the acquisition logic of the connection weight aij is as follows:

[0093] Identify the electrical distance and line impedance between any group of adjacent agents;

[0094] Set a monitoring time zone, and calculate the mean value and standard deviation of the line impedance in the monitoring time zone to obtain the impedance mean value and impedance fluctuation value;

[0095] Perform weighted calculation on the line impedance, impedance mean value, and impedance fluctuation value to obtain the line impedance influence value;

[0096] Perform weighted calculation on the electrical distance and line impedance influence value to obtain the connection weight aij.

[0097] In this application, the acquisition time interval of power operation data is dynamically adjusted according to the power grid load fluctuation situation, specifically as follows:

[0098] Set a real-time load monitoring period, and calculate the change rate of the load in each real-time load monitoring period of the power grid and mark it as the load change value , the formula is expressed as: ; where represents the load value at the current moment t, represents the load value at the previous real-time load monitoring period moment;

[0099] Set an acquisition time interval frequency group, including a normal time interval frequency and a first-level time interval frequency. It should be noted that the normal time interval frequency is less than the first-level time interval frequency;

[0100] If the load change value is within its preset reasonable load change range, it is determined that the load change is within the reasonable range, and the power operation data is collected at the normal time interval frequency;

[0101] If the load change value is not within its preset reasonable load change range, it is determined that the load change is in a large period, and the power operation data is collected at the first-level time interval frequency.

[0102] In this application, based on step S33, the node indicators in the complex network model are used to calculate the structural characteristic indicators of the power grid, specifically:

[0103] The number of edges connected to the node is identified and marked as the node degree value;

[0104] The frequency of nodes with different degree values is counted to obtain the node degree distribution;

[0105] The clustering coefficient of each node is calculated, and the formula is expressed as: ; where j represents the index of the node, Cj represents the clustering coefficient of node j, ej represents the number of edges actually existing between the neighbor nodes of node i, and kj represents the node degree value of node j;

[0106] The node betweenness centrality and edge betweenness centrality of any node and line are calculated respectively; among them, the calculation formula of the node betweenness centrality is: ; the calculation formula of the edge betweenness centrality is: ;

[0107] Set the betweenness centrality threshold, and compare the node betweenness centrality and edge betweenness centrality of the node and line with their corresponding betweenness centrality thresholds. If the node betweenness centrality and edge betweenness centrality are greater than their corresponding betweenness centrality thresholds, the node or line is marked as a critical node or critical line.

[0108] In this application, based on step S34, the robustness and vulnerability of the complex network model of the power grid are analyzed, specifically:

[0109] Identify the current simulated fault scenarios and disturbance situations, where the fault scenarios are such as line short circuits and node failures, and the disturbance situations are such as load mutations and meteorological disaster impacts;

[0110] Calculate the number of nodes included in the largest connected subgraph in the power grid after the fault or disturbance in the complex network model and the total number of nodes The ratio is marked as the connectivity index H, and the formula is expressed as: ;

[0111] Calculate the actual transmission power of the power grid after the fault or disturbance in the complex network model and the transmission power during normal operation The ratio is marked as the power transfer capacity index G, and the formula is expressed as: ;

[0112] Set the expected values corresponding to the connectivity index and the power transfer capacity index, and calculate the connectivity difference LH and the power transfer difference LG by taking the differences between the power transfer capacity index and its corresponding expected value respectively;

[0113] Introduce the concept of time integral to evaluate the changes of the connectivity difference LH and the power transfer difference LG, specifically:

[0114] Let be the function of the connectivity difference changing with time, be the function of the power transfer capacity difference changing with time, t0 is the start time of the fault or disturbance, and t1 is the end time of observation; calculate the time integral of the connectivity difference, and the formula is expressed as: ; calculate the time integral of the power transfer difference, and the formula is expressed as: ;

[0115] Perform weighted calculation on the time integrals of the connectivity difference and the power transfer difference to obtain the coordination evaluation value E, and the formula is expressed as ; where, w1 and w2 respectively represent the weights corresponding to the time integrals of the connectivity difference and the power transfer difference; mark the connectivity index, the power transfer capacity index, the connectivity difference, the power transfer difference, and the corresponding time integrals and the coordination evaluation value as the coordination evaluation information;

[0116] Judge the coordination of the power grid in the simulated scenario or under the disturbance according to the coordination evaluation information, specifically:

[0117] Set the coordination evaluation threshold. If the coordination evaluation value is less than this coordination evaluation threshold, it means that the power grid has good coordination in this simulated scenario or under the disturbance; if the coordination evaluation value is greater than or equal to this coordination evaluation threshold, it means that the power grid has weak coordination, then identify the redundant lines corresponding to the key nodes and key lines in the power grid and enable them in turn;

[0118] Evaluate the coordination of the power grid after enabling the redundant lines in turn to obtain the comprehensive coordination evaluation value; when the comprehensive coordination evaluation value is greater than its preset comprehensive coordination evaluation threshold, it means that the enabled redundant lines are sufficient to supplement the situation of weak coordination of the power grid.

[0119] In this application, to evaluate the coordination of the power grid after enabling the redundant lines in turn, specifically:

[0120] Calculate the evaluation change value by calculating the difference between the grid coordination evaluation value after enabling redundant lines and the coordination evaluation value before enabling; calculate the coordination evaluation difference by calculating the difference between the grid coordination evaluation value after enabling redundant lines and the coordination evaluation threshold, and calculate the coordination threshold difference by calculating the difference between the grid coordination evaluation value after enabling redundant lines and the coordination evaluation threshold; calculate the weighted value of the coordination evaluation difference and the coordination threshold difference to obtain the coordination evaluation value;

[0121] Traverse the coordination evaluation values after enabling the redundant lines of different critical nodes or critical lines in sequence, calculate the mean value and standard deviation of the coordination evaluation values after the redundant lines of different critical nodes or critical lines to obtain the coordination evaluation mean value and the coordination evaluation fluctuation value; calculate the weighted value of the coordination evaluation value, the coordination evaluation mean value and the coordination evaluation fluctuation value to obtain the comprehensive coordination evaluation value.

[0122] After considering the specification and the invention disclosed herein in practice, those skilled in the art will readily conceive of other embodiments of the present invention. This application is intended to cover any variations, uses, or adaptations of the present invention, which follow the general principles of the present invention and include known common knowledge or conventional technical means in the technical field not disclosed in this disclosure. The specification and examples are only regarded as exemplary, and the true scope and spirit of the present invention are pointed out by the following claims.

[0123] It should be understood that the present invention is not limited to the exact structures already described and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present invention is only limited by the appended claims.

Claims

1. A smart grid planning coordination evaluation method, characterized in that: The following steps are involved: S1, collecting grid structure data, power operation data and external environment data related to the grid; forming a grid structure database based on the grid structure data, power operation data and external environment data; S2, extract the features of any data in the power grid structure database: S21, extracting topological feature information based on power grid structure data; S22, extracting operation characteristic information based on the power operation data; S23, extracting environmental feature information based on external environmental data; S3, building a coordination evaluation model based on the topological characteristic information, operation characteristic information, and environmental characteristic information of the power grid; The coordination evaluation model construction process includes: using a multi-agent system to initialize the evaluation model, treating each node in the power grid as an agent, and each agent has state information and behavior rules that match itself; designing a communication protocol based on a message queue between agents to realize information interaction, and using a distributed collaborative control algorithm for interaction between agents; introducing complex network theory, treating the power grid as a complex network, and constructing a complex network model of the power grid; using node indicators in the complex network model to calculate the structural characteristic indicators of the power grid; performing robustness and vulnerability analysis on the complex network model of the power grid to obtain coordination evaluation information; integrating fuzzy logic, establishing a fuzzy rule base, using the extracted feature data as fuzzy input, and performing reasoning calculations through a fuzzy reasoning engine to obtain a fuzzy evaluation result of the coordination of power grid planning; S4, inputting the data obtained from data collection into the coordination evaluation model to obtain evaluation results; and generating evaluation decision recommendations based on the evaluation results.

2. A smart grid planning coordination evaluation method according to claim 1, characterized in that: Based on step S1, the grid structure data, power operation data and external environment data related to the grid are collected, specifically: The grid structure data specifically refers to the connection relationship between each node in the grid, including the topological structure information of substations, transmission lines, and distribution lines; the power operation data specifically refers to the voltage, current, and power of each node in the grid; the external environment data specifically refers to meteorological data, geographical data, and socio-economic data related to grid operation.

3. A smart grid planning coordination evaluation method according to claim 1, characterized in that: Based on step S3, the specific steps of constructing the coordination evaluation model are: S31, uses a multi-agent system to initialize the evaluation model, treating each node in the power grid as an agent, each agent has its own matching state information and behavior rules; The status information includes voltage, active power, and reactive power, and the behavior rules include power regulation strategy, fault response mechanism, and collaboration rules; S32, design a message queue-based communication protocol between intelligent agents to achieve information interaction, and adopt a distributed collaborative control algorithm, specifically a consensus algorithm: Interaction between agents, where xi(t) represents the state variable of agent number i at time t, Ni represents the neighbor set of agent number i, and aij represents the connection weight between agents number i and j; S33, introduces complex network theory, regards the power grid as a complex network, and constructs a complex network model of the power grid. Specifically, each node in the power grid is regarded as a node in the complex network, and the transmission lines and distribution lines connecting the nodes are regarded as edges; S34, using the node index in the complex network model to calculate the structural characteristic index of the power grid; the structural characteristic index includes the node degree value, clustering coefficient, node betweenness centrality, edge betweenness centrality, key nodes or key lines of the node; S35, performing robustness and vulnerability analysis on a complex network model of the power grid to obtain coordination evaluation information; the coordination evaluation information includes a connectivity index, a power transmission capacity index, a connectivity difference and a power transmission difference and corresponding time integrals and coordination evaluation values; S36, integrates fuzzy logic and uses fuzzy logic to process fuzzy information in the evaluation process; establishes a fuzzy rule base and uses the extracted feature data as fuzzy input; for each fuzzy input and output variable, determines the appropriate fuzzy set and membership function, and the membership function is used to describe the boundary and degree of fuzziness of each fuzzy set; inputs the fuzzified topological characteristics, operation characteristics and environmental impact characteristics data into the fuzzy rule base, and performs reasoning calculations through the fuzzy reasoning engine to obtain the fuzzy evaluation results of the coordination of power grid planning.

4. A smart grid planning coordination evaluation method according to claim 3, characterized in that: When integrating fuzzy logic to construct an evaluation model, a quantum fuzzy logic algorithm is used to process fuzzy information using the superposition state and entanglement characteristics of quantum bits; the quantum fuzzy logic algorithm specifically quantizes the fuzzy input through quantum gate operations, and then performs reasoning operations based on quantum fuzzy rules, and finally obtains a fuzzy evaluation result of the coordination of power grid planning.

5. A smart grid planning coordination evaluation method according to claim 3, characterized in that: The acquisition logic of the connection weight aij is: Identify the electrical distance and line impedance between any set of adjacent agents; Set the monitoring time zone, calculate the mean and standard deviation of the line impedance in the monitoring time zone to obtain the impedance mean and impedance fluctuation value; The line impedance, the impedance mean value and the impedance fluctuation value are weighted and calculated to obtain the line impedance influence value; The electrical distance and line impedance influence values ​​are weighted and calculated to obtain the connection weight aij.

6. A smart grid planning coordination evaluation method according to claim 1, characterized in that: The time interval for collecting the power operation data is dynamically adjusted according to the power grid load fluctuation, specifically: Set a real-time load monitoring cycle, calculate the load change rate of the power grid in each real-time load monitoring cycle and mark it as a load change value; Set the collection time interval frequency group, including normal time interval frequency and first-level time interval frequency; If the load change value is within the preset reasonable load change range, it is determined that the load change is within a reasonable range, and the power operation data is collected using a normal time interval frequency; If the load change value is not within the preset reasonable load change range, it is determined to be a period of large load change, and the power operation data is collected using the first-level time interval frequency.

7. A smart grid planning coordination evaluation method according to claim 3, characterized in that: Based on step S33, the structural characteristic index of the power grid is calculated using the node index in the complex network model, specifically: Identify the number of edges connected to the node and mark it as the node degree value; Count the frequencies of nodes with different degree values ​​and get the node degree distribution; Calculate the clustering coefficient of each node, the formula is expressed as: ; Where j represents the index of the node, Cj represents the clustering coefficient of node j, ej represents the number of edges actually existing between the neighboring nodes of node i, and kj represents the node degree value of node j; Calculate the node betweenness centrality and edge betweenness centrality of any node and line respectively; the calculation formula of node betweenness centrality is: ; The calculation formula of edge betweenness centrality is: ; Set the betweenness centrality threshold, compare the node betweenness centrality and edge betweenness centrality of the node or line with their corresponding betweenness centrality threshold. If the node betweenness centrality and edge betweenness centrality are greater than their corresponding betweenness centrality threshold, mark the node or line as a key node or key line.

8. A smart grid planning coordination evaluation method according to claim 3, characterized in that: Based on step S34, the complex network model of the power grid is analyzed for robustness and vulnerability, specifically: Identify the fault scenarios and disturbances currently being simulated; Calculate the number of nodes contained in the largest connected subgraph in the power grid after a fault or disturbance in a complex network model Total number of nodes The ratio is marked as the connectivity index H, and the formula is: ; Calculate the actual power delivered by the grid after a fault or disturbance in complex network models Transmit power during normal operation The ratio of is marked as the power transmission capability index G, and the formula is expressed as: ; Set the expected values ​​corresponding to the connectivity index and the power transmission capacity index, and calculate the connectivity difference LH and the power transmission difference LG by taking the difference between the power transmission capacity index and the power transmission capacity index and the corresponding expected values; The concept of time integration is introduced to evaluate the changes in the connectivity difference LH and the power transfer difference LG, specifically: set up is the function of connectivity difference changing with time, is the function of the power transfer capability difference changing with time, t0 is the start time of the fault or disturbance, and t1 is the end time of the observation; The time integral of the connected difference is calculated as follows: ; The time integral of the power transfer difference is calculated as: ; The time integral of the connectivity difference and the power transfer difference is weighted to obtain the coordination evaluation value E, which is expressed as ; Wherein, w1 and w2 represent the weights corresponding to the time integrals of the connectivity difference and the power transfer difference, respectively; the connectivity index, the power transmission capacity index, the connectivity difference and the power transfer difference and the corresponding time integral and coordination evaluation value are marked as coordination evaluation information; The coordination of the power grid in the simulation scenario or disturbance is judged based on the coordination evaluation information, specifically: A coordination evaluation threshold is set. If the coordination evaluation value is less than the coordination evaluation threshold, it indicates that the power grid has good coordination in the simulation scenario or disturbance. If the coordination evaluation value is greater than or equal to the coordination evaluation threshold, it indicates that the power grid has weak coordination. In this case, the redundant lines at the corresponding positions of the key nodes and key lines in the power grid are identified and enabled in sequence. The coordination of the power grid after the redundant lines are enabled in sequence is evaluated to obtain a comprehensive coordination evaluation value; when the comprehensive coordination evaluation value is greater than its preset comprehensive coordination evaluation threshold, it means that the enabled redundant lines are sufficient to supplement the weak coordination of the power grid.

9. A smart grid planning coordination evaluation method according to claim 8, characterized in that: Evaluate the coordination of the power grid after enabling redundant lines in sequence, specifically: The coordination evaluation value of the power grid after the redundant line is enabled is calculated by difference with the coordination evaluation value before the activation to obtain the evaluation change value; the coordination evaluation value of the power grid after the redundant line is enabled is calculated by difference with the coordination evaluation threshold to obtain the coordination evaluation difference; the coordination evaluation value of the power grid after the redundant line is enabled is calculated by difference with the coordination evaluation threshold to obtain the coordination threshold difference; the coordination evaluation difference and the coordination threshold difference are weighted to obtain the coordination evaluation value; The co-evaluation values ​​after activating the redundant lines of different key nodes or key lines are traversed in turn, and the mean and standard deviation of the co-evaluation values ​​after activating the redundant lines of different key nodes or key lines are calculated to obtain the co-evaluation mean and the co-evaluation fluctuation value; the co-evaluation value, the co-evaluation mean and the co-evaluation fluctuation value are weightedly calculated to obtain the co-evaluation comprehensive value.

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