Power grid weak link identification and optimization method, system, equipment, medium and product

Abstract

The invention belongs to the technical field of power systems, and provides a power grid weak link identification and optimization method, system, device, medium and product, and the method comprises the steps: constructing a weak link candidate set based on multi-source power grid operation data; calculating a comprehensive score of each weak link in the weak link candidate set under different optimization actions; a planning sensitivity coefficient is introduced, and the sensitivity of the optimization action to the comprehensive score is calculated; sorting the weak links based on the comprehensive score and the sensitivity analysis result, and generating a transformation priority sequence; a source network multi-objective optimization model is constructed, and the most potential weak link is mainly transformed according to the transformation priority sequence. By mastering the transformation demand of the weak link in advance, comprehensive evaluation and sensitivity analysis, scientific sorting and strategy guidance of weak link transformation are realized.

Description

Technical Field

[0001] This disclosure belongs to the field of power system technology, and in particular relates to a method for identifying weak links in urban transmission networks and for multi-objective collaborative planning of power sources and grids to improve the comprehensive acceptance capacity of new energy sources. Background Technology

[0002] Traditional power transmission networks primarily consider load power supply security and routine operational constraints in their planning and design, lacking effective assessment methods for the uncertainties of new energy sources and their multi-scenario integration capabilities. This necessitates urban power transmission networks possessing higher flexibility, intelligence, and collaborative optimization capabilities to address issues such as line congestion, node constraint triggering, and uneven distribution of main transformer loads caused by new energy integration.

[0003] Currently, there is limited research on weak link identification and multi-objective planning. There is a lack of systematic methods to quantitatively assess and prioritize weak links in order to guide optimization, transformation, and resource allocation.

[0004] The development of smart grid technology provides a technological foundation for solving the above problems. Through big data analysis, power flow simulation, and capacity assessment models, the operating status of urban power transmission networks can be monitored and analyzed in real time, identifying key nodes, lines, and main transformers that restrict the access of new energy sources. Based on this information, strategies for upgrading weak links can be scientifically formulated, including measures such as line capacity expansion, energy storage configuration, grid reconfiguration, and dispatch optimization, to achieve coordinated planning between power generation and the grid.

[0005] While some existing technologies focus on single planning measures or single-objective optimization, there is a lack of a systematic approach that can simultaneously integrate multiple objectives, scenarios, and optimization actions to improve the comprehensive renewable energy acceptance capacity and operational safety of urban power transmission networks. Summary of the Invention

[0006] To address the aforementioned issues, this disclosure provides a method for identifying weak links in urban power transmission networks and for multi-objective collaborative planning of power sources and grids to enhance the comprehensive acceptance capacity of new energy sources. By understanding the transformation needs of weak links in advance, conducting comprehensive evaluation and sensitivity analysis, it achieves scientific prioritization and strategic guidance for the transformation of weak links.

[0007] Firstly, this disclosure provides a method for identifying and optimizing weak links in a power grid, including: Based on multi-source power grid operation data, a candidate set of weak links is constructed; Calculate the comprehensive score of each weak link in the candidate weak link set under different optimization actions; Introduce a planning sensitivity coefficient to calculate the sensitivity of optimization actions to the overall score; Based on the comprehensive scoring and sensitivity analysis results, the weak links are ranked and a priority sequence for improvement is generated; Construct a source-network multi-objective optimization model, and focus on improving the weakest links with the greatest potential based on the priority sequence of the transformation.

[0008] Furthermore, Based on multi-source power grid operation data, a candidate set of weak links is constructed, including: Collect multi-source data and construct a time-label-based feature mapping matrix; Establish an acceptance capacity margin assessment model; Inputting the feature mapping matrix into the acceptance margin assessment model yields a candidate set of weak links.

[0009] Furthermore, Generate a constraint-triggerable tag for each element in the candidate set of weak links, and bind it to the number of the corresponding triggering scenario.

[0010] Furthermore, Multi-source power grid operation data includes: real-time monitoring data, historical operation data, new energy access prediction data, and planning scenario data.

[0011] Furthermore, Calculate the comprehensive score of each weak link in the candidate weak link set under different optimization actions, including: Define evaluation indicators for new energy consumption efficiency, grid flexibility, and operational risk mitigation; The evaluation indicators for new energy consumption efficiency, grid flexibility, and operational risk mitigation are normalized and weighted accordingly. A multi-objective comprehensive evaluation model for source and network was constructed, and a comprehensive score was calculated for each weak link based on normalized evaluation indicators and weight allocation.

[0012] Furthermore, A multi-objective comprehensive evaluation model for the source network is constructed. Based on normalized evaluation indicators and weight allocation, a comprehensive score is calculated for each weak link, including: For weak links, define a set of candidate optimization actions; A multi-objective comprehensive evaluation model for source network is constructed. For each weak link, the normalized index under each optimization action in the optimization action set is weighted and summed according to the weight to obtain a comprehensive score.

[0013] Furthermore, Construct a source-network multi-objective optimization model, and based on the priority sequence of modifications, focus on modifying the weakest links with the greatest potential, including: Construct a planning action parameter model; Based on the planning action parameter model, an objective function is defined for multi-objective optimization of weak link improvement and planning measures; Set constraints; Solve the source-network multi-objective optimization model composed of the objective function and constraints to generate candidate solutions.

[0014] Furthermore, The planning action parameter model includes: Line capacity expansion model, energy storage configuration model, and grid reconfiguration model.

[0015] Furthermore, The objective function includes: The functions for maximizing new energy consumption, optimizing operational safety, and minimizing investment costs.

[0016] Furthermore, The constraints include: Node power balance constraints, node voltage constraints, line capacity constraints, main transformer rated capacity constraints, energy storage power and capacity constraints, and grid topology connectivity constraints.

[0017] Secondly, based on the same inventive concept, this disclosure also provides a power grid weak link identification and optimization system, including a weak link candidate module, a weak link comprehensive scoring module, a sensitivity calculation module, a renovation priority determination module, and an optimization model construction module; The weak link candidate module is used to construct a weak link candidate set based on multi-source power grid operation data; The comprehensive scoring module for weak links is used to calculate the comprehensive score of each weak link in the candidate set of weak links under different optimization actions; The sensitivity calculation module is used to introduce the planning sensitivity coefficient and calculate the sensitivity of optimization actions to the overall score. The renovation priority determination module is used to rank weak links based on comprehensive scoring and sensitivity analysis results, and generate a renovation priority sequence. The optimization model building module is used to build a multi-objective optimization model for the source network, and to focus on improving the weakest links with the greatest potential according to the priority sequence of the transformation.

[0018] Furthermore, Based on multi-source power grid operation data, a candidate set of weak links is constructed, including: Collect multi-source data and construct a time-label-based feature mapping matrix; Establish an acceptance capacity margin assessment model; Inputting the feature mapping matrix into the acceptance margin assessment model yields a candidate set of weak links.

[0019] Furthermore, Generate a constraint-triggerable tag for each element in the candidate set of weak links, and bind it to the number of the corresponding triggering scenario.

[0020] Furthermore, Calculate the comprehensive score of each weak link in the candidate weak link set under different optimization actions, including: Define evaluation indicators for new energy consumption efficiency, grid flexibility, and operational risk mitigation; The evaluation indicators for new energy consumption efficiency, grid flexibility, and operational risk mitigation are normalized and weighted accordingly. A multi-objective comprehensive evaluation model for source and network was constructed, and a comprehensive score was calculated for each weak link based on normalized evaluation indicators and weight allocation.

[0021] Furthermore, A multi-objective comprehensive evaluation model for the source network is constructed. Based on normalized evaluation indicators and weight allocation, a comprehensive score is calculated for each weak link, including: For weak links, define a set of candidate optimization actions; A multi-objective comprehensive evaluation model for source network is constructed. For each weak link, the normalized index under each optimization action in the optimization action set is weighted and summed according to the weight to obtain a comprehensive score.

[0022] Furthermore, Construct a source-network multi-objective optimization model, and based on the priority sequence of modifications, focus on modifying the weakest links with the greatest potential, including: Construct a planning action parameter model; Based on the planning action parameter model, an objective function is defined for multi-objective optimization of weak link improvement and planning measures; Set constraints; Solve the source-network multi-objective optimization model composed of the objective function and constraints to generate candidate solutions.

[0023] Thirdly, based on the same inventive concept, this disclosure also provides an electronic device, including at least one processor and at least one memory electrically connected; The memory is electrically connected to the processor, wherein the memory stores instructions executable by at least one of the processors, the instructions being executed by at least one of the processors to enable at least one of the processors to perform the power grid weak link identification and optimization method as described above.

[0024] Fourthly, based on the same inventive concept, this disclosure also provides a computer storage medium storing a computer program. When the computer program is executed by the processor, it implements the power grid weak link identification and optimization method as described above.

[0025] Fifthly, based on the same inventive concept, this disclosure also provides a computer program product, which is stored in at least one storage medium; The computer program product includes several instructions to cause at least one electronic device to execute the power grid weak link identification and optimization method as described above.

[0026] Compared with existing technologies, this disclosure provides a method for identifying and optimizing weak links in a power grid, which has the following beneficial effects: 1. Accurately acquire renewable energy acceptance margin indicators for nodes, lines, and main transformers across multiple scenarios, effectively identify areas most prone to constraint triggering, and form a candidate set of weak links. This allows for early understanding of the transformation needs of weak links, providing solid data support and theoretical basis for scientific planning and precise investment.

[0027] 2. Through comprehensive evaluation and sensitivity analysis, a scientific prioritization and strategic guidance for the transformation of weak links were achieved, effectively improving the pertinence and feasibility of planning decisions.

[0028] 3. Parametric modeling of line capacity expansion, energy storage configuration, and grid reconfiguration planning measures, and generating a set of candidate optimization schemes through multi-objective optimization solutions, can improve the renewable energy absorption capacity while ensuring the safety and stability of system operation, and at the same time take into account the economic efficiency of investment, so as to achieve the optimized combination and efficient implementation of weak link transformation.

[0029] Other features and advantages of this disclosure will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the disclosure. The objects and other advantages of this disclosure may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 A schematic diagram of a method for identifying and optimizing weak links in a power grid according to an embodiment of the present disclosure is shown; Figure 2 A schematic diagram illustrating the structural principle of an electronic device according to an embodiment of the present disclosure is shown. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0033] Figure 1 A flowchart illustrating a method for identifying and optimizing weak links in a power grid according to an embodiment of this disclosure is shown, as follows: Figure 1 As shown in the figure, a method for identifying and optimizing weak links in a power grid according to an embodiment of this disclosure includes the following steps: S1, based on multi-source power grid operation data, constructs a candidate set of weak links.

[0034] S11: Collect multi-source power grid operation data and construct a time-label-based feature mapping matrix.

[0035] Multi-source power grid operation data includes, but is not limited to: (1) Real-time monitoring data: node voltage amplitude Line power flow { } Main transformer load rate ; (2) Historical operating data: a set of records of exceeding limits during periods of high renewable energy output. ; (3) New energy access forecast data: wind and solar power output curves Load forecast curve ; (4) Planning scenario data: Power flow distribution matrix under different incremental new energy access scenarios .

[0036] After unifying the time labels of multi-source data, feature data is extracted to construct a feature mapping matrix: , in, In terms of time dimension, Number the scenarios for integrating new energy sources. , Number the nodes; To connect to the node With nodes The lines between them.

[0037] S12, Establish an acceptance capacity margin assessment model.

[0038] The capacity margin indicators for nodes, lines, and main transformers are as follows: , , , in, This represents the maximum renewable energy injection capacity allowed for a node. To limit the thermal stability of the transmission line, The rated capacity of the main transformer. This is the capacity margin index for node i; Contribute to the scenery; This is the acceptance capacity margin index for line ij. Let be the active power of line ij; Let be the reactive power of line ij; For the first k The margin of acceptance capacity of each major variable; The apparent power of the main transformer load; Number the main variable; s is the scene number.

[0039] Introducing the sensitivity coefficient for exceeding limits Used to characterize the tendency of constraint boundary triggering: , in, for ,or ; To inject active power into new energy sources (wind power / photovoltaic).

[0040] like If so, then that location is determined to be the most sensitive to the injection of new energy; The threshold is set in advance.

[0041] The aforementioned capacity margin indicator is only a static indicator and cannot directly reflect the sensitivity of a weak link to the injection of new energy. λ Representing constraints α (Node voltage, line power flow, main transformer load, etc.) in the scenario s The sensitivity to changes in the injection of new energy sources.

[0042] S13. Input the feature mapping matrix into the acceptance margin assessment model to obtain the candidate set of weak links.

[0043] Set tags : , All satisfied The nodes, lines, and main transformers are considered weak points. Add to the candidate set of weak links .in, The threshold for triggering frequency of weak links is used to determine whether the proportion of equipment exceeding the limit in multiple scenarios exceeds the statistically permissible range. This is the overall set of scenes.

[0044] S14 is the candidate set of weak links. Each element in the process generates a constraint trigger tag, which corresponds to the triggering scenario. The number is bound to it.

[0045] S2 calculates the comprehensive score of each weak link in the candidate weak link set under different optimization actions.

[0046] A multi-objective comprehensive evaluation model is constructed that considers the efficiency of new energy consumption, the flexibility of the grid structure, and the suppression of operational risks. A scenario response sensitivity analysis mechanism is introduced to rank the potential for improving the acceptance capacity of each weak link under different optimization actions.

[0047] S21 defines evaluation indicators for new energy consumption efficiency, grid flexibility, and operational risk mitigation.

[0048] (1) New energy consumption efficiency indicators .

[0049] Used to measure the improvement in system capacity brought about by a unit increase in new energy after optimization and upgrading, reflecting the direct contribution of the upgrading measures to the utilization of new energy: , in, For set The weak links within, This refers to the margin increase after the implementation of planning measures; This represents the incremental amount of new energy access, reflecting the actual improvement effect of unit access capacity on the grid structure.

[0050] (2) Grid flexibility index .

[0051] Used to assess the adjustability and responsiveness of the grid structure containing weak links under different power flow distributions or energy storage regulation, reflecting the potential for network reconfiguration or flexible scheduling. The margin changes with power flow regulation at the following rate: , in, It is an indicator of acceptance margin, , or One of them is the weak link. Is it related to weak nodes, weak lines, or weak main transformer? It represents the power flow adjustment capability (such as energy storage regulation, power flow diversion, and main transformer load sharing) and is used to measure structural reconfigurability.

[0052] (3) Operational risk mitigation indicators .

[0053] Used to quantify the degree to which planning measures improve operational constraint exceedances, reflecting the enhanced safety after implementation: , in, This indicates the number of times the planned measures were exceeded. This indicates the number of times the limit was exceeded before the implementation of the planning measures. This represents the number of times the limit was exceeded, reflecting the convergence effect of the planning behavior on the risk distribution; the set of limit exceedance records. The number of times the limit is exceeded is called the number of times the limit is exceeded.

[0054] S22 normalizes the evaluation indicators of new energy consumption efficiency, grid flexibility and operational risk suppression, and sets weight allocation.

[0055] To eliminate differences in the dimensions of different indicators, all indicators are normalized to ensure their values ​​are uniformly within the [0,1] interval. Simultaneously, the information entropy method and grey relational analysis method are combined to determine the weights of each indicator, thereby balancing the impact of each indicator on the overall evaluation and achieving multi-objective quantitative fusion. A linear normalization method is used to transform each objective indicator to the [0,1] interval: , in, Normalized index; for ; for The minimum value in; for The maximum value in.

[0056] Construct a joint weight allocation formula based on information entropy and grey relational degree: , in, These are adaptive weight fusion coefficients used to balance subjective preferences and objective information distribution. As an indicator k The fusion weight; As an indicator k The entropy weight, also known as the objective weight; As an indicator k The grey relational weight.

[0057] S23. Construct a multi-objective comprehensive evaluation model for the source network, and calculate a comprehensive score for each weak link based on normalized evaluation indicators and weight allocation.

[0058] S231, for weak links, set up a set of candidate optimization actions.

[0059] To address weaknesses, several typical optimization actions are defined, including line capacity expansion, energy storage configuration, and grid reconfiguration. A set of candidate optimization actions is provided. A for: .

[0060] S232. Construct a multi-objective comprehensive evaluation model for the source network. For each weak link, the normalized index under each optimization action is weighted and summed according to the weight to obtain a comprehensive score.

[0061] The source-network multi-objective comprehensive evaluation model is as follows: , in, Weak links In optimizing actions The overall score below; As an indicator k The fusion weight; This is a normalized indicator.

[0062] S3 introduces a planning sensitivity coefficient to calculate the sensitivity of optimization actions to the overall score.

[0063] Planning sensitivity coefficient It is used to assess the response potential of weak links to different types of planning adjustments, and to reveal the response potential and room for improvement of weak links under different transformation measures.

[0064] .

[0065] S4. Based on the comprehensive scoring and sensitivity analysis results, the weak links are ranked to generate a priority sequence for improvement.

[0066] Construct sorting function : , in, , The trade-off parameters for adjusting planning priority bias.

[0067] Candidate set of weak links All calculate Arrange them in descending order to form a priority sequence for addressing weaknesses. .

[0068] The priority sequence for transformation provides a reference for subsequent source-network collaborative planning, ensuring the rational allocation of planning resources.

[0069] S5. Construct a multi-objective optimization model for the source network and, based on the priority sequence of modifications, focus on modifying the weakest links with the greatest potential.

[0070] S51, Construct a planning action parameter model.

[0071] This step constructs a model of the parameters involved in implementing the planning actions, which will be used for subsequent optimization of the objective function and constraints.

[0072] (1) Line capacity expansion model.

[0073] Adjustments will be made to the capacity limits of lines with weak links: , in, For the line The new maximum line capacity after the expansion; Original route The baseline capacity limit; This refers to the capacity adjustment amount, which is the numerical value by which the line's capacity is increased. Minimum expandable capacity; This represents the maximum expandable capacity.

[0074] Discrete or continuous capacity expansion schemes can be adopted, derived from power grid capacity expansion standards or investment planning databases.

[0075] (2) Energy storage configuration model.

[0076] Configure energy storage systems at vulnerable nodes, with parameters including energy storage capacity. With charge and discharge power : , , in, This represents the upper limit of the maximum allowable charging / discharging power of the energy storage system. This represents the upper limit of the rated energy capacity of the energy storage system. The configuration range is determined based on the energy storage site selection and capacity selection modules or typical templates.

[0077] (3) Reconstruction model of space frame.

[0078] Perform topology optimization on the network structure containing the weakest link, including switch status. Adjustments and changes to line connection methods: , in, The reconstructed distribution network topology (node-line connection relationships); This is the initial topology; f () is a function that implements topology reconfiguration based on switch switching.

[0079] The network reconfiguration model can ensure that the reconfigured network meets connectivity constraints and safe operation requirements.

[0080] S52, based on the planning action parameter model, defines the objective function for multi-objective optimization of weak link transformation and planning measures.

[0081] New energy consumption maximization function: , Run the security optimization function: , Investment cost minimization function: , in, This indicates the unit price for line capacity expansion; Indicates the line Increased capacity; Indicates the unit price of energy storage; Indicates the energy capacity of the energy storage system This indicates the operational cost of grid reconfiguration or switch scheduling.

[0082] S53, Set constraints.

[0083] The constraints include: (1) Node power balance constraints: , in, Active / reactive power generation for nodes; For node load active / reactive power, Injecting / absorbing power for energy storage; For line power flow; For nodes i The set of adjacent nodes.

[0084] (2) Node voltage constraints: , in, ， These represent the minimum and maximum values ​​of the node voltage.

[0085] (3) Line capacity constraints: , in, These represent the active and reactive power of line ij, respectively.

[0086] (4) Main transformer rated capacity constraint: , in, Main variable load rate This represents the actual flow capacity through the main transformer.

[0087] (5) Energy storage power and capacity constraints: , , (6) Network topology connectivity constraints: The network must remain connected and not be isolated: .

[0088] S54 solves the source-network multi-objective optimization model composed of the objective function and constraints, and generates candidate solutions.

[0089] After parameterizing the planned actions, the objective function is solved using traditional methods to obtain a set of candidate solutions: .

[0090] Candidate solutions are screened and ranked based on factors such as renewable energy consumption, operational safety, and investment costs, resulting in a final set of feasible optimized solutions.

[0091] Based on the same inventive concept as the method disclosed above, this disclosure also provides a power grid weak link identification and optimization system, including a weak link candidate module, a weak link comprehensive scoring module, a sensitivity calculation module, a renovation priority determination module, and an optimization model construction module; The weak link candidate module is used to construct a weak link candidate set based on multi-source power grid operation data; The comprehensive scoring module for weak links is used to calculate the comprehensive score of each weak link in the candidate set of weak links under different optimization actions; The sensitivity calculation module is used to introduce the planning sensitivity coefficient and calculate the sensitivity of optimization actions to the overall score. The renovation priority determination module is used to rank weak links based on comprehensive scoring and sensitivity analysis results, and generate a renovation priority sequence. The optimization model building module is used to construct a multi-objective optimization model for the source network, focusing on improving the most promising weak links according to the priority sequence of modifications. Based on the same inventive concept as the above-disclosed content, this disclosure also provides an electronic device. For example... Figure 2 As shown, the electronic device of this disclosure includes at least one processor and at least one memory electrically connected to the processor. The memory is electrically connected to the processor, and the memory stores instructions that can be executed by the at least one processor. The instructions are executed by the at least one processor to enable the at least one processor to perform the power grid weak link identification and optimization method as described above.

[0092] It should be noted that the electrical connection between the above-mentioned units does not necessarily mean the connection between lines. The indirect connection method can be applied to the embodiments of this disclosure as long as it achieves the purpose of this disclosure.

[0093] Based on the same inventive concept, this disclosure also provides a computer storage medium storing a computer program, which, when executed by a processor, implements the power grid weak link identification and optimization method described above.

[0094] Based on the same inventive concept, this disclosure also provides a computer program product, which is stored in at least one storage medium; the computer program product includes several instructions to cause at least one computer device to execute the power grid weak link identification and optimization method as described above.

[0095] Although the present disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure.

Claims

1. A method for identifying and optimizing weak links in a power grid, characterized in that, The method includes: Based on multi-source power grid operation data, a candidate set of weak links is constructed; Calculate the comprehensive score of each weak link in the candidate weak link set under different optimization actions; Introduce a planning sensitivity coefficient to calculate the sensitivity of optimization actions to the overall score; Based on the comprehensive scoring and sensitivity analysis results, the weak links are ranked and a priority sequence for improvement is generated; Construct a source-network multi-objective optimization model, and focus on improving the weakest links with the greatest potential based on the priority sequence of the transformation.

2. The method for identifying and optimizing weak links in a power grid according to claim 1, characterized in that, Based on multi-source power grid operation data, a candidate set of weak links is constructed, including: Collect multi-source power grid operation data and construct a time-label-based feature mapping matrix; Establish an acceptance capacity margin assessment model; Inputting the feature mapping matrix into the acceptance margin assessment model yields a candidate set of weak links.

3. The method for identifying and optimizing weak links in a power grid according to claim 2, characterized in that, Generate a constraint-triggerable tag for each element in the candidate set of weak links, and bind it to the number of the corresponding triggering scenario.

4. The method for identifying and optimizing weak links in a power grid according to claim 2, characterized in that, Multi-source power grid operation data includes: real-time monitoring data, historical operation data, new energy access prediction data, and planning scenario data.

5. The method for identifying and optimizing weak links in a power grid according to any one of claims 1-4, characterized in that, Calculate the comprehensive score of each weak link in the candidate weak link set under different optimization actions, including: Define evaluation indicators for new energy consumption efficiency, grid flexibility, and operational risk mitigation; The evaluation indicators for new energy consumption efficiency, grid flexibility, and operational risk mitigation are normalized and weighted accordingly. A multi-objective comprehensive evaluation model for source and network was constructed, and a comprehensive score was calculated for each weak link based on normalized evaluation indicators and weight allocation.

6. The method for identifying and optimizing weak links in a power grid according to claim 5, characterized in that, A multi-objective comprehensive evaluation model for the source network is constructed. Based on normalized evaluation indicators and weight allocation, a comprehensive score is calculated for each weak link, including: For weak links, define a set of candidate optimization actions; A multi-objective comprehensive evaluation model for source network is constructed. For each weak link, the normalized index under each optimization action in the optimization action set is weighted and summed according to the weight to obtain a comprehensive score.

7. The method for identifying and optimizing weak links in a power grid according to any one of claims 1-4, characterized in that, Construct a source-network multi-objective optimization model, and based on the priority sequence of modifications, focus on modifying the weakest links with the greatest potential, including: Construct a planning action parameter model; Based on the planning action parameter model, an objective function is defined for multi-objective optimization of weak link improvement and planning measures; Set constraints; Solve the source-network multi-objective optimization model composed of the objective function and constraints to generate candidate solutions.

8. The method for identifying and optimizing weak links in a power grid according to claim 7, characterized in that, The planning action parameter model includes: Line capacity expansion model, energy storage configuration model, and grid reconfiguration model.

9. The method for identifying and optimizing weak links in a power grid according to claim 8, characterized in that, The objective function includes: The functions for maximizing new energy consumption, optimizing operational safety, and minimizing investment costs.

10. The method for identifying and optimizing weak links in a power grid according to claim 9, characterized in that, The constraints include: Node power balance constraints, node voltage constraints, line capacity constraints, main transformer rated capacity constraints, energy storage power and capacity constraints, and grid topology connectivity constraints.

11. A system for identifying and optimizing weak links in a power grid, characterized in that, The system includes a weak link candidate module, a weak link comprehensive scoring module, a sensitivity calculation module, a modification priority determination module, and an optimization model construction module; The weak link candidate module is used to construct a weak link candidate set based on multi-source power grid operation data; The comprehensive scoring module for weak links is used to calculate the comprehensive score of each weak link in the candidate set of weak links under different optimization actions; The sensitivity calculation module is used to introduce the planning sensitivity coefficient and calculate the sensitivity of optimization actions to the overall score. The renovation priority determination module is used to rank weak links based on comprehensive scoring and sensitivity analysis results, and generate a renovation priority sequence. The optimization model building module is used to build a multi-objective optimization model for the source network, and to focus on improving the weakest links with the greatest potential according to the priority sequence of the transformation.

12. The power grid weak link identification and optimization system according to claim 11, characterized in that, Based on multi-source power grid operation data, a candidate set of weak links is constructed, including: Collect multi-source data and construct a time-label-based feature mapping matrix; Establish an acceptance capacity margin assessment model; Inputting the feature mapping matrix into the acceptance margin assessment model yields a candidate set of weak links.

13. The power grid weak link identification and optimization system according to claim 12, characterized in that, Generate a constraint-triggerable tag for each element in the candidate set of weak links, and bind it to the number of the corresponding triggering scenario.

14. The power grid weak link identification and optimization system according to any one of claims 11-13, characterized in that, Calculate the comprehensive score of each weak link in the candidate weak link set under different optimization actions, including: Define evaluation indicators for new energy consumption efficiency, grid flexibility, and operational risk mitigation; The evaluation indicators for new energy consumption efficiency, grid flexibility, and operational risk mitigation are normalized and weighted accordingly. A multi-objective comprehensive evaluation model for source and network was constructed, and a comprehensive score was calculated for each weak link based on normalized evaluation indicators and weight allocation.

15. The power grid weak link identification and optimization system according to claim 14, characterized in that, A multi-objective comprehensive evaluation model for the source network is constructed. Based on normalized evaluation indicators and weight allocation, a comprehensive score is calculated for each weak link, including: For weak links, define a set of candidate optimization actions; A multi-objective comprehensive evaluation model for source network is constructed. For each weak link, the normalized index under each optimization action in the optimization action set is weighted and summed according to the weight to obtain a comprehensive score.

16. The power grid weak link identification and optimization system according to claim 11, characterized in that, Construct a source-network multi-objective optimization model, and based on the priority sequence of modifications, focus on modifying the weakest links with the greatest potential, including: Construct a planning action parameter model; Based on the planning action parameter model, an objective function is defined for multi-objective optimization of weak link improvement and planning measures; Set constraints; Solve the source-network multi-objective optimization model composed of the objective function and constraints to generate candidate solutions.

17. An electronic device, characterized in that, Includes at least one processor and at least one memory electrically connected; The memory is electrically connected to the processor, wherein the memory stores instructions executable by at least one of the processors, the instructions being executed by at least one of the processors to enable at least one of the processors to perform the power grid weak link identification and optimization method as described in any one of claims 1-10.

18. A computer storage medium, characterized in that, The computer storage medium stores a computer program. When the computer program is executed by the processor, it implements the power grid weak link identification and optimization method according to any one of claims 1-10.

19. A computer program product, characterized in that, The computer program product is stored in at least one storage medium; The computer program product includes several instructions for causing at least one electronic device to execute the power grid weak link identification and optimization method according to any one of claims 1-10.

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