A load carrying capacity evaluation method and system based on main and auxiliary micro parallel power flow calculation

Abstract

This invention provides a method and system for carrying capacity assessment based on parallel power flow calculation of the main grid, distribution network, and microgrid. The method generates a task dependency graph by analyzing the equivalent mapping relationship constructed by the topology structure in the power grid, and further generates multiple parallel computing partitions. Representative time-series sections generated based on source-load data within the assessment period are encapsulated as independent simulation tasks and executed in parallel to perform power flow calculations. The system checks the coordination between the system-level carrying capacity interval and the equipment-level carrying capacity interval in parallel. For areas with conflicts, the capacity is redistributed to obtain the allocated carrying capacity intervals at each level. Then, the available capacity is calculated in parallel within each parallel computing partition and the assessment level is divided, along with the installed capacity that has been connected to the grid and filed. The power grid carrying capacity assessment results are output, realizing efficient and accurate carrying capacity assessment based on parallel power flow calculation of the main grid, distribution network, and microgrid.

Description

Technical Field

[0001] This invention relates to the field of distributed power supply capacity assessment, specifically to a capacity assessment method and system based on primary-distribution micro-parallel power flow calculation. Background Technology

[0002] With the large-scale integration of distributed power sources (such as photovoltaic and wind power) into the distribution network, power system carrying capacity assessment has become a key means to ensure the safe and stable operation of the power grid. According to the "Guideline for Capacity Assessment of Distributed Power Source Integration into the Power System" (DL / T 2041—2025), capacity assessment requires system-level and equipment-level calculations, covering multiple levels such as provincial-level administrative regions, county-level administrative regions, and 220kV and below transformers. It also requires step-by-step verification and iterative calculations based on various factors such as time-series production simulation, load forecasting, power output curves, and flexible adjustment resources.

[0003] However, existing load-bearing capacity assessment methods mostly adopt a sequential calculation approach, performing power flow analysis level by level and device by device, which has the following problems: (1) Large amount of calculation: It is necessary to repeatedly verify multiple voltage levels, multiple devices, and multiple time sections (such as quarters and years), resulting in high calculation complexity; (2) Difficulty in scheduling and reconfiguration: The carrying capacity assessment needs to consider the system reconfiguration under different scheduling strategies, and traditional methods are difficult to respond quickly; (3) Long evaluation cycle: It cannot meet the needs of high frequency or high real-time evaluation, which affects the dynamic release and early warning mechanism of the available capacity.

[0004] Therefore, there is an urgent need for a carrying capacity assessment method that can efficiently and accurately perform parallel power flow calculations for the main grid, distribution network, and microgrid. Summary of the Invention

[0005] To address the problems of large computational load, difficult scheduling and reconfiguration, and long evaluation cycle in existing capacity assessment methods, this invention proposes a capacity assessment method and system based on primary-secondary micro-parallel power flow calculation.

[0006] Firstly, a carrying capacity assessment method based on primary-secondary micro-parallel power flow calculation is provided, including: The topology in the power grid is analyzed to construct an equivalent mapping relationship, and multiple parallel computing partitions are generated based on the task dependency graph generated by the equivalent mapping relationship. Representative time series profiles are generated based on the source load data within the evaluation period, and each representative time series profile is encapsulated as an independent simulation task to perform power flow calculations in parallel. The system checks the coordination between the system-level and equipment-level load-bearing capacity ranges in parallel, and redistributes the capacity of conflicting areas to obtain the allocated load-bearing capacity ranges for each level. Based on the allocated carrying capacity ranges of each level and the installed capacity that has been connected to the grid and filed, the available capacity is calculated in parallel within each parallel computing partition and the evaluation level is divided, and the grid carrying capacity evaluation results are output. The system-level carrying capacity range is generated through parallel simulation of multiple scenarios, while the equipment-level carrying capacity range is calculated based on power flow calculation results.

[0007] Secondly, a carrying capacity assessment system based on primary and secondary micro-parallel power flow calculation is provided, including: The parsing module is used to parse the topology in the power grid to construct an equivalent mapping relationship, and generate multiple parallel computing partitions based on the task dependency graph generated by the equivalent mapping relationship; The calculation module is used to generate representative time series sections based on source load data within the evaluation period, and encapsulate each representative time series section into an independent simulation task to perform power flow calculation in parallel. The allocation module is used to verify the coordination between the system-level and equipment-level capacity ranges in parallel, and to redistribute the capacity of conflicting areas to obtain the allocated capacity ranges for each level. The output module is used to calculate the available capacity in parallel within each parallel computing partition and classify the evaluation level based on the allocated carrying capacity intervals of each level and the installed capacity that has been connected to the grid and filed, and output the grid carrying capacity evaluation results. The system-level carrying capacity range is generated through parallel simulation of multiple scenarios, while the equipment-level carrying capacity range is calculated based on power flow calculation results.

[0008] In another aspect, this application also provides an electronic device, comprising: at least one processor and a memory; the memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, a carrying capacity assessment method based on primary-secondary micro-parallel power flow calculation as described above is implemented.

[0009] In another aspect, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the carrying capacity assessment method based on primary-secondary micro-parallel power flow calculation as described above.

[0010] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention provides a method and system for carrying capacity assessment based on parallel power flow calculation of the main grid, distribution network, and microgrid. The method generates a task dependency graph by analyzing the equivalent mapping relationship constructed from the topology of the power grid, and further generates multiple parallel computing partitions. Representative time-series sections are generated based on source-load data within the assessment period, and each representative time-series section is encapsulated as an independent simulation task for parallel power flow calculation. The system then checks the coordination between the system-level and equipment-level carrying capacity intervals in parallel. For conflicting areas, capacity is redistributed to obtain the allocated carrying capacity intervals at each level. Based on the allocated carrying capacity intervals at each level and the installed capacity that has been connected to the grid and registered, the available capacity is calculated in parallel within each parallel computing partition, and the assessment level is divided. The power grid carrying capacity assessment results are output, achieving efficient and accurate carrying capacity assessment based on parallel power flow calculation of the main grid, distribution network, and microgrid.

[0011] By using parallel tasks and parallel data, the carrying capacity assessment time is shortened from several days to hours, significantly improving computational efficiency; it also supports high-frequency assessments, meeting the needs of quarterly or even monthly dynamic carrying capacity assessments; through parallel verification and refactoring optimization, it avoids the cumulative errors caused by step-by-step calculations, further improving the accuracy of the assessment; and it can be extended to different scale power grids and different distributed power penetration scenarios. Attached Figure Description

[0012] Figure 1 This is a schematic diagram of the overall process of the bearing capacity assessment method based on primary and secondary parallel power flow calculation of the present invention; Figure 2 This is a flowchart of the bearing capacity assessment method based on primary and secondary parallel power flow calculation of the present invention; Figure 3 This is a schematic diagram of the main-distribution micro-parallel power flow calculation architecture of the carrying capacity assessment method based on main-distribution micro-parallel power flow calculation of the present invention. Figure 4 This is a schematic diagram of the time-parallel simulation task scheduling process of the carrying capacity assessment method based on primary-secondary micro-parallel power flow calculation of the present invention; Figure 5 This is a schematic diagram of the parallel calculation process for equipment-level bearing capacity of the bearing capacity assessment method based on primary and secondary parallel power flow calculation of the present invention; Figure 6 This is a schematic diagram of the system-level parallel decomposition and verification process of the bearing capacity assessment method based on primary and secondary micro-parallel power flow calculation of the present invention; Figure 7 This is a schematic diagram of the open capacity parallel assessment and classification process of the carrying capacity assessment method based on primary and secondary micro-parallel power flow calculation of the present invention. Figure 8 This is a schematic diagram of the scheduling reconfiguration parallel optimization process of the carrying capacity assessment method based on primary-secondary micro-parallel power flow calculation of the present invention; Figure 9 This is a schematic diagram of the load-bearing capacity assessment system based on primary and secondary parallel power flow calculation according to the present invention. Figure 10 This is a schematic diagram of an electronic device structure according to the present invention. Detailed Implementation

[0013] This invention aims to provide a carrying capacity assessment method and system based on primary-distribution micro-parallel power flow calculation. Through hierarchical and partitioned parallel computing, task scheduling and data collaboration, it enables fast and accurate calculation of carrying capacity assessment in large-scale distributed power access scenarios.

[0014] like Figure 1 As shown, the carrying capacity assessment method based on primary-secondary-micro parallel power flow calculation of the present invention is executed in the following order: First, a primary-secondary-micro hierarchical parallel computing architecture is established and parallel computing partitions are generated; then, a time-series parallel simulation task is constructed and power flow calculation is executed in parallel; then, the device-level carrying capacity interval is calculated in parallel within each partition and the system-level carrying capacity decomposition and verification are completed; finally, based on the allocated carrying capacity intervals, the openable capacity assessment and scheduling reconfiguration optimization are completed in parallel, thereby achieving efficient processing of the entire carrying capacity assessment process.

[0015] To better understand the present invention, the following description, in conjunction with the accompanying drawings and embodiments, will further illustrate the content of the present invention.

[0016] Example 1: A carrying capacity assessment method based on primary-secondary micro-parallel power flow calculation, such as... Figure 2 As shown, it includes: Step S1: Analyze the topology in the power grid to construct an equivalent mapping relationship, and generate multiple parallel computing partitions based on the task dependency graph generated by the equivalent mapping relationship; Step S2: Generate representative time series profiles based on the source load data within the evaluation period, and encapsulate each representative time series profile into an independent simulation task to perform power flow calculations in parallel; Step S3: Parallel verification of the coordination between the system-level and equipment-level load-bearing capacity ranges, and capacity redistribution for conflicting areas to obtain the allocated load-bearing capacity ranges for each level; Step S4: Based on the allocated carrying capacity intervals of each level and the installed capacity that has been connected to the grid and filed, calculate the available capacity in parallel within each parallel computing partition and classify the evaluation level, and output the grid carrying capacity evaluation results.

[0017] Among them, the system-level carrying capacity range is generated through parallel simulation of multiple scenarios, while the equipment-level carrying capacity range is calculated based on the power flow calculation results.

[0018] In this embodiment, during the process of parsing the power grid topology and generating parallel computing partitions in step S1, it is necessary to construct equivalent mapping relationships and intelligently segment the task dependency graph to solve the problem of complex data interaction between the primary, secondary, and micro-levels in traditional methods, thereby providing a foundation for subsequent full-process parallel computing. Specifically, this includes: Analyze the power grid topology and identify the connections and boundary nodes between the main grid, distribution network, and microgrids; The lower-level power grid is equivalent to the load in the upper-level power grid, and the upper-level power grid is equivalent to the power source in the lower-level power grid. An equivalent mapping relationship is constructed, and a task dependency graph is constructed based on the equivalent mapping relationship with a single drawing as a node. The task dependency graph is divided into several parallelizable subtask groups using a graph partitioning algorithm based on the principle of minimizing coupling. These parallelizable subtask groups are then dynamically and adaptively adjusted as parallel computing partitions according to the server's memory status.

[0019] Specifically, when establishing a hierarchical parallel computing architecture for the main grid, distribution grid, and microgrid, this invention first traverses the static data file of the power grid to perform topology analysis on the connection relationships between the main grid, distribution grid, and microgrid, accurately identifying the boundary nodes between each level. Then, the lower-level grid is marked as an equivalent load in the upper-level grid, and the upper-level grid is marked as an equivalent power source in the lower-level grid, forming a one-to-one equivalent mapping relationship. Based on this, a directed task graph containing dependencies is constructed using a single drawing as the basic node, and a graph partitioning algorithm is used to divide the task graph into several parallelizable subtask groups according to the principle of minimizing the coupling between tasks. These subtask groups constitute the parallel computing partitions. At the same time, the number of parallel tasks is dynamically and adaptively adjusted according to the real-time memory status of the server to ensure optimal utilization of computing resources.

[0020] In one specific implementation, this invention addresses the need for large-scale, multi-level grid parallel computing in distributed power source capacity assessment by proposing a topology-aware task segmentation and hierarchical collaborative computing method; such as... Figure 3 As shown, this algorithm can automatically identify the power grid topology, intelligently divide the complex multi-level power grid into parallel computing task units, and establish equivalent mapping relationships between tasks. The architecture uses Flask as the API gateway, employs a file system for data storage and exchange, and ensures the consistency of multi-level power grid computation through an iterative mode of "inner loop computation - outer loop synchronization".

[0021] The algorithm flow is as follows: (1) Topology parsing: Traverse the static JSON file of the power grid, parse the connection relationship between the main grid, distribution network and microgrid, and identify boundary nodes.

[0022] (2) Construction of equivalent pairs: The lower-level power grid is marked as "equivalent load" in the upper-level power grid, and the upper-level power grid is marked as "equivalent power source" in the lower-level power grid, forming a one-to-one "equivalent pair" relationship.

[0023] (3) Task graph generation: Using a single drawing as the basic unit, construct a task dependency graph.

[0024] (4) Intelligent segmentation: Based on the coupling degree between tasks, a graph segmentation algorithm is used to divide the task graph into several sub-task groups to minimize cross-group communication.

[0025] (5) Dynamic scheduling: The number of parallel tasks is dynamically adjusted according to the server memory status to achieve adaptive memory scheduling.

[0026] In this embodiment, during the execution of step S2 to construct the time-series parallel simulation task, it is necessary to intelligently sample, compress, and encapsulate the massive source-load time-series data into independent tasks to solve the problems of huge computational load and excessively long evaluation cycle caused by traditional time-period serial computation. Specifically, this includes: Based on a defined evaluation period and time scale, source-load time-series data and energy storage scheduling strategies are obtained, and feature extraction is performed on the source-load time-series data to obtain source-load time-series data features. Based on the source-load time-series data characteristics and energy storage scheduling strategy, K-means clustering algorithm is used to compress and generate representative time-series profiles. Each representative time series section is encapsulated as an independent simulation task and executed in parallel for power flow calculation.

[0027] Specifically, when constructing a time-series parallel simulation task, this invention first acquires source-load time-series data and energy storage scheduling strategies based on the evaluation period and time scale, and extracts features from the source-load data to obtain representative time-series features. Subsequently, based on the above features and energy storage scheduling strategies, the K-Means clustering algorithm is used to compress the original time-series profiles to generate several representative time-series profiles. Finally, each representative time-series profile is encapsulated as an independent simulation task, so that all drawings under the same profile can constitute a set of fully parallel power flow calculation tasks, thereby realizing parallel processing in the time-series dimension.

[0028] In one specific implementation, this invention considers multiple factors such as load fluctuations, changes in distributed power output, and flexible resource scheduling adjustments, and proposes a method for constructing time-series parallel simulation tasks based on intelligent sampling, such as... Figure 4 As shown, it can significantly reduce the amount of computation while ensuring computational accuracy, and achieve efficient simulation through parallel scheduling.

[0029] (1) Determine the evaluation cycle and time scale: Annual evaluation = 365 days a year × 96 points / day, quarterly evaluation = number of days in the quarter × 96 points / day, custom scenario = specified date set × 96 points / day.

[0030] (2) Time series data preparation: Source load data is directly obtained; energy storage is selected from the classic strategy library, and the charging and discharging time can be set. Assuming that the energy storage charging and discharging power is constant, the section dependency is unlocked to achieve full parallelism.

[0031] (3) Intelligent sampling compression: The original cross-section is feature extracted and compressed into K representative cross-sections by K-Means clustering. The original time index and weight of each representative cross-section are recorded.

[0032] (4) Task encapsulation: Each representative time section is encapsulated as an independent computing task, and all drawings under this section are a set of parallel power flow computing tasks.

[0033] In this embodiment, during the parallel calculation of the equipment-level load-bearing capacity range in step S3, it is necessary to divide the area into zones using a single drawing as the basic unit and clearly define the calculation object of the equipment-level load-bearing capacity range. This solves the problem of unclear equipment-level calculation boundaries and difficulty in parallelization in traditional methods, providing a foundation for subsequent system-level verification. Specifically, this includes: The power grid is decomposed into multiple parallel computing partitions using a single drawing in the task dependency graph as the basic unit. Based on the power flow calculation results, the parallel computing device-level load capacity range is determined within each parallel computing partition. Among them, the equipment-level carrying capacity range is the maximum accessible capacity range of distributed power sources within the power supply range of each parallel computing partition transformer.

[0034] Specifically, when calculating the equipment-level carrying capacity range, this invention uses a single drawing in the task dependency graph as the basic calculation unit, decomposing the power grid into multiple parallel calculation partitions. Within each partition, parallel calculations are performed based on the power flow calculation results to obtain the maximum accessible capacity range of distributed power sources within the transformer power supply range of each parallel calculation partition, thereby providing reliable reference data for the subsequent verification of the system-level carrying capacity range.

[0035] In this embodiment, during the parallel calculation of the device-level bearing capacity range in step S3, it is necessary to handle the coupling between partitions through a boundary condition iteration synchronization mechanism to solve the problem of inconsistent partition calculation results under ring network operation. Specifically, this includes: Based on the power flow calculation results, the bearing capacity range of the parallel computing device-level foundation in each parallel computing partition; The calculation results of each parallel computing partition are iteratively synchronized by pre-set boundary conditions. When the synchronization error of the boundary electrical quantities between each parallel computing partition is less than the convergence threshold, the results of each partition are aggregated to obtain a globally consistent equipment-level load-bearing capacity range.

[0036] Specifically, when calculating the equipment-level bearing capacity of each parallel computing partition based on the power flow calculation results, the present invention first obtains the equipment-level foundation bearing capacity range in parallel within each partition; then, iteratively synchronizes the calculation results of each partition through preset boundary conditions. When the synchronization error of the boundary electrical quantities between each partition is less than the preset convergence threshold, the results of each partition are aggregated to obtain a globally consistent equipment-level bearing capacity range, ensuring that the electrical quantities of the partition calculation results remain consistent at the boundary.

[0037] In one specific implementation, this invention proposes a partitioned parallel computing method based on boundary condition adaptive dynamic synchronization matching technology. This method can handle the partition coupling problem caused by ring network operation and achieve efficient parallel computing across multiple partitions through an iterative synchronization mechanism. Figure 5 As shown, the partitioned parallel computing device-level load capacity includes the following sub-steps: (1) Zoning and boundary identification: Taking the power supply range of 220kV transformer as the calculation object and a single drawing as the basic calculation unit, the power grid is decomposed into multiple parallel calculation zones. Among them, multiple drawings that constitute a ring network are merged into one calculation drawing, and the connection outside the zone is equivalent to a load or power source. (2) Parallel computing initialization: set the maximum number of iterations K_max and the convergence threshold ε, initialize the confidence of boundary nodes C=0.1, and load typical time data; (3) The main loop of partitioned parallel computing includes three sub-steps: partitioned parallel computing, boundary condition synchronization, and convergence check. It is iterated until global convergence.

[0038] Wherein: Boundary condition synchronization confidence model: Define the confidence of boundary node b in the kth iteration as C(b,k)=min(1.0,k / K_max×(1-e^(-α×coupling strength)) / (1+10×ε_total)), where ε_total is the current synchronization error; Boundary condition acquisition strategy: When C < 0.3, use the predicted value; when 0.3 ≤ C < 0.7, use the weighted average of the predicted value and the actual value; when C ≥ 0.7, use the actual calculated value from the previous round. Synchronization of boundary electrical quantities - Calculation of synchronization error: ε_total=w_P×ε_P+ w_Q×ε_Q+ w_V×ε_V +w_θ×ε_θ, where ε_P =|P_A+P_B| / S_base, ε_Q= |Q_A+Q_B| / S_base, ε_V=|V_A-V_B| / V_base, ε_θ=|θ_A-θ_B| / π; Boundary condition update: Based on the calculation results of each partition, the weighted average method is used to calculate the new boundary conditions.

[0039] (4) Bearing capacity calculation: Based on the converged power flow results, the bearing capacity range is calculated for each transformer using formula (1); (1) In the formula: P represents the equipment-level load-bearing capacity range of a certain transformer; P represents the electrical load within the power supply range of the transformer at a typical moment. P G S represents the sum of the output of other types of power sources within the power supply range of this transformer, excluding distributed power sources, under typical conditions; S is the rated capacity of this transformer. The power factor of this transformer should comply with the requirements of DL / T 5554. This is the maximum reverse load rate of the transformer; The maximum output coefficient of distributed power sources within the power supply range of this transformer; P ESS This represents the charging power of the grid-connected, flexibly adjustable resources within the transformer's power supply range at a typical moment; Δ P ESS This represents the expected increase in the installed capacity of flexible regulation resources within the power supply range of the transformer under typical conditions.

[0040] (5) Output the results, and output the statistical indicators and detailed data in JSON and CSV formats.

[0041] In this embodiment, during the parallel verification of system-level carrying capacity intervals in step S3, it is necessary to generate system-level intervals through multi-scenario simulation and perform weight decomposition and conflict redistribution to solve the problem of inconsistency between system-level and equipment-level carrying capacity in traditional methods, and the difficulty in achieving accurate allocation from provincial to county levels. This yields the allocated carrying capacity intervals at each level, providing reliable input for open capacity assessment. Specifically, this includes: Multiple evaluation scenarios are generated by combining the variable range of new energy utilization rate with the range of new energy storage installed capacity. The system-level carrying capacity ranges under each evaluation scenario are calculated in parallel and aggregated to obtain the system-level carrying capacity range of the higher-level administrative region. The decomposition weights are calculated based on the multi-factor comprehensive scores of the lower-level administrative regions, and the system-level carrying capacity interval is decomposed in parallel to each lower-level administrative region according to the decomposition weights to obtain the initial index intervals of each lower-level administrative region. When the upper limit of the equipment-level carrying capacity range is less than the lower limit of the initial index range, it is determined that there is a conflict in the corresponding lower-level administrative region. Based on the transformer ratio of the conflicting lower-level administrative region, the capacity is redistributed to obtain the carrying capacity range of each level after redistribution. The multi-factor comprehensive score is calculated by weighting the electricity load, electricity consumption and number of users of the lower-level administrative regions, the installed capacity of distributed power sources, and the registered scale with the GDP.

[0042] Specifically, when performing parallel verification of the system-level carrying capacity range, this invention first combines the new energy utilization rate variable range and the new energy storage installed capacity range to generate multiple sets of evaluation scenarios. The system-level carrying capacity range under each scenario is calculated in parallel and aggregated to obtain the system-level carrying capacity range of the higher-level administrative region. Then, based on factors such as the electricity load, electricity consumption, number of users, distributed power generation installed capacity, registered capacity, and GDP of the lower-level administrative region, a multi-factor comprehensive score and decomposition weight are calculated. The higher-level system-level carrying capacity range is then decomposed in parallel to each lower-level administrative region according to the weights to obtain the initial indicator range. When the upper limit of the equipment-level carrying capacity range is less than the lower limit of the initial indicator range, a conflict is determined in the corresponding lower-level administrative region, and capacity is redistributed based on the transformer capacity ratio, ultimately obtaining the allocated carrying capacity ranges for each level.

[0043] In this embodiment, during the parallel verification of the system-level carrying capacity range in step S3, it is necessary to further clarify the details of the aggregation calculation of the system-level carrying capacity range under multiple scenarios to solve the problem of inaccurate aggregation of carrying capacity results under different scenarios, thereby accurately obtaining the upper and lower limits of the system-level carrying capacity range of the superior administrative region, and providing an accurate benchmark for subsequent decomposition and verification. Specifically, this includes: Set variable ranges for new energy utilization rates and ranges for new energy storage installed capacity, and combine them to generate multiple sets of evaluation scenarios; Based on the installed capacity of grid-connected distributed power sources as the basic scenario, the system-level basic carrying capacity under each of the aforementioned evaluation scenarios is calculated in parallel. The system-level carrying capacity range under each scenario is determined by iteratively adjusting the installed capacity of distributed power sources until the utilization rate of new energy is equal to the target value. By aggregating the system-level carrying capacity ranges under all scenarios, the minimum carrying capacity at each time point is taken as the lower limit of the system-level carrying capacity range of the superior administrative region, and the maximum carrying capacity is taken as the upper limit of the system-level carrying capacity range of the superior administrative region, thus obtaining the system-level carrying capacity range of the superior administrative region.

[0044] Specifically, when this invention performs parallel calculations of the system-level carrying capacity intervals under various evaluation scenarios and aggregates them to obtain the system-level carrying capacity interval of the higher-level administrative region, it first sets up the variable intervals of new energy utilization rate and the intervals of new energy storage installed capacity, and combines them to generate multiple sets of evaluation scenarios. Taking the installed capacity of grid-connected distributed power sources as the basic scenario, it calculates the system-level basic carrying capacity under each scenario in parallel and iteratively adjusts the installed capacity of distributed power sources until the new energy utilization rate reaches the target value, thereby determining the system-level carrying capacity interval under each scenario. Finally, it aggregates the results of all scenarios, takes the minimum carrying capacity at each moment as the lower limit of the system-level carrying capacity interval of the higher-level administrative region, and the maximum value as the upper limit, to obtain a complete and accurate system-level carrying capacity interval of the higher-level administrative region.

[0045] In one specific implementation, this invention calculates the carrying capacity range of provincial-level administrative regions or independently controlled power dispatch areas through time-series production simulation based on different renewable energy utilization targets and energy storage configuration scenarios, and decomposes the results to county-level administrative regions for verification. It proposes a system-level carrying capacity calculation method based on multi-scenario parallelism, capable of simultaneously processing multiple renewable energy utilization / energy storage configuration scenarios, and achieving efficient allocation from the provincial to the county level through parallel decomposition and verification mechanisms, such as... Figure 6 As shown.

[0046] (1) Parallel calculation of basic values ​​for each scenario, based on the new energy utilization rate variable (adjusted by 1-2 percentage points from the set value) and the new energy storage capacity variable (determined as 5%-10% of the new energy capacity based on existing energy storage). Design cycle assessment: Utilization rate = [Set value - 2%, Set value - 1%, Set value] Energy storage capacity = Existing energy storage + New energy installed capacity × [5%, 7.5%, 10%] Number of scenes: 3 x 3 = 9 scenes Using the grid-connected distributed power generation capacity of this scenario as the basic scenario, and relying on time-series production simulation, the utilization rate of new energy in this scenario is calculated.

[0047] (2) Parallel iterative calculation of the carrying capacity of each scenario. Iterative calculation is performed in parallel at each time point. The simulation is carried out by adjusting the installed capacity of distributed power sources until the utilization rate of new energy is equal to the target value. The carrying capacity calculation results under each scenario are recorded.

[0048] (3) Aggregate the provincial carrying capacity intervals, collect the carrying capacity calculation results at all times, take the minimum value as the lower limit of the provincial carrying capacity, take the maximum value as the upper limit of the provincial carrying capacity, and identify the corresponding boundary scenarios.

[0049] (4) Calculate the weight of each county. Calculate the comprehensive score based on the input elements such as electricity load, electricity consumption, number of users, installed capacity of distributed power sources, registered capacity, and GDP of each county. The score of each county = Σ(element value × corresponding weight), and the weight is the input configuration. The weight of each county = the score of each county / the sum of the scores of all counties. Normalize to obtain the weight of each county.

[0050] (5) Decompose the provincial interval to each county, and allocate the upper and lower limits of the provincial carrying capacity to each county according to the weight of each county. The lower limit of the county system = C_min × county weight, and the upper limit of the county system = C_max × county weight; thus, the carrying capacity interval of each county system is obtained [lower limit of county system, upper limit of county system].

[0051] (6) Verify the results of each county. Apply formula (2) to each county for verification. Compare the system-level interval with the equipment-level interval (from step 3) to determine if there is a conflict. When the upper limit of the equipment-level bearing capacity interval is less than the lower limit of the system-level bearing capacity interval, the system-level bearing capacity should be redistributed.

[0052] (2) In the formula: S represents the distributed power carrying capacity range verification result of the county-level administrative region; S S-min This represents the lower limit of the system-level carrying capacity range for county-level administrative regions. S S-max This represents the upper limit of the system-level carrying capacity range for county-level administrative regions. S D-min This represents the lower limit of the equipment-level load-bearing capacity range for county-level administrative regions. S D-max This represents the upper limit of the equipment-level load-bearing capacity range for county-level administrative regions.

[0053] (7) Conflict County Redistribution: Collect all conflict counties, redistribute system-level capacity according to transformer capacity ratio, and update conflict county results.

[0054] (8) Output system-level carrying capacity results, outputting provincial carrying capacity range and boundary scenario information, and the final carrying capacity range and status of each county in JSON and CSV formats, as inputs for step 5 (open capacity assessment) and step 6 (scheduling reconfiguration optimization).

[0055] In this embodiment, during the parallel computation of available capacity and the division of evaluation levels in step S4, it is necessary to perform level coordination correction based on hierarchical dependencies to solve problems such as inconsistent evaluation results between levels in traditional methods, thereby obtaining stable and reasonable evaluation levels and providing a foundation for subsequent reconstruction and optimization. Specifically, this includes: Based on the allocated carrying capacity intervals of each level and the installed capacity that has been connected to the grid and filed, the grid-connected capacity and the filed capacity that can be opened are calculated in parallel within each parallel computing partition. The evaluation level is determined based on the numerical relationship between the openable filing capacity and the openable grid-connected capacity, and a hierarchical dependency graph of each parallel computing partition is constructed to identify the upper and lower level mapping relationship of each parallel computing partition. When the evaluation level of a lower-level partition is determined to be better than that of a higher-level partition based on the above hierarchical mapping relationship, the evaluation level of the lower-level partition is corrected to be consistent with that of the higher-level partition, and this process is repeated until all partition levels are stable. The open grid-connected capacity is the difference between the lower limit of the carrying capacity range and the already grid-connected installed capacity, and the open filing capacity is the difference between the lower limit of the carrying capacity range and the sum of the already grid-connected and filed installed capacities.

[0056] Specifically, when calculating the available capacity and classifying the evaluation levels based on the allocated carrying capacity intervals of each level, this invention first calculates the available grid-connected capacity and available registration capacity in parallel within each parallel computing partition based on the carrying capacity interval and the installed capacity that has been connected to the grid and filed. Then, it classifies the evaluation levels into three categories: green, yellow, and red, based on the numerical relationship between the two, and constructs a hierarchical dependency graph for each partition to identify the mapping relationship between the upper and lower levels. When it is found that the evaluation level of a lower-level partition is better than that of a higher-level partition, the evaluation level of the lower-level partition is corrected to be consistent with that of the higher-level partition based on the mapping relationship between the upper and lower levels, and this process is recursively executed until all partition levels are stable, ultimately ensuring that the evaluation levels remain consistent across levels.

[0057] In one specific implementation, based on the final carrying capacity range of each county obtained from the aforementioned steps, and combined with the installed capacity of distributed power sources that have been connected to the grid and registered, the available capacity is calculated and classified into levels. A hierarchical dependency-aware parallel scheduling algorithm is proposed, such as... Figure 7 As shown, it can maximize the efficiency of parallel computing at the drawing level while ensuring the principle of "subordinates obeying superiors".

[0058] (1) Construct a hierarchical dependency graph, collect all evaluation objects (county-level administrative regions, 220kV, 110kV, 35kV, 10kV transformer drawings), identify the upper and lower level connection relationships, and construct the parent and child mapping.

[0059] (2) Initialize the parallel computing environment, set the maximum number of outer loop iterations, initialize the status dictionary (status, results, grades), and load the load-bearing capacity range of each drawing. S Installed capacity connected to the grid S _con, installed capacity that has been registered but not yet connected to the grid S _reg.

[0060] (3) The outer loop iteration begins, and (4) to (7) are executed repeatedly until convergence.

[0061] (4) Parallel inner loop computation: Based on the hierarchical dependency-aware scheduling algorithm, all ready drawings from the superior level are selected and computation tasks are submitted in parallel. Each drawing is computed independently, and the grid-connected capacity can be opened. C s1 = S - S _con and available filing capacity C s2 = S - S _con- S _reg.

[0062] (5) Wait for all drawings in this round to be completed, collect the calculation results and update the drawing status.

[0063] (6) External circulation state synchronization, according to C s2min >0 Green C s2min ≤0 and C s1min >0 Yellow C s1min ≤0 (Red): Initially classify the completed drawings into levels, check the red level of the superior and propagate it downwards (the subordinate obeys the superior), and recursively execute until stable.

[0064] (7) Convergence check: Check whether all drawings have been calculated, all drawings have been graded, and there is no grade change in this round. If all conditions are met, converge and exit the outer loop.

[0065] (8) Output the results in JSON and CSV format, including the openable capacity range, assessment level, source of the level, and red warning list for each drawing.

[0066] In this embodiment, after obtaining the openable capacity and evaluation level based on step S4, network reconstruction optimization can be performed in parallel for areas with insufficient load-bearing capacity. Through the parallel optimization mechanism of priority identification and boundary coordination, the problems of low optimization efficiency and difficulty in considering inter-drawing coupling in traditional methods can be solved, thereby outputting an optimization scheme with practical guiding significance and achieving effective improvement in load-bearing capacity. Specifically, it includes: Based on the available capacity and evaluation level, identify objects to be optimized, and perform heuristic switch state search optimization on the objects to be optimized in parallel according to the preset optimization level; Boundary coordination is performed in each parallel computing interval, and the optimized load-bearing capacity improvement, switch state change details, and global switch state table are output as the optimized load-bearing capacity improvement scheme.

[0067] Specifically, when performing network reconfiguration optimization in parallel based on openable capacity and evaluation level, this invention first identifies the objects to be optimized according to the evaluation level, and processes these objects in parallel according to a preset optimization level, using heuristic search to optimize the switch states; at the same time, boundary coordination is performed in each parallel computing interval to ensure that the optimization scheme remains consistent globally; finally, the optimized carrying capacity improvement, switch state change details, and global switch state table are output as an optimization scheme that can directly guide actual power grid dispatch reconfiguration.

[0068] In one specific implementation, for the drawings with insufficient carrying capacity identified in the aforementioned steps (red level, yellow level), the network topology is changed by adjusting the switch states in the network, thereby improving the regional carrying capacity. A multi-drawing parallel optimization algorithm based on priority ranking is proposed, such as... Figure 8 As shown, it is possible to simultaneously perform network reconstruction and optimization on multiple drawings with insufficient load-bearing capacity while taking into account the coupling between drawings, thus greatly improving the optimization efficiency.

[0069] (1) Identify the drawings to be optimized, obtain all red and yellow grade drawings from the openable capacity assessment results in step 5, and distinguish between primary and secondary assessment grade types.

[0070] (2) Primary drawing classification and sorting: classified according to voltage level; primary drawings of the same voltage level are in red with the highest priority, and the optimization order is determined in descending order.

[0071] (3) Process each voltage level in descending order of voltage level.

[0072] (4) In the same voltage level: parallel optimization of drawings, using external loop control, parallel reconstructing optimization of each drawing, searching for the optimal switching state through heuristics, and performing boundary coordination between drawings.

[0073] (5) Within the same voltage level: verify the drawing scheme and verify the capacity constraints and user power supply reliability constraints of all optimized drawings in parallel.

[0074] (6) Output the results in JSON and CSV formats, showing the improved load-bearing capacity of each region, details of the changes in switch status, and the global switch status table.

[0075] Example 2: To implement the bearing capacity assessment method based on primary-secondary micro-parallel power flow calculation in Embodiment 1 above, this invention also provides a bearing capacity assessment system based on primary-secondary micro-parallel power flow calculation, such as... Figure 9 As shown, it includes: The parsing module is used to parse the topology in the power grid to construct an equivalent mapping relationship, and generate multiple parallel computing partitions based on the task dependency graph generated by the equivalent mapping relationship; The calculation module is used to generate representative time series sections based on source load data within the evaluation period, and encapsulate each representative time series section into an independent simulation task to perform power flow calculation in parallel. The allocation module is used to verify the coordination between the system-level and equipment-level capacity ranges in parallel, and to redistribute the capacity of conflicting areas to obtain the allocated capacity ranges for each level. The output module is used to calculate the available capacity in parallel within each parallel computing partition and classify the evaluation level based on the allocated carrying capacity intervals of each level and the installed capacity that has been connected to the grid and filed, and output the grid carrying capacity evaluation results. The system-level carrying capacity range is generated through parallel simulation of multiple scenarios, while the equipment-level carrying capacity range is calculated based on power flow calculation results.

[0076] Preferably, the parsing module is specifically used for: Analyze the power grid topology and identify the connections and boundary nodes between the main grid, distribution network, and microgrids; The lower-level power grid is equivalent to the load in the upper-level power grid, and the upper-level power grid is equivalent to the power source in the lower-level power grid. An equivalent mapping relationship is constructed, and a task dependency graph is constructed based on the equivalent mapping relationship with a single drawing as a node. The task dependency graph is divided into several parallelizable subtask groups using a graph partitioning algorithm based on the principle of minimizing coupling. These parallelizable subtask groups are then dynamically and adaptively adjusted as parallel computing partitions according to the server's memory status.

[0077] Preferably, the computing module is specifically used for: Based on a defined evaluation period and time scale, source-load time-series data and energy storage scheduling strategies are obtained, and feature extraction is performed on the source-load time-series data to obtain source-load time-series data features. Based on the source-load time-series data characteristics and energy storage scheduling strategy, K-means clustering algorithm is used to compress and generate representative time-series profiles. Each representative time series section is encapsulated as an independent simulation task and executed in parallel for power flow calculation.

[0078] Preferably, the calculation of the equipment-level load-bearing capacity range in the allocation module includes: The power grid is decomposed into multiple parallel computing partitions using a single drawing in the task dependency graph as the basic unit. Based on the power flow calculation results, the parallel computing device-level load capacity range is determined within each parallel computing partition. The equipment-level load-bearing capacity range refers to the maximum accessible capacity range of distributed power sources within the power supply range of each parallel computing partition transformer.

[0079] Preferably, the allocation module includes, based on the power flow calculation results, the parallel computing device-level carrying capacity range within each parallel computing partition, comprising: Based on the power flow calculation results, the bearing capacity range of the parallel computing device-level foundation in each parallel computing partition; The calculation results of each parallel computing partition are iteratively synchronized by pre-set boundary conditions. When the synchronization error of the boundary electrical quantities between each parallel computing partition is less than the convergence threshold, the results of each partition are aggregated to obtain a globally consistent equipment-level load-bearing capacity range.

[0080] Preferably, the allocation module is specifically used for: Multiple evaluation scenarios are generated by combining the variable range of new energy utilization rate with the range of new energy storage installed capacity. The system-level carrying capacity ranges under each evaluation scenario are calculated in parallel and aggregated to obtain the system-level carrying capacity range of the higher-level administrative region. The decomposition weights are calculated based on the multi-factor comprehensive scores of the lower-level administrative regions, and the system-level carrying capacity interval is decomposed in parallel to each lower-level administrative region according to the decomposition weights to obtain the initial index intervals of each lower-level administrative region. When the upper limit of the equipment-level carrying capacity range is less than the lower limit of the initial index range, it is determined that there is a conflict in the corresponding lower-level administrative region. Based on the transformer ratio of the conflicting lower-level administrative region, the capacity is redistributed to obtain the carrying capacity range of each level after redistribution. The multi-factor comprehensive score is obtained by weighting the electricity load, electricity consumption and number of users, distributed power generation capacity, and registered capacity of the lower-level administrative regions with the GDP.

[0081] Preferably, the allocation module performs parallel calculations of the system-level carrying capacity intervals under each evaluation scenario and aggregates them to obtain the system-level carrying capacity intervals of the higher-level administrative region, including: Set variable ranges for new energy utilization rates and ranges for new energy storage installed capacity, and combine them to generate multiple sets of evaluation scenarios; Based on the installed capacity of grid-connected distributed power sources as the basic scenario, the system-level basic carrying capacity under each of the aforementioned evaluation scenarios is calculated in parallel. The system-level carrying capacity range under each scenario is determined by iteratively adjusting the installed capacity of distributed power sources until the utilization rate of new energy is equal to the target value. By aggregating the system-level carrying capacity ranges under all scenarios, the minimum carrying capacity at each time point is taken as the lower limit of the system-level carrying capacity range of the superior administrative region, and the maximum carrying capacity is taken as the upper limit of the system-level carrying capacity range of the superior administrative region, thus obtaining the system-level carrying capacity range of the superior administrative region.

[0082] Preferably, the output module, based on the allocated carrying capacity ranges for each level and the installed capacity that has been connected to the grid and registered, performs parallel calculations of the available capacity within each parallel computing partition and classifies the evaluation levels, including: Based on the allocated carrying capacity intervals of each level and the installed capacity that has been connected to the grid and filed, the grid-connected capacity and the filed capacity that can be opened are calculated in parallel within each parallel computing partition. The evaluation level is determined based on the numerical relationship between the openable filing capacity and the openable grid-connected capacity, and a hierarchical dependency graph of each parallel computing partition is constructed to identify the upper and lower level mapping relationship of each parallel computing partition. When the evaluation level of a lower-level partition is determined to be better than that of a higher-level partition based on the above hierarchical mapping relationship, the evaluation level of the lower-level partition is corrected to be consistent with that of the higher-level partition, and this process is repeated until all partition levels are stable. The openable grid-connected capacity is the difference between the lower limit of the carrying capacity range and the already grid-connected installed capacity, and the openable filing capacity is the difference between the lower limit of the carrying capacity range and the sum of the already grid-connected and filed installed capacities.

[0083] Preferably, the system further includes: The optimization module is used to identify objects to be optimized based on the available capacity and evaluation level, and to perform heuristic switch state search optimization on the objects to be optimized in parallel according to a preset optimization level. Boundary coordination is performed in each parallel computing interval, and the optimized load-bearing capacity improvement, switch state change details, and global switch state table are output as the optimized load-bearing capacity improvement scheme.

[0084] Example 3 like Figure 10 As shown, the present invention also provides an electronic device, which may be a computer device, a microcontroller device, a smart mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, processor, and transceiver component are connected via a bus; the memory can be used to store executable programs, and an exemplary executable program may include instructions; the processor is used to execute the instructions stored in the memory. The memory can also be used to store data, which can be accessed and / or modified when instructions are executed.

[0085] The processor may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and it is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions in the storage medium to realize the corresponding method flow or corresponding function, so as to realize the steps of the carrying capacity assessment method based on master-supplier micro parallel power flow calculation in the above embodiments.

[0086] Example 4 Based on the same inventive concept, this invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory). This readable storage medium is a memory device within an electronic device used to store programs and data. It is understood that the storage medium here can include both built-in storage media within the electronic device and extended storage media supported by the electronic device. The storage medium provides storage space, which stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more executable programs (including program code). It should be noted that the storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. Loading and executing one or more instructions stored in the storage medium by the processor can implement the steps of the carrying capacity assessment method based on master-slave micro-parallel power flow calculation in the above embodiments.

[0087] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0088] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0089] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0090] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0091] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of the claims of the present invention pending approval.

Claims

1. A method for carrying capacity assessment based on primary-distribution micro-parallel power flow calculation, characterized in that, include: The topology in the power grid is analyzed to construct an equivalent mapping relationship, and multiple parallel computing partitions are generated based on the task dependency graph generated by the equivalent mapping relationship. Representative time series profiles are generated based on the source load data within the evaluation period, and each representative time series profile is encapsulated as an independent simulation task to perform power flow calculations in parallel. The system checks the coordination between the system-level and equipment-level load-bearing capacity ranges in parallel, and redistributes the capacity of conflicting areas to obtain the allocated load-bearing capacity ranges at each level. Based on the allocated carrying capacity ranges of each level and the installed capacity that has been connected to the grid and filed, the available capacity is calculated in parallel within each parallel computing partition and the evaluation level is divided, and the grid carrying capacity evaluation results are output. The system-level carrying capacity range is generated through parallel simulation of multiple scenarios, while the equipment-level carrying capacity range is calculated based on power flow calculation results.

2. The method according to claim 1, characterized in that, The topology in the analytical power grid is used to construct an equivalent mapping relationship, and multiple parallel computing partitions are generated based on the task dependency graph generated by the equivalent mapping relationship, including: Analyze the power grid topology and identify the connections and boundary nodes between the main grid, distribution network, and microgrids; The lower-level power grid is equivalent to the load in the upper-level power grid, and the upper-level power grid is equivalent to the power source in the lower-level power grid. An equivalent mapping relationship is constructed, and a task dependency graph is constructed based on the equivalent mapping relationship with a single drawing as a node. The task dependency graph is divided into several parallelizable subtask groups using a graph partitioning algorithm based on the principle of minimizing coupling. These parallelizable subtask groups are then dynamically and adaptively adjusted as parallel computing partitions according to the server's memory status.

3. The method according to claim 1, characterized in that, The process of generating representative time series profiles based on source load data within the evaluation period, and encapsulating each representative time series profile into an independent simulation task for parallel power flow calculation, includes: Based on a defined evaluation period and time scale, source-load time-series data and energy storage scheduling strategies are obtained, and feature extraction is performed on the source-load time-series data to obtain source-load time-series data features. Based on the source-load time-series data characteristics and energy storage scheduling strategy, K-means clustering algorithm is used to compress and generate representative time-series profiles. Each representative time series section is encapsulated as an independent simulation task and executed in parallel for power flow calculation.

4. The method according to claim 1, characterized in that, The calculation of the equipment-level load-bearing capacity range includes: The power grid is decomposed into multiple parallel computing partitions using a single drawing in the task dependency graph as the basic unit. Based on the power flow calculation results, the parallel computing device-level load capacity range is determined within each parallel computing partition. The equipment-level load-bearing capacity range refers to the maximum accessible capacity range of distributed power sources within the power supply range of each parallel computing partition transformer.

5. The method according to claim 4, characterized in that, The parallel computing device-level carrying capacity range within each parallel computing partition based on power flow calculation results includes: Based on the power flow calculation results, the bearing capacity range of the parallel computing device-level foundation in each parallel computing partition; The calculation results of each parallel computing partition are iteratively synchronized by pre-set boundary conditions. When the synchronization error of the boundary electrical quantities between each parallel computing partition is less than the convergence threshold, the results of each partition are aggregated to obtain a globally consistent equipment-level load-bearing capacity range.

6. The method according to claim 1, characterized in that, The parallel verification of the system-level and equipment-level load-bearing capacity intervals ensures coordination. Conflicting areas are redistributed to obtain the allocated load-bearing capacity intervals at each level, including: Multiple evaluation scenarios are generated by combining the variable range of new energy utilization rate with the range of new energy storage installed capacity. The system-level carrying capacity ranges under each evaluation scenario are calculated in parallel and aggregated to obtain the system-level carrying capacity range of the higher-level administrative region. The decomposition weights are calculated based on the multi-factor comprehensive scores of the lower-level administrative regions, and the system-level carrying capacity interval is decomposed in parallel to each lower-level administrative region according to the decomposition weights to obtain the initial index intervals of each lower-level administrative region. When the upper limit of the equipment-level carrying capacity range is less than the lower limit of the initial index range, it is determined that there is a conflict in the corresponding lower-level administrative region. Based on the transformer ratio of the conflicting lower-level administrative region, the capacity is redistributed to obtain the carrying capacity range of each level after redistribution. The multi-factor comprehensive score is obtained by weighting the electricity load, electricity consumption and number of users, distributed power generation capacity, and registered capacity of the lower-level administrative regions with the GDP.

7. The method according to claim 6, characterized in that, The parallel computation of system-level carrying capacity intervals under each evaluation scenario and the aggregation to obtain the system-level carrying capacity interval of the higher-level administrative region include: Set variable ranges for new energy utilization rates and ranges for new energy storage installed capacity, and combine them to generate multiple sets of evaluation scenarios; Based on the installed capacity of grid-connected distributed power sources as the basic scenario, the system-level basic carrying capacity under each of the aforementioned evaluation scenarios is calculated in parallel. The system-level carrying capacity range under each scenario is determined by iteratively adjusting the installed capacity of distributed power sources until the utilization rate of new energy is equal to the target value. By aggregating the system-level carrying capacity ranges under all scenarios, the minimum carrying capacity at each time point is taken as the lower limit of the system-level carrying capacity range of the superior administrative region, and the maximum carrying capacity is taken as the upper limit of the system-level carrying capacity range of the superior administrative region, thus obtaining the system-level carrying capacity range of the superior administrative region.

8. The method according to claim 1, characterized in that, The process involves calculating the available capacity and classifying the evaluation levels within each parallel computing partition based on the allocated carrying capacity ranges for each level and the installed capacity that has been connected to the grid and registered. This includes: Based on the allocated carrying capacity intervals of each level and the installed capacity that has been connected to the grid and filed, the grid-connected capacity and the filed capacity that can be opened are calculated in parallel within each parallel computing partition. The evaluation level is determined based on the numerical relationship between the openable filing capacity and the openable grid-connected capacity, and a hierarchical dependency graph of each parallel computing partition is constructed to identify the upper and lower level mapping relationship of each parallel computing partition. When the evaluation level of a lower-level partition is determined to be better than that of a higher-level partition based on the above hierarchical mapping relationship, the evaluation level of the lower-level partition is corrected to be consistent with that of the higher-level partition, and this process is repeated until all partition levels are stable. The openable grid-connected capacity is the difference between the lower limit of the carrying capacity range and the already grid-connected installed capacity, and the openable filing capacity is the difference between the lower limit of the carrying capacity range and the sum of the already grid-connected and filed installed capacities.

9. The method according to claim 1, characterized in that, The method further includes: Based on the available capacity and evaluation level, identify objects to be optimized, and perform heuristic switch state search optimization on the objects to be optimized in parallel according to the preset optimization level; Boundary coordination is performed in each parallel computing interval, and the optimized load-bearing capacity improvement, switch state change details, and global switch state table are output as the optimized load-bearing capacity improvement scheme.

10. A carrying capacity assessment system based on primary-distribution micro-parallel power flow calculation, characterized in that, include: The parsing module is used to parse the topology in the power grid to construct an equivalent mapping relationship, and generate multiple parallel computing partitions based on the task dependency graph generated by the equivalent mapping relationship; The calculation module is used to generate representative time series sections based on source load data within the evaluation period, and encapsulate each representative time series section into an independent simulation task to perform power flow calculation in parallel. The allocation module is used to verify the coordination between the system-level and equipment-level capacity ranges in parallel, and to redistribute the capacity of conflicting areas to obtain the allocated capacity ranges for each level. The output module is used to calculate the available capacity in parallel within each parallel computing partition and classify the evaluation level based on the allocated carrying capacity intervals of each level and the installed capacity that has been connected to the grid and filed, and output the grid carrying capacity evaluation results. The system-level carrying capacity range is generated through parallel simulation of multiple scenarios, while the equipment-level carrying capacity range is calculated based on power flow calculation results.

11. The system according to claim 10, characterized in that, The parsing module is specifically used for: Analyze the power grid topology and identify the connections and boundary nodes between the main grid, distribution network, and microgrids; The lower-level power grid is equivalent to the load in the upper-level power grid, and the upper-level power grid is equivalent to the power source in the lower-level power grid. An equivalent mapping relationship is constructed, and a task dependency graph is constructed based on the equivalent mapping relationship with a single drawing as a node. The task dependency graph is divided into several parallelizable subtask groups using a graph partitioning algorithm based on the principle of minimizing coupling. These parallelizable subtask groups are then dynamically and adaptively adjusted as parallel computing partitions according to the server's memory status.

12. The system according to claim 10, characterized in that, The computing module is specifically used for: Based on a defined evaluation period and time scale, source-load time-series data and energy storage scheduling strategies are obtained, and feature extraction is performed on the source-load time-series data to obtain source-load time-series data features. Based on the source-load time-series data characteristics and energy storage scheduling strategy, K-means clustering algorithm is used to compress and generate representative time-series profiles. Each representative time series section is encapsulated as an independent simulation task and executed in parallel for power flow calculation.

13. The system according to claim 10, characterized in that, The calculation of the equipment-level bearing capacity range in the allocation module includes: The power grid is decomposed into multiple parallel computing partitions using a single drawing in the task dependency graph as the basic unit. Based on the power flow calculation results, the parallel computing device-level load capacity range is determined within each parallel computing partition. The equipment-level load-bearing capacity range refers to the maximum accessible capacity range of distributed power sources within the power supply range of each parallel computing partition transformer.

14. The system according to claim 13, characterized in that, The allocation module, based on power flow calculation results, defines the parallel computing device-level carrying capacity range within each parallel computing partition, including: Based on the power flow calculation results, the bearing capacity range of the parallel computing device-level foundation in each parallel computing partition; The calculation results of each parallel computing partition are iteratively synchronized by pre-set boundary conditions. When the synchronization error of the boundary electrical quantities between each parallel computing partition is less than the convergence threshold, the results of each partition are aggregated to obtain a globally consistent equipment-level load-bearing capacity range.

15. The system according to claim 10, characterized in that, The allocation module is specifically used for: Multiple evaluation scenarios are generated by combining the variable range of new energy utilization rate with the range of new energy storage installed capacity. The system-level carrying capacity ranges under each evaluation scenario are calculated in parallel and aggregated to obtain the system-level carrying capacity range of the higher-level administrative region. The decomposition weights are calculated based on the multi-factor comprehensive scores of the lower-level administrative regions, and the system-level carrying capacity interval is decomposed in parallel to each lower-level administrative region according to the decomposition weights to obtain the initial index intervals of each lower-level administrative region. When the upper limit of the equipment-level carrying capacity range is less than the lower limit of the initial index range, it is determined that there is a conflict in the corresponding lower-level administrative region. Based on the transformer ratio of the conflicting lower-level administrative region, the capacity is redistributed to obtain the carrying capacity range of each level after redistribution. The multi-factor comprehensive score is obtained by weighting the electricity load, electricity consumption and number of users, distributed power generation capacity, and registered capacity of the lower-level administrative regions with the GDP.

16. The system according to claim 15, characterized in that, The allocation module performs parallel calculations of the system-level carrying capacity intervals for each evaluation scenario and aggregates them to obtain the system-level carrying capacity intervals for the higher-level administrative region, including: Set variable ranges for new energy utilization rates and ranges for new energy storage installed capacity, and combine them to generate multiple sets of evaluation scenarios; Based on the installed capacity of grid-connected distributed power sources as the basic scenario, the system-level basic carrying capacity under each of the aforementioned evaluation scenarios is calculated in parallel. The system-level carrying capacity range under each scenario is determined by iteratively adjusting the installed capacity of distributed power sources until the utilization rate of new energy is equal to the target value. By aggregating the system-level carrying capacity ranges under all scenarios, the minimum carrying capacity at each time point is taken as the lower limit of the system-level carrying capacity range of the superior administrative region, and the maximum carrying capacity is taken as the upper limit of the system-level carrying capacity range of the superior administrative region, thus obtaining the system-level carrying capacity range of the superior administrative region.

17. The system according to claim 10, characterized in that, The output module, based on the allocated carrying capacity ranges for each level and the installed capacity that has been connected to the grid and registered, calculates the available capacity in parallel within each parallel computing partition and classifies it into evaluation levels, including: Based on the allocated carrying capacity intervals of each level and the installed capacity that has been connected to the grid and filed, the grid-connected capacity and the filed capacity that can be opened are calculated in parallel within each parallel computing partition. The evaluation level is determined based on the numerical relationship between the openable filing capacity and the openable grid-connected capacity, and a hierarchical dependency graph of each parallel computing partition is constructed to identify the upper and lower level mapping relationship of each parallel computing partition. When the evaluation level of a lower-level partition is determined to be better than that of a higher-level partition based on the above hierarchical mapping relationship, the evaluation level of the lower-level partition is corrected to be consistent with that of the higher-level partition, and this process is repeated until all partition levels are stable. The openable grid-connected capacity is the difference between the lower limit of the carrying capacity range and the already grid-connected installed capacity, and the openable filing capacity is the difference between the lower limit of the carrying capacity range and the sum of the already grid-connected and filed installed capacities.

18. The system according to claim 10, characterized in that, The system also includes: The optimization module is used to identify objects to be optimized based on the available capacity and evaluation level, and to perform heuristic switch state search optimization on the objects to be optimized in parallel according to a preset optimization level. Boundary coordination is performed in each parallel computing interval, and the optimized load-bearing capacity improvement, switch state change details, and global switch state table are output as the optimized load-bearing capacity improvement scheme.

19. An electronic device, characterized in that, include: At least one processor and memory; The memory and processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, the carrying capacity assessment method based on primary-secondary micro-parallel power flow calculation as described in any one of claims 1 to 9 is implemented.

20. A readable storage medium, characterized in that, It contains an execution program, which, when executed, implements the carrying capacity assessment method based on primary-secondary micro-parallel power flow calculation as described in any one of claims 1 to 9.

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