Power distribution network carrying capacity evaluation method and system based on multi-factor coupling

Abstract

The application discloses a power distribution network carrying capacity evaluation method and system based on multi-factor coupling and relates to the technical field of smart grids.The method comprises the following steps: collecting power distribution network operation data, constructing a multi-constraint index system containing electrical safety boundaries and equipment operation limits, generating a sensitivity sorting mapping table through parameter sensitivity identification, constructing a two-stage optimization evaluation model and establishing a regulation cost and fluctuation tolerance mapping, dynamically modifying the plan through multi-time scale hierarchical closed-loop checking, and outputting the final carrying capacity evaluation result.The application solves the technical problem that the existing evaluation method is difficult to distinguish the influence degree of various operation constraints on the carrying capacity of the power grid, lacks a multi-period joint verification mechanism, and the evaluation result is insufficient in accuracy, and cannot support dynamic regulation and control decisions, achieves the technical effects of quantifying the influence weight of different constraint conditions, carrying out multi-level time sequence checking, improving the accuracy of the power grid carrying capacity evaluation result, and reliably outputting the regulation reference basis suitable for the real-time working condition of the power grid.

Description

Technical Field

[0001] This invention relates to the field of smart grid technology, and in particular to a method and system for assessing the carrying capacity of distribution networks based on multi-factor coupling. Background Technology

[0002] In smart grid scenarios, a high proportion of distributed renewable energy is widely integrated into the distribution network. Distribution network capacity is a core indicator for ensuring renewable energy absorption and safe grid operation. Accurate capacity assessment is crucial for grid planning and flexible regulation. Existing distribution network capacity assessment methods often analyze voltage, current, and short-circuit safety constraints in isolation. While suitable for static, closed grid conditions, they have significant shortcomings when facing the complex conditions of dynamic changes in smart grid network topology and the dual random uncertainty of source and load. Current technologies struggle to dynamically generate economically feasible control plans, and the acquired capacity data is incomplete and inaccurate. This fails to support the transformation of smart grids from passive capacity expansion to proactive, flexible regulation, and cannot meet the actual needs of precise control and efficient renewable energy absorption in new distribution networks. Summary of the Invention

[0003] This application provides a method and system for assessing the carrying capacity of distribution networks based on multi-factor coupling. It solves the technical problems of existing assessment methods, such as difficulty in distinguishing the degree of impact of various operational constraints on the carrying capacity of the power grid, lack of multi-time period joint verification mechanism, insufficient accuracy of assessment results, and inability to support dynamic control decision-making.

[0004] The first aspect of this application provides a method for assessing the carrying capacity of a distribution network based on multi-factor coupling. The method includes: collecting distribution network operation data; constructing a multi-constraint index system comprising electrical safety boundaries and equipment operating limits, wherein the multi-constraint index system consists of at least one of voltage constraints, current constraints, and short-circuit constraints; identifying parameter sensitivity of the multi-constraint index system, quantifying the contribution of each parameter to the carrying capacity under different network topologies, and generating a sensitivity ranking mapping table; constructing a two-stage optimization evaluation model based on the constraint priorities defined by the sensitivity ranking mapping table; generating a control plan in the first stage with the goal of maximizing carrying capacity; verifying the feasibility of the control plan based on the most unfavorable operating scenario in the second stage, and establishing a mapping relationship between control costs and fluctuation tolerance; performing a hierarchical closed-loop verification using the sensitivity ranking mapping table and the mapping relationship between control costs and fluctuation tolerance; dynamically correcting the control plan based on operating information at different time scales; and outputting the final evaluation result.

[0005] The second aspect of this application provides a distribution network carrying capacity assessment system based on multi-factor coupling. The system includes: a multi-constraint index system construction module, used to collect distribution network operation data and construct a multi-constraint index system containing electrical safety boundaries and equipment operating limits, wherein the multi-constraint index system consists of at least one of voltage constraints, current constraints, and short-circuit constraints; a sensitivity ranking mapping table generation module, used to identify the parameter sensitivity of the multi-constraint index system, quantify the contribution of each parameter to the carrying capacity under different network topologies, and generate a sensitivity ranking mapping table; a parameter mapping relationship construction module, used to construct a two-stage optimization assessment model with reference to the constraint priorities defined by the sensitivity ranking mapping table, generating a control plan in the first stage with the goal of maximizing carrying capacity, and verifying the feasibility of the control plan based on the most unfavorable operating scenario in the second stage, and establishing a mapping relationship between control costs and fluctuation tolerance; and a final assessment result output module, which applies the sensitivity ranking mapping table and the mapping relationship between control costs and fluctuation tolerance to perform hierarchical closed-loop verification, dynamically corrects the control plan based on operating information at different time scales, and outputs the final assessment result.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] This application obtains the basic assessment results of the power grid carrying capacity by collecting multi-dimensional operation data of the distribution network, relying on the quantitative analysis of the influence of multiple constraints, and carrying out hierarchical time-series joint verification calculations. The assessment values ​​are corrected by combining the grid topology and source-load fluctuation characteristics, thereby dynamically calculating the load that the power grid can accept and the upper limit of new energy sources. This makes the assessment results of the distribution network carrying capacity more accurate and reliable. It achieves the technical effect of quantifying the influence weight of different constraints and carrying out multi-level time-series verification, improving the accuracy of the power grid carrying capacity assessment results, and reliably outputting control reference data adapted to the real-time operating conditions of the power grid. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 This is a flowchart illustrating the distribution network carrying capacity assessment method based on multi-factor coupling provided in the embodiments of this application.

[0010] Figure 2 This is a schematic diagram of the structure of the power distribution network carrying capacity assessment system based on multi-factor coupling provided in the embodiments of this application.

[0011] Figure labeling: Module 1 for constructing a multi-constraint index system, Module 2 for generating a sensitivity ranking mapping table, Module 3 for constructing parameter mapping relationships, and Module 4 for outputting the final evaluation results. Detailed Implementation

[0012] This application provides a method and system for assessing the carrying capacity of distribution networks based on multi-factor coupling. It solves the technical problems of existing assessment methods, such as difficulty in distinguishing the degree of impact of various operational constraints on the carrying capacity of the power grid, lack of multi-time period joint verification mechanism, insufficient accuracy of assessment results, and inability to support dynamic control decision-making.

[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0014] It should be noted that the terms "first," "second," etc., in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to such processes, methods, products, or devices.

[0015] Example 1, as Figure 1 As shown, a distribution network carrying capacity assessment method based on multi-factor coupling is provided, wherein the method includes:

[0016] Collect power distribution network operation data and construct a multi-constraint index system that includes electrical safety boundaries and equipment operating limits. The multi-constraint index system consists of at least one of voltage constraints, current constraints, and short-circuit constraints.

[0017] In this embodiment, the multi-constraint index system is a collection of various electrical safety boundaries and equipment limit indicators, including voltage, current, and short-circuit capacity, which serve as the criterion for assessing load-bearing capacity.

[0018] Specifically, the system first connects to the data acquisition and monitoring control system of the distribution network and reads electrical quantity data according to a preset sampling period. The preset sampling period is pre-configured based on the hardware performance of the target distribution network's data acquisition and monitoring control system and the minimum data refresh rate requirements of the dispatch automation system. The sampling period for steady-state operation monitoring is set to 5 minutes, and the sampling period for over-limit warning and dynamic verification is set to 1 second. The system then reads the voltage amplitude and phase data of each bus node, and the current amplitude, active power, and reactive power data of each feeder branch. Finally, it retrieves the equipment ledger database of the distribution network to obtain the rated current carrying capacity parameters of each distribution line section, the rated capacity and voltage ratio parameters of each distribution transformer, and the rated breaking current parameters of each circuit breaker.

[0019] Next, a network topology identification algorithm based on adjacency matrix is ​​used to analyze the on / off states of all switches in the distribution network and generate the network topology connection relationship at the current moment. This algorithm constructs a Boolean adjacency matrix reflecting the connection relationships of all switching devices, sets the matrix elements corresponding to the closed state of the switches to logical true, and sets the matrix elements corresponding to the open state of the switches to logical false, and performs connectivity traversal on the matrix to output the network topology connection relationship at the current moment. Simultaneously, real-time output data of each distributed power source and real-time power data of each load node are collected. All collected data are timestamped using the system clock of the data acquisition and monitoring control system as a reference. For channel data with sampling time deviations exceeding a preset threshold, linear interpolation is used for resampling to unify them to the same time segment. After timestamping alignment, the numerical range of each channel data is checked, and outliers caused by sensor failures or communication errors are removed. Missing data is marked as null and filled with the value from the previous valid sampling time. Finally, all processed data is stored in a local database, completing the data acquisition process for the distribution network operation.

[0020] Furthermore, when constructing a multi-constraint index system that includes electrical safety boundaries and equipment operating limits, the hierarchy and applicable scenarios of the two types of limits should be clearly defined first. The electrical safety boundary is the limit that each electrical quantity must not exceed under the normal continuous operation of the distribution network, used to ensure the power quality of the power supply and the long-term operating life of the equipment; the equipment operating limit is the maximum allowable electrical quantity limit under short-term extreme operating conditions of the distribution network. Exceeding this limit will cause irreversible damage to the equipment or trigger a power grid safety accident.

[0021] For voltage-related constraints, the allowable deviation range of node voltage is determined according to the operating technical specifications of the corresponding voltage level of the target distribution network. For example, the allowable deviation of node voltage in a 10 kV distribution network is ±7% of the rated voltage, and the allowable deviation of node voltage in a 0.4 kV distribution network is ±10% of the rated voltage. The upper and lower limits of this range are used as the electrical safety boundaries of voltage-related constraints. The limit allowable value of node voltage is determined according to the insulation withstand voltage threshold specified by the manufacturer of the power distribution equipment. This insulation withstand voltage threshold is directly read from the equipment nameplate parameters or the factory test report. This limit value is used as the equipment operating limit for voltage-related constraints.

[0022] For current-related constraints, the long-term allowable heating current value of the distribution line at the corresponding ambient temperature is used as the electrical safety boundary of the current-related constraints, and the current value corresponding to the short-time thermal stability verification of the distribution line is used as the equipment operating limit of the current-related constraints.

[0023] For short-circuit constraints, a safety margin of 80% of the rated breaking current of the circuit breaker is set. The calculated value is used as the short-circuit current safety control threshold and as the electrical safety boundary of the short-circuit constraint. The rated breaking current value of the circuit breaker is used as the equipment operating limit of the short-circuit constraint.

[0024] In practical applications, those skilled in the art can, based on the voltage level of the target distribution network, equipment configuration, and evaluation requirements, have the system initialization module automatically read the constraint type selection flag from the configuration file, or have maintenance personnel select and enable it through the human-machine interface, choosing at least one of voltage constraints, current constraints, and short-circuit constraints to form a complete multi-constraint index system. The completed multi-constraint index system is stored in the form of a structured parameter table, with each record containing the constraint type, limit level, specific value, and corresponding node or equipment number, for direct use in subsequent evaluation steps.

[0025] By collecting comprehensive operational data from the power distribution network and constructing a multi-constraint index system with clearly defined hierarchical levels and clear value rules, standardized input data and unified constraint judgment benchmarks are provided for subsequent carrying capacity assessment.

[0026] The parameter sensitivity of the multi-constraint index system is identified, the contribution of each parameter to the carrying capacity under different network topologies is quantified, and a sensitivity ranking mapping table is generated.

[0027] In this embodiment of the application, the influence contribution is a quantitative value of the magnitude of the impact on the overall carrying capacity or the degree of constraint exceedance of the distribution network when various parameters or branches change.

[0028] Optionally, iterate through various parameters in the multi-constraint index system and extract the operational characteristic data of each network topology. Calculate the magnitude of the carrying capacity change caused by parameter variations to obtain the contribution of each parameter. Compare the contribution within the same topology to determine the highest constraint priority. Summarize the correspondence between topology and priority to generate a sensitivity ranking mapping table. The specific implementation process of this step will be described in detail later.

[0029] Referring to the constraint priorities defined by the sensitivity ranking mapping table, a two-stage optimization evaluation model is constructed. In the first stage, a control plan is generated with the goal of maximizing carrying capacity. In the second stage, the feasibility of the control plan is verified based on the most unfavorable operating scenario, and a mapping relationship between control costs and fluctuation tolerance is established.

[0030] In this embodiment, the two-stage optimization evaluation model is a computational model that breaks down the distribution network carrying capacity assessment into two sequential stages: contingency plan optimization and scenario verification, balancing efficiency and accuracy. The first and second stages are the contingency plan optimization generation stage and the feasibility verification stage based on the most unfavorable scenario, respectively. The most unfavorable operating scenario is the extreme operating condition formed by the combination of source loads and topology within the distribution network, which is most likely to trigger the violation of electrical constraints.

[0031] In one embodiment of this application, a two-stage optimization evaluation model is first constructed, and the specific steps are as follows:

[0032] First, referring to the generated sensitivity ranking mapping table, the constraint priority ranking result corresponding to the current distribution network topology is read. The constraint type with the highest contribution is set as the core rigid constraint, and the remaining constraint types are set as auxiliary verification constraints. This defines the judgment level and tightening degree of the constraint conditions in the model. The tightening degree refers to the fact that the limit of the auxiliary verification constraint can be dynamically relaxed within a certain proportion range during the second stage of verification. The upper limit of relaxation is preset by the operation and maintenance personnel based on the actual operating margin of the equipment. The core rigid constraint is not allowed to relax at any stage. On this basis, a two-stage optimization evaluation model is constructed. This model is a mathematical programming model based on the physical constraints of the distribution network. It belongs to the two-stage decision model under the robust optimization framework. Its solution process does not rely on neural networks or any data-driven black-box fitting methods. All constraints and objective functions follow the power system operation mechanism and operations research optimization theory.

[0033] The two-stage optimization evaluation model adopts a nested structure. The first stage is the bearing capacity maximization optimization sub-model, whose objective function is expressed as follows: , where i represents all distributed generation nodes connected within the distribution network. The target is to maximize the total capacity of distributed power sources that can be connected to the distribution network, where the rated active power of the distributed power sources at the corresponding node is the rated active power. The decision variables of this sub-model include two categories: equipment regulation logic and network connection status. Equipment regulation logic corresponds to variables such as energy storage charging and discharging power, reactive power compensation device switching positions, distributed power source inverter power factor, and on-load tap-changing transformer tap positions. Network connection status corresponds to binary variables such as the on / off states of feeder sectionalizing switches and tie switches. Constraints include node voltage deviation constraints, specifically... j represents all distribution network nodes. and These are the upper and lower limits of the electrical safety boundary corresponding to voltage constraints; including branch current carrying capacity constraints, specifically... , l represents all distribution feeder branches, This refers to the long-term allowable heating current value corresponding to current-type constraints; it includes short-circuit current margin constraints, specifically... k represents all circuit breaker nodes. The safety control thresholds are for short-circuit constraints; at the same time, equipment operation boundary constraints are superimposed, covering the upper and lower limits of energy storage charge state constraints, the tap adjustment range constraints of on-load tap-changing transformers, and the upper limit of the number of daily switch operations constraints.

[0034] The second stage is the sub-model for feasibility verification and control cost optimization under the worst-case scenario. It uses the control plan output from the first stage as input and introduces the worst-case operating scenario as a disturbance condition. Its core objective is to solve the problem of minimizing control cost under the worst-case condition. This sub-model first verifies whether the control plan satisfies all safety constraints under extreme scenarios. If any constraints are exceeded, it simultaneously calculates the total control cost required to eliminate them. The formula for calculating the total control cost is: , The cycle loss cost of energy storage equipment is calculated by multiplying the unit charge-discharge cycle cost by the number of cycles converted from the charge-discharge capacity corresponding to this regulation. The unit charge-discharge cycle cost is obtained by converting the energy storage equipment purchase cost and design cycle life. The cost of wear and tear on the operating life of the voltage regulating equipment is calculated by multiplying the cost of wear and tear per operation by the number of times the voltage regulating gear is operated. The cost of wear and tear per operation is calculated based on the maintenance cycle of the on-load tap-changing transformer and the rated number of operations. The cost of network reconfiguration switching operations is calculated by multiplying the cost per operation by the number of switching actions. The cost per operation is derived from the maintenance cost and mechanical life of the switching equipment. This sub-model simultaneously establishes a functional dependency between fluctuation tolerance and control costs. Fluctuation tolerance is the maximum allowable deviation of electrical quantities from the electrical safety boundary, expressed as a percentage of the corresponding electrical safety boundary limit, ranging from 0% to 5%. The value of this fluctuation tolerance is related to the severity of the sudden drop in irradiance in the most unfavorable operating scenario; the greater the rate of irradiance drop, the greater the allowable voltage deviation of the system. The specific value of the fluctuation tolerance is pre-set by maintenance personnel based on the historical extreme weather statistics of the target distribution network area. The mapping relationship between control costs and fluctuation tolerance is expressed as follows: ,in For fluctuation tolerance, The baseline control cost is denoted as α, which is the attenuation coefficient ranging from 0.2 to 0.5. This expression indicates that as the tolerance for fluctuations increases, the total control cost decreases monotonically according to an exponential law.

[0035] After completing the structural construction of the two-stage model, a deterministic optimization algorithm is used to solve the nested model. The mathematical solution model of the first stage, after second-order cone relaxation, can be transformed into a second-order cone programming problem for solution. The iterative solution of the overall two-stage model uses a column and constraint generation algorithm, obtaining the optimal solution through alternating iterations of the main problem and sub-problems, ultimately yielding an initial control plan that satisfies constraint priorities, and the mapping relationship between control costs and fluctuation tolerance under corresponding operating conditions. Within the constructed two-stage optimization evaluation model framework, the first stage generates the control plan in the following manner, and the second stage verifies the feasibility of the control plan in the following manner. The execution steps of each stage are carried out within a unified model constraint boundary. The specific process of the two-stage execution and the establishment of the mapping relationship is described below:

[0036] Next, the first phase of optimization and evaluation is performed. The sensitivity sorting mapping table is called to identify the highest constraint priority of the current network topology. With the goal of maximizing the carrying capacity of the distribution network, the limit corresponding to the highest constraint priority is used as the core constraint to construct a mathematical solution model containing equipment adjustment logic and topology adjustment logic and solve it. The equipment action sequence and network connection status are output to form a control plan. The specific implementation process of this step will be described in detail later.

[0037] Then, the second-stage optimization evaluation is performed. The source and load power data of the most unfavorable operating scenario are input into the control plan execution logic to simulate the distribution characteristics of electrical quantities in extreme states of the distribution network. The voltage amplitude, current amplitude and short-circuit capacity values ​​are extracted and compared with the electrical safety boundary and equipment operating limit in the multi-constraint index system one by one. When the electrical quantity exceeds the limit, the control plan is determined to be infeasible and relevant information is recorded to generate verification feedback information. The specific implementation process of this step will be described in detail later.

[0038] Finally, the fluctuation tolerance is defined as the maximum deviation limit of the electrical quantity briefly exceeding the electrical safety boundary, and the regulation cost is defined as the total cost of equipment operation required to eliminate the excess quantity, including the loss cost of energy storage equipment, the life loss cost of voltage regulation equipment, and the network reconfiguration operation cost. A functional dependency relationship is established with the fluctuation tolerance as the independent variable and the regulation cost as the dependent variable, and the mapping relationship between the regulation cost and the fluctuation tolerance is obtained. The specific implementation process of this step will also be described in detail later.

[0039] By guiding the model constraint hierarchy with constraint priority, building a nested two-stage mathematical programming model, and clarifying quantitative calculation rules, the system achieves the effect of balancing the potential for increased load-bearing capacity with the safety and reliability under extreme working conditions, and provides a precise quantitative decision-making basis for subsequent graded regulation.

[0040] The sensitivity ranking mapping table and the mapping relationship between the control cost and the fluctuation tolerance are used to perform hierarchical closed-loop verification. The control plan is dynamically modified based on the operating information at different time scales, and the final evaluation result is output.

[0041] Specifically, firstly, in the three stages of day-ahead planning, intraday rolling, and real-time control, the sensitivity ranking mapping table and the mapping relationship between regulation cost and fluctuation tolerance are used to carry out hierarchical closed-loop verification. This process generates the day-ahead energy storage dispatch baseline, adjusts the network power flow distribution, and triggers the inverter reactive voltage regulation. The specific implementation process of this step will be described in detail later.

[0042] Next, the operational information from the three stages is integrated to construct a full-cycle operational data chain. Based on this data chain, the accuracy of the highest constraint priority in the sensitivity ranking mapping table is verified in reverse. The decision variable parameters of the control plan are dynamically adjusted according to the verification results to output the final evaluation results. The specific implementation process of this step will also be described in detail later.

[0043] Furthermore, the method provided in this application embodiment includes:

[0044] The voltage deviation threshold, line current carrying capacity limit, and short-circuit current margin parameters in the multi-constraint index system are traversed to extract operational characteristic data under different network topologies. Based on the operational characteristic data, the carrying capacity change caused by the change of each parameter under each network topology is calculated, and the carrying capacity change is defined as the contribution of the corresponding parameter to the carrying capacity. Under the same network topology, the contribution of all parameters is numerically compared, and the constraint type corresponding to the parameter with the largest contribution is selected and marked as the highest constraint priority under the current network topology. The correspondence between different network topologies and the corresponding highest constraint priority is summarized to generate the sensitivity ranking mapping table.

[0045] In this embodiment, the voltage deviation threshold is the allowable deviation of the node voltage from the rated voltage set during distribution network operation; exceeding this threshold is considered a voltage over-limit. The line current carrying capacity limit is the maximum current value that a distribution line is allowed to carry for a long period under safe operation conditions, determined by the conductor's heating and insulation withstand characteristics. The short-circuit current margin is the difference between the rated breaking current of the circuit breaker at the node and the actual short-circuit current at that node, reflecting the safety margin of the node against short-circuit faults.

[0046] Specifically, the process begins by iterating through the voltage deviation threshold, line current carrying capacity limit, and short-circuit current margin parameters within the multi-constraint index system. Feasible network topologies are obtained by enumerating different combinations of the on / off states of all operable switches within the distribution network and eliminating invalid combinations that do not satisfy the radial operation constraints and connectivity constraints of the distribution network. For each feasible network topology, a forward-backward power flow calculation method is used to obtain the baseline operating state data for that topology. This method calculates the power loss and voltage drop of each branch sequentially from the terminal nodes to the root node, based on the known root node voltage. Then, it calculates the voltage amplitude and phase of each node segment by segment from the root node to the terminal nodes, until the deviation of the node voltage amplitude obtained from two adjacent iterations is less than the preset convergence accuracy.

[0047] Next, operational characteristic data such as voltage amplitude of each node, current amplitude of each branch, and short-circuit capacity of each node are extracted. Based on the obtained baseline operating status data, the output of distributed power sources connected to each node is gradually increased according to the set step size. After each step, the forward-backward power flow calculation is re-executed, and it is verified whether all electrical quantities meet the electrical safety boundaries and equipment operating limits of the multi-constraint index system. When any electrical quantity exceeds its corresponding limit for the first time, the total output of distributed power sources in the entire network corresponding to the previous step is defined as the baseline carrying capacity under this topology, which serves as the reference benchmark for subsequent parameter sensitivity identification.

[0048] Next, based on the operational characteristic data extracted in the above steps, the perturbation method is used to calculate the change in carrying capacity caused by the change of each parameter under each network topology. Specifically: for a single constraint parameter to be analyzed, a small perturbation amplitude of a fixed proportion is applied within its standard allowable value range. The perturbation amplitude is preset to 1% of the corresponding parameter's baseline value. This proportion can be adjusted by maintenance personnel within the range of 1% to 5% based on the parameter measurement accuracy. For the short-circuit current margin parameter, its baseline value is taken as the short-circuit current safety control threshold calculated according to 80% of the circuit breaker's rated breaking current. The values ​​of all other constraint parameters are kept consistent with the baseline state. Here, keeping the values ​​of all other constraint parameters consistent with the baseline state means that the limit values ​​corresponding to the other constraint parameters remain unchanged, not that the values ​​of electrical quantities such as voltage and current in the operating state remain unchanged. During the recalculation of carrying capacity, the values ​​of electrical quantities are naturally determined by the power flow calculation. The maximum carrying capacity of the distribution network under this network topology is recalculated. The difference between the perturbed bearing capacity and the reference bearing capacity is divided by the perturbation amplitude of the corresponding parameter. The calculated result is the contribution of that parameter to the bearing capacity. The dimension of this contribution value is the same as that of the bearing capacity, representing the absolute change in bearing capacity caused by a unit relative change in the parameter. The above perturbation calculation is performed sequentially on all constraint parameters under the same topology to obtain the contribution value of each parameter.

[0049] Within the same network topology, all contribution values ​​corresponding to voltage deviation threshold, line current carrying capacity limit, and short-circuit current margin are sorted in descending order of value, with larger contribution values ​​indicating stronger constraints on the distribution network's carrying capacity. The constraint type corresponding to the parameter with the largest contribution value is selected and marked as the highest constraint priority under the current network topology. The priority ranking results and corresponding contribution values ​​of the remaining constraint types are also retained, forming complete sensitivity ranking data for a single topology.

[0050] Finally, the correspondence between all feasible network topologies and their corresponding highest constraint priorities is summarized, and the complete sensitivity ranking data for each topology is integrated to generate a sensitivity ranking mapping table. This mapping table is stored in the local database as a two-dimensional matrix. The rows of the matrix correspond to different network topology numbers, the columns correspond to different constraint parameter types, and the elements within the matrix store the influence contribution value and priority label of each parameter under the corresponding topology. When calling this mapping table in subsequent steps, the current network topology number is entered as the search condition to directly read the highest constraint priority and the contribution data of each parameter under the corresponding topology, completing the table lookup.

[0051] By quantitatively calculating the influence of various constraint parameters on bearing capacity under different topologies using the perturbation method and generating a structured sensitivity ranking mapping table, the effect of clarifying constraint priorities and improving the pertinence and computational efficiency of bearing capacity assessment is achieved.

[0052] Furthermore, the method provided in this application embodiment includes:

[0053] The sensitivity sorting mapping table is invoked to identify the highest constraint priority under the current network topology; with the maximization of the distribution network carrying capacity as the optimization objective, the limit corresponding to the highest constraint priority is used as the core constraint condition to construct a mathematical solution model that includes equipment adjustment logic and topology adjustment logic; the mathematical solution model is solved to output the equipment action sequence and network connection status that satisfy the core constraint condition, and integrated into the control plan.

[0054] Optionally, within the two-stage optimization evaluation model framework constructed in the aforementioned embodiments, the first stage generates the control plan in the following manner. First, a pre-generated sensitivity ranking mapping table is called to read the unique number corresponding to the current distribution network topology. Using the topology number as the search keyword, a matching query is performed in the mapping table to obtain the ranking results of the influence contribution of three types of constraint parameters—voltage deviation, line current carrying capacity, and short-circuit current—under this topology. The constraint type with the highest contribution value is selected and marked as the highest constraint priority under the current topology. At the same time, the electrical safety boundary limit and equipment operating limit corresponding to this constraint type are extracted as the core judgment threshold for subsequent optimization modeling.

[0055] The optimization objective function is to maximize the total capacity of distributed generation that can be connected to the distribution network. The electrical safety boundary limit corresponding to the highest constraint priority is used as the core hard constraint condition. During the optimization process, it is strictly guaranteed that the electrical quantities do not exceed the limit. At the same time, other constraint types are included as auxiliary verification constraints. In the first stage of the solution process, the limit of the auxiliary verification constraint adopts the original value of the electrical safety boundary in the multi-constraint index system, and relaxation is not allowed. The goal of the solution in this stage is to find a feasible solution that satisfies all constraints, rather than making trade-offs between constraints. The model's decision variables are divided into two categories: one category consists of equipment adjustment logic variables, including continuous and discrete variables such as the charging and discharging active power of energy storage devices, the group switching positions of reactive power compensation devices, the tap positions of on-load tap changers, and the power factor of distributed generation grid-connected inverters. The adjustable range of the power factor of distributed generation grid-connected inverters is 0.8 (leading) to 0.8 (lagging), with a step size of 0.01. The other category consists of topology adjustment logic variables, including binary variables representing the open and closed states of feeder sectionalizing switches and tie switches, where a value of 0 represents an open switch and a value of 1 represents a closed switch. These are then superimposed with constraints on power balance at distribution network nodes, branch power flow, upper and lower limits of equipment operating parameters, and the upper limit of daily switch operation counts, together forming a complete mathematical solution model.

[0056] Next, the completed mathematical model is input into a professional mathematical solver, and a mixed-integer linear programming algorithm is used for calculation. During the solution process, the constraint conditions are checked sequentially according to the constraint priority, prioritizing the satisfaction of the core constraint requirements, and then checking the compliance of auxiliary constraints one by one. After the solution is completed, the optimal calculation result that satisfies all constraints is directly output, which includes the specific action parameters of each adjustable device and the execution order of each device's actions. The execution order is determined based on the speed at which each device's actions affect electrical quantities: the inverter's reactive power regulation has the fastest impact speed and is listed as the first priority; the energy storage charging and discharging power adjustment has the second fastest impact speed and is listed as the second priority; the on-load tap changer's tap changer action has the slowest impact speed and requires coordination with power outages or short-term interruption operations, and is listed as the third priority; the switching operation is generally arranged to be executed last to avoid transient impacts during the operation process affecting the adjustment effect of other devices.

[0057] Finally, after the solver outputs the optimal value in the above order, the execution logic module automatically appends it to the device action sequence according to the fixed priority rules mentioned above. Simultaneously, the network connection status scheme output by the solver includes the combination of the on / off states of each switch node. The two types of output results are integrated to form the initial control plan for the first stage.

[0058] By defining constraint weights based on sensitivity ranking results and calling a mathematical solver to complete optimization calculations, the system achieves the effect of rapidly generating load-bearing capacity maximization control plans and improving evaluation and solution efficiency.

[0059] Furthermore, the method provided in this application embodiment includes:

[0060] The historical deviation characteristics of distributed power generation output and the historical fluctuation characteristics of load electricity consumption behavior are statistically analyzed to construct a two-sided random scenario set containing extreme value samples on the source side and extreme value samples on the load side. Sample combinations covering extreme operating conditions are selected from the two-sided random scenario set, and the irradiance drop characteristics under extreme weather conditions are superimposed on the sample combinations as boundary perturbations to generate the most unfavorable operating scenario.

[0061] Specifically, the generation process for the most unfavorable operating scenario is as follows: First, acquire three consecutive years of historical output data for distributed power sources and three consecutive years of historical power consumption data for each node load within the distribution network. The data sampling interval is set to 15 minutes, and probabilistic feature statistics are performed on a daily basis to extract the probability distribution patterns of source-side output deviation and load-side power consumption fluctuation. These probability distribution patterns are stored in a local database as a cumulative distribution function table. The horizontal axis of the table represents the normalized output value (actual output divided by rated capacity), and the vertical axis represents the cumulative probability value. By performing an inverse transformation lookup based on this cumulative distribution function table, the output and load values ​​corresponding to any quantile can be obtained. The source-side deviation characteristics cover the upper and lower limits of output under different meteorological conditions, while the load-side fluctuation characteristics cover the peak and off-peak load extremes for typical weekdays and holidays. Subsequently, a completeness check is performed on the three consecutive years of historical data, removing invalid data records caused by equipment maintenance, communication interruptions, or sensor failures. Missing periods are not filled with interpolation; the valid data segments are directly used in subsequent probability statistics.

[0062] Next, the Latin hypercube sampling algorithm is used to construct a two-sided random scenario set. First, the range of values ​​for the source-side output random variable and the load-side load random variable is divided into 100 non-overlapping sub-intervals. The upper limit of the range is the maximum value of the variable in the historical data, and the lower limit is the minimum value of the variable in the historical data. If the maintenance personnel believe that the historical extreme values ​​are abnormal or too extreme, the upper limit can be truncated to the 99.5th percentile value and the lower limit can be truncated to the 0.5th percentile value according to the actual situation. Next, a sample point is randomly selected in each sub-interval. The 100 samples selected from the source side and the 100 samples selected from the load side are paired one-to-one according to their position numbers in the sub-interval. That is, the sample of the kth sub-interval on the source side and the sample of the kth sub-interval on the load side are combined into a pair to form 100 basic scenarios. Then, a random shuffling algorithm is used to randomly pair the source samples and load samples again to generate another 100 extended scenarios. Finally, they are merged into a set of 200 bilateral random scenarios, which takes into account both the comprehensiveness of scenario coverage and the efficiency of subsequent verification calculations.

[0063] Next, sample combinations covering extreme operating states are selected from the bilaterally randomized scenario set. The selection criteria are that the distributed power generation output is in a low-output state below the 5th percentile and the load power is in a high-load state above the 95th percentile. The sample combinations that meet the criteria are summarized to form the initial extreme scenario set. If there are fewer than 10 sample combinations that meet both of the above criteria, the selection criteria for distributed power generation output are relaxed to below the 10th percentile and the selection criteria for load power are relaxed to above the 90th percentile to ensure that the initial extreme scenario set has statistical representativeness.

[0064] Then, a threshold for quantifying the sudden drop in irradiance under extreme weather conditions is defined, specifically set at a decrease in irradiance exceeding 30% of the initial irradiance for the corresponding period within 10 consecutive minutes. The irradiance drop curve adopts a linear step-drop shape, with the drop process lasting 10 minutes, decreasing at a constant amplitude every minute during the drop, and maintaining a low irradiance state for 20 minutes after the drop. Based on this threshold and curve shape, a standard irradiance drop time curve is generated. This curve is used as a boundary disturbance condition and superimposed on the distributed power output sequence of the initial extreme scenario set, synchronously updating the node injection power data for the corresponding period. Finally, the most unfavorable operating scenario, which takes into account both extreme values ​​of source and load and sudden weather disturbances, is generated as the input condition for the feasibility verification of the second-stage control plan. The generation process of this most unfavorable operating scenario is constructed in the manner of independent extreme conditions, without considering the power support that energy storage devices may provide during this period. The scenario design goal is to expose the worst electrical quantity distribution of the system under pure source and load conditions to test the inherent robustness of the control plan under extreme conditions.

[0065] By constructing a set of bilateral random scenarios covering three years of historical characteristics through Latin hypercube sampling and superimposing a quantitative irradiance drop disturbance with a clear shape, the comprehensiveness and extremeness of the coverage of the most unfavorable operating scenarios are improved, and the accuracy and reliability of the feasibility verification results of subsequent control plans are ensured.

[0066] Furthermore, the method provided in this application embodiment includes:

[0067] The source-load power data in the most unfavorable operating scenario is input into the execution logic of the control plan to simulate the electrical quantity distribution characteristics of the distribution network under extreme conditions. The voltage amplitude, current amplitude, and short-circuit capacity values ​​in the electrical quantity distribution characteristics are extracted and compared one by one with the electrical safety boundary and equipment operating limit in the multi-constraint index system. If any electrical quantity value exceeds the electrical safety boundary or equipment operating limit, the control plan is determined to be infeasible, and the time of the exceedance, the type of the exceedance parameter, and the magnitude of the exceedance are recorded to generate verification feedback information.

[0068] Specifically, based on the first-stage generation of the control plan, this embodiment performs the second-stage feasibility verification within the established two-stage optimization evaluation model framework in the following manner. First, the distributed power output data and load power data for each time period in the generated worst-case operating scenario are used as node injection values. Combined with the equipment action sequence and network connection status corresponding to the control plan, a deterministic AC power flow calculation is performed using the forward-backward substitution method adapted to the radial distribution network. The initial value of the forward-backward substitution method is set to the voltage of each node as the per-unit value of the rated voltage of 1.0, that is, the voltage amplitude is set to 1.0 times the rated voltage, and the phase is set to 0 degrees. The convergence criterion for the iteration is set to the maximum value of the voltage amplitude deviation of each node in two adjacent iterations being less than 1.0 times the rated voltage. Per-unit value. The calculation process strictly follows the power balance constraints of distribution network nodes and the electrical characteristic constraints of branches. Iteratively solving for each time period, the distribution of electrical quantities of the entire distribution network under extreme operating conditions is obtained, and the voltage phasor of each node and the current phasor of each feeder branch are output. Simultaneously, the short-circuit capacity of each bus node is calculated based on the positive sequence impedance parameters of the nodes obtained from the power flow calculation. The short-circuit capacity is calculated by dividing the square of the rated voltage of the node by the magnitude of the positive sequence impedance of the node, i.e. ,in, Let be the short-circuit capacity of the k-th bus node; The rated voltage of the distribution network busbar node; Let be the positive sequence impedance of the k-th bus node. The positive sequence impedance of a node is the magnitude of the positive sequence impedance. If multiple distributed generation sources operate in parallel in the distribution network, the positive sequence impedance of each node is provided by the diagonal elements of the node impedance matrix obtained from the power flow calculation, thus obtaining the short-circuit capacity value of the corresponding node.

[0069] After completing the power flow calculation and short-circuit capacity determination, three types of core comparison data items are extracted from the electrical quantity distribution results: the voltage amplitude of each node, the current amplitude of each distribution branch, and the short-circuit capacity value of each circuit breaker node. Each extracted electrical quantity value is compared point-by-point with a pre-constructed multi-constraint index system. Specifically, the voltage amplitude is compared sequentially with the upper and lower limits of the allowable voltage deviation corresponding to the electrical safety boundary and the insulation withstand voltage limit corresponding to the equipment operating limit; the current amplitude is compared sequentially with the long-term heating allowable current value of the line corresponding to the electrical safety boundary and the short-time thermal stability current value of the line corresponding to the equipment operating limit; and the short-circuit capacity is compared sequentially with the short-circuit current safety control threshold corresponding to the electrical safety boundary and the rated breaking current value of the circuit breaker corresponding to the equipment operating limit.

[0070] If any electrical quantity value exceeds the constraint limit of the corresponding level during the comparison process, the control plan is determined to be infeasible under the most unfavorable operating scenario. The occurrence time of the limit-breaking event, the type of constraint parameter exceeded, the absolute value of the limit exceeded, and the percentage of the limit exceeded relative to the reference limit are recorded simultaneously. The reference limit is uniformly adopted as the limit value corresponding to the electrical safety boundary, i.e., the percentage of the limit exceeded = (absolute value of the limit exceeded - electrical safety boundary limit) ÷ electrical safety boundary limit × 100%. Finally, all the limit violation information is integrated to generate standardized verification feedback information, which is fed back to the first-stage optimization model for subsequent adjustment of the constraint tightening degree and the value range of optimization variables, supporting the iterative correction of the control plan. If the limit violation occurs on a certain auxiliary verification constraint, the electrical safety boundary limit of that constraint is increased by a margin coefficient corresponding to the limit violation ratio on the original value in the next round of iterative optimization. The margin coefficient is taken as 1.1 to 1.3 in example, and the solution is returned to the first stage. If the limit violation occurs on a core rigid constraint, the combination of decision variables that caused the limit violation is directly removed in the next round of iterative optimization, and no further adjustment is made.

[0071] By substituting extreme scenario data to perform forward-backward alternating current power flow calculations and conducting category-by-category limit-crossing judgments, the system achieves the effect of accurately verifying the safety and feasibility of control plans under extreme operating conditions and precisely locating limit-crossing risk points.

[0072] Furthermore, the method provided in this application embodiment includes:

[0073] The fluctuation tolerance is defined as the maximum deviation limit that allows the electrical quantity value to briefly exceed the electrical safety boundary; the regulation cost is defined as the total cost of equipment operation required to eliminate the excess value, the total cost of equipment operation includes the energy storage equipment loss cost, the voltage regulation equipment life loss cost, and the network reconfiguration operation cost; a functional dependency relationship is established with the fluctuation tolerance as the independent variable and the regulation cost as the dependent variable, forming a mapping relationship between the regulation cost and the fluctuation tolerance.

[0074] Specifically, based on the completion of the second-stage feasibility verification and the acquisition of verification feedback information in the aforementioned steps, this embodiment further establishes a mapping relationship between control costs and fluctuation tolerance, used to quantify the control input costs corresponding to exceeding limits. First, the quantitative definition of fluctuation tolerance is completed. Fluctuation tolerance characterizes the maximum deviation limit of an electrical quantity value from the electrical safety boundary during a brief period. It is valued in scalar form, with the fluctuation tolerance value range set from 0 to 0.1. A value of 0 represents that no electrical quantity is allowed to exceed the limit, and a value of 0.1 represents that the electrical quantity is allowed to deviate from the electrical safety boundary benchmark value by a maximum of 10%. The fluctuation tolerance scalar is uniformly applied to voltage and current constraints, and the corresponding long-term heating allowable current value and voltage deviation upper and lower limits are relaxed by the same proportion. Short-circuit constraints do not participate in fluctuation tolerance relaxation, and their electrical safety boundaries and equipment operating limits are strictly maintained at their original values ​​in all stages. Based on the adjustable equipment type of the distribution network, the control cost is broken down into its components. The control cost is the total cost of various equipment actions to eliminate the over-limit value obtained from the verification. The components include the energy storage equipment loss cost, the voltage regulation equipment life loss cost, and the network reconfiguration operation cost. Independent quantitative mathematical expressions are constructed for each of the three types of components.

[0075] The energy storage equipment loss cost is quantified using a linear function, expressed as: Energy storage equipment loss cost = Single charge / discharge power of energy storage × Duration × Energy storage unit power loss coefficient. The duration is determined based on the control phase: hourly duration (typically 1 hour) for the day-ahead planning phase, 15-minute duration for the intraday rolling phase, and second-level duration (typically 1 second) for the real-time control phase. The duration of each phase is automatically matched by the system clock cycle. The energy storage unit power loss coefficient is pre-calibrated based on the energy storage battery's factory test data, with units of yuan per kilowatt-hour. The voltage regulation equipment lifespan loss cost is quantified using an exponential function, with the complete mathematical expression as follows: N represents the cumulative number of times the pressure regulating equipment has been adjusted during this control period; The unit is the base cost of a single voltage regulation operation, expressed in yuan per operation; α is the exponential decay coefficient, typically 1.02; N-1, as the power exponent, reflects the accelerated aging effect caused by frequent adjustments. The network reconfiguration operation cost is quantified using a linear function, expressed as: Network reconfiguration operation cost = Number of switching actions × Base cost of a single switching operation. The base cost of a single switching operation is determined by combining manual maintenance costs and switch mechanical wear costs, expressed in yuan per operation.

[0076] Finally, the overall control cost is obtained by summing the three types of costs. The complete expression for the total control cost is that the total control cost equals the sum of the energy storage equipment loss cost, the voltage regulation equipment lifespan loss cost, and the network reconfiguration operation cost. A functional dependency relationship is constructed using fluctuation tolerance as the independent variable and control cost as the dependent variable. The larger the fluctuation tolerance value, the higher the allowable exceedance of electrical quantities in the distribution network, and the lower the control cost required to eliminate the exceedance. Conversely, the smaller the fluctuation tolerance value, the stricter the constraints on electrical quantities in the distribution network, and the higher the corresponding control cost. Function calibration parameters are set according to grid operation and management preferences. When the operation and maintenance preference emphasizes safe operation, the upper limit of the fluctuation tolerance range is set to 0.05; when the operation and maintenance preference emphasizes reducing equipment losses, the upper limit of the fluctuation tolerance range is set to 0.1. After parameter calibration, a complete mapping relationship between control cost and fluctuation tolerance is formed. This mapping relationship is stored in the optimization parameter table of the local database in the form of a function expression. When constructing the objective function in the first stage of the optimization model, the control cost is introduced as a penalty term of the objective function by calling the mapping function expression in this parameter table. The penalty coefficient is automatically loaded according to the preset weight corresponding to the current operating stage.

[0077] By constructing cost quantification formulas for energy storage equipment, voltage regulation equipment, and network reconfiguration operations, and deriving the total cost expression, and using fluctuation tolerance as the independent variable to build a cost mapping function, the system achieves the effect of quantifying the equipment regulation input corresponding to different tolerance levels and supporting the optimization model to take into account both safety constraints and operation and maintenance costs.

[0078] Furthermore, the method provided in this application embodiment includes:

[0079] During the day-ahead planning phase, the sensitivity ranking mapping table is used to identify the expected highest constraint priority for future periods, and the economically optimal energy storage equipment output range is determined based on the mapping relationship between the control cost and fluctuation tolerance, generating the day-ahead energy storage scheduling baseline. During the intraday rolling phase, the latest source load forecast information is obtained at a preset cycle. If the source load forecast information indicates that the operating data is about to exceed the day-ahead energy storage scheduling baseline, the adjustable branches are retrieved according to the sensitivity ranking mapping table, and the switching combination mode is changed to adjust the power flow distribution. During the real-time control phase, the instantaneous values ​​of node voltage and branch current are monitored respectively. When the instantaneous value exceeds the electrical safety boundary, the inverter reactive power voltage regulation strategy is immediately triggered.

[0080] In one embodiment, based on the completion of the two-stage optimization evaluation model solution and the construction of the mapping relationship between control costs and fluctuation tolerance, this embodiment further divides the time scale into three levels from long to short: day-ahead planning, intraday rolling, and real-time control, and performs hierarchical closed-loop verification. Each level is connected in sequence, and the adjustment accuracy and response speed are refined layer by layer to form a multi-time-dimensional closed-loop verification system.

[0081] In the current planning phase, the scheduling cycle is based on the entire 24 hours of the following day. First, a pre-generated sensitivity ranking mapping table is called to match the constraint sensitivity ranking results under the current topology of the distribution network. The expected highest constraint priority with the greatest impact on carrying capacity is identified. The expected highest constraint priority is based on the distributed power generation output forecast and load forecast data of the following day. The sensitivity ranking mapping table is called for pre-matching for each preset time period. The constraint priority type that appears most frequently in the 24 hours of the day is counted and marked as the expected highest constraint priority of the following day. This is used as the core constraint boundary for the current day optimization. Subsequently, the mapping relationship between regulation cost and fluctuation tolerance is invoked. With the goal of minimizing the total cost of regulation throughout the entire cycle, the energy storage regulation cost corresponding to different fluctuation tolerance values ​​is traversed. Within the fluctuation tolerance value range of 0 to 0.1, 11 discrete points are selected in sequence with a step size of 0.01. For each discrete point, the mapping relationship function between regulation cost and fluctuation tolerance is invoked to calculate the corresponding energy storage charging and discharging power and state of charge trajectory. From this, the energy storage output range corresponding to the discrete point with the lowest total cost of regulation throughout the entire cycle is selected as the optimal solution. Simultaneously, the baseline of the energy storage state of charge for each time period is determined to form a complete day-ahead energy storage scheduling plan, which serves as the benchmark reference for the subsequent two stages.

[0082] The intraday rolling phase adopts a fixed-cycle rolling update method with a preset rolling cycle of 15 minutes. When each rolling window starts, the latest distributed power generation output forecast and load power consumption forecast data are first obtained. The forecast data is then substituted into the current network topology to perform fast forward and backward power flow calculations to obtain the node voltage and branch current change trends in the short term. The calculation results are compared with the day-ahead energy storage dispatch baseline. The allowable deviation range for charging and discharging power in each time period of the day-ahead energy storage dispatch plan is set at ±5%. If the operating data is determined to be about to exceed this deviation range, the adjustable branch with the highest sensitivity to over-limit constraint adjustment is retrieved according to the sensitivity ranking mapping table. The optimal switch combination scheme is selected based on the principle of minimizing the number of switching actions and achieving the most balanced power flow transfer. The most balanced power flow transfer is determined by minimizing the root mean square error of current changes in adjacent branches before and after topology adjustment. That is, the switch combination scheme with the smallest root mean square error of current changes is prioritized to avoid new overload risks caused by concentrated power flow in local branches. The network topology is reconstructed by changing the open / closed states of sectionalizing switches and tie switches to adjust the power flow distribution path and promptly eliminate potential over-limit risks. Simultaneously, the switching action information and adjustment effect of this topology correction are recorded. After the intraday rolling phase is completed, any over-limit risks not eliminated within the current rolling window will be transferred to the real-time control phase for further processing.

[0083] The real-time control phase employs a millisecond-level continuous monitoring mechanism. Instantaneous voltage values ​​at each node and instantaneous current values ​​in each branch are collected in real-time via distribution network terminal measurement equipment at 10-millisecond intervals. The 10-millisecond acquisition cycle is determined jointly by the hardware sampling capability of the distribution network terminal measurement equipment and the response time constant of the inverter's reactive power voltage regulation strategy, ensuring at least three complete sampling cycles before the inverter completes a single regulation action. The collected data is then compared cycle-by-cycle with preset electrical safety boundary thresholds. When the instantaneous value of any node voltage or branch current exceeds the electrical safety boundary, the reactive power voltage regulation strategy of the distributed power grid-connected inverter is immediately triggered. The inverter's reactive power regulation step size is set to 2% of the distributed power grid inverter's rated capacity, and the single regulation upper limit is 40% of the inverter's rated capacity. The specific single regulation upper limit is determined based on the safe operating range of the distributed power grid-connected inverter's reactive power capacity margin. By rapidly adjusting the inverter's reactive power output and changing the reactive power injected into the nodes, voltage fluctuations are smoothed and current overruns are suppressed within a millisecond timescale, achieving rapid closed-loop correction of fault conditions. The over-limit events detected in the real-time control phase are synchronously transmitted back to the intraday rolling module. The data transmitted back includes the over-limit node number, over-limit time, over-limit electrical quantity type and value, and inverter reactive power regulation action amount. After receiving the above data, the intraday rolling module increases the sensitivity weight coefficient of the corresponding node and branch by an example of 1.2 times in the constraint boundary calculation of the next rolling window to enhance the response priority to the over-limit area and form a closed-loop feedback between levels.

[0084] By conducting hierarchical closed-loop verification of constraint priority identification, topology rolling adjustment, and millisecond-level voltage correction in sequence according to three time scales of day-ahead, intraday, and real-time, the system achieves the effect of balancing operational economy and power supply security, and dynamically improving the accuracy of distribution network carrying capacity assessment results.

[0085] Furthermore, the method provided in this application embodiment includes:

[0086] The sensitivity sorting mapping table is called to retrieve the constraint priority of the current over-limit parameter. If the current over-limit parameter is the highest constraint priority, the network topology adjustment operation is blocked, and the inverter power factor is forcibly adjusted to the target range, using reactive power injection to suppress voltage fluctuations. If the current over-limit parameter is not the highest constraint priority, the mapping relationship between the control cost and the fluctuation tolerance is called to calculate the allowable duration corresponding to the current voltage over-limit. If the voltage recovers to the normal range within the allowable duration, the current switching combination mode is maintained; otherwise, the network reconfiguration operation is initiated.

[0087] Optionally, after detecting an electrical quantity exceeding the limit and triggering the reactive power and voltage regulation strategy of the distributed power grid-connected inverter during the aforementioned real-time control phase, the specific regulation process is executed as follows: After detecting an electrical quantity exceeding the limit and triggering the regulation process during the real-time control phase, the pre-stored sensitivity sorting mapping table is first called. Using the type of the current exceeding-limit parameter as the search keyword, the constraint priority position of the parameter under the current network topology is matched and queried. In the mapping table, all constraint parameters under the same topology are arranged from high to low according to their influence contribution value, and the priority number of each parameter is directly stored, where 1 represents the highest. The sorting results are updated synchronously with the topology structure. Then, the priority position obtained by the query is compared with the highest priority mark of the current topology to determine whether the exceeding-limit parameter belongs to the highest constraint priority.

[0088] If the current out-of-limit parameter is determined to be the highest constraint priority, the system immediately outputs a blocking command, blocking all network topology adjustment operations issued during the day's rolling phase, locking the open / closed status of all current sectionalizing switches and tie switches, and preventing power flow shifts caused by switch actions from further exacerbating the core constraint out-of-limit. Simultaneously, a forced adjustment command is issued to the distributed power source grid-connected inverter to gradually adjust the inverter's power factor. The direction of power factor adjustment is determined based on the out-of-limit type: when the voltage exceeds the upper limit, it is adjusted in the lagging direction, i.e., increasing reactive power absorption; when the voltage exceeds the lower limit, it is adjusted in the leading direction, i.e., increasing reactive power generation. The target power factor range is any value between 0.9 (leading) and 0.95 (lagging). Each adjustment step is 2% of the rated capacity. By increasing the reactive power injection, the node voltage support capability is changed, continuously suppressing voltage fluctuations until the out-of-limit parameter falls back within the electrical safety boundary. After the limit is cleared, the system maintains a 30-second blocking delay. The 30-second delay is determined based on the duration of the transient voltage transition process after the inverter power factor is adjusted, ensuring that the voltage has entered a steady state and there is no rebound trend before the blocking is lifted. This delay value can be adjusted by the operation and maintenance personnel within the range of 15 to 60 seconds according to the voltage response characteristics of the target distribution network. After confirming that the voltage is stable and there are no recurrences, the topology blocking state is gradually lifted and the normal intraday rolling adjustment authority is restored.

[0089] If the current out-of-limit parameter is determined not to be the highest constraint priority, the system invokes the mapping relationship curve between control cost and fluctuation tolerance. First, it calculates the relative deviation ratio of the current out-of-limit amount, i.e., the ratio of the out-of-limit amplitude to the corresponding electrical safety boundary threshold. Then, it substitutes the deviation ratio value into a predefined duration threshold function. Where δ is the deviation ratio, ranging from 0 to 1; The maximum permissible deviation ratio preset for operation and maintenance, typically set to 0.1. The maximum permissible duration is set at a base time, typically 300 seconds, and the final permissible duration threshold is calculated. The greater the deviation, the shorter the permissible duration, and vice versa.

[0090] The system initiates a continuous over-limit timer while maintaining millisecond-level continuous monitoring of the electrical quantity. If the over-limit parameter automatically recovers to within the electrical safety boundary within the allowable duration threshold, it is determined to be a short-term random fluctuation. The current switch combination remains unchanged, and no network reconfiguration operation is performed, thereby reducing switch action losses. During the continuous timer, in addition to monitoring the current over-limit parameter, the voltage of all nodes and the current of all branches are monitored simultaneously. If any other electrical quantity exceeds the equipment operating limit during the waiting period, the self-healing wait is immediately terminated, and the system is treated as a continuous over-limit and network reconfiguration is initiated.

[0091] If the over-limit is not eliminated after the timing duration reaches the allowable duration threshold, it is determined to be a persistent over-limit, and the network reconfiguration operation is formally initiated. Based on the sensitivity ranking results, the branch switch combination with the best regulation efficiency is selected to complete the power flow adjustment. The optimal regulation efficiency is determined by maximizing the improvement effect of the current over-limit node voltage or over-limit branch current after the branch switch is activated. The improvement effect is calculated by substituting the change in the node impedance matrix after the branch switch's open / closed state into the power flow equation. If, after the reconfiguration operation is completed, the power flow distribution is recalculated and the over-limit parameters still have not fallen back to within the electrical safety boundary, the emergency control mode is switched to, and the active power output of the corresponding distributed power source is directly reduced by a reduction step of 10% of the current output until the over-limit is eliminated.

[0092] By combining priority determination with the adaptive decision-making logic of cost mapping curve, the topology blocking rules, voltage regulation parameters and reversion conditions under the highest priority limit exceedance are clarified, as well as the self-healing waiting or reconfiguration trigger determination criteria under the non-highest priority limit exceedance. This achieves the effect of reducing equipment control costs and reducing unnecessary switching actions while ensuring the safety of core constraints.

[0093] Furthermore, the method provided in this application embodiment includes:

[0094] Integrate the day-ahead energy storage scheduling baseline from the day-ahead planning phase, the topology correction records from the intraday rolling phase, and the adjustment logs from the real-time control phase to generate a full-cycle operation data chain; based on the full-cycle operation data chain, reverse verify the accuracy of the highest constraint priority in the sensitivity sorting mapping table, and dynamically adjust the decision variable parameters in the control plan according to the verification results, and output the final evaluation result.

[0095] In one embodiment, after completing the entire 24-hour day-ahead planning, intraday rolling, and real-time control three-level hierarchical closed-loop verification process, the operational data of the three stages are collected and integrated in chronological order. The hourly energy storage scheduling baseline, planned state of charge value, and constraint priority prediction results (24 hours a day, segmented by hour) output from the day-ahead planning stage are extracted. The trigger time, switching action combination, power flow adjustment magnitude, and over-limit handling records for each topology correction in the intraday rolling stage are extracted. The occurrence time, over-limit parameter type, adjustment step size, and voltage recovery time of all inverter regulation events in the real-time control stage are extracted. All data are aligned and matched according to a unified timestamp, and the corresponding source-load measured data and network topology status markers are added to form a full-cycle operational data chain covering the entire 24 hours of the day. Each time segment contains three types of data: planned value, actual executed value, and electrical quantity response results, providing a complete data foundation for subsequent reverse verification.

[0096] Accuracy verification of the sensitivity ranking mapping table is conducted based on the full-cycle operation data chain. Actual limit-crossing events of various constraint parameters are statistically analyzed throughout the entire operation cycle. The statistical scope includes all auxiliary verification constraint parameters. The highest priority constraint is strictly maintained without exceeding limits during operation, and its limit-crossing events are not included in the statistics. Three indicators are calculated for each type of constraint: limit-crossing frequency, maximum single limit-crossing amplitude, and cumulative limit-crossing duration. Fixed weighting coefficients are assigned to these three indicators; for example, limit-crossing frequency corresponds to a weight of 0.3, maximum single limit-crossing amplitude corresponds to a weight of 0.4, and cumulative limit-crossing duration corresponds to a weight of 0.3. The weighted summation is used to calculate the true impact weight of each constraint on the actual operational carrying capacity. The ranking result of the actual impact weights is compared with the originally predicted highest constraint priority in the sensitivity ranking mapping table. The matching degree is calculated as follows: the number of constraint types whose ranking positions are completely consistent with the predicted ranking and the actual ranking is divided by the total number of all constraint types, and the percentage obtained is the matching degree. The preset matching degree qualified threshold is 85%. If the matching degree is lower than this threshold, it is determined that the accuracy of the original ranking result is insufficient, and the parameter correction process needs to be started. The ranking result of the actual impact weights of all constraint types is used as the basis for updating, and a complete sensitivity ranking mapping table is regenerated, rather than only adjusting the highest priority item.

[0097] Then, based on the actual impact weights obtained from verification, the constraint priority ranking in the sensitivity ranking mapping table is dynamically updated. Simultaneously, the decision variable parameters in the control plan are corrected, the allowable range of energy storage device charging and discharging output and voltage regulation priority are adjusted, the branch selection order and switching action sequence of network reconfiguration are optimized, and the trigger threshold and step size parameters of inverter reactive power regulation are corrected. The correction rule for the trigger threshold is as follows: if the actual impact weight of a certain type of constraint is higher than the expected weight corresponding to its predicted ranking position, the reactive power regulation trigger threshold of the inverter corresponding to that type of constraint is corrected downwards, i.e., the trigger threshold is lowered to initiate regulation earlier. The correction magnitude is proportional to the proportion by which the actual impact weight exceeds the expected weight, with an example proportionality coefficient of 1.2. Conversely, if the actual impact weight is lower than the expected weight, the trigger threshold is corrected upwards, i.e., the trigger threshold is raised to reduce unnecessary regulation, with the correction magnitude following the same principle.

[0098] After parameter correction, the updated control plan is substituted into the most unfavorable operating scenario to recalculate the carrying capacity. The corrected total capacity of distributed generation that can be connected to the distribution network is obtained and output as the final carrying capacity assessment result. At the same time, the corrected mapping table and control parameters are synchronized to the day-ahead planning module of the next operating cycle, forming a continuous iterative closed-loop optimization mechanism. If there are still over-limit issues after re-verification, the parameter correction process is repeated. The adjustment range of each round of correction decreases by 50% in each round until all constraints are met or the maximum number of iterations is reached, which is preset to 5. If the verification still fails after 5 iterations, the current carrying capacity assessment result is output and marked as "conservative estimate", and an abnormal carrying capacity assessment alarm is issued to the operation and maintenance personnel.

[0099] By integrating and aggregating operational data across multiple stages throughout the entire lifecycle and performing reverse verification and correction with clearly defined quantitative indicators, dynamic iterative optimization of the sensitivity ranking model and control plan was achieved, resulting in a continuous improvement in the accuracy of distribution network capacity assessment and the adaptability of control scheme operation.

[0100] In summary, the distribution network carrying capacity assessment method based on multi-factor coupling provided in this application has the following technical effects:

[0101] This application employs a multi-timescale hierarchical closed-loop verification system, encompassing day-ahead, intraday, and real-time dimensions. By combining a sensitivity ranking mapping table with the mapping relationship between control costs and fluctuation tolerance, and through limit-over-limit judgment, topology adjustment, and inverter voltage regulation, the control plan is dynamically revised and the accuracy of constraint priority is verified in reverse. This allows for a precise assessment of the distribution network's carrying capacity, making the assessment results more accurate and reliable. It achieves the technical effect of quantifying the impact weights of different constraint conditions and conducting multi-level time-series verification, thereby improving the accuracy of the grid carrying capacity assessment results and reliably outputting control reference data adapted to the real-time operating conditions of the grid.

[0102] Example 2, as Figure 2 As shown, based on the same inventive concept as in Embodiment 1 above, this application provides a distribution network carrying capacity assessment system based on multi-factor coupling, the system comprising:

[0103] Multi-constraint index system construction module 1 is used to collect power distribution network operation data and construct a multi-constraint index system that includes electrical safety boundaries and equipment operation limits. The multi-constraint index system consists of at least one of voltage constraints, current constraints and short-circuit constraints.

[0104] Sensitivity ranking mapping table generation module 2 is used to identify the parameter sensitivity of the multi-constraint index system, quantify the contribution of each parameter to the carrying capacity under different network topologies, and generate a sensitivity ranking mapping table.

[0105] The parameter mapping relationship construction module 3 is used to construct a two-stage optimization evaluation model with reference to the constraint priority defined by the sensitivity ranking mapping table. In the first stage, a control plan is generated with the goal of maximizing the carrying capacity. In the second stage, the feasibility of the control plan is verified based on the most unfavorable operating scenario, and a mapping relationship between control cost and fluctuation tolerance is established.

[0106] The final evaluation result output module 4 applies the sensitivity ranking mapping table and the mapping relationship between the control cost and the fluctuation tolerance to perform hierarchical closed-loop verification, dynamically corrects the control plan based on the operation information at different time scales, and outputs the final evaluation result.

[0107] Furthermore, the sensitivity sorting mapping table generation module 2 is used to perform the following steps:

[0108] The voltage deviation threshold, line current carrying capacity limit, and short-circuit current margin parameters in the multi-constraint index system are traversed to extract operational characteristic data under different network topologies. Based on the operational characteristic data, the carrying capacity change caused by the change of each parameter under each network topology is calculated, and the carrying capacity change is defined as the contribution of the corresponding parameter to the carrying capacity. Under the same network topology, the contribution of all parameters is numerically compared, and the constraint type corresponding to the parameter with the largest contribution is selected and marked as the highest constraint priority under the current network topology. The correspondence between different network topologies and the corresponding highest constraint priority is summarized to generate the sensitivity ranking mapping table.

[0109] Furthermore, the parameter mapping relationship construction module 3 is used to perform the following steps:

[0110] The sensitivity sorting mapping table is invoked to identify the highest constraint priority under the current network topology; with the maximization of the distribution network carrying capacity as the optimization objective, the limit corresponding to the highest constraint priority is used as the core constraint condition to construct a mathematical solution model that includes equipment adjustment logic and topology adjustment logic; the mathematical solution model is solved to output the equipment action sequence and network connection status that satisfy the core constraint condition, and integrated into the control plan.

[0111] Furthermore, the parameter mapping relationship construction module 3 is used to perform the following steps:

[0112] The historical deviation characteristics of distributed power generation output and the historical fluctuation characteristics of load electricity consumption behavior are statistically analyzed to construct a two-sided random scenario set containing extreme value samples on the source side and extreme value samples on the load side. Sample combinations covering extreme operating conditions are selected from the two-sided random scenario set, and the irradiance drop characteristics under extreme weather conditions are superimposed on the sample combinations as boundary perturbations to generate the most unfavorable operating scenario.

[0113] Furthermore, the parameter mapping relationship construction module 3 is used to perform the following steps:

[0114] The source-load power data in the most unfavorable operating scenario is input into the execution logic of the control plan to simulate the electrical quantity distribution characteristics of the distribution network under extreme conditions. The voltage amplitude, current amplitude, and short-circuit capacity values ​​in the electrical quantity distribution characteristics are extracted and compared one by one with the electrical safety boundary and equipment operating limit in the multi-constraint index system. If any electrical quantity value exceeds the electrical safety boundary or equipment operating limit, the control plan is determined to be infeasible, and the time of the exceedance, the type of the exceedance parameter, and the magnitude of the exceedance are recorded to generate verification feedback information.

[0115] Furthermore, the parameter mapping relationship construction module 3 is used to perform the following steps:

[0116] The fluctuation tolerance is defined as the maximum deviation limit that allows the electrical quantity value to briefly exceed the electrical safety boundary; the regulation cost is defined as the total cost of equipment operation required to eliminate the excess value, the total cost of equipment operation includes the energy storage equipment loss cost, the voltage regulation equipment life loss cost, and the network reconfiguration operation cost; a functional dependency relationship is established with the fluctuation tolerance as the independent variable and the regulation cost as the dependent variable, forming a mapping relationship between the regulation cost and the fluctuation tolerance.

[0117] Furthermore, the final evaluation result output module 4 is used to perform the following steps:

[0118] During the day-ahead planning phase, the sensitivity ranking mapping table is used to identify the expected highest constraint priority for future periods, and the economically optimal energy storage equipment output range is determined based on the mapping relationship between the control cost and fluctuation tolerance, generating the day-ahead energy storage scheduling baseline. During the intraday rolling phase, the latest source load forecast information is obtained at a preset cycle. If the source load forecast information indicates that the operating data is about to exceed the day-ahead energy storage scheduling baseline, the adjustable branches are retrieved according to the sensitivity ranking mapping table, and the switching combination mode is changed to adjust the power flow distribution. During the real-time control phase, the instantaneous values ​​of node voltage and branch current are monitored respectively. When the instantaneous value exceeds the electrical safety boundary, the inverter reactive power voltage regulation strategy is immediately triggered.

[0119] Furthermore, the final evaluation result output module 4 is used to perform the following steps:

[0120] The sensitivity sorting mapping table is called to retrieve the constraint priority of the current over-limit parameter. If the current over-limit parameter is the highest constraint priority, the network topology adjustment operation is blocked, and the inverter power factor is forcibly adjusted to the target range, using reactive power injection to suppress voltage fluctuations. If the current over-limit parameter is not the highest constraint priority, the mapping relationship between the control cost and the fluctuation tolerance is called to calculate the allowable duration corresponding to the current voltage over-limit. If the voltage recovers to the normal range within the allowable duration, the current switching combination mode is maintained; otherwise, the network reconfiguration operation is initiated.

[0121] Furthermore, the final evaluation result output module 4 is used to perform the following steps:

[0122] Integrate the day-ahead energy storage scheduling baseline from the day-ahead planning phase, the topology correction records from the intraday rolling phase, and the adjustment logs from the real-time control phase to generate a full-cycle operation data chain; based on the full-cycle operation data chain, reverse verify the accuracy of the highest constraint priority in the sensitivity sorting mapping table, and dynamically adjust the decision variable parameters in the control plan according to the verification results, and output the final evaluation result.

[0123] The distribution network carrying capacity assessment system based on multi-factor coupling provided in this embodiment of the invention can execute the distribution network carrying capacity assessment method based on multi-factor coupling provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0124] Although this application makes various references to certain modules in the system according to the embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy distinction between each other and are not used to limit the scope of protection of this invention.

[0125] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application. In some cases, the actions or steps described in this application can be performed in a different order than that shown in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for assessing the carrying capacity of distribution networks based on multi-factor coupling, characterized in that, The method includes: Collect power distribution network operation data and construct a multi-constraint index system that includes electrical safety boundaries and equipment operating limits. The multi-constraint index system consists of at least one of voltage constraints, current constraints, and short-circuit constraints. The parameter sensitivity of the multi-constraint index system is identified, the influence contribution of each parameter on the carrying capacity under different network topologies is quantified, and a sensitivity ranking mapping table is generated. Referring to the constraint priority defined by the sensitivity ranking mapping table, a two-stage optimization evaluation model is constructed. In the first stage, a control plan is generated with the goal of maximizing the carrying capacity. In the second stage, the feasibility of the control plan is verified based on the most unfavorable operating scenario, and a mapping relationship between control cost and fluctuation tolerance is established. The sensitivity ranking mapping table and the mapping relationship between the control cost and the fluctuation tolerance are used to perform hierarchical closed-loop verification. The control plan is dynamically modified based on the operating information at different time scales, and the final evaluation result is output.

2. The distribution network carrying capacity assessment method based on multi-factor coupling as described in claim 1, characterized in that, The parameter sensitivity of the multi-constraint index system is identified, the contribution of each parameter to the carrying capacity under different network topologies is quantified, and a sensitivity ranking mapping table is generated, including: By iterating through the voltage deviation threshold, line current carrying capacity limit, and short-circuit current margin parameters in the multi-constraint index system, operational characteristic data under different network topologies are extracted. Based on the operational characteristic data, the change in carrying capacity caused by the change of each parameter under each network topology is calculated, and the change in carrying capacity is defined as the contribution of the corresponding parameter to the carrying capacity. Under the same network topology, the influence contribution of all parameters is numerically compared, and the constraint type corresponding to the parameter with the largest influence contribution is selected and marked as the highest constraint priority under the current network topology. The sensitivity ranking mapping table is generated by summarizing the correspondence between different network topologies and their corresponding highest constraint priorities.

3. The distribution network carrying capacity assessment method based on multi-factor coupling as described in claim 1, characterized in that, In the first phase, a control plan is generated with the goal of maximizing carrying capacity, including: The sensitivity sorting mapping table is invoked to identify the highest constraint priority under the current network topology; With the goal of maximizing the carrying capacity of the distribution network, the limit value corresponding to the highest constraint priority is used as the core constraint condition to construct a mathematical solution model that includes equipment adjustment logic and topology adjustment logic. Solve the mathematical model to output the device action sequence and network connection status that meet the core constraints, and integrate them into the control plan.

4. The distribution network carrying capacity assessment method based on multi-factor coupling as described in claim 3, characterized in that, The generation of the worst-case operating scenario includes: By statistically analyzing the historical deviation characteristics of distributed power generation output and the historical fluctuation characteristics of load electricity consumption behavior, a two-sided random scenario set containing extreme value samples from both the source and load sides is constructed. The sample combination covering extreme operating conditions is selected from the set of bilateral random scenarios, and the irradiance drop characteristics under extreme weather conditions are superimposed on the sample combination as a boundary perturbation to generate the most unfavorable operating scenario.

5. The distribution network carrying capacity assessment method based on multi-factor coupling as described in claim 4, characterized in that, In the second phase, the feasibility of the control plan is verified based on the most unfavorable operating scenario, including: The source-load power data in the most unfavorable operating scenario is input into the execution logic of the control plan to simulate the electrical quantity distribution characteristics of the distribution network under extreme conditions. Extract the voltage amplitude, current amplitude, and short-circuit capacity values ​​from the electrical quantity distribution characteristics, and compare them one by one with the electrical safety boundary and equipment operating limit in the multi-constraint index system; If any electrical quantity exceeds the electrical safety boundary or equipment operating limit, the control plan is deemed infeasible, and the time of the exceedance, the type of the exceedance parameter, and the magnitude of the exceedance are recorded, generating verification feedback information.

6. The distribution network carrying capacity assessment method based on multi-factor coupling as described in claim 5, characterized in that, Establish a mapping relationship between the cost of regulation and the tolerance for volatility, including: The fluctuation tolerance is defined as the maximum deviation limit that allows the electrical quantity value to briefly exceed the electrical safety boundary; The regulation cost is defined as the total cost of equipment operation required to eliminate the above-limit size, and the total cost of equipment operation includes the energy storage equipment loss cost, the voltage regulation equipment life loss cost, and the network reconfiguration operation cost. Establish a functional dependency relationship with the volatility tolerance as the independent variable and the regulation cost as the dependent variable, thus forming a mapping relationship between the regulation cost and the volatility tolerance.

7. The distribution network carrying capacity assessment method based on multi-factor coupling as described in claim 1, characterized in that, The graded closed-loop verification is performed by applying the sensitivity ranking mapping table and the mapping relationship between the control cost and the volatility tolerance, including: During the day-ahead planning phase, the sensitivity sorting mapping table is called to identify the expected highest constraint priority for future periods, and the economically optimal energy storage equipment output range is determined based on the mapping relationship between the regulation cost and the fluctuation tolerance, thereby generating the day-ahead energy storage scheduling baseline. During the intraday rolling phase, the latest source load forecast information is obtained according to a preset cycle. If the source load forecast information indicates that the operating data is about to exceed the day-ahead energy storage scheduling baseline, the adjustable branch is retrieved according to the sensitivity sorting mapping table, and the switching combination mode is changed to adjust the power flow distribution. During the real-time control phase, the instantaneous values ​​of node voltage and branch current are monitored respectively. When the instantaneous value exceeds the electrical safety boundary, the inverter reactive voltage regulation strategy is immediately triggered.

8. The distribution network carrying capacity assessment method based on multi-factor coupling as described in claim 7, characterized in that, The reactive voltage regulation strategy for triggering the inverter also includes: The sensitivity sorting mapping table is called to retrieve the constraint priority of the current over-limit parameter. If the current over-limit parameter is the highest constraint priority, the network topology adjustment operation is blocked, the inverter power factor is forcibly adjusted to the target range, and reactive power injection is used to suppress voltage fluctuations. If the current over-limit parameter is not the highest constraint priority, the mapping relationship between the control cost and the fluctuation tolerance is invoked to calculate the allowable duration corresponding to the current voltage over-limit. If the voltage recovers to the normal range within the allowable duration, the current switching combination is maintained; otherwise, a network reconfiguration operation is initiated.

9. The distribution network carrying capacity assessment method based on multi-factor coupling as described in claim 1, characterized in that, The control plan is dynamically revised based on operational information at different time scales, and the final evaluation results are output, including: Integrate the day-ahead energy storage scheduling baseline from the day-ahead planning phase, the topology correction records from the intraday rolling phase, and the adjustment logs from the real-time control phase to generate a full-cycle operation data chain; Based on the full-cycle operation data chain, the accuracy of the highest constraint priority in the sensitivity ranking mapping table is verified in reverse, and the decision variable parameters in the control plan are dynamically corrected according to the verification results, and the final evaluation result is output.

10. A distribution network carrying capacity assessment system based on multi-factor coupling, characterized in that, The system is used to implement the distribution network carrying capacity assessment method based on multi-factor coupling as described in any one of claims 1-9, the system comprising: A multi-constraint index system construction module is used to collect power distribution network operation data and construct a multi-constraint index system that includes electrical safety boundaries and equipment operating limits. The multi-constraint index system consists of at least one of voltage constraints, current constraints, and short-circuit constraints. The sensitivity ranking mapping table generation module is used to identify the parameter sensitivity of the multi-constraint index system, quantify the contribution of each parameter to the carrying capacity under different network topologies, and generate a sensitivity ranking mapping table. The parameter mapping relationship construction module is used to construct a two-stage optimization evaluation model with reference to the constraint priority defined by the sensitivity ranking mapping table. In the first stage, a control plan is generated with the goal of maximizing the carrying capacity. In the second stage, the feasibility of the control plan is verified based on the most unfavorable operating scenario, and a mapping relationship between control cost and fluctuation tolerance is established. The final evaluation result output module applies the sensitivity ranking mapping table and the mapping relationship between the control cost and the fluctuation tolerance to perform hierarchical closed-loop verification, dynamically corrects the control plan based on the operating information at different time scales, and outputs the final evaluation result.

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