Power distribution network source and storage capacity planning and transient stability closed-loop simulation optimization method and system

Abstract

The application discloses a power distribution network source storage capacity planning and transient state closed-loop simulation optimization method and system. The method comprises the following steps: using annual full-time sequence data, constructing a source-storage capacity optimization model, solving and generating an initial capacity configuration scheme and an operation strategy; using an improved K-means clustering algorithm to reduce the dimension of the source-load time sequence scene, and extracting a typical operation scene; for the typical scene, synchronous dual-dimension simulation verification of steady state and electromagnetic transient state is carried out, and two types of safety hazards of steady state and transient state are comprehensively identified; integrating the verification results, constructing a quantitative evaluation model integrating three dimensions of risk probability, severity and influence range, calculating a comprehensive risk value and dividing a risk grade; according to the risk grade and the hazard type, the initial scheme is corrected in a direction according to a preset mapping rule, and the dimension reduction and simulation process are restarted for iterative optimization until the final optimization scheme is output. The application realizes the comprehensive optimization of the economy, safety and adaptability of the planning scheme.

Description

Technical Field

[0001] This invention relates to the field of power system technology, specifically to a method and system for optimizing power distribution network source and storage capacity planning and transient steady-state closed-loop simulation. Background Technology

[0002] Current methods for optimizing source and storage capacity based on typical daily or limited operating data, aiming for optimal economic efficiency, and employing steady-state power flow calculations for safety verification, suffer from a series of fundamental shortcomings when dealing with increasingly prominent source-load scenarios characterized by high randomness and strong fluctuations.

[0003] In terms of data utilization and computational efficiency, existing technologies typically rely on typical daily data that is not highly representative, which cannot fully depict the complex temporal fluctuation characteristics of the entire 8760 hours of the year. Directly introducing full-time-series data for simulation would result in massive computational scale and low solution efficiency, making it difficult to meet the actual engineering application needs of large-scale distribution networks.

[0004] In terms of safety assessment, existing methods mainly rely on steady-state power flow analysis and N-minus-1 verification, with a single simulation dimension and a lack of refined modeling of electromagnetic transient processes. This makes it impossible to effectively identify and assess dynamic safety hazards such as transient voltage drops, inrush currents, and harmonic exceedances caused by short-circuit faults, sudden changes in new energy output, and switching of power electronic equipment, resulting in high safety risks for the planned schemes in actual operation.

[0005] In terms of risk assessment and decision support, existing technologies lack a systematic and quantitative assessment framework. The determination of safety hazards relies heavily on expert experience, failing to construct a comprehensive quantitative model that integrates the probability of risk occurrence, severity, and scope of impact. Furthermore, it does not employ a combined weighting method that combines subjective and objective information, resulting in a crude hazard classification that cannot provide clear and scientific prioritization guidance for subsequent solution optimization.

[0006] In terms of technical process architecture, the planning and simulation stages are independent and have a unidirectional linear relationship. The simulation verification results are only used to determine whether the scheme is qualified or not, while the various steady-state and transient risks identified cannot be automatically and effectively fed back to the planning stage to drive the correction of capacity configuration, operating strategies, or control parameters. This open-loop mode makes the scheme lack adaptive optimization capabilities, has insufficient robustness, and is difficult to adapt to new power system scenarios with highly uncertain source and load.

[0007] Regarding the granularity of the optimization model, existing methods often use relatively coarse constraints. They typically fail to incorporate crucial factors such as the local renewable energy absorption rate, the upper limit of curtailment rate, and the dynamic operating characteristics of equipment efficiency changing with load rate into a unified coupled optimization framework. This can lead to discrepancies between the final capacity configuration and the actual equipment operating characteristics, potentially causing problems such as insufficient renewable energy absorption, high curtailment rates, and significant equipment operating losses, thus impacting the economic efficiency and practicality of the planning scheme.

[0008] In summary, existing technologies have significant shortcomings in terms of computational efficiency, comprehensiveness of security assessment, risk quantification, process closure, and model accuracy when facing scenarios with highly random source loads. These deficiencies together constitute the core technical problem that this patent aims to solve. Summary of the Invention

[0009] This invention provides a method for planning and optimizing distribution network source-storage capacity through metastable closed-loop simulation. It solves the problems of insufficient economic efficiency, incomplete safety assessment, and poor engineering adaptability of traditional source-storage capacity planning methods in high-penetration distribution network scenarios with strong new energy and load randomness, due to simplified modeling, single verification dimension, and lack of closed-loop feedback.

[0010] This invention is achieved through the following technical solution:

[0011] Firstly, this application provides a method for power distribution network source-storage capacity planning and transient steady-state closed-loop simulation optimization, including the following steps:

[0012] Using full-year, full-time data of new energy sources and loads within the planning area, a source-storage capacity optimization model coupled with new energy consumption, curtailment control, and equipment variable operating condition efficiency constraints is constructed and solved to generate an initial capacity configuration scheme and its preliminary operation strategy.

[0013] Using the time-series data of new energy output and load in the preliminary operation strategy as input, the dimensionality of the annual operation scenario is reduced by the improved K-means clustering algorithm, and a set of typical operation scenarios that retain key fluctuation characteristics are extracted.

[0014] For the typical operating scenario, steady-state simulation verification and electromagnetic transient simulation verification are carried out simultaneously on the initial capacity configuration scheme to identify two types of safety hazards: steady-state and transient.

[0015] By integrating the verification results of the steady-state and transient safety hazards, a quantitative assessment model is constructed that integrates three dimensions: risk probability, severity, and scope of impact. Based on this model, the comprehensive risk value of each hazard is calculated and the risk level is classified.

[0016] Based on the risk level and specific type of hidden danger after classification, the initial capacity configuration scheme is modified in a targeted manner according to the preset mapping rules, and the modified scheme is input back into the clustering dimensionality reduction and simulation verification process for iterative optimization until the evaluation result meets the preset convergence condition, and the final optimized planning scheme is output.

[0017] A further optimization scheme is proposed, wherein the objective function of the source-storage capacity optimization model is to minimize the sum of the total investment cost, operating loss cost, and renewable energy curtailment penalty cost within the planning period, as shown in the following formula:

[0018] ;

[0019] In the formula, The cost of the source-storage equipment investment at time t is calculated. Let t be the operating loss cost of equipment such as lines, transformers, and converters at time t. Let t be the penalty cost for abandoning renewable energy.

[0020] A further optimization scheme is that the improved K-means clustering algorithm adaptively determines the optimal number of clusters by calculating the silhouette coefficient.

[0021] A further optimized scheme is that the steady-state simulation verification includes performing time-series power flow calculations and N-1 security analysis for all typical scenarios, and identifying steady-state security risks based on node voltage deviation indicators and line load rate indicators.

[0022] A further optimized solution is that the electromagnetic transient simulation verification includes establishing a refined electromagnetic transient model containing power electronic equipment, simulating preset disturbance events, and identifying transient safety hazards based on transient voltage drop depth and short-circuit inrush current indicators.

[0023] A further optimized approach is to calculate the comprehensive risk value R of a single hidden danger using the following formula:

[0024] The formula for calculating the comprehensive risk value R of a single hidden danger is:

[0025] ;

[0026] Where P is the risk probability, S is the severity, I is the scope of influence, and w1, w2, and w3 are weight coefficients determined by a combination of the analytic hierarchy process and the entropy weight method.

[0027] A further optimized solution is that the risk level classification based on the comprehensive risk value specifically includes:

[0028] Hazards with a comprehensive risk value not lower than the high-risk threshold are classified as high-risk hazards;

[0029] Hazards with a comprehensive risk value between the medium-risk threshold and the high-risk threshold are classified as medium-risk hazards.

[0030] Hazards with a comprehensive risk value lower than the aforementioned medium-risk threshold are classified as low-risk hazards;

[0031] Specifically, for hazards of different risk levels, differentiated correction strategies are implemented in subsequent closed-loop iterative optimizations: high-risk hazards trigger the highest priority correction and must be eliminated or mitigated in the current iteration; medium-risk hazards are corrected in the current iteration based on the optimization resources and the overall risk distribution; low-risk hazards are monitored as operational items, and the correction process is only initiated when they persist for a preset number of consecutive iterations or when their risk level increases.

[0032] A further optimization scheme is that the preset mapping rules include:

[0033] To address the potential risk of line overload, the corresponding corrective actions are to adjust line parameters or optimize the energy storage charging and discharging schedule.

[0034] To address the potential issue of node voltage exceeding limits, the corresponding corrective action is to adjust the reactive power compensation configuration or the parameters for new energy access.

[0035] To address the potential for transient impacts, the corresponding corrective actions are to optimize the control parameters of power electronic equipment or to install dynamic reactive power compensation devices.

[0036] Secondly, this application provides a distribution network source-storage capacity planning and metastable-state closed-loop simulation optimization system, used to implement the distribution network source-storage capacity planning and metastable-state closed-loop simulation optimization method as described above; the system includes:

[0037] The full-time data processing and modeling module is used to construct and solve a source-storage capacity optimization model that couples new energy consumption, curtailment control and equipment variable operating condition efficiency constraints by utilizing the full-year time-series data of new energy and load in the planning area, so as to generate an initial capacity configuration scheme and its preliminary operation strategy.

[0038] The time-series scene intelligent clustering dimensionality reduction module is communicatively connected to the full-time-series data processing and modeling module. It is used to take the new energy output and load time-series data in the preliminary operation strategy as input, and perform dimensionality reduction processing on the annual operation scene through the improved K-means clustering algorithm to extract a set of typical operation scenes that retain key fluctuation characteristics.

[0039] The transient-steady-state joint simulation verification module is communicatively connected to the full-time-series data processing and modeling module and the time-series scenario intelligent clustering and dimensionality reduction module, respectively. It is used to simultaneously perform steady-state simulation verification and electromagnetic transient simulation verification on the initial capacity configuration scheme for the typical operating scenario, and identify two types of safety hazards: steady-state and transient.

[0040] The three-dimensional risk quantification assessment and classification module is communicatively connected to the transient steady-state joint simulation verification module. It is used to integrate the verification results of the steady-state and transient safety hazards, construct a quantitative assessment model that integrates the three dimensions of risk probability, severity and impact range, and calculate the comprehensive risk value of each hazard and classify the risk level accordingly.

[0041] The closed-loop iterative optimization decision and control module is communicatively connected to the three-dimensional risk quantification assessment and classification module and the full-time data processing and modeling module, respectively. It is used to perform targeted correction of the initial capacity configuration scheme according to the risk level and specific type of hidden danger after classification, and input the corrected scheme back into the clustering dimensionality reduction and simulation verification process for iterative optimization until the evaluation result meets the preset convergence condition, and output the final optimized planning scheme.

[0042] Thirdly, this application provides a computer-readable storage medium storing a distribution network source-storage capacity planning and metastable closed-loop simulation optimization program, wherein when the distribution network source-storage capacity planning and metastable closed-loop simulation optimization program is executed by a processor, the steps of the distribution network source-storage capacity planning and metastable closed-loop simulation optimization method as described above are implemented.

[0043] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0044] By constructing a source-storage capacity optimization model that couples renewable energy consumption, curtailment control, and equipment efficiency constraints under varying operating conditions, and solving the model based on full-year, full-time-series data, the economic efficiency and accuracy of the initial planning scheme were ensured.

[0045] An improved K-means clustering algorithm is used to intelligently reduce the dimensionality of source-load time series scenarios, which significantly improves computational efficiency while preserving key fluctuation features and solves the simulation problem of large-scale scenarios.

[0046] By simultaneously conducting steady-state simulation verification and electromagnetic transient simulation verification, comprehensive identification of both steady-state and transient safety hazards was achieved.

[0047] By constructing a quantitative assessment model that integrates three dimensions—risk probability, severity, and scope of impact—hazards can be scientifically classified, providing a clear basis for optimized decision-making.

[0048] Through a closed-loop iterative optimization mechanism driven by potential hazards, the solution can be automatically modified and iterated according to the risk level and hazard type until the final planning solution that meets safety and operational requirements is output.

[0049] The solution provided in this application improves the accuracy, comprehensiveness of simulation evaluation, automation and adaptability of the distribution network source-storage capacity planning scheme for adapting to high random source-load scenarios. Attached Figure Description

[0050] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be considered as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the drawings:

[0051] Figure 1 A flowchart illustrating the distribution network source-storage capacity planning and transient steady-state closed-loop simulation optimization method provided in this application embodiment;

[0052] Figure 2 The functional module block diagram of the distribution network source-storage capacity planning and transient steady-state closed-loop simulation optimization system provided in the embodiments of this application is shown. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0054] First, some of the technical terms used in this application will be explained to help those skilled in the art understand this application.

[0055] AHP: Analytical Hierarchy Process;

[0056] K-means: K-means clustering algorithm;

[0057] P: Probability;

[0058] S: Severity;

[0059] I: Impact Range;

[0060] R: Risk;

[0061] THD: Total Harmonic Distortion.

[0062] Firstly, such as Figure 1As shown, this application provides a method for power distribution network source-storage capacity planning and metastable closed-loop simulation optimization, including the following steps:

[0063] Step S1: Using the full-year, full-time data of new energy sources and loads within the planning area, construct and solve a source-storage capacity optimization model that couples new energy consumption, curtailment control, and equipment variable operating condition efficiency constraints, in order to generate an initial capacity configuration scheme and its preliminary operation strategy.

[0064] Step S2: Using the time-series data of new energy output and load in the preliminary operation strategy as input, the dimensionality of the annual operation scenario is reduced by the improved K-means clustering algorithm to extract a set of typical operation scenarios that retain key fluctuation characteristics.

[0065] Step S3: For the typical operating scenario, simultaneously perform steady-state simulation verification and electromagnetic transient simulation verification on the initial capacity configuration scheme to identify two types of safety hazards: steady-state and transient.

[0066] Step S4: Integrate the verification results of the steady-state and transient safety hazards, construct a quantitative assessment model that integrates three dimensions: risk probability, severity, and scope of impact, and calculate the comprehensive risk value of each hazard and classify the risk level accordingly.

[0067] Step S5: Based on the risk level and specific type of hidden danger after division, the initial capacity configuration scheme is modified in a targeted manner according to the preset mapping rules, and the modified scheme is input back into the clustering dimensionality reduction and simulation verification process for iterative optimization until the evaluation result meets the preset convergence condition, and the final optimized planning scheme is output.

[0068] This embodiment employs an improved K-means clustering algorithm to reduce the dimensionality of the 8760-hour source-load time-series scenario throughout the year, effectively solving the efficiency bottleneck caused by large-scale full-time-series simulation calculations and significantly improving computational efficiency. By constructing a dual-dimensional joint simulation verification system encompassing steady-state power flow and electromagnetic transients, a comprehensive safety assessment of the planning scheme, covering the entire time-series operating state and disturbance transient responses, is conducted, identifying multiple safety hazards, including steady-state limit exceedances and transient impacts. Furthermore, a quantitative assessment model is constructed based on three dimensions: risk probability, severity, and impact scope, accurately classifying hazards and driving an intelligent closed-loop iterative optimization process of "planning-simulation-assessment-correction" according to a preset hazard type-correction measure mapping rule. Ultimately, this method, while ensuring high accuracy and economy, outputs a distribution network source-storage coordinated planning scheme that can adapt to highly random source-load fluctuations and comprehensively optimizes both safety and economy.

[0069] In one embodiment, step S1: Using the full-year, full-time data of new energy sources and loads within the planning area, a source-storage capacity optimization model coupling new energy consumption, curtailment control, and equipment variable operating condition efficiency constraints is constructed and solved to generate an initial capacity configuration scheme and its preliminary operation strategy. This specifically includes the following steps:

[0070] Step S11: Collect and organize the full-year source-load time-series data and equipment parameters required for planning. Through data cleaning, interpolation and standardization, obtain preprocessed time-series data and economic parameters.

[0071] Specifically, the received raw data undergoes quality cleaning and standardized formatting. This process includes detecting and removing outliers and imputing missing data points using interpolation. Afterward, the imputed dataset is normalized and standardized, ultimately resulting in a high-quality, time-series dataset with a unified format and aligned time scales, suitable for subsequent optimization calculations.

[0072] Step S12: Based on the preprocessed time series data and economic parameters, construct a comprehensive cost minimization objective function with the goal of minimizing the sum of the annual equivalent total investment cost, operating loss cost and new energy curtailment cost within the planning period.

[0073] Specifically, the objective function of the source-storage capacity optimization model is to minimize the sum of total investment cost, operating loss cost, and renewable energy curtailment penalty cost within the planning period, as shown in the following formula:

[0074] Equation (1)

[0075] In the formula, The cost of the source-storage equipment investment at time t is calculated. Let t be the operating loss cost of equipment such as lines, transformers, and converters at time t. Let t be the penalty cost for abandoning renewable energy.

[0076] Step S13: Combining the objective function with the physical laws of power grid operation, safety boundaries and management objectives, a complete mathematical framework for the optimization problem is constructed by defining a series of equations and inequalities such as power balance, energy storage operation, equipment variable operating efficiency and new energy absorption rate.

[0077] The process involves defining a series of boundary conditions to ensure the physical feasibility and economic rationality of the solution. These conditions include the real-time balance between active and reactive power of the power grid, the operating boundaries of the energy storage device within the upper and lower limits of charging and discharging power and the safe range of state of charge, and the acceptable range of renewable energy output.

[0078] Preferably, the model further integrates a refined equipment model with renewable energy consumption targets. Specifically, the operating efficiency and loss characteristics of equipment, especially power electronic equipment, are modeled as functions related to their real-time output power, thus taking into account their varying operating conditions. Simultaneously, upper limits are set for the system's local renewable energy consumption rate and curtailment rate, which serve as strong constraints on the optimization model to guide the optimization towards a higher proportion of renewable energy consumption.

[0079] Step S14: Use mathematical programming algorithms to solve the established optimization model and finally output a complete initial planning scheme that includes suggestions for the configuration of new energy and energy storage capacity, as well as a matching preliminary operation strategy.

[0080] The process involves linearizing the mixed-integer nonlinear programming model and calling a mature mathematical programming solver to efficiently search for the optimal solution set that satisfies all boundary conditions under computational resource and time constraints.

[0081] Specifically, the solver outputs a preliminary source-storage capacity configuration scheme. This scheme includes the recommended installed capacity of new energy sources such as photovoltaics and wind power, as well as the rated power and rated capacity of the energy storage system. Simultaneously, the solver generates a preliminary operating strategy that matches the capacity configuration. This strategy details the planned charge / discharge status of the energy storage system for each hour throughout the 8760-hour year, as well as the controlled output curves of the new energy sources.

[0082] This embodiment, by processing full-time-series data, ensures at the planning source that the initial capacity configuration scheme accurately matches the highly random fluctuations in renewable energy output and load demand, thereby improving the accuracy and scenario adaptability of the planning scheme from the data source. The constructed optimization model, coupling renewable energy consumption, curtailment control, and equipment variable operating condition efficiency constraints, enables the generated initial scheme to not only minimize total investment and operating costs but also simultaneously optimize renewable energy consumption levels and actual equipment operating efficiency, laying a solid foundation for the scheme's economic viability, environmental friendliness, and engineering feasibility. Finally, an efficient solution algorithm outputs a complete initial scheme containing specific capacity configurations and hourly operating strategies, providing a high-quality, quantifiable input benchmark for subsequent simulation verification and closed-loop iterative optimization processes, enabling the entire optimization chain to start and operate efficiently.

[0083] In one embodiment, step S2: using the time-series data of new energy output and load in the preliminary operation strategy as input, the dimensionality reduction of the annual operation scenario is performed through an improved K-means clustering algorithm to extract a set of typical operation scenarios that retain key fluctuation characteristics. Specifically, this includes the following steps:

[0084] Step S21: Receive high-precision time-series data of new energy output and various loads throughout the year from the preliminary operation strategy, and quantify the comprehensive differences between different operating day scenarios by constructing a weighted Euclidean distance index that integrates the time-series similarity of active and reactive power.

[0085] Specifically, the distance metric is:

[0086] Equation (2)

[0087] In the formula, , , Let be the weighting coefficient, satisfying The system will be adaptively adjusted based on the penetration rate of new energy sources and load characteristics in the planned area. For the photovoltaic active power output of scenario i at time t, Let be the total active power load of scenario i at time t. Let be the total reactive load of scenario i at time t. This formula, compared to the traditional Euclidean distance, better highlights the similarity between the time-series fluctuations of renewable energy and load.

[0088] Step S22: Using the distance matrix mentioned above, the optimal number of clusters K is adaptively determined by calculating the silhouette coefficients corresponding to different numbers of clusters K, thereby avoiding the subjectivity and bias caused by manually setting the number of clusters.

[0089] Specifically, the optimal number of clusters K is determined by iterating through different K values ​​within a preset range (e.g., 50 to 100), performing clustering for each K value, and calculating its corresponding average silhouette coefficient. The formula for calculating the silhouette coefficient is:

[0090] Equation (3)

[0091] in, Let i be the average distance between scene i and other scenes of the same type. is the average distance from scene i to all scenes in the nearest heterogeneous cluster. The silhouette coefficient SC ranges from [-1, 1]. A larger value indicates better clustering, with tighter intra-cluster clusters and more separated inter-cluster clusters. Finally, the optimal number of clusters is chosen based on the K value that maximizes the average silhouette coefficient.

[0092] Step S23: Using the optimal number of clusters K, and with the above weighted distance as the metric, execute the improved K-means clustering algorithm to divide all 8760 hours of operation throughout the year into segments with the goal of minimizing the sum of squared distances from all scenarios to the center of their respective clusters.

[0093] Specifically, the objective function of clustering is to minimize the sum of squared intra-cluster distances:

[0094] Equation (4)

[0095] Where K is the number of clusters, For the set of scenarios of type k, Let K be the cluster center (centroid) of the k-th scenario. After the clustering algorithm is executed, the scenarios running throughout the year are divided into K clusters.

[0096] Step S24: Calculate and output the clustering results, that is, take the centroid sequence of each cluster as the typical operating scenario of the cluster, and count the number of original scenarios represented by each typical scenario, and calculate its probability of occurrence.

[0097] Specifically, for each cluster, its cluster center vector This represents a typical operational scenario extracted from the data, signifying the average fluctuation characteristics of all scenarios within the cluster. Simultaneously, the number of original scenarios belonging to this cluster is counted. The probability of this typical scenario occurring is: Finally, a set of typical scenarios (usually 50-100) and their corresponding probabilities of occurrence are output. These typical scenarios cover key operating states throughout the year, such as large / small power generation of new energy sources and peak / valley loads. While retaining more than 95% of the time-series distribution characteristics, the simulation computation is reduced by 1-2 orders of magnitude.

[0098] This embodiment achieves high-efficiency and high-fidelity scenario dimensionality reduction by improving K-means clustering. Its core improvement lies in constructing a weighted distance index that integrates the time-series similarity of active and reactive power, thus finely characterizing the fluctuation patterns of renewable energy output and load. Furthermore, it innovatively introduces a silhouette coefficient to adaptively determine the optimal number of clusters, avoiding subjective setting. These two optimizations work synergistically, enabling the method to intelligently extract 50 to 100 typical scenarios from 8760 hours of raw data, retaining over 95% of the time-series fluctuation characteristics. The final result is that, without sacrificing key safety and operational characteristics, the computational load for subsequent simulation verification is reduced by 1-2 orders of magnitude, fundamentally solving the efficiency bottleneck of large-scale time-series simulation and providing an efficient and reliable data foundation for subsequent closed-loop optimization.

[0099] In one embodiment, step S3: For the typical operating scenario, simultaneously perform steady-state simulation verification and electromagnetic transient simulation verification on the initial capacity configuration scheme to identify two types of safety hazards: steady-state and transient. This specifically includes the following steps:

[0100] Step S31: Using the obtained typical operating scenario set and initial capacity configuration scheme as input, establish the corresponding distribution network steady-state simulation model and electromagnetic transient refined simulation model to lay the foundation for dual-dimensional joint verification.

[0101] Specifically, the steady-state simulation model is built based on the admittance matrix and includes components such as lines, transformers, distributed power sources, energy storage, and loads, along with their hourly active and reactive power setpoints under typical scenarios. The electromagnetic transient simulation model includes detailed electromagnetic transient component models such as the distributed parameter model of the power distribution line, the PQ control model of the photovoltaic inverter, and the dual closed-loop control model of the energy storage converter PCS. Both models will share the same network topology, component parameters, and timing operation states under typical scenarios.

[0102] Step S32: Using the steady-state simulation model established in step S31, the Newton-Raphson method is used to calculate the power flow distribution of the entire system for each typical operating scenario's 8760-hour abbreviated time-series operating point.

[0103] The process involves solving the nodal power balance equations, which are in matrix form as follows:

[0104] Equation (5)

[0105] In the formula, , Let i be the active and reactive power injected into node i at time t. , Let be the voltage magnitudes of nodes i and j at time t. , The conductance and susceptance of the nodal admittance matrix are... Let be the voltage phase angle difference between nodes i and j at time t.

[0106] Based on this, for each typical scenario, the N-1 fault state of any component (such as a line or a transformer) being taken out of operation is simulated, and the power flow calculation is re-performed to evaluate the operational safety of the system under fault conditions.

[0107] Step S33: Based on the steady-state power flow calculation results, calculate a series of steady-state safety assessment indicators and compare them with national standards or preset limits to identify whether there are any potential steady-state operation hazards.

[0108] Specifically, the core evaluation indicators include:

[0109] Node voltage deviation: Calculate the relative deviation of the voltage amplitude of each node at each moment from the rated voltage, using the following formula:

[0110] Equation (6)

[0111] When the deviation exceeds the specified range (e.g., ±7%), it is judged as a potential voltage over-limit hazard.

[0112] Line / Transformer Load Rate: Calculated as the ratio of the apparent power of each branch and transformer to its rated capacity at each moment, using the following formula:

[0113] Equation (7)

[0114] When the load rate continuously exceeds 100% or the safety threshold (such as 80%), it is considered a potential equipment overload.

[0115] Network loss rate: The ratio of total active power loss to total injected power in a system is used to assess economic efficiency.

[0116] Renewable energy absorption rate: The ratio of actual grid-connected renewable energy power to theoretically generated renewable energy, used to assess the capacity of planning schemes to accommodate renewable energy.

[0117] Step S34: Using the established electromagnetic transient model, simulate the instantaneous dynamic process of the power grid when it is subjected to typical disturbances in order to assess transient security.

[0118] The process involves setting simulated disturbance events, typical of which include: three-phase short-circuit faults, single-phase ground faults, sudden increases or decreases in the output power of new energy sources (such as photovoltaics), and sudden connection or disconnection of large-capacity loads. The simulation uses numerical integration methods (such as the trapezoidal rule) to solve the differential-algebraic equation system. The discretized equations are:

[0119] Equation (8)

[0120] To obtain the waveforms of electrical quantities such as voltage and current during transient processes.

[0121] Step S35: Extract key transient safety indicators from the electromagnetic transient simulation results and compare them with relevant standard limits to identify transient safety hazards.

[0122] Specifically, the core evaluation indicators include:

[0123] Transient voltage dip depth: Calculate the lowest point of bus voltage during the fault period. With rated voltage relative deviation The formula is:

[0124] Equation (9)

[0125] Excessive depth (e.g., exceeding 20%) can affect the normal operation of sensitive loads.

[0126] Short-circuit surge current peak: Calculates the maximum instantaneous current that may occur at the moment the fault occurs. The formula for calculating the breaking capacity of switchgear is as follows:

[0127] (10)

[0128] in, The impact coefficient is 1.8 to 1.9.

[0129] Total Harmonic Distortion (THD): Calculates the proportion of harmonic components in the total effective value of the voltage waveform to assess whether the power quality meets the standard (e.g., less than 5%).

[0130] Voltage recovery time: This measures the time required for the voltage to return to the normal allowable range (e.g., 90% of the rated voltage) after a fault is cleared. An excessively long recovery time may cause protection malfunctions or load disconnection.

[0131] Step S36: Summarize all verification results of steady-state and transient simulations to form a structured list of safety hazards, which will serve as input for subsequent risk assessment and closed-loop optimization.

[0132] Specifically, the output hazard list should include the following information for each hazard: the typical scenario and time of occurrence of the hazard, the location of the hazard (such as a node or line), the type of hazard (steady-state / transient, such as voltage exceeding limits, overload, voltage drop, harmonic exceedance, etc.), the specific quantitative indicators and the extent of their exceedance, and the possible impact range of the hazard.

[0133] This embodiment employs synchronous dual-dimensional simulation verification to provide a comprehensive, end-to-end safety overview of the planned scheme, encompassing everything from normal operation to extreme disturbances. Steady-state simulation not only verifies the voltage compliance rate and equipment load rate under year-round time-series operation but also assesses the system's structural reliability through N-1 analysis. Furthermore, by establishing detailed models including photovoltaic inverters and energy storage PCS, electromagnetic transient simulation further reveals deeper risks such as voltage drops, inrush currents, and harmonics under transient disturbances like short circuits and sudden power changes. This dual-dimensional verification mechanism of "steady-state time-series + transient disturbances" constitutes a complete safety profile, fundamentally overcoming the limitations of traditional methods that rely on a single verification dimension and cannot identify transient hazards. It provides a solid and comprehensive data foundation for subsequent accurate quantitative risk assessment and targeted scheme modification.

[0134] In one embodiment, step S4: Integrating the verification results of the steady-state and transient security risks, a quantitative assessment model is constructed that integrates three dimensions: risk probability, severity, and scope of impact. Based on this model, the comprehensive risk value of each risk is calculated and the risk level is classified. This specifically includes the following steps:

[0135] Step S41: Process the transient stability verification results. By statistically analyzing the frequency of occurrence of hidden dangers in different typical scenarios, calculating the extent of exceeding the standard of indicators, and assessing their impact range, the raw values ​​of the three dimensions of risk probability, severity, and impact range are quantitatively calculated for each hidden danger.

[0136] Specifically, the risk probability P is determined by the proportion of the potential hazard occurring in all typical scenarios of simulation verification, and its calculation formula is as follows:

[0137] Equation (11)

[0138] in, The number of scenarios where potential hazards occur. This represents the total number of typical scenarios. The severity S is calculated based on the extent to which the actual value of the core safety indicator of the hazard exceeds the national standard or safety limit; its normalization formula is:

[0139] Equation (12)

[0140] in, The actual index values ​​(such as voltage drop depth) obtained from the simulation. This is the limit value for the indicator. The scope of impact I comprehensively assesses the number of power supply nodes affected by the potential hazard and the importance of the loads on these nodes; its calculation formula is:

[0141] Equation (13)

[0142] in, The number of affected nodes. This is a correction factor for critical loads determined based on node load level and power supply importance.

[0143] Step S42: Using the data matrix of risk probability, severity, and scope of impact obtained above as input, calculate the subjective and objective weights using the Analytic Hierarchy Process (AHP) and the entropy weight method respectively, and finally determine a combined weight that takes into account both expert experience and data objectivity for each risk dimension through weighted combination.

[0144] The process begins with domain experts comparing pairwise judgment matrices and then using the Analytic Hierarchy Process (AHP) to calculate subjective weights that reflect subjective perception. Simultaneously, based on the data distribution of each hidden danger across the three elements, information entropy is calculated:

[0145] Equation (14)

[0146] This is used to quantify the dispersion of data across different dimensions, and then calculate the objective weights that reflect the differences in the data.

[0147] Equation (15)

[0148] Finally, the final weights are obtained using a linear combination method:

[0149] Equation (16)

[0150] in, This is a balancing coefficient, usually set to 0.5 to achieve a balance between subjective and objective weights.

[0151] Step S43: Receive the quantified risk three-factor values ​​and the determined combination weights, calculate the comprehensive risk value of each hidden danger through a weighted summation model, and classify the hidden dangers into risk levels according to the preset risk level threshold range.

[0152] Specifically, the comprehensive risk value R for each hidden danger is given by the formula.

[0153] Equation (17)

[0154] The calculation shows that, among which , , These represent the risk probability (P), severity (S), and scope of impact, respectively. The combined weights are calculated in step S42.

[0155] The risk level classification criteria are as follows:

[0156] Hazards with a comprehensive risk value R ≥ 0.7 are classified as high-risk.

[0157] A value of 0.3 ≤ R < 0.7 is considered medium risk.

[0158] A value of R < 0.3 indicates low risk.

[0159] Step S44: Summarize the comprehensive risk value, risk level and key assessment information of all hidden dangers, and generate a structured risk assessment report and hidden danger list as the decision-making basis for closed-loop iterative optimization.

[0160] Specifically, the output report list should include at least the following information: a unique identifier of the hazard, its location (e.g., node, line), type (steady-state / transient, such as voltage exceeding limits, short-circuit current exceeding limits), typical scenario of occurrence, quantitative values ​​of the three risk elements (P, S, I), the calculated comprehensive risk value R, the determined risk level (high / medium / low), and a brief analysis of the triggering mechanism and impact path. This list serves as a crucial bridge connecting "simulation verification" and "closed-loop optimization."

[0161] This embodiment transforms discrete steady-state and transient safety hazards derived from simulation verification into comparable comprehensive risk values ​​through a quantitative model that integrates three dimensions: risk probability, severity, and impact scope. Its core lies in employing a weighting method that combines subjective and objective approaches. Specifically, it utilizes the analytic hierarchy process (AHP) to incorporate expert experience while employing entropy weighting to reflect objective data differences, thereby achieving scientific and accurate hazard rating. Finally, risk levels are automatically assigned based on clearly defined thresholds, and a structured risk assessment list is output. This transforms the traditionally qualitative, experience-based hazard assessment into a data-driven, precise prioritization process, providing a clear and objective decision-making basis for subsequent closed-loop iterative optimization steps. This ensures that optimization resources are efficiently and intelligently focused on the most critical safety issues.

[0162] In one embodiment, step S5: Based on the risk level and specific type of hidden danger after classification, the initial capacity configuration scheme is directionally modified according to the preset mapping rules, and the modified scheme is input back into the clustering dimensionality reduction and simulation verification process for iterative optimization until the evaluation result meets the preset convergence condition, and the final optimized planning scheme is output. This specifically includes the following steps:

[0163] Step S51: Receive the structured risk assessment list, and based on the specific type and risk level of the hidden danger, call the preset standardized mapping rule library to automatically generate targeted planning scheme correction instructions for each identified hidden danger.

[0164] Specifically, this pre-defined mapping rule base associates various steady-state and transient hazards with specific planning decision variables and corrective actions. For line overload and overload hazards, corrective measures include adjusting the conductor cross-section and rated current capacity of the corresponding line, or optimizing the charging and discharging power curve of the energy storage system during the corresponding time period to smooth power fluctuations. For node voltage exceeding limits, corrective measures include adding reactive power compensation devices at the problematic node, adjusting the access capacity and location of nearby renewable energy sources, or modifying the reactive power control strategy of the energy storage converter to provide voltage support. For transient hazards such as transient voltage drops and excessive inrush current, corrective measures include optimizing the control parameters of power electronic devices such as energy storage PCS and photovoltaic inverters, or installing dynamic reactive power compensation devices on key buses. For the hazard of excessive renewable energy curtailment, corrective measures include increasing the rated configuration capacity of the energy storage system or optimizing the regulation strategy of renewable energy output. The risk level determines the priority and magnitude of corrective measures; high-risk hazards typically trigger the highest priority and largest magnitude corrections.

[0165] Step S52: Process the generated correction instructions, following the principle of "high risk first, medium risk second, and low risk last", and orderly adjust the capacity configuration, control parameters and operation strategies in the initial scheme to generate a new round of planning scheme version.

[0166] The process involves first integrating all correction instructions for high-risk hazards and simultaneously updating relevant planning decision variables (such as energy storage capacity, reactive power compensation device location, and control parameters). Then, based on this, correction instructions for medium-risk hazards are integrated and updated. This tiered processing mechanism ensures priority response to the most severe safety hazards. After completing corrections at all levels, a new, optimized, and modified solution is generated.

[0167] Step S53: Using the revised new solution as new input, re-execute the process from steps S1 to S4, that is, re-perform scenario clustering, steady-state and transient simulation verification, and comprehensive risk assessment, and calculate the hazard list and comprehensive risk value under the new solution.

[0168] The process includes: regenerating the initial operational strategy based on the revised plan; performing a new round of scenario clustering on the strategy data to extract a new set of typical operational scenarios; performing steady-state and transient dual-dimensional simulations on the new scenario set; and finally, conducting another risk assessment based on the simulation results. The new assessment results are then compared with preset convergence conditions. Convergence conditions are typically set as follows: no high-risk hazards exist; the number of medium-risk hazards or the total risk value is below a certain preset threshold; and all steady-state and transient safety indicators meet national standards.

[0169] Step S54: Based on the convergence judgment result, decide whether to terminate or continue the process. If the evaluation result of the modified scheme meets all the preset convergence conditions, the optimization process is deemed complete, the process terminates, and the final capacity configuration scheme and supporting operation strategy are output. If not, the current modified scheme is used as the new "initial scheme," and the process returns to step S52 to start the next round of iterative modification based on the latest risk assessment result until the scheme converges.

[0170] The final optimized planning scheme is obtained through multiple closed-loop iterations of "evaluation-correction-re-evaluation" based on the initial scheme. While meeting economic objectives, it has passed comprehensive verification of steady-state and transient safety under typical time-series scenarios, demonstrating stronger robustness and engineering feasibility. This embodiment constructs an intelligent planning closed loop with self-correction and continuous optimization capabilities, transforming the static, one-off planning process into a dynamic, adaptive, and evolutionary optimization process. It establishes a standardized mapping rule base of "hazard type - risk level - correction action," automating and standardizing the decision-making chain from risk quantification assessment to specific scheme adjustments. This method abandons the traditional model of relying on repeated trial and error and debugging based on human experience. By following an orderly correction logic of "high risk first, medium risk second, low risk last," it achieves efficient and accurate allocation of optimization resources and computing resources. Ultimately, driven by preset convergence conditions, the entire system can autonomously conduct multiple rounds of "evaluation-correction-re-evaluation" iterations until it outputs a final planning scheme that has been fully optimized and verified in multiple dimensions such as economy, security, and adaptability to highly random source load fluctuations, thereby significantly improving the reliability, robustness, and feasibility of the planning results in engineering implementation.

[0171] Compared with traditional power distribution network source-storage planning and simulation methods, this invention has the following advantages:

[0172] Planning and modeling were carried out using 8,760 hours of full-time data throughout the year to fully restore the all-weather fluctuation characteristics of highly random source loads. Combined with equipment variable operating condition constraints and multi-cost collaborative optimization, the source-storage capacity configuration error was reduced, the renewable energy curtailment rate was improved, and the equipment utilization rate and investment economy were significantly improved.

[0173] By improving the K-means algorithm to cluster and reduce the dimensionality of large-scale random scenarios, the original 8760 hours of scenarios are compressed into 50 to 100 typical scenarios, reducing the amount of computation and significantly improving the solution efficiency while ensuring planning accuracy, thus meeting the needs of rapid planning for large-scale power distribution network engineering.

[0174] A joint simulation and verification system for steady-state and transient states is constructed, which not only covers potential risks of steady-state operation throughout the year, but also accurately identifies transient safety risks such as short circuits, switching, and sudden changes in power output. This enables full-scenario and full-dimensional safety verification and effectively avoids problems such as substandard power quality and equipment failure after the planning scheme is implemented.

[0175] Establish a three-dimensional risk quantitative assessment model based on probability, severity, and scope of impact. Use a combined weighting approach to achieve scientific classification and positioning of potential hazards, providing a clear decision-making basis for planning optimization and improving the pertinence and reliability of planning schemes.

[0176] This invention innovatively constructs a planning-simulation closed-loop iterative mechanism, using simulation results to drive automatic correction of planning parameters, eliminating the need for repeated manual adjustments and achieving self-optimization and dynamic adaptation of the solution, significantly improving the intelligence level of the planning process. Finally, the overall technical system of this invention is highly adaptable to new distribution network scenarios with high penetration rates of new energy sources and highly volatile loads, and can simultaneously meet multiple objectives such as safety, economy, new energy consumption, and operational reliability. It provides a complete and feasible technical path for the planning, design, safety assessment, and operation optimization of new distribution networks, and has broad engineering application value and promotion prospects.

[0177] Secondly, such as Figure 2 As shown, this application provides a distribution network source-storage capacity planning and metastable state closed-loop simulation optimization system, used to implement the distribution network source-storage capacity planning and metastable state closed-loop simulation optimization method as described above; the system includes a full-time-series data processing and modeling module 100, a time-series scene intelligent clustering and dimensionality reduction module 200, a metastable state joint simulation verification module 300, a three-dimensional risk quantification assessment and classification module 400, and a closed-loop iterative optimization decision and control module 500.

[0178] The full-time data processing and modeling module 100 is used to construct and solve a source-storage capacity optimization model that couples new energy consumption, curtailment control and equipment variable operating condition efficiency constraints by utilizing the full-year time-series data of new energy and load in the planning area, so as to generate an initial capacity configuration scheme and its preliminary operation strategy.

[0179] The time-series scene intelligent clustering dimensionality reduction module 200 is communicatively connected to the full time-series data processing and modeling module 100. It is used to take the new energy output and load time-series data in the preliminary operation strategy as input, and perform dimensionality reduction processing on the annual operation scene through the improved K-means clustering algorithm to extract a set of typical operation scenes that retain key fluctuation characteristics.

[0180] The transient steady-state joint simulation verification module 300 is communicatively connected to the full-time-series data processing and modeling module 100 and the time-series scenario intelligent clustering and dimensionality reduction module 200, respectively, and is used to simultaneously perform steady-state simulation verification and electromagnetic transient simulation verification on the initial capacity configuration scheme for the typical operating scenario, and identify two types of safety hazards: steady-state and transient.

[0181] The three-dimensional risk quantification assessment and classification module 400 is communicatively connected to the transient steady-state joint simulation verification module 300. It is used to integrate the verification results of the steady-state and transient safety hazards, construct a quantitative assessment model that integrates the three dimensions of risk probability, severity and impact range, and calculate the comprehensive risk value of each hazard and classify the risk level accordingly.

[0182] The closed-loop iterative optimization decision and control module 500 is communicatively connected to the three-dimensional risk quantification assessment and classification module 400 and the full-time data processing and modeling module 100, respectively. It is used to perform targeted correction of the initial capacity configuration scheme according to the risk level and specific type of hidden danger after classification, and to re-input the corrected scheme into the time-series scene intelligent clustering dimensionality reduction module 200 to start a new round of iterative optimization process until the evaluation result meets the preset convergence condition and outputs the final optimization planning scheme.

[0183] The functions of each module in the above-mentioned distribution network source-storage capacity planning and metastable closed-loop simulation optimization system correspond to the steps in the above-mentioned distribution network source-storage capacity planning and metastable closed-loop simulation optimization method embodiment. Their functions and implementation processes will not be described in detail here.

[0184] Thirdly, embodiments of this application provide a power distribution network source-storage capacity planning and metastable closed-loop simulation optimization device. The power distribution network source-storage capacity planning and metastable closed-loop simulation optimization device can be a personal computer (PC), laptop computer, server, or other device with data processing capabilities.

[0185] In this embodiment of the application, the distribution network source-storage capacity planning and transient steady-state closed-loop simulation optimization equipment may include a processor, a memory, a communication interface, and a communication bus.

[0186] The communication bus can be of any type and is used to interconnect the processor, memory, and communication interface.

[0187] The communication interface includes input / output (I / O) interfaces, physical interfaces, and logical interfaces. These interfaces are used for interconnecting devices within the power distribution network source-storage capacity planning and metastable-state closed-loop simulation optimization equipment, as well as for interconnecting the equipment with other devices (such as other computing devices or user equipment). Physical interfaces can be Ethernet interfaces, fiber optic interfaces, ATM interfaces, etc.; user equipment can be displays, keyboards, etc.

[0188] Memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0189] The processor can be a general-purpose processor, which can call the distribution network source-storage capacity planning and metastable-state closed-loop simulation optimization program stored in the memory, and execute the distribution network source-storage capacity planning and metastable-state closed-loop simulation optimization method provided in the embodiments of this application. For example, the general-purpose processor can be a central processing unit (CPU). The method executed when the distribution network source-storage capacity planning and metastable-state closed-loop simulation optimization program is called can refer to the various embodiments of the distribution network source-storage capacity planning and metastable-state closed-loop simulation optimization method of this application, and will not be repeated here.

[0190] Fourthly, embodiments of this application also provide a readable storage medium.

[0191] The present application stores a distribution network source-storage capacity planning and metastable closed-loop simulation optimization program on a readable storage medium. When the distribution network source-storage capacity planning and metastable closed-loop simulation optimization program is executed by a processor, it implements the steps of the distribution network source-storage capacity planning and metastable closed-loop simulation optimization method as described above.

[0192] The method implemented when the distribution network source-storage capacity planning and metastable closed-loop simulation optimization program is executed can be referred to in the various embodiments of the distribution network source-storage capacity planning and metastable closed-loop simulation optimization method of this application, and will not be repeated here.

[0193] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for power distribution network source-storage capacity planning and transient steady-state closed-loop simulation optimization, characterized in that, Includes the following steps: Using full-year, full-time data of new energy sources and loads within the planning area, a source-storage capacity optimization model coupled with new energy consumption, curtailment control, and equipment variable operating condition efficiency constraints is constructed and solved to generate an initial capacity configuration scheme and its preliminary operation strategy. Using the time-series data of new energy output and load in the preliminary operation strategy as input, the dimensionality of the annual operation scenario is reduced by the improved K-means clustering algorithm, and a set of typical operation scenarios that retain key fluctuation characteristics are extracted. For the typical operating scenario, steady-state simulation verification and electromagnetic transient simulation verification are carried out simultaneously on the initial capacity configuration scheme to identify two types of safety hazards: steady-state and transient. By integrating the verification results of the steady-state and transient safety hazards, a quantitative assessment model is constructed that integrates three dimensions: risk probability, severity, and scope of impact. Based on this model, the comprehensive risk value of each hazard is calculated and the risk level is classified. Based on the risk level and specific type of hidden danger after classification, the initial capacity configuration scheme is modified in a targeted manner according to the preset mapping rules, and the modified scheme is input back into the clustering dimensionality reduction and simulation verification process for iterative optimization until the evaluation result meets the preset convergence condition, and the final optimized planning scheme is output.

2. The method for power distribution network source-storage capacity planning and transient steady-state closed-loop simulation optimization according to claim 1, characterized in that, The objective function of the source-storage capacity optimization model is to minimize the sum of total investment cost, operating loss cost, and renewable energy curtailment penalty cost within the planning period, as shown in the following formula: ； In the formula, The cost of the source-storage equipment investment at time t is calculated. Let t be the operating loss cost of equipment such as lines, transformers, and converters at time t. Let t be the penalty cost for abandoning renewable energy.

3. The method for power distribution network source-storage capacity planning and transient steady-state closed-loop simulation optimization according to claim 1, characterized in that, The improved K-means clustering algorithm adaptively determines the optimal number of clusters by calculating the silhouette coefficient.

4. The method for power distribution network source-storage capacity planning and transient steady-state closed-loop simulation optimization according to claim 1, characterized in that, The steady-state simulation verification includes performing time-series power flow calculations and N-1 security analysis for all typical scenarios, and identifying steady-state security risks based on node voltage deviation indicators and line load rate indicators.

5. The method for power distribution network source-storage capacity planning and transient steady-state closed-loop simulation optimization according to claim 1, characterized in that, The electromagnetic transient simulation verification includes establishing a refined electromagnetic transient model containing power electronic equipment, simulating preset disturbance events, and identifying transient safety hazards based on transient voltage drop depth index and short-circuit inrush current index.

6. The method for power distribution network source-storage capacity planning and transient steady-state closed-loop simulation optimization according to claim 1, characterized in that, The formula for calculating the comprehensive risk value R of a single hidden danger is: ； Where P is the risk probability, S is the severity, I is the scope of influence, and w1, w2, and w3 are weight coefficients determined by a combination of the analytic hierarchy process and the entropy weight method.

7. The method for power distribution network source-storage capacity planning and transient steady-state closed-loop simulation optimization according to claim 6, characterized in that, The classification of risk levels based on the comprehensive risk value specifically includes: Hazards with a comprehensive risk value not lower than the high-risk threshold are classified as high-risk hazards; Hazards with a comprehensive risk value between the medium-risk threshold and the high-risk threshold are classified as medium-risk hazards. Hazards with a comprehensive risk value lower than the aforementioned medium-risk threshold are classified as low-risk hazards; Specifically, for hazards of different risk levels, differentiated correction strategies are implemented in subsequent closed-loop iterative optimizations: high-risk hazards trigger the highest priority correction and must be eliminated or mitigated in the current iteration; medium-risk hazards are corrected in the current iteration based on the optimization resources and the overall risk distribution; low-risk hazards are monitored as operational items, and the correction process is only initiated when they persist for a preset number of consecutive iterations or when their risk level increases.

8. The method for power distribution network source-storage capacity planning and transient steady-state closed-loop simulation optimization according to claim 1, characterized in that, The preset mapping rules include: To address the potential risk of line overload, the corresponding corrective actions are to adjust line parameters or optimize the energy storage charging and discharging schedule. To address the potential issue of node voltage exceeding limits, the corresponding corrective action is to adjust the reactive power compensation configuration or the parameters for new energy access. To address the potential for transient impacts, the corresponding corrective actions are to optimize the control parameters of power electronic equipment or to install dynamic reactive power compensation devices.

9. A power distribution network source-storage capacity planning and transient steady-state closed-loop simulation optimization system, characterized in that, The system is used to implement the distribution network source-storage capacity planning and transient steady-state closed-loop simulation optimization method according to any one of claims 1-8; the system includes: The full-time data processing and modeling module is used to construct and solve a source-storage capacity optimization model that couples new energy consumption, curtailment control and equipment variable operating condition efficiency constraints by utilizing the full-year time-series data of new energy and load in the planning area, so as to generate an initial capacity configuration scheme and its preliminary operation strategy. The time-series scene intelligent clustering dimensionality reduction module is communicatively connected to the full-time-series data processing and modeling module. It is used to take the new energy output and load time-series data in the preliminary operation strategy as input, and perform dimensionality reduction processing on the annual operation scene through the improved K-means clustering algorithm to extract a set of typical operation scenes that retain key fluctuation characteristics. The transient-steady-state joint simulation verification module is communicatively connected to the full-time-series data processing and modeling module and the time-series scenario intelligent clustering and dimensionality reduction module, respectively. It is used to simultaneously perform steady-state simulation verification and electromagnetic transient simulation verification on the initial capacity configuration scheme for the typical operating scenario, and identify two types of safety hazards: steady-state and transient. The three-dimensional risk quantification assessment and classification module is communicatively connected to the transient steady-state joint simulation verification module. It is used to integrate the verification results of the steady-state and transient safety hazards, construct a quantitative assessment model that integrates the three dimensions of risk probability, severity and impact range, and calculate the comprehensive risk value of each hazard and classify the risk level accordingly. The closed-loop iterative optimization decision and control module is communicatively connected to the three-dimensional risk quantification assessment and classification module and the full-time data processing and modeling module, respectively. It is used to perform targeted correction of the initial capacity configuration scheme according to the risk level and specific type of hidden danger after classification, and input the corrected scheme back into the clustering dimensionality reduction and simulation verification process for iterative optimization until the evaluation result meets the preset convergence condition, and output the final optimized planning scheme.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a distribution network source-storage capacity planning and metastable closed-loop simulation optimization program, wherein when the distribution network source-storage capacity planning and metastable closed-loop simulation optimization program is executed by a processor, it implements the steps of the distribution network source-storage capacity planning and metastable closed-loop simulation optimization method as described in any one of claims 1 to 8.

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