Receiving-end urban power grid energy storage resource allocation method based on voltage-frequency support strength quantitative evaluation

Abstract

The invention relates to a receiving-end urban power grid energy storage resource allocation method based on voltage-frequency support strength quantitative evaluation, and the method comprises the steps: obtaining the data of a receiving-end urban power grid, and obtaining a voltage and frequency support index through calculating a voltage-frequency multi-dimensional index; mapping the energy storage units into marginal contributions to voltage and frequency support indexes, and summarizing the contributions of all the energy storage units at corresponding nodes to obtain a net marginal contribution matrix of the energy storage units to the voltage and frequency support indexes in a given scene; through sensitivity analysis and scene clustering, the minimum energy storage installation capacity required in different representative scenes is obtained; and calculating the comprehensive contribution score of each energy storage unit, sorting the comprehensive contribution scores, generating a priority configuration list, and outputting the minimum capacity and construction position of the energy storage installation meeting the constraint in the corresponding scene. Compared with the prior art, the method has the advantages that unified evaluation of multi-type energy storage supporting capacity and accurate matching of system requirements can be realized, and a landing energy storage resource allocation scheme is output.

Description

Technical Field

[0001] This invention relates to the field of energy storage optimization and scheduling technology, and in particular to a method for allocating energy storage resources in receiving-end urban power grids based on quantitative assessment of voltage-frequency support strength. Background Technology

[0002] With the large-scale access of unstable new energy sources such as wind power and photovoltaics, and the increase in the proportion of imported power (sending-end input), the frequency stability margin of the receiving-end urban power grid is decreasing, and the risks of overvoltage and transient oscillations are increasing. The inertial support, which is mainly based on rotating machines, is gradually becoming scarce.

[0003] To ensure grid operation safety and the absorption of new energy sources, it is necessary to accurately quantify the voltage and frequency support capabilities of various energy storage resources (such as self-contained energy storage at new energy power plants, independent energy storage power stations, and V2G electric vehicles) to guide planning and commissioning. However, current energy storage assessments are mostly based on energy / power / economic aspects, or only consider a single indicator (such as short-circuit ratio improvement or instantaneous active power support). They cannot accurately quantify the marginal contribution and system-level demand of each energy storage unit under different operating scenarios, making it difficult to adapt to actual scenarios and obtain energy storage configuration schemes that can be used for planning and priority construction. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a method for allocating energy storage resources in urban power grids based on voltage-frequency support strength quantitative assessment. This method can achieve unified assessment of the support capabilities of multiple types of energy storage and accurate matching of system requirements, and output a feasible energy storage resource allocation scheme.

[0005] The objective of this invention can be achieved through the following technical solution: a method for allocating energy storage resources in receiving-end urban power grids based on quantitative assessment of voltage-frequency support strength, comprising the following steps: S1. Obtain urban power grid data at the receiving end, including basic data, measurement data, operation data, energy storage equipment data, and operation auxiliary data; S2. Based on the receiving-end urban power grid data, the voltage support index and frequency support index are obtained by calculating the voltage-frequency multidimensional index; S3. Map each energy storage unit to its marginal contribution to the voltage support index and frequency support index, and summarize the contributions of all energy storage units at the corresponding nodes to obtain the net marginal contribution matrix of each energy storage unit to the voltage support index and frequency support index under a given scenario. S4. Use the net marginal contribution matrix to perform sensitivity analysis and scenario clustering to obtain the minimum energy storage capacity required for different representative scenarios. S5. Calculate and sort the comprehensive contribution scores of each energy storage unit, generate a priority configuration list, and output the minimum installed capacity and construction location of energy storage that meet the constraints in the corresponding scenario.

[0006] Furthermore, the basic data in step S1 includes the receiving-end power grid topology (nodes, branches), static synchro parameters, short-circuit capacity, and transformer parameters; Measurement data includes SCADA (Supervisory Control and Data Acquisition) data and PMU (Power Management Unit) data; Operational data includes historical and predicted power sequences of new energy sources, external power input, and load. Energy storage device data includes the rated power of each energy storage device. Rated energy Maximum / minimum active / reactive output, response time constant Control mode and grid connection point location information; Operational support data includes market tags and operational scenario tags.

[0007] Further, step S2 includes the following steps: S21. Preprocess the collected receiving-end power grid data, and then calculate the multidimensional indicators under the voltage support dimension and frequency support dimension according to the voltage-frequency multidimensional indicator system. S22. Normalize the calculated multidimensional indices and obtain the corresponding voltage support index and frequency support index through weighted fusion.

[0008] Furthermore, the multi-dimensional indicators under the voltage support dimension in step S21 include: Static voltage stability margin The node voltage margin before the maximum load increment is defined and calculated using the PV curve sensitivity or minimum eigenvalue method. Transient voltage stability margin Voltage recovery ratio or steady-state recovery time obtained from time-domain / short-circuit-transient simulation; Short-circuit ratio (SCR) = ; Voltage stiffness The linearization sensitivity of node voltage to injected reactive power ; The multidimensional indicators under the frequency support dimension include: Maximum rate of change of frequency (RoCoF) ; Lowest frequency point (nadir) ; steady-state frequency deviation ; Recovery time .

[0009] Furthermore, step S22 specifically involves normalizing the multidimensional index using Min-Max or Z-score methods, and then weighting and summing the results according to the corresponding preset weights to obtain the node's voltage support index. With frequency support index : in, This represents the standardized value. For nodes i Voltage support weight, For nodes j Frequency-supported weights.

[0010] Further, step S3 includes the following steps: S31. Based on the grid Jacobian matrix or sensitivity matrix, calculate the normalized sensitivity of the reactive power injection capacity of energy storage to the voltage support index in order to obtain the marginal contribution of voltage support. S32. By using the equivalent inertia / damping model, correlate the active power injection of energy storage with the system frequency change to obtain the marginal contribution of frequency support. S33. Summarize the contributions of each energy storage unit at the corresponding node, considering SOC constraints, availability ratio, and electrical location attenuation at the grid connection point, to obtain the contribution of each energy storage unit to the grid connection in a given scenario. and The net marginal contribution matrix.

[0011] Furthermore, the specific process of step S31 is as follows: Assume an energy storage unit... k Reactive power injection capability is Calculate the rate of change of voltage parameters using the power grid Jacobian or sensitivity matrix: Define energy storage unit k The contribution to node voltage support is: in, This represents the normalized sensitivity of the voltage index to reactive power.

[0012] Furthermore, the specific process of step S32 is as follows: energy storage provides rapid active power injection through discharge / charge, and the equivalent support for frequency is approximately: in, To calculate the system frequency sensitivity, the energy storage unit... k The contribution to node frequency support is: in, This represents the normalized sensitivity of the frequency index to active power.

[0013] Further, step S4 includes the following steps: S41. Scene Sampling and Feature Construction: Generate N representative scenes from historical and predicted data. Each scene is described by a feature vector, including the proportion of new energy access, external power, load standard deviation, short-circuit capacity, initial SOC distribution, and available energy storage scale. S42. Scene Clustering: Use a clustering algorithm to group N scenes in the feature space and select the representative scene of each cluster as the key scene. S43. Perform sensitivity / perturbation simulations under each representative scenario to calculate the marginal contribution vector of each energy storage unit. and Based on the marginal contribution vector, the minimum required energy storage capacity is calculated according to the preset target values ​​of the voltage support index and the frequency support index.

[0014] Furthermore, the formula for calculating the comprehensive contribution score in step S5 is as follows: in, Annualized cost per unit capacity As the energy storage availability factor, , , For strategy weights.

[0015] Compared with the prior art, the present invention has the following advantages: This invention, based on receiving-end urban power grid data, obtains voltage support index and frequency support index by calculating voltage-frequency multidimensional indicators. Then, each energy storage unit is mapped to its marginal contribution to the voltage support index and frequency support index, and the contributions of all energy storage units at corresponding nodes are summarized to obtain the net marginal contribution matrix of each energy storage unit to the voltage support index and frequency support index under a given scenario. Sensitivity analysis and scenario clustering are then performed using the net marginal contribution matrix to obtain the minimum energy storage capacity required for different representative scenarios. Finally, the comprehensive contribution score of each energy storage unit is calculated and ranked to generate a priority configuration list, outputting the minimum energy storage capacity and construction location that meet the constraints under the corresponding scenario. This constructs a multidimensional indicator system covering static / transient voltage support and frequency support, enabling the mapping of different types of energy storage to comparable support capacity units based on their electrical and control characteristics. Furthermore, through sensitivity analysis and scenario clustering, the marginal contribution and system-level requirements of each energy storage unit under different operating scenarios are quantified, enabling unified evaluation of the support capabilities of multiple types of energy storage and precise matching of system requirements, outputting a feasible energy storage resource allocation scheme.

[0016] This invention establishes a voltage-frequency multi-dimensional index system for receiving-end urban power grids. The voltage dimension index focuses on node voltage amplitude and recovery capability, quantifying the reactive power support value of energy storage. The frequency dimension index focuses on system frequency dynamics and steady-state characteristics, quantifying the active power support value of energy storage. This enables horizontal comparison of the support capabilities of various types of energy storage, ensuring the accuracy and comprehensiveness of the assessment of the support strength of energy storage units.

[0017] This invention addresses uncertainty through scenario clustering, structuring complex operational uncertainties and outputting targeted and interpretable energy storage requirements. Furthermore, based on sensitivity analysis, it can accurately quantify the true value of energy storage under specific nodes and strategies. By employing sensitivity analysis rather than simple capacity indicators, it can obtain the true marginal effect of energy storage on supporting indicators under specific nodes and control strategies. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0019] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0020] Example 1 like Figure 1 As shown, a method for allocating energy storage resources in receiving-end urban power grids based on voltage-frequency support strength quantitative assessment includes the following steps: S1. Obtain urban power grid data at the receiving end, including basic data, measurement data, operation data, energy storage equipment data, and operation auxiliary data; S2. Based on the receiving-end urban power grid data, the voltage support index and frequency support index are obtained by calculating the voltage-frequency multidimensional index; S3. Map each energy storage unit to its marginal contribution to the voltage support index and frequency support index, and summarize the contributions of all energy storage units at the corresponding nodes to obtain the net marginal contribution matrix of each energy storage unit to the voltage support index and frequency support index under a given scenario. S4. Use the net marginal contribution matrix to perform sensitivity analysis and scenario clustering to obtain the minimum energy storage capacity required for different representative scenarios. S5. Calculate and sort the comprehensive contribution scores of each energy storage unit, generate a priority configuration list, and output the minimum installed capacity and construction location of energy storage that meet the constraints in the corresponding scenario.

[0021] In step S1, the basic data includes the receiving-end power grid topology (nodes, branches), synchronous machine parameters, short-circuit capacity, and transformer parameters; Measurement data includes PMU data (50~100ms sampling, used for transient / frequency indicators) and SCADA data (minute-level, used for coupling long-term indicators with the market). Operational data includes historical and predicted power sequences of new energy sources (PV / WT), external power input, and load. Energy storage equipment data includes rated power / energy, active / reactive output limits, response time constant, control mode, grid connection point location, etc. Supporting data includes market tags and operating scenario tags (if any).

[0022] In step S2, the voltage-frequency multidimensional index covers two major dimensions: voltage support and frequency support, and each includes multiple core indicators: Voltage support parameters include static voltage stability margin, transient voltage stability margin, short-circuit ratio, and voltage stiffness. Frequency support indicators include maximum frequency change rate, minimum frequency point, steady-state frequency difference, and recovery time.

[0023] The calculated indicators are standardized and aggregated: Min-Max or Z-score normalization is used, and the weights are determined by expert scoring / AHP, entropy weighting method or supervised learning. The voltage support index and frequency support index are obtained by weighted summation.

[0024] In step S3, a small amount of active / reactive injection / extraction is used as a disturbance to calculate the rate of change of energy storage on the supporting indicators, thereby quantifying the marginal contribution.

[0025] Among them, the marginal contribution of voltage support is based on the grid Jacobian matrix or sensitivity matrix to calculate the normalized sensitivity of the reactive power injection capacity of energy storage to the voltage support index.

[0026] The marginal contribution of frequency support is determined by using an equivalent inertia / damping model to correlate active power injection from energy storage with system frequency changes, and incorporating control parameters such as virtual inertia constant and power limitation duration.

[0027] Finally, the comprehensive capability vector is obtained by summarizing the contribution of energy storage at the node, taking into account SOC constraints, availability ratio, and electrical location attenuation at the grid connection point, to obtain the net marginal contribution matrix.

[0028] Step S4 includes the following process: Scene sampling: Representative scenes (peak hours, valley hours, large external power input, etc.) are generated from historical and predicted data, and feature vectors are generated using the proportion of new energy access and load standard deviation.

[0029] Scene clustering: k-means / hierarchical clustering / DBSCAN algorithm is used to group scenes based on Mahalanobis distance (considering feature covariance). The number of clusters is determined by silhouette or Davies-Bouldin index, and representative scenes are selected.

[0030] Demand quantification: In each representative scenario, the marginal contribution of energy storage is calculated through steady-state power flow / transient simulation, and the minimum installed power / energy required for energy storage is deduced by back-calculating the "indicator compliance threshold".

[0031] In step S5, the comprehensive contribution score is calculated by weighted summation of voltage / frequency marginal contributions, incorporating annualized cost per unit capacity and energy storage availability factor. The output is a priority list for energy storage construction / configuration, sorted by score, providing the minimum energy storage capacity (P / E) requirement to meet constraints under each key scenario.

[0032] It should be noted that in the practical application of the above method, an electronic device including a central processing unit (CPU) can be used. This CPU can execute various appropriate actions and processes based on computer program instructions stored in read-only memory (ROM) or loaded from storage units into random access memory (RAM). RAM can also store various programs and data required for device operation. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0033] Multiple components in the device are connected to an I / O interface, including: input units such as a keyboard, mouse, etc.; output units such as various types of displays, speakers, etc.; storage units such as disks, optical disks, etc.; and communication units such as network interface cards, modems, wireless transceivers, etc. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks. The processing unit performs the various methods and processes described above, such as the method of the present invention. For example, in some embodiments, the method of the present invention may be implemented as a computer software program tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via ROM and / or the communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of the method of the present invention described above may be performed. Alternatively, in other embodiments, the CPU may be configured to execute the method of the present invention by any other suitable means (e.g., by means of firmware).

[0034] The functions described above in this invention can be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that can be used include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0035] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0036] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0037] Example 2 This embodiment applies the scheme described in Embodiment 1, and the main process includes: data acquisition → data preprocessing → index calculation (voltage / frequency multi-dimensional index) → sensitivity analysis (marginal contribution) → scenario clustering (demand scenario division) → energy storage capacity mapping → comprehensive scoring and demand quantification → output (report / API).

[0038] The requirements for the input data are as follows: Topology and parameters: receiving-end grid topology (nodes, branches), static synchronizing machine parameters, short-circuit capacity, and transformer parameters; Measurement data, SCADA (minute level) and PMU (Power Management Unit), power management unit (50–100ms) voltage amplitude, phase angle, frequency; Load and renewable energy data, power series (PV / WT / external power input / load) historical data and forecasts; Energy storage equipment library, rated power of each energy storage unit. Rated energy Maximum / minimum active / reactive output, response time constant Control mode (grid following / grid formation), grid connection point location information; Market / Operational Scenario Tags (if any).

[0039] Data frequency and expectations: PMU is used for transient / frequency indicator calculation (20~50ms sampling), and SCADA is used for long-term indicators (minute level) and market coupling analysis.

[0040] Specifically: 1) Construct a voltage-frequency multidimensional index system to characterize the voltage / frequency support strength using several standardized indicators. Commonly used indicators include: a) Voltage support dimension (calculated for each node or region): Static voltage stability margin Defined as the node voltage margin before the maximum load increment, it can be calculated using PV curve sensitivity or the minimum eigenvalue method; Transient voltage stability margin Voltage recovery ratio or steady-state recovery time obtained from time-domain / short-circuit-transient simulation; Short-circuit ratio (SCR) = ; Voltage stiffness Linearization sensitivity of node voltage to injected reactive power ; b) Frequency support dimension: Maximum rate of change of frequency (RoCoF) ; Lowest frequency point (nadir) ; steady-state frequency deviation ; Recovery time .

[0041] Standardize and aggregate the metrics: Normalize each of the above metrics (e.g., Min-Max or Z-score) and assign them according to the designed weights. The voltage support index of a node / region is obtained by weighted summation. With frequency support index : in, This represents the standardized quantity.

[0042] Weights can be determined using expert scoring / AHP, entropy weighting, or supervised learning based on historical events (e.g., using historical failure events and impact losses to train a regression model to obtain index weights). .

[0043] 2) Mapping of the supporting capacity of energy storage units (marginal contribution model) Map energy storage units to pairs based on their physical and control characteristics. and The marginal contribution value is specifically calculated by using a small amount of injection / extraction (active / reactive) as a disturbance to calculate the rate of change of energy storage on supporting indicators, thereby quantifying the marginal contribution.

[0044] a) Marginal contribution of voltage support (energy storage unit) k At node n) Install energy storage unit k Reactive power injection capability is Using the grid Jacobian (Ybus derived) or sensitivity matrix, calculate: Define energy storage unit k The contribution to node voltage support is: in, For indicator i pair Q Normalized sensitivity.

[0045] b) Marginal contribution of frequency support The frequency response is approximated using an equivalent inertia / damping model. Energy storage provides rapid active power injection through discharging / charging, and the equivalent support for the frequency is approximated as follows: in, The system frequency sensitivity (related to the equivalent inertia H and system damping) is defined as follows: It should be noted that if the energy storage supports virtual inertia or fast charge / discharge strategies, then its control parameters (virtual inertia constant) need to be adjusted. Input the following into the model: power limit duration, etc.

[0046] c) Comprehensive capability vector (node / system level) The contributions of all energy storage units at their respective nodes are summarized, and their availability (SOC constraints, availability ratio) and the electrical location of the grid connection point (affect attenuation due to reactance / impedance) are considered to obtain the contribution of each energy storage unit to the grid connection in a given scenario. and The net marginal contribution matrix.

[0047] 3) Sensitivity analysis and scene clustering (scene-driven demand quantification) To address the diversity of operational scenarios, a two-step approach is adopted: a) Scene sampling and feature construction: N representative scenes (such as daytime peak, nighttime valley, large external power input, storm cloud blockage, etc.) are generated from historical and predicted data. Each scene is described by a feature vector, including the proportion of new energy access, external power, load standard deviation, short-circuit capacity, initial SOC distribution, available energy storage scale, etc.

[0048] b) Scene Clustering: For the N scenes, a clustering algorithm (k-means / hierarchical clustering / DBSCAN) is used to group them in the feature space, and representative scenes from each cluster are selected as key scenes. The Mahalanobis distance is used as the distance metric in clustering to account for feature covariance, and the number of clusters is determined using the Silhouette or Davies-Bouldin index.

[0049] c) Perform sensitivity / disturbance simulations (steady-state power flow / transient simulation / frequency response analysis) under each representative scenario to calculate the marginal contribution vector of each energy storage unit. , Based on these marginal contributions, according to the objective (to make... or (Raise to the corresponding threshold) Calculate the minimum required installed power / energy of energy storage.

[0050] 4) Comprehensive scoring and decision support (ranking / capacity suggestions) By weighted summarizing the marginal contributions of voltage / frequency, and incorporating factors such as annualized cost per unit capacity and energy storage availability factor, a comprehensive contribution score is calculated for each candidate energy storage location / unit. : in, This can be the annualized cost per unit capacity. , , For strategy weights.

[0051] The system generates a priority construction / configuration list based on scores, and simultaneously provides the minimum capacity requirement (P / E) to meet constraints in each key scenario.

[0052] 5) Verification and Fault-Tolerant Design The robustness of the evaluation method is verified through backtesting of historical events (failures, fluctuations) and Monte Carlo uncertainty simulation. For measurement gaps or sparse PMU regions, a degradation strategy is provided: completion based on state estimation / inference or by using an approximate sensitivity correction factor.

[0053] In summary, this scheme establishes a voltage-frequency dual-dimensional characterization system at the receiving-end grid level for the first time, and normalizes the different characteristics of various types of energy storage into a unified marginal contribution framework, which facilitates horizontal comparison and ranking. By structuring complex operational uncertainties through scenario clustering, the output energy storage demand is targeted and interpretable, enabling scenario adaptation; By using sensitivity analysis rather than simple capacity indicators, we can obtain the true marginal effect of energy storage units on supporting indicators under specific nodes and control strategies, and accurately measure the marginal contribution. This solution is easy to integrate with existing SCADA / PMU and planning toolchains, supports automated reports and API calls, and can be directly used for distribution / receiving end planning, energy storage investment prioritization, and distribution dispatch strategy design.

Claims

1. A method for allocating energy storage resources in receiving-end urban power grids based on quantitative assessment of voltage-frequency support strength, characterized in that, Includes the following steps: S1. Obtain urban power grid data at the receiving end, including basic data, measurement data, operation data, energy storage equipment data, and operation auxiliary data; S2. Based on the receiving-end urban power grid data, the voltage support index and frequency support index are obtained by calculating the voltage-frequency multidimensional index; S3. Map each energy storage unit to its marginal contribution to the voltage support index and frequency support index, and summarize the contributions of all energy storage units at the corresponding nodes to obtain the net marginal contribution matrix of each energy storage unit to the voltage support index and frequency support index under a given scenario. S4. Use the net marginal contribution matrix to perform sensitivity analysis and scenario clustering to obtain the minimum energy storage capacity required for different representative scenarios. S5. Calculate and sort the comprehensive contribution scores of each energy storage unit, generate a priority configuration list, and output the minimum installed capacity and construction location of energy storage that meet the constraints in the corresponding scenario.

2. The method for allocating energy storage resources in receiving-end urban power grids based on voltage-frequency support strength quantitative assessment as described in claim 1, characterized in that, The basic data in step S1 includes the receiving-end power grid topology, static synchronous machine parameters, short-circuit capacity, and transformer parameters. Measurement data includes SCADA data and PMU data; Operational data includes historical and predicted power sequences of new energy sources, external power input, and load. Energy storage device data includes the rated power of each energy storage device. Rated energy Maximum / minimum active / reactive output, response time constant Control mode and grid connection point location information; Operational support data includes market tags and operational scenario tags.

3. The method for allocating energy storage resources in receiving-end urban power grids based on voltage-frequency support strength quantitative assessment as described in claim 1, characterized in that, Step S2 includes the following steps: S21. Preprocess the collected receiving-end power grid data, and then calculate the multidimensional indicators under the voltage support dimension and frequency support dimension according to the voltage-frequency multidimensional indicator system. S22. Normalize the calculated multidimensional indices and obtain the corresponding voltage support index and frequency support index through weighted fusion.

4. The method for allocating energy storage resources in receiving-end urban power grids based on voltage-frequency support strength quantitative assessment as described in claim 3, characterized in that, The multi-dimensional indicators under the voltage support dimension in step S21 include: Static voltage stability margin The node voltage margin before the maximum load increment is defined and calculated using the PV curve sensitivity or minimum eigenvalue method. Transient voltage stability margin Voltage recovery ratio or steady-state recovery time obtained from time-domain / short-circuit-transient simulation; Short-circuit ratio (SCR) = ; Voltage stiffness The linearization sensitivity of node voltage to injected reactive power ; The multidimensional indicators under the frequency support dimension include: Maximum frequency change rate ; Lowest frequency point ; steady-state frequency deviation ; Recovery time .

5. The method for allocating energy storage resources in receiving-end urban power grids based on voltage-frequency support strength quantitative assessment as described in claim 4, characterized in that, Specifically, step S22 involves normalizing the multidimensional index using Min-Max or Z-score methods, and then weighting and summing the results according to the corresponding preset weights to obtain the node's voltage support index. With frequency support index : in, This represents the standardized value. For nodes i Voltage support weight, For nodes j Frequency-supported weights.

6. The method for allocating energy storage resources in receiving-end urban power grids based on voltage-frequency support strength quantitative assessment as described in claim 5, characterized in that, Step S3 includes the following steps: S31. Based on the grid Jacobian matrix or sensitivity matrix, calculate the normalized sensitivity of the reactive power injection capacity of energy storage to the voltage support index in order to obtain the marginal contribution of voltage support. S32. By using the equivalent inertia / damping model, correlate the active power injection of energy storage with the system frequency change to obtain the marginal contribution of frequency support. S33. Summarize the contributions of each energy storage unit at the corresponding node, considering SOC constraints, availability ratio, and electrical location attenuation at the grid connection point, to obtain the contribution of each energy storage unit to the grid connection in a given scenario. and The net marginal contribution matrix.

7. The method for allocating energy storage resources in receiving-end urban power grids based on voltage-frequency support strength quantitative assessment as described in claim 6, characterized in that, The specific process of step S31 is as follows: Assume an energy storage unit k Reactive power injection capability is Calculate the rate of change of voltage parameters using the power grid Jacobian or sensitivity matrix: Define energy storage unit k The contribution to node voltage support is: in, This represents the normalized sensitivity of the voltage index to reactive power.

8. The method for allocating energy storage resources in receiving-end urban power grids based on voltage-frequency support strength quantitative assessment as described in claim 7, characterized in that, The specific process of step S32 is as follows: Energy storage provides rapid active power injection through discharge / charge, and the equivalent support for frequency is approximately: in, To calculate the system frequency sensitivity, the energy storage unit... k The contribution to node frequency support is: in, This represents the normalized sensitivity of the frequency index to active power.

9. A method for allocating energy storage resources in receiving-end urban power grids based on voltage-frequency support strength quantitative assessment, as described in claim 8, is characterized in that... Step S4 includes the following steps: S41. Scene Sampling and Feature Construction: Generate N representative scenes from historical and predicted data. Each scene is described by a feature vector, including the proportion of new energy access, external power, load standard deviation, short-circuit capacity, initial SOC distribution, and available energy storage scale. S42. Scene Clustering: Use a clustering algorithm to group N scenes in the feature space and select the representative scene of each cluster as the key scene. S43. Perform sensitivity / perturbation simulations under each representative scenario to calculate the marginal contribution vector of each energy storage unit. and Based on the marginal contribution vector, the minimum required energy storage capacity is calculated according to the preset target values ​​of the voltage support index and the frequency support index.

10. A method for allocating energy storage resources in receiving-end urban power grids based on voltage-frequency support strength quantitative assessment, as described in claim 9, is characterized in that... The formula for calculating the comprehensive contribution score in step S5 is as follows: in, Annualized cost per unit capacity As the energy storage availability factor, , , For strategy weights.

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