Networking type energy storage distribution method and system for enhancing short-circuit ratio and transient voltage stability

Abstract

The invention provides a network construction type energy storage point distribution method and system for enhancing short-circuit ratio and transient voltage stability, and belongs to the technical field of power systems. The method comprises the steps of collecting and preprocessing multi-dimensional data of a power grid, and generating a power grid partition map for guiding energy storage type configuration through computational analysis and mode recognition; based on the power grid partition map, establishing an efficiency quantification model for quantifying the power grid stability improvement efficiency of the network construction type energy storage and the network following type energy storage; according to the efficiency quantification model, constructing and solving an energy storage stationing optimization model considering the collaborative constraint, and obtaining a plurality of alternative optimization stationing schemes; and carrying out time domain simulation verification and parameter cooperative setting covering a preset typical fault scene and a normal operation scene on all the alternative optimization stationing schemes to obtain an energy storage stationing planning scheme. According to the method, a multi-objective optimization model is constructed under the condition of considering the cooperative constraint, and an optimization point distribution scheme which is balanced among cost, strength and stability is solved.

Description

Technical Field

[0001] This invention relates to the field of power system technology, specifically to a grid-type energy storage deployment method and system that enhances short-circuit ratio and transient voltage stability. Background Technology

[0002] With the continuous large-scale integration of high-proportion renewable energy sources, such as wind and solar power, the dynamic characteristics and stability of modern power systems are undergoing fundamental changes. The physical rotational inertia and short-circuit capacity provided by traditional synchronous generators have been significantly weakened, leading to a general decline in the system strength of electronic power grids. Furthermore, the short-circuit ratio, a key quantitative indicator, has approached or fallen below the safe operating threshold in many regions. Simultaneously, the transient voltage stability problem of the power grid after disturbances is becoming increasingly prominent, becoming one of the main bottlenecks restricting the consumption of new energy and the safe operation of the system. Against this backdrop, grid-based technologies capable of independently constructing grid voltage and frequency are considered key enabling technologies for fundamentally reshaping the foundation of grid stability and replacing the function of synchronous generators. Energy storage systems applying grid-based control strategies have received widespread attention for their dual value as both a flexibility resource and an active support unit. Related research focuses on the modeling of individual grid-based energy storage devices, controller design, fault ride-through capabilities, and their improvement effects on the frequency and voltage dynamic response characteristics of local power grids. Preliminary verification of their technical feasibility in improving the short-circuit capacity of specific nodes and suppressing voltage fluctuations has laid a theoretical foundation for their application from the device level to system-level integration.

[0003] However, despite the continuous maturation of the core technologies of grid-based energy storage, its systematic planning and application methods at the grid scale still have significant limitations. Existing research and practice mostly focus on "point-to-point" remedial configuration of known weak nodes, or simply treat them in planning models as a generalized power source that can provide inertia and short-circuit capacity, lacking a top-level planning framework that starts from the physical root causes of grid stability problems and global collaborative decision-making. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for deploying grid-connected energy storage to enhance short-circuit ratio and transient voltage stability. This method is used to systematically solve the problem of synergistic improvement of insufficient short-circuit ratio and transient voltage instability in power systems by diagnosing the root causes of grid stability bottlenecks and accordingly optimizing the configuration of grid-connected and grid-linked energy storage.

[0005] To achieve the above objectives, this invention provides a method for deploying grid-based energy storage to enhance short-circuit ratio and transient voltage stability. The method includes: collecting and preprocessing multi-dimensional grid data; generating a grid partition map to guide energy storage type configuration through calculation, analysis, and pattern recognition; establishing an efficiency quantification model based on the grid partition map to quantify the grid stability improvement effectiveness of grid-based and grid-connected energy storage; constructing and solving an energy storage deployment optimization model considering collaborative constraints based on the efficiency quantification model to obtain multiple alternative optimized deployment schemes; and performing time-domain simulation verification and parameter collaborative tuning covering preset typical fault scenarios and normal operation scenarios for all alternative optimized deployment schemes to obtain an energy storage deployment planning scheme.

[0006] Optionally, the multi-dimensional data of the power grid includes power grid topology and impedance parameters, power output data, and a preset set of typical faults for time-domain simulation analysis.

[0007] Optionally, the step of generating a power grid partition map to guide the configuration of energy storage types through calculation analysis and pattern recognition includes: calculating the short-circuit ratio of all nodes in the power grid under study; performing short-circuit ratio contribution analysis and sensitivity analysis based on the short-circuit ratio calculation results; the short-circuit ratio contribution analysis is used to calculate the contribution ratio of each power source to the short-circuit current of the target node; the sensitivity analysis is used to evaluate the impact of changes in power grid parameters on the node short-circuit ratio to identify key root cause nodes leading to insufficient power grid strength; performing time-domain simulation based on a preset typical fault set to extract the dynamic trajectory features of the voltage waveforms of each node before and after fault clearance; using a pattern recognition method, classifying the transient voltage instability phenomena of different nodes into at least monotonic instability, low-frequency oscillation, and delayed recovery instability modes based on the dynamic trajectory features; and performing spatial correlation and overlay analysis on the regions of the key root cause nodes and the instability modes to generate a power grid partition map.

[0008] Optionally, the step of generating a grid zoning map to guide the configuration of energy storage types through calculation analysis and pattern recognition further includes: spatially associating the region where the key root cause node is located with the region where the monotonic instability mode occurs, and jointly mapping them as a mandatory configuration area for grid-based energy storage; mapping the region where the low-frequency oscillation or delayed recovery instability mode occurs as a priority configuration area for grid-connected energy storage; and mapping the region where multiple instability modes exist simultaneously as a hybrid configuration optimization area that requires coordinated capacity optimization of grid-based and grid-connected energy storage.

[0009] Optionally, the step of establishing an efficiency quantification model for quantifying the grid stability improvement efficiency of grid-connected energy storage and grid-linked energy storage includes: constructing an equivalent virtual synchronous machine model for grid-connected energy storage, wherein the equivalent virtual synchronous machine model sets virtual inertia time constant and transient internal impedance parameters based on the rated capacity parameter of the grid-connected energy storage, so as to equate the grid-connected energy storage to an equivalent synchronous generation unit exhibiting voltage source support characteristics at the grid connection point; based on the equivalent virtual synchronous machine model, defining an intensity improvement efficiency coefficient matrix for grid-connected energy storage, wherein the intensity improvement efficiency coefficient matrix is ​​used to characterize the quantified improvement efficiency on the short-circuit ratio of the target grid node when a unit rated active power capacity of grid-connected energy storage is deployed at the grid node; wherein the intensity improvement efficiency coefficient matrix is ​​obtained by introducing an equivalent unit capacity of grid-connected energy storage in the grid model and performing sensitivity analysis on the change of short-circuit ratio in combination with the grid equivalent impedance parameters.

[0010] Optionally, the step of establishing an efficiency quantification model for improving grid stability using grid-connected energy storage and grid-linked energy storage further includes: constructing a transient power support model for grid-linked energy storage, wherein the transient power support model, based on the grid-connected control characteristics of grid-linked energy storage, establishes an active and reactive power output response model for grid-linked energy storage during grid disturbances or faults, used to characterize the power support behavior of grid-linked energy storage during voltage sags, voltage recovery, and oscillation decay; and, based on the transient power support model, defining a power support efficiency coefficient matrix for grid-linked energy storage, wherein the power support efficiency coefficient matrix is ​​used to quantify the power support performance of grid-connected energy storage during grid disturbances or faults. When a grid-connected energy storage unit of rated capacity is deployed at a grid node, the improvement efficiency on the transient voltage stability index of the target grid node is considered. The transient voltage stability index includes at least one or more of the following: improvement in minimum voltage value, reduction in voltage recovery time, attenuation of voltage oscillation amplitude, and enhancement of oscillation damping. The power support efficiency coefficient matrix is ​​calculated by introducing a grid-connected energy storage transient power support unit of rated capacity into the grid model, and by comparing and analyzing the grid voltage dynamic response before and after energy storage access under typical fault and disturbance scenarios, through a combination of time-domain simulation and sensitivity analysis.

[0011] Optionally, the construction and solution of the energy storage deployment optimization model considering collaborative constraints includes: defining decision variables for the energy storage deployment optimization model, the decision variables including deployment selection variables for each candidate site, energy storage type variables, and power and energy capacity variables; setting collaborative constraints for the energy storage deployment optimization model, the collaborative constraints including at least: partition type constraints based on the power grid partition map, limiting the configuration of grid-type energy storage at sites located in the mandatory configuration zone of grid-type energy storage; global short-circuit ratio constraints based on the efficiency quantification model, ensuring that the short-circuit ratio of all nodes is greater than the safety threshold after planning; and collaborative capacity ratio constraints, used to limit the proportion of the total rated active power of grid-type energy storage to the total rated active power of all energy storage devices to be greater than or equal to a preset lower limit.

[0012] Optionally, the objective function of the energy storage deployment optimization model is: minimizing the total investment cost of the target power system as the first optimization objective; and increasing the short-circuit ratio of the weakest node of the target power system after planning, and / or shortening the longest voltage recovery time of the target power system under the most severe fault as the second optimization objective; the first optimization objective and the second optimization objective together constitute the objective function.

[0013] Optionally, the step of performing time-domain simulation verification and parameter co-tuning covering preset typical fault scenarios and normal operation scenarios for all candidate optimized deployment schemes includes: performing electromagnetic transient simulations on each candidate optimized deployment scheme, the electromagnetic transient simulations covering different locations and different types of fault scenarios; based on the electromagnetic transient simulation results, selecting deployment schemes that meet dynamic safety and stability requirements; configuring initial control parameters for each energy storage site in the selected deployment schemes, including setting virtual inertia parameters and virtual impedance parameters for grid-type energy storage, and setting control parameters for the power outer loop and current inner loop for grid-following energy storage; when configuring initial control parameters, using small-signal stability analysis or time-domain simulation verification, ensuring that the control parameters between adjacent or functionally complementary energy storage sites meet the co-matching requirements, so as to avoid adverse dynamic interaction phenomena between different energy storage sites.

[0014] On the other hand, the present invention provides a grid-type energy storage deployment system that enhances short-circuit ratio and transient voltage stability, and a method for implementing grid-type energy storage deployment that enhances short-circuit ratio and transient voltage stability. The system includes a control module, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the computer program to implement the grid-type energy storage deployment method that enhances short-circuit ratio and transient voltage stability.

[0015] The aforementioned technical solution accurately identifies grid stability bottlenecks through root cause tracing and pattern matching, correlates key nodes with instability modes, and generates a partition map to guide the configuration of grid-based and grid-connected energy storage. By establishing an equivalent virtual synchronous machine model for grid-based energy storage and a transient power support model for grid-connected energy storage, its differentiated improvement efficiency on short-circuit ratio and voltage stability is quantified. Considering collaborative constraints, a multi-objective optimization model is constructed to find an optimal deployment scheme that balances cost, strength, and stability. Finally, after full-condition simulation verification and parameter tuning, an feasible planning scheme integrating deployment and selection is output, systematically improving grid strength and transient voltage stability.

[0016] Other features and advantages of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0017] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the following detailed description to explain the invention, but do not constitute a limitation thereof. In the drawings:

[0018] Figure 1 This is a flowchart of a grid-type energy storage deployment method that enhances short-circuit ratio and transient voltage stability.

[0019] Figure 2 This is a flowchart of the construction and solution process for the energy storage collaborative deployment optimization model. Detailed Implementation

[0020] The following is in conjunction with the appendix Figure 1 - Appendix Figure 2 The specific implementation methods of the embodiments of the present invention will be described in detail below. It should be understood that the specific implementation methods described herein are only for illustrating and explaining the embodiments of the present invention, and are not intended to limit the embodiments of the present invention.

[0021] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of this application all comply with the relevant provisions of national laws and regulations. In the embodiments of this application, certain existing industry solutions such as software, components, and models may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solution of this application, and do not imply that the applicant has already used or necessarily used such solutions.

[0022] In the process of realizing this invention, the inventors of this application discovered that the existing technology for diagnosing grid stability problems lacks a detailed identification of the physical root causes and instability modes, and fails to coordinate and optimize the differentiated functions of grid-connected and grid-linked energy storage in the planning process, resulting in deficiencies in the energy storage configuration scheme in terms of technical relevance, economy and system optimization.

[0023] Example 1

[0024] Reference Figures 1-2 This is the first embodiment of the present invention, which provides a grid-type energy storage deployment method that enhances short-circuit ratio and transient voltage stability, including:

[0025] S100: Collects and preprocesses multi-dimensional power grid data, and generates a power grid zoning map to guide the configuration of energy storage types through calculation analysis and pattern recognition.

[0026] In the embodiments of this application, the multi-dimensional data of the power grid includes power grid topology and impedance parameters, power output data, and a preset typical fault set for time-domain simulation analysis. The preset typical fault set covers serious faults of different fault locations and different fault types, providing comprehensive scenario support for subsequent time-domain simulation analysis.

[0027] In a preferred embodiment of this application, the collected raw data is standardized and preprocessed, including outlier removal and missing data completion for power grid topology, impedance parameters and power output data, uniform format conversion of all parameters to ensure data consistency, and key parameters such as fault duration and triggering conditions of preset typical fault sets are defined.

[0028] In the embodiments of this application, the short-circuit ratio of all nodes in the power grid under study is calculated. Based on the short-circuit ratio calculation results, short-circuit ratio contribution analysis and sensitivity analysis are performed. The short-circuit ratio contribution analysis is used to calculate the contribution ratio of each power source to the short-circuit current of the target node, and the sensitivity analysis is used to evaluate the impact of changes in power grid parameters on the node short-circuit ratio, so as to identify the key root cause nodes leading to insufficient power grid strength. Time-domain simulation is performed based on a preset typical fault set to extract the dynamic trajectory features of the voltage waveforms of each node before and after fault clearance. Using a pattern recognition method, the transient voltage instability phenomena of different nodes are classified into instability modes including at least monotonic instability, low-frequency oscillation, and delayed recovery based on the dynamic trajectory features. The regions of key root cause nodes and instability modes are spatially correlated and superimposed to generate a power grid partition map.

[0029] In a preferred embodiment of this application, the three-phase short-circuit capacity of all nodes is calculated based on the grid node admittance matrix, and the short-circuit ratio distribution of the entire network is obtained by combining the node injected power. For weak nodes with a short-circuit ratio below the threshold of 2.0, a current decomposition method based on the node impedance matrix (such as the superposition theorem) is used to quantitatively calculate the contribution ratio of each power source (generator, new energy power station) in the power system to the short-circuit current of the node. Power sources whose contribution ratio is significantly higher than their power penetration rate are identified (if the contribution ratio of a power source to the short-circuit current of the target weak node, i.e., the percentage of the short-circuit current provided by the power source to the total short-circuit current of the target node, is greater than its power penetration rate in the power system or the power grid of the target node region, i.e., the rated power of the power source accounts for 1.5 to 2.0 times the percentage of the total installed power of the power system or region, it is judged as significantly higher), and their grid connection point is marked as an important source of intensity influence. The reactance parameters of key lines and transformers in the power grid are selected as disturbance variables. A small disturbance of ±5% is applied to each parameter. The change in short-circuit ratio is obtained by recalculating the short-circuit ratio of each node (focusing on the aforementioned weak nodes) before and after the disturbance. Based on this change, the sensitivity coefficient of each branch reactance parameter to the node short-circuit ratio is quantified. The sensitivity coefficient is equal to the ratio of the change in the node short-circuit ratio to the change in the branch reactance parameter. The change in the node short-circuit ratio refers to the change in the short-circuit ratio of the target node (focusing on weak nodes with short-circuit ratios below a threshold) after a small disturbance to the reactance parameters of key lines and transformers in the power grid. The change in the branch reactance parameter refers to the small disturbance to the reactance parameters of the aforementioned key branches themselves. Components with an absolute sensitivity coefficient ≥ 0.4 are marked as structurally sensitive points affecting system strength. These strength-influencing source nodes and structurally sensitive nodes are collectively identified as critical root-cause nodes. These key root causes are the decisive reasons for the insufficient strength of the regional system, rather than simply reflecting its own insufficient strength.

[0030] Furthermore, time-domain simulations are performed under a pre-defined set of typical severe faults. After fault clearance, the transient response waveforms of each node's voltage are extracted. For the voltage recovery curve of each node, a set of quantitative features characterizing its dynamic behavior are calculated and extracted, forming a multi-dimensional feature vector. This multi-dimensional feature vector typically includes the initial voltage drop rate or the average drop slope within a specific time window. The dominant oscillation frequency is extracted through Fast Fourier Transform or Planckian analysis, along with the amplitude or damping ratio corresponding to this frequency component; the time required for the voltage to recover to 0.9 times the rated value (recovery time), the approximate time constant of the recovery phase, and the monotonicity index of the recovery curve (such as whether overshoot or continuous fluctuation occurs). Using the above multi-dimensional feature vectors corresponding to all nodes in the network as input, an unsupervised clustering algorithm or a supervised rule-based classifier is used to automatically classify nodes based on the similarity of the feature vectors. This process categorizes the transient behavior of nodes into a pre-defined, finite number of typical instability modes with clear physical meaning, including:

[0031] The monotonic instability type is characterized by a high initial drop rate, no obvious dominant oscillation frequency (or extremely low amplitude of oscillation components), and an extremely long recovery time or no recovery at all. This mode reflects a fundamental deficiency in system strength and reactive power support.

[0032] Low-frequency oscillation type: The eigenvectors show a dominant oscillation frequency component with significant amplitude in a specific low-frequency range (e.g., 0.1-2.5Hz), and the damping ratio is negative or weakly positive. This mode reflects insufficient system synchronization or damping.

[0033] The delayed recovery type is characterized by a deep initial drop, no obvious sustained oscillation, but a large recovery time constant and a slow recovery rate. This mode mainly reflects insufficient dynamic reactive power support and voltage recovery capability.

[0034] In the embodiments of this application, the region where the critical root cause node is located is spatially associated with the region where the monotonic instability mode occurs, and they are jointly mapped as a grid-type energy storage mandatory configuration area; the region where the low-frequency oscillation or delayed recovery instability mode occurs is mapped as a grid-type energy storage priority configuration area; and the region where multiple instability modes exist simultaneously is mapped as a hybrid configuration optimization area that requires coordinated capacity optimization of grid-type and grid-type energy storage.

[0035] In a preferred embodiment of this application, on the power grid geographic wiring diagram, the obtained key root cause nodes (identified by their geographical location with point coordinates) are overlaid with the obtained typical instability mode regions (colored by the mode to which the node belongs) to form a composite information map integrating static intensity root causes and dynamic instability mechanisms. The area surrounding the geographical location of the key root cause nodes is merged with the area exhibiting monotonic instability modes and jointly mapped as a grid-based energy storage mandatory configuration area, which corresponds to the physical essence of the fundamental lack of voltage source support. The area exhibiting low-frequency oscillation or delayed recovery instability modes (and not containing key root cause nodes) is mapped as a grid-based energy storage priority configuration area, which corresponds to the physical requirement of rapid power damping or injection. The area with multiple instability mode characteristics, or which contains both key root cause nodes and is accompanied by complex phenomena such as oscillations, is mapped as a hybrid configuration optimization area, which corresponds to stability problems with multiple couplings. Finally, a power grid partition map with clear geographical boundaries and functional labels is generated. This power grid partition map is the direct input and key constraint basis for subsequent differentiated and coordinated energy storage planning.

[0036] The aforementioned scheme, through the integration of multi-dimensional power grid data analysis and pattern recognition, achieves precise identification and spatial location of weak links and instability mechanisms in power grid stability. Based on short-circuit ratio contribution and sensitivity analysis, it identifies key root-cause nodes leading to insufficient system strength. Furthermore, based on transient voltage trajectory characteristics, it automatically categorizes node instability behavior into three physically meaningful modes: monotonic instability, low-frequency oscillation, and delayed recovery. The generated power grid zoning map spatially correlates and overlays static strength root causes with dynamic instability modes, providing a direct and scientific basis for subsequent differentiated configuration of grid-connected and grid-linked energy storage, overcoming the blindness of configuring energy storage types solely based on experience.

[0037] S200: Based on the grid partition map, establish an efficiency quantification model to quantify the effectiveness of grid-connected energy storage and grid-linked energy storage in improving grid stability.

[0038] In the embodiments of this application, an equivalent virtual synchronous machine model of grid-connected energy storage is constructed. The equivalent virtual synchronous machine model sets the virtual inertia time constant and transient internal impedance parameters based on the rated capacity of the grid-connected energy storage, so as to equate the grid-connected energy storage to an equivalent synchronous generation unit exhibiting voltage source support characteristics at the grid connection point. Based on the equivalent virtual synchronous machine model, an intensity-enhancing efficiency coefficient matrix of grid-connected energy storage is defined. The intensity-enhancing efficiency coefficient matrix is ​​used to characterize the quantitative enhancement efficiency on the short-circuit ratio of the target grid node when a grid-connected energy storage with a unit rated active power capacity is deployed at the grid node. The intensity-enhancing efficiency coefficient matrix is ​​obtained by introducing an equivalent unit capacity grid-connected energy storage unit into the grid model and performing sensitivity analysis on the change of short-circuit ratio in combination with the grid equivalent impedance parameters.

[0039] In a preferred embodiment of this application, firstly, based on the functional positioning of the power grid zoning map, the efficiency quantitative modeling of grid-based energy storage is carried out. Then, considering the power grid characteristics of the mandatory configuration zone and the hybrid configuration optimization zone for grid-based energy storage, the rated capacity (rated apparent power) of the grid-based energy storage is used. Rated active power Based on this core principle, key parameters of the equivalent virtual synchronous machine are configured parametrically, including setting a virtual inertia time constant that matches the rated capacity. and transient internal impedance The grid-connected energy storage is equivalent to an equivalent synchronous generation unit exhibiting voltage source support characteristics at the grid connection point. Based on the equivalent virtual synchronous machine model, its equivalent short-circuit capacity at the grid connection point is calculated, and the effective inertial time constant considering grid operating characteristics and energy storage control strategies is also calculated. The formula for calculating the equivalent short-circuit capacity of the grid-connected energy storage at the grid connection point is as follows:

[0040]

[0041] in, This represents the equivalent short-circuit capacity at the grid connection point. Indicates the rated voltage at the grid connection point. This represents the equivalent transient internal impedance of a grid-type energy storage system.

[0042] Furthermore, define the intensity enhancement efficiency coefficient matrix. , matrix elements Specifically, this is characterized by the quantified improvement in the short-circuit ratio of the target grid node i when a grid-based energy storage unit with a rated active power capacity (1MW) is deployed at grid node j. The calculation process for this matrix is ​​then initiated. An equivalent unit of grid-based energy storage with a unit capacity is introduced into the original grid model at node j. Combined with the integrated grid equivalent impedance matrix parameters, a sensitivity analysis of the short-circuit ratio change is conducted by applying a unit perturbation. The change in short-circuit capacity of the target node i before and after the introduction of energy storage is compared. Then, based on the definition of short-circuit ratio, the values ​​of matrix elements are calculated using a formula, and each element is substituted into all grid nodes as deployment node j and target node i to complete the entire strength enhancement efficiency coefficient matrix. The construction of the matrix. The numerical calculation formula for the matrix elements is as follows:

[0043]

[0044] in, Elements representing the strength enhancement efficiency coefficient matrix. This represents the change in short-circuit capacity at node i. This represents the equivalent load rating capacity of target node i.

[0045] In the embodiments of this application, a transient power support model for grid-connected energy storage is constructed. Based on the grid-connected control characteristics of grid-connected energy storage, the transient power support model establishes an active and reactive power output response model for grid-connected energy storage during grid disturbances or faults, which is used to characterize the power support behavior of grid-connected energy storage during voltage sags, voltage recovery, and oscillation decay processes. Based on the transient power support model, a power support efficiency coefficient matrix for grid-connected energy storage is defined. The power support efficiency coefficient matrix is ​​used to quantify the improvement efficiency on the transient voltage stability index of the target grid node when a unit rated capacity of grid-connected energy storage is deployed at the grid node. The transient voltage stability index includes at least one or more of the following: improvement in minimum voltage value, reduction in voltage recovery time, attenuation of voltage oscillation amplitude, and enhancement of oscillation damping. The power support efficiency coefficient matrix is ​​obtained by introducing a unit capacity of grid-connected energy storage transient power support unit into the grid model and comparing and analyzing the grid voltage dynamic response before and after energy storage access under typical fault and disturbance scenarios, and by combining time-domain simulation and sensitivity analysis.

[0046] In a preferred embodiment of this application, the efficiency quantitative modeling of grid-connected energy storage is carried out simultaneously, combining the transient instability modes of the priority configuration area and the hybrid configuration optimization area of ​​the grid partition diagram. Based on the grid-connected control characteristics of grid-connected energy storage, an active and reactive power output response model is established during grid disturbances or faults, focusing on characterizing its power support behavior during voltage sag, voltage recovery, and oscillation decay stages. A dynamic power support factor is defined, and its calculation formula is as follows:

[0047]

[0048] in, This indicates the dynamic power support factor of grid-connected energy storage. This represents the dynamic maximum reactive power output within a hundred millisecond timescale. This indicates the rated reactive power capacity of grid-type energy storage.

[0049] The maximum reactive power support capacity of grid-type energy storage is quantified by calculating the dynamic power support factor.

[0050] Furthermore, the power support efficiency coefficient matrix of grid-type energy storage is defined. This matrix quantifies the improvement efficiency of grid-connected energy storage units of a unit rated capacity on the transient voltage stability index of the target grid node. The transient voltage stability index includes at least the improvement in minimum voltage value, the reduction in voltage recovery time, the attenuation of voltage oscillation amplitude, and the enhancement of oscillation damping. The power support efficiency coefficient matrix is ​​then calculated through the following process: A unit capacity of grid-connected energy storage transient power support unit is introduced into the original grid model at the target node. Typical fault and disturbance scenarios covering different locations and types are set. Time-domain simulations are performed on the grid before and after the introduction of energy storage. The voltage dynamic response data from the two simulations are compared. Using sensitivity analysis, the improvement per unit capacity of grid-connected energy storage is calculated as the corresponding matrix element value. This value is then substituted into all grid nodes to complete the calculation, resulting in the complete power support efficiency coefficient matrix. .

[0051] Furthermore, based on the deployment constraints of the power grid zoning map, a quantitative correlation is established between energy storage type, location, and stability benefits. Let the deployment capacity vector of grid-type energy storage at each node in the zoning map be... (The element represents the deployment capacity of node j, and the network configuration mandates that nodes in the configuration area must meet the minimum deployment capacity requirement.) The deployment capacity vector of the network-type energy storage is... (The element represents the deployment capacity of node i, prioritizing matching the network type and the required configuration area), then the vector for improving the overall network short-circuit ratio is... Transient voltage stability margin enhancement vector This directly links energy storage deployment schemes with grid stability improvement benefits, providing key cost-benefit correlation data for subsequent multi-objective deployment optimization.

[0052] The aforementioned scheme constructs a mathematical model capable of accurately quantifying the effectiveness of different types of energy storage in improving grid stability, establishing a direct quantitative correlation between energy storage deployment and stability index improvement. By establishing an equivalent virtual synchronous machine model for grid-based energy storage and a transient power support model for grid-connected energy storage, and further deriving the intensity improvement efficiency coefficient matrix and power support efficiency coefficient matrix, the physical support capabilities of energy storage devices are transformed into quantitative parameters that can be used for optimization calculations. This allows the enhancement effect of grid-based energy storage on the node short-circuit ratio and the improvement effect of grid-connected energy storage on the transient voltage recovery process to be accurately evaluated and mathematically characterized, providing key quantitative inputs and cost-benefit correlation basis for subsequent optimization decisions.

[0053] S300: Based on the efficiency quantification model, construct and solve the energy storage site optimization model considering collaborative constraints to obtain multiple alternative optimization site schemes.

[0054] In the embodiments of this application, decision variables for the energy storage deployment optimization model are defined, including deployment selection variables for each candidate site, energy storage type variables, and power and energy capacity variables; collaborative constraints for the energy storage deployment optimization model are set, including at least: a partition type constraint based on the power grid partition map, which limits the configuration of grid-type energy storage at sites located in the mandatory configuration zone of grid-type energy storage; a global short-circuit ratio constraint based on the efficiency quantification model, which ensures that the short-circuit ratio of all nodes is greater than the safety threshold after planning; and a collaborative capacity ratio constraint, which limits the proportion of the total rated active power of grid-type energy storage to the total rated active power of all energy storage devices to be greater than or equal to a preset lower limit.

[0055] In the preferred embodiment of this application, the power grid zoning map (including the mandatory configuration zone for grid-based energy storage, the priority configuration zone for grid-connected energy storage, and the hybrid configuration optimization zone) and zoning attribute constraints are first analyzed. Simultaneously, the constructed efficiency coefficient matrix for grid-based energy storage intensity enhancement and the efficiency coefficient matrix for grid-connected energy storage power support are invoked. The zoning constraints and efficiency quantification results are used as the core input basis of the optimization model to ensure the effectiveness of constraints and the accuracy of target quantification. Then, the decision variables of the energy storage deployment optimization model are defined, clarifying three core decision variables covering all dimensions of deployment, type, and capacity to meet the optimization decision requirements. Specifically, a binary variable is set for each candidate site to identify whether energy storage is deployed at that site. The energy storage type variable only applies to sites with confirmed deployment, clarifying whether they are configured with grid-based or grid-connected energy storage. Simultaneously, the rated active power and energy capacity of each deployment site are clarified, and their values ​​must meet the technical parameter requirements of the energy storage equipment and match the efficiency characteristics of the selected energy storage type.

[0056] Furthermore, collaborative constraints are set, and based on power grid planning requirements, efficiency quantification models (equivalent virtual synchronous machine model, transient power support model, and deployment constraints based on power grid zoning map), a quantitative correlation is established between energy storage type, location, and stability benefits. Combined with power system stability requirements, three types of core collaborative constraints are established to ensure the technical feasibility and engineering applicability of the optimization scheme from three dimensions: type adaptation, intensity compliance, and support capability. Specific requirements are as follows:

[0057] 1. Follow the functional positioning constraint logic of the power grid zoning map and clarify the configuration rules for different types of energy storage in different areas.

[0058] For the mandatory configuration area of ​​grid-type energy storage, since this area corresponds to the critical root node and the monotonic instability area, the core problem is the lack of voltage source support, which leads to insufficient system strength. Therefore, it is mandatory to require all candidate sites in the area to be configured only with grid-type energy storage.

[0059] For areas where grid-connected energy storage is prioritized, these areas correspond to low-frequency oscillating or delayed-recovery instability modes, requiring rapid damping injection or power replenishment to improve transient voltage trajectories. Therefore, it is recommended that candidate sites in these areas be configured with grid-connected energy storage. At the same time, flexible adjustments are allowed under the premise of multiple objective trade-offs (such as significantly reduced costs and better global stability) to avoid wasting benefits caused by a single type of configuration.

[0060] For the hybrid configuration optimization area, due to the existence of multiple problems such as insufficient strength and slow transient recovery in the area, the energy storage deployment optimization model does not impose restrictions on the type of energy storage. The core input logic is embedded in the energy storage deployment optimization model based on the quantitative correlation between energy storage type, deployment location and stability benefits. During the solution process, the model will automatically compare the short-circuit ratio improvement benefits of configuring grid-type energy storage and the transient voltage improvement benefits of configuring grid-type energy storage in the candidate sites in the area. Combining the cost differences between the two types of energy storage, the global objective function and other constraints, the model will autonomously select the appropriate energy storage type and finally output the configuration scheme that can achieve the global optimum without human intervention.

[0061] 2. The global short-circuit ratio constraint serves as a rigid baseline for ensuring the static strength of the power grid. Its design goal is to ensure that the short-circuit ratio of every node in the planned power system is not lower than the safety threshold determined by the "Guidelines for Power System Security and Stability," thereby preventing voltage instability risks caused by insufficient strength at the root. The feasibility of this constraint is directly based on the constructed efficiency coefficient matrix of grid-based energy storage strength enhancement. This matrix accurately quantifies the efficiency of deploying a unit capacity of grid-based energy storage at any site on the improvement of the short-circuit ratio of all nodes in the entire network. In the energy storage deployment optimization model, this requirement is transformed into the fact that the sum of the baseline short-circuit ratio of any node plus the improvement contribution of all grid-based energy storage deployments must be greater than or equal to the safety threshold. This constraint-forced solver must simultaneously and accurately calculate its cumulative improvement effect on the overall network strength when exploring deployment schemes, thereby anchoring the global safety lower limit of the planning scheme.

[0062] 3. The synergistic capacity ratio constraint aims to coordinate the configuration ratio of the two types of energy storage at the system level, forming a complementary and synergistic overall support system. Its design goal is to prevent planning schemes from being overly biased towards a single technical route, ensuring the system has sufficient voltage source characteristics to support the global stability needs of a high proportion of renewable energy grids, while reserving optimization space for the rapid power regulation advantages of grid-connected energy storage. The lower limit of the power ratio of grid-connected energy storage in the constraint is determined based on the actual operating conditions of the power grid, including the analysis of the existing voltage source deficit in the power system, the mapping between instability modes and stability requirements, and consideration of the fluctuation characteristics of renewable energy output. In the energy storage deployment optimization model, this constraint is manifested as a linear proportional constraint on the total energy storage capacity, requiring that the proportion of the total rated active power of grid-connected energy storage to the total rated active power of all energy storage not be lower than the preset lower limit.

[0063] In the embodiments of this application, the objective function of the energy storage deployment optimization model is as follows: the first optimization objective is to minimize the total investment cost of the target power system (after planning, i.e. after energy storage is connected); the second optimization objective is to increase the short-circuit ratio of the weakest node of the target power system after planning and / or shorten the longest voltage recovery time of the target power system under the most severe fault; the first optimization objective and the second optimization objective together constitute the objective function.

[0064] In a preferred embodiment of this application, the aforementioned decision variables, collaborative constraints, and multi-objective functions are integrated to obtain an energy storage deployment optimization model. Specifically, the value ranges of the decision variables are clearly defined: the deployment selection variable is a binary discrete variable (taking only 0 or 1, corresponding to "no deployment" or "deployment"), the energy storage type variable is a categorical variable (only applicable to "grid-connected" or "grid-following" options), and the power and energy capacity variables are non-negative continuous variables (must be no less than the minimum technical limit of the equipment and no more than the maximum capacity specification of mainstream equipment in the market). The constraint boundaries are clearly defined, including the mandatory and priority adaptation rules for partition type constraints, the safety threshold standard for the global short-circuit ratio constraint (complying with the power grid safety and stability guidelines), and the lower limit range of the collaborative capacity ratio constraint (30%-50%). The priority ranking of objectives is clearly defined, with minimizing the total investment cost as the primary priority objective, and improving the short-circuit ratio of the weakest node and shortening the longest voltage recovery time as secondary core performance objectives, ensuring that the model's logic is closed-loop, boundary parameters are clear, and the objective orientation is clear, meeting the technical requirements for subsequent efficient solution.

[0065] Furthermore, considering the mixed-integer programming characteristics of the energy storage deployment optimization model, which involves both discrete and continuous variables, an appropriate solution method is selected based on the variable size and computational efficiency requirements. If the variable dimensionality is moderate, commercial solvers such as Gurobi and CPLEX can be used directly to ensure solution accuracy. If the variable size is large, improved intelligent optimization algorithms such as decomposition coordination algorithms and multi-objective particle swarm optimization algorithms can be selected to reduce complexity and improve solution efficiency through block-based computation. During the solution process, a set of Pareto optimal solutions is obtained by flexibly adjusting the weight coefficients of the objective function (balancing the importance of cost and stability objectives) or constraint thresholds (such as short-circuit ratio safety threshold and capacity ratio lower limit). Each solution corresponds to a unique energy storage deployment scheme, with differentiated trade-offs among the schemes in three core indicators: total investment cost, short-circuit ratio of the weakest node, and longest voltage recovery time. Subsequently, a comprehensive engineering feasibility verification was conducted on all Pareto optimal solutions. On the one hand, the feasibility of the technical parameters was checked, eliminating schemes whose energy storage power and capacity exceeded the equipment's rated range. On the other hand, the feasibility of project implementation was verified, excluding schemes with limitations such as terrain constraints at the site, land use approval restrictions, difficulties in grid connection due to excessive distance from the grid connection point, and operation and maintenance costs exceeding the reasonable budget. Ultimately, several alternative optimized site deployment schemes were formed, meeting technical parameter compliance, implementation conditions, and a balanced combination of economic efficiency and stability. Each alternative optimized site deployment scheme was labeled with the specific geographical coordinates of each site, energy storage type (grid-based / grid-connected), rated active power, energy capacity, and core technical parameters.

[0066] The above-described scheme provides a systematic method for co-optimizing the deployment capacity and type of energy storage. It can automatically find the Pareto optimal solution that balances economy and stability while meeting multiple technical constraints. By constructing and solving a mixed-integer programming model that aims to minimize total investment cost and optimize system stability, and embedding constraints on partition type, short-circuit ratio, and collaborative capacity ratio, a series of alternative planning schemes can be automatically generated, achieving a continuous distribution from cost-priority to stability-priority, while meeting the overall network strength safety threshold and the requirements for collaborative configuration of different types of energy storage.

[0067] S400: Perform time-domain simulation verification and parameter co-tuning of all alternative optimized deployment schemes, covering preset typical fault scenarios and normal operation scenarios, to obtain an energy storage deployment planning scheme.

[0068] In a preferred embodiment of this application, electromagnetic transient simulations are performed on each candidate optimized deployment scheme, covering different locations and different types of fault scenarios. Based on the electromagnetic transient simulation results, deployment schemes that meet dynamic safety and stability requirements are selected. Initial control parameters are configured for each energy storage site in the selected deployment schemes, including virtual inertia parameters and virtual impedance parameters for grid-type energy storage, and control parameters for the power outer loop and current inner loop for grid-following energy storage. When configuring the initial control parameters, small-signal stability analysis or time-domain simulation verification is used to ensure that the control parameters between adjacent or functionally complementary energy storage sites meet the coordination matching requirements, so as to avoid adverse dynamic interaction phenomena between different energy storage sites.

[0069] In a preferred embodiment of this application, firstly, each generated candidate site layout scheme undergoes electromagnetic transient simulation covering a preset typical fault set to obtain its detailed dynamic response. Based on the simulation results, according to dynamic safety and stability criteria such as voltage recovery time, oscillation amplitude, and frequency deviation, all candidate optimized site layout schemes are rigorously screened, eliminating any schemes with risks such as transient voltage instability, continuous oscillation, or slow recovery, thereby obtaining a set of dynamically safe and feasible preliminary schemes. Next, initial control parameters are configured for each energy storage site in this set of preliminary schemes. For grid-connected energy storage, the virtual inertia time constant, virtual damping coefficient, and virtual impedance parameters in its virtual synchronous machine control mode are mainly tuned; for grid-connected energy storage, the proportional-integral parameters of its power outer loop and current inner loop controllers, high and low voltage ride-through setpoints, and dynamic reactive power support curves are mainly tuned. This configuration is completed based on the site's own model and local stability requirements. Subsequently, the crucial parameter coordination and tuning phase begins. To avoid new stability issues such as subsynchronous oscillations or power oscillations caused by control loop conflicts after multiple sites are integrated, system-level coordination of the control parameters of all energy storage sites within the power system is required. By establishing a small-signal state-space model containing detailed control models of all energy storage sites, eigenvalue analysis and participation factor calculations are performed to identify interaction modes that may lead to weak damping or instability. With the goal of improving the overall damping of the energy storage-integrated power grid system (i.e., the target power grid and the entire system consisting of all energy storage sites and their controllers in the current scheme) and suppressing specific oscillation modes, optimization algorithms (such as non-dominated sorting genetic algorithm-II or multi-objective particle swarm optimization algorithm) are used to coordinate and optimize key parameters, ensuring that the dynamic responses of each site are matched in phase and bandwidth and functionally complementary. Finally, the coordinated control parameter set is substituted into the electromagnetic transient model of the entire system for final time-domain simulation verification. The system verifies whether the target area power grid can maintain stability and excellent dynamic performance under all preset faults and typical operating scenarios, and finally obtains an energy storage deployment plan that has been rigorously verified in terms of dynamic performance and whose control parameters have been coordinated at the system level.

[0070] The above scheme, through time-domain simulation verification and parameter co-tuning, ensures the dynamic safety of the optimized site layout and the dynamic coordination between each energy storage unit, thus outputting a mature scheme that can be directly used for engineering implementation.

[0071] The present invention also provides a grid-type energy storage deployment system that enhances short-circuit ratio and transient voltage stability, and a method for realizing grid-type energy storage deployment that enhances short-circuit ratio and transient voltage stability. The system includes a control module, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the computer program to realize the grid-type energy storage deployment method that enhances short-circuit ratio and transient voltage stability.

[0072] This invention provides a storage medium storing a program that, when executed by a processor, implements a grid-type energy storage deployment method that enhances short-circuit ratio and transient voltage stability.

[0073] This invention provides a processor for running a program, wherein the program executes a grid-type energy storage deployment method that enhances short-circuit ratio and transient voltage stability.

[0074] This invention provides a device including a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements a grid-based energy storage deployment method that enhances short-circuit ratio and transient voltage stability. The device described herein can be a server, PC, PAD, mobile phone, etc.

[0075] This application also provides a computer program product that, when executed on a data processing device, is suitable for implementing a grid-type energy storage deployment method that enhances short-circuit ratio and transient voltage stability.

[0076] Those skilled in the art will understand that embodiments of this application can provide methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0077] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0078] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0079] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0080] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0081] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0082] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0083] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0084] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for deploying grid-type energy storage systems to enhance short-circuit ratio and transient voltage stability, characterized in that, include: Collect and preprocess multi-dimensional power grid data, and generate a power grid zoning map to guide the configuration of energy storage types through calculation analysis and pattern recognition; Based on the aforementioned power grid partitioning map, an efficiency quantification model is established to quantify the effectiveness of grid-connected energy storage and grid-linked energy storage in improving power grid stability. Based on the efficiency quantification model, an energy storage deployment optimization model considering collaborative constraints is constructed and solved to obtain multiple alternative optimization deployment schemes. All alternative optimized deployment schemes are subjected to time-domain simulation verification and parameter co-tuning covering preset typical fault scenarios and normal operation scenarios to obtain an energy storage deployment planning scheme.

2. The grid-type energy storage deployment method for enhancing short-circuit ratio and transient voltage stability according to claim 1, characterized in that, The multi-dimensional data of the power grid includes power grid topology and impedance parameters, power output data, and a set of preset typical faults for time-domain simulation analysis.

3. The grid-type energy storage deployment method for enhancing short-circuit ratio and transient voltage stability according to claim 1, characterized in that, The process of generating a grid zoning map to guide energy storage type configuration through computational analysis and pattern recognition includes: The short-circuit ratio of all nodes in the power grid under study is calculated. Based on the short-circuit ratio calculation results, short-circuit ratio contribution analysis and sensitivity analysis are performed. The short-circuit ratio contribution analysis is used to calculate the contribution ratio of each power source to the short-circuit current of the target node. The sensitivity analysis is used to evaluate the impact of changes in power grid parameters on the node short-circuit ratio in order to identify the key root cause nodes that lead to insufficient power grid strength. Time-domain simulation was performed based on a preset set of typical faults to extract the dynamic trajectory features of the voltage waveforms of each node before and after fault clearance. Using pattern recognition methods, based on the dynamic trajectory characteristics, transient voltage instability phenomena at different nodes are classified into at least three types of instability modes: monotonic instability, low-frequency oscillation, and delayed recovery. Spatial correlation and overlay analysis are performed on the regions of the key root causes and the instability modes to generate a power grid partition map.

4. The grid-type energy storage deployment method for enhancing short-circuit ratio and transient voltage stability according to claim 3, characterized in that, The process of generating a grid zoning map to guide energy storage type configuration through computational analysis and pattern recognition also includes: The regions where the key root cause nodes are located are spatially correlated with the regions where the monotonic instability mode occurs, and are jointly mapped into a grid-type energy storage forced configuration area; The regions where the low-frequency oscillating or delayed-recovery instability modes occur are mapped as priority configuration areas for grid-connected energy storage. Regions with multiple instability modes are mapped as hybrid configuration optimization zones that require capacity optimization of both grid-based and grid-connected energy storage.

5. The grid-type energy storage deployment method for enhancing short-circuit ratio and transient voltage stability according to claim 1, characterized in that, The establishment of an efficiency quantification model for improving grid stability by grid-connected energy storage and grid-linked energy storage includes: An equivalent virtual synchronous machine model for grid-connected energy storage is constructed. The equivalent virtual synchronous machine model is based on the rated capacity parameterization of the grid-connected energy storage to set the virtual inertia time constant and transient internal impedance parameters, so as to convert the grid-connected energy storage into an equivalent synchronous power generation unit that exhibits voltage source support characteristics at the grid connection point. Based on the equivalent virtual synchronous machine model, an intensity improvement efficiency coefficient matrix for grid-type energy storage is defined. The intensity improvement efficiency coefficient matrix is ​​used to characterize the quantitative improvement efficiency on the short-circuit ratio of the target grid node when a grid-type energy storage with a unit rated active power capacity is deployed at the grid node. The strength enhancement efficiency coefficient matrix is ​​obtained by introducing a unit capacity grid-type energy storage equivalent unit into the power grid model and combining the power grid equivalent impedance parameters to perform sensitivity analysis on the change in short-circuit ratio.

6. The grid-type energy storage deployment method for enhancing short-circuit ratio and transient voltage stability according to claim 1, characterized in that, The establishment of an efficiency quantification model for improving grid stability by grid-connected energy storage and grid-linked energy storage also includes: A transient power support model for grid-connected energy storage is constructed. Based on the grid-connected control characteristics of grid-connected energy storage, an active and reactive power output response model for grid-connected energy storage during grid disturbances or faults is established to characterize the power support behavior of grid-connected energy storage during voltage sag, voltage recovery, and oscillation decay processes. Based on the transient power support model, a power support efficiency coefficient matrix for grid-connected energy storage is defined. The power support efficiency coefficient matrix is ​​used to quantify the improvement efficiency of the transient voltage stability index of the target grid node when a unit rated capacity of grid-connected energy storage is deployed at the grid node. The transient voltage stability index includes at least one or more of the following: improvement in minimum voltage value, reduction in voltage recovery time, attenuation of voltage oscillation amplitude, and enhancement of oscillation damping. The power support efficiency coefficient matrix is ​​obtained by introducing a unit capacity grid-connected transient power support unit into the grid model, and comparing and analyzing the grid voltage dynamic response before and after energy storage access under typical fault and disturbance scenarios. It is calculated by combining time-domain simulation and sensitivity analysis.

7. The grid-type energy storage deployment method for enhancing short-circuit ratio and transient voltage stability according to claim 1, characterized in that, The construction and solution of the energy storage deployment optimization model considering collaborative constraints includes: Define the decision variables for the energy storage site optimization model. The decision variables include the site selection variables for each candidate site, the energy storage type variables, and the power and energy capacity variables. The energy storage deployment optimization model is configured with collaborative constraints, which include at least the following: Based on the partition type constraints of the power grid partition map, the configuration of grid-type energy storage is limited to sites located in the mandatory configuration zone of grid-type energy storage; Based on the global constraint of the short-circuit ratio in the efficiency quantification model, it is ensured that the short-circuit ratio of all nodes after planning is greater than the safety threshold. The collaborative capacity ratio constraint is used to limit the ratio of the total rated active power of grid-type energy storage to the total rated active power of all energy storage devices to be greater than or equal to the preset lower limit.

8. The grid-type energy storage deployment method for enhancing short-circuit ratio and transient voltage stability according to claim 1, characterized in that, The objective function of the energy storage deployment optimization model is: The primary optimization objective is to minimize the total investment cost of the target power system. The second optimization objective is to increase the short-circuit ratio of the weakest node in the target power system after planning, and / or shorten the longest voltage recovery time of the target power system under the most severe fault. The first optimization objective and the second optimization objective together constitute the objective function.

9. The grid-type energy storage deployment method for enhancing short-circuit ratio and transient voltage stability according to claim 1, characterized in that, The step of performing time-domain simulation verification and parameter co-tuning for all candidate optimized deployment schemes, covering preset typical fault scenarios and normal operation scenarios, includes: Electromagnetic transient simulations were performed on each of the alternative optimized deployment schemes, covering different locations and different types of fault scenarios. Based on the electromagnetic transient simulation results, a site selection scheme that meets the requirements of dynamic safety and stability was selected. Configure initial control parameters for each energy storage site in the selected deployment scheme, including setting virtual inertia parameters and virtual impedance parameters for grid-type energy storage, and setting control parameters for power outer loop and current inner loop for grid-connected energy storage. When configuring initial control parameters, small-signal stability analysis or time-domain simulation verification is used to ensure that the control parameters of adjacent or functionally complementary energy storage sites meet the requirements for coordinated matching, so as to avoid adverse dynamic interaction phenomena between different energy storage sites.

10. A grid-type energy storage deployment system that enhances short-circuit ratio and transient voltage stability, characterized in that, The system includes a control module, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the computer program to implement the grid-type energy storage deployment method for enhancing short-circuit ratio and transient voltage stability according to any one of claims 1-9.

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