A network configuration method and system for coping with short-time power impact

Abstract

The application discloses a network-configuration type energy storage configuration method and system for short-time power impact, and the method comprises the following steps: acquiring all node information of a target regional power grid, including direct-current feeding nodes, new energy grid-connected nodes and load-side load nodes; sequentially performing N-1 three-phase short-circuit fault simulation on all nodes of the target regional power grid, and determining a fault node set according to frequency response characteristics in a simulation result; clustering the load-side load nodes according to a clustering index and a clustering number to obtain load-side load center nodes; taking the union of the direct-current feeding nodes, the new energy grid-connected nodes and the load-side load center nodes as a candidate configuration node set of the network-configuration type energy storage; solving a network-configuration type energy storage configuration optimization model by using a quantum particle swarm optimization algorithm to obtain a network-configuration type energy storage configuration result; and the network-configuration type energy storage configuration optimization model takes minimizing the sum of network-configuration type energy storage costs of the candidate configuration nodes and safety comprehensive indexes of the fault nodes as a target.

Description

Technical Field

[0001] This application belongs to the field of power system configuration technology, and relates to a grid-type energy storage configuration method and system for dealing with short-term power surges. Background Technology

[0002] In AC / DC receiving-end power grids with a high proportion of renewable energy integration, the DC landing point is located near the load center. The large-scale DC integration and grid connection of renewable energy replaces the start-up of synchronous machines, reducing system inertia and voltage support capacity, and weakening the grid's resilience to fault disturbances. When a short-circuit fault occurs in the receiving-end power grid, voltage drops cause multiple dynamic responses: renewable energy units experience a sharp drop in active power output due to low-voltage ride-through; the DC transmission system experiences reduced active power output after commutation failure and absorbs a large amount of reactive power from the system during the recovery phase; and induction motors also generate high reactive power demand during the recovery process after the voltage drop due to increased slip. Furthermore, the active and reactive power fluctuations caused by the multiple dynamic responses of renewable energy, DC, and loads during system recovery can, in turn, affect the system frequency and voltage response characteristics through network power flow and equipment control loops, further impacting the active and reactive power balance. This interaction creates a short-term active and reactive power coupling impact phenomenon in the receiving-end power grid caused by AC faults. Voltage dynamics and frequency dynamics have multi-timescale interaction effects, and the source-grid-load active and reactive power balance is complex and variable, with the risk of dual frequency and voltage instability, which needs to be given high attention.

[0003] Grid-based energy storage, as a novel type of power electronic device, differs from integrated grid-based energy storage in that it constructs its own reference voltage and frequency, making it unaffected by system fault disturbances. Furthermore, unlike synchronous condensers which can only provide reactive power support, grid-based energy storage can simultaneously provide dynamic active and reactive power support under short-term active-reactive power coupling impact scenarios. Existing research on optimizing system safety configurations generally focuses on optimizing single electrical quantities. However, under short-term active-reactive power coupling impact scenarios, if optimization is solely aimed at improving voltage or frequency response, the configuration result may lead to poor improvement or even deterioration of the response of another electrical quantity. Summary of the Invention

[0004] Objective: In view of at least one of the above technical problems, this application aims to address the issue that in short-term active and reactive power coupling impact scenarios, if optimization configuration is carried out solely with the goal of improving voltage or frequency response, the configuration result may lead to poor improvement effect on the response of another electrical quantity, or even deterioration. This application provides a grid-based energy storage configuration method and system to cope with short-term power impacts, so as to achieve optimal coordination between grid-based energy storage cost and system safety.

[0005] The technical solution adopted in this application is as follows:

[0006] Firstly, this application provides a grid-based energy storage configuration method for coping with short-term power surges, including:

[0007] Obtain information on all nodes of the target area power grid, including DC feed-in nodes, renewable energy grid-connected nodes, and load-side load nodes.

[0008] N-1 three-phase short-circuit fault simulations were performed on all nodes of the target area power grid in sequence, and the fault node set was determined based on the frequency response characteristics in the simulation results; N is the number of all nodes in the target area power grid.

[0009] Based on clustering indices and the number of clusters, the load nodes on the load side are clustered to obtain the load center nodes on the load side; the clustering indices include the load size of each node, the system node impedance matrix, the active power injected by the node, and the reactive power injected by the node.

[0010] The union of DC feed-in nodes, renewable energy grid-connected nodes, and load-side load center nodes is taken as the candidate configuration node set for grid-type energy storage.

[0011] Based on the candidate configuration node set, fault node set, and all node information of the grid-type energy storage, the quantum particle swarm optimization algorithm is used to solve the grid-type energy storage configuration optimization model to obtain the grid-type energy storage configuration result. The grid-type energy storage configuration result includes the configuration nodes and the number of configurations of the grid-type energy storage.

[0012] The optimization model for grid-based energy storage configuration aims to minimize the sum of the grid-based energy storage cost of candidate configuration nodes and the comprehensive safety index of faulty nodes.

[0013] In this embodiment, the candidate configuration node set for grid-type energy storage Represented as:

[0014] ,in For DC feed node, For new energy grid connection nodes, It is the load center node on the load side.

[0015] In some embodiments, the objective function of the grid-based energy storage configuration optimization model Represented as:

[0016] ;

[0017] in, Configure the number of candidate nodes. The cost of grid-type energy storage at the i-th node; As a penalty factor, The number of faulty nodes. Represents the overall safety index of all faulty nodes. sum.

[0018] Furthermore, the cost of grid-based energy storage includes the cost of purchasing energy storage and the cost of operating energy storage. Represented as:

[0019] ;

[0020] ;

[0021] ;

[0022] in, The cost of purchasing energy storage for the i-th node. Energy storage operating cost for the i-th node The purchase cost per megawatt of energy storage, Each grid-connected energy storage unit provides active power. Let i be the number of grid-type energy storage units at the i-th node; , , These are the fitting coefficients, The active power capacity of each grid-connected energy storage unit, This refers to the energy storage operation time.

[0023] Furthermore, the comprehensive safety indicators of the faulty node Integrating transient voltage safety indicators and transient frequency safety indicators , is represented as:

[0024] ;

[0025] , , ;

[0026] , ,

[0027] ;

[0028] in, It is a transient voltage safety indicator, reflecting the degree of voltage drop; This is a transient frequency safety indicator that reflects the depth of frequency drop; N is the total number of nodes in the target area power grid. This is an indicator of the voltage drop at the i-th node; This is the time when the fault is cleared. For time windows; Let be the voltage discrimination function. For time; Set a voltage threshold for each node; Let be the voltage of the i-th node; The set frequency threshold; This is a function of the frequency drop level; The system's center frequency of inertia; Let g be the inertia of the g-th generator; is the frequency of the g-th generator; M is the number of generators.

[0029] In some embodiments, the constraints of the network-type energy storage configuration optimization model include system power flow constraints after energy storage configuration, energy storage transient maximum output constraints, and constraints on the number of energy storage nodes in candidate configuration nodes.

[0030] (1) All nodes of the target area power grid must meet the power flow constraints of the system after energy storage is configured, as follows:

[0031] ;

[0032] in, , Inject active and reactive power into the i-th node; , Let be the voltage magnitude and phase angle of the i-th node; , Let be the voltage magnitude and phase angle of the j-th voltage; , The target area is defined by the admittance magnitude and phase angle of the power grid nodes. The total number of nodes in the target area's power grid;

[0033] (2) The output of each grid-type energy storage unit during the transient process must meet the maximum output constraint of the energy storage during the transient process, expressed as:

[0034] ;

[0035] in, Each grid-type energy storage unit provides active power output; Provide reactive power output for each grid-type energy storage unit; The upper limit of available power for each grid-type energy storage unit;

[0036] (3) The number of grid-type energy storage units in the candidate configuration nodes must meet the energy storage quantity constraint of the candidate configuration nodes, as expressed as:

[0037] ;

[0038] in, Configure the number of candidate nodes. Let i be the number of grid-type energy storage units at the i-th node; This represents the upper limit of the number of grid-type energy storage units at the i-th node.

[0039] In some embodiments, determining the set of fault nodes based on the frequency response characteristics in the simulation results includes:

[0040] Frequency response characteristics are divided into high frequencies where the frequency change is initially positive and then negative, and low frequencies where the frequency change rate is always negative.

[0041] Based on the simulation results, the nodes are divided into high-frequency fault nodes and low-frequency fault nodes.

[0042] Representative fault nodes are selected from high-frequency fault nodes and low-frequency fault nodes respectively, and then merged to obtain a fault node set.

[0043] In some embodiments, the k-medoids method is used to cluster the load nodes on the load side to obtain the load center nodes on the load side.

[0044] Secondly, this application provides a grid-type energy storage configuration device for responding to short-term power surges, including a processor and a storage medium;

[0045] The storage medium is used to store instructions;

[0046] The processor is configured to operate according to the instructions to execute the method according to the first aspect.

[0047] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect.

[0048] Fourthly, this application provides a computer device including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect.

[0049] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the method described in the first aspect.

[0050] Beneficial Effects: The grid-based energy storage configuration method and system provided in this application for addressing short-term power surges have the following advantages: This application leverages the advantage of grid-based energy storage in simultaneously providing dynamic active and reactive power support under short-term active-reactive power coupling surge scenarios, and proposes an optimized configuration method and system for grid-based energy storage to improve the response to short-term active-reactive power coupling surges. Compared with existing approaches that improve system response through energy storage configuration, this application considers the improvement of system frequency characteristics (high frequency followed by low frequency, continuous low frequency) and voltage characteristics (delayed recovery) in the scenario, balances the economy of energy storage configuration with system safety, and considers the requirements for configuration site selection and quantity for short-term active-reactive power coupling surge scenarios in the constraints.

[0051] A comprehensive safety index is proposed in the optimization objective to quantitatively reflect the magnitude of voltage and frequency sags in the system, indirectly reflecting the impact of voltage response on the active power response of equipment, and further reflecting the impact on system frequency. Dynamic weights are introduced to coordinate the improvement effects on both voltage and frequency responses. When a fault impact occurs at a load concentration point, resulting in a poor voltage response, the corresponding voltage index has a higher weight, guiding the algorithm to prioritize improving the voltage response during optimization, thereby reducing the voltage weight. Conversely, when a fault impact occurs at a renewable energy and DC concentration point, resulting in a poor frequency response, the corresponding frequency index has a higher weight, guiding the algorithm to prioritize improving the frequency response during optimization, thereby reducing the frequency weight. By continuously changing the weights during the optimization process, the energy storage configuration can adaptively meet the common safety requirements for both system frequency and voltage, making it suitable for measuring system safety under short-term active and reactive power coupling impact scenarios. Attached Figure Description

[0052] Figure 1 This is a schematic flowchart of a grid-type energy storage configuration method for responding to short-term power surges according to an embodiment of this application;

[0053] Figure 2 This is a schematic diagram illustrating the process of solving a network-type energy storage configuration optimization model using the quantum particle swarm optimization algorithm according to an embodiment of this application;

[0054] Figure 3 This is a schematic diagram of the power grid topology and configuration scheme according to one embodiment of this application;

[0055] Figure 4 This is a schematic diagram comparing the frequency and voltage response before and after optimization according to an embodiment of this application. Detailed Implementation

[0056] The present application will be further described below with reference to the accompanying drawings and embodiments. The following embodiments are only used to more clearly illustrate the technical solutions of the present application, and should not be used to limit the scope of protection of the present application.

[0057] In the description of this application, "several" means one or more, "multiple" means two or more, "greater than," "less than," and "exceeding" are understood to exclude the stated number, while "above," "below," and "within" are understood to include the stated number. The use of "first" and "second" in the description is merely for distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the order of the indicated technical features.

[0058] In the description of this application, the terms "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0059] The term "and / or" simply describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. Additionally, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0060] Example 1: This example provides a grid-based energy storage configuration method to cope with short-term power surges, such as... Figure 1 As shown, it includes:

[0061] S1. Obtain information on all nodes of the target area power grid, including DC feed-in nodes, renewable energy grid-connected nodes, and load-side load nodes.

[0062] In this embodiment, these three types of nodes are extracted based on the equipment access type of the receiving-end power grid to be optimized.

[0063] S2. Perform N-1 three-phase short-circuit fault simulations on all nodes of the target area power grid in sequence. Determine the fault node set based on the frequency response characteristics in the simulation results, where N is the total number of nodes in the target area power grid.

[0064] It should be noted that in the application scenario of this application, the system frequency response and voltage response are highly coupled. The frequency response can reflect the voltage problem to a certain extent, and there are two typical situations. Therefore, both should be taken into account when the fault nodes are concentrated.

[0065] In some embodiments, determining the set of fault nodes based on the frequency response characteristics in the simulation results includes:

[0066] The frequency response characteristics are divided into high-frequency frequencies where the frequency change is first positive and then negative, and low-frequency frequencies where the frequency change rate is always negative. Based on the simulation results, the nodes are divided into high-frequency fault nodes and low-frequency fault nodes. Representative fault nodes are selected from the high-frequency fault nodes and low-frequency fault nodes respectively, and then merged to obtain the fault node set.

[0067] S3. Based on the clustering index and the number of clusters, cluster the load nodes on the load side to obtain the load center nodes on the load side. The clustering indices include the load size of each node, the system node impedance matrix, node injected active power, and node injected reactive power.

[0068] In this embodiment, the load center node on the load side is obtained by using the k-medoids method based on four clustering indicators: the load size of each node, the system node impedance matrix, the node injected active power, and the node injected reactive power, and the number of clusters is specified.

[0069] S4. The union of DC feed-in nodes, new energy grid-connected nodes and load-side load center nodes is taken as the candidate configuration node set for grid-type energy storage.

[0070] Since there are many selectable energy storage nodes in a real power grid, the time complexity of optimization will increase. Considering the characteristics of short-term active and reactive power coupling impact scenarios, the DC feed-in node in the system is selected. New energy grid connection nodes and load center node on the load side Then, the union of the three is taken as the set of candidate nodes for grid-type energy storage configuration. .

[0071] S5. Based on the candidate configuration node set, fault node set and all node information of the grid-type energy storage, the quantum particle swarm optimization algorithm is used to solve the grid-type energy storage configuration optimization model to obtain the grid-type energy storage configuration result. The grid-type energy storage configuration result includes the configuration nodes and configuration quantity of the grid-type energy storage.

[0072] The optimization model for grid-based energy storage configuration aims to minimize the sum of the grid-based energy storage cost of candidate configuration nodes and the comprehensive safety index of faulty nodes.

[0073] In some embodiments, the objective function of the grid-based energy storage configuration optimization model Represented as:

[0074] ;

[0075] in, Configure the number of candidate nodes. The cost of grid-type energy storage at the i-th node; As a penalty factor, The number of faulty nodes. Represents the overall safety index of all faulty nodes. sum.

[0076] It should be noted that, due to the comprehensive safety indicators In actual calculations, this is a very small number, so a penalty factor is used. (Unit: RMB 10,000) This reflects the priority of safety indicators during the optimization process.

[0077] Furthermore, the cost of grid-based energy storage includes the cost of purchasing energy storage and the cost of operating energy storage. Represented as:

[0078] ;

[0079] ;

[0080] ;

[0081] in, The cost of purchasing energy storage for the i-th node. Energy storage operating cost for the i-th node The purchase cost per megawatt of energy storage, Each grid-connected energy storage unit provides active power. Let i be the number of grid-type energy storage units at the i-th node; , , These are the fitting coefficients, The active power capacity of each grid-connected energy storage unit, This refers to the energy storage operation time.

[0082] Furthermore, the comprehensive safety indicators of the faulty node Integrating transient voltage safety indicators and transient frequency safety indicators , is represented as:

[0083] ;

[0084] , , ;

[0085] , ,

[0086] ;

[0087] in, It is a transient voltage safety indicator, reflecting the degree of voltage drop; This is a transient frequency safety indicator that reflects the depth of frequency drop; N is the total number of nodes in the target area power grid. This is an indicator of the voltage drop at the i-th node; This is the time when the fault is cleared. For time windows; Let be the voltage discrimination function. For time; Set a voltage threshold for each node; Let be the voltage of the i-th node; The set frequency threshold; This is a function of the frequency drop level; The system's center frequency of inertia; Let g be the inertia of the g-th generator; is the frequency of the g-th generator; M is the number of generators.

[0088] The purpose of this application is to improve the frequency and voltage response of a system using grid-based energy storage. However, when improving the response based on the safety indicators of a single electrical quantity, it is impossible to fully consider both. To quantitatively measure the combined impact on system voltage and frequency, this application proposes a comprehensive index reflecting voltage and frequency safety: a comprehensive safety index for both frequency and voltage. .

[0089] Due to transient voltage safety indicators and transient frequency safety indicators Since the electrical quantities being measured are different, both indicators have been standardized to make them comparable.

[0090] To effectively utilize the control characteristics of grid-based energy storage for the combined regulation of active and reactive power in the system, and to determine reasonable energy storage locations and capacity configurations, this application proposes an optimized configuration method for grid-based energy storage based on comprehensive safety indicators. Through optimized configuration, site selection can be obtained that significantly improves the system's active and reactive power support, reduces energy storage configuration requirements, and balances economic efficiency and safety.

[0091] In some embodiments, the constraints of the network-type energy storage configuration optimization model include: system power flow constraints after energy storage configuration, maximum transient output constraints of energy storage, and constraints on the number of energy storage nodes in candidate configuration nodes.

[0092] (1) All nodes of the target area power grid must meet the power flow constraints of the system after energy storage is configured, as follows:

[0093] ;

[0094] in, , Inject active and reactive power into the i-th node; , Let be the voltage magnitude and phase angle of the i-th node; , Let be the voltage magnitude and phase angle of the j-th voltage; , The target area is defined by the admittance magnitude and phase angle of the power grid nodes. The total number of nodes in the target area's power grid;

[0095] (2) The output of each grid-type energy storage unit during the transient process must meet the maximum output constraint of the energy storage during the transient process, expressed as:

[0096] ;

[0097] in, Each grid-type energy storage unit provides active power output; Provide reactive power output for each grid-type energy storage unit; The upper limit of available power for each grid-type energy storage unit;

[0098] It should be noted that, since the transient process in the application scenario of this application is very short, the impact of energy storage discharge time is not considered.

[0099] (3) The number of grid-type energy storage units in the candidate configuration nodes must meet the energy storage quantity constraint of the candidate configuration nodes, as expressed as:

[0100] ;

[0101] in, Configure the number of candidate nodes. Let i be the number of grid-type energy storage units at the i-th node; This represents the upper limit of the number of grid-type energy storage units at the i-th node.

[0102] The purpose of the aforementioned grid-type energy storage configuration optimization model is to achieve the optimal balance between cost and system security. The optimization variables are the configuration nodes and their number in the grid-type energy storage system, which is an integer programming problem. Methods for solving integer programming problems include exact methods such as branch and bound, and stochastic algorithms such as particle swarm optimization. Exact algorithms are computationally difficult for large-scale problems, while stochastic algorithms are less likely to find the global optimum. This application employs the quantum particle swarm optimization algorithm and achieves integer-based solutions by rounding down the particle positions. Compared to traditional particle swarm optimization methods, particles in the quantum particle swarm optimization algorithm do not have definite trajectories, allowing them to search for the global optimum within the entire feasible solution. Therefore, its global search capability is far superior to that of traditional particle swarm optimization algorithms. The specific operation process is given below. Figure 2 As shown.

[0103] In this embodiment, the particle swarm is initialized with parameters of 20 particles and 20 iterations. The particle position vector dimension is determined to be 10 based on the candidate node set. Random numbers are generated within the constraint range as the initial positions of the particles. The particle potential well center and average optimal position are iteratively calculated through the quantum particle swarm optimization algorithm. The particle positions are updated and integerized, and the updated particle position vector is output. This vector corresponds to the candidate scheme of the configuration nodes and configuration number of the grid-type energy storage.

[0104] Based on the output particle position vector, configure the corresponding number of grid-type energy storage in the power grid model; traverse all fault scenarios in the fault node set, calculate the comprehensive safety index and cost under each scenario, substitute them into the objective function to solve the objective function value corresponding to each particle, thereby updating the individual optimal position and the global optimal position of the particle, and output the global optimal configuration scheme of the current iteration.

[0105] Determine whether the objective function value of the current globally optimal configuration scheme meets the convergence condition; if the convergence condition is met, output the globally optimal grid-type energy storage configuration scheme; if the convergence condition is not met, feed back the current globally optimal position to the quantum particle swarm optimization algorithm to trigger a new round of particle position update calculation until the result converges.

[0106] Verification Example: The following verification of the optimized configuration of a grid-based energy storage system with transient voltage and frequency safety is performed on a modified IEEE 39-node receiving-end power grid using the PSD-BPA electromechanical simulation platform. The 39-node network topology is as follows: Figure 3 As shown, a comprehensive load model is adopted, with 60% induction motors and 40% constant impedance parallel connection. Photovoltaic power generation at nodes Bus13 and Bus15 is 400MW each; wind turbine power generation at nodes Bus21 and Bus24 is 800MW each; and DC power generation at node Bus16 feeds 800MW into the grid. DC and renewable energy output together account for 37.15% of the system's active power output. Renewable energy parameters, induction motor parameters, node load distribution, and grid-connected energy storage parameters are shown in Tables 1-4.

[0107] Table 1: New Energy Parameters

[0108]

[0109] Table 2: Induction Motor Parameters

[0110]

[0111] Table 3: Load Distribution of 39 Nodes

[0112]

[0113] Table 4: Parameters of Grid-Based Energy Storage

[0114]

[0115] To verify the effectiveness of the grid-based energy storage optimization configuration method, three-phase short-circuit faults at Bus3-4 and Bus10-13 nodes were selected as the fault node set in a 39-node power grid. The three-phase short circuit lasted 0.2 seconds, and the fault was cleared in 0.3 seconds, corresponding to the frequency response characteristics of initial high frequency followed by low frequency and continuous low frequency, respectively. DC feed-in nodes... ={16}, New Energy Grid Connection Nodes ={13, 15, 21, 24}, the load center node on the load side is selected using the k-medoids method. ={3, 4, 8, 14, 16, 20, 24}, then the candidate configuration nodes for grid-type energy storage are... ={3, 4, 8, 13, 14, 15, 16, 20, 21, 24}.

[0116] Optimize parameter settings:

[0117] During configuration, the normal output of the energy storage was set to 0MW to minimize the impact of adding energy storage on the system's power flow distribution. The quantum particle swarm optimization algorithm was used for 20 iterations, with 20 particles and a position vector dimension of 10. In the comprehensive safety indicators, based on relevant national standards, the voltage threshold was set to 0.95 pu, the frequency threshold to 49.8 Hz, and the penalty factor was [not specified]. The cost is 10,000 yuan; in the cost function, the energy storage is configured according to a maximum power of 50MW per unit, and a maximum of 5 units are configured per node. The purchase cost of grid-type energy storage is 75,000 yuan / MW, and the fitting coefficients in the operating cost are 70,000 yuan / (unit·year), 490,000 yuan / (unit·year), and 1,750,000 yuan / year, respectively. The operating time is 10 years.

[0118] Analysis of the optimized configuration results of grid-based energy storage and the optimization effect of system frequency and voltage security:

[0119] Figure 3 Option 1 shows the number and location of the optimized grid-type energy storage units. It can be seen that the configured grid-type energy storage is mainly distributed near the load center, which can more effectively regulate the active and reactive power distribution of the system during transient processes and improve system security.

[0120] Figure 4The system inertia center frequency and bus voltage response were compared before and after optimization of the grid-based energy storage configuration for two types of faults. The response patterns show that in the initial stage of system recovery, the system voltage recovers more slowly under Bus3-4 node faults, resulting in a high-frequency initially followed by a low-frequency decline; while under Bus10-13 node faults, the system voltage recovers relatively quickly, but the system frequency drops rapidly and remains at a low frequency. By increasing the configuration of grid-based energy storage, the voltage delay and high-frequency phenomenon under Bus3-4 node faults are alleviated, and the rapid frequency drop under Bus10-13 node faults is suppressed, indicating that this method has a good adaptability to different frequency response patterns.

[0121] Table 5 compares the voltage and frequency safety indicators and the overall safety indicator before and after optimization. From the safety indicators perspective, the frequency indicator decreased by a maximum of 76.23%, the voltage indicator decreased by a maximum of 46.77%, and the overall indicator decreased by a maximum of 43.77%. This indicates that the overall safety indicator can effectively guide the improvement of system safety. If optimization is only targeted at voltage or frequency, the configuration result may not effectively improve or even worsen the response of other electrical quantities.

[0122] Table 5: Comparison of comprehensive safety indicators before and after optimization for the two types of faults

[0123]

[0124] Analysis of the effective coordination between safety and economy in optimization methods:

[0125] To further illustrate the superiority of this method in balancing safety and economy, three different grid-based energy storage configuration schemes were proposed: Scheme 2: The same number of grid-based energy storage units as the optimized scheme of this method are uniformly deployed at candidate nodes; Scheme 3: Grid-based energy storage configuration considering only voltage safety indicators; Scheme 4: Grid-based energy storage configuration considering only frequency safety indicators. Specific configuration locations and quantities are as follows... Figure 3 As shown in the figure. Table 6 shows a comparison of the voltage frequency, overall safety indicators, and costs of each configuration scheme.

[0126] Table 6 Comparison of safety indicators and costs under different schemes

[0127]

[0128] Comparing Schemes 1 and 2 reveals that, with the same cost, Scheme 1, which optimizes the grid-based energy storage deployment, offers lower overall performance and improves system safety. Comparing Schemes 1 and 3, while Scheme 3 provides some improvement in voltage and frequency safety, its significantly higher cost makes it less economical. Comparing Schemes 1 and 4, Scheme 4, while less expensive and more economical, and significantly improves frequency safety, offers insufficient improvement in voltage safety, failing to achieve a comprehensive improvement in both frequency and voltage safety.

[0129] Example 2: Based on Example 1, this example provides a grid-type energy storage configuration device for dealing with short-term power surges, including a processor and a storage medium;

[0130] The storage medium is used to store instructions;

[0131] The processor is configured to operate according to the instructions to execute the method according to Embodiment 1.

[0132] Example 3: Based on Example 1, this example provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in Example 1.

[0133] Example 4: Based on Example 1, this example provides a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described in Example 1.

[0134] Example 5: Based on Example 1, this example provides a computer program product, including a computer program that, when executed by a processor, implements the method described in Example 1.

[0135] Those skilled in the art will understand that embodiments of this application can be provided as 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.

[0136] 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, as well as 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.

[0137] 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.

[0138] 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.

[0139] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A grid-based energy storage configuration method for responding to short-term power surges, characterized in that, include: Obtain information on all nodes of the target area power grid, including DC feed-in nodes, renewable energy grid-connected nodes, and load-side load nodes. N-1 three-phase short-circuit fault simulations were performed on all nodes of the target area power grid in sequence, and the fault node set was determined based on the frequency response characteristics in the simulation results; N is the number of all nodes in the target area power grid. Based on the clustering index and the number of clusters, the load nodes on the load side are clustered to obtain the load center nodes on the load side. The clustering indicators include the load size of each node, the system node impedance matrix, node injected active power, and node injected reactive power. The union of DC feed-in nodes, renewable energy grid-connected nodes, and load-side load center nodes is taken as the candidate configuration node set for grid-type energy storage. Based on the candidate configuration node set, fault node set, and all node information of the grid-type energy storage, the quantum particle swarm optimization algorithm is used to solve the grid-type energy storage configuration optimization model to obtain the grid-type energy storage configuration result. The grid-type energy storage configuration result includes the configuration nodes and the number of configurations of the grid-type energy storage. The optimization model for grid-based energy storage configuration aims to minimize the sum of the grid-based energy storage cost of candidate configuration nodes and the comprehensive safety index of faulty nodes.

2. The method according to claim 1, characterized in that, The objective function of the grid-type energy storage configuration optimization model Represented as: ； in, Configure the number of candidate nodes. The cost of grid-type energy storage at the i-th node; As a penalty factor, The number of faulty nodes. Represents the overall safety index of all faulty nodes. sum.

3. The method according to claim 2, characterized in that, The cost of grid-based energy storage includes the cost of purchasing energy storage and the cost of operating energy storage. Represented as: ； ； ； in, The cost of purchasing energy storage for the i-th node. Energy storage operating cost for the i-th node The purchase cost per megawatt of energy storage, Each grid-connected energy storage unit provides active power. Let i be the number of grid-type energy storage units at the i-th node; , , These are the fitting coefficients, The active power capacity of each grid-connected energy storage unit, This refers to the energy storage operation time.

4. The method according to claim 2, characterized in that, Comprehensive safety indicators of faulty nodes Integrating transient voltage safety indicators and transient frequency safety indicators , is represented as: ； ， ， ； ， ， ； in, It is a transient voltage safety indicator, reflecting the degree of voltage drop; This is a transient frequency safety indicator that reflects the depth of frequency drop; N is the total number of nodes in the target area power grid. This is an indicator of the voltage drop at the i-th node; This is the time when the fault is cleared. For time windows; Let be the voltage discrimination function. For time; Set a voltage threshold for each node; Let be the voltage of the i-th node; The set frequency threshold; This is a function of the frequency drop level; The system's center frequency of inertia; Let g be the inertia of the g-th generator; is the frequency of the g-th generator; M is the number of generators.

5. The method according to claim 1, characterized in that, The constraints of the network-type energy storage configuration optimization model include system power flow constraints after energy storage configuration, maximum transient output constraints of energy storage, and constraints on the number of energy storage nodes in candidate configuration nodes. (1) All nodes of the target area power grid must meet the power flow constraints of the system after energy storage is configured, as follows: ； in, , Inject active and reactive power into the i-th node; , Let be the voltage magnitude and phase angle of the i-th node; , Let be the voltage magnitude and phase angle of the j-th voltage; , The target area is defined by the admittance magnitude and phase angle of the power grid nodes. The total number of nodes in the target area's power grid; (2) The output of each grid-type energy storage unit during the transient process must meet the maximum output constraint of the energy storage during the transient process, expressed as: ； in, Each grid-type energy storage unit provides active power output; Provide reactive power output for each grid-type energy storage unit; The upper limit of available power for each grid-type energy storage unit; (3) The number of grid-type energy storage units in the candidate configuration nodes must meet the energy storage quantity constraint of the candidate configuration nodes, as expressed as: ； in, Configure the number of candidate nodes. Let i be the number of grid-type energy storage units at the i-th node; This represents the upper limit of the number of grid-type energy storage units at the i-th node.

6. The method according to claim 1, characterized in that, The set of fault nodes is determined based on the frequency response characteristics in the simulation results, including: Frequency response characteristics are divided into high frequencies where the frequency change is initially positive and then negative, and low frequencies where the frequency change rate is always negative. Based on the simulation results, the nodes are divided into high-frequency fault nodes and low-frequency fault nodes. Representative fault nodes are selected from high-frequency fault nodes and low-frequency fault nodes respectively, and then merged to obtain a fault node set.

7. The method according to claim 1, characterized in that, The k-medoids method is used to cluster the load nodes on the load side to obtain the load center nodes on the load side.

8. A grid-type energy storage configuration device for responding to short-term power surges, characterized in that, Including processor and storage media; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method according to any one of claims 1 to 7.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the method according to any one of claims 1 to 7.

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