Networking type energy storage optimization configuration method and system based on short-circuit ratio of new energy station

Abstract

The invention discloses a network construction type energy storage optimal configuration method and system based on a short-circuit ratio of a new energy field station, and is used for solving the technical problem that the comprehensive cost of system investment and operation is relatively high due to an existing network construction type energy storage optimal configuration method. The method comprises the following steps: acquiring original data of the network-building type energy storage system, preprocessing the original data of the network-building type energy storage system, and outputting target data of the network-building type energy storage system; constructing a node power conversion factor matrix and a node impedance matrix based on the target data of the network construction type energy storage system and the topological structure of the network construction type energy storage system; according to the node power conversion factor matrix, the node impedance matrix and the target data of the network construction type energy storage system, constructing a network construction type energy storage optimization configuration model; and solving a deconstruction network type energy storage optimal configuration model, and outputting the short-circuit ratio of the new energy station and an optimal configuration scheme of the energy storage system.

Description

Technical Field

[0001] This invention relates to the field of power system energy storage planning technology, and in particular to a method and system for optimizing the configuration of grid-type energy storage based on the short-circuit ratio of new energy power plants. Background Technology

[0002] In recent years, the global energy transition has accelerated significantly, with renewable energy sources such as wind and solar power playing an increasingly prominent role in the power system. The large-scale integration of multiple renewable energy stations has not only reduced carbon emissions but also brought unprecedented challenges to the stable operation of the power grid. Unlike traditional synchronous generators, renewable energy units are typically connected to the grid via power electronic converters, which have low inertia and weak support for system voltage and frequency. This characteristic makes the power system more susceptible to disturbances, especially with high renewable energy penetration, making issues related to system stability and fault ride-through increasingly prominent.

[0003] Against this backdrop, grid-based energy storage systems have emerged as a promising solution to address the stability challenges of grids with abundant renewable energy sources. Unlike grid-connected converters that rely on grid synchronization, grid-based energy storage systems can independently establish and maintain grid voltage and frequency, providing necessary inertial support, voltage regulation, and fault current contribution. However, in systems with multiple renewable energy stations, the optimal configuration of grid-based energy storage is a complex issue requiring careful consideration of various technical and economic factors. A key parameter significantly influencing this configuration is the short-circuit ratio. Defined as the ratio of the short-circuit capacity at the point of common coupling to the rated capacity of the connected renewable energy units, the short-circuit ratio quantifies the strength of the grid at that node. A higher short-circuit ratio indicates a stronger grid with better voltage support, while a lower short-circuit ratio implies a weaker grid, making it more susceptible to voltage fluctuations and stability issues.

[0004] In power systems with multiple renewable energy stations, the short-circuit ratio varies at different nodes due to differences in grid topology, line parameters, and the distribution of generation resources. This spatial variation in the short-circuit ratio directly affects the performance requirements of grid-based energy storage systems. For example, in weak grid areas with low short-circuit ratios, grid-based energy storage needs to provide stronger voltage support and fault current injection to maintain system stability, which may require larger capacity or higher rated power. Conversely, in strong grid areas with high short-circuit ratios, the demand for grid-based energy storage may be relatively lower, allowing for more cost-effective configurations.

[0005] Existing methods for optimizing the configuration of grid-based energy storage often focus on the control strategies of grid-based converters or the calculation of short-circuit ratios in specific system configurations, while paying limited attention to the comprehensive optimization of the location and capacity of grid-based energy storage considering the spatial distribution of short-circuit ratios across multiple renewable energy stations. This research gap hinders the effective deployment of grid-based energy storage, resulting in higher overall system investment and operating costs. Summary of the Invention

[0006] This invention provides a method and system for optimizing the configuration of grid-type energy storage based on the short-circuit ratio of renewable energy power plants. It addresses the technical problem that existing grid-type energy storage optimization methods have limited attention to the comprehensive optimization of the location and capacity of grid-type energy storage considering the spatial distribution of the short-circuit ratio across multiple renewable energy power plants, resulting in high overall system investment and operation costs.

[0007] The first aspect of this invention provides a method for optimizing the configuration of grid-type energy storage based on the short-circuit ratio of new energy power plants, comprising:

[0008] Acquire the raw data of the grid-type energy storage system, preprocess the raw data of the grid-type energy storage system, and output the target data of the grid-type energy storage system.

[0009] Based on the target data and topology of the grid-type energy storage system, a node power conversion factor matrix and a node impedance matrix are constructed.

[0010] Based on the node power conversion factor matrix, the node impedance matrix, and the target data of the grid-type energy storage system, an optimal configuration model for grid-type energy storage is constructed.

[0011] Solve the grid-type energy storage optimization configuration model to output the short-circuit ratio of the new energy power station and the optimal configuration scheme of the energy storage system.

[0012] Optionally, the step of constructing a node power reduction factor matrix and a node impedance matrix based on the target data of the grid-type energy storage system and the topology of the grid-type energy storage system includes:

[0013] Based on the line reactance in the target data of the grid-type energy storage system and the topology of the grid-type energy storage system, construct the node admittance matrix;

[0014] Based on the nodal admittance matrix, construct the impedance matrix;

[0015] Based on the synchronous generator power, wind turbine power, and impedance matrix in the target data of the grid-type energy storage system, a node power conversion factor matrix is ​​constructed.

[0016] Optionally, the step of constructing a grid-based energy storage optimization configuration model based on the node power reduction factor matrix, the node impedance matrix, and the target data of the grid-based energy storage system includes:

[0017] The node power reduction factor matrix is ​​corrected to output the node power influence factor matrix;

[0018] Based on the node power influence factor matrix, the node impedance matrix, and the target data of the grid-type energy storage system, an optimal configuration model for grid-type energy storage is constructed.

[0019] Optionally, the grid-based energy storage optimization configuration model includes grid physical constraints and an optimization objective function.

[0020] Optionally, the physical constraints of the power grid include constraints of Kirchhoff's current law, node impedance matrix constraints, multi-infeed short-circuit ratio constraints, discretized variable constraints, and safety boundary constraints.

[0021] Specifically, the constraints of Kirchhoff's current law are as follows:

[0022] ;

[0023] ;

[0024] ;

[0025] In the formula, and These are the system nodes and the grid-connected nodes of new energy power plants, respectively. and These are the sets of local branch roads and system branch roads of new energy power stations; It is the branch current that flows from node i to node j in the network; It is the net current injection of node i in the adjoint network; It is the node voltage after net current injection; and It is the ground impedance of the new energy power station and synchronous generator; This refers to the current flowing from the new energy power station to the ground branch, i.e., the current flowing from the new energy grid connection node to the ground. This refers to the branch current flowing from node j to node i in the accompanying network; For the net current injection of node j in the adjoint network; The voltage at node i after net current injection; The voltage at node j after net current injection; Let be the reactance of the branch between node i and node j; Let i be the location of the energy storage at node i. , , These are the ground reactance parameters; The number of energy storage installations;

[0026] The node impedance matrix constraint is specifically as follows:

[0027] ;

[0028] In the formula, Here is the node impedance matrix; It is a current matrix; It is a voltage matrix; Let be the voltage generated at node 1 when only a unit current is injected at node 1, and let represent the self-impedance of node 1. Let be the voltage generated at node 2 when only a unit current is injected at node 1, and let represent the mutual impedance between node 1 and node 2. Let be the voltage generated at node m when only a unit current is injected at node 1, and represent the mutual impedance between node 1 and node m. Let be the voltage generated at node 1 when only a unit current is injected at node 2, and represent the mutual impedance between node 2 and node 1. Let be the voltage generated at node 2 when only a unit current is injected at node 2, and let represent the self-impedance of node 2; Let be the voltage generated at node m when only a unit current is injected at node 2, and represent the mutual impedance between node 2 and node m. Let be the voltage generated at node 1 when only a unit current is injected at node m, and let represent the mutual impedance between node m and node 1. Let be the voltage generated at node 2 when only a unit current is injected at node m, and let represent the mutual impedance between node m and node 2. Let be the voltage generated at node m when only a unit current is injected at node m, and let represent the self-impedance of node m. The current injected into node m; Let be the voltage at node m;

[0029] The multi-feed short-circuit ratio constraint is specifically as follows:

[0030] ;

[0031] In the formula, Short-circuit ratio; for per-unit value, Active power injected into the grid connection points of new energy power plants; Let be the voltage generated at node j when only a unit current is injected at node i, i ≠ j, and represent the mutual impedance between node i and node j. Let be the voltage generated at node i when only a unit current is injected at node i, and let represent the self-impedance of node i.

[0032] The discretization variable constraints are specifically as follows:

[0033] ;

[0034] ;

[0035] In the formula, N is the maximum number of energy storage devices to be installed at the node; For energy storage installation and corresponding capacity identifiers, indicate whether node i has n energy storage units installed. When, it means that n energy storage units are installed, when When M is set to 0, it indicates that the installation will not be performed; M is a constant value. Let be the grounding impedance of the energy storage system at node i; The linearized voltage value when n energy storage units are installed at node i;

[0036] The security boundary constraints are specifically as follows:

[0037] ;

[0038] in, Let be the multi-feed short-circuit ratio of node i.

[0039] Optionally, the optimization objective function is specifically:

[0040] ;

[0041] ;

[0042] In the formula, Cost per unit capacity of energy storage; The capacity of a single energy storage unit; This is the conversion factor for energy storage investment; The annual interest rate for energy storage investment; For energy storage life; For energy storage installation and corresponding capacity identifiers, indicate whether node i has n energy storage units installed. When, it means that n energy storage units are installed, when When the time is set to 0, it indicates that the installation will not be performed. The number of energy storage installations; It is the set of system nodes; N is the maximum number of energy storage devices to be installed on the nodes.

[0043] The second aspect of this invention provides a grid-based energy storage optimization configuration system based on the short-circuit ratio of new energy power plants, comprising:

[0044] The acquisition module is used to acquire the raw data of the grid-type energy storage system, preprocess the raw data of the grid-type energy storage system, and output the target data of the grid-type energy storage system.

[0045] The matrix construction module is used to construct the node power conversion factor matrix and the node impedance matrix based on the target data of the grid-type energy storage system and the topology of the grid-type energy storage system.

[0046] The model building module is used to build an optimal configuration model for grid-type energy storage based on the node power conversion factor matrix, the node impedance matrix, and the target data of the grid-type energy storage system.

[0047] The solution module is used to solve the grid-type energy storage optimization configuration model and output the short-circuit ratio of the new energy power station and the optimal configuration scheme of the energy storage system.

[0048] A computer device provided in a third aspect of the present invention includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor causes the processor to perform the steps of the grid-type energy storage optimization configuration method based on the short-circuit ratio of new energy power stations as described in any of the preceding claims.

[0049] The fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed, it implements the steps of the grid-type energy storage optimization configuration method based on the short-circuit ratio of new energy power stations as described in any of the preceding claims.

[0050] The fifth aspect of the present invention provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein, when the program instructions are executed by a computer, the computer performs the steps of the grid-type energy storage optimization configuration method based on the short-circuit ratio of new energy power stations as described in any of the preceding claims.

[0051] As can be seen from the above technical solutions, the present invention has the following advantages:

[0052] The above-mentioned technical solution of the present invention provides a method for optimizing the configuration of grid-based energy storage based on the short-circuit ratio of new energy power plants. This method involves acquiring the original data of the grid-based energy storage system, preprocessing the original data, and outputting target data for the grid-based energy storage system. Based on the target data and the topology of the grid-based energy storage system, a node power conversion factor matrix and a node impedance matrix are constructed. An optimized configuration model for the grid-based energy storage system is constructed based on the node power conversion factor matrix, the node impedance matrix, and the target data. The optimized configuration model is solved to output the short-circuit ratio of the new energy power plant and the optimal configuration scheme for the energy storage system. Based on this method, the present invention deeply integrates the electrical characteristics of the power grid topology reflected by the node impedance matrix with the coupling relationship of new energy power reflected by the node power conversion factor matrix. This fully incorporates the impact of spatial differences in the short-circuit ratio on energy storage performance requirements during the model construction stage, achieving coordinated optimization of the location selection and capacity determination of grid-based energy storage, thereby reducing the overall cost of system investment and operation. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1 This is a flowchart illustrating the steps of a grid-based energy storage optimization configuration method based on the short-circuit ratio of new energy power stations, provided in Embodiment 1 of the present invention.

[0055] Figure 2 A schematic diagram of the IEEE-39 node system tested according to Embodiment 1 of the present invention;

[0056] Figure 3 This is a flowchart illustrating the configuration mechanism of a novel power system mid-grid energy storage system provided in Embodiment 1 of the present invention.

[0057] Figure 4 This is a schematic diagram of the optimal energy storage configuration under different wind power levels provided in Embodiment 1 of the present invention;

[0058] Figure 5 This is a schematic diagram of the short-circuit ratio of the novel energy station provided in Embodiment 1 of the present invention;

[0059] Figure 6 This is a schematic diagram of the short-circuit ratio of a novel energy station under different wind power outputs, provided in Embodiment 1 of the present invention.

[0060] Figure 7This is a structural block diagram of a grid-type energy storage optimization configuration system based on the short-circuit ratio of new energy power stations, provided in Embodiment 2 of the present invention. Detailed Implementation

[0061] This invention provides a method and system for optimizing the configuration of grid-based energy storage based on the short-circuit ratio of renewable energy power plants. This addresses the technical problem that existing grid-based energy storage optimization methods have limited focus on the comprehensive optimization of the location and capacity of grid-based energy storage considering the spatial distribution of the short-circuit ratio across multiple renewable energy power plants, resulting in high overall system investment and operating costs.

[0062] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0063] Please see Figure 1 , Figure 1 The flowchart illustrates the steps of a grid-based energy storage optimization configuration method based on the short-circuit ratio of new energy power plants, as provided in Embodiment 1 of the present invention.

[0064] This invention provides a method for optimizing the configuration of grid-type energy storage based on the short-circuit ratio of new energy power plants, comprising:

[0065] Step 101: Obtain the raw data of the grid-type energy storage system, preprocess the raw data of the grid-type energy storage system, and output the target data of the grid-type energy storage system.

[0066] The raw data for grid-type energy storage systems include line reactance, synchronous generator power, wind turbine power, system voltage level, grounding impedance of energy storage and wind turbine, capacity of a single energy storage system, and installation cost.

[0067] It should be noted that when acquiring the raw data for a grid-type energy storage system, it is necessary to include grid topology parameters (such as line reactance and connection relationships between system nodes), power parameters (rated active power of synchronous generators and rated active power of wind turbines / photovoltaics in new energy power plants), system baseline parameters (system rated voltage level), and equipment characteristic parameters (grounding impedance of the grid-type energy storage system, grounding impedance of wind turbines, rated capacity and installation cost of a single energy storage unit). After acquisition, the raw data is preprocessed, including data cleaning operations such as removing outliers (such as unreasonable negative values ​​of line reactance) and filling in missing items (such as connection relationship information of individual nodes). The final output of the target data for the grid-type energy storage system supports the subsequent construction of node admittance matrices and impedance matrices.

[0068] In this embodiment, the grid-type energy storage system (e.g., the IEEE 39-Bus Test System) is read into the MATLAB (Matrix Laboratory) software. Figure 2 The data (as shown) was used to complete the code for the proposed mathematical model. Then, GUROBI 11.0.1 software was integrated into MATLAB to solve the mathematical model. Specifically, the input data included: line reactance, synchronous generator power, wind turbine power, system voltage level, grounding impedance of energy storage and wind turbines, capacity of a single energy storage system, and installation cost. After completing the raw data preprocessing, the subsequent construction of the nodal admittance matrix and impedance matrix was performed.

[0069] Step 102: Based on the target data and topology of the grid-type energy storage system, construct the node power conversion factor matrix and the node impedance matrix.

[0070] It should be noted that, based on the line reactance in the target data of the grid-type energy storage system and combined with the system topology (inter-node connection relationship), a node admittance matrix is ​​first constructed. At the same time, using the synchronous generator power and wind turbine power in the target data of the grid-type energy storage system and the voltage-current correlation described by the node impedance matrix, the influence of each node power on other nodes is derived, forming a node power conversion factor matrix, thereby quantifying the coupling effect of multi-node power.

[0071] Specifically, step 102 may include the following sub-steps:

[0072] S21. Construct the node admittance matrix based on the line reactance and topology of the grid-type energy storage system in the target data;

[0073] S22. Construct the impedance matrix based on the nodal admittance matrix;

[0074] S23. Based on the synchronous generator power, wind turbine power, and impedance matrix in the target data of the grid-type energy storage system, construct the node power conversion factor matrix.

[0075] Target data for grid-type energy storage systems: refers to standardized data obtained after preprocessing the raw data of grid-type energy storage systems. It includes line reactance, power in per-unit form, node topology information, etc., which can be directly used for modeling and has consistency and validity.

[0076] Line reactance: The parameter of the inductive resistance of a transmission line to alternating current. It is a core indicator reflecting the electrical characteristics of the line and directly affects the calculation of the line admittance.

[0077] The topology of a grid-type energy storage system refers to the connection method and layout relationship of each node (including power generation node, load node, and energy storage access node) in the power grid to which the grid-type energy storage system is connected, as well as the transmission lines between the nodes.

[0078] Node admittance matrix: A matrix describing the electrical connection characteristics between nodes in a power grid. It uses admittance as its element and includes self-admittance (the equivalent admittance of a node itself) and mutual admittance (the equivalent admittance between nodes). It is used to characterize the relationship between node current and voltage.

[0079] Node impedance matrix: The inverse of the node admittance matrix, with impedance as the element, including self-impedance (the equivalent impedance of the node itself) and mutual impedance (the equivalent impedance between nodes), used to quantify the impact of node injection current on the voltage of each node.

[0080] Node power conversion factor matrix: A matrix that quantifies the influence of the power supply (synchronous generator power, wind turbine power) of a certain node in the power grid on the voltage, current or short-circuit characteristics of other nodes. Its elements are power influence coefficients, supporting the analysis of power coupling relationships among multiple nodes.

[0081] It should be noted that when constructing the node admittance matrix based on the line reactance and system topology in the target data of the grid-type energy storage system, the line reactance is first converted into line admittance (when resistance is ignored, the admittance value is the reciprocal of the reactance value). Then, the connection relationship of each node is determined according to the topology. Here, self-admittance is the sum of the admittances of all lines connected to the corresponding node, and mutual admittance is the negative value of the line admittance between two directly connected nodes, thus forming a complete node admittance matrix. Based on this node admittance matrix, the node impedance matrix is ​​obtained through matrix inversion, and its diagonal elements are the self-impedances of each node (only...). The off-diagonal elements represent the mutual impedance between nodes (the voltage induced in another node when a unit current is injected into a node). Subsequently, combining the synchronous generator power and wind turbine power in the target data of the grid-type energy storage system, and utilizing the voltage-current correlation reflected by the node impedance matrix (the node voltage is equal to the product of the impedance matrix and the injected current), the influence coefficients of the power of different nodes on the voltage and current of the common coupling point are derived. Then, a node power conversion factor matrix is ​​constructed to quantify the coupling effect of multi-node power.

[0082] Step 103: Based on the node power conversion factor matrix, node impedance matrix, and target data of the grid-type energy storage system, construct an optimal configuration model for the grid-type energy storage system.

[0083] The grid-based energy storage optimization configuration model includes grid physical constraints and optimization objective function.

[0084] The physical constraints of the power grid include constraints from Kirchhoff's current law, node impedance matrix constraints, multi-infeed short-circuit ratio constraints, discretized variable constraints, and safety boundary constraints.

[0085] It should be noted that, based on the physical characteristics and economic attributes of power system energy storage planning using multi-energy power plants, a multi-dimensional parameter set is defined to support the construction of the optimization model. Specifically, this includes four core parameters: firstly, basic network parameters, including the number of system nodes and power baseline values; secondly, cost parameters, including energy storage installation costs; and thirdly, energy storage system parameters. Based on these parameters, power grid physical constraints and economic objectives (i.e., power grid physical constraints and optimization objective functions) are established, providing complete decision variables and boundary conditions for subsequent model objective function setting and solution space search.

[0086] Specifically, step 103 may include the following sub-steps:

[0087] S31. Correct the node power reduction factor matrix and output the node power influence factor matrix;

[0088] S32. Based on the node power influence factor matrix, node impedance matrix, and target data of the grid-type energy storage system, construct an optimal configuration model for the grid-type energy storage system.

[0089] It should be noted that, firstly, the node admittance matrix is ​​constructed, and then the corresponding node impedance matrix is ​​obtained by matrix inversion; based on the node impedance matrix and power parameters, the node power reduction factor is derived and the power reduction matrix is ​​constructed. Combined with the system reference voltage and power, it is corrected to the actual ohmic value unit, and the rationality of the reduction value is verified by power conservation verification (such as matching the total power after reduction with the system load); then, the node mapping relationship is created, the self-impedance of each node is extracted from the impedance matrix, and the short-circuit capacity is calculated accordingly. Finally, the corrected power reduction matrix, node short-circuit capacity and self-impedance are integrated to form the corrected node power influence factor matrix.

[0090] Furthermore, in the power system energy storage planning problem, after clearly defining the parameter system, it is necessary to construct complete boundary constraints. This ensures the existence of a solution and provides a feasible search space for algorithm implementation, thereby ensuring that the optimization problem can obtain the theoretically optimal solution. The following formula is based on the adjoint network constraints constructed from energy power plants (using binary variables). The location of the energy storage is marked (where n represents the number of energy storage installations) and a linearized multi-feed short-circuit ratio constraint is introduced, where the accompanying network constraint introduces the line impedance (the ground impedance of the n energy storage installation nodes). ):

[0091] Furthermore, the constraints of Kirchhoff's current law are specifically as follows:

[0092] (1)

[0093] (2)

[0094] (3)

[0095] This formula indicates that the inflow, outflow, and injection currents at each node of the network remain balanced. In the formula, and These are the system nodes and the grid-connected nodes of new energy power plants, respectively. and These are the sets of local branch roads and system branch roads of new energy power stations; It is the branch current that flows from node i to node j in the network; It is the net current injection of node i in the adjoint network; It is the node voltage after net current injection; and It is the ground impedance of the new energy power station and synchronous generator; This refers to the current flowing from the new energy power station to the ground branch, i.e., the current flowing from the new energy grid connection node to the ground. This refers to the branch current flowing from node j to node i in the accompanying network; For the net current injection of node j in the adjoint network; The voltage at node i after net current injection; The voltage at node j after net current injection; Let be the reactance of the branch between node i and node j; Let i be the location of the energy storage at node i. , , These are the ground reactance parameters; The number of energy storage installations;

[0096] Furthermore, the nodal impedance matrix constraints are as follows:

[0097] (4)

[0098] This formula represents the relationship between the injected current at each node of the network and the node impedance and node voltage. When a unit current source is injected into node i, the current sources connected to other nodes are all open circuits, i.e., when... , Sometimes, , , represents the following:

[0099] (5)

[0100] In the formula, Here is the node impedance matrix; It is a current matrix; It is a voltage matrix; Let be the voltage generated at node 1 when only a unit current is injected at node 1, and let represent the self-impedance of node 1. Let be the voltage generated at node 2 when only a unit current is injected at node 1, and let represent the mutual impedance between node 1 and node 2. Let be the voltage generated at node m when only a unit current is injected at node 1, and represent the mutual impedance between node 1 and node m. Let be the voltage generated at node 1 when only a unit current is injected at node 2, and represent the mutual impedance between node 2 and node 1. Let be the voltage generated at node 2 when only a unit current is injected at node 2, and let represent the self-impedance of node 2; Let be the voltage generated at node m when only a unit current is injected at node 2, and represent the mutual impedance between node 2 and node m. Let be the voltage generated at node 1 when only a unit current is injected at node m, and let represent the mutual impedance between node m and node 1. Let be the voltage generated at node 2 when only a unit current is injected at node m, and let represent the mutual impedance between node m and node 2. Let be the voltage generated at node m when only a unit current is injected at node m, and let represent the self-impedance of node m. The current injected into node m; Let be the voltage at node m;

[0101] Furthermore, the multi-infeed short-circuit ratio constraint is specifically as follows:

[0102] (6)

[0103] (7)

[0104] This formula represents the short-circuit ratio of multi-energy power plants. Short-circuit ratio; Short-circuit capacity; Active power injected into the grid connection points of new energy power plants; It is the power conversion factor between the new energy grid-connected bus i and j.

[0105] Based on the above, we can conclude that:

[0106] (8)

[0107] This expression is obtained by letting The short-circuit ratio of the new energy power station can be obtained by standardizing equation (6) and solving equations (1)-(5).

[0108] In the formula, Short-circuit ratio; for per-unit value, Active power injected into the grid connection points of new energy power plants; Let be the voltage generated at node j when only a unit current is injected at node i, i ≠ j, and represent the mutual impedance between node i and node j. Let be the voltage generated at node i when only a unit current is injected at node i, and let represent the self-impedance of node i.

[0109] Furthermore, the discretization variable constraints are specifically as follows:

[0110] (9)

[0111] (10)

[0112] This expression represents the energy storage configuration item in formula (3). Linearization transforms the problem from direct modeling as multiplying multiple continuous variables, which may lead to nonlinear or nonconvex constraints. After linearization, the problem of optimizing the configuration of grid-type energy storage is transformed into a mixed-integer linear programming (MILP) problem, which is easier to solve.

[0113] In the formula, N is the maximum number of energy storage devices to be installed at the node; For energy storage installation and corresponding capacity identifiers, indicate whether node i has n energy storage units installed. When, it means that n energy storage units are installed, when When M is set to 0, it indicates that the installation will not be performed; M is a constant value. Let be the grounding impedance of the energy storage system at node i; The linearized voltage value when n energy storage units are installed at node i;

[0114] Furthermore, the safety boundary constraints are specifically as follows:

[0115] (11)

[0116] This formula is constrained by the physical definition of the multi-infeed short-circuit ratio, requiring that the calculated short-circuit ratio should be greater than the set threshold. The short-circuit ratio threshold is 3; the per-unit node voltage should be greater than 0.

[0117] In the formula, Let be the multi-feed short-circuit ratio of node i.

[0118] Furthermore, the objective function is optimized as follows:

[0119] (12)

[0120] (13)

[0121] This formula represents the objective of minimizing the installation cost of grid-based energy storage. In the formula, Cost per unit capacity of energy storage; The capacity of a single energy storage unit; This is the conversion factor for energy storage investment; The annual interest rate for energy storage investment; For energy storage life; For energy storage installation and corresponding capacity identifiers, indicate whether node i has n energy storage units installed. When, it means that n energy storage units are installed, when When the time is set to 0, it indicates that the installation will not be performed. The number of energy storage installations; It is the set of system nodes; N is the maximum number of energy storage devices to be installed on the nodes.

[0122] In this embodiment, when correcting the node power conversion factor matrix, parameters such as energy storage grounding impedance, wind turbine grounding impedance, and node short-circuit capacity in the target data of the grid-type energy storage system are combined to adjust the influence coefficient of each node power on the common coupling point in the matrix. For example, the conversion factor is corrected by introducing the impedance change after energy storage access, eliminating the error under the assumption of the ideal model, and finally outputting a node power influence factor matrix that can truly reflect the power coupling relationship of multiple nodes. On this basis, the node power influence factor matrix quantifies the role of each node power on system stability, and the node impedance matrix describes the electrical topology characteristics of the power grid. Then, parameters such as energy storage capacity, installation cost, and system voltage level in the target data are integrated to construct a grid-type energy storage optimization configuration model. This model takes minimizing the total installation cost of energy storage as the objective function, and incorporates Kirchhoff's current law constraints (to ensure power conservation), multi-infeed short-circuit ratio constraints (to ensure the stability of new energy grid connection), discretization constraints of energy storage capacity and installation quantity (to conform to engineering reality), and node voltage safety boundary constraints, forming a complete constraint system.

[0123] Step 104: Solve the grid-type energy storage optimization configuration model and output the short-circuit ratio of the new energy power station and the optimal configuration scheme of the energy storage system.

[0124] It should be noted that you should refer to [link / reference]. Figure 3 First, system parameters were extracted and code was written using MATLAB software, and GUROBI software was used to prepare the model for solving. Next, the modeling steps for the optimal configuration of grid-type energy storage were analyzed. Finally, a mathematical optimization problem for the grid-type energy storage configuration was created, which includes constraints such as Kirchhoff's current law, node impedance matrix, multi-feed short-circuit ratio constraints, discretized variable constraints, safety boundary constraints, and optimization objectives. Specifically, based on system parameter extraction, two modeling steps for the optimal configuration of grid-type energy storage were analyzed; then, the mathematical optimization problem for grid-type energy storage was established and solved. The output results include: 1) the optimal configuration scheme of the energy storage system, as shown in Table 1 and... Figure 4 As shown, 2) the short-circuit ratio of new energy power stations, such as Figure 5 and Figure 6 As shown in the figure, this solution process fully realizes the optimal decision-making for the configuration of grid-type energy storage in new power systems.

[0125] Table 1 Optimal Configuration Scheme for Energy Storage System

[0126]

[0127] In this embodiment, the present invention combines the analysis of the modeling steps for the optimal configuration of grid-based energy storage, constructs and solves a mathematical optimization problem that includes constraints such as Kirchhoff's current law and node impedance matrices, as well as optimization objectives. Finally, it outputs the optimal configuration scheme of the energy storage system and the short-circuit ratio of the new energy power station. This fully realizes the optimization decision-making of grid-based energy storage configuration in the new power system, providing accurate quantitative basis and decision support for grid-based energy storage to improve the grid's ability to accept new energy sources and ensure the safe and stable operation of the system. It effectively verifies the engineering practicality and technical effectiveness of the optimization configuration method.

[0128] For comparison of technical effectiveness, existing technologies can be referenced. Current research often focuses on the control strategies of grid-connected converters or the calculation of short-circuit ratios in specific system configurations, while paying limited attention to the comprehensive optimization of the location and capacity of grid-connected energy storage considering the spatial distribution of short-circuit ratios across multiple renewable energy stations. This research gap hinders the effective deployment of grid-connected energy storage, as suboptimal configurations may not fully realize their potential to improve system stability or may lead to unnecessary investment costs.

[0129] To address the aforementioned issues, this invention proposes a grid-based energy storage optimization configuration method based on the short-circuit ratio of new energy power plants. First, a correlation model between energy storage installation state variables and branch impedance is established based on grid topology changes, deriving the sensitivity coefficient of the multi-infeed short-circuit ratio with respect to the energy storage installation state variables. Then, the Big M method is introduced to transform the bilinear constraints into mixed-integer linear constraints, forming a grid-based energy storage location and capacity optimization model that includes multi-infeed safety thresholds. This method focuses on energy storage planning scenarios in new power systems. Based on mathematical modeling under multi-objective constraints, a complete optimization framework is constructed, encompassing parametric modeling (including parameters such as energy storage investment, capacity, and impedance), objective function optimization (minimizing the total energy storage installation cost), and boundary condition constraints (including grid connectivity constraints, multi-infeed short-circuit ratio constraints, and energy storage configuration constraints). By linearizing the nonlinear terms of the model, the economically optimal solution for the grid planning scheme can be obtained, effectively reducing the overall cost of system investment and operation, and providing a theoretical basis and quantitative analysis tool for energy storage planning decisions in new power systems.

[0130] In this embodiment of the invention, a method for optimizing the configuration of grid-based energy storage based on the short-circuit ratio of new energy power plants is provided. The method involves acquiring the original data of the grid-based energy storage system, preprocessing the original data, and outputting target data for the system. Based on the target data and the topology of the grid-based energy storage system, a node power conversion factor matrix and a node impedance matrix are constructed. An optimization configuration model for the grid-based energy storage system is then constructed based on these matrixes. The optimization configuration model is solved to output the short-circuit ratio of the new energy power plant and the optimal configuration scheme for the energy storage system. Based on this scheme, the invention deeply integrates the electrical characteristics of the power grid topology reflected by the node impedance matrix with the coupling relationship of new energy power reflected by the node power conversion factor matrix. This fully incorporates the impact of spatial differences in the short-circuit ratio on energy storage performance requirements during the model construction stage, achieving coordinated optimization of the location selection and capacity determination of grid-based energy storage, thereby reducing the overall cost of system investment and operation.

[0131] Please see Figure 7 , Figure 7 This is a structural block diagram of a grid-type energy storage optimization configuration system based on the short-circuit ratio of new energy power stations, provided in Embodiment 2 of the present invention.

[0132] This invention provides a grid-based energy storage optimization configuration system based on the short-circuit ratio of new energy power plants, comprising:

[0133] The acquisition module 701 is used to acquire the raw data of the grid-type energy storage system, preprocess the raw data of the grid-type energy storage system, and output the target data of the grid-type energy storage system.

[0134] The matrix construction module 702 is used to construct the node power conversion factor matrix and the node impedance matrix based on the target data and topology of the grid-type energy storage system.

[0135] Model building module 703 is used to build an optimal configuration model for grid-type energy storage based on the node power conversion factor matrix, node impedance matrix and target data of grid-type energy storage system;

[0136] Solver module 704 is used to solve the grid-type energy storage optimization configuration model and output the short-circuit ratio of new energy power plants and the optimal configuration scheme of the energy storage system.

[0137] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the system and modules described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0138] This invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program; when the computer program is executed by the processor, the processor performs the steps of the grid-type energy storage optimization configuration method based on the short-circuit ratio of new energy power stations as described in any of the above embodiments.

[0139] This invention also provides a computer-readable storage medium storing a computer program / instruction thereon, which, when executed by a processor, implements the steps of the grid-type energy storage optimization configuration method based on the short-circuit ratio of new energy power stations as described in any of the above embodiments.

[0140] This invention also provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the grid-type energy storage optimization configuration method based on the short-circuit ratio of new energy power stations as described in any of the above embodiments.

[0141] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0142] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0143] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the configuration of grid-based energy storage based on the short-circuit ratio of new energy power plants, characterized in that, include: Acquire the raw data of the grid-type energy storage system, preprocess the raw data of the grid-type energy storage system, and output the target data of the grid-type energy storage system. Based on the target data and topology of the grid-type energy storage system, a node power conversion factor matrix and a node impedance matrix are constructed. Based on the node power conversion factor matrix, the node impedance matrix, and the target data of the grid-type energy storage system, an optimal configuration model for grid-type energy storage is constructed. Solve the grid-type energy storage optimization configuration model to output the short-circuit ratio of the new energy power station and the optimal configuration scheme of the energy storage system.

2. The method for optimizing the configuration of grid-type energy storage based on the short-circuit ratio of new energy power plants according to claim 1, characterized in that, Based on the target data and topology of the grid-type energy storage system, the construction of the node power reduction factor matrix and node impedance matrix includes: Based on the line reactance in the target data of the grid-type energy storage system and the topology of the grid-type energy storage system, construct the node admittance matrix; Based on the nodal admittance matrix, construct the impedance matrix; Based on the synchronous generator power, wind turbine power, and impedance matrix in the target data of the grid-type energy storage system, a node power conversion factor matrix is ​​constructed.

3. The method for optimizing the configuration of grid-type energy storage based on the short-circuit ratio of new energy power plants according to claim 1, characterized in that, The step of constructing an optimal configuration model for grid-based energy storage based on the node power conversion factor matrix, the node impedance matrix, and the target data of the grid-based energy storage system includes: The node power reduction factor matrix is ​​corrected to output the node power influence factor matrix; Based on the node power influence factor matrix, the node impedance matrix, and the target data of the grid-type energy storage system, an optimal configuration model for grid-type energy storage is constructed.

4. The method for optimizing the configuration of grid-type energy storage based on the short-circuit ratio of new energy power plants according to claim 1, characterized in that, The grid-based energy storage optimization configuration model includes grid physical constraints and optimization objective function.

5. The method for optimizing the configuration of grid-type energy storage based on the short-circuit ratio of new energy power stations according to claim 4, characterized in that, The physical constraints of the power grid include constraints from Kirchhoff's current law, node impedance matrix constraints, multi-infeed short-circuit ratio constraints, discretized variable constraints, and safety boundary constraints. Specifically, the constraints of Kirchhoff's current law are as follows: ； ； ； In the formula, and These are the system nodes and the grid-connected nodes of new energy power plants, respectively. and These are the sets of local branch roads and system branch roads of new energy power stations; It is the branch current that flows from node i to node j in the network; It is the net current injection of node i in the adjoint network; It is the node voltage after net current injection; and It is the ground impedance of the new energy power station and synchronous generator; This refers to the current flowing from the new energy power station to the ground branch, i.e., the current flowing from the new energy grid connection node to the ground. This refers to the branch current flowing from node j to node i in the accompanying network; For the net current injection of node j in the adjoint network; The voltage at node i after net current injection; The voltage at node j after net current injection; Let be the reactance of the branch between node i and node j; Let i be the location of the energy storage at node i. , , These are the ground reactance parameters; The number of energy storage installations; The node impedance matrix constraint is specifically as follows: ； In the formula, Here is the node impedance matrix; It is a current matrix; It is a voltage matrix; Let be the voltage generated at node 1 when only a unit current is injected at node 1, and let represent the self-impedance of node 1. Let be the voltage generated at node 2 when only a unit current is injected at node 1, and let represent the mutual impedance between node 1 and node 2. Let be the voltage generated at node m when only a unit current is injected at node 1, and represent the mutual impedance between node 1 and node m. Let be the voltage generated at node 1 when only a unit current is injected at node 2, and represent the mutual impedance between node 2 and node 1. Let be the voltage generated at node 2 when only a unit current is injected at node 2, and let represent the self-impedance of node 2; Let be the voltage generated at node m when only a unit current is injected at node 2, and represent the mutual impedance between node 2 and node m. Let be the voltage generated at node 1 when only a unit current is injected at node m, and let represent the mutual impedance between node m and node 1. Let be the voltage generated at node 2 when only a unit current is injected at node m, and let represent the mutual impedance between node m and node 2. Let be the voltage generated at node m when only a unit current is injected at node m, and let represent the self-impedance of node m. The current injected into node m; Let be the voltage at node m; The multi-feed short-circuit ratio constraint is specifically as follows: ； In the formula, Short-circuit ratio; for per-unit value, Active power injected into the grid connection points of new energy power plants; Let be the voltage generated at node j when only a unit current is injected at node i, i ≠ j, and represent the mutual impedance between node i and node j. Let be the voltage generated at node i when only a unit current is injected at node i, and let represent the self-impedance of node i. The discretization variable constraints are specifically as follows: ； ； In the formula, N is the maximum number of energy storage devices to be installed at the node; For energy storage installation and corresponding capacity identifiers, indicate whether node i has n energy storage units installed. When, it means that n energy storage units are installed, when When M is set to 0, it indicates that the installation will not be performed; M is a constant value. Let be the grounding impedance of the energy storage system at node i; The linearized voltage value when n energy storage units are installed at node i; The security boundary constraints are specifically as follows: ； in, Let be the multi-feed short-circuit ratio of node i.

6. The method for optimizing the configuration of grid-type energy storage based on the short-circuit ratio of new energy power plants according to claim 4, characterized in that, The optimization objective function is as follows: ； ； In the formula, Cost per unit capacity of energy storage; The capacity of a single energy storage unit; This is the conversion factor for energy storage investment; The annual interest rate for energy storage investment; For energy storage life; For energy storage installation and corresponding capacity identifiers, indicate whether node i has n energy storage units installed. When, it means that n energy storage units are installed, when When the time is set to 0, it indicates that the installation will not be performed. The number of energy storage installations; It is the set of system nodes; N is the maximum number of energy storage devices to be installed on the nodes.

7. A grid-based energy storage optimization configuration system based on the short-circuit ratio of new energy power plants, characterized in that, include: The acquisition module is used to acquire the raw data of the grid-type energy storage system, preprocess the raw data of the grid-type energy storage system, and output the target data of the grid-type energy storage system. The matrix construction module is used to construct the node power conversion factor matrix and the node impedance matrix based on the target data of the grid-type energy storage system and the topology of the grid-type energy storage system. The model building module is used to build an optimal configuration model for grid-type energy storage based on the node power conversion factor matrix, the node impedance matrix, and the target data of the grid-type energy storage system. The solution module is used to solve the grid-type energy storage optimization configuration model and output the short-circuit ratio of the new energy power station and the optimal configuration scheme of the energy storage system.

8. A computer device, characterized in that, The device includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor causes the processor to perform the steps of the grid-type energy storage optimization configuration method based on the short-circuit ratio of new energy power stations as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed, it implements the grid-type energy storage optimization configuration method based on the short-circuit ratio of new energy power stations as described in any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, wherein when the program instructions are executed by a computer, the computer performs the grid-type energy storage optimization configuration method based on the short-circuit ratio of new energy power stations as described in any one of claims 1-6.

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