Network construction type energy storage locating and sizing method oriented to power grid strength improvement

Abstract

The invention discloses a network construction type energy storage locating and sizing method oriented to power grid strength improvement. The method comprises the following steps: firstly, carrying out admittance modeling on the new energy multi-infeed system, and deducing a calculation method of a power grid strength characterization index (generalized short-circuit ratio) after network construction type energy storage access; next, taking the minimization of the investment cost of the network construction type energy storage as a target function, and establishing a network construction type energy storage locating and sizing optimization model oriented to the power grid strength improvement; and finally, based on a support function relaxation algorithm, obtaining a final network construction type energy storage locating and sizing result. The addressing and sizing method not only can consider the possibility of accessing network construction type energy storage to the non-new energy station node to improve the power grid strength, but also can process the addressing and sizing optimization problem containing characteristic value constraint to realize the optimal solution, and has practical engineering significance.

Description

Technical Field

[0001] The present invention relates to the field of grid-type energy storage planning, and in particular to a grid-type energy storage site selection and capacity determination method for improving the strength of a power grid. Background Art

[0002] With the increase in the penetration rate of new energy and the corresponding withdrawal of synchronous generators, the grid strength of the new energy power system has dropped significantly, and the safety and stability risks in operation have increased. Grid-connected converters, especially grid-connected energy storage, are considered to be an effective means to provide grid strength support for the "double high" system. Therefore, quantitatively analyzing the impact mechanism of grid-connected energy storage on grid strength and studying the optimal construction plan of grid-connected energy storage has become a key research issue.

[0003] Although a large number of engineering practices and studies have shown that grid-forming converters can improve the strength of the power grid, there is still a lack of accurate quantitative methods to measure their improvement effects, and research on the site selection and capacity determination of grid-forming converters is yet to be carried out. Chaoran Yang et al. extended the small disturbance stability analysis framework based on the generalized short-circuit ratio to a high-proportion renewable energy power system containing grid-forming converters by approximating the grid-forming converters with ideal voltage sources, and proposed an optimal site selection method for grid-forming converters. However, this method only assumes that grid-forming converters can only be configured at renewable energy station nodes and ignores the equipment cost and voltage support capability differences of grid-forming converters (C. Yang, L. Huang, H. Xin, and P. Ju. Placing Grid-Forming Converters to Enhance Small Signal Stability of PLL-Integrated Power Systems [J]. IEEE Trans. Power Syst., 2021, 36(4): 3563–3573).

[0004] Therefore, there is an urgent need for a method for site selection and sizing of grid-connected energy storage for improving grid strength, analyzing the impact of grid-connected energy storage access on grid strength, and providing the optimal site selection and sizing results for grid-connected energy storage. Summary of the invention

[0005] The purpose of the present invention is to overcome the shortcomings of the prior art and propose a method for site selection and sizing of grid-type energy storage for improving grid strength. First, the new energy multi-feedback system is modeled by admittance and the calculation method of the grid strength characterization index (generalized short-circuit ratio) after the grid-type energy storage is connected is derived. Next, taking the minimization of the investment cost of grid-type energy storage as the objective function, an optimization model for site selection and sizing of grid-type energy storage for improving grid strength is established. Finally, the final site selection and sizing result of the grid-type energy storage is obtained based on the support function relaxation algorithm. This site selection and sizing method can not only take into account the possibility of connecting grid-type energy storage to non-new energy station nodes to improve the strength of the grid, but also can handle the site selection and sizing optimization problem with eigenvalue constraints to achieve the optimal solution, which has practical engineering significance.

[0006] The purpose of the present invention is achieved by at least one of the following technical solutions.

[0007] A method for selecting a site and determining the capacity of a grid-connected energy storage system for improving the strength of a power grid comprises the following steps:

[0008] S1. Establish the admittance model of the renewable energy multi-feedback system, including the grid-following converter, grid-forming energy storage and power network;

[0009] S2. Establish a closed-loop system dynamic model including grid-type energy storage, and analyze the stability of the new energy multi-feed system after connecting to the grid-type energy storage;

[0010] S3. Establish the grid strength or stability constraints of the renewable energy multi-feed system;

[0011] S4. Establish upper and lower limit constraints on the grid-type energy storage configuration capacity;

[0012] S5. According to the constraints established in steps S3 and S4, an overall investment cost model for grid-type energy storage planning is constructed, and the optimal location and capacity determination result of the grid-type energy storage is obtained through a support function relaxation algorithm.

[0013] Furthermore, in step S1, the admittance model of the i-th grid-fed converter (GFL) in the renewable energy multi-feed system is specifically as follows:

[0014] ΔI GFLi =-S GFLi Y GFL (s)ΔU i ,i=1,2,…,n

[0015] Among them, ΔI GFLi and ΔU i are the output current and port voltage of the ith grid-connected converter in the global x, y coordinate system; S GFLi is the capacity of the i-th grid-connected converter; s is the Laplace operator; Y GFL(s) is the admittance transfer function matrix of the grid-following converter under unit capacity; n is the number of grid-following converters in the renewable energy multi-feed system.

[0016] Furthermore, in step S1, in the renewable energy multi-feed system, the grid-connected energy storage is connected to the grid through a two-stage transformer, and the specific admittance model of the grid-connected energy storage is as follows:

[0017]

[0018] The grid-type energy storage can be connected to each node j in the renewable energy multi-feed system. The nodes in the renewable energy multi-feed system include the grid-type converter and the non-grid-type converter in the renewable energy multi-feed system. j=1~n+m, where m is the number of non-grid-type converters in the renewable energy multi-feed system. ΔI GFMj and ΔU GFMj is the output current and port voltage in the global x, y coordinate system after the grid-type energy storage is connected at node j; S GFMj is the capacity of the grid-connected energy storage at node j; B GFM is the equivalent constant susceptance of the grid-type energy storage; F(s) is the line transmission equation in the given s frequency domain; B BES is the unit admittance of the grid-type energy storage; B T is the transformer equivalent admittance.

[0019] Furthermore, in step S1, the admittance model on the electrical network side includes:

[0020]

[0021] Among them, Y net (s) is the admittance matrix on the electrical network side; ΔI and is the injected current of n+m nodes in the renewable energy multi-feed system; ΔU and is the voltage vector of n+m nodes in the renewable energy multi-feed system; is the injected current of the grid-following converter at the corresponding node; and Inject current into corresponding nodes for potential configurable grid-connected energy storage; is the Thevenin equivalent susceptance matrix of the renewable energy multi-feed system without grid-connected energy storage, which can be obtained through line parameters; B1, B2, B3, B4 are block matrices of matrix B.

[0022] Furthermore, in step S2, a closed-loop system dynamic model including grid-connected energy storage is established, as follows:

[0023] Considering that the admittance model of the grid-type energy storage and the admittance model of the power network have similar characteristics, it can be considered that the access of the grid-type energy storage does not affect the dynamic characteristics of the device side, but only changes the admittance matrix of the power network side. Therefore, the Thevenin equivalent admittance matrix is ​​converted to:

[0024]

[0025] Among them, S GFMn =diag([S GFM1 ,…,S GFMn ]) and S GFMm =diag([S GFMn+1 ,…,S GFMn+m ]) is the capacity of grid-type energy storage at each node;

[0026] Based on Kron simplification, eliminating the m passive nodes in the electrical network, we can get:

[0027]

[0028] B red (S GFM )=B1+B BES S GFMn -B2(B4+B BES S GFMm ) -1 B3

[0029] Therefore, the updated closed-loop characteristic equation of the renewable energy multi-feedback system with grid-connected energy storage can be written as:

[0030]

[0031] in, is the identity matrix.

[0032] Furthermore, in step S2, the grid strength characterization index of the new energy multi-feed system after access to the grid-building energy storage is analyzed, as follows:

[0033] Considering the closed-loop characteristic equation of the system is diagonalized and its eigenvalues ​​are all positive and satisfy 0<λ1<λ2<…<λ n ,matrix can be broken down into:

[0034]

[0035] Where: P = [x1, x2, ..., x n ] represents the right eigenvector matrix corresponding to the matrix eigenvalues.

[0036] Based on the above formula and matrix decomposition theory, the closed-loop characteristic equation of the system can be transformed into:

[0037]

[0038] The above equation shows that the characteristic equation of the renewable energy multi-feed system can be decoupled into a series of single-feed power systems. Therefore, the stability of the renewable energy multi-feed power system depends on the stability of the weakest single-feed system; and the stability of the single-feed system can be quantified by the short-circuit ratio (SCR). Correspondingly, for the renewable energy multi-feed power system, its stability can be measured by the minimum short-circuit ratio in a series of single-feed systems, namely the generalized short-circuit ratio:

[0039]

[0040] in, is the minimum eigenvalue of the matrix;

[0041] On the other hand, the critical generalized short-circuit ratio, CgSCR, is the value of the short-circuit ratio SCR corresponding to the critical stability of the weakest single-feed system, and its expression is:

[0042]

[0043] in, is the eigenvalue solution of the equation; s c =jω c Denotes the imaginary axis along which the dominant eigenvalue lies in the complex plane.

[0044] Furthermore, in step S3, the grid strength or stability constraint of the renewable energy multi-feed system is established, as follows:

[0045]

[0046] Among them, Δ g is the specific stability margin; this stability constraint ensures the small disturbance stability of the renewable energy multi-feed system.

[0047] Furthermore, in step S4, upper and lower limit constraints of the grid-type energy storage configuration capacity are established, as follows:

[0048]

[0049] in, and The upper and lower limits of the capacity of the grid-connected energy storage that can be connected to node i.

[0050] Furthermore, in step S5, an overall investment cost model for grid-type energy storage planning is constructed, as follows:

[0051]

[0052] Among them, Cost(S GFM ) is the investment cost function with grid-type energy storage capacity as variable; c i is the investment cost of configuring grid-connected energy storage at node i.

[0053] Furthermore, in step S5, since the optimization model of the above-mentioned configuration grid-type energy storage contains eigenvalue constraints, the model becomes non-convex and difficult to solve; therefore, based on the support function algorithm, The kth iteration has the minimum eigenvalue Taylor's theorem is used to derive the corresponding support function expression and relax the eigenvalue constraint, namely:

[0054]

[0055] Among them, q k (S GFM ) is the support function with the grid-type energy storage capacity as the variable; γ is the parameter that affects the number of iterations;

[0056] The expression is as follows:

[0057]

[0058] in,

[0059]

[0060] The original non-convex problem with eigenvalue constraints is transformed into a convex optimization problem based on the support function relaxation algorithm, which can obtain the optimal solution of site selection and capacity determination through iterative solution; when the site selection and capacity determination results of the two previous and subsequent grid-type energy storage satisfy the following formula, the site selection and capacity determination results of the grid-type energy storage converge to the optimal solution:

[0061]

[0062] Where ε is the convergence error.

[0063] Compared with the prior art, the grid-type energy storage site selection and capacity determination method for improving the strength of the power grid of the present invention has the following advantages:

[0064] Beneficial effects:

[0065] (1) In the problem of site selection and capacity determination for grid-type energy storage, it is considered that non-new energy station nodes can also be configured with grid-type energy storage and a more accurate construction plan can be obtained.

[0066] (2) It has good scalability. The method of the present invention can be adapted to other optimization problems with eigenvalue constraints. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 Flow chart for obtaining the optimal location and capacity of grid-type energy storage for this implementation;

[0068] Figure 2 A schematic diagram of a test network structure for a simulation example of the present invention;

[0069] Figure 3 It is a structural schematic diagram of the new energy multi-feed system of the present invention; DETAILED DESCRIPTION

[0070] In order to make the objectives, technical solutions and advantages of the present invention more clear and explicit, the present invention is further described in detail below with reference to the accompanying drawings and embodiments.

[0071] Embodiment 1:

[0072] A method for selecting and sizing grid-connected energy storage for improving grid strength, such as Figure 1 As shown, this example is tested with a modified IEEE 39-node power system to specifically illustrate the grid-type energy storage site selection and capacity determination method for improving grid strength provided by the present invention. The topology of the power network is as follows: Figure 2 As shown, the following steps are included:

[0073] In this embodiment, the capacity of the new energy station and the electrical network parameters of the 39-node system and the equivalent admittance of the grid-type energy storage are obtained, including: the line parameters of the 39-node system, the capacity S of each grid-type converter (GFL) GFLi , Equivalent admittance of grid-type energy storage B BES , the unit investment cost of each node c i .

[0074] 39-node power system with 9 new energy stations, capacity S GFLi =1p.u.; Equivalent admittance B of grid-type energy storage BES =6.25pu; the unit investment cost of each node is c i =1p.u. After testing, it was found that the original generalized short-circuit ratio of the new energy multi-feed system before access to the grid-type energy storage was 1.51, and the generalized short-circuit ratio after access was set to reach 3.5. The line parameters of the 39-node system in this embodiment are shown in Table 1.

[0075] Table 1 39-node power system line data information table

[0076]

[0077]

[0078]

[0079] S1, such as Figure 3 As shown, an admittance model of a renewable energy multi-feedback system is established, including a grid-following converter, a grid-forming energy storage system and an electric network.

[0080] The admittance model of the i-th grid-following converter (GFL) in the renewable energy multi-feed system is as follows:

[0081] ΔI GFLi =-S GFLi Y GFL (s)ΔU i ,i=1,2,…,n

[0082] Among them, ΔI GFLi and ΔU i are the output current and port voltage of the ith grid-connected converter in the global x, y coordinate system; S GFLi is the capacity of the ith grid-connected converter, all set to 1 p.u.; s is the Laplace operator; Y GFL (s) is the admittance transfer function matrix of the grid-following converter under unit capacity; n is the number of grid-following converters in the renewable energy multi-feed system, which is 9 in this example.

[0083] Furthermore, in step S1, in the renewable energy multi-feed system, the grid-connected energy storage is connected to the grid through a two-stage transformer, and the specific admittance model of the grid-connected energy storage is as follows:

[0084]

[0085] The grid-type energy storage can be connected to each node j in the renewable energy multi-feed system. The nodes in the renewable energy multi-feed system include the grid-type converter and the non-grid-type converter in the renewable energy multi-feed system. j=1~n+m, where m is the number of non-grid-type converters in the renewable energy multi-feed system. ΔI GFMj and ΔU GFMj is the output current and port voltage in the global x, y coordinate system after the grid-type energy storage is connected at node j; S GFMj is the capacity of the grid-connected energy storage at node j; B GFM is the equivalent constant susceptance of the grid-type energy storage; F(s) is the line transmission equation in the given s frequency domain; B BES is the unit admittance of the grid-type energy storage; B T is the transformer equivalent admittance.

[0086] Furthermore, in step S1, the admittance model on the electrical network side includes:

[0087]

[0088] Among them, Ynet (s) is the admittance matrix on the electrical network side; ΔI and is the injected current of n+m nodes in the renewable energy multi-feed system; ΔU and is the voltage vector of n+m nodes in the renewable energy multi-feed system; is the injected current of the grid-following converter at the corresponding node; and Inject current into corresponding nodes for potential configurable grid-type energy storage; is the Thevenin equivalent susceptance matrix of the renewable energy multi-infeed system without grid-connected energy storage, which can be obtained through line parameters; B1, B2, B3, B4 are block matrices of matrix B, which can be obtained through IEEE 39-bus system line parameters.

[0089] S2. Establish a closed-loop system dynamic model including grid-type energy storage, and analyze the stability of the new energy multi-feed system after connecting to the grid-type energy storage;

[0090] A closed-loop system dynamic model including grid-connected energy storage is established, as follows:

[0091] Considering that the admittance model of the grid-type energy storage and the admittance model of the power network have similar characteristics, it can be considered that the access of the grid-type energy storage does not affect the dynamic characteristics of the device side, but only changes the admittance matrix of the power network side. Therefore, the Thevenin equivalent admittance matrix is ​​converted to:

[0092]

[0093] Among them, S GFMn =diag([S GFM1 ,…,S GFMn ]) and S GFMm =diag([S GFMn+1 ,…,S GFMn+m ]) is the capacity of grid-type energy storage at each node;

[0094] Based on Kron simplification, eliminating the m passive nodes in the electrical network, we can get:

[0095]

[0096] B red (S GFM )=B1+B BES S GFMn -B2(B4+B BES S GFMm ) -1 B3

[0097] Therefore, the updated closed-loop characteristic equation of the renewable energy multi-feedback system with grid-connected energy storage can be written as:

[0098]

[0099] in,

[0100] is the identity matrix.

[0101] The grid strength characterization indicators of the renewable energy multi-feed system after access to the grid-connected energy storage are analyzed as follows:

[0102] Considering the closed-loop characteristic equation of the system is diagonalized and its eigenvalues ​​are all positive and satisfy 0<λ1<λ2<…<λ n ,matrix can be broken down into:

[0103]

[0104] Where: P = [x1, x2, ..., x n ] represents the right eigenvector matrix corresponding to the matrix eigenvalues.

[0105] Based on the above formula and matrix decomposition theory, the closed-loop characteristic equation of the system can be transformed into:

[0106]

[0107] The above equation shows that the characteristic equation of the renewable energy multi-feed system can be decoupled into a series of single-feed power systems. Therefore, the stability of the renewable energy multi-feed power system depends on the stability of the weakest single-feed system; and the stability of the single-feed system can be quantified by the short-circuit ratio (SCR). Correspondingly, for the renewable energy multi-feed power system, its stability can be measured by the minimum short-circuit ratio in a series of single-feed systems, namely the generalized short-circuit ratio:

[0108]

[0109] in, is the minimum eigenvalue of the matrix;

[0110] On the other hand, the critical generalized short-circuit ratio, CgSCR, is the value of the short-circuit ratio SCR corresponding to the critical stability of the weakest single-feed system, and its expression is:

[0111]

[0112] in, is the eigenvalue solution of the equation; s c =jω c Denotes the imaginary axis along which the dominant eigenvalue lies in the complex plane.

[0113] S3. Establish the grid strength or stability constraints of the renewable energy multi-feed system, as follows:

[0114]

[0115] Among them, Δ g is the specific stability margin; this stability constraint ensures the small disturbance stability of the renewable energy multi-feed system.

[0116] S4. Establish upper and lower limits for the grid-connected energy storage configuration capacity, as follows:

[0117]

[0118] in, and The upper and lower limits of the capacity of the grid-connected energy storage that can be connected to node i.

[0119] S5. According to the constraints established in steps S3 and S4, an overall investment cost model for grid-type energy storage planning is constructed, and the optimal location and capacity determination result of the grid-type energy storage is obtained through a support function relaxation algorithm;

[0120] The overall investment cost model for grid-connected energy storage planning is constructed as follows:

[0121]

[0122] Among them, Cost(S GFM ) is the investment cost function with grid-type energy storage capacity as variable; c i The investment cost of configuring grid-connected energy storage at node i is set to 1 p.u.

[0123] Since the optimization model of the above-mentioned configuration grid energy storage contains eigenvalue constraints, the model becomes non-convex and difficult to solve; therefore, based on the support function algorithm, The kth iteration has the minimum eigenvalue Taylor's theorem is used to derive the corresponding support function expression and relax the eigenvalue constraint, namely:

[0124]

[0125] Among them, q k (S GFM ) is the support function with the grid-type energy storage capacity as the variable; γ is the parameter that affects the number of iterations;

[0126] The expression is as follows:

[0127]

[0128] in,

[0129]

[0130] The original non-convex problem with eigenvalue constraints is transformed into a convex optimization problem based on the support function relaxation algorithm, which can obtain the optimal solution of site selection and capacity determination through iterative solution; when the site selection and capacity determination results of the two previous and subsequent grid-type energy storage satisfy the following formula, the site selection and capacity determination results of the grid-type energy storage converge to the optimal solution:

[0131]

[0132] Where ε is the convergence error.

[0133] In the embodiment, the convergence error ε is set to 10 -5 , and obtain a converged solution It includes the specific node locations and corresponding capacities of the grid-connected energy storage connected to the power network, as shown in Table 2.

[0134] Table 2 Optimal location and capacity determination results and cost table of grid-type energy storage in Example 1

[0135]

[0136]

[0137] As can be seen from Table 2, grid-connected energy storage does not necessarily need to be configured at every new energy station node, and it can also achieve improved grid strength and lower investment costs.

[0138] Embodiment 2:

[0139] In Example 2, the parameters such as equipment, lines and the equivalent impedance of the grid-type energy storage are still the same as those in Example 1, but the investment cost of each node is different, wherein the investment cost of the grid-type energy storage of the new energy site nodes (nodes 1 to 9) is 1 p.u., and that of the non-new energy site nodes (nodes 10 to 39) is 0.9 pu.

[0140] According to the proposed site selection and capacity determination optimization method, the optimal site selection and capacity determination results of the grid-type energy storage in Example 2 are shown in Table 3.

[0141] Table 3 Optimal location and capacity determination results and cost table of grid-type energy storage in Example 2

[0142]

[0143] This value can provide an accurate reference for actual grid-connected energy storage planning, that is, grid-connected energy storage is not only configured at new energy station nodes. When the cost of connecting grid-connected energy storage at non-new energy stations is low, it is also possible to connect it at non-new energy stations to achieve grid strength improvement and cost minimization.

[0144] Embodiment 3:

[0145] In Example 3, the equipment parameters, line parameters and investment costs of each node are still the same as those in Example 1. The equivalent impedance of the grid-type energy storage uses data different from those in Example 1 and Example 2, and is set to 8 p.u.

[0146] According to the grid-type energy storage site selection and sizing method, the optimal site selection and sizing results are shown in Table 4.

[0147] Table 4 Optimal location and capacity determination results and cost table of grid-type energy storage in Example 3

[0148]

[0149] This value can provide an accurate reference for the actual grid-connected energy storage planning process. That is, when the equivalent impedance of the grid-connected energy storage increases accordingly, the grid-connected energy storage capacity required to be configured in the power system decreases accordingly when achieving the same grid strength requirements. This further reflects the voltage support role of the grid-connected energy storage.

[0150] Obviously, the above-mentioned embodiments only express several implementation modes of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the present invention. It should be pointed out that for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the attached claims.

Claims

1. A method for selecting a site and determining the capacity of a grid-connected energy storage system for improving the strength of a power grid, characterized in that: The following steps are involved: S1. Establish the admittance model of the renewable energy multi-feedback system, including the grid-following converter, grid-forming energy storage and power network; S2. Establish a closed-loop system dynamic model including grid-type energy storage, and analyze the stability of the new energy multi-feed system after connecting to the grid-type energy storage; S3. Establish the grid strength or stability constraints of the renewable energy multi-feed system; S4. Establish upper and lower limit constraints on the grid-type energy storage configuration capacity; S5. According to the constraints established in steps S3 and S4, an overall investment cost model for grid-type energy storage planning is constructed, and the optimal location and capacity determination result of the grid-type energy storage is obtained through a support function relaxation algorithm.

2. A method for selecting a site and determining the capacity of a grid-connected energy storage system for improving grid strength according to claim 1, characterized in that: In step S1, the admittance model of the i-th grid-following converter (GFL) in the renewable energy multi-feed system is as follows: ΔI GFLi =-S GFLi Y GFL (s)ΔU i ,i=1,2,…,n Among them, ΔI GFLi and ΔU i are the output current and port voltage of the ith grid-connected converter in the global x, y coordinate system; S GFLi is the capacity of the i-th grid-connected converter; s is the Laplace operator; Y GFL (s) is the admittance transfer function matrix of the grid-following converter under unit capacity; n is the number of grid-following converters in the renewable energy multi-feed system.

3. A method for selecting a site and determining the capacity of a grid-connected energy storage system for improving grid strength according to claim 2, characterized in that: In step S1, in the renewable energy multi-feed system, the grid-connected energy storage is connected to the grid through a two-stage transformer. The specific admittance model of the grid-connected energy storage is as follows: The grid-type energy storage can be connected to each node j in the renewable energy multi-feed system. The nodes in the renewable energy multi-feed system include the grid-type converter and the non-grid-type converter in the renewable energy multi-feed system. j=1~n+m, where m is the number of non-grid-type converters in the renewable energy multi-feed system. ΔI GFMj and ΔU GFMj is the output current and port voltage in the global x, y coordinate system after the grid-type energy storage is connected at node j; S GFMj is the capacity of the grid-connected energy storage at node j; B GFM is the equivalent constant susceptance of the grid-type energy storage; F(s) is the line transmission equation in the given s frequency domain; B BES is the unit admittance of the grid-type energy storage; B T is the transformer equivalent admittance.

4. A method for selecting a site and determining the capacity of a grid-connected energy storage system for improving grid strength according to claim 3, characterized in that: In step S1, the admittance model on the electrical network side includes: Among them, Y net (s) is the admittance matrix on the electrical network side; ΔI and is the injected current of n+m nodes in the renewable energy multi-feed system; ΔU and is the voltage vector of n+m nodes in the renewable energy multi-feed system; is the injected current of the grid-following converter at the corresponding node; and Inject current into corresponding nodes for potential configurable grid-type energy storage; is the Thevenin equivalent susceptance matrix of the renewable energy multi-feed system without grid-connected energy storage, which is obtained through line parameters; B1, B2, B3, and B4 are block matrices of matrix B.

5. A method for selecting a site and determining the capacity of a grid-connected energy storage system for improving grid strength according to claim 3, characterized in that: In step S2, a closed-loop system dynamic model including grid-connected energy storage is established, as follows: Convert the Thevenin equivalent admittance matrix to: Among them, S GFMn =diag([S GFM1 ,…,S GFMn ]) and S GFMm =diag([S GFMn+1 ,…,S GFMn+m ]) is the capacity of grid-type energy storage at each node; Based on Kron simplification, eliminating the m passive nodes in the electrical network, we can obtain: B red (S GFM )=B1+B BES S GFMn -B2(B4+B BES S GFMm ) -1 B3 Therefore, the updated closed-loop characteristic equation of the renewable energy multi-feedback system with grid-connected energy storage can be written as: in, is the identity matrix.

6. A method for selecting a site and determining the capacity of a grid-connected energy storage system for improving the strength of a power grid according to claim 5, characterized in that: In step S2, the grid strength characterization index of the new energy multi-feed system after access to the grid-building energy storage is analyzed, as follows: Considering the closed-loop characteristic equation of the system B red is diagonalized and its eigenvalues ​​are all positive and satisfy 0<λ1<λ2<…<λ n ,matrix B red is broken down into: Where P = [x1, x2, …, x n ] represents the right eigenvector matrix corresponding to the matrix eigenvalue; Based on the above formula and matrix decomposition theory, the closed-loop characteristic equation of the system is transformed into: For renewable energy multi-feed power systems, their stability is measured by the minimum short-circuit ratio in a series of single-feed systems, namely the generalized short-circuit ratio: in, is the minimum eigenvalue of the matrix; On the other hand, the critical generalized short-circuit ratio, CgSCR, is the value of the short-circuit ratio SCR corresponding to the critical stability of the weakest single-feed system, and its expression is: in, is the eigenvalue solution of the equation; s c =jω c Denotes the imaginary axis along which the dominant eigenvalue lies in the complex plane.

7. A method for selecting a site and determining the capacity of a grid-connected energy storage system for improving grid strength according to claim 6, characterized in that: In step S3, establishing the grid strength or stability constraint of the renewable energy multi-feed system includes: Among them, Δ g is the specific stability margin; this stability constraint ensures the small disturbance stability of the renewable energy multi-feed system.

8. A method for selecting a site and determining the capacity of a grid-connected energy storage system for improving grid strength according to claim 3, characterized in that: In step S4, the upper and lower limit constraints of the grid-connected energy storage configuration capacity are established, including: in, and The upper and lower limits of the capacity of the grid-connected energy storage that can be connected to node i.

9. A method for selecting a site and determining the capacity of a grid-connected energy storage system for improving grid strength according to claim 1, characterized in that: In step S5, constructing an overall investment cost model for grid-connected energy storage planning includes: Among them, Cost(S GFM ) is the investment cost function with grid-type energy storage capacity as variable; c i is the investment cost of configuring grid-connected energy storage at node i.

10. A method for selecting a site and determining the capacity of a grid-connected energy storage system for improving grid strength according to claim 1, characterized in that: In step S5, based on the support function algorithm, The kth iteration has the minimum eigenvalue Taylor's theorem is used to derive the corresponding support function expression and relax the eigenvalue constraint, namely: Among them, q k (S GFM ) is the support function with the grid-type energy storage capacity as the variable; γ is the parameter that affects the number of iterations; The expression is as follows: in, The original non-convex problem with eigenvalue constraints is transformed into a convex optimization problem based on the support function relaxation algorithm, which can obtain the optimal solution of site selection and capacity determination through iterative solution; when the site selection and capacity determination results of the two previous and subsequent grid-type energy storage satisfy the following formula, the site selection and capacity determination results of the grid-type energy storage converge to the optimal solution: Where ε is the convergence error.

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