Method for realizing super-large-scale energy storage power station equivalent model based on weighted average

Abstract

The invention relates to a method for realizing an equivalent model of a super-large-scale energy storage power station based on weighted average, which comprises the following steps of: dividing energy storage units by using a plurality of energy storage converters with different topological structures according to an application voltage range of the energy storage units; regularly collecting dynamic indexes and static indexes of an energy storage unit according to preset time, and averagely weighting and integrating the dynamic indexes and the static indexes of the energy storage converters with the same topological structure into parameters of an equivalent model by using a clustering algorithm; according to the parameters of the equivalent model, obtaining a dynamic characteristic index of the system under the action of the step response, and substituting the dynamic characteristic index into the second-order system to obtain a transfer function of the second-order system; and representing the operation characteristics of the energy storage unit according to the transfer function.

Description

Technical Field

[0001] The invention relates to a method for realizing an equivalent model of a super-large-scale energy storage power station based on weighted average, and belongs to the technical field of large-scale battery energy storage. Background Art

[0002] With the rapid development of renewable energy, ultra-large-scale energy storage power stations have become an important infrastructure to support the stability of the power grid and improve energy efficiency. Battery energy storage power stations have great potential in promoting the integration of renewable resources and improving the flexibility of system operation due to their advantages such as flexible resource allocation, fast response speed, and no influence from weather and geographical location. In systems with a high penetration rate of non-dispatchable intermittent power generation resources, large-scale grid-side energy storage power stations can effectively suppress frequency fluctuations and voltage fluctuations, and have the effect of peak shaving and valley filling, which has received much attention in the field of energy storage at home and abroad. However, due to the significant parameter differences and dynamic behavior inconsistencies of the battery packs inside the energy storage power station, traditional simplified models are difficult to accurately reflect their actual operating characteristics.

[0003] Existing technologies such as the Chinese patent number "CN117543628A" disclose a photovoltaic power station partitioning method and system. The method includes the following steps: S1 extracts the dynamic frequency response similarity index from the dynamic frequency response curve of the power system according to the obvious spatial distribution characteristics of the dynamic frequency response characteristics of the nodes in different regions of the power system after the disturbance; S2 constructs the dynamic frequency response similarity index matrix of the photovoltaic power station nodes and performs standardization; S3 uses a clustering algorithm to partition the photovoltaic power station nodes for the power system that does not have its own partitioning attribute.

[0004] The problem with the above-mentioned prior art is that it is necessary to extract and process a large amount of dynamic frequency response data, construct a similarity index matrix, and perform standardization and cluster analysis. This process may involve complex calculations and data processing, and the computational cost may be high. Partitioning by the spatial distribution characteristics of the dynamic frequency response mainly focuses on frequency stability issues. However, it may not fully reflect the dynamic characteristics of the power system in other aspects, such as voltage stability, power angle stability, etc. Summary of the invention

[0005] In order to solve the above problems existing in the prior art, the present invention proposes a method for realizing an equivalent model of a super-large-scale energy storage power station based on weighted average.

[0006] The technical solution of the present invention is as follows:

[0007] On the one hand, the present invention provides a method for realizing an equivalent model of a super-large-scale energy storage power station based on weighted average, comprising the following steps:

[0008] According to the application voltage range of the energy storage unit, the energy storage unit is divided using several energy storage converters with different topologies;

[0009] The dynamic and static indicators of the energy storage unit are collected regularly according to the preset time, and the dynamic and static indicators of the energy storage converters with the same topology are weighted and averaged to form the parameters of the equivalent model using the clustering algorithm;

[0010] According to the parameters of the equivalent model, the dynamic characteristic index of the system under the step response is obtained, and the dynamic characteristic index is substituted into the second-order system to obtain the transfer function of the second-order system; the operation characteristics of the energy storage unit are represented according to the transfer function.

[0011] As a preferred embodiment, the topology structure includes a two-level topology structure, a three-level topology structure and a cascaded H-bridge topology structure;

[0012] For energy storage units with application voltages below 1kV, a two-level topology energy storage converter is used for division;

[0013] For energy storage units with application voltage ranging from 1kV to 35kV, energy storage converters with three-level topology are used for division;

[0014] For energy storage units with application voltage ranging from 36kV to 220kV, energy storage converters with cascaded H-bridge topology are used for division.

[0015] As a preferred embodiment, the static indicators include the battery state of charge SOC and the current maximum charge and discharge power P c , P d ;

[0016] The dynamic indicators include the charging scheduling delay time t dc , discharge scheduling delay time t dd , charging rise time t rc , discharge rise time t rd , charging adjustment time t sc , discharge regulation time t sd , charging regulation overshoot σ c And the discharge regulation overshoot σ d ;

[0017] The parameter integration method of the equivalent model is:

[0018]

[0019] Where k represents the number of equivalent clusters, l represents the number of energy storage units in the equivalent energy storage unit, SOC represents the state of charge of the equivalent battery pack, P represents the equivalent charge and discharge power, t d Denotes the equivalent delay time, tr Denotes the equivalent rise time, t s represents the equivalent adjustment time, σ represents the equivalent overshoot, t rk Indicates the kth equivalent rise time, SOC k represents the state of charge of the kth equivalent battery pack, P k represents the kth equivalent charge and discharge power, t dk represents the kth equivalent delay time, t sk represents the kth equal value adjustment time, σ k represents the kth equivalent overshoot, σ i It represents the equivalent overshoot of the energy storage unit in the i-th equivalent energy storage unit, SOC i represents the charge state of the equivalent battery group of the energy storage unit in the i-th equivalent energy storage unit, t si represents the equivalent adjustment time of the energy storage unit in the i-th equivalent energy storage unit, t ri represents the equivalent rise time of the energy storage unit in the i-th equivalent energy storage unit, t di represents the equivalent delay time of the energy storage unit in the i-th equivalent energy storage unit, P i It represents the equivalent charging and discharging power of the energy storage unit in the i-th equivalent energy storage unit.

[0020] As a preferred implementation, the transfer function calculation method of the second-order system is:

[0021] Equivalent adjustment time t s The relationship between the equivalent overshoot σ and the frequency domain index of the second-order system is:

[0022]

[0023] Among them, ω n represents the natural frequency, ζ represents the damping ratio;

[0024] According to the relationship between the equivalent adjustment time ts and the equivalent overshoot σ and the frequency domain index of the second-order system, the natural frequency ωn is expressed as:

[0025]

[0026] The damping ratio ζ is expressed as:

[0027]

[0028] Substituting the natural frequency ωn and damping ratio ζ into the second-order system, the transfer function of the second-order system is obtained:

[0029]

[0030] Where Φ(s) represents the transfer function of the second-order system, s represents a complex variable, and s2 represents the score of the complex variable and σ represents the overshoot.

[0031] On the other hand, the present invention also provides a system for realizing an equivalent model of a super-large-scale energy storage power station based on weighted average, comprising:

[0032] An energy storage unit division module, which divides the energy storage unit using several energy storage converters with different topologies according to the application voltage range of the energy storage unit;

[0033] The parameter integration module collects the dynamic and static indicators of the energy storage unit at regular intervals according to the preset time, and uses the clustering algorithm to average and weight the dynamic and static indicators of the energy storage converters with the same topology structure into the parameters of the equivalent model;

[0034] The equivalent modeling module obtains the dynamic characteristic index of the system under the step response according to the parameters of the equivalent model, substitutes the dynamic characteristic index into the second-order system to obtain the transfer function of the second-order system; and represents the operating characteristics of the energy storage unit according to the transfer function.

[0035] On the other hand, the present invention further provides an electronic device having a computer program stored thereon, wherein when the computer program is executed by a processor, the method for realizing an equivalent model of a very large-scale energy storage power station based on weighted average as described in any embodiment of the present invention is implemented.

[0036] On the other hand, the present invention also provides a computer-readable medium for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method for realizing an equivalent model of an ultra-large-scale energy storage power station based on weighted average as described in any embodiment of the present invention.

[0037] The present invention has the following beneficial effects:

[0038] The present invention makes a more detailed division by considering the energy storage capacity of the energy storage unit and the energy storage converters of different topological structures. This division method enables each energy storage unit to be modeled according to its actual characteristics and topological structure, thereby improving the accuracy of the model. The dynamic indicators and static indicators of the energy storage converters of the same topological structure are weighted averagely and integrated into the parameters of the equivalent model. This method can more accurately reflect the actual operating characteristics of the energy storage unit in the power system and provide a more reliable basis for system simulation and stability analysis. The equivalent modeling method is adopted to integrate multiple energy storage units with similar characteristics into an equivalent model, thereby reducing the number of nodes and variables in the simulation model. This not only reduces the computational complexity of the simulation, but also improves the efficiency of the simulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1The present invention is implemented in the form of a flow chart.

[0040] Figure 2 This is a topological diagram of the two-level energy storage converter of the present invention.

[0041] Figure 3 This is a topological diagram of the three-level energy storage converter of the present invention.

[0042] Figure 4 This is a topological diagram of the cascaded H-bridge energy storage converter of the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] It should be understood that the step numbers used in this document are only for convenience of description and are not intended to limit the order in which the steps are executed.

[0045] It should be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0046] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0047] The term "and / or" means and includes any and all possible combinations of one or more of the associated listed items.

[0048] Embodiment 1:

[0049] See also Figure 1-4 The present invention provides a method for realizing an equivalent model of a super-large-scale energy storage power station based on weighted average, comprising the following steps:

[0050] According to the application voltage range of the energy storage unit, the energy storage unit is divided using several energy storage converters with different topologies;

[0051] The dynamic and static indicators of the energy storage unit are collected regularly according to the preset time, and the dynamic and static indicators of the energy storage converters with the same topology are weighted and averaged to form the parameters of the equivalent model using the clustering algorithm;

[0052] According to the parameters of the equivalent model, the dynamic characteristic index of the system under the step response is obtained, and the dynamic characteristic index is substituted into the second-order system to obtain the transfer function of the second-order system; the operation characteristics of the energy storage unit are represented according to the transfer function.

[0053] As a preferred embodiment, the topology structure includes a two-level topology structure, a three-level topology structure and a cascaded H-bridge topology structure;

[0054] refer to Figure 2 The topological structure diagram of the two-level energy storage converter is shown in Figure 1. For energy storage units with an application voltage lower than 1kV, a two-level topological structure energy storage converter is used for division;

[0055] Among them, e a 、e b 、e c Indicates the power grid, S1, S2, S3, S4, S5, S6 indicate the power device IGBT switch tube, U dc Indicates DC bus;

[0056] refer to Figure 3 The topological structure diagram of the three-level energy storage converter is shown in Figure 1. For energy storage units with an application voltage range of 1kV to 35kV, a three-level topological structure energy storage converter is used for division;

[0057] Among them, C1 and C2 represent capacitors, O represents the neutral point, D1 and D2 represent diodes, S a1 , S a2 , S a3 , S a4 , S b1 , S b2 , S b3 , S b4 , S c1 , S c2 , S c3 , S c4 It represents the power device IGBT switch tube, and T represents the isolation transformer.

[0058] refer to Figure 4 The topology diagram of the cascaded H-bridge energy storage converter is divided into energy storage converters with cascaded H-bridge topology for energy storage units with application voltage range from 36kV to 220kV.

[0059] Among them, U sa , U sb , U screpresents the grid power supply, C represents the DC voltage capacitor, L represents the filter using inductance to filter out harmonics, U dc Indicates DC voltage.

[0060] This division allows each energy storage unit to be modeled according to its actual characteristics and topological structure, thereby improving the accuracy of the model.

[0061] As a preferred embodiment, the static indicators include the battery state of charge SOC and the current maximum charge and discharge power P c , P d ;

[0062] The dynamic indicators include the charging scheduling delay time t dc , discharge scheduling delay time t dd , charging rise time t rc , discharge rise time t rd , charging adjustment time t sc , discharge regulation time t sd , charging regulation overshoot σ c And the discharge regulation overshoot σ d ;

[0063] The parameter integration method of the equivalent model is:

[0064]

[0065] Where k represents the number of equivalent clusters, l represents the number of energy storage units in the equivalent energy storage unit, SOC represents the state of charge of the equivalent battery pack, P represents the equivalent charge and discharge power, t d Denotes the equivalent delay time, t r represents the equivalent rise time, t s represents the equivalent adjustment time, σ represents the equivalent overshoot, t r x represents the kth equivalent rise time, SOC k represents the charge state of the kth equivalent battery pack, P k represents the kth equivalent charge and discharge power, t dk represents the kth equivalent delay time, t sk represents the kth equal value adjustment time, σ k represents the kth equivalent overshoot, σ i It represents the equivalent overshoot of the energy storage unit in the i-th equivalent energy storage unit, SOC i represents the charge state of the equivalent battery group of the energy storage unit in the i-th equivalent energy storage unit, t si represents the equivalent adjustment time of the energy storage unit in the i-th equivalent energy storage unit, t ri represents the equivalent rise time of the energy storage unit in the i-th equivalent energy storage unit, t direpresents the equivalent delay time of the energy storage unit in the i-th equivalent energy storage unit, P i Represents the equivalent charge and discharge power of the energy storage unit in the i-th equivalent energy storage unit. The dynamic index and static index of the energy storage converter with the same topology are weighted averagely integrated into the parameters of the equivalent model. This method can more accurately reflect the actual operating characteristics of the energy storage unit in the power system and provide a more reliable basis for system simulation and stability analysis.

[0066] As a preferred implementation, the transfer function calculation method of the second-order system is:

[0067] Equivalent adjustment time t s The relationship between the equivalent overshoot σ and the frequency domain index of the second-order system is:

[0068]

[0069] Among them, ω n represents the natural frequency, ζ represents the damping ratio;

[0070] According to the relationship between the equivalent adjustment time ts and the equivalent overshoot σ and the frequency domain index of the second-order system, the natural frequency ωn is expressed as:

[0071]

[0072] The damping ratio ζ is expressed as:

[0073]

[0074] Substituting the natural frequency ωn and the damping ratio ζ into the second-order system, the transfer function of the second-order system is obtained:

[0075]

[0076] Where Φ(s) represents the transfer function of the second-order system, s represents a complex variable, and s 2 represents the score of the complex variable, and σ represents the overshoot. The equivalent modeling method is adopted to integrate multiple energy storage units with similar characteristics into an equivalent model, thereby reducing the number of nodes and variables in the simulation model. This not only reduces the computational complexity of the simulation, but also improves the efficiency of the simulation. Through accurate dynamic characteristic indicators and transfer functions, the dynamic behavior of the system can be better understood, so as to design a more stable control strategy.

[0077] Embodiment 2:

[0078] The present invention also provides a system for realizing an equivalent model of a super-large-scale energy storage power station based on weighted average, comprising:

[0079] An energy storage unit division module, which divides the energy storage unit using several energy storage converters with different topologies according to the application voltage range of the energy storage unit;

[0080] The parameter integration module collects the dynamic and static indicators of the energy storage unit at regular intervals according to the preset time, and uses the clustering algorithm to average and weight the dynamic and static indicators of the energy storage converters with the same topology structure into the parameters of the equivalent model;

[0081] The equivalent modeling module obtains the dynamic characteristic index of the system under the step response according to the parameters of the equivalent model, substitutes the dynamic characteristic index into the second-order system to obtain the transfer function of the second-order system; and represents the operating characteristics of the energy storage unit according to the transfer function.

[0082] Embodiment three:

[0083] This embodiment provides an electronic device having a computer program stored thereon, wherein when the computer program is executed by a processor, the method for realizing an equivalent model of a super-large-scale energy storage power station based on weighted average as described in any embodiment of the present invention is implemented.

[0084] Embodiment 4:

[0085] This embodiment provides a computer-readable medium for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method for realizing an equivalent model of a ultra-large-scale energy storage power station based on weighted average as described in any embodiment of the present invention.

[0086] In the embodiments of the present application, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, c can be single or multiple.

[0087] Those of ordinary skill in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented in a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0088] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0089] In several embodiments provided in the present application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory; hereinafter referred to as: ROM), random access memory (Random Access Memory; hereinafter referred to as: RAM), disk or optical disk, and other media that can store program codes.

[0090] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for realizing an equivalent model of a super-large-scale energy storage power station based on weighted average, characterized in that: The following steps are involved: According to the application voltage range of the energy storage unit, the energy storage unit is divided using several energy storage converters with different topologies; The dynamic and static indicators of the energy storage unit are collected regularly according to the preset time, and the dynamic and static indicators of the energy storage converters with the same topology are weighted and averaged to form the parameters of the equivalent model using the clustering algorithm; According to the parameters of the equivalent model, the dynamic characteristic index of the system under the step response is obtained, and the dynamic characteristic index is substituted into the second-order system to obtain the transfer function of the second-order system; The operating characteristics of the energy storage unit are represented in terms of a transfer function.

2. The method for realizing an equivalent model of a super-large-scale energy storage power station based on weighted average according to claim 1 is characterized in that: The topological structures include a two-level topological structure, a three-level topological structure and a cascaded H-bridge topological structure; For energy storage units with application voltages below 1kV, a two-level topology energy storage converter is used for division; For energy storage units with application voltage ranging from 1kV to 35kV, energy storage converters with three-level topology are used for division; For energy storage units with application voltage ranging from 36kV to 220kV, energy storage converters with cascaded H-bridge topology are used for division.

3. The method for realizing an equivalent model of a super-large-scale energy storage power station based on weighted average according to claim 1 is characterized in that: The static indicators include the battery state of charge SOC and the current maximum charge and discharge power P c , P d ; The dynamic indicators include the charging scheduling delay time t dc , discharge scheduling delay time t dd , charging rise time t rc , discharge rise time t rd , charging adjustment time t sc , discharge regulation time t sd , charging regulation overshoot σ c And the discharge regulation overshoot σ d ; The parameter integration method of the equivalent model is: Where k represents the number of equivalent clusters, l represents the number of energy storage units in the equivalent energy storage unit, SOC represents the state of charge of the equivalent battery pack, P represents the equivalent charge and discharge power, t d Denotes the equivalent delay time, t r represents the equivalent rise time, t s represents the equivalent adjustment time, σ represents the equivalent overshoot, t rk Indicates the kth equivalent rise time, SOC k represents the state of charge of the kth equivalent battery pack, P k represents the kth equivalent charge and discharge power, t dk represents the kth equivalent delay time, t sk represents the kth equal value adjustment time, σ k represents the kth equivalent overshoot, σ i It represents the equivalent overshoot of the energy storage unit in the i-th equivalent energy storage unit, SOC i represents the charge state of the equivalent battery group of the energy storage unit in the i-th equivalent energy storage unit, t si represents the equivalent adjustment time of the energy storage unit in the i-th equivalent energy storage unit, t ri represents the equivalent rise time of the energy storage unit in the i-th equivalent energy storage unit, t di represents the equivalent delay time of the energy storage unit in the i-th equivalent energy storage unit, P i It represents the equivalent charging and discharging power of the energy storage unit in the i-th equivalent energy storage unit.

4. The method for realizing an equivalent model of a super-large-scale energy storage power station based on weighted average according to claim 3 is characterized in that: The transfer function calculation method of the second-order system is: Equivalent adjustment time t s The relationship between the equivalent overshoot σ and the frequency domain index of the second-order system is: Among them, ω n represents the natural frequency, ζ represents the damping ratio; According to the relationship between the equivalent adjustment time ts and the equivalent overshoot σ and the frequency domain index of the second-order system, the natural frequency ωn is expressed as: The damping ratio ζ is expressed as: Substituting the natural frequency ωn and the damping ratio ζ into the second-order system, the transfer function of the second-order system is obtained: Where Φ(s) represents the transfer function of the second-order system, s represents a complex variable, and s 2 represents the score of the complex variable and σ represents the overshoot.

5. A system for realizing an equivalent model of a super-large-scale energy storage power station based on weighted average, characterized in that: include: An energy storage unit division module, which divides the energy storage unit using several energy storage converters with different topologies according to the application voltage range of the energy storage unit; The parameter integration module collects the dynamic and static indicators of the energy storage unit at regular intervals according to the preset time, and uses the clustering algorithm to average and weight the dynamic and static indicators of the energy storage converters with the same topology structure into the parameters of the equivalent model; The equivalent modeling module obtains the dynamic characteristic index of the system under the step response according to the parameters of the equivalent model, and substitutes the dynamic characteristic index into the second-order system to obtain the transfer function of the second-order system; The operating characteristics of the energy storage unit are represented in terms of a transfer function.

6. The system for realizing an equivalent model of a super-large-scale energy storage power station based on weighted average according to claim 5 is characterized in that: The energy storage unit division module has a topology structure including a two-level topology structure, a three-level topology structure and a cascaded H-bridge topology structure; For energy storage units with application voltages below 1kV, a two-level topology energy storage converter is used for division; For energy storage units with application voltage ranging from 1kV to 35kV, energy storage converters with three-level topology are used for division; For energy storage units with application voltage ranging from 36kV to 220kV, energy storage converters with cascaded H-bridge topology are used for division.

7. The system for realizing an equivalent model of a super-large-scale energy storage power station based on weighted average according to claim 5, characterized in that: The parameter integration module includes static indicators such as the battery state of charge SOC and the current maximum charge and discharge power P c , P d ; The dynamic indicators include the charging scheduling delay time t dc , discharge scheduling delay time t dd , charging rise time t rc , discharge rise time t rd , charging adjustment time t sc , discharge regulation time t sd , charging regulation overshoot σ c And the discharge regulation overshoot σ d ; The parameter integration method of the equivalent model is: Where k represents the number of equivalent clusters, l represents the number of energy storage units in the equivalent energy storage unit, SOC represents the state of charge of the equivalent battery pack, P represents the equivalent charge and discharge power, t d Denotes the equivalent delay time, t r Denotes the equivalent rise time, t s represents the equivalent adjustment time, σ represents the equivalent overshoot, t rk Indicates the kth equivalent rise time, SOC k represents the state of charge of the kth equivalent battery pack, P k represents the kth equivalent charge and discharge power, t dk represents the kth equivalent delay time, t sk represents the kth equal value adjustment time, σ k represents the kth equivalent overshoot, σ i It represents the equivalent overshoot of the energy storage unit in the i-th equivalent energy storage unit, SOC i represents the charge state of the equivalent battery group of the energy storage unit in the i-th equivalent energy storage unit, t si represents the equivalent adjustment time of the energy storage unit in the i-th equivalent energy storage unit, t ri represents the equivalent rise time of the energy storage unit in the i-th equivalent energy storage unit, t di represents the equivalent delay time of the energy storage unit in the i-th equivalent energy storage unit, P i It represents the equivalent charging and discharging power of the energy storage unit in the i-th equivalent energy storage unit.

8. The system for realizing an equivalent model of a super-large-scale energy storage power station based on weighted average according to claim 7, characterized in that: In the modeling module, the transfer function calculation method of the second-order system is: Equivalent adjustment time t s The relationship between the equivalent overshoot σ and the frequency domain index of the second-order system is: Among them, ω n represents the natural frequency, ζ represents the damping ratio; According to the relationship between the equivalent adjustment time ts and the equivalent overshoot σ and the frequency domain index of the second-order system, the natural frequency ωn is expressed as: The damping ratio ζ is expressed as: Substituting the natural frequency ωn and the damping ratio ζ into the second-order system, the transfer function of the second-order system is obtained: Where Φ(s) represents the transfer function of the second-order system, s represents a complex variable, and s 2 represents the score of the complex variable and σ represents the overshoot.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method for realizing an equivalent model of a super-large-scale energy storage power station based on weighted average as described in any one of claims 1 to 4 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for realizing an equivalent model of a super-large-scale energy storage power station based on weighted average as described in any one of claims 1 to 4 is implemented.

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