Topology and parameter optimization method of network construction type energy storage main circuit

Abstract

The invention relates to the technical field of network construction type energy storage, in particular to a network construction type energy storage main circuit topology and a parameter optimization method. Comprising the following steps: establishing a plurality of energy storage scenes according to historical parameters, and sequentially generating an optimization sub-model of each energy storage scene; acquiring demand parameters of the energy storage system, and generating a plurality of primary circuit structures according to the demand parameters; selecting a target optimization sub-model according to the demand parameters, and generating an operation evaluation value of each primary circuit structure according to the target optimization sub-model; setting a main circuit topological structure according to all the operation evaluation values; a plurality of energy storage scenes are established according to different construction sites facing the energy storage system, an optimization sub-model of each energy storage scene is set, and the corresponding optimization sub-model is selected according to actual requirements to perform optimization processing on all primary circuit structures, so that the fitness of a main circuit topological structure is improved, the overall operation and maintenance cost of the energy storage system is reduced, and the energy storage efficiency is improved. And the service life of the energy storage system is prolonged.

Description

Technical Field

[0001] The present application relates to the technical field of grid-type energy storage, and in particular to a main circuit topology and parameter optimization method for a grid-type energy storage. Background Art

[0002] In the high-voltage direct-mounted energy storage systems currently being demonstrated in engineering projects, the PCS units all use IGBT devices. The IGBT has large on-state resistance and high switching losses, and the overall converter loss is large, which cannot maximize the advantages of the high-voltage direct-mounted energy storage system.

[0003] In conventional solutions, the PCS unit uses a single-stage AC / DC converter, and the DC side is directly connected to the energy storage battery through a filter inductor. The parameters of the energy storage battery must match the DC design voltage of the PCS unit, and the specifications of the energy storage battery are very limited. For example, in a 35kV direct-mounted energy storage solution, when the PCS unit uses 1700VIGBT, 42 units are connected in series per phase, and the average DC voltage of each PCS unit is designed to be 850V. Regardless of the capacity of the complete energy storage system, the corresponding energy storage battery in the PCS unit requires at least 252 lithium batteries (3.2V) in series, which cannot be flexibly adjusted. In addition, the overall cost is high. Summary of the Invention

[0004] The purpose of this application is: to solve the above technical problems, this application provides a grid-type energy storage main circuit topology and parameter optimization method, aiming to improve the control efficiency of the energy storage system and reduce the operation and maintenance cost of the energy storage system.

[0005] In some embodiments of the present application, a DC / DC link is added to the PCS unit, and flexible access of batteries of different voltages and types to the PCS is achieved by adjusting the DC / DC operating parameters, thereby reducing the number of energy storage batteries connected in series and thus reducing battery loss. Active balancing of battery groups between different PCS units can be achieved through control strategies, thereby improving the service life of the overall energy storage system and reducing overall operation and maintenance costs.

[0006] In some embodiments of the present application, multiple energy storage scenarios are established according to the different construction sites faced by the energy storage system, and an optimization sub-model is set for each energy storage scenario. According to actual needs, the corresponding optimization sub-model is selected to optimize all primary circuit structures, thereby improving the adaptability of the main circuit topology structure, reducing the overall operation and maintenance cost of the energy storage system, and increasing the operating life of the energy storage system.

[0007] This ensures the control efficiency of the energy storage system and reduces the operation and maintenance costs of the energy storage system.

[0008] In some embodiments of the present application, a method for optimizing the topology and parameters of a main circuit of a grid-type energy storage is provided, which is characterized by comprising:

[0009] Establish multiple energy storage scenarios based on historical parameters, and sequentially generate optimal sub-models for each energy storage scenario;

[0010] Obtaining demand parameters of the energy storage system and generating multiple primary circuit structures according to the demand parameters;

[0011] Select a target optimization sub-model according to the required parameters, and generate an operation evaluation value of each primary circuit structure according to the target optimization sub-model;

[0012] Set the main circuit topology according to all operational evaluation values;

[0013] Among them, multiple energy storage scenarios are set, including:

[0014] Establish energy storage scenario sequence A, A=(a1,a2...a i ...a n ), where a i is the i-th energy storage scenario; n is the number of energy storage scenarios.

[0015] In some embodiments of the present application, generating an optimal sub-model for each energy storage scenario includes:

[0016] Establish an initial evaluation model;

[0017] The initial evaluation model includes multiple construction indicators and multiple operation indicators;

[0018] Set a in sequence according to the energy storage scenario sequence A i For target energy storage scenarios;

[0019] Generate an optimization strategy in the target energy storage scenario based on historical parameters;

[0020] Generate an optimization sub-model for the target energy storage scenario based on the optimization strategy and the initial evaluation model; and sequentially generate optimization sub-models for each energy storage scenario;

[0021] Establish the optimal sub-model sequence C, C=(c1,c2...c i ...c n ), where c i is the optimal sub-model for the i-th energy storage scenario.

[0022] In some embodiments of the present application, generating an optimization strategy in a target energy storage scenario includes:

[0023] Obtain all construction indicators in the initial evaluation model;

[0024] Generate the influencing factors of each construction indicator in the target energy storage scenario;

[0025] Establish the impact factor series G1, G1 = (β 11 , β12 ...β 1i ...β 1θ1 ), where β 1i The influencing factor of the i-th constructed indicator; θ1 is the number of constructed indicators;

[0026] Obtain all operating indicators in the initial evaluation indicators;

[0027] Generate influencing factors of various operating indicators in the target energy storage scenario;

[0028] Establish the impact factor series G2, G2 = (β 21 , β 22 ...β 2i .....β 2θ2 ), where β 2i The influencing factor of the i-th operating indicator; θ2 is the number of operating indicators;

[0029] Generate an optimization strategy for the target energy storage scenario based on the impact factor sequence G1 and the impact impression sequence G2.

[0030] In some embodiments of the present application, generating multiple first-level circuit structures includes:

[0031] Generate multiple constraints based on the demand parameters of the energy storage system;

[0032] Establish a constraint model based on all constraint conditions;

[0033] Generate multiple first-level circuit structures based on the feasible domain of the constraint model;

[0034] A single primary circuit structure includes multiple PCS units, where the PCS unit is composed of two-stage converters: AC / DC and DC / DC;

[0035] Establish a first-level circuit structure sequence H, H=(h1, h2...h i ...h r ), where hi is the i-th primary circuit structure; r is the number of primary circuit structures.

[0036] In some embodiments of the present application, the target optimization sub-model is selected according to the demand parameters, including:

[0037] Generate similarity evaluation values ​​for each energy storage scenario based on demand parameters;

[0038] Establish similarity evaluation value sequence F, F=(f1,f2...f i ...f n ), where f i is the similarity evaluation value between the energy storage system and the i-th energy storage scenario;

[0039] Select the maximum value f in the similar evaluation value sequence f max The optimization sub-model corresponding to the energy storage scenario is the target optimization sub-model.

[0040] In some embodiments of the present application, generating similarity evaluation values ​​for various energy storage scenarios includes:

[0041] Set a in sequence according to the energy storage scenario sequence A i For target energy storage scenarios;

[0042] Generate a similarity evaluation value f between the energy storage system and the target energy storage scenario;

[0043]

[0044] Where k is the number of evaluation indicators; μi is the influencing factor of the i-th evaluation indicator; ji is the reference value of the i-th evaluation indicator generated based on the demand parameters of the energy storage system; is the reference value of the i-th evaluation indicator in the target energy storage scenario; and U is the conversion coefficient.

[0045] In some embodiments of the present application, generating an operation evaluation value of each primary circuit structure includes:

[0046] Set h in sequence according to the first-level circuit structure sequence H i is the target first-level circuit structure;

[0047] Generate simulation parameters of the target primary circuit structure according to a preset simulation model;

[0048] Obtaining the construction parameters of the target first-level circuit structure;

[0049] Generate an operation evaluation value b of the target primary circuit structure according to the target optimization sub-model;

[0050] sequentially generating an operational evaluation value for each primary circuit structure;

[0051] Establish the running evaluation value sequence B, B=(b1,b2...b i ...b r ), where b i is the operation evaluation value of the i-th primary circuit structure.

[0052] In some embodiments of the present application, generating the operation evaluation value b includes:

[0053]

[0054] Among them, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; θ1 is the number of indicators to be built; β 1iis the impact factor of the i-th construction indicator generated based on the target optimization sub-model; d 1i is the reference value of the i-th construction index generated based on the construction parameters of the target first-level circuit structure; θ2 is the number of operating indicators; β 2i is the influencing factor of the i-th operating indicator generated based on the target optimization sub-model; d 2i is the reference value of the i-th operating index of the simulation parameters based on the target first-level circuit structure.

[0055] In some embodiments of the present application, the main circuit topology is set according to all operation evaluation values, including:

[0056] Preset operation evaluation value threshold B1;

[0057] Get the maximum value b in the running evaluation value sequence B max ;

[0058] If b max >B1, set the maximum value b max The corresponding primary circuit structure is the main circuit topology;

[0059] If b max <B1, generate a first-level correction instruction, correct the constraint model according to the first-level correction instruction, and set the main circuit topology structure according to the correction result.

[0060] Compared with the prior art, the method for optimizing the main circuit topology and parameters of a grid-type energy storage system according to the embodiment of the present application has the following advantages:

[0061] By adding a DC / DC link to the PCS unit, the flexible access of batteries of different voltages and types to the PCS can be achieved by adjusting the DC / DC operating parameters, reducing the number of energy storage batteries in series, thereby reducing battery loss. In addition, the active balancing of battery groups between different PCS units can be achieved through control strategies, thereby increasing the service life of the overall energy storage system and reducing overall operation and maintenance costs.

[0062] Based on the different construction sites faced by the energy storage system, multiple energy storage scenarios are established, and optimization sub-models are set for each energy storage scenario. According to actual needs, the corresponding optimization sub-model is selected to optimize the entire primary circuit structure, improve the adaptability of the main circuit topology structure, reduce the overall operation and maintenance cost of the energy storage system, and extend the operating life of the energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a flow chart of a method for optimizing the main circuit topology and parameters of a grid-type energy storage system in a preferred embodiment of the present application. DETAILED DESCRIPTION

[0064] The following embodiments are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0065] In the description of this application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on this application.

[0066] The terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, features specified as "first" and "second" may explicitly or implicitly include one or more of such features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0067] In the description of this application, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood broadly. For example, they may refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in this application based on the specific circumstances.

[0068] like Figure 1 As shown, a method for optimizing the topology and parameters of a grid-type energy storage main circuit according to a preferred embodiment of the present application includes:

[0069] S101: Establish multiple energy storage scenarios based on historical parameters, and sequentially generate optimal sub-models for each energy storage scenario;

[0070] S102: Obtaining demand parameters of the energy storage system, and generating multiple primary circuit structures according to the demand parameters;

[0071] S103: selecting a target optimization sub-model according to the demand parameters, and generating an operation evaluation value of each primary circuit structure according to the target optimization sub-model;

[0072] S104: Setting the main circuit topology according to all operation evaluation values;

[0073] Among them, multiple energy storage scenarios are set, including:

[0074] Establish energy storage scenario sequence A, A=(a1,a2...a i ...a n ), where a i is the i-th energy storage scenario; n is the number of energy storage scenarios.

[0075] Specifically, according to the construction scenario faced by the energy storage scenario, several evaluation indicators are generated, and the evaluation indicators include but are limited to multiple parameters such as construction area, construction site, operating environment, etc. Multiple energy storage scenarios are generated based on all the evaluation indicators.

[0076] Specifically, the evaluation index parameters in each energy storage scenario are not exactly the same. Each energy storage scenario has different optimization directions for the energy storage system, and the specific classification basis of the energy storage system can be set based on historical parameters.

[0077] Specifically, the optimal sub-model for each energy storage scenario is generated, including:

[0078] Establish an initial evaluation model;

[0079] The initial evaluation model includes multiple construction indicators and multiple operation indicators;

[0080] Set a in sequence according to the energy storage scenario sequence A i For target energy storage scenarios;

[0081] Generate an optimization strategy in the target energy storage scenario based on historical parameters;

[0082] Generate an optimization sub-model for the target energy storage scenario based on the optimization strategy and the initial evaluation model; and sequentially generate optimization sub-models for each energy storage scenario;

[0083] Establish the optimal sub-model sequence C, C=(c1,c2...c i ...c n ), where c i is the optimal sub-model for the i-th energy storage scenario.

[0084] Specifically, the optimization objectives vary across energy storage scenarios. For example, in scenarios with large construction sites, high maintenance costs, and highly volatile operating environments, the primary optimization objectives are high system stability and low failure rates, thereby reducing maintenance times. In areas with large construction sites, low maintenance costs, and stable operating environments, the primary optimization objectives are cost and service life, thereby reducing overall costs.

[0085] Specifically, according to the specific optimization direction of each energy storage scenario, a corresponding optimization sub-model is generated.

[0086] Specifically, the optimization strategy for the target energy storage scenario is generated, including:

[0087] Obtain all construction indicators in the initial evaluation model;

[0088] Generate the influencing factors of each construction indicator in the target energy storage scenario;

[0089] Establish the impact factor series G1, G1 = (β 11 , β 12 ...β 1i ...β 1θ1 ), where β 1i The influencing factor of the i-th constructed indicator; θ1 is the number of constructed indicators;

[0090] Obtain all operating indicators in the initial evaluation indicators;

[0091] Generate influencing factors of various operating indicators in the target energy storage scenario;

[0092] Establish the impact factor series G2, G2 = (β 21 , β 22 ...β 2i ...β 2θ2 ), where β 2i The influencing factor of the i-th operating indicator; θ2 is the number of operating indicators;

[0093] Generate an optimization strategy for the target energy storage scenario based on the impact factor sequence G1 and the impact impression sequence G2.

[0094] Specifically, by dynamically adjusting the influencing factors of various construction indicators and various operating indicators, different optimization directions can be met, making the evaluation of each primary circuit structure more accurate.

[0095] It is understandable that in the above embodiment, multiple energy storage scenarios are established according to the different construction sites faced by the energy storage system, and an optimization sub-model is set for each energy storage scenario. According to actual needs, the corresponding optimization sub-model is selected to optimize all primary circuit structures to improve the adaptability of the main circuit topology structure.

[0096] In a preferred embodiment of the present application, multiple primary circuit structures are generated, including:

[0097] Generate multiple constraints based on the demand parameters of the energy storage system;

[0098] Establish a constraint model based on all constraint conditions;

[0099] Generate multiple first-level circuit structures based on the feasible domain of the constraint model;

[0100] A single primary circuit structure includes multiple PCS units, where the PCS unit is composed of two-stage converters: AC / DC and DC / DC;

[0101] Establish a first-level circuit structure sequence H, H=(h1, h2...h i ...h r ), where hi is the i-th primary circuit structure; r is the number of primary circuit structures.

[0102] Specifically, after adding a DC / DC link to the PCS unit, batteries of different voltages and types can be flexibly connected to the PCS by adjusting the DC / DC operating parameters, making the PCS converter more versatile.

[0103] Specifically, the number of PCS units in each primary circuit structure is different, and the device parameters of each converter in the PCS unit are also exactly the same.

[0104] Specifically, the constraints include but are not limited to equipment floor space constraints, series battery cell number constraints, construction cost constraints, and other constraints. By solving the constraint model, the corresponding feasible domain is generated, thereby generating multiple first-level circuit structures.

[0105] It is understandable that in the above embodiment, a DC / DC link is added to the PCS unit, and the flexible access of batteries of different voltages and types to the PCS is achieved by adjusting the DC / DC operating parameters, thereby reducing the number of energy storage batteries connected in series, thereby reducing battery loss, and actively balancing the battery groups between different PCS units through control strategies, thereby improving the service life of the overall energy storage system and reducing the overall operation and maintenance costs.

[0106] In a preferred embodiment of the present application, the target optimization sub-model is selected according to the demand parameters, including:

[0107] Generate similarity evaluation values ​​for each energy storage scenario based on demand parameters;

[0108] Establish similarity evaluation value sequence F, F=(f1,f2...f i ...f n ), where f i is the similarity evaluation value between the energy storage system and the i-th energy storage scenario;

[0109] Select the maximum value f in the similar evaluation value sequence f max The optimization sub-model corresponding to the energy storage scenario is the target optimization sub-model.

[0110] Specifically, similar evaluation values ​​for various energy storage scenarios are generated, including:

[0111] Set a in sequence according to the energy storage scenario sequence A i For target energy storage scenarios;

[0112] Generate a similarity evaluation value f between the energy storage system and the target energy storage scenario;

[0113]

[0114] Where k is the number of evaluation indicators; μi is the influencing factor of the i-th evaluation indicator; ji is the reference value of the i-th evaluation indicator generated based on the demand parameters of the energy storage system; is the reference value of the i-th evaluation indicator in the target energy storage scenario; and U is the conversion coefficient.

[0115] Specifically, by presetting the conversion coefficient, the larger the similarity evaluation value is, the greater the similarity between the current energy storage system construction scenario and the target energy storage scenario is.

[0116] Specifically, the evaluation indicators include but are not limited to the construction area, construction site, operating environment and other parameters.

[0117] It is understandable that in the above embodiment, the demand parameters of the energy storage system are processed to generate real-time values ​​of various evaluation indicators, and similar evaluation values ​​are generated for various energy storage scenarios, so as to quickly select the target optimization sub-model and improve the evaluation efficiency and evaluation accuracy of each primary circuit structure.

[0118] In a preferred embodiment of the present application, generating an operation evaluation value of each primary circuit structure includes:

[0119] Set h in sequence according to the first-level circuit structure sequence H i is the target first-level circuit structure;

[0120] Generate simulation parameters of the target primary circuit structure according to a preset simulation model;

[0121] Obtaining the construction parameters of the target first-level circuit structure;

[0122] Generate an operation evaluation value b of the target primary circuit structure according to the target optimization sub-model;

[0123] sequentially generating an operational evaluation value for each primary circuit structure;

[0124] Establish the running evaluation value sequence B, B=(b1,b2...b i ...b r ), where b i is the operation evaluation value of the i-th primary circuit structure.

[0125] Specifically, generating the running evaluation value b includes:

[0126]

[0127] Among them, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; θ1 is the number of indicators to be built; β 1i is the impact factor of the i-th construction indicator generated based on the target optimization sub-model; d 1i is the reference value of the i-th construction index generated based on the construction parameters of the target first-level circuit structure; θ2 is the number of operating indicators; β 2i is the influencing factor of the i-th operating indicator generated based on the target optimization sub-model; d 2i is the reference value of the i-th operating index of the simulation parameters based on the target first-level circuit structure.

[0128] Specifically, all parameters in the model are normalized by presetting a first fixed coefficient and a second fixed coefficient, so that each parameter is within the same value range.

[0129] Specifically, construction indicators include but are not limited to parameters such as land cost, number of PCS units, and construction cost, while operation indicators include but are not limited to parameters such as expected life, failure frequency, control cost, and maintenance cost.

[0130] Specifically, the larger the operation evaluation value, the higher the feasibility and adaptability of the corresponding first-level circuit structure.

[0131] It can be understood that in the above embodiment, the corresponding optimization sub-model is selected according to actual needs to optimize all primary circuit structures, thereby improving the adaptability of the main circuit topology structure, reducing the overall operation and maintenance cost of the energy storage system, and increasing the operating life of the energy storage system.

[0132] Specifically, the main circuit topology is set according to all operational evaluation values, including:

[0133] Preset operation evaluation value threshold B1;

[0134] Get the maximum value b in the running evaluation value sequence B max ;

[0135] If b max >B1, set the maximum value b max The corresponding primary circuit structure is the main circuit topology;

[0136] If b max <B1, generate a first-level correction instruction, correct the constraint model according to the first-level correction instruction, and set the main circuit topology structure according to the correction result.

[0137] Specifically, the operation evaluation value threshold can be set according to historical parameters. max<B1 indicates that all current primary circuit structures are not suitable for the current energy storage system. The original constraint model needs to be modified and a new primary circuit structure needs to be regenerated.

[0138] According to the first concept of the present application, a DC / DC link is added to the PCS unit, and flexible access of batteries of different voltages and types to the PCS is achieved by adjusting the DC / DC operating parameters, thereby reducing the number of energy storage batteries in series and thus reducing battery loss. Active balancing of battery groups between different PCS units can be achieved through control strategies, thereby increasing the service life of the overall energy storage system and reducing overall operation and maintenance costs.

[0139] According to the second concept of the present application, multiple energy storage scenarios are established according to the different construction sites faced by the energy storage system, and an optimization sub-model is set for each energy storage scenario. According to actual needs, the corresponding optimization sub-model is selected to optimize the entire primary circuit structure, thereby improving the adaptability of the main circuit topology structure, reducing the overall operation and maintenance cost of the energy storage system, and increasing the operating life of the energy storage system.

[0140] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and replacements can be made without departing from the technical principles of the present application. These improvements and replacements should also be regarded as the scope of protection of the present application.

Claims

1. A method for optimizing the main circuit topology and parameters of a grid-type energy storage system, characterized in that: include: Establish multiple energy storage scenarios based on historical parameters, and generate optimal sub-models for each energy storage scenario in turn; Obtaining demand parameters of the energy storage system, and generating multiple primary circuit structures according to the demand parameters; A target optimization sub-model is selected according to the demand parameters, and an operation evaluation value of each primary circuit structure is generated according to the target optimization sub-model; Set the main circuit topology according to all operational evaluation values; Among them, multiple energy storage scenarios are set, including: Establish energy storage scenario sequence A, A=(a1,a2...a i ...a n ), where a i is the i-th energy storage scenario; n is the number of energy storage scenarios.

2. The method for optimizing the main circuit topology and parameters of the grid-type energy storage system according to claim 1, characterized in that: Generate optimal sub-models for each energy storage scenario, including: Establishing the initial evaluation model; The initial evaluation model includes multiple construction indicators and multiple operation indicators; According to the energy storage scenario sequence A, set a i For target energy storage scenarios; Generate an optimization strategy in the target energy storage scenario based on historical parameters; Generate an optimization sub-model for the target energy storage scenario based on the optimization strategy and the initial evaluation model; Generate the optimal sub-model for each energy storage scenario in turn; Establish the optimal sub-model sequence C, C = (c1, c2...c i ...c n ), where c i It is the optimal sub-model for the i-th energy storage scenario.

3. The method for optimizing the main circuit topology and parameters of the grid-type energy storage according to claim 2, characterized in that: Generate an optimization strategy for the target energy storage scenario, including: Obtain all construction indicators in the initial evaluation model; Generate the influencing factors of each construction indicator in the target energy storage scenario; Establish the impact factor series G1, G1 = (β 11 , β 12 ...β 1i ...β 1θ1 ), where β 1i The influencing factor of the i-th constructed indicator; θ1 is the number of constructed indicators; Obtain all operating indicators in the initial evaluation indicators; Generate influencing factors of various operating indicators in the target energy storage scenario; Establish the impact factor series G2, G2 = (β 21 , β 22 ...β 2i ...β 2θ2 ), where β 2i The influencing factor of the i-th operating indicator; θ2 is the number of operating indicators; Generate an optimization strategy for the target energy storage scenario based on the impact factor sequence G1 and the impact impression sequence G2.

4. The method for optimizing the main circuit topology and parameters of the grid-type energy storage according to claim 2, characterized in that: Generate multiple first-level circuit structures, including: Generate multiple constraints based on demand parameters of the energy storage system; Establish a constraint model based on all constraint conditions; Generate multiple first-level circuit structures based on the feasible domain of the constraint model; A single primary circuit structure includes multiple PCS units, wherein the PCS unit is composed of two-stage converters, AC / DC and DC / DC; Establish a first-level circuit structure sequence H, H = (h1, h2...h i ...h r ), where hi is the i-th primary circuit structure; r is the number of primary circuit structures.

5. The method for optimizing the main circuit topology and parameters of the grid-type energy storage according to claim 4, characterized in that: Select the target optimization sub-model according to the required parameters, including: Generate similarity evaluation values ​​for each energy storage scenario based on demand parameters; Establish a similar evaluation value sequence F, F = (f1, f2...f i ...f n ), where f i is the similarity evaluation value between the energy storage system and the i-th energy storage scenario; Select the maximum value f in the similar evaluation value sequence f max The optimization sub-model corresponding to the energy storage scenario is the target optimization sub-model.

6. The method for optimizing the main circuit topology and parameters of the grid-type energy storage according to claim 5, characterized in that: Generate similarity ratings for various energy storage scenarios, including: According to the energy storage scenario sequence A, set a i For target energy storage scenarios; Generate a similarity evaluation value f between the energy storage system and the target energy storage scenario; Among them, k is the number of evaluation indicators; μi is the influencing factor of the i-th evaluation indicator; ji is the reference value of the i-th evaluation indicator generated based on the demand parameters of the energy storage system; is the reference value of the i-th evaluation indicator in the target energy storage scenario; and U is the conversion coefficient.

7. The method for optimizing the main circuit topology and parameters of the grid-type energy storage according to claim 6, characterized in that: Generate operational evaluation values ​​for each primary circuit structure, including: According to the first-level circuit structure sequence H, h is set in sequence i is the target primary circuit structure; Generate simulation parameters of a target primary circuit structure according to a preset simulation model; Obtaining the construction parameters of the target primary circuit structure; Generate an operation evaluation value b of the target primary circuit structure according to the target optimization sub-model; Generate the operation evaluation value of each primary circuit structure in sequence; Establish the running evaluation value series B, B = (b1, b2...b i ...b r ), where b i is the operation evaluation value of the i-th primary circuit structure.

8. The method for optimizing the main circuit topology and parameters of the grid-type energy storage according to claim 7, characterized in that: Generate a running evaluation value b, including: Among them, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; θ1 is the number of indicators to be built; β 1i is the influencing factor of the i-th construction indicator generated based on the target optimization sub-model; d 1i is the reference value of the i-th construction index generated based on the construction parameters of the target primary circuit structure; θ2 is the number of operating indicators; β 2i is the influencing factor of the i-th operating indicator generated based on the target optimization sub-model; d 2i is the reference value of the ith operating index of the simulation parameters based on the target primary circuit structure.

9. The method for optimizing the main circuit topology and parameters of a grid-type energy storage system according to claim 7, characterized in that: The main circuit topology is set according to all operational evaluation values, including: Preset operation evaluation value threshold B1; Get the maximum value b in the running evaluation value sequence B max ; If b max >B1, set the maximum value b max The corresponding primary circuit structure is the main circuit topology; If b max <B1, generate a first-level correction instruction, correct the constraint model according to the first-level correction instruction, and set the main circuit topology structure according to the correction result.

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