Method and system for optimizing new energy consumption capability

Abstract

The invention discloses a method and a system for optimizing new energy consumption capability, and belongs to the technical field of new energy capability evaluation. The method comprises the following steps: acquiring information data of a new energy system related to the absorption capability, and establishing an energy storage system operation optimization model for optimizing the new energy absorption capability according to the information data by using a pre-established learning model; performing decoupling simplification on the energy storage system operation optimization model to obtain a simplified linear programming model, and solving the linear programming model to obtain an optimal solution of the linear programming model; and based on the optimal solution, establishing an operation strategy for optimizing the new energy consumption capability, and controlling the operation of the new energy system according to the operation strategy to optimize the consumption capability of the new energy. The method is wide in application range, can optimize the new energy consumption capability of different scenes, is high in efficiency, and is simple in calculation.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy capacity assessment, and more specifically, to a method and system for optimizing new energy consumption capacity. Background Art

[0002] With the rapid development of renewable energy power generation, when the output characteristics and load characteristics differ greatly, large-scale renewable energy power generation has encountered power rationing problems in actual operation, hindering the realization of the dual carbon goals. How to improve the new energy absorption capacity is a problem that needs to be solved at present. Summary of the invention

[0003] In view of the above problems, the present invention proposes a method for optimizing the new energy consumption capacity, comprising:

[0004] Acquire information data related to the new energy system and the absorption capacity, and use a pre-established learning model to establish an energy storage system operation optimization model for optimizing the new energy absorption capacity based on the information data;

[0005] Decoupling and simplifying the energy storage system operation optimization model to obtain a simplified linear programming model, and solving the linear programming model to obtain an optimal solution to the linear programming model;

[0006] Based on the optimal solution, an operation strategy for optimizing the new energy absorption capacity is established, and the operation of the new energy system is controlled by the operation strategy to optimize the new energy absorption capacity.

[0007] Optional, information data, including: cost data and grid parameter data;

[0008] The cost data include: new energy power curtailment costs and energy storage charging costs;

[0009] The grid parameter data includes: grid power data, energy storage system power data, node voltage data, new energy power restriction data and energy storage charging and discharging data.

[0010] Optional, pre-built learning models including:

[0011] Obtain information data related to new energy systems and consumption capacity under various structures / scenarios, and establish energy storage system operation optimization models for new energy under various structures / scenarios based on the information data;

[0012] Using the information data as input data of a preset learning structure and using the output data of the energy storage system operation optimization model, dividing the input data and the output data into a training set and a verification set, inputting the training set into the preset learning structure for learning, so as to adjust the structural parameters of the preset learning structure and generate an initial learning model;

[0013] The initial learning model is verified with the verification set. If the verification result meets the preset requirements, the initial learning model is used as the learning model. If the verification result does not meet the preset requirements, the model parameters of the initial learning model are adjusted until the verification result meets the preset requirements.

[0014] Among them, when the similarity between the output data in the verification set and the output data obtained by the initial learning model based on the input data in the verification set is greater than 95%, the verification result meets the preset requirements.

[0015] Optionally, the energy storage system operation optimization model includes:

[0016] An objective function and constraints of the objective function;

[0017] The objective function includes: an objective function with the minimum total cost as the goal;

[0018] The constraints include: power balance constraints of the power grid, power constraints of the energy storage system, power constraints of the energy storage system, power constraints of the power grid, and power limitation variable constraints of new energy power sources.

[0019] Optionally, after establishing the energy storage system operation optimization model for optimizing the new energy consumption capacity, the method further includes:

[0020] Verifying the energy storage system operation optimization model to determine whether the energy storage system operation optimization model meets the requirements;

[0021] Wherein, if at least one of the objective function or constraint conditions in the energy storage system operation optimization model does not meet the requirements, then the energy storage system operation optimization model does not meet the requirements;

[0022] When the objective function of the energy storage system operation optimization model does not meet the requirements, the parameters of the objective function are adjusted according to the information data and the absorption capacity requirements of the new energy;

[0023] When the constraints of the energy storage system operation optimization model do not meet the requirements, the constraints are increased or decreased, or the parameters of the current constraints are adjusted according to the information data and the absorption capacity requirements of the new energy.

[0024] Optionally, the optimal solution of the linear programming model meets the upper and lower power limits of the actual energy storage of new energy.

[0025] Optionally, an operation strategy is adopted to meet the energy storage power constraints and capacity constraints with the goal of optimizing the new energy absorption capacity.

[0026] Optionally, after establishing an operation strategy for optimizing the new energy consumption capacity, the method further includes:

[0027] A simulation scenario of new energy is established, and the operation strategy is operated in the simulation scenario. Based on the operation result, the simulation operation result is determined. Based on the operation result, the effectiveness of the operation strategy is determined. If the effectiveness result does not meet the requirements, the model parameters of the energy storage system operation optimization model are readjusted until the effectiveness of the operation strategy meets the requirements.

[0028] On the other hand, the present invention also proposes a system for optimizing the new energy consumption capacity, comprising:

[0029] A model building unit, used to obtain information data related to the new energy system and the absorption capacity, and use a pre-established learning model to establish an energy storage system operation optimization model for optimizing the new energy absorption capacity according to the information data;

[0030] A solving unit, used for decoupling and simplifying the energy storage system operation optimization model to obtain a simplified linear programming model, and solving the linear programming model to obtain an optimal solution of the linear programming model;

[0031] The optimization operation unit is used to establish an operation strategy for optimizing the new energy absorption capacity based on the optimal solution, and control the operation of the new energy system with the operation strategy to optimize the new energy absorption capacity.

[0032] Optional, information data, including: cost data and grid parameter data;

[0033] The cost data include: new energy power curtailment costs and energy storage charging costs;

[0034] The grid parameter data includes: grid power data, energy storage system power data, node voltage data, new energy power restriction data and energy storage charging and discharging data.

[0035] Optional, pre-built learning models including:

[0036] Obtain information data related to new energy systems and consumption capacity under various structures / scenarios, and establish energy storage system operation optimization models for new energy under various structures / scenarios based on the information data;

[0037] Using the information data as input data of a preset learning structure and using the output data of the energy storage system operation optimization model, dividing the input data and the output data into a training set and a verification set, inputting the training set into the preset learning structure for learning, so as to adjust the structural parameters of the preset learning structure and generate an initial learning model;

[0038] The initial learning model is verified with the verification set. If the verification result meets the preset requirements, the initial learning model is used as the learning model. If the verification result does not meet the preset requirements, the model parameters of the initial learning model are adjusted until the verification result meets the preset requirements.

[0039] Among them, when the similarity between the output data in the verification set and the output data obtained by the initial learning model based on the input data in the verification set is greater than 95%, the verification result meets the preset requirements.

[0040] Optionally, the energy storage system operation optimization model includes:

[0041] An objective function and constraints of the objective function;

[0042] The objective function includes: an objective function with the minimum total cost as the goal;

[0043] The constraints include: power balance constraints of the power grid, power constraints of the energy storage system, power constraints of the energy storage system, power constraints of the power grid, and power limitation variable constraints of new energy power sources.

[0044] Optionally, a model building unit is used to establish an energy storage system operation optimization model for optimizing the new energy consumption capacity, and then verify the energy storage system operation optimization model to determine whether the energy storage system operation optimization model meets the requirements;

[0045] Wherein, if at least one of the objective function or constraint conditions in the energy storage system operation optimization model does not meet the requirements, then the energy storage system operation optimization model does not meet the requirements;

[0046] When the objective function of the energy storage system operation optimization model does not meet the requirements, the parameters of the objective function are adjusted according to the information data and the absorption capacity requirements of the new energy;

[0047] When the constraints of the energy storage system operation optimization model do not meet the requirements, the constraints are increased or decreased, or the parameters of the current constraints are adjusted according to the information data and the absorption capacity requirements of the new energy.

[0048] Optionally, the optimal solution of the linear programming model meets the upper and lower power limits of the actual energy storage of new energy.

[0049] Optionally, an operation strategy is adopted to meet the energy storage power constraints and capacity constraints with the goal of optimizing the new energy absorption capacity.

[0050] Optionally, the optimization operation unit is used to establish a simulation scenario of new energy after establishing an operation strategy for optimizing the new energy absorption capacity, so that the operation strategy is operated in the simulation scenario, and the simulation operation result is determined based on the operation result, and the effectiveness of the operation strategy is determined based on the operation result. If the effectiveness result does not meet the requirements, the model parameters of the energy storage system operation optimization model are readjusted until the effectiveness of the operation strategy meets the requirements.

[0051] In yet another aspect, the present invention further provides a computing device, comprising: one or more processors;

[0052] a processor for executing one or more programs;

[0053] When the one or more programs are executed by the one or more processors, the above-described method is implemented.

[0054] In yet another aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed, the method described above is implemented.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] The present invention provides a method for optimizing the absorption capacity of new energy, comprising: obtaining information data related to the absorption capacity of the new energy system, using a pre-established learning model, and establishing an energy storage system operation optimization model for optimizing the absorption capacity of new energy according to the information data; decoupling and simplifying the energy storage system operation optimization model to obtain a simplified linear programming model, and solving the linear programming model to obtain the optimal solution of the linear programming model; based on the optimal solution, establishing an operation strategy for optimizing the absorption capacity of new energy, and controlling the operation of the new energy system with the operation strategy to optimize the absorption capacity of new energy. The present invention has a wide range of applications, can optimize the absorption capacity of new energy in different scenarios, has high efficiency, and is simple to calculate. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 A flow chart of a method for optimizing the new energy consumption capacity according to the present invention;

[0058] Figure 2 This is a structural diagram of a system for optimizing the new energy consumption capacity according to the present invention. DETAILED DESCRIPTION

[0059] Now, exemplary embodiments of the present invention are described with reference to the accompanying drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. These embodiments are provided to disclose the present invention in detail and completely and to fully convey the scope of the present invention to those skilled in the art. The terms used in the exemplary embodiments shown in the accompanying drawings are not intended to limit the present invention. In the accompanying drawings, the same units / elements are marked with the same reference numerals.

[0060] Unless otherwise specified, the terms (including technical terms) used herein have the commonly understood meanings to those skilled in the art. In addition, it is understood that the terms defined in commonly used dictionaries should be understood to have the same meanings as those in the context of the relevant fields, and should not be understood as idealized or overly formal meanings.

[0061] Embodiment 1:

[0062] The present invention proposes a method for optimizing the new energy consumption capacity, such as Figure 1 As shown, including:

[0063] Step 1: Acquire information data related to the new energy system and the absorption capacity, and use a pre-established learning model to establish an energy storage system operation optimization model for optimizing the new energy absorption capacity according to the information data;

[0064] Step 2: decoupling and simplifying the energy storage system operation optimization model to obtain a simplified linear programming model, and solving the linear programming model to obtain an optimal solution of the linear programming model;

[0065] Step 3: Based on the optimal solution, establish an operation strategy for optimizing the new energy absorption capacity, and use the operation strategy to control the operation of the new energy system to optimize the new energy absorption capacity.

[0066] Among them, information data includes: cost data and power grid parameter data;

[0067] The cost data include: new energy power curtailment costs and energy storage charging costs;

[0068] The grid parameter data includes: grid power data, energy storage system power data, node voltage data, new energy power restriction data and energy storage charging and discharging data.

[0069] Among them, the learning model is pre-established, including:

[0070] Obtain information data related to new energy systems and consumption capacity under various structures / scenarios, and establish energy storage system operation optimization models for new energy under various structures / scenarios based on the information data;

[0071] Using the information data as input data of a preset learning structure and using the output data of the energy storage system operation optimization model, dividing the input data and the output data into a training set and a verification set, inputting the training set into the preset learning structure for learning, so as to adjust the structural parameters of the preset learning structure and generate an initial learning model;

[0072] The initial learning model is verified with the verification set. If the verification result meets the preset requirements, the initial learning model is used as the learning model. If the verification result does not meet the preset requirements, the model parameters of the initial learning model are adjusted until the verification result meets the preset requirements.

[0073] Among them, when the similarity between the output data in the verification set and the output data obtained by the initial learning model based on the input data in the verification set is greater than 95%, the verification result meets the preset requirements.

[0074] Among them, the energy storage system operation optimization model includes:

[0075] An objective function and constraints of the objective function;

[0076] The objective function includes: an objective function with the minimum total cost as the goal;

[0077] The constraints include: power balance constraints of the power grid, power constraints of the energy storage system, power constraints of the energy storage system, power constraints of the power grid, and power limitation variable constraints of new energy power sources.

[0078] After establishing the energy storage system operation optimization model for optimizing the new energy consumption capacity, the method further includes:

[0079] Verifying the energy storage system operation optimization model to determine whether the energy storage system operation optimization model meets the requirements;

[0080] Wherein, if at least one of the objective function or constraint conditions in the energy storage system operation optimization model does not meet the requirements, then the energy storage system operation optimization model does not meet the requirements;

[0081] When the objective function of the energy storage system operation optimization model does not meet the requirements, the parameters of the objective function are adjusted according to the information data and the absorption capacity requirements of the new energy;

[0082] When the constraints of the energy storage system operation optimization model do not meet the requirements, the constraints are increased or decreased, or the parameters of the current constraints are adjusted according to the information data and the absorption capacity requirements of the new energy.

[0083] Among them, the optimal solution of the linear programming model meets the upper and lower limits of the actual energy storage power of new energy.

[0084] Among them, the operation strategy meets the energy storage power constraints and capacity constraints with the goal of optimizing the new energy absorption capacity.

[0085] Among them, after establishing the operation strategy for optimizing the new energy consumption capacity, the method also includes:

[0086] A simulation scenario of new energy is established, and the operation strategy is operated in the simulation scenario. Based on the operation result, the simulation operation result is determined. Based on the operation result, the effectiveness of the operation strategy is determined. If the effectiveness result does not meet the requirements, the model parameters of the energy storage system operation optimization model are readjusted until the effectiveness of the operation strategy meets the requirements.

[0087] Embodiment 2:

[0088] The present invention also proposes a system 200 for optimizing the new energy consumption capacity, such as Figure 2 As shown, including:

[0089] The model building unit 201 is used to obtain information data related to the new energy system and the absorption capacity, and use a pre-established learning model to establish an energy storage system operation optimization model for optimizing the new energy absorption capacity according to the information data;

[0090] A solving unit 202 is used to decouple and simplify the energy storage system operation optimization model to obtain a simplified linear programming model, and solve the linear programming model to obtain an optimal solution of the linear programming model;

[0091] The optimization operation unit 203 is used to establish an operation strategy for optimizing the new energy absorption capacity based on the optimal solution, and control the operation of the new energy system with the operation strategy to optimize the new energy absorption capacity.

[0092] Among them, information data includes: cost data and power grid parameter data;

[0093] The cost data include: new energy power curtailment costs and energy storage charging costs;

[0094] The grid parameter data includes: grid power data, energy storage system power data, node voltage data, new energy power restriction data and energy storage charging and discharging data.

[0095] Among them, the learning model is pre-established, including:

[0096] Obtain information data related to new energy systems and consumption capacity under various structures / scenarios, and establish energy storage system operation optimization models for new energy under various structures / scenarios based on the information data;

[0097] Using the information data as input data of a preset learning structure and using the output data of the energy storage system operation optimization model, dividing the input data and the output data into a training set and a verification set, inputting the training set into the preset learning structure for learning, so as to adjust the structural parameters of the preset learning structure and generate an initial learning model;

[0098] The initial learning model is verified with the verification set. If the verification result meets the preset requirements, the initial learning model is used as the learning model. If the verification result does not meet the preset requirements, the model parameters of the initial learning model are adjusted until the verification result meets the preset requirements.

[0099] Among them, when the similarity between the output data in the verification set and the output data obtained by the initial learning model based on the input data in the verification set is greater than 95%, the verification result meets the preset requirements.

[0100] Among them, the energy storage system operation optimization model includes:

[0101] An objective function and constraints of the objective function;

[0102] The objective function includes: an objective function with the minimum total cost as the goal;

[0103] The constraints include: power balance constraints of the power grid, power constraints of the energy storage system, power constraints of the energy storage system, power constraints of the power grid, and power limitation variable constraints of new energy power sources.

[0104] The model building unit 201 is used to establish an energy storage system operation optimization model for optimizing the new energy consumption capacity, and then verify the energy storage system operation optimization model to determine whether the energy storage system operation optimization model meets the requirements;

[0105] Wherein, if at least one of the objective function or constraint conditions in the energy storage system operation optimization model does not meet the requirements, then the energy storage system operation optimization model does not meet the requirements;

[0106] When the objective function of the energy storage system operation optimization model does not meet the requirements, the parameters of the objective function are adjusted according to the information data and the absorption capacity requirements of the new energy;

[0107] When the constraints of the energy storage system operation optimization model do not meet the requirements, the constraints are increased or decreased, or the parameters of the current constraints are adjusted according to the information data and the absorption capacity requirements of the new energy.

[0108] Among them, the optimal solution of the linear programming model meets the upper and lower limits of the actual energy storage power of new energy.

[0109] Among them, the operation strategy meets the energy storage power constraints and capacity constraints with the goal of optimizing the new energy absorption capacity.

[0110] Among them, the optimization operation unit 203 is used to establish a simulation scenario of new energy after establishing an operation strategy for optimizing the new energy absorption capacity, so that the operation strategy is operated in the simulation scenario, and the simulation operation result is determined according to the operation result, and the effectiveness of the operation strategy is determined according to the operation result. If the effectiveness result does not meet the requirements, the model parameters of the energy storage system operation optimization model are readjusted until the effectiveness of the operation strategy meets the requirements.

[0111] The present invention has a wide range of applications, can optimize the absorption capacity of new energy in different scenarios, has high efficiency and simple calculation.

[0112] Embodiment 3:

[0113] Based on the same inventive concept, the present invention also provides a computer device, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding functions, so as to implement the steps of the method in the above embodiment.

[0114] Embodiment 4:

[0115] Based on the same inventive concept, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both a built-in storage medium in a computer device and an extended storage medium supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of the method in the above embodiment.

[0116] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The schemes in the embodiments of the present invention may be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0117] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0118] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0119] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0120] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0121] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for optimizing the new energy consumption capacity, characterized in that: include: Acquire information data related to the new energy system and the absorption capacity, and use a pre-established learning model to establish an energy storage system operation optimization model for optimizing the new energy absorption capacity based on the information data; Decoupling and simplifying the energy storage system operation optimization model to obtain a simplified linear programming model, and solving the linear programming model to obtain an optimal solution to the linear programming model; Based on the optimal solution, an operation strategy for optimizing the new energy absorption capacity is established, and the operation of the new energy system is controlled by the operation strategy to optimize the new energy absorption capacity.

2. The method according to claim 1, characterized in that The information data includes: cost data and power grid parameter data; The cost data include: new energy power curtailment costs and energy storage charging costs; The grid parameter data includes: grid power data, energy storage system power data, node voltage data, new energy power restriction data and energy storage charging and discharging data.

3. The method according to claim 1, characterized in that Pre-built learning models including: Obtain information data related to new energy systems and consumption capacity under various structures / scenarios, and establish energy storage system operation optimization models for new energy under various structures / scenarios based on the information data; Using the information data as input data of a preset learning structure and using the output data of the energy storage system operation optimization model, dividing the input data and the output data into a training set and a verification set, inputting the training set into the preset learning structure for learning, so as to adjust the structural parameters of the preset learning structure and generate an initial learning model; The initial learning model is verified with the verification set. If the verification result meets the preset requirements, the initial learning model is used as the learning model. If the verification result does not meet the preset requirements, the model parameters of the initial learning model are adjusted until the verification result meets the preset requirements. Among them, when the similarity between the output data in the verification set and the output data obtained by the initial learning model based on the input data in the verification set is greater than 95%, the verification result meets the preset requirements.

4. The method according to claim 1, characterized in that: The energy storage system operation optimization model includes: An objective function and constraints of the objective function; The objective function includes: an objective function with the minimum total cost as the goal; The constraints include: power balance constraints of the power grid, power constraints of the energy storage system, power constraints of the energy storage system, power constraints of the power grid, and power limitation variable constraints of new energy power sources.

5. The method according to claim 1, characterized in that After establishing the energy storage system operation optimization model for optimizing the new energy consumption capacity, the method further includes: Verifying the energy storage system operation optimization model to determine whether the energy storage system operation optimization model meets the requirements; Wherein, if at least one of the objective function or constraint conditions in the energy storage system operation optimization model does not meet the requirements, then the energy storage system operation optimization model does not meet the requirements; When the objective function of the energy storage system operation optimization model does not meet the requirements, the parameters of the objective function are adjusted according to the information data and the absorption capacity requirements of the new energy; When the constraints of the energy storage system operation optimization model do not meet the requirements, the constraints are increased or decreased, or the parameters of the current constraints are adjusted according to the information data and the absorption capacity requirements of the new energy.

6. The method according to claim 1, characterized in that The optimal solution of the linear programming model meets the upper and lower limits of the power of the actual energy storage of new energy.

7. The method according to claim 1, characterized in that The operation strategy meets the energy storage power constraint and capacity constraint under the goal of optimizing the new energy consumption capacity.

8. A system for optimizing the capacity of absorbing new energy, characterized in that: include: A model building unit, used to obtain information data related to the new energy system and the absorption capacity, and use a pre-established learning model to establish an energy storage system operation optimization model for optimizing the new energy absorption capacity according to the information data; A solving unit, used for decoupling and simplifying the energy storage system operation optimization model to obtain a simplified linear programming model, and solving the linear programming model to obtain an optimal solution of the linear programming model; The optimization operation unit is used to establish an operation strategy for optimizing the new energy absorption capacity based on the optimal solution, and control the operation of the new energy system with the operation strategy to optimize the new energy absorption capacity.

9. A computer device, characterized in that: include: one or more processors; a processor for executing one or more programs; When the one or more programs are executed by the one or more processors, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed, the method according to any one of claims 1 to 7 is implemented.

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