Performance optimization method of wind and light energy storage system and related equipment

Abstract

The embodiment of the invention discloses a performance optimization method for a wind and light energy storage system and related equipment, and the method comprises the steps: carrying out the evaluation of operation data through employing a preset data evaluation function and a preset performance standard index, and obtaining a performance evaluation result of the energy storage system; and judging the operation data by using a preset trend judgment function and a preset trend change standard index to obtain a trend change evaluation result of the energy storage system, and optimizing the performance of the energy storage system according to the performance evaluation result and the trend change evaluation result. The performance evaluation result and the trend change evaluation result can comprehensively and deeply analyze and evaluate the performance of the energy storage equipment, and the actual effect of optimization measures is ensured, so that the overall performance and the use efficiency of the energy storage equipment are improved, and reliable guarantee and basis are provided for performance optimization of the energy storage equipment; and the optimal performance output of the energy storage system is realized.

Description

[Technical field]

[0001] The present invention relates to the technical field of energy storage system performance optimization, and in particular to a performance optimization method and related equipment for a wind-solar energy storage system. [Background technology]

[0002] As the global demand for renewable energy grows, wind and solar energy, as representatives of clean energy, are increasingly being developed and applied in power generation equipment. At the same time, due to the intermittent nature of wind and solar energy, energy storage technology has become the key to ensuring the stability of energy supply. Therefore, energy storage equipment plays an increasingly important role in modern power systems, and its performance optimization is crucial to improving the efficiency and stability of energy systems. However, there is a key problem that is currently prevalent, that is, there is a lack of effective evaluation and verification methods for the performance optimization of energy storage equipment. Existing technologies often rely on experience or single parameter analysis when optimizing the performance of energy storage equipment, lacking a comprehensive and systematic evaluation method and verification platform, which makes it difficult to ensure the effectiveness of the optimization solution, and ultimately may not achieve the best performance output. [Summary of the invention]

[0003] In view of this, the present invention provides a performance optimization method and related equipment for a wind-solar energy storage system.

[0004] The specific technical solution of the first embodiment of the present invention is: a performance optimization method for a wind-solar energy storage system, the method comprising: obtaining operating data of the energy storage system; the energy storage system comprises a wind energy storage system and a photovoltaic energy storage system; evaluating the operating data using a preset data evaluation function to obtain performance indicators of the energy storage system; obtaining performance evaluation results of the energy storage system based on the performance indicators and preset performance standard indicators; judging the operating data based on a preset trend judgment function to obtain operating data change values ​​of the energy storage system; obtaining trend change evaluation results of the energy storage system based on the operating data change values ​​of the energy storage system and preset trend change standard indicators; optimizing the performance of the energy storage system based on the performance evaluation results and the trend change evaluation results.

[0005] Preferably, the preset data evaluation function is obtained using the following formula:

[0006]

[0007] Wherein: Sp is the preset data evaluation function, ε is the evaluation factor, α i is the operating data of the wind energy storage system, β i is the operating data of the optical energy storage system, and ζ is the preset performance standard indicator.

[0008] Preferably, the preset trend judgment function is obtained using the following formula:

[0009]

[0010] Wherein, Qs is the preset trend judgment function, θ i is the operating data of the wind energy storage system and the solar energy storage system, i is the order of the current operating data, K is the preset trend change standard indicator, and η is the data change factor.

[0011] Preferably, after obtaining the operating data of the energy storage system, it also includes: performing data range verification, consistency verification and logical relationship verification on the operating data; obtaining first operating data that failed the verification and second operating data that passed the verification; using a preset analysis modulation function to adaptively adjust the first operating data to obtain adjusted operating data; using the adjusted operating data and the second operating data as the operating data, returning to the step of evaluating the operating data using a preset data evaluation function to obtain performance indicators of the energy storage system.

[0012] Preferably, the preset analysis modulation function is obtained using the following formula:

[0013]

[0014] Wherein, Tz is the preset analysis modulation function, n is the number of first operation data, ξ is the adjustment coefficient, ρ i is the data value of the first running data, It is the preset data comparison value.

[0015] Preferably, the method also includes: performing cyclic redundancy verification on the operating data using a preset verification function to obtain a cyclic redundancy verification result of the energy storage system; then optimizing the performance of the energy storage system according to the performance evaluation result and the trend change evaluation result, including: optimizing the performance of the energy storage system according to the performance evaluation result, the trend change evaluation result and the cyclic redundancy verification result.

[0016] Preferably, the preset verification function is obtained using the following formula:

[0017]

[0018] Wherein, Y is the preset verification function, ω is the verification coefficient, is the operating data to be verified, i is the verification order of the current operating data, and ψ is the preset verification comparison value.

[0019] The specific technical scheme of the second embodiment of the present invention is: a performance optimization system for a wind-solar energy storage system, the system comprising: a data acquisition module, a performance index acquisition module, a first evaluation result acquisition module, a data change value acquisition module, a second evaluation result acquisition module and an optimization module; the data acquisition module is used to acquire the operating data of the energy storage system; the energy storage system comprises a wind energy storage system and a light energy storage system; the performance index acquisition module is used to evaluate the operating data using a preset data evaluation function to obtain the performance index of the energy storage system; the first evaluation result acquisition module is used to obtain the performance evaluation result of the energy storage system according to the performance index and the preset performance standard index; the data change value acquisition module is used to judge the operating data according to a preset trend judgment function to obtain the operating data change value of the energy storage system; the second evaluation result acquisition module is used to obtain the trend change evaluation result of the energy storage system according to the operating data change value of the energy storage system and the preset trend change standard index; the optimization module is used to optimize the performance of the energy storage system according to the performance evaluation result and the trend change evaluation result.

[0020] The specific technical solution of the third embodiment of the present invention is: a performance optimization device for a wind-solar-energy storage system, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method described in any one of the first embodiments of the present application.

[0021] The specific technical solution of the fourth embodiment of the present invention is: a computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to perform the steps of the method described in any one of the first embodiments of the present application.

[0022] Implementing the embodiments of the present invention will have the following beneficial effects:

[0023] The present invention obtains the performance evaluation result of the energy storage system by evaluating the operating data using a preset data evaluation function and a preset performance standard index, and obtains the trend change evaluation result of the energy storage system by judging the operating data using a preset trend judgment function and a preset trend change standard index, and optimizes the performance of the energy storage system according to the performance evaluation result and the trend change evaluation result.

[0024] The performance evaluation results and trend change evaluation results can conduct a comprehensive and in-depth analysis and evaluation of the performance of the energy storage equipment, ensure the actual effect of the optimization measures, thereby improving the overall performance and utilization efficiency of the energy storage equipment, and provide a reliable guarantee and basis for the performance optimization of the energy storage equipment, so as to achieve the best performance output of the energy storage system.

Brief Description of the Drawings

[0025] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0026] Figure 1 A flow chart of the steps of the performance optimization method of the wind-solar energy storage system;

[0027] Figure 2 A schematic diagram of the performance optimization system for wind-solar energy storage system;

[0028] Figure 3 A diagram of the internal structure of a computer device;

[0029] Among them, 201, data acquisition module; 202, performance indicator acquisition module; 203, first evaluation result acquisition module; 204, data change value acquisition module; 205, second evaluation result acquisition module; 206, optimization module. [Specific implementation method]

[0030] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0031] The terms "first", "second", etc. in the specification, claims and drawings of this application are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or modules is not limited to the listed steps or modules, but optionally includes steps or modules that are not listed, or optionally includes other steps or modules that are inherent to these processes, methods, products or devices.

[0032] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0033] See also Figure 1 , is a flowchart of the steps of a performance optimization method for a wind-solar energy storage system in the first embodiment of the present application, which provides a reliable guarantee and basis for the performance optimization of the energy storage device. The method includes:

[0034] Step 101, obtaining operation data of an energy storage system; the energy storage system includes a wind energy storage system and a solar energy storage system;

[0035] Step 102: using a preset data evaluation function to evaluate the operating data to obtain performance indicators of the energy storage system;

[0036] Step 103: Obtaining a performance evaluation result of the energy storage system according to the performance index and a preset performance standard index;

[0037] Step 104: judging the operation data according to a preset trend judgment function to obtain a change value of the operation data of the energy storage system;

[0038] Step 105: obtaining a trend change evaluation result of the energy storage system according to the operating data change value of the energy storage system and a preset trend change standard index;

[0039] Step 106: Optimize the performance of the energy storage system according to the performance evaluation result and the trend change evaluation result.

[0040] Specifically, before performance optimization, the wind energy storage system and the solar energy storage system are deployed according to their requirements, test environment and standards; the performance, appearance and connection of the equipment are tested and inspected; the operation data of the equipment is collected using laboratory test methods, and the collected operation data is stored; the problems in the operation data are sorted out and modified after the collected operation data information is analyzed; the wind speed changes, light intensity fluctuations and load demand changes of the energy storage system are simulated and tested; the operation data of different simulation conditions are obtained, and the operation data is analyzed through data analysis and machine learning to predict the failure of the energy storage system equipment, and the operation performance changes of the energy storage system are monitored in real time through the loop tracking algorithm; the three types of wind, solar and storage equipment are optimized and adjusted through manual operation and intelligent maintenance instruments.

[0041] The cyclic tracking algorithm includes: standard setting and adjustment of the initial parameters of the algorithm, including performance indicator thresholds, tracking cycles and data collection frequency; collecting operation data from the wind energy storage system and the photovoltaic energy storage system at regular intervals according to the set tracking cycle, including power output, voltage, current, temperature, wind speed and light intensity; calculating the performance indicators of the wind energy storage system and the photovoltaic energy storage system according to the data evaluation function, and comparing them with the preset performance standard indicators to obtain the performance evaluation results of the wind energy storage system and the photovoltaic energy storage system; analyzing the change trend of the performance indicators over time through the data evaluation results, and judging the performance of the wind energy storage system and the photovoltaic energy storage system according to the trend judgment function. The system can judge the data changes generated during the operation of the wind energy storage system and the photovoltaic energy storage system, and obtain the trend change evaluation results of the wind energy storage system and the photovoltaic energy storage system by comparing with the preset trend change standard indicators; put forward optimization adjustment suggestions based on the results of performance evaluation and trend analysis, including adjusting equipment parameters, replacing damaged components and improving maintenance strategies; convert the optimization suggestions into specific operation steps through manual and automated equipment to optimize and adjust the wind energy storage system and the photovoltaic energy storage system; feed back the adjusted results to the algorithm model, and further adjust the parameters and optimization strategies according to the feedback information to form a continuous improvement cycle.

[0042] At the same time, there are differences in the performance of the loop tracking algorithm and the particle swarm optimization algorithm. The specific performance differences are shown in Table 1:

[0043]

[0044]

[0045] Table 1: Differences between the Cycle Tracking Algorithm and the Particle Swarm Optimization Algorithm

[0046] In summary, the cycle tracking algorithm is superior to the particle swarm optimization algorithm in both performance evaluation efficiency and trend judgment efficiency. It can be seen that the cycle tracking algorithm is the best choice for the present invention.

[0047] The method in this embodiment obtains the performance evaluation result of the energy storage system by evaluating the operating data using a preset data evaluation function and a preset performance standard indicator, and obtains the trend change evaluation result of the energy storage system by judging the operating data using a preset trend judgment function and a preset trend change standard indicator, and optimizes the performance of the energy storage system according to the performance evaluation result and the trend change evaluation result.

[0048] The performance evaluation results and trend change evaluation results can conduct a comprehensive and in-depth analysis and evaluation of the performance of the energy storage equipment, ensure the actual effect of the optimization measures, thereby improving the overall performance and utilization efficiency of the energy storage equipment, and provide a reliable guarantee and basis for the performance optimization of the energy storage equipment, so as to achieve the best performance output of the energy storage system.

[0049] In a specific embodiment, the preset data evaluation function is obtained using the following formula:

[0050]

[0051] Wherein: Sp is the preset data evaluation function, ε is the evaluation factor, α i is the operating data of the wind energy storage system, β i is the operating data of the solar energy storage system, and ζ is the preset performance standard index. Through the data evaluation function, the actual performance index of the device can be compared with the preset performance standard index, which helps to ensure that the device meets or exceeds the preset standard in various performance indicators, thereby meeting specific usage requirements.

[0052] In a specific embodiment, the preset trend judgment function is obtained using the following formula:

[0053]

[0054] Wherein, Qs is the preset trend judgment function, θ i is the operating data of the wind energy storage system and the optical energy storage system, i is the order of the current operating data, κ is the preset trend change standard indicator, and η is the data change factor. Through the trend judgment function, the operating data changes of the equipment can be monitored in real time, and the equipment can be judged whether it is in a normal state according to the preset trend change standard indicator. Once the data changes exceed the preset range, the system can immediately issue an early warning to remind the operator or maintenance personnel to take timely measures to prevent equipment failure or accidents. This real-time monitoring and early warning mechanism can greatly improve the safety and reliability of the equipment.

[0055] In a specific embodiment, after obtaining the operating data of the energy storage system, it also includes: performing data range verification, consistency verification and logical relationship verification on the operating data; obtaining first operating data that failed the verification and second operating data that passed the verification; using a preset analysis modulation function to adaptively adjust the first operating data to obtain adjusted operating data; using the adjusted operating data and the second operating data as the operating data, returning to the step of evaluating the operating data using a preset data evaluation function to obtain performance indicators of the energy storage system.

[0056] Specifically, set the thresholds, weights and standards of the compliance verification algorithm, collect verification data from the running data, including equipment performance data, environmental data and operation data; clean and format the collected verification data to remove noise and outliers to ensure data quality; extract key features from the processed data, and perform preliminary verification of the features according to preset rules, including data range, consistency and logical relationships; adjust the rules and parameters for data that cannot be effectively verified and new pattern data according to the analysis modulation function to improve the accuracy of verification; use the updated rules to perform verification on all data, and check the data according to the verification function; analyze and evaluate the verification results, judge the verification results of the data and continuously iterate and optimize the rule set and parameters, and output the verification results for subsequent compliance checks.

[0057] In a specific embodiment, the preset analysis modulation function is obtained using the following formula:

[0058]

[0059] Wherein, Tz is the preset analysis modulation function, n is the number of first operation data, ξ is the adjustment coefficient, ρ i is the data value of the first running data, It is the preset data comparison value.

[0060] In a specific embodiment, the method further includes: performing cyclic redundancy verification on the operating data using a preset verification function to obtain a cyclic redundancy verification result of the energy storage system; then optimizing the performance of the energy storage system according to the performance evaluation result and the trend change evaluation result, including: optimizing the performance of the energy storage system according to the performance evaluation result, the trend change evaluation result and the cyclic redundancy verification result. Specifically, during network transmission or data storage, data errors may occur due to various reasons (such as physical layer interference, circuit failure, etc.). Cyclic redundancy verification can enhance the anti-interference ability of data, so that the receiving end can effectively detect errors in the data and take corresponding error correction measures. This greatly improves the reliability of data transmission and reduces system failures or functional abnormalities caused by data errors.

[0061] In a specific embodiment, the preset verification function is obtained using the following formula:

[0062]

[0063] Wherein, Y is the preset verification function, ω is the verification coefficient, is the operating data to be verified, i is the verification order of the current operating data, and ψ is the preset verification comparison value.

[0064] In the specific embodiments, see Figure 2 , is a structural diagram of a performance optimization system for a wind-solar energy storage system in the second example of the present application, the system comprising: a data acquisition module 201, a performance index acquisition module 202, a first evaluation result acquisition module 203, a data change value acquisition module 204, a second evaluation result acquisition module 205 and an optimization module 206; the data acquisition module 201 is used to acquire the operating data of the energy storage system; the energy storage system comprises a wind energy storage system and a light energy storage system; the performance index acquisition module 202 is used to evaluate the operating data using a preset data evaluation function to obtain the performance index of the energy storage system; the first evaluation The evaluation result acquisition module 203 is used to obtain the performance evaluation result of the energy storage system according to the performance index and the preset performance standard index; the data change value acquisition module 204 is used to judge the operation data according to the preset trend judgment function to obtain the operation data change value of the energy storage system; the second evaluation result acquisition module 205 is used to obtain the trend change evaluation result of the energy storage system according to the operation data change value of the energy storage system and the preset trend change standard index; the optimization module 206 is used to optimize the performance of the energy storage system according to the performance evaluation result and the trend change evaluation result.

[0065] In a specific embodiment, the performance optimization system also includes a test optimization module, which organizes and modifies problems arising from device collection information; the test optimization module includes a simulation unit, a fault prediction unit, a performance tracking unit and an optimization adjustment unit, the simulation unit tests wind speed changes, light intensity fluctuations and load demand changes of three types of wind, solar and storage equipment; the fault prediction unit predicts faults of the three types of wind, solar and storage equipment through data analysis and machine learning; the performance tracking unit monitors the changes in the operating performance of the three types of wind, solar and storage equipment in real time through a cyclic tracking algorithm; the optimization adjustment unit optimizes and adjusts the three types of wind, solar and storage equipment through manual operation and intelligent maintenance instruments, the output end of the simulation unit is connected to the input end of the fault prediction unit, the output end of the fault prediction unit is connected to the input end of the performance tracking unit, and the output end of the performance tracking unit is connected to the input end of the optimization adjustment unit.

[0066] In a specific embodiment, the performance optimization system also includes an acceptance module, through which the results of laboratory tests of the three types of wind, solar and storage equipment are accepted and recorded, and a data report is generated; the acceptance module includes a data verification unit, a compliance check unit and a report generation unit, and the data verification unit verifies the accuracy and completeness of the data during the test process through an adaptive verification algorithm; the compliance check checks the manufacturing, installation and operation status of the three types of wind, solar and storage equipment through inspection standards and equipment evaluation tools; the report generation unit generates a report on the test data and analysis results in the form of graphics, charts and text descriptions, and the output end of the data verification unit is connected to the input end of the compliance check unit, and the output end of the compliance check unit is connected to the input end of the report generation unit.

[0067] At the same time, the difference in verification performance between the adaptive verification algorithm and the cyclic redundancy check algorithm is shown in Table 2:

[0068]

[0069]

[0070] Table 2: Performance differences between the adaptive verification algorithm and the cyclic redundancy check algorithm

[0071] In summary, the adaptive verification algorithm is superior to the cyclic redundancy check algorithm in terms of feature extraction efficiency, adjustment efficiency and verification efficiency. It can be seen that the adaptive verification algorithm is the best choice for the present invention.

[0072] In a specific embodiment, the performance optimization system is applied in a laboratory, and a laboratory management module is also provided in the laboratory. The laboratory management module includes a laboratory resource scheduling unit, an environment control unit, a security management unit and a document management unit. The resource scheduling unit allocates and schedules equipment, tools and space in the laboratory through the equipment cloud service center; the environment control unit monitors and adjusts the environmental conditions of the laboratory through a programmable logic controller; the security management unit uses cameras, smoke detectors and emergency stop buttons to monitor the safety status of the laboratory in real time; the document management unit manages the laboratory information through the electronic document management system EDMS.

[0073] The laboratory is also equipped with an equipment pre-inspection module, which includes a performance detection unit, an appearance detection unit, an interface detection unit and an electrical connection detection unit. The performance detection unit uses a power meter and an oscilloscope to detect the output power and response time of the three types of wind, solar and storage equipment; the appearance detection unit detects the surface, assembly and manufacturing defects of the three types of wind, solar and storage equipment through manual inspection and detection tools; the interface detection unit inspects the communication interface of the three types of wind, solar and storage equipment through a communication tester and cable testing tools; the electrical connection detection unit uses a multimeter and a circuit analyzer to detect the electrical connection of the three types of wind, solar and storage equipment.

[0074] The data acquisition module is used to collect the operating data of the energy storage system. The data acquisition module includes a sensor unit, a signal conditioning unit, an analog-to-digital conversion unit and a data storage unit. The sensor unit uses a photoelectric sensor and a Hall sensor to collect data information of three types of equipment: wind, solar and storage; the signal conditioning unit amplifies, filters and linearizes the collected analog signal through an operational amplifier; the analog-to-digital conversion unit converts the collected analog signal into a digital signal through an analog-to-digital converter; the data storage unit uses a random access memory RAM and a solid-state hard disk SSD to store the converted digital signal, the output end of the sensor unit is connected to the input end of the signal conditioning unit, the output end of the signal conditioning unit is connected to the input end of the analog-to-digital conversion unit, and the output end of the analog-to-digital conversion unit is connected to the input end of the data storage unit.

[0075] The operation data is analyzed by using a data analysis module, which includes a data preprocessing unit, a feature extraction unit, an analysis and evaluation unit, and a visualization unit. The data preprocessing unit cleans, removes noise, and performs numerical conversion operations on the data through data anomaly detection; the feature extraction unit extracts equipment feature information from the preprocessed data through principal component analysis; the analysis and evaluation unit analyzes and evaluates the processed equipment data through historical data and equipment standard lists; the visualization unit displays the data evaluation results to the user using a liquid crystal display screen, the output end of the data preprocessing unit is connected to the input end of the feature extraction unit, the output end of the feature extraction unit is connected to the input end of the analysis and evaluation unit, and the output end of the analysis and evaluation unit is connected to the input end of the visualization unit.

[0076] The system in this embodiment obtains the performance evaluation result of the energy storage system by evaluating the operating data using a preset data evaluation function and a preset performance standard index, and obtains the trend change evaluation result of the energy storage system by judging the operating data using a preset trend judgment function and a preset trend change standard index, and optimizes the performance of the energy storage system according to the performance evaluation result and the trend change evaluation result. The performance evaluation results and the trend change evaluation results can conduct a comprehensive and in-depth analysis and evaluation of the performance of the energy storage equipment, ensure the actual effect of the optimization measures, thereby improving the overall performance and utilization efficiency of the energy storage equipment, providing a reliable guarantee and basis for the performance optimization of the energy storage equipment, and achieving the best performance output of the energy storage system.

[0077] In a specific embodiment, the third embodiment of the present application provides a performance optimization device for a wind-solar-energy storage system, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method described in any one of the first embodiments of the present application.

[0078] Specifically, according to the specific test requirements of wind power, photovoltaic and energy storage equipment, the corresponding environment is built, a wind tunnel laboratory is prepared for wind power equipment, a light and temperature controllable test room is prepared for photovoltaic equipment, and a battery charging and discharging test platform is prepared for energy storage equipment; the equipment is pre-tested and checked. Before the formal test, the pre-inspection module is used to conduct a comprehensive inspection of the equipment to ensure that the appearance of the equipment is intact, the connection is correct, and all sensors and instruments are in good working condition; then, data collection and storage: start the data acquisition module, and collect the operating parameters of the equipment in real time according to the pre-defined test plan, such as wind speed, power generation, battery efficiency, etc., and store these data in the database for subsequent analysis; perform data analysis and report generation, use the data analysis module to conduct in-depth analysis of the collected data, and evaluate whether the equipment performance meets the expected goals. The analysis results are used to generate a detailed test report, which should include equipment performance evaluation, existing problems and improvement suggestions; carry out problem rectification and retest, and adjust and repair the equipment according to the problems pointed out in the test report. After the rectification is completed, retesting is performed to confirm whether the problem has been solved and to verify whether the equipment performance has been improved; finally, the test results are accepted and recorded. The acceptance module reviews the test results to confirm whether the equipment has passed all tests. The equipment that passes will be awarded a certificate of conformity, and the relevant data and reports will be archived in the laboratory test records for future reference and traceability.

[0079] In a specific embodiment, the fourth embodiment of the present application provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of the method as described in any one of the first embodiments of the present application.

[0080] Figure 3 The internal structure of a computer device in one embodiment is shown. The computer device can be a terminal or a server. Figure 3 The computer device includes a processor, a memory, etc. connected via a system bus. The memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program. When the computer program is executed by the processor, the processor can implement the method in this embodiment. The internal memory may also store a computer program. When the computer program is executed by the processor, the processor can implement the method in this embodiment. Those skilled in the art can understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0081] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

[0082] The above description is only a preferred embodiment of the present invention and does not limit the present invention in other forms. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A performance optimization method for a wind-solar energy storage system, characterized in that: The method comprises: Acquiring operating data of an energy storage system; the energy storage system includes a wind energy storage system and a light energy storage system; Using a preset data evaluation function to evaluate the operating data to obtain performance indicators of the energy storage system; Obtaining a performance evaluation result of the energy storage system according to the performance indicator and a preset performance standard indicator; Judging the operating data according to a preset trend judgment function to obtain a change value of the operating data of the energy storage system; Obtaining a trend change evaluation result of the energy storage system according to the operating data change value of the energy storage system and a preset trend change standard indicator; The performance of the energy storage system is optimized according to the performance evaluation result and the trend change evaluation result.

2. The performance optimization method of the wind-solar energy storage system according to claim 1, characterized in that: The preset data evaluation function is obtained using the following formula: Wherein: Sp is the preset data evaluation function, ε is the evaluation factor, α i is the operating data of the wind energy storage system, β i is the operating data of the optical energy storage system, and ζ is the preset performance standard indicator.

3. The performance optimization method of the wind-solar energy storage system according to claim 2, characterized in that: The preset trend judgment function is obtained using the following formula: Wherein, Qs is the preset trend judgment function, θ i is the operating data of the wind energy storage system and the solar energy storage system, i is the order of the current operating data, κ is the preset trend change standard indicator, and η is the data change factor.

4. The performance optimization method of the wind-solar energy storage system according to claim 1, characterized in that: After obtaining the operating data of the energy storage system, the method further includes: Performing data range verification, consistency verification and logical relationship verification on the operation data; Acquire the first operation data that failed the verification and the second operation data that passed the verification; Adaptively adjusting the first operating data using a preset analysis modulation function to obtain adjusted operating data; The adjusted operating data and the second operating data are used as the operating data, and the step of evaluating the operating data using a preset data evaluation function is returned to obtain the performance index of the energy storage system.

5. The performance optimization method of the wind-solar energy storage system according to claim 4, characterized in that: The preset analysis modulation function is obtained using the following formula: Wherein, Tz is the preset analysis modulation function, n is the number of first operation data, ξ is the adjustment coefficient, ρ i is the data value of the first running data, It is the preset data comparison value.

6. The performance optimization method of the wind-solar energy storage system according to claim 1, characterized in that: The method further comprises: Performing cyclic redundancy verification on the operating data using a preset verification function to obtain a cyclic redundancy verification result of the energy storage system; Then, optimizing the performance of the energy storage system according to the performance evaluation result and the trend change evaluation result includes: The performance of the energy storage system is optimized according to the performance evaluation result, the trend change evaluation result and the cyclic redundancy verification result.

7. The performance optimization method of the wind-solar energy storage system according to claim 6, characterized in that: The preset verification function is obtained using the following formula: Wherein, Y is the preset verification function, ω is the verification coefficient, is the operating data to be verified, i is the verification order of the current operating data, and ψ is the preset verification comparison value.

8. A performance optimization system for a wind-solar energy storage system, characterized in that: The system comprises: a data acquisition module, a performance indicator acquisition module, a first evaluation result acquisition module, a data change value acquisition module, a second evaluation result acquisition module and an optimization module; The data acquisition module is used to acquire the operating data of the energy storage system; the energy storage system includes a wind energy storage system and a light energy storage system; The performance index acquisition module is used to evaluate the operating data using a preset data evaluation function to obtain the performance index of the energy storage system; The first evaluation result acquisition module is used to obtain the performance evaluation result of the energy storage system according to the performance index and the preset performance standard index; The data change value acquisition module is used to judge the operation data according to a preset trend judgment function to obtain the operation data change value of the energy storage system; The second evaluation result acquisition module is used to obtain the trend change evaluation result of the energy storage system according to the operation data change value of the energy storage system and the preset trend change standard index; The optimization module is used to optimize the performance of the energy storage system according to the performance evaluation result and the trend change evaluation result.

9. A performance optimization device for a wind-solar energy storage system, comprising a memory and a processor, characterized in that: The memory stores a computer program, and when the computer program is executed by the processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 7.

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