Distributed compressed air energy storage system design method and system

Abstract

The invention provides a distributed compressed air energy storage system design method and system, and relates to the technical field of energy storage, and the design method comprises the steps: obtaining electric power residual prediction data of a power grid and historical pumping data of each compressor; according to the historical fatigue degree, corresponding to the historical pumping data, of each compressor, a pumping speed threshold value of each compressor and an air energy storage pressure threshold value of an energy storage device corresponding to each compressor are obtained through prediction; based on the pumping speed threshold value, the air energy storage pressure threshold value and the energy storage priority of each compressor and the electric power remaining prediction data, allocation optimization of the compressors is carried out, and allocation data of the compressors are obtained; and controlling a corresponding compressor to perform compressed air energy storage on the power of the power grid according to the allocation data. According to the method and the system provided by the invention, the service life and the working efficiency of the compressor can be ensured, and meanwhile, the working mode is more reliable and stable.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy storage, in particular to a distributed compressed air energy storage system design method and system. BACKGROUND

[0002] The compressed air energy storage system is a kind of high-efficiency, environmentally friendly energy storage technology, which stores energy in the process of air compression and releases energy to generate electricity when needed. The core of the compressed air energy storage system is the compression and expansion process of air. During the energy storage stage, the system uses an electric motor to drive a compressor to compress air to a high pressure state and store it in a specific air storage facility, such as an underground cave or a high-pressure tank. During this process, the temperature of the air rises, and part of the energy is lost in the form of heat. In addition, the compressed air energy storage system recovers this part of heat through regenerative technology and uses it for the subsequent expansion process, improving the overall efficiency of the system. During the energy release stage, high-pressure air is expanded by a turbine to do work, driving a generator to generate electricity, converting the stored energy into electrical energy.

[0003] However, in the existing compressed air energy storage process, random control of the compressor is used for energy storage work, which can lead to the use of high-load compressors that are not clear about the historical operation status of the compressor, resulting in a decrease in air compression energy storage efficiency and a significant increase in the risk of compressor damage. SUMMARY

[0004] The present application provides a distributed compressed air energy storage system design method and system to solve the technical problem that in the existing compressed air energy storage process, random control of the compressor is used for energy storage work, which can lead to the use of high-load compressors that are not clear about the historical operation status of the compressor, resulting in a decrease in air compression energy storage efficiency and a significant increase in the risk of compressor damage.

[0005] To achieve the above-mentioned purposes and other related purposes, the present application provides a distributed compressed air energy storage system design method, comprising: obtaining power surplus prediction data of a power grid and historical pumping data of each compressor; predicting the pumping speed threshold of each compressor and the air energy storage pressure threshold of the energy storage device corresponding to each compressor according to the historical fatigue degree of each compressor corresponding to the historical pumping data; based on the pumping speed threshold, air energy storage pressure threshold and energy storage priority of each compressor, and the power surplus prediction data, optimizing the deployment of the compressor to obtain deployment data of the compressor; and controlling the corresponding compressor to compress air energy storage of the power grid power according to the deployment data.

[0006] In one embodiment of the present invention, obtaining power surplus prediction data of the power grid includes: obtaining total power generation data of the power grid, which includes power from the main power grid and renewable power data connected to the power grid; obtaining power surplus prediction data of the power grid based on the total power generation data of the power grid, power consumption data of each region to which the power grid needs to supply electricity, and power loss data during corresponding power transmission; the calculation formula for the power surplus prediction data is: , This represents the total electricity generated by the power grid. This represents regional electricity consumption data. This represents power loss data.

[0007] In one embodiment of the present invention, after obtaining the historical pumping data of each compressor, the method further includes: extracting the historical pumping speed and historical pumping operation time corresponding to each compressor from the historical pumping data; filtering out the historical non-operation time between the historical pumping operation time based on the historical pumping operation time; and predicting the historical fatigue level of the current compressor based on the historical pumping speed, historical pumping operation time, and historical non-operation time.

[0008] In one embodiment of the present invention, the historical fatigue level of the current compressor is predicted based on historical pumping speed, historical pumping operation time, and historical non-operation time. This includes: obtaining the speed fatigue increase factor corresponding to each historical pumping time period by looking up a table based on the speed range of the historical pumping speed corresponding to different historical pumping time periods in the historical pumping operation time; calculating the historical fatigue increment based on the historical air energy storage pressure of the energy storage device and the corresponding fatigue conversion coefficient for each historical pumping time period; and predicting the historical fatigue level of the current compressor based on the historical pumping time period, speed fatigue increase factor, cumulative fatigue increase factor, historical non-operation time, fatigue decrease factor corresponding to the historical non-operation time, and historical fatigue increment.

[0009] In one embodiment of the present invention, the formula for calculating historical fatigue level is: , Indicates the first A historical pumping period, Indicates the first A historical non-running time, Indicates the first The sum of historical non-operational time and the accumulated historical fatigue level before that, Indicates the fatigue increase factor. Indicates the fatigue decline factor. This represents the fatigue increase factor of speed. This represents the cumulative fatigue increase factor, where each The range of fatigue levels One , represents historical fatigue value, represents historical air energy storage pressure, represents fatigue conversion coefficient.

[0010] In an embodiment of the present application, the pump speed threshold of each compressor and the air energy storage pressure threshold of the energy storage device corresponding to each compressor are predicted according to the historical fatigue degree of each compressor corresponding to the historical pumping data, comprising: calculating the first pump speed influence quantity corresponding to the corresponding compressor according to the historical fatigue degree of each compressor corresponding to the historical pumping data and the corresponding first speed adjustment coefficient; calculating the second pump speed influence quantity corresponding to the corresponding energy storage device according to the current air energy storage pressure of the energy storage device corresponding to each compressor corresponding to the historical pumping data and the corresponding second speed adjustment coefficient; predicting the pump speed threshold of each compressor according to the reference pump speed, the first pump speed influence quantity and the second pump speed influence quantity; and looking up the air energy storage pressure threshold of the energy storage device corresponding to each compressor according to the pump speed threshold.

[0011] In an embodiment of the present application, the calculation formula of the pump speed threshold is: ; represents reference pump speed, represents historical fatigue degree, represents first speed adjustment coefficient, represents first pump speed influence quantity, represents current air energy storage pressure, represents second speed adjustment coefficient, represents second pump speed influence quantity.

[0012] In an embodiment of the present application, based on the pump speed threshold, the air energy storage pressure threshold and the energy storage priority of each compressor, and the power surplus prediction data, the deployment optimization of the compressor is carried out to obtain the deployment data of the compressor, comprising: monitoring the air energy storage pressure state of each energy storage device according to the air energy storage pressure threshold to obtain an energy storage device set whose air energy storage pressure is less than the air energy storage pressure threshold; sorting the energy storage device set according to the energy storage priority of each energy storage device to obtain an energy storage device list; predicting the air compression amount corresponding to the energy storage device according to the power surplus prediction data; according to the air compression amount, the residual volume corresponding to each energy storage device in the energy storage device list is called in turn in the order of priority from high to low, and the superposition processing is carried out to obtain a superposition compression volume; the superposition compression volume is detected; when the superposition compression volume is greater than the air compression amount for the first time, the compressor set corresponding to the superposition compression volume and the pump speed threshold of the compressor are taken as the deployment data.

[0013] In an embodiment of the present application, according to the air compression amount, the residual volume corresponding to each energy storage device in the energy storage device list is sequentially called in order of priority from high to low, and superposition processing is performed to obtain a superimposed compression volume, including: calculating the difference between the air energy storage pressure threshold and the air energy storage pressure of the corresponding energy storage device to obtain a pressure difference; according to the pressure difference and the corresponding volume conversion coefficient, the residual volume corresponding to each energy storage device is calculated, and the calculation formula of the residual volume is: , represents the volume conversion coefficient, represents the pressure difference, represents the air energy storage pressure, represents the air energy storage pressure threshold; according to the air compression amount, the residual volume corresponding to each energy storage device in the energy storage device list is sequentially called in order of priority from high to low, and superposition processing is performed to obtain a superimposed compression volume.

[0014] To achieve the above object and other related objects, the present application also provides a distributed compressed air energy storage system design system, comprising: an acquisition unit configured to acquire power surplus prediction data of a power grid and historical pumping data of each compressor; a prediction unit configured to predict a pumping speed threshold of each compressor and an air energy storage pressure threshold of an energy storage device corresponding to each compressor according to a historical fatigue degree of each compressor corresponding to the historical pumping data; an optimization unit configured to perform optimization of deployment of the compressors based on the pumping speed threshold of each compressor, the air energy storage pressure threshold of each compressor, and an energy storage priority, and the power surplus prediction data, to obtain deployment data of the compressors; and a control unit configured to control the corresponding compressors to compress air energy storage of power of the power grid according to the deployment data.

[0015] The present application has the following advantages: the distributed compressed air energy storage system design method and system provided by the present application can determine the historical fatigue degree of each compressor based on the historical pumping data of each compressor after predicting the power surplus prediction data that needs to be additionally stored, and then can realize prediction of the pumping speed threshold of the compressor for compressed air energy storage and the air energy storage pressure threshold of the corresponding energy storage device in combination with the current air energy storage pressure of the corresponding energy storage device, so as to provide optimized compressor deployment data for the power surplus prediction data by using the predicted pumping speed threshold of each compressor and the air energy storage pressure threshold of each energy storage device, to realize compressed air energy storage of the surplus power of the power grid. Through this monitoring mode of the compressor and the energy storage device at the same time, it can be ensured that the pumping speed of the compressor and the air energy storage pressure threshold of the energy storage device are within a reasonable range before each energy storage, so as to ensure the service life and working efficiency of the compressor, and also make the working mode more reliable and stable. BRIEF DESCRIPTION OF DRAWINGS

[0016] The accompanying drawings, which are incorporated herein and form a part of the specification, illustrate embodiments consistent with the present application and, together with the description, further serve to explain the principles of the application. It is to be understood that the drawings are designed solely for purposes of illustration to be used in conjunction with the description in

[0017] In the drawings: Figure 1 A flowchart of a design method of a distributed compressed air energy storage system according to an embodiment of the present application is shown.

[0018] Figure 2 A block diagram of a design system of a distributed compressed air energy storage system according to an embodiment of the present application is shown.

[0019] Figure 3 A structural diagram of an electronic device according to an embodiment of the present application is shown.

[0020] Reference signs are as follows: Electronic device 1; design system of a distributed compressed air energy storage system 11; memory 12; processor 13; acquisition unit 111; prediction unit 112; optimization unit 113; control unit 114. DETAILED DESCRIPTION

[0021] The above embodiments of the present application are described with reference to the drawings, and other advantages and effects of the present application will be more clearly understood from the following examples. The present application can be variously embodied, and the following examples and features thereof can be combined with each other to the extent not departing from the scope of the present application.

[0022] It should be noted that the drawings provided in the following embodiments only schematically illustrate the basic concept of the present application, and the drawings only show the components related to the present application, not the number, shape and size of the components when actually implemented. The actual implementation of each component can be randomly changed in shape, number and ratio, and the layout pattern of the components can be more complex.

[0023] In the following description, numerous specific details are discussed in order to provide a thorough explanation of embodiments of the application. It will be apparent, however, to one of ordinary skill in the art that embodiments of the application can be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to avoid obscuring the application embodiments being described.

[0024] The application provides a distributed compressed air energy storage system design method. After predicting power surplus prediction data that needs to be additionally stored, the historical fatigue degree of each compressor can be determined based on the historical pumping data of each compressor. Then, in combination with the current air energy storage pressure of the corresponding energy storage device, the pumping speed threshold of the compressor for compressed air energy storage and the air energy storage pressure threshold of the corresponding energy storage device can be predicted, so as to provide optimized compressor allocation data for the power surplus prediction data by using the predicted pumping speed threshold of each compressor and the air energy storage pressure threshold of each energy storage device, and to realize compressed air energy storage of the power surplus. Through this monitoring method of the compressor and the energy storage device, the pumping speed of the compressor and the air energy storage pressure threshold of the energy storage device can be ensured to be within a reasonable range before each energy storage, so that the service life of the compressor is ensured, and the working mode is more reliable, and the problem of low air compression energy storage efficiency and accelerated compressor damage caused by the continuous use of the compressor with high load is effectively prevented.

[0025] Figure 1 A flowchart of the distributed compressed air energy storage system design method in an example embodiment of the application is shown, which is applied to a design system and includes steps S10-S40. The technical solutions of the application will be described in detail below in combination with Figure 1

[0026] First, step S10 is performed to obtain power surplus prediction data of a power grid and historical pumping data of each compressor.

[0027] The power surplus prediction data of the power grid can be the power surplus prediction data that is further predicted after the power grid predicts the power consumption of each region. Of course, after the total power generation data of the power grid is deducted according to the predicted power consumption, the power surplus prediction data can be further predicted by other energy storage methods before being input into the design system of the application. For example, the power surplus prediction data for compressed air energy storage can be predicted after the energy storage prediction of other energy storage methods such as battery energy storage, and the power surplus prediction data for compressed air energy storage can be obtained by subtracting the previous power surplus prediction data after the energy storage prediction of other energy storage methods.

[0028] In step S10, the power surplus prediction data of the power grid is obtained, including: ​The total power generation data of the power grid includes main grid power and renewable power data incorporated into the power grid. According to the total power generation data of the power grid, the power consumption data of each region supplied by the power grid, and the power loss data when corresponding power transmission is performed, power surplus prediction data of the power grid is obtained. The calculation formula of the power surplus prediction data is: represents the total power generation data of the power grid, represents the power consumption data of each region, represents the power loss data.

[0029] In the design system of the present application, when the power surplus prediction data is used for compressed air energy storage, the total power generation data of the power grid is first obtained, which can be obtained by superimposing the main grid power and the renewable power data incorporated into the power grid. The renewable power data can be wind power data, hydroelectric power data, solar power data, etc. After determining the total power generation data of the power grid, the power consumption data of each region supplied by the power grid and the power loss data when corresponding power transmission is performed can be further predicted, and the power surplus prediction data is obtained by calculating the formula The power consumption is removed. It is worth noting that the calculation of the power surplus prediction data can be directly used for compressed air energy storage after removing the power consumption. Of course, it can also be the remaining power surplus prediction data obtained after completing energy storage by other energy storage means, and then further completing compressed air energy storage.

[0030] After step S10, that is, after obtaining the historical pumping data of each compressor, it further includes: Extracting the historical pumping speed and historical pumping running time corresponding to each compressor in the historical pumping data; According to the historical pumping running time, the historical non-running time between the historical pumping running time is filtered to obtain; According to the historical pumping speed, the historical pumping running time and the historical non-running time, the historical fatigue degree of the current compressor is predicted.

[0031] ​After the historical pumping data of the compressor is acquired, the historical fatigue degree of each compressor can be calculated, so that the executable pumping speed threshold of the compressor can be more accurately predicted, and the air energy storage pressure threshold of the energy storage device can be predicted, so that the reliable air compression work that the compressor can perform can be better reflected. Specifically, the historical pumping speed corresponding to each compressor in the historical pumping data can be extracted, and then the historical non-running time between adjacent two historical pumping running times can be found according to the extracted historical pumping running time. After the historical non-running time is obtained, the historical fatigue degree of the current compressor can be more accurately estimated by combining the historical pumping speed, the historical pumping running time and the historical non-running time.

[0032] The historical fatigue degree of the current compressor is predicted according to the historical pumping speed, the historical pumping running time and the historical non-running time, and includes: According to the speed interval in which the historical pumping speed corresponding to each historical pumping time period in the historical pumping running time is located, a speed fatigue increasing factor corresponding to each historical pumping time period is obtained by table lookup; According to the historical air energy storage pressure of the energy storage device in each historical pumping time period and the corresponding fatigue conversion coefficient, a historical fatigue increment value is calculated; According to the historical pumping time period, the speed fatigue increasing factor, the fatigue cumulative increasing factor, the historical non-running time, the fatigue decreasing factor corresponding to the historical non-running time and the historical fatigue increment value, the historical fatigue degree of the current compressor is predicted.

[0033] In the historical fatigue degree prediction using the historical pumping running time and the historical non-running time, the speed interval in which the historical pumping speed corresponding to each historical pumping time period in the historical pumping running time is located can be found based on the historical pumping speed, so that the speed fatigue increasing factor corresponding to the speed interval can be further found as the speed fatigue increasing factor of the corresponding historical pumping time period. In the design system of the present application, a corresponding relationship table of speed interval and speed fatigue increasing factor is artificially stored in advance, so that the corresponding speed interval can be found from the corresponding relationship table based on the historical pumping speed, and then the speed fatigue increasing factor corresponding to the speed interval can be found. In addition, the historical pumping speed can be measured by a speed sensor for speed detection of the compressor, and each time information can be obtained by a timer when the compressor is working.

[0034] Then, the historical air energy storage pressure of the energy storage device for each historical pumping period can be obtained through pressure sensors, etc. Then, the historical air energy storage pressure is multiplied with the corresponding fatigue conversion coefficient to calculate the historical fatigue increase brought to the compressor by the historical air energy storage pressure.

[0035] Finally, by combining historical pumping time periods with speed fatigue increase factors and cumulative fatigue increase factors, combining historical non-operation time with corresponding fatigue decrease factors, and considering the historical fatigue increment caused by the historical air energy storage pressure of the energy storage device, a comprehensive calculation is performed to predict the current historical fatigue level of the compressor, thus ensuring the accuracy of the compressor fatigue load prediction.

[0036] Preferably, the formula for calculating historical fatigue level is: , Indicates the first A historical pumping period, Indicates the first A historical non-running time, Indicates the first The sum of historical non-operational time and the accumulated historical fatigue level before that, Indicates the fatigue increase factor. Indicates the fatigue decline factor. This represents the fatigue increase factor of speed. This represents the cumulative fatigue increase factor, where each The range of fatigue levels Corresponding to one , This indicates historical fatigue and appreciation. Indicates historical air energy storage pressure, This represents the fatigue conversion factor.

[0037] When calculating the fatigue escalation factor resulting from compressor fatigue during historical non-operational periods, this fatigue escalation factor can be calculated by combining the fatigue escalation factor generated by the compressor's own operation with the historical air storage pressure of the energy storage device based on the compressor's cumulative fatigue escalation factor. Since each The range of fatigue levels Corresponding to one Therefore, this fatigue increase factor was manually calibrated. The historical fatigue increase per unit time... Since the historical air storage pressure is constantly changing, the compressor can also adjust its operation based on historical pumping time periods. Historical fatigue value-added per unit time Integrating is performed to calculate the results over the historical pumping period. Historical fatigue value-added total The fatigue conversion coefficient It can be calibrated manually based on the conversion relationship between energy storage pressure and fatigue level.

[0038] Next, step S20 is executed, whereby the pumping speed threshold for each compressor and the air storage pressure threshold for the corresponding energy storage device are predicted based on the historical fatigue level of each compressor corresponding to the historical pumping data. The energy storage device can be an air tank, or other devices used for compressed air storage.

[0039] After calculating the historical fatigue level of each compressor, the design system of this invention can further predict the pumping speed threshold of each compressor and the air storage pressure threshold of the corresponding energy storage device based on the historical fatigue level of the compressor and the air storage pressure information of the energy storage device, thereby ensuring the operational reliability of the compressor when it continues to compress air.

[0040] In step S20, based on the historical fatigue level of each compressor corresponding to the historical pumping data, the pumping speed threshold of each compressor and the air energy storage pressure threshold of the energy storage device corresponding to each compressor are predicted, including: Based on the historical fatigue level of each compressor and the corresponding first speed adjustment coefficient corresponding to the historical pumping data, the first pumping speed influence of the corresponding compressor is calculated. Based on the current air energy storage pressure of the energy storage device corresponding to each compressor and the corresponding second speed adjustment coefficient, the influence of the second pumping speed of the corresponding energy storage device is calculated. Based on the baseline pumping speed, the influence of the first pumping speed, and the influence of the second pumping speed, the pumping speed threshold for each compressor is predicted. Based on the pumping speed threshold, the air storage pressure threshold of the energy storage device corresponding to each compressor is obtained by looking up the table.

[0041] In the process of predicting the pumping speed threshold and the air energy storage pressure threshold, the calculated historical fatigue degree can be used in combination with the corresponding first speed adjustment coefficient to calculate the corresponding first pumping speed influence quantity, and then the current air energy storage pressure of the corresponding energy storage device of each compressor corresponding to the historical pumping data is combined with the corresponding second speed adjustment coefficient to calculate the corresponding second pumping speed influence quantity of the corresponding energy storage device. The first speed adjustment coefficient can be determined by the range of the fatigue degree corresponding to the historical fatigue degree, that is, according to the fatigue degree range of the historical fatigue degree, the speed adjustment coefficient corresponding to the fatigue degree range is found as the first speed adjustment coefficient, and the corresponding first pumping speed influence quantity is calculated by multiplying the historical fatigue degree. Similarly, the second speed adjustment coefficient can also be found according to the energy storage pressure range of the current air energy storage pressure to find the corresponding speed adjustment coefficient as the second speed adjustment coefficient, and the second pumping speed influence quantity is calculated in combination with the corresponding current air energy storage pressure. Then, the reasonable prediction adjustment of the pumping speed prediction is realized by combining the reference pumping speed in the compressor pumping process, so as to obtain the pumping speed threshold of each compressor. Finally, the air energy storage pressure threshold of the energy storage device corresponding to the compressor under the corresponding pumping speed threshold is found through the corresponding relationship table of the pumping speed threshold and the air energy storage pressure threshold, so as to realize the reasonable control of the compressor and the energy storage device through the pumping speed threshold and the air energy storage pressure threshold, and ensure the reliability of the compressor operation.

[0042] Preferably, the calculation formula of the pumping speed threshold is: ; represents the reference pumping speed, represents the historical fatigue degree, represents the first speed adjustment coefficient, represents the first pumping speed influence quantity, represents the current air energy storage pressure, represents the second speed adjustment coefficient, represents the second pumping speed influence quantity.

[0043] Then, step S30 is performed to perform allocation optimization of the compressor based on the pumping speed threshold, the air energy storage pressure threshold and the energy storage priority of each compressor, and the power remaining prediction data, to obtain the allocation data of the compressor.

[0044] After obtaining the pumping speed threshold of each compressor, the air energy storage pressure threshold of the energy storage device and the energy storage priority by designing the system, the deployment optimization of the compressor in need of work can be further combined with the power surplus prediction data, so as to obtain the deployment data which can meet the requirements of the pumping speed threshold, the air energy storage pressure threshold, the energy storage priority and the power surplus prediction data, so as to more reliably operate the compressor and realize the compressed air energy storage.

[0045] In step S30, based on the pumping speed threshold, the air energy storage pressure threshold and the energy storage priority of each compressor, and the power surplus prediction data, the deployment optimization of the compressor is carried out to obtain the deployment data of the compressor, including: According to the air energy storage pressure threshold, the air energy storage pressure state of each energy storage device is monitored to obtain an energy storage device set whose air energy storage pressure is less than the air energy storage pressure threshold; According to the energy storage priority of each energy storage device, the energy storage device set is sorted to obtain an energy storage device list; According to the power surplus prediction data, the air compression amount corresponding to the energy storage device is predicted; According to the air compression amount, the residual volume corresponding to each energy storage device in the energy storage device list is called in turn in the order of priority from high to low, and the superposition processing is carried out to obtain a superposition compression volume; The superposition compression volume is detected; When the superposition compression volume is greater than the air compression amount for the first time, the compressor set corresponding to the superposition compression volume and the pumping speed threshold corresponding to the compressor are taken as the deployment data.

[0046] In the optimization of the compressor, the air energy storage pressure threshold of the energy storage device can be used to monitor the air energy storage pressure state of each energy storage device, so as to determine whether the air energy storage pressure of each energy storage device meets the air energy storage pressure threshold. If it meets, the corresponding energy storage device data is extracted to construct an energy storage device set. After obtaining the energy storage device set, each energy storage device data in the energy storage device set is sorted based on the artificial set energy storage priority of the energy storage device, so as to obtain an energy storage device list. Then, the air compression amount of the energy storage device can be roughly predicted by the power surplus prediction data. Specifically, the historical power surplus prediction data set and the air compression amount data set can be used for model training to obtain an air compression amount prediction model. Then, the corresponding air compression amount is directly predicted by inputting the power surplus prediction data. After determining the air compression amount, the residual volume in the energy storage device list is sequentially added based on the priority from high to low, so as to obtain the added compression volume after each addition. And the added compression volume is continuously detected, and when the added compression volume is greater than the air compression amount for the first time, the compressor set corresponding to the added compression volume and the pumping speed threshold corresponding to the compressor are taken as the deployment data to control and manage the compressor.

[0047] According to the air compression amount, the residual volume corresponding to each energy storage device in the energy storage device list is sequentially called for addition processing in the order of priority from high to low, to obtain an added compression volume, including: The pressure difference between the air energy storage pressure threshold and the air energy storage pressure of the corresponding energy storage device is calculated to obtain a pressure difference value; According to the pressure difference value and the corresponding volume conversion coefficient, the residual volume corresponding to each energy storage device is calculated, and the calculation formula of the residual volume is: , The volume conversion coefficient is represented by V, The pressure difference value is represented by P, The air energy storage pressure is represented by P, The air energy storage pressure threshold is represented by P0; According to the air compression amount, the residual volume corresponding to each energy storage device in the energy storage device list is sequentially called for addition processing in the order of priority from high to low, to obtain an added compression volume.

[0048] In the process of calculating the superimposed compression volume, the difference between the air energy storage pressure threshold and the air energy storage pressure of the corresponding energy storage device can be calculated as a pressure difference value, and then the pressure difference value is converted into the residual volume corresponding to each energy storage device by combining the manually set volume conversion coefficient, and then the residual volume is sequentially superimposed based on the residual volume in the energy storage device list in descending order of priority, so that the superimposed compression volume after each superposition is obtained. And through continuous detection of the superimposed compression volume, when the superimposed compression volume is greater than the air compression amount for the first time, the compressor set corresponding to the superimposed compression volume and the pumping speed threshold corresponding to the compressor are taken as the deployment data to realize the control and management of the compressor.

[0049] Then, step S40 is performed, and the corresponding compressor is controlled according to the deployment data to compress the grid power into compressed air energy storage.

[0050] After obtaining the deployment data through the design system, the deployment data can be directly used to control the corresponding compressor to realize the compressed air energy storage of the excess power, thereby improving the efficiency and operation reliability of the compressor in the compressed air energy storage.

[0051] Please refer to Figure 2 The application also provides a distributed compressed air energy storage system design system 11, comprising: an acquisition unit 111 configured to acquire power surplus prediction data of a power grid and historical pumping data of each compressor; a prediction unit 112 configured to predict a pumping speed threshold of each compressor and an air energy storage pressure threshold of an energy storage device corresponding to each compressor according to a historical fatigue degree of each compressor corresponding to the historical pumping data; an optimization unit 113 configured to perform deployment optimization of the compressor based on the pumping speed threshold of each compressor, the air energy storage pressure threshold, the energy storage priority, and the power surplus prediction data, to obtain deployment data of the compressor; and a control unit 114 configured to control the corresponding compressor to compress the grid power into compressed air energy storage according to the deployment data.

[0052] It should be noted that the distributed compressed air energy storage system design system 11 provided by the above embodiment and the distributed compressed air energy storage system design method provided by the above embodiment belong to the same concept, wherein the specific operation of each module and unit has been described in detail in the method embodiment, which will not be repeated here. The distributed compressed air energy storage system design system 11 provided by the above embodiment can allocate the above functions to different functional modules as needed in actual application, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above, and this is not limited herein.

[0053] Please refer toFigure 3 The electronic device 1 can include a memory 12, a processor 13 and a bus, and can further include a computer program, such as a distributed compressed air energy storage system design program, stored in the memory 12 and executable on the processor 13.

[0054] The memory 12 includes at least one type of readable storage medium, such as a flash memory, a mobile hard disk, a multimedia card, a card-type memory (e.g., an SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory 12 can be an internal storage unit of the electronic device 1, such as a mobile hard disk of the electronic device 1. In other embodiments, the memory 12 can also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Further, the memory 12 can include both an internal storage unit and an external storage device of the electronic device 1. The memory 12 can be used to store application software installed in the electronic device 1 and various data, such as codes for distributed compressed air energy storage system design, and can also be used to temporarily store data that has been output or will be output.

[0055] The processor 13 can be composed of an integrated circuit in some embodiments, such as a single packaged integrated circuit or a plurality of packaged integrated circuits with the same or different functions, including one or more combinations of a central processing unit (CPU), a microprocessor, a digital processing chip, a graphics processor and various control chips, etc. The processor 13 is a control unit of the electronic device 1, which connects various components of the electronic device 1 through various interfaces and lines, executes programs or modules stored in the memory 12 (such as a distributed compressed air energy storage system design program, etc.), and calls data stored in the memory 12 to perform various functions and process data of the electronic device 1.

[0056] The processor 13 executes an operating system and various application programs installed in the electronic device 1. The processor 13 executes the application programs to implement the steps in the above-mentioned distributed compressed air energy storage system design method.

[0057] The computer program can be divided into one or more modules, which are stored in the memory 12 and executed by the processor 13 to complete the present application. The one or more modules can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program in the electronic device 1. For example, the computer program can be divided into units in the distributed compressed air energy storage system design system.

[0058] The integrated units implemented in the form of software function modules described above can be stored in a computer readable storage medium, which can be nonvolatile or volatile. The software function modules described above are stored in a storage medium, including a plurality of instructions for causing a computer device (which can be a personal computer, a computer device, or a network device, etc.) or a processor to execute part of the functions of the distributed compressed air energy storage system design method described in various embodiments of the present application.

[0059] In summary, the distributed compressed air energy storage system design method and system disclosed by the present application can determine the historical fatigue degree of each compressor based on the historical pumping data of each compressor after predicting the power surplus prediction data that needs additional storage. Then, by combining the current air energy storage pressure of the corresponding energy storage device, the pumping speed threshold of the compressor for compressed air energy storage and the air energy storage pressure threshold of the corresponding energy storage device can be predicted to provide optimized compressor allocation data for the power surplus prediction data by using the predicted pumping speed threshold of each compressor and the air energy storage pressure threshold of each energy storage device, so as to realize compressed air energy storage of the power surplus. Through this monitoring method of the compressor and the energy storage device, the pumping speed of the compressor and the air energy storage pressure threshold of the energy storage device can be ensured to be within a reasonable range before each energy storage, so as to ensure the service life and working efficiency of the compressor, and make the working mode more reliable and stable. Therefore, the present application effectively overcomes the shortcomings of the prior art and has high industrial utilization value.

[0060] The above embodiments only exemplarily illustrate the principles and effects of the present application, and are not used to limit the present application. Any person skilled in the art can modify or change the above embodiments without departing from the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by those skilled in the art without departing from the spirit and technical thought disclosed by the present application should be covered by the claims of the present application.

Claims

1. A method of designing a distributed compressed air energy storage system, characterized in that, The method comprises the following steps: obtaining power surplus prediction data of a power grid and historical pumping data of each compressor; predicting a pumping speed threshold of each compressor and an air energy storage pressure threshold of an energy storage device corresponding to each compressor according to the historical fatigue degree of each compressor corresponding to the historical pumping data; performing optimization of the deployment of the compressor based on the pumping speed threshold, the air energy storage pressure threshold and the energy storage priority of each compressor, and the power surplus prediction data, to obtain deployment data of the compressor; controlling the corresponding compressor to compress air energy storage of power grid power according to the deployment data.

2. The distributed compressed air energy storage system design method of claim 1, wherein, The method comprises the following steps: obtaining power surplus prediction data of a power grid, comprising: obtaining total power generation data of the power grid, including main grid power and renewable power data integrated into the power grid; The calculation formula of the power residual prediction data is: , represents grid total power production data, represents regional power consumption data, represents power loss data.

3. The distributed compressed air energy storage system design method of claim 1, wherein, obtaining the power surplus prediction data of the power grid according to the total power generation data of the power grid, the power consumption data of each region to be supplied with power by the power grid, and the power loss data during corresponding power transmission; after obtaining the historical pumping data of each compressor, further comprising: extracting the historical pumping speed and historical pumping operation time corresponding to each compressor in the historical pumping data; filtering to obtain historical non-operation time between the historical pumping operation time according to the historical pumping operation time; 4. The distributed compressed air energy storage system design method of claim 3, wherein, predicting the historical fatigue degree of the current compressor according to the historical pumping speed, the historical pumping operation time and the historical non-operation time. The method for predicting the historical fatigue degree of the current compressor according to the historical pumping speed, the historical pumping operation time and the historical non-operation time comprises the following steps: looking up the speed fatigue incremental factor corresponding to each historical pumping time period according to the speed interval in which the historical pumping speed corresponding to the different historical pumping time periods in the historical pumping operation time is located; calculating the historical fatigue increment value according to the historical air energy storage pressure of the energy storage device in each historical pumping time period and the corresponding fatigue conversion coefficient; 5. The distributed compressed air energy storage system design method of claim 4, wherein, predicting the historical fatigue degree of the current compressor according to the historical pumping time period, the speed fatigue incremental factor, the fatigue cumulative incremental factor, the historical non-operation time, the fatigue decrement factor corresponding to the historical non-operation time and the historical fatigue increment value. ， represents the first historical pumping time period, represents the first historical non-running time, represents the first historical non-running time, represents the first historical non-running time, represents the first historical non-running time, represents the first historical non-running time and its previous accumulated historical fatigue degree sum, represents the fatigue increasing factor, represents the fatigue decreasing factor, represents the speed fatigue increasing factor, represents the fatigue cumulative increasing factor, wherein each fatigue degree range value corresponds to one , , represents the historical fatigue increase value, represents the historical air energy storage pressure, represents the fatigue conversion coefficient.

6. The distributed compressed air energy storage system design method of claim 1, wherein, The calculation formula of the historical fatigue degree is: The method for predicting the pumping speed threshold of each compressor and the air energy storage pressure threshold of the energy storage device corresponding to each compressor according to the historical fatigue degree of each compressor corresponding to the historical pumping data comprises the following steps: calculating the first pumping speed influence amount corresponding to each compressor according to the historical fatigue degree of each compressor corresponding to the historical pumping data and the corresponding first speed adjustment coefficient; calculating the second pumping speed influence amount corresponding to the energy storage device according to the current air energy storage pressure of the energy storage device corresponding to each compressor corresponding to the historical pumping data and the corresponding second speed adjustment coefficient; According to the reference pumping speed, the first pumping speed influence quantity and the second pumping speed influence quantity, a pumping speed threshold of each compressor is predicted; According to the pumping speed threshold, an air energy storage pressure threshold of a corresponding energy storage device of each compressor is obtained by table lookup.

7. The distributed compressed air energy storage system design method of claim 6, wherein, The calculation formula of the pumping speed threshold is: ； represents a reference pumping speed, represents a historical fatigue level, represents a first speed adjustment coefficient, represents a first pumping speed influence quantity, represents a current air reservoir pressure, represents a second speed adjustment coefficient, represents a second pumping speed influence quantity.

8. The distributed compressed air energy storage system design method of claim 1, wherein, Based on the pumping speed threshold, the air energy storage pressure threshold and the energy storage priority of each compressor, and the power surplus prediction data, the deployment optimization of the compressor is performed to obtain the deployment data of the compressor, including: According to the air energy storage pressure threshold, the air energy storage pressure state of each energy storage device is monitored to obtain an energy storage device set whose air energy storage pressure is less than the air energy storage pressure threshold; According to the energy storage priority of each energy storage device, the energy storage device set is sorted to obtain an energy storage device list; According to the air compression amount corresponding to the energy storage device, the residual volume corresponding to each energy storage device in the energy storage device list is called in turn according to the priority from high to low to obtain a superimposed compression volume. The superimposed compression volume is detected. When the superimposed compression volume is greater than the air compression amount for the first time, a compressor set corresponding to the superimposed compression volume and the pumping speed threshold corresponding to the compressor are taken as the deployment data. According to the air compression amount, the residual volume corresponding to each energy storage device in the energy storage device list is called in turn according to the priority from high to low to obtain a superimposed compression volume, including:

9. The distributed compressed air energy storage system design method of claim 8, wherein, The air energy storage pressure threshold and the air energy storage pressure of the corresponding energy storage device are calculated to obtain a pressure difference; According to the air compression amount, the residual volume corresponding to each energy storage device in the energy storage device list is called in turn according to the priority from high to low to obtain a superimposed compression volume. According to the pressure difference value and the corresponding volume conversion coefficient, the residual volume corresponding to each energy storage device is calculated, and the calculation formula of the residual volume is: , represents the volume conversion coefficient, represents the pressure difference value, represents the air energy storage pressure, represents the air energy storage pressure threshold value; Including:

10. A distributed compressed air energy storage system design system, characterized by, An acquisition unit is configured to acquire power surplus prediction data of a power grid and historical pumping data of each compressor; A prediction unit is configured to predict a pumping speed threshold of each compressor and an air energy storage pressure threshold of an energy storage device corresponding to each compressor according to a historical fatigue degree of each compressor corresponding to the historical pumping data; An optimization unit is configured to perform deployment optimization of the compressor based on the pumping speed threshold, the air energy storage pressure threshold and the energy storage priority of each compressor, and the power surplus prediction data to obtain deployment data of the compressor; And A control unit is configured to control the corresponding compressor to compress air energy storage of power grid power according to the deployment data. ​

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