Cooperative control method for energy storage power station system based on Carnot cycle battery

Abstract

The invention relates to the technical field of energy storage power station system control, in particular to an energy storage power station system cooperative control method based on a Carnot cycle battery, and the method comprises the steps: determining a change characteristic value of each parameter of each air chamber in each Carnot cycle process, and carrying out the clustering of all historical Carnot cycle processes; whether the historical Carnot cycle process can be used for parameter control in the subsequent Carnot cycle process or not is judged based on the clustering result; the method comprises the following steps: clustering all parameters in a Carnot cycle process, determining the effective degree of each cluster, carrying out weighted averaging on the average values of all parameters in the Carnot cycle process in the corresponding cluster to obtain the optimal values of all parameters, and controlling all parameters in the subsequent Carnot cycle process through a controller. The invention aims to reduce the instability of air chamber parameters in the subsequent working process of the solid heat storage system and improve the overall heat utilization efficiency.

Description

Technical Field

[0001] This application relates to the technical field of energy storage power station system control, and specifically relates to a cooperative control method for an energy storage power station system based on a Carnot cycle battery. Background Art

[0002] An energy storage power station is a facility that converts renewable energy into electrical energy and stores it for supplying power during peak demand periods or power system failures. With the rapid development of renewable energy, energy storage technology has become the key to balancing the power grid supply and demand and improving energy utilization efficiency. The Carnot cycle battery energy storage technology is mainly based on the storage and conversion of thermal energy. Through a heat pump cycle and a heat engine cycle, electrical energy is stored and released by absorbing and releasing heat at different temperatures, thereby realizing the conversion between electrical energy and thermal energy. The cooperative control of the energy storage power station system aims to improve the stability and reliability of the power grid by optimizing the coordinated operation of each component in the energy storage power station system. Among them, the heat storage type (solid heat storage) energy storage power station is a relatively common technology. Currently, the Carnot cycle principle is widely applied in solid heat storage systems.

[0003] The high-temperature hot air generated from the high-temperature air chamber of the solid heat storage device enters the waste heat boiler. After heat exchange through the shell system, the low-temperature air enters the low-temperature air chamber of the solid heat accumulator through the low-temperature air duct, and then enters the high-temperature air chamber again after being heated by the solid heat accumulator, and continuously enters the shell system of the heat boiler again, completing a closed cycle in a cycle. During the whole cycle process, due to irreversible losses such as the thermal resistance of the heat exchanger, the parameters of the air chamber will produce unstable fluctuations during the working process, resulting in insufficient heat exchange and low overall heat utilization efficiency. Summary of the Invention

[0004] In order to solve the above technical problems, this application provides a cooperative control method for an energy storage power station system based on a Carnot cycle battery to solve the existing problems.

[0005] The cooperative control method for the energy storage power station system based on the Carnot cycle battery of this application adopts the following technical solutions:

[0006] An embodiment of this application provides a cooperative control method for an energy storage power station system based on a Carnot cycle battery. The method includes the following steps:

[0007] S1. Obtain various parameters collected at all acquisition moments of each air chamber during several historical Carnot cycles of the energy storage power station coefficient;

[0008] S2. Based on the range and overall change trend of the change curves of various parameters of each air chamber during the Carnot cycle, determine the change characteristic values of various parameters of each air chamber during each Carnot cycle;

[0009] S3. Cluster all historical Carnot cycle processes based on the average level of the differences in the characteristic value changes of each parameter under the same air chamber during the Carnot cycle process;

[0010] S4. Determine the stability based on the number of clusters in the clustering result, the difference between the maximum number of elements in the cluster and the number of all historical Carnot cycle processes, and judge whether the historical Carnot cycle process can be used for parameter control in subsequent Carnot cycle processes;

[0011] S5. Determine the effectiveness of each cluster based on the number of elements in each cluster and the degree of disorder in the order distribution of all elements corresponding to the Carnot cycle process in the cluster according to the acquisition order; perform normalization processing on the effectiveness and record it as the weight value;

[0012] S6. Use the weight value to perform weighted averaging on the average values of each parameter in all Carnot cycle processes within the corresponding cluster to obtain the optimal values of each parameter, and control each parameter in subsequent Carnot cycle processes through a controller.

[0013] Preferably, the types of the air chambers include a high-temperature air chamber and a low-temperature air chamber.

[0014] Preferably, the change curve is obtained by curve fitting of the data collected for each parameter of each air chamber during the Carnot cycle process at all acquisition times.

[0015] Preferably, the change characteristic value is determined by the product result of the range and the overall change trend.

[0016] Preferably, the overall change trend is further determined by the average value of the slopes of the corresponding points at all acquisition times on the change curve.

[0017] Preferably, when clustering all historical Carnot cycle processes, the clustering distance between Carnot cycle processes is further determined as:

[0018] Under the same parameter of the same air chamber, calculate the difference between the change characteristic values in any two historical Carnot cycle processes; take the average level of the differences of all parameters under all air chambers as the clustering distance between any two historical Carnot cycle processes.

[0019] Preferably, in step S4, the stability is further determined by the ratio result of the difference and the number of clusters.

[0020] Preferably, when the stability is greater than or equal to a preset stability threshold, the historical Carnot cycle process can be used for parameter control in subsequent Carnot cycle processes.

[0021] Preferably, the effectiveness degree is determined by the ratio of the number of elements in the corresponding clustering cluster to the degree of disorder of the order distribution.

[0022] Preferably, the method for the controller to control the parameters in the subsequent Carnot cycle process is as follows:

[0023] Take the optimal value as the ideal value of the corresponding parameter, take the deviation between the ideal value and the measured value as the input of the controller, and the controller controls the corresponding parameter in the subsequent Carnot cycle process.

[0024] This application has at least the following beneficial effects:

[0025] In this application, first, by analyzing the fluctuation of each parameter in the high-temperature air chamber and the low-temperature air chamber during each historical Carnot cycle process, the change characteristics of each parameter are characterized; secondly, according to the average difference of the change characteristic values of each parameter under the same air chamber as the clustering distance, all historical Carnot cycle processes are clustered to cluster the Carnot cycle processes with smaller distances of the change characteristic values of the parameters into one category, which is convenient for subsequent overall analysis of the same type of Carnot cycle processes and reduces the problem of inaccurate prediction results caused by the analysis of a single Carnot cycle process; then, by analyzing the clustering results, the stability of the solid heat storage system during the historical Carnot cycle process is quantified, and the accuracy of parameter control judgment in the subsequent Carnot cycle process is improved; again, by analyzing the continuity between the Carnot cycle processes in each clustering cluster, the effectiveness degree of the Carnot cycle processes in the clustering cluster is characterized, and the reference degree of the clustering cluster to the air chamber parameters in the subsequent Carnot cycle process is calculated; finally, by performing weighted analysis on the air chamber parameters in the historical Carnot cycle process, the optimal value of the air chamber parameters in the subsequent Carnot cycle process is obtained, thereby reducing the instability of the air chamber parameters during the operation of the subsequent solid heat storage system, and thus improving the overall thermal utilization efficiency. Description of the Drawings

[0026] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0027] Figure 1 It is a flowchart of the collaborative control method for the energy storage power station system based on the Carnot cycle battery provided by the present application;

[0028] Figure 2 It is a schematic diagram of the solid heat storage system based on the Carnot cycle provided by an embodiment of the present application. Detailed Embodiments

[0029] To further elaborate on the technical means and effects adopted by this application to achieve the intended invention purpose, the following will, in conjunction with the accompanying drawings and preferred embodiments, elaborate in detail on the specific implementation manner, structure, features, and effects of the collaborative control method for an energy storage power station system based on a Carnot cycle battery proposed according to this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0030] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs.

[0031] The following will specifically describe the specific solution of the collaborative control method for an energy storage power station system based on a Carnot cycle battery provided by this application in conjunction with the accompanying drawings.

[0032] A collaborative control method for an energy storage power station system based on a Carnot cycle battery provided by one embodiment of this application.

[0033] Specifically, the following collaborative control method for an energy storage power station system based on a Carnot cycle battery is provided. Please refer to Figure 1 , and this method includes the following steps:

[0034] S1. Obtain various parameters collected by each air chamber at all acquisition moments during several historical Carnot cycle processes of the energy storage power station system.

[0035] This embodiment takes a heat storage type (solid heat storage) energy storage power station as an example for analysis. In this embodiment, the schematic diagram of the solid heat storage system based on the Carnot cycle is as shown in Figure 2 . In Figure 2 , the solid heat storage system mainly consists of 1. solid heat storage equipment, 2. waste heat boiler, 3. return air duct, 4. outlet air duct, and 5. fan.

[0036] During the entire working process of the solid heat storage system, it is very important to monitor the key parameters such as the temperature, flow rate, and pressure data of the hot air or cold air generated by the high-temperature air chamber and the low-temperature air chamber.

[0037] Accordingly, this application first obtains various parameters in the high-temperature air chamber and the low-temperature air chamber at all acquisition moments during each historical Carnot cycle process, where the data acquisition frequency is 1 second and the acquisition period is a complete Carnot cycle process.

[0038] So far, through the above method, various parameters collected by each air chamber at all acquisition moments during several historical Carnot cycle processes of the energy storage power station system can be obtained, and the historical number of acquisitions during the Carnot cycle process is set by the implementer himself.

[0039] S2. Based on the range and overall change trend of the parameter change curves of each air chamber during the Carnot cycle, determine the change characteristic values of the parameters of each air chamber during each Carnot cycle.

[0040] The parameter data of the high-temperature air chamber and the low-temperature air chamber during the historical M Carnot cycles are obtained. First, by analyzing the fluctuation of each parameter in the high-temperature air chamber and the low-temperature air chamber during each historical Carnot cycle, the change characteristics of each parameter are characterized.

[0041] Preferably, in this embodiment, the change characteristic value is determined by the product of the range and the overall change trend.

[0042] Specifically, as an implementation manner, taking the temperature parameter during the mth historical Carnot cycle as an example, the mathematical formula for the change characteristic value of the corresponding temperature parameter is:

[0043] θ m,a =(Te max -Te min )×l m,a

[0044] In the formula, θ m,a represents the change characteristic value of the temperature parameter of the ath air chamber during the mth Carnot cycle; when a = 1, it represents the high-temperature air chamber, and when a = 2, it represents the low-temperature air chamber; Te max represents the maximum temperature in the change curve of the temperature parameter of the ath air chamber during the mth Carnot cycle, and Te min represents the minimum temperature in the change curve of the temperature parameter of the ath air chamber during the mth Carnot cycle; l m,a represents the slope of the change curve of the temperature parameter of the ath air chamber during the mth Carnot cycle, and this value is represented by the average value of the slopes of all the corresponding points at all the acquisition times on the change curve.

[0045] Among them, the change curve is obtained by curve fitting of the data collected for each parameter of each air chamber at all acquisition times during the Carnot cycle. In this embodiment, the least squares method is used for curve fitting, and the least squares method is a well-known technology and will not be elaborated here.

[0046] It should be understood that the smaller the value of |Te max -Te min |, the smaller the temperature fluctuation range of the ath air chamber during the mth Carnot cycle, and the smaller the value of l m,a , the more stable the corresponding temperature fluctuation. Therefore, the smaller the value of θ m,a , the smaller the change characteristic value of the temperature parameter of the ath air chamber during the mth Carnot cycle.

[0047] As other embodiments, the overall change trend l m,a It can also be determined by the slope of the straight line obtained by linearly fitting the change curve of the temperature parameter of the a-th air chamber in the m-th Carnot cycle process.

[0048] Similarly, the change characteristic values of each parameter of each air chamber in the historical M Carnot cycle processes can be calculated.

[0049] S3. Based on the average level of the differences in the change characteristic values of each parameter under the same air chamber between Carnot cycle processes, cluster all historical Carnot cycle processes.

[0050] According to the method in step S2, the change characteristic values of each parameter of each air chamber in the historical M Carnot cycle processes can be calculated. Then, for all historical Carnot cycle processes, using the average difference of the change characteristic values of each parameter under the same air chamber as the clustering distance, cluster all Carnot cycle processes, so as to cluster the Carnot cycle processes with smaller distances of the change characteristic values of the parameters into one category, which is convenient for subsequent overall analysis of the same category of Carnot cycle processes and reduces the problem of inaccurate prediction results caused by the analysis of a single Carnot cycle process.

[0051] The specific clustering process is as follows. First, calculate the differences in the change characteristic values between the parameters under the same air chamber in different Carnot cycle processes to obtain the clustering distance between the Carnot cycle processes.

[0052] Preferably, in this embodiment, under the same parameter of the same air chamber, calculate the difference between the change characteristic values in any two historical Carnot cycle processes; take the average level of the differences of all parameters under all air chambers as the clustering distance between any two historical Carnot cycle processes.

[0053] As an embodiment, taking the clustering distance ρ between the p-th and q-th Carnot cycle processes p,q as an example, the expression is as follows: In the formula, ρ p,q represents the clustering distance between the p-th and q-th Carnot cycle processes, K represents the number of items of parameters in the Carnot cycle process, and θ p,a,k , θ q,a,k respectively represent the change characteristic values of the k-th parameter of the a-th air chamber in the p-th and q-th Carnot cycle processes.

[0054] Then, use the K-means clustering algorithm to cluster all historical Carnot cycle processes, where the clustering distance between different Carnot cycle processes is ρ calculated above. p,qTo quantify, the number of clustering clusters is determined using the silhouette coefficient method. Among them, the silhouette coefficient method and the K-means clustering algorithm are both well-known technologies and will not be elaborated here. In other embodiments of the present application, other suitable clustering algorithms can also be selected, such as: density clustering algorithm.

[0055] Suppose that after clustering, a total of N clustering clusters are obtained. Among them, the change characteristic values of the air chamber parameters in each clustering cluster during the Carnot cycle process are relatively similar, that is, the difference between the change characteristic values is small.

[0056] S4. Based on the number of clustering clusters in the clustering result, the difference between the maximum number of elements in the clustering cluster and the number of all historical Carnot cycle processes, determine the stability, and judge whether the historical Carnot cycle process can be used for parameter control in the subsequent Carnot cycle process.

[0057] Since in a solid heat storage system based on the Carnot cycle, the fluctuations of the air chamber parameters in different Carnot cycle processes should be relatively small. When the parameters show large fluctuations, it may lead to unstable operation of the system and affect the continuity and reliability during the heat storage and heat release processes. Controlling the parameters in the subsequent Carnot cycle process with the parameter data obtained from unreliable historical Carnot cycle processes will affect the judgment of parameter control in the subsequent Carnot cycle process.

[0058] Therefore, the present application first determines the stability of the solid heat storage system in the historical Carnot cycle process by calculating the stability of the air chamber parameter fluctuations between the Carnot cycle processes in the clustering result, so as to judge whether the historical Carnot cycle process can be used for parameter control in the subsequent Carnot cycle process.

[0059] It should be noted that the fewer the number of clustering clusters in the clustering result, the more concentrated the fluctuations of the air chamber parameters in the historical Carnot cycle process, and at the same time, the Carnot cycle process is more concentrated in a certain cluster, the smaller the difference in the fluctuations of the air chamber parameters in the corresponding historical Carnot cycle process, and the more stable the operation of the corresponding solid heat storage system.

[0060] Preferably, in this embodiment, the method for determining the stability is specifically: calculate the difference between the maximum number of elements in the clustering cluster and the number of all historical Carnot cycle processes; use the ratio result of this difference to the number of clustering clusters as the stability.

[0061] As an implementation manner, the mathematical formula for the stability of the solid heat storage system in the specific historical Carnot cycle process is as follows: In the formula, α represents the stability of the solid heat storage system in the historical Carnot cycle process, N represents the number of clustering clusters after clustering all historical Carnot cycle processes, M represents the number of historical Carnot cycle processes, represents the maximum value of the number of Carnot cycle processes included in N clustering clusters.

[0062] It should be understood that The larger the value of , the fewer the number of categories obtained after clustering, and the more concentrated the parameter changes in the Carnot cycle process; The larger the value of , the greater the proportion of the Carnot cycle processes in the clustering cluster with the largest number of Carnot cycle processes in the Carnot cycle process, and the corresponding Carnot cycle processes are more concentrated in a certain cluster. Therefore, the larger the value of α, the better the stability of the solid heat storage system in the historical Carnot cycle process.

[0063] In other embodiments of the present application, the mathematical formula for the stability can also be determined by determined.

[0064] Furthermore, if the stability is less than the stability threshold, it indicates that the stability of the solid heat storage system in the historical Carnot cycle process is relatively poor, which may be caused by reasons such as material problems and design defects, resulting in a poor heat exchange efficiency of the entire system. It is necessary to arrange maintenance personnel to overhaul the entire heat storage system.

[0065] If the stability is greater than or equal to the stability threshold, the air chamber parameters in the subsequent Carnot cycle process are found out for collaborative control by analyzing the changes in the air chamber parameters in the historical Carnot cycle process.

[0066] Among them, the stability threshold takes a value of 0.4 in this embodiment, and the implementer can set it by himself.

[0067] S5. Based on the number of elements in each clustering cluster and the degree of disorder in the order distribution of all elements corresponding to the Carnot cycle process according to the acquisition order, determine the effectiveness of each clustering cluster; perform normalization processing on the effectiveness and record it as the weight value.

[0068] Specifically, first for the Carnot cycle processes in each clustering cluster, if the continuity between different Carnot cycle processes is higher, it indicates that during the operation of the solid heat storage system, the change difference of the air chamber parameters in adjacent Carnot cycle processes is smaller, indicating that the effectiveness of the Carnot cycle processes in the corresponding clustering cluster is higher, and its reference degree to the air chamber parameters in the subsequent Carnot cycle process is also greater. Therefore, the effectiveness of the Carnot cycle processes in each clustering cluster can be calculated.

[0069] Preferably, in this embodiment, the ratio result of the number of elements in each clustering cluster to the degree of disorder in the order distribution of all elements corresponding to the Carnot cycle process in the clustering cluster is used as the effectiveness of the clustering cluster.

[0070] As an implementation manner, specifically, for the nth clustering cluster, the mathematical formula for the effectiveness of the clustering cluster is:

[0071]

[0072] In the formula, d n represents the effectiveness of the nth clustering cluster, and C n represents the number of Carnot cycle processes in the nth clustering cluster. σ n represents the variance value of the order of all Carnot cycle processes in the nth clustering cluster according to the acquisition order. For example: if the Carnot cycle processes in this clustering cluster are the 2nd, 4th, 5th, and 6th Carnot cycle processes respectively, then the variance of the numbers 2, 4, 5, and 6 is used as σ n .

[0073] It should be understood that the larger the value of C n , the more Carnot cycle processes are included in this clustering cluster; the smaller the value of σ n , the closer the distances between the Carnot cycle processes in the nth clustering cluster are. Therefore, the larger the value of d n , the greater the effectiveness in the Carnot cycle processes of the nth clustering cluster.

[0074] As another implementation, the standard deviation of the order of all Carnot cycle processes in the nth clustering cluster according to the acquisition order can also be used as σ n .

[0075] According to the above method, the effectiveness of each clustering cluster can be calculated. The greater the effectiveness of a certain clustering cluster, the greater the reference degree of the air chamber parameters corresponding to the Carnot cycle processes in this clustering cluster to the subsequent Carnot cycle processes. Therefore, the effectiveness is normalized and denoted as the weight value.

[0076] Taking the nth clustering cluster as an example, the mathematical formula for its corresponding weight value is: In the formula, λ n represents the weight value of the nth clustering cluster, d n represents the effectiveness of the nth clustering cluster, and N represents the number of clustering clusters after clustering all historical Carnot cycle processes.

[0077] S6. Use the weight value to perform weighted averaging on the average values of each parameter in all Carnot cycle processes within the corresponding clustering cluster to obtain the optimal values of each parameter, and control each parameter in the subsequent Carnot cycle processes through the controller.

[0078] According to the above method, the weight value of each clustering cluster can be calculated, and then the air chamber parameters in all Carnot cycle processes within the corresponding clustering cluster are weighted averaged to determine the optimal parameter values of the air chamber in each parameter for the subsequent Carnot cycle processes.

[0079] As an implementation, specifically, the mathematical formula for the optimal value of the kth parameter is:

[0080]

[0081] In the formula, V k represents the optimal value of the k-th parameter, N represents the number of clustering clusters after clustering all historical Carnot cycle processes, and λ n represents the weight value of the n-th clustering cluster, and μ n,k represents the average value of the k-th parameter in all Carnot cycle processes of the n-th clustering cluster.

[0082] According to the above method, the optimal value of each parameter can be calculated. In the subsequent Carnot cycle process, the calculated optimal value is set as the ideal value for the corresponding type of parameter in the air chamber, and the deviation between the ideal value and the measured value is used as the input of the PID controller, and the PID controller controls the corresponding parameter in the subsequent Carnot cycle process.

[0083] In other embodiments of the present application, a PI controller can also be used to control the corresponding parameter.

[0084] Thus far, the collaborative control in the working process of the solid heat storage system can be completed through the above method.

[0085] Each embodiment in the present application is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the key points of each embodiment are the differences from other embodiments.

[0086] It should be noted that unless otherwise specified and limited, terms such as "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a circuit structure, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the article or device including the element. In addition, the term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0087] Those skilled in the art will readily think of other implementation schemes of the present application after considering the specification and practicing the invention herein. The present application aims to cover any variations, uses or adaptive changes of the present application, and these variations, uses or adaptive changes follow the general principles of the present application and include common general knowledge or conventional technical means in the technical field not invented by the present application.

[0088] It should be understood that the present application is not limited to the exact structure already described and shown in the drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. A coordinated control method for an energy storage power station system based on a Carnot cycle battery, characterized in that: The method comprises the following steps: S1, obtaining various parameters collected by each wind chamber at all collection times during several historical Carnot cycles of the energy storage power station coefficient; S2, based on the range and overall change trend of the change curves of various parameters of each air chamber during the Carnot cycle, determine the change characteristic values ​​of various parameters of each air chamber during each Carnot cycle; S3, based on the average level of the difference in the characteristic values ​​of various parameters between Carnot cycle processes under the same wind chamber, all historical Carnot cycle processes are clustered; S4, based on the number of clusters in the clustering results, the difference between the maximum number of elements in the clusters and the number of all historical Carnot cycle processes, determine the stability and judge whether the historical Carnot cycle process can be used for parameter control in subsequent Carnot cycle processes; S5, determining the effectiveness of each cluster based on the number of elements in each cluster and the degree of disorder of the order distribution of all its elements in the Carnot cycle process according to the order of collection; normalizing the effectiveness and recording it as a weight value; S6, using the weight value to perform weighted average on the average values ​​of various parameters in all Carnot cycle processes in the corresponding cluster, to obtain the optimal value of various parameters, and to control various parameters in subsequent Carnot cycle processes through the controller.

2. The coordinated control method of the energy storage power station system based on the Carnot cycle battery according to claim 1, characterized in that: The types of wind chambers include high-temperature wind chambers and low-temperature wind chambers.

3. The coordinated control method of the energy storage power station system based on the Carnot cycle battery according to claim 1, characterized in that: The variation curve is obtained by curve fitting the data collected at all collection times for each parameter of each air chamber during the Carnot cycle.

4. The coordinated control method of the energy storage power station system based on the Carnot cycle battery according to claim 1, characterized in that: The change characteristic value is determined by the product of the range and the overall change trend.

5. The coordinated control method of the energy storage power station system based on Carnot cycle battery according to claim 3, characterized in that: The overall change trend is further determined by the average of the slopes of all points corresponding to the acquisition moments on the change curve.

6. The coordinated control method of the energy storage power station system based on Carnot cycle battery according to claim 1, characterized in that: When clustering all historical Carnot cycle processes, the clustering distance between the Carnot cycle processes is further determined as: Under the same parameter of the same wind chamber, the difference between the change characteristic values ​​in any two historical Carnot cycle processes is calculated; the average level of the difference of all parameters under all wind chambers is taken as the clustering distance between any two historical Carnot cycle processes.

7. The coordinated control method of the energy storage power station system based on Carnot cycle battery according to claim 1, characterized in that: In step S4, the stability is further determined by the ratio of the difference to the number of clusters.

8. The coordinated control method of the energy storage power station system based on Carnot cycle battery according to claim 7, characterized in that: When the stability is greater than or equal to a preset stability threshold, the historical Carnot cycle process can be used for parameter control in subsequent Carnot cycle processes.

9. The coordinated control method of the energy storage power station system based on Carnot cycle battery according to claim 1, characterized in that: The effectiveness is determined by the ratio of the number of elements in the corresponding cluster to the degree of disorder of the order distribution.

10. The coordinated control method of the energy storage power station system based on Carnot cycle battery according to claim 1, characterized in that: The method for controlling various parameters in the subsequent Carnot cycle process by the controller is: The optimal value is used as the ideal value of the corresponding parameter, and the deviation between the ideal value and the measured value is used as the input of the controller, and the controller controls the corresponding parameter in the subsequent Carnot cycle process.

Images