Excitation parameter setting method for compression air energy storage unit peak regulation and phase modulation mode

Abstract

This application relates to the field of new energy technology, and in particular to a method for tuning excitation parameters in peak-shaving and phase-modulation modes of a compressed air energy storage unit. The method includes: acquiring key parameters of the first excitation system in peak-shaving mode and key parameters of the second excitation system in phase-modulation mode; performing cluster analysis on the key parameters of the first and second excitation systems using the optimal peak-shaving strategy and the maximum phase-modulation strategy, respectively, to obtain the highest quality core; further clustering the key parameters of the first and second excitation systems based on the distance between the vectors of each parameter and the highest quality core, obtaining the first key excitation parameter and the second key excitation parameter; tuning these parameters; and then integrating them to obtain the excitation parameters of the compressed air energy storage unit that accommodate both peak-shaving and phase-modulation modes. This solves the problem that, during multi-mode switching, the excitation parameters tuned in peak-shaving mode of the compressed air energy storage system are not effective in phase-modulation mode.

Description

Technical Field

[0001] This application relates to the field of new energy technology, and in particular to a method for setting the excitation parameters of a compressed air energy storage unit in peak-shaving and phase-shaving mode. Background Technology

[0002] In recent years, new energy power generation technologies, represented by wind power and photovoltaic power generation, have developed rapidly, and the proportion of new energy in building a low-carbon power system has gradually increased. However, the inherent uncertainties of wind power and photovoltaic power have brought unprecedented challenges to the operation of the power system. Among these challenges, the dynamic support capability of conventional power supply voltage in new energy bases is particularly prominent. In order to smooth the output fluctuations of renewable energy and improve the flexibility and stability of the power system, energy storage has become the main means of peak shaving in the new power system, and its importance in the energy balance of the power system is becoming increasingly prominent. Compressed air energy storage technology is an emerging energy storage technology that stores pressure potential energy by compressing air. It has the characteristics of large capacity and long life and has broad application prospects.

[0003] In related technologies, the expansion and power generation section of compressed air energy storage uses a synchronous motor, which can serve as a synchronous condenser to provide dynamic voltage support for new energy bases. During operation, the compressed air energy storage unit needs to flexibly switch between different working modes according to the needs of the power system, such as peak shaving mode and phase regulation mode. Peak shaving mode is mainly used to balance the supply and demand of the power system, while phase regulation mode is used to regulate the reactive power of the power system and improve power quality. In order to ensure that the compressed air energy storage unit can operate efficiently and stably in both modes, the parameter setting of the excitation system is particularly important.

[0004] However, the excitation parameter setting principles of compressed air energy storage systems differ between peak shaving and phase modulation modes. They are mostly designed for a single operating mode and lack adaptability and robustness when switching between multiple modes. As a result, the excitation parameters set in peak shaving mode are not effective in phase modulation, which urgently needs to be addressed. Summary of the Invention

[0005] This application provides a method for setting the excitation parameters of a compressed air energy storage unit in peak-shaving and phase-modulation mode, in order to solve the problem that the excitation parameters set in the peak-shaving mode of the compressed air energy storage system are not effective in the phase-modulation mode when switching between multiple modes.

[0006] The first aspect of this application provides a method for tuning the excitation parameters of a compressed air energy storage unit in peak-shaving and phase-modulation mode, including the following steps:

[0007] Acquire the key parameters of the first excitation system in peak shaving mode and the key parameters of the second excitation system in phase modulation mode in the compressed air energy storage unit;

[0008] Based on a preset clustering analysis method, the key parameters of the first excitation system are clustered using the optimal peak-shaving strategy, and the key parameters of the second excitation system are clustered using the maximum phase-shaving strategy. The optimal centroids of the key parameters of the first and second excitation systems are obtained. The key parameters of the first and second excitation systems are clustered according to the first distance between the vector of the key parameter of the first excitation system and the optimal centroid and the second distance between the vector of the key parameter of the second excitation system and the optimal centroid, so as to obtain the first key excitation parameter under the peak-shaving mode and the second key excitation parameter under the phase-shaving mode.

[0009] The first key excitation parameter and the second key excitation parameter are tuned, and the tuned first key excitation parameter and the tuned second key excitation parameter are integrated to obtain the excitation parameters of the compressed air energy storage unit that take into account both peak shaving and phase modulation modes.

[0010] According to one embodiment of this application, the step of performing cluster analysis on the key parameters of the first excitation system using the optimal peak-shaving strategy and on the key parameters of the second excitation system using the maximum phase-shaving strategy includes:

[0011] Based on the preset clustering analysis method, the key parameters of the first excitation system are clustered using the optimal small-signal dynamic adjustment strategy to obtain the first key excitation parameters.

[0012] Based on the preset clustering analysis method, the key parameters of the second excitation system are clustered using the strategy of maximizing effective reactive current gain to obtain the second key excitation parameters.

[0013] According to one embodiment of this application, the tuning of the first key excitation parameter includes:

[0014] Determine whether the turbine system in the compressed air energy storage unit is in power generation mode under the peak shaving mode;

[0015] If the turbine system operates in the power generation state under the peak shaving mode, the setting target of the first key excitation parameter is determined, and the potential energy of compressed air is converted into electrical energy; otherwise, a fault alert is generated.

[0016] According to one embodiment of this application, the tuning of the second key excitation parameter includes:

[0017] Determine whether the turbine system in the compressed air energy storage unit is in phase modulation mode under the peak shaving mode;

[0018] If the turbine system is in the phase modulation state under the peak shaving mode, the tuning target of the second key excitation parameter is determined, and the turbine system is used to adjust the second key excitation parameter to provide reactive power.

[0019] According to one embodiment of this application, the objective function corresponding to the tuning target of the first key excitation parameter is:

[0020] minJ=∫0 T e 2 (t)dt

[0021] The objective function corresponding to the setting target of the second key excitation parameter is:

[0022]

[0023] Where J is the objective function, T is the time interval, and e 2 (t) represents the square of the active power deviation, Δi d Δu represents the reactive current increment along the d-axis, and Δu represents the voltage change.

[0024] According to an embodiment of the present invention, the method for tuning excitation parameters of a compressed air energy storage unit in peak-shaving and phase-modulation modes involves obtaining key parameters of the first excitation system in peak-shaving mode and key parameters of the second excitation system in phase-modulation mode. Cluster analysis is then performed on the key parameters of the first and second excitation systems using the optimal peak-shaving strategy and the maximum phase-modulation strategy, respectively, to obtain the optimal centroid. Based on the distance between the vector of each parameter and the optimal centroid, the key parameters of the first and second excitation systems are further clustered to obtain the first and second key excitation parameters. These parameters are then tuned and integrated to obtain the excitation parameters of the compressed air energy storage unit that accommodate both peak-shaving and phase-modulation modes. This solves the problem that, during multi-mode switching, the excitation parameters tuned in peak-shaving mode do not perform well in phase-modulation mode. By using cluster analysis to analyze the key parameters of the excitation system in both peak-shaving and phase-modulation modes, the key excitation parameters in both modes are determined, tuned, and integrated. This ensures that the excitation system of the compressed air energy storage system exhibits good performance in both peak-shaving and phase-modulation modes during frequent switching scenarios.

[0025] A second aspect of this application provides an excitation parameter tuning device for a compressed air energy storage unit in peak-shaving and phase-modulation mode, comprising:

[0026] The acquisition module is used to acquire key parameters of the first excitation system in peak shaving mode and key parameters of the second excitation system in phase shaving mode of the compressed air energy storage unit.

[0027] The clustering analysis module is used to perform clustering analysis on the key parameters of the first excitation system based on a preset clustering analysis method and the optimal peak-shaving strategy, and to perform clustering analysis on the key parameters of the second excitation system based on the maximum phase-shaving strategy, so as to obtain the optimal centroids of the key parameters of the first excitation system and the key parameters of the second excitation system. Based on the first distance between the vector of the key parameter of the first excitation system and the optimal centroid and the second distance between the vector of the key parameter of the second excitation system and the optimal centroid, the key parameters of the first excitation system and the key parameters of the second excitation system are clustered and divided to obtain the first key excitation parameter under the peak-shaving mode and the second key excitation parameter under the phase-shaving mode.

[0028] The tuning module is used to tune the first key excitation parameter and the second key excitation parameter, and to integrate the tuned first key excitation parameter and the tuned second key excitation parameter to obtain the excitation parameters of the compressed air energy storage unit that take into account both peak-shaving and phase-modulation modes.

[0029] According to an embodiment of the present invention, the clustering analysis module includes:

[0030] The first clustering analysis unit is used to perform clustering analysis on the key parameters of the first excitation system based on the preset clustering analysis method and using the optimal small-signal dynamic adjustment strategy to obtain the first key excitation parameters.

[0031] The second clustering analysis unit is used to perform clustering analysis on the key parameters of the second excitation system based on the preset clustering analysis method and using the maximum effective reactive current gain strategy to obtain the second key excitation parameters.

[0032] According to one embodiment of the present invention, the tuning module includes:

[0033] The first judgment unit is used to determine whether the turbine system in the compressed air energy storage unit is in the power generation state under the peak shaving mode.

[0034] The first determining unit is used to determine the setting target of the first key excitation parameter if the turbine system is in the power generation state under the peak shaving mode, and convert the potential energy of compressed air into electrical energy; otherwise, it generates a fault reminder.

[0035] According to one embodiment of the present invention, the tuning module includes:

[0036] The second judgment unit is used to determine whether the turbine system in the compressed air energy storage unit is in phase modulation state under the peak shaving mode.

[0037] The second determining unit is used to determine the tuning target of the second key excitation parameter if the turbine system is in the phase modulation state under the peak shaving mode, and to adjust the second key excitation parameter using the turbine system to provide reactive power.

[0038] According to one embodiment of the present invention, the objective function corresponding to the tuning target of the first key excitation parameter is:

[0039] minJ=∫0 T e 2 (t)dt

[0040] The objective function corresponding to the setting target of the second key excitation parameter is:

[0041]

[0042] Where J is the objective function, T is the time interval, and e 2 (t) represents the square of the active power deviation, Δi d Δu represents the reactive current increment along the d-axis, and Δu represents the voltage change.

[0043] According to an embodiment of the present invention, the excitation parameter tuning device for a compressed air energy storage unit in peak-shaving and phase-modulation modes acquires key parameters of the first excitation system in peak-shaving mode and key parameters of the second excitation system in phase-modulation mode. Cluster analysis is performed on the key parameters of the first and second excitation systems using the optimal peak-shaving strategy and the maximum phase-modulation strategy, respectively, to obtain the optimal centroid. Based on the distance between the vector of each parameter and the optimal centroid, the key parameters of the first and second excitation systems are further clustered to obtain the first and second key excitation parameters, which are then tuned and integrated to obtain the excitation parameters of the compressed air energy storage unit that accommodate both peak-shaving and phase-modulation modes. This solves the problem that, during multi-mode switching, the excitation parameters tuned in peak-shaving mode of the compressed air energy storage system are not effective in phase-modulation mode. By using cluster analysis to analyze the key parameters of the excitation system in both peak-shaving and phase-modulation modes, the key excitation parameters in both modes are determined, tuned, and integrated, thus achieving good performance of the excitation system in both peak-shaving and phase-modulation modes of the compressed air energy storage system during frequent switching scenarios.

[0044] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to implement the excitation parameter tuning method for the peak-shaving-phase-modulation mode of a compressed air energy storage unit as described in the above embodiments.

[0045] A fourth aspect of this application provides a computer-readable storage medium storing computer instructions for causing the computer to execute the excitation parameter tuning method for the peak-shaving and phase-shaving mode of a compressed air energy storage unit as described in the above embodiments.

[0046] A fifth aspect of the present invention provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the method described in the above embodiments.

[0047] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0048] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0049] Figure 1 This is a flowchart of a method for setting excitation parameters in peak-shaving and phase-modulation mode of a compressed air energy storage unit according to an embodiment of this application;

[0050] Figure 2 This is an overall flowchart of a method for tuning excitation parameters of a compressed air energy storage system in peak-shaving-phase-modulation mode according to an embodiment of this application;

[0051] Figure 3 This is a block diagram illustrating the excitation parameter setting device for a compressed air energy storage unit in peak-shaving and phase-modulation mode according to an embodiment of this application.

[0052] Figure 4 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application.

[0053] Explanation of reference numerals in the attached figures: 10 - Excitation parameter setting device for peak-shaving and phase-shaving mode of compressed air energy storage unit; 100 - Acquisition module; 200 - Cluster analysis module; 300 - Setting module. Detailed Implementation

[0054] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0055] The following describes a method for tuning excitation parameters in peak-shaving and phase-modulation modes of a compressed air energy storage unit according to embodiments of this application, with reference to the accompanying drawings. Addressing the problem mentioned in the background art that the excitation parameters tuned in peak-shaving mode of a compressed air energy storage system do not perform well in phase-modulation mode during multi-mode switching, this application provides a method for tuning excitation parameters in peak-shaving and phase-modulation modes of a compressed air energy storage unit. In this method, key parameters of the first excitation system in peak-shaving mode and key parameters of the second excitation system in phase-modulation mode are obtained. Cluster analysis is performed on the key parameters of the first and second excitation systems using the optimal peak-shaving strategy and the maximum phase-modulation strategy, respectively, to obtain the highest quality core. The key parameters of the first and second excitation systems are then clustered based on the distance between the vector of each parameter and the highest quality core to obtain the first key excitation parameter and the second key excitation parameter. These are then tuned and integrated to obtain the excitation parameters of the compressed air energy storage unit that accommodate both peak-shaving and phase-modulation modes. This solves the problem that the excitation parameters of the compressed air energy storage system tuned in peak shaving mode do not perform well in phase modulation mode when switching between multiple modes. By using cluster analysis to perform cluster analysis on the key parameters of the excitation system in peak shaving and phase modulation modes, the key excitation parameters in the two modes are determined, tuned and integrated, thus achieving good performance of the excitation system in both peak shaving and phase modulation modes of the compressed air energy storage system when frequently switching scenarios.

[0056] Specifically, Figure 1 This is a flowchart illustrating a method for setting excitation parameters in peak-shaving and phase-shaving mode for a compressed air energy storage unit, as provided in an embodiment of this application.

[0057] like Figure 1 As shown, the excitation parameter tuning method for the peak-shaving-phase-modulation mode of this compressed air energy storage unit includes the following steps:

[0058] In step S101, the key parameters of the first excitation system in peak shaving mode and the key parameters of the second excitation system in phase shaving mode of the compressed air energy storage unit are obtained.

[0059] Specifically, since the excitation parameter tuning principles of compressed air energy storage systems differ between peak shaving mode and phase modulation mode, the excitation parameters tuned in peak shaving mode may not perform well in phase modulation. Therefore, in order to ensure that the excitation system of compressed air energy storage systems has good performance in both peak shaving and phase modulation modes when frequently switching scenarios, this application proposes a method for tuning excitation parameters of compressed air energy storage units that takes into account both peak shaving and phase modulation modes to solve the above problems.

[0060] Specifically, in this embodiment, firstly, it is necessary to obtain the key parameter data of the excitation system of the compressed air energy storage unit in peak shaving and phase modulation modes, that is, to obtain the key parameters of the first excitation system in peak shaving mode and the key parameters of the second excitation system in phase modulation mode. The key parameters of the first excitation system and the key parameters of the second excitation system may include, but are not limited to, excitation current, excitation voltage, excitation time constant, etc. Then, the obtained key parameters of the first excitation system and the key parameters of the second excitation system are preprocessed, such as by data cleaning and normalization, to ensure the accuracy and consistency of the data, and then cluster analysis is performed on the processed key parameters of the first excitation system and the key parameters of the second excitation system.

[0061] In step S102, based on a preset clustering analysis method, the key parameters of the first excitation system are clustered using the optimal peak-shaving strategy, and the key parameters of the second excitation system are clustered using the maximum phase-shaving strategy. The optimal centroids of the key parameters of the first and second excitation systems are obtained. The key parameters of the first and second excitation systems are clustered according to the first distance between the vector of the key parameter of the first excitation system and the optimal centroid, and the second distance between the vector of the key parameter of the second excitation system and the optimal centroid, so as to obtain the first key excitation parameter under the peak-shaving mode and the second key excitation parameter under the phase-shaving mode.

[0062] According to one embodiment of this application, cluster analysis is performed on key parameters of the first excitation system using an optimal peak-shaving strategy, and cluster analysis is performed on key parameters of the second excitation system using a maximum phase modulation strategy. This includes: performing cluster analysis on key parameters of the first excitation system using an optimal small-signal dynamic adjustment strategy based on a preset cluster analysis method to obtain first key excitation parameters; and performing cluster analysis on key parameters of the second excitation system using a maximum effective reactive current gain strategy based on a preset cluster analysis method to obtain second key excitation parameters.

[0063] The preset clustering analysis method can be selected by those skilled in the art according to actual testing needs, and is not specifically limited here. Preferably, the preset clustering analysis method used in the embodiments of this application can be a clustering analysis method that combines the K-means++ algorithm with the iterative self-organizing data analysis method.

[0064] Specifically, in this embodiment of the application, after obtaining the key parameters of the first excitation system and the key parameters of the second excitation system, the key parameters of the excitation system under the peak shaving mode and the phase modulation mode are clustered based on the method of combining the K-means++ algorithm and the iterative self-organizing data analysis method, and the key excitation parameters under the peak shaving mode and the phase modulation mode are divided accordingly.

[0065] Specifically, this application's embodiments utilize a method combining the K-means++ algorithm and iterative self-organizing data analysis. First, it employs an optimal peak-shaving strategy (i.e., an optimal small-signal dynamic adjustment strategy) to cluster the key parameters of the first excitation system. Then, it employs a maximum phase-modulation strategy (i.e., a strategy using the maximum effective reactive current gain) to cluster the key parameters of the second excitation system, obtaining the centroids of both systems. Second, based on the distance between each parameter vector and its centroid—specifically, the first distance between the vector of the first excitation system's key parameter and its centroid, and the second distance between the vector of the second excitation system's key parameter and its centroid—various types of typical data are assigned to the peak-shaving and phase-modulation modes. In other words, the key parameters of the first and second excitation systems are clustered and assigned to the corresponding peak-shaving and phase-modulation modes, completing AACAES (Advanced Adiabatic Compressed Air Energy) clustering. Clustering and partitioning of key data representations for each mode of the advanced thermally insulated compressed air energy storage (ECS) system are used to obtain the first key excitation parameter in peak shaving mode and the second key excitation parameter in phase shaving mode.

[0066] In step S103, the first key excitation parameter and the second key excitation parameter are adjusted, and the adjusted first key excitation parameter and the adjusted second key excitation parameter are integrated to obtain the excitation parameters of the compressed air energy storage unit that take into account both peak-shaving and phase-shaving modes.

[0067] According to one embodiment of this application, the first key excitation parameter is tuned, including: determining whether the turbine system in the compressed air energy storage unit is in a power generation state under peak shaving mode; if the turbine system is in a power generation state under peak shaving mode, then the tuning target of the first key excitation parameter is determined, and the potential energy of compressed air is converted into electrical energy; otherwise, a fault alert is generated.

[0068] According to one embodiment of this application, the tuning of the second key excitation parameter includes: determining whether the turbine system in the compressed air energy storage unit is in phase modulation state in peak shaving mode; if the turbine system is in phase modulation state in peak shaving mode, determining the tuning target of the second key excitation parameter, and using the turbine system to adjust the second key excitation parameter to provide reactive power.

[0069] Specifically, in order to ensure that the excitation system of the compressed air energy storage system has good performance in both peak shaving mode and phase modulation mode when frequently switching scenarios, after obtaining the first key excitation parameter in peak shaving mode and the second key excitation parameter in phase modulation mode, the key excitation parameters of the two modes need to be tuned.

[0070] Specifically, in the embodiments of this application, the excitation parameter tuning target of the compressed air energy storage unit in peak shaving mode is to achieve optimal small-signal dynamic regulation performance, and the excitation parameter tuning target of the compressed air energy storage unit in phase modulation mode is to achieve maximum effective reactive current gain. Therefore, for peak shaving mode, if the compressed air energy storage unit turbine system operates in power generation mode, the first key excitation parameter tuning of the compressed air energy storage unit aims at optimal small-signal dynamic regulation performance, using the first key excitation parameter as a variable, and adjusting the potential energy of the compressed air... Converting to electrical energy reduces frequency and active power fluctuations to meet peak grid demand, thus setting the first key excitation parameter. Otherwise, a fault alert is generated, and the turbine system is maintained and repaired. In phase-shifting mode, the turbine system is in phase-shifting state under peak-shaving mode. At this time, the excitation parameter setting of the compressed air energy storage unit aims at maximizing the effective reactive current gain, using the second key excitation parameter as a variable. By adjusting the excitation system, reactive power can be provided or absorbed to maintain grid voltage stability.

[0071] The objective function corresponding to the tuning target of the first key excitation parameter is:

[0072] minJ=∫0 T e 2 (t)dt

[0073] The objective function corresponding to the setting target of the second key excitation parameter is:

[0074]

[0075] Where J is the objective function, which is the integral of the squared active power deviation over the time interval 0-T, and T is the time interval. 2 (t) represents the square of the active power deviation, Δi d Δu represents the reactive current increment along the d-axis, and Δu represents the voltage change.

[0076] Furthermore, in this embodiment of the application, after the first key excitation parameter and the second key excitation parameter are tuned, the tuned first key excitation parameter and the tuned second key excitation parameter need to be integrated and input into the compressed air energy storage excitation system, so as to obtain excitation parameters that have good performance in both peak shaving and phase modulation modes of the compressed air energy storage unit when frequently switching scenarios.

[0077] To enable those skilled in the art to more clearly understand the excitation parameter tuning method of the compressed air energy storage unit in peak-shaving-phase-modulation mode of this application, the following is combined with... Figure 2 Explanation:

[0078] Step S201: Collect key parameters of the excitation system and perform data clustering analysis with the objectives of optimal peak regulation and maximum phase regulation respectively;

[0079] Step S202: Determine the optimal centroid by combining K-means++ with iterative self-organizing data analysis, and assign various typical data to the corresponding modes of peak adjustment and phase adjustment according to the distance between each parameter vector and the centroid.

[0080] Step S203: The excitation system parameters are tuned with the optimal small-signal dynamic adjustment performance and the maximum effective reactive current gain as optimization objectives, and the main sensitive parameters in each mode as variables. Two sets of excitation parameters are obtained and then integrated into the compressed air energy storage excitation system.

[0081] In summary, based on the detailed description of the specific embodiments above, the embodiments of this application can achieve the following beneficial effects:

[0082] (1) Improve system stability. By optimizing excitation parameters, the stability of CAES (Compressed Air Energy Storage) units in peak shaving and phase modulation modes has been improved.

[0083] (2) Improve system efficiency. By adjusting and optimizing the excitation parameters, the overall efficiency of the CAES unit is improved. Optimizing the excitation parameters makes the turbine system more efficient in the power generation process, reduces energy loss, improves the efficiency of converting compressed air potential energy into electrical energy, and reduces reactive power loss, thereby improving the overall efficiency of the system.

[0084] (3) Improve system flexibility and optimize excitation parameters so that the turbine system can switch quickly between different modes, improve the system response speed, adapt to changes in grid demand, and operate efficiently in peak shaving mode and phase shaving mode, thereby improving the system's multi-mode operation capability.

[0085] According to an embodiment of the present invention, the method for tuning excitation parameters of a compressed air energy storage unit in peak-shaving and phase-modulation modes involves obtaining key parameters of the first excitation system in peak-shaving mode and key parameters of the second excitation system in phase-modulation mode. Cluster analysis is then performed on the key parameters of the first and second excitation systems using the optimal peak-shaving strategy and the maximum phase-modulation strategy, respectively, to obtain the optimal centroid. Based on the distance between the vector of each parameter and the optimal centroid, the key parameters of the first and second excitation systems are further clustered to obtain the first and second key excitation parameters. These parameters are then tuned and integrated to obtain the excitation parameters of the compressed air energy storage unit that accommodate both peak-shaving and phase-modulation modes. This solves the problem that, during multi-mode switching, the excitation parameters tuned in peak-shaving mode do not perform well in phase-modulation mode. By using cluster analysis to analyze the key parameters of the excitation system in both peak-shaving and phase-modulation modes, the key excitation parameters in both modes are determined, tuned, and integrated. This ensures that the excitation system of the compressed air energy storage system exhibits good performance in both peak-shaving and phase-modulation modes during frequent switching scenarios.

[0086] Next, referring to the accompanying drawings, the excitation parameter setting device for the peak-shaving-phase-modulation mode of the compressed air energy storage unit according to the embodiments of this application is described.

[0087] Figure 3 This is a block diagram of the excitation parameter setting device for the peak-shaving-phase-modulation mode of the compressed air energy storage unit according to an embodiment of this application.

[0088] like Figure 3 As shown, the excitation parameter setting device 10 of the compressed air energy storage unit in peak-shaving-phase-modulation mode includes: an acquisition module 100, a cluster analysis module 200, and a setting module 300.

[0089] Among them, the acquisition module 100 is used to acquire the key parameters of the first excitation system in peak shaving mode and the key parameters of the second excitation system in phase shaving mode in compressed air energy storage unit.

[0090] The clustering analysis module 200 is used to perform clustering analysis on the key parameters of the first excitation system based on a preset clustering analysis method and the optimal peak-shaving strategy, and to perform clustering analysis on the key parameters of the second excitation system based on the maximum phase-shaving strategy, so as to obtain the optimal centroids of the key parameters of the first excitation system and the key parameters of the second excitation system. The key parameters of the first excitation system and the key parameters of the second excitation system are clustered and divided according to the first distance between the vector of the key parameter of the first excitation system and the optimal centroid and the second distance between the vector of the key parameter of the second excitation system and the optimal centroid, so as to obtain the first key excitation parameter under the peak-shaving mode and the second key excitation parameter under the phase-shaving mode.

[0091] The tuning module 300 is used to tune the first key excitation parameter and the second key excitation parameter, and to integrate the tuned first key excitation parameter and the tuned second key excitation parameter to obtain the excitation parameters of the compressed air energy storage unit that take into account both peak-shaving and phase-modulation modes.

[0092] According to one embodiment of the present invention, the clustering analysis module 200 includes:

[0093] The first clustering analysis unit is used to perform clustering analysis on the key parameters of the first excitation system based on a preset clustering analysis method and the optimal small-signal dynamic adjustment strategy to obtain the first key excitation parameters.

[0094] The second clustering analysis unit is used to perform clustering analysis on the key parameters of the second excitation system based on a preset clustering analysis method and using the strategy of maximizing the effective reactive current gain, so as to obtain the second key excitation parameters.

[0095] According to one embodiment of the present invention, the tuning module 300 includes:

[0096] The first judgment unit is used to determine whether the turbine system in the compressed air energy storage unit is in the power generation state under the peak shaving mode.

[0097] The first determining unit is used to determine the setting target of the first key excitation parameter if the turbine system is in the power generation state under peak shaving mode, and to convert the potential energy of compressed air into electrical energy; otherwise, it generates a fault alert.

[0098] According to one embodiment of the present invention, the tuning module 300 includes:

[0099] The second judgment unit is used to determine whether the turbine system in the compressed air energy storage unit is in phase modulation mode under peak shaving mode.

[0100] The second determining unit is used to determine the setting target of the second key excitation parameter if the turbine system is in phase modulation state in peak shaving mode, and to adjust the second key excitation parameter using the turbine system to provide reactive power.

[0101] According to one embodiment of the present invention, the objective function corresponding to the tuning target of the first key excitation parameter is:

[0102] minJ=∫0 T e 2 (t)dt

[0103] The objective function corresponding to the setting target of the second key excitation parameter is:

[0104]

[0105] Where J is the objective function, T is the time interval, and e 2 (t) represents the square of the active power deviation, Δi d Δu represents the reactive current increment along the d-axis, and Δu represents the voltage change.

[0106] According to an embodiment of the present invention, the excitation parameter tuning device for a compressed air energy storage unit in peak-shaving and phase-modulation modes acquires key parameters of the first excitation system in peak-shaving mode and key parameters of the second excitation system in phase-modulation mode. Cluster analysis is performed on the key parameters of the first and second excitation systems using the optimal peak-shaving strategy and the maximum phase-modulation strategy, respectively, to obtain the optimal centroid. Based on the distance between the vector of each parameter and the optimal centroid, the key parameters of the first and second excitation systems are further clustered to obtain the first and second key excitation parameters, which are then tuned and integrated to obtain the excitation parameters of the compressed air energy storage unit that accommodate both peak-shaving and phase-modulation modes. This solves the problem that, during multi-mode switching, the excitation parameters tuned in peak-shaving mode of the compressed air energy storage system are not effective in phase-modulation mode. By using cluster analysis to analyze the key parameters of the excitation system in both peak-shaving and phase-modulation modes, the key excitation parameters in both modes are determined, tuned, and integrated, thus achieving good performance of the excitation system in both peak-shaving and phase-modulation modes of the compressed air energy storage system during frequent switching scenarios.

[0107] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0108] The memory 401, the processor 402, and the computer program stored on the memory 401 and capable of running on the processor 402.

[0109] When the processor 402 executes the program, it implements the excitation parameter tuning method for the peak-shaving-phase-tuning mode of the compressed air energy storage unit provided in the above embodiments.

[0110] Furthermore, electronic devices also include:

[0111] Communication interface 403 is used for communication between memory 401 and processor 402.

[0112] The memory 401 is used to store computer programs that can run on the processor 402.

[0113] The memory 401 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0114] If the memory 401, processor 402, and communication interface 403 are implemented independently, then the communication interface 403, memory 401, and processor 402 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized into address buses, data buses, control buses, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0115] Optionally, in a specific implementation, if the memory 401, processor 402, and communication interface 403 are integrated on a single chip, then the memory 401, processor 402, and communication interface 403 can communicate with each other through an internal interface.

[0116] Processor 402 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0117] This embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for setting excitation parameters in the peak-shaving-phase-modulation mode of a compressed air energy storage unit.

[0118] This invention also provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the method as described in the above embodiments.

[0119] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0120] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0121] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0122] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0123] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0124] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware, and the program can be stored in a computer-readable storage medium. When executed, the program includes one or a combination of the steps of the method embodiments.

[0125] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0126] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for tuning excitation parameters in peak-shaving and phase-modulation mode of a compressed air energy storage unit, characterized in that, Includes the following steps: Acquire the key parameters of the first excitation system in peak shaving mode and the key parameters of the second excitation system in phase modulation mode in the compressed air energy storage unit; Based on a preset clustering analysis method, the key parameters of the first excitation system are clustered using the optimal peak-shaving strategy, and the key parameters of the second excitation system are clustered using the maximum phase-shaving strategy. The optimal centroids of the key parameters of the first and second excitation systems are obtained. The key parameters of the first and second excitation systems are clustered according to the first distance between the vector of the key parameter of the first excitation system and the optimal centroid and the second distance between the vector of the key parameter of the second excitation system and the optimal centroid, so as to obtain the first key excitation parameter under the peak-shaving mode and the second key excitation parameter under the phase-shaving mode. The first key excitation parameter and the second key excitation parameter are tuned, and the tuned first key excitation parameter and the tuned second key excitation parameter are integrated to obtain the excitation parameters of the compressed air energy storage unit that take into account both peak shaving and phase modulation modes. The step of performing cluster analysis on the key parameters of the first excitation system using the optimal peak-shaving strategy and on the key parameters of the second excitation system using the maximum phase-shaving strategy includes: performing cluster analysis on the key parameters of the first excitation system using the optimal small-signal dynamic adjustment strategy based on the preset cluster analysis method to obtain the first key excitation parameters; and performing cluster analysis on the key parameters of the second excitation system using the maximum effective reactive current gain strategy based on the preset cluster analysis method to obtain the second key excitation parameters. The step of setting the first key excitation parameter includes: determining whether the turbine system in the compressed air energy storage unit is in the power generation state under the peak shaving mode; if the turbine system is in the power generation state under the peak shaving mode, then the setting target of the first key excitation parameter is determined, and the potential energy of the compressed air is converted into electrical energy; otherwise, a fault reminder is generated. The step of setting the second key excitation parameter includes: determining whether the turbine system in the compressed air energy storage unit is in phase modulation state under the peak shaving mode; if the turbine system is in phase modulation state under the peak shaving mode, then determining the setting target of the second key excitation parameter, and using the turbine system to adjust the second key excitation parameter to provide reactive power.

2. The method according to claim 1, characterized in that, The objective function corresponding to the tuning target of the first key excitation parameter is: The objective function corresponding to the setting target of the second key excitation parameter is: in, Let T be the objective function, and T be the time interval. This is the square of the active power deviation. This represents the reactive current increment along the d-axis. This represents the change in voltage.

3. An excitation parameter setting device for a compressed air energy storage unit in peak-shaving and phase-modulation mode, characterized in that, include: The acquisition module is used to acquire key parameters of the first excitation system in peak shaving mode and key parameters of the second excitation system in phase shaving mode of the compressed air energy storage unit. The clustering analysis module is used to perform clustering analysis on the key parameters of the first excitation system based on a preset clustering analysis method and the optimal peak-shaving strategy, and to perform clustering analysis on the key parameters of the second excitation system based on the maximum phase-shaving strategy, so as to obtain the optimal centroids of the key parameters of the first excitation system and the key parameters of the second excitation system. Based on the first distance between the vector of the key parameter of the first excitation system and the optimal centroid and the second distance between the vector of the key parameter of the second excitation system and the optimal centroid, the key parameters of the first excitation system and the key parameters of the second excitation system are clustered and divided to obtain the first key excitation parameter under the peak-shaving mode and the second key excitation parameter under the phase-shaving mode. The tuning module is used to tune the first key excitation parameter and the second key excitation parameter, and to integrate the tuned first key excitation parameter and the tuned second key excitation parameter to obtain the excitation parameters of the compressed air energy storage unit that take into account both peak-shaving and phase-modulation modes. The clustering analysis module includes: a first clustering analysis unit, used to perform clustering analysis on the key parameters of the first excitation system based on the preset clustering analysis method and using the optimal small-signal dynamic adjustment strategy to obtain the first key excitation parameters; and a second clustering analysis unit, used to perform clustering analysis on the key parameters of the second excitation system based on the preset clustering analysis method and using the maximum effective reactive current gain strategy to obtain the second key excitation parameters. The tuning module includes: a first judgment unit, used to judge whether the working state of the turbine system in the compressed air energy storage unit in the peak shaving mode is the power generation state; and a first determination unit, used to determine the tuning target of the first key excitation parameter if the working state of the turbine system in the peak shaving mode is the power generation state, and convert the potential energy of the compressed air into electrical energy; otherwise, generate a fault reminder. The tuning module includes: a second judgment unit, used to judge whether the turbine system in the compressed air energy storage unit is in phase modulation state under the peak shaving mode; and a second determination unit, used to determine the tuning target of the second key excitation parameter if the turbine system is in the phase modulation state under the peak shaving mode, and to use the turbine system to adjust the second key excitation parameter to provide reactive power.

4. The apparatus according to claim 3, characterized in that, The objective function corresponding to the tuning target of the first key excitation parameter is: The objective function corresponding to the setting target of the second key excitation parameter is: in, Let T be the objective function, and T be the time interval. This is the square of the active power deviation. This represents the reactive current increment along the d-axis. This represents the change in voltage.

5. An electronic device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the excitation parameter tuning method for the peak-shaving-phase-tuning mode of the compressed air energy storage unit as described in any one of claims 1-2.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the excitation parameter tuning method for the peak-shaving-phase-tuning mode of the compressed air energy storage unit as described in any one of claims 1-2.

7. A computer program product, comprising a computer program, characterized in that, The computer program is executed to implement the excitation parameter tuning method for the peak-shaving-phase-tuning mode of the compressed air energy storage unit as described in any one of claims 1-2.

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