Super-capacitor coupling lithium battery energy storage system and method based on frequency modulation instruction prediction

Abstract

The invention relates to the technical field of power grid frequency modulation, and discloses a super capacitor coupling lithium battery energy storage system and method based on frequency modulation instruction prediction, and the system comprises a data collection module, a frequency modulation instruction prediction module, a sub-sequence decomposition module, a phasor conversion module, an independence calculation module, and a power distribution decision module. The data acquisition module acquires historical frequency modulation instruction data, real-time power grid frequency data, supercapacitor data, lithium battery state data and environmental parameters of a thermal power generating unit, the frequency modulation instruction prediction module is responsible for predicting a frequency modulation instruction of the thermal power generating unit, and the sub-sequence decomposition module is responsible for decomposing the frequency modulation instruction into multiple sub-sequences. The phasor conversion module is responsible for converting the subsequences into phasors, the independence calculation module is responsible for calculating angles between the phasors to evaluate the independence degree between the two phasors, and the power distribution decision module is responsible for deciding power distribution of the capacitor and the lithium battery according to a frequency modulation instruction prediction result and an independence calculation result between the phasors.

Description

Technical Field

[0001] This invention relates to the field of power grid frequency regulation technology, specifically to a supercapacitor-coupled lithium battery energy storage system and method based on frequency regulation command prediction. Background Technology

[0002] As the primary frequency-regulating power source in the power system, thermal power units face increased coal consumption, reduced reliability, and shortened operational lifespan due to long-term frequency regulation. Despite the increasing demand for frequency regulation, high-quality and efficient frequency-regulating power sources remain scarce. The large-scale grid connection of new energy sources further exacerbates this problem, increasing the demand for frequency regulation in thermal power units. However, due to environmental pressures and the "heat-driven power generation" principle for heating units, the regulation capacity of thermal power units is constrained. To improve the frequency regulation capability of thermal power units, various auxiliary frequency-regulating energy storage devices have been developed in existing technologies. However, the performance of these devices cannot meet the efficiency and reliability requirements of the units themselves, resulting in reduced grid subsidy revenue and low economic efficiency.

[0003] Existing frequency regulation command prediction methods suffer from significant errors, relying primarily on current prediction error feedback and failing to fully utilize historical error data, resulting in insufficient prediction accuracy. Error feedback models, based solely on current prediction errors, are subject to randomness and struggle to consistently improve prediction accuracy. These issues collectively lead to low economic efficiency of thermal power units, hindering their ability to fully utilize their frequency regulation capabilities.

[0004] To address the aforementioned issues, a supercapacitor-coupled lithium battery energy storage system based on frequency modulation command prediction is proposed. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a supercapacitor-coupled lithium battery energy storage system and method based on frequency modulation command prediction. It has the advantage of smaller error in existing frequency modulation command prediction methods and solves the problem of larger error in existing frequency modulation command prediction methods.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a supercapacitor-coupled lithium battery energy storage system based on frequency modulation command prediction, comprising a data acquisition module, a frequency modulation command prediction module, a subsequence decomposition module, a phasor conversion module, an independence calculation module, and a power allocation decision module; The data acquisition module is responsible for collecting historical frequency regulation command data of thermal power units, real-time grid frequency data, supercapacitor data, lithium battery status data, and environmental parameters. The frequency regulation command prediction module is responsible for predicting the frequency regulation command of the thermal power unit and then making a decision on the power allocation of the capacitor and lithium battery. The subsequence decomposition module is responsible for decomposing the frequency modulation command into multiple subsequences to prepare for subsequent analysis and processing. The phasor conversion module is responsible for converting subsequences into phasors; The irrelevance calculation module is responsible for calculating the angle between phasors to evaluate the degree of irrelevance between the two phasors. The larger the angle, the stronger the irrelevance. The power allocation decision module is responsible for making decisions on the power allocation of the capacitor and lithium battery based on the prediction results of the frequency modulation command and the calculation results of the phasor independence.

[0007] Preferably, the angular frequency predicted by the frequency modulation command prediction module is: .

[0008] Preferably, the sequence decomposition module decomposes the frequency modulation command into multiple sub-sequences, the sub-sequences being... , , … .

[0009] Preferably, the formula for calculating the subsequence is:

[0010] In the formula, , , … Represents a subsequence. Indicates the first Subsequences and All indicate the first phasor coefficients of each subsequence This represents the angular frequency predicted by the frequency modulation command prediction module, indicating the periodic change of the frequency modulation command. The frequency modulation command prediction module predicts the first... angular frequency, This represents the total number of angular frequencies counted.

[0011] Preferably, the phasor transformation module sums the subsequences using the following formula:

[0012] In the formula, The intrinsic mode function is represented by the frequency modulation command data, which is approximated by converting it into a sum of trigonometric functions. , , … Represents a subsequence. Indicates the first Subsequences, and All indicate the first phasor coefficients of each subsequence This represents the angular frequency predicted by the frequency modulation command prediction module, indicating the periodic change of the frequency modulation command. The frequency modulation command prediction module predicts the first... angular frequency, This represents the total number of angular frequencies counted.

[0013] Preferably, the phasor conversion module converts the subsequence into phasors. The calculation formula is as follows:

[0014]

[0015]

[0016] In the formula, To represent a phasor, that is, a complex number representing a phasor. , The complex numbers representing the first and second phasors, respectively. This represents the amplitude, i.e., the maximum strength of the signal. , represents the phase angle, , These represent the first and second phase angles, respectively, which are the offsets of the signal relative to the reference signal. Represents the imaginary unit, satisfying =-1.

[0017] Preferably, the independence calculation module is responsible for calculating the angles between phasors. The calculation formula is as follows:

[0018] In the formula, Indicates the angle between phasors. To represent a phasor, that is, a complex number representing a phasor. , The complex numbers representing the first and second phasors, respectively. , represents the phase angle, , These represent the first and second phase angles, respectively. and From and Extract the phase angle from the complex representation of the phasor.

[0019] Preferably, the angle between the phasors The phase difference directly reflects the phase relationship between two signals. Close to 0 or 360 ∘ If the two signals are close to being in phase, it means that their changes over time are very similar. Approximately 180 ∘ If the two signals are out of phase, then this is a specific type of correlation. When In 180 ∘ -360 ∘ When the two values ​​are in between, it indicates that the signals are partially correlated.

[0020] Preferably, the angle between the phasors Orthogonality: when =90 ∘ or =270 ∘ When the two signals are considered orthogonal, it means that the changes of the two phasors over time are completely independent and have no correlation.

[0021] A preferred method for supercapacitor-coupled lithium battery energy storage based on frequency modulation command prediction includes the following steps: Step 1: Establish the data acquisition module, frequency modulation command prediction module, subsequence decomposition module, phasor transformation module, independence calculation module, and power allocation decision module; Step 2: The data acquisition module collects historical frequency regulation command data of the thermal power unit, real-time grid frequency data, supercapacitor data, lithium battery status data, and environmental parameters; Step 3: The frequency regulation command prediction module predicts the frequency regulation command of the thermal power unit and then makes a decision on the power allocation of the capacitor and lithium battery. Step 4: The subsequence decomposition module is responsible for decomposing the frequency modulation command into multiple subsequences to prepare for subsequent analysis and processing. Step 5: The phasor conversion module is responsible for converting the subsequence into phasors; Step 6: The irrelevance calculation module calculates the angle between phasors to evaluate the degree of irrelevance between the two phasors. The larger the angle, the stronger the irrelevance. Step 7: The power allocation decision module performs power allocation for the capacitor and lithium battery based on the frequency modulation command prediction results and the phasor independence calculation results.

[0022] Compared with the prior art, the present invention provides a supercapacitor-coupled lithium battery energy storage system and method based on frequency modulation command prediction, which has the following beneficial effects: This invention relates to a supercapacitor-coupled lithium battery energy storage system based on frequency regulation command prediction. It adjusts the data acquisition frequency by modifying environmental parameters to more accurately capture changes in equipment status. It optimizes the power allocation strategy based on the impact of environmental factors on the performance of the lithium battery and supercapacitor. Finally, it adjusts the prediction model parameters based on changes in environmental parameters to improve prediction accuracy. By calculating subsequences, it not only effectively extracts relevant phasor information, providing a foundation for subsequent phasor calculations, but also helps improve the response speed of the energy storage system, ensuring better adaptation to changes in frequency regulation requirements during power system operation.

[0023] Preferably, the phasors of all subsequences are summed using a phasor transformation module to obtain the eigenmode functions. The complex frequency modulation command data, which is organized by the angular frequency predicted by the frequency modulation command prediction module, is decomposed into a series of simple sine waves. Each sine wave has its specific frequency, amplitude and phase. This decomposition helps to understand and analyze the different frequency components in the signal, so that the system can make accurate predictions to control the changes in the frequency modulation command. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the supercapacitor-coupled lithium battery energy storage system based on frequency modulation command prediction according to the present invention.

[0025] Figure 2 This is a schematic diagram of the supercapacitor-coupled lithium battery energy storage method based on frequency modulation command prediction according to the present invention. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] Please see Figures 1-2 A supercapacitor-coupled lithium battery energy storage system based on frequency modulation command prediction includes a data acquisition module, a frequency modulation command prediction module, a subsequence decomposition module, a phasor conversion module, an independence calculation module, and a power allocation decision module. The data acquisition module is responsible for collecting historical frequency regulation command data of thermal power units, real-time grid frequency data, supercapacitor data, lithium battery status data (such as voltage, current, temperature, and remaining capacity), and environmental parameters (such as temperature and humidity, which affect equipment performance and lifespan). Through sensors and data acquisition systems, it ensures that the acquired data is accurate and reliable, which helps to improve the operating efficiency and safety of the entire system. The frequency regulation command prediction module is responsible for predicting the frequency regulation commands of thermal power units and then making decisions on the power allocation of capacitors and lithium batteries. By predicting the frequency regulation commands of thermal power units, decisions can be made in advance to optimize the power allocation of capacitors and lithium batteries, thereby improving the system's response speed and frequency regulation effect. The subsequence decomposition module is responsible for decomposing the frequency modulation command into multiple subsequences to prepare for subsequent analysis and processing. Decomposing the frequency modulation command into multiple subsequences helps to accurately analyze the frequency modulation requirements at each time point and improves the flexibility of the system. The phasor transformation module is responsible for converting subsequences into phasors. Through phasor representation, signals with different frequency components can be unified onto a complex plane for analysis, which simplifies the signal processing process. The irrelevance calculation module is responsible for calculating the angle between phasors to evaluate the degree of irrelevance between two phasors. The larger the angle, the stronger the irrelevance. The phasor diagram method can intuitively show the phase relationship between different signals, which is convenient for system analysis. The power allocation decision module is responsible for making decisions on the power allocation of capacitors and lithium batteries based on the prediction results of frequency modulation commands and the calculation results of phasor independence. It can dynamically adjust the power allocation strategy according to real-time data and prediction results to adapt to different operating conditions and needs.

[0028] The angular frequency predicted by the frequency modulation command prediction module is .

[0029] The sequence decomposition module decomposes the frequency modulation command into multiple subsequences, the subsequences being... , , … .

[0030] The formula for calculating the subsequence is:

[0031] In the formula, , , … Represents a subsequence. Indicates the first Subsequences, and All indicate the first phasor coefficients of each subsequence This represents the angular frequency predicted by the frequency modulation command prediction module, indicating the periodic change of the frequency modulation command. The frequency modulation command prediction module predicts the first... angular frequency, This represents the total number of angular frequencies counted.

[0032] By calculating subsequences, not only can relevant phasor information be effectively extracted, providing a basis for subsequent phasor calculations, but it also helps to improve the response speed of energy storage systems and ensure that they can better adapt to changes in frequency regulation requirements during power system operation.

[0033] The phasor transformation module sums the subsequences using the following formula:

[0034] In the formula, The intrinsic mode function is represented by the frequency modulation command data, which is approximated by converting it into a sum of trigonometric functions. , , … Represents a subsequence. Indicates the first Subsequences, and All indicate the first phasor coefficients of each subsequence This represents the angular frequency predicted by the frequency modulation command prediction module, indicating the periodic change of the frequency modulation command. The frequency modulation command prediction module predicts the first... angular frequency, This represents the total number of angular frequencies counted. The phasors of all subsequences are summed using the phasor transformation module to obtain the eigenmode functions. The complex frequency modulation command data, which is organized by the angular frequency predicted by the frequency modulation command prediction module, is decomposed into a series of simple sine waves. Each sine wave has its specific frequency, amplitude and phase. This decomposition helps to understand and analyze the different frequency components in the signal, so that the system can make accurate predictions to control the changes in the frequency modulation command.

[0035] The phasor transformation module converts subsequences into phasors. The calculation formula is as follows:

[0036]

[0037]

[0038] In the formula, To represent a phasor, that is, a complex number representing a phasor. , The complex numbers representing the first and second phasors, respectively. This represents the amplitude, i.e., the maximum strength of the signal. , represents the phase angle, , These represent the first and second phase angles, respectively, which are the offsets of the signal relative to the reference signal. Represents the imaginary unit, satisfying =-1.

[0039] By calculating phasors For the angle between subsequent phasors The calculation is based on the principle that when dealing with the superposition of multiple sinusoidal waveforms, the phasor method can represent them as a sum of complex numbers, thereby simplifying the calculation process and improving computational efficiency.

[0040] The independence calculation module is responsible for calculating the angles between phasors. The calculation formula is as follows:

[0041] In the formula, Indicates the angle between phasors. To represent a phasor, that is, a complex number representing a phasor. , The complex numbers representing the first and second phasors, respectively. , represents the phase angle, , These represent the first and second phase angles, respectively. and From and Extract the phase angle from the complex representation of the phasor.

[0042] Angle between phasors The phase difference directly reflects the phase relationship between two signals. Close to 0 or 360 ∘ If the two signals are close to being in phase, it means that their changes over time are very similar. Approximately 180 ∘ If the two signals are out of phase, then this is a specific type of correlation. When In 180 ∘ -360 ∘ When the two values ​​are in between, it indicates that the signals are partially correlated.

[0043] Angle between phasors Orthogonality: when =90 ∘ or =270 ∘ When the two signals are considered orthogonal, it means that the changes of the two phasors over time are completely independent and have no correlation.

[0044] The formula for calculating the angle between phasors can not only directly reflect the phase relationship between two signals, but also be used to determine the orthogonality of signals.

[0045] In another embodiment of the present invention, a supercapacitor-coupled lithium battery energy storage method based on frequency modulation command prediction is provided, comprising the following steps: Step 1: Establish the data acquisition module, frequency modulation command prediction module, subsequence decomposition module, phasor transformation module, independence calculation module, and power allocation decision module; Step 2: The data acquisition module collects historical frequency regulation command data of thermal power units, real-time grid frequency data, supercapacitor data, lithium battery status data (such as voltage, current, temperature, and remaining capacity), and environmental parameters (such as air temperature and humidity, which affect equipment performance and lifespan). Step 3: The frequency regulation command prediction module predicts the frequency regulation command of the thermal power unit and then makes a decision on the power allocation of the capacitor and lithium battery. Step 4: The subsequence decomposition module is responsible for decomposing the frequency modulation command into multiple subsequences to prepare for subsequent analysis and processing. Step 5: The phasor conversion module is responsible for converting the subsequence into phasors; Step 6: The irrelevance calculation module calculates the angle between phasors to evaluate the degree of irrelevance between the two phasors. The larger the angle, the stronger the irrelevance. Step 7: The power allocation decision module performs power allocation for the capacitor and lithium battery based on the frequency modulation command prediction results and the phasor independence calculation results.

[0046] Example 1 S1.1 Establish a data acquisition module and configure the corresponding sensors and data acquisition devices to ensure that the required data can be acquired in real time and accurately; S1.2 Develop a frequency modulation command prediction module, use historical data to train a machine learning model, and verify and optimize the model; S1.3 Design a subsequence decomposition algorithm to decompose the frequency modulation command into multiple subsequences and extract the feature parameters of each subsequence; S1.4 Implement the phasor conversion module to convert the subsequence into a complex phasor representation and perform necessary mathematical operations and physical interpretations; S1.5 Develop an independence calculation module to calculate the angles between different phasors and evaluate the degree of independence between them; S1.6 Design a power allocation decision module to formulate a reasonable power allocation strategy based on the frequency modulation command prediction results and the phasor independence calculation results. S1.7 Integrate all modules into a complete system and conduct comprehensive testing and verification. Ensure the system can operate stably and reliably in actual operation; S1.8 Continuously monitor and maintain the system, and adjust model parameters and strategies according to actual conditions to adapt to changes and development needs of the power system.

[0047] Example 2 S2.1 Data Acquisition: Collect all necessary data through the data acquisition module; S2.2 Frequency Modulation Command Prediction: The frequency modulation command prediction module is used to predict the frequency modulation commands of thermal power units; S2.3 Subsequence Decomposition: The frequency modulation command is decomposed into multiple subsequences using the subsequence decomposition module; S2.4 Phasor Conversion: The phasor conversion module converts the subsequence into phasors; S2.5 Irrelevantity Calculation: Use the irrelevance calculation module to calculate the angles between phasors and evaluate irrelevance; S2.6 Power Allocation Decision: Based on the prediction results and irrelevance calculation results, the power allocation of the supercapacitor and lithium battery is performed through the power allocation decision module.

[0048] Environmental parameter adjustment: In Example 2, the impact of environmental parameters on equipment performance and lifespan was specifically considered. For example, in high temperature and high humidity environments, the acquisition frequency of the data acquisition module was adjusted to more accurately capture changes in equipment status.

[0049] Power allocation optimization: In the power allocation decision module, the power allocation strategy is optimized based on the impact of high temperature and high humidity environments on the performance of lithium batteries and supercapacitors to ensure the stability and efficiency of the energy storage system.

[0050] Prediction model adjustment: The frequency modulation command prediction module adjusts the parameters of the prediction model according to changes in environmental parameters to improve the accuracy of prediction.

[0051] Comparative Example 1 (Traditional FM command prediction method) Steps and procedures T1.1 Data Acquisition: Using traditional data acquisition methods, including manual recording or non-real-time automated systems, historical frequency regulation command data of thermal power units, power grid frequency data, energy storage device status data and environmental parameters are collected. T1.2 FM command prediction: Instead of using machine learning models, it relies on expert experience or simple statistical methods to predict FM commands; T1.3 Subsequence processing: Analyzing the overall data without subsequence decomposition may not effectively extract local features and trends; T1.4 Phasor Transformation: Analyzing data directly in the time domain without converting subsequences into phasors may not fully utilize phase information. T1.5 Irrelevantity assessment: If the angle between phasors is not calculated and instead traditional correlation analysis methods are used, the irrelevance between signals may not be accurately assessed. T1.6 Power Allocation Decision: Power allocation is not based on the calculation results of phasor independence, but on simple rules or experience. T1.7 System Integration and Testing: Integrating traditional methods into the system and performing basic testing may lack comprehensive performance verification; T1.8 System Maintenance: System maintenance and adjustments based on experience may not be able to adapt to changes in the power system in a timely manner.

[0052] Comparative Example 2 (Frequency Modulation Command Prediction Method Without Considering the Influence of Environmental Parameters) Steps and procedures T2.1 Data Acquisition: Same as in Example 2, all necessary data are collected through the data acquisition module; T2.2 Frequency modulation command prediction: Similar to Example 2, the frequency modulation command prediction module is used to predict the frequency modulation command of the thermal power unit; T2.3 Subsequence Decomposition: Similar to Example 2, the frequency modulation command is decomposed into multiple subsequences using the subsequence decomposition module; T2.4 Phasor Conversion: Similar to Example 2, the subsequence is converted into phasors through the phasor conversion module; T2.5 Irrelevantity Calculation: Similar to Example 2, the irrelevance calculation module is used to calculate the angle between phasors and evaluate irrelevance; T2.6 Power Allocation Decision: Similar to Example 2, the power allocation between the supercapacitor and the lithium battery is performed through the power allocation decision module based on the prediction results and irrelevance calculation results. Environmental parameters are not considered T2.7 Environmental parameters are not considered: During the power allocation decision-making process, the impact of environmental parameters on equipment performance and lifespan is not considered, the acquisition frequency of the data acquisition module is not adjusted, and the power allocation strategy and prediction model parameters are not optimized.

[0053]

[0054] The tables above clearly show the significant improvement in frequency modulation command prediction accuracy of the present invention (Example 1 and Example 2) compared to the traditional model (Comparative Example 1). Furthermore, Example 2, compared to Comparative Example 2, can consider the influence of environmental parameters and adjust the data acquisition frequency according to the environmental parameters, thereby accurately capturing changes in equipment status. Therefore, the data from the examples demonstrate the present invention's ability to improve prediction accuracy, optimize power allocation strategies, and adapt to environmental changes.

[0055] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A supercapacitor-coupled lithium battery energy storage system based on frequency modulation command prediction, characterized in that, It includes a data acquisition module, a frequency modulation command prediction module, a subsequence decomposition module, a phasor conversion module, an independence calculation module, and a power allocation decision module; The data acquisition module is responsible for collecting historical frequency regulation command data of thermal power units, real-time grid frequency data, supercapacitor data, lithium battery status data, and environmental parameters. The frequency regulation command prediction module is responsible for predicting the frequency regulation command of the thermal power unit and then making a decision on the power allocation of the capacitor and lithium battery. The subsequence decomposition module is responsible for decomposing the frequency modulation command into multiple subsequences to prepare for subsequent analysis and processing. The phasor conversion module is responsible for converting subsequences into phasors; The irrelevance calculation module is responsible for calculating the angle between phasors to evaluate the degree of irrelevance between the two phasors. The larger the angle, the stronger the irrelevance. The power allocation decision module is responsible for making decisions on the power allocation of the capacitor and lithium battery based on the prediction results of the frequency modulation command and the calculation results of the phasor independence.

2. The supercapacitor-coupled lithium battery energy storage system based on frequency modulation command prediction according to claim 1, characterized in that: The angular frequency predicted by the frequency modulation command prediction module is .

3. The supercapacitor-coupled lithium battery energy storage system based on frequency modulation command prediction according to claim 1, characterized in that: The sequence decomposition module decomposes the frequency modulation command into multiple sub-sequences, the sub-sequences being... , , ... .

4. The supercapacitor-coupled lithium battery energy storage system based on frequency modulation command prediction according to claim 3, characterized in that: The formula for calculating the subsequence is: In the formula, , , ... Represents a subsequence. Indicates the first Subsequences, and All indicate the first phasor coefficients of each subsequence This represents the angular frequency predicted by the frequency modulation command prediction module, indicating the periodic change of the frequency modulation command. The frequency modulation command prediction module predicts the first... angular frequency, This represents the total number of angular frequencies counted.

5. The supercapacitor-coupled lithium battery energy storage system based on frequency modulation command prediction according to claim 4, characterized in that: The phasor transformation module sums the subsequences using the following formula: In the formula, The intrinsic mode function is represented by the frequency modulation command data, which is approximated by converting it into a sum of trigonometric functions. , , ... Represents a subsequence. Indicates the first Subsequences, and All indicate the first phasor coefficients of each subsequence This represents the angular frequency predicted by the frequency modulation command prediction module, indicating the periodic change of the frequency modulation command. The frequency modulation command prediction module predicts the first... angular frequency, This represents the total number of angular frequencies counted.

6. The supercapacitor-coupled lithium battery energy storage system based on frequency modulation command prediction according to claim 5, characterized in that: The phasor conversion module converts the subsequence into phasors. The calculation formula is as follows: In the formula, To represent a phasor, that is, a complex number representing a phasor. , Represent the complex numbers of the first and second phasors, respectively. This represents the amplitude, i.e., the maximum strength of the signal. , represents the phase angle, , These represent the first and second phase angles, respectively, which are the offsets of the signal relative to the reference signal. Represents the imaginary unit, satisfying =-1.

7. The supercapacitor-coupled lithium battery energy storage system based on frequency modulation command prediction according to claim 6, characterized in that: The independence calculation module is responsible for calculating the angles between phasors. The calculation formula is as follows: In the formula, Indicates the angle between phasors. To represent a phasor, that is, a complex number representing a phasor. , Represent the complex numbers of the first and second phasors, respectively. , represents the phase angle, , These represent the first and second phase angles, respectively. and From and Extract the phase angle from the complex representation of the phasor.

8. The supercapacitor-coupled lithium battery energy storage system based on frequency modulation command prediction according to claim 7, characterized in that: The angle between the phasors The phase difference directly reflects the phase relationship between two signals. Close to 0 or 360 ∘ If the two signals are close to being in phase, it means that their changes over time are very similar. Approximately 180 ∘ If the two signals are out of phase, then this is a specific type of correlation. When In 180 ∘ -360 ∘ When the two values ​​are in between, it indicates that the signals are partially correlated.

9. The supercapacitor-coupled lithium battery energy storage system based on frequency modulation command prediction according to claim 8, characterized in that, The angle between the phasors Orthogonality: when =90 ∘ or =270 ∘ When the two signals are considered orthogonal, it means that the changes of the two phasors over time are completely independent and have no correlation.

10. A supercapacitor-coupled lithium battery energy storage method based on frequency modulation command prediction, characterized in that, Includes the following steps: Collect historical frequency regulation command data of thermal power units, real-time grid frequency data, supercapacitor data, lithium battery status data, and environmental parameters; Predict the frequency regulation commands of thermal power units, and then make decisions on the power allocation of capacitors and lithium batteries. Decompose the frequency modulation command into multiple subsequences; convert the subsequences into phasors; The angle between phasors is calculated to assess the degree of independence between the two phasors; the larger the angle, the stronger the independence. Based on the frequency modulation command prediction results and the phasor independence calculation results, the power allocation of the decision capacitor and lithium battery is performed.

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