Synchronization method for parallel operation control of multiple energy storage devices of power grid

Abstract

The invention discloses a synchronization method for parallel operation control of multiple energy storage devices of a power grid, which relates to the technical field of intelligent power grids and comprises the following steps of: acquiring output current of an energy storage unit in real time, calculating a circulating current value of the energy storage unit, optimizing the circulating current value through a multi-objective optimization function by a quantum optimization algorithm, and generating a dynamic virtual impedance parameter; calculating the initial phase difference of the energy storage units according to the dynamic virtual impedance parameters, and adjusting the carrier density of the metasurface array between the energy storage units to generate a microwave calibration signal; and transmitting a microwave calibration signal, detecting an actual phase error of the energy storage unit, collecting temperature data of the metasurface array, correcting the dynamic virtual impedance parameter according to the temperature data, and generating a corrected actual phase error. The circulating current phenomenon caused by inconsistent parameters of the energy storage units is effectively compensated, and the overall stability and efficiency during parallel operation of the multiple energy storage devices are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart grids, and in particular to a synchronization method for controlling parallel operation of multiple energy storage devices in a power grid. Background Art

[0002] As the global demand for renewable energy increases, energy storage technology, as a key link in solving the problem of intermittent energy supply, has become increasingly important. In power systems, energy storage devices can not only store excess electricity for use during peak hours, but also improve grid stability by adjusting grid frequency and voltage. Especially in large-scale power grid applications, the parallel operation of multiple energy storage devices has become one of the important strategies to improve overall flexibility and efficiency. However, synchronous control between energy storage units has always been one of the core challenges of research in this field. Traditional methods mainly rely on hardware circuit design to achieve current sharing and harmonic suppression. Although it improves stability and efficiency to a certain extent, it still has limitations when facing complex working conditions. For example, in the case of dynamic load changes or large differences in the characteristics of different energy storage units, it is difficult for existing technologies to achieve ideal synchronization effects, mainly due to the lack of an effective real-time feedback adjustment mechanism and the failure to fully utilize the advantages of modern optimization algorithms.

[0003] The existing parallel energy storage system control technology generally adopts methods based on classical control theory, such as proportional-integral-differential (PID) controller, droop control, etc. Although these methods can meet the basic power distribution requirements to a certain extent, they perform poorly in dealing with complex nonlinear dynamic processes. Specifically, on the one hand, it is difficult for traditional control strategies to accurately compensate for the circulation phenomenon caused by inconsistent parameters of energy storage units; on the other hand, it also faces great challenges in high-precision phase synchronization control. In view of the above shortcomings, the present invention proposes an innovative solution-by collecting the output current of the energy storage unit in real time, and using the quantum optimization algorithm to perform multi-objective optimization on the circulation value, thereby generating dynamic virtual impedance parameters. This method not only overcomes the limitations of traditional control strategies in dealing with complex working conditions, but also significantly improves the synchronization accuracy and overall performance between energy storage units. In addition, the present invention also involves the regulation of the carrier density of the metasurface array and the technology of correcting the dynamic virtual impedance parameters based on temperature data, which further enhances the robustness and adaptability. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a synchronization method for parallel operation control of multiple energy storage devices in a power grid to solve the problem that traditional control strategies are difficult to accurately compensate for the circulation phenomenon caused by inconsistent energy storage unit parameters and high-precision phase synchronization control.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, the present invention provides a synchronization method for parallel operation control of multiple energy storage devices in a power grid, which includes: real-time collection of the output current of the energy storage unit, and calculation of the circulating current value of the energy storage unit, optimizing the circulating current value through a multi-objective optimization function by a quantum optimization algorithm, and generating dynamic virtual impedance parameters; calculating the initial phase difference of the energy storage unit according to the dynamic virtual impedance parameters, and adjusting the carrier density of the metasurface array between the energy storage units to generate a microwave calibration signal; emitting a microwave calibration signal, and detecting the actual phase error of the energy storage unit, collecting temperature data of the metasurface array, correcting the dynamic virtual impedance parameters according to the temperature data, and generating a corrected actual phase error; feeding back the corrected actual phase error to the quantum optimization algorithm to reconstruct the multi-objective optimization function, and maintaining synchronous operation based on the time series prediction of the historical dynamic virtual impedance parameters.

[0007] As a preferred solution of the synchronization method for parallel operation control of multiple energy storage devices in the power grid of the present invention, the real-time acquisition of the output current of the energy storage unit and the calculation of the circulating current value of the energy storage unit are carried out in the following specific steps: The current signal is collected in real time through the Hall current sensor, and the current signal is conditioned through noise suppression, analog-to-digital conversion and time synchronization; The circulating current value of the energy storage unit is generated through average current calculation and instantaneous current calculation.

[0008] As a preferred solution of the synchronization method for parallel operation control of multiple energy storage devices in the power grid described in the present invention, wherein: the circulating current value is optimized by a quantum optimization algorithm through a multi-objective optimization function to generate a dynamic virtual impedance parameter, and the specific steps are as follows: Based on the quantum mechanics potential well theory, a quantum mechanics potential field model is constructed, and the local optimal solution of the energy storage unit is dynamically updated; Generate globally optimized virtual impedance parameters through a hybrid mechanism of quantum tunneling and classical gradient descent; Dynamic virtual impedance parameters are generated through dynamic imaginary impedance adjustment.

[0009] As a preferred solution of the synchronization method for parallel operation control of multiple energy storage devices in the power grid described in the present invention, wherein: the initial phase difference of the energy storage unit is calculated according to the dynamic virtual impedance parameter, the carrier density of the metasurface array between the energy storage units is adjusted, and a microwave calibration signal is generated. The specific steps are as follows: The dynamic virtual impedance parameters are converted into the initial phase difference between energy storage units through the impedance-phase mapping model and the gradient smoothing constraint; The initial phase difference is mapped to a carrier density reference value through an exponential function, and the carrier density reference value is adjusted through power dynamic feedback compensation and physical limiting protection; Based on the adjusted carrier density reference value, the phase shift is calculated by exponential function mapping; The phase offset is synthesized through the calibration phase to obtain the calibration phase parameter, and the calibration phase parameter is used to generate a microwave calibration signal through a microwave transmitter.

[0010] As a preferred solution of the synchronization method for parallel operation control of multiple energy storage devices in the power grid of the present invention, the specific steps of transmitting the microwave calibration signal and detecting the actual phase error of the energy storage unit are as follows: transmitting a microwave calibration signal via a microwave transmitter; The microwave calibration signal is collected in real time by a vector network analyzer, and a local oscillator is used to mix the microwave calibration signal to generate an intermediate frequency signal, and the phase of the intermediate frequency signal is extracted; Phase resolution and synchronization are performed through phase-locked loop tracking and actual phase error calculation, and error compensation and filtering are performed through anti-aliasing filtering and temperature compensation correction to generate the actual phase error.

[0011] As a preferred solution of the synchronization method for parallel operation control of multiple energy storage devices in the power grid of the present invention, wherein: the temperature data of the metasurface array is collected, the dynamic virtual impedance parameters are corrected according to the temperature data, and the corrected actual phase error is generated. The specific steps are as follows: The digital temperature sensor collects temperature data in real time, and performs outlier elimination and smoothing. The dynamic virtual impedance parameters are dynamically corrected through a linear temperature coefficient model, and a corrected actual phase error is generated based on the corrected dynamic virtual impedance parameters.

[0012] As a preferred solution of the synchronization method for parallel operation control of multiple energy storage devices in the power grid described in the present invention, wherein: the corrected actual phase error is fed back to the quantum optimization algorithm to reconstruct the multi-objective optimization function, and the specific steps are as follows: By establishing a dynamic coupling mechanism between phase error and quantum potential field, the corrected actual phase error is injected into the quantum optimization algorithm after normalization to obtain the dynamic influencing factor. Reconstruct multi-objective functions based on dynamic impact factors.

[0013] As a preferred solution of the synchronization method for parallel operation control of multiple energy storage devices in the power grid of the present invention, wherein: the time series prediction based on the historical dynamic virtual impedance parameters maintains the synchronous operation, and the specific steps are as follows: A bidirectional long short-term memory network is used to construct a time series prediction model to perform multi-step advance prediction of dynamic virtual impedance parameters. Based on the predicted value, the carrier density of the metasurface array is feedforward controlled to generate a pre-compensated microwave calibration signal, and the energy storage unit is operated synchronously by combining the prediction-optimization fusion and the residual feedback mechanism.

[0014] In a second aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the synchronization method for controlling parallel operation of multiple energy storage devices in a power grid as described in the first aspect of the present invention.

[0015] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the synchronization method for controlling parallel operation of multiple energy storage devices in a power grid as described in the first aspect of the present invention.

[0016] The beneficial effects of the present invention are: by real-time acquisition of the output current of the energy storage unit and calculation of the circulating current value, a quantum optimization algorithm is used to perform multi-objective optimization to generate dynamic virtual impedance parameters, and then the initial phase difference between the energy storage units is accurately calculated, and a microwave calibration signal is generated by adjusting the carrier density of the metasurface array, thereby achieving high-precision phase synchronization control. This method not only effectively compensates for the circulating current phenomenon caused by inconsistent energy storage unit parameters, but also enhances robustness and adaptability, significantly improves the overall stability and efficiency when multiple energy storage devices are operated in parallel, and provides a more reliable and efficient energy storage system control solution for smart grids. Ultimately, the present invention solves the limitations of traditional control strategies under complex working conditions and greatly improves the synchronization accuracy and response speed of the energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0018] Figure 1 This is a flow chart of the synchronization method for controlling the parallel operation of multiple energy storage devices in a power grid in Example 1.

[0019] Figure 2 This is a flow chart for generating dynamic virtual impedance parameters in Example 1. DETAILED DESCRIPTION

[0020] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0021] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0022] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0023] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, provides a synchronization method for parallel operation control of multiple energy storage devices in a power grid, comprising the following steps: S1: Collect the output current of the energy storage unit in real time, calculate the circulating current value of the energy storage unit, optimize the circulating current value through the multi-objective optimization function through the quantum optimization algorithm, and generate dynamic virtual impedance parameters; Furthermore, the current signal is collected in real time by the Hall current sensor, and the current signal is conditioned by noise suppression, analog-to-digital conversion, and time synchronization; It should be noted that noise suppression is achieved by filtering out high-frequency switching noise (such as the noise caused by the switching of the inverter IGBT) through an anti-aliasing filter (cut-off frequency 10kHz); Analog-to-digital conversion is to perform AD conversion on the analog signal at a sampling rate of 20kHz to generate a discrete current sequence; Time synchronization uses the IEEE1588 Precision Time Protocol (PTP) to align the data collection time of all units, ensuring that the global clock deviation is less than 1μs.

[0024] The circulating current value of the energy storage unit is generated through average current calculation and instantaneous current calculation.

[0025] It should be noted that the average current is the arithmetic mean of the output currents of all energy storage units, which is calculated every 10 milliseconds (i.e. 200 sampling points). The instantaneous circulating current value of each energy storage unit is the difference between its output current and the average current. The circulating current value of the energy storage unit is the instantaneous circulating current value of the energy storage unit.

[0026] Based on the quantum mechanics potential well theory, a quantum mechanics potential field model is constructed, and the local optimal solution of the energy storage unit is dynamically updated. The expression is: ; in, is the potential energy value corresponding to the virtual impedance parameter of the energy storage unit at time t, is the virtual impedance parameter of the energy storage unit, is the circulating current value of the kth energy storage unit, is the circulation quantum wave function, is the reduced Planck constant, is the equivalent particle mass, α=0.6 is the circulation suppression weight, β= is the time attenuation coefficient, γ=0.3 is the impedance constraint strength, is the reference impedance, is the standard deviation of impedance distribution, t is the time, N is the total number of energy storage units, is the virtual impedance parameter of the kth energy storage unit; The expression of the local optimal solution update rule of the energy storage unit is: ; in, is the local optimal solution of the kth energy storage unit at time t, and the historical data is the virtual impedance parameters and corresponding potential energy values ​​of each energy storage unit saved in the past 20 iterations ; It should be noted that , the smaller the value, the closer it is to the ideal state (the circulating current is minimum and the impedance is stable); The quantum kinetic energy term represents the penalty for the change of circulation gradient, forcing the parameters to converge smoothly; The circulation suppression term represents an exponential term that decays with time and preferentially suppresses early circulation; The impedance constraint term represents a quartic term that enhances the sensitivity to impedance deviation.

[0027] Through the hybrid mechanism of quantum tunneling and classical gradient descent, a globally optimized virtual impedance parameter is generated, which is expressed as: ; in, is the virtual impedance parameter for global optimization, λ=0.5 is the tunneling intensity coefficient, ξ~Uniform(0,1) is a random variable, η=0.01 is the gradient learning rate, Δt is the control period, represents virtual impedance; Through dynamic imaginary impedance adjustment, dynamic virtual impedance parameters are generated, and the expression is: ; ; in, is the dynamic imaginary impedance of the kth energy storage unit, is the basic imaginary impedance of the kth energy storage unit, is the power change sensitivity coefficient, is the actual power of the kth energy storage unit, is the average power, is the rated power, is the dynamic virtual impedance parameter, is the real impedance and j is the imaginary unit.

[0028] S2: Calculate the initial phase difference of the energy storage unit according to the dynamic virtual impedance parameter, and adjust the carrier density of the metasurface array between the energy storage units to generate a microwave calibration signal; Furthermore, the dynamic virtual impedance parameters are converted into the initial phase difference between the energy storage units through the impedance-phase mapping model and the gradient smoothing constraint; It should be noted that the impedance-phase mapping model is based on the transmission line theory, and the phase difference caused by the dynamic virtual impedance parameter is expressed as: ; in, is the initial phase difference of the kth energy storage unit, Z=50Ω is the characteristic impedance of the transmission line, and G=0.8 is the normalization coefficient; The gradient smoothing constraint is to avoid phase mutations in adjacent control cycles and impose a rate of change limit. The expression is: ; in, is the initial phase difference of the kth energy storage unit at time t, and t-Δt represents time t of the previous cycle; The initial phase difference is mapped to a carrier density reference value through an exponential function, and the carrier density reference value is adjusted through power dynamic feedback compensation and physical limiting protection; It should be noted that the power dynamic feedback compensation is to monitor the power change rate of the energy storage unit in real time. If the power increases or decreases suddenly, the additional density adjustment amount is added proportionally to predict the phase lag and compensate in advance; Physical limiting protection is to constrain the carrier density within the physical carrying range of the metasurface material to prevent device damage due to over-adjustment. The higher the carrier density, the lower the metasurface dielectric constant and the greater the phase advance of the microwave calibration signal.

[0029] Based on the adjusted carrier density reference value, the phase shift is calculated by exponential function mapping; It should be noted that the carrier density reference value is first converted into a theoretical phase offset through a pre-calibrated logarithmic relationship model, which contains nonlinear relationship parameters between the carrier density and the phase offset determined by the initial calibration stage; the theoretical phase offset is then superimposed with an error compensation term generated by a closed-loop control algorithm based on the phase error monitored in real time to form the final calibrated phase offset.

[0030] The phase offset is synthesized through the calibration phase to obtain the calibration phase parameter, and the generated calibration phase parameter is used to generate a microwave calibration signal through a microwave transmitter.

[0031] It should be noted that the calibration phase parameter is generated by superimposing the reference phase and the phase offset, wherein the reference phase is aligned with the grid synchronization signal in real time through a phase-locked loop to ensure the consistency of the reference phase of each energy storage unit; the superimposed calibration phase parameter is input into the phase modulation module of the microwave transmitter, and the phase parameter is mapped to the instantaneous phase angle of the high-frequency continuous wave signal generated by the microwave transmitter through a voltage-controlled phase shifter or digital direct frequency synthesis technology to complete the phase modulation; The modulated high-frequency continuous wave signal further passes through the dynamic amplitude control module to adjust the signal amplitude according to the real-time phase error partition. Linear gain amplification is used in the low-error area, and an exponential enhancement item is superimposed in the high-error area to improve the calibration response speed. The final output microwave calibration signal is injected into the transmission line between the energy storage units through the power amplifier and the directional coupler. At the same time, the vector network analyzer monitors the actual phase parameters in real time and feeds back to the closed-loop control loop to achieve iterative optimization of the phase synchronization error and dynamic adjustment of the microwave calibration signal.

[0032] S3: transmitting a microwave calibration signal, detecting the actual phase error of the energy storage unit, collecting temperature data of the metasurface array, correcting the dynamic virtual impedance parameters according to the temperature data, and generating a corrected actual phase error; Furthermore, a microwave calibration signal is transmitted via a microwave transmitter; It should be noted that when the microwave calibration signal is transmitted through a microwave transmitter, the microwave calibration signal is generated by a voltage-controlled phase shifter or digital direct frequency synthesis technology according to the calibration phase parameters, the carrier frequency is fixed to a preset value (for example, 2.4 GHz), and the amplitude of the microwave calibration signal is adjusted by the dynamic amplitude control module according to the real-time phase error partition and then output.

[0033] The microwave calibration signal is collected in real time by a vector network analyzer, and mixed with the received microwave calibration signal by a local oscillator to generate an intermediate frequency signal, and the amplitude and phase of the intermediate frequency signal are extracted; It should be noted that the vector network analyzer collects the microwave calibration signal in the transmission line in real time through a directional coupler. The microwave calibration signal at the receiving end is mixed with the same-frequency reference signal generated by the local oscillator to generate an intermediate frequency signal. The intermediate frequency signal is separated into the in-phase component and the orthogonal component by an orthogonal demodulator, and converted into a digital signal by an analog-to-digital converter with a sampling rate of not less than 40kHz. The digital signal processor extracts the instantaneous phase angle and signal amplitude based on the inverse tangent operation, and uses a phase-locked loop to track the grid synchronization reference phase to calculate the actual phase error. The error data is filtered out by a finite impulse response filter with a cutoff frequency of 10kHz to remove high-frequency noise and is stored in the historical database for closed-loop control and quantum optimization algorithm calls.

[0034] Through phase-locked loop tracking and actual phase error calculation, phase resolution and synchronization are performed, and error compensation and filtering are performed through anti-aliasing filtering and temperature compensation correction to generate the actual phase error; It should be noted that the grid synchronization reference signal is tracked by a phase-locked loop, and the phase comparator of the phase-locked loop detects the phase difference between the local oscillator output signal and the grid synchronization signal, generates an error voltage to drive the voltage-controlled oscillator to adjust the frequency, so that the local reference phase is aligned with the grid phase in real time; The phase solution module extracts the in-phase component and the orthogonal component of the microwave calibration signal through an orthogonal demodulator, calculates the instantaneous measurement phase using the inverse tangent operation, and obtains the original phase error by subtracting it from the reference phase. The original phase error is input into a finite impulse response anti-aliasing filter with a cutoff frequency of 10 kHz to filter out high-frequency switching noise and measurement interference, and output a smoothed error signal. At the same time, the phase error is corrected according to the linear compensation model based on the temperature data collected by the metasurface array temperature sensor, and the corrected phase error is used as the actual phase error.

[0035] The digital temperature sensor collects temperature data in real time, and performs outlier elimination and smoothing. It should be noted that the digital temperature sensor collects the raw data of the surface temperature of the metasurface array at a sampling frequency of 1000 times per second; first, outliers are eliminated based on the Raida criterion; secondly, sliding window adaptive smoothing is performed on the valid data, and a Savitzky-Golay filter with dynamically adjusted window width is used; finally, zero phase distortion filtering is used to eliminate delay accumulation.

[0036] The dynamic virtual impedance parameters are dynamically corrected through a linear temperature coefficient model, and a corrected actual phase error is generated according to the corrected dynamic virtual impedance parameters.

[0037] It should be noted that the linear temperature coefficient model receives the pre-processed temperature data and performs dynamic virtual impedance parameter correction based on the temperature drift characteristics; The corrected dynamic virtual impedance parameters are input into the impedance-phase mapping model in real time. Combined with the phase change rate limiting threshold output by the gradient smoothing constraint module, the theoretical phase difference between the energy storage units is recalculated. The theoretical phase difference is subtracted from the actual phase measurement value extracted by the vector network analyzer in the phase solution module, and the corrected actual phase error is generated after the high-frequency components are filtered out by the anti-aliasing filter.

[0038] S4: Feedback the corrected actual phase error to the quantum optimization algorithm to reconstruct the multi-objective optimization function, and maintain synchronous operation based on the time series prediction of historical dynamic virtual impedance parameters; Furthermore, by establishing a dynamic coupling mechanism between the phase error and the quantum potential field, the corrected actual phase error is injected into the quantum optimization algorithm after normalization to obtain the dynamic impact factor; Reconstruct multi-objective functions based on dynamic impact factors; It should be noted that the dynamic influence factor of the actual phase error on the virtual impedance optimization is defined as follows: ; in, is the dynamic influence factor of the corrected actual phase on the virtual impedance optimization, =0.2rad is the maximum allowable phase deviation, T is the integration period, is the corrected actual phase error of the kth energy storage unit at time t, τ is the time integral variable; The reconstructed multi-objective function expression is: ; in, is the reconstructed multi-objective function, and Φ is the phase difference.

[0039] A bidirectional long short-term memory network is used to construct a time series prediction model to perform multi-step advance prediction of dynamic virtual impedance parameters. It should be noted that the time series prediction model of the bidirectional long short-term memory network takes the historical dynamic virtual impedance parameter sequence as the main input feature, and constructs a bidirectional encoding structure including a forward propagation chain and a backward propagation chain: the input layer receives the multidimensional feature vector composed of the dynamic virtual impedance parameter time series, real-time power change rate and temperature gradient of the past 20 control cycles, and inputs it into the bidirectional stacking layer after normalization; The forward layer extracts historical trend features by forward propagation in time, the backward layer captures lagged correlation by reverse propagation, the hidden state fuses bidirectional features through a fully connected gating mechanism, and the output layer uses a multi-step prediction structure to synchronously generate dynamic virtual impedance parameter prediction values ​​for the next five control cycles; In the training phase, a mean square error loss function with residual connection is used, and 500 rounds of iterative training are performed based on the Adam optimizer. Random deactivation and early stopping strategies with a Dropout rate of 0.2 are simultaneously applied to prevent overfitting. In the prediction phase, the dynamic virtual impedance parameters, power sensor data, and temperature data collected in real time are updated with a sliding input window according to a 1ms control period. The output prediction value is corrected by the Kalman filter residual to generate the final multi-step advance prediction result. The prediction data is used to adjust the carrier density baseline value of the metasurface array in advance through the feedforward channel to achieve phase-synchronized pre-compensation control.

[0040] Based on the predicted value, the pre-compensated microwave calibration signal is generated by feed-forward controlling the carrier density of the metasurface array, and the energy storage unit is operated synchronously by combining the prediction-optimization fusion and the residual feedback mechanism; It should be noted that based on the multi-step advance prediction value of the dynamic virtual impedance parameter output by the bidirectional long short-term memory network prediction model, the feedforward control module converts the prediction value into the carrier density reference value adjustment amount through the pre-calibrated impedance-carrier density nonlinear mapping model, and inputs the n-step advance prediction value into the carrier density increment calculation unit, and performs nonlinear transformation, and generates a carrier density reference value sequence after superimposing the power dynamic feedback compensation amount; the microwave calibration signal generation unit adjusts the carrier phase of the metasurface array n control cycles in advance according to the reference value sequence, and injects the phase offset into the microwave carrier through the voltage-controlled phase shifter to form a pre-compensation calibration signal and inject it into the transmission line; The prediction-optimization fusion mechanism performs confidence-weighted fusion of the dynamic virtual impedance parameters currently output by the quantum optimization algorithm and the predicted value. When the prediction confidence factor is higher than the threshold of 0.85, a hybrid virtual impedance dominated by the predicted value is used, otherwise it switches to the quantum optimization value-dominated mode. The residual feedback mechanism calculates the deviation between the actual phase error and the predicted phase difference in real time, generates a model correction term through a proportional-integral regulator to dynamically update the weight parameters of the bidirectional long-short-term memory network, synchronously triggers the quantum potential field reconstruction event, and injects the residual energy into the potential function to form a constraint term. Finally, the amplitude of the microwave calibration signal is adjusted through the dynamic amplitude control module. When the phase mutation rate exceeds 100 krad / s, the hardware protection circuit is activated to force the carrier density to be locked, realizing the triple coordinated control of prediction feedforward, optimization iteration and residual feedback.

[0041] This embodiment also provides a computer device, which is suitable for the case of a synchronization method for controlling the parallel operation of multiple energy storage devices in a power grid, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the synchronization method for controlling the parallel operation of multiple energy storage devices in a power grid as proposed in the above embodiment.

[0042] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0043] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the synchronization method for realizing parallel operation control of multiple energy storage devices in a power grid as proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0044] In summary, the present invention achieves high-precision phase synchronization control by: real-time acquisition of the output current of the energy storage unit and calculation of the circulating current value, using the quantum optimization algorithm for multi-objective optimization to generate dynamic virtual impedance parameters, and then accurately calculating the initial phase difference between the energy storage units, and generating a microwave calibration signal by adjusting the carrier density of the metasurface array. This method not only effectively compensates for the circulating current phenomenon caused by inconsistent energy storage unit parameters, but also enhances robustness and adaptability, significantly improves the overall stability and efficiency when multiple energy storage devices are operated in parallel, and provides a more reliable and efficient energy storage system control solution for smart grids. Finally, the present invention solves the limitations of traditional control strategies under complex working conditions and greatly improves the synchronization accuracy and response speed of the energy storage system.

[0045] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A synchronization method for parallel operation control of multiple energy storage devices in a power grid, characterized by: include, Collect the output current of the energy storage unit in real time, calculate the circulating current value of the energy storage unit, optimize the circulating current value through the multi-objective optimization function through the quantum optimization algorithm, and generate dynamic virtual impedance parameters; The initial phase difference of the energy storage unit is calculated according to the dynamic virtual impedance parameter, and the carrier density of the metasurface array between the energy storage units is adjusted to generate a microwave calibration signal; Transmitting a microwave calibration signal, detecting the actual phase error of the energy storage unit, collecting temperature data of the metasurface array, correcting the dynamic virtual impedance parameters according to the temperature data, and generating a corrected actual phase error; The corrected actual phase error is fed back to the quantum optimization algorithm to reconstruct the multi-objective optimization function, and the synchronous operation is maintained based on the time series prediction of the historical dynamic virtual impedance parameters.

2. The synchronization method for controlling parallel operation of multiple energy storage devices in a power grid according to claim 1, characterized in that: The real-time collection of the output current of the energy storage unit and calculation of the circulating current value of the energy storage unit are carried out in the following specific steps: The current signal is collected in real time through the Hall current sensor, and the current signal is conditioned through noise suppression, analog-to-digital conversion and time synchronization; The circulating current value of the energy storage unit is generated through average current calculation and instantaneous current calculation.

3. The synchronization method for controlling parallel operation of multiple energy storage devices in a power grid as claimed in claim 2, characterized in that: The quantum optimization algorithm is used to optimize the circulating current value through a multi-objective optimization function to generate dynamic virtual impedance parameters. The specific steps are as follows: Based on the quantum mechanics potential well theory, a quantum mechanics potential field model is constructed, and the local optimal solution of the energy storage unit is dynamically updated; Generate globally optimized virtual impedance parameters through a hybrid mechanism of quantum tunneling and classical gradient descent; Dynamic virtual impedance parameters are generated through dynamic imaginary impedance adjustment.

4. The synchronization method for controlling parallel operation of multiple energy storage devices in a power grid as claimed in claim 3, characterized in that: The initial phase difference of the energy storage unit is calculated according to the dynamic virtual impedance parameter, the carrier density of the metasurface array between the energy storage units is adjusted, and a microwave calibration signal is generated. The specific steps are as follows: The dynamic virtual impedance parameters are converted into the initial phase difference between energy storage units through the impedance-phase mapping model and the gradient smoothing constraint; The initial phase difference is mapped to a carrier density reference value through an exponential function, and the carrier density reference value is adjusted through power dynamic feedback compensation and physical limiting protection; Based on the adjusted carrier density reference value, the phase shift is calculated by exponential function mapping; The phase offset is synthesized through the calibration phase to obtain the calibration phase parameter, and the calibration phase parameter is used to generate a microwave calibration signal through a microwave transmitter.

5. The synchronization method for parallel operation control of multiple energy storage devices in a power grid as claimed in claim 4, characterized in that: The specific steps of transmitting the microwave calibration signal and detecting the actual phase error of the energy storage unit are as follows: transmitting a microwave calibration signal via a microwave transmitter; The microwave calibration signal is collected in real time by a vector network analyzer, and a local oscillator is used to mix the microwave calibration signal to generate an intermediate frequency signal, and the phase of the intermediate frequency signal is extracted; Phase resolution and synchronization are performed through phase-locked loop tracking and actual phase error calculation, and error compensation and filtering are performed through anti-aliasing filtering and temperature compensation correction to generate the actual phase error.

6. The synchronization method for controlling parallel operation of multiple energy storage devices in a power grid as claimed in claim 5, characterized in that: The temperature data of the metasurface array is collected, the dynamic virtual impedance parameters are corrected according to the temperature data, and the corrected actual phase error is generated. The specific steps are as follows: The digital temperature sensor collects temperature data in real time, and performs outlier elimination and smoothing. The dynamic virtual impedance parameters are dynamically corrected through a linear temperature coefficient model, and a corrected actual phase error is generated according to the corrected dynamic virtual impedance parameters.

7. The synchronization method for controlling parallel operation of multiple energy storage devices in a power grid according to claim 6, characterized in that: The corrected actual phase error is fed back to the quantum optimization algorithm to reconstruct the multi-objective optimization function. The specific steps are as follows: By establishing a dynamic coupling mechanism between phase error and quantum potential field, the corrected actual phase error is injected into the quantum optimization algorithm after normalization to obtain the dynamic influencing factor. Reconstruct multi-objective functions based on dynamic impact factors.

8. The synchronization method for controlling parallel operation of multiple energy storage devices in a power grid according to claim 7, characterized in that: The time series prediction based on historical dynamic virtual impedance parameters maintains synchronous operation, and the specific steps are as follows: A bidirectional long short-term memory network is used to construct a time series prediction model to perform multi-step advance prediction of dynamic virtual impedance parameters. Based on the predicted value, the carrier density of the metasurface array is feedforward controlled to generate a pre-compensated microwave calibration signal, and the energy storage unit is operated synchronously by combining the prediction-optimization fusion and the residual feedback mechanism.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the synchronization method for controlling the parallel operation of multiple energy storage devices in a power grid as described in any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the synchronization method for controlling the parallel operation of multiple energy storage devices in a power grid as described in any one of claims 1 to 8 are implemented.

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