Power distribution method, device and equipment of electricity-hydrogen hybrid energy storage system and medium

Abstract

The invention provides a power distribution method, device and equipment of an electricity-hydrogen hybrid energy storage system and a medium, and belongs to the technical field of energy storage. The electricity-hydrogen hybrid energy storage system comprises a super capacitor, a lithium battery and a hydrogen energy storage subsystem, and the method comprises the following steps: decomposing the total regulation power of the electricity-hydrogen hybrid energy storage system to obtain high-frequency power, intermediate-frequency power and low-frequency power; correcting the high-frequency power according to a first charge state value of the super capacitor to obtain a first power correction value and a first optimal adjustment power of the super capacitor; according to the first power correction value and a second charge state value of the lithium battery, correcting the intermediate frequency power to obtain a second power correction value and a second optimal adjustment power of the lithium battery; and correcting the low-frequency power according to the second power correction value and the health state value of the hydrogen energy storage subsystem to obtain third optimal adjustment power of the hydrogen energy storage subsystem. The energy utilization rate and the operation stability of the electricity-hydrogen hybrid energy storage system can be improved.

Description

Technical Field

[0001] This application relates to the field of energy storage technology, and in particular to power distribution methods, devices, equipment and media for hybrid electric-hydrogen energy storage systems. Background Technology

[0002] In existing technologies, different types of filters are typically used to decompose the total power of an energy storage system into power components of different frequency bands, and then distribute them according to the frequency response characteristics of each energy storage unit in the energy storage system. However, this implementation process does not take into account the state constraints of each energy storage unit in the energy storage system, which can easily lead to problems such as unstable operation of the energy storage system and low energy utilization. Summary of the Invention

[0003] The main purpose of this application is to propose a power distribution method, device, equipment and medium for an electric-hydrogen hybrid energy storage system, aiming to improve the energy utilization rate and operational stability of the electric-hydrogen hybrid energy storage system.

[0004] To achieve the above objectives, one aspect of this application proposes a power distribution method for an electro-hydrogen hybrid energy storage system, wherein the electro-hydrogen hybrid energy storage system includes a supercapacitor, a lithium battery, and a hydrogen energy storage subsystem, and the method includes: The total regulating power of the electric-hydrogen hybrid energy storage system, the first state of charge value of the supercapacitor, the second state of charge value of the lithium battery, and the health status value of the hydrogen energy storage subsystem are obtained. The total regulated power is decomposed to obtain high-frequency power, mid-frequency power and low-frequency power; Based on the first state of charge value, the high-frequency power is corrected to obtain the first power correction value and the first optimal adjustment power of the supercapacitor; Based on the second state of charge value and the first power correction value, the intermediate frequency power is corrected to obtain the second power correction value and the second optimal regulation power of the lithium battery; Based on the health status value and the second power correction value, the low-frequency power is corrected to obtain the third optimal regulation power of the hydrogen energy storage subsystem.

[0005] Furthermore, the decomposition of the total regulated power to obtain high-frequency power, mid-frequency power, and low-frequency power includes: Variational mode decomposition is performed on the total regulating power, and an improved gray wolf algorithm is used to optimize the penalty factor and number of modes required for the decomposition process, resulting in multiple power components. The multiple power components are grouped and merged to obtain the high-frequency power, the mid-frequency power, and the low-frequency power.

[0006] Furthermore, the total regulating power is subjected to variational mode decomposition, and an improved gray wolf algorithm is used to optimize the penalty factor and the number of modes required for the decomposition process, resulting in multiple power components including: The improved gray wolf algorithm is initialized with parameters, and then the gray wolf population is initialized with a chaotic mapping strategy. The position of each gray wolf represents the value of the penalty factor and the number of modes. The fitness value of each gray wolf in the gray wolf population is calculated based on a preset fitness function and the position of each gray wolf in the gray wolf population; wherein, the fitness function is used to evaluate the effect of variational mode decomposition of the total regulation power based on the position of the gray wolf. Select the three gray wolves with the best fitness values ​​from the gray wolf population, and then update the positions of the other gray wolves in the gray wolf population other than the three gray wolves using the position update strategy of particle swarm optimization based on the positions of the three gray wolves. Determine whether the preset termination condition is met; if not, return to the step of calculating the fitness value of each gray wolf in the gray wolf population based on the preset fitness function and the position of each gray wolf in the gray wolf population; if yes, perform variational mode decomposition on the total regulation power based on the positions of the three gray wolves to obtain the multiple power components.

[0007] Further, the step of correcting the high-frequency power based on the first state of charge value to obtain the first power correction value and the first optimal adjustment power of the supercapacitor includes: Based on the first state of charge value and the high-frequency power, fuzzy reasoning is performed to obtain the first power correction coefficient; The first power correction value and the first optimal adjustment power of the supercapacitor are calculated based on the high-frequency power and the first power correction coefficient.

[0008] Further, the step of correcting the intermediate frequency power based on the second state of charge value and the first power correction value to obtain the second power correction value and the second optimal regulation power of the lithium battery includes: The first intermediate frequency power is obtained by calculating based on the intermediate frequency power and the first power correction value; Based on the second state of charge value and the first intermediate frequency power, fuzzy reasoning is performed to obtain the second power correction coefficient; The second power correction value and the second optimal regulation power of the lithium battery are obtained by calculating based on the first intermediate frequency power and the second power correction coefficient.

[0009] Further, the step of correcting the low-frequency power based on the health status value and the second power correction value to obtain the third optimal regulation power of the hydrogen energy storage subsystem includes: The first low-frequency power is obtained by calculating based on the low-frequency power and the second power correction value; Based on the health status value and the first low-frequency power, fuzzy inference is performed to obtain the third power correction coefficient; The third optimal regulation power of the hydrogen energy storage subsystem is obtained by calculating based on the first low-frequency power and the third power correction coefficient.

[0010] Furthermore, the total regulating power of the electro-hydrogen hybrid energy storage system is obtained in the following manner: The output power of the photovoltaic power generation system and the required power of the DC load are obtained and the deviation is calculated to obtain the total regulation power of the electric-hydrogen hybrid energy storage system.

[0011] To achieve the above objectives, another aspect of this application proposes a power distribution device for an electro-hydrogen hybrid energy storage system, the electro-hydrogen hybrid energy storage system comprising a supercapacitor, a lithium battery, and a hydrogen energy storage subsystem, the device comprising: The first module is used to obtain the total regulating power of the electric-hydrogen hybrid energy storage system, the first state of charge value of the supercapacitor, the second state of charge value of the lithium battery, and the health status value of the hydrogen energy storage subsystem. The second module is used to decompose the total regulating power to obtain high-frequency power, medium-frequency power and low-frequency power; The third module is used to correct the high-frequency power based on the first state of charge value to obtain the first power correction value and the first optimal adjustment power of the supercapacitor. The fourth module is used to correct the intermediate frequency power based on the second state of charge value and the first power correction value to obtain the second power correction value and the second optimal adjustment power of the lithium battery. The fifth module is used to correct the low-frequency power based on the health status value and the second power correction value to obtain the third optimal regulation power of the hydrogen energy storage subsystem.

[0012] To achieve the above objectives, another aspect of this application proposes an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the power distribution method of the above-described electro-hydrogen hybrid energy storage system.

[0013] To achieve the above objectives, another aspect of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the power distribution method of the above-described electro-hydrogen hybrid energy storage system.

[0014] This application includes at least the following beneficial effects: by decomposing the total regulating power of the electric-hydrogen hybrid energy storage system into high-frequency power, medium-frequency power, and low-frequency power, and then incorporating the state of charge (SOC) value of the supercapacitor into the process of correcting the high-frequency power to determine the optimal regulating power of the supercapacitor, incorporating the SOC value of the lithium battery into the process of correcting the medium-frequency power to determine the optimal regulating power of the lithium battery, and incorporating the health status value of the hydrogen energy storage subsystem into the process of correcting the low-frequency power to determine the optimal regulating power of the hydrogen energy storage subsystem, the energy utilization efficiency and operational stability of the electric-hydrogen hybrid energy storage system can be improved. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the composition of the DC microgrid provided in the embodiments of this application; Figure 2 This is a schematic flowchart of a power distribution method for an electro-hydrogen hybrid energy storage system provided in an embodiment of this application; Figure 3 This is a schematic diagram of the composition of a power distribution device for an electro-hydrogen hybrid energy storage system provided in an embodiment of this application; Figure 4 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of systems and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0017] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various concepts, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to a determination” as used herein may be interpreted as “when…” or “when…” or “in response to a determination.”

[0018] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0020] Hybrid Energy Storage Systems (HESS) integrate multiple energy storage technologies to effectively mitigate photovoltaic power output fluctuations in microgrids, improve power supply reliability, and reduce dependence on the external power grid. For example, the combination of batteries and supercapacitors can smooth short-term and long-term power fluctuations, enhancing microgrid stability. A key to achieving efficient operation of HESS systems lies in rational power allocation. In hydrogen-containing HESS systems, the implemented power allocation strategy must consider not only the slow charging and discharging rates and nonlinearity of hydrogen storage, but also the lifespan management of storage devices and multi-timescale adjustment requirements. This ensures efficient response to power demand changes, avoids over-reliance on any single energy storage technology, and improves the system's energy utilization rate.

[0021] In existing technologies, different types of filters are typically used to decompose the total power of an energy storage system into power components of different frequency bands. Then, the power is allocated according to the frequency response characteristics of each energy storage unit in the energy storage system. For example, low-frequency power components are handled by energy-type energy storage (such as batteries), and high-frequency power components are handled by power-type energy storage (such as supercapacitors). However, this implementation process does not take into account the state constraints of each energy storage unit in the energy storage system, which can easily lead to problems such as unstable operation of the energy storage system, low energy utilization, and adverse effects on equipment lifespan.

[0022] In view of this, embodiments of this application provide a power distribution method, apparatus, device, and medium for an electro-hydrogen hybrid energy storage system. This scheme decomposes the total regulating power of the electro-hydrogen hybrid energy storage system into high-frequency power, medium-frequency power, and low-frequency power. Then, in the process of correcting the high-frequency power to determine the optimal regulating power of the supercapacitor, the state of charge (SOC) value of the supercapacitor is analyzed; in the process of correcting the medium-frequency power to determine the optimal regulating power of the lithium battery, the SOC value of the lithium battery is analyzed; and in the process of correcting the low-frequency power to determine the optimal regulating power of the hydrogen energy storage subsystem, the health status value of the hydrogen energy storage subsystem is analyzed. This improves the energy utilization rate and operational stability of the electro-hydrogen hybrid energy storage system, and enhances its flexibility and responsiveness.

[0023] Please see Figure 1 , Figure 1 This is an optional schematic diagram of a DC microgrid provided in an embodiment of this application. The DC microgrid includes an energy router, a DC bus, a photovoltaic power generation system, an electro-hydrogen hybrid energy storage system, and DC loads. The electro-hydrogen hybrid energy storage system includes a supercapacitor, a lithium battery, and a hydrogen energy storage subsystem. The hydrogen energy storage subsystem includes an electrolyzer, a hydrogen storage tank, and a fuel cell. The energy router includes a first unidirectional Boost converter, a second unidirectional Boost converter, a first bidirectional Buck / Boost converter, a second bidirectional Buck / Boost converter, a first unidirectional DC / DC converter, and a second unidirectional DC / DC converter.

[0024] The core component of a photovoltaic power generation system is the photovoltaic array. The photovoltaic array is used as the main power source. The photovoltaic array is connected to the DC bus through the first unidirectional Boost converter and uses the MPPT (Maximum power point tracking) algorithm to maximize solar energy capture.

[0025] The DC load is connected to the DC bus via a second unidirectional Boost converter for DC power consumption.

[0026] As a power-type energy storage device, the supercapacitor has high power density and millisecond-level dynamic response capability. It is connected to the DC bus through the first bidirectional Buck / Boost converter to handle millisecond-level power fluctuation smoothing. It can quickly absorb or release electrical energy and is suitable for transient power regulation and short-term energy storage.

[0027] Lithium batteries, as short- to medium-term energy storage devices, have both high energy density and power density. They are connected to the DC bus through a second bidirectional Buck / Boost converter to provide energy throughput from minutes to hours, enabling them to respond quickly to power changes and provide stable power support.

[0028] The hydrogen energy storage subsystem is a long-term energy storage system with high energy density. The electrolyzer is connected to the DC bus via a first unidirectional DC / DC converter to produce hydrogen from electricity. The electrolyzer is connected to a hydrogen storage tank to store hydrogen. The hydrogen storage tank is connected to a fuel cell to supply hydrogen. The fuel cell is connected to the DC bus via a second unidirectional DC / DC converter to generate electricity. This forms a closed-loop energy flow of "electricity-hydrogen-electricity", achieving efficient energy storage and utilization, and providing stable power support for long periods and large scale.

[0029] In summary, the hybrid electric-hydrogen energy storage system, by integrating supercapacitors, lithium batteries, and hydrogen energy storage subsystems, can effectively combine short-term, medium-term, and long-term energy storage. Specifically, supercapacitors provide transient power regulation, lithium batteries handle short- to medium-term energy fluctuations, and the hydrogen energy storage subsystem supports long-term, large-scale energy storage. To further improve the operational stability, reliability, and energy efficiency of this multi-layered energy storage architecture, a reasonable power allocation strategy needs to be developed, as described below.

[0030] Please see Figure 2 , Figure 2 This is an optional flowchart illustrating a power distribution method for an electro-hydrogen hybrid energy storage system provided in an embodiment of this application. The method may include, but is not limited to, the following steps S101 to S105: Step S101: Obtain the total regulation power of the electric-hydrogen hybrid energy storage system, the first state of charge value of the supercapacitor, the second state of charge value of the lithium battery, and the health status value of the hydrogen energy storage subsystem. Step S102: Decompose the total regulating power of the electric-hydrogen hybrid energy storage system to obtain high-frequency power, medium-frequency power and low-frequency power; Step S103: Based on the first state of charge value of the supercapacitor, the high-frequency power is corrected to obtain the first power correction value and the first optimal regulation power of the supercapacitor. Step S104: Based on the second state of charge value of the lithium battery and the first power correction value of the supercapacitor, the intermediate frequency power is corrected to obtain the second power correction value and the second optimal regulation power of the lithium battery. Step S105: Based on the health status value of the hydrogen energy storage subsystem and the second power correction value of the lithium battery, the low-frequency power is corrected to obtain the third optimal regulation power of the hydrogen energy storage subsystem.

[0031] Steps S101 to S105 as shown in the embodiments of this application, by taking into account the state of charge value when determining the optimal regulation power of the supercapacitor, the state of charge value when determining the optimal regulation power of the lithium battery, and the health status value when determining the optimal regulation power of the hydrogen energy storage subsystem, are beneficial to improving the energy utilization rate and operational stability of the electric-hydrogen hybrid energy storage system.

[0032] In step S101 of some embodiments, the total regulating power of the electro-hydrogen hybrid energy storage system can be obtained in the following way: By obtaining the output power of the photovoltaic power generation system and the power demand of the DC load, and then using the first expression to calculate the deviation, the total regulating power of the electric-hydrogen hybrid energy storage system can be obtained. This first expression is: ; In the formula, for The total regulation power of the instantaneous electro-hydrogen hybrid energy storage system can be understood as: Power deviation within a DC microgrid at any given time for The power demand of the DC load at any given time. for The output power of the photovoltaic power generation system at any given time.

[0033] In step S101 of some embodiments, the first state of charge (SOC) of the supercapacitor refers to the ratio of the current stored charge of the supercapacitor to its maximum nominal capacity. It is an important parameter for measuring the current remaining energy state of the supercapacitor and can be estimated by voltage method, ampere-hour integration method, Kalman filter algorithm, model fitting method, etc.

[0034] In step S101 of some embodiments, the second state of charge value of the lithium battery refers to the ratio of the current remaining charge of the lithium battery to its total charge in a fully charged state, which directly affects the working state of the lithium battery. It can be estimated by methods such as open-circuit voltage method, ampere-hour integration method, and Kalman filtering method.

[0035] In step S101 of some embodiments, the health status value of the hydrogen energy storage subsystem is actually the state of health (SOH) value of the hydrogen storage tank. It usually represents the ratio of the current performance (such as capacity, internal resistance, peak power, etc.) of the hydrogen storage tank to its initial performance. It can reflect the aging degree, performance degradation and changes in internal physicochemical properties of the hydrogen storage tank. It is an important basis for assessing whether the hydrogen storage tank needs maintenance or replacement. It can be estimated by internal resistance measurement method, capacity degradation method, machine learning algorithm, etc.

[0036] In some embodiments, step S102 may include, but is not limited to, the following steps S201 to S202: Step S201: Perform variational mode decomposition on the total regulation power of the electric-hydrogen hybrid energy storage system. At the same time, use the improved gray wolf algorithm to optimize the penalty factor and the number of modes required for the variational mode decomposition process to obtain multiple power components. The improved gray wolf algorithm is mainly reflected in the optimization of the position initialization strategy and position update strategy in the traditional gray wolf algorithm.

[0037] Step S202: Group and merge the multiple power components obtained from variational mode decomposition to obtain high-frequency power, mid-frequency power and low-frequency power.

[0038] In this step, multiple power components can first be arranged in descending order of their center frequencies. Then, the number of the arranged power components is counted and denoted as N. Based on the remainder of N / 3, the arranged power components are grouped and merged to determine the power of different frequency bands. Specifically, there are three cases: The first case: When the remainder of N / 3 is 0, the first N / 3 power components arranged from the 1st to the N / 3rd position are superimposed or combined in other ways to obtain high-frequency power, the middle N / 3 power components arranged from the N / 3+1st to the 2N / 3rd position are superimposed or combined in other ways to obtain mid-frequency power, and the last N / 3 power components arranged from the 2N / 3+1st to the Nth position are superimposed or combined in other ways to obtain low-frequency power. The second case: When the remainder of N / 3 is 1, the quotient of N / 3 is denoted as K, where K is a positive integer. The first K+1 power components from the 1st to the K+1th position are superimposed or combined in other ways to obtain the high-frequency power. The middle K power components from the K+2th to the 2K+1th position are superimposed or combined in other ways to obtain the mid-frequency power. The last K power components from the 2K+2th to the Nth position are superimposed or combined in other ways to obtain the low-frequency power. The third case: When the remainder of N / 3 is 2, the quotient of N / 3 is denoted as K, where K is a positive integer. The first K+1 power components from the 1st to the K+1th position are superimposed or combined in other ways to obtain the high-frequency power. The middle K+1 power components from the K+2th to the 2K+2nd position are superimposed or combined in other ways to obtain the mid-frequency power. The last K power components from the 2K+3th to the Nth position are superimposed or combined in other ways to obtain the low-frequency power.

[0039] In this application, the total regulation power task of the electric-hydrogen hybrid energy storage system is decomposed into high-frequency power, medium-frequency power and low-frequency power by using the variational mode decomposition algorithm. Subsequently, the power is allocated according to the frequency response characteristics of the supercapacitor, lithium battery and hydrogen energy storage subsystem to give full play to their energy storage advantages. This can realize power regulation on multiple time scales and ensure that the system can respond quickly to different frequency power demands.

[0040] Optionally, wavelet packet decomposition algorithm, Fourier transform algorithm, adaptive filtering algorithm, etc., can be used to directly decompose the total regulating power of the electric hydrogen hybrid energy storage system into high-frequency power, medium-frequency power and low-frequency power, which is not limited in this application.

[0041] In some embodiments, step S201 may include, but is not limited to, the following steps S301 to S305: Step S301: Initialize the parameters of the improved gray wolf algorithm, and then use the chaotic mapping strategy to initialize the position of the gray wolf population. The position of each gray wolf represents the value of the penalty factor and the value of the mode number.

[0042] In this step, parameter initialization of the improved gray wolf algorithm refers to initializing the size of the gray wolf population, the maximum number of iterations during algorithm operation, the number of optimization variables, the value range of optimization variables, and the parameters required when implementing the position update strategy. The optimization variables include the penalty factor and the number of modes.

[0043] In this step, the Tent chaotic mapping strategy is introduced to address the problem of uneven population initialization distribution in the traditional gray wolf algorithm. The second expression can be used to initialize the position of the gray wolf population, with the corresponding chaotic values ​​uniformly distributed within the range [0,1]. This second expression is: , ; In the formula, Let be the initial position of the i-th gray wolf. These are the control parameters for chaotic mapping. Let be the initial state of the chaotic variable corresponding to the i-th gray wolf, which is randomly generated in the range [0,1]. Let be the chaos value corresponding to the i-th gray wolf. To optimize the upper bound of the variable's value range, To optimize the lower bound of the range of values ​​for variables.

[0044] Step S302: Calculate the fitness value of each gray wolf in the gray wolf population based on the preset fitness function and the position of each gray wolf in the gray wolf population.

[0045] In this step, the fitness function is mainly used to evaluate the effectiveness of variational mode decomposition of the total regulation power of the hybrid electric-hydrogen energy storage system based on the location of the gray wolf. The fitness function can be one or a weighted sum of at least two of the following: envelope entropy minimization function, information entropy minimization function, and reconstruction error minimization function. Envelope entropy measures the sparsity and periodicity of the modal components and is mainly related to the normalized probability distribution of the envelope signal of the modal components; information entropy measures the uncertainty of the modal components and is mainly related to the effective information contained in the modal components; reconstruction error measures the error between the sum of all modal components and the original signal.

[0046] Step S303: Select the three gray wolves with the best fitness values ​​from the gray wolf population, and then update the positions of the other gray wolves in the gray wolf population other than the three selected gray wolves using the position update strategy of particle swarm optimization.

[0047] In this step, a dual-guidance mechanism of individual historical optimality and population optimality from the Particle Swarm Optimization (PSO) algorithm is introduced. This combines the memory capability of the PSO algorithm with the hierarchical cooperation of the Gray Wolf Algorithm, overcoming the problem of insufficient inter-population information interaction in the traditional Gray Wolf Algorithm and improving the algorithm's ability to escape local optima. Specifically, the third expression can be used to update the positions of the gray wolves in the population other than the three selected wolves. This third expression is:

[0048] In the formula, the three selected gray wolves are defined as follows: Wolf, wolves and Wolf, The wolf has the best fitness value. The wolf's fitness value is second best. The wolf's fitness value was the third best. All gray wolves in the population other than the three selected individuals were defined as ordinary gray wolves. Let be the speed of the i-th ordinary gray wolf in the k-th iteration. Let be the speed of the i-th ordinary gray wolf before entering the (k+1)-th iteration. The inertia weight is used to control the influence of the velocity before the iteration on the velocity after the iteration. The first learning factor is used to characterize the learning of ordinary gray wolves. The intensity of wolf behavior For the selection at the k-th iteration The wolf's position The second learning factor is used to characterize the learning of ordinary gray wolves. The intensity of wolf behavior For the selection at the k-th iteration The wolf's position As the third learning factor, For the selection at the k-th iteration The wolf's position Let i be the position of the i-th ordinary gray wolf in the k-th iteration. Let i be the position of the i-th ordinary gray wolf before entering the (k+1)-th iteration. , and All values ​​are random numbers between 0 and 1. It should be noted that, due to the additional introduction of a speed parameter during the gray wolf position update process, the speed of the gray wolf population will also be randomly initialized during the execution of step S301 above.

[0049] Among these, the cosine law can be introduced to adjust the inertial weight. Setting it to a non-linear state is used to balance the algorithm's global and local search capabilities, improving its convergence speed and accuracy. Specifically, the inertia weight required for each iteration is calculated using the fourth expression. The fourth expression is: ; In the formula, The maximum inertia weight is preferably set to 2. The minimum inertia weight is preferably set to 0. This represents the current iteration number. The maximum number of iterations, It is a decreasing coefficient and its value range is .

[0050] Step S304: Determine whether the preset termination condition is met. The termination condition is preferably set to reach the maximum number of iterations. If yes, proceed to step S305. If no, return to step S302.

[0051] Step S305: Based on the positions of the three selected gray wolves, perform variational mode decomposition on the total regulating power of the electric-hydrogen hybrid energy storage system to obtain multiple power components.

[0052] In this step, the position of the single gray wolf with the best fitness value among the three selected gray wolves is first taken as the optimal solution, or the optimal solution is obtained by averaging the positions of the three selected gray wolves. The optimal solution includes the optimal value of the penalty factor and the optimal value of the number of modes. Then, the total regulation power of the electric-hydrogen hybrid energy storage system is decomposed by variational mode based on the optimal solution to obtain multiple power components.

[0053] In this application, the penalty factor and the number of modes involved in the variational mode decomposition algorithm are optimized by adopting the improved gray wolf algorithm, and then the total regulation power of the electric hydrogen hybrid energy storage system is processed by the parameter-optimized variational mode decomposition algorithm, which can improve the adaptability and accuracy of power decomposition.

[0054] The implementation principle of the variational mode decomposition algorithm is explained below, specifically including the following: The core of variational mode decomposition (VMD) lies in constructing and solving a constrained optimization problem. Its goal is to minimize the total bandwidth of all mode functions and constrain the sum of all mode functions to be as equal as possible to the original signal, so as to ensure that each mode function has an accurate distribution in the frequency domain and avoid mode aliasing and irrelevant frequency band interference.

[0055] First, the constrained variational problem can be represented by the following fifth expression: ; In the formula, For the k-th mode function, The original signal, Let be the center frequency of the k-th modal function. For modal number, The imaginary unit, For the Dirac function, Denotes the square of the L2 norm. Describes the differential operator with respect to time t. This represents the convolution operator; in, This represents the one-sided spectrum of the k-th mode function generated by the Hilbert transform. This indicates the introduction of the exponential operator. To adjust the center frequency of the k-th mode function so that it is moved to the fundamental frequency band.

[0056] Secondly, by introducing the Lagrange multiplication operator and the penalty factor, the constrained variational problem is transformed into an unconstrained variational problem, which can be represented by the following sixth expression: ; In the formula, Refers to the Lagrange function, The Lagrange multiplier operator is used to ensure that all modal functions can more accurately reconstruct the original signal, reducing intermodal coupling and information loss. This is a penalty factor used to control the strictness of the signal reconstruction constraints, in order to balance convergence speed and computational stability. This represents the vector dot product operator.

[0057] Finally, the alternating direction multiplier method is used to iteratively solve the above unconstrained variational problem, which yields the optimal K modal functions and their center frequencies. Specifically, after initializing the Lagrange multiplier operator and each modal function and its center frequency, alternating iterative updates are performed until the preset convergence condition is met. In each iteration, the seventh expression is used to update each modal function. This seventh expression is: ; The center frequencies of each modal function are updated using the eighth expression, which is: ; The Lagrange multiplication operator is updated using the ninth expression, which is: ; The preset convergence condition is used to constrain the changes in each modal function during two adjacent iterations, and can be expressed by the following tenth expression: ; In the formula, This represents the Fourier transform result of the k-th mode function in the (n+1)-th iteration. The result is the Fourier transform of the original signal. The Fourier transform result of the Lagrange multiplication operator. Let be the center frequency of the k-th modal function in the (n+1)-th iteration. This is the update step size for the Lagrange multiplier operator, which is used to adjust the convergence speed of the constraints. The convergence threshold is used. In this seventh expression, the mode function in the time domain is transformed to the frequency domain using Fourier transform, and then further transformed into a half-space integral in the non-negative frequency range using Hermitian conjugate symmetry, thus obtaining the mode function in the frequency domain.

[0058] In some embodiments, step S103 may include, but is not limited to, the following steps S401 to S402: Step S401: Perform fuzzy reasoning based on the first state of charge value of the supercapacitor and the high-frequency power to obtain the first power correction coefficient.

[0059] In this step, a pre-created first fuzzy controller is first invoked. Its inputs are the state of charge and initial power command of the supercapacitor, and its output is the power correction coefficient of the supercapacitor. The first fuzzy controller uses "if-then" inference rules to construct a first fuzzy rule base. The first fuzzy rule base is used to reflect the nonlinear mapping relationship between the two input variables, the state of charge and initial power command of the supercapacitor, and the output variable, the power correction coefficient of the supercapacitor. The number of fuzzy subsets divided for the physical range of each variable can be limited within the range of [3,7]. The selected fuzzy membership function can be a single-point membership function, a triangular membership function, or a trapezoidal membership function. Then, the high-frequency power is used as the initial power command of the supercapacitor and input together with the first state of charge value of the supercapacitor into the first fuzzy controller for analysis and processing to obtain the first power correction coefficient.

[0060] Step S402: Calculate the first power correction value and the first optimal adjustment power of the supercapacitor based on the high-frequency power and the first power correction coefficient.

[0061] In this step, the first power correction value of the supercapacitor is calculated based on the high-frequency power and the first power correction coefficient using the eleventh expression, which is: ; Based on the high-frequency power and the first power correction value of the supercapacitor, the first optimal regulating power of the supercapacitor is calculated using the twelfth expression, which is: ; In the formula, For high-frequency power, This is the first power correction factor. This is the first power correction value for the supercapacitor. This is the first optimal regulating power for a supercapacitor.

[0062] In this application, by taking into account the first state of charge value of the supercapacitor and combining it with a fuzzy inference mechanism to reasonably adjust the high-frequency power allocated to the supercapacitor, overcharging or over-discharging of the supercapacitor can be avoided, thereby extending the service life of the supercapacitor and improving energy utilization.

[0063] In some embodiments, step S104 may include, but is not limited to, steps S501 to S503: Step S501: Based on the intermediate frequency power and the first power correction value of the supercapacitor, calculate the first intermediate frequency power using the thirteenth expression, which is: ; In the formula, For intermediate frequency power, This is the first intermediate frequency power.

[0064] Step S502: Perform fuzzy inference based on the second state of charge value of the lithium battery and the first intermediate frequency power to obtain the second power correction coefficient.

[0065] In this step, a pre-created second fuzzy controller is first invoked. Its inputs are the state of charge (SOC) and initial power command of the lithium battery, and its output is the power correction coefficient of the lithium battery. The second fuzzy controller uses "if-then" inference rules to construct a second fuzzy rule library. The second fuzzy rule library is used to reflect the nonlinear mapping relationship between the two input variables, the SOC and initial power command of the lithium battery, and the output variable, the power correction coefficient of the lithium battery. The number of fuzzy subsets divided for the physical range of each variable can be limited within the range of [3,7]. The selected fuzzy membership function can be a single-point membership function, a triangular membership function, or a trapezoidal membership function. Then, the first intermediate frequency power is used as the initial power command of the lithium battery and input together with the second SOC value of the lithium battery into the second fuzzy controller for analysis and processing to obtain the second power correction coefficient.

[0066] Step S503: Calculate the second power correction value and the second optimal regulation power of the lithium battery based on the first intermediate frequency power and the second power correction coefficient.

[0067] In this step, the second power correction value of the lithium battery is first calculated based on the first intermediate frequency power and the second power correction coefficient using the fourteenth expression, which is: ; Then, based on the first intermediate frequency power and the second power correction value of the lithium battery, the second optimal regulation power of the lithium battery is calculated using the fifteenth expression, which is: ; In the formula, This is the second power correction factor. This is the second power correction value for lithium batteries. This is the second optimal regulated power for lithium batteries.

[0068] In this application, by taking into account the synergistic effect between the supercapacitor and the lithium battery, the first power correction value of the supercapacitor is used to make an initial adjustment to the intermediate frequency power originally allocated to the lithium battery. Then, taking into account the second state of charge value of the lithium battery, the first intermediate frequency power obtained by the initial adjustment is further reasonably adjusted by combining the fuzzy inference mechanism. This can avoid overcharging or over-discharging of the lithium battery, thereby extending the service life of the lithium battery and improving energy utilization.

[0069] In some embodiments, step S105 may include, but is not limited to, the following steps S601 to S603: Step S601: Based on the low-frequency power and the second power correction value of the lithium battery, calculate the first low-frequency power using the sixteenth expression, which is: ; In the formula, For low-frequency power, This is the first low-frequency power.

[0070] Step S602: Based on the health status value of the hydrogen energy storage subsystem and the first low-frequency power, perform fuzzy reasoning to obtain the third power correction coefficient.

[0071] In this step, a pre-created third fuzzy controller is first invoked. Its inputs are the health status and initial power command of the hydrogen energy storage subsystem, and its output is the power correction coefficient of the hydrogen energy storage subsystem. The third fuzzy controller uses "if-then" inference rules to construct a third fuzzy rule base. The third fuzzy rule base is used to reflect the nonlinear mapping relationship between the two input variables, the health status and initial power command of the hydrogen energy storage subsystem, and the output variable, the power correction coefficient of the hydrogen energy storage subsystem. The number of fuzzy subsets divided for the physical range of each variable can be limited within the range of [3,7]. The selected fuzzy membership function can be a single-point membership function, a triangular membership function, or a trapezoidal membership function. Then, the first low-frequency power is used as the initial power command of the hydrogen energy storage subsystem and input together with the health status value of the hydrogen energy storage subsystem into the third fuzzy controller for analysis and processing to obtain the third power correction coefficient.

[0072] Step S603: Calculate the third optimal regulation power of the hydrogen energy storage subsystem based on the first low-frequency power and the third power correction coefficient.

[0073] In this step, the third power correction value of the hydrogen energy storage subsystem is first calculated based on the first low-frequency power and the third power correction coefficient using the seventeenth expression, which is: ; Based on the first low-frequency power and the third power correction value of the hydrogen energy storage subsystem, the third optimal regulation power of the hydrogen energy storage subsystem is calculated using the eighteenth expression, which is: ; In the formula, This is the third power correction factor. This is the third power correction value for the hydrogen energy storage subsystem. This is the third optimal regulating power for the hydrogen energy storage subsystem.

[0074] In this application, by taking into account the synergistic effect between the lithium battery and the hydrogen energy storage subsystem, the low-frequency power originally allocated to the hydrogen energy storage subsystem is first adjusted using the second power correction value of the lithium battery. Then, taking into account the health status value of the hydrogen energy storage subsystem, the first low-frequency power obtained from the initial adjustment is further adjusted reasonably using a fuzzy inference mechanism. This can prevent the hydrogen storage tank from overfilling or over-discharging hydrogen, thereby extending the service life of the hydrogen storage tank and improving energy utilization.

[0075] In some embodiments, after determining the first optimal regulation power of the supercapacitor, the second optimal regulation power of the lithium battery, and the third optimal regulation power of the hydrogen energy storage subsystem, it is necessary to control the supercapacitor to operate at the first optimal regulation power, control the lithium battery to operate at the second optimal regulation power, and control the hydrogen energy storage subsystem to operate at the third optimal regulation power.

[0076] Specifically, when the total regulating power of the hybrid electric-hydrogen energy storage system is less than zero (i.e., the photovoltaic power generation is greater than the load demand), the hybrid electric-hydrogen energy storage system is controlled to enter the charging mode to absorb the excess power of the DC microgrid. That is, the supercapacitor is controlled to charge at the first optimal regulating power to absorb transient power fluctuations and quickly smooth out high-frequency fluctuations; the lithium battery is controlled to charge at the second optimal regulating power to store short-term excess electrical energy to meet energy demands from minutes to hours; and the electrolyzer is controlled to operate at the third optimal regulating power to use excess electrical energy to electrolyze hydrogen and store the hydrogen in a hydrogen storage tank to achieve long-term energy storage.

[0077] Specifically, when the total regulation power of the hybrid electric-hydrogen energy storage system is greater than zero (i.e., the photovoltaic power generation is insufficient to meet the load demand), the hybrid electric-hydrogen energy storage system is controlled to enter the discharge mode to compensate for the power gap of the DC microgrid. That is: the supercapacitor is controlled to discharge at the first optimal regulation power to respond to transient power demand and provide millisecond-level power support; the lithium battery is controlled to discharge at the second optimal regulation power to release the stored electrical energy and compensate for the short-to-medium-term energy gap; and the fuel cell is controlled to operate at the third optimal regulation power to convert the hydrogen in the hydrogen storage tank into electrical energy to provide long-term, large-scale power support.

[0078] It should be noted that when the total regulation power of the hybrid energy storage system is equal to zero (i.e., the photovoltaic power generation is basically balanced with the load demand), the hybrid energy storage system is controlled to enter standby mode. At this time, the hybrid energy storage system maintains a low power consumption operation state and does not need to participate in power regulation. That is, the supercapacitor and lithium battery are controlled to be in a silent state, the electrolyzer is controlled to stop producing hydrogen and / or the fuel cell is controlled to stop generating electricity.

[0079] The power allocation method for the hybrid energy storage system provided in this application employs a variable mode decomposition algorithm that incorporates an improved gray wolf algorithm for key parameter optimization. This algorithm further decomposes the total regulating power of the hybrid energy storage system into high-frequency power, mid-frequency power, and low-frequency power. Combined with a fuzzy control strategy, the method incorporates the state of charge (SOC) value of the supercapacitor for analysis when correcting the high-frequency power to determine the optimal regulating power; the SOC value of the lithium battery for analysis when correcting the mid-frequency power to determine the optimal regulating power; and the health status value of the hydrogen energy storage subsystem for analysis when correcting the low-frequency power to determine the optimal regulating power. This ensures that the hybrid energy storage system can respond quickly to power demands at different frequencies, improving its energy utilization and operational stability, and enhancing its flexibility and responsiveness.

[0080] Please see Figure 3 , Figure 3 This is a schematic diagram of an optional power distribution device for an electro-hydrogen hybrid energy storage system provided in this application embodiment. This device can implement the aforementioned power distribution method for the electro-hydrogen hybrid energy storage system and may include, but is not limited to, the following: The first module 701 is used to obtain the total regulation power of the electric-hydrogen hybrid energy storage system, the first state of charge value of the supercapacitor, the second state of charge value of the lithium battery, and the health status value of the hydrogen energy storage subsystem. The second module 702 is used to decompose the total regulation power of the electric-hydrogen hybrid energy storage system to obtain high-frequency power, medium-frequency power and low-frequency power; The third module 703 is used to correct the high-frequency power based on the first state of charge value of the supercapacitor, so as to obtain the first power correction value and the first optimal regulation power of the supercapacitor. The fourth module 704 is used to correct the intermediate frequency power based on the second state of charge value of the lithium battery and the first power correction value of the supercapacitor, so as to obtain the second power correction value and the second optimal regulation power of the lithium battery. The fifth module 705 is used to correct the low-frequency power based on the health status value of the hydrogen energy storage subsystem and the second power correction value of the lithium battery, so as to obtain the third optimal regulation power of the hydrogen energy storage subsystem.

[0081] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The functions specifically implemented by the present device embodiments are the same as those specifically implemented by the above method embodiments, and the beneficial effects achieved by the present device embodiments are also the same as those achieved by the above method embodiments.

[0082] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the power distribution method of the above-described electro-hydrogen hybrid energy storage system. The electronic device may include any smart terminal such as a tablet computer, laptop computer, or desktop computer.

[0083] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those implemented by the above method embodiments, and the beneficial effects achieved by the present device embodiments are also the same as those achieved by the above method embodiments.

[0084] Please see Figure 4 , Figure 4 This is a schematic diagram illustrating the hardware structure of an electronic device according to another embodiment. The electronic device includes: The processor 801 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 802 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 802 can store the operating system and other applications. When the technical solutions provided in the embodiments of this application are implemented through software or firmware, the relevant program code is stored in the memory 802 and is called and executed by the processor 801. The 803 input / output interface is used to implement information input and output. The communication interface 804 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 805 transmits information between various components of the device (e.g., processor 801, memory 802, input / output interface 803, and communication interface 804); The processor 801, memory 802, input / output interface 803, and communication interface 804 are connected to each other within the device via bus 805.

[0085] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the power distribution method of the above-described electro-hydrogen hybrid energy storage system.

[0086] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented by this storage medium embodiment are the same as those implemented by the above method embodiments, and the beneficial effects achieved by this storage medium embodiment are also the same as those achieved by the above method embodiments.

[0087] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0088] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0089] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0090] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0091] Those skilled in the art will understand that all or some of the steps, apparatuses, or functional modules / units in the methods disclosed above can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0092] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, apparatus, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, apparatus, products, or devices.

[0093] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0094] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed between the devices or units may be through some interfaces, and the indirect coupling or communication connection may be electrical, mechanical, or other forms.

[0095] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0096] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0097] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0098] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A power distribution method for an electro-hydrogen hybrid energy storage system, characterized in that, The hybrid electric-hydrogen energy storage system includes a supercapacitor, a lithium battery, and a hydrogen energy storage subsystem, and the method includes: The total regulating power of the electric-hydrogen hybrid energy storage system, the first state of charge value of the supercapacitor, the second state of charge value of the lithium battery, and the health status value of the hydrogen energy storage subsystem are obtained. The total regulated power is decomposed to obtain high-frequency power, mid-frequency power and low-frequency power; Based on the first state of charge value, the high-frequency power is corrected to obtain the first power correction value and the first optimal adjustment power of the supercapacitor; Based on the second state of charge value and the first power correction value, the intermediate frequency power is corrected to obtain the second power correction value and the second optimal regulation power of the lithium battery; Based on the health status value and the second power correction value, the low-frequency power is corrected to obtain the third optimal regulation power of the hydrogen energy storage subsystem.

2. The power distribution method for the electro-hydrogen hybrid energy storage system according to claim 1, characterized in that, The decomposition of the total regulated power to obtain high-frequency power, mid-frequency power, and low-frequency power includes: Variational mode decomposition is performed on the total regulating power, and an improved gray wolf algorithm is used to optimize the penalty factor and number of modes required for the decomposition process, resulting in multiple power components. The multiple power components are grouped and merged to obtain the high-frequency power, the mid-frequency power, and the low-frequency power.

3. The power distribution method for the electro-hydrogen hybrid energy storage system according to claim 2, characterized in that, The total regulated power is subjected to variational mode decomposition, and an improved gray wolf algorithm is used to optimize the penalty factor and number of modes required for the decomposition process, resulting in multiple power components including: The improved gray wolf algorithm is initialized with parameters, and then the gray wolf population is initialized with a chaotic mapping strategy. The position of each gray wolf represents the value of the penalty factor and the number of modes. The fitness value of each gray wolf in the gray wolf population is calculated based on a preset fitness function and the position of each gray wolf in the gray wolf population; wherein, the fitness function is used to evaluate the effect of variational mode decomposition of the total regulation power based on the position of the gray wolf. Select the three gray wolves with the best fitness values ​​from the gray wolf population, and then update the positions of the other gray wolves in the gray wolf population other than the three gray wolves using the position update strategy of particle swarm optimization based on the positions of the three gray wolves. Determine whether the preset termination condition is met; if not, return to the step of calculating the fitness value of each gray wolf in the gray wolf population based on the preset fitness function and the position of each gray wolf in the gray wolf population; if yes, perform variational mode decomposition on the total regulation power based on the positions of the three gray wolves to obtain the multiple power components.

4. The power distribution method for the electro-hydrogen hybrid energy storage system according to claim 1, characterized in that, The step of correcting the high-frequency power based on the first state of charge value to obtain the first power correction value and the first optimal adjustment power of the supercapacitor includes: Based on the first state of charge value and the high-frequency power, fuzzy reasoning is performed to obtain the first power correction coefficient; The first power correction value and the first optimal adjustment power of the supercapacitor are calculated based on the high-frequency power and the first power correction coefficient.

5. The power distribution method for the electro-hydrogen hybrid energy storage system according to claim 1, characterized in that, The step of correcting the intermediate frequency power based on the second state of charge value and the first power correction value to obtain the second power correction value and the second optimal regulation power of the lithium battery includes: The first intermediate frequency power is obtained by calculating based on the intermediate frequency power and the first power correction value; Based on the second state of charge value and the first intermediate frequency power, fuzzy reasoning is performed to obtain the second power correction coefficient; The second power correction value and the second optimal regulation power of the lithium battery are obtained by calculating based on the first intermediate frequency power and the second power correction coefficient.

6. The power distribution method for the electro-hydrogen hybrid energy storage system according to claim 1, characterized in that, The step of correcting the low-frequency power based on the health status value and the second power correction value to obtain the third optimal regulation power of the hydrogen energy storage subsystem includes: The first low-frequency power is obtained by calculating based on the low-frequency power and the second power correction value; Based on the health status value and the first low-frequency power, fuzzy inference is performed to obtain the third power correction coefficient; The third optimal regulation power of the hydrogen energy storage subsystem is obtained by calculating based on the first low-frequency power and the third power correction coefficient.

7. The power distribution method for the electro-hydrogen hybrid energy storage system according to claim 1, characterized in that, The total regulating power of the electro-hydrogen hybrid energy storage system is obtained in the following way: The output power of the photovoltaic power generation system and the required power of the DC load are obtained and the deviation is calculated to obtain the total regulation power of the electric-hydrogen hybrid energy storage system.

8. A power distribution device for an electro-hydrogen hybrid energy storage system, characterized in that, The hybrid electric-hydrogen energy storage system includes a supercapacitor, a lithium battery, and a hydrogen energy storage subsystem. The device includes: The first module is used to obtain the total regulating power of the electric-hydrogen hybrid energy storage system, the first state of charge value of the supercapacitor, the second state of charge value of the lithium battery, and the health status value of the hydrogen energy storage subsystem. The second module is used to decompose the total regulating power to obtain high-frequency power, medium-frequency power and low-frequency power; The third module is used to correct the high-frequency power based on the first state of charge value to obtain the first power correction value and the first optimal adjustment power of the supercapacitor. The fourth module is used to correct the intermediate frequency power based on the second state of charge value and the first power correction value to obtain the second power correction value and the second optimal adjustment power of the lithium battery. The fifth module is used to correct the low-frequency power based on the health status value and the second power correction value to obtain the third optimal regulation power of the hydrogen energy storage subsystem.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the power distribution method of the electro-hydrogen hybrid energy storage system according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the power distribution method of the electro-hydrogen hybrid energy storage system as described in any one of claims 1 to 7.

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