A power dynamic allocation method and device for a new energy hybrid hydrogen production system

Abstract

The present invention discloses a method and device for power dynamic allocation of a new energy hybrid hydrogen production system. The method includes: obtaining new energy fluctuation power prediction data; analyzing the new energy fluctuation power prediction data to generate IMF intrinsic signals of multiple frequency bands; based on the load response characteristics of each type of hydrogen production device, giving corresponding fluctuation constraints, performing signal reconstruction on the IMF intrinsic signals, and adapting the input power to each type of hydrogen production device; combining the operating characteristics of the new energy hybrid hydrogen production system to construct a multi-objective optimization function; based on the analysis of the multi-objective optimization function, giving the dynamic power allocation information of each type of hydrogen production device. The present invention provides a power dynamic allocation scheme for new energy hybrid hydrogen production, effectively improving the energy balance level of hybrid hydrogen production, and solving problems such as intermittent and fluctuating new energy in off-grid / weak grid-connected engineering applications affecting hydrogen production efficiency, shortening the service life of hydrogen production devices, and increasing the requirement for energy storage capacity.

Description

Technical Field

[0001] The present invention belongs to the technical field of new energy, and particularly relates to a method and device for dynamically allocating the power of a new energy hybrid hydrogen production system. Background Art

[0002] With the rapid development of renewable energy, the proportion of wind and solar power generation in the power system is increasing day by day, gradually forming a new power system. However, the randomness and volatility of wind and solar power generation in the new power system also pose challenges to the stable operation of the power system.

[0003] The volatility and intermittency of new energy also pose significant challenges to the production of green hydrogen energy. For example, firstly, the instability of energy supply will affect the efficiency and quality of hydrogen production; secondly, the frequent adjustment of the operating state of hydrogen production devices (such as electrolyzers) will also increase the risk of failure and shorten the life of the devices; in addition, it will also increase the precise control of hydrogen production operating conditions, which will also hinder the optimization of hydrogen production processes and the guarantee of hydrogen production quality; finally, if the storage demand for new energy is increased, it will also increase the cost of hydrogen production and the complexity of hydrogen production devices.

[0004] In recent years, some studies have been conducted on how to allocate the operating power in the production of green hydrogen energy with new energy.

[0005] For example, Patent CN116054129A provides a method for allocating the combined operating power of multiple electrolyzers based on photovoltaic hydrogen production, including: establishing a photovoltaic power generation prediction model, predicting the photovoltaic power generation at the target moment according to the photovoltaic power generation prediction model; classifying the electrolyzers in the electrolyzer group, and establishing at least one operating characteristic model for each category of the electrolyzer group according to the operating data, where the operating data includes the operating power and hydrogen production data of each electrolyzer; superimposing all the operating characteristic models to obtain a comprehensive simulation operating model; determining the optimal power allocation strategy of the comprehensive simulation operating model under the photovoltaic power generation through a preset optimization method to ensure optimal power allocation when multiple electrolyzers are operating in combination.

[0006] Another patent CN117767352A provides an operation optimization control method for a wind-solar-hydrogen energy storage system, including: establishing a model for suppressing wind-solar fluctuations based on the mathematical model of the wind-solar-hydrogen energy storage system to obtain the output power of the model for suppressing wind-solar fluctuations; inputting the output power of the model for suppressing wind-solar fluctuations into an electrolyzer that operates by coupling alkaline water electrolysis for hydrogen production and proton exchange membrane water electrolysis for hydrogen production to optimize the coupling operation strategy of the electrolyzer; respectively establishing a two-layer optimization model with the maximum total wind-solar consumption power and the minimum total operating cost of the wind-solar-hydrogen energy storage system as the objective functions; using an improved pelican optimization algorithm to solve the two-layer optimization model to obtain the total wind-solar consumption power and operating cost of the wind-solar-hydrogen energy storage system. This solution improves the hydrogen production efficiency of the wind-solar-hydrogen energy storage system, realizes the operation optimization of the wind-solar-hydrogen energy storage system, greatly saves economic costs, and ensures the stability of the system.

[0007] However, in the existing technologies such as the above solution, there are still problems of insufficient matching accuracy and poor real-time performance in the coupling ability between new energy with volatility and intermittency and the operating characteristics of the hybrid electrolyzer hydrogen production device.

[0008] Therefore, how to dynamically allocate the power of the new energy hybrid hydrogen production system to improve the source-load balance level between the fluctuating power of new energy and hybrid hydrogen production, the stability of the hydrogen production microgrid, and achieve a high level of hybrid hydrogen production energy balance, high hydrogen production efficiency, and low hydrogen production cost is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0009] Aiming at the defects existing in the above-mentioned existing technologies, the present invention provides a method and device for dynamically allocating the power of a new energy hybrid hydrogen production system. The method includes: obtaining new energy fluctuating power prediction data; analyzing the new energy fluctuating power prediction data to generate IMF eigen-signals of multiple frequency bands; based on the load response characteristics of each type of hydrogen production device, giving corresponding fluctuation limits, reconstructing the IMF eigen-signals, and performing power adaptation with each type of hydrogen production device; combining the operating characteristics of the new energy hybrid hydrogen production system to construct a multi-objective optimization function; based on the analysis of the multi-objective optimization function, giving the dynamic power allocation information of each type of hydrogen production device. The present invention effectively improves the hybrid hydrogen production energy balance level, solves problems such as intermittent and fluctuating new energy affecting hydrogen production efficiency, shortening the service life of hydrogen production devices, and high requirements for energy storage capacity in off-grid / weak grid connection engineering applications.

[0010] In the first aspect, the present invention provides a method for dynamically allocating the power of a new energy hybrid hydrogen production system, specifically including the following steps:

[0011] Obtaining new energy fluctuating power prediction data;

[0012] Analyzing the new energy fluctuating power prediction data to generate IMF eigen-signals of multiple frequency bands;

[0013] Based on the load response characteristics of various types of hydrogen production devices, corresponding fluctuation constraints are given, the IMF intrinsic signals are reconstructed, and the input power is adapted to various types of hydrogen production devices;

[0014] Combined with the operating characteristics of the new energy hybrid hydrogen production system, a multi-objective optimization function is constructed;

[0015] Based on the analysis of the multi-objective optimization function, the dynamic power distribution information of various types of hydrogen production devices is given.

[0016] Furthermore, the new energy hybrid hydrogen production system includes a wind power generation device, a photovoltaic power generation device, and a hybrid electrolytic cell hydrogen production device, and the hybrid electrolytic cell hydrogen production device includes at least two types of hydrogen production devices.

[0017] Furthermore, the new energy fluctuation power prediction data is analyzed to generate IMF intrinsic signals of multiple frequency bands, specifically including:

[0018] Based on the frequency difference, the new energy fluctuation power prediction data is decomposed by VMD to generate IMF intrinsic signals of multiple frequency bands corresponding to each fluctuation component.

[0019] Furthermore, the hybrid electrolytic cell hydrogen production device includes a PEM electrolytic cell device and an ALK electrolytic cell device;

[0020] The fluctuation constraints specifically include:

[0021] The allowable power change of the PEM electrolytic cell device is 10 - 20% Pe / s, and the allowable power change of the ALK electrolytic cell device is not more than 1% Pe / s.

[0022] Furthermore, the IMF intrinsic signals are reconstructed and the input power is adapted to various types of hydrogen production devices, specifically including:

[0023] Based on the load response characteristics of the PEM electrolytic cell device and the ALK electrolytic cell device, the IMF intrinsic signals of multiple frequency bands are screened to obtain multiple IMF intrinsic signals to be signal-reconstructed;

[0024] The multiple IMF intrinsic signals to be signal-reconstructed are demarcated into low-frequency and high-frequency, and signal reconstruction is performed respectively to form a low-frequency change component and a high-frequency change component;

[0025] The low-frequency change component and the high-frequency change component are respectively adapted to the input power of the ALK electrolytic cell device and the input power of the PEM electrolytic cell device.

[0026] Furthermore, the multiple IMF intrinsic signals to be signal-reconstructed are demarcated into low-frequency and high-frequency, and signal reconstruction is performed respectively to form a low-frequency change component and a high-frequency change component, which are specifically expressed as:

[0027] P LP = IMF 1,1 + IMF 1,2 + … + IMF 1,m

[0028] P LH = IMF 2,1 + IMF 2,2 + … + IMF 2,n

[0029] Among them, P LP is the low-frequency variation component, and P LH is the high-frequency variation component. IMF 1,1 is the IMF eigen-signal numbered 1 designated as low frequency, and IMF 1,2 is the IMF eigen-signal numbered 2 designated as low frequency, and IMF 1,m is the IMF eigen-signal numbered m designated as low frequency, and IMF 2,1 is the IMF eigen-signal numbered 1 designated as high frequency, and IMF 2,2 is the IMF eigen-signal numbered 2 designated as high frequency, and IMF 2,n is the IMF eigen-signal numbered n designated as high frequency.

[0030] Furthermore, combining with the operating characteristics of the new energy hybrid hydrogen production system, a multi-objective optimization function is constructed, specifically including:

[0031] Based on the operating characteristics of the new energy hybrid hydrogen production system, multiple objective functions are given;

[0032] Based on each objective function, corresponding weight factors are given;

[0033] Combining each objective function and the corresponding weight factors, a multi-objective optimization function is constructed.

[0034] Furthermore, the operating characteristics of the new energy hybrid hydrogen production system include: the new energy abandonment rate, the service life of the hydrogen production device, and the hydrogen production amount of the hydrogen production device in the new energy hybrid hydrogen production system. The multiple objective functions include the first objective function, the second objective function, and the third objective function;

[0035] The first objective function aims to minimize the new energy abandonment rate, the second objective function aims to minimize the loss of the service life of the ALK electrolyzer device, and the third objective function aims to maximize the hydrogen production amount of the hybrid electrolyzer hydrogen production device.

[0036] Furthermore, the first objective function is specifically expressed as:

[0037]

[0038] Among them, f1 is the first objective function, i is the serial number of the time series of the predicted data of the new energy fluctuating power during the hydrogen production period, T is the total number of times of the power data time series during the hydrogen production period, and P PV,i is the power generation power of the wind power generation device at time i, and P Wind,i is the power generation power of the photovoltaic power generation device at time i, and P ALK,i is the input power of the ALK electrolyzer device at time i, and P PEM,i is the input power of the PEM electrolyzer device at time i, and min is the minimum value function;

[0039] The second objective function is specifically expressed as:

[0040]

[0041] Among them, f 2 is the second objective function, and P LP,i is the low-frequency change component at time i;

[0042] The third objective function is specifically expressed as;

[0043]

[0044] Among them, f 3 is the third objective function, max is the maximum value function, and η ALK,i is the hydrogen production efficiency of the ALK electrolyzer device at time i, and η PEM,i is the hydrogen production efficiency of the PEM electrolyzer device at time i;

[0045] Combining each objective function and the corresponding weight factor, a multi-objective optimization function is constructed, which is specifically expressed as:

[0046] J = ε 1 *f 1 + ε 2 *f 2 + ε 3 *f 3

[0047] Among them, J is the multi-objective optimization function, and ε 1 is the weight factor of the first objective function, and ε 2 is the weight factor of the second objective function, and ε 3 is the weight factor of the third objective function.

[0048] In a second aspect, the present invention also provides a power dynamic distribution device for a new energy hybrid hydrogen production system, which adopts the power dynamic distribution method of the above new energy hybrid hydrogen production system, and specifically includes:

[0049] An acquisition unit for obtaining predicted data of new energy fluctuating power;

[0050] An analysis unit for parsing new energy fluctuating power prediction data to generate IMF eigen-signals of multiple frequency bands; based on the load response characteristics of various types of hydrogen production devices, corresponding fluctuation constraints are given, the IMF eigen-signals are signal-reconstructed, and the input power is adapted to each type of hydrogen production device; combining the operating characteristics of the new energy hybrid hydrogen production system, a multi-objective optimization function is constructed.

[0051] A power dynamic allocation unit for giving dynamic power allocation information for each type of hydrogen production device based on the analysis of the multi-objective optimization function.

[0052] A power dynamic allocation method and device for a new energy hybrid hydrogen production system provided by the present invention has at least the following beneficial effects:

[0053] (1) The power dynamic allocation scheme for input power adaptation according to various types of hydrogen production devices given by the present invention effectively solves the problem that the intermittent and fluctuating new energy in off-grid / weak grid-connected engineering applications affects the hydrogen production efficiency.

[0054] (2) Based on the load response characteristics of various types of hydrogen production devices, a multi-objective optimization function is constructed to achieve dynamic power allocation for hybrid hydrogen production, effectively solving the problem of shortened service life of hydrogen production devices caused by intermittent and fluctuating new energy in off-grid / weak grid-connected engineering applications.

[0055] (3) The power dynamic allocation scheme for hybrid hydrogen production given by the present invention effectively improves the energy balance level of hybrid hydrogen production and reduces the configuration requirements for the energy storage capacity by the intermittent and fluctuating new energy in engineering applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a schematic flow chart of a power dynamic allocation method for a new energy hybrid hydrogen production system provided by the present invention;

[0057] Figure 2 It is an example diagram of IMF eigen-signals of multiple frequency bands in a certain embodiment provided by the present invention;

[0058] Figure 3 It is a schematic flow chart of signal reconstruction of IMF eigen-signals and input power adaptation to various types of hydrogen production devices in a certain embodiment provided by the present invention;

[0059] Figure 4 It is a schematic flow chart of constructing a multi-objective optimization function in a certain embodiment provided by the present invention;

[0060] Figure 5 It is a schematic architecture diagram of a power dynamic allocation device for a new energy hybrid hydrogen production system provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0061] To better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings of the specification and specific implementation manners. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0062] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. The singular forms "a", "the" and "said" used in the embodiments of the present invention and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. "Plurality" generally includes at least two.

[0063] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover a non-exclusive inclusion, so that a commodity or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such commodity or device. Without further limitation, the element defined by the statement "including one..." does not exclude the existence of another identical element in the commodity or device including the said element.

[0064] There are fluctuations and intermittencies in the new energy in the new energy hybrid hydrogen production system. When it is coupled with the hybrid electrolyzer hydrogen production device, there are still deficiencies in both the matching accuracy and real-time performance. As a result, the hybrid electrolyzer hydrogen production device is insufficiently adapted to the power fluctuations of the new energy in terms of its power dynamic distribution and operating characteristics.

[0065] Currently, the rapidly developing hybrid electrolyzer hydrogen production devices include ALK electrolyzer devices and PEM electrolyzer devices, etc. Among them, the low-cost ALK electrolyzer device can be used to absorb the stable renewable energy component in the new energy, while the PEM electrolyzer device with good rapid response ability can be used to absorb the fluctuating renewable energy component in the new energy. Therefore, the power signal of the new energy in the new energy hybrid hydrogen production system can be decomposed, and then based on the decomposed change and fluctuation components, it can be adapted to the input power of each type of electrolyzer in the hybrid electrolyzer hydrogen production device, and the power distribution can be optimized according to the multi-objective function, and finally the power dynamic distribution strategy for hybrid hydrogen production can be realized.

[0066] As Figure 1 shown, the present invention provides a method for power dynamic distribution of a new energy hybrid hydrogen production system, which specifically includes the following steps:

[0067] Obtain new energy fluctuation power prediction data;

[0068] Analyze the new energy fluctuating power prediction data and generate IMF eigen-signals of multiple frequency bands;

[0069] Based on the load response characteristics of each type of hydrogen production device, give corresponding fluctuation constraints, reconstruct the signal of the IMF eigen-signal, and adapt the input power to each type of hydrogen production device;

[0070] Combine the operating characteristics of the new energy hybrid hydrogen production system to construct a multi-objective optimization function;

[0071] Based on the analysis of the multi-objective optimization function, give the dynamic power distribution information of each type of hydrogen production device.

[0072] The present invention provides a dynamic power distribution scheme for hybrid hydrogen production, effectively improving the energy balance level of hybrid hydrogen production, and solving problems such as intermittent and fluctuating new energy in off-grid / weak grid-connected engineering applications that can affect hydrogen production efficiency, shorten the service life of hydrogen production devices, and increase the requirement for energy storage capacity.

[0073] The new energy hybrid hydrogen production system includes a wind power generation device, a photovoltaic power generation device, and a hybrid electrolytic cell hydrogen production device, and the hybrid electrolytic cell hydrogen production device includes at least two types of hydrogen production devices.

[0074] The hybrid electrolytic cell hydrogen production device includes a PEM electrolytic cell device and an ALK electrolytic cell device.

[0075] There is a certain proportion of new energy power generation in the new energy hybrid hydrogen production system. For example, wind power generation devices, photovoltaic power generation devices, etc. New energy power generation has characteristics such as volatility, randomness, and intermittency. Accurate prediction of new energy power generation power is the premise for hybrid hydrogen production power distribution and serves as the input data for hybrid hydrogen production capacity optimization. The prediction method for new energy fluctuating power prediction data can adopt a method combining a model and historical data. The specific prediction value of the new energy fluctuating power prediction data is not further limited here. The shorter the time granularity of the new energy fluctuating power prediction data, the better, in order to better achieve source-load balance.

[0076] In the hybrid electrolytic cell hydrogen production device, the characteristics of different types of electrolytic cells are combined (for example, the PEM electrolytic cell device has a higher current density, and the ALK electrolytic cell device has mature technology and lower cost) to adapt to the volatility of renewable energy, improve hydrogen production efficiency, ensure hydrogen purity, enhance reliability, and balance cost-effectiveness, etc.

[0077] Analyze the new energy fluctuating power prediction data and generate IMF eigen-signals of multiple frequency bands, specifically including:

[0078] Based on the frequency difference, decompose the new energy fluctuating power prediction data by VMD to generate IMF eigen-signals of multiple frequency bands corresponding to each fluctuation component.

[0079] The analysis of new energy fluctuating power prediction data mainly considers the frequency aspect. For different frequencies, the variational mode decomposition (VMD) technology is used to decompose the new energy fluctuating power prediction data into multiple IMF eigen-signals for different frequencies, that is, for each fluctuating component after the decomposition of the new energy fluctuating power.

[0080] The variational mode decomposition technology has the characteristics of non-recursion and variable modes, and can decompose the new energy fluctuating power prediction data into a series of finite-bandwidth self-sequences with frequency scales from low to high (or from high to low).

[0081] By using VMD to decompose the new energy fluctuating power prediction data, multiple frequency-band IMF eigen-signals corresponding to each fluctuating component are generated, specifically including:

[0082] Through random initialization, multiple initial mode signals and corresponding central frequencies for the new energy fluctuating power prediction data are determined;

[0083] The objective function and constraint conditions for the decomposition of the new energy fluctuating power prediction data are given. Among them, the objective function includes the fidelity and bandwidth terms of the decomposition of the new energy fluctuating power prediction data, and the constraint condition is that the sum of each mode signal is equal to the new energy fluctuating power prediction data, specifically expressed as:

[0084]

[0085] P = ∑ k IMF k

[0086] where F is the objective function for the decomposition of the new energy fluctuating power prediction data, min is the function to take the minimum value, IMF k is the k-th mode signal, ω k is the central frequency corresponding to IMF k , is the square of the Euclidean length of the vector, is the time derivative operator, t is the time variable, δ is the Dirac function, j is the imaginary unit, P is the new energy fluctuating power prediction data, e is the base of the natural logarithm, approximately equal to 2.71828;

[0087] In addition, represents the convolution operation of the two, is the exponential term, representing the phase change related to the central frequency;

[0088] Based on the iterative update of the mode signal and the corresponding central frequency respectively, the objective function is minimized;

[0089] According to the determined bandwidth and the number of fluctuation components, combined with the threshold condition of minimizing the objective function, multiple-frequency-band IMF eigen-signals corresponding to each fluctuation component are generated.

[0090] The threshold condition for minimizing the objective function can be iterative update until the objective function converges or reaches a preset precision tolerance.

[0091] Through VMD decomposition, different frequency components in the new energy fluctuation power prediction data signal can be effectively extracted, providing important input information for the operation of new energy and the power dynamic allocation of hybrid hydrogen production. Precise quantization decomposition of the new energy fluctuation power prediction data signal and signal reconstruction adapted to the characteristics of the hybrid electrolytic cell hydrogen production device enable the fluctuation power to be maximally absorbed by the hybrid electrolytic cell hydrogen production device.

[0092] In the process of decomposing the new energy fluctuation power prediction data, the objective function and constraint conditions of the VMD algorithm are jointly defined by the modal signal, central frequency, combined with the Dirac function, time derivative operator, time variable, etc. By optimizing these parameters, the signal can be effectively and accurately decomposed.

[0093] As Figure 2 shown, in a certain embodiment, the obtained new energy fluctuation power prediction data is decomposed to generate IMF eigen-signals of two frequency bands. That is, IMF1 reflects the overall change trend of the new energy fluctuation power prediction data with respect to the time variable, while IMF2 and IMF3 are the IMF eigen-signals of two frequency bands obtained after VMD decomposition, and IMF2 and IMF3 reflect the fluctuation components of the new energy fluctuation power prediction data with respect to time change. In addition, the r-modal signal is the parameter signal obtained after IMF1 is decomposed into IMF2 and IMF3 by VMD.

[0094] According to the load response characteristics of each type of hydrogen production device, the respective fluctuation constraints of each type of hydrogen production device can be determined. The fluctuation constraints specifically include:

[0095] The allowable power change of the PEM electrolytic cell device is 10 - 20% Pe / s, and the allowable power change of the ALK electrolytic cell device is not greater than 1% Pe / s.

[0096] Precise constraint control is performed on the input power of the PEM electrolytic cell device and the ALK electrolytic cell device, that is, the ratios of the input power of the PEM electrolytic cell device and the ALK electrolytic cell device to the rated power per second are respectively limited and constrained.

[0097] As Figure 3 shown, signal reconstruction is performed on the IMF eigen-signal and input power adaptation is carried out with each type of hydrogen production device, specifically including:

[0098] Based on the load response characteristics of the PEM electrolyzer device and the ALK electrolyzer device, the IMF intrinsic signals of multiple frequency bands are screened to obtain multiple IMF intrinsic signals to be signal reconstructed;

[0099] The multiple IMF intrinsic signals to be signal reconstructed are delimited into low-frequency and high-frequency, and signal reconstruction is performed respectively to form a low-frequency change component and a high-frequency change component;

[0100] The low-frequency change component and the high-frequency change component are respectively adapted to the input power of the ALK electrolyzer device and the input power of the PEM electrolyzer device.

[0101] The multiple IMF intrinsic signals to be signal reconstructed are delimited into low-frequency and high-frequency, and signal reconstruction is performed respectively to form a low-frequency change component and a high-frequency change component, specifically expressed as:

[0102] P LP = IMF 1,1 + IMF 1,2 + … + IMF 1,m

[0103] P LH = IMF 2,1 + IMF 2,2 + … + IMF 2,n

[0104] Among them, P LP is the low-frequency change component, P LH is the high-frequency change component, IMF 1,1 is the IMF intrinsic signal numbered 1 delimited as low-frequency, IMF 1,2 is the IMF intrinsic signal numbered 2 delimited as low-frequency, IMF 1,m is the IMF intrinsic signal numbered m delimited as low-frequency, IMF 2,1 is the IMF intrinsic signal numbered 1 delimited as high-frequency, IMF 2,2 is the IMF intrinsic signal numbered 2 delimited as high-frequency, IMF 2,n is the IMF intrinsic signal numbered n delimited as high-frequency.

[0105] For different application scenarios, the number of IMF intrinsic signals is different, and no specific limitation is made here. To achieve the adaptation of the IMF intrinsic signals of different frequency bands to the PEM electrolyzer device and the ALK electrolyzer device, it is necessary to perform low-frequency and high-frequency division processing on the IMF intrinsic signals of each frequency band to form a low-frequency change amount and a high-frequency change amount. Of course, the specific frequency values of the low-frequency and high-frequency divisions are also related to the specific application scenarios, and no specific limitation is made on the frequency values here.

[0106] The input power of the ALK electrolyzer device is adapted by the low-frequency variation amount, and the input power of the PEM electrolyzer device is adapted by the high-frequency variation amount, giving full play to the cost-effectiveness of the ALK electrolyzer device and the fast response advantage of the PEM electrolyzer device. The input power of the ALK electrolyzer device is adapted by the low-frequency variation amount, and the input power of the PEM electrolyzer device is adapted by the high-frequency variation amount. Specifically, the specific value of the low-frequency variation amount is used as a reference value to correspondingly set the specific value of the input power of the ALK electrolyzer device, and the specific value of the high-frequency variation amount is used as a reference value to correspondingly set the specific value of the input power of the PEM electrolyzer device.

[0107] The specific correspondence between the variation amount and the input power can be achieved in various ways and set according to different requirements, and no specific limitation is made here. In a specific application scenario, according to the determined correspondence between the variation amount and the input power, the power dynamic distribution from the new energy fluctuation power prediction data to the hybrid hydrogen production can be realized.

[0108] To further optimize the power dynamic distribution of the hybrid hydrogen production, as Figure 4 shown, combining the operating characteristics of the new energy hybrid hydrogen production system, a multi-objective optimization function is constructed, specifically including:

[0109] Based on the operating characteristics of the new energy hybrid hydrogen production system, multiple objective functions are given;

[0110] Based on each objective function, the corresponding weight factors are given;

[0111] Combining each objective function and the corresponding weight factors, a multi-objective optimization function is constructed.

[0112] Construct a multi-objective optimization function, determine the objective of the optimization function, and set the corresponding weight factors according to different application scenarios. Using the dynamic distribution power values of the ALK electrolyzer device and the PEM electrolyzer device in the hybrid electrolyzer hydrogen production device as decision variables, the optimal solution of the dynamic power distribution is given.

[0113] The operating characteristics of the new energy hybrid hydrogen production system include: the new energy abandonment rate, the service life of the hydrogen production device, and the hydrogen production amount of the hydrogen production device in the new energy hybrid hydrogen production system. The multiple objective functions include the first objective function, the second objective function, and the third objective function;

[0114] The first objective function aims to minimize the new energy abandonment rate, the second objective function aims to minimize the life loss of the ALK electrolyzer device, and the third objective function aims to maximize the hydrogen production amount of the hybrid electrolyzer hydrogen production device.

[0115] Specifically, the first objective function is specifically expressed as:

[0116]

[0117] Among them, f 1 is the first objective function, i is the serial number of the new energy fluctuation power prediction data time series within the hydrogen production cycle, T is the total number of times of the power data time series within the hydrogen production cycle, P PV,i is the power generation power of the wind power generation device at the i-th moment, P Wind,i is the power generation power of the photovoltaic power generation device at the i-th moment, P ALK,i is the input power of the ALK electrolyzer device at the i-th moment, P PEM,i is the input power of the PEM electrolyzer device at the i-th moment, and min is the minimum value function;

[0118] Taking the minimum new energy abandonment rate as the goal of the first objective function, by improving the follow-up response energy of the hybrid electrolyzer hydrogen production device to the new energy fluctuation power prediction data, the consumption level of new energy is improved.

[0119] The second objective function is specifically expressed as:

[0120]

[0121] Among them, f 2 is the second objective function, P LP,i is the low-frequency change component at the i-th moment;

[0122] The service life of the hydrogen production device mainly considers the matching degree between the input power of the ALK electrolyzer device and the low-frequency change component. Reducing the volatility during the adaptation process of the input power of the ALK electrolyzer device and the low-frequency change component can reduce the life loss of the ALK electrolyzer device.

[0123] The third objective function is specifically expressed as;

[0124]

[0125] Among them, f 3 is the third objective function, max is the maximum value function, η ALK,i is the hydrogen production efficiency of the ALK electrolyzer device at the i-th moment, η PEM,i is the hydrogen production efficiency of the PEM electrolyzer device at the i-th moment;

[0126] The third objective function takes the overall hydrogen production volume of the hybrid electrolyzer hydrogen production device as the goal to improve the economic benefits of hybrid electrolytic hydrogen production.

[0127] Combining each objective function and the corresponding weight factors, a multi-objective optimization function is constructed, specifically expressed as:

[0128] J = ε 1 * f 1 + ε 2 * f 2 + ε3 *f 3

[0129] Among them, J is a multi-objective optimization function, and ε 1 is the weight factor of the first objective function, and ε 2 is the weight factor of the second objective function, and ε 3 is the weight factor of the third objective function.

[0130] As Figure 5 shown, the present invention also provides a power dynamic distribution device for a new energy hybrid hydrogen production system, adopting the power dynamic distribution method of the above new energy hybrid hydrogen production system, specifically including:

[0131] An acquisition unit for obtaining new energy fluctuating power prediction data;

[0132] An analysis unit for analyzing the new energy fluctuating power prediction data to generate IMF eigen-signals of multiple frequency bands; based on the load response characteristics of each type of hydrogen production device, giving corresponding fluctuation constraints, performing signal reconstruction on the IMF eigen-signals, and adapting the input power to each type of hydrogen production device; combining the operating characteristics of the new energy hybrid hydrogen production system to construct a multi-objective optimization function;

[0133] A power dynamic distribution unit for giving dynamic power distribution information for each type of hydrogen production device based on the analysis of the multi-objective optimization function.

[0134] A power dynamic distribution method and device for a new energy hybrid hydrogen production system provided by the present invention have at least the following beneficial effects:

[0135] (1) The power dynamic distribution scheme for input power adaptation according to each type of hydrogen production device given by the present invention effectively solves the problem that the intermittent fluctuating new energy in off-grid / weak grid-connected engineering applications affects the hydrogen production efficiency.

[0136] (2) Based on the load response characteristics of each type of hydrogen production device and constructing a multi-objective optimization function, the power dynamic distribution for hybrid hydrogen production is realized, effectively solving the problem that the service life of the hydrogen production device is shortened due to the intermittent fluctuating new energy in off-grid / weak grid-connected engineering applications.

[0137] (3) The power dynamic distribution scheme for hybrid hydrogen production given by the present invention effectively improves the energy balance level of hybrid hydrogen production and reduces the configuration requirements for the energy storage capacity by the intermittent fluctuating new energy in engineering applications.

[0138] Although the preferred embodiments of the present invention have been described, additional changes and modifications can be made to these embodiments by those skilled in the art once they learn the basic inventive concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications that fall within the scope of the present invention. Obviously, those skilled in the art can make various changes and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A method for dynamic power allocation of a new energy hybrid hydrogen production system, characterized in that: The new energy hybrid hydrogen production system includes a wind power generation device, a photovoltaic power generation device and a hybrid electrolyzer hydrogen production device. The hybrid electrolyzer hydrogen production device includes a PEM electrolyzer device and an ALK electrolyzer device, and specifically includes the following steps: Obtain new energy fluctuation power forecast data; Analyze the forecast data of renewable energy fluctuation power and generate IMF eigensignals in multiple frequency bands; Based on the load response characteristics of various types of hydrogen production devices, the corresponding fluctuation constraints are given, the IMF intrinsic signal is reconstructed, and the input power is adapted to various types of hydrogen production devices; Based on the operation characteristics of the new energy hybrid hydrogen production system, multiple objective functions are given, wherein the operation characteristics of the new energy hybrid hydrogen production system include: the new energy abandonment rate, the life of the hydrogen production device and the hydrogen production of the hydrogen production device in the new energy hybrid hydrogen production system, and the multiple objective functions include a first objective function, a second objective function and a third objective function; The first objective function aims to minimize the abandonment rate of new energy, the second objective function aims to minimize the life loss of the ALK electrolyzer device, and the third objective function aims to maximize the hydrogen production of the hybrid electrolyzer hydrogen production device; Based on each objective function, the corresponding weight factor is given; Combine each objective function and the corresponding weight factors to construct a multi-objective optimization function; Based on the analysis of multi-objective optimization functions, the dynamic power allocation information of various types of hydrogen production devices is given.

2. The power dynamic allocation method of the new energy hybrid hydrogen production system according to claim 1 is characterized in that: Analyze the new energy fluctuation power forecast data and generate IMF intrinsic signals in multiple frequency bands, including: Based on the frequency difference, the new energy fluctuation power prediction data is decomposed through VMD to generate IMF eigensignals of multiple frequency bands corresponding to each fluctuation component.

3. The power dynamic allocation method of the new energy hybrid hydrogen production system according to claim 1 is characterized in that: The volatility constraints include: The allowable power variation of the PEM electrolyzer device is 10-20% Pe / s, and the allowable power variation of the ALK electrolyzer device is no more than 1% Pe / s.

4. The power dynamic allocation method of the new energy hybrid hydrogen production system as claimed in claim 3 is characterized in that: The IMF intrinsic signal is reconstructed and the input power is adapted to various types of hydrogen production devices, including: Based on the load response characteristics of the PEM electrolyzer device and the ALK electrolyzer device, the IMF intrinsic signals of multiple frequency bands are screened to obtain multiple IMF intrinsic signals to be reconstructed; The multiple IMF eigensignals to be reconstructed are demarcated into low-frequency and high-frequency ones, and the signals are reconstructed respectively to form low-frequency variation components and high-frequency variation components; The low-frequency variation component and the high-frequency variation component are adapted to the input power of the ALK electrolyzer device and the input power of the PEM electrolyzer device respectively.

5. The power dynamic allocation method of the new energy hybrid hydrogen production system as claimed in claim 4 is characterized in that: The multiple IMF eigensignals to be reconstructed are demarcated into low-frequency and high-frequency components, and the signals are reconstructed respectively to form low-frequency variation components and high-frequency variation components, which are specifically expressed as follows: P LP =IMF 1,1 +IMF 1,2 +…+IMF 1,m P LH =IMF 2,1 +IMF 2,2 +…+IMF 2,n Among them, P LP is the low-frequency variation component, P LH is the high frequency variation component, IMF 1,1 is the IMF eigensignal numbered 1, which is classified as low frequency. 1,2 is the IMF eigensignal numbered 2, which is classified as low frequency. 1,m is the eigensignal of IMF number m which is classified as low frequency, IMF 2,1 is the IMF eigensignal numbered 1, which is classified as high frequency. 2,2 is the IMF eigensignal numbered 2, which is classified as high frequency. 2,n is the IMF eigensignal numbered n, which is classified as high frequency.

6. The power dynamic allocation method of the new energy hybrid hydrogen production system according to claim 1, characterized in that: The first objective function is specifically expressed as: Among them, f1 is the first objective function, i is the number of the new energy fluctuation power prediction data time series within the hydrogen production cycle, T is the total number of power data time series within the hydrogen production cycle, P PV,i is the power generated by the wind power generation device at time i, P Wind,i is the power generated by the photovoltaic power generation device at time i, P ALK,i is the input power of the ALK electrolyzer at time i, P PEM,i is the input power of the PEM electrolyzer at time i, and min is the minimum value function; The second objective function is specifically expressed as: Among them, f2 is the second objective function, P LP,i is the low-frequency variation component at time i; The third objective function is specifically expressed as; Among them, f3 is the third objective function, max is the maximum value function, η ALK,i is the hydrogen production efficiency of the ALK electrolyzer at time i, η PEM,i is the hydrogen production efficiency of the PEM electrolyzer at time i; Combining each objective function and the corresponding weight factor, a multi-objective optimization function is constructed, which is specifically expressed as: J=ε1*f1+ε2*f2+ε3*f3 Wherein, J is a multi-objective optimization function, ε1 is a weight factor of the first objective function, ε2 is a weight factor of the second objective function, and ε3 is a weight factor of the third objective function.

7. A power dynamic distribution device for a new energy hybrid hydrogen production system, characterized in that: The method for dynamic power allocation of a new energy hybrid hydrogen production system as claimed in any one of claims 1 to 6 specifically comprises: A collection unit, used to obtain new energy fluctuation power prediction data; The analysis unit is used to analyze the prediction data of new energy fluctuation power and generate IMF intrinsic signals of multiple frequency bands; based on the load response characteristics of various types of hydrogen production devices, the corresponding fluctuation constraints are given, the IMF intrinsic signals are reconstructed, and the input power is adapted to various types of hydrogen production devices; combined with the operating characteristics of the new energy hybrid hydrogen production system, a multi-objective optimization function is constructed; The power dynamic allocation unit is used to provide dynamic power allocation information of various types of hydrogen production devices based on the analysis of the multi-objective optimization function.

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