Wind-solar combined water electrolysis hydrogen production method and system

Abstract

The invention relates to the technical field of wind-solar power generation hydrogen production, in particular to a wind-solar combined water electrolysis hydrogen production method and system.The method comprises the steps that output power data of wind power generation equipment and photovoltaic power generation equipment and input power data of an alkaline electrolytic bath are collected, and the collected data are preprocessed; respectively acquiring comprehensive adjustment coefficients of the wind power generation equipment and the photovoltaic power generation equipment; and determining the disturbance step length of the corresponding power generation equipment in the maximum power point tracking algorithm based on the perturbation and observation method at the current moment by using the comprehensive adjustment coefficient. The invention aims to dynamically adjust the disturbance step length, prevent the output power from wandering near the maximum power point to generate oscillation, and improve the stability and hydrogen production efficiency of a hydrogen production system.

Description

Technical Field

[0001] This application relates to the technical field of hydrogen production by wind and solar power generation, and specifically relates to a method and system for electrolytic water hydrogen production by combining wind and solar energy. Background Art

[0002] The method of electrolytic water hydrogen production by combining wind and solar energy is a technology that uses renewable energy such as wind energy and solar energy for power generation, and then prepares hydrogen through electrolysis of water. By converting wind energy and solar energy into hydrogen through electrolytic water hydrogen production for storage, it can effectively alleviate the problems of abandoned wind and abandoned light, realize the sustainable utilization of wind energy and solar energy, and reduce the dependence on traditional fossil energy.

[0003] Existing methods for electrolytic water hydrogen production by combining wind and solar energy usually use the maximum power point tracking technology (Maximum power point tracking, MPPT) to improve the utilization rate of wind energy and solar energy resources by the system, and thus improve the hydrogen production efficiency. For example, in a method and device for wind and solar power generation hydrogen production and hydrogen mixing control with the publication number CN117498389A, electrolytic hydrogen is obtained by electrolyzing wind and solar off-grid electricity based on maximum power control.

[0004] In the maximum power point tracking technology, the perturbation observation method is the most commonly used maximum power tracking method because of its simple control and easy implementation. Usually, a perturbation step size is preset in the perturbation observation method. However, in the electrolytic water hydrogen production system combined with wind and solar energy, due to the strong uncertainty of wind power generation and photovoltaic power generation affected by uncontrollable factors such as wind speed, light, and temperature in the environment, it is difficult to pre-select a suitable perturbation step size. An inappropriate selection of the perturbation step size will not only cause the output power of wind power generation and photovoltaic power generation to fail to converge to their respective maximum powers in time, making the electrolytic water hydrogen production system combined with wind and solar energy unable to effectively utilize wind energy and solar energy resources, thereby affecting the hydrogen production efficiency, but also may cause the output power of wind power generation and photovoltaic power generation to hover at their respective maximum power points, resulting in oscillations of the output power, making the electrolytic water hydrogen production system combined with wind and solar energy unable to operate safely and stably, and thus also affecting the hydrogen production efficiency. Summary of the Invention

[0005] In order to solve the above technical problems, this application provides a method and system for electrolytic water hydrogen production by combining wind and solar energy, and the specific technical solutions adopted are as follows: In a first aspect, an embodiment of this application provides a method for electrolytic water hydrogen production by combining wind and solar energy, and the method includes the following steps: Collect the output power data of wind power generation equipment and photovoltaic power generation equipment and the input power data of the alkaline electrolyzer, preprocess the collected data, and construct a sequence of the data collected at the current moment and its historical time period; The average level of the local standard deviations of all data points in the electrolyzer input power sequence and the coefficient of the trend term are positively fused, and the result is used as the fluctuation eigenvalue of the electrolyzer input power sequence; the product of the normalized value of the mean of the output power sequences of various power generation devices and the fluctuation eigenvalue is denoted as the first adjustment coefficient of the corresponding type of power generation device at the current moment; After smoothing the output power sequences of various power generation devices, all peaks are extracted; the result of the inverse proportional mapping of the product of the standard deviation between all peaks and the number of peaks is used as the second adjustment coefficient of the corresponding type of power generation device at the current moment; The first and second adjustment coefficients are positively fused to obtain the comprehensive adjustment coefficient of the corresponding type of power generation device at the current moment, so as to determine the perturbation step size in the maximum power point tracking algorithm based on the perturbation observation method for the corresponding type of power generation device at the current moment.

[0006] Preferably, the preprocessing method is to perform normalization processing on each power data respectively by using the Min-Max normalization method.

[0007] Preferably, the historical time period is the time period between the current moment and the previous adjustment moment.

[0008] Preferably, the coefficient of the trend term is determined by the exponential result of the fitting line slope of the trend term sequence after extracting the trend term sequence from the local standard deviations of all data points in the electrolyzer input power sequence.

[0009] Preferably, the fluctuation eigenvalue is further determined by the product result of the average level and the coefficient of the trend term.

[0010] Preferably, the calculation method of the normalized value of the mean is: calculate the sum value of the means of all data points in the output power sequences of all types of power generation devices; the ratio result of the mean calculated for each type of power generation device to the sum value is used as the normalized value of the mean.

[0011] Preferably, the method for smoothing the output power sequences of various power generation devices is to perform smoothing processing by using the SG filtering algorithm.

[0012] Preferably, the second adjustment coefficient is determined by the reciprocal of the product result of the standard deviation between all peaks and the number of peaks.

[0013] Preferably, the comprehensive adjustment coefficient is further determined by the product result of the first adjustment coefficient and the second adjustment coefficient.

[0014] Preferably, the method for determining the perturbation step size in the maximum power point tracking algorithm based on the perturbation observation method for the corresponding type of power generation device at the current moment is: The sum of the product result of normalizing the comprehensive adjustment coefficient of various power generation devices at the current moment and the preset maximum perturbation step size and the preset minimum perturbation step size is used as the perturbation step size value of the corresponding power generation device at the current moment.

[0015] In a second aspect, another embodiment of the present application further provides a wind-solar combined electrolytic water hydrogen production system, which implements the wind-solar combined electrolytic water hydrogen production method described in any one of the above. The system includes a wind-solar power generation module, an electrolytic water hydrogen production module, a hydrogen storage module, a wind-solar power generation control module, and a step size adjustment module; The wind-solar power generation module is used to convert wind energy and light energy into electrical energy and supply it to the electrolytic water hydrogen production module; The electrolytic water hydrogen production module is used to produce hydrogen using the electrical energy generated by the wind-solar power generation module; The hydrogen storage module is used to store the hydrogen produced in the electrolytic water hydrogen production module; The wind-solar power generation control module is used to maximize the output power of the wind power generation device and the photovoltaic power generation device in the wind-solar power generation module by using a hydrogen production power supply with MPPT tracking function; The hydrogen production power supply includes: an inverter, an inverter, a rectifier, a step size adjustment module, and an MPPT tracking conversion module. The step size adjustment module is used to adjust the perturbation step size in the maximum power point tracking algorithm based on the perturbation observation method in the MPPT tracking conversion module to improve the stability and hydrogen production efficiency of the wind-solar combined electrolytic water hydrogen production system during its hydrogen production process.

[0016] Preferably, the step size adjustment module includes a data acquisition unit, a data processing unit, and a step size calculation unit, which are used to sequentially implement steps one to three in the above-mentioned wind-solar combined electrolytic water hydrogen production method.

[0017] The present application has at least the following beneficial effects: 1. According to the obtained first adjustment coefficient, the present application respectively adjusts the perturbation step size values in the MPPT tracking conversion modules for the wind power generation device and the photovoltaic power generation device in the subsequent MPPT hydrogen production power supply, which can effectively avoid the phenomenon that the output power of the wind power generation device and the photovoltaic power generation device hovers near their respective maximum power points and cannot converge in time, resulting in large oscillations. Furthermore, it suppresses the data fluctuations in the total output power of the subsequent wind power generation device and photovoltaic power generation device, thereby reducing the degree of data fluctuations in the input power of the alkaline electrolytic cell, and improving the stability of the operation of the hydrogen production system in the subsequent electrolytic water hydrogen production module; 2. The present application adjusts the values of the perturbation step sizes in the MPPT tracking conversion modules for wind power generation equipment and photovoltaic power generation equipment in the subsequent MPPT hydrogen production power supply according to the obtained second adjustment coefficient. On the premise of ensuring the stable operation of the hydrogen production system in the electrolytic water hydrogen production module, by reducing the time for the output power of the wind power generation equipment or photovoltaic power generation equipment to hover near their respective local maximum power points, the output power of the wind power generation equipment and photovoltaic power generation equipment can quickly approach their respective maximum power points, so that they can output at the maximum power as early as possible during the power generation process, and further effectively improve the utilization rate of wind energy and light energy resources by the electrolytic water hydrogen production module, thereby improving the subsequent hydrogen production efficiency; 3. The present application dynamically adjusts the perturbation step sizes of the wind power generation equipment and photovoltaic power generation equipment in the maximum power point tracking algorithm based on the perturbation observation method according to the comprehensive adjustment coefficient obtained from the first and second adjustment coefficients. On the premise of ensuring the stable operation of the hydrogen production system in the electrolytic water hydrogen production module, it can effectively improve the utilization of wind energy and light energy resources by the electrolytic water hydrogen production module, and further improve the hydrogen production efficiency of the wind-solar combined electrolytic water hydrogen production system. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0019] Figure 1 is a flowchart of a wind-solar combined electrolytic water hydrogen production method provided by an embodiment of the present application; Figure 2 is a schematic diagram of the modules of a wind-solar combined electrolytic water hydrogen production system provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] Embodiment 1 A wind-solar combined electrolytic water hydrogen production method provided by an embodiment of the present application is specifically referred to Figure 1 , and the method includes the following steps: Step 1: Collect the output power data of the wind power generation equipment and photovoltaic power generation equipment and the input power data of the alkaline electrolyzer, and preprocess the collected data.

[0021] In this embodiment, any current moment t in the step adjustment module is taken as an example, and the time period between the current moment t and its previous adjustment moment is denoted as the historical time period of the current moment t. In the data acquisition unit of the step adjustment module, a power sensor is used to collect the output power data of the wind power generation equipment and the photovoltaic power generation equipment in the wind-solar power generation module and the input power data of the alkaline electrolyzer in the water electrolysis hydrogen production module, hereinafter referred to as the wind power output data, the photovoltaic power output data, and the electrolyzer input power data, and all the collected data is transmitted to the data processing unit in the step adjustment module.

[0022] In this embodiment, the data sampling frequency of the power sensor is 10 Hz, which can be set by the implementer himself.

[0023] In this application, both the wind power generation equipment and the photovoltaic power generation equipment are referred to as power generation equipment, that is, the wind power output data and the photovoltaic power output data are collectively referred to as the output power data of the power generation equipment.

[0024] In the data processing unit, all the data collected during the historical time period of the current moment t is obtained, and each type of power data is respectively preprocessed by normalization using the Min-Max normalization method. Each type of power data after the normalization process is sorted in ascending order according to the data sampling time to obtain the wind power output sequence A, the photovoltaic power output sequence B, and the electrolyzer input power sequence C respectively. The Min-Max normalization method is a well-known technology, and the specific process will not be elaborated here.

[0025] Step 2: Obtain the comprehensive adjustment coefficients of the wind power generation equipment and the photovoltaic power generation equipment respectively.

[0026] S1: To ensure the stability of the hydrogen production system operation in the water electrolysis hydrogen production module, the input power of the alkaline electrolyzer should have small data fluctuations. When the total output power has large data fluctuations, the disturbance step size in the disturbance observation method can be reduced to avoid the phenomenon that the output powers of the wind power generation equipment and the photovoltaic power generation equipment hover near their respective maximum power points and cannot converge in time, resulting in large oscillations, thereby suppressing the data fluctuations in the total output power of the subsequent wind power generation equipment and the photovoltaic power generation equipment, and thus reducing the degree of data fluctuations in the input power of the alkaline electrolyzer to improve the stability of the subsequent hydrogen production system operation.

[0027] Based on the above analysis, in the data processing unit, the result of the positive fusion of the average level of the local standard deviations of all data points and the trend term coefficients in the electrolyzer input power sequence is used as the fluctuation eigenvalue of the electrolyzer input power sequence.

[0028] It can be understood that the forward fusion is a fusion method such as addition and multiplication between data. The specific forward fusion method is determined by the implementer according to the actual situation, and this application does not make special restrictions.

[0029] As a preferred embodiment, the trend term coefficient is determined by the exponential result of the slope of the fitting line of the trend term sequence after extracting the trend term sequence from the local standard deviations of all data points in the input power sequence of the electrolytic cell; the fluctuation eigenvalue is further determined by the product of the average level and the trend term coefficient.

[0030] In this embodiment, the sliding window technique is used to traverse the data points in the input power sequence C of the electrolytic cell, and the standard deviations of all data points in the window where each data point is located are calculated respectively, that is, the local standard deviation of each data point. All the obtained standard deviations are sorted in ascending order according to the sampling time of the data points corresponding to the standard deviations, and a standard deviation sequence C1 is obtained, which is used to evaluate whether the input power of the alkaline electrolytic cell shows a trend of increasing data fluctuation degree at the current evaluation time t.

[0031] Among them, the sliding window technique is a well-known technique, and the specific process will not be elaborated.

[0032] At the same time, the mean value of all the obtained standard deviations is recorded as the data fluctuation degree of the input power sequence C of the electrolytic cell, which is used to evaluate the data fluctuation degree of the input power of the alkaline electrolytic cell in the historical time period at the current evaluation time t.

[0033] Furthermore, in this embodiment, the STL (Seasonal and Trend decomposition using Loess) time series decomposition algorithm is used to extract the trend term sequence of the standard deviation sequence C1, and the linear fitting algorithm based on the least squares method is used to linearly fit the data points in the trend term sequence. The exponential result of the slope of the obtained fitting line is recorded as the fluctuation trend of the input power sequence C of the electrolytic cell, and is also recorded as the trend term coefficient of the local standard deviations of all data points in the input power sequence of the electrolytic cell, which is used to evaluate whether the input power of the alkaline electrolytic cell shows a trend of increasing data fluctuation degree at the current evaluation time t. The exponential result is to make the fluctuation trend have the same sign as the data fluctuation degree of the input power sequence C of the electrolytic cell.

[0034] Among them, both the STL time series decomposition algorithm and the linear fitting algorithm based on the least squares method are well-known techniques, and the specific process will not be elaborated.

[0035] In this embodiment, the product of the data fluctuation degree and the fluctuation trend of the electrolyzer input power sequence C is denoted as the fluctuation characteristic value of the electrolyzer input power sequence C, which is used to evaluate the data fluctuation degree and the fluctuation change trend of the input power of the alkaline electrolyzer at the current moment t.

[0036] Secondly, since the input power of the alkaline electrolyzer is obtained by rectifying the wind power generation equipment and the photovoltaic power generation equipment by the MPPT hydrogen production power supply, if the output power of the wind power generation equipment is larger than that of the photovoltaic power generation equipment, the greater the influence of the wind power generation equipment on the input power of the alkaline electrolyzer compared to the photovoltaic power generation equipment, the more the perturbation step size in the MPPT tracking conversion module for the wind power generation equipment in the subsequent MPPT hydrogen production power supply should be reduced to improve the stability of the input power of the subsequent alkaline electrolyzer, and vice versa.

[0037] Based on the above analysis, the present application denotes the product between the normalized value of the mean of the output power sequences of various power generation equipment and the fluctuation characteristic value as the first adjustment coefficient of the corresponding power generation equipment at the current moment.

[0038] Among them, as a preferred embodiment, the calculation method of the normalized value of the mean is as follows: calculate the sum value of the means of all data points in the output power sequences of all types of power generation equipment; take the ratio result of the mean calculated for each type of power generation equipment to the sum value as the normalized value of the mean.

[0039] Taking the wind power output sequence A as an example in this embodiment, calculate the mean a of all data points in the wind power output sequence A and the mean b of all data points in the photovoltaic output sequence B. Through the formula WA = a / (a + b), perform a normalization operation on the mean a to obtain the electrolyzer influence coefficient WA of the wind power output sequence A, which is used to evaluate the influence degree of the output power of the wind power generation equipment on the input power of the alkaline electrolyzer during the historical time period at the current moment t.

[0040] Furthermore, the product of the electrolyzer influence coefficient WA and the fluctuation characteristic value of the electrolyzer input power sequence C is denoted as the first adjustment coefficient of the wind power generation equipment at the current moment t, which is used to subsequently adjust the value of the perturbation step size in the MPPT tracking conversion module for the wind power generation equipment in the MPPT hydrogen production power supply at the current moment t to improve the stability of the operation of the hydrogen production system in the electrolytic water hydrogen production module. The larger the first adjustment coefficient, the smaller the perturbation step size should be.

[0041] Using the same method as the first adjustment coefficient of the wind power generation equipment at the current moment t, obtain the first adjustment coefficient of the photovoltaic power generation equipment at the current moment t, which is used to subsequently adjust the value of the perturbation step in the MPPT tracking conversion module for the photovoltaic power generation equipment in the MPPT hydrogen production power supply respectively at the current moment t, so as to improve the operation stability of the hydrogen production system in the electrolysis water hydrogen production module. Only the photovoltaic output power sequence B needs to be replaced with the wind power output power sequence A.

[0042] S2: On the premise that the hydrogen production system in the electrolysis water hydrogen production module operates stably, it is necessary to improve the utilization rate of wind energy and light energy resources by the electrolysis water hydrogen production module as much as possible, so as to improve the hydrogen production efficiency. This requires the output powers of the wind power generation equipment and the photovoltaic power generation equipment to quickly approach their respective maximum power points, so that they can output at the maximum power during the power generation process.

[0043] However, the wind power generation equipment and the photovoltaic power generation equipment will be affected by uncontrollable factors such as wind speed, light, and temperature in the environment, making their output powers have strong uncertainties, resulting in multiple local peak points in their output power data. This will easily cause the maximum power point tracking algorithm based on the perturbation observation method to fall into the local maximum power point, thus unable to find the global maximum power point.

[0044] Therefore, when there are multiple local peak points with large differences in the output power data of the wind power generation equipment or the photovoltaic power generation equipment, the perturbation step in the perturbation observation method can be increased to reduce the time for the output power of the wind power generation equipment or the photovoltaic power generation equipment to hover near its respective local maximum power point, thereby increasing the speed of finding the global maximum power point and enabling them to output at the maximum power.

[0045] Based on the above analysis, the present application smooths the output power sequences of various power generation equipment and extracts all the peaks therein; the result of the inverse proportional mapping of the product of the standard deviation between all the peaks and the number of peaks is used as the second adjustment coefficient of the corresponding power generation equipment at the current moment.

[0046] Optionally, the inverse proportional mapping can be implemented by methods such as negative linear mapping, negative exponential mapping, or setting adjustment parameters.

[0047] As a preferred implementation manner, the second adjustment coefficient is determined by the reciprocal of the product result of the standard deviation between all the peaks and the number of peaks.

[0048] In this embodiment, taking the wind power output sequence A as an example, the SG (Savitzky Golay Filter) filtering algorithm is used to smooth the wind power output sequence A, so as to reduce the influence of the noise interference suffered by the power data in the wind power output sequence A during data acquisition and transmission on the subsequent extraction of local peak points therein, and obtain the smoothed wind power output sequence A1.

[0049] Among them, the SG filtering algorithm is a well-known technology, and the specific process will not be elaborated here.

[0050] Furthermore, in this embodiment, the AMPD (Automatic multiscale-based peak detection) peak detection algorithm is used to extract all the peaks in the wind power output sequence A1. Among them, the APMD peak detection algorithm is a well-known technology, and the specific process will not be elaborated here.

[0051] In this embodiment, the product of the standard deviation between all the obtained peaks and the number of peaks, plus the reciprocal of a constant is denoted as the second adjustment coefficient of the wind power generation device at the current moment t, which is used to adjust the value of the perturbation step in the MPPT tracking transformation module for the wind power generation device in the MPPT hydrogen production power supply at the current moment t, so as to improve the utilization rate of wind energy resources by the hydrogen production system in the electrolytic water hydrogen production module, thereby improving the hydrogen production efficiency. Adding the constant is to avoid the denominator being 0. The constant needs to be set as a positive number close to 0. In this embodiment, the constant is set to 0.01, and the larger the first adjustment coefficient, the smaller the perturbation step should be.

[0052] Using the same method as the second adjustment coefficient of the wind power generation device at the current moment t, the second adjustment coefficient of the photovoltaic power generation device at the current moment t is obtained, which is used to adjust the value of the perturbation step in the MPPT tracking transformation module for the wind power generation device in the MPPT hydrogen production power supply at the current moment t, so as to improve the utilization rate of wind energy resources by the hydrogen production system in the electrolytic water hydrogen production module, thereby improving the hydrogen production efficiency. Among them, only the wind power output sequence A needs to be replaced with the photovoltaic power output sequence B.

[0053] S3: In this application, the result of positively fusing the first adjustment coefficient and the second adjustment coefficient of the corresponding type of power generation device at the current moment is used as the comprehensive adjustment coefficient of the corresponding type of power generation device at the current moment.

[0054] As a preferred implementation manner, the comprehensive adjustment coefficient is further determined by the product result of the first adjustment coefficient and the second adjustment coefficient of the corresponding type of power generation device at the current moment.

[0055] In this embodiment, the product of the first and second adjustment coefficients of the wind power generation device at the current moment t is denoted as the comprehensive adjustment coefficient of the wind power generation device at the current moment t, which is used for subsequent adjustment of the MPPT hydrogen production power supply for the wind power generation device.

[0056] Using the same method as the comprehensive adjustment coefficient of the wind power generation device at the current moment t, the comprehensive adjustment coefficient of the photovoltaic power generation device at the current moment t is obtained.

[0057] It should be noted that the comprehensive adjustment coefficients of the wind power generation device and the photovoltaic power generation device at the current moment t are used to subsequently adjust the value of the perturbation step at the current moment t in the MPPT tracking conversion module of the MPPT hydrogen production power supply for the wind power generation device and the photovoltaic power generation device, and the two obtained comprehensive adjustment coefficients are both transmitted to the step calculation unit.

[0058] Step 3: Use the comprehensive adjustment coefficient to determine the perturbation step in the maximum power point tracking algorithm based on the perturbation observation method for the corresponding type of power generation device at the current moment.

[0059] In the step calculation unit of the step adjustment module, the perturbation step values UA(t) and UB(t) of the wind power generation device and the photovoltaic power generation device at the current moment t are respectively calculated, which are used as the values of the perturbation step in the MPPT tracking conversion module of the MPPT hydrogen production power supply for the wind power generation device and the photovoltaic power generation device at the current moment t.

[0060] In this application, the comprehensive adjustment coefficient is used to determine the perturbation step in the maximum power point tracking algorithm based on the perturbation observation method for the corresponding type of power generation device at the current moment.

[0061] As a preferred implementation manner, the method for determining the perturbation step in the maximum power point tracking algorithm based on the perturbation observation method for the corresponding type of power generation device at the current moment by using the comprehensive adjustment coefficient is: the sum value of the product result of normalizing the comprehensive adjustment coefficient of each type of power generation device at the current moment and the preset maximum perturbation step, and the preset minimum perturbation step is used as the perturbation step value of the corresponding type of power generation device at the current moment.

[0062] In this embodiment, the calculation methods of the perturbation step values UA(t) and UB(t) are as follows: , , where UA(t) and HA(t) respectively represent the disturbance step value and the comprehensive adjustment coefficient of the wind power generation equipment at the current moment t; UB(t) and HB(t) respectively represent the disturbance step value and the comprehensive adjustment coefficient of the photovoltaic power generation equipment at the current moment t; a1 is a preset minimum disturbance step, which is 0.001 in this embodiment, and a2 is a preset maximum disturbance step, which is 0.01 in this embodiment; Sigmoid() represents the Sigmoid function, which is used to limit the value of HA(t) between (0, 1).

[0063] Among them, this formula restricts both UA(t) and UB(t) to be within (0.001, 0.011), and this range has a better effect on adjusting the disturbance step in the maximum power point tracking algorithm based on the disturbance observation method.

[0064] The obtained disturbance step values UA(t) and UB(t) are respectively transmitted to the MPPT tracking conversion modules for the wind power generation equipment and the photovoltaic power generation equipment in the MPPT hydrogen production power supply, and are respectively used as the values of the disturbance step in the maximum power point tracking algorithm based on the disturbance observation method in the MPPT tracking conversion module at the current moment t. Then, the MPPT tracking conversion module with the adjusted disturbance step is used to optimize the output power of the wind power generation equipment and the photovoltaic power generation equipment in the subsequent process, and alkaline electrolyzers are used to produce hydrogen based on the wind-solar rectified electricity obtained from the subsequent MPPT hydrogen production power supply.

[0065] Among them, the maximum power point tracking algorithm based on the disturbance observation method is a well-known technology, and the specific process will not be elaborated here.

[0066] Embodiment 2 As shown in the attached Figure 2 figure, a schematic diagram of the modules of a wind-solar combined electrolytic water hydrogen production system provided by an embodiment of the present application. The system includes: a wind-solar power generation module, an electrolytic water hydrogen production module, a hydrogen storage module, a wind-solar power generation control module, and a step adjustment module.

[0067] The wind-solar power generation module is a power generation system formed by a wind power generation equipment and a photovoltaic power generation equipment, and is used to convert wind energy and light energy into electrical energy and supply it to the electrolytic water hydrogen production module. The wind power generation equipment and the photovoltaic power generation equipment are a wind power generation unit and a photovoltaic power generation unit respectively; The electrolytic water hydrogen production module uses the electrical energy generated by the wind-solar power generation module and an electrolytic water hydrogen production device to produce hydrogen. The electrolytic water hydrogen production device is an alkaline electrolyzer; The hydrogen storage module is used to store the hydrogen produced in the electrolytic water hydrogen production module in a hydrogen storage device. The hydrogen storage device is a hydrogen storage tank; The wind-solar power generation control module maximizes the output power of wind power generation equipment and photovoltaic power generation equipment in the wind-solar power generation module by using a hydrogen-producing power source with MPPT tracking function (referred to as MPPT hydrogen-producing power source). The MPPT hydrogen-producing power source mainly includes a converter, an inverter, a rectifier, a step adjustment module and an MPPT tracking conversion module.

[0068] Among them, the MPPT hydrogen production power supply is directly coupled and connected with the wind power generation equipment and the photovoltaic power generation equipment respectively, and the MPPT hydrogen production power supply is connected with the alkaline electrolyzer. Through the converter, inverter and MPPT tracking conversion module, the wind power generation equipment and the photovoltaic power generation equipment output direct current at their respective maximum powers, and the wind and solar rectified electricity is obtained through the rectifier, and finally the wind and solar rectified electricity is transmitted to the input end of the alkaline electrolyzer through the MPPT hydrogen production power supply; Among them, the step adjustment module includes a data acquisition unit, a data processing unit and a step calculation unit, which are used to sequentially implement steps one to three in the above-mentioned wind-solar combined water electrolysis hydrogen production method. The perturbation step size in the maximum power point tracking algorithm based on the perturbation observation method in the MPPT tracking conversion module is adjusted at regular intervals to improve the stability and hydrogen production efficiency of the wind-solar combined water electrolysis hydrogen production system in its hydrogen production process, wherein the control unit is connected to the MPPT tracking conversion module in the hydrogen production power supply, and the time interval set in this embodiment is 5 minutes, which can be set by the implementer.

[0069] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary technical means in the art that are not invented by the present application.

[0070] It will be appreciated that the present application is not limited to the exact construction that has been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof.

Claims

1. A method for producing hydrogen by electrolyzing water in combination with wind and light, characterized in that, The method includes the following steps: Collect the output power data of wind power generation equipment and photovoltaic power generation equipment and the input power data of the alkaline electrolyzer, preprocess the collected data, and construct a sequence of the data collected at the current moment and its historical time period; Take the result of the positive fusion of the average level of the local standard deviations of all data points in the electrolyzer input power sequence and the trend term coefficient as the fluctuation characteristic value of the electrolyzer input power sequence; Denote the product between the normalized value of the mean of the output power sequences of various power generation equipment and the fluctuation characteristic value as the first adjustment coefficient of the corresponding type of power generation equipment at the current moment; Smooth the output power sequences of various power generation equipment and then extract all peaks; Take the result of the inverse proportional mapping of the product of the standard deviation between all peaks and the number of peaks as the second adjustment coefficient of the corresponding type of power generation equipment at the current moment; Positively fuse the first and second adjustment coefficients to obtain the comprehensive adjustment coefficient of the corresponding type of power generation equipment at the current moment, so as to determine the perturbation step size in the maximum power point tracking algorithm based on the perturbation observation method for the corresponding type of power generation equipment at the current moment.

2. The method for producing hydrogen by electrolyzing water in combination with wind and light according to claim 1, characterized in that, The preprocessing method is to perform normalization processing on each type of power data respectively using the Min - Max normalization method.

3. The method for producing hydrogen by electrolyzing water in combination with wind and light according to claim 1, characterized in that, The historical time period is the time period between the current moment and its previous adjustment moment.

4. The method for producing hydrogen by electrolyzing water by combining wind energy and solar energy according to claim 1, characterized in that, The trend term coefficient is determined by the exponential result of the fitting line slope of the trend term sequence after extracting the trend term sequence from the local standard deviations of all data points in the electrolyzer input power sequence.

5. The method for producing hydrogen by electrolyzing water in combination with wind and light according to claim 1, characterized in that, The fluctuation characteristic value is further determined by the product result of the average level and the trend term coefficient.

6. The method for producing hydrogen by electrolyzing water by combining wind energy and solar energy according to claim 1, wherein The calculation method of the normalized value of the mean is: Calculate the sum value of the means of all data points in the output power sequences of all types of power generation equipment; Take the ratio result of the mean calculated for each type of power generation equipment to the sum value as the normalized value of the mean.

7. The method for producing hydrogen by electrolyzing water by combining wind and light according to claim 1, characterized in that, The method for smoothing the output power sequences of various power generation equipment uses the SG filtering algorithm for smoothing processing.

8. The method for producing hydrogen by electrolyzing water in combination with wind and light according to claim 1, characterized in that, The second adjustment coefficient is determined by the reciprocal of the product result of the standard deviation between all peaks and the number of peaks.

9. The method for producing hydrogen by electrolyzing water in combination with wind and light according to claim 1, wherein, The comprehensive adjustment coefficient is further determined by the product result of the first adjustment coefficient and the second adjustment coefficient.

10. The method for producing hydrogen by electrolyzing water in combination with wind and light according to claim 1, characterized in that, The determination method for determining the perturbation step size in the maximum power point tracking algorithm based on the perturbation observation method for the corresponding type of power generation equipment at the current moment is: Take the sum value of the product result of the normalized comprehensive adjustment coefficient of various power generation equipment at the current moment and the preset maximum perturbation step size and the preset minimum perturbation step size as the perturbation step size value of the corresponding type of power generation equipment at the current moment.

11. A hydrogen production system by electrolyzing water combining wind and light, characterized in that, The system implements a method for producing hydrogen by electrolyzing water with combined wind and light as described in any one of claims 1 - 10. The system includes a wind - solar power generation module, an electrolyzed water hydrogen production module, a hydrogen storage module, a wind - solar power generation control module, and a step size adjustment module; The wind - solar power generation module is used to convert wind energy and light energy into electrical energy and supply it to the electrolyzed water hydrogen production module; The electrolyzed water hydrogen production module is used to produce hydrogen using the electrical energy generated by the wind - solar power generation module; The hydrogen storage module is used to store the hydrogen produced in the electrolyzed water hydrogen production module; The wind-solar power generation control module is used to maximize the output power of the wind power generation equipment and the photovoltaic power generation equipment in the wind-solar power generation module by using a hydrogen production power supply with MPPT tracking function; The hydrogen production power supply includes: an inverter, an inverter, a rectifier, a step adjustment module, and an MPPT tracking conversion module. The step adjustment module is used to adjust the perturbation step in the maximum power point tracking algorithm based on the perturbation observation method in the MPPT tracking conversion module, so as to improve the stability and hydrogen production efficiency of the wind-solar combined electrolytic water hydrogen production system during its hydrogen production process.

12. The integrated wind-solar water electrolysis hydrogen production system according to claim 11, wherein, The step adjustment module includes a data acquisition unit, a data processing unit, and a step calculation unit, which are used to sequentially implement steps 1 to 3 in the above-mentioned wind-solar combined electrolytic water hydrogen production method.

Images