A wind-solar combined water electrolysis hydrogen production method and system

Abstract

The present application relates to the technical field of wind and solar power generation and hydrogen production technology, and specifically to a wind and solar combined water electrolysis hydrogen production method and system, the method comprising: collecting output power data of wind power generation equipment and photovoltaic power generation equipment and input power data of alkaline electrolyzers, and preprocessing the collected data; obtaining comprehensive adjustment coefficients of wind power generation equipment and photovoltaic power generation equipment respectively; and using the comprehensive adjustment coefficients to determine the perturbation step size of the maximum power point tracking algorithm based on the perturbation observation method for the corresponding power generation equipment at the current moment. The present application aims to dynamically adjust the perturbation step size to avoid oscillation of the output power hovering near the maximum power point, thereby improving the stability and efficiency of the hydrogen production system.

Description

Technical Field

[0001] The present application relates to the technical field of hydrogen production by wind and solar power generation, and specifically to a method and system for hydrogen production by water electrolysis using a combination of wind and solar power. Background Art

[0002] The wind-solar combined electrolysis method uses renewable energy sources such as wind and solar energy to generate electricity, then produces hydrogen through water electrolysis. By converting wind and solar energy into hydrogen for storage, this method can effectively alleviate the problem of wind and solar power curtailment, enabling the sustainable utilization of wind and solar energy and reducing reliance on traditional fossil fuels.

[0003] Existing wind-solar combined water electrolysis hydrogen production methods usually use maximum power point tracking (MPPT) technology to improve the system's utilization of wind and solar energy resources, thereby improving hydrogen production efficiency. For example, publication number CN117498389A describes a wind-solar power generation hydrogen production and hydrogen mixing control method and device, which uses maximum power control to electrolyze off-grid wind and solar power to produce electrolytic hydrogen.

[0004] In 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. A perturbation step size is usually set in advance in the perturbation observation method. However, in the wind-solar combined water electrolysis hydrogen production system, wind power generation and photovoltaic power generation are affected by uncontrollable factors such as wind speed, light and temperature in the environment and have strong uncertainty. This makes it difficult to pre-select a suitable perturbation step size. Improper 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 power in time, making the wind-solar combined water electrolysis hydrogen production system unable to effectively utilize wind energy and light energy resources, thereby affecting hydrogen production efficiency, but may also cause the output power of wind power generation and photovoltaic power generation to hover at their respective maximum power points, causing output power oscillations, making the wind-solar combined water electrolysis hydrogen production system unable to operate safely and stably, thereby also affecting hydrogen production efficiency. Summary of the Invention

[0005] In order to solve the above technical problems, the present application provides a method and system for producing hydrogen by electrolysis of water using a combination of wind and solar power. The technical solutions adopted are as follows:

[0006] In a first aspect, an embodiment of the present application provides a method for producing hydrogen by electrolysis of water using a combination of wind and solar power, the method comprising the following steps:

[0007] Collect output power data of wind power generation equipment and photovoltaic power generation equipment, as well as input power data of alkaline electrolyzers, pre-process the collected data, and construct a sequence of the data collected at the current time and in historical time periods;

[0008] The result of forward fusion of the average level of the local standard deviation of all data points in the electrolytic cell input power series and the trend term coefficient is used as the fluctuation characteristic value of the electrolytic cell input power series; the product of the normalized value of the mean of the output power series of various power generation equipment and the fluctuation characteristic value is recorded as the first adjustment coefficient of the corresponding power generation equipment at the current moment;

[0009] After smoothing the output power sequences of various power generation equipment, all peak values ​​are extracted; the result of inversely proportional mapping of the product of the standard deviation between all peak values ​​and the number of peak values ​​is used as the second adjustment coefficient of the corresponding power generation equipment at the current moment;

[0010] The first and second adjustment coefficients are forwardly fused to obtain the comprehensive adjustment coefficient of the corresponding power generation equipment at the current moment, so as to determine the perturbation step size of the maximum power point tracking algorithm based on the perturbation observation method of the corresponding power generation equipment at the current moment.

[0011] Preferably, the preprocessing method is to use the Min-Max normalization method to perform normalization processing on each type of power data respectively.

[0012] Preferably, the historical time period is the time period between the current moment and the previous adjustment moment.

[0013] Preferably, the trend term coefficient is determined by exponentially extracting the trend term sequence from the local standard deviation of all data points in the electrolytic cell input power sequence and then extracting the trend term sequence.

[0014] Preferably, the fluctuation characteristic value is further determined by the product of the average level and the trend term coefficient.

[0015] Preferably, the method for calculating the normalized value of the mean is: calculating the sum of the means of all data points in the output power sequences of all types of power generation equipment; and taking the ratio of the means calculated by various power generation equipment to the sum as the normalized value of the mean.

[0016] Preferably, the method for smoothing the output power sequences of various power generation equipment adopts an SG filtering algorithm for smoothing.

[0017] Preferably, the second adjustment coefficient is determined by the inverse of the product of the standard deviation between all the peaks and the number of peaks.

[0018] Preferably, the comprehensive adjustment coefficient is further determined by the product of the first adjustment coefficient and the second adjustment coefficient.

[0019] Preferably, the method for determining the perturbation step size of the maximum power point tracking algorithm based on the perturbation-observation method at the current moment for the corresponding type of power generation equipment is:

[0020] The product of the normalized comprehensive adjustment coefficients of various power generation equipment at the current moment and the preset maximum disturbance step length and the sum of the preset minimum disturbance step length are used as the disturbance step length value of the corresponding power generation equipment at the current moment.

[0021] In a second aspect, another embodiment of the present application further provides a wind-solar combined water electrolysis hydrogen production system, which implements any of the wind-solar combined water electrolysis hydrogen production methods described above, the system comprising a wind-solar power generation module, a water electrolysis hydrogen production module, a hydrogen storage module, a wind-solar power generation control module, and a step size adjustment module;

[0022] Wind and solar power generation module, used to convert wind energy and light energy into electrical energy and supply it to the water electrolysis hydrogen production module;

[0023] Water electrolysis hydrogen production module, used to produce hydrogen using electricity generated by wind and solar power generation modules;

[0024] A hydrogen storage module is used to store the hydrogen produced by the water electrolysis hydrogen production module;

[0025] A wind-solar power generation control module for maximizing the output power of wind power generation equipment and photovoltaic power generation equipment in the wind-solar power generation module by using a hydrogen-generating power source with an MPPT tracking function;

[0026] The hydrogen production power supply includes: a converter, 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 water electrolysis hydrogen production system during its hydrogen production process.

[0027] Preferably, the step length adjustment module includes a data acquisition unit, a data processing unit and a step length calculation unit, which are used to sequentially implement steps one to three in the above-mentioned wind-solar combined water electrolysis hydrogen production method.

[0028] This application has at least the following beneficial effects:

[0029] 1. The present application adjusts the disturbance step values ​​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 first adjustment coefficient, which can effectively prevent the output power of the wind power generation equipment and the photovoltaic power generation equipment from hovering near their respective maximum power points and failing to converge in time, resulting in large oscillations. This can suppress the data fluctuations in the total output power of the subsequent wind power generation equipment and the photovoltaic power generation equipment, thereby reducing the degree of data fluctuations in the input power of the alkaline electrolyzer, and improving the stability of the hydrogen production system operation in the subsequent water electrolysis hydrogen production module.

[0030] 2. The present application adjusts the disturbance step value in the MPPT tracking conversion module for the wind power generation equipment and the photovoltaic power generation equipment in the subsequent MPPT hydrogen production power supply according to the obtained second adjustment coefficient. Under the premise of ensuring the stable operation of the hydrogen production system in the water electrolysis hydrogen production module, by reducing the time that the output power of the wind power generation equipment or the photovoltaic power generation equipment lingers near its respective local maximum power point, the output power of the wind power generation equipment and the photovoltaic power generation equipment can quickly approach their respective maximum power points, thereby enabling them to output at maximum power as early as possible during the power generation process, thereby effectively improving the utilization rate of wind energy and light energy resources of the water electrolysis hydrogen production module, thereby improving the subsequent hydrogen production efficiency;

[0031] 3. This application dynamically adjusts the perturbation step size 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. It can effectively improve the utilization of wind energy and light energy resources by the water electrolysis hydrogen production module while ensuring the stable operation of the hydrogen production system in the water electrolysis hydrogen production module, thereby improving the hydrogen production efficiency of the wind-solar combined water electrolysis hydrogen production system. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0033] Figure 1 A flow chart of a method for producing hydrogen by electrolysis of water using a combination of wind and solar power, provided in one embodiment of the present application;

[0034] Figure 2 A schematic diagram of the modules of a wind-solar combined water electrolysis hydrogen production system provided in one embodiment of the present application. DETAILED DESCRIPTION

[0035] Example 1

[0036] An embodiment of the present application provides a method for producing hydrogen by electrolysis of water combined with wind and solar power. Figure 1 , the method comprises the following steps:

[0037] Step 1: Collect output power data of wind power generation equipment and photovoltaic power generation equipment and input power data of alkaline electrolyzer, and pre-process the collected data.

[0038] This embodiment takes any current moment t in the step size adjustment module as an example, and records the time period between the current moment t and the previous adjustment moment as the historical time period of the current moment t. In the data acquisition unit in the step size adjustment module, power sensors are used to respectively collect the output power data of the wind power generation equipment and the photovoltaic power generation equipment in the wind and solar power generation module and the input power data of the alkaline electrolyzer in the water electrolysis hydrogen production module, which are subsequently referred to as wind output power data, photovoltaic output power data, and electrolyzer input power data, and all collected data are transmitted to the data processing unit in the step size adjustment module.

[0039] In this embodiment, the data sampling frequency of the power sensor is 10 Hz, which can be set by the implementer.

[0040] In this application, wind power generation equipment and photovoltaic power generation equipment are both referred to as power generation equipment, that is, wind output power data and photovoltaic output power data are collectively referred to as output power data of power generation equipment.

[0041] In the data processing unit, all data collected in the historical time period at the current time t are obtained, and the Min-Max normalization method is used to perform a pre-processing operation on each type of power data to normalize them. Each type of power data after normalization is arranged in ascending order according to the data sampling time, and the wind output power sequence A, the photovoltaic output power sequence B and the electrolyzer input power sequence C are obtained respectively. The Min-Max normalization method is a well-known technology, and the specific process will not be repeated here.

[0042] Step 2: Obtain the comprehensive adjustment coefficients of wind power generation equipment and photovoltaic power generation equipment respectively.

[0043] S1: In order to ensure the stability of the operation of the hydrogen production system in the water electrolysis hydrogen production module, the input power of the alkaline electrolyzer should have a smaller data fluctuation. When the total output power has a larger data fluctuation, the perturbation step size in the perturbation observation method can be reduced to avoid the output power of the wind power generation equipment and the photovoltaic power generation equipment from hovering around their respective maximum power points and unable to converge in time, resulting in large oscillations. This can suppress the subsequent data fluctuations in the total output power of the wind power generation equipment and the photovoltaic power generation equipment, thereby reducing the degree of data fluctuations in the input power of the alkaline electrolyzer, and improving the stability of the subsequent hydrogen production system operation.

[0044] Based on the above analysis, in the data processing unit, the average level of the local standard deviation of all data points in the electrolytic cell input power series and the trend term coefficient are forward fused as the fluctuation characteristic value of the electrolytic cell input power series.

[0045] It can be understood that 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 impose any special restrictions.

[0046] 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 deviation of all data points in the electrolytic cell input power sequence; the fluctuation characteristic value is further determined by the product of the average level and the trend term coefficient.

[0047] In this embodiment, the sliding window technology is used to traverse the data points in the electrolytic cell input power sequence C, and the standard deviation of all data points in the window where each data point is located is calculated respectively, that is, the local standard deviation of each data point. All the obtained standard deviations are arranged in ascending order according to the sampling time of the data point corresponding to the standard deviation to obtain a standard deviation sequence C1, which is used for subsequent evaluation of whether the input power of the alkaline electrolytic cell shows a trend of increasing data fluctuation at the current evaluation time t.

[0048] The sliding window technology is a well-known technology, and the specific process will not be described in detail.

[0049] At the same time, the mean of all obtained standard deviations is recorded as the data fluctuation degree of the electrolytic cell input power sequence C, 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.

[0050] Furthermore, this embodiment uses the STL (Seasonal and Trend decomposition using Loess) time series decomposition algorithm to extract the trend item sequence of the standard deviation sequence C1, and uses a linear fitting algorithm based on the least squares method to perform linear fitting on the data points in the trend item sequence. The exponential result of the slope of the obtained fitting straight line is recorded as the fluctuation trend of the electrolytic cell input power sequence C, and is also recorded as the trend item coefficient of the local standard deviation of all data points in the electrolytic cell input power sequence, which is used to evaluate whether the input power of the alkaline electrolytic cell shows a trend of increasing data fluctuation 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 electrolytic cell input power sequence C.

[0051] The STL time series decomposition algorithm and the linear fitting algorithm based on the least squares method are both well-known technologies, and the specific processes will not be described in detail.

[0052] In this embodiment, the product of the data fluctuation degree and the fluctuation trend of the electrolytic cell input power sequence C is recorded as the fluctuation characteristic value of the electrolytic cell input power sequence C, which is used to evaluate the data fluctuation degree and fluctuation change trend of the input power of the alkaline electrolytic cell at the current evaluation time t.

[0053] 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 greater than the output power of the photovoltaic power generation equipment, the greater the impact of the wind power generation equipment on the input power of the alkaline electrolyzer than the photovoltaic power generation equipment, the more the disturbance 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 subsequent input power of the alkaline electrolyzer, and vice versa.

[0054] Based on the above analysis, the present application records the product of the normalized value of the mean of the output power sequence 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.

[0055] Among them, as a preferred embodiment, the method for calculating the normalized value of the mean is: calculating the sum of the means of all data points in the output power sequence of all types of power generation equipment; and taking the ratio of the mean calculated by various power generation equipment to the sum as the normalized value of the mean.

[0056] This embodiment takes wind power output A as an example, calculates 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 power output sequence B, and normalizes the mean a using the formula WA=a / (a+b) to obtain the electrolytic cell influence coefficient WA of the wind power output sequence A. This coefficient is used to assess the degree of influence of the output power of the wind power generation equipment on the input power of the alkaline electrolytic cell within the historical time period at the current time t.

[0057] Furthermore, the product of the electrolyzer influence coefficient WA and the fluctuation characteristic value of the electrolyzer input power sequence C is recorded as the first adjustment coefficient of the wind power generation equipment at the current time t, which is used to subsequently adjust the disturbance step size in the MPPT tracking conversion module for the wind power generation equipment in the MPPT hydrogen production power supply at the current time t to improve the stability of the hydrogen production system operation in the water electrolysis hydrogen production module, wherein the larger the first adjustment coefficient is, the smaller the disturbance step size should be.

[0058] The same method as the first adjustment coefficient of the wind power generation equipment at the current time t is used to obtain the first adjustment coefficient of the photovoltaic power generation equipment at the current time t, which is used to subsequently adjust the disturbance step size in the MPPT tracking conversion module for the photovoltaic power generation equipment in the MPPT hydrogen production power supply at the current time t, so as to improve the stability of the hydrogen production system operation in the water electrolysis hydrogen production module, wherein it is only necessary to replace the photovoltaic output power sequence B with the wind output power sequence A.

[0059] S2: Under the premise of stable operation of the hydrogen production system in the water electrolysis hydrogen production module, it is necessary to increase the utilization rate of wind energy and light energy resources in the water electrolysis hydrogen production module as much as possible, so as to improve the hydrogen production efficiency. This requires that the output power of wind power generation equipment and photovoltaic power generation equipment can quickly approach their respective maximum power points, so that they can output at maximum power during the power generation process.

[0060] However, wind power generation equipment and photovoltaic power generation equipment are affected by uncontrollable factors in the environment, such as wind speed, light and temperature, which makes their output power highly uncertain, resulting in multiple local peak points in their output power data. This can easily cause the maximum power point tracking algorithm based on the perturbation observation method to fall into the local maximum power point, making it impossible to find the global maximum power point.

[0061] Therefore, when multiple local peak points with large differences appear in the output power data of wind power generation equipment or photovoltaic power generation equipment, the perturbation step size in the perturbation observation method can be increased to reduce the time that the output power of the wind power generation equipment or photovoltaic power generation equipment lingers near its respective local maximum power point, thereby increasing the speed of finding the global maximum power point, so that they can output at maximum power.

[0062] Based on the above analysis, this application smoothes the output power sequences of various power generation equipment and extracts all the peaks therein; the result of inversely proportional mapping of the standard deviation between all peaks and the product of the number of peaks is used as the second adjustment coefficient of the corresponding power generation equipment at the current moment.

[0063] Optionally, the inverse proportional mapping may be implemented by negative linear mapping, negative exponential mapping, or by setting adjustment parameters.

[0064] As a preferred implementation manner, the second adjustment coefficient is determined by the inverse of the product of the standard deviation between all the peaks and the number of peaks.

[0065] This embodiment uses wind power output sequence A as an example and uses the SG (Savitzky Golay Filter) algorithm to smooth wind power output sequence A. This reduces the impact of noise interference on the power data in wind power output sequence A during data acquisition and transmission on the subsequent extraction of local peak points, thereby obtaining a smoothed wind power output sequence A1.

[0066] The SG filtering algorithm is a well-known technology, and the specific process will not be described in detail.

[0067] Furthermore, this embodiment uses an AMPD (Automatic multiscale-based peak detection) peak detection algorithm to extract all peaks in the wind output power sequence A1. The APPD peak detection algorithm is a well-known technology, and the specific process will not be repeated here.

[0068] In this embodiment, the product of the obtained standard deviation between all peaks and the number of peaks is added to the constant The reciprocal of is recorded as the second adjustment coefficient of the wind power generation equipment at the current time t, which is used to subsequently adjust the disturbance step size in the MPPT tracking conversion module for wind power generation equipment in the MPPT hydrogen production power supply at the current time t, so as to improve the utilization rate of wind energy resources by the hydrogen production system in the water electrolysis hydrogen production module, thereby improving the hydrogen production efficiency, where the constant is added. To avoid the denominator being 0, the constant It should be set to a positive number close to 0. In this embodiment, the constant It is set to 0.01, and the larger the first adjustment coefficient is, the smaller the disturbance step should be.

[0069] The same method as the second adjustment coefficient of the wind power generation equipment at the current moment t is used to obtain the second adjustment coefficient of the photovoltaic power generation equipment at the current moment t, which is used to subsequently adjust the disturbance 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, so as to improve the utilization rate of wind energy resources by the hydrogen production system in the water electrolysis hydrogen production module, thereby improving the hydrogen production efficiency, wherein it is only necessary to replace the wind output power sequence A with the photovoltaic output power sequence B.

[0070] S3: In the present application, the result of forward fusion of the first adjustment coefficient and the second adjustment coefficient of the corresponding type of power generation equipment at the current moment is used as the comprehensive adjustment coefficient of the corresponding type of power generation equipment at the current moment.

[0071] As a preferred embodiment, the comprehensive adjustment coefficient is further determined by the product of the first adjustment coefficient and the second adjustment coefficient of the corresponding power generation equipment at the current moment.

[0072] In this embodiment, the product of the first and second adjustment coefficients of the wind power generation equipment at the current time t is recorded as the comprehensive adjustment coefficient of the wind power generation equipment at the current time t, which is used for subsequent adjustment of the MPPT hydrogen production power supply for the wind power generation equipment.

[0073] The same method as the comprehensive adjustment coefficient of the wind power generation equipment at the current time t is used to obtain the comprehensive adjustment coefficient of the photovoltaic power generation equipment at the current time t.

[0074] It should be noted that the comprehensive adjustment coefficient of the wind power generation equipment and the photovoltaic power generation equipment at the current time t is used to subsequently adjust the disturbance step value at the current time t in the MPPT tracking transformation module for the wind power generation equipment and the photovoltaic power generation equipment in the MPPT hydrogen production power supply, and the two obtained comprehensive adjustment coefficients are transmitted to the step calculation unit.

[0075] Step 3: Use the comprehensive adjustment coefficient 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.

[0076] In the step length calculation unit of the step length adjustment module, the disturbance step length values ​​UA(t) and UB(t) of the wind power generation equipment and the photovoltaic power generation equipment at the current time t are calculated respectively, and are used as the value of the disturbance step length at the current time t in the MPPT tracking conversion module for the wind power generation equipment and the photovoltaic power generation equipment in the MPPT hydrogen production power supply.

[0077] In the present application, a comprehensive adjustment coefficient is used 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.

[0078] As a preferred embodiment, the method for determining the disturbance step length in the maximum power point tracking algorithm based on the disturbance observation method of the corresponding type of power generation equipment at the current moment by using the comprehensive adjustment coefficient is: the comprehensive adjustment coefficient of various power generation equipment at the current moment is normalized, and the product of the preset maximum disturbance step length and the preset minimum disturbance step length is summed as the disturbance step length value of the corresponding type of power generation equipment at the current moment.

[0079] In this embodiment, the calculation method of the disturbance step values ​​UA(t) and UB(t) is as follows:

[0080] , , where UA(t) and HA(t) represent the disturbance step value and comprehensive adjustment coefficient of the wind power generation equipment at the current time t, respectively; UB(t) and HB(t) represent the disturbance step value and comprehensive adjustment coefficient of the photovoltaic power generation equipment at the current time t, respectively; a1 is the preset minimum disturbance step value, which is 0.001 in this embodiment; a2 is the preset maximum disturbance step value, which is 0.01 in this embodiment; Sigmoid() represents the Sigmoid function, which is used to limit the value of HA(t) to between (0,1).

[0081] The formula limits UA(t) and UB(t) to between (0.001, 0.011). This range is more effective for adjusting the perturbation step size in the maximum power point tracking algorithm based on the perturbation-observation method.

[0082] The obtained disturbance step values ​​UA(t) and UB(t) are respectively transmitted to the MPPT tracking conversion modules for wind power generation equipment and photovoltaic power generation equipment of the MPPT hydrogen production power supply, and are respectively used as the values ​​of the disturbance step at the current moment t in the maximum power point tracking algorithm based on the disturbance observation method in the MPPT tracking conversion module. The MPPT tracking conversion module after adjusting the disturbance step is used to optimize the subsequent output power of the wind power generation equipment and photovoltaic power generation equipment, and hydrogen is produced using an alkaline electrolyzer based on the wind-solar rectified electricity obtained from the subsequent MPPT hydrogen production power supply.

[0083] The maximum power point tracking algorithm based on the perturbation and observation method is a well-known technology, and the specific process will not be described in detail.

[0084] Example 2

[0085] As attached Figure 2 As shown, a module schematic diagram of a wind-solar combined water electrolysis hydrogen production system provided in one embodiment of the present application includes: a wind-solar power generation module, a water electrolysis hydrogen production module, a hydrogen storage module, a wind-solar power generation control module and a step size adjustment module.

[0086] The wind-solar power generation module is a power generation system formed by wind power generation equipment and photovoltaic power generation equipment, which is used to convert wind energy and light energy into electrical energy and supply it to the water electrolysis hydrogen production module. The wind power generation equipment and photovoltaic power generation equipment are wind turbine generator sets and photovoltaic generator sets respectively;

[0087] The water electrolysis hydrogen production module produces hydrogen by using the electricity generated by the wind and solar power generation module and the water electrolysis hydrogen production equipment, which is an alkaline electrolyzer;

[0088] A hydrogen storage module is used to store the hydrogen generated by the water electrolysis hydrogen production module in a hydrogen storage device, wherein the hydrogen storage device is a hydrogen storage tank;

[0089] 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-size adjustment module, and an MPPT tracking conversion module.

[0090] The MPPT hydrogen production power supply is directly coupled to the wind power generation equipment and photovoltaic power generation equipment respectively, and the MPPT hydrogen production power supply is connected to the alkaline electrolyzer. Through the converter, inverter and MPPT tracking conversion module, the wind power generation equipment and photovoltaic power generation equipment output DC power at their respective maximum power, and the wind and solar rectified power is obtained through the rectifier. Finally, the wind and solar rectified power is transmitted to the input end of the alkaline electrolyzer through the MPPT hydrogen production power supply;

[0091] Among them, 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 water electrolysis hydrogen production method. By adjusting the perturbation step size in the maximum power point tracking algorithm based on the perturbation observation method in the MPPT tracking conversion module at regular intervals, the stability and hydrogen production efficiency of the wind-solar combined water electrolysis hydrogen production system during its hydrogen production process can be improved, wherein the control unit is connected to the MPPT tracking conversion module in the hydrogen production power supply. In this embodiment, the period of time set is 5 minutes, which can be set by the implementer.

[0092] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of the present application and include common knowledge or customary techniques in the art not invented herein.

[0093] It will be understood 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 electrolysis of water using a combination of wind and solar power, characterized in that: The method comprises the following steps: Collect output power data of wind power generation equipment and photovoltaic power generation equipment, as well as input power data of alkaline electrolyzers, pre-process the collected data, and construct a sequence of the data collected at the current time and in historical time periods; The product of the average level of the local standard deviation of all data points in the electrolytic cell input power sequence and the trend term coefficient is used as the fluctuation characteristic value of the electrolytic cell input power sequence; the product of the normalized value of the mean of the output power sequence of each power generation device and the fluctuation characteristic value is recorded as the first adjustment coefficient of the corresponding power generation device at the current moment; After smoothing the output power sequences of various power generation equipment, all peak values ​​are extracted; the product of the standard deviation between all peak values ​​and the number of peak values ​​is added to the inverse of a constant α, which is used as the second adjustment coefficient of the corresponding power generation equipment at the current moment; the constant α is 0.01; The product of the first and second adjustment coefficients is used as the comprehensive adjustment coefficient of the corresponding type of power generation equipment at the current moment to determine the perturbation step size of the maximum power point tracking algorithm based on the perturbation and observation method for the corresponding type of power generation equipment at the current moment; The trend term coefficient is determined by exponentially calculating the slope of the fitted line of the trend term sequence after extracting the trend term sequence from the local standard deviation of all data points in the electrolytic cell input power sequence; The normalized value of the mean is calculated by: calculating the sum of the mean values ​​of all data points in the output power sequences of all types of power generation equipment; and taking the ratio of the mean values ​​calculated for each type of power generation equipment to the sum value as the normalized value of the mean; The method for determining the disturbance step length is as follows: the product of the comprehensive adjustment coefficient of various power generation equipment at the current moment normalized by the Sigmoid function and the preset maximum disturbance step length, and the sum of the product and the preset minimum disturbance step length are used as the disturbance step length value of the corresponding power generation equipment at the current moment.

2. The method for producing hydrogen by electrolysis of water by combining wind and solar power as claimed in claim 1, characterized in that: The preprocessing method is to use the Min-Max normalization method to normalize each type of power data separately.

3. The method for producing hydrogen by water electrolysis by combining wind and solar power as claimed in claim 1, characterized in that: The historical time period is the time period between the current moment and the previous adjustment moment.

4. The method for producing hydrogen by electrolysis of water by combining wind and solar power as claimed in claim 1, characterized in that: The method for smoothing the output power sequences of various power generation equipment adopts the SG filtering algorithm for smoothing.

5. A wind-solar combined water electrolysis hydrogen production system, characterized in that: The system implements a wind-solar combined water electrolysis hydrogen production method according to any one of claims 1 to 4, the system comprising a wind-solar power generation module, a water electrolysis hydrogen production module, a hydrogen storage module, a wind-solar power generation control module, and a step size adjustment module; Wind and solar power generation module, used to convert wind energy and light energy into electrical energy and supply it to the water electrolysis hydrogen production module; Water electrolysis hydrogen production module, used to produce hydrogen using electricity generated by wind and solar power generation modules; A hydrogen storage module is used to store the hydrogen produced by the water electrolysis hydrogen production module; A wind-solar power generation control module for maximizing the output power of wind power generation equipment and photovoltaic power generation equipment in the wind-solar power generation module by using a hydrogen-generating power source with an MPPT tracking function; The hydrogen production power supply includes: a converter, 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 water electrolysis hydrogen production system during its hydrogen production process.

6. The wind-solar combined water electrolysis hydrogen production system according to claim 5, characterized in that: The step length adjustment module includes a data acquisition unit, a data processing unit and a step length calculation unit, which are used to sequentially implement the steps in the above-mentioned wind-solar combined water electrolysis hydrogen production method.

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