Fuzzy self-adaptive electricity-hydrogen hybrid energy storage power distribution method

Abstract

The invention relates to the technical field of electric energy storage, and aims to provide a fuzzy self-adaptive electricity-hydrogen hybrid energy storage power distribution method. According to the method, in a micro-grid system comprising an electricity-hydrogen hybrid energy storage system, a Butterworth low-pass filter capable of adaptively adjusting parameters is used for filtering power to be consumed, and low-frequency power and filtered power are distributed to a hydrogen production electrolytic bath and an energy storage battery respectively to achieve electricity-hydrogen hybrid energy storage combined consumption; and in the filtering processing process, the cut-off frequency of a filter is obtained based on a fuzzy inference rule by taking the temperature of the electrolytic cell and the SOC of the battery as input variables, and the consumption power of the electrolytic cell and the SOC of the battery are maintained in a preset range by adaptively adjusting parameters of the filter. According to the invention, energy waste caused by response delay can be reduced, and the stability of the system can be improved; compared with a traditional power distribution mode, the service life of the battery is prolonged; the adaptability of the system is enhanced, and the overall operation efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric energy storage, and specifically relates to an electric-hydrogen hybrid energy storage power distribution technology, and in particular to a fuzzy adaptive electric-hydrogen hybrid energy storage power distribution method based on the dynamic response characteristics of an electrolyzer and the charge state of a battery. Background Art

[0002] With the growth of renewable energy installed capacity and the diversified energy demand on the power load side, the role of energy storage in the power system has become increasingly prominent. The electric-hydrogen hybrid energy storage system based on hydrogen electrolyzers and lithium batteries is gradually becoming a research hotspot because it combines the rapid response characteristics of batteries with the long-term energy storage capacity of electrolyzers.

[0003] For hybrid energy storage systems, the commonly used power allocation methods currently include the following categories: (1) Power allocation methods based on signal processing, such as variational mode decomposition, etc., which distinguish the power of different frequency components by signal decomposition; (2) Power allocation methods based on hierarchical control, which coordinate the power response of batteries and electrolyzers through a multi-layer control architecture; (3) Power allocation methods based on optimization algorithms, such as model predictive control, etc., which determine the power allocation strategy by optimizing the objective function; (4) Power allocation methods based on filters, which use the frequency separation characteristics of filters to allocate power fluctuations of different frequencies to different energy storage components. Among these methods, filters have great application potential in hybrid energy storage systems composed of energy storage units with similar or identical performance due to their advantages such as small computational complexity, strong real-time performance, and the ability to achieve efficient power division when designed reasonably.

[0004] However, for electric-hydrogen hybrid energy storage systems, the electrolyzer has a slow dynamic response speed, which is significantly different from the fast response characteristics of batteries. The existing power allocation methods often result in energy waste during application, or the electrolyzer cannot keep up with the input power, resulting in unstable operation.

[0005] Therefore, it is necessary to propose a new solution to achieve reasonable power allocation in order to solve the above key problems. Summary of the invention

[0006] The technical problem to be solved by the present invention is to overcome the deficiencies in the prior art and provide a fuzzy adaptive electric-hydrogen hybrid energy storage power distribution method.

[0007] To solve the technical problem, the solution of the present invention is:

[0008] A fuzzy adaptive electric-hydrogen hybrid energy storage power distribution method is provided. In a microgrid system including an electric-hydrogen hybrid energy storage system, a Butterworth low-pass filter with adaptively adjusted parameters is used to filter the power to be absorbed, and the low-frequency power and the filtered power are respectively allocated to the hydrogen production electrolyzer and the energy storage battery to realize the joint absorption of the electric-hydrogen hybrid energy storage; in the filtering process, the electrolyzer temperature and the battery SOC are used as input variables and the cutoff frequency of the filter is obtained based on fuzzy inference rules, and the parameters of the filter are adaptively adjusted to maintain the electrolyzer absorption power and the battery SOC within a preset range.

[0009] As a preferred solution of the present invention, the method of obtaining the cutoff frequency of the filter based on the fuzzy inference rule specifically includes the following operations:

[0010] (1) Define the input variables as the electrolyzer temperature and the battery SOC, and define the output variable as the filter cutoff frequency ;

[0011] (2) defining membership functions, including the electrolytic cell temperature membership function, the SOC membership function, and the filter cutoff frequency membership function;

[0012] (3) Define the evaluation rules in the rule base;

[0013] (4) Merge the output fuzzy sets of the rules, and use the centroid method to defuzzify the merged membership function to obtain the final filter cutoff frequency .

[0014] As a preferred embodiment of the present invention, the range of the electrolytic cell temperature is arrive , the battery SOC ranges from 0 to 1;

[0015] The electrolytic cell temperature membership function includes a low temperature membership function, a medium temperature membership function and a high temperature membership function, wherein:

[0016] Low temperature membership function : °C to °C, triangular function, peak at °C;

[0017]

[0018] Medium temperature membership function : °C to °C, triangular function, peak at °C;

[0019]

[0020] High temperature membership function : °C to °C, triangular function, peak at °C;

[0021]

[0022] The SOC membership function includes a low SOC membership function, a moderate SOC membership function and a high SOC membership function, wherein:

[0023] Low SOC membership function : 0% to 70%, triangular function, peak at 0%;

[0024]

[0025] Moderate SOC membership function : 40% to 60%, triangular function, peak at 50%;

[0026]

[0027] High SOC membership function : 30% to 100%, triangular function, peak at 100%;

[0028]

[0029] The cutoff frequency membership function of the filter includes a low cutoff frequency membership function, a medium cutoff frequency membership function and a high cutoff frequency membership function, wherein:

[0030] Low cutoff frequency membership function : Hz to Hz, triangular function, peak at Hz;

[0031]

[0032] Medium cutoff frequency membership function : Hz to Hz, triangular function, peak at Hz;

[0033]

[0034] High cutoff frequency membership function : Hz to Hz, triangular function, peak at Hz;

[0035]

[0036] The rule base includes the following evaluation rules:

[0037] Rule 1: If the cell temperature is high and the SOC is moderate, then the filter cutoff frequency is high; the output high cutoff frequency membership generated by this rule is: ;

[0038] Rule 2: If the cell temperature is low and the SOC is moderate, then the filter cutoff frequency is low; the output low cutoff frequency membership generated by this rule is: ;

[0039] Rule 3: If the cell temperature is high and the SOC is low or high, then the filter cutoff frequency is moderate; the output moderate cutoff frequency membership generated by this rule is: ;

[0040] Rule 4: If the cell temperature is low and the SOC is low or high, then the filter cutoff frequency is low; the output low cutoff frequency membership generated by this rule is: ;

[0041] Rule 5: If the cell temperature is moderate and the SOC is low or high, then the filter cutoff frequency is low; this rule produces an output moderate cutoff frequency membership of: ;

[0042] Rule 6: If the cell temperature is moderate and the SOC is moderate, then the filter cutoff frequency is moderate; the output moderate cutoff frequency membership generated by this rule is: ;

[0043] The membership function after merging the output fuzzy sets is:

[0044]

[0045] The final filter cutoff frequency obtained by defuzzification using the centroid method As shown below:

[0046] ;

[0047] In the above formulas, T is the electrolytic cell temperature; S is the battery SOC; f is the filter cutoff frequency; and are the filter cutoff frequencies corresponding to the lower and upper operating temperature limits of the electrolytic cell respectively; It refers to the rule intersection operation; It refers to the rule merging operation; and are the lower and upper limits of the cutoff frequency range respectively.

[0048] As a preferred solution of the present invention, the membership function after merging the output fuzzy sets is is a piecewise function, which can be further The segmented intervals are split, and the cutoff frequencies of the segmented intervals are calculated;

[0049] Assumptions exist There are different expressions on , then:

[0050]

[0051]

[0052] for In the interval The expression on The total number of different expressions.

[0053] As a preferred solution of the present invention, a frequency response test of a rated voltage step input is performed on the electrolyzer in the electric-hydrogen hybrid energy storage system. and upper operating temperature limit Carry out step response experiments respectively; identify data points and analyze frequency response of the experimental results to obtain the cutoff frequency corresponding to the lower and upper operating temperature limits of the electrolyzer and .

[0054] As a preferred embodiment of the present invention, the cutoff frequency corresponding to the lower limit and upper limit of the operating temperature of the electrolytic cell is obtained. and The methods include:

[0055] (1) Multiply the voltage and current output by the electrolyzer to obtain the power curve:

[0056]

[0057] in, is the voltage value of the voltage data point, is the current value of the current data point, for time point;

[0058] (2) Using image processing software to read the step response results in the power curve to obtain step response data;

[0059] (3) The step response data was used as the input of the MATLAB software and the tfest function was used for fitting. The number of zeros was set to 1, and the number of poles was set to 2 and 1 for fitting respectively. The one with the best fitting effect, i.e., R 2 The number of poles closest to 1, to obtain the fitted transfer function;

[0060] If the number of poles is 1 and the number of zeros is 1, then the transfer function is:

[0061] (1)

[0062] in, is the zero position, is the pole position, is the transfer function gain factor;

[0063] If the number of poles is 2 and the number of zeros is 1, the general formula of its transfer function is:

[0064] (2)

[0065] in, is the zero position, and is the pole position, is the transfer function gain factor;

[0066] According to the above fitting process, the parameter values ​​in the general formula of the transfer function are determined;

[0067] (4) Based on the fitted transfer function, the Bode function is used to draw the Bode plot. According to the logarithmic amplitude-frequency characteristics of the Bode plot, the frequency corresponding to the gain attenuation of -3dB is found, that is, the cut-off frequencies corresponding to the lower and upper operating temperatures of the electrolytic cell are obtained. and .

[0068] As a preferred solution of the present invention, during the operation of the electric-hydrogen hybrid energy storage system, the temperature of the electrolyzer and the SOC of the battery are measured regularly according to the set time interval; the latest measurement data are used to calculate the cutoff frequency corresponding to the lower and upper operating temperature limits of the electrolyzer. and , and then further recalculate the cutoff frequency of the filter according to the fuzzy inference rule, thereby realizing adaptive adjustment of the filter parameters.

[0069] As a preferred solution of the present invention, the cutoff frequency obtained based on the fuzzy inference rule is suitable for an analog Butterworth filter, which needs to be further converted into a digital filter by a bilinear transformation; specifically,

[0070] The transfer function of the analog Butterworth filter is:

[0071]

[0072] ,

[0073] In the formula, is the angular frequency of the transfer function; is the filter cutoff frequency obtained based on the fuzzy inference rule; s is the complex frequency variable used for Laplace transform;

[0074] make , substituting into the above transfer function, the transfer function of the digital filter is obtained as follows:

[0075]

[0076] Where M is the sampling period; , ; z is the complex variable used for z-transformation.

[0077] As a preferred embodiment of the present invention, the hydrogen production electrolyzer is an alkaline solution electrolyzer; the energy storage battery is a lithium battery cell, or a battery pack composed of a plurality of lithium battery cells.

[0078] Description of the invention principle:

[0079] After in-depth research, the inventor team of the applicant found that the dynamic response characteristics of the alkaline solution electrolyzer are closely related to the operating temperature. Generally, the electrolyzer at a higher temperature responds faster, while the response speed at a lower temperature is slower. Therefore, in the power distribution process, it is necessary to adaptively adjust the cutoff frequency of the filter according to the operating temperature of the electrolyzer to match the dynamic characteristics of the electrolyzer and optimize the power distribution effect. In the electric-hydrogen hybrid energy storage system containing an alkaline solution electrolyzer, the design of a suitable filter requires an in-depth study of the dynamic characteristics of the electrolyzer and the determination of the frequency boundary of the filter based on its dynamic response capability. In addition, the battery state of charge (SOC) in the hybrid energy storage system is crucial to the stable operation of the system. Overcharging or over-discharging will affect the battery life and reduce the long-term benefits of the system. Therefore, in the filter design process, it is also necessary to consider the SOC state of the battery and dynamically adjust the cutoff frequency of the filter according to the SOC level.

[0080] Based on the above findings, the applicant proposed that by designing a filter with a variable cutoff frequency, the power allocation strategy can be adjusted in real time according to the temperature of the electrolyzer and the changes in the battery SOC, thereby optimizing the operation effect of the hybrid energy storage system. Compared with the traditional fixed parameter filter, this method can dynamically adapt to changes in the system state and improve the rationality of power allocation.

[0081] Compared with the prior art, the present invention has the following beneficial effects:

[0082] 1. The present invention adopts fuzzy reasoning to adaptively adjust the filter cutoff frequency according to the dynamic response capability of the electrolytic cell at different temperatures, so that the load of the electrolytic cell is more in line with its dynamic characteristics, reducing energy waste caused by response delay and improving the stability of the system;

[0083] 2. The present invention takes into account the battery SOC to adjust the filter cutoff frequency, which can reduce the risk of battery overcharge and over-discharge, and can extend the battery life compared to the traditional power distribution method.

[0084] 3. The variable parameter filter proposed in the present invention can adjust parameters according to the system status, enhance the adaptability of the system, enable the battery and the electrolyzer to perform their respective functions, reduce the energy conversion loss of the energy storage system, enable it to achieve better power distribution under different operating conditions, and improve the overall operating efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] Figure 1 The microgrid system structure is an electric-hydrogen hybrid energy storage system containing an electrolyzer.

[0086] Figure 2 This is the voltage-current response diagram of a 10kW electrolyzer at 30 degrees Celsius and 92V voltage step.

[0087] Figure 3 This is the voltage-current response diagram of a 10kW electrolyzer at 75 degrees Celsius and 92V voltage step.

[0088] Figure 4 This is the power response diagram of a 10kW electrolyzer at 30 degrees Celsius and 92V voltage step.

[0089] Figure 5 This is the power response diagram of a 10kW electrolyzer at 75 degrees Celsius and 92V voltage step.

[0090] Figure 6 Bode plot for a 10kW electrolyzer at 30 degrees Celsius.

[0091] Figure 7 Bode plot for a 10kW electrolyzer at 75 degrees Celsius.

[0092] Figure 8 The photovoltaic power variation diagram is a specific example.

[0093] Fig. 9 The figure is a graph of electrolytic cell power results for a specific example.

[0094] Fig.10 The figure is a battery power result diagram for a specific example.

[0095] Fig.11The figure is a graph showing the battery SOC change results for a specific example.

[0096] Fig.12 This is the SOC change result diagram of the traditional fixed-frequency filter example. DETAILED DESCRIPTION

[0097] Part I Implementation of the Invention

[0098] Based on the dynamic response characteristics of the electrolyzer and the battery state of charge (SOC), the present invention proposes a fuzzy adaptive electric-hydrogen hybrid energy storage power allocation method. Specifically, for a hybrid energy storage system including a hydrogen production electrolyzer and an energy storage battery, a Butterworth low-pass filter with adaptive parameter adjustment is used to filter the power to be absorbed, giving the low-frequency power to the electrolyzer and filtering out the power to the battery, thereby realizing the combined absorption of hybrid energy storage; for the Butterworth low-pass filter, it is proposed to obtain the cutoff frequency of the filter based on the frequency response characteristics of the electrolyzer, taking into account the electrolyzer temperature and the battery SOC and according to the fuzzy reasoning rules, so that the electrolyzer can absorb appropriate power by adaptively adjusting the parameters of the filter, while maintaining the battery SOC at a reasonable level as much as possible.

[0099] The hydrogen production electrolyzer described in the present invention may be an alkaline solution electrolyzer; the energy storage battery may be a lithium battery cell, or a battery pack composed of multiple lithium battery cells.

[0100] Based on the above innovative ideas, the implementation scheme of the present invention is specifically as follows:

[0101] 1. Calculate the cutoff frequency corresponding to the lower and upper operating temperature limits of the electrolytic cell and .

[0102] The frequency response test of the rated voltage step input of the hydrogen production electrolyzer is carried out at the lower limit of the electrolyzer operating temperature. and upper operating temperature limit Carry out step response experiments respectively; identify data points and analyze frequency response of the experimental results to obtain the cutoff frequency corresponding to the lower and upper operating temperature limits of the electrolyzer and .

[0103] Specifically include:

[0104] (1) Multiply the voltage and current output by the electrolyzer to obtain the power curve:

[0105]

[0106] in, is the voltage value of the voltage data point, is the current value of the current data point, for time point;

[0107] (2) Using image processing software to read the step response results in the power curve to obtain step response data;

[0108] (3) The step response data was used as the input of the MATLAB software and the tfest function was used for fitting. The number of zeros was set to 1, and the number of poles was set to 2 and 1 for fitting respectively. The one with the best fitting effect, i.e., R 2 The number of poles closest to 1, to obtain the fitted transfer function;

[0109] If the number of poles is 1 and the number of zeros is 1, then the transfer function is:

[0110] (1)

[0111] in, is the zero position, is the pole position, is the transfer function gain factor; s is the complex frequency variable used for Laplace transform;

[0112] If the number of poles is 2 and the number of zeros is 1, the general formula of its transfer function is:

[0113] (2)

[0114] in, is the zero position, and is the pole position, is the transfer function gain factor;

[0115] According to the above fitting process, the parameter values ​​in the general formula of the transfer function are determined;

[0116] (4) Based on the fitted transfer function, the Bode function is used to draw the Bode plot. According to the logarithmic amplitude-frequency characteristics of the Bode plot, the frequency corresponding to the gain attenuation of -3dB is found, that is, the cut-off frequencies corresponding to the lower and upper operating temperatures of the electrolytic cell are obtained. and .

[0117] 2. Define the input variables as the electrolyzer temperature and the battery SOC, and define the output variable as the cutoff frequency of the filter The range of the electrolytic cell temperature is arrive , the battery SOC ranges from 0 to 1.

[0118] 3. Define membership functions, including: electrolytic cell temperature membership function, SOC membership function and filter cutoff frequency membership function.

[0119] (1) The temperature membership function of the electrolytic cell includes low temperature membership function, medium temperature membership function and high temperature membership function, where:

[0120] Low temperature membership function : °C to °C, triangular function, peak at °C;

[0121]

[0122] Medium temperature membership function : °C to °C, triangular function, peak at °C;

[0123]

[0124] High temperature membership function : °C to °C, triangular function, peak at °C;

[0125]

[0126] (2) The SOC membership function includes low SOC membership function, moderate SOC membership function and high SOC membership function, where:

[0127] Low SOC membership function : 0% to 70%, triangular function, peak at 0%;

[0128]

[0129] Moderate SOC membership function : 40% to 60%, triangular function, peak at 50%;

[0130]

[0131] High SOC membership function : 30% to 100%, triangular function, peak at 100%;

[0132]

[0133] (3) The cutoff frequency membership function of the filter includes a low cutoff frequency membership function, a medium cutoff frequency membership function and a high cutoff frequency membership function, where:

[0134] Low cutoff frequency membership function : Hz to Hz, triangular function, peak at Hz;

[0135]

[0136] Medium cutoff frequency membership function : Hz to Hz, triangular function, peak at Hz;

[0137]

[0138] High cutoff frequency membership function : Hz to Hz, triangular function, peak at Hz;

[0139]

[0140] 4. Define the evaluation rules in the rule base.

[0141] The rule base includes the following evaluation rules:

[0142] Rule 1: If the cell temperature is high and the SOC is moderate, then the filter cutoff frequency is high; the output high cutoff frequency membership generated by this rule is: ;

[0143] Rule 2: If the cell temperature is low and the SOC is moderate, then the filter cutoff frequency is low; the output low cutoff frequency membership generated by this rule is: ;

[0144] Rule 3: If the cell temperature is high and the SOC is low or high, then the filter cutoff frequency is moderate; the output moderate cutoff frequency membership generated by this rule is: ;

[0145] Rule 4: If the cell temperature is low and the SOC is low or high, then the filter cutoff frequency is low; the output low cutoff frequency membership generated by this rule is: ;

[0146] Rule 5: If the cell temperature is moderate and the SOC is low or high, then the filter cutoff frequency is low; this rule produces an output moderate cutoff frequency membership of: ;

[0147] Rule 6: If the cell temperature is moderate and the SOC is moderate, then the filter cutoff frequency is moderate; the output moderate cutoff frequency membership generated by this rule is: ;

[0148] 5. Merge the output fuzzy sets of the rules, use the centroid method to defuzzify the merged membership function, and obtain the final filter cutoff frequency .

[0149] The membership function after merging the output fuzzy sets is:

[0150]

[0151] The final filter cutoff frequency obtained by defuzzification using the centroid method As shown below:

[0152] ;

[0153] In the above formulas, T is the electrolytic cell temperature; S is the battery SOC; f is the filter cutoff frequency; and are the filter cutoff frequencies corresponding to the lower and upper operating temperature limits of the electrolytic cell respectively; It refers to the rule intersection operation; It refers to the rule merging operation; and are the lower and upper limits of the cutoff frequency range respectively.

[0154] The membership function after merging the output fuzzy sets is a piecewise function, which can be further The segmented intervals are split, and the cutoff frequencies of the segmented intervals are calculated;

[0155] Assumptions exist There are different expressions on , then:

[0156]

[0157]

[0158] in, for In the interval The expression on The total number of different expressions.

[0159] The above cutoff frequency obtained based on fuzzy inference rules is suitable for analog Butterworth filter, which needs to be further converted into digital filter by bilinear transformation;

[0160] First, the second-order Butterworth low-pass filter has a flat passband response. The second-order Butterworth low-pass filter is designed according to the above-obtained cutoff frequency; for the analog Butterworth filter, the transfer function is:

[0161]

[0162] ,

[0163] In the formula, is the angular frequency of the transfer function; is the filter cutoff frequency obtained based on the fuzzy inference rule; s is the complex frequency variable used for Laplace transform;

[0164] Then, the above analog filter is converted into a digital filter using a bilinear transformation:

[0165] make , substituting into the above transfer function, the transfer function of the digital filter is obtained as follows:

[0166]

[0167] Where M is the sampling period; , ; z is the complex variable used for z-transformation.

[0168] During the operation of the electric-hydrogen hybrid energy storage system, it is necessary to measure the temperature of the electrolyzer and the SOC of the battery at set time intervals; the latest measurement data is used to calculate the cutoff frequency corresponding to the lower and upper operating temperature limits of the electrolyzer. and , and then further calculate the cutoff frequency of the filter according to the fuzzy inference rule, so as to realize the adaptive adjustment of the filter parameters.

[0169] As common sense that can be understood by those skilled in the art, in order to implement the power distribution method of the electric-hydrogen hybrid energy storage described in the present invention, it is necessary to apply a computing device, which includes: a memory configured to store instructions; and a processor, which is configured to call the instructions from the memory and can implement the aforementioned power distribution method of the electric-hydrogen hybrid energy storage when executing the instructions. At the same time, it is also necessary to apply a computer-readable storage medium, which stores instructions, and the instructions are used to enable the computer to execute the aforementioned power distribution method of the electric-hydrogen hybrid energy storage. The computing device can be a personal computer, a server, or a network device, etc. The computer storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0170] In the specific implementation process, the above calculation and control process can be realized by embedding a software program in the control device of the microgrid system. Of course, the measurement data can also be uploaded to a local computer or cloud server through networking, and after completing the corresponding calculation, the control signal is fed back to the hydrogen production electrolyzer and energy storage battery to realize power distribution and maintain the electrolyzer consumption power and battery SOC within the preset range.

[0171] Part II Application Examples and Verification

[0172] 1. Figure 1 The structure of a microgrid system containing an electric-hydrogen hybrid energy storage system is shown as an example, which is mainly composed of photovoltaic arrays, energy storage batteries (such as lithium batteries), hydrogen production electrolyzers, loads, and DC-DC converters. The energy storage system includes an electric energy storage system based on batteries and a hydrogen energy storage system based on electrolyzers. In actual microgrid systems, fuel cells can be optional, but in the present invention, only hydrogen production electrolyzers and energy storage batteries are used as electric-hydrogen hybrid energy storage coupling elements.

[0173] 2. Take a 10kW alkali solution electrolyzer as an example. Its temperature operating range is 30 degrees Celsius to 70 degrees Celsius. At 30 degrees Celsius and 70 degrees Celsius, a step signal of 92V voltage (slightly lower than the rated voltage 100V for safety reasons) is input to obtain the voltage and current curve at the output end, such as Figure 2 and Figure 3 The upper line is the voltage, the lower line is the current, the voltage is 40V per grid, the current is 10A per grid, the horizontal axis is the time, each grid is 1s, and the power response curve is obtained by reading the voltage and current curve data and multiplying them, as shown in Figure 4 and Figure 5According to the power response curve, the transfer function and Bode diagram corresponding to the two curves can be obtained by using the tfest and bode functions in MATLAB. After comparison, the transfer function with 2 poles and 1 zeros is the most suitable, as shown in Figure 6 and Figure 7 As shown, according to the Bode diagram, we can know the highest frequency that the alkali solution electrolyzer can respond to at these two temperatures, that is, the cutoff frequency corresponding to the lower temperature limit and the upper temperature limit. and , 0.132Hz and 0.22Hz respectively.

[0174] 3. Taking the light intensity data of a certain area in Zhejiang Province as an example, the maximum power of the photovoltaic panel is 20kW. The change of its photovoltaic power within about 75 minutes is as follows: Figure 8 As shown, the battery capacity is set to 0.05kWh. Due to instrument limitations, the sampling period is set to 4Hz. The method proposed in the present invention uses a Butterworth filter to decompose the photovoltaic power, input low-frequency power to the electrolyzer, and input high-frequency power to the battery. The input variables of the fuzzy reasoning model are re-measured every 5 minutes: the electrolyzer temperature and the battery SOC, and the filter cutoff frequency is adaptively adjusted. According to the fuzzy reasoning method proposed in the present invention, the corresponding filter cutoff frequency is inferred according to the electrolyzer temperature and SOC at the acquisition time point. The reasoning results are shown below.

[0175] Based on the input temperature of 50.00 °C and SOC of 0.50, the filter cutoff frequency is adjusted to: 0.17 Hz;

[0176] Based on the input temperature of 51.08 °C and SOC of 0.55, the filter cutoff frequency is adjusted to: 0.17 Hz;

[0177] Based on the input temperature of 52.19 °C and SOC of 0.53, the filter cutoff frequency is adjusted to: 0.18 Hz;

[0178] Based on the input temperature of 53.35 °C and SOC of 0.51, the filter cutoff frequency is adjusted to: 0.18 Hz;

[0179] According to the input temperature of 54.57 °C and SOC of 0.48, the filter cutoff frequency is adjusted to: 0.18 Hz;

[0180] Based on the input temperature of 55.82 °C and SOC of 0.58, the filter cutoff frequency is adjusted to: 0.17 Hz;

[0181] Based on the input temperature of 57.08 °C and SOC of 0.59, the filter cutoff frequency is adjusted to: 0.17 Hz;

[0182] Based on the input temperature of 58.42 °C and SOC of 0.62, the filter cutoff frequency is adjusted to: 0.17 Hz;

[0183] Based on the input temperature of 59.75 °C and SOC of 0.37, the filter cutoff frequency is adjusted to: 0.17 Hz;

[0184] According to the input temperature of 61.19 °C and SOC 0.07, the filter cutoff frequency is adjusted to: 0.18 Hz;

[0185] According to the input temperature of 62.73 °C and SOC 0.13, the filter cutoff frequency is adjusted to: 0.18 Hz;

[0186] Based on the input temperature of 64.32 °C and SOC of 0.22, the filter cutoff frequency is adjusted to: 0.18 Hz;

[0187] According to the input temperature of 65.93 °C and SOC 0.23, the filter cutoff frequency is adjusted to: 0.18 Hz;

[0188] Based on the input temperature of 67.55 °C and SOC 0.30, the filter cutoff frequency is adjusted to: 0.18 Hz.

[0189] 4. In the power distribution process, the Butterworth filter is used and the filter parameters are adaptively adjusted, which can filter in real time and realize reasonable and effective power distribution of electrolyzers and batteries.

[0190] Based on the regulation of the method of the present invention, the power consumption curve of the electrolytic cell and the power consumption curve of the battery are obtained by Fig. 9 and Fig.10 As shown, Fig.11 This is the SOC curve of the battery. It can be seen that the SOC is maintained within an appropriate range. When it is approaching the lower limit, it can be adjusted back by adjusting the filter cutoff frequency.

[0191] 5. Use a fixed-frequency Butterworth filter to distribute and regulate power for the same electric-hydrogen hybrid energy storage system. The experimental results show that the fixed-frequency filter cannot adjust the frequency according to the battery SOC and temperature. When the set frequency is too low, assuming it is 0.14Hz, the battery SOC result is as follows: Fig.12 As shown, the battery SOC will be lower than the lower limit, which will have an adverse effect on the battery life and the safety and stability of the system; when the set frequency is too high, assuming it is 0.2Hz, when the electrolytic cell temperature is low, it will not be able to follow and cause energy waste, and may also affect its operating stability.

[0192] In contrast, the adaptive Butterworth filter proposed in the present invention can ensure that the SOC is within a safe range and the cutoff frequency ensures that the electrolyzer follows its power input as closely as possible.

[0193] After comparison and verification, the advantages and effects of the present invention over the prior art are mainly reflected in the following aspects: (1) improving the overall efficiency of the energy storage system and reducing unnecessary energy loss; (2) extending the battery life and avoiding performance degradation caused by overcharging or over-discharging; (3) improving the responsiveness of the electrolyzer so that it can better adapt to power fluctuations and improve the operating stability of the system; (4) enhancing the adaptability of the system so that it can achieve optimal power allocation under different operating conditions and improve the economic benefits and reliability of the energy storage system.

[0194] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art may make various modifications or variations within the scope of the claims, which do not affect the essence of the present invention.

Claims

1. A fuzzy adaptive electric-hydrogen hybrid energy storage power allocation method, characterized in that: In a microgrid system including an electric-hydrogen hybrid energy storage system, a Butterworth low-pass filter with adaptively adjusted parameters is used to filter the power to be absorbed, and the low-frequency power and the filtered power are respectively allocated to the hydrogen production electrolyzer and the energy storage battery to realize the joint absorption of the electric-hydrogen hybrid energy storage; in the filtering process, the electrolyzer temperature and the battery SOC are used as input variables and the cutoff frequency of the filter is obtained based on fuzzy inference rules, and the parameters of the filter are adaptively adjusted to maintain the electrolyzer absorption power and the battery SOC within a preset range.

2. The method according to claim 1, characterized in that The method of obtaining the cutoff frequency of the filter based on the fuzzy inference rule specifically includes the following operations: (1) Define the input variables as the electrolyzer temperature and the battery SOC, and define the output variable as the filter cutoff frequency ; (2) defining membership functions, including the electrolytic cell temperature membership function, the SOC membership function, and the filter cutoff frequency membership function; (3) Define the evaluation rules in the rule base; (4) Merge the output fuzzy sets of the rules, and use the centroid method to defuzzify the merged membership function to obtain the final filter cutoff frequency .

3. The method according to claim 2, characterized in that The range of the electrolytic cell temperature is arrive , the battery SOC ranges from 0 to 1; The electrolytic cell temperature membership function includes a low temperature membership function, a medium temperature membership function and a high temperature membership function, wherein: Low temperature membership function : °C to °C, triangular function, peak at °C; ； Medium temperature membership function : °C to °C, triangular function, peak at °C; ； High temperature membership function : °C to °C, triangular function, peak at °C; ； The SOC membership function includes a low SOC membership function, a moderate SOC membership function and a high SOC membership function, wherein: Low SOC membership function : 0% to 70%, triangular function, peak at 0%; ； Moderate SOC membership function : 40% to 60%, triangular function, peak at 50%; ； High SOC membership function : 30% to 100%, triangular function, peak at 100%; ； The cutoff frequency membership function of the filter includes a low cutoff frequency membership function, a medium cutoff frequency membership function and a high cutoff frequency membership function, wherein: Low cutoff frequency membership function : Hz to Hz, triangular function, peak at Hz; ； Medium cutoff frequency membership function : Hz to Hz, triangular function, peak at Hz; ； High cutoff frequency membership function : Hz to Hz, triangular function, peak at Hz; ； The rule base includes the following evaluation rules: Rule 1: If the cell temperature is high and the SOC is moderate, then the filter cutoff frequency is high; the output high cutoff frequency membership generated by this rule is: ; Rule 2: If the cell temperature is low and the SOC is moderate, then the filter cutoff frequency is low; the output low cutoff frequency membership generated by this rule is: ; Rule 3: If the cell temperature is high and the SOC is low or high, then the filter cutoff frequency is moderate; the output moderate cutoff frequency membership generated by this rule is: ; Rule 4: If the cell temperature is low and the SOC is low or high, then the filter cutoff frequency is low; the output low cutoff frequency membership generated by this rule is: ; Rule 5: If the cell temperature is moderate and the SOC is low or high, then the filter cutoff frequency is low; this rule produces an output moderate cutoff frequency membership of: ; Rule 6: If the cell temperature is moderate and the SOC is moderate, then the filter cutoff frequency is moderate; the output moderate cutoff frequency membership generated by this rule is: ; The membership function after merging the output fuzzy sets is: ； The final filter cutoff frequency obtained by defuzzification using the centroid method As shown below: ； In the above formulas, T is the electrolytic cell temperature; S is the battery SOC; f is the filter cutoff frequency; and are the filter cutoff frequencies corresponding to the lower and upper operating temperature limits of the electrolytic cell respectively; It refers to the rule intersection operation; It refers to the rule merging operation; and are the lower and upper limits of the cutoff frequency range respectively.

4. The method according to claim 3, characterized in that The membership function after merging the output fuzzy sets is a piecewise function, which can be further The segmented intervals are split, and the cutoff frequencies of the segmented intervals are calculated; Assumptions exist There are different expressions on , then: ； ； in, for In the interval The expression on The total number of different expressions.

5. The method according to claim 3, characterized in that: The frequency response test of the rated voltage step input of the electrolyzer in the electric-hydrogen hybrid energy storage system is carried out at the lower limit of the electrolyzer operating temperature. and upper operating temperature limit Carry out step response experiments respectively; identify data points and analyze frequency response of the experimental results to obtain the cutoff frequency corresponding to the lower and upper operating temperature limits of the electrolyzer and .

6. The method according to claim 5, characterized in that Get the cutoff frequency corresponding to the lower and upper operating temperature limits of the electrolytic cell and The methods include: (1) Multiply the voltage and current output by the electrolyzer to obtain the power curve: ； in, is the voltage value of the voltage data point, is the current value of the current data point, for time point; (2) Using image processing software to read the step response results in the power curve to obtain step response data; (3) The step response data was used as the input of the MATLAB software and the tfest function was used for fitting. The number of zeros was set to 1, and the number of poles was set to 2 and 1 for fitting respectively. The one with the best fitting effect, i.e., R 2 The number of poles closest to 1, to obtain the fitted transfer function; If the number of poles is 1 and the number of zeros is 1, then the transfer function is: ； in, is the zero position, is the pole position, is the transfer function gain factor; If the number of poles is 2 and the number of zeros is 1, the general formula of its transfer function is: ； in, is the zero position, and is the pole position, is the transfer function gain factor; According to the above fitting process, the parameter values ​​in the general formula of the transfer function are determined; (4) Based on the fitted transfer function, the Bode function is used to draw the Bode plot. According to the logarithmic amplitude-frequency characteristics of the Bode plot, the frequency corresponding to the gain attenuation of -3dB is found, that is, the cut-off frequencies corresponding to the lower and upper operating temperatures of the electrolytic cell are obtained. and .

7. The method according to any one of claims 1 to 6, characterized in that During the operation of the electric-hydrogen hybrid energy storage system, the temperature of the electrolyzer and the SOC of the battery are measured regularly according to the set time intervals; The latest measurement data is used to calculate the cutoff frequency corresponding to the lower and upper operating temperature limits of the electrolyzer. and , and then further recalculate the cutoff frequency of the filter according to the fuzzy inference rule, thereby realizing adaptive adjustment of the filter parameters.

8. The method according to claims 1 to 6, characterized in that The cutoff frequency obtained based on the fuzzy inference rule is suitable for an analog Butterworth filter, and needs to be further converted into a digital filter using a bilinear transformation; specifically, The transfer function of the analog Butterworth filter is: ； ； In the formula, is the angular frequency of the transfer function; is the filter cutoff frequency obtained based on fuzzy inference rules; s is the complex frequency variable used for Laplace transform; make , substituting into the above transfer function, the transfer function of the digital filter is obtained as follows: ； Where M is the sampling period; , ; z is the complex variable used for z-transformation.

9. The method according to any one of claims 1 to 6, characterized in that The hydrogen production electrolyzer is an alkaline solution electrolyzer; the energy storage battery is a lithium battery cell, or a battery pack composed of multiple lithium battery cells.

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