Energy management and control method and equipment for electricity-hydrogen hybrid micro-grid based on fuel cell

Abstract

The invention relates to an electricity-hydrogen hybrid micro-grid energy management and control method and equipment based on a fuel cell. The method comprises the following steps: acquiring first required power of a load, the hydrogen storage content of a hydrogen production machine in the electricity-hydrogen hybrid energy storage system and the charge state of a lithium battery, and performing fuzzy processing on the hydrogen storage content, the charge state and the first required power to obtain first output power of a first energy supply device in the electricity-hydrogen hybrid energy storage system; and based on the first demand power and the first output power, determining second demand power of the load, and performing fuzzy processing on the hydrogen storage content, the state of charge and the second demand power to obtain second output power of a second energy supply device in the electricity-hydrogen hybrid energy storage system. According to the embodiment of the invention, aiming at a micro-grid system formed by photovoltaic solar energy, a fuel cell subsystem, a lithium battery and a hydrogen production machine, a double-layer fuzzy logic strategy is adopted, and energy scheduling among various energy sources is realized, so that the required power of a user side load is met.

Description

Technical Field

[0001] The present application relates to the field of new energy technology, and in particular to a method and device for energy management and control of an electric-hydrogen hybrid microgrid based on a fuel cell. Background Art

[0002] Due to the continued deterioration of the ecological environment, the integrated utilization of clean energy sources such as hydrogen and solar energy in energy systems has become a research hotspot. For example, electric-hydrogen hybrid energy storage systems (HESSs) leverage the reliability and flexibility of microgrid systems. However, to achieve the complementary advantages of hybrid energy storage systems (HESSs) and improve their performance, reliability, and service life, effective energy management strategies (EMSs) are required to regulate HESS power flow to meet load demands.

[0003] Therefore, the present application urgently needs an energy management strategy that can adjust the power flow of the HESS to meet the load demand. Summary of the Invention

[0004] Based on this, it is necessary to provide an energy management method and device for a fuel cell-based electric-hydrogen hybrid microgrid that can adjust the power flow of the HESS to meet the load demand in order to address the above technical problems.

[0005] In a first aspect, the present application provides a method for energy management and control of an electric-hydrogen hybrid microgrid based on a fuel cell, comprising:

[0006] Obtaining the first required power of the load, the hydrogen storage content of the hydrogen generator in the electric-hydrogen hybrid energy storage system, and the state of charge of the lithium battery;

[0007] Performing fuzzy processing on the hydrogen storage content, state of charge, and first required power to obtain a first output power of a first energy supply device in the electric-hydrogen hybrid energy storage system; the first energy supply device includes a fuel cell subsystem;

[0008] determining a second required power of the load based on the first required power and the first output power;

[0009] The hydrogen storage content, the state of charge and the second required power are fuzzy processed to obtain the second output power of the second energy supply device in the electric-hydrogen hybrid energy storage system; the second energy supply device includes a lithium battery and the hydrogen generator.

[0010] In one embodiment, the method further comprises:

[0011] determining an operating mode of the first energy supply device according to a first output power of the first energy supply device and a power range of a preset operating mode;

[0012] Based on the working mode, the first energy supply device is controlled to output the first output power.

[0013] In one embodiment, controlling the first energy supply device to output the first output power based on the operating mode includes:

[0014] When the working mode is a single stack mode, obtaining a first performance value of each container in the fuel cell subsystem, and taking a container corresponding to the best first performance value as a target container;

[0015] The fuel cell stack with the longest remaining life in the target container is used as the target fuel cell stack;

[0016] The target fuel cell stack is controlled to output the first output power.

[0017] In one embodiment, controlling the first energy supply device to output the first output power based on the operating mode includes:

[0018] When the working mode is a single container mode, determining the required number of fuel cell stacks according to the first output power of the first energy supply device;

[0019] For each container in the fuel cell subsystem, determining a first fuel cell stack combination corresponding to each container according to the quantity;

[0020] Obtaining attenuation values ​​corresponding to each of the first fuel cell stack combinations, and using a container corresponding to a minimum attenuation value as a target container;

[0021] Determining a target fuel cell combination from the first fuel cell combinations in the target container according to the remaining life, hydrogen consumption, and attenuation value of each first fuel cell combination in the target container;

[0022] The battery stack corresponding to the target battery stack combination is controlled to output the first output power.

[0023] In one embodiment, the method further comprises:

[0024] When the working mode is the full container mode, determining a second stack combination corresponding to the fuel cell subsystem according to the quantity;

[0025] Determining a target fuel cell stack combination from each of the second fuel cell stack combinations according to the remaining life, hydrogen consumption, and attenuation value of the second fuel cell stack combinations corresponding to the fuel cell subsystem;

[0026] The battery stack corresponding to the target battery stack combination is controlled to output the first output power.

[0027] In one embodiment, the method further comprises:

[0028] When the working mode is the mixed container mode, arbitrarily combining the containers in the fuel cell subsystem to determine initial container combinations, and obtaining second performance values ​​of the initial container combinations;

[0029] taking the best performance value among the first performance values ​​and the second performance values ​​as the target performance value;

[0030] The container corresponding to the target performance value or each container in the initial container combination is used as the target container;

[0031] Control the fuel cell stack corresponding to the target container to output the first output power.

[0032] In one embodiment, the fuzzy processing of the hydrogen storage content, the state of charge, and the first required power to obtain the first output power of the first energy supply device in the electric-hydrogen hybrid energy storage system includes:

[0033] Performing fuzzy quantization processing on the hydrogen storage content, the state of charge, and the first required power using an optimization algorithm and a corresponding first membership function to obtain a corresponding first fuzzy value;

[0034] Performing fuzzy control processing based on each of the first fuzzy values ​​and a first preset fuzzy rule to obtain a second fuzzy value;

[0035] The second fuzzy value is defuzzified to obtain a first output power of the first energy supply device.

[0036] In one embodiment, the fuzzy processing of the hydrogen storage content, the state of charge, and the second required power to obtain the second output power of the second energy supply device in the electric-hydrogen hybrid energy storage system includes:

[0037] Performing fuzzy quantization processing on the hydrogen storage content, the state of charge, and the second required power using an optimization algorithm and a corresponding second membership function to obtain a corresponding third fuzzy value;

[0038] Performing fuzzy control processing based on each of the third fuzzy values ​​and the corresponding second preset fuzzy rule to obtain a fourth fuzzy value and a fifth fuzzy value;

[0039] The fourth fuzzy value and the fifth fuzzy value are defuzzified respectively to obtain the second output power of the lithium battery and the second output power of the hydrogen generator.

[0040] In a second aspect, the present application also provides an energy management and control device for an electric-hydrogen hybrid microgrid based on a fuel cell, comprising:

[0041] An acquisition module is used to obtain a first required power of the load, a hydrogen storage content of the hydrogen generator in the electric-hydrogen hybrid energy storage system, and a state of charge of the lithium battery;

[0042] a first fuzzy processing module, configured to perform fuzzy processing on the hydrogen storage content, the state of charge, and the first required power to obtain a first output power of a first energy supply device in the electric-hydrogen hybrid energy storage system; the first energy supply device includes a fuel cell subsystem;

[0043] a first determining module, configured to determine a second required power of the load based on the first required power and the first output power;

[0044] The second fuzzy processing module is used to perform fuzzy processing on the hydrogen storage content, the state of charge and the second required power to obtain the second output power of the second energy supply device in the electric-hydrogen hybrid energy storage system; the second energy supply device includes a lithium battery and the hydrogen generator.

[0045] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the method steps provided in the first aspect when executing the computer program.

[0046] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which implements the method steps provided in the first aspect when the computer program is executed by a processor.

[0047] In a fifth aspect, the present application also provides a computer program product, comprising a computer program, which implements the method steps provided in the first aspect when executed by a processor.

[0048] The above-mentioned fuel cell-based electric-hydrogen hybrid microgrid energy management and control method and device obtains the first required power of the load, the hydrogen storage content of the hydrogen generator in the electric-hydrogen hybrid energy storage system, and the state of charge of the lithium battery, performs fuzzy processing on the hydrogen storage content, state of charge, and the first required power, and obtains the first output power of the first energy supply device in the electric-hydrogen hybrid energy storage system. Based on the first required power and the first output power, the second required power of the load is determined, and the hydrogen storage content, state of charge, and second required power are fuzzy processed to obtain the second output power of the second energy supply device in the electric-hydrogen hybrid energy storage system; the first energy supply device includes a fuel cell subsystem, and the second energy supply device includes a lithium battery and a hydrogen generator. The embodiment of the present application adopts a two-layer fuzzy logic strategy for a microgrid system composed of photovoltaic solar energy, a fuel cell subsystem, a lithium battery, and a hydrogen generator to realize energy scheduling between various energy sources to meet the required power of the user-side load. Moreover, while maximizing the utilization of renewable energy, it reduces hydrogen consumption, improves the efficiency of clean energy use, and provides a green and efficient solution for ensuring the power demand on the user side. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0050] Figure 1 This is a diagram of an application environment for a fuel cell-based electric-hydrogen hybrid microgrid energy management and control method according to an embodiment;

[0051] Figure 2 1 is a flow chart of a method for energy management and control of an electric-hydrogen hybrid microgrid based on a fuel cell in one embodiment;

[0052] Figure 3 is a schematic diagram of photovoltaic solar power generation in one embodiment;

[0053] Figure 4 1 is a flow chart of a first output power output method in one embodiment;

[0054] Figure 5 is a schematic diagram of the net output power and operating efficiency of a fuel cell subsystem in one embodiment;

[0055] Figure 6 is a schematic diagram of power ranges of a preset working mode in one embodiment;

[0056] Figure 71 is a flow chart of a method for determining a first output power in one embodiment;

[0057] Figure 8 This is a structural block diagram of an energy management and control device for an electric-hydrogen hybrid microgrid based on a fuel cell in one embodiment;

[0058] Figure 9 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0060] The energy management method of the electric-hydrogen hybrid microgrid based on fuel cells provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown, the electric-hydrogen hybrid microgrid system includes an electric-hydrogen hybrid energy storage system, a microgrid system, and a computer device (device for implementing double-layer fuzzy control). The electric-hydrogen hybrid energy storage system includes a photovoltaic module, a fuel cell (fuel cell subsystem), a lithium battery, and an electrolyzer (hydrogen generator). The computer device obtains a first power demand of the load in the microgrid, the hydrogen storage content of the hydrogen generator in the electric-hydrogen hybrid energy storage system, and the state of charge of the lithium battery. It performs fuzzy processing on the hydrogen storage content, state of charge, and first power demand to obtain a first output power of a first energy supply device in the electric-hydrogen hybrid energy storage system. Based on the first power demand and the first output power, it determines a second power demand of the load. It performs fuzzy processing on the hydrogen storage content, state of charge, and second power demand to obtain a second output power of a second energy supply device in the electric-hydrogen hybrid energy storage system. The first energy supply device includes a fuel cell subsystem, and the second energy supply device includes a lithium battery and a hydrogen generator.

[0061] In an exemplary embodiment, Figure 2 As shown, a method for energy management and control of an electric-hydrogen hybrid microgrid based on a fuel cell is provided, comprising the following steps S201 to S204.

[0062] S201, obtaining a first required power of a load, a hydrogen storage content of a hydrogen generator in an electric-hydrogen hybrid energy storage system, and a state of charge of a lithium battery.

[0063] The computer device obtains the first required power Preq, the hydrogen storage content (HSC), and the state of charge (SOC) of the lithium battery. The first required power Preq is the total required power of the load minus the power generated by the photovoltaic module. The photovoltaic module can be a photovoltaic solar cell. The power generation of photovoltaic solar cell depends on the light intensity and the ambient temperature. Since the light intensity and the ambient temperature show seasonal changes, the power generation of photovoltaic solar cell is obtained based on historical data, such as Figure 3 Show.

[0064] S202, fuzzy processing is performed on the hydrogen storage content, the state of charge, and the first required power to obtain a first output power of a first energy supply device in the electric-hydrogen hybrid energy storage system; the first energy supply device includes a fuel cell subsystem.

[0065] Optionally, the fuel cell subsystem may be a multi-stack fuel cell system (MFCS).

[0066] In the embodiment of the present application, the optimization algorithm and the corresponding first membership function are used to perform fuzzy quantization processing on the hydrogen storage content, the state of charge, and the first required power to obtain the corresponding first fuzzy value. Based on each first fuzzy value and the first preset fuzzy rule, fuzzy control processing is performed to obtain a second fuzzy value. The second fuzzy value is defuzzified to obtain the first output power P of the first energy supply device. MFCS Optionally, the fuzzy set of hydrogen storage content can be divided into four levels: very low / low / medium / high; the fuzzy set of state of charge can be divided into three levels: low / medium / high; and the fuzzy set of first required power can be divided into five levels: very low / low / medium / high / very high.

[0067] In a possible implementation, fuzzy mapping rules between different collected data and fuzzy values ​​can also be pre-set. The corresponding fuzzy mapping rules are used to perform fuzzy processing on the hydrogen storage content, the state of charge, and the first required power to obtain a first fuzzy value, and then fuzzy control processing is performed based on each first fuzzy value and the first preset fuzzy rule to obtain a second fuzzy value. The second fuzzy value is defuzzified to obtain the first output power P of the first energy supply device. MFCS .

[0068] In another possible implementation, a neural network model can be used to automatically learn the mapping of hydrogen storage content, state of charge, and first required power to a fuzzy set to obtain a first fuzzy value. Fuzzy control processing is then performed based on each first fuzzy value and a first preset fuzzy rule to obtain a second fuzzy value. The second fuzzy value is then defuzzified to obtain the first output power P of the first energy supply device. MFCS.

[0069] S203: Determine a second required power of the load based on the first required power and the first output power.

[0070] In the embodiment of the present application, the difference between the first required power and the first output power is used as the second required power Prem.

[0071] S204, performing fuzzy processing on the hydrogen storage content, the state of charge, and the second required power to obtain a second output power of a second energy supply device in the electric-hydrogen hybrid energy storage system; the second energy supply device includes a lithium battery and a hydrogen generator.

[0072] In the embodiment of the present application, the optimization algorithm and the corresponding second membership function are used to perform fuzzy quantization processing on the hydrogen storage content, the state of charge, and the second required power to obtain the corresponding third fuzzy value; based on each third fuzzy value and the corresponding second preset fuzzy rule, fuzzy control processing is performed to obtain a fourth fuzzy value and a fifth fuzzy value; the fourth fuzzy value and the fifth fuzzy value are defuzzified respectively to obtain the second output power P of the lithium battery bat and the second output power P of the hydrogen generator EC .

[0073] Similarly, the embodiment of the present application may also adopt the other two implementation methods of the above-mentioned S202 to obtain the third fuzzy value.

[0074] If the photovoltaic module's generated power exceeds the primary power requirement of the current load, or if there's no need for energy from the lithium battery or hydrogen generator, the lithium battery must maintain a minimum state of charge to ensure the hybrid energy storage system can readily respond to fluctuations or failures. If the lithium battery's state of charge is less than 20%, excess energy is prioritized for charging the lithium battery. If the hydrogen storage content is less than 20%, excess energy is prioritized for hydrogen storage.

[0075] In the above-mentioned fuel cell-based electric-hydrogen hybrid microgrid energy control method, the first required power of the load, the hydrogen storage content of the hydrogen generator in the electric-hydrogen hybrid energy storage system, and the state of charge of the lithium battery are obtained, and the hydrogen storage content, state of charge and first required power are fuzzy processed to obtain the first output power of the first energy supply device in the electric-hydrogen hybrid energy storage system. Based on the first required power and the first output power, the second required power of the load is determined, and the hydrogen storage content, state of charge and second required power are fuzzy processed to obtain the second output power of the second energy supply device in the electric-hydrogen hybrid energy storage system; the first energy supply device includes a fuel cell subsystem, and the second energy supply device includes a lithium battery and a hydrogen generator. The embodiment of the present application adopts a two-layer fuzzy logic strategy for a microgrid system composed of photovoltaic solar energy, a fuel cell subsystem, a lithium battery and a hydrogen generator to realize energy scheduling between various energy sources to meet the required power of the user-side load. Moreover, while maximizing the utilization of renewable energy, it reduces hydrogen consumption, improves the efficiency of clean energy use, and provides a green and efficient solution for ensuring the power demand on the user side.

[0076] Figure 4 FIG. 1 is a flow chart of a first output power output method in an embodiment, as shown in FIG. Figure 4 As shown, the following steps are included:

[0077] S401 : Determine an operating mode of the first energy supply device according to a first output power of the first energy supply device and a power range of a preset operating mode.

[0078] In the embodiment of the present application, the relationship between the net output power and the operating efficiency of the fuel cell subsystem is as follows: Figure 5 As shown in the figure, when the operating efficiency of the fuel cell subsystem is above 50%, it is defined as the high efficiency region (HER). At this time, the net output power of the fuel cell subsystem is .

[0079] Among them, the preset working modes include single stack mode, single container mode, full container mode and mixed container mode. Figure 6 As shown, the power range of the single stack mode is set to: ;The power range of single container mode is: ; The power range of the full container mode is ; The power range of the hybrid container mode is: .in, is the output power of the fuel cell subsystem; The maximum output power of the fuel cell subsystem within the optimal efficiency range; is the minimum output power of the fuel cell subsystem within the optimal efficiency range; is the maximum output power of the fuel cell subsystem; numbox is the number of operating fuel cell stacks.

[0080] In the embodiment of the present application, based on the first output power of the fuel cell subsystem, it is determined in which of the above power intervals the first output power is located, thereby determining the operating mode of the fuel cell subsystem.

[0081] S402: Based on the working mode, control the first energy supply device to output a first output power.

[0082] In the embodiment of the present application, the first energy supply device includes a fuel cell subsystem, which is composed of M containers, each of which contains N fcs The first output power is composed of n fuel cell stacks. The actual operation of the first stack combination is If the operating mode is single-stack mode, that is, n is 1, the container with the best performance is selected to operate a single stack to output the first output power. If the operating mode is single-container mode, the container with the best performance is selected to operate multiple stacks to output the first output power. If the operating mode is mixed container mode, the single container or multi-container combination with the best performance is selected from each single container and multi-container combination to output the first output power. If the operating mode is full container mode, multiple stacks are selected from all containers to output the first output power.

[0083] Specifically, based on the working mode, controlling the first energy supply device to output the first output power includes the following steps:

[0084] Step 1: When the working mode is single stack mode, obtain the first performance value of each container in the fuel cell subsystem, and use the container corresponding to the optimal first performance value as the target container; use the stack with the longest remaining life in the target container as the target stack; control the target stack to output the first output power.

[0085] In the embodiment of the present application, the first performance value is based on the performance consistency of the container , the remaining useful life of the container and the attenuation value of the container Sure, , a, b, c are weight coefficients.

[0086] The performance consistency of the container is specifically expressed as: ;

[0087] The remaining useful life of a container is specifically expressed as: ;

[0088] The attenuation value of a container is the attenuation value of different battery stack combinations during operation, that is, the attenuation value corresponding to different battery stack combinations. It is specifically expressed as follows (taking the container containing two battery stacks as an example): ; ;in, is the average attenuation value of the battery stack in the container; represents the total number of battery stacks in a single container; is the attenuation value of the f-th battery stack; is the attenuation value of the stack at the end of its life; The attenuation value of the battery stack that provides the least power in the container; is the initial impedance; is the attenuation value of all stacks in each of the H stack combinations in the next stage; is the attenuation value of all stacks in the next stage; h is the hth stack combination among H stack combinations; n is the number of running stacks; stack is the stack; R is the current attenuation impedance; con1 is the first condition; con2 is the second condition; o is the stack o in the container; p is the stack p in the container; t is the time; is the operating status of the battery stack o in the container; It is the operating status of the battery stack p in the container.

[0089] In the single stack mode, the first performance value of each container is obtained based on the above formula, the container corresponding to the optimal first performance value is used as the target container, the stack with the longest life is obtained from the target container as the target stack, and the target stack is controlled to output the first output power.

[0090] Step 2: When the working mode is single container mode, determine the required number of fuel cell stacks according to the first output power of the first energy supply device; for each container in the fuel cell subsystem, determine the first fuel cell stack combination corresponding to each container according to the number; obtain the attenuation value corresponding to each first fuel cell stack combination, and use the container corresponding to the minimum attenuation value as the target container; determine the target fuel cell stack combination from the first fuel cell stack combinations in the target container according to the remaining life, hydrogen consumption and attenuation value of each first fuel cell stack combination in the target container; control the fuel cell stack corresponding to the target fuel cell stack combination to output the first output power.

[0091] In the embodiment of the present application, the number of battery stacks required for determining the first output power is n, and the first battery stack combinations corresponding to each container have a total of According to the above method for determining the attenuation value of the container, the attenuation value corresponding to each first stack combination under each container is obtained, and the container corresponding to the minimum attenuation value is used as the target container.

[0092] After obtaining the target container, for each first stack combination under the target container, the performance value of the first stack combination is determined according to the remaining life, hydrogen consumption and attenuation value of the first stack combination. ,in, is the remaining life of the first stack combination, is the hydrogen consumption of the corresponding first fuel cell combination; is the attenuation value of the corresponding first stack combination, and d, e, and f are weight coefficients.

[0093] Each fuel cell stack in the first fuel cell combination corresponding to the optimal performance value is used as a target fuel cell stack, and the target fuel cell stack is controlled to output a first output power.

[0094] Step three: When the working mode is the full container mode, determine the second stack combination corresponding to the fuel cell subsystem according to the quantity; determine the target stack combination from each of the second stack combinations according to the remaining life, hydrogen consumption and attenuation value of the second stack combination corresponding to the fuel cell subsystem; control the stack corresponding to the target stack combination to output the first output power.

[0095] In the embodiment of the present application, due to the large power generation capacity in the full container mode, it is not necessary to determine the working state of the container, and multiple stacks are selected from all containers as the target stacks for current power generation. Therefore, in the above step 2, the target first stack combination is determined from each first stack combination under the target container, while in the embodiment of the present application, the second stack combination is determined from the stacks under all containers according to the number of required stacks. The second stack combination includes The corresponding performance value is determined according to the second battery stack combination to obtain the target battery stack combination corresponding to the optimal performance value, and the battery stack corresponding to the target battery stack combination is controlled to output the first output power.

[0096] Step 4: When the working mode is the mixed container mode, arbitrarily combine the containers in the fuel cell subsystem, determine the initial container combinations, and obtain the second performance value of each initial container combination; use the optimal performance value among the first performance values ​​and the second performance values ​​as the target performance value; use the container corresponding to the target performance value or each container in the initial container combination as the target container; control the stack corresponding to the target container to output the first output power.

[0097] In the embodiment of the present application, the containers in the fuel cell subsystem are randomly combined to determine the initial container combinations. For example, if there are two containers, the initial container combination includes one; if there are three containers, the initial container combination includes three, namely, the combination of container 1 and container 2, the combination of container 2 and container 3, and the combination of container 1, container 2, and container 3.

[0098] Based on the above-mentioned method of obtaining the first performance value, the second performance value corresponding to each initial container combination is obtained respectively, and the optimal performance value is selected from each first performance value and each second performance value as the target performance value. The target performance value may correspond to a single container or a container combination. A single container or each container in the container combination is used as the target container, and the battery stack corresponding to the target container is controlled to output the first output power.

[0099] In an embodiment of the present application, the operating mode of the first energy supply device is determined based on the first output power of the first energy supply device and the power range of the preset operating mode; based on the operating mode, the first energy supply device is controlled to output the first output power. In an embodiment of the present application, for a fuel cell subsystem composed of a container-type multi-stack group, the performance index is determined based on the attenuation consistency, remaining life, and attenuation value between the stacks within the container to determine a template container. Based on the determination of the upper target container, the optimal operating state combination and power distribution for the next stage are determined taking into account the attenuation value, hydrogen consumption, and remaining life of the stacks, thereby achieving energy distribution between the fuel cell subsystems.

[0100] Figure 7 FIG. 1 is a flow chart of a method for determining a first output power in an embodiment. Figure 7 As shown, the embodiment of the present application relates to a possible implementation method of how to perform fuzzy processing on the hydrogen storage content, the state of charge, and the first required power to obtain the first output power of the first energy supply device in the electric-hydrogen hybrid energy storage system, including the following steps:

[0101] S701 , using an optimization algorithm and a corresponding first membership function to perform fuzzy quantization processing on the hydrogen storage content, the state of charge, and the first required power to obtain a corresponding first fuzzy value.

[0102] Optionally, the first membership function can be a Gaussian membership function, a generalized bell-shaped membership function, a triangular membership function, a trapezoidal membership function, a bilateral Gaussian membership function, etc. Since the power generated by the photovoltaic module may be greater than the first required power of the current load, the range of the first membership function is [-1, 1]. Considering the simplicity and fast response of the algorithm, the Gaussian membership function can be preferably used as the first membership function:

[0103] In an embodiment of the present application, the hydrogen storage content, the state of charge, and the first required power each correspond to a first membership function. The center point of the first membership function is adaptively shifted based on the first required power, the hydrogen storage content, and the state of charge of the lithium battery. The standard deviation of the first membership function is positively correlated with the variance of the first required power.

[0104] Taking the efficiency of the hydrogen generator, the cycle life of the lithium battery and the loss rate of the fuel cell subsystem as the objective functions, the non-dominated sorting genetic algorithm II (NSGA-II) is used to optimize the center point and standard deviation of the first membership function, thereby obtaining the first fuzzy values ​​corresponding to the hydrogen storage content, state of charge and the first required power.

[0105] S702 , performing fuzzy control processing based on each first fuzzy value and a first preset fuzzy rule to obtain a second fuzzy value.

[0106] Optionally, the second fuzzy value may also be classified as low / medium / high etc.

[0107] The first preset fuzzy rule may be a correspondence between the fuzzy value of the hydrogen storage content, the fuzzy value of the state of charge, the fuzzy value of the first required power, and the fuzzy value of the first energy supply device. For example, the fuzzy value of the hydrogen storage content is low, the fuzzy value of the state of charge is low, the fuzzy value of the first required power is low, and the fuzzy value of the first energy supply device is high.

[0108] Optionally, the first fuzzy rule may also be in other forms, such as a neural network.

[0109] In the embodiment of the present application, fuzzy control processing is performed based on each first fuzzy value and the first preset fuzzy rule to obtain a second fuzzy value.

[0110] S703: Defuzzify the second fuzzy value to obtain a first output power of the first energy supply device.

[0111] In the embodiment of the present application, the second fuzzy value can be defuzzified using a truncated centroid method to obtain the first output power of the first energy supply device. The truncated centroid method achieves defuzzification by calculating the centroid of the first membership function. This method is very intuitive and has good performance, and is particularly suitable for output conversion of complex fuzzy control systems.

[0112] In an embodiment of the present application, an optimization algorithm and a corresponding first membership function are used to perform fuzzy quantization processing on the hydrogen storage content, the state of charge, and the first required power to obtain corresponding first fuzzy values. Fuzzy control processing is performed based on each first fuzzy value and a first preset fuzzy rule to obtain a second fuzzy value. The second fuzzy value is defuzzified to obtain the first output power of the first energy supply device, thereby improving the accuracy of determining the first output power.

[0113] In one embodiment, fuzzy processing is performed on the hydrogen storage content, the state of charge, and the second required power to obtain the second output power of the second energy supply device in the electric-hydrogen hybrid energy storage system, including:

[0114] An optimization algorithm and a corresponding second membership function are used to perform fuzzy quantization processing on the hydrogen storage content, the state of charge, and the second required power to obtain a corresponding third fuzzy value; fuzzy control processing is performed based on each third fuzzy value and the corresponding second preset fuzzy rule to obtain a fourth fuzzy value and a fifth fuzzy value; the fourth fuzzy value and the fifth fuzzy value are respectively defuzzified to obtain the second output power of the lithium battery and the second output power of the hydrogen generator.

[0115] For specific implementation methods, please refer to the above Figure 7 Corresponding embodiments.

[0116] It should be understood that, although the steps in the flowcharts of the above embodiments are shown in sequence as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be performed in other orders. Moreover, at least a portion of the steps in the flowcharts of the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these steps or stages is not necessarily to be performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0117] Based on the same inventive concept, the embodiments of the present application also provide a fuel cell-based electric-hydrogen hybrid microgrid energy control device for implementing the above-mentioned fuel cell-based electric-hydrogen hybrid microgrid energy control method. The implementation solution provided by the device is similar to the implementation solution described in the above-mentioned method. Therefore, the specific limitations of one or more fuel cell-based electric-hydrogen hybrid microgrid energy control device embodiments provided below can be found in the above-mentioned limitations of the fuel cell-based electric-hydrogen hybrid microgrid energy control method, and will not be repeated here.

[0118] In an exemplary embodiment, Figure 8 As shown, an energy management and control device for an electric-hydrogen hybrid microgrid based on a fuel cell is provided, comprising: an acquisition module 11, a first fuzzy processing module 12, a first determination module 13 and a second fuzzy processing module 14, wherein:

[0119] An acquisition module 11 is configured to acquire a first required power of a load, a hydrogen storage content of a hydrogen generator in an electric-hydrogen hybrid energy storage system, and a state of charge of a lithium battery;

[0120] A first fuzzy processing module 12 is configured to perform fuzzy processing on the hydrogen storage content, the state of charge, and the first required power to obtain a first output power of a first energy supply device in the electric-hydrogen hybrid energy storage system; the first energy supply device includes a fuel cell subsystem;

[0121] A first determining module 13 is configured to determine a second required power of the load based on the first required power and the first output power;

[0122] The second fuzzy processing module 14 is used to perform fuzzy processing on the hydrogen storage content, the state of charge and the second required power to obtain the second output power of the second energy supply device in the electric-hydrogen hybrid energy storage system; the second energy supply device includes a lithium battery and a hydrogen generator.

[0123] In one embodiment, the apparatus further comprises:

[0124] a second determining module, configured to determine an operating mode of the first energy supply device according to the first output power of the first energy supply device and a power range of a preset operating mode;

[0125] The control module is used to control the first energy supply device to output a first output power based on the working mode.

[0126] In one embodiment, the control module is specifically used to obtain the first performance value of each container in the fuel cell subsystem when the working mode is a single stack mode, and use the container corresponding to the optimal first performance value as the target container; use the stack with the longest remaining life in the target container as the target stack; and control the target stack to output the first output power.

[0127] In one embodiment, the control module is specifically used to determine the required number of fuel cell stacks according to the first output power of the first energy supply device when the working mode is a single container mode; for each container in the fuel cell subsystem, determine the first fuel cell stack combination corresponding to each container according to the number; obtain the attenuation value corresponding to each first fuel cell stack combination, and use the container corresponding to the minimum attenuation value as the target container; determine the target fuel cell stack combination from the first fuel cell stack combinations in the target container according to the remaining life, hydrogen consumption and attenuation value of each first fuel cell stack combination in the target container; and control the fuel cell stack corresponding to the target fuel cell stack combination to output the first output power.

[0128] In one embodiment, the control module is specifically used to determine the second stack combination corresponding to the fuel cell subsystem according to the quantity when the working mode is the full container mode; determine the target stack combination from each of the second stack combinations according to the remaining life, hydrogen consumption and attenuation value of the second stack combination corresponding to the fuel cell subsystem; and control the stack corresponding to the target stack combination to output the first output power.

[0129] In one embodiment, the control module is specifically used to arbitrarily combine the containers in the fuel cell subsystem when the working mode is a mixed container mode, determine the initial container combinations, and obtain the second performance value of each initial container combination; use the optimal performance value among the first performance values ​​and the second performance values ​​as the target performance value; use the container corresponding to the target performance value or each container in the initial container combination as the target container; and control the fuel cell stack corresponding to the target container to output the first output power.

[0130] In one embodiment, the first fuzzy processing module 12 is specifically used to use an optimization algorithm and a corresponding first membership function to perform fuzzy quantization processing on the hydrogen storage content, the state of charge, and the first required power to obtain corresponding first fuzzy values; perform fuzzy control processing based on each first fuzzy value and a first preset fuzzy rule to obtain a second fuzzy value; and defuzzify the second fuzzy value to obtain the first output power of the first energy supply device.

[0131] In one embodiment, the second fuzzy processing module 14 is specifically used to use an optimization algorithm and a corresponding second membership function to perform fuzzy quantization processing on the hydrogen storage content, the state of charge, and the second required power to obtain a corresponding third fuzzy value; perform fuzzy control processing based on each third fuzzy value and the corresponding second preset fuzzy rule to obtain a fourth fuzzy value and a fifth fuzzy value; and defuzzify the fourth fuzzy value and the fifth fuzzy value respectively to obtain the second output power of the lithium battery and the second output power of the hydrogen generator.

[0132] Each module in the aforementioned fuel cell-based electric-hydrogen hybrid microgrid energy management and control device can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a computer device's memory in software form, allowing the processor to call and execute the corresponding operations of each module.

[0133] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 9As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store relevant data for energy management and control. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for energy management and control of an electric-hydrogen hybrid microgrid based on a fuel cell is implemented.

[0134] Those skilled in the art will understand that Figure 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0135] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps of any of the above method embodiments when executing the computer program.

[0136] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above method embodiments are implemented.

[0137] In one embodiment, a computer program product is provided, comprising a computer program, which implements the steps of any of the above method embodiments when executed by a processor.

[0138] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0139] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), quantum computing-based data processing logic devices, artificial intelligence (AI) processors, and the like.

[0140] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0141] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for energy management and control of an electric-hydrogen hybrid microgrid based on a fuel cell, characterized in that: The method comprises: Obtaining the first required power of the load, the hydrogen storage content of the hydrogen generator in the electric-hydrogen hybrid energy storage system, and the state of charge of the lithium battery; Performing fuzzy processing on the hydrogen storage content, state of charge, and first required power to obtain a first output power of a first energy supply device in the electric-hydrogen hybrid energy storage system; the first energy supply device includes a fuel cell subsystem; determining a second required power of the load based on the first required power and the first output power; The hydrogen storage content, the state of charge and the second required power are fuzzy processed to obtain a second output power of a second energy supply device in the electric-hydrogen hybrid energy storage system; the second energy supply device includes a lithium battery and the hydrogen generator.

2. The method according to claim 1, characterized in that The method further comprises: determining an operating mode of the first energy supply device according to a first output power of the first energy supply device and a power range of a preset operating mode; Based on the working mode, the first energy supply device is controlled to output the first output power.

3. The method according to claim 2, characterized in that Based on the working mode, controlling the first energy supply device to output the first output power includes: When the working mode is a single stack mode, obtaining a first performance value of each container in the fuel cell subsystem, and taking a container corresponding to the best first performance value as a target container; The fuel cell stack with the longest remaining life in the target container is used as the target fuel cell stack; The target fuel cell stack is controlled to output the first output power.

4. The method according to claim 2, characterized in that Based on the working mode, controlling the first energy supply device to output the first output power includes: When the working mode is a single container mode, determining the required number of fuel cell stacks according to the first output power of the first energy supply device; For each container in the fuel cell subsystem, determining a first fuel cell stack combination corresponding to each container according to the quantity; Obtaining attenuation values ​​corresponding to each of the first fuel cell stack combinations, and using a container corresponding to a minimum attenuation value as a target container; Determining a target fuel cell combination from the first fuel cell combinations in the target container according to the remaining life, hydrogen consumption, and attenuation value of each first fuel cell combination in the target container; The battery stack corresponding to the target battery stack combination is controlled to output the first output power.

5. The method according to claim 4, characterized in that The method further comprises: When the working mode is the full container mode, determining a second stack combination corresponding to the fuel cell subsystem according to the quantity; Determining a target fuel cell stack combination from each of the second fuel cell stack combinations according to the remaining life, hydrogen consumption, and attenuation value of the second fuel cell stack combinations corresponding to the fuel cell subsystem; The battery stack corresponding to the target battery stack combination is controlled to output the first output power.

6. The method according to claim 3, characterized in that The method further comprises: When the working mode is the mixed container mode, arbitrarily combining the containers in the fuel cell subsystem to determine initial container combinations, and obtaining second performance values ​​of the initial container combinations; taking the best performance value among the first performance values ​​and the second performance values ​​as the target performance value; The container corresponding to the target performance value or each container in the initial container combination is used as the target container; Control the fuel cell stack corresponding to the target container to output the first output power.

7. The method according to claim 1, characterized in that The fuzzy processing of the hydrogen storage content, the state of charge, and the first required power to obtain the first output power of the first energy supply device in the electric-hydrogen hybrid energy storage system includes: Performing fuzzy quantization processing on the hydrogen storage content, the state of charge, and the first required power using an optimization algorithm and a corresponding first membership function to obtain a corresponding first fuzzy value; Performing fuzzy control processing based on each of the first fuzzy values ​​and a first preset fuzzy rule to obtain a second fuzzy value; The second fuzzy value is defuzzified to obtain a first output power of the first energy supply device.

8. The method according to claim 1, characterized in that The fuzzy processing of the hydrogen storage content, the state of charge, and the second required power to obtain the second output power of the second energy supply device in the electric-hydrogen hybrid energy storage system includes: Performing fuzzy quantization processing on the hydrogen storage content, the state of charge, and the second required power using an optimization algorithm and a corresponding second membership function to obtain a corresponding third fuzzy value; Performing fuzzy control processing based on each of the third fuzzy values ​​and the corresponding second preset fuzzy rule to obtain a fourth fuzzy value and a fifth fuzzy value; The fourth fuzzy value and the fifth fuzzy value are defuzzified respectively to obtain the second output power of the lithium battery and the second output power of the hydrogen generator.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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