Capacity optimization method and system for efficient electrohydrogen catalyst-based multi-energy flow coupled systems

Abstract

A method and system for optimizing the capacity of an efficient electrohydrogen-catalyst-based multi-energy flow-coupled system is provided. [Solution] A capacity optimization method for a multi-energy flow coupled system based on an efficient electrohydrogen catalyst, comprising the steps of: treating a transition metal catalyst through defect engineering and interface engineering, and then constructing a high-definition model; constructing a multi-energy flow coupled model using energy storage equipment based on the constructed high-definition model, and creating a multi-energy flow coupled collaborative optimization structure with the constraints of reserve, carbon emissions, renewable energy allocation standards, and the real-time balance of electricity, heat, and hydrogen supply and demand power, and minimizing total annual costs as the goal; and obtaining operating data and cost data, inputting them into the multi-energy flow coupled collaborative optimization structure, solving it, and outputting an optimal capacity configuration strategy.

Description

[Technical Field]

[0001] (CROSS-REFERENCE TO RELATED APPLICATIONS) This invention claims priority to a Chinese patent application bearing application number 202510396758.X and entitled "Efficient electrohydrogen catalyst-based capacity optimization method and system for multi-energy flow combined system" filed with the State Intellectual Property Office of the People's Republic of China on April 1, 2025, the entire contents of which are incorporated herein by reference for all purposes and constitute a part of the present invention.

[0002] The present invention relates to the technical field of high-proportion new energy power system planning, and in particular to a capacity optimization method and system for a multi-energy flow combined system based on efficient electrohydrogen catalyst. [Background technology]

[0003] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0004] Currently, the global energy system is still dominated by fossil fuels, and greenhouse gas emissions from fossil fuel combustion have serious impacts on the environment and climate. Against this backdrop, renewable energy sources such as wind and solar energy are rapidly developing. However, with the rapid increase in renewable energy power generation capacity and grid connection capacity, grid transport capacity is facing major challenges, and issues such as wind and solar waste and declining utilization rates of new energy sources are becoming more serious. Hydrogen energy has become an increasingly attractive energy carrier due to its advantages of flexibility, high energy density, abundant supply sources, long-term storage, and inter-regional transport. To achieve effective collaboration between hydrogen energy and existing power systems (electricity-hydrogen coupling), experts have proposed the concept of a multi-energy flow coupled system.

[0005] However, existing electrohydrogen bonding technologies, such as water electrolyzers and hydrogen fuel cells, still rely primarily on precious metal catalysts (e.g., platinum-based catalysts and iridium-based catalysts) that are scarce, expensive, and have poor electrocatalytic efficiency. Furthermore, existing multi-energy flow-coupled systems are unable to fully realize the flexibility of electrohydrogen bonding technology in the modeling process, preventing the technology from fully utilizing its potential to solve the problems of wind and light waste. These technological bottlenecks limit the deep integration of hydrogen energy with existing power systems. Therefore, improving the catalytic efficiency of existing electrohydrogen bonding technology, conducting high-definition modeling of electrohydrogen bonding technology, fully realizing the flexibility of electrohydrogen bonding technology, and realizing an efficient collaboration plan for multi-energy flow-coupled systems are urgent challenges to be resolved. Summary of the Invention

[0006] To solve the above problems, the present invention proposes a method and system for optimizing the capacity of a multi-energy flow coupled system based on an efficient electro-hydrogen catalyst, incorporating defect engineering and interface engineering to obtain an electro-hydrogen bonding technology with efficient catalytic performance, and constructing a high-definition model, building a multi-energy flow coupled model based on the model, and creating a multi-energy flow coupled collaborative optimization structure. The present invention improves the catalytic efficiency of the electro-hydrogen bonding technology and realizes deep integration of hydrogen energy with existing power systems.

[0007] To achieve the above object, the present invention adopts the following technical solutions.

[0008] In a first aspect, the present invention provides a method for producing a composite catalyst by processing a transition metal catalyst by defect engineering and interface engineering; using the composite catalyst in a water electrolyzer and a hydrogen fuel cell to respectively construct a high-resolution model of the water electrolyzer and a high-resolution model of the hydrogen fuel cell; Based on the high-definition model of the water electrolyzer and the high-definition model of the hydrogen fuel cell, a multi-energy flow coupled model is constructed with energy storage devices, and a multi-energy flow coupled collaborative optimization structure is created with the constraints of reserve, carbon emission, renewable energy allocation standard, and real-time balance of electricity, heat, and hydrogen supply and demand power, and with the goal of minimizing total annual cost; obtaining operation data and cost data, inputting them into a multi-energy flow coupled collaborative optimization structure, solving it, and outputting an optimal capacity configuration strategy; The present invention provides a capacity optimization method for an efficient electrohydrogen catalyst-based multi-energy flow coupled system, including:

[0009] In a further technical solution, the defect engineering incorporates disordered structures into the perfect lattice of the transition metal catalyst, and the defect engineering includes three-dimensional defects, namely vacancies, screw dislocations, and stacking faults; and the interface engineering forms a multilayer porous heterogeneous heterostructure on the transition metal catalyst by chemical vapor deposition.

[0010] In another technical solution, the high-definition model of the water electrolyzer is an output characteristic model of the water electrolyzer, specifically:

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[0011] In another technical solution, the high-definition model of the hydrogen fuel cell is an output characteristic model of the hydrogen fuel cell, specifically:

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[0012] In a further technical solution, based on a high-definition model of a water electrolyzer and a high-definition model of a hydrogen fuel cell, a multi-energy flow coupled model is constructed using energy storage equipment, specifically including a renewable energy model, a hydrogen storage container model, a heat storage tank model, an electrical storage equipment model, an electric heating device model, and a compressor model.

[0013] In a further technical solution, the total annual cost to be minimized is the sum of investment costs, operation and maintenance costs, grid power purchase costs and penalty costs minus grid power sales revenue.

[0014] In a further technical solution, the water electrolyzer includes an alkaline water electrolyzer, a proton exchange membrane water electrolyzer, and a solid oxide water electrolyzer, and the hydrogen fuel cell includes an alkaline hydrogen fuel cell, a proton exchange membrane hydrogen fuel cell, a solid oxide hydrogen fuel cell, and a molten carbonate hydrogen fuel cell.

[0015] In a second aspect, the present invention provides a catalyst efficiency enhancement module configured to process a transition metal catalyst by defect engineering and interface engineering to obtain a composite catalyst; an electric-hydrogen bond model construction module configured to use the composite catalyst in a water electrolyzer and a hydrogen fuel cell to construct a high-definition model of the water electrolyzer and a high-definition model of the hydrogen fuel cell, respectively; a multi-energy flow coupled collaborative optimization structure creation module configured to build a multi-energy flow coupled model using energy storage devices based on the high-definition model of the water electrolyzer and the high-definition model of the hydrogen fuel cell, and create a multi-energy flow coupled collaborative optimization structure with the constraints of reserve, carbon emission, renewable energy allocation standard, and real-time balance of electricity, heat, and hydrogen supply and demand power, with the goal of minimizing total annual cost; a strategy output module configured to acquire and input operational data and cost data into a multi-energy flow coupled collaborative optimization structure, solve the structure, and output an optimal capacity configuration strategy; The present invention provides a capacity optimization system for an efficient electrohydrogen catalyst-based multi-energy flow coupled system, including:

[0016] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0017] 1. This invention builds high-definition models of water electrolyzers and hydrogen fuel cells that not only reflect the output characteristics of electrohydrogen bonding technology, but also fully demonstrate the flexibility of electrohydrogen bonding technology. By examining the cooperative and complementary relationship with the variability and unpredictability of renewable resources such as wind and solar power, it is possible to maximize the use of renewable energy, reduce the rate of wind and solar waste, and fully utilize the function and potential of hydrogen energy as an energy carrier.

[0018] 2. This invention combines defect engineering and interface engineering with two transition metal catalysts, Fe and Ni, which have abundant storage capacity and are environmentally friendly, to obtain transition metal catalysts that are competitive in both cost and catalytic performance, thereby achieving water electrolyzers and hydrogen fuel cells with efficient catalytic performance, providing technical support for the efficient conversion and storage of new energy power generation. The use of the improved Fe-Ni composite catalyst in water electrolyzers and hydrogen fuel cells significantly improves the output performance of electrohydrogen bonding technology, significantly improves the efficiency of electricity-hydrogen and hydrogen-electricity conversion, and further provides electrohydrogen bonding technology with efficient catalytic performance, laying a solid foundation for the deep integration of hydrogen energy into existing power systems.

[0019] 3. The multi-energy flow coupling collaborative optimization architecture created by this invention integrates multiple electro-hydrogen coupling technologies with efficient catalytic performance, comprehensively coordinating three different energy sources (electricity, heat, and hydrogen) and multiple energy conversion and storage devices, efficiently meeting users' diverse energy needs such as electricity, heat, and hydrogen, and achieving stable performance with very high cost-effectiveness and environmental friendliness. Importantly, the method proposed by this invention clarifies the optimal combination and type selection of electro-hydrogen conversion devices under different operating conditions, providing accurate guidance for energy scheduling and equipment configuration in different scenarios.

[0020] The accompanying drawings, which form a part of this specification and are intended to be used for a better understanding of the invention, are illustrative of the invention and are not to be construed as limiting the invention. [Brief explanation of the drawings]

[0021] [Figure 1] FIG. 1 is a logical structural diagram of the capacity optimization method for a multi-energy flow coupling system based on efficient electrohydrogen catalysts in the present invention. [Figure 2] FIG. 1 is a structural diagram of the overall energy system of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0022] It should be noted that the following detailed description is all illustrative and is intended to further explain the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art.

[0023] Unless a contradiction occurs, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0024] Example 1 As shown in FIG. 1, this embodiment proposes a capacity optimization method for a multi-energy flow combined system based on an efficient electrohydrogen catalyst, and specifically adopts the following technical solution:

[0025] S1, Technology to improve catalytic efficiency in energy conversion devices (water electrolyzers and hydrogen fuel cells). Renewable energy drives the water electrolyzer to split water into high-purity hydrogen and oxygen gases, converting the renewable energy into chemical energy. The hydrogen fuel cell then uses hydrogen and oxygen gases in an electrochemical reaction to synthesize water and release electrical energy, further converting the chemical energy of the hydrogen gas into electrical energy. This process is completely fossil-free and effectively avoids the carbon emissions issues associated with traditional energy conversion. Therefore, the renewable energy-water-hydrogen energy system provides an important technological path for promoting the transition of existing energy systems to low carbon.

[0026] On the other hand, the processes of hydrogen generation in a water electrolyzer and electricity generation in a hydrogen fuel cell both depend on four types of electrode electrochemical reactions that occur on the surface of the catalyst, as shown in the following equations:

[0027] Hydrogen Evolution Reaction (HER) and Oxygen Evolution Reaction (OER) in Acidic Electrolytes: [ka] Hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) in alkaline electrolytes: [ka] Hydrogen Oxidation Reaction (HOR) and Oxygen Reduction Reaction (ORR) in Acidic Electrolytes: [ka] Hydrogen oxidation reaction (HOR) and oxygen reduction reaction (ORR) in alkaline electrolytes: [ka]

[0028] To effectively catalyze the four electrochemical reactions, a high energy barrier must be overcome. For example, the water splitting reaction in a water electrolyzer requires a Gibbs free energy of 237.2 kJ mol under ideal conditions. -1 and the driving potential is 1.23 V. However, in the actual reaction process, due to factors such as ohmic resistance, concentration difference resistance, and activation resistance, the reaction requires additional potential to break the HO covalent bond in the water molecule and drive the reaction to occur.

[0029] Furthermore, analyzing these three resistances, it can be seen that the ohmic resistance mainly comes from the obstacles to the transport of electron flow caused by the cathode and anode materials and external conductors, and the obstacles to the transport of ion flow caused by the electrolyte, and this problem can be effectively addressed by improving the conductivity of the electrode materials and electrolyte or reducing the distance between the two plates.

[0030] The concentration difference resistance can be reduced by increasing the pressure of the gas reactants or the concentration of the liquid electrolyte. Therefore, although the ohmic resistance and the concentration difference resistance are mainly due to the selection of the electrode material and electrolyte, as well as the device operating conditions, the catalytic performance of the catalyst is particularly important in driving the kinetic process of the electrochemical reaction, since activation resistance accounts for more than 60% of the energy barrier of the electrochemical reaction.

[0031] However, the catalysts currently commonly used in electrohydrogen bonding technology are mainly precious metals such as platinum group metals and iridium group metals, which are rare and expensive, severely limiting the large-scale popularization and commercialization of electrohydrogen bonding technology. Obtaining cost-effective catalysts that can effectively reduce the activation energy of the reaction has become a key challenge in the development of electrohydrogen bonding technology.

[0032] In this example, defect engineering and interface engineering are combined to process the transition metal catalyst to obtain a composite catalyst, and a transition metal catalyst that is competitive in both cost and catalytic performance is obtained, thereby obtaining a water electrolyzer and a hydrogen fuel cell with efficient catalytic performance, providing technical support for the efficient conversion and storage of new energy power generation. Here, the transition metal catalyst is a two-type transition metal catalyst of Fe and Ni, which are abundant in storage and environmentally friendly.

[0033] Specifically, this process is as follows:

[0034] First, defect engineering optimizes the electron distribution within Fe and Ni by intentionally incorporating disordered structures into the perfect Fe and Ni lattice and disrupting the long-term periodicity of the matrix. In this example, defect engineering disrupts the electron distribution within the Fe and Ni lattice by introducing three types of defects: vacancies, screw dislocations, and stacking faults. Specifically, vacancies are point defects, screw dislocations are line defects, and stacking faults are planar defects. The introduction of these three types of defects effectively induces additional band alignment between the catalyst and reactants, reducing the resistance to electron transfer and facilitating the transfer and transport of electrons and charges during electrochemical reactions. Furthermore, the incorporation of three-dimensional defects shifts the d-band center of Fe and Ni, shifting the position of the center of gravity of the d-orbital electron energy. This promotes interactions between the catalyst's d-orbital and the reactant's electron orbital, facilitating the formation of new bonds, altering the binding energy of key intermediates, and further promoting the adsorption and desorption of reaction intermediates. Furthermore, the incorporation of three-dimensional defects significantly reduces the band gap of Fe and Ni electrocatalysts, reducing the energy barrier for electrons transitioning from the valence band to the conduction band. This allows the d-orbital electrons of Fe and Ni catalysts to more easily participate in electrode reactions, improving electron conduction efficiency and accelerating the kinetics of electrochemical reactions. Finally, the incorporation of defects alters the lattice structure of Fe and Ni, exposing more active sites and increasing the density of active sites, effectively improving the internal activity of transition metal catalysts.

[0035] Next, the incorporation of three-dimensional defects enhances the electrocatalytic activity of Fe and Ni transition metal catalysts. In this example, interface engineering is incorporated into defect engineering to further optimize the catalytic performance of electrohydrogen bonding technology from a different angle. In this example, interface engineering is performed by transporting defect-engineered Fe and Ni precursors to the base surface with N2 via chemical vapor deposition (CVD). At high temperatures approaching 1000°C, the Fe and Ni precursors are decomposed and deposited on the base surface, leading to the formation of a multilayer porous Fe-Ni heterogeneous structure (HS).

[0036] Both Fe and Ni possess unfilled d orbitals and unpaired electrons, resulting in high electron density. This provides ample electron sites for interaction with the electron clouds of reactants or key reaction intermediates, forming coordinate bonds. This optimizes the adsorption and desorption energies during the reaction process and facilitates electron flow between reactants and products. More importantly, the differences in band alignment and electronegativity between the different crystalline phases of Fe and Ni induce electronic modulation at the surface of Fe-Ni heterostructures, resulting in significant electronic effects. Furthermore, the atomic orbitals of Fe and Ni overlap and collide with each other during heterointerface formation, resulting in mutually filled bonding and antibonding states, which inevitably affects the local electron alignment. At the same time, the lattice constants of Fe and Ni are not perfectly matched, which causes lattice distortion at the heterointerface during crystal growth, directly affecting the geometry and coordination environment of the active sites. As described above, the electronic effects, interfacial bonding, lattice distortion, and synergistic effects of interface engineering each optimize the electron arrangement of the active sites and the Fermi level of the catalyst to different degrees, thereby increasing the specific surface area of ​​the catalyst, significantly improving the internal activity and conductivity of the transition metal catalyst, and increasing the density of the active sites.

[0037] Finally, in this example, to avoid the occurrence of electrolytic reactions on the surface of the Fe-Ni heterostructure in acidic / alkaline electrolytes, which can dissolve and aggregate the active components, leading to catalyst inactivation and shortened service life, this example uses a solvothermal synthesis method to cover the surface of the Fe-Ni heterostructure with a carbon coating, which effectively prevents the Fe-Ni composite catalyst from oxidizing and corroding in strong acid or alkaline environments, ensuring the high activity and stability of the Fe-Ni heterostructure and extending its service life.

[0038] S2. Using the above composite catalyst in a water electrolyzer and a hydrogen fuel cell, we construct a high-resolution model of the water electrolyzer and a high-resolution model of the hydrogen fuel cell, respectively. By using the improved Fe-Ni composite catalyst in water electrolyzers and hydrogen fuel cells, the output performance of the electrohydrogen bonding technology can be significantly improved, the electricity-hydrogen and hydrogen-electricity conversion efficiency can be significantly improved, and an electrohydrogen bonding technology with efficient catalytic performance can be obtained.

[0039] First, the output characteristic model of the water electrolyzer is as shown in the following equation.

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[0040]

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[0041] Next, the output characteristic model of a hydrogen fuel cell is as shown in the following equation.

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[0042]

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[0043] This embodiment fully demonstrates the flexibility of the water electrolyzer and hydrogen fuel cell, and is specifically as shown in the following formula.

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[0044]

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[0045]

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[0047] In this embodiment, there are three types of water electrolyzers: alkaline water electrolyzer (AEL), proton exchange membrane water electrolyzer (PEMEL), and solid oxide water electrolyzer (SOEL), and there are four types of hydrogen fuel cells: alkaline hydrogen fuel cell (AFC), proton exchange membrane hydrogen fuel cell (PEMFC), solid oxide hydrogen fuel cell (SOFC), and molten carbonate hydrogen fuel cell (MCFC).

[0048] As described above, this embodiment establishes an output characteristic model and a flexible expression model of the electric-hydrogen bonding technology, and calculates the electric-hydrogen conversion efficiency in the model.

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[0049] S3, Construction of multi-energy flow coupled collaborative optimization structure To ensure the efficient operation of the multi-energy flow coupled system and fully utilize the collaborative effects between different energy sources, the system must include multiple types of energy storage technologies and other energy conversion technologies to ensure the operational stability and energy supply reliability of the multi-energy flow coupled system. Therefore, in this embodiment, based on the high-definition model of the electric-hydrogen coupled technology, a multi-energy flow coupled model is constructed using energy storage devices, and a multi-energy flow coupled collaborative optimization structure is created with the constraints of reserves, carbon emissions, renewable energy allocation standards, and the real-time balance of electricity, heat, and hydrogen supply and demand power, with the goal of minimizing total annual costs.

[0050] In this embodiment, renewable energy includes wind energy and solar energy, and is specifically modeled as follows.

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[0051] As shown in Figure 2, to ensure the reliability of a multi-energy flow coupled system, it is necessary to integrate various energy storage devices into the system, such as hydrogen storage vessels (HSVs), thermal storage tanks (TSTs), and battery energy storage devices (BESs), to respond to the variability of renewable energy, fully exert their regulating function, ensure supply and demand balance at any time, stabilize the system output, and further improve the robustness of the system when facing power load perturbations.

[0052] Hydrogen storage vessel model:

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[0053] Thermal storage tank model:

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[0054] Electricity storage equipment model:

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[0055] At the same time, the electrical storage device must provide spinning reserve, which is modeled as follows:

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[0056] In addition, in order to ensure that the multi-energy flow combined system can maintain the supply and demand balance even when the heat load needs increase in winter, the present invention incorporates an electric heating device into the system, the model of which is as follows:

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[0057] In addition, when the hydrogen gas generated in the water electrolyzer enters the hydrogen storage container, the hydrogen storage container needs to compress the hydrogen gas in order to maximize the storage density of the hydrogen gas within a limited volume. Therefore, the present invention provides a compressor model to explain this process in detail.

[0058]

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[0059] In addition, the optimized capacity of each energy conversion device and energy storage device must satisfy the following constraints:

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[0060] In this embodiment, as shown in FIG. 1, in order to realize an orderly and complementary coupling between different types of energy in the system and efficient cooperative operation between each device, the model needs to satisfy the following constraints.

[0061] Real-time balance of electricity, heat and hydrogen supply and demand:

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[0062] Reserve constraints:

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[0063] Carbon Emissions Constraints:

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[0064] However, e Grid represents the carbon emission factor, and A EM represents the maximum allowable carbon emissions, and S EM represents the carbon emissions target shortfall.

[0065] Renewable Energy Allocation Standards:

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[0066] In this embodiment, the goal is to minimize the total annual cost (TAC), which includes the investment cost of each type of equipment, operation and maintenance cost, grid power purchase cost, and penalty cost. At the same time, in this embodiment, the revenue source is the income from selling electricity to the grid, which is specifically shown as follows:

[0067]

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[0068] The formula for calculating the Capital Recovery Factor (CRF) is as follows:

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[0069] The electricity purchase cost and electricity sales revenue are as follows:

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[0070] Penalty Cost:

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[0071] S4, Output of optimal capacity configuration strategy The operational data and cost data are acquired and input into a multi-energy flow coupled collaborative optimization structure, which is solved and outputs an optimal capacity configuration strategy.

[0072] The optimization area's time-of-day power purchase and sales prices, as well as the operation and cost data for wind power equipment units, solar power generation equipment units, different types of water electrolyzers and hydrogen fuel cells, compressors, and electrothermal devices, are used as input parameters for a multi-energy flow coupled collaborative optimization structure. A commercially available solver is used to solve the problem, and optimal capacity configuration and type selection combination schemes for each type of equipment unit are obtained, providing model support for subsequent real-time scheduling of the equipment units. During the scheduling phase, dynamic parameters such as fluctuations in wind and solar resources on the power source side, the storage status of electricity, heat, and hydrogen on the energy storage side, and changes in the load needs of electricity, heat, and hydrogen are monitored in real time. This helps the energy aggregator formulate an optimal scheduling plan and determine the optimal real-time output of each type of equipment unit and the optimal energy charging / discharging strategy for energy storage devices, effectively dealing with real-time uncertainties on the power source, storage, and load sides.

[0073] Example 2 This embodiment provides a capacity optimization system for an efficient electrohydrogen catalyst-based multi-energy flow coupled system, which specifically includes: a catalyst efficiency enhancement module configured to process the transition metal catalyst by defect engineering and interface engineering to obtain a composite catalyst; an electric-hydrogen bond model construction module configured to use the composite catalyst in a water electrolyzer and a hydrogen fuel cell to construct a high-definition model of the water electrolyzer and a high-definition model of the hydrogen fuel cell, respectively; a multi-energy flow coupled collaborative optimization structure creation module configured to build a multi-energy flow coupled model using energy storage devices based on the high-definition model of the water electrolyzer and the high-definition model of the hydrogen fuel cell, and create a multi-energy flow coupled collaborative optimization structure with the constraints of reserve, carbon emission, renewable energy allocation standard, and real-time balance of electricity, heat, and hydrogen supply and demand power, with the goal of minimizing total annual cost; and a strategy output module configured to obtain the operational data and the cost data, input them into a multi-energy flow coupled collaborative optimization structure, solve it, and output an optimal capacity configuration strategy.

[0074] For the specific implementation of the modules in this embodiment, please refer to the steps of the capacity optimization method for the multi-energy flow combined system based on efficient electrohydrogen catalyst described in Example 1, and will not be specifically described here.

[0075] The above examples merely illustrate some embodiments of the present invention, and although the descriptions are specific and detailed, they should not be understood as limiting the scope of the present invention. Those skilled in the art should note that various modifications and improvements that can be made without departing from the concept of the present invention are all intended to be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined in accordance with the appended claims.

Claims

1. 1. A computer-implemented method for capacity optimization of an efficient electrohydrogen catalysis based multi-energy flow coupled system, comprising: treating the transition metal catalyst by defect engineering and interface engineering to obtain a composite catalyst; using the composite catalyst in a water electrolyzer and a hydrogen fuel cell to respectively construct a high-resolution model of the water electrolyzer and a high-resolution model of the hydrogen fuel cell; Based on the high-definition model of the water electrolyzer and the high-definition model of the hydrogen fuel cell, a multi-energy flow coupled model is constructed using energy storage devices, and a multi-energy flow coupled collaborative optimization structure is created with the constraints of reserve, carbon emission, renewable energy allocation standard, and real-time balance of electricity, heat, and hydrogen supply and demand power, and with the goal of minimizing total annual cost; Taking the operational data and cost data as input to a multi-energy flow coupled collaborative optimization structure, solving it, and outputting an optimal capacity configuration strategy; A method for capacity optimization of a multi-energy flow coupled system based on an efficient electrohydrogen catalyst, comprising:

2. The capacity optimization method for a multi-energy flow coupled system based on an efficient electrohydrogen catalyst according to claim 1, characterized in that the defect engineering incorporates disordered structures into the perfect lattice of the transition metal catalyst, the defect engineering includes three-dimensional defects, namely, vacancies, screw dislocations, and stacking faults, and the interface engineering forms a multilayer porous heterogeneous heterostructure on the transition metal catalyst by chemical vapor deposition.

3. The high-resolution model of the water electrolytic cell is an output characteristic model of the water electrolytic cell, and specifically, [Number 130] [Number 131] where: [Number 132] represents the driving power per hour required for the nth type of water electrolyzer, [Number 133] represents the rated power of the nth water electrolyzer, [Number 134] represents the minimum operation limit of the nth type of water electrolyzer, where n represents the type of water electrolyzer; [Number 135] represents the amount of hydrogen generated per hour by the nth water electrolyzer, [Number 136] represents the electricity-hydrogen conversion efficiency of the nth water electrolyzer, and LCVH ２ The capacity optimization method of an efficient electrohydrogen catalyst based multi-energy flow coupled system according to claim 1, characterized in that: represents the lower heating value of hydrogen gas.

4. The high-resolution model of the hydrogen fuel cell is an output characteristic model of the hydrogen fuel cell, and specifically, [Number 137] [Number 138] where: [Number 139] represents the amount of electricity generated per hour by the mth hydrogen fuel cell, [Number 140] represents the hydrogen consumption per hour of the mth type of hydrogen fuel cell, [Number 141] represents the hydrogen-electricity conversion efficiency of the m-th type of hydrogen fuel cell, m represents the type of hydrogen fuel cell, [Number 142] represents the amount of heat generated per hour by the mth hydrogen fuel cell, [Number 143] represents the thermal conversion efficiency of the m-th hydrogen fuel cell, [Number 144] The capacity optimization method for a multi-energy flow coupled system based on efficient electro-hydrogen catalysts as claimed in claim 1, wherein m represents the dissipation rate of the m-th hydrogen fuel cell.

5. The capacity optimization method for a multi-energy flow coupled system based on an efficient electrohydrogen catalyst according to claim 1, characterized in that, based on a high-definition model of a water electrolyzer and a high-definition model of a hydrogen fuel cell, a multi-energy flow coupled model is constructed using energy storage devices, specifically including a renewable energy model, a hydrogen storage container model, a heat storage tank model, an electrical storage device model, an electric heating device model, and a compressor model.

6. 2. The capacity optimization method for a multi-energy flow-coupled system based on an efficient electrohydrogen catalyst according to claim 1, wherein the minimized total annual cost is the sum of investment cost, operation and maintenance cost, grid power purchase cost, and penalty cost minus grid power sales revenue.

7. 2. The capacity optimization method for a multi-energy flow-coupled system based on an efficient electro-hydrogen catalyst according to claim 1, wherein the water electrolyzer comprises an alkaline water electrolyzer, a proton exchange membrane water electrolyzer, and a solid oxide water electrolyzer, and the hydrogen fuel cell comprises an alkaline hydrogen fuel cell, a proton exchange membrane hydrogen fuel cell, a solid oxide hydrogen fuel cell, and a molten carbonate hydrogen fuel cell.

8. a catalyst efficiency enhancement module configured to process the transition metal catalyst by defect engineering and interface engineering to obtain a composite catalyst; an electric-hydrogen bond model construction module configured to use the composite catalyst in a water electrolyzer and a hydrogen fuel cell to construct a high-definition model of the water electrolyzer and a high-definition model of the hydrogen fuel cell, respectively; a multi-energy flow coupled collaborative optimization structure creation module configured to build a multi-energy flow coupled model using energy storage devices based on the high-definition model of the water electrolyzer and the high-definition model of the hydrogen fuel cell, and create a multi-energy flow coupled collaborative optimization structure with constraints on reserves, carbon emissions, renewable energy allocation standards, and real-time balance of electricity, heat, and hydrogen supply and demand power, with the goal of minimizing total annual costs; a strategy output module configured to acquire and input operation data and cost data into a multi-energy flow coupled collaborative optimization structure, solve the structure, and output an optimal capacity configuration strategy; A capacity optimization system for a multi-energy flow coupled system based on an efficient electrohydrogen catalyst, comprising:

9. 8. A computer-readable storage medium having a program stored thereon, characterized in that, when the program is executed by a processor, the steps of the method for capacity optimization of an efficient electrohydrogen-catalyst-based multi-energy flow-coupled system according to any one of claims 1 to 7 are realized.

10. 8. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that, when the processor executes the program, the steps of the method for capacity optimization of an efficient electrohydrogen catalyst based multi-energy flow coupled system according to any one of claims 1 to 7 are realized.

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