Method and system for optimizing the capacity of a multi-energy flow coupling system based on efficient electrohydrogen catalysis

Abstract

The present invention relates to a method and system for optimizing the capacity of a multi-energy flow coupling system based on efficient electro-hydrogen catalysis, belonging to the technical field of high-proportion new energy power system planning, including: treating transition metal catalysts by defect engineering and interface engineering, and respectively constructing refined models after treatment; based on the constructed refined models, constructing a multi-energy flow coupling model in combination with energy storage devices, and taking reserves, carbon emissions, renewable energy quota standards, and real-time balance of power supply and demand of electricity, heat, and hydrogen as constraint conditions, and minimizing the annual total cost as the goal, building a multi-energy flow coupling collaborative optimization framework; obtaining operation data and cost data, inputting them into the multi-energy flow coupling collaborative optimization framework and solving, and outputting an optimal capacity configuration strategy. The present invention improves the catalytic efficiency of the electro-hydrogen coupling technology and realizes the deep integration of hydrogen energy and the existing power system.

Description

Technical Field

[0001] The present invention relates to the technical field of high - proportion new - energy power system planning, and particularly to a method and system for optimizing the capacity of a multi - energy - flow coupling system based on efficient electro - hydrogen catalysis. Background Art

[0002] The statements in this part merely provide background - technical information related to the present invention and do not necessarily constitute prior art.

[0003] Currently, the global energy system still mainly relies on fossil energy, and the global energy system is still dominated by fossil energy. The greenhouse gases emitted by its combustion pose a serious threat to the environment and climate. Against this background, renewable energy such as wind energy and solar energy has developed rapidly. However, with the sharp increase in the installed capacity and grid - connected capacity of renewable - energy power generation, while the grid - bearing capacity faces huge challenges, the problems of wind and light curtailment and the reduction of new - energy utilization rate have become more and more serious. Hydrogen energy, with its advantages such as flexibility, high energy density, rich sources, long - term storage, and cross - space transportation, has gradually become an energy carrier that has attracted much attention. To achieve the effective coordination (electro - hydrogen coupling) between hydrogen energy and the existing power system, relevant technical personnel have proposed the concept of a multi - energy - flow coupling system.

[0004] However, existing electro - hydrogen coupling technologies, such as water electrolyzers and hydrogen fuel cells, still mainly rely on noble - metal catalysts (such as platinum - based catalysts and iridium - based catalysts). Such catalyst materials are scarce, expensive, and have unsatisfactory electro - catalytic efficiency. In addition, the existing multi - energy - flow coupling systems fail to fully reflect the flexibility of electro - hydrogen coupling technology during the modeling process and cannot fully explore the potential of electro - hydrogen coupling technology in solving the problems of wind and light curtailment. It can be seen that the above - mentioned technical bottlenecks restrict the deep integration of hydrogen energy and the existing power system. Therefore, how to improve the catalytic efficiency of existing electro - hydrogen coupling technologies, while carrying out refined modeling of electro - hydrogen coupling technologies, fully reflecting the flexibility of electro - hydrogen coupling technologies, and realizing the efficient collaborative planning of multi - energy - flow coupling systems has become an urgent problem to be solved. Summary of the Invention

[0005] To solve the above problems, the present invention proposes a method and system for optimizing the capacity of a multi - energy - flow coupling system based on efficient electro - hydrogen catalysis. By introducing defect engineering and interface engineering, an electro - hydrogen coupling technology with high - efficiency catalytic performance is obtained. At the same time, a refined model is constructed, and based on this model, a multi - energy - flow coupling model is constructed and a multi - energy - flow coupling collaborative optimization framework is built. The present invention improves the catalytic efficiency of electro - hydrogen coupling technology and realizes the deep integration of hydrogen energy and the existing power system.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] In a first aspect, the present invention provides a method for optimizing the capacity of a multi-energy flow coupling system based on efficient electro-hydrogen catalysis, including:

[0008] Treating a transition metal catalyst by defect engineering and interface engineering to obtain a composite catalyst;

[0009] Using the composite catalyst in a water electrolyzer and a hydrogen fuel cell, and respectively constructing refined models of the water electrolyzer and the hydrogen fuel cell;

[0010] Based on the refined models of the water electrolyzer and the hydrogen fuel cell, constructing a multi-energy flow coupling model in combination with energy storage devices, and taking reserve, carbon emissions, renewable energy quota standards, and real-time balance of power supply and demand of electricity, heat, and hydrogen as constraint conditions, and aiming at minimizing the annual total cost, building a multi-energy flow coupling collaborative optimization framework;

[0011] Obtaining operation data and cost data, inputting them into the multi-energy flow coupling collaborative optimization framework and solving, and outputting an optimal capacity configuration strategy.

[0012] In a further technical solution, the defect engineering introduces irregular structures into the perfect lattice of the transition metal catalyst, and the defect engineering includes defects in three dimensions, namely: vacancy defect, screw dislocation, and stacking fault; the interface engineering is to use chemical vapor deposition to make the transition metal catalyst form a multi-layer porous heterogeneous structure.

[0013] In a further technical solution, the refined model of the water electrolyzer is the output characteristic model of the water electrolyzer, specifically:

[0014] ; ; where represents the hourly driving electric power required for the nth type of water electrolyzer; represents the rated power of the nth type of water electrolyzer; represents the lowest limit for the operation of the nth type of water electrolyzer; n represents the type of water electrolyzer; represents the hourly hydrogen production of the nth type of water electrolyzer; represents the electricity-hydrogen conversion efficiency of the nth type of water electrolyzer; represents the low calorific value of hydrogen.

[0015] In a further technical solution, the refined model of the hydrogen fuel cell is the output characteristic model of the hydrogen fuel cell, specifically:

[0016] ; ; where represents the hourly electricity generation of the mth type of hydrogen fuel cell; represents the hourly hydrogen consumption of the mth type of hydrogen fuel cell; represents the hydrogen - electricity conversion efficiency of the m - th type of hydrogen fuel cell; m represents the type of hydrogen fuel cell; represents the hourly heat production of the m - th type of hydrogen fuel cell; represents the heat conversion efficiency of the m - th type of hydrogen fuel cell; represents the dissipation rate of the m - th type of hydrogen fuel cell.

[0017] A further technical solution is to construct a multi - energy flow coupling model based on the refined models of the water electrolyzer and the hydrogen fuel cell, combined with energy storage devices, specifically including: a renewable energy model, a hydrogen storage tank model, a heat storage tank model, an electricity storage device model, an electro - thermal device model, and a compressor model.

[0018] A further technical solution is that the minimized annual total cost is the sum of the investment cost, operation and maintenance cost, main - grid power purchase cost, and penalty cost minus the main - grid power selling revenue.

[0019] A further technical solution is that the water electrolyzer includes: an alkaline water electrolyzer, a proton - exchange membrane water electrolyzer, and a solid - oxide water electrolyzer; 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.

[0020] In the second aspect, the present invention provides a multi - energy flow coupling system capacity optimization system based on efficient electro - hydrogen catalysis, including:

[0021] A catalytic efficiency improvement module, configured to: treat the transition - metal catalyst by defect engineering and interface engineering to obtain a composite catalyst;

[0022] An electro - hydrogen coupling model construction module, configured to: use the composite catalyst in the water electrolyzer and the hydrogen fuel cell, and respectively construct refined models of the water electrolyzer and the hydrogen fuel cell;

[0023] A multi - energy flow coupling collaborative optimization architecture building module, configured to: construct a multi - energy flow coupling model based on the refined models of the water electrolyzer and the hydrogen fuel cell, combined with energy storage devices, and build a multi - energy flow coupling collaborative optimization architecture with reserve, carbon emissions, renewable energy quota standards, and real - time balance of electricity, heat, and hydrogen supply - demand power as constraints and minimizing the annual total cost as the objective;

[0024] A strategy output module, configured to: obtain operation data and cost data, input them into the multi - energy flow coupling collaborative optimization architecture for solution, and output the optimal capacity configuration strategy.

[0025] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0026] 1. The present invention constructs refined models of a water electrolysis cell and a hydrogen fuel cell. These models not only reflect the output characteristics of the power-to-hydrogen coupling technology but also fully demonstrate the flexibility of this technology, exploring the synergistic and complementary relationships with the volatility and unpredictability of wind and solar renewable resources, so as to maximize the utilization of renewable energy, reduce the curtailment rates of wind and solar power, and at the same time give full play to the function and potential of hydrogen as an energy carrier.

[0027] 2. The present invention combines defect engineering and interface engineering and applies them to two transition metal catalysts, Fe and Ni, which are rich in reserves and environmentally friendly, to obtain transition metal catalysts that are competitive in terms of cost and catalytic performance, so as to obtain a water electrolysis cell and a hydrogen fuel cell with high catalytic performance, providing technical support for the efficient conversion and storage of new energy power generation. After applying the modified Fe-Ni composite catalyst to the water electrolysis cell and the hydrogen fuel cell, the output performance of the power-to-hydrogen coupling technology has been greatly improved, and the power-to-hydrogen and hydrogen-to-power conversion efficiencies have been significantly improved. Furthermore, a power-to-hydrogen coupling technology with high catalytic performance has been obtained, laying a solid foundation for the deep integration of hydrogen energy and the existing power system.

[0028] 3. The multi-energy flow coupling and collaborative optimization architecture built by the present invention integrates various power-to-hydrogen coupling technologies with high catalytic performance, comprehensively coordinates three heterogeneous energies, namely electricity, heat, and hydrogen, as well as various energy conversion and storage devices, and efficiently meets the diverse energy demands of the user side for electricity, heat, hydrogen, etc., achieving stable operation with high cost and environmental benefits. Importantly, the method proposed by the present invention clarifies the optimal combination and selection of power-to-hydrogen conversion devices under different working conditions, providing precise guidance for energy scheduling and equipment configuration in different scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The accompanying drawings forming a part of this specification are used to provide a further understanding of the present invention. The schematic embodiments and descriptions thereof of the present invention are used to explain the present invention and do not constitute a limitation to the present invention.

[0030] Figure 1 is the logical structure diagram of the multi-energy flow coupling system capacity optimization method based on high-efficiency power-to-hydrogen catalysis in the present invention;

[0031] Figure 2 is the structure diagram of the integrated energy system of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0032] It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0033] Without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0034] Embodiment 1

[0035] As Figure 1 shown, this embodiment proposes a method for optimizing the capacity of a multi-energy flow coupling system based on efficient electro-hydrogen catalysis, and specifically adopts the following technical solutions:

[0036] S1. A technology for improving the catalytic efficiency of energy conversion devices (water electrolyzers and hydrogen fuel cells).

[0037] Renewable energy drives the water electrolyzer to decompose water into high-purity hydrogen and oxygen, converting the energy contained in renewable energy into chemical energy. Subsequently, the hydrogen fuel cell uses hydrogen and oxygen for an electrochemical reaction to resynthesize water and release electrical energy, thereby realizing the further conversion of chemical energy in hydrogen into electrical energy. This process is completely independent of fossil fuels and effectively avoids the carbon emission problem in traditional energy conversion. Therefore, the renewable energy-water-hydrogen energy system provides an important technical path for promoting the low-carbon transformation of the existing energy system.

[0038] The processes of hydrogen production by the water electrolyzer and power generation by the hydrogen fuel cell both depend on four electrode electrochemical reactions occurring on the catalyst surface, as specifically shown in the following equations:

[0039] Hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) in acidic electrolyte:

[0040] ;

[0041] Hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) in alkaline electrolyte:

[0042] ;

[0043] Hydrogen oxidation reaction (HOR) and oxygen reduction reaction (ORR) in acidic electrolyte:

[0044] ;

[0045] Hydrogen oxidation reaction (HOR) and oxygen reduction reaction (ORR) in alkaline electrolyte:

[0046] ;

[0047] To achieve effective catalysis of four electrochemical reactions, it is necessary to overcome a relatively high energy barrier. Taking the water splitting reaction in a water electrolyzer as an example, under ideal conditions, the Gibbs free energy change of the water splitting reaction is 237.2 kJ mol-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 an additional potential to break the H-O covalent bond in water molecules to drive the reaction.

[0048] Further analyzing these three types of resistances, the ohmic resistance mainly comes from the hindrance of the cathode and anode materials and external wires to the transmission of electron flow, as well as the hindrance of the electrolyte to the transmission of ion flow. This problem can be effectively addressed by improving the conductivity of the electrode materials and electrolytes, or reducing the distance between the two electrodes.

[0049] The concentration difference resistance can be reduced by increasing the pressure of gaseous reactants or the concentration of liquid electrolytes. Therefore, the ohmic resistance and concentration difference resistance mainly depend on the selection of electrode materials and electrolytes and the operating conditions of the equipment. More than 60% of the energy barriers in electrochemical reactions come from the activation resistance, which makes the catalytic performance of the catalyst particularly crucial for promoting the electrochemical reaction kinetics process.

[0050] However, the catalysts commonly used in current electrohydrogen coupling technologies are mostly noble metals, such as platinum group metals and iridium group metals. These precious metals are not only scarce in materials but also expensive in price, severely restricting the large-scale promotion and commercialization process of electrohydrogen coupling technologies. How to obtain a catalyst with cost-effectiveness and capable of effectively reducing the reaction activation energy has become an important challenge in the development of electrohydrogen coupling technologies.

[0051] In this embodiment, a composite catalyst is obtained by treating a transition metal catalyst through a combination of defect engineering and interface engineering, and a transition metal catalyst with competitiveness in terms of cost and catalytic performance is obtained to obtain a water electrolyzer and a hydrogen fuel cell with high catalytic performance, providing technical support for the efficient conversion and storage of new energy power generation. The transition metal catalysts are two transition metal catalysts of Fe and Ni with abundant reserves and environmental friendliness.

[0052] The specific process is as follows:

[0053] First, defect engineering breaks the long-range periodicity of the matrix by intentionally introducing irregular structures into the perfect lattices of Fe and Ni to optimize the electron distribution states within Fe and Ni. In this embodiment, defect engineering combines three-dimensional defects: vacancy defects, screw dislocations, and stacking faults to perturb the electron distribution within the Fe and Ni lattices. Specifically, vacancy defects belong to point defects, screw dislocations belong to line defects, and stacking faults belong to plane defects. The introduction of these three-dimensional defects effectively induces an additional energy band alignment between the catalyst and the reactants, thereby reducing the resistance of electron transfer and promoting the transfer and transport of electrons and charges during the electrochemical reaction process. At the same time, the introduction of three-dimensional defects shifts the d-band centers of Fe and Ni, changes the position of the energy center of gravity of the d-orbital electrons, promotes the interaction between the d-orbitals of the catalyst and the electron orbitals of the reactants, making it easier to form new bonds, and changes the binding energy of the key intermediates, thereby facilitating the adsorption and desorption of reaction intermediates. In addition, the combination of three-dimensional defects significantly reduces the band gap of the Fe and Ni electrocatalysts, reduces the energy barrier for electrons to jump from the valence band to the conduction band, makes the electrons in the d-orbitals of the Fe and Ni catalysts more likely to participate in the electrode reaction, improves the electron conduction efficiency, and promotes the kinetic process of the electrochemical reaction. Finally, the introduction of defects changes the structure of the Fe and Ni lattices, exposes more active sites, increases the density of active sites, and effectively improves the intrinsic activity of the transition metal catalyst.

[0054] Second, the introduction of three-dimensional defects respectively improves the electrocatalytic activities of the Fe and Ni transition metal catalysts. In this embodiment, based on defect engineering, interface engineering is introduced to further optimize the catalytic performance of the electrohydrogen coupling technology from different perspectives. In this embodiment, interface engineering uses chemical vapor deposition (CVD) to transport the Fe and Ni precursors treated by defect engineering and N2 to the surface of the substrate together. The high temperature of nearly 1000 °C promotes the decomposition and deposition of the Fe and Ni precursors on the surface of the substrate, thereby forming a multi-layered porous Fe-Ni heterogeneous structure (HS).

[0055] Since both Fe and Ni have unfilled d electron orbitals and unpaired electrons, they have a relatively high electron density, which can provide sufficient electron sites to interact with the electron clouds of reactants or key reaction intermediates and form coordination bonds with them, thereby optimizing the adsorption energy and desorption energy during the reaction process and promoting the electron flow between reactants and products. More importantly, due to the differences in the energy band arrangements and electronegativities between different crystal phases of Fe and Ni, the electron modulation on the surface of the Fe-Ni heterostructure is induced, forming an obvious electron effect; and when the atomic orbitals of Fe and Ni form a heterointerface, they will overlap and collide with each other, resulting in the mutual filling of bonding states and antibonding states and forming interface bonds, which inevitably affects the local arrangement of electrons; at the same time, since the lattice constants of Fe and Ni are not exactly the same, lattice strain will occur at the heterointerface during the quasi-growth of the crystal, directly interfering with the geometric structure and coordination environment of the active sites. In summary, the electron effect, interface bonding, lattice strain, and synergistic effect caused by interface engineering all optimize the electron arrangement of the active sites and the Fermi level of the catalyst to varying degrees, increase the specific surface area of the catalyst, significantly improve the intrinsic activity and conductivity of the transition metal catalyst, and increase the density of active sites.

[0056] Finally, in this embodiment, in order to prevent the electrolyte reaction from occurring on the surface of the Fe-Ni heterostructure in acid / alkali electrolytes, resulting in the dissolution and agglomeration of the active components, passivating the catalyst and reducing its lifespan, this embodiment uses the solvothermal synthesis method to coat a carbon material coating on the surface of the Fe-Ni heterostructure, effectively preventing the oxidative corrosion of the Fe-Ni composite catalyst in strong acid or strong alkali environments, ensuring the high activity and stability of the Fe-Ni heterostructure, and extending its lifespan.

[0057] S2. Use the above composite catalyst in a water electrolyzer and a hydrogen fuel cell, and respectively construct refined models of the water electrolyzer and the hydrogen fuel cell.

[0058] After using the modified Fe-Ni composite catalyst in the water electrolyzer and the hydrogen fuel cell, the output performance of the electro-hydrogen coupling technology has been greatly improved, and the electro-hydrogen and hydrogen-electric conversion efficiencies have been significantly improved, thereby obtaining an electro-hydrogen coupling technology with high catalytic performance.

[0059] First, the output characteristic model of the water electrolyzer is shown as follows:

[0060] ;

[0061] In the formula, represents the hourly driving electric power required for the nth water electrolyzer; represents the rated power of the nth water electrolyzer; Represents the minimum limit for the operation of the nth type of water electrolysis cell; n represents the type of water electrolysis cell, and there are N types of water electrolysis cells in the present invention.

[0062] ;

[0063] In the formula, Represents the hourly hydrogen production of the nth type of water electrolysis cell; Represents the electro-hydrogen conversion efficiency of the nth type of water electrolysis cell; Represents the lower heating value of hydrogen.

[0064] Secondly, the output characteristic model of the hydrogen fuel cell is shown as follows:

[0065] ;

[0066] In the formula, Represents the hourly electricity production of the mth type of hydrogen fuel cell; Represents the hourly hydrogen consumption of the mth type of hydrogen fuel cell; Represents the hydrogen-electric conversion efficiency of the mth type of hydrogen fuel cell; m represents the type of hydrogen fuel cell, and there are M types of hydrogen fuel cells in total.

[0067] ;

[0068] In the formula, Represents the hourly heat production of the mth type of hydrogen fuel cell; Represents the heat conversion efficiency of the mth type of hydrogen fuel cell; Represents the dissipation rate of the mth type of hydrogen fuel cell.

[0069] Next, this embodiment fully demonstrates the flexibility of the water electrolysis cell and the hydrogen fuel cell, as shown specifically in the following formula:

[0070] ;

[0071] , ;

[0072] In the formula, Represents the online capacity of the water electrolysis cell or the hydrogen fuel cell t at time Represents the startup capacity of the water electrolysis cell or the hydrogen fuel cell t at time Represents the shutdown capacity of the water electrolysis cell or the hydrogen fuel cell t at time Represents the total investment capacity of the water electrolysis cell or the hydrogen fuel cell; Represents N types of water electrolysis cells and M types of hydrogen fuel cells.

[0073] ;

[0074] In the formula, represents the real-time power consumption / power generation of the water electrolysis cell or hydrogen fuel cell t at a certain moment; , respectively represent the output lower limit and output upper limit of the water electrolysis cell or hydrogen fuel cell.

[0075] ;

[0076] ;

[0077] In the formula, , respectively represent the start-up ramp limit and shutdown ramp limit of the water electrolysis cell or hydrogen fuel cell; , respectively represent the up-ramp limit and down-ramp limit of the water electrolysis cell or hydrogen fuel cell.

[0078] ;

[0079] ;

[0080] In the formula, , respectively represent the minimum start-up time and minimum shutdown time of the water electrolysis cell or hydrogen fuel cell; T represents the total planned time.

[0081] In this embodiment, there are three types of water electrolysis cells, namely: alkaline water electrolyzer (AEL), proton exchange membrane water electrolyzer (PEMEL), and solid oxide water electrolyzer (SOEL); there are four types of hydrogen fuel cells, namely: alkaline hydrogen fuel cell (AFC), proton exchange membrane hydrogen fuel cell (PEMFC), solid oxide hydrogen fuel cell (SOFC), and molten carbonate fuel cell (MCFC).

[0082] In summary, the output characteristic model and flexibility characterization model of the electric-hydrogen coupling technology are constructed in this embodiment, and the electric-hydrogen conversion efficiency is reflected in the models. And the significant improvement of the hydrogen-electric conversion efficiency effectively reduces the efficiency differences among the three types of water electrolyzers and the four types of hydrogen fuel cells, and further highlights the differences in flexibility constraints such as the minimum start / stop time and ramp limit caused by factors such as the operating temperature of different water electrolyzers and hydrogen fuel cells. Through the in-depth characterization of the flexibility differences of different devices, the present invention further proposes a strategy for device selection in different working scenarios, that is, according to the performance of various water electrolyzers and hydrogen fuel cells under different working conditions, evaluate their adaptability, and select the device type that best matches the specific scenario according to the flexibility requirements of the system. This selection process not only optimizes the device configuration, gives full play to the efficiency advantages of the electric-hydrogen coupling device, but also ensures the reliability and stability of the system under various operating conditions.

[0083] S3. Establishment of a multi-energy flow coupling and collaborative optimization architecture.

[0084] To ensure the efficient operation of the multi-energy flow coupling system and give full play to the synergistic effect among heterogeneous energies, the system also needs to include various energy storage technologies and other energy conversion technologies, etc., to ensure the operation stability and energy supply reliability of the multi-energy flow coupling system. Therefore, in this embodiment, a multi-energy flow coupling model is constructed based on the refined model of the aforementioned electric-hydrogen coupling technology combined with energy storage devices, and a multi-energy flow coupling collaborative optimization architecture is established with reserve, carbon emission, renewable energy quota standard, and real-time balance of electric, heat, and hydrogen supply and demand power as constraints and minimizing the annual total cost as the goal.

[0085] In this embodiment, the renewable energy includes wind energy and solar energy, and the specific modeling method is as follows:

[0086] ;

[0087] ;

[0088] In the formula, , respectively represent the real-time output of the wind turbine and the photovoltaic unit t at time , respectively represent the time capacity factors characterizing the wind energy resource and the photovoltaic resource endowment in the optimization area; , , , , , respectively represent the total capacity, newly built capacity, and existing capacity of the wind turbines and photovoltaic units in the optimization area.

[0089] As Figure 2 shown, to ensure the reliability of the multi - energy - flow coupling system, it is necessary to integrate multiple energy storage devices in the system, such as hydrogen storage vessels (HSV), thermal storage tanks (TST), and battery energy storage (BES), to better cope with the volatility of renewable energy, give full play to their regulating role, ensure the supply - demand balance at any time, stabilize the system output, and further enhance the robustness of the system in the face of source - load disturbances.

[0090] Hydrogen storage tank model:

[0091] ;

[0092] ;

[0093] , ≥0;

[0094] ;

[0095] ;

[0096] ;

[0097] ;

[0098] In the formula, , respectively represent the hydrogen charging amount and hydrogen discharging amount of the hydrogen storage tank at t time; , respectively represent the maximum charging / discharging power and the maximum hydrogen storage capacity of the hydrogen storage tank; , , respectively represent the hydrogen charging rate, hydrogen discharging rate, and self - loss rate of the hydrogen storage tank; represents the hydrogen storage state of the hydrogen storage tank at t time; represents the time interval.

[0099] Thermal storage tank model:

[0100] ;

[0101] ;

[0102] ≥0, ≥0;

[0103] ;

[0104] ;

[0105] ;

[0106] ;

[0107] In the formula, , respectively represent the heat charging amount and heat discharging amount of the heat storage tank t at the moment; , respectively represent the maximum charging / discharging power and the maximum heat storage capacity of the heat storage tank; , , respectively represent the heat charging rate, heat discharging rate and self-loss rate of the heat storage tank; represents the heat storage state of the heat storage tank t at the moment.

[0108] Electric energy storage device model:

[0109] ;

[0110] ;

[0111] ≥0, ≥0;

[0112] ;

[0113] ;

[0114] ;

[0115] Meanwhile, the electric energy storage device needs to provide spinning reserve, and the modeling is as follows:

[0116] ;

[0117] ;

[0118] ;

[0119] In the formula, , respectively represent the charging amount and discharging amount of the electric energy storage device t at the moment; , respectively represent the maximum charging / discharging power and the maximum electric energy storage capacity of the electric energy storage device; , , respectively represent the charging rate, discharging rate, and self-loss rate of the electricity storage device; represents the t state of charge of the electricity storage device at time represents the t rotating reserve provided by the electricity storage device at time

[0120] In addition, to ensure that the multi-energy flow coupling system can still maintain the supply-demand balance when the winter heat load demand is large, an electro-thermal device is introduced into the system, and its model is as follows:

[0121] ;

[0122] In the formula, represents the t real-time output thermal power of the electro-thermal device at time represents the t power consumption of the electro-thermal device at time represents the energy conversion efficiency of the electro-thermal device.

[0123] In addition, when the hydrogen generated by the water electrolyzer enters the hydrogen storage tank, in order to maximize the hydrogen storage density within a limited volume, the hydrogen storage tank needs to compress the hydrogen. Therefore, the present invention gives a compressor model to describe this process in detail:

[0124] ;

[0125] In the formula, represents the t power consumption of the compressor at time represents the t intake air volume of the compressor at time represents the specific heat capacity of hydrogen at constant pressure; represents the operating temperature of the compressor; represents the operating efficiency of the compressor; , respectively represent the intake pressure and outlet pressure of the compressor; r represents the isentropic index of hydrogen.

[0126] In addition, the optimized capacities of each energy conversion device and energy storage device need to satisfy the following constraints:

[0127] ;

[0128] In the formula, represents the t real-time output of the device at time represents the t online power of the device at time , , respectively represent the total capacity, newly built capacity and existing capacity of the device; represents the set of all energy conversion devices and energy storage devices in the multi-energy flow coupling system.

[0129] In this embodiment, as Figure 1 shown, to achieve the orderly complementary coupling between heterogeneous energies in the system and the efficient collaborative operation between devices, the model still needs to meet the following constraint conditions:

[0130] Real-time balance of power supply and demand for electricity, heat, and hydrogen:

[0131] ;

[0132] ;

[0133] ;

[0134] In the formula, , , respectively represent t the electricity load, heat load, and hydrogen load at time , respectively represent t the electricity purchase amount from the main grid and the electricity sales amount to the main grid at time

[0135] Reserve constraint:

[0136] ;

[0137] In the formula, represents the load reserve capacity; , respectively represent the prediction errors of the wind turbine and the photovoltaic unit.

[0138] Carbon emission constraint:

[0139] ;

[0140] In the formula, represents the carbon emission factor; represents the maximum allowable value of carbon emissions; represents the carbon emission target deficit.

[0141] Renewable energy quota standard:

[0142] ;

[0143] = ;

[0144] Wherein, represents the renewable energy quota standard; represents the shortfall of the renewable energy quota standard.

[0145] In this embodiment, with the goal of minimizing the total annual cost (TAC), TAC covers the investment cost, operation and maintenance cost, main grid power purchase cost, and penalty cost of various equipment. At the same time, the income from selling electricity to the main grid is used as the income source, and the specific expression is as follows:

[0146] ;

[0147] = ;

[0148] = ;

[0149] = ;

[0150] ;

[0151] ;

[0152] ;

[0153] ;

[0154] + + ;

[0155] + + ;

[0156] + + ;

[0157] Wherein, represents the unit investment cost of equipment x ; represents the fixed operation and maintenance cost of equipment x ; represents the variable operation cost of equipment x ; represents the start-up cost of equipment x ;

[0158] The calculation formula of the capital recovery factor (CRF) is as follows:

[0159] ;

[0160] Wherein, r represents the discount rate; represents the life of the multi - energy flow coupling system.

[0161] The electricity purchase cost and the electricity sale revenue are as follows:

[0162] ;

[0163] ;

[0164] Wherein, , respectively represent the time - of - use electricity purchase and sale prices.

[0165] Penalty cost:

[0166] ;

[0167] Wherein, represents the carbon tax of the planned area; represents the penalty cost for the shortage of renewable energy quota standard.

[0168] S4. Output of the optimal capacity configuration strategy

[0169] Obtain the operation data and cost data, input them into the multi - energy flow coupling collaborative optimization framework and solve, and output the optimal capacity configuration strategy.

[0170] Obtain the time - of - use electricity purchase and sale prices, the operation and cost data of wind turbines, photovoltaic units, different types of electrolyzers and hydrogen fuel cells, compressors and electro - thermal devices in the optimization area, and use them as the input parameters of the multi - energy flow coupling collaborative optimization framework. Use a commercial solver to solve to obtain the optimal capacity configuration and combined selection scheme of various units, providing model support for the real - time scheduling of subsequent units. In the scheduling stage, by real - time monitoring of dynamic parameters such as the fluctuations of wind and light resources on the source side, the electro - thermal - hydrogen storage status on the energy storage side, and the changes in electro - thermal - hydrogen load demand on the load side, assist the energy aggregator to formulate the optimal scheduling plan, determine the optimal real - time output of various units and the optimal charging and discharging energy storage strategy of energy storage devices, so as to effectively cope with the real - time uncertainties on the source, storage, and load sides.

[0171] Embodiment 2

[0172] This embodiment provides a multi - energy flow coupling system capacity optimization system based on efficient electro - hydrogen catalysis, which specifically includes the following modules:

[0173] Catalytic efficiency improvement module, configured to: process the transition metal catalyst by defect engineering and interface engineering to obtain a composite catalyst;

[0174] An electrolytic hydrogen coupling model construction module, configured to: use the composite catalyst in a water electrolysis cell and a hydrogen fuel cell, and respectively construct refined models of the water electrolysis cell and the hydrogen fuel cell;

[0175] A multi-energy flow coupling collaborative optimization architecture building module, configured to: based on the refined models of the water electrolysis cell and the hydrogen fuel cell, construct a multi-energy flow coupling model in combination with energy storage devices, and use reserve, carbon emissions, renewable energy quota standards, and real-time balance of power supply and demand of electricity, heat, and hydrogen as constraint conditions, and minimize the annual total cost as the goal to build a multi-energy flow coupling collaborative optimization architecture;

[0176] A strategy output module, configured to: obtain operation data and cost data, input them into the multi-energy flow coupling collaborative optimization architecture for solution, and output an optimal capacity configuration strategy.

[0177] For the implementation of specific modules in this embodiment, refer to the steps of a method for identifying types of transmission line losses described in Embodiment 1, and specific descriptions will not be provided here.

[0178] The above embodiments only represent several implementation manners of the present invention, and the descriptions thereof are relatively specific and detailed, but should not be construed as limiting the scope of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the appended claims.

Claims

1. A capacity optimization method for a multi-energy flow coupling system based on efficient electro-hydrogen catalysis, characterized in that: include: Defect engineering and interface engineering are used to treat transition metal catalysts to obtain composite catalysts; The composite catalyst is used in a water electrolysis cell and a hydrogen fuel cell, and refined models of the water electrolysis cell and the hydrogen fuel cell are constructed respectively; Based on the refined models of water electrolyzers and hydrogen fuel cells, a multi-energy flow coupling model is constructed in combination with energy storage equipment. With the constraints of reserves, carbon emissions, renewable energy quota standards, and real-time balance of electricity, heat, and hydrogen supply and demand, and the goal of minimizing the total annual cost, a multi-energy flow coupling collaborative optimization architecture is built; The operation data and cost data are used as inputs of the multi-energy flow coupling collaborative optimization framework and solved to output the optimal capacity configuration strategy.

2. The method for optimizing the capacity of a multi-energy flow coupling system based on efficient electric hydrogen catalysis according to claim 1, characterized in that: The defect engineering introduces an irregular structure into the perfect lattice of the transition metal catalyst, and the defect engineering includes defects in three dimensions, namely: vacancy defects, screw dislocations, and stacking faults; the interface engineering uses chemical vapor deposition to form a multi-layer porous heterogeneous structure of the transition metal catalyst.

3. The method for optimizing the capacity of a multi-energy flow coupling system based on efficient electric hydrogen catalysis according to claim 1, characterized in that: The refined model of the water electrolysis cell is an output characteristic model of the water electrolysis cell, specifically: ; ;in, represents the hourly driving electric power required by the n-th water electrolysis cell; It represents the rated power of the nth water electrolysis cell; Indicates the minimum limit of the operation of the nth water electrolysis cell; n indicates the type of water electrolysis cell; represents the hourly hydrogen production of the nth water electrolysis cell; represents the electricity-to-hydrogen conversion efficiency of the nth water electrolyzer; Indicates the lower heating value of hydrogen.

4. The method for optimizing the capacity of a multi-energy flow coupling system based on efficient electric hydrogen catalysis according to claim 1, characterized in that: The refined model of the hydrogen fuel cell is an output characteristic model of the hydrogen fuel cell, specifically: ; ;in, represents the hourly power generation of the mth hydrogen fuel cell; Indicates the hourly hydrogen consumption of the mth hydrogen fuel cell; represents the hydrogen-electricity conversion efficiency of the mth hydrogen fuel cell; m represents the type of hydrogen fuel cell; represents the hourly heat production of the mth hydrogen fuel cell; represents the thermal conversion efficiency of the mth hydrogen fuel cell; represents the dissipation rate of the mth hydrogen fuel cell.

5. The method for optimizing the capacity of a multi-energy flow coupling system based on efficient electric hydrogen catalysis according to claim 1, characterized in that: Based on the refined models of water electrolyzers and hydrogen fuel cells, a multi-element energy flow coupling model is constructed in combination with energy storage equipment, including: renewable energy model, hydrogen storage tank model, heat storage tank model, power storage equipment model, electric heating device model and compressor model.

6. The method for optimizing the capacity of a multi-energy flow coupling system based on efficient electric hydrogen catalysis according to claim 1, characterized in that: The minimized annual total cost is the sum of investment cost, operation and maintenance cost, main grid electricity purchase cost and penalty cost minus the main grid electricity sales income.

7. The method for optimizing the capacity of a multi-energy flow coupling system based on efficient electric hydrogen catalysis according to claim 1, characterized in that: The water electrolysis cell includes: an alkaline water electrolysis cell, a proton exchange membrane water electrolysis cell and a solid oxide water electrolysis cell; 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.

8. Capacity optimization system of multi-energy flow coupling system based on efficient electric hydrogen catalysis, characterized in that: include: The catalytic efficiency improvement module is configured to: process the transition metal catalyst by using defect engineering and interface engineering to obtain a composite catalyst; The electric-hydrogen coupling model building module is configured to: use the composite catalyst in a water electrolysis cell and a hydrogen fuel cell, and respectively build refined models of the water electrolysis cell and the hydrogen fuel cell; The module for building a multi-energy flow coupling collaborative optimization framework is configured as follows: Based on the refined models of water electrolysis cells and hydrogen fuel cells, a multi-energy flow coupling model is built in combination with energy storage equipment, and a multi-energy flow coupling collaborative optimization framework is built with the constraints of reserves, carbon emissions, renewable energy quota standards, and real-time balance of electricity, heat, and hydrogen supply and demand power, with the goal of minimizing the annual total cost; The strategy output module is configured to: obtain operation data and cost data, input and solve the multi-energy flow coupling collaborative optimization framework, and output the optimal capacity configuration strategy.

9. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the steps in the method for optimizing the capacity of a multi-energy flow coupling system based on efficient electric hydrogen catalysis as described in any one of claims 1 to 7 are implemented.

10. 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 in the capacity optimization method of a multi-energy flow coupling system based on efficient electric hydrogen catalysis as described in any one of claims 1 to 7 are implemented.

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