Multi-element energy storage coupling double-layer planning method, system, equipment and medium

Abstract

The invention belongs to the technical field of power systems, and discloses a multi-element energy storage coupling double-layer planning method, system, equipment and medium, and the method comprises the following steps: based on the structural characteristics of a wind and light off-grid hydrogen production ammonia synthesis system, obtaining process data of an alkaline electric hydrogen production system, and constructing an electrolytic tank model by analyzing the reaction characteristics of electrolysis; according to the electrolytic tank model, analyzing a heat transfer mechanism in the alkaline electric hydrogen production system, and combining an energy conservation mechanism to construct a first-order energy conservation difference equation of the alkaline electric hydrogen production system; according to the method, a multi-element energy storage double-layer planning model of the wind-solar off-grid hydrogen production synthetic ammonia system is constructed, the multi-element energy storage double-layer planning model of the wind-solar off-grid hydrogen production synthetic ammonia system is solved through a first-order energy conservation difference equation of an alkaline electric hydrogen production system, and an optimal energy storage planning scheme of the wind-solar off-grid hydrogen production synthetic ammonia system is obtained. The method is beneficial for promoting consumption of large-scale renewable energy sources and green transformation of an energy system.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a multi-element energy storage coupled two-layer planning method, system, equipment and medium. Background Technology

[0002] With the continuous increase in the penetration rate of new energy sources, the inherent uncertainty, volatility, and anti-peak-shaving characteristics of their output will increasingly pose challenges to the safe and stable operation of the power system. Against this backdrop, flexible resources, as a key means of regulating power output and load demand and smoothing system net load fluctuations, are becoming increasingly strategically valuable.

[0003] In the chemical industry, the green transformation of the chemical sector, represented by petroleum refining, is key to my country's achievement of its dual-carbon goals. In my country, heavy chemical industrial parks, as major clusters of traditional chemical industries, have long faced significant energy consumption and environmental pollution, resulting in immense pressure on carbon emissions. Under international carbon emission reduction conventions and my country's low-carbon policies, heavy industry, as a high-energy-consuming and high-carbon-emission sector, faces stringent requirements. The EU's carbon border adjustment mechanism clearly stipulates that only industrial products with zero direct and indirect emissions during production can be exempted from carbon pricing. The EU's Renewable Energy Directive (REDII), adopted in February 2023, strictly regulates the certification scheme for green fuel chemicals, requiring only three types of products—hydrogen produced entirely through a green grid, hydrogen produced through direct wind-solar connection, and hydrogen produced through participation in PPA electricity trading—to be certified as "green hydrogen." This means that the entire heavy chemical industry needs to achieve 100% green energy use to gain a competitive edge in development.

[0004] Currently, achieving green electricity and green hydrogen substitution in the chemical industry still faces numerous challenges. These challenges primarily stem from the inherent volatility and uncertainty of renewable energy sources, which conflict with the requirements for safety and stable operation in chemical production processes. The output of renewable energy sources such as wind and solar power is significantly affected by weather conditions, leading to intermittent and unstable supply. However, traditional chemical industry process design and operating logic prioritize "safety, stability, long-term operation, full capacity, and optimization." These complex processes are subject to intricate safety constraints related to operating variables such as temperature and pressure, resulting in limited flexibility and extremely high demands for continuous and stable energy supply, directly conflicting with the volatility of renewable energy. Furthermore, the existing energy infrastructure in the chemical industry is largely based on fossil fuel design; achieving widespread application of green electricity and green hydrogen requires a large-scale restructuring of existing industrial park energy supply systems.

[0005] Therefore, promoting the transformation of chemical industrial parks towards a fully green electricity and green hydrogen power supply is not only a necessity to adapt to the times but also an inevitable requirement for the chemical industry to achieve sustainable development. At the same time, the power industry also faces challenges to the safety and stability of the power system brought about by the large-scale, fluctuating integration of wind and solar renewable energy. Utilizing wind and solar renewable energy to generate hydrogen on a large scale and driving a new green system for hydrogen production, storage, transmission, and utilization is a key technology for achieving large-scale utilization of renewable energy.

[0006] Therefore, how to provide a multi-element energy storage coupled two-layer planning method, system, equipment and medium is an urgent problem to be solved. Summary of the Invention

[0007] This invention provides a multi-element energy storage coupled two-layer planning method, system, device, and medium to solve the problems mentioned above in the prior art.

[0008] According to a first aspect of the present invention, a multi-element energy storage coupled two-layer planning method is provided.

[0009] In one embodiment, the multi-element energy storage coupled two-layer planning method includes the following steps:

[0010] Based on the structural characteristics of the off-grid hydrogen production and ammonia synthesis system, process data of the alkaline electro-hydrogen production system were obtained, and an electrolytic cell model was constructed by analyzing the reaction characteristics of electrolysis.

[0011] Based on the electrolytic cell model, the heat transfer mechanism inside the alkaline electro-hydrogen production system is analyzed, and combined with the energy conservation mechanism, a first-order energy conservation difference equation for the alkaline electro-hydrogen production system is constructed.

[0012] A multi-element energy storage bi-layer planning model for a wind-solar off-grid hydrogen production and ammonia synthesis system was constructed. The optimal energy storage planning scheme for the wind-solar off-grid hydrogen production and ammonia synthesis system was obtained by solving the first-order energy conservation difference equation of the alkaline electro-hydrogen production system.

[0013] In one embodiment, the process data of the alkaline electro-hydrogen production system obtained based on the structural characteristics of the off-grid wind-solar hydrogen production and ammonia synthesis system, and the construction of an electrolyzer model by analyzing the reaction characteristics of electrolysis, includes the following steps:

[0014] Based on the structural characteristics of the off-grid hydrogen production and ammonia synthesis system, the process flow of the alkaline electro-hydrogen production system is determined, and process data of the alkaline electro-hydrogen production system is collected according to the process flow of the alkaline electro-hydrogen production system. The process data of the alkaline electro-hydrogen production system includes the electrical energy consumed by electrolysis, the heat energy generated by electrolysis reaction, the voltage across the electrolytic cell, and the current passing through the electrolytic cell.

[0015] Based on the process data of the alkaline electrolytic hydrogen production system, calculate the heat generated by the electrolytic reaction in the electrolytic cell of the alkaline electrolytic hydrogen production system;

[0016] An electrolytic cell model is constructed based on the heat generated by the electrolytic reaction in the electrolytic cell.

[0017] In one embodiment, the step of analyzing the heat transfer mechanism inside the alkaline electrolytic hydrogen production system based on the electrolytic cell model and constructing the first-order energy conservation difference equation for the alkaline electrolytic hydrogen production system in conjunction with the energy conservation mechanism includes the following steps:

[0018] Based on the electrolytic cell model, the limiting conditions for the alkaline electrolytic hydrogen production system are configured, and a system thermal dynamic model including the electrolytic cell and cooling coil is constructed by analyzing the heat transfer mechanism inside the alkaline electrolytic hydrogen production system.

[0019] Based on the system thermal dynamics model including the electrolyzer and cooling coil, a linear energy conservation equation for the alkaline electro-hydrogen production system is constructed by analyzing the energy balance between the electrolyzer and cooling coil.

[0020] The linear energy conservation equation of the alkaline electro-hydrogen production system is processed by first-order difference to obtain the first-order energy conservation difference equation of the alkaline electro-hydrogen production system.

[0021] In one embodiment, the multi-element energy storage dual-layer planning model of the off-grid hydrogen production and ammonia synthesis system includes: an upper-layer planning model and a lower-layer planning model;

[0022] The upper-level planning model is used to solve the capacity configuration of the photo-ionized hydrogen production and ammonia synthesis system;

[0023] The lower-level planning model is used to solve the optimal operation strategy of the photo-off-grid hydrogen production and ammonia synthesis system.

[0024] In one embodiment, the objective function of the upper-level planning model includes: a first objective function that minimizes the total amount of electricity drawn from the power grid and a second objective function that minimizes the annualized total investment cost;

[0025] The expressions for the first objective function and the second objective function are as follows:

[0026]

[0027] In the formula, f1 represents the total amount of electricity drawn from the power grid, and P grid,t Let f2 represent the total power drawn from the power grid, Δt represent the total power output, and f2 represent the total investment cost. The investment recovery factor is represented by r, the annual bank interest rate is represented by n, and the system lifespan is represented by n. Indicates the rated capacity of wind power. Indicates the rated capacity of photovoltaic power. Indicates the rated power of energy storage. Indicates the rated capacity of energy storage. Indicates the rated power of the electrolytic cell. Indicates the volume of the hydrogen storage tank. k represents the rated yield of ammonia synthesis. wind Indicates the unit capacity cost of wind power, k solar Indicates the unit capacity cost of photovoltaic power, k es_P Indicates the unit power of the energy storage battery, k es_E Indicates the capacity cost of energy storage batteries, Indicates the unit power cost of the electrolytic cell, Indicates the unit volume cost of hydrogen storage tanks, This indicates the unit output cost of an ammonia synthesis unit.

[0028] In one embodiment, the objective function of the lower-level planning model is: an objective function that maximizes the renewable energy absorption rate, and the expression of the objective function that maximizes the renewable energy absorption rate is:

[0029]

[0030] In the formula, f3 represents the renewable energy integration rate. Indicates the power of the electrolytic cell. P represents the ammonia synthesis power. grid,t P represents the power drawn from the power grid. wind,t P represents wind power output. solar,t This indicates the output of photovoltaic power.

[0031] In one embodiment, solving the multi-element energy storage bi-level planning model of the off-grid hydrogen production and ammonia synthesis system to obtain the optimal energy storage planning scheme for the off-grid hydrogen production and ammonia synthesis system includes the following steps:

[0032] Based on the priority method of the main objective function, a preset solver is used to solve the upper-level planning model in the multi-element energy storage two-level planning model to obtain the capacity configuration results of the upper-level decision variables.

[0033] The known constraints of the capacity allocation results of the upper-level decision variables are substituted into the lower-level planning model to obtain the lower-level planning model based on the solution results of the upper level.

[0034] The lower-level planning model is solved based on the solution results of the upper-level model to obtain the operational data results of the lower-level decision variables. The capacity configuration results of the upper-level decision variables and the operational data results of the lower-level decision variables are taken as the optimal solution to obtain the optimal energy storage planning scheme of the wind-solar off-grid hydrogen production and ammonia synthesis system.

[0035] According to a second aspect of the present invention, a multi-element energy storage coupled two-layer planning system is provided.

[0036] In one embodiment, a multi-element energy storage coupled two-layer planning system includes:

[0037] The electrolytic cell model building module is used to obtain process data of the alkaline electro-hydrogen production system based on the structural characteristics of the off-grid hydrogen production and ammonia synthesis system of wind and solar power, and to build an electrolytic cell model by analyzing the reaction characteristics of electrolysis.

[0038] The equation construction module is used to analyze the heat transfer mechanism inside the alkaline electric hydrogen production system based on the electrolytic cell model, and to construct the first-order energy conservation difference equation of the alkaline electric hydrogen production system in combination with the energy conservation mechanism.

[0039] The optimal solution module is used to construct a multi-element energy storage dual-layer planning model for the off-grid hydrogen production and ammonia synthesis system of wind and solar power. It solves the multi-element energy storage dual-layer planning model of the off-grid hydrogen production and ammonia synthesis system of wind and solar power through the first-order energy conservation difference equation of the alkaline electro-hydrogen production system, and obtains the optimal energy storage planning scheme of the off-grid hydrogen production and ammonia synthesis system of wind and solar power.

[0040] In one embodiment, the process data of the alkaline electro-hydrogen production system obtained based on the structural characteristics of the off-grid wind-solar hydrogen production and ammonia synthesis system, and the construction of an electrolytic cell model by analyzing the reaction characteristics of electrolysis, includes:

[0041] Based on the structural characteristics of the off-grid hydrogen production and ammonia synthesis system, the process flow of the alkaline electro-hydrogen production system is determined, and process data of the alkaline electro-hydrogen production system is collected according to the process flow of the alkaline electro-hydrogen production system. The process data of the alkaline electro-hydrogen production system includes the electrical energy consumed by electrolysis, the heat energy generated by electrolysis reaction, the voltage across the electrolytic cell, and the current passing through the electrolytic cell.

[0042] Based on the process data of the alkaline electrolytic hydrogen production system, calculate the heat generated by the electrolytic reaction in the electrolytic cell of the alkaline electrolytic hydrogen production system;

[0043] An electrolytic cell model is constructed based on the heat generated by the electrolytic reaction in the electrolytic cell.

[0044] In one embodiment, the step of analyzing the heat transfer mechanism inside the alkaline electrolytic hydrogen production system based on the electrolytic cell model, and constructing the first-order energy conservation difference equation for the alkaline electrolytic hydrogen production system in conjunction with the energy conservation mechanism, includes:

[0045] Based on the electrolytic cell model, the limiting conditions for the alkaline electrolytic hydrogen production system are configured, and a system thermal dynamic model including the electrolytic cell and cooling coil is constructed by analyzing the heat transfer mechanism inside the alkaline electrolytic hydrogen production system.

[0046] Based on the system thermal dynamics model including the electrolyzer and cooling coil, a linear energy conservation equation for the alkaline electro-hydrogen production system is constructed by analyzing the energy balance between the electrolyzer and cooling coil.

[0047] The linear energy conservation equation of the alkaline electro-hydrogen production system is processed by first-order difference to obtain the first-order energy conservation difference equation of the alkaline electro-hydrogen production system.

[0048] In one embodiment, the multi-element energy storage dual-layer planning model of the off-grid hydrogen production and ammonia synthesis system includes: an upper-layer planning model and a lower-layer planning model;

[0049] The upper-level planning model is used to solve the capacity configuration of the photo-ionized hydrogen production and ammonia synthesis system;

[0050] The lower-level planning model is used to solve the optimal operation strategy of the photo-off-grid hydrogen production and ammonia synthesis system.

[0051] In one embodiment, the objective function of the upper-level planning model includes: a first objective function that minimizes the total amount of electricity drawn from the power grid and a second objective function that minimizes the annualized total investment cost;

[0052] The expressions for the first objective function and the second objective function are as follows:

[0053]

[0054] In the formula, f1 represents the total amount of electricity drawn from the power grid, and P grid,t Let f2 represent the total power drawn from the power grid, Δt represent the total power output, and f2 represent the total investment cost. The investment recovery factor is represented by r, the annual bank interest rate is represented by n, and the system lifespan is represented by n. Indicates the rated capacity of wind power. Indicates the rated capacity of photovoltaic power. Indicates the rated power of energy storage. Indicates the rated capacity of energy storage. Indicates the rated power of the electrolytic cell. Indicates the volume of the hydrogen storage tank. k represents the rated yield of ammonia synthesis. wind Indicates the unit capacity cost of wind power, k solar Indicates the unit capacity cost of photovoltaic power, k es_P Indicates the unit power of the energy storage battery, k es_E Indicates the capacity cost of energy storage batteries, Indicates the unit power cost of the electrolytic cell, Indicates the unit volume cost of hydrogen storage tanks, This indicates the unit output cost of an ammonia synthesis unit.

[0055] In one embodiment, the objective function of the lower-level planning model is: an objective function that maximizes the renewable energy absorption rate, and the expression of the objective function that maximizes the renewable energy absorption rate is:

[0056]

[0057] In the formula, f3 represents the renewable energy integration rate. Indicates the power of the electrolytic cell. P represents the ammonia synthesis power. grid,t P represents the power drawn from the power grid. wind,t P represents wind power output. solar,t This indicates the output of photovoltaic power.

[0058] In one embodiment, solving the multi-element energy storage bi-layer planning model of the off-grid hydrogen production and ammonia synthesis system to obtain the optimal energy storage planning scheme for the off-grid hydrogen production and ammonia synthesis system includes:

[0059] Based on the priority method of the main objective function, a preset solver is used to solve the upper-level planning model in the multi-element energy storage two-level planning model to obtain the capacity configuration results of the upper-level decision variables.

[0060] The known constraints of the capacity allocation results of the upper-level decision variables are substituted into the lower-level planning model to obtain the lower-level planning model based on the solution results of the upper level.

[0061] The lower-level planning model is solved based on the solution results of the upper-level model to obtain the operational data results of the lower-level decision variables. The capacity configuration results of the upper-level decision variables and the operational data results of the lower-level decision variables are taken as the optimal solution to obtain the optimal energy storage planning scheme of the wind-solar off-grid hydrogen production and ammonia synthesis system.

[0062] According to a third aspect of the present invention, a computer device is provided.

[0063] In some embodiments, the computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method described above.

[0064] According to a fourth aspect of the present invention, a computer-readable storage medium is provided.

[0065] In one embodiment, a computer program is stored on the computer-readable storage medium, which, when executed by a processor, implements the steps of the above method.

[0066] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0067] This invention explores the application of a new green electro-hydrogen coupling technology in the chemical industry for green electricity and green hydrogen substitution. This is not only an important way for the green transformation of the chemical industry, but also conducive to promoting the large-scale consumption of renewable energy and the green transformation of the energy system.

[0068] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0069] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0070] Figure 1 This is a flowchart illustrating a multi-element energy storage coupled two-layer planning method according to an exemplary embodiment;

[0071] Figure 2 This is a schematic diagram of a multi-element energy storage coupled two-layer planning system according to an exemplary embodiment;

[0072] Figure 3 This is a schematic diagram of the structure of a computer device according to an exemplary embodiment;

[0073] Figure 4 This is a schematic diagram illustrating the structural principle of a wind-solar off-grid hydrogen production and ammonia synthesis system in a multi-element energy storage coupled dual-layer planning method according to an exemplary embodiment;

[0074] Figure 5 This is a schematic diagram of the heat transfer process of a first-order thermal dynamic model of an alkaline electro-hydrogen production system in a multi-element energy storage coupled bi-layer planning method according to an exemplary embodiment.

[0075] Figure 6 This is a schematic diagram of the system bi-level programming solution structure in a multi-element energy storage coupled bi-level programming method according to an exemplary embodiment;

[0076] Figure 7 This is a graph showing the solution results of electrolyzer power and temperature in a multi-element energy storage coupled bilayer planning method according to an exemplary embodiment;

[0077] Figure 8 This is a graph showing the power balance optimization results (positive half-axis represents power generation, negative half-axis represents power absorption) in a multi-element energy storage coupled bi-layer planning method according to an exemplary embodiment.

[0078] Figure 9 This is a diagram illustrating the optimization results of hydrogen storage state in a multi-element energy storage coupled bi-layer planning method according to an exemplary embodiment;

[0079] Figure 10 This is a diagram illustrating the optimization results of hydrogen and ammonia production in a multi-element energy storage coupled bilayer planning method according to an exemplary embodiment;

[0080] Figure 11This is a diagram illustrating the impact of wind and solar volatility on energy storage and hydrogen production in a multi-element energy storage coupled bi-layer planning method according to an exemplary embodiment.

[0081] Figure 12 This is a schematic diagram illustrating the relationship between energy storage and hydrogen storage states and with hydrogen and ammonia production in a multi-element energy storage coupled bilayer planning method according to an exemplary embodiment. Detailed Implementation

[0082] Figure 1 An embodiment of a multi-element energy storage coupled two-layer planning method of the present invention is shown.

[0083] In this optional embodiment, the multi-element energy storage coupled two-layer planning method includes the following steps:

[0084] Step S101: Based on the structural characteristics of the off-grid hydrogen production and ammonia synthesis system, process data of the alkaline electro-hydrogen production system are obtained, and an electrolytic cell model is constructed by analyzing the reaction characteristics of electrolysis.

[0085] Step S102: Based on the electrolysis cell model, analyze the heat transfer mechanism inside the alkaline electro-hydrogen production system, and in conjunction with the energy conservation mechanism, construct the first-order energy conservation difference equation for the alkaline electro-hydrogen production system.

[0086] Step S103: Construct a multi-element energy storage dual-layer planning model for the off-grid hydrogen production and ammonia synthesis system of wind and solar power, and solve the multi-element energy storage dual-layer planning model of the off-grid hydrogen production and ammonia synthesis system of wind and solar power through the first-order energy conservation difference equation of the alkaline electro-hydrogen production system to obtain the optimal energy storage planning scheme of the off-grid hydrogen production and ammonia synthesis system of wind and solar power.

[0087] In this optional embodiment, when acquiring process data of the alkaline electro-hydrogen production system based on the structural characteristics of the off-grid wind-solar hydrogen production and ammonia synthesis system, and constructing an electrolytic cell model by analyzing the reaction characteristics of electrolysis, the process flow of the alkaline electro-hydrogen production system can be determined based on the structural characteristics of the off-grid wind-solar hydrogen production and ammonia synthesis system. Based on the process flow of the alkaline electro-hydrogen production system, process data of the alkaline electro-hydrogen production system is collected. This process data includes the electrical energy consumed by electrolysis, the heat generated by the electrolysis reaction, the voltage across the electrolytic cell, and the current flowing through the electrolytic cell. Based on the process data of the alkaline electro-hydrogen production system, the heat generated by the electrolysis reaction in the electrolytic cell of the alkaline electro-hydrogen production system is calculated. Based on the heat generated by the electrolysis reaction in the electrolytic cell, an electrolytic cell model is constructed.

[0088] It should be noted that the key energy conversion part of a new energy-driven chemical energy system lies in the coupling between the new energy power generation device on the source side and the chemical load equipment on the load side. The wind-solar off-grid ammonia production system, the subject of this invention, is an example. Figure 4As shown, the “source-grid-load-storage” system can be decomposed into the following four main parts: (1) Source: The power source of the system is composed of wind power and photovoltaic power generation output; (2) Grid: The small microgrid formed by the whole system is weakly connected to the large power grid, and it is required that power cannot be sent back to the grid, but power can be taken from the grid according to certain requirements; (3) Load: The load is mainly the chemical load of electric hydrogen production and synthetic ammonia, of which electric hydrogen production includes supporting cooling and heat dissipation equipment, and synthetic ammonia includes supporting air separation, compression and other equipment; (4) Storage: The energy storage and material storage part of the system is composed of electricity storage and hydrogen storage, which are used for electricity buffering and hydrogen buffering respectively.

[0089] The alkaline electrolytic hydrogen production system consists of two main components: the main process unit and auxiliary equipment. The main process unit is centered around an electrolyzer, which is formed by several small electrolytic cells connected in series electrically. Each small electrolytic cell, as the basic functional unit of the electrolysis reaction, comprises key components such as bipolar electrode pairs (cathode / anode), an ion-conducting medium (electrolyte), and a porous membrane material. Auxiliary equipment includes key auxiliary devices such as gas-liquid separation units, electrolyte circulation pump sets, and cooling devices. An integrated control system enables the coordinated operation of multiple devices, creating a steady-state operating environment for the electrolysis process.

[0090] Specifically, the core of an electrolytic cell is its various electrolytic cells. Therefore, the following section will first introduce the electrolytic cell model (i.e., the electrolytic cell model). In the following symbols, the subscript 'c' represents a cell.

[0091] The overall reaction equation for water electrolysis in a small electrolytic cell is:

[0092] H2O→H2+0.5O2;

[0093] In the formula, H2O represents the electrolysis of water, H2 represents hydrogen, and O2 represents oxygen.

[0094] In the electrolysis of water reaction, part of the electrical energy input to the cell is used for electrolysis, that is, electrical energy is converted into chemical energy in the products, and the other part is used to generate heat, that is, heat energy dissipated into the environment, which can be expressed as:

[0095]

[0096] In the formula, P c This indicates the electrical energy input to the electrolytic cell; This indicates the electrical energy consumed in electrolysis; H c This indicates that the electrolysis reaction generates heat energy. For ease of representation, power will be used to express energy in the following text.

[0097] The electrical energy input for the water electrolysis reaction in the electrolysis cell can be determined by the external voltage and current:

[0098] P c =U cI c ;

[0099] In the formula, P c U represents the electrical energy input to the electrolytic cell; c I represents the voltage applied across the electrolytic cell. c This indicates the current passing through the electrolysis cell.

[0100] According to Faraday's law, the amount of hydrogen produced during the electrolysis of water is directly proportional to the electrolysis current:

[0101]

[0102] In the formula, This indicates the molar flow rate of hydrogen, expressed in mol / s; n e The value is 2, which represents the number of electrons transferred in a single hydrogen molecule during the reaction; F represents the Faraday constant.

[0103] Since the portion of the electrical energy input to the small cell used for electrolysis is equal to the chemical energy contained in the products, and this chemical energy is numerically equal to the molar enthalpy change of the electrolysis reaction, the following relationship exists:

[0104]

[0105] In the formula: The molar enthalpy change of an electrolytic reaction is expressed in J / mol. Indicates the molar flow rate of hydrogen. This indicates that electrolysis consumes electrical energy.

[0106] Therefore, given the input voltage and current of the small cell, the heat generated by the reaction can be obtained:

[0107]

[0108] Therefore, for an electrolyzer consisting of N small cells connected in series, the input voltage, current, power, hydrogen production, and heat generated by the electrolyzer satisfy the following:

[0109] U s =NU c ;

[0110] I s =I c ;

[0111] P s =NP c ;

[0112]

[0113] H s =NH c ;

[0114] In the formula and the following symbols, the subscript 's' represents an electrolytic cell (stack); U s Indicates the input voltage of the electrolytic cell; I s P represents the input current of the electrolytic cell; s This indicates the input power of the electrolytic cell; Indicates the hydrogen production of the electrolyzer; H s This indicates the heat generated by the electrolytic cell.

[0115] In this optional embodiment, when analyzing the heat transfer mechanism inside the alkaline electrolytic hydrogen production system based on the electrolytic cell model and constructing the first-order energy conservation difference equation of the alkaline electrolytic hydrogen production system in conjunction with the energy conservation mechanism, the limiting conditions of the alkaline electrolytic hydrogen production system can be configured according to the electrolytic cell model. A system thermal dynamic model including the electrolytic cell and cooling coil can be constructed by analyzing the heat transfer mechanism inside the alkaline electrolytic hydrogen production system. Based on the system thermal dynamic model including the electrolytic cell and cooling coil, a linear energy conservation equation of the alkaline electrolytic hydrogen production system can be constructed by analyzing the energy balance between the electrolytic cell and the cooling coil. A first-order difference processing is performed on the linear energy conservation equation of the alkaline electrolytic hydrogen production system to obtain the first-order energy conservation difference equation of the alkaline electrolytic hydrogen production system.

[0116] It should be noted that, based on the small electrolysis cell model, the following modeling assumptions for the alkaline electrolytic hydrogen production system are proposed (the constraints of the alkaline electrolytic hydrogen production system):

[0117] (1) Mass changes within the open system are not considered;

[0118] (2) The temperature, voltage and current of the electrolytic cell are uniformly distributed in space;

[0119] (3) The cooling water inlet temperature is constant. Based on this, the following first-order energy conservation equation considering temperature dynamics is established.

[0120] Based on the internal heat transfer mechanism of the alkaline electrolytic hydrogen production system, the electrolyzer mainly consists of three mass elements: the electrolyzer, the gas-liquid separator, and the cooling coil. Simplifying the electrolyzer and gas-liquid separator, considering only the electrolyzer temperature and its heat exchange process with the cooling coil, a system thermal dynamic model is established, comprising two mass elements: the electrolyzer and the cooling coil. The system heat transfer process is as follows: Figure 5 As shown. The heat capacities of the electrolytic cell and the cooling coil are C, respectively. s With C cool The electrolytic cell temperature is T. s The outlet temperature of the cooling coil is T. cool .

[0121] Based on the system thermal dynamics model that includes two mass elements, the electrolytic cell and the cooling coil, the energy balance equations for the electrolytic cell and the cooling coil are as follows.

[0122] (1) Energy conservation equation for electrolytic cells:

[0123]

[0124] In the formula, C s T represents the heat capacity of the electrolytic cell. s,t H represents the temperature of the electrolytic cell at time t. s,t H represents the heat generation term in electrolysis. dis,s,t k represents the natural heat dissipation term. cool A represents the heat transfer coefficient between the electrolytic cell and the cooling coil. cool ΔT represents the heat exchange area between the electrolytic cell and the cooling coil. t This represents the logarithmic heat transfer temperature difference between the electrolytic cell and the cooling coil.

[0125] For the electrolysis heat generation term, based on the heat generated by the reaction, we obtain:

[0126]

[0127] In the formula, H s,t P represents the heat generation term in electrolysis. s,t Indicates the input power of the electrolytic cell. Indicates the molar flow rate of hydrogen. This represents the molar enthalpy change of an electrolytic reaction.

[0128] Since the molar enthalpy change of the electrolytic reaction differs very little at different temperatures, the molar enthalpy change at 80℃ is taken as .

[0129] For natural heat dissipation, the thermal resistance of the electrolytic cell is used for calculation:

[0130]

[0131] In the formula, H dis,s,t T represents the natural heat dissipation term. s,t T represents the temperature of the electrolytic cell at time t. amb Rs represents the ambient temperature and Rs represents the thermal resistance of the electrolytic cell.

[0132] (2) The energy conservation equation for the cooling coil is:

[0133] C cool (T cool,in -T cool,t )=k cool A cool ΔT t ;

[0134] In the formula, C cool T represents the heat capacity of the cooling coil. cool,in Indicates the inlet temperature of the cooling coil, Tcool,t This indicates the cooling water temperature at the cooling coil outlet, kJ. cool A represents the heat transfer coefficient between the electrolytic cell and the cooling coil. cool ΔT represents the heat exchange area between the electrolytic cell and the cooling coil. t This represents the logarithmic heat transfer temperature difference between the electrolytic cell and the cooling coil.

[0135] The formula for calculating the logarithmic heat transfer temperature difference is as follows:

[0136]

[0137] In the formula, ΔT t T represents the logarithmic heat transfer temperature difference between the electrolytic cell and the cooling coil. cool,in Indicates the inlet temperature of the cooling coil, T s,t T represents the temperature of the electrolytic cell at time t. cool,t This indicates the temperature of the cooling water at the outlet of the cooling coil.

[0138] Therefore, based on the energy conservation equation of the cooling coil and the calculation formula for the logarithmic heat transfer temperature difference, the cooling water temperature at the cooling coil outlet can be obtained:

[0139]

[0140] For the logarithmic mean temperature difference ΔT t According to the simulation results, the inlet and outlet temperatures of the cooling water differ by only about 2℃, and the temperature difference between the electrolytic cell and the cooling water exceeds 40℃, which meets the requirement of using the arithmetic mean temperature difference to replace the logarithmic mean temperature difference. Therefore, the arithmetic mean temperature difference is used for linearization.

[0141]

[0142] Substituting the formula for calculating the cooling water temperature at the cooling coil outlet and the energy conservation equation for the cooling coil into the linearization formula for the arithmetic mean temperature difference yields the arithmetic mean temperature difference, which is calculated as follows:

[0143]

[0144] Therefore, substituting the above arithmetic mean temperature difference into the energy conservation equation of the electrolytic cell, we get:

[0145]

[0146] From this, we obtain information about P. s,t , and T s,t The linear energy conservation equation for an alkaline electro-hydrogen production system.

[0147] A first-order difference approximation of the linear energy conservation equation for an alkaline electrochemical hydrogen production system yields the energy conservation difference equation:

[0148]

[0149] Thus, the first-order energy conservation difference equation for the alkaline electrohydrogen production system, which can be substituted into the bilevel programming model for solution, was obtained.

[0150] In addition, for the bi-level programming problem of the multi-element energy storage bi-level programming model of the wind-solar off-grid hydrogen production and ammonia synthesis system, the upper level solves the capacity configuration and the lower level solves the optimization operation strategy. Therefore, the decision variables are classified into upper-level decision variables and lower-level decision variables, as shown in Table 1 and Table 2.

[0151] Table 1 Upper-level decision variables

[0152]

[0153] Table 2 Lower-level decision variables

[0154]

[0155]

[0156] In Table 1, the upper-level decision variables are the rated values ​​of each part of the system, which are 1×1 matrices; in Table 2, the lower-level variables are the actual operating values ​​of each part of the system at each moment, which are T×1 matrices, where T is the known normalized power output data of wind and solar power. If it is the actual data for the whole year, then T = 35040.

[0157] For upper-level planning, on the one hand, it is necessary to minimize the cost of capacity configuration, and on the other hand, to operate as independently as possible, drawing power from the grid as little as possible. Therefore, the two objective functions are set to minimize the total amount of power drawn from the grid and minimize the annualized total investment cost, respectively.

[0158] In this optional embodiment, the multi-element energy storage dual-layer planning model of the photo-off-grid hydrogen production and ammonia synthesis system includes: an upper-layer planning model and a lower-layer planning model;

[0159] The upper-level planning model is used to solve the capacity configuration of the photo-ionized hydrogen production and ammonia synthesis system;

[0160] The lower-level planning model is used to solve the optimal operation strategy of the photo-off-grid hydrogen production and ammonia synthesis system.

[0161] In this optional embodiment, the objective function of the upper-level planning model includes: a first objective function that minimizes the total amount of electricity drawn from the power grid and a second objective function that minimizes the annualized total investment cost;

[0162] The expressions for the first objective function and the second objective function are as follows:

[0163]

[0164] In the formula, f1 represents the total amount of electricity drawn from the power grid, and P grid,t Let f2 represent the total power drawn from the power grid, Δt represent the total power output, and f2 represent the total investment cost. denoted by , r represents the bank's annual interest rate, taken as 1.5%, and n represents the operating life of the off-grid hydrogen production and ammonia synthesis system, taken as 20 years. Indicates the rated capacity of wind power. Indicates the rated capacity of photovoltaic power. Indicates the rated power of energy storage. Indicates the rated capacity of energy storage. Indicates the rated power of the electrolytic cell. Indicates the volume of the hydrogen storage tank. k represents the rated yield of ammonia synthesis. wind Indicates the unit capacity cost of wind power, k solar Indicates the unit capacity cost of photovoltaic power, k es_P Indicates the unit power of the energy storage battery, k es_E Indicates the capacity cost of energy storage batteries, Indicates the unit power cost of the electrolytic cell, Indicates the unit volume cost of hydrogen storage tanks, This indicates the unit output cost of an ammonia synthesis unit.

[0165] Specifically, for multi-objective function problems at the upper level, the priority method of the main objective function is adopted. First, the optimization is performed with the minimum total power consumption of the power grid as the single objective, and the result is used as the constraint on the total power consumption of the power grid. Then, the optimization problem is solved again with the minimum annualized total investment cost as the single objective.

[0166] It should be noted that for lower-level planning, the renewable energy absorption rate needs to be as high as possible during system operation, so the objective function is set to maximize the renewable energy absorption rate.

[0167] In this optional embodiment, the objective function of the lower-level planning model is: an objective function that maximizes the renewable energy absorption rate, and the expression of the objective function that maximizes the renewable energy absorption rate is:

[0168]

[0169] In the formula, f3 represents the renewable energy integration rate. Indicates the power of the electrolytic cell. P represents the ammonia synthesis power. grid,t P represents the power drawn from the power grid. wind,t P represents wind power output. solar,t This indicates the output of photovoltaic power.

[0170] In addition, the constraints on the multi-element energy storage two-layer planning model for off-grid hydrogen production and ammonia synthesis systems include:

[0171] (1) Wind farm and photovoltaic power station model;

[0172] The wind turbine's output power P at each moment wind,t The relationship between wind speed v(t) and the turbine output-wind speed operating characteristic curve can be approximated by a piecewise function as shown in the following formula:

[0173]

[0174] In the formula, The rated output power of the wind turbine is expressed in MW; v(t) represents the wind speed at any given moment, in m / s; v C Indicates the wind turbine cut-in speed, v F Indicates the cut-out speed of the wind turbine, v R This indicates the rated wind speed of the fan.

[0175] Therefore, based on the formula for calculating the output power of a wind turbine at each moment, it can be assumed that, given the normalized power output curve of the wind farm, the wind power output is proportional to the rated power, i.e., the wind power output constraint is:

[0176]

[0177] In the formula: This represents the normalized wind power output at each known moment.

[0178] The expression for the basic output model of a photovoltaic power generation system is as follows:

[0179]

[0180] In the formula: P solar,t This indicates the output of a photovoltaic power station at any given moment, expressed in MW. R(t) represents the rated output of the photovoltaic panel; R(t) represents the solar radiation intensity at each moment, in W / m². 2 ;R STC This represents the solar radiation intensity under standard test conditions, at 1000 W / m². 2 ;α P The power temperature coefficient of the photovoltaic panel is represented by T(t); T(t) represents the temperature of the photovoltaic panel at each moment, in °C. STC This indicates the temperature of the photovoltaic panel under standard test conditions, which is 25°C.

[0181] Therefore, based on the expression of the basic output model of a photovoltaic power generation system, it can be assumed that, given the normalized output curve of a photovoltaic power station, the photovoltaic output is proportional to the rated power, i.e., the photovoltaic output constraint is:

[0182]

[0183] In the formula: For each known moment, the photovoltaic normalized output is provided.

[0184] (2) Microgrid model;

[0185] For a microgrid system, the energy balance condition is that energy generation equals energy consumption at every moment. Therefore, the energy balance constraint for a microgrid system is defined by the following formula:

[0186]

[0187] In the formula: the left side of the equal sign represents the electrical quantity generated, P wind,t P solar,t P grid,t P es_discharge,t These represent the wind power and solar power output, the power drawn from the main grid, and the energy storage discharge power at each moment, respectively; the right side of the equal sign represents the power consumption. P es_charge,t P loss,t These represent the power output for hydrogen production by electricity, ammonia synthesis, energy storage charging, and power curtailment, respectively. All power units are in MW.

[0188] Secondly, for scenarios where power is drawn from the power grid, there is a power transmission limit on the distribution network transmission lines, thus constraining the power drawn from the power grid:

[0189]

[0190] In the formula, P grid,t This represents the power output taken by the power grid at each moment; This represents the upper limit of power transmission in the power distribution network transmission lines, taken as 10MW.

[0191] Considering the practical interaction between microgrids and the main power grid, interaction with the main power grid should be minimized. Therefore, drawing power from the main power grid cannot be done arbitrarily. Only when the discharge power of wind power, photovoltaic power, and energy storage cannot sustain the power consumption at a certain moment is it permissible to draw as little power from the main power grid as possible. Therefore, the following formula defines the constraints on the power grid drawing strategy:

[0192]

[0193] Similarly, the definition of annual power grid withdrawal time stipulates that the system's independent operating time throughout the year must be no less than 4500 hours, meaning the annual power grid withdrawal time must not exceed 4260 hours. Therefore, there is a constraint on the number of hours of power grid withdrawal.

[0194]

[0195] Where: STATE grid,t Δt represents the 01 variable, used to record the power grid's power intake status; Δt represents the time granularity, which is 15 min in the model. This indicates the maximum number of hours of electricity to be drawn throughout the year, which is 4260 hours.

[0196] (3) Load model;

[0197] This invention utilizes the alkaline electrolyzer, which is currently the lowest-cost and most maturely applied. Under the premise of primarily considering the steady-state external characteristic model, it is assumed that the conversion efficiency remains constant during operation. Therefore, the electro-hydrogen coupling relationship of the electro-hydrogen production load can be defined as the electro-hydrogen conversion constraint of the electrolyzer as follows:

[0198]

[0199] In the formula: This represents the amount of hydrogen produced at each moment, in units of m. 3 ; This represents the hydrogen production power at any given moment, expressed in MW. This represents the electro-hydrogen conversion coefficient, taken as 0.00477 MWh / m 3 This is equivalent to producing 1m 3 The production of hydrogen requires the consumption of 0.00477 MWh of electricity.

[0200] The load adjustment range for a single alkaline electrolyzer is 20%-100%, and the upward and downward ramp rates of the load power are both set at 20% of the rated power / s. The power adjustment range constraints and power ramp constraints for the electrolyzer are specified as follows:

[0201]

[0202] In the formula, Indicates the rated power of the electrolytic cell; This indicates the power ramp-up rate of the electrolytic cell. This indicates the power of hydrogen production at any given moment. This indicates the power of hydrogen production at the previous moment.

[0203] Furthermore, considering the temperature dynamics of the alkaline electrolytic hydrogen production system and the energy balance constraints of the electrolyzer derived from the first-order energy conservation difference equation of the alkaline electrolytic hydrogen production system, the two operating indicators are the variable temperature range and the temperature ramp-up capability. The variable temperature range of the alkaline electrolyzer is taken as 65-100℃, and the upward and downward temperature ramp-up rates are both taken as 10℃ / 15min. Therefore, the variable temperature range constraints and ramp-up constraints of the electrolyzer are specified as follows:

[0204] 338.15≤T s,t ≤373.15;

[0205] -10≤T s,t -T s,t-1 ≤10;

[0206] In the formula, Ts,t T represents the temperature of the electrolytic cell at time t. s,t-1 This indicates the temperature of the electrolytic cell at time t-1.

[0207] Since ammonia synthesis is primarily a chemical reaction process, the main electricity consumption in the chemical synthesis process comes from air separation and air compression units, and the electricity consumption is directly proportional to the ammonia production. The electro-ammonia conversion constraint of the ammonia synthesis unit can be defined by the following formula:

[0208]

[0209] In the formula, The power of the ammonia synthesis unit at any given moment is expressed in MW. This represents the ammonia production at each moment, in kg; P asac This represents the power of air separation and air compression, taken as 0.0009 MWh / kg.

[0210] Based on the chemical formula for ammonia synthesis, a simple expression for the hydrogen-ammonia conversion can be obtained, specifying the constraints for the hydrogen-ammonia conversion in the ammonia synthesis unit:

[0211]

[0212] In the formula, This indicates the mass of hydrogen used at each moment, expressed in kg.

[0213] The load power adjustment range for the ammonia synthesis unit is set at 30%-110%, with both the upward and downward ramp rates set at 20% of the rated power / h. Therefore, the power adjustment range constraints and power ramp constraints for the ammonia synthesis unit are specified separately:

[0214]

[0215] In the formula: Indicates rated ammonia production, kg ( / 15min); This represents the ammonia production ramp-up rate of the ammonia synthesis unit, taken as 5% ( / 15min).

[0216] In addition, regarding the product definition and output requirements, the annual ammonia production needs to reach 0.1 million tons, therefore, a total ammonia production constraint is stipulated:

[0217]

[0218] In the formula: This represents the lower limit of total ammonia production, taken as 10. 6 kg.

[0219] (4) Energy storage and hydrogen storage models;

[0220] Regarding energy storage capacity indicators, energy storage capacity constraints are specified:

[0221]

[0222] In the formula, This indicates the rated capacity of the energy storage battery, in MWh; E es,t This indicates the battery charge level at any given moment. Because overcharging and over-discharging of energy storage batteries can affect their lifespan, energy storage capacity constraints stipulate that the battery charge level should be between 10% and 90% of its rated capacity.

[0223] Regarding energy storage power specifications, the energy storage charging and discharging power constraints are as follows:

[0224]

[0225] In the formula, This represents the upper limit of the charging and discharging power of energy storage batteries, measured in MW.

[0226] Regarding the relationship between capacity and power indicators, capacity-power balance constraints for energy storage batteries can be defined:

[0227]

[0228] In the formula, E es,t E represents the battery charge at any given moment. es,t-1 The energy storage battery charge τ represents the charge level τ of the battery at the previous moment. es Indicates the self-discharge rate of the energy storage battery; η es This represents the charge / discharge efficiency of the energy storage battery. Considering the most common lithium battery, the two indicators are taken as 3% and 90%, respectively. The capacity-power balance constraint can be understood as the relationship between the difference in battery capacity between two time points and the difference in charge / discharge during that time period. Considering the continuity of time, it is also necessary to specify the initial and final state constraints of the energy storage battery capacity:

[0229]

[0230] In the formula, E es,1 This represents the initial state constraint of the energy storage battery's charge level; E es,end This represents the final state constraint indicating the energy storage battery's charge level. This indicates the rated capacity of the energy storage battery, expressed in MWh.

[0231] Furthermore, frequent charging and discharging can significantly impair the performance and lifespan of energy storage batteries. Therefore, when the output of wind and solar power exceeds the power output of hydrogen production and ammonia synthesis, the energy storage battery is not allowed to discharge; conversely, when the output of wind and solar power is less than the power output of hydrogen production and ammonia synthesis, the energy storage battery is not allowed to charge. There are possible constraints on the energy storage battery charging and discharging strategy:

[0232]

[0233] Regarding the capacity index of hydrogen storage tanks, describing the hydrogen storage capacity using intuitive physical quantities such as amount of substance or mass is not practical for engineering applications. Furthermore, considering that the hydrogen storage tank is a constant-volume container, if the specified hydrogen storage pressure is below 10 MPa, the conversion between different physical quantities of hydrogen can be achieved using the ideal gas law pV = n′RT′ (where p represents gas pressure, V represents gas volume, n′ represents the amount of substance, R represents the gas constant, and T′ represents gas temperature). Therefore, the hydrogen storage tank pressure can be used to represent the hydrogen storage capacity. Thus, the hydrogen storage capacity of a hydrogen storage tank is constrained by the hydrogen storage pressure.

[0234]

[0235] In the formula, This indicates the pressure of the hydrogen storage tank at any given moment, expressed in MPa. and These are the lower and upper limits of the hydrogen storage tank pressure, respectively, taken as 1 MPa and 10 MPa.

[0236] For the inlet and outlet gas flow rates of the hydrogen storage tank, constraints on the hydrogen production and consumption flow rates of the hydrogen storage tank can be specified:

[0237]

[0238] In the formula: and These represent the amount of hydrogen produced and consumed at time t, in mol. This indicates the upper limit of hydrogen flow rate, taken as 25%. This indicates the volume of the hydrogen storage tank, in meters (m³). 3 .

[0239] Regarding the relationship between hydrogen storage capacity and inlet / outlet gas flow rate, a hydrogen storage tank capacity-flow balance constraint can be defined:

[0240]

[0241] The capacity-flow balance constraint can be understood as the relationship between the difference in hydrogen storage capacity in the hydrogen storage tank between two points in time and the difference in hydrogen production and consumption during that time period. Considering the continuity of time, it is also necessary to specify the initial and final state constraints on the hydrogen storage capacity in the hydrogen storage tank:

[0242]

[0243] In the formula, This indicates the initial state of the hydrogen storage tank's hydrogen capacity. This indicates the final state of the hydrogen storage tank's hydrogen level.

[0244] In this optional embodiment, when solving the multi-element energy storage bi-level planning model of the off-grid hydrogen production and ammonia synthesis system to obtain the optimal energy storage planning scheme of the system, the upper-level planning model in the multi-element energy storage bi-level planning model can be solved using a preset solver based on the priority method of the main objective function to obtain the capacity configuration results of the upper-level decision variables. The known constraints of the capacity configuration results of the upper-level decision variables are then substituted into the lower-level planning model to obtain the lower-level planning model based on the upper-level solution results. The lower-level planning model based on the upper-level solution results is then solved to obtain the operational data results of the lower-level decision variables. The capacity configuration results of the upper-level decision variables and the operational data results of the lower-level decision variables are used as the optimal solution to obtain the optimal energy storage planning scheme of the off-grid hydrogen production and ammonia synthesis system.

[0245] It should be noted that the solution structure of the upper-level planning-lower-level execution two-level planning method designed in this invention is as follows: Figure 6 As shown, based on the known conditions, it mainly consists of the following two parts:

[0246] (1) Perform capacity planning on the upper-level model;

[0247] Since all constraints of the bilevel programming problem are set to linear constraints, the decision variables, constraints and the upper and lower level objective function are combined, and the priority method of the main objective function is adopted. The bilevel programming problem is substituted into MATLAB and the optimization result is solved by the CPLEX solver in Yalmip. The result of the capacity configuration of the upper level decision variables is obtained first.

[0248] (2) Run the simulation of the lower-level model;

[0249] After obtaining the optimization results of the upper-level decision variables, these results are treated as known conditions and used as constraints, then re-introduced into the optimization model. By combining the upper and lower-level decision variables, constraints, and the lower-level objective function, the optimization problem is solved again, yielding the results of the lower-level decision variable execution data. By solving the upper and lower-level objective functions separately, the coupling of the two levels of decision variables is achieved, resulting in an optimal solution that simultaneously satisfies the constraints of both levels.

[0250] To facilitate understanding of the above-mentioned technical solutions of the present invention, the following is a further explanation of the above-mentioned technical solutions of the present invention from the perspective of numerical example analysis, as follows:

[0251] (1) Case Setting; This invention uses the power output curves of a wind farm and photovoltaic power station in Dongying City, Shandong Province, China as a case study; for example, Figure 1A two-tiered planning configuration is implemented for the off-grid ammonia production microgrid consisting of wind and solar power. The normalized power output curves of local wind farms and photovoltaic power stations for the entire year of 2024 are used. 1440 data points from July 1st to 15th (15 days in summer) are taken as a case study for verification calculations. Corresponding annual indicators are proportionally reduced with wind and solar power output, such as annual independent operating hours and total annual ammonia production. Rated hydrogen production is 500 Nm³. 3 The relevant parameters of the first-order linearized thermal dynamic model of the alkaline electrohydrogen production system (simplified alkaline electrohydrogen production system parameters) are shown in Table 3.

[0252] Table 3 Simplified parameters of alkaline electrochemical hydrogen production system

[0253] parameter Value Number of cascaded small pools N 298 <![CDATA[Small pond area A c > <![CDATA[2m 2 ]]> <![CDATA[Thermal resistance R of electrolytic cell s > 0.001K / W <![CDATA[Total heat transfer coefficient k of the cooling coil cool A cool > 10kW / K <![CDATA[Cooling coil heat capacity C cool > 1.15 MJ / K

[0254] (2) Analysis of the temperature and thermal dynamic characteristics of the alkaline electro-hydrogen production system; First, 192 data points over 48 hours from July 1st to 2nd in summer were used as a case study for verification calculation. The results of the electrolyzer power and temperature calculation are as follows: Figure 7 The left axis represents the electrolytic cell power, corresponding to the green line; the right axis represents the electrolytic cell temperature, corresponding to the blue line.

[0255] Figure 7 The left line of the red dashed box in the figure represents the moment when the power of the electrolytic cell changes, and the right line represents the moment when the temperature of the electrolytic cell begins to respond. There is a time delay of about 30 minutes between the two, which illustrates the lag in the response of thermodynamic quantities to electrical quantities. Figure 7 The left line of the red solid box in the image represents the moment when the electrolytic cell power completes its change, and the right line represents the moment when the electrolytic cell temperature response ends. The dynamic change process of temperature can be clearly observed. Compared with the power change, the temperature dynamic has a time constant on the order of hours. In many cases, the temperature directly enters the next dynamic response process before it reaches a steady state due to the power change.

[0256] This fully illustrates that although the power of the electrolyzer is highly flexible and can be matched with the fluctuations of renewable energy, its temperature response is a dynamic process with a large time constant, demonstrating the necessity of considering the temperature thermal dynamics of the electro-hydrogen production system during system planning and configuration.

[0257] (3) Analysis of system optimization results without considering the temperature dynamic constraints of the alkaline electric hydrogen production system; Without considering the temperature dynamic constraints of the alkaline electric hydrogen production system, the normalized output data of wind and solar power over 15 days are substituted to solve the bi-level programming problem in a coupled manner. The optimization results of the upper-level capacity configuration and objective function are shown in Table 4. According to the optimization results in Table 4, the wind power capacity is greater than the photovoltaic capacity, which corresponds to the stability of wind power and the volatility of photovoltaic power.

[0258] Table 4. Results of Upper-Level Capacity Configuration and Objective Function Optimization

[0259] Solving variables Optimization results Rated wind power capacity (MW) 3.6205 Rated photovoltaic capacity (MW) 2.2532 Rated energy storage capacity (MW) 1.2973 Rated energy storage capacity (MWh) 4.0475 Rated power of electrolytic cell (MW) 2.0750 <![CDATA[Volume of hydrogen storage tank (m 3 )]]> 94.6011 Rated ammonia synthesis yield (kg / h) 142.4451 Investment cost (million RMB) 9.9582 Renewable energy consumption rate 91.9100％

[0260] The optimization results for the lower-level operation strategy power balance, energy storage hydrogen storage status, and hydrogen ammonia production are as follows: Figure 8 , Figure 9 , Figure 10 As shown.

[0261] according to Figure 8 The optimized power balance results in the majority of power being used for hydrogen production and consumption in electrolyzers, a small portion for ammonia synthesis, and a tiny fraction for energy storage, resulting in a very low curtailment rate. This aligns with the nearly 92% renewable energy consumption rate shown in Table 4. However, even with this high rate, the wind and solar power consumption rate has not reached an even higher level. This is because when wind and solar power output is low, such as during certain periods from day 9 to 13, electricity needs to be drawn from the grid to compensate for the minimum compliance requirements of hydrogen production and ammonia synthesis. This increase in electricity drawage prevents the wind and solar power consumption rate from reaching a higher level of over 95%.

[0262] according to Figure 10 The optimization results for hydrogen and ammonia production show that ammonia production is significantly less flexible than hydrogen production, aiming to maintain stable operation for as long as possible, which is consistent with the difference in flexibility between the two loads.

[0263] (4) Analysis of the operating status of the source-load-storage system;

[0264] 1) The impact of wind and solar volatility on energy storage and hydrogen production by electricity. Comparison curves of normalized wind and solar power output with normalized energy storage capacity, and hydrogen production by electricity power curves are plotted, such as... Figure 11 Based on the relationship between wind and solar power output and energy storage, as well as the changes in hydrogen production power, three typical scenarios can be analyzed:

[0265] Intermittent type, corresponding Figure 11 Scenario 1: Wind and solar power output is low, accompanied by obvious periodic fluctuations, with periods of simultaneous wind and solar deprivation occurring at night (on the nights of the 8th, 9th, 10th, and 12th). Although wind and solar power output is low, in order to maintain the minimum output for nighttime load, it is still necessary to consume a portion of wind and solar power output for energy storage during the day and release the stored energy at night to balance the minimum load.

[0266] Fluctuation type, corresponding to Figure 11 Scenario 2: The wind and solar power output is relatively high, the wind and solar complementarity is weak, the fluctuation is strong and the peak-valley difference is obvious. Under the condition of meeting the load conditions and capacity, the load follows the source as much as possible, and the excess output after full load operation is used for energy storage (noon on the second day).

[0267] Big hairstyle, corresponding Figure 11Scenario 3: The wind and solar power output is relatively high, and the wind and solar complementarity is strong. It can maintain stable output for a long time. Excess output is stored to ensure stable operation at full load for as long as possible. Power is abandoned only after the energy storage capacity reaches the upper limit (noon on the 4th day).

[0268] 2) The relationship between energy storage and hydrogen storage status, and between energy storage and hydrogen and ammonia production, is illustrated by plotting the comparison curves of normalized energy storage capacity and normalized hydrogen storage capacity, as well as the comparison curve of normalized hydrogen and ammonia production. Figure 12 .

[0269] Regarding the correspondence between energy storage (hydrogen storage) and hydrogen / ammonia production, in Figure 12 In the diagram above, the release of energy storage often corresponds to the hydrogen production in the diagram below maintaining full-load or minimum-load operation, and the release of hydrogen storage in the diagram above often corresponds to the ammonia production in the diagram below maintaining full-load or minimum-load operation. This fully illustrates the buffering role of energy storage and hydrogen storage in the fluctuations of wind and solar power output and the fluctuations of hydrogen production by electricity, respectively.

[0270] It is worth noting that the release of stored hydrogen always has a certain time lag compared to energy storage, such as... Figure 12 The left and right borders of the red box. Since the ramp-up performance of the ammonia synthesis unit is significantly lower than that of the electrolyzer, in order to maintain the ammonia synthesis unit under stable operating conditions as much as possible, the first priority is to maintain stable hydrogen production by releasing energy storage, and the second priority is to maintain stable hydrogen consumption by releasing stored hydrogen. That is, energy storage release takes precedence over hydrogen storage release. The electrolyzer matches the source-side fluctuations with its relatively high dynamic performance, and the release of energy storage and stored hydrogen matches the low flexibility of ammonia synthesis.

[0271] (5) Comparative analysis of optimization results before and after considering the temperature dynamic constraints of the alkaline electro-hydrogen production system;

[0272] Without considering the dynamic temperature constraints of the alkaline electrohydrogen production system, the normalized power output data of wind and solar power over 15 days is substituted to solve the bi-level programming problem in a coupled manner. Combining the optimization results in section 4.3, and considering the solution speed, the wind and solar capacities are fixed at 4MW and 2MW, respectively. The optimization results of the upper-level capacity configuration and objective function are shown in Table 5.

[0273] Table 5 shows that after considering the dynamic characteristics of electro-hydrogen production, the most significant difference in capacity configuration occurs between the energy storage and electro-hydrogen production stages, with little impact on subsequent hydrogen storage and ammonia synthesis. This aligns with the conclusion that energy storage and electro-hydrogen production are strongly coupled, as are hydrogen storage and ammonia synthesis, indicating that the dynamic process of electro-hydrogen production primarily affects the electro-hydrogen conversion process. Increasing energy storage power actually leads to a decrease in capacity; simultaneously, increasing electrolyzer power is also beneficial. On one hand, increasing electrolyzer power requires a larger adjustment range to adapt to the dynamic process; on the other hand, the decrease in energy storage capacity is due to the increased rated power of the electrolyzer reducing the required storage capacity. Furthermore, considering the dynamic characteristics actually increases the wind and solar energy absorption rate, which is also related to the increased rated power of the electrolyzer.

[0274] Table 5 Comparison of optimization results before and after considering the dynamic characteristics of electro-hydrogen production.

[0275] Solving variables Original optimization results Optimization results considering the dynamic characteristics of electro-hydrogen production Rated energy storage capacity (MW) 1.2073 1.5386 Rated energy storage capacity (MWh) 3.8390 3.0068 Rated power of electrolytic cell (MW) 2.1329 2.3309 <![CDATA[Volume of hydrogen storage tank (m 3 )]]> 96.1090 99.4687 Rated ammonia synthesis yield (kg / h) 147.9598 151.1119 Investment cost (million RMB) 10.3493 10.5484 Renewable energy consumption rate 91.9300％ 93.3100％

[0276] Considering the fluctuations in hydrogen production power and energy storage capacity, Table 6 shows the variances of normalized hydrogen production power and normalized energy storage capacity before and after considering the dynamic characteristics of hydrogen production. It is evident that the variance of normalized hydrogen production power decreased by 20.1265%, indicating that the fluctuations in hydrogen production power are smaller due to the inclusion of the electrolyzer's dynamic characteristics, resulting in a more stable operating strategy compared to when dynamic characteristics are not considered. Conversely, the variance of normalized energy storage capacity increased by 23.2108%, indicating a stronger degree of variation in energy storage capacity and more frequent energy storage utilization. This is also intended to mitigate the fluctuations caused by the electrolyzer's dynamic characteristics.

[0277] Table 6 Comparison of the variances of hydrogen production power and energy storage capacity before and after considering the dynamic characteristics of hydrogen production by electrolysis.

[0278]

[0279] This invention, based on a multi-element energy storage coupled bi-layer planning method considering the continuity of new energy and load output, incorporates a first-order thermodynamic model of an alkaline electro-hydrogen system considering temperature. Using a wind-solar off-grid ammonia production system as an example, the model is validated through numerical examples. Coupled bi-layer planning is then performed on the wind-solar off-grid ammonia production system before and after considering the temperature dynamic constraints of the alkaline electro-hydrogen system. Electrolyzer verification results show that the electrolyzer temperature response lags behind the power response and exhibits a dynamic process with a large time constant, illustrating the necessity of considering the thermodynamics of the electro-hydrogen system during system planning and configuration. System optimization results indicate that different wind and solar power output scenarios are key factors affecting the operation of energy storage and load power. It was found that hydrogen release always has a certain time delay relative to energy storage, indicating that energy storage release has a higher priority than hydrogen release. The electrolyzer matches the source-side volatility with relatively high dynamic performance, while the release of energy and hydrogen matches the low flexibility of ammonia synthesis. Finally, comparing the optimization results before and after considering the dynamic characteristics of electro-hydrogen, it was found that while the rated power of the electrolyzer increased, the rated capacity of the energy storage decreased. This is due to the electrolyzer needing a larger power adjustment range to adapt to the dynamic process. Meanwhile, by analyzing the fluctuations in hydrogen production power and energy storage capacity, it was found that the fluctuations in hydrogen production power decreased, and the electrolyzer operation strategy became more stable; the fluctuations in energy storage capacity increased, and energy storage was called up more frequently, which is also to smooth out the fluctuations caused by the dynamic characteristics of the electrolyzer.

[0280] Figure 2 An embodiment of a multi-element energy storage coupled two-layer planning system of the present invention is shown.

[0281] In this optional embodiment, the multi-element energy storage coupled two-layer planning system includes:

[0282] The electrolytic cell model construction module 201 is used to obtain process data of the alkaline electro-hydrogen production system based on the structural characteristics of the off-grid hydrogen production and ammonia synthesis system of wind and solar power, and to construct an electrolytic cell model by analyzing the reaction characteristics of electrolysis.

[0283] The equation construction module 202 is used to analyze the heat transfer mechanism inside the alkaline electric hydrogen production system based on the electrolytic cell model, and to construct the first-order energy conservation difference equation of the alkaline electric hydrogen production system in combination with the energy conservation mechanism.

[0284] The optimal solution solution module 203 is used to construct a multi-element energy storage dual-layer planning model for the wind-solar off-grid hydrogen production and ammonia synthesis system. It solves the multi-element energy storage dual-layer planning model for the wind-solar off-grid hydrogen production and ammonia synthesis system through the first-order energy conservation difference equation of the alkaline electro-hydrogen production system, and obtains the optimal energy storage planning scheme for the wind-solar off-grid hydrogen production and ammonia synthesis system.

[0285] In this optional embodiment, the step of acquiring process data of the alkaline electro-hydrogen production system based on the structural characteristics of the off-grid wind-solar hydrogen production and ammonia synthesis system, and constructing an electrolytic cell model by analyzing the reaction characteristics of electrolysis, includes: determining the process flow of the alkaline electro-hydrogen production system based on the structural characteristics of the off-grid wind-solar hydrogen production and ammonia synthesis system; collecting process data of the alkaline electro-hydrogen production system according to the process flow, wherein the process data of the alkaline electro-hydrogen production system includes the electrical energy consumed by electrolysis, the heat energy generated by the electrolysis reaction, the voltage across the electrolytic cell, and the current flowing through the electrolytic cell; calculating the heat generated by the electrolysis reaction in the alkaline electro-hydrogen production system based on the process data of the alkaline electro-hydrogen production system; and constructing an electrolytic cell model based on the heat generated by the electrolysis reaction in the electrolytic cell.

[0286] In this optional embodiment, the step of analyzing the heat transfer mechanism inside the alkaline electrolytic hydrogen production system based on the electrolytic cell model and constructing the first-order energy conservation difference equation of the alkaline electrolytic hydrogen production system in conjunction with the energy conservation mechanism includes: configuring the limiting conditions of the alkaline electrolytic hydrogen production system according to the electrolytic cell model, and constructing a system thermal dynamic model including the electrolytic cell and cooling coil by analyzing the heat transfer mechanism inside the alkaline electrolytic hydrogen production system; constructing a linear energy conservation equation of the alkaline electrolytic hydrogen production system by analyzing the energy balance between the electrolytic cell and the cooling coil based on the system thermal dynamic model including the electrolytic cell and cooling coil; and performing a first-order difference processing on the linear energy conservation equation of the alkaline electrolytic hydrogen production system to obtain the first-order energy conservation difference equation of the alkaline electrolytic hydrogen production system.

[0287] In this optional embodiment, the multi-element energy storage bi-layer planning model of the photovoltaic off-grid hydrogen production and ammonia synthesis system includes: an upper-layer planning model and a lower-layer planning model; the upper-layer planning model is used to solve for the capacity configuration of the photovoltaic off-grid hydrogen production and ammonia synthesis system; the lower-layer planning model is used to solve for the optimal operation strategy of the photovoltaic off-grid hydrogen production and ammonia synthesis system.

[0288] In this optional embodiment, the objective function of the upper-level planning model includes: a first objective function that minimizes the total amount of electricity drawn from the power grid and a second objective function that minimizes the annualized total investment cost;

[0289] The expressions for the first objective function and the second objective function are as follows:

[0290]

[0291] In the formula, f1 represents the total amount of electricity drawn from the power grid, and P grid,t Let f2 represent the total power drawn from the power grid, Δt represent the total power output, and f2 represent the total investment cost. The investment recovery factor is represented by r, the annual bank interest rate is represented by n, and the system lifespan is represented by n. Indicates the rated capacity of wind power. Indicates the rated capacity of photovoltaic power. Indicates the rated power of energy storage. Indicates the rated capacity of energy storage. Indicates the rated power of the electrolytic cell. Indicates the volume of the hydrogen storage tank. k represents the rated yield of ammonia synthesis. wind Indicates the unit capacity cost of wind power, k solar Indicates the unit capacity cost of photovoltaic power, k es_P Indicates the unit power of the energy storage battery, k es_E Indicates the capacity cost of energy storage batteries, Indicates the unit power cost of the electrolytic cell, Indicates the unit volume cost of hydrogen storage tanks, This indicates the unit output cost of an ammonia synthesis unit.

[0292] In this optional embodiment, the objective function of the lower-level planning model is: an objective function that maximizes the renewable energy absorption rate, and the expression of the objective function that maximizes the renewable energy absorption rate is:

[0293]

[0294] In the formula, f3 represents the renewable energy integration rate. Indicates the power of the electrolytic cell. P represents the ammonia synthesis power. grid,t P represents the power drawn from the power grid. wind,t P represents wind power output.solar,t This indicates the output of photovoltaic power.

[0295] In this optional embodiment, solving the multi-element energy storage bi-level planning model of the off-grid hydrogen production and ammonia synthesis system to obtain the optimal energy storage planning scheme for the system includes: using a preset solver based on the priority method of the main objective function to solve the upper-level planning model of the multi-element energy storage bi-level planning model, obtaining the capacity configuration results of the upper-level decision variables; substituting the known constraints of the capacity configuration results of the upper-level decision variables into the lower-level planning model to obtain the lower-level planning model based on the upper-level solution results; solving the lower-level planning model based on the upper-level solution results to obtain the operating data results of the lower-level decision variables; and using the capacity configuration results of the upper-level decision variables and the operating data results of the lower-level decision variables as the optimal solution to obtain the optimal energy storage planning scheme for the off-grid hydrogen production and ammonia synthesis system.

[0296] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 3 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores static and dynamic information data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements the steps in the above method embodiments.

[0297] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the computer device to which the present invention is applied. A specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0298] In addition, the present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0299] In addition, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0300] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0301] This invention is not limited to the structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this invention is limited only by the appended claims.

Claims

1. A multi-element energy storage coupled bi-level programming method, characterized in that, The method comprises the following steps: Based on the structural characteristics of the wind-solar off-grid hydrogen synthesis ammonia system, process data of the alkaline electrolytic hydrogen production system are obtained, and an electrolytic cell model is constructed by analyzing the reaction characteristics of electrolysis; According to the electrolytic cell model, the heat transfer mechanism inside the alkaline electrolytic hydrogen production system is analyzed, and a first-order energy conservation difference equation of the alkaline electrolytic hydrogen production system is constructed in combination with the energy conservation mechanism; A multi-energy storage double-layer planning model of the wind-solar off-grid hydrogen synthesis ammonia system is constructed, and the multi-energy storage double-layer planning model of the wind-solar off-grid hydrogen synthesis ammonia system is solved through the first-order energy conservation difference equation of the alkaline electrolytic hydrogen production system to obtain an optimal energy storage planning scheme of the wind-solar off-grid hydrogen synthesis ammonia system.

2. The multi-energy coupled bi-level programming method of claim 1, wherein, The method comprises the following steps: Based on the structural characteristics of the wind-solar off-grid hydrogen synthesis ammonia system, the process data of the alkaline electrolytic hydrogen production system are obtained, and an electrolytic cell model is constructed by analyzing the reaction characteristics of electrolysis; Based on the structural characteristics of the wind-solar off-grid hydrogen synthesis ammonia system, the process flow of the alkaline electrolytic hydrogen production system is determined, and the process data of the alkaline electrolytic hydrogen production system are collected according to the process flow of the alkaline electrolytic hydrogen production system, wherein the process data of the alkaline electrolytic hydrogen production system include electrolytic power consumption, electrolytic reaction generated heat, electrolytic cell voltage, and electrolytic cell current; According to the process data of the alkaline electrolytic hydrogen production system, the heat generated by the electrolytic reaction of the electrolytic cell in the alkaline electrolytic hydrogen production system is calculated; 3. The multi-energy coupled bi-level programming method of claim 1, wherein, Based on the heat generated by the electrolytic reaction of the electrolytic cell, an electrolytic cell model is constructed. The method comprises the following steps: According to the electrolytic cell model, the limited conditions of the alkaline electrolytic hydrogen production system are configured, and a system thermal dynamic model including an electrolytic tank and a cooling coil is constructed by analyzing the heat transfer mechanism inside the alkaline electrolytic hydrogen production system; According to the system thermal dynamic model including the electrolytic tank and the cooling coil, a linear energy conservation equation of the alkaline electrolytic hydrogen production system is constructed by analyzing the energy balance of the electrolytic tank and the cooling coil; 4. The method of claim 1, wherein, The linear energy conservation equation of the alkaline electrolytic hydrogen production system is first-order difference processed to obtain a first-order energy conservation difference equation of the alkaline electrolytic hydrogen production system. The multi-energy storage double-layer planning model of the wind-solar off-grid hydrogen synthesis ammonia system comprises an upper-layer planning model and a lower-layer planning model; The upper-layer planning model is used for solving the capacity configuration of the wind-solar off-grid hydrogen synthesis ammonia system; 5. The method of claim 4, wherein, The lower-layer planning model is used for solving the optimal operation strategy of the wind-solar off-grid hydrogen synthesis ammonia system. The objective function of the upper-layer planning model comprises a first objective function of minimizing the total amount of power grid electricity and a second objective function of minimizing the annual total investment cost; In the formula, f1 represents the total amount of power taken from the grid, P grid,t represents the power taken from the grid, Δt represents the time period, f2 represents the total investment cost, represents the investment recovery coefficient, r represents the annual interest rate of the bank, and n represents the system operation life, represents the wind power rated capacity, represents the photovoltaic rated capacity, represents the energy storage rated power, represents the energy storage rated capacity, represents the electrolyzer rated power, represents the hydrogen storage tank volume, represents the synthetic ammonia rated yield, k wind represents the wind power unit capacity cost, k solar represents the photovoltaic unit capacity cost, k es_P represents the energy storage battery unit power cost, k es_E represents the energy storage battery capacity cost, represents the electrolyzer unit power cost, represents the hydrogen storage tank unit volume cost, represents the synthetic ammonia device unit yield cost.

6. The multi-energy coupled bi-level programming method of claim 4, wherein, The expressions of the first objective function and the second objective function are respectively as follows: In the formula, f3 represents the renewable energy consumption rate, represents the electrolytic tank power, represents the synthetic ammonia power, P grid,t represents the power grid power, P wind,t represents the wind power output, P solar,t represents the photovoltaic output.

7. The method of claim 4, wherein, The objective function of the lower-layer planning model is a target function of maximizing the renewable energy consumption rate, and the expression of the target function of maximizing the renewable energy consumption rate is as follows: The method comprises the following steps: Based on the main objective function priority method, a preset solver is used to solve the upper layer planning model in the multi-element energy storage bi-level planning model, and the capacity configuration result of the upper layer decision variable is obtained; The capacity configuration result of the upper layer decision variable is known as a constraint condition, which is substituted into the lower layer planning model to obtain the lower layer planning model based on the above solving result; The lower layer planning model based on the above solving result is solved to obtain the operation data result of the lower layer decision variable, and the capacity configuration result of the upper layer decision variable and the operation data result of the lower layer decision variable are taken as the optimal solution to obtain the optimal energy storage planning scheme of the wind-solar off-grid hydrogen synthesis ammonia system.

8. A multi-energy storage coupled bi-level planning system, characterized in that, The system comprises: An electrolytic cell model construction module is configured to obtain process data of the alkaline electrolytic hydrogen production system based on structural characteristics of the wind-solar off-grid hydrogen synthesis ammonia system, and construct an electrolytic cell model by analyzing reaction characteristics of electrolysis; An equation construction module is configured to analyze heat transfer mechanisms inside the alkaline electrolytic hydrogen production system according to the electrolytic cell model, and construct a first-order energy conservation difference equation of the alkaline electrolytic hydrogen production system in combination with an energy conservation mechanism; An optimal scheme solving module is configured to construct a multi-element energy storage bi-level planning model of the wind-solar off-grid hydrogen synthesis ammonia system, and solve the multi-element energy storage bi-level planning model of the wind-solar off-grid hydrogen synthesis ammonia system by the first-order energy conservation difference equation of the alkaline electrolytic hydrogen production system to obtain an optimal energy storage planning scheme of the wind-solar off-grid hydrogen synthesis ammonia system.

9. The multi-energy coupled bi-level programming system of claim 8, wherein, The alkaline electrolytic hydrogen production system based on the structural characteristics of the wind-solar off-grid hydrogen synthesis ammonia system, obtaining process data of the alkaline electrolytic hydrogen production system, and constructing an electrolytic cell model by analyzing reaction characteristics of electrolysis comprises: Based on the structural characteristics of the wind-solar off-grid hydrogen synthesis ammonia system, the process flow of the alkaline electrolytic hydrogen production system is determined, and process data of the alkaline electrolytic hydrogen production system is collected according to the process flow of the alkaline electrolytic hydrogen production system, wherein the process data of the alkaline electrolytic hydrogen production system includes electrolytic power consumption, electrolytic reaction generated heat, electrolytic cell voltage, and electrolytic current passing through the electrolytic cell; According to the process data of the alkaline electrolytic hydrogen production system, the heat generated by the electrolytic reaction of the electrolytic cell in the alkaline electrolytic hydrogen production system is calculated; Based on the heat generated by the electrolytic reaction of the electrolytic cell, the electrolytic cell model is constructed.

10. The multi-energy coupled bi-level programming system of claim 8, wherein, The alkaline electrolytic hydrogen production system based on the structural characteristics of the wind-solar off-grid hydrogen synthesis ammonia system, obtaining process data of the alkaline electrolytic hydrogen production system, and constructing an electrolytic cell model by analyzing reaction characteristics of electrolysis comprises: According to the electrolytic cell model, the limited conditions of the alkaline electrolytic hydrogen production system are configured, and a system thermal dynamic model including an electrolytic cell and a cooling coil is constructed by analyzing the heat transfer mechanism inside the alkaline electrolytic hydrogen production system; According to the system thermal dynamic model including the electrolytic cell and the cooling coil, the alkaline electrolytic hydrogen production system linear energy conservation equation is constructed by analyzing the energy balance of the electrolytic cell and the cooling coil; The alkaline electrolytic hydrogen production system linear energy conservation equation is first-order difference processed to obtain the first-order energy conservation difference equation of the alkaline electrolytic hydrogen production system.

11. The multi-energy coupled bi-level programming system of claim 8, wherein, The multi-element energy storage bi-level planning model of the wind-solar off-grid hydrogen synthesis ammonia system comprises an upper layer planning model and a lower layer planning model; The upper layer planning model is configured to solve the capacity configuration of the wind-solar off-grid hydrogen synthesis ammonia system; The lower-layer planning model is used for solving an optimal operation strategy of the off-grid hydrogen synthesis ammonia system.

12. The multi-energy coupled bi-level programming system of claim 11, wherein, The objective function of the upper-layer planning model comprises a first objective function of minimizing total power grid electricity consumption and a second objective function of minimizing annual total investment cost; Expressions of the first objective function and the second objective function are respectively as follows: wherein f1 represents the total amount of power taken from the grid, P grid,t represents the power taken from the grid, Δt represents the time period, f2 represents the total investment cost, represents the investment recovery coefficient, r represents the annual interest rate of the bank, and n represents the system operation life, represents the rated capacity of the wind power, represents the rated capacity of the photovoltaic, represents the rated power of the energy storage, represents the rated capacity of the energy storage, represents the rated power of the electrolyzer, represents the volume of the hydrogen storage tank, represents the rated production rate of the ammonia synthesis, k wind represents the unit capacity cost of the wind power, k solar represents the unit capacity cost of the photovoltaic, k es_P represents the unit power cost of the energy storage battery, k es_E represents the capacity cost of the energy storage battery, represents the unit power cost of the electrolyzer, represents the unit volume cost of the hydrogen storage tank, represents the unit production cost of the ammonia synthesis device.

13. The multi-energy coupled bi-level programming system of claim 12, wherein, An objective function of the lower-layer planning model is a highest renewable energy consumption rate objective function, and an expression of the highest renewable energy consumption rate objective function is as follows: In the formula, f3 represents the renewable energy consumption rate, represents the electrolytic tank power, represents the synthetic ammonia power, P grid,t represents the power grid power, P wind,t represents the wind power output, P solar,t represents the photovoltaic output.

14. The multi-energy coupled bi-level programming system of claim 12, wherein, Solving the multi-energy storage double-layer planning model of the wind-solar off-grid hydrogen synthesis ammonia system, an optimal energy storage planning scheme of the wind-solar off-grid hydrogen synthesis ammonia system is obtained, which comprises: Based on a main objective function priority method, a preset solver is used to solve the upper-layer planning model in the multi-energy storage double-layer planning model, and a capacity configuration result of the upper-layer decision variable is obtained; The capacity configuration result of the upper-layer decision variable is substituted into the lower-layer planning model as a known constraint condition, and the lower-layer planning model based on the upper-layer solving result is obtained; The lower-layer planning model based on the upper-layer solving result is solved, and an operation data result of the lower-layer decision variable is obtained; the capacity configuration result of the upper-layer decision variable and the operation data result of the lower-layer decision variable are taken as an optimal solution, and the optimal energy storage planning scheme of the wind-solar off-grid hydrogen synthesis ammonia system is obtained.

15. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that, The processor executes the computer program to implement the steps of the method in any one of claims 1 to 7.

16. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method in any one of claims 1 to 7.

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