Configuration method, device, and medium for a hydrogen production system integrating wind energy and solar energy

Abstract

The embodiments of this specification specifically relate to a configuration method, device, and medium for a hydrogen production system that couples wind energy and solar energy. In the method, based on the constraint information, the external environmental parameter information, hydrogen demand information, and real-time power consumption of the electrolyzer module at each moment within a preset future time period, a multi-objective genetic algorithm is used to solve the objective function to obtain the configured installed capacity of the power generation module, the configured upper limit of the charge of the energy storage module, and the configured rated power of the energy storage module. The configured installed capacity of the power generation module, the configured upper limit of the charge of the energy storage module, and the configured rated power of the energy storage module obtained by the solution can not only ensure the electrolysis power requirements of the electrolyzer corresponding to the hydrogen demand information, but also ensure that the total construction cost of the hydrogen production system is lower, and the power loss is smaller when the power generation module and the energy storage module jointly supply the electrolysis power of the electrolyzer.

Description

Technical Field

[0001] Multiple embodiments of this specification relate to the technical field of system configuration, and specifically relate to a configuration method, device, and medium for a hydrogen production system that couples wind energy and solar energy. Background Art

[0002] In recent years, the demand for energy in various countries around the world has been continuously climbing, and the energy consumption has been increasing. However, the reserves of traditional fossil fuels are decreasing day by day, and the contradiction between insufficient energy supply capacity and strong energy demand continues to intensify. At the same time, the problem of global warming caused by the combustion of fossil fuels is also becoming increasingly serious. As the main consumption field of fossil fuels, the carbon emissions and carbon-containing by-products generated during the production process of the heavy chemical industry are not conducive to the realization of the green and low-carbon goal.

[0003] Existing methanol and methane production technologies rely heavily on raw materials such as natural gas and coal, which results in a large amount of carbon emissions during the production process and is not conducive to the realization of the green and low-carbon goal. To solve the carbon emission problem, renewable energy can be introduced during the production process. However, the inherent randomness and volatility of renewable energy will affect its power generation, thereby having a negative impact on the production process.

[0004] To achieve the goal of green and low-carbon production, there is currently an improved methane and methanol production method. In this method, external hydrogen is introduced to reduce the carbon content in the coal chemical process. To ensure that the introduced hydrogen does not cause carbon emissions, renewable energy can be used for power generation, and then water electrolysis is carried out to produce hydrogen, ensuring that all the hydrogen used in the coal chemical process is green hydrogen. In addition, the by-product oxygen generated by water electrolysis for hydrogen production is also used in the coal processing process, and together with the raw coal, it is used for water coal slurry gasification to produce synthesis gas mainly composed of carbon monoxide and hydrogen. This processing technology itself has the advantages of stable combustion and less pollution emissions, and is more energy-saving and environmentally friendly than traditional methods. Moreover, this production process effectively utilizes the by-products of water electrolysis for hydrogen production, avoiding waste of resources. However, due to environmental factors, the power generation power of renewable energy fluctuates, so it is impossible to always ensure that the power generation power is equal to the power required for electrolysis. Therefore, an additional energy storage module needs to be set up under this method to store the excess electric energy generated by the power generation module, or use the stored excess electric energy to supply power to the electrolyzer when the electric energy generated by the power generation module is insufficient to supply power for the electrolyzer to carry out electrolysis. And there is a large gap in the upfront construction costs required for renewable energy power generation modules with different installed capacities and energy storage modules with different upper charge limits and different rated powers; in addition, for renewable energy power generation modules with different installed capacities and energy storage modules with different upper charge limits and different rated powers, when jointly supplying power to the electrolysis module, the amount of resource abandonment is also different. Therefore, before applying this method for methane and methanol production, it is necessary to effectively configure the installed capacity of the renewable energy power generation module, the upper charge limit of the energy storage module, and the rated power of the energy storage module. Summary of the Invention

[0005] The embodiments of this specification provide a configuration method, device, and medium for a hydrogen production system that couples wind energy and solar energy.

[0006] The technical solution is as follows:

[0007] In a first aspect, the embodiments of this specification provide a configuration method for a hydrogen production system that couples wind energy and solar energy. The hydrogen production system includes a power generation module, an energy storage module, and an electrolyzer module. The power generation module is respectively connected to the energy storage module and the electrolyzer module, and the energy storage module is also connected to the electrolyzer module. The power generation module is used to generate electrical energy for electrolysis by the electrolyzer, the energy storage module is used to store the excess electrical energy generated by the power generation module, and the energy storage module is also used to supply power to the electrolyzer when the electrical energy generated by the power generation module is insufficient for electrolysis by using the stored excess electrical energy. The configuration method includes:

[0008] Obtain the external environment parameter information and hydrogen demand information at each moment within a future preset time period;

[0009] Based on the hydrogen demand information at each moment within the future preset time period, determine the real-time consumption power of the electrolyzer module at each moment within the future preset time period;

[0010] Establish an objective function with the total system construction cost and resource abandonment rate as the optimization objectives;

[0011] Obtain the constraint information, and on the basis of the constraint information, the external environment parameter information, hydrogen demand information, and real-time consumption power of the electrolyzer module at each moment within the future preset time period, use a multi-objective genetic algorithm to solve the objective function to obtain the configured installed capacity of the power generation module, the configured upper limit charge capacity of the energy storage module, and the configured rated power of the energy storage module;

[0012] The constraint information includes:

[0013] At each moment within the future preset time period, the maximum power supply of the system obtained from the power generation power of the power generation module, the real-time charge capacity of the energy storage module, the time interval between the current moment and the next moment, and the configured rated power of the energy storage module is greater than or equal to the real-time consumption power of the electrolyzer module;

[0014] At each moment within the future preset time period, the real-time charge capacity of the energy storage module is less than or equal to the configured upper limit charge capacity;

[0015] At each moment within the future preset time period, the real-time charge and discharge power of the energy storage module is less than or equal to the configured rated power;

[0016] The total construction cost of the system includes the construction cost of the power generation module and the construction cost of the energy storage module. The construction cost of the power generation module is obtained based on the configured installed capacity of the power generation module, and the construction cost of the energy storage module is obtained based on the configured upper limit of the charge and the configured rated power of the energy storage module;

[0017] The resource abandonment rate is calculated based on the configured installed capacity, the external environment parameter information at each moment within a future preset time period, the real-time charge of the energy storage module at each moment within the future preset time period, and the real-time power consumption of the electrolyzer module.

[0018] As a preferred solution, the power generation module includes a wind power generation unit and a solar power generation unit, and the configured installed capacity includes the installed capacity of the wind power generation unit and the installed capacity of the solar power generation unit;

[0019] The construction cost of the power generation module includes the construction cost of the wind power generation unit and the construction cost of the solar power generation unit;

[0020] The construction cost of the wind power generation unit is obtained based on the installed capacity of the wind power generation unit;

[0021] The construction cost of the solar power generation unit is obtained based on the installed capacity of the solar power generation unit.

[0022] As a preferred solution, the external environment parameter information includes wind speed information and solar irradiance information;

[0023] The power generation power of the power generation module is the sum of the power generation power of the wind power generation unit and the power generation power of the solar power generation unit;

[0024] The power generation power of the wind power generation unit is calculated based on the wind speed information and the installed capacity of the wind power generation unit;

[0025] The power generation power of the solar power generation unit is calculated based on the solar irradiance information and the installed capacity of the solar power generation unit.

[0026] As a preferred solution, the real-time power consumption of the electrolyzer module at each moment within the future preset time period is equal to the electrolysis power required for the electrolyzer module to generate the hydrogen demand at that moment.

[0027] As a preferred solution, the calculation formula for the resource abandonment rate is:

[0028]

[0029] Among them, L represents the resource abandonment rate, P loss represents the amount of resource abandonment, P use represents the total power generation, T represents the total number of moments within the future preset time period, P W(i) represents the power generation of the wind power generation unit at the future i-th moment, ρ W represents the energy conversion efficiency of the wind power generation unit, P P (i) represents the power generation of the solar power generation unit at the future i-th moment, ρ P represents the energy conversion efficiency of the solar power generation unit, P EL (i) represents the real-time power consumption of the electrolyzer module at the future i-th moment, Y(i) represents the time interval between the future i-th moment and the (i + 1)-th moment, α represents the energy conversion efficiency of the energy storage module, E B (i + 1) represents the real-time state of charge of the energy storage module at the future (i + 1)-th moment, E B (i) represents the real-time state of charge of the energy storage module at the future i-th moment. The resource abandonment amount is calculated for the moments when the power generation of all power generation modules is greater than the real-time power consumption of the electrolyzer module within a preset future time length.

[0030] As a preferred solution:

[0031] P W (i) = f(S i , P W ) * P W ;

[0032] P P (i) = X(T i , P P ) * P P ;

[0033] Among them, S i represents the wind speed at the future i-th moment, T i represents the solar irradiance at the future i-th moment, f(·) represents the wind speed energy efficiency conversion ratio function, X(·) represents the solar irradiance energy efficiency conversion ratio function, P W represents the installed capacity of the wind power generation unit, P P represents the installed capacity of the solar power generation unit.

[0034] As a preferred solution, constructing the wind speed energy efficiency conversion ratio function includes:

[0035] Obtain a first data set, where the first data set includes multiple first data subsets, and each first data subset includes the power generation data of the wind power generation unit under different wind speed environmental conditions corresponding to the installed capacity of the wind power generation unit;

[0036] Construct a first multiple regression function, where the independent variables in the first multiple regression function are wind speed and the installed capacity of the wind power generation unit, and the dependent variable in the first multiple regression function is the wind speed energy efficiency conversion ratio;

[0037] Solve the first multiple regression function based on the first data set to construct a wind speed energy efficiency conversion ratio function.

[0038] As a preferred solution, constructing a solar energy efficiency conversion ratio function includes:

[0039] Obtain a second data set, where the second data set includes multiple second data subsets, and each second data subset includes the power generation data of the solar power generation unit under different solar irradiation environmental conditions at the corresponding installed capacity of the solar power generation unit;

[0040] Construct a second multiple regression function, where the independent variables in the second multiple regression function are solar irradiation and the installed capacity of the solar power generation unit, and the dependent variable in the second multiple regression function is the solar energy efficiency conversion ratio;

[0041] Solve the second multiple regression function based on the second data set to construct a solar energy efficiency conversion ratio function.

[0042] In a second aspect, an embodiment of the present specification provides an electronic device, including a processor and a memory; the processor is connected to the memory; the memory is used to store executable program code; the processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory to execute the steps described in the first aspect of the above embodiment.

[0043] In a third aspect, an embodiment of the present specification provides a computer storage medium, which stores multiple instructions, and the instructions are suitable for being loaded and executed by a processor to execute the steps described in the first aspect of the above embodiment.

[0044] The beneficial effects brought by the technical solutions provided by some embodiments of the present specification at least include:

[0045] Based on the constraint information, the external environment parameter information, hydrogen demand information, and the real-time power consumption of the electrolyzer module at each moment within the future preset time length, use the multi-objective genetic algorithm to solve the objective function to obtain the configured installed capacity of the power generation module, the configured upper limit charge of the energy storage module, and the configured rated power of the energy storage module. The obtained configured installed capacity of the power generation module, the configured upper limit charge of the energy storage module, and the configured rated power of the energy storage module can not only ensure the electrolysis power requirements of the electrolyzer corresponding to the hydrogen demand information, but also ensure that the total construction cost of the hydrogen production system is lower, and the power loss during the combined supply of the electrolysis power of the electrolyzer by the power generation module and the energy storage module is smaller. Description of the Drawings

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0047] Figure 1 It is a schematic flowchart of a configuration method for a hydrogen production system coupling wind energy and solar energy provided by an embodiment of this specification.

[0048] Figure 2 It is a schematic structural diagram of a configuration system for a hydrogen production system coupling wind energy and solar energy provided by an embodiment of this specification.

[0049] Figure 3 It is a schematic structural diagram of an electronic device provided by an embodiment of this specification. Detailed implementation manners

[0050] The following will clearly and completely describe the technical solutions in the embodiments of this specification with reference to the accompanying drawings in the embodiments of this specification.

[0051] The terms "first", "second", "third", etc. in the specification, claims and the above accompanying drawings of this specification are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or devices.

[0052] The following description provides examples and does not limit the scope, applicability or examples set forth in the claims. Changes can be made to the functions and arrangements of the described elements without departing from the scope of the content of this specification. Various processes or components can be appropriately omitted, substituted or added to each example. For example, the described methods can be executed in a different order from the described order, and various steps can be added, omitted or combined. In addition, the features described in some examples can be combined into other examples.

[0053] Before explaining the configuration method of the hydrogen production system coupling wind energy and solar energy through multiple embodiments of this specification, the methanol and methane preparation technologies will be explained first:

[0054] Methanol preparation technology:

[0055] The preparation of methanol generally uses natural gas and coal as raw materials for production.

[0056] The process of producing methanol from natural gas is as follows: Natural gas and steam undergo a reforming reaction to produce a mixed gas mainly composed of hydrogen, carbon monoxide, and carbon dioxide; then a methanol synthesis reaction is carried out, and carbon monoxide, carbon dioxide, and hydrogen react to produce methanol under specific temperature and pressure conditions. The reaction process is as follows:

[0057] CO2 + 3H2 → CH3OH + H2O

[0058] C0 + 2H2 → CH3OH

[0059] The process of producing methanol from coal is as follows: First, the raw coal is converted into coal gas through a gasification process, and then sulfur dioxide and hydrogen sulfide in the coal gas are removed. At this time, the main components of the mixed gas are carbon monoxide, carbon dioxide, and hydrogen. At this time, carbon monoxide and hydrogen react to synthesize methane, and then the methane steam is converted into methanol.

[0060] Methane preparation technology:

[0061] Methane production generally uses coal as a raw material, reacts with oxygen and steam under high temperature and high pressure conditions to obtain syngas. The main components of syngas are gases such as carbon monoxide, hydrogen, carbon dioxide, and hydrogen sulfide. After steps such as purification and impurity removal of the syngas, carbon monoxide and hydrogen in the syngas react under high temperature and high pressure conditions to obtain methane.

[0062] In order to achieve the production goal of green and low-carbon, there is currently an improved methane and methanol production system. This production system includes a power generation and hydrogen production part and a coal chemical part.

[0063] The power generation and hydrogen production part (i.e., the hydrogen production system involved in multiple embodiments of this specification) includes a renewable energy power generation module, an energy storage module, and an electrolyzer module. The electric power generated by the renewable energy power generation module is used for electrolyzing water to produce hydrogen in the electrolyzer module, and there is also electric energy transmission between the renewable energy power generation module and the energy storage module. The renewable energy power generation module, the energy storage module, and the electrolyzer module are connected by a power electronic converter. The renewable energy power generation module can be any one of a combination of a wind energy and solar energy power generation unit, a wind energy power generation unit, and a solar energy power generation unit. The electrolyzer module uses a proton exchange membrane electrolyzer and is connected to the renewable energy power generation module and the energy storage module respectively. When the power generation power of the renewable energy power generation module is insufficient, the energy storage module supplies power; when the power generation power of the renewable energy power generation module is excessive, the energy storage module stores the excess power, so as to ensure the long-term and stable supply of hydrogen and oxygen.

[0064] The coal chemical section includes a raw gas preparation module, an air separation module, a raw gas purification module, a CO / H2 cryogenic separation module, a methanol synthesis module, and a methane production module. The air separation module is used to collect the oxygen from the electrolyzer module and the oxygen separated from the air. The raw gas preparation module is used to carry out a water coal slurry gasification reaction between the coal raw material and the oxygen from the air separation module to obtain a raw gas mainly composed of carbon monoxide and hydrogen. The raw gas purification unit is used to remove impurities from the raw gas and desulfurize it to obtain syngas. The CO / H2 cryogenic separation module is used to separate carbon monoxide and hydrogen in the syngas. The methanol synthesis module is used to synthesize methanol based on the syngas from the raw gas purification module. The methane production module is used to produce methane based on the syngas from the raw gas purification module. Among them, the methanol synthesis module includes a syngas compression section, a recycle gas recovery section, and a hydrogen recovery section; the methane production module includes a high-temperature methanation section and an LNG refrigeration section. The air separation module includes an air compression section, a cooling section, and a rectification section.

[0065] The working process of the production system is as follows:

[0066] The power generation module generates electricity to produce electric energy and supplies power to the electrolyzer module to electrolyze water to produce oxygen and hydrogen. The oxygen from the electrolyzer module passes through the air separation module and then undergoes water coal slurry gasification with the coal raw material in the raw gas preparation section to obtain raw gas. The raw gas undergoes impurity removal through the raw gas purification module to obtain syngas. The syngas is respectively used in the CO / H2 cryogenic separation module to extract carbon monoxide and hydrogen, the methanol synthesis module to synthesize methanol, and the methane production module to produce methane. The hydrogen extracted by the CO / H2 cryogenic separation module and the hydrogen obtained from electrolyzing water are jointly used for methanol synthesis and methane production. The carbon monoxide obtained by the CO / H2 cryogenic separation module is used to obtain acetic acid. The syngas passes through the methanol synthesis module to obtain crude methanol, and the unutilized syngas and hydrogen are both recycled. The syngas passes through the methane production section to obtain liquefied natural gas.

[0067] Referring to Figure 1 as shown, Figure 1 is a schematic flow chart of a configuration method for a hydrogen production system coupling wind energy and solar energy provided by an embodiment of this specification. The hydrogen production system includes a power generation module, an energy storage module, and an electrolyzer module. The power generation module is respectively connected to the energy storage module and the electrolyzer module. The energy storage module is also connected to the electrolyzer module. The power generation module is used to generate the electric energy for the electrolyzer to electrolyze. The energy storage module is used to store the excess electric energy generated by the power generation module. The energy storage module is also used to supply power to the electrolyzer for electrolysis when the electric energy generated by the power generation module is insufficient. Its characteristics are that the configuration method can at least include the following steps:

[0068] Step 102: Obtain the external environment parameter information and hydrogen demand information at each moment within a preset future time length.

[0069] Step 104: Based on the hydrogen demand information at each moment within a preset future time length, determine the real-time consumption power of the electrolyzer module at each moment within a preset future time length.

[0070] Step 106: Establish an objective function with the total system construction cost and resource abandonment rate as the optimization objectives. The objective of the objective function is to minimize the sum of the normalized total system construction cost and the normalized resource abandonment rate.

[0071] Step 108: Obtain the constraint information, and on the basis of the constraint information, the external environment parameter information, hydrogen demand information, and real-time consumption power of the electrolyzer module at each moment within a preset future time length, use the multi-objective genetic algorithm to solve the objective function to obtain the configured installed capacity of the power generation module, the configured upper limit of the charge of the energy storage module, and the configured rated power of the energy storage module.

[0072] The constraint information includes:

[0073] Constraint 1: At each moment within a preset future time length, the maximum system power supply obtained from the power generation power of the power generation module, the real-time charge of the energy storage module, the time interval between the current moment and the next moment, and the configured rated power of the energy storage module is greater than or equal to the real-time consumption power of the electrolyzer module.

[0074] Constraint 2: At each moment within a preset future time length, the real-time charge of the energy storage module is less than or equal to the configured upper limit of the charge.

[0075] Constraint 3: At each moment within a preset future time length, the real-time charge and discharge power of the energy storage module is less than or equal to the configured rated power.

[0076] The total system construction cost includes the construction cost of the power generation module and the construction cost of the energy storage module. The construction cost of the power generation module is obtained based on the configured installed capacity of the power generation module, and the construction cost of the energy storage module is obtained based on the configured upper limit of the charge and the configured rated power of the energy storage module.

[0077] The resource abandonment rate is calculated based on the configured installed capacity, the external environment parameter information at each moment within a preset future time length, the real-time charge of the energy storage module at each moment within a preset future time length, and the real-time consumption power of the electrolyzer module.

[0078] Specifically, obtaining the maximum system power supply from the power generation power of the power generation module, the real-time charge of the energy storage module, the time interval between the current moment and the next moment, and the configured rated power of the energy storage module includes:

[0079] Based on the state of charge of the energy storage module, and the time interval between the current moment and the next moment, obtain the first maximum output power of the energy storage module;

[0080] Based on the first maximum output power of the energy storage module and the configured rated power of the energy storage module, obtain the second maximum output power of the energy storage module;

[0081] Based on the power generation power of the power generation module and the second maximum output power of the energy storage module, obtain the maximum power supply of the system.

[0082] More specifically, the obtaining of the second maximum output power of the energy storage module based on the first maximum output power of the energy storage module and the configured rated power of the energy storage module includes:

[0083] When the first maximum output power is greater than the configured rated power, use the configured rated power as the second maximum output power;

[0084] When the first maximum output power is less than the configured rated power, use the first maximum output power as the second maximum output power.

[0085] More specifically, the obtaining of the maximum power supply of the system based on the power generation power of the power generation module and the second maximum output power of the energy storage module is specifically to perform a summation operation on the power generation power of the power generation module and the second maximum output power of the energy storage module to obtain the maximum power supply of the system.

[0086] For Constraint 1, several examples are used for illustration:

[0087] Example 1: Assume that the time interval between the current moment and the next moment is 1 hour, and assume that at the current moment, the power generation power of the power generation module is 5 kw, the real-time consumption power of the electrolyzer module is 20 kw, the state of charge of the energy storage module is 10 kwh, and the configured rated power of the energy storage module is 10 kw. Then, the maximum power supply of the system obtained from the power generation power of the power generation module, the state of charge of the energy storage module, the time interval between the current moment and the next moment, and the configured rated power of the energy storage module is 15 kw, which is less than the real-time consumption power of the electrolyzer module, 20 kw, at this time.

[0088] Example 2: Assume that the time interval between the current moment and the next moment is 1 hour, and assume that at the current moment, the power generation power of the power generation module is 10 kw, the real-time consumption power of the electrolyzer module is 20 kw, the state of charge of the energy storage module is 10 kwh, and the configured rated power of the energy storage module is 10 kw. Then, the maximum power supply of the system obtained from the power generation power of the power generation module, the state of charge of the energy storage module, the time interval between the current moment and the next moment, and the configured rated power of the energy storage module is 20 kw, which is equal to the real-time consumption power of the electrolyzer module, 20 kw, at this time.

[0089] Example 3: Assume that the time interval between the current moment and the next moment is 1 hour. Also assume that at the current moment, the power generation power of the power generation module is 15 kw, the real-time power consumption of the electrolyzer module is 20 kw, the real-time state of charge of the energy storage module is 10 kwh, and the configured rated power of the energy storage module is 10 kw. Then, the maximum power supply of the system obtained from the power generation power of the power generation module, the real-time state of charge of the energy storage module, the time interval between the current moment and the next moment, and the configured rated power of the energy storage module is 25 kw, which is greater than the real-time power consumption of 20 kw of the electrolyzer module at this time.

[0090] That is, the situations described in Example 2 and Example 3 should be satisfied during the solution process.

[0091] It should be noted that when considering the energy efficiency conversion efficiency of the power generation module, the energy efficiency conversion efficiency also needs to be considered in the calculation process of the maximum power supply. Taking Example 2 as an example, assume that the energy efficiency conversion efficiency of the power generation module is 0.9. Then, the maximum power supply of the system is 19 kw, which is less than the real-time power consumption of 20 kw of the electrolyzer module at this time (note: the same applies when considering the energy efficiency conversion efficiency of the energy storage module, and no more elaboration will be made here).

[0092] Based on the constraint information, the external environment parameter information, the hydrogen demand information, and the real-time power consumption of the electrolyzer module at each moment within the future preset time length, the multi-objective genetic algorithm is used to solve the objective function to obtain the configured installed capacity of the power generation module, the configured upper limit state of charge of the energy storage module, and the configured rated power of the energy storage module. The configured installed capacity of the power generation module, the configured upper limit state of charge of the energy storage module, and the configured rated power of the energy storage module obtained by the solution can not only ensure the electrolysis power requirement of the electrolyzer corresponding to the hydrogen demand information, but also ensure that the total construction cost of the hydrogen production system is lower, and the power loss during the combined supply of the electrolysis power of the electrolyzer by the power generation module and the energy storage module is smaller.

[0093] Based on the above constraint information, the external environment parameter information, and the hydrogen demand information at each moment within the future preset time length, the NSGA-II algorithm is used to solve the objective function. The specific steps are as follows:

[0094] Initialize the population. Let the initial population size be N, the crossover probability and mutation probability be θ and μ respectively, and the maximum number of iterations be max_N;

[0095] Randomly generate the initial population so that it is randomly distributed in the entire solution space;

[0096] Fast non-dominated sorting: Calculate the number of dominated solutions and the number of dominating solutions for each solution according to the objective function, and perform Pareto ranking on them until all solutions are ranked;

[0097] Crowding degree calculation and sorting: For two objective functions, calculate the crowding degree for the solutions of each Pareto rank, then sum up the crowding degrees and sort them;

[0098] Generate a new generation of population through selection, crossover, and mutation: Implement the selection operation using the binary tournament method. Randomly select N / 2 solutions from all solutions and compare their fitness values, then select the solution with the best fitness value and put it into the next generation population. Repeat this step multiple times until a solution space with N new populations is obtained; Implement the crossover operation using the simulated binary method; Implement the mutation operation using the polynomial mutation method;

[0099] Merge the new generation of population with the initial population;

[0100] Perform a fast non-dominated sorting on the merged population, calculate the crowding degree for the solutions in each non-dominated layer, and select appropriate solutions according to the non-dominated relationship and the crowding degree results of each solution to form a new parent population;

[0101] Generate a new subpopulation according to the above corresponding step methods;

[0102] Merge the new parent population and the subpopulation, and select appropriate solutions based on the objective function values of each solution after merging;

[0103] Compare whether the number of iterations reaches the maximum number of iterations max_N. If so, output the result. If it is less than the maximum number of iterations, repeat the above corresponding steps until the number of iterations meets the maximum number of iterations.

[0104] It should be noted that the above steps are only the conventional methods of the multi-objective genetic algorithm, so no more details will be elaborated.

[0105] In an embodiment of this specification, the power generation module includes a wind power generation unit and a solar power generation unit, and the configured installed capacity includes the installed capacity of the wind power generation unit and the installed capacity of the solar power generation unit;

[0106] The construction cost of the power generation module includes the construction cost of the wind power generation unit and the construction cost of the solar power generation unit;

[0107] The construction cost of the wind power generation unit is obtained based on the installed capacity of the wind power generation unit;

[0108] The construction cost of the solar power generation unit is obtained based on the installed capacity of the solar power generation unit.

[0109] It can be understood that the construction cost per unit installed capacity of the wind power generation unit can be obtained in advance, and further, the construction cost of the wind power generation unit can be obtained only based on the construction cost per unit installed capacity and the installed capacity of the wind power generation unit. The same applies to the calculation of the construction cost of the solar power generation unit, which will not be elaborated here.

[0110] It should be added that for obtaining the construction cost of the energy storage module, the pre-obtained cost calculation formula can be used for calculation, and the configured upper limit charge capacity and the configured rated power are substituted into the cost calculation formula to calculate the construction cost of the energy storage module. And the configured upper limit charge capacity, the configured rated power and the construction cost of the energy storage module should all show a positive correlation. That is, the higher the configured upper limit charge capacity, the higher the construction cost of the energy storage module, and the higher the configured rated power, the higher the construction cost of the energy storage module.

[0111] In an embodiment of the present specification, the external environment parameter information includes wind speed information and solar irradiance information;

[0112] The power generation power of the power generation module is the sum of the power generation power of the wind power generation unit and the power generation power of the solar power generation unit;

[0113] The power generation power of the wind power generation unit is calculated based on the wind speed information and the installed capacity of the wind power generation unit;

[0114] The power generation power of the solar power generation unit is calculated based on the solar irradiance information and the installed capacity of the solar power generation unit.

[0115] It can be understood that the installed capacity of the wind power generation unit is the power generation power that the wind power generation unit can reach under standard conditions, and different wind speeds result in different power generation powers that the wind power generation unit can reach. Therefore, in this embodiment, the power generation power of the wind power generation unit is calculated based on the wind speed information and the installed capacity of the wind power generation unit. The same applies to the calculation of the power generation power of the solar power generation unit, which will not be elaborated here. The specific calculation method will be introduced in the corresponding embodiments below.

[0116] In an embodiment of the present specification, the real-time power consumption of the electrolyzer module at each moment within the future preset time length is equal to the electrolysis power required for the electrolyzer module to generate the hydrogen demand at that moment.

[0117] Therefore, it can be understood that after obtaining the hydrogen demand information at each moment within the future preset time length, the real-time power consumption of the electrolyzer module at each moment within the future preset time length will also be determined.

[0118] It should be noted here that the external environment parameter information and hydrogen demand information at each moment within the future preset time length can be obtained through model prediction based on historical data, such as a neural network model.

[0119] In an embodiment of the present specification, the calculation formula for the resource abandonment rate is:

[0120]

[0121] Wherein, L represents the resource abandonment rate, P loss represents the amount of resource abandonment, P use represents the total power generation, T represents the total number of moments within the future preset time length, P W (i) represents the power generation of the wind power generation unit at the i-th moment in the future, ρ W represents the energy conversion efficiency of the wind power generation unit, P P (i) represents the power generation of the solar power generation unit at the i-th moment in the future, ρ P represents the energy conversion efficiency of the solar power generation unit, P EL (i) represents the real-time consumption power of the electrolyzer module at the i-th moment in the future, Y(i) represents the time interval between the i-th moment and the (i + 1)-th moment in the future (note that because there is a unit conversion between power and electricity, the time interval needs to be multiplied), α represents the energy conversion efficiency of the energy storage module, E B (i + 1) represents the real-time state of charge of the energy storage module at the (i + 1)-th moment in the future, E B (i) represents the real-time state of charge of the energy storage module at the i-th moment in the future. The amount of resource abandonment is calculated for the moments when the power generation of all power generation modules within the future preset time length is greater than the real-time consumption power of the electrolyzer module, that is, when calculating the amount of resource abandonment P loss the time data of the moments when the power generation of the power generation module is less than the real-time consumption power of the electrolyzer module is not brought in, because the generated electricity is all consumed in this case and there is no situation of resource abandonment.

[0122] It can be understood that when the power generation is greater than the real-time consumption power of the electrolyzer module, the excess power will be preferentially stored in the energy storage module. When the energy storage module supplies the excess power for the second time, there is a certain energy conversion efficiency and it cannot be utilized 100%. For example, if it stores 100 kWh of electricity, when it supplies this part of the electricity to the electrolyzer module, it may only convey 98 kWh of electricity, and 2 kWh may be lost during the transmission process. Therefore, the parameter α is set in the above formula. Similarly, this is even more true for the wind power generation unit and the solar power generation unit. Therefore, ρ W and ρ PIt is also understandable that if the energy storage module reaches its full charge capacity, this excess power will be directly discarded.

[0123] In one embodiment of this specification:

[0124] P W (i) = f(S i , P W ) * P W ;

[0125] P P (i) = X(T i , P P ) * P P ;

[0126] Wherein, S i represents the wind speed at the i-th future moment, T i represents the solar irradiance at the i-th future moment, f(·) represents the wind speed energy efficiency conversion ratio function, X(·) represents the solar irradiance energy efficiency conversion ratio function, P W represents the installed capacity of the wind power generation unit, P P represents the installed capacity of the solar power generation unit.

[0127] In one embodiment of this specification, constructing the wind speed energy efficiency conversion ratio function includes:

[0128] Obtaining a first data set, where the first data set includes a plurality of first data subsets, and each first data subset includes the power generation data of the wind power generation unit under different wind speed environmental conditions at the corresponding installed capacity of the wind power generation unit;

[0129] Constructing a first multiple regression function, where the independent variables in the first multiple regression function are wind speed and the installed capacity of the wind power generation unit, and the dependent variable in the first multiple regression function is the wind speed energy efficiency conversion ratio;

[0130] Solving the first multiple regression function based on the first data set to construct the wind speed energy efficiency conversion ratio function.

[0131] In one embodiment of this specification, constructing the solar energy efficiency conversion ratio function includes:

[0132] Obtaining a second data set, where the second data set includes a plurality of second data subsets, and each second data subset includes the power generation data of the solar power generation unit under different solar irradiance environmental conditions at the corresponding installed capacity of the solar power generation unit;

[0133] Constructing a second multiple regression function, where the independent variables in the second multiple regression function are solar irradiance and the installed capacity of the solar power generation unit, and the dependent variable in the second multiple regression function is the solar energy efficiency conversion ratio;

[0134] Solve the second multiple regression function based on the second data set to construct a solar energy efficiency conversion ratio function.

[0135] It should be noted that a multiple regression function is a statistical model used to describe the relationship between two or more independent variables (explanatory variables) and a dependent variable (response variable). In mathematical terms, the multiple regression function model includes a multiple linear regression function model and a multiple non-linear regression function model. The multiple linear regression function model can be written in the form of the following equation:

[0136] Y = β0 + β1X1 + β2X2 +... + β n X n + Φ,

[0137] where Y is the dependent variable, representing the variable we want to predict or explain; X1, X2,..., X n are independent variables, which are explanatory variables affecting the dependent variable; β0 is the intercept, which is the expected value of the dependent variable when all independent variables are 0; β1, β2,..., β n are the coefficients of each independent variable, representing the expected change in the dependent variable when the corresponding independent variable changes by one unit; Φ is the error term, representing the random variation that the model fails to explain.

[0138] In multiple regression, our goal is to find the best coefficients β1, β2,..., β n so as to accurately predict the dependent variable.

[0139] Therefore, taking the first multiple regression function as an example for explanation, the first multiple regression function is as follows:

[0140] B = β0 + β1S + β2P + Φ,

[0141] where B represents the wind speed energy efficiency conversion ratio; S represents the wind speed; P represents the installed capacity of the wind power generation unit; Φ represents the error term; β0 is the intercept; β1 is the coefficient corresponding to the wind speed; β2 is the coefficient corresponding to the installed capacity of the wind power generation unit.

[0142] Therefore, based on the above first data set, the first multiple regression function can be solved to construct a wind speed energy efficiency conversion ratio function.

[0143] For the solution of the multiple linear regression function model, the least squares method is usually used to estimate the coefficients.

[0144] Of course, if there is no obvious linear relationship between the independent variable and the dependent variable, a multiple non-linear regression function model needs to be adopted. The multiple non-linear regression function model is similar to the above-mentioned multiple linear regression function model, but the difference is that it allows the relationship between the independent variable and the dependent variable to be represented by a non-linear equation, which means that the relationship between one or more independent variables and the dependent variable in the function model is not linear, but follows a certain non-linear function, which can be but is not limited to any one of exponential functions, logarithmic functions, and power functions.

[0145] When establishing the multiple regression function, specifically, the corresponding multiple regression function model can be directly selected according to the actual situation, or the multiple linear regression function model and the multiple non-linear regression function model can be used respectively for solution to obtain a wind speed energy efficiency conversion ratio function that is more in line with the actual situation.

[0146] The solution of the multiple non-linear regression function model is usually more complex than that of the linear model because it involves non-linear optimization problems. In practical applications, computer algorithms such as the Newton-Raphson method, the gradient descent method, and the genetic algorithm are usually used to estimate the model parameters.

[0147] For the second multiple regression function, its solution process is the same as that of the first multiple regression function, so it will not be elaborated here. It should also be added that, similarly, the cost calculation formula of the above energy storage module can also be obtained by solving the multiple regression function.

[0148] The specific embodiments of this specification have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve the desired results. Additionally, the processes depicted in the figures do not necessarily require the particular order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0149] Next, please refer to Figure 2 , Figure 2 which shows a schematic structural diagram of a configuration system 200 of a hydrogen production system coupling wind energy and solar energy provided by an embodiment of this specification.

[0150] The configuration system 200 may at least include:

[0151] An acquisition module 201, which acquires the external environment parameter information and hydrogen demand information at each moment within a future preset time length;

[0152] A determination module 202, which determines the real-time power consumption of the electrolyzer module at each moment within a future preset time length based on the hydrogen demand information at each moment within a future preset time length;

[0153] A building module 203 that builds an objective function with the total system construction cost and the resource abandonment rate as optimization objectives;

[0154] A solving module 204 that obtains constraint information, and on the basis of the constraint information, the external environment parameter information, the hydrogen demand information, and the real-time power consumption of the electrolyzer module at each moment within a preset future time length, uses a multi-objective genetic algorithm to solve the objective function to obtain the configured installed capacity of the power generation module, the configured upper limit charge of the energy storage module, and the configured rated power of the energy storage module.

[0155] Each embodiment in this specification is described in a progressive manner. For the same or similar parts between the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the configuration system embodiment, since it is basically similar to the configuration method embodiment, the description is relatively simple, and reference can be made to the corresponding part of the configuration method embodiment for relevant parts.

[0156] Please refer to Figure 3 The schematic structural diagram of an electronic device provided by the embodiment of the present specification shown.

[0157] As Figure 3 shown, the electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302.

[0158] Among them, the communication bus 302 can be used to realize the connection and communication of the above-mentioned various components.

[0159] Among them, the user interface 303 may include buttons, and the optional user interface may further include a standard wired interface and a wireless interface.

[0160] Among them, the network interface 304 may include, but is not limited to, a Bluetooth module, an NFC module, a Wi-Fi module, etc.

[0161] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and circuits to connect various parts within the entire electronic device 300. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and by calling the data stored in the memory 305, it executes various functions of the electronic device 300 and processes data. Optionally, the processor 301 may be implemented in at least one hardware form of DSP, FPGA, or PLC. The processor 301 may integrate a combination of one or several of CPU, GPU, and modem, etc. Among them, the CPU mainly processes the operating system, user interface, application programs, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communication. It can be understood that the above-mentioned modem may not be integrated into the processor 301 and may be implemented separately by a single chip.

[0162] Among them, the memory 305 may include RAM and may also include ROM. Optionally, the memory 305 includes a non-transitory computer-readable medium. The memory 305 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area. Among them, the program storage area may store instructions for implementing the operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store the data involved in the above-mentioned various method embodiments. Optionally, the memory 305 may also be at least one storage device located far from the aforementioned processor 301. As a computer storage medium, the memory 305 may include an operating system, a network communication module, a user interface module, and a configuration application program. The processor 301 may be used to call the configuration program stored in the memory 305 and execute the steps of the configuration method mentioned in the foregoing embodiments.

[0163] The embodiments of this specification also provide a computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When they run on a computer or a processor, they cause the computer or the processor to execute one or more steps of the above-mentioned configuration method embodiments. If the various component modules of the above-mentioned electronic device are implemented in the form of software functional units and sold or used as independent products, they can be stored in the computer-readable storage medium.

[0164] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of this specification are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired manner (such as coaxial cable, optical fiber, Digital Subscriber Line (DSL)) or wirelessly (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that the computer can access or a data storage device such as a server or data center that includes one or more integrated available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as Digital Versatile Disc (DVD)), or semiconductor media (such as Solid State Disk (SSD)), etc.

[0165] Those of ordinary skill in the art can understand that all or part of the processes in the above embodiments of the method can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. The foregoing storage media include: various media such as ROM, RAM, magnetic disks, or optical discs that can store program codes. Without conflict, the technical features in this embodiment and the implementation solutions can be combined arbitrarily.

[0166] The above-described embodiments are merely described in a preferred embodiment manner of this specification and do not limit the scope of this specification. Without departing from the design spirit of this specification, various deformations and improvements made by those of ordinary skill in the art to the technical solutions of this specification shall fall within the protection scope determined by the claims of this specification.

Claims

1. A configuration method for a hydrogen production system coupled with wind energy and solar energy, wherein the hydrogen production system comprises a power generation module, an energy storage module, and an electrolyzer module, wherein the power generation module is connected to the energy storage module and the electrolyzer module respectively, and the energy storage module is also connected to the electrolyzer module, wherein the power generation module is used to generate electric energy for electrolysis in the electrolyzer, and the energy storage module is used to store excess electric energy generated by the power generation module, and the energy storage module is also used to use the excess electric energy stored in the power generation module to power the electrolyzer for electrolysis when the electric energy generated by the power generation module is insufficient for electrolysis in the electrolyzer, wherein the power generation module comprises: Configuration methods include: Obtain the external environment parameter information and hydrogen demand information at each moment within the future preset time length; Based on the hydrogen demand information at each moment within the future preset time length, determine the real-time power consumption of the electrolyzer module at each moment within the future preset time length; Establish an objective function with the total system construction cost and resource abandonment rate as optimization targets; Obtain constraint information, and use a multi-objective genetic algorithm to solve the objective function based on the constraint information and the external environmental parameter information, hydrogen demand information, and real-time power consumption of the electrolyzer module at each moment within a preset time length in the future, so as to obtain the configured installed capacity of the power generation module, the configured upper limit load of the energy storage module, and the configured rated power of the energy storage module; The constraint information includes: At each moment within a preset time length in the future, the maximum power supply of the system obtained by the power generation power of the power generation module, the real-time charge of the energy storage module, the time interval between the current moment and the next moment, and the configured rated power of the energy storage module is greater than or equal to the real-time power consumption of the electrolyzer module; At each moment within a preset time period in the future, the real-time charge of the energy storage module is less than or equal to the configured upper limit charge; At each moment within a preset time period in the future, the real-time charging and discharging power of the energy storage module is less than or equal to the configured rated power; The total construction cost of the system includes the construction cost of the power generation module and the construction cost of the energy storage module. The construction cost of the power generation module is obtained based on the configured installed capacity of the power generation module, and the construction cost of the energy storage module is obtained based on the configured upper limit load and the configured rated power of the energy storage module. The resource abandonment rate is calculated based on the configured installed capacity, the external environmental parameter information at each moment within the future preset time length, the real-time charge of the energy storage module at each moment within the future preset time length, and the real-time power consumption of the electrolyzer module; The power generation module includes a wind power generation unit and a solar power generation unit, and the configured installed capacity includes the installed capacity of the wind power generation unit and the installed capacity of the solar power generation unit; The construction cost of the power generation module includes the construction cost of the wind power generation unit and the construction cost of the solar power generation unit; The construction cost of the wind power generation unit is obtained based on the installed capacity of the wind power generation unit; The construction cost of the solar power generation unit is obtained based on the installed capacity of the solar power generation unit; The external environment parameter information includes wind speed information and solar radiation information; The power generation power of the power generation module is the sum of the power generation power of the wind power generation unit and the power generation power of the solar power generation unit; The power generation power of the wind power generation unit is calculated based on wind speed information and installed capacity of the wind power generation unit; The power generation power of the solar power generation unit is calculated based on the solar irradiation information and the installed capacity of the solar power generation unit; The calculation formula of the resource abandonment rate is: Among them, L represents the resource abandonment rate, P loss Indicates the amount of resource abandonment, P use represents the total amount of power generation, T represents the total number of moments within the preset time length in the future, and P W (i) represents the power generation of the wind power generation unit at the i-th moment in the future, ρ W Represents the energy conversion efficiency of the wind power generation unit, P P (i) represents the power generation of the solar power generation unit at the i-th moment in the future, ρ P Represents the energy conversion efficiency of the solar power generation unit, P EL (i) represents the real-time power consumption of the electrolyzer module at the future i-th moment, Y(i) represents the time interval between the future i-th moment and the i+1-th moment, α represents the energy conversion efficiency of the energy storage module, E B (i+1) represents the real-time charge of the energy storage module at the future i+1th moment, E B (i) represents the real-time charge of the energy storage module at the i-th moment in the future, and the resource abandonment amount is calculated for the moment when the power generation power of all power generation modules within a preset time length in the future is greater than the real-time power consumption of the electrolyzer module; P W (i)=f(S i ,P W )*P W ; P P (i)=X(T i ,P P )*P P ; Among them, S i represents the wind speed at the i-th moment in the future, T i represents the solar radiation at the i-th moment in the future, f(·) represents the wind speed energy efficiency conversion ratio function, X(·) represents the solar radiation energy efficiency conversion ratio function, P W represents the installed capacity of wind power generation unit, P P represents the installed capacity of solar power generation units; Construct the wind speed energy efficiency conversion ratio function, including: Acquire a first data set, wherein the first data set includes a plurality of first data subsets, each of which includes power generation data of a wind power generation unit under different wind speed environmental conditions at an installed capacity of a corresponding wind power generation unit; Constructing a first multiple regression function, wherein the independent variables of the first multiple regression function are wind speed and installed capacity of wind power generation units, and the dependent variable of the first multiple regression function is wind speed energy efficiency conversion ratio; Solving the first multivariate regression function based on the first data set to construct a wind speed energy efficiency conversion ratio function; Construct a solar energy efficiency conversion ratio function, including: Acquire a second data set, wherein the second data set includes a plurality of second data subsets, each of which includes power generation data of a solar power generation unit under different solar irradiation environment conditions at the installed capacity of the corresponding solar power generation unit; constructing a second multiple regression function, wherein the independent variables of the second multiple regression function are solar irradiation and installed capacity of solar power generation units, and the dependent variable of the second multiple regression function is solar energy efficiency conversion ratio; The second multivariate regression function is solved based on the second data set to construct a solar energy efficiency conversion ratio function.

2. The configuration method of the hydrogen production system coupled with wind energy and solar energy according to claim 1 is characterized in that: The real-time power consumption of the electrolyzer module at each moment within the future preset time length is equal to the electrolysis power required by the electrolyzer module to generate the hydrogen demand at that moment.

3. An electronic device, characterized in that: including a processor and a memory; The processor is connected to the memory; The memory is used to store executable program code; The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the method according to any one of claims 1 to 2.

4. A computer-readable storage medium, characterized in that: A computer program is stored thereon, wherein when the computer program is executed by a processor, the method according to any one of claims 1 to 2 is implemented.

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