Capacity configuration optimization method, device, equipment, medium and program product

Abstract

The invention relates to the technical field of power systems, and discloses a capacity configuration optimization method and device, equipment, a medium and a program product, and the method can deeply master the operation rules and mutual relations of all components in a system through obtaining a plurality of operation models. Furthermore, a target function is established by taking the highest renewable energy consumption proportion as a target and combining a plurality of operation models and a plurality of initial installation scale values, so that the complex operation condition and optimization demand of the system can be converted into a mathematical expression, and effective integration of system information is realized; therefore, the objective function can comprehensively reflect the operation characteristics and constraint conditions of the system, so that the consumption conditions of the renewable energy sources by the system under different installation scales can be determined. Therefore, through the particle swarm algorithm, the installation scale combination which enables the renewable energy consumption proportion to reach the highest can be quickly searched in a huge installation scale combination space, and a final capacity configuration optimization result is determined, so that the renewable energy consumption proportion is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a capacity configuration optimization method, device, equipment, medium and program product. Background Art

[0002] With the rapid increase in the proportion of wind and solar power installed, the problem of wind and solar power abandonment caused by the randomness, volatility and intermittency of renewable energy output has become increasingly prominent, so there is an urgent need to explore new methods to solve the problem of new energy consumption. The production of green hydrogen by electrolyzing water with renewable energy and combining it with nitrogen to form green ammonia has become an important direction of exploration at home and abroad, and it is also an important way for the synthetic ammonia industry to achieve carbon emission reduction.

[0003] The hydrogen production and ammonia synthesis system based on renewable energy needs to be able to adapt to the volatility and intermittency of renewable energy, and also needs to meet the safety production requirements of hydrogen production and ammonia synthesis. Therefore, in order to maximize the utilization of renewable energy in off-grid systems, it is necessary to first solve the optimization of system operation and scheduling methods, and then improve the proportion of renewable energy consumption in hydrogen production and ammonia synthesis coupled with hydrogen power generation through system capacity configuration optimization. Summary of the invention

[0004] In view of this, the present invention provides a capacity configuration optimization method, device, equipment, medium and program product to solve the problem of low absorption ratio of renewable energy in hydrogen production and ammonia synthesis coupled with hydrogen power generation caused by system operation scheduling optimization problems.

[0005] In a first aspect, the present invention provides a capacity configuration optimization method for a control system, wherein the control system is connected to a renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system; the method comprises:

[0006] Acquire multiple operation models and multiple initial installed capacity values ​​of the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system; establish an objective function based on multiple operation models and multiple initial installed capacity values ​​with the goal of maximizing the renewable energy consumption ratio of the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system; solve the objective function based on the multiple initial installed capacity values ​​using a particle swarm algorithm to obtain a capacity configuration optimization result of the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system.

[0007] The capacity configuration optimization method provided by the present invention can deeply grasp the operation rules and mutual relations of each component in the system by obtaining multiple operation models. Further, taking the highest proportion of renewable energy consumption as the goal and combining multiple operation models and multiple initial installed capacity values ​​to establish an objective function, the complex operation of the system and the optimization requirements can be converted into mathematical expressions, which is convenient for subsequent solving and optimization using algorithms. Further, the effective integration of system information is achieved, so that the objective function can fully reflect the operating characteristics and constraints of the system, and then the system's consumption of renewable energy under different installed capacity scales can be determined, so that the particle swarm algorithm can quickly search for the installed capacity combination that makes the renewable energy consumption ratio reach the highest in the huge installed capacity combination space and determine the final capacity configuration optimization result. Therefore, by implementing the present invention, it is possible to comprehensively consider all aspects of the system, optimize the capacity configuration of the system as a whole, adapt to the characteristics of renewable energy, and improve the consumption ratio of renewable energy, thereby alleviating the problem of wind and light abandonment caused by the randomness, volatility and intermittency of renewable energy output, and provide technical support for the efficient utilization and sustainable development of renewable energy.

[0008] In an optional embodiment, with the goal of maximizing the renewable energy consumption ratio of the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system, an objective function is established according to multiple operation models and multiple initial installed capacity values, including:

[0009] According to multiple operation models and multiple initial installed capacity values, the operating power relationship of each unit in the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system is established; with the highest renewable energy consumption ratio in the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system as the goal, an objective function is established according to the operating power relationship of each unit.

[0010] The capacity configuration optimization method provided by the present invention can more accurately reflect the power relationship and mutual influence between the various units in the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system by establishing the operating power relationship of each unit. Furthermore, the objective function is established according to the operating power relationship of each unit, which can make the objective function more accurately reflect the operating characteristics and optimization requirements of the system, and provide support for the subsequent more effective guidance of the algorithm to find the optimal capacity configuration plan.

[0011] In an optional embodiment, the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system includes a renewable energy power generation unit; according to multiple operation models and multiple initial installed capacity values, an operating power relationship of each unit in the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system is established, including:

[0012] Determine multiple installed power values ​​based on multiple initial installed capacity scale values; establish a renewable energy power generation power relationship of the renewable energy power generation unit based on multiple initial installed capacity scale values ​​and multiple installed power values; establish an operating power relationship of each unit based on multiple installed power values, multiple operating models and the renewable energy power generation power relationship.

[0013] The capacity configuration optimization method provided by the present invention establishes a renewable energy power generation power relationship of a renewable energy power generation unit by combining the initial installed capacity scale value and the installed power value, taking into account the performance parameters of the equipment itself and the impact of external environmental factors on the power generation, and can more accurately reflect the power output characteristics of the renewable energy power generation unit. Further, by combining multiple installed power values, multiple operating models and renewable energy power generation relationship to establish the operating power relationship of each unit, it is possible to establish an operating power association between the units of the entire system, and then through this association, it is possible to more clearly understand the transmission and distribution process of renewable energy power generation in the system, as well as its impact on the operating power of other units, so that when optimizing capacity configuration, it is possible to better coordinate the relationship between renewable energy power generation and other units, improve the system's adaptability and absorption capacity to renewable energy, and reduce system instability and energy waste caused by renewable energy fluctuations.

[0014] In an optional embodiment, the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system further includes a water electrolysis hydrogen production unit, an ammonia synthesis unit, a hydrogen fuel cell power generation unit and an electrochemical energy storage unit;

[0015] According to multiple installed power values, multiple operation models and renewable energy power generation power relationship, the operation power relationship of each unit is established, including:

[0016] According to the renewable energy power generation power relationship, the installed power value and the operation model of the water electrolysis hydrogen production unit, a first operation power relationship of the water electrolysis hydrogen production unit is established; according to the renewable energy power generation power relationship, the installed power value and the operation model of the hydrogen fuel cell power generation unit, a second operation power relationship of the hydrogen fuel cell power generation unit is established; according to the renewable energy power generation power relationship, the installed power value and the operation model of the electrochemical energy storage unit, a third operation power relationship of the electrochemical energy storage unit is established; according to the renewable energy power generation power relationship, the first operation power relationship, the second operation power relationship and the third operation power relationship, a fourth operation power relationship of the synthetic ammonia unit is established.

[0017] The capacity configuration optimization method provided by the present invention can deeply analyze the power interaction and energy flow between different units by comprehensively considering the operating characteristics and mutual relationships of each key unit in the system and establishing the operating power relationship formula of each unit in the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system. In the subsequent optimization of capacity configuration, the scale of each unit can be adjusted more accurately, so that the system can achieve efficient and stable operation under various operating conditions, maximize the absorption ratio of renewable energy, and meet the needs of safe production of hydrogen production and synthetic ammonia chemical industry.

[0018] In an optional embodiment, a fourth operating power relationship of the synthetic ammonia unit is established according to the renewable energy power generation power relationship, the first operating power relationship, the second operating power relationship and the third operating power relationship, including:

[0019] The synthetic ammonia power range is determined according to the renewable energy power generation power relationship, the first operating power relationship, the second operating power relationship and the third operating power relationship; and the second operating power relationship is established according to the synthetic ammonia power range.

[0020] The capacity configuration optimization method provided by the present invention determines the ammonia power range by combining the operating power relationship of each unit in the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system, which can more accurately grasp the operating power characteristics of the ammonia synthesis unit, so that the established second operating power relationship can better reflect the actual operating status and power demand of the ammonia synthesis unit in the entire system, and then in the subsequent optimization of capacity configuration, the parameters and scale related to the ammonia synthesis unit can be adjusted more specifically to ensure that the ammonia synthesis unit can operate efficiently and safely, and work in coordination with other units to jointly improve the renewable energy absorption efficiency and the overall performance of the system.

[0021] In an optional implementation, based on multiple initial installed capacity values, a particle swarm algorithm is used to solve the objective function to obtain a capacity configuration optimization result of a renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system, including:

[0022] Based on multiple initial installed capacity values, the particle swarm algorithm is used to solve the objective function, and multiple target installed capacity values ​​of the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system are obtained; according to the multiple target installed capacity values, the capacity configuration optimization results of the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system are determined.

[0023] The capacity configuration optimization method provided by the present invention, by utilizing the efficient search and optimization capabilities of the particle swarm algorithm, can quickly search for the installed capacity scale combination that maximizes the renewable energy consumption ratio in a huge installed capacity scale combination space and determine the final capacity configuration optimization result, thereby enabling the system to achieve efficient energy utilization and stable operation in off-grid mode, thereby increasing the renewable energy consumption ratio.

[0024] In a second aspect, the present invention provides a capacity configuration optimization device for controlling a system, wherein the control system is connected to a renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system; the device comprises:

[0025] The acquisition module is used to obtain multiple operation models and multiple initial installed capacity scale values ​​of the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system; the establishment module is used to establish an objective function based on multiple operation models and multiple initial installed capacity scale values ​​with the goal of maximizing the renewable energy consumption ratio of the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system; the solution module is used to solve the objective function based on multiple initial installed capacity scale values ​​using a particle swarm algorithm to obtain a capacity configuration optimization result of the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system.

[0026] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the capacity configuration optimization method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0027] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the capacity configuration optimization method of the first aspect or any corresponding embodiment thereof.

[0028] In a fifth aspect, the present invention provides a computer program product, comprising computer instructions for causing a computer to execute the capacity configuration optimization method of the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0030] Figure 1 is a flow chart of a capacity configuration optimization method according to an embodiment of the present invention;

[0031] Figure 2 is a flow chart of another capacity configuration optimization method according to an embodiment of the present invention;

[0032] Figure 3 is a flow chart of another capacity configuration optimization method according to an embodiment of the present invention;

[0033] Figure 4 is a structural block diagram of a capacity configuration optimization device according to an embodiment of the present invention;

[0034] Figure 5 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0036] An embodiment of the present invention provides a capacity configuration optimization method, which takes the highest renewable energy consumption ratio as the goal and combines multiple operation models and multiple initial installed capacity values ​​to establish an objective function to achieve the effect of optimizing the capacity configuration of the system and improving the renewable energy consumption ratio.

[0037] According to an embodiment of the present invention, an embodiment of a capacity configuration optimization method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0038] In this embodiment, a capacity configuration optimization method is provided for a control system, wherein the control system is connected to a renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system.

[0039] Specifically, the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system may include a renewable energy power generation unit, a water electrolysis hydrogen production unit, an ammonia synthesis unit, a hydrogen fuel cell power generation unit, a hydrogen storage unit, and an electrochemical energy storage unit.

[0040] Among them, renewable energy power generation supplies water electrolysis hydrogen production units and ammonia synthesis units with certain flexible response capabilities. The hydrogen fuel cell power generation unit provides guaranteed electricity for the entire system when renewable energy power is lacking. The hydrogen production unit produces hydrogen and stores it in the hydrogen storage unit, and then supplies it to the ammonia synthesis unit and the hydrogen fuel cell power generation unit. The electrochemical energy storage unit is used to smooth the renewable energy power generation output and provide a part of the guaranteed electricity.

[0041] Furthermore, the renewable energy power generation unit may include one or more of a hydropower generation unit, a wind power generation unit, a photovoltaic power generation unit, a solar thermal power generation unit, etc.; the water electrolysis hydrogen production unit may include one or more of alkaline water electrolysis hydrogen production, proton exchange membrane water electrolysis hydrogen production, alkaline anion exchange membrane water electrolysis hydrogen production, solid oxide water electrolysis hydrogen production, etc.; the ammonia synthesis unit may include an air separation nitrogen production system, a nitrogen compression system and a hydrogen compression system.

[0042] Figure 1 is a flow chart of a capacity configuration optimization method according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0043] Step S101, obtaining multiple operation models and multiple initial installed capacity values ​​of a renewable energy hydrogen production and ammonia synthesis coupled with hydrogen power generation system.

[0044] Among them, multiple operating models represent the operating models of each unit in the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system. They are mathematical descriptions or logical structures of the relationships and changing laws among key parameters (such as power, rate, capacity, etc.) of each unit under different operating conditions. They are used to reflect the energy conversion, material flow and synergy of each unit with other units, and help understand, predict and optimize the system operation status, so as to achieve the system while ensuring safety and reliability, while improving energy utilization efficiency and economy.

[0045] Multiple installed capacity values ​​represent the installed capacity of each unit in the renewable energy hydrogen production, ammonia synthesis and hydrogen power generation system. It is a comprehensive expression of the capacity-related parameters of each unit equipment, reflecting the installed capacity of each unit equipment. Furthermore, the installed capacity can directly determine the production capacity, energy supply or storage capacity of each unit, thereby affecting the energy conversion efficiency, material output capacity, energy balance and economic operation of the entire system.

[0046] Specifically, the multiple operation models may include:

[0047] (1) Operation model of water electrolysis hydrogen production unit: used to describe the relationship between parameters such as hydrogen production power and hydrogen production rate and factors such as renewable energy power generation, equipment installed power, and operating time during the water electrolysis hydrogen production process.

[0048] (2) Ammonia synthesis unit operation model: used to represent the relationship between parameters such as ammonia synthesis power and hydrogen consumption rate in the ammonia synthesis process and other system factors (such as renewable energy power generation, hydrogen storage capacity, hydrogen power generation, etc.).

[0049] (3) Hydrogen fuel cell power generation unit operation model: It is used to represent the relationship between parameters such as hydrogen power generation power, hydrogen consumption rate, and other factors during the hydrogen fuel cell power generation process, such as the operating power of the water electrolysis hydrogen production unit, the operating power of the synthetic ammonia unit, and the power generation power of renewable energy. It can reflect the utilization of hydrogen in the power generation process and the energy balance relationship with other units.

[0050] (4) Hydrogen storage unit operation model: used to describe the relationship between parameters such as hydrogen storage capacity, hydrogen charging rate, hydrogen desorption rate, etc. in the hydrogen storage unit and the operating status of other parts of the system.

[0051] (5) Electrochemical energy storage unit operation model: used to represent the relationship between the parameters of the electrochemical energy storage unit, such as energy storage discharge power, energy storage charging power, and energy storage charge capacity, and the overall operation of the system. Furthermore, by simulating the charging and discharging process of the electrochemical energy storage unit under different energy input and output conditions, it can reflect its role in balancing the energy supply and demand of the system.

[0052] Further, the multiple installed capacity values ​​may include:

[0053] (1) Renewable energy power generation installed capacity: It represents the total installed capacity of renewable energy power generation units (such as wind turbines and solar photovoltaic panels), which can reflect the system's ability to obtain electricity from renewable energy.

[0054] (2) Installed capacity of water electrolysis hydrogen production: It indicates the total power or production capacity of the water electrolysis hydrogen production unit. It can determine the system's ability to produce hydrogen, which in turn directly affects the supply of hydrogen required for synthetic ammonia production.

[0055] (3) Synthetic ammonia installed capacity value: It represents the total production capacity of the synthetic ammonia unit, which can determine the maximum amount of synthetic ammonia that the system can produce and is directly related to the production capacity of synthetic ammonia.

[0056] (4) Hydrogen fuel cell power generation installed capacity value: It represents the total power of the hydrogen fuel cell power generation unit, which can reflect the system's ability to use hydrogen to generate electricity, and thus affects the redistribution and utilization efficiency of energy within the system.

[0057] (5) Installed capacity of hydrogen storage unit: It indicates the total storage capacity of the hydrogen storage unit, which determines the system's ability to store hydrogen and plays a role in regulating the balance between hydrogen supply and demand when renewable energy generation is unstable or the demand for synthetic ammonia production changes.

[0058] (6) Installed capacity of electrochemical energy storage unit: represents the total energy storage capacity of the electrochemical energy storage unit.

[0059] Step S102, with the goal of maximizing the renewable energy consumption ratio of the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system, establish an objective function based on multiple operation models and multiple initial installed capacity values.

[0060] Among them, the objective function is used to characterize the renewable energy consumption ratio of the renewable energy hydrogen production and ammonia synthesis coupled with hydrogen power generation system.

[0061] Specifically, by taking the maximum proportion of renewable energy consumption as the goal and combining multiple operation models and multiple initial installed capacity values ​​to establish an objective function, the complex operation of the system and the optimization requirements can be converted into mathematical expressions, which is convenient for subsequent solution and optimization using algorithms. Furthermore, the effective integration of system information is achieved, so that the objective function can fully reflect the operating characteristics and constraints of the system, and then determine the system's consumption of renewable energy under different installed capacity scales.

[0062] Step S103, based on multiple initial installed capacity values, the particle swarm algorithm is used to solve the objective function to obtain a capacity configuration optimization result of the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system.

[0063] Among them, Particle Swarm Optimization (PSO) is a random search optimization algorithm based on swarm intelligence, which is used to simulate the foraging behavior of biological groups such as bird flocks and fish schools, and regards the solution of the problem as particles in the search space, where each particle represents a potential solution.

[0064] Specifically, the efficient search and optimization capabilities of the particle swarm algorithm can be used to quickly search for the installed capacity combination that maximizes the renewable energy consumption ratio in the huge installed capacity combination space and determine the final capacity configuration optimization result.

[0065] The capacity configuration optimization method provided in this embodiment can deeply grasp the operation rules and mutual relationships of each component in the system by obtaining multiple operation models. Further, taking the highest proportion of renewable energy consumption as the goal and combining multiple operation models and multiple initial installed capacity values ​​to establish an objective function can convert the complex operation of the system and the optimization requirements into mathematical expressions, which is convenient for subsequent solving and optimization using algorithms. Further, the effective integration of system information is achieved, so that the objective function can fully reflect the operating characteristics and constraints of the system, and then the system's consumption of renewable energy under different installed capacity scales can be determined, so that the particle swarm algorithm can quickly search for the installed capacity combination that makes the renewable energy consumption ratio reach the highest in the huge installed capacity combination space and determine the final capacity configuration optimization result. Therefore, by implementing the present invention, it is possible to comprehensively consider all aspects of the system, optimize the capacity configuration of the system as a whole, adapt to the characteristics of renewable energy, and improve the consumption ratio of renewable energy, thereby alleviating the problem of wind and light abandonment caused by the randomness, volatility and intermittency of renewable energy output, and provide technical support for the efficient utilization and sustainable development of renewable energy.

[0066] In this embodiment, a capacity configuration optimization method is provided for a control system, wherein the control system is connected to a renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system.

[0067] Figure 2 is a flow chart of a capacity configuration optimization method according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0068] Step S201, obtain multiple operation models and multiple initial installed capacity values ​​of the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system. Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0069] Step S202, with the goal of maximizing the renewable energy consumption ratio of the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system, establish an objective function based on multiple operation models and multiple initial installed capacity values.

[0070] Specifically, the above step S202 includes:

[0071] Step S2021, based on multiple operation models and multiple initial installed capacity values, establish an operation power relationship of each unit in the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system.

[0072] Specifically, by establishing the operating power relationship of each unit in the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system, the power relationship and mutual influence between the various units of the system can be more accurately reflected.

[0073] In some optional implementations, the above step S2021 includes:

[0074] Step a1, determining multiple installed capacity values ​​according to multiple initial installed capacity scale values.

[0075] Step a2: establishing a renewable energy generation power relationship of a renewable energy generation unit according to a plurality of initial installed capacity values ​​and a plurality of installed capacity power values.

[0076] Step a3, establishing an operating power relationship of each unit according to multiple installed power values, multiple operating models and renewable energy power generation relationship.

[0077] Specifically, installed capacity is a measure of the capacity or production / storage capability of each unit of equipment, while installed power is related to the actual rate of energy conversion, production or storage of the equipment.

[0078] Furthermore, for renewable energy power generation units, the installed scale determines the capacity of the power generation equipment, and the installed power is its actual power generation capacity, which is affected by environmental factors; the installed scale of the water electrolysis hydrogen production unit is used to indicate the hydrogen production capacity, and the installed power has a proportional relationship with it based on the equipment parameters, which is affected by the electrolysis efficiency and operating conditions; in the synthetic ammonia unit, the installed scale indicates the production capacity, and the installed power provides energy for the reaction, which is affected by the process conditions and the catalyst performance; the installed scale of the hydrogen fuel cell power generation unit is the power generation capacity, and the installed power is the rated power, and the actual output is affected by the type of fuel cell and the hydrogen supply conditions; the installed scale of the hydrogen storage unit is the hydrogen storage capacity, and the installed power is related to the hydrogen compression / liquefaction equipment, which is affected by the hydrogen storage method and conditions; the installed scale of the electrochemical energy storage unit indicates the energy storage capacity, and the installed power involves charging and discharging, which is affected by the battery characteristics.

[0079] Furthermore, according to the relationship between the installed capacity scale and installed power of each unit, the corresponding installed power value can be determined according to the installed capacity scale value of each unit.

[0080] Furthermore, the power generation relationship of renewable energy is shown in the following relationship (1):

[0081] P RE，t =P HY，t +P W，t +P PV，t +P ST，t (1)

[0082] Where: P RE，t represents the renewable energy power generation at time t; P HY，t represents the hydroelectric power at time t, as shown in the following equation (2); P W，t represents the wind power generation at time t, as shown in the following equation (3); PPV，t represents the photovoltaic power generation at time t, as shown in the following equation (4); P ST，t represents the CSP power at time t, as shown in the following equation (5).

[0083] P HY，t =I HY ·p HY,t (2)

[0084] P W，t =I W ·p W，t (3)

[0085] P PV，t =I PV ·p PV,t (4)

[0086] P ST，t =I ST ·p ST,t (5)

[0087] Where: I HY represents the installed hydroelectric power, p HY，t represents the hydroelectric power output curve at time t; I W represents the installed power of wind power; p W，t represents the wind power output curve at time t; I PV represents the installed power of photovoltaic power generation; p PV，t Represents the photovoltaic power generation output curve at time t; I ST represents the installed power of CSP; p ST，t Represents the CSP output curve at time t.

[0088] Finally, by combining the multiple installed power values, multiple operating models and renewable energy power generation relationship, the operating power relationship of each unit can be established, and then the operating power association between the units of the entire system can be established. Furthermore, through this association, we can more clearly understand the transmission and distribution process of renewable energy power generation in the system, as well as its impact on the operating power of other units, so that when optimizing capacity configuration, we can better coordinate the relationship between renewable energy power generation and other units, improve the system's adaptability and absorption capacity for renewable energy, and reduce system instability and energy waste caused by renewable energy fluctuations.

[0089] In some optional implementations, the above step a3 includes:

[0090] Step a31, establishing a first operating power relationship formula of the water electrolysis hydrogen production unit according to the renewable energy power generation power relationship formula, the installed power value and the operating model of the water electrolysis hydrogen production unit.

[0091] Specifically, the first operating power relationship is used to characterize the operating power of the water electrolysis hydrogen production unit, as shown in the following relationship (6):

[0092]

[0093] Where: P H，t P represents the hydrogen production power at time t, i.e., the operating power; Hmax represents the maximum hydrogen production power, as shown in the following equation (7); P Hmin represents the minimum hydrogen production power, as shown in the following equation (8); P′ H，t represents the hydrogen production power generated at time t, as shown in the following equation (9); P Hs Indicates the hot standby power of the hydrogen production system.

[0094] P Hmax =I H ·F Hmax (7)

[0095] P Hmin =I H ·F Hmin (8)

[0096]

[0097] Where: P A，t represents the ammonia synthesis power at time t; I H Indicates the installed power of hydrogen production; F Hmax Indicates the maximum hydrogen production power range; F Hmin Indicates the minimum hydrogen production power range; E H Indicates the unit energy consumption of hydrogen production; H S，t-1 Indicates the amount of hydrogen stored at time t-1; H SC Indicates the rated hydrogen storage capacity; η HS Indicates the hydrogen storage adjustment factor; H A，t It represents the rate of hydrogen consumption in synthesizing ammonia at time t.

[0098] Furthermore, the hydrogen production rate at time t can also be calculated, as shown in the following equation (10):

[0099]

[0100] Where: H P，t represents the hydrogen production rate at time t.

[0101] Specifically, in the water electrolysis hydrogen production unit, the hydrogen production rate can affect the operating power through the system feedback mechanism, equipment operating efficiency, and energy balance requirements, and thus can affect the renewable energy consumption ratio of the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system.

[0102] Furthermore, the hydrogen storage capacity H at time t can be calculated S，t , as shown in the following relation (11):

[0103] H S，t =H S，t-1 +H P，t -H A，t -H HP，t (11)

[0104] Where: H HP，t Indicates the rate at which hydrogen is consumed in hydrogen power generation at time t.

[0105] Specifically, the amount of hydrogen storage can be affected by H，t This in turn affects the hydrogen production power, and further, can affect the renewable energy consumption ratio of the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system.

[0106] Furthermore, the amount of hydrogen stored will also affect the operational decisions of the hydrogen fuel cell power generation unit. When the amount of hydrogen stored is sufficient, the hydrogen fuel cell power generation unit can generate electricity more flexibly according to system requirements, and its power generation will change. The change in its power generation will indirectly affect the calculation of the proportion of renewable energy consumption by affecting the system power balance.

[0107] Step a32, establishing a second operating power relationship equation for the hydrogen fuel cell power generation unit according to the renewable energy power generation power relationship equation, the installed power value and the operating model of the hydrogen fuel cell power generation unit.

[0108] Specifically, the second operating power relationship is shown in the following relationship (12):

[0109] P HP，t =max(P H，t +P A，t -P RE，t ,0) (12)

[0110] Where: P HP，t represents the hydrogen power generation power at time t; E HP Indicates the amount of electricity generated per unit of hydrogen.

[0111] Further, P HP，t The range of is shown in the following relation (13):

[0112] P HPmin ≤P HP，t ≤P HPmax (13)

[0113] Where: P HPmin represents the minimum hydrogen power generation power, as shown in the following equation (14); P HPmaxrepresents the maximum hydrogen power generation power, as shown in the following equation (15).

[0114] P HPmax =I HP ·F HPmax (14)

[0115] P HPmin =I HP ·F HPmin (15)

[0116] Where: I HP Indicates the installed capacity of hydrogen power generation.

[0117] Furthermore, the hydrogen consumption rate of hydrogen power generation by the hydrogen fuel cell power generation unit is shown in the following relation (16):

[0118] H HP，t =P HP，t / E HP (16)

[0119] Specifically, the rate at which hydrogen is consumed through hydrogen power generation can affect the operating power of the hydrogen fuel cell power generation unit, and thus can affect the renewable energy absorption ratio of the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system.

[0120] Step a33, establishing a third operating power relationship equation of the electrochemical energy storage unit according to the renewable energy power generation power relationship equation, the installed power value of the electrochemical energy storage unit and the operating model.

[0121] Specifically, the third operating power relationship is shown in the following relationships (17) to (19):

[0122] P Bdischa，t =min(max(max(P H，t +P A，t -P RE，t ,0)-P HP，t ,0),I B ) (17)

[0123] P Bcha，t =min(max(P RE，t -P H，t -P A，t ,0),I B ) (18)

[0124]

[0125] Where: P Bdischa，t Represents the energy storage discharge power at time t; I B Represents the installed power of energy storage; P Bcha，t represents the energy storage charging power at time t; Bt Represents the energy storage charge capacity at time t; η B Indicates the energy efficiency of energy storage charging and discharging; B max Represents the installed capacity of energy storage.

[0126] Step a34, establishing a fourth operating power relationship equation for the synthetic ammonia unit according to the renewable energy power generation power relationship equation, the first operating power relationship equation, the second operating power relationship equation and the third operating power relationship equation.

[0127] Specifically, by comprehensively considering the operating characteristics and interrelationships of each key unit in the system and establishing the operating power relationship of each unit in the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system, it is possible to deeply analyze the power interaction and energy flow between different units, and more accurately adjust the scale of each unit during the subsequent optimization of capacity configuration, so that the system can achieve efficient and stable operation under various operating conditions, maximize the absorption ratio of renewable energy, and meet the needs of safe production of hydrogen production and synthetic ammonia chemical industry.

[0128] In some optional implementations, the above step a34 includes:

[0129] Step a341, determining the synthetic ammonia power range according to the renewable energy power generation power relationship formula, the first operating power relationship formula, the second operating power relationship formula and the third operating power relationship formula.

[0130] First, the power range F of synthetic ammonia is generated according to the power of renewable energy generation. A ′ ,t , as shown in the following relation (20):

[0131]

[0132] Where: ΔF A Indicates the hourly synthetic ammonia adjustment ratio; F Amin Indicates the minimum synthetic ammonia power range; F Amax Indicates the maximum synthetic ammonia power range; P REmax represents the maximum renewable energy power generation power; α represents the renewable energy power generation power segmentation coefficient calculated according to the synthetic ammonia adjustment ratio, as shown in the following equation (21); N represents the number of renewable energy power generation segments calculated according to the synthetic ammonia adjustment ratio, as shown in the following equation (22).

[0133] α=ΔF A / (F Amax -F Amin +ΔF A ) (twenty one)

[0134] N=1,2,...,(FAmax -F Amin -ΔF A ) / ΔF A (twenty two)

[0135] Secondly, the synthetic ammonia power range is adjusted according to the hydrogen storage capacity and the hydrogen power generation power, as shown in the following equations (23) to (25):

[0136]

[0137] Where: F A,t Indicates the adjusted synthetic ammonia power range at time t; Indicates the synthetic ammonia power range adjusted according to the hydrogen storage capacity; η HSU Indicates the upper limit coefficient of hydrogen storage regulation; η HSL Indicates the lower limit coefficient of hydrogen storage regulation; H SCmin Indicates the minimum hydrogen storage capacity; Indicates the synthetic ammonia power range adjusted according to the hydrogen power generation power; P Amin Indicates the minimum ammonia synthesis power.

[0138] Finally, the synthetic ammonia power range can be constrained according to the load regulation rate and the safety of key infrastructure equipment, as shown in the following equations (26) to (28):

[0139] F A，t-1 -ΔF A ≤F A，t ≤F A，t-1 +ΔF A (26)

[0140] F As ≤F A，t ≤F Amax (27)

[0141] F As <F Amin (28)

[0142] Where: F As Indicates the minimum power range required to ensure the safety of key infrastructure equipment in the synthetic ammonia system.

[0143] Step a342, establishing a second operating power relationship according to the synthetic ammonia power range.

[0144] Specifically, the second operating power relationship is shown in the following relationship (29):

[0145]

[0146] Where: I A Indicates the installed power of synthetic ammonia.

[0147] Furthermore, since the rate at which hydrogen is consumed by synthetic ammonia affects the power of synthetic ammonia, it is also necessary to calculate the rate at which hydrogen is consumed by the synthetic ammonia unit, as shown in the following equation (30):

[0148]

[0149] Where: η HA Indicates the hydrogen-ammonia mass conversion coefficient; E A Indicates the unit energy consumption of synthetic ammonia.

[0150] Specifically, the rate at which hydrogen is consumed by synthesizing ammonia can affect the operating power of the synthesizing ammonia unit, and thus can affect the proportion of renewable energy consumption in the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system.

[0151] Step S2022, with the goal of maximizing the renewable energy consumption ratio of the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system, establish an objective function according to the operating power relationship of each unit.

[0152] Specifically, the objective function is used to characterize the renewable energy consumption ratio of the renewable energy hydrogen production and ammonia synthesis coupled with hydrogen power generation system.

[0153] Furthermore, the proportion of renewable energy consumption is shown in the following equation (31):

[0154]

[0155] Where: RC represents the proportion of renewable energy consumption.

[0156] Step S203, based on multiple initial installed capacity values, the particle swarm algorithm is used to solve the objective function to obtain the capacity configuration optimization result of the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system. Figure 1 Step S103 of the illustrated embodiment will not be described in detail here.

[0157] The capacity configuration optimization method provided in this embodiment establishes the renewable energy power generation relationship of the renewable energy power generation unit by combining the initial installed capacity scale value and the installed power value, taking into account the performance parameters of the equipment itself and the influence of external environmental factors on the power generation, and can more accurately reflect the power output characteristics of the renewable energy power generation unit. Further, by comprehensively considering the operating characteristics and mutual relationships of each key unit in the system and establishing the operating power relationship of each unit in the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system, it is possible to deeply analyze the power interaction and energy flow between different units, and more accurately adjust the scale of each unit when optimizing the capacity configuration in the subsequent process, so that the system can achieve efficient and stable operation under various operating conditions, maximize the absorption ratio of renewable energy, and meet the safety production requirements of hydrogen production and ammonia synthesis chemical industry at the same time. Further, establishing the objective function according to the operating power relationship of each unit can make the objective function more accurately reflect the operating characteristics and optimization requirements of the system, and provide support for the subsequent more effective guidance of the algorithm to find the optimal capacity configuration plan.

[0158] In this embodiment, a capacity configuration optimization method is provided for a control system, wherein the control system is connected to a renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system.

[0159] Figure 3 is a flow chart of a capacity configuration optimization method according to an embodiment of the present invention. Figure 3 As shown, the process includes the following steps:

[0160] Step S301, obtain multiple operation models and multiple initial installed capacity values ​​of the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system. Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0161] Step S302, with the goal of maximizing the renewable energy consumption ratio of the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system, establish an objective function based on multiple operation models and multiple initial installed capacity values. Figure 2 Step S202 of the illustrated embodiment will not be described in detail here.

[0162] Step S303, based on multiple initial installed capacity values, the particle swarm algorithm is used to solve the objective function to obtain a capacity configuration optimization result of the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system.

[0163] Specifically, the above step S303 includes:

[0164] Step S3031, based on multiple initial installed capacity values, the particle swarm algorithm is used to solve the objective function to obtain multiple target installed capacity values ​​of the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system.

[0165] Specifically, multiple initial installed capacity values ​​can be used as initial particles in the particle swarm. Each particle represents an installed capacity combination of various units (such as renewable energy power generation unit, water electrolysis hydrogen production unit, ammonia synthesis unit, hydrogen fuel cell power generation unit, electrochemical energy storage unit, etc.) in the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system. At the same time, the speed is randomly initialized for each particle, and the speed determines the moving direction and step size of the particle in the search space.

[0166] Furthermore, the fitness value corresponding to each particle can be calculated according to the objective function.

[0167] Among them, the fitness value reflects the superiority of the installed capacity combination represented by the particle in maximizing the proportion of renewable energy consumption.

[0168] Furthermore, update individual extrema, global extrema, particle speed and position:

[0169] (1) Update individual extreme values: For each particle, compare its current fitness value with the optimal fitness value in its own historical record. If the current fitness value is better, update the individual extreme value of the particle, that is, record the current installed capacity combination as the optimal position of the particle.

[0170] (2) Update the global extreme value: Compare the fitness values ​​of all particles and find the particle with the highest fitness value. The corresponding installed capacity combination is the current global extreme value. Furthermore, the global extreme value represents the optimal installed capacity configuration currently found by the entire particle swarm.

[0171] (3) Update particle speed and position: The speed and position of the particle can be updated according to the speed update formula and position update formula of the particle swarm algorithm.

[0172] Furthermore, the steps of calculating the fitness value to updating the particle speed and position are repeated for multiple iterations. After each iteration, check whether the termination condition is met, such as reaching the preset maximum number of iterations, the change in the fitness value is less than a certain threshold, etc. If the termination condition is met, the iteration is stopped, and the multiple particle positions in the particle swarm obtained at this time are multiple target installed capacity values.

[0173] Step S3032, determining the capacity configuration optimization result of the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system according to multiple target installed capacity values.

[0174] Specifically, for the multiple calculated target installed capacity values, their corresponding fitness values ​​can be calculated again according to the objective function.

[0175] Furthermore, the target installed capacity value with the highest fitness value can be found by comparing the fitness values ​​of all target installed capacity values.

[0176] Furthermore, the combination of installed capacity scales of each unit represented by the target installed capacity scale value is the capacity configuration optimization result of the renewable energy hydrogen production and ammonia synthesis coupled with hydrogen power generation system.

[0177] Furthermore, in some optional implementations, the obtained capacity configuration optimization results may be further verified and adjusted. For example, it may be checked whether the configuration meets other constraints of the system, such as physical limitations of the equipment, cost budget, safety requirements, etc. If certain constraints are not met, it may be necessary to appropriately adjust the results or re-perform the optimization calculation.

[0178] The capacity configuration optimization method provided in this embodiment, by utilizing the efficient search and optimization capabilities of the particle swarm algorithm, can quickly search for the installed capacity scale combination that maximizes the renewable energy consumption ratio in a huge installed capacity scale combination space and determine the final capacity configuration optimization result, thereby enabling the system to achieve efficient energy utilization and stable operation in off-grid mode, thereby increasing the renewable energy consumption ratio.

[0179] In one example, a method for optimizing capacity configuration of a renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system is provided, comprising the following steps:

[0180] (1) Obtain the power generation of renewable energy based on the installed capacity and output curve of renewable energy;

[0181] (2) Determine the operating power of each unit based on the installed power of the water electrolysis hydrogen production unit and the operating model of the water electrolysis hydrogen production unit, the installed power of the synthetic ammonia unit and the operating model of the synthetic ammonia unit, the installed power of the hydrogen fuel cell power generation unit and the operating model of the hydrogen fuel cell power generation unit, and the installed power of the electrochemical energy storage unit and the operating model of the electrochemical energy storage unit, and calculate the proportion of renewable energy consumption;

[0182] (3) Taking the highest proportion of renewable energy consumption as the optimization goal and the installed capacity of each unit in the system, such as renewable energy, water electrolysis hydrogen production, synthetic ammonia, hydrogen fuel cell power generation, and electrochemical energy storage, as variables, the particle swarm algorithm is used to solve the optimal capacity configuration plan of the renewable energy hydrogen production and synthetic ammonia coupled hydrogen power generation system.

[0183] Among them, the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system includes a renewable energy power generation unit, a water electrolysis hydrogen production unit, ammonia synthesis unit, a hydrogen fuel cell power generation unit, a hydrogen storage unit, and an electrochemical energy storage unit. The renewable energy power generation supplies the water electrolysis hydrogen production unit and ammonia synthesis unit with a certain flexible response capability. The hydrogen fuel cell power generation unit provides guaranteed power for the entire system when renewable energy power is lacking. The hydrogen production unit produces hydrogen and stores it in the hydrogen storage unit, and then supplies it to the ammonia synthesis unit and the hydrogen fuel cell power generation unit. The electrochemical energy storage unit is used to smooth the renewable energy power generation output and provide a part of guaranteed power.

[0184] Furthermore, the renewable energy power generation unit includes one or more of a hydropower generation unit, a wind power generation unit, a photovoltaic power generation unit, a solar thermal power generation unit, etc., the water electrolysis hydrogen production unit includes one or more of alkaline water electrolysis hydrogen production, proton exchange membrane water electrolysis hydrogen production, alkaline anion exchange membrane water electrolysis hydrogen production, solid oxide water electrolysis hydrogen production, etc., and the synthetic ammonia unit includes an air separation nitrogen production system, a nitrogen compression system and a hydrogen compression system.

[0185] Furthermore, the corresponding calculations in steps (1) to (3) are shown in the above equations (1) to (31).

[0186] The capacity configuration optimization method of the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system provided in this example has the following effects:

[0187] (1) By optimizing the operation method of the renewable energy hydrogen production and ammonia synthesis coupled with hydrogen power generation system, efficient energy utilization and stable system operation in off-grid mode can be achieved;

[0188] (2) Based on the interlocking operation of each unit of the system and the flexible corresponding hydrogen production unit and ammonia synthesis unit, the particle swarm algorithm is used to solve the optimal installed capacity of each unit such as renewable energy, water electrolysis hydrogen production, ammonia synthesis, hydrogen fuel cell power generation, hydrogen storage, and electrochemical energy storage, effectively promoting the consumption of renewable energy in the off-grid system.

[0189] In this embodiment, a capacity configuration optimization device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0190] This embodiment provides a capacity configuration optimization device for controlling a system, wherein the control system is connected to a renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system; Figure 4 As shown, the device comprises:

[0191] The acquisition module 401 is used to obtain multiple operation models and multiple initial installed capacity scale values ​​of the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system.

[0192] A module 402 is established for establishing an objective function based on a plurality of operation models and a plurality of initial installed capacity values ​​with the goal of maximizing the renewable energy consumption ratio of the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system.

[0193] The solution module 403 is used to solve the objective function based on multiple initial installed capacity values ​​using a particle swarm algorithm to obtain a capacity configuration optimization result of the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system.

[0194] In some optional implementations, the establishing module 402 includes:

[0195] The first submodule is used to establish the operating power relationship of each unit in the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system according to multiple operating models and multiple initial installed capacity values.

[0196] The second submodule is used to establish an objective function based on the operating power relationship of each unit with the goal of maximizing the renewable energy consumption ratio of the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system.

[0197] In some optional embodiments, the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system includes a renewable energy power generation unit; the first establishment submodule includes:

[0198] The determination unit is used to determine multiple installed power values ​​according to multiple initial installed capacity scale values.

[0199] The first establishing unit is used to establish a renewable energy power generation relationship of the renewable energy power generation unit according to a plurality of initial installed capacity scale values ​​and a plurality of installed capacity power values.

[0200] The second establishing unit is used to establish an operating power relationship formula of each unit according to multiple installed power values, multiple operating models and renewable energy power generation power relationship formula.

[0201] In some optional embodiments, the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system further includes a water electrolysis hydrogen production unit, an ammonia synthesis unit, a hydrogen fuel cell power generation unit and an electrochemical energy storage unit; the second establishment unit includes:

[0202] The first establishing subunit is used to establish a first operating power relationship formula of the water electrolysis hydrogen production unit according to the renewable energy power generation power relationship formula, the installed power value and the operating model of the water electrolysis hydrogen production unit.

[0203] The second establishing subunit is used to establish a second operating power relationship of the hydrogen fuel cell power generation unit according to the renewable energy power generation power relationship, the installed power value and the operating model of the hydrogen fuel cell power generation unit.

[0204] The third establishing subunit is used to establish a third operating power relationship of the electrochemical energy storage unit according to the renewable energy power generation power relationship, the installed power value and the operating model of the electrochemical energy storage unit.

[0205] The fourth establishing subunit is used to establish a fourth operating power relationship equation for the synthetic ammonia unit according to the renewable energy power generation power relationship equation, the first operating power relationship equation, the second operating power relationship equation and the third operating power relationship equation.

[0206] In some optional implementations, the fourth establishing subunit includes:

[0207] The determination subunit is used to determine the synthetic ammonia power range according to the renewable energy power generation power relationship formula, the first operating power relationship formula, the second operating power relationship formula and the third operating power relationship formula.

[0208] The fifth establishing subunit is used to establish a second operating power relationship according to the synthetic ammonia power range.

[0209] In some optional implementations, the solution module 403 includes:

[0210] The solving submodule is used to solve the objective function based on multiple initial installed capacity values ​​using a particle swarm algorithm to obtain multiple target installed capacity values ​​for the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system.

[0211] The submodule is used to determine the capacity configuration optimization result of the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system according to multiple target installed capacity values.

[0212] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0213] The capacity configuration optimization device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0214] The embodiment of the present invention also provides a computer device having the above Figure 4 The capacity configuration shown is optimized for the device.

[0215] See also Figure 5 , Figure 5 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 5 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 5 A processor 10 is taken as an example.

[0216] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0217] The memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiment.

[0218] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0219] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0220] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0221] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0222] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0223] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A capacity configuration optimization method, characterized in that: Used for a control system, the control system is connected to a renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system; the method comprises: Acquire multiple operation models and multiple initial installed capacity scale values ​​of the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system; Taking the maximum renewable energy consumption ratio of the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system as the goal, establishing an objective function according to the multiple operation models and the multiple initial installed capacity values; Based on the multiple initial installed capacity values, the objective function is solved using a particle swarm algorithm to obtain a capacity configuration optimization result of the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system.

2. The method according to claim 1, characterized in that Taking the maximum renewable energy consumption ratio of the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system as the goal, an objective function is established according to the multiple operation models and the multiple initial installed capacity values, including: According to the multiple operation models and the multiple initial installed capacity values, an operation power relationship formula of each unit in the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system is established; With the goal of maximizing the renewable energy consumption ratio of the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system, the objective function is established according to the operating power relationship formula of each unit.

3. The method according to claim 2, characterized in that The renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system includes a renewable energy power generation unit; according to the multiple operation models and the multiple initial installed capacity values, an operation power relationship of each unit in the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system is established, including: Determining a plurality of installed power values ​​according to the plurality of initial installed capacity scale values; Establishing a renewable energy generation power relationship of the renewable energy generation unit according to the multiple initial installed capacity scale values ​​and the multiple installed capacity power values; The operating power relationship of each unit is established according to the multiple installed power values, the multiple operating models and the renewable energy generation power relationship.

4. The method according to claim 3, characterized in that The renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system also includes a water electrolysis hydrogen production unit, an ammonia synthesis unit, a hydrogen fuel cell power generation unit and an electrochemical energy storage unit; According to the multiple installed power values, the multiple operation models and the renewable energy generation power relationship, the operation power relationship of each unit is established, including: According to the renewable energy power generation power relationship, the installed power value and the operation model of the water electrolysis hydrogen production unit, a first operation power relationship of the water electrolysis hydrogen production unit is established; Establishing a second operating power relationship formula of the hydrogen fuel cell power generation unit according to the renewable energy power generation power relationship formula, the installed power value and the operating model of the hydrogen fuel cell power generation unit; Establishing a third operating power relationship formula of the electrochemical energy storage unit according to the renewable energy power generation power relationship formula, the installed power value and the operating model of the electrochemical energy storage unit; A fourth operating power relationship equation of the synthetic ammonia unit is established based on the renewable energy generation power relationship equation, the first operating power relationship equation, the second operating power relationship equation, and the third operating power relationship equation.

5. The method according to claim 4, characterized in that According to the renewable energy power generation power relationship formula, the first operating power relationship formula, the second operating power relationship formula and the third operating power relationship formula, a fourth operating power relationship formula of the synthetic ammonia unit is established, including: Determining a synthetic ammonia power range according to the renewable energy power generation power relationship formula, the first operating power relationship formula, the second operating power relationship formula, and the third operating power relationship formula; The second operating power relationship is established according to the synthetic ammonia power range.

6. The method according to claim 1, characterized in that Based on the multiple initial installed capacity values, the objective function is solved using a particle swarm algorithm to obtain a capacity configuration optimization result of the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system, including: Based on the multiple initial installed capacity values, the objective function is solved using a particle swarm algorithm to obtain multiple target installed capacity values ​​of the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system; According to the multiple target installed capacity values, the capacity configuration optimization result of the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system is determined.

7. A capacity configuration optimization device, characterized in that: Used for a control system, the control system is connected to a renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system; the device comprises: An acquisition module, used to acquire multiple operation models and multiple initial installed capacity scale values ​​of the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system; Establishing a module for establishing an objective function based on the multiple operation models and the multiple initial installed capacity values ​​with the goal of maximizing the renewable energy consumption ratio of the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system; A solution module is used to solve the objective function based on the multiple initial installed capacity values ​​using a particle swarm algorithm to obtain a capacity configuration optimization result of the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the capacity configuration optimization method according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the capacity configuration optimization method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises computer instructions, wherein the computer instructions are used to enable a computer to execute the capacity configuration optimization method according to any one of claims 1 to 6.

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