A capacity configuration optimization method, apparatus, equipment, medium, and program product.

Abstract

This invention relates to the field of power system technology and discloses a capacity configuration optimization method, device, equipment, medium, and program product. By acquiring multiple operating models, this invention can gain a deep understanding of the operating patterns and interrelationships of various components within the system. Furthermore, by setting the highest renewable energy consumption ratio as the objective and establishing an objective function based on multiple operating models and initial installed capacity values, the complex operating conditions and optimization requirements of the system can be transformed into mathematical expressions. This achieves effective integration of system information, enabling the objective function to comprehensively reflect the system's operating characteristics and constraints. Consequently, it can determine the system's renewable energy consumption under different installed capacity scales. Through particle swarm optimization, it can quickly search within a vast space of installed capacity combinations to find the combination of installed capacity that maximizes the renewable energy consumption ratio and determine the final capacity configuration optimization result, thereby improving the renewable energy consumption ratio.

Description

Technical Field

[0001] This invention relates to the field of power system technology, specifically to a capacity configuration optimization method, apparatus, equipment, medium, and program product. Background Technology

[0002] With the rapid increase in the proportion of wind and solar installed capacity, the problem of wind and solar curtailment caused by the randomness, volatility, and intermittency of renewable energy output has become increasingly prominent. Therefore, it is urgent to explore new methods to solve the problem of renewable energy consumption. The production of green hydrogen through renewable energy electrolysis of water and its combination with nitrogen to form green ammonia has become an important direction of exploration both domestically and internationally. At the same time, this is also an important way for the synthetic ammonia industry to achieve carbon emission reduction.

[0003] Hydrogen production and ammonia synthesis systems based on renewable energy sources need to be able to adapt to the volatility and intermittency of renewable energy, while also meeting the safety requirements of hydrogen production and ammonia synthesis in chemical production. Therefore, to maximize the utilization of renewable energy in off-grid systems, it is first necessary to optimize system operation and scheduling methods, and then improve the proportion of renewable energy consumption through hydrogen production and ammonia synthesis coupled with hydrogen power generation by optimizing system capacity configuration. Summary of the Invention

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

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

[0006] Multiple operating models and initial installed capacity values ​​for a renewable energy-based hydrogen production and ammonia synthesis coupled with hydrogen power generation system are obtained. With the goal of maximizing the renewable energy consumption ratio of the renewable energy-based hydrogen production and ammonia synthesis coupled with hydrogen power generation system, an objective function is established based on the multiple operating models and initial installed capacity values. Based on the multiple initial installed capacity values, the objective function is solved using a particle swarm optimization algorithm to obtain the capacity configuration optimization results of the renewable energy-based hydrogen production and ammonia synthesis coupled with hydrogen power generation system.

[0007] The capacity configuration optimization method provided by this invention, by acquiring multiple operating models, can deeply understand the operating rules and interrelationships of various components in the system. Furthermore, by setting the highest renewable energy consumption ratio as the objective and establishing an objective function based on multiple operating models and multiple initial installed capacity values, the complex operating conditions and optimization requirements of the system can be transformed into mathematical expressions, facilitating subsequent solution and optimization using algorithms. Furthermore, it achieves effective integration of system information, enabling the objective function to comprehensively reflect the system's operating characteristics and constraints. This allows for the determination of the system's renewable energy consumption under different installed capacity scales. Thus, the particle swarm optimization algorithm can quickly search the vast space of installed capacity combinations to find the combination of installed capacity that maximizes the renewable energy consumption ratio and determine the final capacity configuration optimization result. Therefore, by implementing this invention, various aspects of the system can be comprehensively considered to optimize the system's capacity configuration as a whole, adapting to the characteristics of renewable energy, improving the renewable energy consumption ratio, and alleviating the problem of wind and solar curtailment caused by the randomness, fluctuation, and intermittency of renewable energy output. This provides technical support for achieving efficient utilization and sustainable development of renewable energy.

[0008] In one optional implementation, with the goal of maximizing the renewable energy consumption ratio of the renewable energy-to-hydrogen-to-ammonia-synthetic-hydrogen-power-generation system, an objective function is established based on multiple operating models and multiple initial installed capacity values, including:

[0009] Based on multiple operating models and multiple initial installed capacity values, the operating power relationship of each unit in the renewable energy hydrogen production and ammonia synthesis coupled with 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 with hydrogen power generation system, an objective function is established based on the operating power relationship of each unit.

[0010] The capacity configuration optimization method provided by this invention, by establishing the operating power relationship formulas of each unit in a renewable energy hydrogen production and ammonia synthesis coupled with hydrogen power generation system, can more accurately reflect the power relationships and mutual influences between the units in the system. Furthermore, by establishing an objective function based on the operating power relationship formulas of each unit, the objective function can more accurately reflect the system's operating characteristics and optimization requirements, providing support for subsequently guiding the algorithm to find the optimal capacity configuration scheme more effectively.

[0011] In one optional implementation, the renewable energy-to-hydrogen-to-ammonia-synthetic-hydrogen-power-coupled system includes renewable energy power generation units; based on multiple operating models and multiple initial installed capacity values, the operating power relationships of each unit within the renewable energy-to-hydrogen-to-ammonia-synthetic-hydrogen-power-coupled system are established, including:

[0012] Multiple installed capacity values ​​are determined based on multiple initial installed capacity values; based on multiple initial installed capacity values ​​and multiple installed capacity values, the renewable energy power generation relationship of the renewable energy power generation unit is established; based on multiple installed capacity values, multiple operating models and renewable energy power generation relationship, the operating power relationship of each unit is established.

[0013] The capacity configuration optimization method provided by this invention establishes a renewable energy power generation relationship for renewable energy power generation units by combining initial installed capacity and installed power values. This takes into account the performance parameters of the equipment itself as well as the impact of external environmental factors on power generation, thus more accurately reflecting the power output characteristics of the renewable energy power generation units. Furthermore, by combining multiple installed power values, multiple operating models, and renewable energy power generation relationships to establish operating power relationships for each unit, the method establishes an operational power correlation between all units in the entire system. This correlation allows for a clearer understanding of the transmission and distribution process of renewable energy power generation within the system, as well as its impact on the operating power of other units. Therefore, when optimizing capacity configuration, this method 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.

[0014] In one optional implementation, 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] Based on multiple installed capacity values, multiple operating models, and renewable energy power generation formulas, the operating power relationships for each unit are established, including:

[0016] Based on the renewable energy power generation relationship, the installed power value and operation model of the water electrolysis hydrogen production unit, a first operating power relationship is established for the water electrolysis hydrogen production unit; based on the renewable energy power generation relationship, the installed power value and operation model of the hydrogen fuel cell power generation unit, a second operating power relationship is established for the hydrogen fuel cell power generation unit; based on the renewable energy power generation relationship, the installed power value and operation model of the electrochemical energy storage unit, a third operating power relationship is established for the electrochemical energy storage unit; based on the renewable energy power generation relationship, the first operating power relationship, the second operating power relationship, and the third operating power relationship, a fourth operating power relationship is established for the ammonia synthesis unit.

[0017] The capacity configuration optimization method provided by this invention comprehensively considers the operating characteristics and interrelationships of each key unit in the system and establishes the operating power relationship between each unit in the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system. It can deeply analyze the power interaction and energy flow between different units, and can more accurately adjust the scale of each unit when optimizing the capacity configuration in the future. This enables the system to achieve efficient and stable operation under various operating conditions, maximize the proportion of renewable energy consumption, and meet the needs of safe production in hydrogen production and ammonia synthesis.

[0018] In one optional implementation, a fourth operating power relationship for the ammonia synthesis unit is established based on the renewable energy power generation relationship, the first operating power relationship, the second operating power relationship, and the third operating power relationship, including:

[0019] Based on the power generation relationship of renewable energy, the first operating power relationship, the second operating power relationship, and the third operating power relationship, the power range of ammonia synthesis is determined; based on the power range of ammonia synthesis, the second operating power relationship is established.

[0020] The capacity configuration optimization method provided by this invention determines the ammonia synthesis 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. This allows for a more accurate grasp of the operating power characteristics of the ammonia synthesis unit, thereby enabling the established second operating power relationship to better reflect the actual operating status and power demand of the ammonia synthesis unit in the entire system. Consequently, in subsequent capacity configuration optimization, 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, while working in conjunction with other units to jointly improve the renewable energy absorption efficiency and the overall system performance.

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

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

[0023] The capacity configuration optimization method provided by this invention utilizes the efficient search and optimization capabilities of the particle swarm optimization algorithm to quickly search for the combination of installed capacity that maximizes the renewable energy consumption ratio within a vast space of installed capacity combinations and determine the final capacity configuration optimization result. This enables the system to achieve efficient energy utilization and stable operation in off-grid mode, thereby improving the renewable energy consumption ratio.

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

[0025] The system comprises the following modules: an acquisition module for acquiring multiple operating models and initial installed capacity values ​​for a renewable energy-to-hydrogen-to-ammonia-to-hydrogen-power-generating system; an establishment module for establishing an objective function based on the multiple operating models and initial installed capacity values, with the goal of maximizing the renewable energy consumption ratio of the renewable energy-to-hydrogen-to-ammonia-to-hydrogen-power-generating system; and a solution module for solving the objective function using a particle swarm optimization algorithm based on the multiple initial installed capacity values, thereby obtaining the capacity configuration optimization results for the renewable energy-to-hydrogen-to-ammonia-to-hydrogen-power-generating system.

[0026] Thirdly, the present invention provides a computer device, comprising: 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 computer instructions to perform the capacity configuration optimization method of the first aspect or any corresponding embodiment described above.

[0027] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to perform the capacity configuration optimization method of the first aspect or any corresponding embodiment thereof.

[0028] Fifthly, the present invention provides a computer program product, including computer instructions for causing a computer to execute the capacity configuration optimization method of the first aspect or any corresponding embodiment thereof. Attached Figure Description

[0029] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

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

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

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

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

[0034] Figure 5 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] This invention provides a capacity configuration optimization method, which aims to optimize the system's capacity configuration and improve the renewable energy consumption ratio by taking the highest renewable energy consumption ratio as the objective and combining multiple operating models and multiple initial installed capacity values ​​to establish an objective function.

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

[0038] This embodiment provides a capacity configuration optimization method for controlling a system that is connected to a renewable energy-based hydrogen production and ammonia synthesis coupled hydrogen power generation system.

[0039] Specifically, a renewable energy-based hydrogen production and ammonia synthesis coupled with 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 the water electrolysis hydrogen production unit and the ammonia synthesis unit with a certain degree of flexible response capability. The hydrogen fuel cell power generation unit provides backup power for the entire system when renewable energy power is scarce. The hydrogen production unit produces hydrogen, which is stored in the hydrogen storage unit and then supplied to the ammonia synthesis unit and the hydrogen fuel cell power generation unit. The electrochemical energy storage unit is used to smooth the output of renewable energy power generation and provide a portion of backup power.

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

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

[0043] Step S101: Obtain multiple operating models and multiple initial installed capacity values ​​for 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 frameworks of the interrelationships and changing patterns of key parameters (such as power, rate, capacity, etc.) between each unit under different operating conditions. They are used to reflect the energy conversion, material flow and synergistic effects of each unit with other units, and help to understand, predict and optimize the system's operating status, so as to improve energy utilization efficiency and economy while ensuring the system's safety and reliability.

[0045] Multiple installed capacity values ​​represent the installed capacity of each unit in a renewable energy hydrogen production and ammonia synthesis coupled with hydrogen power generation system. They are a comprehensive description of the capacity-related parameters of each unit's equipment, reflecting the installed capacity of each unit's 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 operational economy of the entire system.

[0046] Specifically, multiple operating 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 capacity 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 during ammonia synthesis and other factors of the system (such as renewable energy power generation, hydrogen storage, 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 and hydrogen consumption rate during hydrogen fuel cell power generation and factors such as the operating power of water electrolysis hydrogen production unit, the operating power of ammonia synthesis unit, and renewable energy power generation. 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, and hydrogen release rate 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 parameters such as energy storage discharge power, energy storage charging power, and energy storage charge capacity of the electrochemical energy storage unit 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] Furthermore, multiple installed capacity values ​​can include:

[0053] (1) Renewable energy power generation capacity: This 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 for hydrogen production: This represents the total power or production capacity of the water electrolysis hydrogen production unit. It determines the system's ability to produce hydrogen, and thus directly affects the supply of hydrogen required for ammonia synthesis.

[0055] (3) Ammonia synthesis capacity: This represents the total production capacity of the ammonia synthesis unit, which determines the maximum amount of ammonia that the system can produce and is directly related to the ammonia synthesis capacity.

[0056] (4) Installed capacity of hydrogen fuel cell power generation: This represents the total power of the hydrogen fuel cell power generation unit, which reflects the system's ability to generate electricity using hydrogen, and thus affects the redistribution and utilization efficiency of energy within the system.

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

[0058] (6) Installed capacity of electrochemical energy storage unit: This 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-to-hydrogen-to-ammonia-synthetic-hydrogen-power-generation system, an objective function is established based on multiple operating models and multiple initial installed capacity values.

[0060] The objective function is used to characterize the proportion of renewable energy consumption in the renewable energy-to-hydrogen-to-ammonia-synthetic-hydrogen-power-generation system.

[0061] Specifically, by setting the highest renewable energy consumption ratio as the objective and establishing an objective function that combines multiple operating models and initial installed capacity values, the complex operating conditions and optimization requirements of the system can be transformed into mathematical expressions, facilitating subsequent solution and optimization using algorithms. Furthermore, it achieves effective integration of system information, enabling the objective function to comprehensively reflect the system's operating characteristics and constraints, thereby determining the system's renewable energy consumption under different installed capacity scales.

[0062] Step S103: Based on multiple initial installed capacity values, the objective function is solved using the particle swarm optimization algorithm to obtain the capacity configuration optimization results of the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system.

[0063] Particle Swarm Optimization (PSO) is a stochastic search optimization algorithm based on swarm intelligence. It simulates the foraging behavior of groups of organisms such as flocks of birds and schools of fish, and treats the solutions to the problem as particles in the search space. Each particle represents a potential solution.

[0064] Specifically, the efficient search and optimization capabilities of the particle swarm optimization algorithm can be utilized to quickly search for the combination of installed capacity that maximizes the proportion of renewable energy consumption within a vast space of installed capacity combinations and determine the final capacity configuration optimization result.

[0065] The capacity configuration optimization method provided in this embodiment can deeply understand the operating rules and interrelationships of various components in the system by acquiring multiple operating models. Furthermore, by setting the highest renewable energy consumption ratio as the objective and establishing an objective function based on multiple operating models and multiple initial installed capacity values, the complex operating conditions and optimization requirements of the system can be transformed into mathematical expressions, facilitating subsequent solution and optimization using algorithms. Furthermore, it achieves effective integration of system information, enabling the objective function to comprehensively reflect the system's operating characteristics and constraints. This allows for the determination of the system's renewable energy consumption under different installed capacity scales. Thus, the particle swarm optimization algorithm can quickly search the vast space of installed capacity combinations to find the combination of installed capacity that maximizes the renewable energy consumption ratio and determine the final capacity configuration optimization result. Therefore, by implementing this invention, various aspects of the system can be comprehensively considered to optimize the system's capacity configuration as a whole, adapting to the characteristics of renewable energy, improving the renewable energy consumption ratio, and alleviating the problem of wind and solar curtailment caused by the randomness, fluctuation, and intermittency of renewable energy output. This provides technical support for achieving efficient utilization and sustainable development of renewable energy.

[0066] This embodiment provides a capacity configuration optimization method for controlling a system that is connected to a renewable energy-based hydrogen production and ammonia synthesis coupled hydrogen power generation system.

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

[0068] Step S201: Obtain multiple operating models and multiple initial installed capacity values ​​for a renewable energy-to-hydrogen-to-ammonia-synthetic-hydrogen-power-generation system. For details, please refer to [link to relevant documentation]. Figure 1 Step S101 of the illustrated embodiment will not be described again here.

[0069] Step S202: With the goal of maximizing the renewable energy consumption ratio of the renewable energy-to-hydrogen-to-ammonia-coupled-hydrogen-power-generation system, an objective function is established based on multiple operating models and multiple initial installed capacity values.

[0070] Specifically, step S202 includes:

[0071] Step S2021: Based on multiple operating models and multiple initial installed capacity values, establish the operating 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 formulas 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 units of the system can be reflected more accurately.

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

[0074] Step a1: Determine multiple installed power values ​​based on multiple initial installed capacity values.

[0075] Step a2: Based on multiple initial installed capacity values ​​and multiple installed power values, establish the renewable energy power generation relationship of the renewable energy power generation unit.

[0076] Step a3: Based on multiple installed capacity values, multiple operating models, and renewable energy power generation power relationship formulas, establish the operating power relationship formulas for each unit.

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

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

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

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

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

[0082] In the formula: P RE，t P represents the renewable energy power generation at time t; HY，t Let P represent the hydroelectric power generation at time t, as shown in equation (2); W，t Let P represent the wind power generation at time t, as shown in equation (3);PV，t Let P represent the photovoltaic power generation at time t, as shown in equation (4); ST，t Let t represent the solar thermal power generation at time t, as shown in 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] In the formula: I HY p represents the installed capacity of hydropower generation. HY，t Represents the hydroelectric power output curve at time t; I W Indicates the installed capacity of wind power generation; p W，t Represents the wind power output curve at time t; I PV Indicates the installed capacity of photovoltaic power generation; p PV，t Represents the photovoltaic power output curve at time t; I ST Indicates the installed capacity of concentrated solar power (CSP); p ST，t This represents the solar thermal power output curve at time t.

[0088] Finally, by combining the obtained multiple installed capacity values, multiple operating models, and renewable energy power generation relationship formulas, the operating power relationship formulas for each unit can be established, thereby establishing the operating power correlation between all units in the entire system. Furthermore, through this correlation, 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, can be more clearly understood. This allows for better coordination of the relationship between renewable energy power generation and other units when optimizing capacity configuration, improving the system's adaptability and absorption capacity for renewable energy, and reducing system instability and energy waste caused by renewable energy fluctuations.

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

[0090] Step a31: Based on the renewable energy power generation power relationship, the installed power value and operation model of the water electrolysis hydrogen production unit, establish the first operating power relationship 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] In the formula: P H，t P represents the hydrogen production power at time t, which is also the operating power. Hmax The maximum hydrogen production power is represented by the following relationship (7); P Hmin The minimum hydrogen production power is represented by the following relationship (8); P′ H，t Let P represent the hydrogen production power at time t, as shown in equation (9); Hs This 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] In the formula: P A，t I represents the ammonia synthesis power at time t; H Indicates the installed capacity of hydrogen production facilities; F Hmax Indicates the maximum hydrogen production capacity range; F Hmin Indicates the minimum hydrogen production power range; E H Indicates the energy consumption per unit of hydrogen production; H S，t-1 H represents the amount of hydrogen stored at time t-1; SC Indicates the rated hydrogen storage capacity; η HS Indicates the hydrogen storage adjustment factor; H A，t This represents the rate at which hydrogen is consumed in the synthesis of 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] In the formula: H P，t This 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 system feedback mechanisms, equipment operating efficiency, and energy balance requirements, which in turn can affect the renewable energy consumption ratio of the renewable energy hydrogen production to ammonia synthesis coupled with hydrogen power generation system.

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

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

[0104] In the formula: H HP，t This represents the rate at which hydrogen is consumed in hydrogen power generation at time t.

[0105] Specifically, hydrogen storage capacity can be affected by P′ H，t This, in turn, affects hydrogen production capacity, and further, it can affect the proportion of renewable energy consumption in renewable energy-to-hydrogen-ammonia-synthetic-hydrogen-power-generation systems.

[0106] Furthermore, hydrogen storage capacity also influences the operational decisions of hydrogen fuel cell power generation units. When hydrogen storage is sufficient, the hydrogen fuel cell power generation unit can generate electricity more flexibly according to system demand, resulting in variations in its power output. These variations in power output, in turn, affect the calculation of the renewable energy consumption ratio by influencing the system's power balance.

[0107] Step a32: Based on the renewable energy power generation power relationship, the installed power value of the hydrogen fuel cell power generation unit and the operation model, establish the second operating power relationship of the hydrogen fuel cell power generation unit.

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

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

[0110] In the formula: P HP，t E represents the hydrogen power generation capacity at time t. HP This indicates the amount of electricity generated per unit of hydrogen gas.

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

[0112] P HPmin ≤P HP，t ≤P HPmax (13)

[0113] In the formula: P HPmin The minimum hydrogen power generation capacity is represented by the following relationship (14); P HPmaxThe maximum hydrogen power generation capacity is represented by the following relationship (15).

[0114] P HPmax =I HP ·F HPmax (14)

[0115] P HPmin =I HP ·F HPmin (15)

[0116] In the formula: I HP This indicates the installed capacity of hydrogen power generation.

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

[0118] H HP，t =P HP，t / E HP (16)

[0119] Specifically, the rate at which hydrogen is consumed in hydrogen power generation can affect the operating power of hydrogen fuel cell power generation units, and in turn, the proportion of renewable energy consumption in renewable energy-to-hydrogen-ammonia-synthetic-hydrogen-power-generation systems.

[0120] Step a33: Based on the renewable energy power generation relationship, the installed power value of the electrochemical energy storage unit and the operation model, establish the third operating power relationship of the electrochemical energy storage unit.

[0121] Specifically, the third operating power relationship is shown in the following equations (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] In the formula: P Bdischa，t I represents the energy storage discharge power at time t; B P represents the installed capacity of energy storage; Bcha，t B represents the energy storage charging power at time t;t η represents the energy storage charge capacity at time t; B Indicates the energy storage charging and discharging efficiency; B max This indicates the installed capacity of energy storage.

[0126] Step a34: Based on the renewable energy power generation power relationship, the first operating power relationship, the second operating power relationship, and the third operating power relationship, establish the fourth operating power relationship for the ammonia synthesis unit.

[0127] Specifically, by comprehensively considering the operating characteristics and interrelationships of each key unit in the system and establishing the operating power relationship between 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. In subsequent capacity optimization, the scale of each unit can be adjusted more precisely, enabling the system to achieve efficient and stable operation under various operating conditions, maximizing the proportion of renewable energy consumption, and meeting the needs of safe production in hydrogen production and ammonia synthesis.

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

[0129] Step a341: Determine the power range for ammonia synthesis based on the renewable energy power generation relationship, the first operating power relationship, the second operating power relationship, and the third operating power relationship.

[0130] First, based on the range F of ammonia synthesis power generated by renewable energy power generation. A ′ ,t The following relation (20) is shown:

[0131]

[0132] In the formula: ΔF A Indicates the hourly ammonia synthesis adjustment ratio; F Amin Indicates the minimum ammonia synthesis power range; F Amax Indicates the maximum ammonia synthesis power range; P REmax α represents the maximum renewable energy power generation; α represents the segmentation coefficient of renewable energy power generation calculated based on the synthetic ammonia adjustment ratio, as shown in the following relationship (21); N represents the number of segments of renewable energy power generation calculated based on the synthetic ammonia adjustment ratio, as shown in the following relationship (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 range of ammonia synthesis power is adjusted according to the hydrogen storage capacity and hydrogen power generation capacity, as shown in the following equations (23) to (25):

[0136]

[0137] In the formula: F A,t This represents the range of ammonia synthesis power after adjustment at time t; This indicates the range of ammonia synthesis power adjusted based on hydrogen storage capacity; η HSU Indicates the upper limit coefficient for hydrogen storage regulation; η HSL Indicates the lower limit coefficient for hydrogen storage regulation; H SCmin Indicates the minimum hydrogen storage capacity; This indicates the range of ammonia synthesis power adjusted based on hydrogen power generation capacity; P Amin This represents the minimum ammonia synthesis power.

[0138] Finally, the power range for ammonia synthesis can be constrained based on the load regulation rate and the need to ensure the safety of key infrastructure, 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] In the formula: F As This indicates the minimum power range required to ensure the safety of key basic equipment in the ammonia synthesis system.

[0143] Step a342: Establish a second operating power relationship based on the ammonia synthesis power range.

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

[0145]

[0146] In the formula: I A This indicates the installed capacity of the ammonia synthesis plant.

[0147] Furthermore, since the rate of hydrogen consumption in ammonia synthesis affects the ammonia synthesis power, it is also necessary to calculate the rate of hydrogen consumption in ammonia synthesis of the ammonia synthesis unit, as shown in the following relationship (30):

[0148]

[0149] In the formula: η HA E represents the hydrogen-ammonia mass conversion factor. A This indicates the energy consumption per unit of ammonia synthesis.

[0150] Specifically, the rate at which hydrogen is consumed in ammonia synthesis can affect the operating power of the ammonia synthesis unit, which in turn can affect the renewable energy consumption ratio of the renewable energy-to-hydrogen-ammonia-coupled-hydrogen-power-generation system.

[0151] Step S2022: With the goal of maximizing the renewable energy consumption ratio of the renewable energy-to-hydrogen-to-ammonia-synthetic-hydrogen-power-generation system, an objective function is established based on the operating power relationship of each unit.

[0152] Specifically, the objective function is used to characterize the proportion of renewable energy consumption in the renewable energy-to-hydrogen-to-ammonia-synthetic-hydrogen-power-generation system.

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

[0154]

[0155] In the formula: RC represents the renewable energy consumption ratio.

[0156] Step S203: Based on multiple initial installed capacity values, the objective function is solved using the particle swarm optimization algorithm to obtain the capacity configuration optimization results of the renewable energy hydrogen production and ammonia synthesis coupled with hydrogen power generation system. For details, please refer to [link to relevant documentation]. Figure 1 Step S103 of the illustrated embodiment will not be described again here.

[0157] The capacity configuration optimization method provided in this embodiment establishes a renewable energy power generation relationship between renewable energy power generation units by combining the initial installed capacity scale and installed power values. This takes into account the performance parameters of the equipment itself and the impact of external environmental factors on power generation, thus more accurately reflecting the power output characteristics of the renewable energy power generation units. Furthermore, by comprehensively considering the operating characteristics and interrelationships of each key unit in the system and establishing operating power relationships between each unit within the renewable energy hydrogen production / ammonia synthesis coupled hydrogen power generation system, it is possible to deeply analyze the power interaction and energy flow between different units. This allows for more precise adjustment of the scale of each unit during subsequent capacity configuration optimization, enabling the system to achieve efficient and stable operation under various operating conditions, maximizing the proportion of renewable energy consumption, and simultaneously meeting the safety requirements of hydrogen production and ammonia synthesis chemical production. Furthermore, establishing an objective function based on the operating power relationships of each unit allows the objective function to more accurately reflect the system's operating characteristics and optimization requirements, providing support for more effectively guiding the algorithm to find the optimal capacity configuration scheme.

[0158] This embodiment provides a capacity configuration optimization method for controlling a system that is connected to a renewable energy-based hydrogen production and ammonia synthesis coupled hydrogen power generation system.

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

[0160] Step S301: Obtain multiple operating models and multiple initial installed capacity values ​​for a renewable energy-to-hydrogen-to-ammonia-synthetic-hydrogen-power-generation system. For details, please refer to [link to relevant documentation]. Figure 1 Step S101 of the illustrated embodiment will not be described again here.

[0161] Step S302: With the goal of maximizing the renewable energy consumption ratio of the renewable energy-to-hydrogen-to-ammonia-synthetic-hydrogen-power-generation system, an objective function is established based on multiple operating models and multiple initial installed capacity values. For details, please refer to [link to relevant documentation]. Figure 2 Step S202 of the illustrated embodiment will not be described again here.

[0162] Step S303: Based on multiple initial installed capacity values, the objective function is solved using the particle swarm optimization algorithm to obtain the capacity configuration optimization results of the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system.

[0163] Specifically, step S303 includes:

[0164] Step S3031: Based on multiple initial installed capacity values, the objective function is solved using the particle swarm optimization algorithm to obtain multiple target installed capacity values ​​for 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 a combination of installed capacity from various units (such as renewable energy power generation units, water electrolysis hydrogen production units, ammonia synthesis units, hydrogen fuel cell power generation units, electrochemical energy storage units, etc.) in a renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system. Simultaneously, a velocity is randomly initialized for each particle, which determines the particle's direction of movement and step size in the search space.

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

[0167] The fitness value reflects the merits of the combination of installed capacity represented by the particle in maximizing the proportion of renewable energy consumption.

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

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

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

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

[0172] Furthermore, the steps of calculating fitness values ​​and updating particle velocities and positions are repeated multiple times. After each iteration, it is checked whether the termination condition is met, such as reaching the preset maximum number of iterations or the change in fitness values ​​being less than a certain threshold. If the termination condition is met, the iteration stops, and the multiple particle positions obtained in the particle swarm at this point represent the multiple target device size values.

[0173] Step S3032: Determine the capacity configuration optimization result of the renewable energy hydrogen production and ammonia synthesis coupled hydrogen power generation system based on multiple target installed capacity values.

[0174] Specifically, for the multiple target installed capacity values ​​obtained from the calculation, their corresponding fitness values ​​can be calculated again based on the objective function.

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

[0176] Furthermore, the combination of the installed capacity of each unit represented by the target installed capacity 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 can be further verified and adjusted. For example, it can be checked whether the configuration meets other system constraints, such as physical limitations of the equipment, cost budget, and security requirements. If certain constraints are not met, it may be necessary to adjust the results appropriately or recalculate the optimization.

[0178] The capacity configuration optimization method provided in this embodiment utilizes the efficient search and optimization capabilities of the particle swarm optimization algorithm to quickly search for the combination of installed capacity that maximizes the renewable energy consumption ratio within a vast space of installed capacity combinations and determine the final capacity configuration optimization result. This enables the system to achieve efficient energy utilization and stable operation in off-grid mode, thereby improving the renewable energy consumption ratio.

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

[0180] (1) Obtain the renewable energy power generation capacity 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 and operation model of the water electrolysis hydrogen production unit, the installed power and operation model of the ammonia synthesis unit, the installed power and operation model of the hydrogen fuel cell power generation unit, and the installed power and operation model of the electrochemical energy storage unit, and calculate the renewable energy consumption ratio.

[0182] (3) Taking the highest renewable energy consumption ratio as the optimization objective, and using 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 for the optimal capacity configuration scheme of the renewable energy hydrogen production and synthetic ammonia coupled hydrogen power generation system.

[0183] The renewable energy-to-hydrogen and ammonia-synthesis coupled hydrogen power generation system includes 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. Renewable energy power generation supplies the water electrolysis hydrogen production unit and the ammonia synthesis unit, which have a certain degree of flexible response capability. The hydrogen fuel cell power generation unit provides backup power for the entire system when renewable energy power is scarce. The hydrogen produced by the hydrogen production unit is stored in the hydrogen storage unit and then supplied to the ammonia synthesis unit and the hydrogen fuel cell power generation unit. The electrochemical energy storage unit is used to smooth the output of renewable energy power generation and provide a portion of backup power.

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

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

[0186] The capacity configuration optimization method for a renewable energy hydrogen production and ammonia synthesis coupled with 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 based on the flexible and corresponding hydrogen production unit and ammonia synthesis unit, 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 is solved by particle swarm algorithm, which effectively promotes the consumption of renewable energy in the off-grid system.

[0189] This embodiment also provides a capacity configuration optimization device for implementing the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

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

[0191] The acquisition module 401 is used to acquire multiple operating models and multiple initial installed capacity values ​​of a renewable energy hydrogen production and ammonia synthesis coupled with hydrogen power generation system.

[0192] Module 402 is established to create an objective function based on multiple operating models and multiple initial installed capacity values, with the goal of maximizing the renewable energy consumption ratio of the renewable energy-to-hydrogen-to-ammonia-synthetic-hydrogen-power-generation system.

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

[0194] In some alternative implementations, the establishment 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 based on 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-to-hydrogen-to-ammonia-synthetic-hydrogen-power-generation system.

[0197] In some optional implementations, the renewable energy-to-hydrogen-to-ammonia-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 ​​based on multiple initial installed capacity values.

[0199] The first establishment unit is used to establish the renewable energy power generation relationship of the renewable energy power generation unit based on multiple initial installed capacity values ​​and multiple installed power values.

[0200] The second establishment unit is used to establish the operating power relationship of each unit based on multiple installed power values, multiple operating models and renewable energy power generation relationship formulas.

[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 building unit includes:

[0202] The first sub-unit is used to establish the first operating power relationship of the water electrolysis hydrogen production unit based on the renewable energy power generation power relationship, the installed power value and operating model of the water electrolysis hydrogen production unit.

[0203] The second sub-unit is used to establish the second operating power relationship of the hydrogen fuel cell power generation unit based on the renewable energy power generation power relationship, the installed power value of the hydrogen fuel cell power generation unit, and the operating model.

[0204] The third sub-unit is used to establish the third operating power relationship of the electrochemical energy storage unit based on the renewable energy power generation relationship, the installed power value of the electrochemical energy storage unit, and the operating model.

[0205] The fourth sub-unit is used to establish the fourth operating power relationship of the ammonia synthesis unit based on the renewable energy power generation relationship, the first operating power relationship, the second operating power relationship, and the third operating power relationship.

[0206] In some alternative implementations, the fourth establishment subunit includes:

[0207] The sub-unit is determined to determine the power range of ammonia synthesis based on the renewable energy power generation relationship, the first operating power relationship, the second operating power relationship, and the third operating power relationship.

[0208] The fifth sub-unit is used to establish the second operating power relationship based on the ammonia synthesis power range.

[0209] In some alternative implementations, the solver module 403 includes:

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

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

[0212] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

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

[0214] This invention also provides a computer device having the above-described features. Figure 4 The capacity configuration optimization device shown.

[0215] Please see Figure 5 , Figure 5 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 5 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 5 Take a processor 10 as an example.

[0216] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0217] The memory 20 stores instructions executable by at least one processor 10 to cause at least one processor 10 to perform the method shown in the above embodiments.

[0218] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

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

[0220] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0221] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0222] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

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

Claims

1. A capacity configuration optimization method characterized by, A method for controlling a system connected with a renewable energy hydrogen synthesis ammonia coupling hydrogen power generation system, the renewable energy hydrogen synthesis ammonia coupling hydrogen power generation system comprising a renewable energy power generation unit, a water electrolysis hydrogen production unit, a synthetic ammonia unit, a hydrogen fuel cell power generation unit and an electrochemical energy storage unit; the method comprising: obtaining a plurality of operating models and a plurality of initial installed capacity values of the renewable energy hydrogen synthesis ammonia coupling hydrogen power generation system; establishing a target function according to the plurality of operating models and the plurality of initial installed capacity values, with the highest renewable energy consumption ratio of the renewable energy hydrogen synthesis ammonia coupling hydrogen power generation system as the target; solving the target function based on the plurality of initial installed capacity values using a particle swarm algorithm to obtain a capacity configuration optimization result of the renewable energy hydrogen synthesis ammonia coupling hydrogen power generation system; wherein, according to the plurality of operating models and the plurality of initial installed capacity values, a target function is established with the highest renewable energy consumption ratio of the renewable energy hydrogen synthesis ammonia coupling hydrogen power generation system as the target, comprising: establishing an operating power relationship of each unit in the renewable energy hydrogen synthesis ammonia coupling hydrogen power generation system according to the plurality of operating models and the plurality of initial installed capacity values; establishing the target function according to the operating power relationship of each unit, with the highest renewable energy consumption ratio of the renewable energy hydrogen synthesis ammonia coupling hydrogen power generation system as the target; wherein, according to the plurality of operating models and the plurality of initial installed capacity values, the operating power relationship of each unit in the renewable energy hydrogen synthesis ammonia coupling hydrogen power generation system is established, comprising: determining a plurality of installed power values according to the plurality of initial installed capacity values; establishing a renewable energy power generation power relationship of the renewable energy power generation unit according to the plurality of initial installed capacity values and the plurality of installed power values; establishing the operating power relationship of each unit according to the plurality of installed power values, the plurality of operating models and the renewable energy power generation power relationship; wherein, according to the plurality of installed power values, the plurality of operating models and the renewable energy power generation power relationship, the operating power relationship of each unit is established, comprising: establishing a first operating power relationship of the water electrolysis hydrogen production unit according to the renewable energy power generation power relationship, the installed power value and the operating model of the water electrolysis hydrogen production unit; establishing 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; establishing 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; determining a synthetic ammonia power range 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; establishing a fourth operating power relationship according to the synthetic ammonia power range.

2. The method of claim 1, wherein, Based on the plurality of initial installed capacity values, the objective function is solved by using a particle swarm algorithm to obtain a capacity configuration optimization result of the renewable energy hydrogen synthesis ammonia coupling hydrogen power generation system, including: Based on the plurality of initial installed capacity values, the objective function is solved by using a particle swarm algorithm to obtain a plurality of target installed capacity values of the renewable energy hydrogen synthesis ammonia coupling hydrogen power generation system; According to the plurality of target installed capacity values, the capacity configuration optimization result of the renewable energy hydrogen synthesis ammonia coupling hydrogen power generation system is determined.

3. A capacity configuration optimization apparatus characterized by comprising: A device for controlling a system connected with a renewable energy hydrogen synthesis ammonia coupling hydrogen power generation system, the renewable energy hydrogen synthesis ammonia coupling hydrogen power generation system including a renewable energy power generation unit, a water electrolysis hydrogen production unit, a synthetic ammonia unit, a hydrogen fuel cell power generation unit and an electrochemical energy storage unit; the device includes: An acquisition module for acquiring a plurality of operating models and a plurality of initial installed capacity values of the renewable energy hydrogen synthesis ammonia coupling hydrogen power generation system; An establishment module for establishing an objective function according to the plurality of operating models and the plurality of initial installed capacity values, with the highest renewable energy consumption ratio of the renewable energy hydrogen synthesis ammonia coupling hydrogen power generation system as the target; A solving module for solving the objective function by using a particle swarm algorithm based on the plurality of initial installed capacity values to obtain a capacity configuration optimization result of the renewable energy hydrogen synthesis ammonia coupling hydrogen power generation system; The establishment module includes: A first establishment submodule for establishing an operating power relationship of each unit in the renewable energy hydrogen synthesis ammonia coupling hydrogen power generation system according to the plurality of operating models and the plurality of initial installed capacity values; A second establishment submodule for establishing the objective function according to the operating power relationship of each unit, with the highest renewable energy consumption ratio of the renewable energy hydrogen synthesis ammonia coupling hydrogen power generation system as the target; The first establishment submodule includes: A determination unit for determining a plurality of installed power values according to the plurality of initial installed capacity values; A first establishment unit for establishing a renewable energy power generation power relationship of the renewable energy power generation unit according to the plurality of initial installed capacity values and the plurality of installed power values; A second establishment unit for establishing the operating power relationship of each unit according to the plurality of installed power values, the plurality of operating models and the renewable energy power generation power relationship; The second establishment unit includes: A first establishment submodule for establishing a first operating power relationship of the water electrolysis hydrogen production unit according to the renewable energy power generation power relationship, an installed power value and an operating model of the water electrolysis hydrogen production unit; A second establishment submodule for establishing a second operating power relationship of the hydrogen fuel cell power generation unit according to the renewable energy power generation power relationship, an installed power value and an operating model of the hydrogen fuel cell power generation unit; The third establishing sub-unit is configured to establish a third operation power relationship of the electrochemical energy storage unit according to the renewable energy power generation power relationship, the installed power value and the operation model of the electrochemical energy storage unit; The determining sub-unit is configured to determine a synthetic ammonia power range 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. The fifth establishing sub-unit is configured to establish a fourth operation power relationship according to the synthetic ammonia power range.

4. A computer device, comprising: The computer readable storage medium has computer instructions stored thereon, and the computer instructions are used to make a computer execute the capacity configuration optimization method in claim 1 or 2. The computer readable storage medium has computer instructions stored thereon, and the computer instructions are used to make a computer execute the capacity configuration optimization method in claim 1 or 2.

5. A computer readable storage medium, characterized in that, The computer readable storage medium has computer instructions stored thereon, and the computer instructions are used to make a computer execute the capacity configuration optimization method in claim 1 or 2.

6. A computer program product, characterised in that, ​

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