Flexible ammonia synthesis system capacity configuration optimization method based on wind-solar combined power generation

Abstract

The invention discloses a flexible ammonia synthesis system capacity configuration optimization method based on wind-solar combined power generation. Comprising the following steps: constructing a wind-solar hydrogen production coupling ammonia synthesis system; determining flexible boundary parameters of modules for producing hydrogen by electrolyzing water, synthesizing ammonia and the like; building an energy mass flow model of the flexible system under variable working conditions by utilizing dynamic analogue simulation of coupling of water electrolysis hydrogen production and ammonia synthesis; and by taking the minimum leveling production cost as a target parameter, constructing a wind-solar hydrogen production coupling ammonia synthesis system capacity configuration optimization model, and solving a target function to obtain wind-solar installed capacity, energy storage, the scale of hydrogen storage equipment, rated power of an electrolytic bath and ammonia synthesis equipment, and the optimal leveling production cost of ammonia. The universal capacity configuration optimization method of the green ammonia synthesis system for the flexible process is realized, the leveling production cost of the green ammonia is reduced, and the method is suitable for guiding the construction, popularization and application of an actual industrial green ammonia synthesis system.

Description

Technical Field

[0001] This invention relates to the field of renewable energy synthesis of green fuels, specifically to a method for optimizing the capacity configuration of a flexible ammonia synthesis system based on combined wind and solar power generation. Background Technology

[0002] Ammonia is the world's second-largest produced basic chemical, widely used in agricultural fertilizers, chemicals, and textiles. Currently, ammonia is primarily synthesized via the Haber process. Traditional Haber process ammonia synthesis relies on high-temperature, high-pressure catalytic processes and complex multi-stage recycling equipment. With the escalating global energy crisis and climate change, actively exploring cleaner and more efficient alternatives to ammonia synthesis has become crucial. Recent breakthroughs in renewable energy power generation and electrolyzer technologies, along with their continuous cost reduction, have made "renewable energy hydrogen production + Haber process thermocatalytic ammonia synthesis" the most technologically mature, economically feasible, and industrially promising green synthesis alternative. This approach not only significantly reduces fossil fuel consumption and carbon emissions associated with traditional ammonia synthesis but also provides a potential solution for the flexible regulation of renewable energy-dominated power systems.

[0003] However, the intermittency and instability of renewable energy sources are inherently contradictory to the high-temperature, high-pressure, steady-state operation of chemical processes. This leads to risks such as frequent equipment start-ups and shutdowns and catalyst deactivation in actual industrial production. Existing designs mitigate fluctuations through "over-installed capacity + large energy storage / hydrogen," but this increases the levelized production cost by 30-50%, hindering the large-scale industrial application of green synthetic ammonia. Therefore, there is an urgent need for a capacity configuration optimization method that considers the multi-dimensional coupling of "renewable energy fluctuations - flexible chemical regulation - system levelized production cost" to overcome the bottleneck of large-scale economic development of green ammonia. Summary of the Invention

[0004] To address the shortcomings of the existing technologies, this invention provides a method for optimizing the capacity configuration of a flexible ammonia synthesis system based on wind and solar power generation. This method can optimize the system capacity configuration of wind turbines, photovoltaics, batteries, electrolyzers, hydrogen storage tanks, air separation devices, and ammonia synthesis equipment, adapting to renewable energy fluctuations and based on flexible chemical control, as well as calculate the levelized production cost of ammonia.

[0005] To achieve the above objectives, this invention provides a method for optimizing the capacity configuration of a flexible ammonia synthesis system based on combined wind and solar power generation. The technical solution adopted includes the following steps: (1) Construct a wind-solar hydrogen production coupled ammonia synthesis system, the system including a wind turbine and photovoltaic power generation system, a battery subsystem, an alkaline electrolyzer hydrogen production subsystem, a hydrogen storage subsystem, an air separation nitrogen production subsystem, and an ammonia synthesis subsystem; (2) Based on the boundary conditions of flexible operation of electrolyzer and ammonia synthesis tower equipment, dynamic simulation study of hydrogen production by electrolysis of water coupled with ammonia synthesis is carried out, energy and mass flow mathematical model of system operation under different load conditions is established, and the corresponding energy and mass flow mathematical model parameters are determined. (3) Construct an optimization model for the capacity configuration of a wind-solar hydrogen production and ammonia synthesis system, specifically as follows: 3.1) The optimization objective is to minimize the levelized production cost of ammonia products; 3.2) Set the optimization parameters for the optimization model, including the rated installed capacity of wind turbines and photovoltaics, the storage capacity of batteries and hydrogen storage tanks, and the rated power of electrolyzers and ammonia synthesis towers; 3.3) Set boundary constraints for the optimization model, including supply and demand balance constraints, flexible control constraints, and safe production constraints; (4) Data collection and organization, specifically: 4.1) Collect and normalize the time-series data of wind and solar power output; 4.2) Configure the cost parameters of each subsystem in step (1), including the initial investment cost, operation and maintenance cost, lifespan and energy efficiency parameters of wind and solar power generation, batteries, electrolyzers, air separation nitrogen production, hydrogen storage and ammonia synthesis; (5) Using the energy-mass flow mathematical model parameters determined in step (2) and the data collected and organized in step (4) as input parameters, the capacity optimization model of the wind-solar hydrogen production coupled ammonia synthesis system is solved based on the mixed integer nonlinear programming algorithm. The optimal levelized production cost under the current boundary conditions is calculated, and the corresponding wind and solar installed capacity, battery and hydrogen storage tank equipment scale, electrolyzer and ammonia synthesis tower equipment rated power under the optimal cost are selected as the optimal scheme for the capacity configuration of wind-solar hydrogen production and ammonia synthesis.

[0006] Furthermore, the material flow and energy flow mathematical models in step (2) are based on the simulated material and energy consumption values ​​under different operating conditions, and are obtained by fitting the least squares method to obtain a linear mathematical model in two variables, including a material flow and energy flow mathematical model. The material flow model includes the water consumption and oxygen production per unit mass of hydrogen produced under different loads of the electrolyzer, and the hydrogen and nitrogen consumption per unit mass of ammonia produced under different loads of the ammonia synthesis tower. The energy flow mathematical model includes the energy consumption per unit mass of hydrogen and ammonia produced under different loads of the electrolyzer and the ammonia synthesis tower, respectively. The energy flow mathematical model is specifically expressed as follows: In the formula, y i The output results of different energy-mass flow models, i For different types of energy and mass flows, including unit energy consumption for hydrogen / ammonia production, and unit hydrogen / water / nitrogen / oxygen production of the system, i=1, 2, ..., 6, where a, b, and c are model parameters. x j For different load operating conditions, j This represents two different pieces of equipment: an electrolytic cell and an ammonia synthesis tower. j =1, 2.

[0007] Furthermore, the optimization objective in step (3), which is to minimize the levelized production cost of ammonia products, is specifically expressed as follows: In the formula, LCOA To standardize the production cost of ammonia products, P C This is the sum of the initial investment costs of each subsystem. t Representing the year, P DC This is the annual depreciation value of the fixed asset. R tax and R dis These represent the tax rate and the discount rate, respectively. P OM For the annual operation and maintenance costs of the system, P RV Residual value P BP For the potential benefits of by-products, Q NH3，t This represents the annual production of ammonia products.

[0008] Furthermore, the initial investment cost P C The sum of the products of the initial investment cost and the optimal scale of each subsystem is expressed as follows: In the formula, p i The initial investment cost per unit of the subsystem. Q i To optimize the optimal size of each subsystem in the model results, n The number of subsystems in the wind-solar hydrogen production and ammonia synthesis system is n=7.

[0009] Furthermore, the annual depreciation value of the fixed assets mentioned above... P DC The depreciation amount for each subsystem over the useful life of the fixed asset is calculated and allocated system-wide, as follows: In the formula, T i This refers to the equipment's lifespan.

[0010] Furthermore, the aforementioned operation and maintenance costs P OM The sum of the system's operating, maintenance, and raw material consumption costs is specifically expressed as: In the formula, λ i For the operation and maintenance coefficients of each subsystem, C k The unit price of the raw materials. M k This represents the annual consumption of raw materials. r This represents the types of raw materials consumed during system operation, including fresh water and recycled water. r =2.

[0011] Furthermore, the residual value P RV The residual value that the system is expected to recover at the end of its service life is specifically expressed as: In the formula, η i This represents the projected net residual value rate for each subsystem.

[0012] Furthermore, the aforementioned by-product revenue P BP The potential economic benefits created by the system's byproducts, oxygen and saturated steam, are specifically expressed as follows: In the formula, P O2 The market price of oxygen. M O2 This represents the annual production of oxygen. P H2O The market price of saturated steam. M H2O This represents the annual production of saturated steam.

[0013] Furthermore, step (3) supply and demand balance constraints include two major constraints: mass conservation and energy conservation. The mass conservation constraint means that the total mass of each substance before the reaction in each subsystem is equal to the total mass of each substance produced after the reaction. The energy conservation constraint means that the total power supply of wind and solar power generation and energy storage systems at each moment is equal to the sum of the energy consumption of each subsystem, the energy stored in the energy storage system, the energy loss, and the amount of abandoned electricity, specifically expressed as follows: In the formula, t This represents the system's uptime in hours over a one-year period. P W and P SThese represent the equivalent full-load hours for wind and solar power generation, respectively. Q W and Q S These represent the optimal rated power of wind and solar power generation equipment. α out For the discharge efficiency of the energy storage system, P ESS,out This refers to the battery's discharge capacity. P ESS,in The amount of charge on the battery. P ASS This refers to the energy consumption of the air separation nitrogen production subsystem. P EC The energy consumption of the water electrolysis hydrogen production subsystem. P SAS This represents the energy consumption of the ammonia synthesis subsystem.

[0014] Furthermore, step (3) flexibly controls the electrolyzer and ammonia synthesis tower equipment with different flexible load ranges and multiple load ranges, specifically expressed as follows: ① The electrolytic cell and ammonia synthesis tower have adjustable load ranges: In the formula, v This refers to the flexible load range of the equipment. v min and v max These are the lower and upper limits of the load control range, respectively. ② The multi-load range variable speed control method involves quickly adjusting the load in the medium load range to fully utilize its variable load operation capability, and slowly adjusting the load in the high and low load ranges to ensure operational safety. In the formula, i This is a device with multi-load, variable-speed control capabilities, comprising an electrolytic cell and an ammonia synthesis tower. i =1, 2, r up,i and r down,i These represent the ramp-up and unload rates, respectively. r up,min and r down,max These are limits for high and low load ramp-up and unloading rates. r up,max and r down,min These are limits for the ramp-up and unload-down rates under medium loads. v mid min and vmid max These represent the lower and upper limits of the medium load control range.

[0015] Furthermore, step (3) safety production constraints include energy storage system safety constraints and equipment operation safety constraints, which can be specifically expressed as: The safety constraints for energy storage systems are safety limits imposed on the charge / discharge rate and energy storage capacity of the batteries. In the formula, and These are the charging and discharging rates of the battery, respectively. and These are the maximum charge and discharge rates of the battery, respectively. For the battery's charge, This is the maximum capacity of the battery. β The battery's charge safety factor; The safety constraints for equipment operation are to impose safety constraints on the continuous operating time of the equipment under low load: In the formula, and These represent the times when the k-th departure and entry into low-load operation, respectively. This is the maximum time that continuous operation is allowed in the low-load range.

[0016] Furthermore, in step (4), the wind and solar power output time series data are normalized to obtain the hourly equivalent full-load hours of wind and solar power generation within one year, specifically expressed as: In the formula, t The hourly rate of wind and solar power generation over a one-year period. Q W,out and Q S,out These represent the actual output power of wind and solar power generation, respectively. T e This represents the actual operating time of wind and solar power generation.

[0017] The advantages of this invention are: (1) High fidelity and universality. This invention uses the energy-mass flow mathematical model constructed based on the multi-steady-state simulation method and the actual wind and solar power output data of various regions as input parameters, making the calculation results of the model more reasonable and accurate, and applicable to different scenarios with different wind and solar resource endowments and process routes; (2) Multi-steady-state flexible collaborative mechanism. The system constructed by this invention not only includes an energy storage and hydrogen storage subsystem module to smooth the volatility of renewable energy power generation, but also takes into account the flexible control of electrolyzers and ammonia synthesis equipment to enhance the stability of chemical processes. Based on the three-in-one complementary architecture of "energy storage-hydrogen storage-chemical flexibility", it significantly reduces the wind and solar curtailment rate and the number of equipment start-ups and shutdowns, extends the life of equipment and catalysts, and realizes the full-link energy efficiency improvement of "source-load-storage-chemical". (3) Optimal solution with dual economic and safety objectives. The optimization method developed in this invention embeds a set of safety constraints from actual industrial operation under the optimization objective of minimizing the levelized production cost of ammonia. The resulting system capacity configuration optimization scheme can directly guide the modular design of thousand-ton-level green ammonia production sites, and is especially suitable for remote areas with abundant renewable energy but weak power grids. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating a method for capacity configuration optimization and economic analysis of a flexible ammonia synthesis system based on combined wind and solar power generation, according to the present invention. Figure 2 This is a schematic diagram of the multi-load range differential speed control method of the present invention; Figure 3 This is a schematic diagram of a flexible simulation system for electrolytic water hydrogen production coupled with ammonia synthesis in one embodiment of the present invention; Figure 4 This is a schematic diagram of an energy-mass flow mathematical model in one embodiment of the present invention; Figure 5 This is a distribution map of historical power output data for wind and solar power in one embodiment of the present invention; Figure 6 This is a schematic diagram of the capacity configuration of a flexible ammonia synthesis system based on wind and solar power generation in one embodiment of the present invention; Detailed Implementation

[0019] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and examples. The embodiments of the present invention include, but are not limited to, the following examples.

[0020] like Figure 1 As shown, this invention provides a method for optimizing the capacity configuration of a flexible ammonia synthesis system based on combined wind and solar power generation, comprising the following steps: (1) Construct a wind-solar hydrogen production coupled ammonia synthesis system, the system including a wind turbine and photovoltaic power generation system, a battery subsystem, an alkaline electrolyzer hydrogen production subsystem, a hydrogen storage subsystem, an air separation nitrogen production subsystem, and an ammonia synthesis subsystem.

[0021] The specific process of the system is as follows: the wind turbine, photovoltaic power generation system and battery subsystem constitute the power supply module of the system, which provides power to the subsequent electrolyzer, air separation and ammonia synthesis unit; the hydrogen produced by electrolyzing water in the alkaline electrolyzer unit enters the hydrogen storage buffer tank to stabilize the system fluctuations, and the other part is mixed with the nitrogen obtained by separating air in the air separation nitrogen generation unit to obtain syngas; the syngas is pressurized and heated and then transported to the ammonia synthesis subsystem, where it is circulated and separated to obtain the chemical ammonia.

[0022] (2) Based on the boundary conditions of flexible operation of equipment such as electrolyzers and ammonia synthesis towers, dynamic simulation research was carried out on the electrolysis of water to produce hydrogen coupled with ammonia synthesis system. Mathematical models of material flow and energy flow of the system under different load conditions were established, and the corresponding energy and mass flow model parameters were determined.

[0023] The mathematical models for material flow and energy flow are linear models in two variables, obtained by fitting simulated material and energy consumption values ​​under different operating conditions using the least squares method. The material flow model includes the water consumption and oxygen production per unit mass of hydrogen produced in the electrolyzer under different loads, and the hydrogen and nitrogen consumption per unit mass of ammonia produced in the ammonia synthesis tower under different loads. The energy flow mathematical model includes the energy consumption per unit mass of hydrogen and ammonia produced in the electrolyzer and ammonia synthesis tower under different loads, respectively. The energy-mass flow model is specifically expressed as follows: In the formula, y i The output results of different energy-mass flow models, i For different types of energy and mass flows, including unit energy consumption for hydrogen / ammonia production, and unit hydrogen / water / nitrogen / oxygen production of the system, i =1, 2, ..., 6, where a, b, and c are model parameters. x j For different load operating conditions, j This represents two different pieces of equipment: an electrolytic cell and an ammonia synthesis tower. j =1, 2.

[0024] (3) Construct an optimization model for the capacity configuration of a wind-solar hydrogen production and ammonia synthesis system, specifically as follows: 3.1) The optimization objective is to minimize the levelized production cost of ammonia products; 3.2) Set the optimization parameters of the model, including the rated installed capacity of wind turbines and photovoltaics, the storage capacity of batteries and hydrogen storage tanks, and the rated power of electrolyzers and ammonia synthesis towers; 3.3) Set boundary constraints for the optimization model, including supply and demand balance constraints, flexible control constraints, and safe production constraints.

[0025] Furthermore, step 3.1), with the minimum levelized production cost of ammonia products as the optimization objective, is specifically expressed as follows: In the formula, LCOA To standardize the production cost of ammonia products, P C This is the sum of the initial investment costs of each subsystem. t Representing the year, P DC This is the annual depreciation value of the fixed asset. R tax and R dis These represent the tax rate and the discount rate, respectively. P OM The annual operation and maintenance costs of the system, P RV Residual value P BP For the potential benefits of by-products, Q NH3，t This represents the annual production of ammonia products.

[0026] Furthermore, the aforementioned initial investment costs P C Annual depreciation value of fixed assets P DC Operation and maintenance costs P OM residual value P RV and by-product income P BP Specifically, it is expressed as follows: Initial investment cost P C The sum of the products of the initial investment cost per unit of each subsystem and the optimal size: In the formula, p i The initial investment cost per unit of the subsystem. Q i To optimize the optimal size of each subsystem in the model results, n The number of subsystems in the wind-solar hydrogen production and ammonia synthesis system is n=7.

[0027] Annual depreciation value of fixed assets P DC For each subsystem, calculate the depreciation expense over the useful life of the fixed asset and allocate it system-wide: In the formula, T i This refers to the equipment's lifespan.

[0028] Operation and maintenance costsP OM The sum of the costs of system operation, maintenance, and raw material consumption: In the formula, λ i For the operation and maintenance coefficients of each subsystem, C k The unit price of the raw materials. M k This represents the annual consumption of raw materials. r This represents the types of raw materials consumed during system operation, including fresh water and recycled water. r =2.

[0029] residual value P RV The residual value that the system is expected to recover at the end of its service life: In the formula, η i This represents the projected net residual value rate for each subsystem.

[0030] by-product income P BP The potential economic benefits created by the system's byproducts, oxygen and saturated steam, are specifically expressed as follows: In the formula, P O2 The market price of oxygen. M O2 This represents the annual production of oxygen. P H2O The market price of saturated steam. M H2O This represents the annual production of saturated steam.

[0031] Furthermore, the boundary constraints in step 3.3), including supply and demand balance constraints, flexible control constraints, and safe production constraints, are specifically represented as follows: The supply and demand balance constraint includes two major constraints: mass conservation and energy conservation. The mass conservation constraint states that the total mass of all substances in each subsystem before the reaction is equal to the total mass of all substances produced after the reaction. The energy conservation constraint states that the total power supply from wind and solar power generation and energy storage systems at each moment is equal to the sum of the energy consumption of each subsystem, the stored energy of the energy storage system, energy losses, and wasted electricity. Specifically, it is expressed as follows: In the formula, t This represents the system's uptime in hours over a one-year period. P W and PS These represent the equivalent full-load hours for wind and solar power generation, respectively. Q W and Q S These represent the optimal rated power of wind and solar power generation equipment. α out For the discharge efficiency of the energy storage system, P ESS,out This refers to the battery's discharge capacity. P ESS,in The amount of charge on the battery. P ASS This refers to the energy consumption of the air separation nitrogen production subsystem. P EC The energy consumption of the water electrolysis hydrogen production subsystem. P SAS This represents the energy consumption of the ammonia synthesis subsystem.

[0032] Flexible control constraints refer to the different speed regulation methods for electrolyzers and ammonia synthesis towers with different flexible load ranges and multiple load ranges, specifically expressed as follows: ① The electrolytic cell and ammonia synthesis tower have adjustable load ranges: In the formula, v This refers to the flexible load range of the equipment. v min and v max These are the lower and upper limits of the load control range, respectively. ②For example Figure 2 As shown, the multi-load range variable speed control method involves quickly adjusting the load in the medium load range to fully utilize its variable load operation capability, and slowly adjusting the load in the high and low load ranges to ensure operational safety. In the formula, i This is a device with multi-load, variable-speed control capabilities, comprising an electrolytic cell and an ammonia synthesis tower. i =1, 2, r up,i and r down,i These represent the ramp-up and unload rates, respectively. r up,min and r down,max These are limits for high and low load ramp-up and unloading rates. r up,max and r down,min These are limits for the ramp-up and unload-down rates under medium loads. v mid minand v mid max These represent the lower and upper limits of the medium load control range.

[0033] Safety constraints in production include safety constraints for energy storage systems and safety constraints for equipment operation, specifically expressed as follows: The safety constraints for energy storage systems are safety limits imposed on the charge / discharge rate and energy storage capacity of the batteries. In the formula, and These are the charging and discharging rates of the battery, respectively. and These are the maximum charge and discharge rates of the battery, respectively. For the battery's charge, This is the maximum capacity of the battery. β The battery's charge safety factor; The safety constraints for equipment operation are to impose safety constraints on the continuous operating time of the equipment under low load: In the formula, and These represent the times when the k-th departure and entry into low-load operation, respectively. This is the maximum time that continuous operation is allowed in the low-load range.

[0034] (4) Data collection and organization, specifically: 4.1) Collect and normalize the time-series data of wind and solar power output; 4.2) Configure the cost parameters of each subsystem in step (1), including the initial investment cost, operation and maintenance cost, lifespan and energy efficiency parameters of wind and solar power generation, storage battery, electrolyzer, air separation nitrogen production unit, hydrogen storage unit and synthetic ammonia; Furthermore, the wind and solar power output time-series data in step 4.1) are normalized to obtain the hourly equivalent full-load hours of wind and solar power generation within one year, specifically expressed as: In the formula, t This represents the time allotted for wind and solar power generation, measured in hours, over a one-year period. Q W,out and Q S,out These represent the actual output power of wind and solar power generation, respectively. T e This represents the actual operating time of wind and solar power generation.

[0035] (5) Using the energy-mass flow mathematical model parameters determined in step (2) and the data collected and organized in step (4) as input parameters, the capacity optimization model of the wind-solar hydrogen production coupled ammonia synthesis system is solved based on the mixed integer nonlinear programming algorithm. The optimal levelized production cost under the current boundary conditions is calculated, and the corresponding wind and solar installed capacity, battery and hydrogen storage tank equipment scale, electrolyzer and ammonia synthesis tower rated power under the optimal cost are selected as the optimal scheme for the wind-solar hydrogen production ammonia synthesis capacity configuration.

[0036] The above technical solution is illustrated using a proposed 3,000-ton-per-year synthetic ammonia demonstration project in a certain region of Inner Mongolia Autonomous Region as an example.

[0037] like Figure 3 As shown, a dynamic simulation model for hydrogen production via water electrolysis and ammonia synthesis via the Haber process was constructed, obtaining the energy and mass flow mathematical model and dynamic response curves of the system under different load conditions (e.g., Figure 4 (As shown).

[0038] The original time-series data of wind and solar power output for 8760 hours were collected locally and normalized to obtain the equivalent full-load operating hours of wind and solar power generation for the whole year (e.g., Figure 5 (As shown).

[0039] The cost parameters of each piece of equipment in the system, including the initial investment cost, operation and maintenance cost, depreciation, residual value, lifespan and energy efficiency parameters of wind and solar power generation, batteries, electrolyzers, air separation nitrogen production unit, hydrogen storage unit and ammonia synthesis module, are shown in Table 1.

[0040] Table 1 Parameters of each device To calculate the potential revenue from by-products, the prices of industrial high-purity oxygen and saturated steam were set at RMB 750 / ton and RMB 245 / ton, respectively, based on local market conditions. Considering local policies and the level of economic development, the main economic indicators of the model were set at tax rates and discount rates of 15% and 7%, respectively.

[0041] The boundary constraints for the optimization model are set as follows: the equipment load range and multi-load regulation rate of the flexible control constraints are shown in Table 2; the battery power safety factor and the maximum allowable continuous operation time under low load in the safety production constraints are 0.98 and 24h, respectively. Table 2 Flexible Control Constraint Parameters like Figure 6As shown, the optimization model is solved using a mixed-integer nonlinear programming algorithm to obtain the optimal levelized cost of ammonia production and the corresponding wind and solar power capacity, battery and hydrogen storage tank equipment scale, and rated power of equipment such as electrolyzers and ammonia synthesis towers as the optimal scheme for the capacity configuration of wind and solar hydrogen production for ammonia synthesis. This example, by optimizing the capacity configuration of the ammonia synthesis system, achieves an ammonia levelized cost of 3601 yuan / ton, significantly lower than the current cost range of 4200-5600 yuan / ton for low-priced renewable energy ammonia synthesis, reaching the cost reduction target (2800-4200 yuan / ton) for large-scale commercialization of green ammonia by 2030 predicted by the International Energy Agency (IEA). Therefore, the optimization results based on this embodiment of the invention have significant economic and commercial feasibility.

[0042] The above embodiments are one of the preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. A method for optimizing the capacity configuration of a flexible ammonia synthesis system based on combined wind and solar power generation, characterized in that, The method includes the following steps: (1) Construct a wind-solar hydrogen production coupled ammonia synthesis system, the system including a wind turbine and photovoltaic power generation system, a battery subsystem, an alkaline electrolyzer hydrogen production subsystem, a hydrogen storage subsystem, an air separation nitrogen production subsystem and an ammonia synthesis subsystem; (2) Based on the boundary conditions of flexible operation of electrolyzer and ammonia synthesis tower equipment, dynamic simulation study of hydrogen production by electrolysis of water coupled with ammonia synthesis is carried out, energy and mass flow mathematical model of system operation under different load conditions is established, and the corresponding energy and mass flow mathematical model parameters are determined. (3) Construct an optimization model for the capacity configuration of a wind-solar hydrogen production coupled ammonia synthesis system, specifically as follows: 3.1) The optimization objective is to minimize the levelized production cost of ammonia products; 3.2) Set the optimization parameters for the optimization model, including the rated installed capacity of wind turbines and photovoltaics, the storage capacity of batteries and hydrogen storage tanks, and the rated power of electrolyzers and ammonia synthesis towers; 3.3) Set boundary constraints for the optimization model, including supply and demand balance constraints, flexible control constraints, and safe production constraints; (4) Data collection and organization, specifically: 4.1) Collect and normalize the time-series data of wind and solar power output; 4.2) Configure the cost parameters of each subsystem in step (1), including the initial investment cost, operation and maintenance cost, lifespan and energy efficiency parameters of wind and solar power generation, batteries, electrolyzers, air separation nitrogen production, hydrogen storage and ammonia synthesis; (5) Using the energy-mass flow mathematical model parameters determined in step (2) and the data collected and organized in step (4) as input parameters, the capacity optimization model of the wind-solar hydrogen production coupled ammonia synthesis system is solved based on the mixed integer nonlinear programming algorithm. The optimal levelized production cost under the current boundary conditions is calculated, and the corresponding wind and solar installed capacity, battery and hydrogen storage tank equipment scale, electrolyzer and ammonia synthesis tower equipment rated power under the optimal cost are selected as the optimal scheme for the capacity configuration of wind-solar hydrogen production and ammonia synthesis.

2. The capacity configuration optimization method for a flexible ammonia synthesis system based on combined wind and solar power generation according to claim 1, characterized in that, The energy-mass flow mathematical model in step (2) is a two-variable linear mathematical model obtained by fitting the simulated values ​​of material consumption and energy consumption under different working conditions using the least squares method, including the material flow and energy flow mathematical models. The mass flow model includes the water consumption and oxygen production per unit mass of hydrogen produced in the electrolyzer under different loads, and the hydrogen and nitrogen consumption per unit mass of ammonia produced in the ammonia synthesis tower under different loads. The energy flow mathematical model includes the energy consumption per unit mass of hydrogen and ammonia produced in the electrolyzer and ammonia synthesis tower under different loads, respectively. The energy-mass flow mathematical model is specifically expressed as follows: In the formula, y i The output results of different energy-mass flow models, i For different types of energy and mass flows, including unit energy consumption for hydrogen / ammonia production, and unit hydrogen / water / nitrogen / oxygen production of the system, i =1, 2, ..., 6, where a, b, and c are model parameters. x j For different load operating conditions, j This represents two different pieces of equipment: an electrolytic cell and an ammonia synthesis tower. j =1, 2.

3. The capacity configuration optimization method for a flexible ammonia synthesis system based on combined wind and solar power generation according to claim 1, characterized in that, In step (3), the optimization objective of minimizing the levelized production cost of ammonia products is specifically expressed as follows: In the formula, LCOA To standardize the production cost of ammonia products, P C This is the sum of the initial investment costs of each subsystem. t Representing the year, P DC This is the annual depreciation value of the fixed asset. R tax and R dis These represent the tax rate and the discount rate, respectively. P OM The annual operation and maintenance costs of the system, P RV Residual value P BP For the potential benefits of by-products, Q NH3，t This represents the annual production of ammonia products.

4. The capacity configuration optimization method for a flexible ammonia synthesis system based on wind and solar combined power generation according to claim 3, characterized in that, Initial investment cost P C The sum of the products of the initial investment cost and the optimal scale of each subsystem is expressed as follows: In the formula, p i The initial investment cost per unit of the subsystem. Q i To optimize the optimal size of each subsystem in the model results, n The number of subsystems in the wind-solar hydrogen production and ammonia synthesis system is n=7.

5. The capacity configuration optimization method for a flexible ammonia synthesis system based on wind and solar combined power generation according to claim 3, characterized in that, Annual depreciation value of fixed assets P DC The depreciation amount for each subsystem over the useful life of the fixed asset is calculated and allocated system-wide, as follows: In the formula, T i This refers to the equipment's lifespan.

6. The capacity configuration optimization method for a flexible ammonia synthesis system based on wind and solar combined power generation according to claim 3, characterized in that, Operation and maintenance costs P OM The sum of the system's operating, maintenance, and raw material consumption costs is specifically expressed as: In the formula, λ i For the operation and maintenance coefficients of each subsystem, C k The unit price of the raw materials. M k This represents the annual consumption of raw materials. r This represents the types of raw materials consumed during system operation, including fresh water and recycled water. r =2.

7. The capacity configuration optimization method for a flexible ammonia synthesis system based on wind and solar combined power generation according to claim 1, characterized in that, Step (3) The supply and demand balance constraint includes two major constraints: mass conservation and energy conservation. The mass conservation constraint means that the total mass of each substance before the reaction in each subsystem is equal to the total mass of each substance produced after the reaction. The energy conservation constraint means that the total power supply of wind and solar power generation and energy storage system at each moment is equal to the sum of the energy consumption of each subsystem, the energy stored in the energy storage system, the energy loss, and the amount of abandoned electricity, specifically expressed as follows: In the formula, t This represents the system's uptime in hours over a one-year period. P W and P S These represent the equivalent full-load hours for wind and solar power generation, respectively. Q W and Q S These represent the optimal rated power of wind and solar power generation equipment. α out For the discharge efficiency of the energy storage system P ESS,out This refers to the battery's discharge capacity. P ESS,in The amount of charge on the battery. P ASS This refers to the energy consumption of the air separation nitrogen production subsystem. P EC The energy consumption of the water electrolysis hydrogen production subsystem. P SAS This represents the energy consumption of the ammonia synthesis subsystem.

8. The capacity configuration optimization method for a flexible ammonia synthesis system based on combined wind and solar power generation according to claim 1, characterized in that, Step (3) Flexible control constraints are different flexible load ranges and multiple load ranges for the electrolyzer and ammonia synthesis tower equipment, specifically expressed as follows: (1) The electrolytic cell and ammonia synthesis tower have adjustable load ranges: In the formula, v This refers to the flexible load range of the equipment. v min and v max These are the lower and upper limits of the load control range, respectively. (2) The variable speed control method for multiple load ranges is to quickly adjust the load when the equipment is operating in the medium load range to fully utilize its variable load operation capability, and to slowly adjust the load in the high and low load ranges to ensure operational safety: In the formula, i This is a device with multi-load, variable-speed control capabilities, comprising an electrolytic cell and an ammonia synthesis tower. i =1, 2, r up,i and r down,i These represent the ramp-up and unload rates, respectively. r up,min and r down,max These are limits for high and low load ramp-up and unloading rates. r up,max and r down,min These are limits for the ramp-up and unload-down rates under medium loads. v mid min and v mid max These represent the lower and upper limits of the medium load control range.

9. The capacity configuration optimization method for a flexible ammonia synthesis system based on combined wind and solar power generation according to claim 1, characterized in that, Step (3) Safety production constraints include safety constraints for energy storage systems and safety constraints for equipment operation; (1) The safety constraints of the energy storage system are to impose safety limits on the charge and discharge rates and energy storage capacity of the battery: In the formula, and These are the charging and discharging rates of the battery, respectively. and These are the maximum charge and discharge rates of the battery, respectively. For the battery's charge, This is the maximum capacity of the battery. β The battery's charge safety factor; (2) The safety constraints for equipment operation are to impose safety constraints on the continuous operating time of the equipment under low load: In the formula, and These represent the times when the k-th departure and entry into low-load operation, respectively. This is the maximum time that continuous operation is allowed in the low-load range.

10. The capacity configuration optimization method for a flexible ammonia synthesis system based on wind and solar combined power generation according to claim 7, characterized in that, Step (4) Normalize the wind and solar power output time series data to obtain the hourly equivalent full-load hours of wind and solar power generation within one year, specifically expressed as: In the formula, t This represents the time allotted for wind and solar power generation, measured in hours, over a one-year period. Q W,out and Q S,out These represent the actual output power of wind and solar power generation, respectively. T e This represents the actual operating time of wind and solar power generation.

Images