Green ammonia synthesis system cooperative control method, equipment and medium
By establishing a model and constraints for the green ammonia synthesis system, determining the ammonia capacity configuration scheme, and using a mixed-integer linear programming algorithm to optimize the equipment operation load scheduling, the problem of output fluctuation caused by the uncertainty of wind and solar power output in the green ammonia synthesis system was solved, realizing flexible load adjustment and safe continuous production of chemical equipment.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- ZHEJIANG ZHENTAI ENERGY TECH CO LTD
- Filing Date
- 2024-11-13
- Publication Date
- 2026-05-15
AI Technical Summary
The green hydrogen production in the green ammonia synthesis system fluctuates due to the uncertainty of wind and solar power output. The start-up and shutdown costs of chemical equipment are high, and it cannot respond quickly to changes in the power system, resulting in poor safety and flexibility.
A model and constraints for the green ammonia synthesis system are established. Based on the maximum ammonia production as the fitness function, the ammonia capacity configuration scheme is determined. The equipment operation load scheduling strategy is solved by a mixed integer linear programming algorithm to achieve coordinated control of each piece of equipment.
It improves the stability and flexibility of the green ammonia synthesis system, enables flexible load adjustment of chemical equipment, reduces the start-up and shutdown costs of chemical equipment, and enhances the safety and response speed of the system.
Smart Images
Figure CN122044091A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of green ammonia synthesis control, and in particular to a method, equipment and medium for coordinated control of a green ammonia synthesis system. Background Technology
[0002] Green energy technologies such as wind power and photovoltaic power for hydrogen production are constantly developing and will become a key path for the clean energy transition in the future. However, large-scale, intertemporal and spatial storage and transportation of hydrogen still faces many problems in terms of safety and economy. In the near to medium term, comprehensive utilization of green hydrogen can be achieved by using electrochemical technologies, such as electro-to-methanol, electro-to-ammonia, and electro-to-methane, to store electrical energy in green chemical products using hydrogen as a medium, thereby realizing the large-scale consumption of new energy.
[0003] Conventional power systems can determine power generation and dispatch plans based on electricity demand, while chemical production typically operates in one or more steady states, with electricity consumption predictable and requests submitted to the power system in advance. In green electricity-green hydrogen-green ammonia plants, renewable energy generation varies with weather conditions, requiring downstream chemical production to adjust in real time in coordination with the power system; the load lacks a traditional steady state. Furthermore, the dynamic response speeds and characteristics of the power system and the chemical process system are drastically different, and their dynamic responses are mutually restrictive and influential, exhibiting strong coupling.
[0004] Due to the significant uncertainty in the power output from wind and solar sources in the electrochemical system, green hydrogen production fluctuates randomly. While green hydrogen electrolyzers can flexibly address this issue through start / stop / standby and rapid load adjustments, the start-up and shutdown costs and operational inertia of chemical equipment such as methanol and ammonia synthesis plants are far greater than those of power equipment. These require continuous, stable operation and flexible load adjustments, making rapid response to power output fluctuations impossible. Furthermore, improper capacity matching among equipment in the electrochemical system can lead to issues of poor safety and flexibility. Therefore, the green ammonia synthesis system needs a capacity adjustment function to adapt to changes in its operating conditions. Summary of the Invention
[0005] The purpose of this application is to provide a method, equipment, and medium for coordinated control of a green ammonia synthesis system, which can coordinately control various devices in the green ammonia synthesis system to adapt to changes in the operating conditions of the green ammonia synthesis system.
[0006] To achieve the above objectives, this application provides the following solution:
[0007] Firstly, this application provides a method for the coordinated control of a green ammonia synthesis system, comprising:
[0008] Establish equipment models and constraints for a green ammonia synthesis system. The green ammonia synthesis system includes multiple devices, including wind and solar power generation equipment, hydrogen production equipment, green ammonia synthesis equipment, and hydrogen storage equipment. The equipment models include models for both the wind and solar power generation equipment and the hydrogen production equipment. The constraints include constraints for the hydrogen production equipment, the green ammonia synthesis equipment, the hydrogen storage equipment, and system constraints. The system constraints include capacity constraints, electrical balance constraints, and hydrogen balance constraints for each device.
[0009] Based on the equipment model and the constraints, the ammonia capacity configuration scheme of the green ammonia synthesis system is determined using the maximum ammonia production of the green ammonia synthesis system as the fitness function.
[0010] Generate scheduling scenarios based on scenario requirements;
[0011] Using the ammonia capacity configuration scheme and the operating characteristics of each device in the green ammonia synthesis system as boundary conditions, and taking the maximum operating benefit in the scheduling scenario as the objective function, the operating load scheduling strategy of each device is solved to determine the optimal ammonia capacity configuration scheme and the optimal operating load scheduling strategy.
[0012] Control each device in the green ammonia synthesis system according to the optimal ammonia capacity configuration scheme and the optimal operating load scheduling strategy.
[0013] In a second aspect, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the green ammonia synthesis system collaborative control method described in any one of the above.
[0014] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the cooperative control method for the green ammonia synthesis system described above.
[0015] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application establishes equipment models and constraints for a green ammonia synthesis system. Based on the equipment models and constraints, and using the maximum benefit of the green ammonia synthesis system as the fitness function, the target system function of the green ammonia synthesis system is determined. Based on each equipment model, an ammonia capacity configuration scheme is formulated, reducing the safety issues caused by frequent startups of hydrogen production equipment. Then, a scheduling scenario is generated according to scenario requirements. Using the ammonia capacity configuration scheme and the operating characteristics of each piece of equipment in the green ammonia synthesis system as boundary conditions, and using the maximum operating benefit in the scheduling scenario as the objective function, the operating load scheduling strategy of each piece of equipment is solved to determine the optimal ammonia capacity configuration scheme and the optimal operating load scheduling strategy. Each piece of equipment in the green ammonia synthesis system is controlled according to the optimal ammonia capacity configuration scheme and the optimal operating load scheduling strategy, thereby realizing the coordinated control optimization between each piece of equipment, giving full play to the role of hydrogen energy storage, smoothing unstable wind and solar output, reducing dependence on the power grid, improving the stability of the green ammonia synthesis system, and realizing flexible load regulation and safe continuous production of chemical equipment such as methanol and ammonia synthesis in the green ammonia synthesis system. Attached Figure Description
[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0017] Figure 1 A schematic diagram of the green ammonia system structure provided in an embodiment of this application;
[0018] Figure 2 This is a schematic flowchart of a collaborative control method for a green ammonia synthesis system provided in an embodiment of this application. Detailed Implementation
[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0020] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0021] Green ammonia systems, such as Figure 1As shown, the green ammonia system includes wind and solar power generation equipment, hydrogen production equipment, green ammonia synthesis equipment, and hydrogen storage equipment; in this application, the wind and solar power generation equipment includes wind power generation equipment and photovoltaic power generation equipment; the hydrogen production equipment can be an electrolyzer hydrogen production equipment, and the hydrogen storage equipment can be a hydrogen storage tank; furthermore, the electrolyzer hydrogen production equipment is an alkaline electrolyzer.
[0022] This application utilizes wind and solar power generation and grid-purchased electricity to electrolyze water to produce hydrogen, and then catalytically synthesizes green ammonia with nitrogen, achieving comprehensive conversion and utilization of green electricity.
[0023] Wind and solar energy provide electricity, which is then inverted, transformed, and transmitted. Part of the electricity powers the hydrogen production equipment in the electrolyzer, while another part enters the inverter to charge the upstream power source. The power generation, transmission, and transformation unit also includes a grid power input to ensure the system's input power when the wind farm and photovoltaic power station generate insufficient power.
[0024] The oxygen at the anode of the electrolytic cell hydrogen production equipment is vented after gas-liquid separation. Part of the hydrogen at the cathode enters the green ammonia synthesis equipment and is compressed to the pressure required for ammonia synthesis by a compressor. The other part enters the hydrogen storage device to consume the upstream hydrogen or to supplement the downstream hydrogen. After being mixed and compressed with nitrogen, the hydrogen enters the ammonia synthesis reactor in the gas phase and leaves the boundary area in the form of liquid ammonia after gas-liquid separation at the outlet.
[0025] This application provides a collaborative control method for a green ammonia synthesis system. This method is executed by a computer device, specifically a terminal or server, or both. In this application embodiment, for example... Figure 2 As shown, the method includes the following steps.
[0026] S1: Establish the equipment model and constraints of the green ammonia synthesis system; the green ammonia synthesis system includes multiple devices, including wind and solar power generation equipment, hydrogen production equipment, green ammonia synthesis equipment, and hydrogen storage equipment; the equipment model includes a wind and solar power generation equipment model and a hydrogen production equipment model; the constraints include constraints for the hydrogen production equipment, constraints for the green ammonia synthesis equipment, constraints for the hydrogen storage equipment, and system constraints; the system constraints include capacity constraints, power balance constraints, and hydrogen balance constraints for each device.
[0027] S2: Based on the equipment model and the constraints, the ammonia capacity configuration scheme of the green ammonia synthesis system is determined using the maximum ammonia production of the green ammonia synthesis system as the fitness function.
[0028] S3: Generate scheduling scenarios based on scenario requirements.
[0029] S4: Using the ammonia capacity configuration scheme and the operating characteristics of each device in the green ammonia synthesis system as boundary conditions, and taking the maximum benefit within the scheduling scenario as the objective function, solve for the operating load scheduling strategy of each device to determine the optimal ammonia capacity configuration scheme and the optimal operating load scheduling strategy. Specifically, a mixed-integer linear programming (MILP) algorithm is used to solve for the operating load scheduling strategy of each device.
[0030] S5: Control each device in the green ammonia synthesis system according to the optimal ammonia capacity configuration scheme and the optimal operating load scheduling strategy.
[0031] In an exemplary embodiment, the wind and solar power generation equipment model is as follows:
[0032]
[0033] in, Let t be the actual power output of the photovoltaic power generation equipment. E represents the unit installed photovoltaic power generation at time t. PV The installed capacity of photovoltaic power generation equipment; Let t be the actual power output of the wind turbine at time t; E represents the unit installed power generation of wind power at time t; WT This refers to the installed capacity of wind power generation equipment.
[0034] In one exemplary embodiment, the hydrogen production equipment model is as follows:
[0035] in, Let t be the hydrogen production power. Energy consumption of hydrogen production equipment; Let t be the amount of hydrogen produced at time t;
[0036] The electrolyzer hydrogen production equipment exhibits a startup lag. The startup state of this hydrogen production equipment during operation is as follows:
[0037] in, β is the power of the hydrogen production equipment at time t; EL,L The load factor during the startup process of the hydrogen production equipment; The rated power of the hydrogen production equipment; L t The binary variable for starting the hydrogen production equipment at time t;
[0038] To avoid the risk of explosion caused by excessive hydrogen content in oxygen at low loads during the operation of an electrolyzer hydrogen production equipment, the effective hydrogen production power of the equipment has a minimum operating limit. The operating state can be described as the constraint conditions of the hydrogen production equipment during operation. These constraint conditions are as follows:
[0039] in, Lower limit of effective hydrogen production power of electrolyzer hydrogen production equipment.
[0040] In one exemplary embodiment, the main objective of this application is to achieve stable and continuous ammonia production. The green ammonia synthesis equipment needs to meet the constraints of operating load range and flexible load adjustment.
[0041] The constraints of the green ammonia synthesis equipment are as follows:
[0042] in, The minimum operating load rate for green ammonia synthesis equipment; Rated output of green ammonia synthesis equipment; Let t be the green ammonia yield; The green ammonia yield at time t-1; The flexible variable load adjustment rate for green ammonia synthesis equipment.
[0043] In one exemplary embodiment, the hydrogen storage device serves as the main buffer between the electrolyzer and the methanol synthesis equipment, and its charging / discharging state and operational constraints are the constraints of the hydrogen storage device.
[0044] The constraints of this hydrogen storage device are:
[0045] in, The maximum hydrogen charge for the hydrogen storage equipment; This represents the maximum hydrogen release mass of the hydrogen storage device. Let be the hydrogen charging state variable of the hydrogen storage device at time t; Let t be the hydrogen release state variable of the hydrogen storage device at time t, where 0 indicates stop and 1 indicates operation; The amount of hydrogen at the initial state of the hydrogen storage equipment during the scheduling cycle; The hydrogen quantity at the end of the scheduling cycle; Let t be the amount of hydrogen stored inside the hydrogen storage device; Let t be the amount of gas supplied to the hydrogen storage device. Let t be the gas release rate of the hydrogen storage device; This represents the maximum amount of hydrogen the system can use.
[0046] In one exemplary embodiment, the capacity constraint is:
[0047] in, This represents the upper limit of the capacity constraint for the i-th device; This represents the lower limit of the capacity constraint for the i-th device. Let i be the capacity of the i-th device;
[0048] The electrical balance constraint condition is as follows:
[0049] in, Let t be the actual power output of the photovoltaic power generation equipment. Let t be the actual power output of the wind turbine at time t; Let t be the amount of electricity consumed by the power grid. The grid-connected power at time t; Let t be the hydrogen production power. Ammonia power consumption per unit mass of production; The amount of electricity discarded by the system at time t;
[0050] The hydrogen balance constraint condition is as follows:
[0051] in, Let t be the amount of gas supplied to the hydrogen storage device. Let t be the gas release rate of the hydrogen storage device; Let t be the amount of hydrogen produced at time t; Let t be the green ammonia production at time t.
[0052] In an exemplary embodiment, before S3, the method further includes: obtaining the total number of wind-solar-sunshine power generation scheduling scenarios based on the scenario requirements; determining the number of wind-solar-sunshine power generation scenarios required to construct all wind-solar-sunshine power generation scheduling scenarios in each weather type feature category based on the probability of different wind-solar-sunshine power generation feature categories and the total number of wind-solar-sunshine power generation scheduling scenarios; sampling the joint probability density distribution of the optimal joint Copula function using Monte Carlo simulation based on the number of wind-solar-sunshine power generation scenarios required for all wind-solar-sunshine power generation scheduling scenarios; and determining the sampling samples using the synchronous back-substitution reduction method based on the sampling samples; wherein, Di is the number of wind-solar-sunshine power generation scenarios required for all wind-solar-sunshine power generation scheduling scenarios.
[0053] In one exemplary embodiment, S3 can be replaced by the following steps.
[0054] S31: Initialize the original array; the original array includes multiple wind, solar and solar power data.
[0055] S32: Shuffle the initialized original array, randomly generate a data index n from the unprocessed k wind, solar and solar power data, and swap the positions of the kth wind, solar and solar power data and the nth wind, solar and solar power data in the original data, until all the wind, solar and solar power data in the original data are scrambled, and a new array is generated.
[0056] S33: Based on the scenario requirements, the new array is divided in order to generate a scheduling scenario that meets the scenario requirements.
[0057] In an exemplary embodiment, the benefits involved in S4 specifically include:
[0058] maxN P =C INC -C INV -C OM -C ING In the formula, C INC For system product revenue; C INV C represents the investment cost of system equipment. OM For system equipment operation and maintenance costs; C ING This refers to the system's raw material costs.
[0059] The revenue from the system products is as follows:
[0060] In the formula: c NH3 c is the selling price of ammonia. GRI,S This refers to the price at which electricity is sold to the grid.
[0061] System operation and maintenance costs include equipment maintenance and electrolytic cell start-up and shutdown costs.
[0062] Its expression is:
[0063] In the formula: k inv,i The unit capacity operation and maintenance cost of each device in the system; wi is the annual operation and maintenance cost rate of each device; N represents the capacity of each device in the system; N is the total number of devices; c SW For the start-up and shutdown costs of electrolytic cell equipment; L t Let t be the device startup rate at time t.
[0064] The system's raw material cost is as follows:
[0065] In the formula: c N Purchase slot N; The mass N consumed at time t; Let t be the electricity purchase price at time t. Let t be the amount of electricity consumed by the power grid.
[0066] Furthermore, let's take the 7×24h wind and solar power output scheduling scenario generated based on the requirements of this scenario as an example.
[0067] The number of 7×24h wind and solar power output scheduling scenarios to be generated is set to K, and K=5 is taken to obtain the total number of wind and solar power output scheduling scenarios M. Then, based on the probability Pi of different wind and solar power output feature classes and the total number of scenarios M, the number of wind and solar power output scenarios Di required to construct all scheduling scenarios in each weather type feature category is determined. Then, the Monte Carlo simulation method is used to sample the joint probability density distribution of the optimal joint Copula function n times to obtain the sampled samples. Finally, the synchronous back-substitution reduction method is used to obtain the wind and solar power output data of each weather type in group Di. In this application, n is taken as 100.
[0068] By using the shuffle algorithm module, a set of equally probable random sequences is generated to randomly generate continuous scenes of wind and solar power output, which can effectively avoid the pseudo-random number problem caused by using built-in functions.
[0069] The specific steps are as follows:
[0070] 1) Initialize a raw array consisting of M data points on wind, sunlight, and sunrise power, with the array length set to m.
[0071] 2) Prepare to shuffle the array by randomly generating a number n between [0, k] from the k unprocessed data.
[0072] 3) Swap the kth and nth sunrise power data in their original array positions.
[0073] 4) Repeat steps 2) and 3) until all the data in the array is scrambled, and you get a new array randomly generated by the algorithm.
[0074] 5) Divide the power output data of 7 adjacent wind and solar days into a group to generate K groups of 7×24h wind and solar related power output scheduling scenarios.
[0075] Furthermore, after obtaining the scheduling scenarios, system optimization and adjustment are carried out in two stages. In the first stage, the maximum ammonia production of the system is used as the fitness function to determine the capacity configuration scheme of the ammonia synthesis system. In the second stage, the capacity configuration results and the operating characteristics of the equipment are used as boundary conditions, and the maximum operating benefit within all scheduling scenarios is used as the objective function. A mixed-integer linear programming algorithm is then used to solve the operating load scheduling strategy for each piece of equipment. Finally, when the algorithm reaches the maximum number of iterations, it simultaneously outputs the optimal ammonia capacity configuration and the optimal operating load scheduling strategy, and sends the configuration and strategy parameters to the control systems of each submodule through the communication interface for implementation.
[0076] This application presents a multi-steady-state and dynamic tracking system for the green ammonia synthesis production process under varying wind and solar power conditions, from the power generation end to the chemical processing end, and coordinates the system control and scheduling of grid power, hydrogen storage, and electrochemical energy storage devices. For the established typical basic green hydrogen-green ammonia process model, with the goal of stabilizing ammonia production, appropriate grid power supplementation is a necessary condition. Reasonable deployment of hydrogen storage devices can reduce grid power fluctuations and load, while reasonable deployment of electrochemical energy storage devices helps to quickly stabilize the system and reduce the complexity of overall scheduling and control. Through the coordinated operation of various devices, the role of hydrogen energy storage is fully utilized, mitigating unstable wind and solar power output and achieving flexible load regulation and safe continuous production of methanol, ammonia, and other chemical equipment in the green ammonia synthesis system.
[0077] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal. The computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is connected to the system bus via the I / O interfaces. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the computer device stores collaborative control data for a green ammonia synthesis system. The I / O interfaces of the computer device are used for exchanging information between the processor and external devices. The communication interface of the computer device is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a collaborative control method for a green ammonia synthesis system.
[0078] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described above.
[0079] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the methods described above.
[0080] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0081] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.
Claims
1. A method for coordinated control of a green ammonia synthesis system, characterized in that, The coordinated control method for the green ammonia synthesis system includes: Establish equipment models and constraints for a green ammonia synthesis system. The green ammonia synthesis system includes multiple devices, including wind and solar power generation equipment, hydrogen production equipment, green ammonia synthesis equipment, and hydrogen storage equipment. The equipment models include models for both the wind and solar power generation equipment and the hydrogen production equipment. The constraints include constraints for the hydrogen production equipment, the green ammonia synthesis equipment, the hydrogen storage equipment, and system constraints. The system constraints include capacity constraints, electrical balance constraints, and hydrogen balance constraints for each device. Based on the equipment model and the constraints, the ammonia capacity configuration scheme of the green ammonia synthesis system is determined using the maximum ammonia production of the green ammonia synthesis system as the fitness function. Generate scheduling scenarios based on scenario requirements; Using the ammonia capacity configuration scheme and the operating characteristics of each device in the green ammonia synthesis system as boundary conditions, and taking the maximum operating benefit in the scheduling scenario as the objective function, the operating load scheduling strategy of each device is solved to determine the optimal ammonia capacity configuration scheme and the optimal operating load scheduling strategy. Control each device in the green ammonia synthesis system according to the optimal ammonia capacity configuration scheme and the optimal operating load scheduling strategy.
2. The method for coordinated control of the green ammonia synthesis system according to claim 1, characterized in that, The model of the wind and solar power generation equipment is as follows: in, Let t be the actual power output of the photovoltaic power generation equipment. E represents the unit installed photovoltaic power generation at time t. PV The installed capacity of photovoltaic power generation equipment; Let t be the actual power output of the wind turbine at time t; E represents the unit installed power generation of wind power at time t; WT This refers to the installed capacity of wind power generation equipment.
3. The method for coordinated control of the green ammonia synthesis system according to claim 1, characterized in that, The hydrogen production equipment model is as follows: in, Let t be the hydrogen production power. Energy consumption of hydrogen production equipment; Let t be the amount of hydrogen produced at time t; The startup status of the hydrogen production equipment is as follows: in, β is the power of the hydrogen production equipment at time t; EL,L The load factor during the startup process of the hydrogen production equipment; The rated power of the hydrogen production equipment; L t The binary variable for starting the hydrogen production equipment at time t; The constraints on the hydrogen production equipment during its operation are as follows: in, Lower limit of effective hydrogen production power of electrolyzer hydrogen production equipment.
4. The method for coordinated control of the green ammonia synthesis system according to claim 1, characterized in that, The constraints of the green ammonia synthesis equipment are as follows: in, The minimum operating load rate for green ammonia synthesis equipment; Rated output of green ammonia synthesis equipment; Let t be the green ammonia yield; The green ammonia yield at time t-1; The flexible variable load adjustment rate for green ammonia synthesis equipment.
5. The method for coordinated control of the green ammonia synthesis system according to claim 1, characterized in that, The constraints for hydrogen storage equipment are: in, The maximum hydrogen charge for the hydrogen storage equipment; This represents the maximum hydrogen release mass of the hydrogen storage device. Let be the hydrogen charging state variable of the hydrogen storage device at time t; Let t be the hydrogen release state variable of the hydrogen storage device at time t, where 0 indicates stop and 1 indicates operation; The amount of hydrogen at the initial state of the hydrogen storage equipment during the scheduling cycle; The hydrogen quantity at the end of the scheduling cycle; Let t be the amount of hydrogen stored inside the hydrogen storage device; Let t be the amount of gas supplied to the hydrogen storage device. Let t be the gas release rate of the hydrogen storage device; This represents the maximum amount of hydrogen the system can use.
6. The method for coordinated control of the green ammonia synthesis system according to claim 1, characterized in that, The capacity constraint is as follows: in, This represents the upper limit of the capacity constraint for the i-th device. This represents the lower limit of the capacity constraint for the i-th device. Let i be the capacity of the i-th device; The electrical balance constraint condition is as follows: in, Let t be the actual power output of the photovoltaic power generation equipment. Let t be the actual power output of the wind turbine at time t; Let t be the amount of electricity consumed by the power grid. The grid-connected power at time t; Let t be the hydrogen production power. Ammonia power consumption per unit mass of production; The amount of electricity discarded by the system at time t; The hydrogen balance constraint condition is as follows: in, Let t be the amount of gas supplied to the hydrogen storage device. Let t be the gas release rate of the hydrogen storage device; Let t be the amount of hydrogen produced at time t; Let t be the green ammonia production at time t.
7. The method for coordinated control of the green ammonia synthesis system according to claim 1, characterized in that, Based on the scenario requirements, a scheduling scenario is generated, which previously included: Based on the aforementioned scenario requirements, obtain the total number of scenarios for power scheduling based on weather and solar power generation. Based on the probability of different wind and solar power output characteristic classes and the total number of wind and solar power output scheduling scenarios, the number of wind and solar power output scenarios required to construct all wind and solar power output scheduling scenarios in each weather classification characteristic class is determined; based on the number of wind and solar power output scenarios required for all wind and solar power output scheduling scenarios, the Monte Carlo simulation method is used to sample the joint probability density distribution of the optimal joint Copula function to determine the sampling sample. Based on the aforementioned sample, the synchronous back-substitution reduction method is used to determine the wind, solar and solar power generation data for various weather types in group Di; where Di is the number of wind, solar and solar power generation scenarios required for all wind, solar and solar power generation scheduling scenarios.
8. The method for coordinated control of the green ammonia synthesis system according to claim 7, characterized in that, Based on the scenario requirements, a scheduling scenario is generated, specifically including: Initialize the original array; the original array includes multiple wind, solar, and sunrise power data. The initialized original array is shuffled. A data index n is randomly generated from the k unprocessed wind, solar and solar power data, and the positions of the kth wind, solar and solar power data and the nth wind, solar and solar power data in the original data are swapped until all the wind, solar and solar power data in the original data are scrambled, and a new array is generated. Based on the scenario requirements, the new array is divided in order to generate a scheduling scenario that meets the scenario requirements.
9. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the collaborative control method for the green ammonia synthesis system according to any one of claims 1-8.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the collaborative control method for the green ammonia synthesis system as described in any one of claims 1-8.