Dynamic design and operation method of green electricity-green hydrogen-green ammonia integrated system

Abstract

The invention relates to the technical field of energy system integration and optimization, in particular to a dynamic design and operation method of a green power-green hydrogen-green ammonia integrated system, which comprises a multi-steady-state optimization model, a dynamic and steady-state evaluation index and an optimal dynamic regulation and control model of a hybrid causal relationship algorithm. According to the method, the problem that the traditional steady-state design cannot adapt to the fluctuation of renewable energy sources is solved by constructing the wind and light resource allocation analysis, the dynamic material and energy balance model and the load regulation and control path optimization method, the leveling ammonia production cost can be remarkably reduced by 10.2%, the electrolytic hydrogen production energy storage capacity is reduced by 50% or above, and the method is suitable for large-scale industrial production. And the steady-state operation time of 4 hours and the load adjustment rate of 1.0%-1.5% per minute are realized, and the flexibility and the economical efficiency of the system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy system integration and optimization, and in particular to a dynamic design and operation method of a green electricity-green hydrogen-green ammonia integrated system. Background Art

[0002] With the rapid transition of the global energy mix toward renewable energy, diversified renewable energy sources such as wind and solar have attracted widespread attention due to their low-carbon characteristics and long-term energy storage potential, becoming an essential component of promoting sustainable development. However, the inherent intermittent, volatile, and random nature of these energy sources poses severe challenges to the stable operation of traditional energy systems. This is particularly true in integrated electricity-hydrogen-ammonia systems (IEHAs), where their continuous non-steady-state (CNSS) operating characteristics make existing steady-state design frameworks difficult to adapt to actual needs. Green hydrogen and green ammonia, as key hydrogen-based green energy carriers, are gradually becoming core components of renewable energy systems due to their advantages in long-term energy storage and efficient energy conversion.

[0003] However, integrating systems that couple variable renewable energy (VRE) with chemical synthesis processes faces numerous technical bottlenecks, particularly the lack of a theoretical framework and technical methods appropriate for the dynamic characteristics of IEHA. Existing technologies primarily rely on traditional steady-state design theory, which is suitable for continuous chemical processes but cannot effectively address the frequent load regulation and multi-timescale control challenges inherent in IEHA systems due to the uncertainties of wind and solar power generation.

[0004] In addition, the technical specifications and standard systems of traditional engineering projects are difficult to meet the requirements of the IEHA system in terms of wide load range, fast adjustment rate and high reliability, resulting in significant deficiencies in economy, safety and operational efficiency. Summary of the Invention

[0005] Through research, the inventors discovered that the dynamic operation of the IEHA system involves complex cross-scale energy transfer and multi-level material-energy coupling mechanisms. The steady-state operation of the ammonia synthesis process, a core component of the continuous chemical process, directly impacts the system's economic efficiency and safety. However, under the direct supply of green electricity, the uncertainty of wind and solar power generation forces the hydrogen production and ammonia synthesis processes into a non-steady-state operation, which conflicts with the traditional steady-state operation requirements.

[0006] The purpose of the present invention is to provide a dynamic design and operation method for a green electricity-green hydrogen-green ammonia integrated system. By constructing a multi-stable optimization model, a dynamic material and energy balance model, and a hybrid causal relationship model, it solves the technical problem in the existing technology that the traditional steady-state design framework cannot adapt to the intermittency, volatility and uncertainty of wind and solar resources.

[0007] According to one aspect of the present invention, a dynamic design and operation method of a green electricity-green hydrogen-green ammonia integrated system is provided, comprising: Analyze input and boundary conditions to obtain wind and solar resource configuration data and its economic requirements, and evaluate the uncertainty characteristics of wind and solar resources; Construct a dynamic steady-state model, establish an optimization model based on the multi-stable characteristics of the system, and define the flexible steady-state operation cycle and allowable fluctuation range; Establish dynamic material balance and energy balance models to ensure the system maintains stable operation under changing working conditions; Build a dynamic load control model to achieve flexible load adjustment and optimized operation; Scenario application and key parameter identification: verify the model validity and identify key parameters in the system through simulation; Optimize the capacity configuration of energy storage equipment and formulate control modes; Comprehensively evaluate the cost-effectiveness and safety of the system; Output optimal design and operation strategy.

[0008] In some embodiments, the uncertainty characteristics of the wind and solar resources are expressed in the form of a normalized output curve, which is used to quantify the fluctuation characteristics of the wind and solar resources. In some embodiments, the normalized output curve is derived based on statistical analysis of historical data, specifically including: the fluctuation range, duration distribution, and instantaneous change rate of the generated power.

[0009] In some embodiments, the calculation formula of the flexible steady-state operation period is:

[0010] in, Represents the state variable in the time interval The amount of change within.

[0011] In some embodiments, the dynamic material balance equation is:

[0012] Among them, C i represents the concentration of the i-th substance; F in and F out Represent the input and output flow of substances respectively; r i represents the reaction rate; V represents the reaction volume.

[0013] In some embodiments, the dynamic energy balance equation is:

[0014] in, represents the fluid density; C prepresents specific heat capacity; T represents temperature; and They represent heat generation and heat loss respectively.

[0015] In some embodiments, the dynamic load control model combines reaction kinetics equations, material balance equations, and energy balance equations to form comprehensive optimization constraints.

[0016] In some embodiments, the energy storage device capacity optimization model aims to minimize the levelized ammonia production cost, and the calculation formula for the electrochemical energy storage capacity is:

[0017] in, Indicates the energy difference caused by fluctuations in wind and solar power generation; Indicates energy storage efficiency.

[0018] In some embodiments, the calculation formula for the hydrogen storage capacity is:

[0019] in, Indicates the fluctuation of hydrogen demand; Indicates the hydrogen storage efficiency.

[0020] In some embodiments, the load adjustment rate is calculated as follows:

[0021] in, Indicates the pressure change of the synthesis tower; Indicates a time interval.

[0022] Compared with the existing technology, the present invention has the following beneficial effects: the present invention reduces the levelized cost of ammonia production by 10.2%, significantly improving the economic efficiency of the system; the present invention reduces the energy storage capacity of electrolytic hydrogen production by more than 50%, effectively reducing energy storage investment; The present invention achieves a steady-state operating time of 4 hours and a load adjustment rate of 1.0%-1.5% per minute, greatly improving the flexibility of the system; the present invention expands the load range of synthetic ammonia production to 30%-110%, enhancing adaptability to fluctuations in renewable energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0024] Figure 1 This is a schematic diagram of the integrated topology structure of the green electricity-green hydrogen-green ammonia integrated system of the present invention; Figure 2 This is a dynamic theoretical architecture diagram of the electricity-hydrogen-ammonia integrated system of the present invention; Figure 3 It is a flow chart of the dynamic design and operation method of the present invention. DETAILED DESCRIPTION

[0025] The following is a combination of the embodiments of the present invention Figure 1-3 The technical solutions in the embodiments of the present invention are described clearly and completely. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0026] This paper takes an actual project in northern China as an example to demonstrate the design optimization and operation control of the system under conditions of fluctuating wind and solar resources.

[0027] Example The Integrated Green Electricity, Green Hydrogen, and Green Ammonia (IEHA) system consists of a wind power generation system, a photovoltaic power generation system, an electrochemical energy storage system, a hydrogen storage system, a water electrolysis hydrogen production system, and a Haber-Bosch ammonia synthesis system. The system captures renewable energy from the wind and photovoltaic power generation systems, converts the electricity into hydrogen, and then generates ammonia through the ammonia synthesis system. The electrochemical energy storage and hydrogen storage systems serve as buffers to decouple the fluctuations in wind and solar power generation from the steady-state requirements of subsequent chemical processes. The water electrolysis hydrogen production system utilizes a hybrid ALK and PEM structure to enhance adaptability to diverse operating conditions. The ammonia synthesis system utilizes a multi-stable flexible design to meet the requirements of continuous, non-steady-state operation under fluctuating renewable energy conditions.

[0028] First, the specific implementation of the system input and boundary condition analysis was clarified during the input and boundary condition analysis, which involved obtaining wind and solar resource configuration data and its economic requirements, and evaluating the uncertainty characteristics of wind and solar resources. The system design was based on wind and solar resource configuration data and its economic requirements. The system first conducted a statistical analysis of historical wind and solar resource output curves. A normalized output curve based on 8,760 hours of data showed that the average annual fluctuation range of wind and solar resources was -11.5% to +15.6%, with over 1,000 cumulative hours of low output. This data was used to quantify the uncertainty characteristics of wind and solar resources and generate a normalized output curve. This curve, with time as the horizontal axis and normalized power output as the vertical axis, intuitively illustrates the fluctuating characteristics of wind and solar resources. Furthermore, the system considered equipment performance constraints and economic safety requirements, such as the efficiency decay rate of the electrolyzer, changes in catalyst activity, and the dynamic balancing capacity of the heat exchange network.

[0029] Next, we construct a dynamic and steady-state model, establish an optimization model based on the system's multi-stable characteristics, and define the flexible steady-state operating cycle and allowable fluctuation range. The system state variables X include temperature, pressure, and production load, and the operating variables U include voltage, current, and flow. The function f describes the change of state variables over time, and the flexible steady-state operating cycle SOD is used to quantify the system's steady-state operating capability. The calculation formula for the flexible steady-state operating cycle is:

[0030] in, Represents the state variable in the time interval The flexible operating range δ is defined as the maximum allowable fluctuation, and its value is determined by system design parameters. In practice, the flexible operating range can be expanded by adjusting the cooling medium flow rate and catalyst stacking method to optimize the synthesis tower temperature control.

[0031] Next, a dynamic material balance and energy balance model is established to ensure that the system maintains stable operation under changing working conditions. The dynamic material balance equation is as follows:

[0032] Among them, C i represents the concentration of the i-th substance; F in and F out Represent the input and output flow of substances respectively; r i represents the reaction rate; V represents the reaction volume. An example of a dynamic energy balance equation is as follows:

[0033] in, represents the fluid density; C p represents specific heat capacity; T represents temperature; and The above simulations are used to simulate the operation of the system under different working conditions to verify its robustness and generalization ability.

[0034] Furthermore, in constructing a dynamic load control model to achieve flexible load adjustment and optimized operation, the load adjustment cycle T reg The optimization objective function expression is:

[0035] in, and They represent the changes in pressure and temperature respectively. The model combines the reaction kinetics equation, material balance equation and energy balance equation to form comprehensive optimization constraints.

[0036] Then, in scenario application and key parameter identification, simulation was used to verify the model's effectiveness and identify key system parameters, including catalyst activity, equipment performance, and heat exchange network balance capacity. Using the hybrid causal relationship model (HCRM), the causal relationship between these parameters and their time lag effects were revealed. For example, the mathematical expression for the causal relationship and lag time between the synthesis tower temperature and the cold medium flow rate is as follows:

[0037] in, Indicates the temperature change of the synthesis tower; Indicates the change in cold medium flow rate. Through dynamic process simulation, the system accurately describes the transient pressure distribution rules, thereby optimizing operational efficiency.

[0038] Furthermore, in optimizing the energy storage equipment capacity configuration and formulating the control mode, the energy storage capacity optimization model aims to minimize the levelized cost of ammonia production (LCOA), taking into account factors such as equipment depreciation, manufacturing costs, and energy consumption costs. The calculation formula for electrochemical energy storage capacity is:

[0039] in, Indicates the energy difference caused by fluctuations in wind and solar power generation; Indicates energy storage efficiency. The calculation formula for hydrogen storage capacity is as follows:

[0040] in, Indicates the fluctuation of hydrogen demand; Indicates hydrogen storage efficiency. By optimizing storage capacity configuration, the system achieves the best balance between economy and safety.

[0041] Furthermore, in the comprehensive evaluation of the system's cost-effectiveness and safety, the cost-effectiveness evaluation model takes unit product cost as its core and combines parameters such as energy storage capacity, equipment performance, and operating conditions to form a multi-objective optimization function. The safety evaluation model includes constraints such as equipment load margin and heat exchange network balancing capacity.

[0042] Finally, in outputting the optimal design and operation strategy, based on the above analysis and optimization, the system determines the energy storage equipment capacity configuration, equipment selection, process path optimization, etc., and formulates operation strategies such as dynamic load adjustment path, control parameter optimization, and safety measures. For example, the calculation formula for load adjustment rate is as follows:

[0043] in, Indicates the pressure change of the synthesis tower; By optimizing the load adjustment path, the system achieves a load adjustment rate of 1.0%-1.5% per minute.

[0044] In summary, this implementation verified the feasibility of the dynamic theoretical framework through actual projects. The results showed that the present invention reduced the levelized ammonia production cost by 10.2%, reduced the electrolytic hydrogen storage capacity by more than 50%, and achieved a steady-state operation time of 4 hours and a synthetic ammonia production load range of 30%-110%, significantly improving the economy and flexibility of the system, and providing important guidance for the engineering application of the green electricity-green hydrogen-green ammonia integrated system.

[0045] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention. Therefore, from all points of view, the embodiments should be regarded as illustrative and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes that fall within the meaning and range of equivalents of the claims are included in the present invention. Any reference signs in the claims should not be construed as limiting the claim to which they relate.

[0046] In addition, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. A dynamic design and operation method for a green electricity-green hydrogen-green ammonia integrated system, characterized in that: include: Analyze input and boundary conditions to obtain wind and solar resource configuration data and its economic requirements, and evaluate the uncertainty characteristics of wind and solar resources; Construct a dynamic steady-state model, establish an optimization model based on the multi-stable characteristics of the system, and define the flexible steady-state operation cycle and allowable fluctuation range; Establish dynamic material balance and energy balance models to ensure the system maintains stable operation under changing working conditions; Build a dynamic load control model to achieve flexible load adjustment and optimized operation; Scenario application and key parameter identification: verify the model validity and identify key parameters in the system through simulation; Optimize the capacity configuration of energy storage equipment and formulate control modes; Comprehensively evaluate the cost-effectiveness and safety of the system; Output optimal design and operation strategy.

2. The method according to claim 1, characterized in that The uncertainty characteristics of the wind and solar resources are expressed in the form of a normalized output curve, which is used to quantify the fluctuation characteristics of the wind and solar resources.

3. The method according to claim 2, characterized in that The normalized output curve is obtained based on statistical analysis of historical data, and specifically includes: the fluctuation range, duration distribution and instantaneous change rate of the generated power.

4. The method according to claim 1, wherein The calculation formula of the flexible steady-state operation period is: in, Represents the state variable in the time interval The amount of change within.

5. The method according to claim 1, wherein The dynamic material balance equation is: Among them, C i represents the concentration of the i-th substance; F in and F out Represent the input and output flow of substances respectively; r i represents the reaction rate; V represents the reaction volume.

6. The method according to claim 1, characterized in that The dynamic energy balance equation is: in, represents the fluid density; C p represents specific heat capacity; T represents temperature; and They represent heat generation and heat loss respectively.

7. The method according to claim 1, characterized in that The dynamic load control model combines the reaction kinetics equation, the material balance equation and the energy balance equation to form comprehensive optimization constraints.

8. The method according to claim 1, characterized in that The energy storage equipment capacity optimization model aims to minimize the levelized ammonia production cost. The calculation formula for electrochemical energy storage capacity is: in, Indicates the energy difference caused by fluctuations in wind and solar power generation; Indicates energy storage efficiency.

9. The method according to claim 1, characterized in that The calculation formula for the hydrogen storage capacity is: in, Indicates the fluctuation of hydrogen demand; Indicates the hydrogen storage efficiency.

10. The method according to claim 1, characterized in that The calculation formula of the load adjustment rate is: in, Indicates the pressure change of the synthesis tower; Indicates a time interval.

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