Control Method, Control System, and Computer-Readable Medium for an Integrated Electric-Hydrogen-Ammonia Energy System

Abstract

The present invention provides a control method for an integrated electric-hydrogen-ammonia energy system. The control method includes the following steps: S0: determining the architecture of the integrated electric-hydrogen-ammonia energy system; S1: establishing a model of the integrated electric-hydrogen-ammonia energy system; S2: training the model; S3: establishing constraint conditions and an objective function based on the trained model; S4: obtaining the optimization parameters of the objective function through a non-linear optimization algorithm based on the gradient descent method; and S5: using the optimization parameters to control the integrated electric-hydrogen-ammonia energy system.

Description

Technical Field

[0001] The present invention relates to an integrated power-to-hydrogen-to-ammonia energy system. More specifically, the present invention relates to a control method, a control system, and a computer-readable medium for an integrated power-to-hydrogen-to-ammonia energy system. Background Art

[0002] With the rapid development of renewable energy sources such as wind power and photovoltaic power generation, the supply-demand relationship of the power grid has also changed significantly. In a traditional power system, the power source and the load are not directly linked, and the power grid acts as a buffer between the power source and the load. Therefore, as long as the fluctuations of the power source and the load are not large, the power grid can perform a certain degree of dispatching control to adjust the output of the power source, thereby ensuring the real-time balance between the power source and the load.

[0003] For the above reasons, the planning and design of traditional electrical devices (loads) usually do not need to consider the fluctuations of the power source input, but regard the power source as a stable input. Specifically, when considering electrical devices such as hydrogen production and ammonia synthesis, if the power supply mode of the traditional power grid is adopted, then when planning hydrogen production, ammonia synthesis and other electrical devices, it is only necessary to consider operating stably at the rated load, and there is no need to consider the load regulation ability of the devices. Except for maintenance and other times, the annual full-load production time is usually counted as 8,000 hours, and indicators such as the annual output and equipment utilization rate of the device are calculated based on this.

[0004] However, when directly coupling renewable energy sources such as wind power and photovoltaic power generation for power supply or when there is a large proportion of renewable energy in a regional power grid, the power grid can no longer provide a stable power source for electrical devices. When the output of renewable energy is large, the electrical device can operate at a higher load; conversely, when the output of renewable energy is small, the electrical device also needs to reduce the load accordingly. In such an application scenario, the planning of electrical devices such as hydrogen production and ammonia synthesis also has new characteristics different from the traditional ones:

[0005] (1) It is necessary to consider that the hydrogen production and ammonia synthesis devices can operate continuously at different loads, and the efficiency of the hydrogen production and ammonia synthesis devices is different when operating at different loads;

[0006] (2) It is necessary to consider that the load regulation speed of the hydrogen production and ammonia synthesis devices is limited by the technical level;

[0007] (3) The annual equivalent full-load operation time of the hydrogen production and ammonia synthesis devices is lower than the traditional 8,000 hours, and indicators such as the annual output and equipment utilization rate need to be calculated according to the actual operation time.

[0008] Traditional integrated energy planning emphasizes the "energy demand" as the core and usually considers energy demand to be "rigid". For example, CN 108537409 B discloses a collaborative planning method for industrial park distribution networks considering multi-energy coupling characteristics, including the following steps: S1. Conduct research and data collection on the user scale and industrial development overview of the industrial park; S2. Forecast the energy demand of the industrial park; S3. Establish output characteristic models for various energy resources; S4. According to the energy characteristics of the industrial park, construct the objective function of the planning scheme and establish energy resource constraint conditions; S5. Analyze and match various resources based on the analysis of the integrated energy resource coupling characteristics; S6. Evaluate the economic and social benefits of the integrated energy planning scheme to obtain the final integrated energy collaboration scheme. It can be seen that after the research and data collection are completed, the next step is to predict the energy demand of the industrial park, which reflects the optimization idea centered on demand (referred to as "source follows load"), that is, by optimizing the power output to meet the load demand. Then establish the output characteristic model of the integrated energy multi-energy coupling resources, construct the objective function, establish the constraint conditions, and finally conduct analysis and evaluation.

[0009] However, in the application scenarios of direct coupling of renewable energy for hydrogen production and ammonia synthesis, the current integrated energy planning methods have the following limitations:

[0010] (1) The existing planning models assume that the load demand is "rigid", that is, a reasonable integrated energy system must be configured to meet the load demand, reflecting the "source follows load" planning idea. However, in the integrated energy system of power-to-hydrogen-to-ammonia with direct coupling of renewable energy, the hydrogen production and ammonia synthesis loads are not rigid and constant. On the contrary, the loads of hydrogen production and ammonia synthesis can be dynamically adjusted according to the characteristics of renewable energy output, which is a "flexible" load of "load follows source". The traditional planning methods are not applicable to the "flexible" load of "load follows source".

[0011] (2) The traditional planning methods do not consider that the regulation ability of the load is limited, but assume that at any moment t, the output and the load can reach balance. This limitation stems from two factors:

[0012] a) There is no "flexible" load of "load follows source" in the traditional planning methods, and there is no need for rapid load regulation originally;

[0013] b) The time accuracy of the traditional planning methods is usually at the hour level and cannot reflect the changes in the device regulation speed at the minute level.

[0014] (3) Traditional integrated energy planning uses mixed-integer linear programming, such as the patent CN110163411 A of North China Electric Power University. Since the linear programming algorithm is adopted, the construction of the model is restricted to a certain extent and a large number of simplifications are made compared with the actual situation. The control method proposed by the present invention uses a non-linear optimization algorithm based on the gradient descent method, which can greatly improve the degree of freedom of modeling and construct non-linear equations. Summary of the Invention

[0015] Based on the dynamic output characteristics of renewable energy, the present invention simulates the annual operation curves of hydrogen production and ammonia synthesis devices in the mode of direct coupling power supply of renewable energy, and calculates the economic indicators of the overall project based on information such as initial investment, operating cost, annual output, and market price, and uses them as the objective function of optimization. Then, the parameters affecting the economic indicators of the overall project are set as optimization parameters, such as the ratio of new energy, the ratio of wind and light, the ratio of hydrogen production, the ratio of energy storage / hydrogen storage, etc., and a non-linear optimization algorithm based on the gradient descent method is used to obtain the optimal result.

[0016] The present invention aims to realize the consideration of the adjustable load of hydrogen production and ammonia synthesis devices in the control of the integrated electric-hydrogen-ammonia energy system with direct coupling power supply of renewable energy. The load of hydrogen production and ammonia synthesis devices is no longer a "rigid" demand, but a "flexible" or adjustable load that can change with the volatility of renewable energy output, realizing "load moving with the source".

[0017] In addition, the present invention considers the limited load adjustment ability of hydrogen production and ammonia synthesis devices, that is, limited by the current technical conditions, the change rate of the load cannot be higher than a predetermined value.

[0018] In addition, the present invention uses a non-linear optimization algorithm based on the gradient descent method, which can improve the degree of freedom of modeling and more accurately simulate the actual situation.

[0019] According to an example of the present invention, a control method for an integrated electric-hydrogen-ammonia energy system is provided, and the control method includes the following steps:

[0020] S0: Determine the architecture of the integrated electric-hydrogen-ammonia energy system;

[0021] S1: Establish a model of the integrated electric-hydrogen-ammonia energy system;

[0022] S2: Train the model;

[0023] S3: Establish constraint conditions and an objective function based on the trained model;

[0024] S4: Obtain the optimization parameters of the objective function through a non-linear optimization algorithm based on the gradient descent method; and

[0025] S5: Use the optimization parameters to control the integrated electric-hydrogen-ammonia energy system.

[0026] Preferably, the architecture of the integrated electricity-hydrogen-ammonia energy system includes a renewable energy power generation device, an energy storage device, a hydrogen production device, an ammonia synthesis device, and a hydrogen storage device. The electric power generated by the renewable energy power generation device is supplied to the hydrogen production device and the ammonia synthesis device via a local power grid.

[0027] Preferably, the model of the integrated electricity-hydrogen-ammonia energy system includes a hydrogen production model, an ammonia synthesis model, an energy storage model, and a hydrogen storage model. Among them, the electrolytic water hydrogen production model and the ammonia synthesis model are non-linear functions of the load.

[0028] Preferably, training the hydrogen production model includes:

[0029] Obtaining the historical data of the hydrogen production device; and

[0030] Training the hydrogen production model based on the historical data of the hydrogen production device;

[0031] Among them, training the ammonia synthesis model includes:

[0032] Obtaining the historical data of the ammonia synthesis device; and

[0033] Training the ammonia synthesis model based on the historical data of the ammonia synthesis device.

[0034] Preferably, the constraint conditions include load constraint conditions. The load constraint conditions include load regulation speed constraint conditions and load rate constraint conditions. The load regulation speed constraint conditions define the corresponding upper and lower limits of the load regulation speeds of the hydrogen production device and the ammonia synthesis device, and the load rate constraint conditions define the corresponding upper and lower limits of the load rates of the hydrogen production device and the ammonia synthesis device.

[0035] Preferably, the control method further includes: obtaining the output power curve of the renewable energy power generation device in minutes, and the non-linear optimization algorithm considers the output power curve.

[0036] Preferably, the optimization parameters include at least one of the scale of the wind power generation device, the scale of the photovoltaic power generation device, the scale of the hydrogen production device, the scale of the ammonia synthesis device, the scale of the energy storage device, the scale of the hydrogen storage device, and the minimum load of the hydrogen production device and the ammonia synthesis device.

[0037] Preferably, the learning rate of the gradient descent method takes the following values:

[0038] S41: Set the initial learning rate η0;

[0039] S42: The learning rate increases by m%, where 0 < m < 30;

[0040] S43: Determine whether the IRR is satisfied t <IRRt-1 wherein, IRR t and IRR t-1 are the IRR values at times t and t-1 respectively;

[0041] S44: If IRR t < IRR t-1 is satisfied, then divide the learning rate by n, where n > 1; otherwise, return to step S42;

[0042] S45: Determine whether the exit condition

[0043] is satisfied. If so, the optimization process is completed. If not, return to step S42.

[0044] If the objective function has a converging trend, gradually increase the learning rate to improve the iteration speed; if the objective function has a diverging trend, decrease the learning rate to improve the convergence.

[0045] Preferably, η0 is 0.4 - 0.6, 1 < m < 10, 1.2 < n < 3.

[0046] Preferably, η0 is 0.5, m = 5, n = 2.

[0047] According to another example of the present invention, there is provided a control system for an integrated electric-hydrogen-ammonia energy system, including a processor and a memory, wherein an application program is stored in the memory, and when the application program is executed by the processor, the processor is caused to execute the control method as described above.

[0048] According to another example of the present invention, there is provided a control system for an integrated electric-hydrogen-ammonia energy system, including:

[0049] An architecture determination module configured to determine the architecture of the integrated electric-hydrogen-ammonia energy system;

[0050] A model establishment module configured to establish a model of the integrated electric-hydrogen-ammonia energy system;

[0051] A model training module configured to train the model;

[0052] A constraint condition and objective function establishment module configured to establish constraint conditions and an objective function based on the trained model;

[0053] An optimization module configured to obtain the optimization parameters of the objective function through a non-linear optimization algorithm based on the gradient descent method; and

[0054] A control module configured to control the integrated electric-hydrogen-ammonia energy system using the optimization parameters.

[0055] Preferably, the architecture of the integrated electric-hydrogen-ammonia energy system includes a renewable energy power generation device, an energy storage device, a hydrogen production device, an ammonia synthesis device, and a hydrogen storage device. The power generated by the renewable energy power generation device is supplied to the hydrogen production device and the ammonia synthesis device via a local power grid.

[0056] Preferably, the model of the integrated electric-hydrogen-ammonia energy system includes a hydrogen production model, an ammonia synthesis model, an energy storage model, and a hydrogen storage model. Among them, the electrolytic water hydrogen production model and the ammonia synthesis model are non-linear functions of the load.

[0057] Preferably, the model training module is configured to:

[0058] Obtain the historical data of the hydrogen production device; and

[0059] Train the hydrogen production model based on the historical data of the hydrogen production device;

[0060] Among them, the model training module is further configured to:

[0061] Obtain the historical data of the ammonia synthesis device; and

[0062] Train the ammonia synthesis model based on the historical data of the ammonia synthesis device.

[0063] Preferably, the constraint conditions include load constraint conditions. The load constraint conditions include load regulation speed constraint conditions and load rate constraint conditions. The load regulation speed constraint conditions define the corresponding upper and lower limits of the load regulation speed of the hydrogen production device and the ammonia synthesis device, and the load rate constraint conditions define the corresponding upper and lower limits of the load rate of the hydrogen production device and the ammonia synthesis device.

[0064] Preferably, the control system further includes an output curve acquisition module configured to acquire the output curve of the renewable energy power generation device in minutes, and among them, the optimization module considers the output curve.

[0065] Preferably, the optimization parameters include at least one of the scale of the wind power generation device, the scale of the photovoltaic power generation device, the scale of the hydrogen production device, the scale of the ammonia synthesis device, the scale of the energy storage device, the scale of the hydrogen storage device, and the minimum load of the hydrogen production device and the ammonia synthesis device.

[0066] Preferably, the learning rate of the gradient descent method takes the following values:

[0067] S41: Set the initial learning rate η0;

[0068] S42: The learning rate increases by m%, where 0 < m < 30;

[0069] S43: Determine whether IRR is satisfied t <IRRt-1 Among them, IRR t and IRR t-1 are the IRR values at times t and t - 1 respectively;

[0070] S44: If IRR t < IRR t-1 is satisfied, then divide the learning rate by n, where n > 1; otherwise, return to step S42;

[0071] S45: Determine whether the exit condition

[0072] is satisfied. If so, the optimization process is completed. If not, return to step S42.

[0073] If the objective function has a converging trend, gradually increase the learning rate to improve the iteration speed; if the objective function has a diverging trend, decrease the learning rate to improve the convergence.

[0074] Preferably, η0 is 0.4 - 0.6, 1 < m < 10, 1.2 < n < 3.

[0075] Preferably, η0 is 0.5, m = 5, n = 2.

[0076] According to another example of the present invention, there is provided a computer-readable medium storing computer program code, which, when executed by a processor, causes the processor to execute the control method as described above.

[0077] By optimizing the device scale of the direct coupling of renewable energy for hydrogen production and ammonia synthesis, the present invention can increase the utilization ratio of renewable energy, reduce the rate of abandoned electricity, and improve the utilization rate of hydrogen production and ammonia synthesis devices, thereby improving the economic benefits of the integrated electricity-hydrogen-ammonia system.

[0078] The present invention can achieve diverse energy supply strategies. Electricity, hydrogen, and ammonia are all forms of energy. Energy enterprises can, through the present invention, reasonably select the supply ratios of the three types of energy under different resource conditions, market conditions, and investment costs to maximize the benefits.

[0079] Other exemplary embodiments of the present invention are apparent from the detailed description provided below. It should be understood that the detailed description and specific examples, although disclosing exemplary embodiments of the present invention, are for illustrative purposes only and are not intended to limit the scope of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] At least one embodiment will be described below in conjunction with the following drawings, in which like reference numerals represent like elements.

[0081] Figure 1 is an integrated electricity, hydrogen, and ammonia energy system powered directly by renewable energy according to an example of the present invention.

[0082] Figure 2A is a flowchart of a control method for an integrated electricity, hydrogen, and ammonia energy system according to an example of the present invention.

[0083] Figure 2B is a flowchart of the gradient descent method according to an example of the present invention.

[0084] Figure 2C is a schematic diagram of a control system for an integrated electricity, hydrogen, and ammonia energy system according to an example of the present invention.

[0085] Figure 3A is a schematic diagram of a standardized energy conversion model.

[0086] Figure 3B is a schematic diagram of an energy conversion model for hydrogen production by electrolyzing water.

[0087] Figure 3C is a schematic diagram of an energy conversion model for ammonia synthesis.

[0088] Figure 3D is a schematic diagram of an energy conversion model for electricity storage.

[0089] Figure 4 is a schematic diagram of energy and material balance according to an example of the present invention.

[0090] Figure 5 is an annual output curve of wind power and photovoltaic power generation according to an example of the present invention.

[0091] Figure 6 is a simulated operation diagram of an integrated electricity, hydrogen, and ammonia energy system according to an example of the present invention.

[0092] Figure 7 shows the iterative optimization process of the objective function of a non - linear optimization algorithm according to an example of the present invention.

[0093] Figure 8A shows the iterative optimization process of the optimization parameters of a non - linear optimization algorithm according to an example of the present invention.

[0094] Figure 8B shows the iterative optimization process of other parameters of a non - linear optimization algorithm according to an example of the present invention.

[0095] Figure 9 shows the optimal value of the objective function under given boundary conditions in the form of a three - dimensional graph. Detailed implementation manners

[0096] The following description is merely exemplary in nature and is in no way intended to limit the invention, its application, or its use. Example embodiments are provided so that this disclosure will be thorough and will fully convey the scope to those skilled in the art. Numerous specific details are set forth such as examples of specific components, devices, and methods to provide a thorough understanding of embodiments of the present disclosure. It will be apparent to those skilled in the art that specific details need not be employed, that the example embodiments may be embodied in many different forms, and that neither should be construed to limit the scope of the present disclosure. In some example embodiments, well-known processes, well-known device structures, and well-known technologies have not been described in detail.

[0097] The terms "module" and related terms (such as, control module, controller, control member, control unit, processor, and similar terms) refer to one or various combinations of the following: application specific integrated circuit (ASIC), field programmable gate array (FPGA), electronic circuit, central processing unit (e.g., microprocessor), and associated non-transitory memory components, the non-transitory memory components being in the form of memory and storage devices (read only, programmable read only, random access, hard disk drive, etc.). The non-transitory memory components are capable of storing machine-readable instructions in the form of: one or more software or firmware programs or routines, combinational logic circuits, input / output circuits and devices, signal conditioning and buffering circuits, and other components accessible by one or more processors to provide the described functionality. The input / output circuits and devices include analog / digital converters and associated devices that monitor input from sensors, where such input is monitored at a preset sampling frequency or in response to a triggering event. Software, firmware, programs, instructions, control routines, code, algorithms, and similar terms mean sets of controller-executable instructions, including calibration and look-up tables.

[0098] This disclosure may describe the present teachings in terms of functional and / or logical block components and / or various processing steps. It should be recognized that such block components may consist of hardware, software, and / or firmware components that have been configured to perform the specified functions. Embodiments may also be implemented in a cloud computing environment. The flowcharts and block diagrams in the process illustrations depict the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, segment, or portion of code that includes one or more executable instructions for implementing the specified logical function. It will also be noted that each block of the block diagrams and / or flowchart illustrations, and combinations of blocks in the block diagrams and / or flowchart illustrations, can be implemented by a dedicated hardware-based system that performs the specified functions or acts, or by a combination of dedicated hardware and computer instructions. These computer program instructions may also be stored in a computer-readable medium that can direct a controller or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable medium produce an article of manufacture including instructions for implementing the functions / acts specified in one or more blocks of the flowchart and / or block diagram.

[0099] Furthermore, any method steps, processes, and operations described herein will not be construed as necessarily requiring them to be performed in the particular order discussed or illustrated, unless specifically identified as the order of execution. It will also be understood that additional or alternative steps may be employed, unless otherwise indicated.

[0100] Figure 1 is an integrated electric-hydrogen-ammonia energy system directly coupled with renewable energy power supply according to an example. The integrated electric-hydrogen-ammonia energy system includes a wind power generation device, a photovoltaic power generation device, an energy storage device, a hydrogen production device, a synthetic ammonia device, and a hydrogen storage device. The electric power generated by the wind power generation device and the photovoltaic power generation device is supplied to the hydrogen production device and the synthetic ammonia device via a local power grid. The wind power generation device and the photovoltaic power generation device are collectively referred to as renewable energy power generation devices. It should be understood that the renewable energy power generation devices may include other types of renewable energy power generation devices, such as a hydroelectric power generation device, without departing from the scope of the present invention. In this example, renewable energy green power is provided by the wind power generation device and the photovoltaic power generation device. However, before optimization is implemented, the optimal ratio of wind power to photovoltaic power is uncertain. Using wind power and photovoltaic power as the main energy sources, power is supplied to the hydrogen production device and the synthetic ammonia device through the local power grid. The local power grid is connected to the large power grid and can perform power exchange. At the same time, the system is configured with a certain capacity of energy storage device and hydrogen storage device to smooth the volatility of the output of renewable energy. Most of the electric power is used for electrolyzing water to produce hydrogen, and the reaction process is as follows:

[0101] (1).

[0102] The hydrogen produced by electrolyzing water is used as one of the raw materials and reacts with nitrogen captured from the air under certain reaction conditions to produce ammonia. This process also requires a certain amount of electrical input. The reaction process is as follows:

[0103] (2).

[0104] A certain capacity of energy storage devices and hydrogen storage devices will be configured in the system to smooth the volatility of wind power and photovoltaic power generation. In addition, the system will also be connected to the large power grid. When there is surplus power generated by wind power and photovoltaic power generation, the excess power can be sent into the large power grid. However, when the output of wind power and photovoltaic power generation decreases, the large power grid may not be able to provide enough power to keep the hydrogen production and ammonia synthesis load at the rated load. This is because:

[0105] (a) The influence range of meteorological conditions is large. When the proportion of renewable energy in the power grid is high to a certain extent, when the output of wind power and photovoltaic power generation in a certain part of the large power grid is insufficient, it is very likely that power supply shortages will occur in a relatively large area. Therefore, the hydrogen production and ammonia synthesis load must decrease with the output of wind power and photovoltaic power generation, and grid power supply can only ensure that hydrogen production and ammonia synthesis maintain the minimum load (usually 20% - 30% of the rated load).

[0106] (b) In the current power system structure, the proportion of thermal power is still very high. Using grid power to maintain the operation of hydrogen production and ammonia synthesis at a high load will significantly increase carbon emissions.

[0107] Figure 2A is a flowchart of a control method for an integrated electric-hydrogen-ammonia energy system according to an example of the present invention. According to an example of the present invention, a control method for an integrated electric-hydrogen-ammonia energy system is provided. The control method includes the following steps:

[0108] S0: Determine the architecture of the integrated electric-hydrogen-ammonia energy system;

[0109] S1: Establish a model of the integrated electric-hydrogen-ammonia energy system;

[0110] S2: Train the model;

[0111] S3: Establish constraint conditions and an objective function based on the trained model;

[0112] S4: Obtain the optimization parameters of the objective function through a non-linear optimization algorithm based on the gradient descent method; and

[0113] S5: Use the optimization parameters to control the integrated electric-hydrogen-ammonia energy system.

[0114] According to an example of the present invention, the architecture of the integrated electric-hydrogen-ammonia energy system includes a renewable energy power generation device, an energy storage device, a hydrogen production device, an ammonia synthesis device, and a hydrogen storage device. The electric power generated by the renewable energy power generation device is supplied to the hydrogen production device and the ammonia synthesis device via a local power grid. According to an example of the present invention, the renewable energy includes at least one of wind power generation and photovoltaic power generation. It should be understood that the present invention can also be applied to any other suitable renewable energy direct-coupled power supply system without departing from the scope of the present invention.

[0115] According to an example of the present invention, the model of the integrated electric-hydrogen-ammonia energy system includes a hydrogen production model, an ammonia synthesis model, an energy storage model, and a hydrogen storage model. Among them, the electrolytic water hydrogen production model and the ammonia synthesis model are non-linear functions of the load.

[0116] According to an example of the present invention, training the hydrogen production model includes:

[0117] Obtaining the historical data of the hydrogen production device; and

[0118] Training the hydrogen production model based on the historical data of the hydrogen production device;

[0119] Among them, training the ammonia synthesis model includes:

[0120] Obtaining the historical data of the ammonia synthesis device; and

[0121] Training the ammonia synthesis model based on the historical data of the ammonia synthesis device.

[0122] According to an example of the present invention, the constraint conditions include load constraint conditions. The load constraint conditions include load regulation speed constraint conditions and load rate constraint conditions. The load regulation speed constraint conditions define the corresponding upper and lower limits of the load regulation speed of the hydrogen production device and the ammonia synthesis device, and the load rate constraint conditions define the corresponding upper and lower limits of the load rate of the hydrogen production device and the ammonia synthesis device.

[0123] According to an example of the present invention, the control method further includes: obtaining the output power curve of the renewable energy power generation device in minutes, and the non-linear optimization algorithm takes into account the output power curve. Therefore, the method according to the present invention can reflect the change of the device regulation speed on the minute scale.

[0124] According to an example of the present invention, the optimization parameters include at least one of the scale of the wind power generation device, the scale of the photovoltaic power generation device, the scale of the hydrogen production device, the scale of the ammonia synthesis device, the scale of the energy storage device, the scale of the hydrogen storage device, and the minimum load of the hydrogen production device and the ammonia synthesis device.

[0125] Figure 2CIt is a schematic diagram of a control system for an integrated electric-hydrogen-ammonia energy system according to an example of the present invention. The control system 100 includes: an architecture determination module 101 configured to determine the architecture of the integrated electric-hydrogen-ammonia energy system; a model establishment module 102 configured to establish a model of the integrated electric-hydrogen-ammonia energy system; a model training module 103 configured to train the model; a constraint condition and objective function establishment module 104 configured to establish constraint conditions and an objective function based on the trained model; an optimization module 105 configured to obtain optimization parameters of the objective function through a non-linear optimization algorithm based on the gradient descent method; and a control module 106 configured to control the integrated electric-hydrogen-ammonia energy system using the optimization parameters.

[0126] According to an example of the present invention, the model training module 103 is configured to: obtain historical data of a hydrogen production device; and train a hydrogen production model based on the historical data of the hydrogen production device.

[0127] According to an example of the present invention, the model training module 103 is further configured to: obtain historical data of an ammonia synthesis device; and train an ammonia synthesis model based on the historical data of the ammonia synthesis device.

[0128] The control system 100 further includes an output curve acquisition module 107 configured to acquire an output curve of a renewable energy power generation device in minutes, wherein the optimization module 105 takes into account the output curve.

[0129] Now refer to Figures 3A - 3D , Figure 3A is a schematic diagram of a standardized energy conversion model; Figure 3B is a schematic diagram of an energy conversion model for hydrogen production by electrolyzing water; Figure 3C is a schematic diagram of an energy conversion model for ammonia synthesis; Figure 3D is a schematic diagram of an energy conversion model for electricity storage.

[0130] Essentially, hydrogen production by electrolyzing water, ammonia synthesis, energy storage and hydrogen storage are all processes of energy conversion. For example, hydrogen production by electrolyzing water is to convert electrical energy into hydrogen; ammonia synthesis is to convert electrical energy, hydrogen and nitrogen into ammonia and release a certain amount of heat. Therefore, first, the above-mentioned devices are represented as the same standardized energy conversion model, such as Figure 3A shown.

[0131] The energy conversion model for hydrogen production by electrolyzing water is as Figure 3B shown, with electricity and heat as inputs and heat and gas (hydrogen) as outputs. Since hydrogen production by electrolyzing water itself is an exothermic reaction, heat always exists in the reaction output. However, when the load of hydrogen production by electrolyzing water is lower than a certain level, the reaction heat release is not sufficient to maintain the optimal temperature of the system, and at this time, heat needs to be supplemented to the system as an energy input.

[0132] The energy conversion model of ammonia synthesis is as shown in Figure 3C . The inputs are electricity and gas (hydrogen and nitrogen), and the outputs are heat and ammonia (gaseous). Similarly, when ammonia synthesis operates at a relatively high level, the exothermic reaction can drive a waste heat boiler, so electricity exists in the outputs, while there is no electricity output when the ammonia synthesis load is low. On the other hand, when the ammonia synthesis load is low, the exothermic reaction cannot keep the system at the optimal temperature, so heat needs to be supplemented as an energy input.

[0133] The energy conversion model of electricity storage is as shown in Figure 3D . Its inputs and outputs are only electricity, without considering the heat release of the system. Similarly, hydrogen storage can also be expressed in a form similar to Figure 3D . Its inputs and outputs are both hydrogen. In the present invention, since the energy loss during hydrogen storage is not considered, the input and output are both the same hydrogen, and the hydrogen storage model will not be elaborated further below.

[0134] The above-mentioned various models are expressed by the following mathematical expressions:

[0135] (3)

[0136] Wherein, respectively represent the inputs of electric energy, heat and gas, respectively represent the outputs of electric energy, heat and gas, and the subscript represents time. is a state variable, indicating the operating state of this energy conversion model at this time, including four states: shutdown, hot standby, following, and active.

[0137] According to an example of the present invention, the hydrogen production model by electrolyzing water indicates that the hydrogen production is a function of the hydrogen production power input, the unit hydrogen production power consumption under the rated load, and the hydrogen production efficiency, and the hydrogen production efficiency is a non-linear function of the load. For example, the formula of the hydrogen production model by electrolyzing water can be expressed as:

[0138] (4)

[0139] Wherein, is the hydrogen production; is the hydrogen production power input; is the unit hydrogen production power consumption under the rated load; is the hydrogen production efficiency, which is a function of the load. According to an example of the present invention, is a non-linear function of the load. It should be understood that formula (4) is only an example of the hydrogen production model by electrolyzing water, and the present invention can adopt any other suitable hydrogen production model by electrolyzing water without departing from the scope of the present invention.

[0140] According to an example of the present invention, the ammonia synthesis model represents the ammonia output as a function of the hydrogen input and power consumption, where the power consumption is a non-linear function of the load. For example, the formula of the ammonia synthesis model can be expressed as:

[0141] H nh3 (5)

[0142] (6)

[0143] where, is the ammonia output; is the hydrogen input, is the power consumption, f P is a function of the load, is the hydrogen-ammonia conversion function. According to an example of the present invention, f P is a non-linear function of the load. It should be understood that the formulas (5) and (6) are merely an example of the ammonia synthesis model, and the present invention can adopt any other suitable ammonia synthesis model without departing from the scope of the present invention.

[0144] According to an example of the present invention, the energy storage model represents the state of charge at time t + 1 as a function of the state of charge at time t, the charging efficiency and power of the energy storage, the discharging efficiency and power of the energy storage, the time interval between time t and time t + 1, and the discharging loss at time t. For example, the formula of the energy storage model can be expressed as:

[0145] (7)

[0146] where, and are the state of charge at time t and time t + 1 respectively; and are the charging efficiency and power of the energy storage respectively; and are the discharging efficiency and power of the energy storage respectively; is the time interval between time t and time t + 1; is the discharging loss at time t. It should be understood that the formula (7) is merely an example of the energy storage model, and the present invention can adopt any other suitable energy storage model without departing from the scope of the present invention.

[0147] According to an example of the present invention, the constraint conditions include real-time energy balance constraint conditions, real-time material balance constraint conditions, and load constraint conditions.

[0148] Figure 4It is a schematic diagram of energy and material balance according to an example of the present invention. According to an example of the present invention, the real-time energy balance constraint conditions are as follows:

[0149] (8)

[0150] Wherein, is the power of the renewable energy power generation device, is the discharge power of the energy storage device, is the power of the power grid connection, is the electric power of the hydrogen production device, is the electric power of the ammonia synthesis device, is the charging power of the energy storage device, is the power of the power grid injection, is the curtailed power.

[0151] According to an example of the present invention, the real-time material balance constraint conditions are as follows:

[0152] (9)

[0153] Wherein, is the hydrogen production rate, is the inlet rate of the hydrogen storage device, is the consumption rate of hydrogen, is the outlet rate of the hydrogen storage device, is the venting rate of hydrogen.

[0154] In addition, according to an example of the present invention, the load constraint conditions include the load regulation speed constraint condition and the load rate constraint condition.

[0155] For example, the load regulation speed constraint condition is as follows:

[0156] (10)

[0157] (11)

[0158] Wherein, and are the load rates of hydrogen production at times t and t-1 respectively, is the limit value of hydrogen production load reduction, is the limit value of hydrogen production load increase; and are the load rates of ammonia synthesis at times t and t-1 respectively, is the limit value of ammonia synthesis load reduction, is the limit value of ammonia synthesis load increase.

[0159] According to an example of the present invention, the load constraint conditions:

[0160] (12)

[0161] (13)

[0162] Among them, and are the minimum loads of the hydrogen production device and the ammonia synthesis device respectively, and are the maximum loads of the hydrogen production device and the ammonia synthesis device respectively.

[0163] According to an example of the present invention, the objective function is:[

[0164] (14)

[0165] IRR is the internal rate of return calculated according to the cash flow, CAPEX is the initial investment, the operating cost is OPEX, INCOME is the income, EXPENSE is the function of the expenditure, and n is the financial measurement period.

[0166] According to an example of the present invention, the optimization parameters of the non - linear optimization algorithm include at least one of the scale of the wind power generation device, the scale of the photovoltaic power generation device, the scale of the hydrogen production device, the scale of the ammonia synthesis device, the scale of the energy storage device, the scale of the hydrogen storage device, and the minimum load of the hydrogen production device and the ammonia synthesis device. It should be understood that,

[0167] According to an example of the present invention, the learning rate of the gradient descent method takes the following values:[

[0168] S41: Set the initial learning rate η0;

[0169] S42: The learning rate increases by m%, where 0 < m < 30;

[0170] S43: Determine whether it satisfies IRR t < IRR t-1 Among them, IRR t and IRR t-1 are the IRR values at times t and t - 1 respectively;

[0171] S44: If it satisfies IRR t < IRR t-1 , then divide the learning rate by n, where n > 1; otherwise return to step S42;

[0172] S45: Determine whether the exit condition is satisfied;

[0173] If it is yes, the optimization process is completed. If it is no, return to step S42.

[0174] According to an example of the present invention, η0 is 0.4 - 0.6, 1 < m < 10, 1.2 < n < 3. Preferably, η0 is 0.5, η0 is 0.5, m = 5, n = 2. It should be understood that m, n, and η0 can take any other suitable values without departing from the scope of the present invention.

[0175] The present invention also provides a control system for an integrated electric-hydrogen-ammonia energy system, including a processor and a memory. An application program is stored in the memory. When the application program is executed by the processor, the processor is caused to execute the control method of the present invention.

[0176] The present invention also provides a computer-readable medium storing computer program code. When the computer program code is executed by a processor, the processor is caused to execute the control method of the present invention.

[0177] In addition, simulation result analysis is also carried out, considering the following application scenarios: Assume that the output of the ammonia synthesis unit at rated load is 50 t / h, and the rated hydrogen production rate of the hydrogen production unit is configured according to 1.2 times the hydrogen demand of ammonia synthesis. The total installed capacity of new energy is configured according to 1.8 times the total power load of the hydrogen production unit and the ammonia synthesis unit, where the ratio of wind power to photovoltaic power generation is 6:4. In addition, in this example, it is assumed that the minimum load of the hydrogen production unit is 30%, the minimum load of the ammonia synthesis unit is 50%, and the load adjustment speed limits of both are 1% / min. The hydrogen storage scale is 120,000 N . Figure 5 is the annual output curve graph of a renewable energy power generation device (wind power and photovoltaic power generation) according to an example of the present invention.

[0178] In this example, the annual output curve of the renewable energy power generation device (wind power and photovoltaic power generation) is as Figure 5 shown. Figure 6 is the simulation operation graph according to an example of the present invention (only the first 500 hours are intercepted for display).

[0179] The calculation results show that the annual production capacity utilization rates of the hydrogen production unit and the ammonia synthesis unit can reach 5214 h and 6258 h respectively, and the annual ammonia production is 312,900 tons.

[0180] Meanwhile, assume the following financial parameters:

[0181] Initial investment First-year operating cost Annual growth rate of operating cost Wind power 7000 yuan / kW 100 yuan / kW 5% Photovoltaic power generation 5000 yuan / kW 50 yuan / kW 5% Hydrogen production 20000 yuan / N#timg# / h 5% of the initial investment 3% Ammonia synthesis 1 billion yuan 5% of the initial investment 3%

[0182] The selling price of ammonia is assumed to be 4500 yuan / ton. According to the simulation results, the overall investment return rate IRR of the project is 5.3%.

[0183] Optimize using the method described in the present invention. According to an example of the present invention, assuming that the scale of the ammonia synthesis plant remains fixed, a total of 5 parameters to be optimized are set, namely:

[0184] (1) x1: The scale of the hydrogen production plant;

[0185] (2) x2: The total installed capacity of new energy;

[0186] (3) x3: The proportion of wind power installed capacity in new energy. 1 - x3 is the proportion of photovoltaic power generation;

[0187] (4) x4: The scale of the energy storage device / hydrogen storage device;

[0188] (5) x5: The minimum load of the hydrogen production plant and the ammonia synthesis plant.

[0189] The constraints for optimization are:

[0190] (1) The total investment, operating cost, financial cost, etc. of various devices;

[0191] (2) The technical parameters of various devices, such as power consumption, conversion efficiency, etc.;

[0192] (3) The prices of electricity, hydrogen, ammonia, etc.

[0193] In this example, assume that the new energy grid connection ratio is limited to 20%, that is, the grid-connected electricity does not exceed 20% of the total new energy power generation.

[0194] Establish mathematical models for subsystems such as wind power generation devices, photovoltaic power generation devices, hydrogen production devices, ammonia synthesis devices, energy storage devices, and hydrogen storage devices according to the method described above, and establish constraint relationships, and then run the gradient descent method to solve the optimal value.

[0195] Figure 7 Shows the iterative optimization process of the objective function of the non-linear optimization algorithm according to an example of the present invention, where η0 is 0.5. "*" represents the internal rate of return on project investment IRR, and "Δ" represents the iterative error. It can be seen that IRR finally converges to 6.2%, which is about 17% higher than that before optimization.

[0196] Figure 8A Shows the iterative optimization process of the optimization parameters of the non-linear optimization algorithm according to an example of the present invention. Figure 8B Shows the iterative optimization process of other parameters of the non-linear optimization algorithm according to an example of the present invention. In Figure 8A The changes of x1~x5 during the iteration are shown from top to bottom in sequence, and it can be seen that they all finally converge to a certain specific value. Figure 8BThe 5 curves therein respectively reflect the changes that occur to other system parameters during the iteration process. It can be seen that the final grid-connected power, off-grid power, and the annual operating hours of the hydrogen production and ammonia synthesis plants all converge. Figure 9 The optimal value of the objective function under given boundary conditions is shown in the form of a three-dimensional graph.

[0197] The present invention has described certain preferred embodiments and their variations. Those skilled in the art can conceive of other variations and changes after reading and understanding the specification. Therefore, the present invention is not limited to the specific embodiments disclosed as the best mode for carrying out the present invention, and the present invention will include all embodiments falling within the scope of the claims.

Claims

1. A control method for an integrated electric-hydrogen-ammonia energy system, the control method comprising the following steps: S0: Determine the architecture of the integrated electric-hydrogen-ammonia energy system; S1: Establish a model of the integrated electric-hydrogen-ammonia energy system; S2: Train the model; S3: Based on the trained model, establish constraint conditions and an objective function; S4: Obtain the optimized parameters of the objective function through a non-linear optimization algorithm based on the gradient descent method; and S5: Use the optimized parameters to control the integrated electric-hydrogen-ammonia energy system, Among them, The architecture of the integrated electric-hydrogen-ammonia energy system includes a renewable energy power generation device, an energy storage device, a hydrogen production device, an ammonia synthesis device, and a hydrogen storage device. The electric power generated by the renewable energy power generation device is supplied to the hydrogen production device and the ammonia synthesis device via a local power grid. The constraint conditions include load constraint conditions. The load constraint conditions include a load adjustment speed constraint condition and a load rate constraint condition. The load adjustment speed constraint condition defines the corresponding upper and lower limits of the load adjustment speeds of the hydrogen production device and the ammonia synthesis device, and the load rate constraint condition defines the corresponding upper and lower limits of the load rates of the hydrogen production device and the ammonia synthesis device.

2. The control method according to claim 1, wherein, The model of the integrated electric-hydrogen-ammonia energy system includes an electrolytic water hydrogen production model, an ammonia synthesis model, an energy storage model, and a hydrogen storage model. Among them, the electrolytic water hydrogen production model and the ammonia synthesis model are non-linear functions of the load.

3. The control method according to claim 2, wherein, Training the electrolytic water hydrogen production model includes: Obtain the historical data of the hydrogen production device; and Train the electrolytic water hydrogen production model based on the historical data of the hydrogen production device; Among them, training the ammonia synthesis model includes: Obtain the historical data of the ammonia synthesis device; and Train the ammonia synthesis model based on the historical data of the ammonia synthesis device.

4. The control method according to claim 3 further includes: Obtain the output power curve of the renewable energy power generation device in minutes. Among them, the non-linear optimization algorithm considers the output power curve.

5. The control method according to claim 4, wherein, The optimized parameters include at least one of the scale of the wind power generation device, the scale of the photovoltaic power generation device, the scale of the hydrogen production device, the scale of the ammonia synthesis device, the scale of the energy storage device, the scale of the hydrogen storage device, and the minimum load of the hydrogen production device and the ammonia synthesis device.

6. The control method according to claim 1, wherein The learning rate η of the gradient descent method takes the following values: S41: Set an initial learning rate η0; S42: The learning rate η increases by m%, where 0 < m < 30; S43: Determine whether the IRR is satisfied t <IRR t-1 , where IRR t and IRR t-1 are the IRR values at times t and t - 1 respectively; S44: If IRR is satisfied t <IRR t-1 , then divide the learning rate η by n, where n > 1; otherwise, return to step S42; S45: Determine whether the exit condition is satisfied, If yes, the optimization process is completed; if no, return to step S42, IRR is the internal rate of return calculated according to the cash flow, and n is the financial calculation period.

7. The control method according to claim 6, wherein η0 is 0.4 - 0.6, 1 < m < 10, 1.2 < n < 3.

8. A control system for an integrated electric-hydrogen-ammonia energy system, including a processor and a memory. An application program is stored in the memory. When the application program is executed by the processor, the processor executes the control method according to any one of claims 1 to 7.

9. A control system for an integrated electric-hydrogen-ammonia energy system, including: An architecture determination module configured to determine the architecture of the integrated electric-hydrogen-ammonia energy system; A model establishment module configured to establish a model of the integrated electric-hydrogen-ammonia energy system; A model training module configured to train the model; A constraint condition and objective function establishment module configured to establish constraint conditions and an objective function based on a trained model; An optimization module configured to obtain optimization parameters of the objective function through a non-linear optimization algorithm based on the gradient descent method; And A control module configured to control the integrated electric-hydrogen-ammonia energy system by using the optimization parameters, wherein the architecture of the integrated electric-hydrogen-ammonia energy system includes a renewable energy power generation device, an energy storage device, a hydrogen production device, an ammonia synthesis device, and a hydrogen storage device, and the electric power generated by the renewable energy power generation device is supplied to the hydrogen production device and the ammonia synthesis device via a local power grid, The constraint conditions include load constraint conditions, and the load constraint conditions include a load regulation speed constraint condition and a load rate constraint condition. The load regulation speed constraint condition defines the corresponding upper and lower limits of the load regulation speeds of the hydrogen production device and the ammonia synthesis device, and the load rate constraint condition defines the corresponding upper and lower limits of the load rates of the hydrogen production device and the ammonia synthesis device.

10. The control system according to claim 9, wherein, The model of the integrated electric-hydrogen-ammonia energy system includes an electrolytic water hydrogen production model, an ammonia synthesis model, an energy storage model, and a hydrogen storage model, wherein the electrolytic water hydrogen production model and the ammonia synthesis model are non-linear functions of the load.

11. The control system according to claim 10, wherein, The model training module is configured to: Obtain historical data of the hydrogen production device; and Train the electrolytic water hydrogen production model based on the historical data of the hydrogen production device; wherein the model training module is further configured to: Obtain historical data of the ammonia synthesis device; and Train the ammonia synthesis model based on the historical data of the ammonia synthesis device.

12. The control system according to claim 11 further includes an output curve acquisition module configured to acquire an output curve of the renewable energy power generation device in minutes, wherein, The optimization module considers the output curve.

13. The control system according to claim 12, wherein, The optimization parameters include at least one of the scale of the wind power generation device, the scale of the photovoltaic power generation device, the scale of the hydrogen production device, the scale of the ammonia synthesis device, the scale of the energy storage device, the scale of the hydrogen storage device, and the minimum load of the hydrogen production device and the ammonia synthesis device.

14. The control system according to claim 9, wherein, The learning rate η of the gradient descent method takes values as follows: S41: Set an initial learning rate η0; S42: The learning rate η increases by m%, where 0 < m < 30; S43: Determine whether the IRR is satisfied t <IRR t-1 , where IRR t and IRR t-1 are the IRR values at times t and t - 1 respectively; S44: If the IRR is satisfied t <IRR t-1 , then divide the learning rate η by n, where n > 1; otherwise, return to step S42; S45: Determine whether the exit condition is satisfied; If yes, the optimization process is completed; if no, return to step S42, IRR is the internal rate of return calculated according to the cash flow, and n is the financial calculation period.

15. The control system according to claim 14, wherein, η0 is 0.4 - 0.6, 1 < m < 10, 1.2 < n < 3.

16. A computer-readable medium storing computer program code, which when executed by a processor causes the processor to execute the control method according to any one of claims 1 to 7.

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