Intelligent group control method, device and electronic equipment for multi-tank hydrogen production system of renewable energy

Abstract

This invention relates to the field of new energy technology, and particularly to a method, apparatus, and electronic device for intelligent group control of multiple electrolyzers in a renewable energy hydrogen production system. The method includes: acquiring the renewable energy power of the renewable energy hydrogen production system; inputting the renewable energy power into a target optimization model, which outputs the battery's charge / discharge power and state of charge, as well as the operating power and start-up, shutdown, and production states of multiple electrolyzers in the renewable energy hydrogen production system; and correcting the cold start state, standby state, and shutdown state of the multiple electrolyzers in the renewable energy hydrogen production system based on their operating power and operating states. This solves the problems of frequent start-ups and shutdowns in renewable energy hydrogen production systems and the increased energy consumption and shortened equipment lifespan caused by non-optimal operation of electrolyzers in related technologies.

Description

Technical Field

[0001] This invention relates to the field of new energy technology, and in particular to a method, apparatus and electronic equipment for intelligent group control of multiple tanks in a renewable energy hydrogen production system. Background Technology

[0002] With the rapid development of renewable energy hydrogen production technology, systems that directly power hydrolysis cells with fluctuating power sources such as renewable energy and photovoltaics face severe challenges: frequent start-ups and shutdowns caused by power fluctuations and low-load operation lead to a surge in energy consumption and a shortened equipment lifespan, which seriously restricts the economic viability of green hydrogen.

[0003] In related technologies, simple start-stop logic control or array rotation control strategies are employed, lacking comprehensive consideration of power fluctuation buffering capabilities and hydrogen production response characteristics, making it difficult to balance system operating efficiency and response flexibility. Furthermore, while energy storage systems possess energy regulation capabilities, the coordinated control between them and hydrogen production systems lacks a unified optimization framework, failing to maximize overall benefits at the system level. Summary of the Invention

[0004] This invention provides a method, apparatus, and electronic device for intelligent group control of multiple cells in a renewable energy hydrogen production system, in order to solve the problems of frequent start-up and shutdown of renewable energy hydrogen production systems and increased energy consumption and shortened equipment life caused by the electrolyzer operating at non-optimal points.

[0005] The first aspect of this invention provides a multi-cell intelligent group control method for a renewable energy hydrogen production system, comprising the following steps: acquiring the renewable energy power of the renewable energy hydrogen production system; inputting the renewable energy power into a target optimization model, wherein the target optimization model outputs the charge / discharge power and state of charge of the battery, as well as the operating power and start-up time, shutdown time, and production status of multiple electrolyzers in the renewable energy hydrogen production system; correcting the cold start state, standby state, and shutdown state of multiple electrolyzers in the renewable energy hydrogen production system based on the operating power and start-up time, shutdown time, and production status of multiple electrolyzers in the renewable energy hydrogen production system, and finally outputting a complete multi-electrolyzer production plan.

[0006] Optionally, the target optimization model includes a first optimization model and a second optimization model. The renewable energy power is input into the first optimization model, and the first optimization model outputs the smoothed renewable energy power, as well as the battery's charge and discharge power and state of charge. The smoothed renewable energy power is input into the second optimization model, and the second optimization model outputs the operating power of multiple electrolyzers and the start-up time, shutdown time, and production status in the operating status.

[0007] Optionally, the first optimization model includes: a first objective function and a first constraint model, wherein the first objective function is a function aimed at minimizing the tracking error of the battery for the high-frequency reconstructed set data and ensuring that the smoothed renewable energy power is not lower than the minimum operating power of the electrolyzer, and the first constraint model includes charge and discharge power constraints and charge and discharge state constraints, smoothed renewable energy constraints, battery state of charge equation, limit constraints, and state of charge adjustment margin constraints.

[0008] Optionally, the first optimization model smooths the fluctuations in the renewable energy power, including: performing empirical mode decomposition on the renewable energy power to generate intrinsic mode functions and residuals of different frequencies; performing high-frequency reconstruction from the high-frequency fluctuation components to the low-frequency fluctuation components one by one according to the intrinsic mode functions of different frequencies to generate a high-frequency reconstruction data set; optimizing the high-frequency reconstruction data set of battery energy storage tracking renewable energy power through the first optimization model to obtain the smoothed renewable energy power, and generating the battery's charge and discharge power and state of charge.

[0009] Optionally, the second optimization model includes: a second objective function and a second constraint model, wherein the second objective function is a function aimed at minimizing the power curtailment of renewable energy, minimizing the total number of start-ups and shutdowns of the electrolyzer, and maximizing the total hydrogen production of the system, and the second constraint model includes power balance constraints, wind curtailment power boundary constraints, electrolyzer power limit constraints, and electrolyzer state switching constraints.

[0010] Optionally, the step of correcting the cold start state, standby state, and shutdown state of the multiple electrolyzers in the renewable energy hydrogen production system based on their operating power and the start-up time, shutdown time, and production state in the operating state includes: calculating the maximum standby time based on the cumulative energy consumption required for cold start and standby energy consumption of the electrolyzers in the renewable energy hydrogen production system; and correcting the cold start state, standby state, and shutdown state of the multiple electrolyzers in the renewable energy hydrogen production system using the target standby time of the electrolyzers.

[0011] Optionally, the step of correcting the cold start state, standby state, and shutdown state of multiple electrolyzers in the renewable energy hydrogen production system based on the calculated maximum standby time of the electrolyzer includes: calculating the interval between the shutdown time of any electrolyzer and the nearest adjacent startup time; determining whether the interval exceeds the maximum standby time of the electrolyzer; if there is an interval that does not exceed the maximum standby time of the electrolyzer, then correcting the state of the electrolyzer within the interval between the current shutdown time and startup time to the standby state; if there is an interval that exceeds the maximum standby time of the electrolyzer, then maintaining the shutdown state, and setting the state of the electrolyzer for consecutive target times before the startup time to the cold start state; setting the state of the electrolyzer at any non-cold start, running, or standby time to the shutdown state.

[0012] A second aspect of the present invention provides a multi-cell intelligent group control device for a renewable energy hydrogen production system, comprising: an acquisition module for acquiring the renewable energy power of the renewable energy hydrogen production system; a group control module for inputting the renewable energy power into a target optimization model, wherein the target optimization model outputs the charge / discharge power and state of charge of the battery, as well as the operating power and start-up time, shutdown time, and production status of multiple electrolyzers in the renewable energy hydrogen production system; and correcting the cold start state, standby state, and shutdown state of multiple electrolyzers in the renewable energy hydrogen production system according to the operating power and start-up time, shutdown time, and production status of multiple electrolyzers in the renewable energy hydrogen production system, and finally outputting a complete multi-cell production plan.

[0013] A third aspect of the present invention provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to perform the multi-tank intelligent group control method for a renewable energy hydrogen production system as described in the above embodiments.

[0014] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to perform the multi-tank intelligent group control method for a renewable energy hydrogen production system as described in the above embodiments.

[0015] Therefore, the present invention has at least the following beneficial effects:

[0016] This invention utilizes a battery energy storage system to mitigate power fluctuations from renewable energy sources, ensuring a more stable input power to the hydrogen electrolysis system. This not only improves the efficiency of the electrolyzer but also reduces equipment wear caused by power fluctuations. By precisely controlling and correcting the operating power and status of the electrolyzer, the total number of start-ups and shutdowns is reduced, thereby decreasing equipment wear caused by frequent start-ups and shutdowns, extending equipment lifespan, and fully utilizing the power regulation capabilities of the energy storage system. This achieves complementary power regulation between the hydrogen electrolysis system and the energy storage system, improving the overall efficiency and stability of the system.

[0017] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0018] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0019] Figure 1 A flowchart illustrating a multi-tank intelligent group control method for a renewable energy hydrogen production system according to an embodiment of the present invention;

[0020] Figure 2 This is an example diagram of the standby-cold start-stop correction process of an electrolytic cell according to an embodiment of the present invention;

[0021] Figure 3 A schematic diagram of the power output of a renewable energy hydrogen production system provided according to an embodiment of the present invention;

[0022] Figure 4 This is a schematic diagram of the power of multiple electrolytic cells provided according to an embodiment of the present invention;

[0023] Figure 5 This is a schematic diagram of a multi-tank intelligent group control device for a renewable energy hydrogen production system provided in an embodiment of the present invention;

[0024] Figure 6 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present invention. Detailed Implementation

[0025] Embodiments of the present invention are described in detail below. Examples of these embodiments are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0026] The following description, with reference to the accompanying drawings, outlines an embodiment of the present invention of a multi-tank intelligent group control method, apparatus, electronic device, and storage medium for a renewable energy hydrogen production system.

[0027] Specifically, Figure 1 This is a schematic flowchart of a multi-tank intelligent group control method for a renewable energy hydrogen production system provided in an embodiment of the present invention.

[0028] like Figure 1 As shown, the multi-tank intelligent group control method for this renewable energy hydrogen production system includes the following steps:

[0029] In step S101, the renewable energy power of the renewable energy hydrogen production system is obtained.

[0030] It is understood that the embodiments of the present invention can obtain the renewable energy power of the renewable energy hydrogen production system in order to smooth the fluctuations in renewable energy power in the future.

[0031] In step S102, the renewable energy power is input into the target optimization model, and the target optimization model outputs the battery's charge and discharge power and state of charge, as well as the operating power and start-up time, shutdown time and production status of multiple electrolyzers in the renewable energy hydrogen production system.

[0032] It is understood that the embodiments of the present invention can process the input renewable energy power through a target optimization model, which can effectively smooth out its inherent intermittency and volatility. Based on the battery charging and discharging power and state of charge output by the optimization model, it can help regulate the power balance of the entire system, ensure that the electrolyzer obtains a more stable power supply, thereby improving the stability and efficiency of the hydrogen production process. By precisely controlling the electrolyzer's operating power, start-up time, shutdown time, and production status, not only can the hydrogen production be maximized, but the number of electrolyzer start-ups and shutdowns can also be reduced, equipment wear can be reduced, and service life can be extended.

[0033] In this embodiment of the invention, the target optimization model includes a first optimization model and a second optimization model. The renewable energy power is input into the first optimization model, and the first optimization model outputs the smoothed renewable energy power, as well as the charge and discharge power and state of charge of the battery. The smoothed renewable energy power is input into the second optimization model, and the second optimization model outputs the operating power of multiple electrolyzers and the start-up time, shutdown time and production status in the operating status.

[0034] It is understood that the first optimization model in this embodiment of the invention smooths the original renewable energy power and optimizes the charging and discharging power and state of charge of the battery energy storage system to achieve stability control on the power side. The second optimization model further optimizes the operating power, start-up and shutdown, and production status of multiple electrolyzers based on the smoothed power to achieve efficient scheduling on the hydrogen production side. Through hierarchical modeling, power scheduling and production scheduling are decoupled, improving the controllability and optimization accuracy of the system. It avoids convergence difficulties caused by excessive complexity of single model optimization, significantly reduces frequent adjustments or unplanned shutdowns of electrolyzers caused by renewable energy fluctuations, improves electrolysis efficiency and hydrogen production stability, achieves coordinated scheduling of multiple electrolyzers, and enhances system flexibility and capacity.

[0035] In this embodiment of the invention, the first optimization model smooths the fluctuations in renewable energy power, including: performing empirical mode decomposition on the renewable energy power to generate intrinsic mode functions and residuals of different frequencies; performing high-frequency reconstruction from the high-frequency fluctuation components to the low-frequency fluctuation components one by one according to the intrinsic mode functions of different frequencies to generate a high-frequency reconstruction data set; optimizing the high-frequency reconstruction data set of battery energy storage tracking renewable energy power through the first optimization model to obtain the smoothed renewable energy power, and generating the battery's charge and discharge power and state of charge.

[0036] It is understood that the embodiments of the present invention can accurately extract high-frequency fluctuations in the original renewable energy power by layer-by-layer superposition of high-frequency intrinsic mode functions through high-frequency reconstruction, thereby improving the smoothing accuracy. The smoothed renewable energy power has higher stability and predictability, providing stable and controllable input power for downstream equipment such as electrolyzers, avoiding frequent start-ups or efficiency reductions caused by fluctuations, reducing system response delays caused by power fluctuations, and improving the adaptability to renewable energy.

[0037] Specifically, EMD (Empirical Mode Decomposition) is a time-frequency analysis method that decomposes complex signals into a series of IMFs (Intrinsic Mode Functions). IMFs better reflect the local fluctuation components of a signal. Performing EMD decomposition on a renewable energy power curve yields the IMFs of the original power curve at different frequencies, expressed as:

[0038] (1)

[0039] In the formula: P wt For renewable energy power; I MF,j For the first j One IMF; res It represents the residual.

[0040] To smooth out the high-frequency fluctuations in the renewable energy power curve, the IMF (Integrated Motion Flow Model) is first reconstructed from the high-frequency fluctuation components to the low-frequency fluctuation components, resulting in... n A reconstructed data set is represented as follows:

[0041] (2)

[0042] In the formula: P IMF,k For the first k One reconstructed data; I high To reconstruct the dataset, I MF,j For the first j One IMF.

[0043] In this embodiment of the invention, the first optimization model includes: a first objective function and a first constraint model. The first objective function is a function that aims to minimize the tracking error of the battery for the high-frequency reconstructed set data and ensure that the smoothed renewable energy power is not lower than the minimum operating power of the electrolyzer. The first constraint model includes charge and discharge power constraints and charge and discharge state constraints, smoothed renewable energy constraints, battery state of charge equation, limit constraints, and state of charge adjustment margin constraints.

[0044] It is understood that the embodiments of the present invention can optimize the battery charging and discharging power, enabling the battery to accurately track the high-frequency fluctuation components of renewable energy power, thereby effectively smoothing the fluctuations, ensuring a more stable power output to the electrolyzer, avoiding frequent start-ups or efficiency reductions in the electrolyzer due to fluctuations, ensuring that the smoothed power meets the minimum operating requirements of the electrolyzer, avoiding electrolyzer shutdowns due to insufficient power, limiting the maximum charging and discharging power and state of charge of the battery, thereby preventing battery overload operation, extending battery life, avoiding thermal runaway or equipment damage, ensuring that the smoothed renewable energy power is always positive, providing effective input power to the electrolyzer, supporting its stable operation, limiting the state of charge within a safe range and setting adjustment margins, ensuring the flexibility and robustness of the system in dynamic environments.

[0045] It should be noted that this invention is illustrated using renewable energy power as an example, and is not intended to be specific.

[0046] Specifically, the structure of the first optimization model is as follows:

[0047] (1) Objective function:

[0048] 1) Sub-objective 1 takes the battery's tracking error with high-frequency reconstructed set data as the objective, and constructs a set of optimization problems for smoothing battery power fluctuations, represented as:

[0049] (3)

[0050] In the formula: T It is a set of discrete moments; and These represent the battery charging and discharging power at time t, respectively. I MF,j For the first j One IMF, P IMF,k For the first k Reconstructed data, Let n be the nth objective function.

[0051] 2) Sub-objective 2 sets a penalty target based on the smoothed power range and matches it with the dimension of sub-objective 1, as follows:

[0052] (4)

[0053] in,

[0054] (5)

[0055] In the formula: This represents a penalty state in the low-power range (0-1 variables). This represents the low-power interval penalty state at time t; P sys,min This represents the minimum operating power of the electrolysis hydrogen production system. P wt (t) represents the renewable energy power at time t. and t represents the battery charging and discharging power at time t, respectively.

[0056] The battery power optimization objective set is a dynamically normalized weighted sum of the above two sub-objectives, expressed as:

[0057] (6)

[0058] In the formula: For the first j Weights of the sub-objective function for battery power optimization Let be the theoretical minimum value of the j-th sub-objective. The theoretical maximum value of the j-th sub-objective. Let j be the value of the sub-objective function. This is the combined error threshold for the values ​​of each sub-objective function.

[0059] (2) Constraint Model

[0060] In battery power optimization, both charge / discharge power constraints and charge / discharge state constraints must be met, as follows:

[0061] (7)

[0062] In the formula: and These represent the battery charging and discharging states at time t (0-1 variables). P bat,max This refers to the battery's maximum charge and discharge power.

[0063] The smoothed renewable energy power, obtained from power balance calculations, is expressed as follows:

[0064] (8)

[0065] In the formula: P net (t) represents the renewable energy power at time t after smoothing, P bc (t) and P bd (t) represents the battery charging and discharging power at time t, respectively, P wt (t) represents the renewable energy power at time t.

[0066] The smoothed renewable energy power will subsequently be used as the input power for the electrolysis hydrogen production system, and must be non-negative, expressed as:

[0067] (9)

[0068] The battery state of charge equation and its limit constraints are expressed as follows:

[0069] (10)

[0070] In the formula: S OC (t) represents the battery state of charge at time t; S OC,min and S OC,max These are the lower and upper limits of the battery's state of charge. and These refer to the battery charge and discharge efficiency; E bat This refers to the battery's rated capacity; Δ T For discrete duration, and These represent the battery charging and discharging power at time t-1, respectively.

[0071] At the last moment of any optimization window, the state of charge adjustment margin constraint must be satisfied, expressed as:

[0072] (11)

[0073] In the formula: For battery capacity adjustment margin, S OC,min and S OC,max S represents the upper and lower limits of the battery's state of charge. OC (t) end To optimize the battery state of charge at the end of the window.

[0074] In this embodiment of the invention, the second optimization model includes: a second objective function and a second constraint model. The second objective function is a function aimed at minimizing the power curtailment of renewable energy, minimizing the total number of start-ups and shutdowns of the electrolyzer, and maximizing the total hydrogen production of the system. The second constraint model includes power balance constraints, wind curtailment power boundary constraints, electrolyzer power limit constraints, and electrolyzer state switching constraints.

[0075] It is understood that the embodiments of the present invention can minimize the power curtailment of renewable energy, thereby maximizing the absorption of renewable energy power and reducing wind and solar curtailment by optimizing the power allocation of electrolyzers and the coordinated scheduling of energy storage systems. This improves the utilization rate of renewable energy, reduces energy waste, and enhances the overall energy efficiency of the system. Minimizing the total number of start-ups and shutdowns of the electrolyzers can reduce equipment wear and increased energy consumption caused by frequent start-ups and shutdowns, extending equipment life. Maximizing the total hydrogen production of the system can maximize the hydrogen production efficiency of the electrolyzers while meeting power balance and equipment operation constraints, thereby maximizing economic benefits and improving the return on investment. Power balance constraints support the dynamic adjustment of electrolyzer power to adapt to the volatility of renewable energy. Wind curtailment power boundary constraints can improve the renewable energy absorption rate. Electrolyzer power limit constraints can ensure that the electrolyzers operate within a safe operating range. Electrolyzer state switching constraints avoid state conflicts and ensure the consistency of system logic.

[0076] Specifically, the structure of the second optimization model is as follows:

[0077] (1) Objective function

[0078] The overall objective function comprises three sub-optimization objectives. The first sub-optimization objective is to minimize the curtailment of renewable energy power in order to maximize the utilization of renewable energy, expressed as:

[0079] (12)

[0080] In the formula: This is the first optimization sub-objective; Pwt,loss (t) represents the amount of renewable energy curtailed at time t. t is time; min is the minimum.

[0081] The second sub-optimization objective is to minimize the total number of start-ups and shutdowns of the electrolyzer, expressed as:

[0082] (13)

[0083] In the formula: f 2 represents the second optimization sub-objective; N This represents the total number of electrolytic cells; For the first i The operating status of the TECHNOLOGY electrolytic cell at time t; For the first i The shutdown state of the electrolytic cell at time t; max represents maximization.

[0084] The third sub-optimization objective is to maximize the total hydrogen production of the system, in order to achieve optimal power allocation among multiple electrolyzers, expressed as:

[0085] (14)

[0086] In the formula: f 3 represents the third optimization sub-objective; Q el,i (t) For the first i Hydrogen production of the electrolyzer at time t; Δ T Sampling time.

[0087] The overall objective function is a dynamically normalized weighted sum of the three sub-optimization sub-objectives, expressed as:

[0088] (15)

[0089] In the formula: G To optimize the overall objective; For the first j Each optimization sub-objective weight coefficient Let be the theoretical minimum value of the j-th optimization sub-objective. The theoretical maximum value of the j-th optimization sub-objective is... Let be the value of the j-th optimization sub-objective function.

[0090] (2) Constraint Model

[0091] In the optimization problem, the system must satisfy power balance, expressed as:

[0092] (16)

[0093] In the formula: Power connected to the grid; This refers to the power output of the wind curtailment. For the first i The operating power of the electrolytic cell.

[0094] When the wind curtailment power is less than the smoothed renewable energy power value at any given time, it is expressed as:

[0095] (17)

[0096] To ensure the safe operation of the electrolytic cell, the electrolytic cell must meet power limit constraints, as shown below:

[0097] (18)

[0098] In the formula: For the first i Minimum operating power of the TSMC electrolytic cell; For the first i Taiwan Electrolytic Cell's maximum operating power For the first i Operating status of the Taiwan electrolytic cell.

[0099] An electrolytic cell can only be in one operating state at any given time, as represented by:

[0100] (19)

[0101] In the formula: For the first i TECO electrolytic cell operating status Let represent the operating state of the i-th electrolytic cell at time t. Let represent the operating state of the i-th electrolytic cell at time t-1.

[0102] The hot start time, cold start time, and shutdown time of the electrolytic cell can be represented by switching between adjacent times of production, cold start, and standby states, as follows:

[0103] (20)

[0104] in, For the first i TECO electrolytic cell operating status Let represent the operating state of the i-th electrolytic cell at time t. Let represent the operating state of the i-th electrolytic cell at time t-1. For the first j The weight coefficients of each optimization sub-objective.

[0105] In step S103, the cold start state, standby state, and shutdown state of the multiple electrolyzers in the renewable energy hydrogen production system are corrected according to the operating power and start-up time, shutdown time, and production state of the multiple electrolyzers in the renewable energy hydrogen production system, and finally a complete multi-electrolyzer production plan is output.

[0106] It is understood that the embodiments of the present invention can make corrections for cold start, standby and shutdown states based on the operating power, start-up time, shutdown time and production status of multiple electrolyzers in the renewable energy hydrogen production system. The resulting multi-electrolyzer production plan achieves load balancing of power distribution, maximizes hydrogen production, reduces cold start energy consumption and equipment wear, and reduces renewable energy abandonment, significantly improving the economy, stability and sustainability of the renewable energy hydrogen production system.

[0107] In this embodiment of the invention, the cold start state, standby state, and shutdown state of multiple electrolyzers in the renewable energy hydrogen production system are corrected based on the operating power and start-up time, shutdown time, and production state of the electrolyzers in the renewable energy hydrogen production system. This includes: calculating the maximum standby time based on the cumulative energy consumption required for cold start and standby energy consumption of the electrolyzers in the renewable energy hydrogen production system; and correcting the cold start state, standby state, and shutdown state of multiple electrolyzers in the renewable energy hydrogen production system using the target standby time of the electrolyzers.

[0108] It is understood that the embodiments of the present invention can calculate the maximum standby time based on the cumulative energy consumption required for cold start and the standby energy consumption, and combine the target standby time to correct the cold start, standby and shutdown states of the electrolyzer, which can significantly improve the operating efficiency and economy of the renewable energy hydrogen production system.

[0109] In this embodiment of the invention, the cold start state, standby state, and shutdown state of multiple electrolyzers in a renewable energy hydrogen production system are corrected based on the calculated maximum standby time of the electrolyzers. This includes: calculating the interval between the shutdown time of any electrolyzer and the nearest adjacent startup time; determining whether the interval exceeds the maximum standby time of the electrolyzer; if the interval does not exceed the maximum standby time of the electrolyzer, the state of the electrolyzer within the interval between the current shutdown time and startup time is corrected to the standby state; if the interval exceeds the maximum standby time of the electrolyzer, the shutdown state is maintained, and the state of the electrolyzer at consecutive target times before the startup time is set to the cold start state; and setting any non-cold start, running, or standby state of the electrolyzer is set to the shutdown state.

[0110] It is understood that the embodiments of the present invention can significantly reduce the frequency of cold starts by setting a maximum standby time and prioritizing hot starts. Cold start energy consumption is only triggered when necessary, avoiding accelerated lifespan degradation caused by frequent cold starts and extending the lifespan of the electrolyzer. Unnecessary standby periods are clearly marked as shutdown states to avoid the electrolyzer maintaining standby power consumption during low-power periods. Combined with battery energy storage to supplement power during low-power periods to the minimum operating power of the electrolyzer, zero abandonment of renewable energy is achieved, significantly improving the economy, stability and sustainability of renewable energy hydrogen production systems.

[0111] Specifically, based on the optimization results of the second stage regarding electrolytic cell power, operating status, start-up time, and shutdown time, the third stage modifies the electrolytic cell cold start, standby state, and shutdown state plans. A maximum allowable standby time for the electrolytic cell is set. If this time is exceeded, the standby power consumption of the electrolytic cell will exceed the energy consumption required for the cold start process. This value can be calculated based on the ratio of the cumulative energy consumption required for cold start to the standby energy consumption, expressed as:

[0112] ;(twenty one)

[0113] In the formula: P cs This refers to the cold start power of the electrolytic cell; P sb This refers to the standby power of the electrolytic cell. T cs This refers to the cold start time of the electrolytic cell; T sb This represents the longest standby time for the electrolytic cell. This is for floor function.

[0114] The logic flow for correcting the working status plan of electrolytic hydrogen production is as follows: Figure 2 As shown. The longest standby time of the electrolytic cell is calculated based on equation (21). T sb First, calculate whether the nearest start-up time adjacent to the shutdown time of any electrolytic cell exceeds [a certain threshold]. T sb Determine whether this startup is allowed as a standby warm start; if it does not exceed [a certain threshold], [the system will proceed]. T sb Then the standby state during this period δ el,sb Set to 1, if it exceeds T sb If this is a cold start, then the time before this start time will be considered a cold start. T cs Cold start status at any moment δ el,cs Set to 1. Based on the above correction results for the cold start process and standby state of the electrolytic cell, set the shutdown state of the electrolytic cell for any non-cold start, running, and standby time. δ el,stop The value is 1.

[0115] In summary, this invention proposes a multi-tank intelligent group control method, device, and electronic equipment for a renewable energy hydrogen production system. It fully utilizes the power regulation capability of the energy storage system to achieve complementary power regulation between the electrolysis hydrogen production and energy storage systems, thereby improving the overall efficiency and stability of the system and providing key support for the deep consumption of renewable energy and efficient hydrogen production.

[0116] The multi-cell intelligent group control method for renewable energy hydrogen production systems proposed in this invention utilizes a battery energy storage system to smooth out power fluctuations in renewable energy sources, ensuring a more stable input power supplied to the electrolysis hydrogen production system. This not only helps improve the working efficiency of the electrolyzers but also reduces equipment wear caused by power fluctuations. By precisely controlling and correcting the operating power and operating status of the electrolyzers, the total number of start-ups and shutdowns of the electrolyzers is reduced, thereby reducing equipment wear caused by frequent start-ups and shutdowns, helping to extend equipment lifespan. It also fully utilizes the power regulation capability of the energy storage system to achieve complementary power regulation between the electrolysis hydrogen production system and the energy storage system, improving the overall efficiency and stability of the system.

[0117] The multi-tank intelligent group control method for renewable energy hydrogen production systems of the present invention will be described in detail below with specific embodiments, as follows:

[0118] This invention employs an optimization framework to uniformly optimize hydrogen production power, electrolyzer power, electrical energy storage power, and the sequential production status of the electrolyzer. The optimization process uses a rolling solution method to establish the optimization objective function and constraint model respectively.

[0119] The optimization objectives include two sub-optimization objectives in the first stage and three sub-optimization objectives in the second stage. In the first stage: the first sub-objective is the tracking error of the high-frequency reconstructed data set, and the second sub-objective is a penalty objective for the smoothed power range. In the second stage: the first sub-objective is to minimize the curtailment of renewable energy power, the second sub-objective is to minimize the total number of start-ups and shutdowns of the electrolysis hydrogen production system units, and the third sub-objective is to maximize the total hydrogen production of the system. The constraint model includes power balance constraints, hydrogen production power curtailment constraints, electrolyzer power constraints, electrolyzer state constraints, electrolyzer time-series production state constraints, energy storage power constraints, energy storage state of charge constraints, and energy storage state of charge adjustment margin constraints. Mathematical programming methods are used to solve the model, obtaining the operating power of each device in the system and the state of the electrolyzer.

[0120] To verify the effectiveness and superiority of the method of this invention, this section uses a wind power hydrogen production system and conducts a specific implementation and analysis of the method through a typical daily calculation example. In the example, the installed wind power capacity is set to 25MW. To ensure that the electrolysis hydrogen production subsystem has the capacity to fully absorb wind power, five 5MW electrolyzers are configured with the same capacity, and the battery energy storage capacity is configured to 1.75MWh. The sampling time is set to 15min. The system parameters and economic parameters set in the example are shown in Tables 1 and 2 below.

[0121] Table 1 Example Parameters

[0122]

[0123] Table 2 Economic Parameters

[0124]

[0125] This invention reduces wind power curtailment and increases total hydrogen production by optimizing the power allocation of the electrolyzer, thereby decreasing the total number of start-ups and shutdowns. Furthermore, it reduces cold start and standby energy consumption by optimizing standby and shutdown states. With battery energy storage configured, [the energy is then distributed as follows]. Figure 3 and 4 It can be seen that the battery smooths out the fluctuations in wind power, making the electrolyzer's operating power smoother. At the same time, during the period from 06:00 to 07:00, when wind power is in a low power range and has not reached the minimum operating power of the electrolyzer, the battery discharges to supplement the power to the minimum operating power of the electrolyzer, thus realizing the full absorption of wind power.

[0126] Next, referring to the accompanying drawings, a multi-tank intelligent group control device for a renewable energy hydrogen production system according to an embodiment of the present invention is described.

[0127] Figure 5 This is a block diagram of a multi-tank intelligent group control device for a renewable energy hydrogen production system according to an embodiment of the present invention.

[0128] like Figure 5 As shown, the multi-tank intelligent group control device 10 of the renewable energy hydrogen production system includes: an acquisition module 100 and a group control module 200.

[0129] The acquisition module 100 is used to acquire the renewable energy power of the renewable energy hydrogen production system; the group control module 200 is used to input the renewable energy power into the target optimization model, the target optimization model outputs the battery's charge and discharge power and state of charge, as well as the operating power and start-up time, shutdown time and production status of multiple electrolyzers in the renewable energy hydrogen production system; based on the operating power and start-up time, shutdown time and production status of multiple electrolyzers in the renewable energy hydrogen production system, the cold start status, standby status and shutdown status of multiple electrolyzers in the renewable energy hydrogen production system are corrected, and finally a complete multi-electrolyzer production plan is output.

[0130] It should be noted that the foregoing explanation of the embodiment of the multi-tank intelligent group control method for renewable energy hydrogen production system also applies to the multi-tank intelligent group control device of the renewable energy hydrogen production system in this embodiment, and will not be repeated here.

[0131] The multi-cell intelligent group control device for renewable energy hydrogen production system proposed in this embodiment of the invention utilizes a battery energy storage system to smooth out power fluctuations in renewable energy, ensuring a more stable input power supplied to the electrolysis hydrogen production system. This not only helps improve the working efficiency of the electrolyzers but also reduces equipment wear caused by power fluctuations. By precisely controlling and correcting the operating power and operating status of the electrolyzers, the total number of start-ups and shutdowns of the electrolyzers is reduced, thereby reducing equipment wear caused by frequent start-ups and shutdowns, helping to extend equipment lifespan. It also fully utilizes the power regulation capability of the energy storage system to achieve complementary power regulation between the electrolysis hydrogen production system and the energy storage system, improving the overall efficiency and stability of the system.

[0132] Figure 6 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. The electronic device may include:

[0133] The memory 601, the processor 602, and the computer program stored on the memory 601 and capable of running on the processor 602.

[0134] When the processor 602 executes the program, it implements the multi-slot intelligent group control method for the renewable energy hydrogen production system provided in the above embodiments.

[0135] Furthermore, electronic devices also include:

[0136] Communication interface 603 is used for communication between memory 601 and processor 602.

[0137] The memory 601 is used to store computer programs that can run on the processor 602.

[0138] The memory 601 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0139] If the memory 601, processor 602, and communication interface 603 are implemented independently, then the communication interface 603, memory 601, and processor 602 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 6 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0140] Optionally, in a specific implementation, if the memory 601, processor 602, and communication interface 603 are integrated on a single chip, then the memory 601, processor 602, and communication interface 603 can communicate with each other through an internal interface.

[0141] Processor 602 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention.

[0142] This invention also provides a computer-readable storage medium storing a computer program or instructions thereon, which, when executed by a processor, implements the above-described intelligent group control method for multi-slot hydrogen production systems based on renewable energy.

[0143] This invention also provides a computer program product, including a computer program or instructions, which, when executed, implement the above-described intelligent group control method for multi-tank hydrogen production systems based on renewable energy.

[0144] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0145] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0146] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0147] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0148] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

Claims

1. A method for intelligent group control of multiple tanks in a renewable energy hydrogen production system, characterized in that, Includes the following steps: Obtain renewable energy power from renewable energy hydrogen production systems; The renewable energy power is input into a target optimization model, which outputs the battery's charge / discharge power and state of charge, as well as the operating power and start-up, shutdown, and production status of multiple electrolyzers in the renewable energy hydrogen production system. The target optimization model includes a first optimization model and a second optimization model. The renewable energy power is input into the first optimization model, which outputs smoothed renewable energy power, as well as the battery's charge / discharge power and state of charge. The smoothed renewable energy power is input into the second optimization model, which outputs the operating power and start-up, shutdown, and production status of multiple electrolyzers. The first optimization model includes: a first target... The system comprises a target function and a first constraint model. The first objective function aims to minimize the tracking error of the battery for the high-frequency reconstructed data set and ensure that the smoothed renewable energy power is not lower than the minimum operating power of the electrolyzer. The first constraint model includes charging and discharging power constraints and charging and discharging state constraints, smoothed renewable energy constraints, battery state of charge equation, limit constraints, and state of charge adjustment margin constraints. The second optimization model comprises a second objective function and a second constraint model. The second objective function aims to minimize the renewable energy curtailment power, minimize the total number of electrolyzer start-ups and shutdowns, and maximize the total hydrogen production of the system. The second constraint model includes power balance constraints, wind curtailment power boundary constraints, electrolyzer power limit constraints, and electrolyzer state switching constraints. Based on the operating power and start-up, shutdown, and production status of multiple electrolyzers in the renewable energy hydrogen production system, the cold start, standby, and shutdown states of the multiple electrolyzers in the renewable energy hydrogen production system are corrected, ultimately outputting a complete multi-electrolyzer production plan. The correction of the cold start, standby, and shutdown states of the multiple electrolyzers in the renewable energy hydrogen production system based on their operating power and start-up, shutdown, and production status includes: calculating the maximum standby time based on the cumulative energy consumption required for cold start and standby energy consumption of the electrolyzers in the renewable energy hydrogen production system; and correcting the cold start, standby, and shutdown states of the multiple electrolyzers in the renewable energy hydrogen production system using the target standby time of the electrolyzers.

2. The multi-tank intelligent group control method for a renewable energy hydrogen production system according to claim 1, characterized in that, The first optimization model smooths out fluctuations in the renewable energy power, including: The renewable energy power is subjected to empirical mode decomposition to generate intrinsic mode functions and residuals of different frequencies; Based on the intrinsic mode functions of different frequencies, high-frequency wave components are successively reconstructed into low-frequency wave components to generate a high-frequency reconstructed data set. The smoothed renewable energy power is obtained by optimizing the high-frequency reconstructed data set of battery energy storage tracking renewable energy power using the first optimization model, and the battery's charge and discharge power and state of charge are generated.

3. The multi-tank intelligent group control method for a renewable energy hydrogen production system according to claim 1, characterized in that, The method of correcting the cold start state, standby state, and shutdown state of multiple electrolyzers in the renewable energy hydrogen production system using the target standby time of the electrolyzer includes: Calculate the time interval between the shutdown time of any electrolytic cell and the nearest adjacent startup time; Determine whether the interval duration exceeds the maximum standby time of the electrolytic cell; If there is an interval that does not exceed the maximum standby time of the electrolytic cell, then the state of the electrolytic cell within the interval between the current shutdown time and the startup time is corrected to the standby state. If the interval exceeds the maximum standby time of the electrolytic cell, the cell remains in a stopped state, and the state of the electrolytic cell for consecutive target times before the start-up time is set to a cold start state. Set the electrolytic cell to a shutdown state at any non-cold start, running, or standby time.

4. A multi-tank intelligent group control device for a renewable energy hydrogen production system, characterized in that, include: The acquisition module is used to acquire the renewable energy power of the renewable energy hydrogen production system; A group control module is used to input the renewable energy power into a target optimization model. The target optimization model outputs the battery's charge / discharge power and state of charge, as well as the operating power and start-up, shutdown, and production status of multiple electrolyzers in the renewable energy hydrogen production system. The target optimization model includes a first optimization model and a second optimization model. The renewable energy power is input into the first optimization model, which outputs the smoothed renewable energy power, as well as the battery's charge / discharge power and state of charge. The smoothed renewable energy power is input into the second optimization model, which outputs the operating power and start-up, shutdown, and production status of multiple electrolyzers. The first optimization model includes a first objective function and a first constraint model. The first objective function aims to minimize the battery's tracking error for high-frequency reconstructed data and ensure that the smoothed renewable energy power is not lower than the minimum operating power of the electrolyzers. The first constraint model includes charge / discharge power constraints, charge / discharge state constraints, smoothed renewable energy constraints, a battery state of charge equation, limit constraints, and state of charge adjustment margin constraints. The optimization model includes a second objective function and a second constraint model. The second objective function aims to minimize renewable energy curtailment power, minimize the total number of electrolyzer start-ups and shutdowns, and maximize the total hydrogen production of the system. The second constraint model includes power balance constraints, wind curtailment power boundary constraints, electrolyzer power limit constraints, and electrolyzer state switching constraints. Based on the operating power and start-up, shutdown, and production states of multiple electrolyzers in the renewable energy hydrogen production system, the cold start state, standby state, and shutdown state of the multiple electrolyzers in the renewable energy hydrogen production system are corrected. The final output is a complete multi-electrolyzer production plan. The step of correcting the cold start, standby, and shutdown states of the multiple electrolyzers in the renewable energy hydrogen production system based on their operating power and the start-up, shutdown, and production states includes: calculating the maximum standby time based on the cumulative energy consumption required for cold start and standby energy consumption of the electrolyzers in the renewable energy hydrogen production system; and correcting the cold start, standby, and shutdown states of the multiple electrolyzers in the renewable energy hydrogen production system using the target standby time of the electrolyzers.

5. An electronic device, characterized in that, include: The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the multi-tank intelligent group control method for a renewable energy hydrogen production system as described in any one of claims 1-3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the multi-tank intelligent group control method for renewable energy hydrogen production systems as described in any one of claims 1-3.

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