Renewable energy hydrogen production system multi-tank intelligent group control method and device and electronic equipment

Abstract

The invention relates to the technical field of new energy, in particular to a multi-tank intelligent group control method and device for a renewable energy source hydrogen production system and electronic equipment, and the method comprises the steps that renewable energy source power of the renewable energy source hydrogen production system is obtained; the renewable energy power is input into a target optimization model, and the target optimization model outputs the charging and discharging power and the charge state of a battery, the corresponding operation power of a plurality of electrolytic cells in the renewable energy hydrogen production system and the starting time, the shutdown time and the production state in the operation state; and correcting a cold start state, a standby state and a shutdown state in the operation states of the plurality of electrolytic cells in the renewable energy hydrogen production system according to the corresponding operation power of the plurality of electrolytic cells in the renewable energy hydrogen production system and the start time, the shutdown time and the production state in the operation states. Therefore, the problems of high energy consumption, short service life of equipment and the like caused by frequent start and stop of a renewable energy hydrogen production system and operation of the electrolytic cell at a non-optimal point in the related technology are solved.

Description

Technical Field

[0001] The present invention relates to the field of new energy technology, and in particular to a multi-tank intelligent group control method, device and electronic equipment for a renewable energy hydrogen production system. Background Art

[0002] With the rapid development of renewable energy hydrogen production technology, systems that directly supply hydrolyzers with fluctuating power sources such as renewable energy and photovoltaics face severe challenges: frequent starts and stops and low-load operation caused by power fluctuations lead to a surge in energy consumption and shortened equipment life, seriously restricting the economic viability of green hydrogen.

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

[0004] The present invention provides a multi-tank intelligent group control method, device and electronic equipment for a renewable energy hydrogen production system to solve the problems in the related art such as frequent start and stop of the renewable energy hydrogen production system and operation of the electrolyzer at a suboptimal point, resulting in increased energy consumption and shortened equipment life.

[0005] A first aspect of the present invention provides a multi-tank intelligent group control method for a renewable energy hydrogen production system, comprising the following steps: obtaining the renewable energy power of the renewable energy hydrogen production system; inputting the renewable energy power into a target optimization model, the target optimization model outputting the charge and discharge power and charge state of the battery, as well as the operating power corresponding to multiple electrolyzers in the renewable energy hydrogen production system and the start time, shutdown time and production status in the operating state; correcting the cold start state, standby state and shutdown state in the operating state of multiple electrolyzers in the renewable energy hydrogen production system according to the operating power corresponding to the multiple electrolyzers in the renewable energy hydrogen production system and the start time, shutdown time and production status in the operating state, 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 charge and discharge power and charge state 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 electrolytic cells and the start-up time, shutdown time and production status in the operating state.

[0007] Optionally, the first optimization model includes: a first objective function and a first constraint model, wherein the first objective function is a function whose goal is to minimize the tracking error of the battery for high-frequency reconstructed set data, and the smoothed renewable energy power cannot be lower than the minimum operating power of the electrolyzer, and the first constraint model includes charging and discharging power constraints and charging and discharging state constraints, smoothed renewable energy constraints, battery state of charge equations, limit constraints, and state of charge adjustment margin constraints.

[0008] Optionally, the first optimization model smoothes the fluctuations of 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 high-frequency fluctuation components to low-frequency fluctuation components one by one according to the intrinsic mode functions of different frequencies to generate a high-frequency reconstructed data set; optimizing the battery energy storage through the first optimization model to track the renewable energy power high-frequency reconstruction data set to obtain the smoothed renewable energy power, and generate the battery charging and discharging 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 abandoned power of renewable energy, minimizing the total number of electrolyzer starts and stops, and maximizing the total hydrogen production of the system, and the second constraint model includes power balance constraints, wind power abandonment boundary constraints, electrolyzer power limit constraints and electrolyzer state switching constraints.

[0010] Optionally, the method corrects the cold start state, standby state and shutdown state in the operating state of multiple electrolyzers in the renewable energy hydrogen production system according to the operating power corresponding to the multiple electrolyzers in the renewable energy hydrogen production system and the start time, shutdown time and production state in the operating state, including: calculating the maximum standby time according to the cumulative energy consumption required for the cold start of the electrolyzer in the renewable energy hydrogen production system and the standby energy consumption; and correcting the cold start state, standby state and shutdown state in the operating state of multiple electrolyzers in the renewable energy hydrogen production system using the target standby time of the electrolyzer.

[0011] Optionally, the method corrects the cold start state, standby state and shutdown state of the operating states of multiple electrolyzers in the renewable energy hydrogen production system based on the calculated maximum standby time of the electrolyzer, including: calculating the interval time between the shutdown moment of any electrolyzer and the nearest adjacent startup moment; judging whether the interval time exceeds the maximum standby time of the electrolyzer; if there is an interval time that does not exceed the maximum standby time of the electrolyzer, correcting the electrolyzer state within the interval time between the current shutdown moment and the startup moment to the standby state; if there is an interval time that exceeds the maximum standby time of the electrolyzer, maintaining the shutdown state, and setting the electrolyzer state at consecutive target moments before the startup moment to the cold start state; setting the electrolyzer in any non-cold start, operation and standby moment states to the shutdown state.

[0012] A second aspect of the present invention provides an intelligent group control device for multiple tanks in a renewable energy hydrogen production system, including: 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, the target optimization model outputting the charge and discharge power and charge state of the battery, as well as the operating power corresponding to multiple electrolyzers in the renewable energy hydrogen production system and the start time, shutdown time and production status in the operating state; correcting the cold start state, standby state and shutdown state in the operating state of multiple electrolyzers in the renewable energy hydrogen production system according to the operating power corresponding to the multiple electrolyzers in the renewable energy hydrogen production system and the start time, shutdown time and production status in the operating state, and finally outputting a complete multi-electrolyzer production plan.

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

[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 embodiment.

[0015] Therefore, the present invention has at least the following beneficial effects: The embodiments of the present invention can utilize a battery energy storage system to smooth out power fluctuations of renewable energy, ensuring that the input power provided to the electrolysis hydrogen production system is more stable. This not only helps to improve the working efficiency of the electrolyzer, but also reduces equipment losses caused by power fluctuations. By accurately controlling and correcting the operating power and operating status of the electrolyzer, the total number of starts and stops of the electrolyzer is reduced, thereby reducing equipment wear caused by frequent starts and stops, helping to extend the service life of the equipment. The power regulation capability of the energy storage system is fully utilized to achieve power complementary regulation between electrolysis hydrogen production and the energy storage system, thereby improving the efficiency and stability of the overall operation of the system.

[0016] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] 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 in conjunction with the accompanying drawings, in which: Figure 1 This is a flow chart of a multi-tank intelligent group control method for a renewable energy hydrogen production system according to an embodiment of the present invention; Figure 2 This is an example diagram of a correction process for electrolytic cell standby-cold start-shutdown according to an embodiment of the present invention; Figure 3 A power diagram of a renewable energy hydrogen production system according to an embodiment of the present invention; Figure 4 A power diagram of multiple electrolytic cells provided according to an embodiment of the present invention; Figure 5 A schematic diagram of a multi-tank intelligent group control device for a renewable energy hydrogen production system according to an embodiment of the present invention; Figure 6 A schematic structural diagram of an electronic device provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present invention, but are not to be construed as limiting the present invention.

[0019] The following describes, with reference to the accompanying drawings, a method, device, electronic device, and storage medium for intelligent group control of multiple tanks in a renewable energy hydrogen production system according to embodiments of the present invention.

[0020] Specifically, Figure 1A schematic flow chart of a multi-tank intelligent group control method for a renewable energy hydrogen production system provided by an embodiment of the present invention.

[0021] like Figure 1 As shown, the multi-tank intelligent group control method of the renewable energy hydrogen production system includes the following steps: In step S101 , the renewable energy power of the renewable energy hydrogen production system is obtained. It is understandable that the embodiment of the present invention can obtain the renewable energy power of the renewable energy hydrogen production system to facilitate the subsequent smoothing of the fluctuating power of the renewable energy.

[0022] In step S102, the renewable energy power is input into the target optimization model, and the target optimization model outputs the charge and discharge power and charge state of the battery, as well as the operating power and start-up time, shutdown time and production status of the operating state corresponding to multiple electrolyzers in the renewable energy hydrogen production system. It can be understood that the embodiments of the present invention can process the input renewable energy power through the target optimization model, which can effectively smooth out its inherent intermittent and volatility. The battery charging and discharging power and state of charge output based on the optimization model 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 operating power, start-up time, shutdown time and production status of the electrolyzer, not only can the hydrogen production be maximized, but also the number of starts and stops of the electrolyzer can be reduced, equipment wear can be reduced, and the service life can be extended.

[0023] In an embodiment of the present 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 charge state 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 electrolytic cells and the start-up time, shutdown time and production status in the operating state. It can be understood that the first optimization model of the embodiment of the present invention smoothes the original renewable energy power and optimizes the charging and discharging power and charge state of the battery energy storage system to achieve stability control on the power side. The second optimization model further optimizes the operating power and start and stop, production status of multiple electrolyzers on the basis of the smoothed power to achieve efficient scheduling on the hydrogen production side. Through hierarchical modeling, power scheduling and production scheduling are decoupled, the controllability and optimization accuracy of the system are improved, and the convergence difficulties caused by the high optimization complexity of a single model are avoided. The frequent adjustment or unplanned shutdown of the electrolyzer caused by renewable energy fluctuations is significantly reduced, the electrolysis efficiency and hydrogen production stability are improved, the coordinated scheduling of multiple electrolyzers is achieved, and the system flexibility and production capacity are improved.

[0024] In an embodiment of the present invention, the first optimization model smoothes the fluctuations of 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 high-frequency fluctuation components to low-frequency fluctuation components one by one according to the intrinsic mode functions of different frequencies to generate a high-frequency reconstructed data set; optimizing the battery energy storage through the first optimization model to track the renewable energy power high-frequency reconstruction data set to obtain the smoothed renewable energy power, and generate the battery charging and discharging power and charge state.

[0025] It can be understood that the embodiments of the present invention can reconstruct the original renewable energy power through high-frequency reconstruction by layer-by-layer superposition of high-frequency intrinsic mode functions, accurately extract high-frequency fluctuations in the renewable energy power, and improve 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 and stop of equipment or reduced efficiency due to fluctuations, reducing system response delays caused by power fluctuations, and improving adaptability to renewable energy.

[0026] Specifically, EMD (Empirical Mode Decomposition) is a time-frequency analysis method that decomposes complex signals into a series of IMFs (Intrinsic Mode Functions). IMFs can better reflect the local fluctuation components of the signal. Applying EMD decomposition to the renewable energy power curve yields IMFs at different frequencies of the original power curve, expressed as: ; (1) Where: P wt is renewable energy power; I MF,j For the j IMF; r es is the residual.

[0027] In order to realize the smoothing of the high-frequency fluctuation part of the renewable energy power curve by the battery, the IMF is first reconstructed from the high-frequency fluctuation component to the low-frequency fluctuation component one by one. n The dimensionally reconstructed data set is expressed as: ; (2) Where: P IMF,k For the k Reconstructed data; I high To reconstruct the data set, I MF,j For the j IMF.

[0028] In an embodiment of the present invention, the first optimization model includes: a first objective function and a first constraint model, wherein the first objective function is a function whose goal is to minimize the tracking error of the battery for the high-frequency reconstructed set data, and the smoothed renewable energy power cannot be 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 equations, limit constraints, and state of charge adjustment margin constraints.

[0029] It can be understood that the embodiments of the present invention can optimize the battery charging and discharging power so that the battery can accurately track the high-frequency fluctuation component of the renewable energy power, thereby effectively smoothing the fluctuation, ensuring that the power output to the electrolyzer is more stable, avoiding frequent start and stop of the electrolyzer or reduced efficiency due to fluctuations, ensuring that the smoothed power meets the minimum operating requirements of the electrolyzer, avoiding electrolyzer shutdown due to insufficient power, limiting the maximum charge and discharge power and charge and discharge state 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 for the electrolyzer, supporting its stable operation, limiting the state of charge within a safe range and setting an adjustment margin, ensuring the flexibility and robustness of the system in a dynamic environment.

[0030] It should be noted that the present invention is described with reference to renewable energy power as an example, without any specific limitation.

[0031] Specifically, the structure of the first optimization model is: (1) Objective function: 1) Sub-goal 1 takes the tracking error of the battery for high-frequency reconstructed data as the target and constructs a set of optimization problems for smoothing battery power fluctuations, which can be expressed as: ; (3) Where: T is a set of discrete moments; and are the battery charging and discharging powers at time t respectively; I MF,j For the j IMF, P IMF,k For the k Reconstructed data, is the nth objective function.

[0032] 2) Sub-goal 2 sets the penalty target based on the smoothed power range and matches it with the dimension of sub-goal 1, expressed as: ; (4) in, ; (5) Where: is the low power interval penalty state (0-1 variable), is the low power interval penalty state at time t; P sys,min is the minimum operating power of the electrolysis hydrogen production system, P wt (t) is the renewable energy power at time t, and are the battery charging and discharging powers at time t respectively.

[0033] The battery power optimization target set is the dynamic normalized weighted sum of the above two sub-targets, expressed as: ; (6) Where: For the j The weight of the battery power optimization sub-objective function, is the theoretical minimum value of the j-th sub-goal, is the theoretical maximum value of the j-th sub-goal, is the j-th sub-objective function value, is the comprehensive error threshold of each sub-objective function value.

[0034] (2) Constraint model In battery power optimization, the charge and discharge power constraints and charge and discharge state constraints must be met, which can be expressed as: ; (7) Where: and are the battery charging and discharging states at time t (0-1 variables); P bat,max It is the maximum charge and discharge power of the battery.

[0035] The smoothed renewable energy power is calculated based on the power balance and is expressed as: ; (8) Where: P net (t) is the renewable energy power at time t after smoothing, P bc (t) and P bd (t) are the battery charging and discharging power at time t, P wt (t) is the renewable energy power at time t.

[0036] The smoothed renewable energy power will be used as the input power for the electrolysis hydrogen production system and must be non-negative, expressed as: ; (9) The battery state of charge equation and its limit constraints are expressed as: ; (10) Where: S OC (t) is the battery state of charge at time t; S OC,min and S OC,max The lower and upper limits of the battery state of charge; and are the battery charge and discharge efficiency; E bat is the rated capacity of the battery; Δ T is a discrete duration, and are the battery charging and discharging powers at time t-1 respectively.

[0037] At the last moment of any optimization window, the state of charge regulation margin constraint must be satisfied, which is expressed as: ; (11) Where: Adjust margin for battery charge, S OC,min and S OC,max S is the lower and upper limit of the battery state of charge, OC (t end ) is the battery state of charge at the end of the optimization window.

[0038] In an embodiment of the present invention, the second optimization model includes: a second objective function and a second constraint model, wherein the second objective function is a function whose goal is to minimize the abandoned power of renewable energy, minimize the total number of start-up and shutdown times of the electrolyzer, and maximize the total hydrogen production of the system; the second constraint model includes power balance constraints, wind power abandonment boundary constraints, electrolyzer power limit constraints, and electrolyzer state switching constraints.

[0039] It can be understood that the embodiments of the present invention can minimize the abandoned power of renewable energy, thereby maximizing the absorption of renewable energy power and reducing wind and solar power abandonment by optimizing the power distribution of the electrolyzer and the coordinated scheduling of the energy storage system, thereby improving the utilization rate of renewable energy, reducing energy waste, and improving the overall energy efficiency of the system. Minimizing the total number of start-up and shutdown times of the electrolyzer can reduce equipment wear and increased energy consumption caused by frequent start-up and shutdown of the electrolyzer, extend the life of the equipment, and maximizing the total hydrogen production of the system can maximize the hydrogen production efficiency of the electrolyzer while meeting the power balance and equipment operation constraints, thereby maximizing economic benefits and improving the return on investment; power balance constraints support dynamic adjustment of electrolyzer power and adapt to the volatility of renewable energy; wind power abandonment boundary constraints can improve the absorption rate of renewable energy; electrolyzer power limit constraints can ensure that the electrolyzer operates in a safe operating range; electrolyzer state switching constraints avoid state conflicts and ensure system logical consistency.

[0040] Specifically, the structure of the second optimization model is: (1) Objective function The overall objective function includes three sub-optimization objectives. The first sub-optimization objective is to minimize the power curtailment of renewable energy to achieve the maximum absorption of renewable energy, which is expressed as: ; (12) Where: is the first optimization sub-goal; P wt,loss (t) is the renewable energy curtailment power at time t, t is time; min is minimization.

[0041] The second sub-optimization goal is to minimize the total number of starts and stops of the electrolyzer, which can be expressed as: ; (13) Where: f 2 is the second optimization sub-goal; N is the total number of electrolytic cells; For the i The power-on status of the electrolytic cell at time t; For the i The shutdown state of the electrolytic cell at time t; max is the maximum.

[0042] The third sub-optimization objective is to maximize the total hydrogen production of the system to achieve optimal power distribution among multiple electrolyzers, which can be expressed as: ; (14) Where: f 3 is the third optimization sub-goal; Q el,i (t) For thei The hydrogen production of the electrolyzer at time t; Δ T is the sampling time.

[0043] The overall objective function is the dynamic normalized weighted sum of the above three sub-optimization sub-objectives, expressed as: ; (15) Where: G To optimize the overall goal; For the j Optimization sub-objective weight coefficients, is the theoretical minimum value of the jth optimization sub-goal, is the theoretical maximum value of the j-th optimization sub-goal, is the value of the j-th optimized sub-objective function.

[0044] (2) Constraint model In the optimization problem, the system needs to satisfy the power balance, which can be expressed as: ; (16) Where: is the grid-connected power; is the wind power curtailment; For the i The operating power of the electrolyzer.

[0045] At any time, the wind power curtailment is less than the smoothed renewable energy power value, which is expressed as: ; (17) To ensure the safe operation of the electrolyzer, the electrolyzer must meet the power limit constraint, which is expressed as: ; (18) Where: For the i Minimum operating power of an electrolyzer; For the i Maximum operating power of electrolyzer, For the i The operating status of the electrolyzer.

[0046] The electrolytic cell can only be in one operating state at any time, which is expressed as: ; (19) Where: For the i The operating status of the electrolyzer, is the operating status of the i-th electrolytic cell at time t, is the operating status of the i-th electrolytic cell at time t-1.

[0047] The hot start time, cold start time and shutdown time of the electrolyzer can be represented by switching between adjacent moments of production, cold start and standby states, as follows: ; (20) in, For the i The operating status of the electrolyzer, is the operating status of the i-th electrolytic cell at time t, is the operating status of the i-th electrolytic cell at time t-1, For the j The weight coefficients of the optimization sub-objectives.

[0048] In step S103, the cold start state, standby state and shutdown state in the operating states of the multiple electrolyzers in the renewable energy hydrogen production system are corrected according to the operating power corresponding to the multiple electrolyzers in the renewable energy hydrogen production system and the start time, shutdown time and production state in the operating state, and finally a complete multi-electrolyzer production plan is output. It can be understood that the embodiments of the present invention can perform cold start, standby state and shutdown state corrections based on the operating power, start time, shutdown time and production status of multiple electrolyzers in the renewable energy hydrogen production system. The multi-electrolyzer production plan finally generated realizes load balancing of power distribution, maximizes hydrogen production, reduces cold start energy consumption and equipment loss, and reduces renewable energy abandonment, significantly improving the economy, stability and sustainability of the renewable energy hydrogen production system.

[0049] In an embodiment of the present invention, the cold start state, standby state and shutdown state in the operating state of multiple electrolyzers in the renewable energy hydrogen production system are corrected according to the operating power corresponding to the multiple electrolyzers in the renewable energy hydrogen production system and the start time, shutdown time and production state in the operating state, including: calculating the maximum standby time according to the cumulative energy consumption required for the cold start of the electrolyzer in the renewable energy hydrogen production system and the standby energy consumption; and using the target standby time of the electrolyzer to correct the cold start state, standby state and shutdown state in the operating state of multiple electrolyzers in the renewable energy hydrogen production system. It can be understood that the embodiment 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 correct the cold start, standby and shutdown states of the electrolyzer in combination with the target standby time, which can significantly improve the operating efficiency and economy of the renewable energy hydrogen production system.

[0050] In an embodiment of the present invention, the cold start state, standby state and shutdown state in the operating states of multiple electrolyzers in the renewable energy hydrogen production system are corrected according to the calculated maximum standby time of the electrolyzer, including: calculating the interval time between the shutdown moment of any electrolyzer and the nearest adjacent startup moment; judging whether the interval time exceeds the maximum standby time of the electrolyzer; if there is an interval time that does not exceed the maximum standby time of the electrolyzer, the electrolyzer state within the interval time between the current shutdown moment and the startup moment is corrected to the standby state; if there is an interval time that exceeds the maximum standby time of the electrolyzer, the shutdown state is maintained, and the electrolyzer state at the consecutive target moments before the startup moment is set to the cold start state; the electrolyzer is set to the shutdown state at any non-cold start, operation and standby moment states.

[0051] It can be understood that the embodiments of the present invention can significantly reduce the frequency of cold starts by setting the maximum standby time and giving priority to hot starts. Cold start energy consumption is triggered only when necessary, avoiding accelerated life decay due to frequent cold starts, extending the life of the electrolyzer, and clearly marking non-essential standby periods as shutdown states to avoid the electrolyzer maintaining standby power consumption during low-power periods. Combined with battery energy storage, the power during low-power periods is supplemented to the minimum operating power of the electrolyzer to achieve zero waste of renewable energy, significantly improving the economy, stability and sustainability of the renewable energy hydrogen production system.

[0052] Specifically, based on the optimization results of the electrolytic cell power, operating status, start time, and shutdown time in the second phase, the electrolytic cell cold start, standby state, and shutdown state plans are revised in the third phase. The maximum allowable standby time of the electrolytic cell is set. When this time is exceeded, the electrolytic cell standby power consumption will be greater than the energy consumption required for the cold start process. The value can be calculated based on the ratio of the cumulative energy consumption required for cold start to the standby energy consumption, expressed as: ;(twenty one) Where: P cs is the cold start power of the electrolyzer; P sb is the standby power of the electrolyzer; T cs is the cold start time of the electrolyzer; T sb The maximum standby time of the electrolytic cell; This is a floor operation.

[0053] The logic flow of the electrolysis hydrogen production working state plan correction is as follows Figure 2 The maximum standby time of the electrolytic cell calculated based on formula (21) is shown as follows. T sb First, calculate whether the shutdown time of any electrolytic cell and its nearest startup time exceed T sb, judge whether this startup is allowed to be a standby hot start, if it does not exceed T sb , then the standby state during this period δ el,sb Set to 1, if more than T sb , then this startup is a cold start, and the startup time before T cs Cold start state at a moment δ el,cs Set to 1. According to the above electrolytic cell cold start process and standby state correction results, set the electrolytic cell's shutdown state at any non-cold start, operation and standby time δ el,stop is 1.

[0054] In summary, the present invention proposes a multi-tank intelligent group control method, device and electronic equipment for a renewable energy hydrogen production system, which fully utilizes the power regulation capability of the energy storage system, realizes power complementary regulation between electrolytic hydrogen production and the energy storage system, improves the efficiency and stability of the overall operation of the system, and provides key support for the deep consumption of renewable energy and efficient hydrogen production.

[0055] According to the multi-tank intelligent group control method for a renewable energy hydrogen production system proposed in an embodiment of the present invention, a battery energy storage system is used to smooth out power fluctuations of renewable energy, ensuring that the input power provided to the electrolytic hydrogen production system is more stable. This not only helps to improve the working efficiency of the electrolyzer, but also reduces equipment losses caused by power fluctuations. By accurately controlling and correcting the operating power and operating status of the electrolyzer, the total number of starts and stops of the electrolyzer is reduced, thereby reducing equipment wear caused by frequent starts and stops, helping to extend the service life of the equipment, and making full use of the power regulation capability of the energy storage system to achieve power complementary regulation between electrolytic hydrogen production and the energy storage system, thereby improving the efficiency and stability of the overall operation of the system.

[0056] The following is a detailed description of the multi-tank intelligent group control method for a renewable energy hydrogen production system according to the present invention using specific embodiments, as follows: The present invention adopts an optimization framework to uniformly optimize hydrogen production power, electrolyzer power, electric energy storage power, and electrolyzer sequential production status. The rolling solution method is adopted in the optimization to establish the optimization objective function and constraint model respectively.

[0057] The optimization objectives include two sub-optimization objectives in the first phase and three sub-optimization objectives in the second phase. In the first phase, the first sub-objective is the tracking error of the high-frequency reconstructed ensemble data, and the second sub-objective is a penalty objective for the smoothed power range. In the second phase, the first sub-objective is to minimize renewable energy power curtailment, the second sub-objective is to minimize the total number of starts and shutdowns of the electrolysis hydrogen production system units, and the third sub-objective is to maximize the total system hydrogen production. The constraint model includes power balance constraints, hydrogen production power curtailment constraints, electrolyzer power constraints, electrolyzer state constraints, electrolyzer sequential 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, determining the operating power of each device in the system and the state of the electrolyzer.

[0058] To demonstrate the effectiveness and superiority of the proposed method, this section uses a wind power hydrogen production system and demonstrates its specific implementation and analysis through a typical daily calculation example. In this example, the installed wind power capacity is set at 25 MW. To ensure that the electrolysis hydrogen production subsystem can fully absorb the wind power, five 5 MW electrolyzers are configured with equal capacity, and the battery energy storage capacity is configured to be 1.75 MWh. The sampling time is set to 15 minutes. The system parameters and economic parameters for this example are shown in Tables 1 and 2, respectively.

[0059] Table 1 Example parameters

[0060] Table 2 Economic parameters

[0061] The present invention reduces the amount of wind power abandoned by optimizing the power distribution of the electrolyzer, improves the total hydrogen production of the system, reduces the total number of electrolyzer start-up and shutdown times, and reduces the total energy consumption of the electrolyzer during cold start and standby by optimizing the standby and shutdown states. Figure 3 and 4 It can be seen that the battery smoothes the fluctuations of wind power, making the operating power of the electrolyzer smoother. At the same time, during the period of 06:00-07:00, wind power is in a low power range and has not reached the minimum operating power of the electrolyzer. At this time, the battery discharges to supplement the power to the minimum operating power of the electrolyzer, realizing the full absorption of wind power.

[0062] Next, a multi-tank intelligent group control device for a renewable energy hydrogen production system according to an embodiment of the present invention will be described with reference to the accompanying drawings.

[0063] Figure 5 Schematic diagram of a multi-tank intelligent group control device for a renewable energy hydrogen production system according to an embodiment of the present invention.

[0064] like Figure 5As 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.

[0065] Among them, the acquisition module 100 is used to obtain 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, and the target optimization model outputs the charging and discharging power and charge state of the battery, as well as the operating power corresponding to multiple electrolyzers in the renewable energy hydrogen production system and the start time, shutdown time and production status in the operating state; according to the operating power corresponding to multiple electrolyzers in the renewable energy hydrogen production system and the start time, shutdown time and production status in the operating state, the cold start state, standby state and shutdown state in the operating state of multiple electrolyzers in the renewable energy hydrogen production system are corrected, and finally a complete multi-electrolyzer production plan is output.

[0066] It should be noted that the aforementioned explanation of the embodiment of the multi-tank intelligent group control method for a renewable energy hydrogen production system is also applicable to the multi-tank intelligent group control device for a renewable energy hydrogen production system of this embodiment, and will not be repeated here.

[0067] The multi-tank intelligent group control device for a renewable energy hydrogen production system proposed in an embodiment of the present invention utilizes a battery energy storage system to smooth out power fluctuations of renewable energy, thereby ensuring that the input power provided to the electrolytic hydrogen production system is more stable. This not only helps to improve the working efficiency of the electrolyzer, but also reduces equipment losses caused by power fluctuations. By precisely controlling and correcting the operating power and operating status of the electrolyzer, the total number of starts and stops of the electrolyzer is reduced, thereby reducing equipment wear caused by frequent starts and stops, helping to extend the service life of the equipment, and making full use of the power regulation capability of the energy storage system to achieve power complementary regulation between electrolytic hydrogen production and the energy storage system, thereby improving the efficiency and stability of the overall operation of the system.

[0068] Figure 6 This is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device may include: A memory 601 , a processor 602 , and a computer program stored in the memory 601 and executable on the processor 602 .

[0069] When the processor 602 executes the program, the multi-tank intelligent group control method for the renewable energy hydrogen production system provided in the above embodiment is implemented.

[0070] Furthermore, the electronic device further includes: The communication interface 603 is used for communication between the memory 601 and the processor 602 .

[0071] The memory 601 is used to store computer programs that can be run on the processor 602 .

[0072] The memory 601 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0073] If the memory 601, processor 602, and communication interface 603 are implemented independently, the communication interface 603, memory 601, and processor 602 can be interconnected via a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be divided into address buses, data buses, control buses, etc. For ease of representation, Figure 6 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0074] Optionally, in a specific implementation, if the memory 601, the processor 602 and the communication interface 603 are integrated on a chip, the memory 601, the processor 602 and the communication interface 603 can communicate with each other through an internal interface.

[0075] The processor 602 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0076] An embodiment of the present invention further provides a computer-readable storage medium having a computer program or instruction stored thereon. When the computer program or instruction is executed by a processor, the multi-tank intelligent group control method of the renewable energy hydrogen production system as described above is implemented.

[0077] An embodiment of the present invention further provides a computer program product, including a computer program or instructions, which, when executed, implements the above-mentioned multi-tank intelligent group control method for a renewable energy hydrogen production system.

[0078] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples without contradiction.

[0079] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "N" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0080] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or N executable instructions for implementing a custom logical function or step of a process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0081] It should be understood that various components of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiment, the N steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, it can be implemented using any one or a combination of the following technologies known in the art: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having suitable combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0082] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

Claims

1. A multi-tank intelligent group control method for a renewable energy hydrogen production system, characterized in that: The following steps are involved: Obtaining renewable energy power for renewable energy hydrogen production systems; The renewable energy power is input into a target optimization model, and the target optimization model outputs the charge and 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 the multiple electrolyzers in the renewable energy hydrogen production system; According to the operating power and start-up time, shutdown time and production status of the 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, and finally a complete multi-electrolyzer production plan is output.

2. The multi-tank intelligent group control method for a renewable energy hydrogen production system according to claim 1 is characterized in that: 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 charge state 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 electrolytic cells and the start-up time, shutdown time and production status in the operating state.

3. The multi-tank intelligent group control method for renewable energy hydrogen production system according to claim 2 is characterized in that: The first optimization model includes: a first objective function and a first constraint model, wherein the first objective function is a function whose goal is to minimize the tracking error of the battery for the high-frequency reconstructed set data, and the smoothed renewable energy power cannot be 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 equations, limit constraints, and state of charge adjustment margin constraints.

4. The multi-tank intelligent group control method for a renewable energy hydrogen production system according to claim 3 is characterized in that: The first optimization model smoothes 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 high-frequency fluctuation components to low-frequency fluctuation components one by one according to the intrinsic mode functions of different frequencies to generate a high-frequency reconstruction data set; The first optimization model is used to optimize the battery energy storage to track the renewable energy power high-frequency reconstruction data set to obtain the smoothed renewable energy power, and generate the battery charging and discharging power and charge state.

5. The multi-tank intelligent group control method for a renewable energy hydrogen production system according to claim 2, characterized in that: The second optimization model includes: a second objective function and a second constraint model, wherein the second objective function is a function whose goal is to minimize the abandoned power of renewable energy, minimize the total number of electrolyzer starts and stops, and maximize the total hydrogen production of the system; the second constraint model includes power balance constraints, wind power abandonment boundary constraints, electrolyzer power limit constraints, and electrolyzer state switching constraints.

6. 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 the multiple electrolyzers in the renewable energy hydrogen production system according to the operating power and the start time, shutdown time and production state of the multiple electrolyzers in the renewable energy hydrogen production system includes: Calculating a maximum standby time based on the cumulative energy consumption required for cold start and the standby energy consumption of the electrolyzer in the renewable energy hydrogen production system; The target standby time of the electrolyzer is used to correct the cold start state, the standby state and the shutdown state in the operating states of multiple electrolyzers in the renewable energy hydrogen production system.

7. The multi-tank intelligent group control method for a renewable energy hydrogen production system according to claim 6, characterized in that: The method of using the target standby time of the electrolyzer to correct the cold start state, the standby state and the shutdown state in the operating states of the plurality of electrolyzers in the renewable energy hydrogen production system includes: Calculate the time interval between the shutdown moment of any electrolytic cell and the nearest startup moment; Determining whether the interval duration exceeds the maximum standby duration of the electrolytic cell; If the interval time does not exceed the maximum standby time of the electrolytic cell, the electrolytic cell state within the interval time between the current shutdown time and the startup time is corrected to the standby state; If the interval time exceeds the maximum standby time of the electrolytic cell, the electrolytic cell is kept in the shutdown state, and the state of the electrolytic cell at the target time before the start time is set to the cold start state; The electrolyzer is set to shutdown state at any time other than cold start, operation and standby.

8. A multi-tank intelligent group control device for a renewable energy hydrogen production system, characterized in that: include: an acquisition module, used for acquiring renewable energy power of a renewable energy hydrogen production system; The group control module is used to input the renewable energy power into the target optimization model, and the target optimization model outputs the charge and discharge power and charge state of the battery, as well as the operating power corresponding to multiple electrolyzers in the renewable energy hydrogen production system and the start time, shutdown time and production status in the operating state; according to the operating power corresponding to multiple electrolyzers in the renewable energy hydrogen production system and the start time, shutdown time and production status in the operating state, the cold start state, standby state and shutdown state in the operating state of multiple electrolyzers in the renewable energy hydrogen production system are corrected, and finally a complete multi-electrolyzer production plan is output.

9. An electronic device, characterized in that: include: 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 according to any one of claims 1 to 6.

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

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