Hydrogen production scheduling optimization method and system based on electrolytic cell performance analysis

Abstract

The invention discloses a hydrogen production scheduling optimization method and system based on electrolytic bath performance analysis, and belongs to the technical field of new energy hydrogen production. The method comprises the steps that hydrogen production data are collected through a DCS of a hydrogen production control system, and performance data of an electrolytic bath are obtained; calculating the comprehensive performance score of each electrolytic bath based on the performance data of the electrolytic bath, and setting the initial working state of the electrolytic bath according to the comprehensive performance score; and constructing an electrolytic cell scheduling optimization model by using the comprehensive performance score of the electrolytic cell, obtaining an optimal electrolytic cell scheduling scheme through the electrolytic cell scheduling optimization model, and dynamically adjusting the working state of the electrolytic cell. According to the method, hydrogen production scheduling optimization is performed according to the performance characteristics of the electrolytic cell, the working state of the electrolytic cell is dynamically adjusted, the hydrogen production efficiency is improved, the service life of the electrolytic cell is prolonged, and overall efficient and stable operation of a hydrogen production system is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of hydrogen production from renewable energy, and in particular to a hydrogen production scheduling optimization method and system based on electrolyzer performance analysis. Background Art

[0002] Currently, with the growing demand for clean energy, the scale of wind and solar power generation is rapidly expanding. However, the randomness and volatility of the output of renewable energy sources such as wind and solar power pose a challenge in safely and stably absorbing them on a large scale. Hydrogen, as an efficient and clean energy carrier, has attracted widespread attention due to its significant advantages in low carbon emissions, cleanliness, energy density, storage time, and spatial flexibility. Electrolyzer hydrogen production technology, due to its relatively low cost and high technical maturity, occupies a key position in the hydrogen energy production field and is an effective way to consume renewable energy on a large scale.

[0003] As the core equipment of hydrogen production systems, electrolyzers vary in performance due to differences in materials and operating time. Electrolyzer performance directly impacts the efficiency of the hydrogen production system. Traditional hydrogen production systems typically employ a fixed rotation or random switching strategy for multiple electrolyzers, failing to account for performance degradation, efficiency differences, and load imbalances. This leaves room for improvement in overall system efficiency. Traditional hydrogen production systems lack an optimization method that integrates electrolyzer performance with dynamic rotation scheduling. Therefore, it is necessary to optimize electrolyzer hydrogen production scheduling by taking into account electrolyzer performance characteristics and dynamically adjusting electrolyzer operating conditions to improve hydrogen production efficiency, ensure the overall efficient and stable operation of the hydrogen production system, and achieve large-scale renewable energy consumption. Summary of the Invention

[0004] In view of the above technical problems, the present invention provides a hydrogen production scheduling optimization method and system based on electrolyzer performance analysis that solves at least some of the above technical problems. The method takes into account the performance characteristics of the electrolyzer to optimize hydrogen production scheduling and dynamically adjust the working state of the electrolyzer, which helps to improve hydrogen production efficiency, extend the life of the electrolyzer, and ensure the overall efficient and stable operation of the hydrogen production system.

[0005] To achieve the above object, the technical solution adopted by the present invention is:

[0006] In a first aspect, the present invention provides a method for optimizing hydrogen production scheduling based on electrolyzer performance analysis, the method comprising the following steps:

[0007] S1. Collect hydrogen production data through the DCS system of the hydrogen production control system to obtain the performance data of the electrolyzer;

[0008] S2. Calculating a comprehensive performance score for each electrolytic cell based on the performance data of the electrolytic cell, and setting an initial working state of the electrolytic cell according to the comprehensive performance score;

[0009] S3. Utilize the comprehensive performance score of the electrolytic cell to construct an electrolytic cell scheduling optimization model, obtain the optimal electrolytic cell scheduling plan through the electrolytic cell scheduling optimization model, and dynamically adjust the working state of the electrolytic cell.

[0010] In a specific embodiment, in said S1, the performance data of the electrolyzer obtained includes: current efficiency, operating voltage, electrolyte temperature, electrolyzer liquid level, and hydrogen production; and the data is stored in a cloud database server.

[0011] In a specific embodiment, in S2, before calculating the comprehensive performance score of each electrolytic cell, the performance data of the electrolytic cell is first preprocessed, and the data preprocessing includes: data cleaning, data conversion and data smoothing.

[0012] In a specific embodiment, in S2, the comprehensive performance score of each electrolytic cell is calculated based on the performance data of the electrolytic cell, and the calculation formula is:

[0013]

[0014] Among them, Z represents the comprehensive performance score of the electrolyzer, η represents the current efficiency, V represents the operating voltage, T represents the operating temperature, L represents the electrolyzer liquid level, Q represents the hydrogen production, α, β, γ, λ, and δ represent weight coefficients, which are dynamically adjusted according to the electrolyzer type.

[0015] In a specific embodiment, in S2, the initial working state of the electrolytic cell is set according to the comprehensive performance score, and the working state includes: operating state, standby state and unavailable state; and the electrolytic cell is classified, and the classification includes: static cell, dynamic cell, hot standby cell, cold standby cell, maintenance or fault cell.

[0016] In a specific embodiment, in S3, the electrolyzer scheduling optimization model constructed includes an objective function and constraints, and the optimal solution set is solved by a genetic algorithm, which is converted into a scheduling instruction by a group control device and transmitted to the hydrogen production control system to adjust the working state of the electrolyzer; wherein:

[0017] The objective function includes:

[0018] Maximum electrolyzer hydrogen production rate:

[0019]

[0020] Among them, Z i represents the comprehensive performance score of electrolytic cell i, X i Indicates the load ratio of electrolytic cell i, D i represents the predicted decay rate of electrolytic cell i, W represents the life balancing weight, and n represents the number of electrolytic cells;

[0021] Minimum curtailment rate of solar and wind power:

[0022]

[0023] Among them, P PV (t) represents the output power of photovoltaic power generation at time t; represents the maximum photovoltaic power generation at time t; P WD (t) represents the output power of wind power generation at time t; represents the maximum wind power generation at time t; T is the time interval from t = 0 to t = T of the entire scheduling optimization process;

[0024] Constraints include:

[0025] Constraints of electrolyzer hydrogen production:

[0026]

[0027] in, represents the hydrogen production power consumed by the electrolyzer at time t; Indicates the minimum input power of the electrolyzer; Indicates the maximum input power of the electrolytic cell; R adjust (t) represents the power regulation rate of the electrolytic cell at time t; Indicates the maximum power reduction rate of the electrolytic cell; Indicates the maximum rising power regulation rate of the electrolytic cell;

[0028] Energy balance constraints:

[0029]

[0030] Among them, P PV (t) represents the output power of the photovoltaic power generation system at time t; P WD (t) represents the output power of the wind power generation system at time t; P ES (t) represents the charge and discharge power of the energy storage system at time t. A positive value indicates that the energy storage system is discharging, and a negative value indicates that the energy storage system is charging. P represents the hydrogen production power consumed by the electrolyzer at time t; grid (t) represents the power exchange of the power grid at time t, a positive value indicates power purchase from the power grid, and a negative value indicates power transmission to the power grid; P oth (t) represents the power consumption of other load equipment;

[0031] Material balance constraints:

[0032] Q elc (t)+QCD (t)+P DE (t) = 0

[0033] Among them, Q elc (t) represents the flow rate of hydrogen produced by the electrolyzer at time t; Q CD (t) represents the hydrogen charging and discharging flow rate of the hydrogen storage device at time t, where hydrogen discharging is positive and hydrogen charging is negative; P DE (t) represents the hydrogen flow rate transmitted by the hydrogen transmission device at time t.

[0034] In a specific embodiment, a neural network model is used to obtain a predicted attenuation rate of the electrolytic cell based on the operating time, standby time, and start-stop times of the electrolytic cell.

[0035] In a specific embodiment, each electrolytic cell is provided with a manual control and automatic control selection button, which can be set to a manual control mode or an automatic control mode.

[0036] In a second aspect, an embodiment of the present invention further provides a hydrogen production scheduling optimization system based on electrolyzer performance analysis, which applies the above-mentioned hydrogen production scheduling optimization method based on electrolyzer performance analysis to perform hydrogen production scheduling optimization. The system includes:

[0037] The data acquisition module is used to collect hydrogen production data through the DCS system of the hydrogen production control system and obtain the performance data of the electrolyzer;

[0038] a data calculation module, configured to calculate a comprehensive performance score of each electrolytic cell based on the performance data of the electrolytic cell, and to set an initial working state of the electrolytic cell according to the comprehensive performance score;

[0039] The scheduling optimization module is used to construct an electrolytic cell scheduling optimization model using the comprehensive performance score of the electrolytic cell, obtain the optimal electrolytic cell scheduling plan through the electrolytic cell scheduling optimization model, and dynamically adjust the working state of the electrolytic cell.

[0040] Compared with the prior art, the present invention has at least the following beneficial effects:

[0041] The embodiments of the present invention provide a hydrogen production scheduling optimization method and system based on electrolyzer performance analysis, which takes into account the performance characteristics of the electrolyzer to optimize hydrogen production scheduling and dynamically adjust the working state of the electrolyzer, thereby helping to improve hydrogen production efficiency, extend the life of the electrolyzer, and ensure the overall efficient and stable operation of the hydrogen production system.

[0042] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0043] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0045] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0046] Figure 1 A schematic flow chart of a hydrogen production scheduling optimization method based on electrolyzer performance analysis provided in an embodiment of the present invention.

[0047] Figure 2 A schematic diagram of electrolytic cell status classification provided by an embodiment of the present invention.

[0048] Figure 3 A schematic diagram of dynamic allocation of electrolytic cells provided in an embodiment of the present invention.

[0049] Figure 4 A schematic diagram of electrolytic cell start-stop control provided in an embodiment of the present invention.

[0050] Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments.

[0052] In describing the present invention, it should be noted that some processes described in this specification and accompanying drawings include multiple operations that appear in a specific order. However, it should be understood that these operations may be performed in a different order than the order in which they appear, or may be performed in parallel. Furthermore, the use of various sequence numbers is for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0053] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort are intended to fall within the scope of protection of the present invention.

[0054] Reference Figure 1 As shown, an embodiment of the present invention provides a hydrogen production scheduling optimization method based on electrolyzer performance analysis, which mainly includes the following steps:

[0055] S1. Collect hydrogen production data through the DCS system of the hydrogen production control system to obtain the performance data of the electrolyzer;

[0056] S2. Calculating a comprehensive performance score for each electrolytic cell based on the performance data of the electrolytic cell, and setting an initial working state of the electrolytic cell according to the comprehensive performance score;

[0057] S3. Utilize the comprehensive performance score of the electrolytic cell to construct an electrolytic cell scheduling optimization model, obtain the optimal electrolytic cell scheduling plan through the electrolytic cell scheduling optimization model, and dynamically adjust the working state of the electrolytic cell.

[0058] The relevant working principles and specific implementation methods of the present invention are described in detail below:

[0059] In an embodiment of the present invention, data is collected through the DCS (Distributed Control System) of the hydrogen production control system. The data collected includes electrolyzer operating parameters: real-time power, current efficiency, operating voltage, electrolyte temperature, electrolyzer liquid level, electrolyzer hydrogen production, power supply status, etc.; hydrogen production unit parameters: load up and down rate of change, hydrogen-electricity characteristic curve, startup characteristic curve, hydrogen production main pipe pressure, hydrogen production flow rate, etc.; hydrogen storage tank parameters: hydrogen storage tank pressure, hydrogen storage tank safety pressure limit, hydrogen transmission flow rate, etc. The collected data is stored in a cloud database server to support the functional data call of the hydrogen production system management and control platform. The present invention mainly considers the performance parameters of the electrolyzer to carry out scheduling optimization.

[0060] In a specific embodiment, the acquired performance data of the electrolytic cell is first preprocessed. The data preprocessing mainly includes:

[0061] Data cleaning: Addressing errors and inconsistencies in data to improve data quality; for example, missing value and outlier handling. Data transformation: Converting data into a form suitable for analysis to enhance its expressiveness; examples include min-max normalization or Z-score standardization. Data smoothing: Reducing the impact of noise and revealing underlying patterns in the data; examples include moving average and regression.

[0062] Furthermore, the performance data of the pretreated electrolytic cells are used to calculate the comprehensive performance score of each electrolytic cell. The calculation formula is:

[0063]

[0064] Among them, Z represents the comprehensive performance score of the electrolyzer, η represents the current efficiency, V represents the operating voltage, T represents the operating temperature, L represents the electrolyzer liquid level, Q represents the hydrogen production, α, β, γ, λ, and δ represent weight coefficients, which are dynamically adjusted according to the electrolyzer type.

[0065] Furthermore, according to the calculated comprehensive performance score of each electrolytic cell, the initial working state of the electrolytic cell is set to be the operating state, the standby state and the unavailable state, such as Figure 2 As shown. Then, they are arranged by score, first set as static tank, dynamic tank, hot standby tank, and cold standby tank in sequence. The static tank is used to maintain the base load and remain essentially stable, while the dynamic tank is used to absorb the load fluctuations between the next moment and the current moment. Normal dynamic tanks operate within an intermediate load range (which can be set manually). In principle, the dynamic tank absorbs the load fluctuations between the previous moment and the next moment, and the static tank maintains the base load. When the number of dynamic tanks currently in operation is insufficient to absorb the fluctuations, the hot standby tank absorbs the remaining fluctuations. The valves and power supply status of the hot standby tank are both in hot standby mode and can be started at any time, and the time from startup to load is less than the set time (the time can be set). The cold standby tank is a tank with a longer startup to load time, and the on-site valve status requires time to be reserved for verification. In addition, it is preferred to increase the load of the high-scoring electrolytic cells in the static tanks to 80%-100% of the rated value, and reduce the load of the low-scoring electrolytic cells in the static tanks to below 80%. When an abnormal voltage surge is detected in an electrolytic cell, it is immediately isolated and the standby tank is activated. In this embodiment, the electrolytic cells whose calculated scores are much lower than the preset standard value (for example, the scores are 60% of the standard value) are set as maintenance or fault cells to remind the staff to carry out maintenance to ensure efficient and safe operation.

[0066] Furthermore, the performance values ​​of each electrolyzer vary during operation. To improve the operating efficiency and safety of the hydrogen production system, the present invention also constructs an electrolyzer scheduling optimization model based on the real-time comprehensive performance scores of the electrolyzers. The electrolyzer scheduling optimization model is used to obtain the optimal scheduling plan and dynamically adjust the working state of the electrolyzers. The constructed electrolyzer scheduling optimization model includes an objective function and constraints, wherein:

[0067] The objective function includes:

[0068] Maximum electrolyzer hydrogen production rate:

[0069]

[0070] Among them, Zi represents the comprehensive performance score of electrolytic cell i, X i Indicates the load ratio of electrolytic cell i, D i represents the predicted decay rate of electrolytic cell i (the predicted decay rate of electrolytic cell i can be obtained using a neural network model based on factors such as the operating time, standby time, and number of starts and stops of the electrolytic cell. If the predicted value is lower than the threshold, a maintenance reminder is triggered and its load priority is reduced). W represents the life balancing weight, and n represents the number of electrolytic cells.

[0071] Minimum curtailment rate of solar and wind power:

[0072]

[0073] Among them, P PV (t) represents the output power of photovoltaic power generation at time t; represents the maximum photovoltaic power generation at time t; P WD (t) represents the output power of wind power generation at time t; represents the maximum wind power generation at time t; T is the time interval from t = 0 to t = T of the entire scheduling optimization process;

[0074] Constraints include:

[0075] Constraints of electrolyzer hydrogen production:

[0076]

[0077] in, represents the hydrogen production power consumed by the electrolyzer at time t; Indicates the minimum input power of the electrolyzer; Indicates the maximum input power of the electrolytic cell; R adjust (t) represents the power regulation rate of the electrolytic cell at time t; Indicates the maximum power reduction rate of the electrolytic cell; Indicates the maximum rising power regulation rate of the electrolytic cell;

[0078] Energy balance constraints:

[0079]

[0080] Among them, P PV (t) represents the output power of the photovoltaic power generation system at time t; P WD (t) represents the output power of the wind power generation system at time t; P ES (t) represents the charge and discharge power of the energy storage system at time t. A positive value indicates that the energy storage system is discharging, and a negative value indicates that the energy storage system is charging. P represents the hydrogen production power consumed by the electrolyzer at time t; grid (t) represents the power exchange of the power grid at time t, a positive value indicates power purchase from the power grid, and a negative value indicates power transmission to the power grid; P oth (t) represents the power consumption of other load equipment;

[0081] Material balance constraints:

[0082] Q elc (t)+Q CD (t)+P DE (t) = 0

[0083] Among them, Q elc (t) represents the flow rate of hydrogen produced by the electrolyzer at time t; Q CD (t) represents the hydrogen charging and discharging flow rate of the hydrogen storage device at time t, where hydrogen discharging is positive and hydrogen charging is negative; P DE (t) represents the hydrogen flow rate transmitted by the hydrogen transmission device at time t.

[0084] Furthermore, according to the objective function and constraints, the Pareto optimal solution set is solved by genetic algorithm, the solution with the highest comprehensive score is selected, and the solution is converted into a dispatching instruction through the group control device and transmitted to the hydrogen production control system to dynamically adjust the working state of the electrolyzer and perform rotation optimization dynamic allocation among the static tank, dynamic tank, hot standby tank and cold standby tank; dynamic allocation see Figure 3 shown.

[0085] In a specific embodiment, each electrolytic cell is provided with a manual control and automatic control selection button, which can be set to manual control mode or automatic control mode. In automatic mode, the load instructions and start-stop instructions issued by the group control can be tracked to achieve automatic load adjustment and automatic start-stop control. Figure 4 As shown in the figure, condition A is that the start sequence is completed, the start power end point is reached, the oxygen tank pressure reaches the rated pressure, and the inlet purification valve is open (the purity has passed the test); condition B is that the stop sequence is completed, the tank stops feedback, and the inlet purification valve is closed.

[0086] Through the description of the above embodiments, those skilled in the art can know that the embodiments of the present invention provide a hydrogen production scheduling optimization method based on electrolyzer performance analysis. This method takes into account the performance characteristics of the electrolyzer to optimize hydrogen production scheduling, dynamically adjusts the working state of the electrolyzer, helps to improve hydrogen production efficiency, extend the life of the electrolyzer, and is conducive to ensuring the overall efficient and stable operation of the hydrogen production system.

[0087] Furthermore, an embodiment of the present invention further provides a hydrogen production scheduling optimization system based on electrolyzer performance analysis, which applies the above-mentioned hydrogen production scheduling optimization method based on electrolyzer performance analysis to perform hydrogen production scheduling optimization. The system includes:

[0088] The data acquisition module is used to collect hydrogen production data through the DCS system of the hydrogen production control system and obtain the performance data of the electrolyzer;

[0089] A data calculation module is used to calculate the comprehensive performance score of each electrolytic cell based on the performance data of the electrolytic cell, and set the initial working state of the electrolytic cell according to the comprehensive performance score;

[0090] The scheduling optimization module is used to construct an electrolytic cell scheduling optimization model using the comprehensive performance score of the electrolytic cell, obtain the optimal electrolytic cell scheduling plan through the electrolytic cell scheduling optimization model, and dynamically adjust the working state of the electrolytic cell.

[0091] Further, refer to Figure 5 As shown, an embodiment of the present invention also provides an electronic device, which applies the above-mentioned hydrogen production scheduling optimization method based on electrolyzer performance analysis to complete the scheduling optimization of the hydrogen production system; the electronic device may include a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may also include a computer program stored in the memory 11 and capable of running on the processor 10.

[0092] In some embodiments, the processor 10 may be composed of an integrated circuit, such as a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and a combination of various control chips. The processor 10 is the control core (Control Unit) of the electronic device, connecting the various components of the entire electronic device using various interfaces and circuits, and executing or executing programs or modules stored in the memory 11 and calling data stored in the memory 11 to perform various functions of the electronic device and process data.

[0093] The contents or technical means not mentioned in the embodiments of the present invention can be obtained by referring to the existing technology, and are not limited by the embodiments of the present disclosure, so they will not be described in detail.

[0094] The above describes in detail the various embodiments of the present invention, and explains the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core ideas. Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, computer software program products or electronic devices, etc. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0095] It should be noted that the word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer.

[0096] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0097] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A hydrogen production scheduling optimization method based on electrolyzer performance analysis, characterized in that: The method includes: S1. Collect hydrogen production data through the DCS system of the hydrogen production control system to obtain the performance data of the electrolyzer; S2. Calculating a comprehensive performance score for each electrolytic cell based on the performance data of the electrolytic cell, and setting an initial working state of the electrolytic cell according to the comprehensive performance score; S3. Utilize the comprehensive performance score of the electrolytic cell to construct an electrolytic cell scheduling optimization model, obtain the optimal electrolytic cell scheduling plan through the electrolytic cell scheduling optimization model, and dynamically adjust the working state of the electrolytic cell.

2. The hydrogen production scheduling optimization method based on electrolyzer performance analysis according to claim 1 is characterized in that: In said S1, the performance data of the electrolyzer obtained include: current efficiency, operating voltage, electrolyte temperature, electrolyzer liquid level, and hydrogen production; and the data is stored in a cloud database server.

3. The hydrogen production scheduling optimization method based on electrolyzer performance analysis according to claim 1 is characterized in that: In S2, before calculating the comprehensive performance score of each electrolytic cell, the performance data of the electrolytic cell is preprocessed, and the data preprocessing includes: data cleaning, data conversion and data smoothing.

4. The hydrogen production scheduling optimization method based on electrolyzer performance analysis according to claim 1 is characterized in that: In S2, the comprehensive performance score of each electrolytic cell is calculated based on the performance data of the electrolytic cell, and the calculation formula is: Among them, Z represents the comprehensive performance score of the electrolyzer, η represents the current efficiency, V represents the operating voltage, T represents the operating temperature, L represents the electrolyzer liquid level, Q represents the hydrogen production, α, β, γ, λ, and δ represent weight coefficients, which are dynamically adjusted according to the electrolyzer type.

5. The hydrogen production scheduling optimization method based on electrolyzer performance analysis according to claim 4 is characterized in that: In S2, the initial working state of the electrolytic cell is set according to the comprehensive performance score, and the working state includes: operating state, standby state and unavailable state; and the electrolytic cell is classified into: static cell, dynamic cell, hot standby cell, cold standby cell, maintenance or fault cell.

6. A hydrogen production scheduling optimization method based on electrolyzer performance analysis according to claim 5, characterized in that: In S3, the electrolyzer scheduling optimization model constructed includes an objective function and constraints, and the optimal solution set is solved by a genetic algorithm. The optimal solution set is converted into a scheduling instruction by a group control device and transmitted to the hydrogen production control system to adjust the working state of the electrolyzer; wherein: The objective function includes: Maximum electrolyzer hydrogen production rate: Among them, Z i represents the comprehensive performance score of electrolytic cell i, X i Indicates the load ratio of electrolytic cell i, D i represents the predicted decay rate of electrolytic cell i, W represents the life balancing weight, and n represents the number of electrolytic cells; Minimum curtailment rate of solar and wind power: Among them, P PV (t) represents the output power of photovoltaic power generation at time t; represents the maximum photovoltaic power generation at time t; P WD (t) represents the output power of wind power generation at time t; represents the maximum wind power generation at time t; T is the time interval from t = 0 to t = T of the entire scheduling optimization process; Constraints include: Constraints of electrolyzer hydrogen production: in, represents the hydrogen production power consumed by the electrolyzer at time t; Indicates the minimum input power of the electrolyzer; Indicates the maximum input power of the electrolytic cell; R adjust (t) represents the power regulation rate of the electrolytic cell at time t; Indicates the maximum power reduction rate of the electrolytic cell; Indicates the maximum rising power regulation rate of the electrolytic cell; Energy balance constraints: Among them, P PV (t) represents the output power of the photovoltaic power generation system at time t; P WD (t) represents the output power of the wind power generation system at time t; P ES (t) represents the charge and discharge power of the energy storage system at time t. A positive value indicates that the energy storage system is discharging, and a negative value indicates that the energy storage system is charging. P represents the hydrogen production power consumed by the electrolyzer at time t; grid (t) represents the power exchange of the power grid at time t, a positive value indicates power purchase from the power grid, and a negative value indicates power transmission to the power grid; P oth (t) represents the power consumption of other load equipment; Material balance constraints: Q elc (t)+Q CD (t)+P DE (t)=0 Among them, Q elc (t) represents the flow rate of hydrogen produced by the electrolyzer at time t; Q CD (t) represents the hydrogen charging and discharging flow rate of the hydrogen storage device at time t, where hydrogen discharging is positive and hydrogen charging is negative; P DE (t) represents the hydrogen flow rate transmitted by the hydrogen transmission device at time t.

7. The hydrogen production scheduling optimization method based on electrolyzer performance analysis according to claim 6 is characterized in that: Based on the operating time, standby time, and number of starts and stops of the electrolyzer, a neural network model is used to obtain the predicted attenuation rate of the electrolyzer.

8. The hydrogen production scheduling optimization method based on electrolyzer performance analysis according to claim 6 is characterized in that: Each electrolytic cell is provided with a manual control and automatic control selection button, which can be set to manual control mode or automatic control mode.

9. A hydrogen production scheduling optimization system based on electrolyzer performance analysis, characterized in that: A hydrogen production scheduling optimization method based on electrolyzer performance analysis according to any one of claims 1 to 8 is applied to perform hydrogen production scheduling optimization, the system comprising: The data acquisition module is used to collect hydrogen production data through the DCS system of the hydrogen production control system and obtain the performance data of the electrolyzer; a data calculation module, configured to calculate a comprehensive performance score of each electrolytic cell based on the performance data of the electrolytic cell, and to set an initial working state of the electrolytic cell according to the comprehensive performance score; The scheduling optimization module is used to construct an electrolytic cell scheduling optimization model using the comprehensive performance score of the electrolytic cell, obtain the optimal electrolytic cell scheduling plan through the electrolytic cell scheduling optimization model, and dynamically adjust the working state of the electrolytic cell.

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