Method for determining optimal matching of multi-type electrolytic hydrogen production equipment based on timing production

Abstract

The application relates to a method for determining optimal matching of multiple types of electrolytic hydrogen production equipment based on timing production. The method comprises the following steps: constructing an alkaline water electrolysis operation model and a proton exchange membrane water electrolysis operation model according to upper and lower limits of equipment power constraints, equipment hydrogen production output constraints and equipment operation logic state constraints of electrolytic hydrogen production equipment; modeling the response of the alkaline water electrolysis operation model and the response of the proton exchange membrane water electrolysis operation model according to actual scheduling signal constraints to obtain an initial matching model of the electrolytic hydrogen production equipment; constructing an objective function of the initial matching model of the electrolytic hydrogen production equipment according to power resource environment parameters of the electrolytic hydrogen production equipment to obtain a target matching model of the electrolytic hydrogen production equipment; and solving the target matching model of the electrolytic hydrogen production equipment to obtain target matching parameters of the electrolytic hydrogen production equipment. The method can realize optimal matching of multiple types of electrolytic hydrogen production equipment, thereby improving the accuracy of the planning stage of the electrolytic hydrogen production equipment.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a method, apparatus, computer equipment, storage medium and computer program product for determining the optimal ratio of multiple types of electrolytic hydrogen production equipment based on time-series production. Background Technology

[0002] The global energy transition is accelerating, and hydrogen energy is attracting increasing attention due to its diverse sources, clean and low-carbon characteristics, flexibility, efficiency, and wide range of applications. In the hydrogen energy sector, electrolysis technologies, such as alkaline water electrolysis (ALK), proton exchange membrane water electrolysis (PEM), and solid oxide electrolysis (SOEC), are developing rapidly and are entering a critical window for large-scale deployment. On the other hand, to support a high proportion of renewable energy in the power system, the electricity market reform is accelerating, leading to a growing demand for peak-shaving and frequency regulation ancillary services, which will further incentivize new and high-quality regulation resources such as electrolysis hydrogen production equipment.

[0003] However, the techno-economic viability of different types of electrolytic hydrogen production equipment varies. In terms of equipment cost, ALK is relatively lower than PEM. PEM is more techno-economical in terms of energy efficiency, hydrogen production energy consumption, and operation and maintenance costs. Regarding performance parameters, PEM has stronger dynamic response capabilities than ALK in terms of load range, start-up and shutdown speed, and load change speed, resulting in significant performance advantages. However, regardless of whether ALK or PEM is used, it is impossible to achieve the optimal ratio of multiple types of electrolytic hydrogen production equipment, leading to insufficient accuracy in the planning stage of electrolytic hydrogen production equipment. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for determining the optimal ratio of multiple types of electrolytic hydrogen production equipment based on time-series production, which can improve the accuracy of the planning stage of electrolytic hydrogen production equipment.

[0005] In a first aspect, this application provides a method for determining the optimal ratio of multiple types of electrolytic hydrogen production equipment based on time-series production, including:

[0006] Obtain the upper and lower limits of equipment power, equipment hydrogen production output constraints, equipment operation logic state constraints, and actual scheduling signal constraints of the electrolysis hydrogen production equipment;

[0007] Based on the upper and lower limits of the equipment power, the hydrogen production capacity of the equipment, and the operating logic state constraints of the equipment, an alkaline water electrolysis operation model and a proton exchange membrane water electrolysis operation model are constructed.

[0008] Based on the actual scheduling signal constraints, the response of the alkaline water electrolysis operation model and the response of the proton exchange membrane water electrolysis operation model are modeled to obtain the initial ratio model of the electrolysis hydrogen production equipment.

[0009] Based on the power resource and environmental parameters of the electrolytic hydrogen production equipment, an objective function is constructed for the initial ratio model of the electrolytic hydrogen production equipment, and the target ratio model of the electrolytic hydrogen production equipment is obtained.

[0010] Solve the target ratio model of the electrolytic hydrogen production equipment to obtain the target ratio parameters of the electrolytic hydrogen production equipment.

[0011] Secondly, this application also provides an optimal ratio determination device for multiple types of electrolytic hydrogen production equipment based on time-series production, comprising:

[0012] The constraint data acquisition module is used to acquire the upper and lower limits of equipment power, equipment hydrogen production output constraints, equipment operating logic state constraints, and actual scheduling signal constraints of the electrolytic hydrogen production equipment.

[0013] The operation model construction module is used to construct an alkaline water electrolysis operation model and a proton exchange membrane water electrolysis operation model based on the upper and lower limits of the equipment power, the hydrogen production capacity constraints of the equipment, and the operation logic state constraints of the equipment.

[0014] The proportioning model acquisition module is used to model the response of the alkaline water electrolysis operation model and the proton exchange membrane water electrolysis operation model based on the actual scheduling signal constraints, and to obtain the initial proportioning model of the electrolysis hydrogen production equipment.

[0015] The proportioning model acquisition module is also used to construct the objective function of the initial proportioning model of the electrolytic hydrogen production equipment based on the power resource and environmental parameters of the electrolytic hydrogen production equipment, and obtain the target proportioning model of the electrolytic hydrogen production equipment;

[0016] The proportioning parameter acquisition module is used to solve the target proportioning model of the electrolytic hydrogen production equipment and obtain the target proportioning parameters of the electrolytic hydrogen production equipment.

[0017] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0018] Obtain the upper and lower limits of equipment power, equipment hydrogen production output constraints, equipment operation logic state constraints, and actual scheduling signal constraints of the electrolysis hydrogen production equipment;

[0019] Based on the upper and lower limits of the equipment power, the hydrogen production capacity of the equipment, and the operating logic state constraints of the equipment, an alkaline water electrolysis operation model and a proton exchange membrane water electrolysis operation model are constructed.

[0020] Based on the actual scheduling signal constraints, the response of the alkaline water electrolysis operation model and the response of the proton exchange membrane water electrolysis operation model are modeled to obtain the initial ratio model of the electrolysis hydrogen production equipment.

[0021] Based on the power resource and environmental parameters of the electrolytic hydrogen production equipment, an objective function is constructed for the initial ratio model of the electrolytic hydrogen production equipment, and the target ratio model of the electrolytic hydrogen production equipment is obtained.

[0022] Solve the target ratio model of the electrolytic hydrogen production equipment to obtain the target ratio parameters of the electrolytic hydrogen production equipment.

[0023] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0024] Obtain the upper and lower limits of equipment power, equipment hydrogen production output constraints, equipment operation logic state constraints, and actual scheduling signal constraints of the electrolysis hydrogen production equipment;

[0025] Based on the upper and lower limits of the equipment power, the hydrogen production capacity of the equipment, and the operating logic state constraints of the equipment, an alkaline water electrolysis operation model and a proton exchange membrane water electrolysis operation model are constructed.

[0026] Based on the actual scheduling signal constraints, the response of the alkaline water electrolysis operation model and the response of the proton exchange membrane water electrolysis operation model are modeled to obtain the initial ratio model of the electrolysis hydrogen production equipment.

[0027] Based on the power resource and environmental parameters of the electrolytic hydrogen production equipment, an objective function is constructed for the initial ratio model of the electrolytic hydrogen production equipment, and the target ratio model of the electrolytic hydrogen production equipment is obtained.

[0028] Solve the target ratio model of the electrolytic hydrogen production equipment to obtain the target ratio parameters of the electrolytic hydrogen production equipment.

[0029] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0030] Obtain the upper and lower limits of equipment power, equipment hydrogen production output constraints, equipment operation logic state constraints, and actual scheduling signal constraints of the electrolysis hydrogen production equipment;

[0031] Based on the upper and lower limits of the equipment power, the hydrogen production capacity of the equipment, and the operating logic state constraints of the equipment, an alkaline water electrolysis operation model and a proton exchange membrane water electrolysis operation model are constructed.

[0032] Based on the actual scheduling signal constraints, the response of the alkaline water electrolysis operation model and the response of the proton exchange membrane water electrolysis operation model are modeled to obtain the initial ratio model of the electrolysis hydrogen production equipment.

[0033] Based on the power resource and environmental parameters of the electrolytic hydrogen production equipment, an objective function is constructed for the initial ratio model of the electrolytic hydrogen production equipment, and the target ratio model of the electrolytic hydrogen production equipment is obtained.

[0034] Solve the target ratio model of the electrolytic hydrogen production equipment to obtain the target ratio parameters of the electrolytic hydrogen production equipment.

[0035] The aforementioned method, apparatus, computer equipment, storage medium, and computer program product for determining the optimal ratio of multiple types of electrolytic hydrogen production equipment based on time-series production involves: acquiring the upper and lower limits of equipment power, equipment hydrogen production output constraints, equipment operating logic state constraints, and actual scheduling signal constraints of the electrolytic hydrogen production equipment; constructing alkaline water electrolysis operation models and proton exchange membrane water electrolysis operation models based on the upper and lower limits of equipment power, equipment hydrogen production output constraints, and equipment operating logic state constraints; modeling the response of the alkaline water electrolysis operation models and the proton exchange membrane water electrolysis operation models based on the actual scheduling signal constraints to obtain the initial ratio model of the electrolytic hydrogen production equipment; constructing the objective function of the initial ratio model of the electrolytic hydrogen production equipment based on the power resource environmental parameters of the electrolytic hydrogen production equipment to obtain the target ratio model of the electrolytic hydrogen production equipment; and solving the target ratio model of the electrolytic hydrogen production equipment to obtain the target ratio parameters of the electrolytic hydrogen production equipment.

[0036] By acquiring the upper and lower limits of equipment power, hydrogen production output, operational logic state, and actual scheduling signal constraints of the electrolytic hydrogen production equipment, and constructing corresponding operational models for alkaline water electrolysis and proton exchange membrane water electrolysis, the response of the electrolytic hydrogen production equipment under actual scheduling signals can be accurately modeled. Based on this model, further considering power resource and environmental parameters, a target ratio model for the electrolytic hydrogen production equipment is constructed and solved to determine the optimal equipment ratio parameters. This method not only enables the optimal ratio of various types of electrolytic hydrogen production equipment, improving the accuracy of the planning stage, but also optimizes the operating efficiency of the equipment, increases hydrogen production, effectively reduces energy consumption and operating costs, and significantly improves the overall performance and economic benefits of the electrolytic hydrogen production equipment. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is an application environment diagram of a method for determining the optimal ratio of multiple types of electrolytic hydrogen production equipment based on time-series production in one embodiment.

[0039] Figure 2 This is a flowchart illustrating a method for determining the optimal ratio of multiple types of electrolytic hydrogen production equipment based on time-series production in one embodiment.

[0040] Figure 3 This is a flowchart illustrating the method for obtaining the target proportioning model of an electrolytic hydrogen production device in one embodiment.

[0041] Figure 4 This is a flowchart illustrating the methods for each power resource change item in one embodiment;

[0042] Figure 5 This is a flowchart illustrating a method for obtaining target proportioning parameters in one embodiment;

[0043] Figure 6 This is a flowchart illustrating the method for obtaining the target proportion parameters in another embodiment;

[0044] Figure 7 This is a flowchart illustrating the construction methods of an alkaline water electrolysis operation model and a proton exchange membrane water electrolysis operation model in one embodiment;

[0045] Figure 8 This is a schematic diagram of the operating principle of an alkaline water electrolysis device in one embodiment;

[0046] Figure 9 This is a schematic diagram of the execution logic of a method for determining the optimal ratio of multiple types of electrolytic hydrogen production equipment based on time-series production in one embodiment;

[0047] Figure 10 This is a structural block diagram of an optimal ratio determination device for multi-type electrolytic hydrogen production equipment based on time-series production in one embodiment;

[0048] Figure 11 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0050] This application provides a method for determining the optimal ratio of various types of electrolytic hydrogen production equipment based on time-series production, which can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or placed on a cloud or other network server. Server 104 obtains the upper and lower limits of the electrolytic hydrogen production equipment's power, hydrogen production output, operational logic state, and actual scheduling signal constraints from terminal 102. Based on these constraints, server 104 constructs an alkaline water electrolysis operation model and a proton exchange membrane water electrolysis operation model. Based on the actual scheduling signal constraints, it models the response of the alkaline water electrolysis operation model and the proton exchange membrane water electrolysis operation model to obtain the initial ratio model of the electrolytic hydrogen production equipment. Based on the power resource environmental parameters of the electrolytic hydrogen production equipment, it constructs the objective function of the initial ratio model to obtain the target ratio model. Finally, it solves the target ratio model to obtain the target ratio parameters of the electrolytic hydrogen production equipment. The terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, and smart in-vehicle systems. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted devices. The server 104 can be implemented using a standalone server or a server cluster consisting of multiple servers.

[0051] In one exemplary embodiment, such as Figure 2 As shown, a method for determining the optimal ratio of various types of electrolytic hydrogen production equipment based on time-series production is provided, and this method is applied to... Figure 1 Taking the server in the example, the explanation includes the following steps 202 to 210. Wherein:

[0052] Step 202: Obtain the upper and lower limits of the equipment power, the equipment hydrogen production output, the equipment operation logic state, and the actual scheduling signal constraints of the electrolytic hydrogen production equipment.

[0053] Among them, the upper and lower limits of equipment power can be the upper and lower limits of power for ALK devices or / and PEM devices.

[0054] Among them, the hydrogen production output constraint of the equipment can be the hydrogen production output constraint of ALK equipment or / and PEM equipment.

[0055] Among them, the device operation logic state constraint can be the operation logic state constraint of the ALK device.

[0056] The actual scheduling signal constraints can be constraints generated by the ALK device or / and the PEM device based on the actual scheduling signals.

[0057] Specifically, the rated power range (upper and lower limits) of the equipment is determined to ensure that the equipment operates within a safe and effective power range, thus obtaining the upper and lower power limit constraints. Based on the equipment's technical parameters and external conditions, a hydrogen production output model is established to obtain the equipment's hydrogen production output constraints, ensuring that the hydrogen production volume fluctuates within the set range. The equipment's operating logic states (such as start-up, stop, standby, etc.) are defined, and operating rules for the equipment in different states are established according to the equipment's working principle and system requirements, thus obtaining the equipment's operating logic state constraints. Combined with actual dispatch signals, a dispatch strategy for the equipment is formulated to ensure that the equipment can respond flexibly under different dispatch signals, thus obtaining the actual dispatch signal constraints.

[0058] Step 204: Based on the upper and lower limits of equipment power, the hydrogen production capacity of equipment, and the operating logic state of equipment, construct the alkaline water electrolysis operation model and the proton exchange membrane water electrolysis operation model.

[0059] Among them, the alkaline water electrolysis operation model can be a model that describes the operating status of the ALK equipment.

[0060] Among them, the proton exchange membrane electrolysis water operation model can be a model that describes the operating status of the PEM equipment.

[0061] Specifically, the ALK equipment is modeled, and its operation is mainly divided into three states: working, standby, and idle, represented by L, S, and I, respectively. These states are binary variables; a value of 1 indicates the device is in that state, and a value of 0 indicates it is not in that state. For example, if the alkaline electrolyzer is in the working state, then L=1; otherwise, L=0. To clearly represent the state transition process of the alkaline electrolyzer, two more binary variables are introduced: Y represents whether it is in a start-up state, and Z represents whether it is in a shutdown state. A value of 1 indicates that it is in this state, and a value of 0 indicates that it is not. For example, if the alkaline electrolyzer transitions from standby to working state, then Y=1 and Z=0. Finally, two continuous variables are needed to represent the input-output relationship of the alkaline electrolyzer: F represents the hydrogen production of the alkaline electrolyzer, and U represents the electricity consumed by the alkaline electrolyzer. The logical relationships of the variables are shown in the appendix. Figure 8 As shown. Based on this, the overall equipment constraints of the alkaline electrolyzer are as follows:

[0062] ALK device power upper and lower limit constraints

[0063]

[0064] In the formula: This indicates the operating power of the ALK device when it is in standby mode, in MW; and This represents the minimum and maximum operating power (MW) of the ALK device when it is in operation.

[0065] Hydrogen production capacity constraints of ALK equipment

[0066] (2)

[0067] In the formula: a and b are constants, representing the conversion relationship between the power of the ALK equipment module and the hydrogen production rate.

[0068] 3) ALK device operating logic state constraints

[0069]

[0070]

[0071]

[0072] (3)

[0073] Equation (3) constitutes the logical constraints of the working state, standby state, idle state, start state, and stop state of the ALK device module, ensuring that each state variable of the ALK device conforms to the logical constraints.

[0074] The PEM device is modeled as follows:

[0075] PEM equipment power upper and lower limits constraints

[0076] (4)

[0077] In the formula: The maximum operating power of the PEM equipment is expressed in MW. Let be the operating power of the PEM device at time t, in MW.

[0078] PEM equipment hydrogen production capacity constraints

[0079] (5)

[0080] In the formula: a and b are constants; U is the rated power of the PEM electrolyzer; is the current electrical load of the PEM electrolyzer; and is the conversion efficiency of the PEM electrolyzer. In actual calculations, piecewise linearization can be considered.

[0081] Step 206: Based on the actual scheduling signal constraints, model the response of the alkaline water electrolysis operation model and the proton exchange membrane water electrolysis operation model to obtain the initial ratio model of the electrolysis hydrogen production equipment.

[0082] The initial ratio model for the electrolytic hydrogen production equipment can be a model framework for calculating the ratio of the electrolytic hydrogen production equipment, but the objective function of the model has not yet been constructed.

[0083] Specifically, it is necessary to collect and analyze actual dispatch signal data to determine the response characteristics of the equipment under different dispatch signals. Next, for the alkaline water electrolysis equipment, an operating model is established under different power and load conditions, considering its hydrogen production efficiency and energy consumption characteristics. Then, a similar model is created for the proton exchange membrane water electrolysis equipment, focusing on its performance and energy conversion efficiency under different operating conditions. Combining the models of the two equipment, the response under various dispatch signals is comprehensively analyzed, the operating parameters of the two equipment are optimized, and an initial ratio model for the electrolysis hydrogen production equipment suitable for actual needs is formulated. The response of ALK and PEM is modeled as follows:

[0084] 6) Actual scheduling signal constraints

[0085] (6)

[0086] In the formula: for Peak shaving signal of the time system, MW; The installed capacity of ALK equipment is expressed in MW. The installed capacity of PEM equipment is expressed in MW.

[0087] Step 208: Based on the power resource and environmental parameters of the electrolysis hydrogen production equipment, construct the objective function of the initial ratio model of the electrolysis hydrogen production equipment to obtain the target ratio model of the electrolysis hydrogen production equipment.

[0088] Among them, the power resource environmental parameters can be parameters of the environment in which the electrolysis hydrogen production equipment is located, and are related to the current scenario.

[0089] The objective function can be a function of the proportions used in the electrolysis hydrogen production equipment in the model.

[0090] Among them, the target ratio model for the electrolytic hydrogen production equipment can be a model used to calculate the ratio of the electrolytic hydrogen production equipment, and the objective function has been constructed in this model.

[0091] Specifically, the objective function of the initial proportioning model for the electrolytic hydrogen production equipment aims to maximize the overall benefits of the electrolytic hydrogen production equipment under the electricity market environment.

[0092] 7) The objective function is as follows:

[0093]

[0094]

[0095]

[0096]

[0097]

[0098] (8)

[0099] In the formula: This represents the total length of the simulation time. For time indexing; The selling price of hydrogen is in yuan / Nm3; For ALK module Hydrogen production at any given time, in yuan / Nm3; For PEM module Hydrogen production at any given time, in yuan / Nm3; The investment cost per ALK module is expressed in yuan. For the entire lifecycle of the electrolytic cell module, in years; The investment cost per PEM module is expressed in yuan. For the entire lifecycle of the electrolytic cell module, in years; and Benefits of ALK equipment participating in frequency modulation up and down, in MW / yuan; and Benefits of PEM equipment participating in frequency regulation upwards and downwards, in MW / yuan; For the entire lifecycle of the electrolytic cell module, in years; and ALK devices The startup and shutdown status at all times. and The starting and stopping costs for alkaline electrolyzers are listed separately, in yuan per cycle. The simulation time step is in hours.

[0100] Step 210: Solve the target ratio model of the electrolysis hydrogen production equipment to obtain the target ratio parameters of the electrolysis hydrogen production equipment.

[0101] Among them, the target ratio parameter is used to achieve the optimal ratio parameter for the electrolytic hydrogen production equipment.

[0102] Specifically, based on the objective function, the model parameters and constraints of the target ratio model for the electrolytic hydrogen production equipment, an appropriate optimization algorithm, such as linear programming, nonlinear programming, or genetic algorithm, is selected to solve the optimization problem. Using actual power resource environmental parameters and equipment characteristic data as input, the optimization algorithm and iterative calculations are run to find the parameter combination that optimizes the objective function, thus obtaining the target ratio parameters for the electrolytic hydrogen production equipment. A detailed execution logic diagram of the method for determining the optimal ratio of multiple types of electrolytic hydrogen production equipment based on time-series production is shown below. Figure 9 As shown

[0103] In the aforementioned method for determining the optimal ratio of multiple types of electrolytic hydrogen production equipment based on time-series production, the following steps are taken: First, constraints on the upper and lower limits of the equipment power, the equipment hydrogen production output, the equipment operating logic state, and the actual scheduling signal constraints are obtained. Based on these constraints, alkaline water electrolysis and proton exchange membrane water electrolysis operation models are constructed. Then, based on the actual scheduling signal constraints, the responses of the alkaline and proton exchange membrane water electrolysis operation models are modeled to obtain the initial ratio model for the electrolytic hydrogen production equipment. Finally, based on the power resource environmental parameters of the electrolytic hydrogen production equipment, an objective function for the initial ratio model is constructed to obtain the target ratio model. Finally, the target ratio model is solved to obtain the target ratio parameters for the electrolytic hydrogen production equipment.

[0104] By acquiring the upper and lower limits of equipment power, hydrogen production output, operational logic state, and actual scheduling signal constraints of the electrolytic hydrogen production equipment, and constructing corresponding operational models for alkaline water electrolysis and proton exchange membrane water electrolysis, the response of the electrolytic hydrogen production equipment under actual scheduling signals can be accurately modeled. Based on this model, further considering power resource and environmental parameters, a target ratio model for the electrolytic hydrogen production equipment is constructed and solved to determine the optimal equipment ratio parameters. This method not only enables the optimal ratio of various types of electrolytic hydrogen production equipment, improving the accuracy of the planning stage, but also optimizes the operating efficiency of the equipment, increases hydrogen production, effectively reduces energy consumption and operating costs, and significantly improves the overall performance and economic benefits of the electrolytic hydrogen production equipment.

[0105] In one exemplary embodiment, such as Figure 3 As shown, based on the power resource and environmental parameters of the electrolytic hydrogen production equipment, an objective function is constructed for the initial proportioning model of the electrolytic hydrogen production equipment, resulting in the target proportioning model of the electrolytic hydrogen production equipment, including steps 302 to 306. Wherein:

[0106] Step 302: Based on the power resource environmental parameters, construct the power resource scheduling input, power resource scheduling output, and power resource consumption items for the initial ratio model of the electrolysis hydrogen production equipment.

[0107] Among them, the power resource scheduling input item can be the power resources input into the power grid in actual application, which are based on the initial allocation model of the electrolysis hydrogen production equipment.

[0108] Among them, the power resource dispatch output item can be the power resources output to the power grid in actual application from the initial ratio model of the electrolysis hydrogen production equipment.

[0109] Among them, the power resource consumption item can be the power resource consumption or resource interaction of the special power grid in actual application, as described in the initial ratio model of the electrolysis hydrogen production equipment.

[0110] Specifically, based on relevant data on power resources and the environment, including electricity price fluctuations, renewable energy supply, and load demand, the power resource dispatch input items are defined, namely, those in formula (8).

[0111]

[0112]

[0113]

[0114] Similarly, the output term for power resource dispatch is determined, i.e., formula (8).

[0115]

[0116] Similarly, the electricity resource consumption item is determined, i.e., formula (8).

[0117] .

[0118] Step 304: Construct the objective function based on the power resource scheduling input, power resource scheduling output, and power resource consumption.

[0119] Specifically, in order to construct an objective function based on the power resource dispatch inputs, power resource dispatch outputs, and power resource consumption, the optimization objective is first defined, such as minimizing power consumption, reducing hydrogen production costs, or maximizing hydrogen production efficiency. Then, the mathematical expression of the objective function is established by using the power resource dispatch inputs (such as power supply, electricity price, and load forecast), power resource dispatch outputs (such as equipment power output, hydrogen production, and system efficiency), and power resource consumption as variables.

[0120] Step 306: Connect the objective function to the initial ratio model of the electrolytic hydrogen production equipment to obtain the target ratio model of the electrolytic hydrogen production equipment.

[0121] Specifically, the constructed objective function is integrated with the inputs, outputs, and consumption terms of the initial proportioning model of the electrolysis hydrogen production equipment to form a complete mathematical model. Through mathematical optimization methods, such as linear programming or nonlinear programming, the objective function is embedded into the initial proportioning model of the electrolysis hydrogen production equipment, making the objective function a part of the model. Simulations or solutions are then performed using actual power resource environmental parameters to optimize the model parameters to meet the requirements of the objective function, thus obtaining the target proportioning model for the electrolysis hydrogen production equipment.

[0122] In this embodiment, the power resource scheduling input, output, and consumption items of the initial ratio model of the electrolytic hydrogen production equipment are constructed based on power resource environmental parameters. Furthermore, an objective function is constructed based on these items, and then the objective function is integrated into the initial ratio model to obtain the target ratio model of the electrolytic hydrogen production equipment. This process can accurately optimize the operating parameters of the electrolytic hydrogen production equipment under different power resource conditions, maximize hydrogen production efficiency and economy, ensure the stability and efficiency of the equipment under various operating conditions, thereby achieving optimal energy utilization, reducing operating costs, and improving the reliability and sustainability of the overall system.

[0123] In one exemplary embodiment, such as Figure 4 As shown, based on power resource environmental parameters, the power resource scheduling input, power resource scheduling output, and power resource consumption items of the initial configuration model for the electrolysis hydrogen production equipment are constructed, including steps 402 to 408. Wherein:

[0124] Step 402: Based on the power resource environmental parameters, determine the power resource scheduling input coefficient, power resource scheduling output coefficient, and power resource consumption characteristic parameters of the initial ratio model for the electrolysis hydrogen production equipment.

[0125] Among them, the power resource dispatch input coefficient can be the coefficient of the power resource dispatch input item.

[0126] Among them, the power resource dispatch output coefficient can be the coefficient of the power resource dispatch output item.

[0127] Among them, the characteristic parameters of electricity resource consumption can be the coefficients of electricity resource consumption items.

[0128] Specifically, relevant power resource data in the power resource environmental parameters are collected and analyzed, including electricity price, load demand, renewable energy supply, etc. Power resource dispatch input coefficients are defined, such as electricity price fluctuation coefficient and load demand coefficient, to quantify the impact of input parameters on the system. In formula (8), these can be used as follows: , , , and Next, the power resource dispatch output coefficients are determined, including the power output coefficients of the equipment, the hydrogen production efficiency coefficients, etc., to describe the response characteristics of the output parameters. In formula (8), these can be represented as follows: and Finally, calculate the characteristic parameters of each power resource consumption, such as the power consumption coefficient and the hydrogen production cost coefficient, to reflect the energy consumption and cost characteristics of the system. These parameters can be represented in formula (8). , , and .

[0129] Step 404: Based on the equipment hydrogen production output constraints and actual dispatch signal constraints, determine the inherent operating parameters and floating operating parameters of the initial ratio model of the electrolytic hydrogen production equipment.

[0130] Among them, the inherent operating parameters of the equipment can be parameters that do not change with the environment.

[0131] Among them, the floating parameters of equipment operation can be parameters that change during the operation of the equipment.

[0132] Specifically, the technical specifications and performance parameters of the equipment in the constraints of hydrogen production capacity and actual dispatch signals are analyzed to determine the maximum and minimum values ​​of hydrogen production capacity and their corresponding operating conditions, which constitute the inherent parameters of equipment operation. Then, in combination with the fluctuation of actual dispatch signals, the response characteristics of the equipment under different dispatch signals are identified, such as load changes and start-stop frequency, and these variables are determined as floating parameters of equipment operation. From formula (8), it can be seen that, except for the power resource dispatch input coefficient, the power resource dispatch output coefficient and the characteristic parameters of each power resource consumption, they are all inherent parameters of equipment operation or floating parameters of equipment operation.

[0133] Step 406: Based on the power resource dispatch input coefficient, the inherent parameters of equipment operation, and the floating parameters of equipment operation, construct the power resource dispatch input items and the power resource consumption items.

[0134] Specifically, by combining power resource dispatch input coefficients (such as electricity price fluctuation coefficients and load demand coefficients), inherent equipment operating parameters (such as rated power and maximum hydrogen production capacity), and equipment operating floating parameters (such as load changes and start-stop frequency), the power resource dispatch input items are determined, such as the optimal power input of the equipment under different electricity prices and load conditions. Similarly, by combining power resource dispatch input coefficients (such as electricity price fluctuation coefficients and load demand coefficients), inherent equipment operating parameters (such as rated power and maximum hydrogen production capacity), and equipment operating floating parameters (such as load changes and start-stop frequency), the power resource consumption items under actual dispatch conditions are calculated, including total electricity consumption, electricity utilization rate, and hydrogen production cost.

[0135] Step 408: Construct power resource dispatch output items based on power resource dispatch input coefficients and inherent equipment operating parameters.

[0136] Specifically, the impact of power resource dispatch input coefficients (such as electricity price fluctuation coefficients and load demand coefficients) on equipment operation is analyzed. Combined with the inherent parameters of equipment operation (such as the rated power, maximum hydrogen production, and efficiency curve of the equipment), the impact of these input coefficients on the equipment output under different conditions is quantified through mathematical modeling or simulation. The actual output power, hydrogen production, and efficiency of the equipment under various dispatch input conditions are determined, and power resource dispatch output items are constructed.

[0137] In this embodiment, the power resource scheduling input coefficients, output coefficients, and power resource consumption characteristic parameters of the initial ratio model of the electrolytic hydrogen production equipment are determined based on power resource environmental parameters. Combined with the equipment's hydrogen production output constraints and actual scheduling signal constraints, the inherent and floating parameters of the equipment's operation are determined. Based on these parameters, power resource scheduling input, consumption, and output items are constructed. This process can systematically and accurately model the operation of the electrolytic hydrogen production equipment, enabling the equipment's operating parameters to be dynamically adjusted and optimized under different power resource conditions. This improves hydrogen production efficiency, reduces energy consumption and costs, and achieves efficient and stable equipment operation and optimized resource utilization.

[0138] In one exemplary embodiment, such as Figure 5 As shown, the target proportioning model for the electrolytic hydrogen production equipment is solved to obtain the target proportioning parameters for the electrolytic hydrogen production equipment, including steps 502 to 504. Wherein:

[0139] Step 502: Select a solution algorithm for the target ratio model of the electrolytic hydrogen production equipment based on the power resource scheduling input, power resource scheduling output, and power resource consumption.

[0140] Specifically, based on the power resource scheduling inputs, outputs, and consumptions, the complexity and constraints of the target ratio model for the electrolytic hydrogen production equipment are analyzed to determine the optimization objective (such as minimizing energy consumption or maximizing hydrogen production efficiency). According to the characteristics of the model, the applicability of different optimization algorithms is evaluated; for example, linear programming is suitable for linear models, nonlinear programming is suitable for nonlinear models, and metaheuristic algorithms such as genetic algorithms and particle swarm optimization are suitable for complex multi-peak problems. Finally, the most suitable solution algorithm is selected.

[0141] Step 504: Use the solution algorithm to solve the target ratio model of the electrolytic hydrogen production equipment until the output value of the objective function reaches the maximum value, and obtain the target ratio parameters of the electrolytic hydrogen production equipment.

[0142] Specifically, the target proportioning model of the electrolytic hydrogen production equipment is iteratively solved using a selected solution algorithm (such as linear programming, nonlinear programming, or genetic algorithm). By continuously adjusting the proportioning parameters in the target proportioning model, the output value of the objective function is evaluated after each iteration. This iteration continues until the objective function reaches its maximum value, indicating that the optimal solution has been found. Finally, the proportioning parameters at this point are recorded, and these are the target proportioning parameters of the electrolytic hydrogen production equipment.

[0143] In this embodiment, based on the input, output, and consumption items of power resource scheduling, a solution algorithm for the target ratio model of the electrolytic hydrogen production equipment is selected. This algorithm is then used to solve the model until the output value of the objective function is maximized, thus obtaining the target ratio parameters for the equipment. This process ensures that the electrolytic hydrogen production equipment achieves optimal performance in actual operation, maximizing hydrogen production efficiency and minimizing energy consumption and operating costs. Through precise optimization algorithms, the equipment ratio can be dynamically adjusted to adapt to different power resource conditions, ensuring optimal resource utilization and high equipment operating efficiency.

[0144] In one exemplary embodiment, such as Figure 6 As shown, the target proportioning model of the electrolytic hydrogen production equipment is solved using a solution algorithm until the output value of the objective function converges, thus obtaining the target proportioning parameters of the electrolytic hydrogen production equipment, including steps 602 to 606. Wherein:

[0145] Step 602: Use the solution algorithm to solve the target ratio model of the electrolysis hydrogen production equipment to obtain the initial resource optimization value corresponding to the objective function.

[0146] The initial resource optimization value can be the result obtained by solving the target ratio model of the electrolytic hydrogen production equipment using a solution algorithm, but the objective function has not reached its maximum value.

[0147] Specifically, the target proportioning model of the electrolytic hydrogen production equipment is iteratively solved using a selected solution algorithm (such as linear programming, nonlinear programming, or genetic algorithm). By continuously adjusting the proportioning parameters in the target proportioning model of the electrolytic hydrogen production equipment, the output value of the objective function after each iteration is evaluated as the initial resource optimization value corresponding to the objective function.

[0148] Step 604: If the initial resource optimization value fails to converge, modify the model parameters of the target ratio model of the electrolytic hydrogen production equipment according to the initial resource optimization value, and return to the step of using the solution algorithm to solve the target ratio model of the electrolytic hydrogen production equipment to obtain the initial resource optimization value corresponding to the objective function, until the initial resource optimization value converges and the target resource optimization value is obtained.

[0149] Specifically, if the initial resource optimization value fails to converge, the gap between the initial resource optimization value and the convergence criterion is assessed to determine the model parameters that need adjustment (such as the proportioning coefficient, operating constraints, etc.). Next, the model parameters of the target proportioning model for the electrolytic hydrogen production equipment are modified based on the initial resource optimization value, and the selected solution algorithm is applied iteratively again. This process is repeated to continuously optimize the model parameters and the solution until the initial resource optimization value reaches the convergence criterion, obtaining a stable target resource optimization value.

[0150] Step 606: Calculate the target allocation parameters based on the target resource optimization value.

[0151] Specifically, the converged target resource optimization value is used as input to the target proportioning model of the electrolytic hydrogen production equipment. The specific values ​​of each proportioning parameter are then calculated by back-calculation using the model. These parameters include key parameters such as power input, equipment load, and operating time. In this way, it is ensured that each parameter can reach its optimal state while optimizing resource utilization, ultimately yielding the target proportioning parameters.

[0152] In this embodiment, the target ratio model of the electrolytic hydrogen production equipment is solved by using a solution algorithm to obtain the initial resource optimization value corresponding to the objective function. If the initial resource optimization value does not converge, the model parameters are modified and the solution process is repeated until the initial resource optimization value converges. Finally, the target resource optimization value is obtained and the target ratio parameters are calculated. This process can flexibly adapt to changes in different power resource environments, dynamically optimize the equipment's operating strategy, and improve the system's response speed and adaptability. This ensures that the equipment always maintains optimal performance and efficiency under various complex and changing operating conditions, thereby improving the system's reliability and lifespan.

[0153] In one exemplary embodiment, such as Figure 7As shown, the upper and lower limits of equipment power constraints include those for alkaline water electrolysis and proton exchange membrane water electrolysis; the hydrogen production capacity constraints include those for both alkaline and proton exchange membrane water electrolysis. Based on these constraints, the hydrogen production capacity constraints, and the equipment operating logic state constraints, an alkaline water electrolysis operation model and a proton exchange membrane water electrolysis operation model are constructed, including steps 702 to 704. Wherein:

[0154] Step 702: Based on the upper and lower limits of the power of the alkaline water electrolysis equipment, the hydrogen production capacity of the alkaline water electrolysis equipment, and the operating logic state constraints of the equipment, construct the alkaline water electrolysis operation model.

[0155] Specifically, based on the upper and lower power limits and hydrogen production capacity constraints of the alkaline water electrolysis equipment, the upper and lower power limits of the equipment are determined to ensure that the equipment operates within a safe power range. Secondly, based on the equipment's technical parameters and actual operating data, a hydrogen production capacity model is established to ensure hydrogen production efficiency and yield under different power inputs. Next, based on the equipment's operational logic state constraints (such as start-up, stop, and standby) and their transition conditions, the model is ensured to accurately reflect the equipment's operating characteristics under different states. Finally, these constraints and conditions are integrated into the mathematical model, and the accuracy and reliability of the model are verified through simulation or actual data, thereby constructing a complete alkaline water electrolysis operation model.

[0156] Step 704: Based on the upper and lower limits of the power of the proton exchange membrane water electrolysis equipment and the hydrogen production capacity constraint of the proton exchange membrane water electrolysis equipment, construct the operation model of the proton exchange membrane water electrolysis equipment.

[0157] Specifically, based on the upper and lower power limits of the proton exchange membrane (PEM) water electrolysis equipment and its hydrogen production capacity constraints, the upper and lower power limits of the equipment are determined to ensure that the equipment operates within a safe power range. Next, based on the equipment's technical specifications and actual operating data, a hydrogen production capacity model is established to ensure hydrogen production efficiency and yield under different power inputs. Then, these power and output constraints are integrated into the model, and mathematical expressions or simulation methods are used to ensure that the model accurately reflects the hydrogen production performance of the equipment under different power inputs. Finally, the model is verified and calibrated to ensure that it can reliably predict the equipment's operation under different operating conditions, thus constructing a complete PEM water electrolysis operation model.

[0158] In this embodiment, an alkaline water electrolysis operation model is constructed based on the upper and lower limits of power, hydrogen production output, and equipment operation logic constraints of the alkaline water electrolysis equipment. Similarly, a proton exchange membrane water electrolysis operation model is constructed based on the upper and lower limits of power and hydrogen production output constraints of the proton exchange membrane water electrolysis equipment. The beneficial effects of this process are: it provides refined management and control strategies for both types of water electrolysis equipment, enabling them to operate optimally under different load and power supply conditions, maximizing hydrogen production efficiency, extending equipment lifespan, reducing maintenance costs, and improving the overall system reliability and economy, thereby achieving more efficient and sustainable hydrogen production.

[0159] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0160] Based on the same inventive concept, this application also provides an apparatus for determining the optimal ratio of multiple types of electrolytic hydrogen production equipment based on time-series production, used to implement the aforementioned method for determining the optimal ratio of multiple types of electrolytic hydrogen production equipment. The solution provided by this apparatus is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the apparatus for determining the optimal ratio of multiple types of electrolytic hydrogen production equipment based on time-series production provided below can be found in the limitations of the method for determining the optimal ratio of multiple types of electrolytic hydrogen production equipment based on time-series production described above, and will not be repeated here.

[0161] In one exemplary embodiment, such as Figure 10 As shown, an optimal ratio determination device for multiple types of electrolytic hydrogen production equipment based on time-series production is provided, including: a constraint data acquisition module 1002, an operation model construction module 1004, a ratio model acquisition module 1006, and a ratio parameter acquisition module 1008, wherein:

[0162] The constraint data acquisition module 1002 is used to acquire the upper and lower limits of the equipment power, the equipment hydrogen production output constraints, the equipment operation logic state constraints, and the actual scheduling signal constraints of the electrolytic hydrogen production equipment.

[0163] The operation model construction module 1004 is used to construct alkaline water electrolysis operation models and proton exchange membrane water electrolysis operation models based on the upper and lower limits of equipment power, the hydrogen production capacity of equipment, and the operation logic state constraints of equipment.

[0164] The proportioning model module 1006 is used to model the response of the alkaline water electrolysis operation model and the proton exchange membrane water electrolysis operation model according to the actual scheduling signal constraints, so as to obtain the initial proportioning model of the electrolysis hydrogen production equipment.

[0165] The proportioning model module 1006 is also used to construct the objective function of the initial proportioning model of the electrolytic hydrogen production equipment based on the power resource and environmental parameters of the electrolytic hydrogen production equipment, and obtain the target proportioning model of the electrolytic hydrogen production equipment.

[0166] The proportioning parameter acquisition module 1008 is used to solve the target proportioning model of the electrolytic hydrogen production equipment and obtain the target proportioning parameters of the electrolytic hydrogen production equipment.

[0167] In one embodiment, the proportioning model obtaining module 1006 is further configured to construct, based on power resource scheduling inputs, power resource scheduling outputs, and power resource consumption items of the initial proportioning model for the electrolytic hydrogen production equipment, according to power resource environmental parameters; construct an objective function based on the power resource scheduling inputs, power resource scheduling outputs, and power resource consumption items; and connect the objective function to the initial proportioning model for the electrolytic hydrogen production equipment to obtain the target proportioning model for the electrolytic hydrogen production equipment.

[0168] In one embodiment, the proportioning model obtaining module 1006 is further configured to: determine the power resource dispatching input coefficient, power resource dispatching output coefficient, and power resource consumption characteristic parameters of the initial proportioning model of the electrolytic hydrogen production equipment based on power resource environmental parameters; determine the inherent operating parameters and floating operating parameters of the equipment in the initial proportioning model of the electrolytic hydrogen production equipment based on the equipment hydrogen production output constraints and actual dispatching signal constraints; construct power resource dispatching input items and power resource consumption items based on the power resource dispatching input coefficient, inherent operating parameters, and floating operating parameters; and construct power resource dispatching output items based on the power resource dispatching input coefficient and inherent operating parameters.

[0169] In one embodiment, the proportioning model obtaining module 1006 is further configured to select a solution algorithm for the target proportioning model of the electrolytic hydrogen production equipment based on the power resource scheduling input, power resource scheduling output, and power resource consumption; and use the solution algorithm to solve the target proportioning model of the electrolytic hydrogen production equipment until the output value of the objective function is the maximum value, thereby obtaining the target proportioning parameters of the electrolytic hydrogen production equipment.

[0170] In one embodiment, the proportioning parameter obtaining module 1008 is further configured to use a solving algorithm to solve the target proportioning model of the electrolytic hydrogen production equipment to obtain the initial resource optimization value corresponding to the objective function; if the initial resource optimization value fails to converge, the model parameters of the target proportioning model of the electrolytic hydrogen production equipment are modified according to the initial resource optimization value, and the process returns to the step of using a solving algorithm to solve the target proportioning model of the electrolytic hydrogen production equipment to obtain the initial resource optimization value corresponding to the objective function, until the initial resource optimization value converges to obtain the target resource optimization value; and the target proportioning parameter is calculated based on the target resource optimization value.

[0171] In one embodiment, the operating model construction module 1004 is further configured to construct an alkaline water electrolysis operating model based on the upper and lower limits of the power of the alkaline water electrolysis equipment, the hydrogen production capacity constraint of the alkaline water electrolysis equipment, and the equipment operating logic state constraint; and to construct a proton exchange membrane water electrolysis operating model based on the upper and lower limits of the power of the proton exchange membrane water electrolysis equipment and the hydrogen production capacity constraint of the proton exchange membrane water electrolysis equipment.

[0172] The various modules in the aforementioned device for determining the optimal ratio of multiple types of electrolytic hydrogen production equipment based on time-series production can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0173] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 11 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores server data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a method for determining the optimal proportions of multiple types of electrolytic hydrogen production equipment based on time-series production.

[0174] Those skilled in the art will understand that Figure 11The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0175] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0176] In one embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0177] In one embodiment, a computer program product or computer program is provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and executes the computer instructions, causing the computer device to perform the steps in the above method embodiments.

[0178] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0179] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0180] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0181] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for determining the optimal proportions of multiple types of electrolytic hydrogen production equipment based on time-series production, characterized in that, The method includes: Obtain the upper and lower limits of equipment power, equipment hydrogen production output constraints, equipment operation logic state constraints, and actual scheduling signal constraints of the electrolysis hydrogen production equipment; Based on the upper and lower limits of the equipment power, the hydrogen production capacity of the equipment, and the operating logic state constraints of the equipment, an alkaline water electrolysis operation model and a proton exchange membrane water electrolysis operation model are constructed. Based on the actual scheduling signal constraints, the response of the alkaline water electrolysis operation model and the response of the proton exchange membrane water electrolysis operation model are modeled to obtain the initial ratio model of the electrolysis hydrogen production equipment. Based on the power resource and environmental parameters of the electrolytic hydrogen production equipment, an objective function is constructed for the initial ratio model of the electrolytic hydrogen production equipment, and the target ratio model of the electrolytic hydrogen production equipment is obtained. Solve the target ratio model of the electrolytic hydrogen production equipment to obtain the target ratio parameters of the electrolytic hydrogen production equipment.

2. The method according to claim 1, characterized in that, The step of constructing the objective function of the initial proportioning model of the electrolytic hydrogen production equipment based on the power resource environmental parameters of the electrolytic hydrogen production equipment, and obtaining the target proportioning model of the electrolytic hydrogen production equipment, includes: Based on the power resource environment parameters, construct the power resource scheduling input, power resource scheduling output, and power resource consumption items of the initial ratio model of the electrolytic hydrogen production equipment. The objective function is constructed based on the power resource scheduling input, the power resource scheduling output, and the power resource consumption. The objective function is input into the initial ratio model of the electrolytic hydrogen production equipment to obtain the target ratio model of the electrolytic hydrogen production equipment.

3. The method according to claim 2, characterized in that, The step of constructing the initial ratio model of the electrolytic hydrogen production equipment based on the power resource environmental parameters includes the following: power resource scheduling input items, power resource scheduling output items, and power resource consumption items. Based on the power resource environment parameters, determine the power resource scheduling input coefficient, power resource scheduling output coefficient, and power resource consumption characteristic parameters of the initial ratio model of the electrolysis hydrogen production equipment; Based on the hydrogen production capacity constraints of the equipment and the actual scheduling signal constraints, determine the inherent operating parameters and floating operating parameters of the initial ratio model of the electrolytic hydrogen production equipment. Based on the power resource scheduling input coefficient, the inherent operating parameters of the equipment, and the floating operating parameters of the equipment, the power resource scheduling input item and the power resource consumption item are constructed. The power resource scheduling output item is constructed based on the power resource scheduling input coefficient and the inherent operating parameters of the equipment.

4. The method according to claim 2, characterized in that, The process of solving the target proportion model of the electrolytic hydrogen production equipment to obtain the target proportion parameters of the electrolytic hydrogen production equipment includes: Based on the power resource scheduling input, the power resource scheduling output, and the power resource consumption, select a solution algorithm for the target ratio model of the electrolysis hydrogen production equipment; The target proportioning model of the electrolytic hydrogen production equipment is solved using the aforementioned algorithm until the output value of the objective function reaches its maximum value, thereby obtaining the target proportioning parameters of the electrolytic hydrogen production equipment.

5. The method according to claim 4, characterized in that, The process of using the aforementioned solution algorithm to solve the target proportion model of the electrolytic hydrogen production equipment until the output value of the objective function converges, thereby obtaining the target proportion parameters of the electrolytic hydrogen production equipment, includes: The solution algorithm is used to solve the target ratio model of the electrolytic hydrogen production equipment to obtain the initial resource optimization value corresponding to the objective function; If the initial resource optimization value fails to converge, the model parameters of the target ratio model of the electrolytic hydrogen production equipment are modified according to the initial resource optimization value, and the process returns to the step of using the solution algorithm to solve the target ratio model of the electrolytic hydrogen production equipment to obtain the initial resource optimization value corresponding to the objective function, until the initial resource optimization value converges and the target resource optimization value is obtained. The target allocation parameters are calculated based on the target resource optimization value.

6. The method according to claim 1, characterized in that, The power limits for the equipment include power limits for both alkaline water electrolysis and proton exchange membrane water electrolysis; the hydrogen production capacity constraints include hydrogen production capacity constraints for both alkaline water electrolysis and proton exchange membrane water electrolysis; the construction of alkaline water electrolysis and proton exchange membrane water electrolysis operation models based on the power limits, hydrogen production capacity constraints, and operational logic state constraints includes: Based on the upper and lower limits of the power of the alkaline water electrolysis equipment, the hydrogen production capacity constraint of the alkaline water electrolysis equipment, and the operating logic state constraint of the equipment, the alkaline water electrolysis operation model is constructed. Based on the upper and lower power limits of the proton exchange membrane water electrolysis equipment and the hydrogen production capacity constraints of the proton exchange membrane water electrolysis equipment, an operating model for the proton exchange membrane water electrolysis equipment is constructed.

7. A device for determining the optimal proportion of multiple types of electrolytic hydrogen production equipment based on time-series production, characterized in that, The device includes: The constraint data acquisition module is used to acquire the upper and lower limits of equipment power, equipment hydrogen production output constraints, equipment operating logic state constraints, and actual scheduling signal constraints of the electrolytic hydrogen production equipment. The operation model construction module is used to construct an alkaline water electrolysis operation model and a proton exchange membrane water electrolysis operation model based on the upper and lower limits of the equipment power, the hydrogen production capacity constraints of the equipment, and the operation logic state constraints of the equipment. The proportioning model acquisition module is used to model the response of the alkaline water electrolysis operation model and the proton exchange membrane water electrolysis operation model based on the actual scheduling signal constraints, and to obtain the initial proportioning model of the electrolysis hydrogen production equipment. The proportioning model acquisition module is also used to construct the objective function of the initial proportioning model of the electrolytic hydrogen production equipment based on the power resource and environmental parameters of the electrolytic hydrogen production equipment, and obtain the target proportioning model of the electrolytic hydrogen production equipment; The proportioning parameter acquisition module is used to solve the target proportioning model of the electrolytic hydrogen production equipment and obtain the target proportioning parameters of the electrolytic hydrogen production equipment.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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