Double-layer optimization method, device and equipment for off-grid photovoltaic hydrogen production system and storage medium

Abstract

The invention discloses an off-grid photovoltaic hydrogen production system double-layer optimization method, device and equipment and a storage medium, and the method comprises the steps: constructing an off-grid photovoltaic hydrogen production system based on a battery energy storage unit, a photovoltaic power generation unit and a hydrogen energy conversion unit, and constructing a double-layer optimization model comprising an upper-layer constant volume optimization model and a lower-layer operation optimization model; the upper-layer constant volume optimization model takes the maximization of the total yield of green hydrogen in the whole life cycle of the system as a target and takes the system capacity configuration as a decision variable; the lower-layer operation optimization model takes green hydrogen yield maximization in the current scheduling time period of the system as a target and takes a system operation strategy as a decision variable; and an optimal capacity configuration scheme and an optimal operation strategy are obtained by solving the double-layer optimization model, so that the utilization efficiency of the system on renewable energy sources is improved, the overall low-carbon benefit is enhanced, and the potential of the electrolytic cell is fully played.

Description

Technical Field

[0001] This invention relates to the field of power grid technology, and more specifically, to a two-layer optimization method, apparatus, equipment, and storage medium for an off-grid photovoltaic hydrogen production system. Background Technology

[0002] To address environmental challenges and promote low-carbon transformation, industrial parks are widely deploying renewable energy systems centered on distributed photovoltaics, supplemented by off-grid "photovoltaic + energy storage" systems to mitigate the intermittency and volatility of photovoltaic output. However, existing technologies generally prioritize maximizing economic efficiency in system capacity configuration and operation scheduling, neglecting the core value of hydrogen energy conversion units (especially the electrolyzers within these units).

[0003] Driven by the logic of prioritizing economic benefits, to avoid the marginal costs of starting up, stopping, maintaining, and operating hydrogen energy conversion equipment such as electrolyzers under low load, the system typically keeps the hydrogen energy conversion unit in standby mode. Hydrogen production is only initiated when battery storage is at full capacity and grid electricity sales revenue is extremely low. This operating mode prevents the hydrogen energy conversion unit from fully utilizing its inherent advantages of large capacity and long-term hydrogen storage, severely suppressing its green hydrogen production capacity and energy conversion value. This not only restricts the system's efficiency in utilizing renewable energy but also weakens its overall low-carbon benefits, greatly limiting the potential of the hydrogen energy conversion unit. Summary of the Invention

[0004] Based on this, the present invention provides a two-layer optimization method, apparatus, equipment and storage medium for off-grid photovoltaic hydrogen production systems, in order to solve the defects of existing technologies that limit the potential of hydrogen energy conversion units and result in insufficient renewable energy utilization efficiency and low-carbon benefits due to the economic priority logic constraining the operation of hydrogen energy conversion units.

[0005] To achieve the above objectives, embodiments of the present invention provide a two-layer optimization method for off-grid photovoltaic hydrogen production systems, comprising: Integrate battery energy storage units, photovoltaic power generation units, and hydrogen energy conversion units to construct an off-grid photovoltaic hydrogen production system; Based on the off-grid photovoltaic hydrogen production system, a two-layer optimization model is constructed. The two-layer optimization model includes an upper-layer fixed-capacity optimization model and a lower-layer operation optimization model. The upper-layer fixed-capacity optimization model aims to maximize the total green hydrogen production over the entire life cycle of the system, with system capacity configuration as the decision variable. The lower-layer operation optimization model aims to maximize the green hydrogen production during the current scheduling period of the system, with system operation strategy as the decision variable. Solve the two-layer optimization model to obtain the optimal capacity configuration scheme and the optimal operation strategy.

[0006] To achieve the above objectives, embodiments of the present invention also provide a dual-layer optimization device for an off-grid photovoltaic hydrogen production system, comprising: The system building module is used to integrate battery energy storage units, photovoltaic power generation units, and hydrogen energy conversion units to build an off-grid photovoltaic hydrogen production system; The model building module is used to construct a two-layer optimization model based on the off-grid photovoltaic hydrogen production system. The two-layer optimization model includes an upper-layer capacity optimization model and a lower-layer operation optimization model. The upper-layer capacity optimization model aims to maximize the total green hydrogen production over the entire life cycle of the system, with system capacity configuration as the decision variable. The lower-layer operation optimization model aims to maximize the green hydrogen production during the current scheduling period of the system, with system operation strategy as the decision variable. The solution module is used to solve the two-layer optimization model to obtain the optimal capacity configuration scheme and the optimal operation strategy.

[0007] To achieve the above objectives, embodiments of the present invention also provide a two-layer optimization device for an off-grid photovoltaic hydrogen production system, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the two-layer optimization method for the off-grid photovoltaic hydrogen production system as described in any of the above embodiments.

[0008] To achieve the above objectives, embodiments of the present invention also provide a computer-readable storage medium, the computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the off-grid photovoltaic hydrogen production system two-layer optimization method as described in any of the above embodiments.

[0009] Compared with existing technologies, the dual-layer optimization method, apparatus, equipment, and storage medium for off-grid photovoltaic hydrogen production systems disclosed in this invention first integrates battery energy storage units, photovoltaic power generation units, and hydrogen energy conversion units to construct a complete off-grid photovoltaic hydrogen production system. Based on this, a dual-layer optimization model is further constructed, comprising an upper-layer constant-capacity optimization model and a lower-layer operation optimization model. The upper-layer constant-capacity optimization model aims to maximize the total green hydrogen production over the system's entire lifecycle, using system capacity configuration as the decision variable. The lower-layer operation optimization model aims to maximize the green hydrogen production during the current scheduling period, using system operation strategy as the decision variable. Subsequently, by solving this dual-layer optimization model, the optimal capacity configuration scheme and optimal operation strategy are obtained. Therefore, this invention, by constructing a multi-unit collaborative off-grid photovoltaic hydrogen production system and building and solving a dual-layer optimization model with maximizing green hydrogen production as the core objective, obtains the optimal capacity configuration scheme and optimal operation strategy, thereby improving the system's utilization efficiency of renewable energy, enhancing overall low-carbon benefits, and fully leveraging the role of the hydrogen energy conversion unit, especially fully utilizing the potential of the electrolyzer. Attached Figure Description

[0010] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a schematic flowchart of a two-layer optimization method for an off-grid photovoltaic hydrogen production system provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of a dual-layer optimization device for an off-grid photovoltaic hydrogen production system according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a dual-layer optimization device for an off-grid photovoltaic hydrogen production system provided in an embodiment of the present invention. Detailed Implementation

[0012] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0013] See Figure 1This is a flowchart illustrating a two-layer optimization method for an off-grid photovoltaic hydrogen production system according to an embodiment of the present invention. Specifically, the two-layer optimization method for the off-grid photovoltaic hydrogen production system includes steps S1 to S3: S1. Integrate battery energy storage unit, photovoltaic power generation unit and hydrogen energy conversion unit to build an off-grid photovoltaic hydrogen production system; S2. Based on the off-grid photovoltaic hydrogen production system, a two-layer optimization model is constructed; wherein, the two-layer optimization model includes an upper-layer fixed-capacity optimization model and a lower-layer operation optimization model, the upper-layer fixed-capacity optimization model aims to maximize the total green hydrogen production over the entire life cycle of the system, with system capacity configuration as the decision variable; the lower-layer operation optimization model aims to maximize the green hydrogen production during the current scheduling period of the system, with system operation strategy as the decision variable; S3. Solve the two-layer optimization model to obtain the optimal capacity configuration scheme and the optimal operation strategy.

[0014] Specifically, for off-grid photovoltaic hydrogen production systems in industrial parks, the logic of "upper-level capacity optimization - lower-level operation optimization - bidirectional iterative convergence" is used to maximize green hydrogen production. The specific technical details include four main parts: system modeling, upper-level capacity optimization, lower-level scheduling, and iterative convergence, as follows: I. System Mathematical Modeling A mathematical model of the system (i.e., off-grid photovoltaic hydrogen production system) containing multiple core units was constructed, and the input-output relationship and key parameters of each unit were clarified. The core units include battery energy storage unit, energy production unit (such as photovoltaic power generation unit) and hydrogen energy conversion unit. Among them, the energy production unit includes photovoltaic power generation unit, and the hydrogen energy conversion unit includes electrolyzer array unit. The electrolyzer array unit includes several independently controllable electrolyzers.

[0015] II. Upper-level volumetric optimization With the goal of maximizing the total green hydrogen production throughout the system's lifecycle, the rated capacity of each unit is used as the decision variable. An optimization algorithm is employed to find the optimal capacity configuration scheme. Specifically, an improved particle swarm optimization-genetic algorithm hybrid intelligent algorithm (PSOGA) can be used. This hybrid algorithm combines the global search capability of particle swarm optimization with the crossover and mutation mechanisms of genetic algorithms, improving the efficiency and accuracy of solving complex optimization problems. The decision variables of the upper-level fixed-capacity optimization model include the rated power of the electrolyzer, the number of electrolyzers, and the rated capacity of the battery energy storage, which are not limited here.

[0016] III. Optimization of Lower-Level Operations Using the capacity configuration scheme output by the upper-level constant-capacity optimization model as the boundary, and with the objective of "maximizing the total green hydrogen production during the current scheduling period," a real-time equipment operation strategy is formulated. The decision variables of the lower-level operation optimization model include the real-time operating power of the electrolyzer, the charging and discharging power of the battery, and the power of wasted solar power, etc., which are not limited here.

[0017] IV. Iterative Convergence Through a bidirectional iterative process of "lower-level feedback - upper-level update," the capacity configuration and scheduling strategy are corrected until the algorithm converges. After the lower-level operation optimization model completes its calculations, the green hydrogen and equipment technical status parameters are returned to the upper-level capacity optimization model. The upper-level capacity optimization model adjusts its decision variables based on the feedback. Through multiple iterations, the final optimal capacity configuration scheme and optimal operation strategy are output. An off-grid photovoltaic hydrogen production system is constructed based on the optimal capacity configuration scheme and optimal operation strategy, and power dispatch is implemented.

[0018] It is worth noting that, in addition to maximizing green hydrogen production, the objective functions of the upper-level constant-capacity optimization model and the lower-level operation optimization model can also take into account other factors, such as maximizing economic benefits and minimizing active power losses.

[0019] Compared with existing technologies, the embodiments of the present invention construct a multi-unit collaborative off-grid photovoltaic hydrogen production system, and build and solve a two-layer optimization model with the core objective of maximizing green hydrogen production to obtain the optimal capacity configuration scheme and the optimal operation strategy, thereby improving the system's utilization efficiency of renewable energy, enhancing the overall low-carbon benefits, and fully leveraging the potential of the hydrogen energy conversion unit.

[0020] In a preferred embodiment, based on steps S1-S3, the hydrogen energy conversion unit includes an electrolyzer array unit, which is a collection of several independently controllable electrolyzers; the green hydrogen production during the current system scheduling period is calculated as follows: for each time step during the current system scheduling period, the real-time operating power of the electrolyzer at that time step is multiplied by the real-time operating efficiency of the same period to obtain the effective electrical energy of the electrolyzer at that time step; the effective electrical energy of all the electrolyzers at all time steps is summed to obtain the total effective electrical energy; the total effective electrical energy is divided by the higher calorific value of hydrogen to obtain the green hydrogen production during the current system scheduling period.

[0021] For example, the objective function of the lower-level running optimization model can be set as: ; in, The maximum value operator, The green hydrogen production during the current scheduling period of the system; for Time Electrolyzer Real-time operating power, for Time Electrolyzer Real-time operating efficiency; The high calorific value of hydrogen; For time step.

[0022] System operation constraints include energy storage state constraints, operating power constraints, energy balance constraints, and grid interaction constraints.

[0023] Equipment capacity constraints are: ; In the formula, Representing the The capacity of the equipment and These are the minimum and maximum allowable capacity values ​​for the device, respectively.

[0024] Energy storage state constraints are: ; in, This refers to the state of charge of the battery energy storage unit. This refers to the minimum permissible state of charge of a battery energy storage unit. This represents the maximum permissible state of charge of the battery energy storage unit. The operating power constraint is: ; ; ; ; in, The charging power of the battery energy storage unit, The charging state variable (binary variable) of the battery energy storage unit. This refers to the rated power of the battery energy storage unit; This refers to the discharge power of the battery energy storage unit. The discharge state variable (binary variable) of the battery energy storage unit. Electrolytic cell Real-time operating power, Electrolytic cell The running state variables (i.e., binary variables of running state). Electrolytic cell Rated power.

[0025] The energy balance constraint is: ; ; ; ; in, The total input power of the system. This refers to the real-time output power of the photovoltaic power generation unit. This represents the total output power of the system. For abandoned light power, It is the real-time operating power of the electrolytic cell array unit, which is the sum of the real-time operating power of all independently controllable electrolytic cells.

[0026] Preferably, the upper-level fixed-capacity optimization model is constrained by the total life-cycle cost constraint to ensure that the total expenditure of the project does not exceed the preset investment budget. The specific cost constraint formula is as follows: ; ; ; ; ; in, Investment costs, including the investment costs of the electrolytic cell array units. Investment cost of photovoltaic power generation units Investment cost of battery energy storage units , It is the total number of electrolytic cells. For maintenance costs, , and These are the operation and maintenance cost factors for electrolyzers, battery energy storage units, and photovoltaic power generation units, respectively. The degradation cost of battery energy storage units, The degradation cost factor per unit charge / discharge power of a battery energy storage unit. The degradation cost of the electrolytic cell array unit, for The voltage decay of the electrolytic cell at any given time. This is the voltage decay threshold at the end of the electrolytic cell's lifespan. Indicates time , It is the time step.

[0027] In a preferred embodiment, based on steps S1-S3, the total energy throughput capacity of the battery energy storage unit is equal to 2 multiplied by the average depth of discharge of the battery energy storage unit, and then multiplied sequentially by the total energy of the battery energy storage unit, the total life cycle number of the battery energy storage unit at the average depth of discharge, and the charge / discharge efficiency of the battery energy storage unit; wherein, the total life cycle number of the battery energy storage unit at the average depth of discharge is equal to the first cycle life fitting coefficient divided by the average depth of discharge. Power; It is the second cycle life fitting coefficient, and the first cycle life fitting coefficient and the second cycle life fitting coefficient are determined according to the battery type of the battery energy storage unit; In the dual-layer optimization model, the total energy throughput capacity of the battery energy storage unit is limited to be less than or equal to the pre-designed energy throughput capacity threshold. And / or, the hydrogen energy conversion unit includes an electrolyzer array unit, which is a collection of several independently controllable electrolyzers. The voltage decay of the electrolytic cell at that moment is equal to The sum of the voltage decay at start-up and shutdown, voltage decay at variable power, voltage decay at low power operation, and voltage decay at normal operation of the electrolytic cell at any given time. The voltage decay of the electrolytic cell at the start and stop times is equal to A binary variable representing the operating state of the electrolyzer at any given time. and A binary variable representing the operating state of the electrolyzer at any given time. The absolute value of the difference is multiplied by the time step and the start-up / shutdown degradation coefficient determined according to the type of electrolyzer; The voltage decay of the electrolytic cell at that moment is equal to The real-time operating power of the electrolytic cell at that time and The absolute value of the difference in real-time operating power of the electrolytic cell at any given time is multiplied by the time step and the variable power degradation coefficient determined according to the type of electrolytic cell. The voltage decay of the low-power operating cell at that time is equal to The binary variable of the electrolytic cell operating state at time [time]. Multiply by the time step and the low-power operation degradation factor determined according to the type of electrolyzer; The normal operating voltage decay of the electrolytic cell at that time is equal to Real-time operating power of the electrolytic cell Multiply by the time step and the normal operation degradation coefficient determined according to the type of electrolyzer; In the dual-layer optimization model, the cumulative voltage decay of any electrolytic cell in the electrolytic cell array unit is limited to being less than or equal to a pre-designed voltage decay threshold.

[0028] For example, the degradation of the battery energy storage unit is determined by its total energy throughput capacity: ; ; in, The total energy throughput capacity of the battery energy storage unit; The average depth of discharge of the battery energy storage unit; The number of cycles throughout the entire life cycle of the battery energy storage unit at this average depth of discharge. The total energy of the battery energy storage unit; The charge / discharge efficiency of the battery energy storage unit; the first cycle life fitting coefficient. Second cycle lifetime fitting coefficient Determined based on battery type.

[0029] The degradation of the electrolytic cell is determined by the amount of decline in its physical properties: ; ; ; ; ; in, for Time Electrolyzer The amount of voltage attenuation; Electrolytic cell The amount of voltage attenuation during startup and shutdown; Electrolytic cell The voltage attenuation of variable power; Electrolytic cell Low-power operating voltage attenuation; Electrolytic cell The normal operating voltage attenuation. for A binary variable representing the operating state of the electrolytic cell at any given time; for A binary variable representing the operating state of the electrolytic cell at any given time; for Real-time operating power of the electrolytic cell; For time step; , , , These are the start-up / shutdown degradation coefficient, variable power degradation coefficient, low power operation degradation coefficient, and normal operation degradation coefficient, determined according to the type of electrolytic cell.

[0030] It is worth noting that the binary variable for the electrolytic cell's operating status is a state parameter describing whether the equipment is operating as a whole. When the equipment is in operation, the binary variable is 1, and when the equipment is in a stopped state, the binary variable is 0.

[0031] In a preferred embodiment, based on any of the above embodiments, the hydrogen energy conversion unit includes an electrolyzer array unit, which is a collection of several independently controllable electrolyzers. The constraints of the off-grid photovoltaic hydrogen production system include safety constraints: the ratio of hydrogen impurity content on the anode side of the electrolyzer to oxygen production on the anode side of the electrolyzer needs to be less than or equal to a preset safety threshold. The hydrogen impurity content is calculated based on the hydrogen cross-flow constant, the oxygen yield on the anode side of the electrolyzer, and the rate at which hydrogen is discharged from the anode side of the electrolyzer. The hydrogen cross-flow constant is calculated based on the diffusion cross-flow, the convection cross-flow, and the circulating electrolyte mixing flow. The diffusion crossflow rate is calculated based on the effective diffusion coefficient of hydrogen in the membrane, the solubility of hydrogen in the electrolyte, the working pressure of the electrolyzer, and the thickness of the membrane. The convective crossflow rate is calculated based on the permeability of the diaphragm, the solubility of hydrogen in the electrolyte, the working pressure of the electrolytic cell, the pressure difference across the diaphragm, the dynamic viscosity of the electrolyte, and the thickness of the diaphragm. The circulating electrolyte mixing flow rate is calculated based on the solubility of hydrogen in the electrolyte, the working pressure of the electrolytic cell, and the circulation speed of the electrolyte.

[0032] Specifically, given the highly dangerous nature of hydrogen energy, safety constraints are necessary to ensure... The hydrogen impurity content on the anode side of the electrolyzer at any time and The electrolytic cell described at the time Oxygen production The ratio in the values ​​does not exceed the safety threshold, the specific value of which can be set according to actual conditions and is not limited here. The hydrogen impurity content on the anode side of the electrolyzer is positively correlated with the hydrogen impurity content and hydrogen cross-flow constant at the previous moment. The hydrogen cross-flow constant is positively correlated with diffusion cross-flow, convection cross-flow, and circulating electrolyte mixing flow. The diffusion cross-flow is positively correlated with the effective diffusion coefficient of hydrogen in the diaphragm, the solubility of hydrogen in the electrolyte, and the working pressure of the electrolyzer, and negatively correlated with the thickness of the diaphragm. The convection cross-flow is positively correlated with the permeability of the diaphragm, the solubility of hydrogen in the electrolyte, the working pressure of the electrolyzer, and the pressure difference across the diaphragm, and negatively correlated with the dynamic viscosity of the electrolyte and the thickness of the diaphragm. The circulating electrolyte mixing flow is positively correlated with the solubility of hydrogen in the electrolyte, the working pressure of the electrolyzer, and the circulation speed of the electrolyte. The hydrogen emission rate (i.e., the rate at which hydrogen is discharged from the anode side of the electrolyzer) is positively correlated with the electrolyzer's operating pressure and the volume of the upper gas phase space of the separator, and negatively correlated with the gas constant and operating temperature. The hydrogen emission rate can be determined by obtaining parameters such as the electrolyzer's operating pressure, the volume of the upper gas phase space of the separator, and the operating temperature, and then substituting them into a pre-constructed rate calculation formula. This rate calculation formula can be obtained by fitting the results of multiple experimental tests. It is worth noting that the core components of the electrolyzer include a diaphragm, an anode-side gas-liquid separator, and an electrolyte circulation system.

[0033] In a preferred embodiment, based on the previous embodiment, the hydrogen impurity content on the anode side of the electrolytic cell is calculated as follows: The hydrogen impurity content on the anode side of the electrolyzer at that time is equal to The hydrogen impurity content on the anode side of the electrolyzer at that time, plus the hydrogen cross-flow constant, and then subtracted The oxygen yield at the anode side of the electrolyzer at the specified time and The product of the hydrogen impurity content at the anode side of the electrolyzer at any given time and the quotient of the rate at which hydrogen is discharged from the anode side of the electrolyzer; wherein, The oxygen yield at the anode side of the electrolyzer at that time is equal to The product of the real-time operating power, real-time operating efficiency and time step of the electrolyzer at that moment, divided by 2, and the product of the higher calorific value of the hydrogen and the molar mass of the hydrogen; The hydrogen cross-flow constant is equal to the sum of the diffusion cross-flow, the convection cross-flow, and the circulating electrolyte mixing flow. The diffusion crossflow rate is equal to the product of the effective diffusion coefficient of hydrogen in the diaphragm, the solubility of hydrogen in the electrolyte, and the working pressure of the electrolyzer, divided by the thickness of the diaphragm. The convective crossflow rate is equal to the product of the membrane permeability, the hydrogen solubility in the electrolyte, the working pressure of the electrolyzer, and the pressure difference across the membrane, divided by the product of the electrolyte dynamic viscosity and the membrane thickness. The circulating electrolyte mixing flow rate is equal to the product of the hydrogen solubility in the electrolyte, the working pressure of the electrolytic cell, and the electrolyte circulation rate, divided by 4.

[0034] Specifically, the formula for calculating the hydrogen impurity content on the anode side of the electrolyzer is as follows: ; ; ; ; ; ; in, express The hydrogen impurity content on the anode side of the electrolyzer at all times. express Time Electrolyzer Oxygen yield on the anode side, Here is the molar mass of hydrogen. for Time Electrolyzer The hydrogen impurity content on the anode side, For time step, Hydrogen impurities from the electrolyzer The rate of discharge from the anode side; for Time Electrolyzer Real-time operating power, for Time Electrolyzer Real-time operating efficiency, The high calorific value of hydrogen; The hydrogen cross-flow constant is derived from the diffusion cross-flow. Cross-flow of convection Mixing flow rate with circulating electrolyte Composed of various elements; This represents the effective diffusion coefficient of hydrogen in the membrane. This refers to the solubility of hydrogen in the electrolyte. The working pressure of the electrolytic cell. The thickness of the diaphragm. The permeability of the diaphragm, This represents the pressure difference across the diaphragm. This refers to the dynamic viscosity of the electrolyte. This represents the electrolyte circulation rate.

[0035] In a preferred embodiment, based on any of the above embodiments, the real-time operating efficiency of the electrolytic cell is calculated in the following manner: when When the real-time operating power of the electrolytic cell is greater than zero, the real-time operating efficiency of the electrolytic cell is equal to: Multiply by the natural constant of times ( The ratio of the real-time operating power of the electrolytic cell to its rated power at any given time, raised to the power of ( ), minus Multiply The ratio of the real-time operating power of the electrolytic cell to its rated power, plus... ;in, , , and The efficiency influence coefficient of the electrolytic cell; when When the real-time operating power of the electrolytic cell is zero, the real-time operating efficiency of the electrolytic cell is zero.

[0036] For example, the hydrogen energy conversion unit includes an electrolyzer array unit, which includes multiple independently controllable electrolyzers. The real-time operating efficiency of the electrolyzers is calculated using the following formula: ; in, for Time Electrolyzer Real-time operating power, Electrolytic cell Rated power. , , and Electrolytic cell The efficiency impact coefficient.

[0037] Specifically, an empirical fitting formula for the real-time operating efficiency of the electrolyzer is constructed based on experimental data. This formula accurately fits the typical characteristics of efficiency changes in the electrolyzer under different loads, and clarifies the boundary condition where the efficiency is 0 during shutdown. This formula combines exponential and linear terms, allowing it to better reflect the actual efficiency characteristics of the equipment. By adjusting... ~ It can be adapted to electrolytic cells with different technical routes and has a wide range of engineering application value.

[0038] Compared with existing technologies, the dual-layer optimization method for off-grid photovoltaic hydrogen production systems disclosed in this invention first constructs a complete off-grid photovoltaic hydrogen production system, comprising an integrated battery energy storage unit, a photovoltaic power generation unit, and a hydrogen energy conversion unit. Based on this system architecture, a dual-layer optimization model is further established, consisting of an upper-layer capacity optimization model and a lower-layer operation optimization model, with maximizing green hydrogen production as the core objective. By solving the dual-layer optimization model, the optimal capacity configuration scheme and optimal operation strategy are obtained, thereby improving the system's utilization efficiency of renewable energy, enhancing overall low-carbon benefits, and fully leveraging the potential of the hydrogen energy conversion unit. The core feature of this dual-layer optimization model is that it fully incorporates factors related to equipment performance degradation and clarifies their direct impact on equipment lifespan. Specifically, the upper-layer capacity optimization model aims to maximize the total green hydrogen production throughout the system's entire lifespan, globally optimizing the capacity of the battery energy storage unit and the hydrogen energy conversion unit, thereby ensuring the long-term feasibility of the formulated capacity configuration scheme. The lower-level operation optimization model is based on the capacity configuration determined by the upper-level planning, with the goal of maximizing the green hydrogen production in the current scheduling cycle to achieve optimal energy scheduling of the system. To improve calculation accuracy, the lower-level operation optimization model accurately introduces the nonlinear dynamic efficiency characteristics of the electrolyzer. To ensure operational safety, safety constraints are specifically set.

[0039] See Figure 2 This invention also provides a dual-layer optimization device for an off-grid photovoltaic hydrogen production system, comprising: System building module 21 is used to integrate battery energy storage unit, photovoltaic power generation unit and hydrogen energy conversion unit to build off-grid photovoltaic hydrogen production system; The model building module 22 is used to construct a two-layer optimization model based on the off-grid photovoltaic hydrogen production system. The two-layer optimization model includes an upper-layer fixed-capacity optimization model and a lower-layer operation optimization model. The upper-layer fixed-capacity optimization model aims to maximize the total green hydrogen production over the entire life cycle of the system, with system capacity configuration as the decision variable. The lower-layer operation optimization model aims to maximize the green hydrogen production during the current scheduling period of the system, with system operation strategy as the decision variable. The solver module 23 is used to solve the two-layer optimization model to obtain the optimal capacity configuration scheme and the optimal operation strategy.

[0040] It is worth noting that the specific working process of the off-grid photovoltaic hydrogen production system dual-layer optimization device can be referred to the working process of the off-grid photovoltaic hydrogen production system dual-layer optimization method described in the above embodiments, and will not be repeated here.

[0041] See Figure 3This invention also provides a two-layer optimization device for an off-grid photovoltaic hydrogen production system, including a processor 31, a memory 32, and a computer program stored in the memory 32 and configured to be executed by the processor 31. When the processor 31 executes the computer program, it implements the steps described in the above embodiments of the two-layer optimization method for off-grid photovoltaic hydrogen production systems, for example... Figure 1 The steps S1 to S3 described above; or, when the processor 31 executes the computer program, it implements the functions of each module in the above-described device embodiments.

[0042] For example, the computer program can be divided into one or more modules, which are stored in the memory 32 and executed by the processor 31 to complete the present invention. The one or more modules can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the off-grid photovoltaic hydrogen production system's two-layer optimization device. For example, the computer program can be divided into multiple modules. The specific working process of each module can be referred to the working process of the off-grid photovoltaic hydrogen production system's two-layer optimization model described in the above embodiments, and will not be repeated here.

[0043] The off-grid photovoltaic hydrogen production system dual-layer optimization device can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. The off-grid photovoltaic hydrogen production system dual-layer optimization device may include, but is not limited to, a processor 31 and a memory 32. Those skilled in the art will understand that the off-grid photovoltaic hydrogen production system dual-layer optimization device may also include input / output devices, network access devices, buses, etc.

[0044] The processor 31 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor 31 is the control center of the off-grid photovoltaic hydrogen production system's dual-layer optimization equipment, connecting various parts of the entire off-grid photovoltaic hydrogen production system's dual-layer optimization equipment via various interfaces and lines.

[0045] The memory 32 can be used to store the computer programs and / or modules. The processor 31 realizes various functions of the off-grid photovoltaic hydrogen production system's dual-layer optimization equipment by running or executing the computer programs and / or modules stored in the memory 32 and calling the data stored in the memory 32. The memory 32 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as image playback function), etc.; the data storage area may store data created based on the use of the mobile phone, etc. In addition, the memory 32 may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0046] If the modules integrated in the off-grid photovoltaic hydrogen production system's dual-layer optimization equipment are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by the processor 31, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0047] Compared with existing technologies, the off-grid photovoltaic hydrogen production system dual-layer optimization device, equipment, and storage medium disclosed in this invention first integrates a hydrogen energy conversion unit, a battery energy storage unit, and a photovoltaic power generation unit to construct a complete off-grid photovoltaic hydrogen production system. Based on this, a dual-layer optimization model is further constructed, comprising an upper-layer capacity optimization model and a lower-layer operation optimization model. The upper-layer capacity optimization model aims to maximize the total green hydrogen production over the system's entire lifecycle, using system capacity configuration as the decision variable. The lower-layer operation optimization model aims to maximize the green hydrogen production during the current scheduling period, using system operation strategy as the decision variable. Subsequently, by solving this dual-layer optimization model, the optimal capacity configuration scheme and the optimal operation strategy are obtained. Therefore, this invention, by constructing a multi-unit collaborative off-grid photovoltaic hydrogen production system and building and solving a dual-layer optimization model with maximizing green hydrogen production as the core objective, obtains the optimal capacity configuration scheme and the optimal operation strategy, thereby improving the system's utilization efficiency of renewable energy, enhancing overall low-carbon benefits, and fully leveraging the potential of the hydrogen energy conversion unit.

[0048] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A two-layer optimization method for an off-grid photovoltaic hydrogen production system, characterized in that, include: Integrate battery energy storage units, photovoltaic power generation units, and hydrogen energy conversion units to construct an off-grid photovoltaic hydrogen production system; Based on the off-grid photovoltaic hydrogen production system, a two-layer optimization model is constructed; wherein, the two-layer optimization model includes an upper-layer constant-capacity optimization model and a lower-layer operation optimization model, the upper-layer constant-capacity optimization model aims to maximize the total green hydrogen production throughout the system's entire life cycle, and uses the system capacity configuration as the decision variable; The lower-level operation optimization model aims to maximize the green hydrogen production during the current system scheduling period, and uses the system operation strategy as the decision variable. Solve the two-layer optimization model to obtain the optimal capacity configuration scheme and the optimal operation strategy.

2. The two-layer optimization method for off-grid photovoltaic hydrogen production systems as described in claim 1, characterized in that, The hydrogen energy conversion unit includes an electrolyzer array unit, which is a collection of several independently controllable electrolyzers. The green hydrogen production during the current system scheduling period is calculated as follows: for each time step during the current system scheduling period, the real-time operating power of the electrolyzer at that time step is multiplied by the real-time operating efficiency of the same period to obtain the effective electrical energy of the electrolyzer at that time step; the total effective electrical energy is obtained by summing the effective electrical energy of all the electrolyzers at all the time steps. The total effective electrical energy is divided by the high calorific value of hydrogen to obtain the green hydrogen production during the current scheduling period of the system.

3. The two-layer optimization method for off-grid photovoltaic hydrogen production systems as described in claim 1, characterized in that, The total energy throughput capacity of the battery energy storage unit is equal to 2 multiplied by the average depth of discharge of the battery energy storage unit, and then multiplied by the total energy of the battery energy storage unit, the total life cycle count of the battery energy storage unit at the average depth of discharge, and the charge / discharge efficiency of the battery energy storage unit; wherein, the total life cycle count of the battery energy storage unit at the average depth of discharge is equal to the first cycle life fitting coefficient divided by the average depth of discharge. Power; It is the second cycle life fitting coefficient, and the first cycle life fitting coefficient and the second cycle life fitting coefficient are determined according to the battery type of the battery energy storage unit; In the two-layer optimization model, the total energy throughput capacity of the battery energy storage unit is limited to be less than or equal to a pre-designed energy throughput capacity threshold.

4. The two-layer optimization method for off-grid photovoltaic hydrogen production systems as described in claim 1, characterized in that, The hydrogen energy conversion unit includes an electrolyzer array unit, which is a collection of several independently controllable electrolyzers. The voltage decay of the electrolytic cell at that moment is equal to The sum of the voltage decay at start-up and shutdown, voltage decay at variable power, voltage decay at low power operation, and voltage decay at normal operation of the electrolytic cell at any given time. The voltage decay of the electrolytic cell at the start and stop times is equal to The sum of two variables representing the operating state of the electrolyzer at any given time The absolute value of the difference between the two variables of the electrolyzer's operating state at any given time is multiplied by the time step and the start-up / shutdown degradation coefficient determined according to the type of electrolyzer. The voltage decay of the electrolytic cell at that moment is equal to The real-time operating power of the electrolytic cell at that time and The absolute value of the difference in real-time operating power of the electrolytic cell at any given time is multiplied by the time step and the variable power degradation coefficient determined according to the type of electrolytic cell. The voltage decay of the low-power operating cell at that time is equal to The product of the binary variable representing the operating state of the electrolyzer at any given time, the time step, and the low-power operation degradation coefficient determined according to the type of the electrolyzer; The normal operating voltage decay of the electrolytic cell at that time is equal to The real-time operating power of the electrolytic cell at any given time is multiplied by the time step and the normal operation degradation coefficient determined according to the type of the electrolytic cell; The cumulative voltage decay of any electrolytic cell in the electrolytic cell array unit is limited to being less than or equal to a pre-designed voltage decay threshold.

5. The two-layer optimization method for off-grid photovoltaic hydrogen production systems as described in claim 1, characterized in that, The hydrogen energy conversion unit includes an electrolyzer array unit, which is a collection of several independently controllable electrolyzers; the constraints of the off-grid photovoltaic hydrogen production system include safety constraints: the ratio of hydrogen impurity content on the anode side of the electrolyzer to oxygen production on the anode side of the electrolyzer needs to be less than or equal to a preset safety threshold. The hydrogen impurity content is calculated based on the hydrogen cross-flow constant, the oxygen yield on the anode side of the electrolyzer, and the rate at which hydrogen is discharged from the anode side of the electrolyzer. The hydrogen cross-flow constant is calculated based on the diffusion cross-flow, the convection cross-flow, and the circulating electrolyte mixing flow. The diffusion crossflow rate is calculated based on the effective diffusion coefficient of hydrogen in the membrane, the solubility of hydrogen in the electrolyte, the working pressure of the electrolyzer, and the thickness of the membrane. The convective crossflow rate is calculated based on the permeability of the diaphragm, the solubility of hydrogen in the electrolyte, the working pressure of the electrolytic cell, the pressure difference across the diaphragm, the dynamic viscosity of the electrolyte, and the thickness of the diaphragm. The circulating electrolyte mixing flow rate is calculated based on the solubility of hydrogen in the electrolyte, the working pressure of the electrolytic cell, and the circulation speed of the electrolyte.

6. The two-layer optimization method for off-grid photovoltaic hydrogen production systems as described in claim 5, characterized in that, The hydrogen impurity content on the anode side of the electrolyzer is calculated as follows: The hydrogen impurity content on the anode side of the electrolyzer at that time is equal to The hydrogen impurity content on the anode side of the electrolyzer at that time, plus the hydrogen cross-flow constant, and then subtracted The oxygen yield at the anode side of the electrolyzer at the specified time and The product of the hydrogen impurity content at the anode side of the electrolyzer at any given time and the quotient of the rate at which hydrogen is discharged from the anode side of the electrolyzer; wherein, The oxygen yield at the anode side of the electrolyzer at that time is equal to The product of the real-time operating power, real-time operating efficiency and time step of the electrolyzer at any given time, divided by 2, and the product of the higher calorific value of the hydrogen and the molar mass of the hydrogen; The hydrogen cross-flow constant is equal to the sum of the diffusion cross-flow, the convection cross-flow, and the circulating electrolyte mixing flow. The diffusion crossflow rate is equal to the product of the effective diffusion coefficient of hydrogen in the diaphragm, the solubility of hydrogen in the electrolyte, and the working pressure of the electrolyzer, divided by the thickness of the diaphragm. The convective crossflow rate is equal to the product of the membrane permeability, the hydrogen solubility in the electrolyte, the working pressure of the electrolyzer, and the pressure difference across the membrane, divided by the product of the electrolyte dynamic viscosity and the membrane thickness. The circulating electrolyte mixing flow rate is equal to the product of the hydrogen solubility in the electrolyte, the working pressure of the electrolytic cell, and the electrolyte circulation rate, divided by 4.

7. The two-layer optimization method for off-grid photovoltaic hydrogen production systems as described in claim 2, characterized in that, The real-time operating efficiency of the electrolytic cell is calculated in the following way: when When the real-time operating power of the electrolytic cell is greater than zero, the real-time operating efficiency of the electrolytic cell is equal to: Multiply by the natural constant of times ( The ratio of the real-time operating power of the electrolytic cell to its rated power at any given time, raised to the power of ( ), minus Multiply The ratio of the real-time operating power of the electrolytic cell to its rated power, plus... ;in, , , and The efficiency influence coefficient of the electrolytic cell; when When the real-time operating power of the electrolytic cell is zero, the real-time operating efficiency of the electrolytic cell is zero.

8. A dual-layer optimization device for an off-grid photovoltaic hydrogen production system, characterized in that, include: The system building module is used to integrate battery energy storage units, photovoltaic power generation units, and hydrogen energy conversion units to build an off-grid photovoltaic hydrogen production system; The model building module is used to construct a two-layer optimization model based on the off-grid photovoltaic hydrogen production system. The two-layer optimization model includes an upper-layer capacity optimization model and a lower-layer operation optimization model. The upper-layer capacity optimization model aims to maximize the total green hydrogen production over the entire life cycle of the system, with system capacity configuration as the decision variable. The lower-layer operation optimization model aims to maximize the green hydrogen production during the current scheduling period of the system, with system operation strategy as the decision variable. The solution module is used to solve the two-layer optimization model to obtain the optimal capacity configuration scheme and the optimal operation strategy.

9. A dual-layer optimization device for an off-grid photovoltaic hydrogen production system, characterized in that, The system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the two-layer optimization method for off-grid photovoltaic hydrogen production systems as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the two-layer optimization method for off-grid photovoltaic hydrogen production system as described in any one of claims 1 to 7.

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