Electro-hydrogen dynamic collaborative coupling calculation method

Abstract

The invention provides an electricity-hydrogen dynamic collaborative coupling calculation method, which comprises the following steps: S10: inputting basic data including full-time-sequence wind and light resource data, hydrogen load demand data, technical economic parameters of target equipment and project financial parameters; s20, constructing an upper-layer capacity configuration optimization model with the lowest system life cycle leveling cost of the hydrogen production system as a target; s30, constructing a lower-layer operation simulation model based on a capacity configuration scheme obtained by the upper-layer capacity configuration optimization model; s40, according to the annual operation key economic and technical indexes output by the lower-layer operation simulation model, calculating the system full-life-cycle leveling cost under the current capacity configuration scheme; and S50, judging whether the optimization process is converged or not. By constructing a framework of upper-layer planning optimization-lower-layer operation simulation-economical efficiency closed-loop feedback, dynamic association of system planning and operation is realized, the problem that early-stage planning capacity configuration is separated from later-stage actual operation scheduling is solved, and the economical efficiency of the whole life cycle of the electro-hydrogen system is improved.

Description

Technical Field

[0001] This application relates to the field of hydrogen energy storage technology, and in particular to a dynamic synergistic coupling calculation method for electro-hydrogen. Background Technology

[0002] As the global energy structure shifts towards cleaner and lower-carbon energy, the development and utilization of renewable energy sources such as wind and solar power are becoming increasingly widespread. Wind-solar hydrogen production systems, as an important pathway to convert fluctuating renewable energy into hydrogen energy, offer advantages such as long energy storage cycles, high energy density, and diverse application scenarios, and have become one of the key directions for the coordinated development of the hydrogen energy industry and renewable energy.

[0003] Existing wind-solar hydrogen production systems typically include wind power generation units, photovoltaic power generation units, water electrolysis hydrogen production units, energy storage units, and hydrogen load units. During system operation, wind and solar power output is intermittent and fluctuates due to natural conditions, while hydrogen load (such as industrial hydrogen and transportation hydrogen) usually has a certain degree of stability or predictability. Achieving dynamic matching between wind and solar power generation and hydrogen load, and rationally allocating energy storage capacity, has become a key issue affecting the system's economics and operational efficiency.

[0004] In actual operation, the system is prone to unreasonable equipment capacity configuration, high initial investment or insufficient operating benefits due to the lack of coordination between the planning stage and the operation strategy, which affects the total life cycle cost of the project. The lack of refined simulation of the dynamic matching between wind and solar power output fluctuations and load demand leads to serious wind and solar curtailment and low resource utilization. The capacity of energy storage facilities is often set based on experience or simple rules, which fails to give full play to their regulatory role in cross-time energy transfer and makes it difficult to balance investment costs and the value of benefits. Summary of the Invention

[0005] In view of this, embodiments of this application provide a method for calculating the dynamic synergistic coupling of electricity and hydrogen. One or more embodiments of this application also relate to a computing device, a computing apparatus, a computer-readable storage medium, and a computer program for calculating the dynamic synergistic coupling of electricity and hydrogen, in order to address the technical deficiencies existing in the prior art.

[0006] In a first aspect, embodiments of this application provide a dynamic synergistic coupling calculation method for electro-hydrogen, applied to a hydrogen production system, including:

[0007] S10: Input basic data, including full-time wind and solar resource data, hydrogen load demand data, technical and economic parameters of target equipment and project financial parameters. Target equipment includes wind turbines, photovoltaic modules, energy storage facilities and electrolyzers.

[0008] S20: Construct an upper-level capacity configuration optimization model with the goal of minimizing the levelized cost of the hydrogen production system throughout its entire life cycle. The decision variables include the rated power of the wind turbine, the rated power of the photovoltaic system, the rated power of the electrolyzer, and the rated capacity of the energy storage facility. The upper-level capacity configuration optimization model adopts an intelligent optimization algorithm to output one or more preliminary capacity configuration schemes. Among them, the intelligent optimization algorithm includes particle swarm optimization, genetic algorithm, or differential evolution algorithm.

[0009] S30: Based on the capacity configuration scheme output by the upper-level capacity configuration optimization model, a lower-level operation simulation model is constructed. The lower-level operation simulation model uses hourly time-series data throughout the year and performs power balance calculations based on the actual operating constraints of the target equipment to simulate the dynamic behavior of the hydrogen production system in actual operation, thereby outputting annual key economic and technical indicators for operation. The annual key economic and technical indicators for operation include: annual hydrogen production, wind and solar curtailment rate, and equipment utilization rate.

[0010] S40: Calculate the levelized cost of the system's entire lifecycle under the current capacity configuration scheme based on the annual key economic and technical indicators output by the lower-level operation simulation model;

[0011] S50: Determine whether the optimization process has converged. If it has converged, output the optimal capacity configuration scheme and the corresponding operating strategy. If it has not converged, update the capacity configuration scheme based on the intelligent optimization algorithm and return to step S20 for the next round of iterative optimization.

[0012] In one possible implementation, the lower-level simulation model achieves real-time balance between electrical and hydrogen power by solving an optimization problem within each time unit t. The objective function of the optimization problem is to minimize the sum of the cost of wind and solar curtailment penalties and the cost of hydrogen load shortage penalties.

[0013] In one possible implementation, the changes in the total input energy, total output energy, energy loss, and stored energy of the hydrogen production system within a preset time period are monitored to achieve energy balance.

[0014] The energy balance is expressed by the following formula:

[0015]

[0016] Among them, E wind E pv These represent the total power generation of wind power and photovoltaic power during the calculation period, respectively.

[0017] This represents the total energy consumed in producing hydrogen during the calculation period.

[0018] E curtail This represents the total amount of wind and solar power curtailed within the calculation period.

[0019] E BESS,Loss This represents the net change in energy of the battery energy storage system during the calculation period.

[0020] In one possible implementation, the optimization problem of the lower-level running simulation model needs to satisfy the following constraints:

[0021] P wind (t)+P pv (t)+P BESS,discharge (t)=P elec (t)+P BESS,charge (t)+P curtailment (t)+P BESS,Loss (t)

[0022] Among them, P wind (t) represents the wind power at time t, P pv (t) represents the photovoltaic power at time t, P BESS,discharge (t) represents the discharge power of the battery energy storage system at time t, P BESS,charge (t) represents the charging power of the battery energy storage system at time t, P elec (t) represents the power consumed by the electrolytic cell at time t, P curtailment (t) represents the power of wind and solar power curtailment at time t, P BESS,Loss (t) represents the power loss of the energy storage system at time t.

[0023] In one possible implementation, the constraints further include state update constraints for the energy storage facility, wherein the calculation formula for the state update constraints of the energy storage facility includes:

[0024] SOC(t)=SOC(t-1)+C(t)-D(t)-Loss(t)

[0025] Wherein, SOC(T) represents the energy stored in the energy storage facility at time t, SOC(T-1) represents the energy stored in the energy storage facility at time t-1, C(T) represents the energy flow to the energy storage facility at time t, D(t) represents the energy consumption flow of the energy storage facility at time t, and Loss(t) represents the energy loss of the energy storage system at time t.

[0026] In one possible implementation, the objective function of the upper-layer capacity configuration optimization model is to minimize the system's total lifecycle leveled cost, wherein the formula for calculating the minimum system lifecycle leveled cost includes:

[0027]

[0028] Where C represents the power plant's fixed costs, R represents the power plant's residual value of fixed assets, t represents the year index, and Ot F represents the annual operating and maintenance cost of a power plant in year t. t T represents the interest payable by the power plant in year t. t H represents the tax payable by the power plant in year t, r represents the benchmark discount rate, and H represents the tax payable by the power plant in year t. t Let t be the annual hydrogen production of the electrolysis system in year t.

[0029] In one possible implementation, the criterion for determining whether the optimization process has converged includes: when the rate of change of the levelized cost of the system's entire lifecycle obtained from two consecutive iterations is less than a rate of change threshold, the optimization process is determined to have converged.

[0030] In one possible implementation, the updated capacity configuration scheme includes automatically adjusting the capacity configuration of wind turbines, photovoltaics, electrolyzers, and energy storage facilities using particle swarm optimization, genetic algorithms, or differential algorithms.

[0031] In one possible implementation, the technical and economic parameters of the target equipment include the unit power investment cost and operation and maintenance cost of wind turbines, photovoltaic modules, and electrolyzers, as well as the unit capacity investment cost and operation and maintenance cost of energy storage facilities.

[0032] In one possible implementation, the project financial parameters include the project operating period, benchmark discount rate, equity ratio, and financing interest rate.

[0033] Secondly, embodiments of this application provide an electro-hydrogen dynamic cooperative coupling computing device, comprising:

[0034] The data input module takes in basic data, including real-time wind and solar resource data, hydrogen load demand data, technical and economic parameters of the target equipment, and project financial parameters. The target equipment includes wind turbines, photovoltaic modules, energy storage facilities, and electrolyzers.

[0035] The capacity configuration optimization module constructs an upper-level capacity configuration optimization model with the goal of minimizing the levelized cost of the hydrogen production system throughout its entire life cycle. The decision variables include the rated power of the wind turbine, the rated power of the photovoltaic system, the rated power of the electrolyzer, and the rated capacity of the energy storage facility. The upper-level capacity configuration optimization model adopts an intelligent optimization algorithm to output one or more preliminary capacity configuration schemes. The intelligent optimization algorithm includes particle swarm optimization, genetic algorithm, or differential evolution algorithm.

[0036] The simulation module operates by constructing a lower-level operation simulation model based on the capacity configuration scheme output by the upper-level capacity configuration optimization model. The lower-level operation simulation model uses hourly time-series data throughout the year and performs power balance calculations based on the actual operating constraints of the target equipment to simulate the dynamic behavior of the hydrogen production system in actual operation, thereby outputting annual key economic and technical indicators for operation. The annual key economic and technical indicators for operation include: annual hydrogen production, wind and solar curtailment rate, and equipment utilization rate.

[0037] The economic evaluation module calculates the levelized cost of the system's entire lifecycle under the current capacity configuration scheme based on the annual key economic and technical indicators output by the lower-level operation simulation model.

[0038] The iterative control module determines whether the optimization process has converged. If it has converged, it outputs the optimal capacity configuration scheme and the corresponding operating strategy. If it has not converged, it updates the capacity configuration scheme based on the intelligent optimization algorithm and returns to step S20 for the next round of iterative optimization.

[0039] Thirdly, embodiments of this application provide a computing device, including:

[0040] Memory and processor;

[0041] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the above-described electro-hydrogen dynamic synergistic coupling calculation method.

[0042] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the above-described electro-hydrogen dynamic cooperative coupling calculation method.

[0043] Fifthly, embodiments of this application provide a computer program, wherein when the computer program is executed in a computer, the computer is instructed to perform the steps of the above-described electro-hydrogen dynamic synergistic coupling calculation method.

[0044] The technical solution provided in this application, through the construction of a two-layer iterative optimization framework of "upper-layer planning optimization - lower-layer operation simulation - economic closed-loop feedback," achieves dynamic correlation between system planning and operation. Technically, it solves the problem of the disconnect between early-stage planned capacity configuration and later-stage actual operation scheduling, improving the economic efficiency of the hydrogen-electric system throughout its entire lifecycle. Specifically, in stage S10, the system inputs basic data such as full-time wind and solar resources, hydrogen load demand, and the technical and economic parameters of the target equipment; in stage S20, an upper-layer capacity configuration model is constructed with the goal of minimizing the levelized cost throughout the system's lifecycle, using intelligent optimization algorithms such as particle swarm optimization to generate preliminary capacity configuration schemes for wind turbines, photovoltaics, electrolyzers, and energy storage facilities; in stage S30, a lower-layer operation simulation model is constructed based on the configuration scheme, performing hourly simulations for 8760 hours throughout the year, calculating power balance under the condition of meeting actual equipment operation constraints, and outputting key operational indicators such as annual hydrogen production and wind / solar curtailment rates; in stage S40, the levelized cost throughout the system's lifecycle is calculated back based on the operational indicators; and in stage S50, convergence judgment is used to iteratively optimize the scheme until the optimal configuration is output. First, this scheme achieves dynamic coupling between capacity configuration and actual operational characteristics through closed-loop iteration of upper-level planning and lower-level simulation, thus solving the problem of the separation between planning and operation from a methodological perspective. Second, it adopts levelized cost throughout the entire life cycle as a unified evaluation index, ensuring that economic factors at different time scales are optimized in a coordinated manner. Finally, by combining intelligent algorithms with time-by-time simulation, it improves the engineering applicability of the scheme while ensuring solution efficiency, providing a scientific basis for system investment decisions that combines economy and reliability. Attached Figure Description

[0045] Figure 1 This is a flowchart of a dynamic synergistic coupling calculation method for electro-hydrogen provided in one embodiment of this application;

[0046] Figure 2 This is a schematic diagram of the structure of an electro-hydrogen dynamic cooperative coupling computing device provided in one embodiment of this application;

[0047] Figure 3 This is a structural block diagram of a computing device provided in one embodiment of this application. Detailed Implementation

[0048] Many specific details are set forth in the following description to provide a full understanding of this application. However, this application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of this application; therefore, this application is not limited to the specific embodiments disclosed below.

[0049] The terminology used in one or more embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the scope of one or more embodiments of this application. The singular forms “a” and “the” as used in one or more embodiments of this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in one or more embodiments of this application refers to and includes any or all possible combinations of one or more associated listed items.

[0050] It should be understood that although the terms first, second, etc., may be used to describe various information in one or more embodiments of this application, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first may also be referred to as second without departing from the scope of one or more embodiments of this application, and similarly, second may also be referred to as first. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0051] This application provides a method for calculating the dynamic synergistic coupling of electricity and hydrogen. This application also relates to a computing device, a computer-readable storage medium, and a computer program for calculating the dynamic synergistic coupling of electricity and hydrogen, which will be described in detail in the following embodiments.

[0052] Figure 1 A flowchart of a dynamic synergistic coupling calculation method for electro-hydrogen provided according to an embodiment of this application is shown.

[0053] See Figure 1 The method specifically includes the following steps:

[0054] S10: Input basic data, including full-time wind and solar resource data, hydrogen load demand data, technical and economic parameters of target equipment and project financial parameters. Target equipment includes wind turbines, photovoltaic modules, energy storage facilities and electrolyzers.

[0055] S20: Construct an upper-level capacity configuration optimization model with the goal of minimizing the levelized cost of the hydrogen production system throughout its entire life cycle. The decision variables include the rated power of the wind turbine, the rated power of the photovoltaic system, the rated power of the electrolyzer, and the rated capacity of the energy storage facility. The upper-level capacity configuration optimization model adopts an intelligent optimization algorithm to output one or more preliminary capacity configuration schemes. Among them, the intelligent optimization algorithm includes particle swarm optimization, genetic algorithm, or differential evolution algorithm.

[0056] S30: Based on the capacity configuration scheme output by the upper-level capacity configuration optimization model, a lower-level operation simulation model is constructed. The lower-level operation simulation model uses hourly time-series data throughout the year and performs power balance calculations based on the actual operating constraints of the target equipment to simulate the dynamic behavior of the hydrogen production system in actual operation, thereby outputting annual key economic and technical indicators for operation. The annual key economic and technical indicators for operation include: annual hydrogen production, wind and solar curtailment rate, and equipment utilization rate.

[0057] S40: Calculate the levelized cost of the system's entire lifecycle under the current capacity configuration scheme based on the annual key economic and technical indicators output by the lower-level operation simulation model;

[0058] S50: Determine whether the optimization process has converged. If it has converged, output the optimal capacity configuration scheme and the corresponding operating strategy. If it has not converged, update the capacity configuration scheme based on the intelligent optimization algorithm and return to step S20 for the next round of iterative optimization.

[0059] Regarding S10

[0060] In some embodiments, basic data required for optimization calculations can be collected and input, which may include wind and solar resource data, hydrogen load demand data, technical and economic parameters of the target equipment, and project financial parameters.

[0061] The specific data is as follows:

[0062] (1) Resource and load data, including wind speed and solar irradiance data for 8760 hours in a typical year; hydrogen load demand data for 8760 hours; hydrogen sales electricity price or equivalent revenue parameters.

[0063] (2) Technical and economic parameters of the target equipment: Key equipment of the wind-solar hydrogen production system, including the unit power investment and operation and maintenance costs, efficiency, and lifespan of wind turbines, photovoltaic modules, and electrolyzers. Key equipment of the energy storage system: Unit capacity investment cost, operation and maintenance costs, and maximum hydrogen charging / discharging rate of energy storage facilities.

[0064] (3) Project financial boundary parameters: total project investment, capital ratio, financing interest rate, operating period, taxes and fees, etc.

[0065] Optimize algorithm parameters: convergence judgment threshold, etc.

[0066] Step S10 lays the foundation for the entire optimization process by systematically collecting and inputting multi-source data. Specifically, this process includes: collecting hourly wind speed and solar irradiance data for typical years to characterize the spatiotemporal fluctuations of wind and solar resources; obtaining corresponding time-series hydrogen load demand data to reflect dynamic demand at the hydrogen consumption end; and inputting technical and economic parameters such as unit investment cost, operation and maintenance cost, and operating efficiency of wind turbines, photovoltaic modules, electrolyzers, and energy storage facilities, as well as setting project financial boundary parameters including discount rate and operating period. This step, by establishing a complete and accurate parameter database, ensures that the subsequent optimization model accurately reflects the actual operating environment and economic conditions of the system. By constructing a comprehensive and time-aligned basic data system, the adaptability of the capacity configuration scheme to wind and solar fluctuations and dynamic load changes is guaranteed from the source, providing a reliable input foundation for subsequent two-layer optimization. This effectively avoids planning deviations caused by incomplete data or time-series mismatches, providing data support for improving the overall economic efficiency and operational reliability of the system.

[0067] In some embodiments, the technical and economic parameters of the target equipment include the unit power investment cost and operation and maintenance cost of wind turbines, photovoltaic modules, and electrolyzers, as well as the unit capacity investment cost and operation and maintenance cost of energy storage facilities. By fully importing these quantified cost and efficiency data into the optimization model, a precise cost calculation basis and efficiency constraint are provided for subsequent capacity configuration optimization and operational simulation. The direct technical effect of this claim is that, by accurately quantifying the full-cycle cost and performance of key equipment, it ensures that the upper-level capacity configuration model can accurately balance initial investment and long-term operating costs during the optimization process, thereby guaranteeing the economic rationality and engineering feasibility of the final optimized solution from the data source.

[0068] In some embodiments, project financial parameters include the project operating period, benchmark discount rate, equity ratio, and financing interest rate. The data input phase of system optimization initialization is fully imported, where the operating period defines the time boundary for the full life-cycle cost-benefit analysis; the benchmark discount rate, as a quantitative benchmark for the time value of money, directly affects the levelized cost calculation; and the equity ratio and financing interest rate jointly determine the project's capital structure characteristics and financial costs. Through the synergistic effect of these parameters, a full life-cycle economic evaluation framework that conforms to the project's actual financial conditions is constructed. Its technical effect is to ensure that the capacity configuration scheme output by the optimized model not only meets technical feasibility requirements but also possesses financial rationality and investment decision value, thereby fundamentally avoiding the problem of planning schemes being out of touch with economic reality due to ambiguous financial boundary conditions.

[0069] Regarding S20

[0070] In some embodiments, step 20 achieves preliminary optimization of capacity configuration by constructing a systematic mathematical optimization framework. The specific implementation process is as follows: First, the lowest levelized cost of operation (LCOH) over the entire system's lifecycle is established as the single quantitative objective. The rated power of wind turbines, photovoltaics, electrolyzers, and energy storage facilities are used as decision variables to construct a complete economic model that includes investment costs, operation and maintenance costs, and equipment lifespan. Then, intelligent optimization algorithms such as particle swarm optimization, genetic algorithms, or differential evolution algorithms are employed. Through mechanisms such as population initialization, fitness evaluation, and iterative updates, a global exploration is conducted in a multi-dimensional solution space that satisfies equipment physical constraints, automatically generating a preliminary capacity configuration scheme that balances investment economics and technical feasibility. The technical effect of this process is that the parallel search capability of intelligent algorithms significantly improves the solution efficiency of complex nonlinear optimization problems. Simultaneously, using LCOH as a unified evaluation standard ensures the comparability between different configuration schemes, providing a high-quality candidate solution set for subsequent refined operation simulations, thereby improving the convergence speed and scheme quality of the overall optimization process from the source.

[0071] In some embodiments, the objective function of the upper-layer capacity configuration optimization model is to minimize the system's total lifecycle leveled cost, wherein the formula for calculating the minimum system lifecycle leveled cost includes:

[0072]

[0073] Where C represents the power plant's fixed costs, R represents the power plant's residual value of fixed assets, t represents the year index, and O t F represents the annual operating and maintenance cost of a power plant in year t. t T represents the interest payable by the power plant in year t. t H represents the tax payable by the power plant in year t, r represents the benchmark discount rate, and H represents the tax payable by the power plant in year t. t Let t be the annual hydrogen production of the electrolysis system in year t.

[0074] The upper-level capacity configuration optimization model achieves optimal system planning by constructing a comprehensive objective function that integrates the economics and technological output throughout the entire lifecycle. Specifically, this process involves discounting the initial total investment cost, annual operation and maintenance costs, financial expenses, and taxes (cash outflows) with the residual value of fixed assets and annual net cash inflows from hydrogen sales (cash inflows). Simultaneously, it incorporates time-series discounted data on annual hydrogen production to construct a lifecycle levelized cost index centered on minimizing the unit hydrogen production cost. This model deeply integrates long-term technical performance (hydrogen production capacity) with full-cycle economic factors (investment, operation and maintenance, financing, and taxes) through dynamic financial evaluation. This process overcomes the limitation of traditional planning where economic evaluation and technical performance are disconnected, enabling comprehensive comparison and selection of system solutions within a unified quantitative framework. It ensures both the economic feasibility of the solutions throughout their lifecycle and the accuracy and comparability of hydrogen production cost accounting, providing a scientific basis for investment decisions that combines financial rigor and technological rationality.

[0075] On the other hand, by integrating a steady-state equilibrium long-term feedback mechanism, the specific implementation process is as follows: When constructing the upper-level capacity configuration optimization model, the system, based on the input wind and solar resource data, first extracts the peak and valley power prediction characteristics of long-term wind and solar output. Then, based on these characteristics, it automatically generates deep hydrogen charging strategies for energy storage facilities during periods of high wind and solar output and deep hydrogen discharging strategies during periods of low wind and solar output. These strategies are quantified into target charging and discharging depth constraints and directly embedded into the optimization model with the goal of minimizing the levelized cost over the entire life cycle. This allows capacity configuration decisions and long-term operation strategies to form a closed-loop feedback. The technical effect of this step is that by transforming long-term power prediction characteristics into specific energy storage operation constraints, the energy storage capacity determined in the planning stage can proactively adapt to the seasonal and long-term fluctuation characteristics of wind and solar resources. From the system architecture level, this solves the problem of the disconnect between capacity configuration and long-term operation needs in traditional planning, significantly improving the system's adaptability to renewable energy fluctuations, the economic efficiency of the entire life cycle investment, and the long-term reliability of hydrogen supply.

[0076] Regarding S30

[0077] In some embodiments, step 30 achieves accurate evaluation of capacity configuration schemes by constructing a refined operational simulation environment. Specifically, the process involves: based on the equipment capacity parameters output from the upper-level model, such as establishing a dynamic simulation model with hourly wind and solar power output data and hydrogen load data for 8760 hours per year as input and equipment operating characteristics (such as electrolyzer start-up and shutdown constraints, energy storage charge-discharge rates, etc.) as boundary conditions, solving the power balance equation at each moment accurately simulates the energy flow and state transitions between wind, solar, storage, hydrogen, and load in the system. Ultimately, it outputs three key economic and technical indicators: annual hydrogen production (reflecting system output efficiency), wind and solar curtailment rate (characterizing resource utilization efficiency), and equipment utilization rate (reflecting investment effectiveness). This process transforms static capacity parameters into dynamic operational performance through full-time operational simulation, providing a reliable quantitative basis for upper-level optimization. This effectively avoids capacity redundancy or insufficiency caused by the disconnect between planning and operation, significantly improving the practical operability and life-cycle economic benefits of the system design scheme.

[0078] In some embodiments, hourly operation simulations are conducted for a given capacity configuration scheme in a typical year. The optimization problem for each time series unit t aims to achieve the optimal balance between electricity and hydrogen power at the technical level, providing accurate annual key economic and technical indicators (such as annual hydrogen production, wind and solar curtailment rates, and equipment utilization rates) for the levelized cost of the upper-level computing system throughout its entire lifecycle. Its mathematical model is expressed as follows:

[0079] Objective function: Minimize the [penalty cost for wind and solar power curtailment + penalty cost for hydrogen supply shortage]

[0080] Constraints:

[0081] (1) Energy balance of hydrogen production system: E_wind+E_pv=E_H2+E_curtail+E_BESS,Loss.

[0082] Meaning: Within a preset time period, the total energy input to the hydrogen production system is equal to the change in total output energy, energy loss, and stored energy.

[0083] (2) Power balance of hydrogen production system: P_wind(t) + P_pv(t) + P_BESS,discharge(t) = P_elec(t) + P_BESS,charge(t) + P_curtailment(t) + P_BESS,Loss(t)

[0084] Meaning: The sum of total wind and solar power generation and energy storage discharge power equals the sum of electrolyzer power consumption, energy storage charging power, wind and solar curtailment power, and energy storage system losses. This is the core constraint, reflecting the dynamic matching relationship between electricity, hydrogen, and energy storage.

[0085] (3) Dynamic constraints of motor slot equipment:

[0086] The power of an electrolytic cell is between its minimum technical output and its rated power (the operating range of an electrolytic cell is 30-110%).

[0087] (4) Dynamic constraints of energy storage devices:

[0088] State update: SOC(t) = SOC(t-1) + C(t) - D(t) - Loss(t). Where SOC(t) is the state (stored energy) of the energy storage facility at time t.

[0089] Capacity constraint: 0 ≤ SOC(t) ≤ C_cap (C_cap is the rated capacity of the energy storage facility).

[0090] Charge / discharge rate constraint: The charge / discharge rate of hydrogen shall not exceed the equipment limit.

[0091] In some embodiments, the lower-level simulation model achieves real-time balance between electrical and hydrogen power by solving an optimization problem within each time unit t. The objective function of the optimization problem is to minimize the sum of the cost of wind and solar curtailment penalties and the cost of hydrogen load shortage penalties.

[0092] Step S30 involves a crucial expansion of the lower-level operational simulation model, clarifying its specific implementation within each time unit t. This step achieves a dynamic balance between electrical and hydrogen power by constructing and solving a real-time optimization problem aimed at minimizing the sum of "wind and solar curtailment penalty costs" and "hydrogen load shortage penalty costs." Specifically, within 8760 hourly time steps throughout the year, the algorithm, based on current wind and solar power generation and hydrogen load demand, dynamically allocates the power consumption of the electrolyzer, the power curtailment of wind and solar power, and the hydrogen charging / discharging flow of the energy storage facility through optimized calculations. This prioritizes hydrogen load supply and maximizes wind and solar power consumption while meeting all equipment operational constraints. The direct technical effect of this claim is that it transforms macro-level operational strategies into executable hourly optimization instructions, forcing the system to proactively avoid energy waste and supply shortages at the operational level through an economic penalty mechanism. This technically ensures that the capacity configuration determined by the upper-level planning can achieve its expected economic and reliability value in actual operation.

[0093] In some embodiments, the changes in the total input energy, total output energy, energy loss, and stored energy of the hydrogen production system are used to achieve energy balance.

[0094] The energy balance is expressed by the following formula:

[0095]

[0096] Among them, E wind Epv These represent the total power generation of wind power and photovoltaic power during the calculation period, respectively.

[0097] This represents the total energy consumed in producing hydrogen during the calculation period.

[0098] E curtail This represents the total amount of wind and solar power curtailed within the calculation period.

[0099] E BESS,Loss This represents the net change in energy of the battery energy storage system during the calculation period.

[0100] In some embodiments, the optimization problem of the lower-level running simulation model needs to satisfy the following constraints:

[0101] P wind (t)+P pv (t)+P BESS,discharge (t)=P elec (t)+P BESS,charge (t)+P curtailment (t)+P BESS,Loss (t)

[0102] Among them, P wind (t) represents the wind power at time t, P pv (t) represents the photovoltaic power at time t, P BESS,discharge (t) represents the discharge power of the battery energy storage system at time t, P BESS,charge (t) represents the charging power of the battery energy storage system at time t, P elec (t) represents the power consumed by the electrolytic cell at time t, P curtailment (t) represents the power of wind and solar power curtailment at time t, P BESS,Loss (t) represents the power loss of the energy storage system at time t.

[0103] In some embodiments, lower-level operational simulations ensure the physical rationality of system operation by establishing a multi-timescale energy balance system. This process involves two levels: at the long-term energy balance level, the system requires that the total power generation from wind and solar power must equal the sum of the effective energy consumed in hydrogen production (considering electrolyzer efficiency and auxiliary system losses), the energy lost due to wind and solar curtailment, and the net change in energy stored in the battery energy storage system; at the instantaneous power balance level, the system requires that the sum of wind power, solar power, and battery discharge power at each moment must equal the sum of the power consumed by the electrolyzer, the battery charging power, the power lost due to wind and solar curtailment, and the power lost through energy storage. The technical effect of this multi-level balance mechanism is that it ensures the overall energy efficiency of the system through long-term energy conservation and ensures operational stability through instantaneous power balance. This verifies the rationality of capacity configuration at the macro level and guarantees the feasibility of real-time system operation at the micro level, thus establishing a rigorous mathematical bridge between planning and operation and significantly enhancing the engineering practical value of the optimization results.

[0104] In other embodiments, by introducing the charge and discharge power term of the battery energy storage system (BESS) into the power balance constraint on the hydrogen production side, a refined and coordinated optimization of the system energy flow description is achieved. Specifically, the power balance equation at each time t includes not only wind power, photovoltaic power, electrolyzer power consumption, and curtailed wind and solar power, but also explicitly incorporates the charge power of the battery energy storage system. BESS,charge (t) and release P BESS,discharge (t) Electrical power, thus mathematically characterizing the instantaneous power balance relationship of the multi-energy flow coupling node of "wind and solar power generation - battery energy storage - electrolysis hydrogen production". This enables the dynamic incorporation of fast-response battery energy storage into the core constraints, establishing its crucial role at the model level in smoothing second- or minute-level fluctuations in wind and solar power and complementing the slow-response hydrogen production facilities on a time scale. This allows the optimization algorithm to collaboratively configure the capacity of both electric and hydrogen energy storage and formulate their joint operation strategy, thereby ensuring stable system operation while further improving the immediate absorption capacity of renewable energy and the overall operational economy.

[0105] In some embodiments, the constraints further include state update constraints for the energy storage facility, wherein the calculation formula for the state update constraints of the energy storage facility includes:

[0106] SOC(t)=SOC(t-1)+C(t)-D(t)-Loss(t)

[0107] Wherein, SOC(t) represents the energy stored in the energy storage facility at time t, SOC(t-1) represents the energy stored in the energy storage facility at time t-1, C(t) represents the energy flow to the energy storage facility at time t, D(t) represents the energy consumption flow of the energy storage facility at time t, and Loss(t) represents the energy loss of the energy storage system at time t.

[0108] This constraint, based on the principle of energy conservation, defines the stored energy SOC(t) at the current time t as the algebraic sum of the stored energy SOC(t-1) at the previous time, the net storage capacity (stored energy flow C(t) minus consumed energy flow D(t)) during the current period, and the system loss Loss(t). This enables the recursive calculation of the energy storage facility's state. Its technical advantage lies in accurately characterizing the dynamic characteristics of the energy storage facility during time-series operation through mathematical modeling. This ensures both the continuity of the energy account and reflects the loss characteristics of the actual system, providing an accurate operational simulation basis for upper-level capacity optimization. This allows the energy storage capacity configuration scheme to truly reflect its actual adjustment capability during cross-period energy transfer.

[0109] Regarding S40

[0110] In some embodiments, key economic and technical indicators (KPIs) for calculating the levelized cost of the entire lifecycle of the system are extracted from the results of lower-level operational simulations.

[0111] Annual hydrogen sales revenue: calculated based on the actual hydrogen load met.

[0112] Annual maintenance cost: calculated based on actual equipment runtime and operating conditions.

[0113] Annual wind and solar curtailment losses: used to assess resource utilization efficiency.

[0114] The above indicators are fed back to the upper-level model to calculate the LCOH under this configuration scheme.

[0115] Based on the hourly operating data of 8760 hours throughout the year output in step S30, key performance indicators such as annual actual hydrogen sales revenue, cumulative operation and maintenance costs of each device, and total wind and solar curtailment are accurately calculated. These time-series operating results are then converted into financial data such as power plant fixed costs required for calculating levelized cost, and finally fed back to the upper-level capacity configuration model. The technical effect of this step is that it accurately quantifies physical quantities such as equipment operating status and energy utilization efficiency into economic benefit indicators, establishing a direct mapping relationship between "operating performance and economic benefits." This allows the optimization algorithm to evaluate the merits of the configuration scheme based on accurate economic feedback, thereby ensuring the economic rationality of the iterative optimization direction and providing key data support for the scientific decision-making of system capacity configuration schemes.

[0116] Regarding S50

[0117] In some embodiments, it can be determined whether the optimization process has converged, where the main criterion is whether the rate of change of the levelized cost over the entire system lifecycle is less than a preset threshold. If converged, the final optimization result is output, including:

[0118] (1) Optimal capacity configuration scheme.

[0119] (2) Corresponding typical annual hourly operation strategy.

[0120] (3) Comprehensive evaluation report on system economy and reliability.

[0121] If convergence fails, an intelligent optimization algorithm is used to automatically adjust the capacity configuration of wind turbines, photovoltaics, electrolyzers, and energy storage facilities based on the feedback performance indicators, generate a new generation configuration scheme, and return to step S20 for a new round of iterative calculation.

[0122] In some embodiments, the criterion for determining whether the optimization process has converged includes: when the rate of change of the system's total lifecycle leveled cost obtained from two consecutive iterations is less than a rate of change threshold, the optimization process is considered converged. This convergence criterion achieves intelligent termination decision-making for the optimization process by establishing a quantitative numerical stability standard. Specifically, during the iteration process, the system's total lifecycle leveled cost obtained from two adjacent iterations is continuously recorded, and the relative rate of change is calculated and compared with a preset rate of change threshold. When the rate of change remains below the threshold, it indicates that the objective function has entered a stable plateau region, and further iterations have negligible impact on the quality improvement of the solution. This mechanism, by establishing a precise numerical convergence standard, ensures that the optimization results meet the preset accuracy requirements while effectively avoiding the resource waste or insufficient convergence problems that may be caused by traditional fixed-iteration-number methods, thus ensuring the efficiency of the optimization process and the reliability of the solution quality from an algorithmic perspective.

[0123] In some embodiments, updating the capacity configuration scheme includes automatically adjusting the capacity configuration of wind turbines, photovoltaics, electrolyzers, and energy storage facilities using intelligent optimization algorithms such as particle swarm optimization, genetic algorithms, or differential algorithms. The introduction of intelligent optimization algorithms enables automated iterative improvement of the configuration scheme. Specifically, when the optimization process fails to converge, the system calls a genetic algorithm or particle swarm optimization algorithm as a search engine to encode the capacity configuration of wind turbines, photovoltaics, electrolyzers, and energy storage facilities as individual or particle positions in the solution space. The algorithm automatically generates a new generation of capacity configuration schemes by simulating selection, crossover, and mutation operations in biological evolution or individual cooperation mechanisms in swarm intelligence, while satisfying equipment capacity constraints. This technical solution effectively avoids the shortcomings of traditional gradient optimization methods that are prone to getting trapped in local optima by constructing a population-based parallel search strategy. Its technical effect lies in significantly improving the global exploration capability of complex nonlinear optimization problems, ensuring that the system can find a planning scheme that approximates the global optimum in the multi-dimensional capacity configuration space, thus providing reliable algorithmic support for the collaborative optimization of wind-solar-hydrogen production systems.

[0124] Corresponding to the above method embodiments, this application also provides an embodiment of an electro-hydrogen dynamic cooperative coupling computing device. Figure 2A schematic diagram of the structure of an electro-hydrogen dynamic cooperative coupling computing device according to an embodiment of this application is shown. Figure 2 As shown, the device includes:

[0125] The data input module takes in basic data, including full-time wind and solar resource data, hydrogen load demand data, technical and economic parameters of the target equipment, and project financial parameters. The target equipment includes wind turbines, photovoltaic modules, energy storage facilities, and electrolyzers.

[0126] The capacity configuration optimization module constructs an upper-level capacity configuration optimization model with the goal of minimizing the levelized cost of the hydrogen production system throughout its entire life cycle. The decision variables include the rated power of the wind turbine, the rated power of the photovoltaic system, the rated power of the electrolyzer, and the rated capacity of the energy storage facility. The upper-level capacity configuration optimization model adopts an intelligent optimization algorithm to output one or more preliminary capacity configuration schemes. Among them, the intelligent optimization algorithm includes particle swarm optimization, genetic algorithm, or differential evolution algorithm, etc.

[0127] The simulation module operates by constructing a lower-level operation simulation model based on the capacity configuration scheme output by the upper-level capacity configuration optimization model. The lower-level operation simulation model uses hourly time-series data throughout the year and performs power balance calculations based on the actual operating constraints of the target equipment to simulate the dynamic behavior of the hydrogen production system in actual operation, thereby outputting annual key economic and technical indicators for operation. These annual key economic and technical indicators include: annual hydrogen production, wind and solar curtailment rate, and equipment utilization rate.

[0128] The economic evaluation module calculates the levelized cost of the system's entire lifecycle under the current capacity configuration scheme based on the annual key economic and technical indicators output by the lower-level operation simulation model.

[0129] The iterative control module determines whether the optimization process has converged. If it has converged, it outputs the optimal capacity configuration scheme and the corresponding operating strategy. If it has not converged, it updates the capacity configuration scheme based on the intelligent optimization algorithm and returns to step S20 for the next round of iterative optimization.

[0130] In one possible implementation, the lower-level operational simulation model achieves real-time balance between electrical and hydrogen power by solving an optimization problem within each time unit t. The objective function of the optimization problem is to minimize the sum of the cost of wind and solar curtailment penalties and the cost of hydrogen load shortage penalties.

[0131] In one possible implementation, the changes in the total input energy, total output energy, energy loss, and stored energy of the hydrogen production system within a preset time period are monitored to achieve energy balance.

[0132] The energy balance is expressed by the following formula:

[0133]

[0134] Among them, E wind E pv These represent the total power generation of wind power and photovoltaic power during the calculation period, respectively.

[0135] This represents the total energy consumed in producing hydrogen during the calculation period.

[0136] E curtail This represents the total amount of wind and solar power curtailed within the calculation period.

[0137] E BESS,Loss This represents the net change in energy of the battery energy storage system during the calculation period.

[0138] In one possible implementation, the optimization problem of the lower-level running simulation model needs to satisfy the following constraints:

[0139] P wind (t)+P pv (t)+P BESS,discharge (t)=P elec (t)+P BESS,charge (t)+P curtailment (t)+P BESS,Loss (t)

[0140] Among them, P wind (t) represents the wind power at time t, P pv (t) represents the photovoltaic power at time t, P BESS,discharge (t) represents the discharge power of the battery energy storage system at time t, P BESS,charge (t) represents the charging power of the battery energy storage system at time t, P elec (t) represents the power consumed by the electrolytic cell at time t, P curtailment (t) represents the power of wind and solar power curtailment at time t, P BEsS,Loss (t) represents the power loss of the energy storage system at time t.

[0141] In one possible implementation, the constraints further include state update constraints for the energy storage facility, wherein the calculation formula for the state update constraints of the energy storage facility includes:

[0142] SOC(t)=SOC(t-1)+C(t)-D(t)-Loss(t)

[0143] Wherein, SOC(t) represents the energy stored in the energy storage facility at time t, SOC(t-1) represents the energy stored in the energy storage facility at time t-1, C(t) represents the energy flow to the energy storage facility at time t, D(t) represents the energy consumption flow of the energy storage facility at time t, and Loss(t) represents the energy loss of the energy storage system at time t.

[0144] In one possible implementation, the objective function of the upper-layer capacity configuration optimization model is to minimize the system's total lifecycle leveled cost, wherein the formula for calculating the minimum system lifecycle leveled cost includes:

[0145]

[0146] Where C represents the power plant's fixed costs, R represents the power plant's residual value of fixed assets, t represents the year index, and O t F represents the annual operating and maintenance cost of a power plant in year t. t T represents the interest payable by the power plant in year t. t H represents the tax payable by the power plant in year t, r represents the benchmark discount rate, and H represents the tax payable by the power plant in year t. t Let t be the annual hydrogen production of the electrolysis system in year t.

[0147] In one possible implementation, the criterion for determining whether the optimization process has converged includes: when the rate of change of the levelized cost of the system's entire lifecycle obtained from two consecutive iterations is less than a rate of change threshold, the optimization process is determined to have converged.

[0148] In one possible implementation, the updated capacity configuration scheme includes automatically adjusting the capacity configuration of wind turbines, photovoltaics, electrolyzers, and energy storage facilities using intelligent optimization algorithms such as particle swarm optimization, genetic algorithms, or differential algorithms.

[0149] In one possible implementation, the technical and economic parameters of the target equipment include the unit power investment cost and operation and maintenance cost of wind turbines, photovoltaic modules, and electrolyzers, as well as the unit capacity investment cost and operation and maintenance cost of energy storage facilities.

[0150] In one possible implementation, the project financial parameters include the project operating period, benchmark discount rate, equity ratio, and financing interest rate.

[0151] The above is a schematic scheme of an electro-hydrogen dynamic synergistic coupling calculation device according to this embodiment. It should be noted that the technical solution of this electro-hydrogen dynamic synergistic coupling calculation device and the technical solution of the above-described electro-hydrogen dynamic synergistic coupling calculation method belong to the same concept. For details not described in detail in the technical solution of the electro-hydrogen dynamic synergistic coupling calculation device, please refer to the description of the technical solution of the above-described electro-hydrogen dynamic synergistic coupling calculation method.

[0152] Figure 3 A structural block diagram of a computing device 300 according to an embodiment of this application is shown. The components of the computing device 300 include, but are not limited to, a memory 310 and a processor 320. The processor 320 is connected to the memory 310 via a bus 330, and a database 350 is used to store data.

[0153] The computing device 300 also includes an access device 340, which enables the computing device 300 to communicate via one or more networks 360. Examples of these networks include Public Switched Telephone Network (PSTN), Local Area Network (LAN), Wide Area Network (WAN), Personal Area Network (PAN), or combinations of communication networks such as the Internet. The access device 340 may include one or more of any type of wired or wireless network interface (e.g., a network interface card (NIC)), such as an IEEE 802.11 Wireless Local Area Network (WLAN) wireless interface, a Wi-MAX (Worldwide Interoperability for Microwave Access) interface, an Ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a Bluetooth interface, or a Near Field Communication (NFC) interface.

[0154] In one embodiment of this application, the aforementioned components of the computing device 300 and Figure 3 Other components, not shown, can also be connected to each other, for example, via a bus. It should be understood that... Figure 3 The block diagram of the computing device shown is for illustrative purposes only and is not intended to limit the scope of this application. Those skilled in the art can add or replace other components as needed.

[0155] The computing device 300 can be any type of stationary or mobile computing device, including mobile computers or mobile computing devices (e.g., tablet computers, personal digital assistants, laptop computers, notebook computers, netbooks, etc.), mobile phones (e.g., smartphones), wearable computing devices (e.g., smartwatches, smart glasses, etc.) or other types of mobile devices, or stationary computing devices such as desktop computers or personal computers (PCs). The computing device 300 can also be a mobile or stationary server.

[0156] The processor 320 is configured to execute the following computer-executable instructions, which, when executed by the processor, implement the steps of the aforementioned electro-hydrogen dynamic synergistic coupling calculation method. The above is an illustrative scheme of a computing device according to this embodiment. It should be noted that the technical solution of this computing device and the technical solution of the aforementioned electro-hydrogen dynamic synergistic coupling calculation method belong to the same concept. Details not described in detail in the technical solution of the computing device can be found in the description of the technical solution of the aforementioned electro-hydrogen dynamic synergistic coupling calculation method.

[0157] An embodiment of this application also provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the above-described electro-hydrogen dynamic cooperative coupling calculation method.

[0158] The above is an illustrative scheme of a computer-readable storage medium according to this embodiment. It should be noted that the technical solution of this storage medium belongs to the same concept as the technical solution of the above-described electro-hydrogen dynamic cooperative coupling calculation method. For details not described in detail in the technical solution of the storage medium, please refer to the description of the technical solution of the above-described electro-hydrogen dynamic cooperative coupling calculation method.

[0159] An embodiment of this application also provides a computer program, wherein when the computer program is executed in a computer, the computer is instructed to perform the steps of the above-described electro-hydrogen dynamic cooperative coupling calculation method.

[0160] The above is an illustrative scheme of a computer program according to this embodiment. It should be noted that the technical solution of this computer program belongs to the same concept as the technical solution of the above-described electro-hydrogen dynamic synergistic coupling calculation method. For details not described in detail in the technical solution of the computer program, please refer to the description of the technical solution of the above-described electro-hydrogen dynamic synergistic coupling calculation method.

[0161] The foregoing has described specific embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired results. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0162] The computer instructions include computer program code, which may be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium may be appropriately added to or subtracted according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media may not include electrical carrier signals and telecommunication signals.

[0163] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments of this application are not limited to the described order of actions, because according to the embodiments of this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the embodiments of this application.

[0164] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0165] The preferred embodiments disclosed above are merely illustrative of this application. The optional embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the embodiments of this application. These embodiments are selected and specifically described in this application to better explain the principles and practical applications of the embodiments of this application, thereby enabling those skilled in the art to better understand and utilize this application. This application is limited only by the claims and their full scope and equivalents.

Claims

1. A calculation method for dynamic coordinated coupling of electro-hydrogen, characterized in that, Applications in hydrogen production systems, including: S10: Input basic data, including full-time wind and solar resource data, hydrogen load demand data, technical and economic parameters of the target equipment and project financial parameters, wherein the target equipment includes wind turbines, photovoltaic modules, energy storage facilities and electrolyzers; S20: Construct an upper-level capacity configuration optimization model with the goal of minimizing the levelized cost of the hydrogen production system throughout its entire life cycle. The decision variables include the rated power of the wind turbine, the rated power of the photovoltaic system, the rated power of the electrolyzer, and the rated capacity of the energy storage facility. The upper-level capacity configuration optimization model adopts an intelligent optimization algorithm to output one or more preliminary capacity configuration schemes. The intelligent optimization algorithm includes particle swarm optimization, genetic algorithm, or differential evolution algorithm. S30: Based on the capacity configuration scheme output by the upper-level capacity configuration optimization model, a lower-level operation simulation model is constructed. The lower-level operation simulation model uses hourly time-series data throughout the year and performs power balance calculations based on the actual operating constraints of the target equipment to simulate the dynamic behavior of the hydrogen production system in actual operation, thereby outputting annual key economic and technical indicators for operation. The annual key economic and technical indicators for operation include: annual hydrogen production, wind and solar curtailment rate, and equipment utilization rate. S40: Calculate the levelized cost of the system's entire lifecycle under the current capacity configuration scheme based on the annual key economic and technical indicators output by the lower-level operation simulation model; S50: Determine whether the optimization process has converged. If it has converged, output the optimal capacity configuration scheme and the corresponding operating strategy. If it has not converged, update the capacity configuration scheme based on the intelligent optimization algorithm and return to step S20 for the next round of iterative optimization.

2. The method according to claim 1, characterized in that, The lower-level operation simulation model achieves real-time balance between electrical energy and hydrogen production power by solving an optimization problem within each time unit t. The objective function of the optimization problem is to minimize the sum of the cost of wind and solar curtailment penalties and the cost of hydrogen load shortage penalties.

3. The method according to claim 2, characterized in that, Within a preset time period, the changes in the total input energy, total output energy, energy loss, and stored energy of the hydrogen production system are recorded to achieve energy balance. The energy balance is expressed by the following formula: wherein E wind , E pv respectively represent the total power generation of wind power and photovoltaic in the calculation period, This represents the total energy consumed in producing hydrogen during the calculation period. E curtail represents the total curtailed wind and solar energy within the calculation period, E BESS,Loss represents the net change in the energy of the battery energy storage system over the calculation period.

4. The method according to claim 2, characterized in that, The optimization problem of the lower-level simulation model must satisfy the following constraints: P wind (t)+P pv (t)+P BESS,discharge (t)=P elec (t)+P BESS,charge (t)+P curtailment (t)+P BESS,Loss (t) Among them, P wind (t) represents the wind power at time t, P pv (t) represents the photovoltaic power at time t, P BESS,discharge (t) represents the discharge power of the battery energy storage system at time t, P BESS,charge (t) represents the charging power of the battery energy storage system at time t, P elec (t) represents the power consumed by the electrolytic cell at time t, P curtailment (t) represents the power of wind and solar power curtailment at time t, P BESS,Loss (t) represents the power loss of the energy storage system at time t.

5. The method according to claim 4, characterized in that, The constraints include state update constraints for the energy storage facility, wherein the calculation formula for the state update constraints of the energy storage facility includes: SOC(t)=SOC(t-1)+C(t)-D(t)-Loss(t) Wherein, SOC(t) represents the energy stored in the energy storage facility at time t, SOC(t-1) represents the energy stored in the energy storage facility at time t-1, C(t) represents the energy flow to the energy storage facility at time t, D(t) represents the energy consumption flow of the energy storage facility at time t, and Loss(t) represents the energy loss of the energy storage system at time t.

6. The method according to claim 1, characterized in that, The objective function of the upper-layer capacity configuration optimization model is to minimize the levelized cost over the entire system lifecycle. The formula for calculating the minimum levelized cost over the entire system lifecycle includes: Where C represents the power plant's fixed costs, R represents the power plant's residual value of fixed assets, t represents the year index, and O t F represents the annual operating and maintenance cost of a power plant in year t. t T represents the interest payable by the power plant in year t. t H represents the tax payable by the power plant in year t, r represents the benchmark discount rate, and H represents the tax payable by the power plant in year t. t Let t be the annual hydrogen production of the electrolysis system in year t.

7. The method according to claim 1, characterized in that, The criteria for determining whether the optimization process has converged include: when the rate of change of the levelized cost of the system's entire life cycle obtained from two consecutive iterations is less than the rate of change threshold, the optimization process is determined to have converged.

8. The method according to claim 1, characterized in that, The updated capacity configuration scheme includes using particle swarm optimization, genetic algorithm, or differential algorithm to automatically adjust the capacity configuration of wind turbines, photovoltaics, electrolyzers, and energy storage facilities.

9. The method according to claim 1, characterized in that, The technical and economic parameters of the target equipment include the unit power investment cost and operation and maintenance cost of wind turbines, photovoltaic modules, and electrolyzers, as well as the unit capacity investment cost and operation and maintenance cost of energy storage facilities.

10. The method according to claim 1, characterized in that, The project's financial parameters include the project's operating period, benchmark discount rate, equity ratio, and financing interest rate.

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