Green electricity hydrogen production system full life cycle cost configuration optimization method and system

Abstract

The invention discloses a green electricity hydrogen production system full life cycle cost configuration optimization method and system, and the method comprises the steps: collecting equipment investment parameters and life cycle operation and maintenance cost, and carrying out the preprocessing; constructing an optimization function with the unit hydrogen production cost as a target; equipment operation start-stop information and operation duration are collected, period division and start-stop frequency statistics are carried out, and an operation duration sequence is obtained; embedding the loss factor into a hydrogen yield calculation model through a hydrogen production correction coupling method; setting a residual value rate function, and executing replacement judgment by combining the output of the hydrogen yield model and the running state of the equipment as judgment triggering conditions; and dynamically updating the configuration according to an execution replacement judgment result and feeding back to the optimization process. Good effects are achieved in the aspects of cost accounting precision, configuration response timeliness and electrolytic cell service life management strategies.

Description

Technical Field

[0001] This invention relates to the field of optimization calculation technology for the life cycle cost configuration of new energy hydrogen production systems, and in particular to a method and system for optimizing the life cycle cost configuration of green electricity hydrogen production systems. Background Technology

[0002] As hydrogen energy gains increasing importance in the new energy system, water electrolysis for hydrogen production, as a major green hydrogen production route, is receiving more and more attention from new energy power plant projects. A typical wind-solar-hydrogen co-generation station usually involves multiple subsystems, including wind or photovoltaic power generation equipment, electrolyzer systems, energy storage units, and back-end compression and hydrogen storage facilities. The initial investment and life-cycle operation and maintenance costs of each piece of equipment have a direct impact on the overall economic benefits.

[0003] Existing methods for configuring hydrogen production from new energy sources primarily focus on optimizing the electrical side of operations, such as maximizing the tracking of renewable energy output and increasing the hydrogen production load factor to improve annual hydrogen production. However, in actual engineering construction, equipment investment costs vary significantly, and electrolyzers and energy storage systems suffer severe lifespan reductions due to frequent start-ups and shutdowns, significantly impacting the unit cost of hydrogen production. Furthermore, factors such as the replacement frequency, residual value handling, and operation and maintenance strategies of different equipment throughout their lifecycles all introduce complex interferences into the overall cost structure of the hydrogen production system. Traditional single cost indicators or economic evaluation methods such as static investment payback periods are insufficient to accurately reflect the merits of configuration schemes. Summary of the Invention

[0004] Purpose of the invention: The purpose of this invention is to provide an optimized calculation method for the full life cycle cost configuration of a green electricity hydrogen production system that integrates equipment start-up and shutdown loss modeling, unit hydrogen production cost optimization, and residual value rate dynamic judgment mechanism.

[0005] Technical solution: The optimized calculation method for the full life-cycle cost configuration of the green electricity hydrogen production system described in this invention is characterized by the following steps:

[0006] (1) Collect equipment investment parameters and life cycle operation and maintenance costs, and preprocess the equipment investment parameters and life cycle operation and maintenance costs;

[0007] (2) Combine the life cycle cost modeling method with the normalized unit output cost calculation method to construct an optimization function with the unit hydrogen production cost as the objective;

[0008] (3) Collect equipment start-up and shutdown information and runtime, divide the equipment start-up and shutdown information and runtime into periods and count the number of start-ups and shutdowns to obtain runtime sequence;

[0009] (4) The start-up and shutdown loss factor of the electrolyzer is obtained by constructing the exponential recovery response function, and the loss factor is embedded into the hydrogen yield calculation model by the hydrogen production correction coupling method.

[0010] (5) Based on the life loss modeling method of equipment life cycle and operation behavior, set the residual value rate function, embed the residual value rate function into the replacement judgment, and combine the output of hydrogen production rate model and equipment operation status as the judgment trigger condition to execute the replacement judgment;

[0011] (6) Update the configuration dynamically based on the result of the replacement judgment and feed it back to the optimization process.

[0012] Furthermore, in step (1), the investment parameters and lifecycle maintenance costs of the data acquisition equipment include:

[0013] Standardized project data interface collection: Retrieve unit quotations, installation costs, and annual operation and maintenance contract quotations for electrolyzer systems, energy storage units, and wind and solar power generation systems from the new energy hydrogen production project investment database and industry benchmark quotation database;

[0014] Historical construction case data backtracking and extraction: Extract the complete account cost of historically operating power plant projects, including the breakdown of initial construction period capital expenditures and subsequent operating expenses;

[0015] Connect with the equipment supplier's technical and economic parameter database: extract typical values, life cycle and annual maintenance cost ratio parameters from the tender documents and equipment manuals provided by the manufacturer.

[0016] Furthermore, preprocessing of equipment investment parameters and lifecycle maintenance costs includes:

[0017] Structured organization: Electrolyzers, energy storage equipment, wind turbines, and photovoltaic modules are classified and assigned investment identifiers and annual operation and maintenance parameters respectively;

[0018] Time normalization: All cost parameters are uniformly converted to the annual dimension or the total life cycle dimension. When there is a phased depreciation or an increasing trend, the present value is processed according to the life cycle discount factor.

[0019] Construct a standardized input vector: Integrate investment identifiers, annual operation and maintenance parameters, and life cycle discount factors to form the input data set for the optimization function, and construct the optimization function accordingly.

[0020] Furthermore, the construction optimization function includes:

[0021] The preprocessed data is input into the life cycle cost modeling framework, and the normalized unit output cost calculation method is integrated into the modeling process to form an optimized structure with the unit hydrogen production cost as the target.

[0022] Lifecycle cost modeling method: The initial investment cost of equipment and the lifecycle operation and maintenance cost are used as inputs, and the present value is discounted according to the lifecycle time axis to construct the cost expression of the economic expenditure of the whole lifecycle.

[0023] By combining the lifecycle hydrogen production output dynamically output by the hydrogen production model, the unit output dimension is embedded into the modeling process, and the unit hydrogen production is used as the normalized granularity to perform unit cost decomposition on the discounted cost.

[0024] The expression for the unit hydrogen production cost is used as the optimization objective function, and iterative optimization is performed to minimize the unit output cost.

[0025] Furthermore, in step (3), the start / stop information and runtime of the data acquisition device include:

[0026] The start / stop flag is obtained through the controller interface of the electrolytic cell; the status is recorded each time the power-on operation and shutdown signal are triggered; and the start time stamp and stop time stamp are recorded.

[0027] Start and stop commands are extracted from the scheduling log, and the duration of continuous operation periods is accumulated to obtain the actual running time of each start and stop segment. The sampling period is set to the minute level.

[0028] Further, in step (3), obtaining the actual runtime sequence of each start-stop segment includes:

[0029] Divide the continuous start-stop state period into a cycle, and calculate the corresponding runtime for each cycle;

[0030] Each cycle from power-on to power-off is recorded as one start-stop behavior. The start-stop frequency throughout the entire lifecycle is statistically analyzed as an auxiliary indicator to obtain a sequence of operating cycles with the duration of each run as an element.

[0031] Further, in step (4), obtaining the electrolytic cell start-up and shutdown loss factor includes:

[0032] A power response model is constructed using the exponential recovery response function method, and the start-stop efficiency factor is calculated using the duration of each run as input.

[0033] The response time constant is set as a fixed technical parameter, and the efficiency recovery ratio corresponding to each runtime is obtained through exponential calculation.

[0034] The efficiency recovery ratio corresponding to each runtime segment is converted into a start-stop loss factor.

[0035] Embedding loss factors into the hydrogen production rate calculation model includes: the start-stop loss factor characterizes the intensity of the decline in hydrogen production capacity caused by start-stop behavior;

[0036] The start-stop loss factor is embedded into the hydrogen production rate model using a hydrogen production correction coupling method, and then corrected in combination with the theoretical hydrogen production rate to obtain the actual hydrogen production rate output after considering the start-stop effect.

[0037] Further, in step (5), the setting of the residual rate function includes:

[0038] A performance degradation expression for equipment is constructed based on the equipment's years of operation, number of start-ups and shutdowns, and average operating time.

[0039] The residual value rate function adopts a combined modeling method that couples depreciation over years, technological substitution trend and fatigue factor. It uses depreciation rate and number of years of operation as static inputs and start-up and shutdown frequency and operation cycle as dynamic operation behavior inputs to construct the residual value function expression.

[0040] Replacement determination: Set a residual value threshold using the economic life ratio method. When the residual value rate is less than the residual value threshold, the equipment performance is determined to have deteriorated to a replaceable state. When the residual value rate is greater than or equal to the set threshold, the equipment performance is determined to be still acceptable and does not need to be replaced.

[0041] The replacement judgment also incorporates the annual output of the hydrogen production rate model and the actual hydrogen production efficiency as auxiliary judgment indicators to determine whether the actual efficiency of the equipment is lower than the set hydrogen production efficiency for a long period of time, and then performs the replacement operation.

[0042] Further, in step (6), the dynamic configuration update and feedback includes:

[0043] After performing the replacement judgment, the configuration combination parameters in the current optimization variables are updated according to the equipment replacement results. The equipment's initial lifespan, investment cost, and capacity parameters are reset, and the reconstructed and updated configuration is used to perform hydrogen production simulation and cost function.

[0044] The results of the new round of hydrogen production and the life cycle cost are used as inputs to the optimization function, driving the optimizer to use a swarm intelligence optimization algorithm to search the next generation solution space;

[0045] The update process forms a closed-loop mechanism of configuration parameters, hydrogen production capacity, cost function, judgment logic, and feedback optimization, realizing a lifecycle adaptive configuration optimization strategy based on operational behavior, and forming the optimal equipment configuration combination output that meets economic requirements.

[0046] A system for optimizing the lifecycle cost of a green electricity-to-hydrogen system includes:

[0047] Data acquisition and processing module: used to collect and preprocess equipment investment parameters and lifecycle maintenance costs;

[0048] Optimization Modeling and Solving Module: This module combines lifecycle cost modeling methods with normalized unit output cost calculation methods to construct an optimization function with unit hydrogen production cost as the objective.

[0049] Start-stop behavior and runtime statistics module: used to collect equipment start-stop information and runtime, divide the equipment start-stop information and runtime into periods and count the number of start-stops to obtain a runtime sequence;

[0050] Start-up and shutdown loss modeling and hydrogen yield coupling module: This module is used to obtain the electrolyzer start-up and shutdown loss factor by constructing an exponential recovery response function, and to embed the loss factor into the hydrogen yield calculation model through a hydrogen production correction coupling method to form a dynamic hydrogen production rate model.

[0051] Lifetime depreciation modeling and replacement judgment module: It is used to set the residual value rate function based on the lifetime depreciation modeling method based on equipment life cycle and operating behavior, embed the residual value rate function into the replacement judgment, and combine the hydrogen production rate model output and equipment operating status as the judgment trigger conditions to execute the replacement judgment;

[0052] Configuration update and feedback optimization module: It is used to dynamically update the configuration based on the result of the execution replacement judgment and feed it back to the optimization process until the optimal configuration combination that meets the termination conditions is output.

[0053] Beneficial Effects: Compared with existing technologies, this invention has the following significant advantages: 1. The fluctuation range of the unit hydrogen production cost assessment results can be reduced by 10% to 30%; 2. The calculation deviation of the unit hydrogen production cost relative to the benchmark payment period statistics can converge to within ±5%; 3. The convergence algebra of the optimization process under the same constraints can be reduced by 15% to 40%, improving configuration iteration efficiency and engineering usability; 4. It effectively improves the uniformity and comparability of equipment cost parameters in optimization calculation, overcomes the problems of incomplete information and low accuracy in traditional single static cost data-driven methods, and significantly enhances the computability and adaptability of life cycle cost analysis; 5. It breaks through the limitations of existing methods that only use economic indicators or installed capacity as the objective. The target's one-sidedness enables dynamic comparison and iterative optimization of configuration schemes throughout the entire life cycle from the perspective of unit output, improving configuration efficiency and economic benefits; 6. It significantly improves the sensitivity of the hydrogen production model to operational behavior, overcomes the ideal assumption problem of traditional models not considering start-up and shutdown losses, and realizes fine modeling and correction of the impact of start-up and shutdown on hydrogen production efficiency; 7. It adopts an exponential efficiency recovery function to model the performance loss trend after the electrolyzer starts and stops, and dynamically embeds it into the hydrogen yield calculation process through a hydrogen production correction coupling method, significantly improving the sensitivity of the hydrogen production model to operational behavior, overcoming the ideal assumption problem of traditional models not considering start-up and shutdown losses, and realizing fine modeling and correction of the impact of start-up and shutdown on hydrogen production efficiency. Attached Figure Description

[0054] Figure 1 This is the overall flowchart of the present invention. Detailed Implementation

[0055] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0056] like Figure 1 As shown, an optimized calculation method for the life-cycle cost configuration of a green electricity-to-hydrogen system is provided, including the following steps:

[0057] Step 1: Preprocess the equipment investment parameters and lifecycle maintenance costs by collecting them;

[0058] The collection of equipment investment parameters and lifecycle operation and maintenance costs includes: standardized project data interface collection, historical construction case data backtracking and extraction, and connection to the equipment supplier's technical and economic parameter database;

[0059] Standardized project data interface collection: Retrieve unit quotations, installation costs, and annual operation and maintenance contract quotations for electrolyzer systems, energy storage units, and wind and solar power generation systems from the new energy hydrogen production project investment database and industry benchmark quotation database;

[0060] Historical construction case data backtracking and extraction: Extract the complete account cost of historically operating power plant projects, including the breakdown of initial construction period capital expenditures and subsequent operating expenses;

[0061] Connect with the equipment supplier's technical and economic parameter database: extract typical values, life cycle and annual maintenance cost ratio parameters from the manufacturer's tender documents and equipment manuals;

[0062] The process involves structuring, time normalization, and constructing standardized input vectors.

[0063] The standardized input vector is constructed as follows:

[0064]

[0065]

[0066]

[0067]

[0068] in, Represents a structured device parameter matrix. Indicates the first Initial investment cost of such equipment Indicates the first Annual operation and maintenance costs of this type of equipment Indicates the first Device lifecycle, Indicates the first Discount factor for the life cycle of the equipment Indicates the order of discount years. Indicates the first Class lifecycle, Indicates the first Annual discount rate for this type of equipment Indicates the first Equipment present value eigenvector Represents the standardized input vector. This refers to a structured storage electrolytic cell. Indicates energy storage, Indicates wind power, Indicates photovoltaic;

[0069] When using structured storage electrolytic cells, energy storage, wind power, and photovoltaics as structured equipment parameter matrices, it is necessary to clearly define the parameters of initial investment cost, annual operation and maintenance cost, and life cycle.

[0070] By calculating the structured device parameter matrix, different data sources from different devices can have a unified data structure;

[0071] Structured organization: Electrolyzers, energy storage equipment, wind turbines, and photovoltaic modules are classified and assigned investment identifiers and annual operation and maintenance parameters respectively;

[0072] Time normalization: All cost parameters are uniformly converted to the annual dimension or the total life cycle dimension. When there is a phased depreciation or an increasing trend, the present value is processed according to the life cycle discount factor.

[0073] Construct a standardized input vector: Integrate investment identifiers, annual operation and maintenance parameters, and life cycle discount factors to form the input data set for the optimization function, and construct the optimization function accordingly;

[0074] Step 2: Combine the life cycle cost modeling method with the normalized unit output cost calculation method to construct an optimization function with the unit hydrogen production cost as the objective.

[0075] The preprocessed data is input into the life cycle cost modeling framework, and the normalized unit output cost calculation method is integrated into the modeling process to form an optimized structure with the unit hydrogen production cost as the objective.

[0076] One scheme for optimizing the structure with the goal of reducing the unit hydrogen production cost is as follows:

[0077]

[0078] in, The unit cost of hydrogen production, For actual hydrogen production, Cost over the entire lifecycle;

[0079] Lifecycle cost modeling method: The initial investment cost of equipment and the lifecycle operation and maintenance cost are used as inputs, and the present value is discounted according to the lifecycle time axis to construct the cost expression of the economic expenditure of the whole lifecycle.

[0080] One approach to constructing the cost expression for total life-cycle economic expenditures is as follows:

[0081]

[0082] in, Indicates the device category index. Indicates the first Class of equipment Annual maintenance costs Indicates lifecycle, Indicates the discount rate;

[0083] By combining the lifecycle hydrogen production output dynamically output by the hydrogen production model, the unit output dimension is embedded into the modeling process, and the unit hydrogen production is used as the normalized granularity to perform unit cost decomposition on the discounted cost.

[0084] The expression for the unit hydrogen production cost is used as the optimization objective function, and iterative optimization is performed to minimize the unit output cost.

[0085] One approach to iterative optimization that aims to minimize the cost per unit output is as follows:

[0086]

[0087] in, Represents the input vector Minimize the variables. This represents the optimization objective unit cost function;

[0088] Minimizing only the total lifecycle cost may lead to a configuration that results in "lower cost but lower hydrogen production / greater start-up and shutdown losses", which degrades the economic efficiency per unit output. By targeting the unit hydrogen production cost, the optimizer simultaneously constrains cost and actual output during the search process, avoiding the reverse result of "total cost decreasing but unit cost increasing".

[0089] Traditional LCOH typically relies on fixed efficiency or fixed loading factor assumptions, while the present invention... and All of them introduce dynamic factors such as start-stop loss modeling, lifespan depreciation and replacement judgment / residual value rate, so that the unit cost index can be updated iteratively with the operation behavior, which is more suitable for scenarios where wind and solar fluctuations lead to frequent start-stops;

[0090] Step 3: Collect historical or simulated start-stop information and runtime of the equipment, divide the equipment start-stop information and runtime into periods and count the number of start-stops to obtain the runtime sequence;

[0091] The start / stop flag is obtained through the controller interface of the electrolytic cell; the status is recorded each time the power-on operation and shutdown signal are triggered; and the start time stamp and stop time stamp are recorded.

[0092] Extract start and stop commands from the scheduling log, accumulate the duration of continuous running periods, and obtain the actual running time of each start and stop segment. The sampling period is set to the level of 5 minutes.

[0093] Divide the continuous start-stop state period into a cycle, and calculate the corresponding runtime for each cycle;

[0094] One approach to calculating the runtime for each cycle is as follows:

[0095]

[0096] in, Indicates the first Duration of each start-stop cycle Indicates the first End timestamp Indicates the first The next start timestamp;

[0097] Each cycle from power-on to power-off is recorded as one start-stop behavior. The start-stop frequency throughout the entire lifecycle is statistically analyzed as an auxiliary indicator to obtain a sequence of operating cycles with the duration of each run as the element.

[0098] One method to obtain the running cycle sequence is:

[0099]

[0100] in, Represents the sequence of runtime throughout the entire lifecycle. Indicates the start / stop cycle;

[0101] Step 4: Obtain the electrolyzer start-up and shutdown loss factor by constructing an exponential recovery response function, and embed this loss factor into the hydrogen yield calculation model through a hydrogen production correction coupling method to form a dynamic hydrogen production rate model.

[0102] In the operation log with a sampling period of 5 minutes, the runtime after each start and stop and the corresponding start and stop efficiency factor are extracted. Least squares fitting is used to minimize the residual of the start and stop efficiency factor, thereby obtaining... , ;

[0103] In a simulation driven by runtime logs with a sampling period of 5 minutes, the introduction of start-stop loss factors and replacement judgments reduces the fluctuation of the unit hydrogen production cost assessment by 10% to 30% compared to the comparative scheme that "does not consider start-stop losses and only estimates hydrogen production based on static efficiency." Under frequent start-stop conditions, the deviation in hydrogen production caused by start-stop can be explicitly corrected, allowing the calculation deviation of unit hydrogen production cost to converge to within ±5% relative to the baseline accounting period. At the same time, due to the introduction of configuration updates and feedback loops, the convergence algebra of the optimization process under the same constraints can be reduced by 15% to 40%, improving configuration iteration efficiency and engineering availability.

[0104] The power response model is constructed using the exponential recovery response function method;

[0105] One approach to constructing a power response model is as follows:

[0106]

[0107] in, Indicates the first Secondary start-stop efficiency factor This indicates the initial rate of efficiency decay. Indicates the recovery time constant;

[0108] The start-stop efficiency factor is calculated by using the duration of each run as input.

[0109] One method for calculating the start-stop efficiency factor is as follows:

[0110]

[0111] in, Indicates average start-stop efficiency;

[0112] The response time constant is set as a fixed technical parameter, and the efficiency recovery ratio corresponding to each runtime is obtained through exponential calculation.

[0113] One approach to obtaining the efficiency recovery ratio is:

[0114]

[0115] in, Indicates the first The efficiency recovery rate;

[0116] The efficiency recovery ratio corresponding to each runtime segment is converted into a start-stop loss factor.

[0117] The loss factor is embedded into the hydrogen production rate calculation model, including the start-stop loss factor to characterize the intensity of the decline in hydrogen production capacity caused by start-stop behavior.

[0118] The start-stop loss factor is embedded into the hydrogen production rate model by adopting the hydrogen production correction coupling method, and the model is corrected by combining it with the theoretical hydrogen production rate to obtain the actual hydrogen production rate output after considering the start-stop effect.

[0119] One approach to correct for the theoretical hydrogen production rate is as follows:

[0120]

[0121] in, This indicates the corrected actual hydrogen production.

[0122] Step 5: Construct an equipment performance degradation model based on the equipment lifecycle and operating behavior, and set a residual value rate function based on this model. Combine the residual value rate function with the output of the dynamic hydrogen production rate model, and perform equipment replacement judgment with optimal economic efficiency as the core.

[0123] A performance degradation expression for equipment is constructed based on the equipment's years of operation, number of start-ups and shutdowns, and average operating time.

[0124] One approach to constructing the expression for device performance degradation is as follows:

[0125]

[0126] in, Indicates the first Current performance of this type of device Indicates the initial performance value. Indicates the performance degradation coefficient. Indicates the current service life. Indicates the design life of the equipment;

[0127] The residual value rate function adopts a combined modeling method that couples depreciation over years, technological substitution trend and fatigue factor. It uses depreciation rate and number of years of operation as static inputs and start-up and shutdown frequency and operation cycle as dynamic operation behavior inputs to construct the residual value function expression.

[0128] One approach to constructing a residual function expression is as follows:

[0129]

[0130] in, Indicates the equipment residual value rate. Indicates the depreciation factor. Indicates the fatigue depreciation factor. Indicates the number of starts and stops. This indicates the maximum number of start-stop thresholds;

[0131] The maximum number of start-stop cycles is set manually based on the specific equipment.

[0132] Replacement determination: Set a residual value threshold using the economic life ratio method. When the residual value rate is less than the residual value threshold, the equipment performance is determined to have deteriorated to a replaceable state. When the residual value rate is greater than or equal to the set threshold, the equipment performance is determined to be still acceptable and does not need to be replaced.

[0133] One approach to setting the residual value threshold using the economic life ratio method is as follows:

[0134]

[0135] in, Indicates the residual threshold. This represents the equipment tolerance threshold coefficient;

[0136] The equipment tolerance threshold coefficient is set to 0.8. The equipment tolerance threshold is set by the experimenter based on experience.

[0137] The replacement judgment also incorporates the annual output of the hydrogen production rate model and the actual hydrogen production efficiency as auxiliary judgment indicators to determine whether the actual efficiency of the equipment is lower than the set hydrogen production efficiency for a long period of time, and then performs the replacement operation.

[0138] One approach to perform the judgment and replacement operation is as follows:

[0139]

[0140]

[0141] in, This indicates the current hydrogen production efficiency. This indicates the lower limit of permissible hydrogen production efficiency;

[0142] If the current hydrogen production efficiency is less than the residual value threshold, or the hydrogen production efficiency is less than the allowable lower limit of hydrogen production efficiency, a replacement operation is performed.

[0143] The lower limit of hydrogen production efficiency is 0.75, which is determined by the experimenters based on experience. The current hydrogen production efficiency is equal to the actual operating efficiency of the equipment in the current cycle.

[0144] Step six: Based on the result of the replacement judgment, dynamically update the system combination parameters in the optimization function and feed them back to the optimization solution process in step two for iteration until the optimal configuration combination that meets the termination condition is output.

[0145] After performing the replacement judgment, the configuration combination parameters in the current optimization variables are updated according to the equipment replacement results, the equipment start life, investment cost and capacity parameters are reset, and hydrogen production simulation and cost function reconstruction are performed on the updated configuration;

[0146] The results of the new round of hydrogen production and the life cycle cost are used as the input of the optimization function. The optimizer uses a swarm intelligence optimization algorithm to search the next generation solution space. The swarm intelligence optimization algorithm is preferably a particle swarm optimization algorithm. The particle position vector is composed of configuration combination parameters. The objective function value of the unit hydrogen production cost is used as the fitness. The speed and position are updated according to the inertia weight and the individual / group learning factor.

[0147] The update process forms a closed-loop mechanism of configuration parameters, hydrogen production capacity, cost function, judgment logic, and feedback optimization, realizing a life-cycle adaptive configuration optimization strategy based on operational behavior, and forming the optimal equipment configuration combination output that meets economic requirements;

[0148] When dealing with high-dimensional nonlinear optimization problems involving the configuration and replacement of various types of equipment such as wind power, photovoltaics, energy storage, and electrolytic cells, the driving optimizer further combines decomposition and constraint embedding strategies. By hierarchically modeling equipment selection and operation strategies, solving them separately and updating them interactively in the iteration, the search dimension is reduced.

[0149] Energy balance, start-stop constraints, and lifetime thresholds are introduced as hard or penal constraints in the swarm intelligence search process to ensure the feasibility of the solution. A maximum number of iterations and convergence criteria are set to ensure that a stable solution is obtained within a reasonable time.

[0150] This invention also provides a green electricity hydrogen production system full life cycle cost configuration optimization system, including a data acquisition and processing module, an optimization modeling and solving module, a start-up and shutdown behavior and runtime statistics module, a start-up and shutdown loss modeling and hydrogen yield coupling module, a lifespan loss modeling and replacement judgment module, and a configuration update and feedback optimization module.

[0151] The data acquisition and processing module is used to preprocess the equipment investment parameters and lifecycle maintenance costs by collecting them.

[0152] Optimization Modeling and Solving Module: This module combines lifecycle cost modeling methods with normalized unit output cost calculation methods to construct an optimization function with unit hydrogen production cost as the objective.

[0153] Start-stop behavior and runtime statistics module: used to collect equipment start-stop information and runtime, divide the equipment start-stop information and runtime into periods and count the number of start-stops to obtain a runtime sequence;

[0154] Start-up and shutdown loss modeling and hydrogen yield coupling module: This module is used to obtain the electrolyzer start-up and shutdown loss factor by constructing an exponential recovery response function, and to embed the loss factor into the hydrogen yield calculation model through a hydrogen production correction coupling method to form a dynamic hydrogen production rate model.

[0155] Lifetime depreciation modeling and replacement judgment module: It is used to set the residual value rate function based on the lifetime depreciation modeling method based on equipment life cycle and operating behavior, embed the residual value rate function into the replacement judgment, and combine the hydrogen production rate model output and equipment operating status as the judgment trigger conditions to execute the replacement judgment;

[0156] Configuration update and feedback optimization module: It is used to dynamically update the configuration based on the result of the execution replacement judgment and feed it back to the optimization process until the optimal configuration combination that meets the termination conditions is output.

Claims

1. A method for optimizing the life-cycle cost configuration of a green electricity-to-hydrogen system, characterized in that, Includes the following steps: (1) Collect equipment investment parameters and life cycle operation and maintenance costs, and preprocess the equipment investment parameters and life cycle operation and maintenance costs; (2) Combine the life cycle cost modeling method with the normalized unit output cost calculation method to construct an optimization function with the unit hydrogen production cost as the objective; (3) Collect historical or simulated start-up and shutdown information and runtime of the equipment, divide the equipment start-up and shutdown information and runtime into periods and count the number of start-ups and shutdowns to obtain the runtime sequence; (4) The start-up and shutdown loss factor of the electrolyzer is obtained by constructing the exponential recovery response function, and the loss factor is embedded into the hydrogen production rate calculation model by the hydrogen production correction coupling method to form a dynamic hydrogen production rate model. (5) Construct a device performance degradation model based on the device life cycle and operating behavior, and set a residual value rate function based on this. Combine the residual value rate function with the output of the dynamic hydrogen production rate model, and perform device replacement judgment with the core of optimal economic efficiency. (6) Based on the result of the replacement judgment, the system combination parameters in the optimization function are dynamically updated and fed back to the optimization solution process in step (2) for iteration until the optimal configuration combination that meets the termination condition is output.

2. The method for optimizing the life-cycle cost configuration of a green electricity-to-hydrogen system according to claim 1, characterized in that, In step (1), the investment parameters and lifecycle maintenance costs of the data acquisition equipment include: Standardized project data interface collection: Retrieve unit quotations, installation costs, and annual operation and maintenance contract quotations for electrolyzer systems, energy storage units, and wind and solar power generation systems from the new energy hydrogen production project investment database and industry benchmark quotation database; Historical construction case data backtracking and extraction: Extract the complete account cost of historically operating power plant projects, including the breakdown of initial construction period capital expenditures and subsequent operating expenses; Connect with the equipment supplier's technical and economic parameter database: extract typical values, life cycle and annual maintenance cost ratio parameters from the tender documents and equipment manuals provided by the manufacturer.

3. The method for optimizing the life-cycle cost configuration of a green electricity-to-hydrogen system according to claim 1, characterized in that, In step (1), the preprocessing of equipment investment parameters and lifecycle operation and maintenance costs includes: Structured organization: Electrolyzers, energy storage equipment, wind turbines, and photovoltaic modules are classified and assigned investment identifiers and annual operation and maintenance parameters respectively; Time normalization: All cost parameters are uniformly converted to the annual dimension or the total life cycle dimension. When there is a phased depreciation or an increasing trend, the present value is processed according to the life cycle discount factor. Construct a standardized input vector: Integrate investment identifiers, annual operation and maintenance parameters, and life cycle discount factors to form the input data set for the optimization function, and construct the optimization function accordingly.

4. The method for optimizing the life-cycle cost configuration of a green electricity-to-hydrogen system according to claim 1, characterized in that, In step (2), the construction of the optimization function includes: The preprocessed data is input into the life cycle cost modeling framework, and the normalized unit output cost calculation method is integrated into the modeling process to form an optimized structure with the unit hydrogen production cost as the target. Lifecycle cost modeling method: The initial investment cost of equipment and the lifecycle operation and maintenance cost are used as inputs, and the present value is discounted according to the lifecycle time axis to construct the cost expression of the economic expenditure of the whole lifecycle. By combining the lifecycle hydrogen production output dynamically output by the hydrogen production model, the unit output dimension is embedded into the modeling process, and the unit hydrogen production is used as the normalized granularity to perform unit cost decomposition on the discounted cost. The expression for the unit hydrogen production cost is used as the optimization objective function, and iterative optimization is performed to minimize the unit output cost.

5. The method for optimizing the life-cycle cost configuration of a green electricity-to-hydrogen system according to claim 1, characterized in that, In step (3), the start / stop information and runtime of the data acquisition device include: The start / stop flag is obtained through the controller interface of the electrolytic cell; the status is recorded each time the power-on operation and shutdown signal are triggered; and the start time stamp and stop time stamp are recorded. Start and stop commands are extracted from the scheduling log, and the duration of continuous operation periods is accumulated to obtain the actual running time of each start and stop segment. The sampling period is set to the minute level.

6. The method for optimizing the life-cycle cost configuration of a green electricity-to-hydrogen system according to claim 1, characterized in that, In step (3), obtaining the actual runtime sequence of each start-stop segment includes: Divide the continuous start-stop state period into a cycle, and calculate the corresponding runtime for each cycle; Each cycle from power-on to power-off is recorded as one start-stop behavior. The start-stop frequency throughout the entire lifecycle is statistically analyzed as an auxiliary indicator to obtain a sequence of operating cycles with the duration of each run as an element.

7. The method for optimizing the life-cycle cost configuration of a green electricity-to-hydrogen system according to claim 1, characterized in that, In step (4), obtaining the electrolytic cell start-up and shutdown loss factor includes: A power response model is constructed using the exponential recovery response function method, and the start-stop efficiency factor is calculated using the duration of each run as input. The response time constant is set as a fixed technical parameter, and the efficiency recovery ratio corresponding to each runtime is obtained through exponential calculation. The efficiency recovery ratio corresponding to each runtime segment is converted into a start-stop loss factor. Embedding the loss factor into the hydrogen production rate calculation model: the start-stop loss factor characterizes the intensity of the decline in hydrogen production capacity caused by start-stop behavior; The start-stop loss factor is embedded into the hydrogen production rate model using a hydrogen production correction coupling method, and then corrected in combination with the theoretical hydrogen production rate to obtain the actual hydrogen production rate output after considering the start-stop effect.

8. The method for optimizing the life-cycle cost configuration of a green electricity-to-hydrogen system according to claim 1, characterized in that, In step (5), the setting of the residual rate function includes: A performance degradation expression for equipment is constructed based on the equipment's years of operation, number of start-ups and shutdowns, and average operating time. The residual value rate function adopts a combined modeling method that couples depreciation over years, technological substitution trend and fatigue factor. It uses depreciation rate and number of years of operation as static inputs and start-up and shutdown frequency and operation cycle as dynamic operation behavior inputs to construct the residual value function expression. Replacement determination: Set a residual value threshold using the economic life ratio method. When the residual value rate is less than the residual value threshold, the equipment performance is determined to have deteriorated to a replaceable state. When the residual value rate is greater than or equal to the set threshold, the equipment performance is determined to be still acceptable and does not need to be replaced. The replacement judgment also incorporates the annual output of the hydrogen production rate model and the actual hydrogen production efficiency as auxiliary judgment indicators to determine whether the actual efficiency of the equipment is lower than the set hydrogen production efficiency for a long period of time, and then performs the replacement operation.

9. The method for optimizing the life-cycle cost configuration of a green electricity-to-hydrogen system according to claim 1, characterized in that, In step (6), the dynamic configuration update and feedback includes: After performing the replacement judgment, the configuration combination parameters in the current optimization variables are updated according to the equipment replacement results. The equipment's initial lifespan, investment cost, and capacity parameters are reset, and the reconstructed and updated configuration is used to perform hydrogen production simulation and cost function. The results of the new round of hydrogen production and the life cycle cost are used as inputs to the optimization function, driving the optimizer to use a swarm intelligence optimization algorithm to search the next generation solution space; The update process forms a closed-loop mechanism of configuration parameters, hydrogen production capacity, cost function, judgment logic, and feedback optimization, realizing a lifecycle adaptive configuration optimization strategy based on operational behavior, and forming the optimal equipment configuration combination output that meets economic requirements.

10. A life-cycle cost optimization system for a green electricity-to-hydrogen system, characterized in that, include: Data acquisition and processing module: used to collect and preprocess equipment investment parameters and lifecycle maintenance costs; Optimization Modeling and Solving Module: This module combines lifecycle cost modeling methods with normalized unit output cost calculation methods to construct an optimization function with unit hydrogen production cost as the objective. Start-stop behavior and runtime statistics module: used to collect equipment start-stop information and runtime, divide the equipment start-stop information and runtime into periods and count the number of start-stops to obtain a runtime sequence; Start-up and shutdown loss modeling and hydrogen yield coupling module: This module is used to obtain the electrolyzer start-up and shutdown loss factor by constructing an exponential recovery response function, and to embed the loss factor into the hydrogen yield calculation model through a hydrogen production correction coupling method to form a dynamic hydrogen production rate model. Lifetime depreciation modeling and replacement judgment module: It is used to set the residual value rate function based on the lifetime depreciation modeling method based on equipment life cycle and operating behavior, embed the residual value rate function into the replacement judgment, and combine the hydrogen production rate model output and equipment operating status as the judgment trigger conditions to execute the replacement judgment; Configuration update and feedback optimization module: It is used to dynamically update the configuration based on the result of the execution replacement judgment and feed it back to the optimization process until the optimal configuration combination that meets the termination conditions is output.

Images