Green hydrogen system configuration optimization method and system considering carbon emission reduction income

Abstract

The invention belongs to the technical field of green hydrogen production and energy system optimization, and provides a green hydrogen system configuration optimization method and system considering carbon emission reduction income. Building a multi-objective optimization model by taking the full-life-cycle comprehensive benefit maximization of the green hydrogen system as an objective function; wherein indexes of the objective function comprise green hydrogen sales income, carbon emission reduction income, whole life cycle investment cost, annual operation and maintenance cost and hydrogen energy storage and transportation cost; solving the multi-objective optimization model by adopting a particle swarm algorithm, and determining a green hydrogen system configuration scheme; economic benefits corresponding to carbon emission reduction in the green hydrogen production process are fully considered, collaborative optimization of economy and carbon emission reduction benefits is achieved, and the problem that green hydrogen project economic feasibility and environmental benefits are difficult to balance is solved.

Description

Technical Field

[0001] This invention belongs to the field of green hydrogen production and energy system optimization technology, and particularly relates to a green hydrogen system configuration optimization method and system that takes into account carbon emission reduction benefits. Background Technology

[0002] As a zero-carbon energy carrier, green hydrogen production relies on the coordinated operation of renewable energy power generation and water electrolysis hydrogen production technology.

[0003] Current green hydrogen system configuration methods mainly focus on minimizing the costs of renewable energy output, electrolyzer operation, and energy storage matching, without fully considering the economic benefits of carbon emission reduction during green hydrogen production. This leads to an underestimation of the overall system benefits, potentially resulting in insufficient renewable energy consumption or unrealized carbon emission reduction potential, making it difficult to directly connect with carbon market trading rules. Furthermore, the lack of carbon emission reduction benefits in the green hydrogen system optimization objectives makes it difficult to balance the economic feasibility and environmental benefits of green hydrogen projects, thus limiting the large-scale promotion of green hydrogen technology. Summary of the Invention

[0004] To address the aforementioned problems, this invention proposes a green hydrogen system configuration optimization method and system that takes into account carbon emission reduction benefits. This invention fully considers the economic benefits corresponding to carbon emission reduction during the green hydrogen production process, achieving synergistic optimization of economic efficiency and carbon emission reduction benefits, and solving the problem of balancing the economic feasibility and environmental benefits of green hydrogen projects.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solution: In a first aspect, the present invention provides a method for optimizing the configuration of a green hydrogen system that takes into account carbon emission reduction benefits, including: Build a green hydrogen system architecture; Based on the green hydrogen system architecture, a multi-objective optimization model is constructed with power balance, component capacity, operating characteristics, and carbon emission reduction accounting as constraints, and maximizing the comprehensive benefits of the green hydrogen system throughout its entire life cycle as the objective function. The indicators of the objective function include green hydrogen sales revenue, carbon emission reduction revenue, total life cycle investment cost, annual operation and maintenance cost, and hydrogen energy storage and transportation cost. The particle swarm optimization algorithm is used to solve the multi-objective optimization model and determine the configuration scheme of the green hydrogen system.

[0006] Furthermore, the carbon emission reduction benefit is the product of the carbon emission reduction amount and the carbon price.

[0007] Furthermore, the carbon emission reduction for: ; in, For the annual production of green hydrogen; As a benchmark carbon emission level; This represents the actual carbon emissions of green hydrogen.

[0008] Furthermore, the objective function is: ; in, For the comprehensive benefits of the green hydrogen system throughout its entire life cycle; For green hydrogen sales revenue; For carbon emission reduction benefits; Cost of investment over the entire life cycle; Annual operating and maintenance costs; Costs associated with hydrogen storage and transportation.

[0009] Furthermore, the green hydrogen sales revenue, the total life-cycle investment cost, the annual operation and maintenance cost, and the hydrogen energy storage and transportation cost are respectively: ; ; ; ; in, For the first Hourly hydrogen production; For the first The hourly price of green hydrogen; The unit investment cost of photovoltaic modules; This refers to the unit investment cost of wind turbine units; This refers to the unit investment cost of the electrolytic cell; Unit investment cost for energy storage equipment; For the installed capacity of photovoltaic power plants; For wind farm installed capacity; This refers to the rated power of the electrolytic cell; Rated capacity of the energy storage device; This is the capital recovery coefficient; Annual operating and maintenance rate per unit for photovoltaic modules; The unit annual operation and maintenance cost rate for wind turbine units; The annual operating and maintenance cost per unit of electrolytic cell; The power purchased by the grid in hour t; Time-of-use pricing; Unit cost of hydrogen storage tanks; Leakage rate; For conveying distance; This refers to the energy consumption cost of transmission.

[0010] Furthermore, the power balance constraint, the component capacity constraint, the operating characteristic constraint, and the carbon emission reduction accounting constraint are respectively: ; , ; ; Electrolytic cell Number of start-stop cycles per year Energy storage , ; , ; in, For the first The actual output power of the hourly photovoltaic power station; For the first The actual output power of the wind farm in one hour; For the first Discharge power of the hourly energy storage system; The charging power of the energy storage system in hour t; For the first The actual operating power of the hourly electrolytic cell; This refers to the rated power of the electrolytic cell; For the first The actual energy storage capacity of the hourly energy storage system; This refers to the rated capacity of the energy storage system. The charging efficiency of the energy storage system; This refers to the discharge efficiency of the energy storage system.

[0011] Furthermore, the particle swarm optimization algorithm introduces adaptive inertia weights and chaotic mutation operators. The solution process adopts two stages: coarse calculation and fine calculation. The coarse calculation stage aims to quickly obtain a feasible solution, simplify some constraints, and determine the range of component capacity. The fine calculation stage refines the constraints based on the coarse calculation results. The optimization results include the specific installed capacity of photovoltaic / wind power, the number of electrolyzers and the power of a single electrolyzer, the capacity and type ratio of energy storage, as well as the generation of hourly electrolyzer load adjustment curves, energy storage charging and discharging time series tables, and grid power purchase / sale timing and power plans.

[0012] Secondly, the present invention also provides a green hydrogen system configuration optimization system that takes into account carbon emission reduction benefits, comprising: The system architecture creation module is configured to: build the green hydrogen system architecture; The optimization model building module is configured to: construct a multi-objective optimization model based on the green hydrogen system architecture, with power balance, component capacity, operating characteristics, and carbon emission reduction accounting as constraints, and maximizing the comprehensive benefits of the green hydrogen system throughout its entire life cycle as the objective function; wherein, the indicators of the objective function include green hydrogen sales revenue, carbon emission reduction revenue, total life cycle investment cost, annual operation and maintenance cost, and hydrogen energy storage and transportation cost; The configuration scheme optimization module is configured to use the particle swarm optimization algorithm to solve the multi-objective optimization model and determine the green hydrogen system configuration scheme.

[0013] Thirdly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the green hydrogen system configuration optimization method taking into account carbon emission reduction benefits as described in the first aspect.

[0014] Fourthly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the program to implement the steps of the green hydrogen system configuration optimization method taking into account carbon emission reduction benefits as described in the first aspect.

[0015] Fifthly, the present invention also provides a computer program product, the computer program product comprising a computer program, which, when executed by a processor, implements the steps of the green hydrogen system configuration optimization method taking into account carbon emission reduction benefits as described in the first aspect.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention constructs a multi-objective optimization model with power balance, component capacity, operating characteristics, and carbon emission reduction accounting as constraints, and maximizing the comprehensive benefits of the green hydrogen system throughout its entire life cycle as the objective function. The indicators of the objective function include green hydrogen sales revenue, carbon emission reduction revenue, total life cycle investment cost, annual operation and maintenance cost, and hydrogen storage and transportation cost. A particle swarm optimization algorithm is used to solve the multi-objective optimization model to determine the green hydrogen system configuration scheme. By fully considering the economic benefits corresponding to carbon emission reductions during green hydrogen production, this invention achieves synergistic optimization of economic efficiency and carbon emission reduction benefits, solving the problem of balancing the economic feasibility and environmental benefits of green hydrogen projects. Attached Figure Description

[0017] The accompanying drawings, which form part of this embodiment, are used to provide a further understanding of this embodiment. The illustrative embodiments and their descriptions are used to explain this embodiment and do not constitute an improper limitation of this embodiment.

[0018] Figure 1 This is a flowchart of the method in Embodiment 1 of the present invention. Detailed Implementation

[0019] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0020] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0021] Example 1: As described in the background section, current green hydrogen system configuration methods primarily focus on minimizing the costs of renewable energy output, electrolyzer operation, and energy storage matching, without fully considering the economic benefits (such as carbon trading revenue and carbon emission reduction subsidies) corresponding to carbon emission reductions during green hydrogen production. This leads to the following defects in the optimization results: The economic assessment is one-sided: it only calculates investment costs, operation and maintenance costs, and energy consumption costs, ignoring the value of carbon assets, which leads to an underestimation of the overall system benefits; the configuration plan is unreasonable: it does not adjust the installed capacity of renewable energy, electrolyzer power and energy storage configuration in conjunction with carbon emission reduction benefits, which may result in insufficient renewable energy consumption or insufficient release of carbon emission reduction potential; the carbon emission reduction accounting is not standardized: it lacks a quantitative method for carbon emission reduction that matches the entire green hydrogen production process, making it difficult to directly connect with carbon market trading rules or policy subsidy standards.

[0022] To address the aforementioned issues, this embodiment provides a green hydrogen system configuration optimization method that takes into account carbon emission reduction benefits. This method achieves optimal synergy between economic efficiency and carbon emission reduction benefits, and is applicable to distributed or centralized green hydrogen projects such as photovoltaic / wind power-driven water electrolysis for hydrogen production.

[0023] like Figure 1 As shown, the green hydrogen system configuration optimization method in this embodiment includes a green hydrogen system configuration architecture, a carbon emission reduction benefit accounting model, a multi-objective optimization model, and solution steps, as detailed below: S1, Green Hydrogen System Configuration Architecture: S1.1 Renewable Energy Power Generation Unit: Optionally, the core configuration of the power generation unit consists of a photovoltaic power station and a wind farm, which can be configured individually or in combination depending on the resource endowment of the project area. In desert and Gobi regions with abundant sunshine, photovoltaic power stations are prioritized, using high-efficiency PERC or TOPCon photovoltaic modules to improve photoelectric conversion efficiency. In grassland and coastal areas with excellent wind resources, the focus is on wind farm construction, using 2.5MW or higher permanent magnet direct-drive wind turbine units to enhance low-wind-speed power generation capabilities. The core function of this unit is to provide a continuous and stable supply of clean electricity to the electrolyzer, minimizing the fossil fuel dependence on green hydrogen production and ensuring the zero-carbon nature of green hydrogen from the source.

[0024] S1.2, Electrolysis of water to produce hydrogen: Optionally, the mainstream configurations for water electrolysis hydrogen production units are alkaline electrolyzers (AE) and proton exchange membrane (PEM) electrolyzers. The choice between these two should be based on the project scale, load characteristics, and investment budget. For large-scale centralized projects with an annual output of 10,000 tons or more, alkaline electrolyzers are preferred due to their advantages of high single-cell power (up to 1000kW or more), low investment cost, and strong operational stability. For distributed projects or scenarios requiring frequent start-ups and shutdowns, PEM electrolyzers are preferred due to their fast response speed (start-up time ≤ 10 minutes), wide load adjustment range (10%-100%), and excellent adaptability to renewable energy fluctuations. This unit directly converts electrical energy into hydrogen through water electrolysis, producing only hydrogen and oxygen in the process, with no other pollutant emissions.

[0025] S1.3, Energy Storage Unit: Optionally, a hybrid energy storage architecture combining electrochemical and hydrogen energy storage can be adopted. Electrochemical energy storage primarily utilizes lithium-ion battery systems, employing lithium iron phosphate batteries to ensure charge-discharge cycle life (≥6000 cycles) and safety. This is mainly used to mitigate minute-level and hourly output fluctuations in renewable energy. Hydrogen energy storage uses high-pressure hydrogen storage tanks as its core equipment, employing 45MPa or 90MPa carbon fiber fully wound hydrogen storage tanks. These tanks combine energy storage and hydrogen storage functions, absorbing any long-term excess output from renewable energy sources while ensuring the supply of hydrogen for subsequent transportation and utilization. The coordinated operation of these two types of energy storage effectively ensures the electrolyzer operates under stable loads, avoiding equipment damage caused by frequent start-ups and shutdowns.

[0026] S1.4 Carbon Emission Reduction Accounting Module: As the core module connecting green hydrogen production and the carbon market, the carbon emission reduction accounting module is equipped with a high-precision data acquisition terminal and dedicated accounting software. It collects real-time operational data from the entire process, including renewable energy power generation, electrolyzer power consumption, hydrogen production, and equipment energy consumption. Through the built-in carbon emission reduction quantification model (based on the baseline method), it automatically calculates core indicators such as carbon emission reduction per unit of green hydrogen and total carbon emission reduction, and generates accounting reports that meet the requirements of carbon market trading, thus achieving real-time, accurate, and standardized management of carbon emission reduction data.

[0027] S1.5, Optimized Control Module: The carbon emission reduction accounting module integrates an industrial-grade controller and a dedicated optimization algorithm, serving as the brain of the green hydrogen system. The controller is multi-protocol compatible, enabling real-time communication with various components such as the power generation unit, electrolyzer, and energy storage equipment. The optimization algorithm dynamically updates the optimization target based on real-time collected resource data and market data (carbon price, electricity price, etc.), and outputs the optimal component capacity configuration scheme (such as photovoltaic / wind power installation ratio and electrolyzer power selection) and real-time operation strategy (such as electrolyzer load adjustment commands and energy storage charging and discharging scheduling commands) through a rolling optimization strategy, ensuring that the system maximizes its comprehensive benefits throughout its entire life cycle.

[0028] S2, Carbon Emission Reduction Benefit Calculation Model: S2.1 Carbon emission reduction revenue = carbon emission reduction × carbon price (carbon trading price or policy subsidy standard), where the carbon emission reduction is calculated using the baseline method, and the specific formula is as follows: ; in, For carbon emission reductions; For carbon price A dual-track pricing system is adopted, allowing projects to choose between carbon trading prices or policy subsidy standards based on their specific circumstances. If a project connects to the carbon market or a regional carbon market, The average price over the trading period (such as monthly or quarterly average price) is used for real-time access and dynamic updates via the carbon market trading system API interface; if the project enjoys local government carbon emission reduction subsidies, then... When the subsidy standards clearly defined in the policy are met, and both trading and subsidy conditions are satisfied, a combined subsidy plus trading premium calculation model can be used. For example, if a project reduces carbon emissions by 13,300 tons of CO2, the average carbon market price is 50 yuan / ton of CO2, and the local subsidy is 200 yuan / ton of CO2, then... =1.33×10 4 × (50 + 200) = 3.325 × 10 6 In addition, a carbon price risk early warning mechanism needs to be established. When the carbon price fluctuates by more than ±20%, the optimization control module will automatically adjust its operating strategy to ensure the stability of carbon emission reduction benefits.

[0029] S2.2, Baseline Carbon Emissions As a core benchmark for measuring the carbon emission reduction value of green hydrogen, its value must be strictly anchored to the mainstream technological routes of fossil fuel hydrogen production and industry carbon accounting standards. Specifically, it is the weighted average of the unit carbon emissions of hydrogen produced by mainstream fossil fuel hydrogen production methods such as coal-to-hydrogen and natural gas-to-hydrogen. The weighting should be determined with reference to the proportion of fossil fuel hydrogen production capacity in the project's region—for example, in areas rich in coal resources, the weight of coal-to-hydrogen can be increased to 60%-70%; in coastal areas with sufficient natural gas supply, the weight of natural gas-to-hydrogen can be adjusted to over 50%. Relevant standards should be prioritized when determining the value. If the standards do not explicitly specify values, default values ​​in relevant greenhouse gas inventory guidelines can be referenced, such as approximately 18-20 kg CO2 / kg H2 carbon emissions per unit for coal-to-hydrogen and approximately 8-10 kg CO2 / kg H2 for natural gas-to-hydrogen (without carbon capture). A comprehensive weighted average is commonly found... The value is taken as 13.5 kg CO2 / kg H2. Meanwhile, A dynamic updating mechanism needs to be established, and the baseline should be calibrated annually based on the latest energy statistics and carbon accounting standards to ensure its timeliness.

[0030] S2.3, Actual carbon emissions of green hydrogen Adopting a full life-cycle accounting approach, this method covers the entire process from renewable energy power generation and water electrolysis to hydrogen production and initial hydrogen storage. Specifically, it includes three main categories of emission sources: First, indirect emissions from renewable energy power generation (such as implicit carbon emissions from photovoltaic module production and wind turbine manufacturing), which can be determined through relevant reporting guidelines. If the project adopts green energy traceability certification (such as green certificates), this part can be accounted for as zero-carbon. Second, emissions from electrolyzer operation, mainly indirect emissions from the energy consumption of electrolyzer cooling systems and auxiliary equipment, accounting for less than 5% of total emissions. Third, emissions from other auxiliary processes (such as energy consumption emissions from hydrogen compression). Through full-process accounting, when renewable energy power generation is certified as zero-carbon, The concentration is typically controlled within the range of 0.1-0.3 kg CO2 / kg H2, with alkaline electrolyzers having lower energy consumption due to their auxiliary systems. It is closer to 0.1 kg CO2 / kg H2. The PEM electrolyzer has slightly higher emissions due to the implicit emissions from proton exchange membrane production. The figure is approximately 0.2-0.3 kg CO2 / kg H2. To improve the accuracy of the calculation, each emission source needs to be equipped with dedicated metering equipment, such as installing an energy consumption monitoring meter at the auxiliary power supply of the electrolyzer, to achieve accurate traceability of emission data.

[0031] S2.4, carbon emission reduction The core calculation basis for carbon emission reduction benefits is as follows: ; in, The annual green hydrogen production (kgH2) should be based on the hydrogen flow meter data at the electrolyzer outlet, and corrected by combining the hydrogen purity test results (≥99.97%) to avoid production calculation deviations caused by impurity content. and Values ​​from the same accounting period (e.g., annually) must be used to ensure the reasonableness of the difference calculation. For example, a project producing 10,000 tons of green hydrogen annually should use... =13.5kgCO2 / kgH2 =0.2kgCO2 / kgH2, then the annual carbon emission reduction ΔE = (13.5 - 0.2) × 10 6 =1.33×10 7 kgCO2, or 13,300 tons of CO2, must be included in the project's carbon asset ledger as the core basis for carbon trading or subsidy applications.

[0032] S3. Multi-objective optimization model: Comprehensive benefits throughout the entire life cycle of green hydrogen systems With maximization as the objective, while taking into account technological constraints and market rules, the model is as follows: S3.1 Objective Function: ; in, For green hydrogen sales revenue; For carbon emission reduction benefits; Cost of investment over the entire life cycle; Annual operating and maintenance costs; Costs associated with hydrogen storage and transportation.

[0033] The quantitative calculation formulas and explanations for each parameter are as follows: S3.1.1, Revenue from the sale of green hydrogen is: ; in, The hydrogen production (kg) in hour t is determined by the electrolyzer power. Electrolysis efficiency Calculation yields ( (2860 is the lower calorific value of hydrogen, kJ / kg). The hourly price of green hydrogen in hour t is RMB 35 / kg for industrial use and RMB 45 / kg for transportation use.

[0034] S3.1.2, The total life-cycle investment cost is:

[0035] in, The unit investment cost of photovoltaic modules (unit: yuan / W) means the total investment cost for each watt of photovoltaic module, including all related costs such as the purchase, transportation, installation and commissioning of the photovoltaic module itself; The unit investment cost of wind turbine (unit: yuan / W) means the total investment cost for each 1 watt of wind turbine, covering the procurement, transportation, installation and commissioning costs of equipment such as the wind turbine body, hub, and tower. The unit investment cost of an electrolytic cell (unit: yuan / W) means the total investment cost corresponding to each watt of rated power of the electrolytic cell, including the purchase, installation and commissioning costs of equipment such as the electrolytic cell body, auxiliary control system, and pipelines. The unit investment cost of energy storage equipment (unit: yuan / Wh) means the total investment cost corresponding to 1 watt-hour of energy storage capacity. For the lithium battery energy storage system in this solution, it includes the procurement, installation and commissioning costs of equipment such as battery modules, BMS management system, and energy storage converter (PCS). The installed capacity of a photovoltaic power station (unit: W or MW) refers to the total power generation corresponding to the photovoltaic module cluster configured in the project, and is the core indicator for measuring the scale of a photovoltaic power generation unit. Installed capacity of a wind farm (unit: W or MW) refers to the total power generation capacity corresponding to the wind turbine cluster configured in the project, and is a core indicator for measuring the scale of wind power generation units; Rated power of the electrolyzer (unit: W or MW) refers to the upper limit of the design power of the electrolyzer during normal and stable operation, which determines the maximum hydrogen production capacity of the electrolyzer. Rated capacity (unit: Wh or MWh) refers to the maximum electrical energy capacity that an energy storage system (lithium battery energy storage in the document) can store. It is a core indicator for measuring the electrical energy storage capacity of an energy storage unit. This is the capital recovery coefficient. The annual interest rate is 4.9%. Lifecycle (20 years).

[0036] S3.1.3, Annual operation and maintenance cost is: ; in, Annual operating and maintenance cost per unit of photovoltaic modules (unit: yuan / W) (Annual cost), meaning the annual operation and maintenance cost required for each watt of photovoltaic (PV) module, covering the total annual operation and maintenance costs for PV modules, including daily inspections, cleaning, fault repairs, and parts replacement. (Optional) =0.02 yuan / W·year; Annual operating and maintenance cost per unit of wind turbine (unit: yuan / W) (Annual cost) refers to the annual operating and maintenance costs required for each 1 watt of wind turbine, including blade cleaning, gearbox maintenance, generator overhaul, tower maintenance, and other full-year operating and maintenance costs. =0.03 yuan / W·year; Annual operating and maintenance cost per unit of electrolytic cell (unit: RMB / W) (Annual) means the annual operating and maintenance cost required for each watt of rated power of the electrolyzer, covering the full year's operating and maintenance costs such as electrolyte replacement, electrode maintenance, control system overhaul, and pipeline maintenance. The power purchased by the grid in hour t; It is a time-of-use electricity price.

[0037] S3.1.4 The cost of hydrogen energy storage and transportation is: ; in, Unit cost of hydrogen storage tanks; For leakage rate, optional =0.5%; The transport distance is in kilometers. For transmission energy consumption costs, optional =0.01 yuan / kWh·km.

[0038] S3.2, Constraints: S3.2.1 Power balance constraint: ; in, For the first The actual output power of a photovoltaic power station in one hour (unit: W or MW) refers to the photovoltaic modules in the first hour. The actual electrical power generated by photoelectric conversion per hour varies with environmental conditions such as irradiance and temperature during that period, and is one of the core sources of clean electricity for the green hydrogen system. For the first The actual output power of a wind farm in hours (unit: W or MW) refers to the actual electrical power generated by the wind turbine in hour t through wind energy conversion. Its value varies with environmental conditions such as wind speed and wind direction during that period, and works in conjunction with photovoltaic output to supply power to the electrolyzer. For the first The discharge power (unit: W or MW) of the hourly energy storage system specifically refers to the discharge power of the energy storage device (lithium battery energy storage in this solution) on the [number]th [hour]. The power released to the system every hour is used to smooth out fluctuations in photovoltaic / wind power output and ensure stable operation of the electrolytic cell; The charging power of the energy storage system in hour t (unit: W or MW) specifically refers to the power of the energy storage device absorbing electrical energy from the system in hour t, mainly used to store excess electrical energy during peak photovoltaic / wind power output; meeting the requirements of... (Charging and discharging are mutually exclusive); all power is non-negative.

[0039] S3.2.2, Component capacity constraints: , ; ; (Energy storage SOC constraint).

[0040] in, For the first The actual operating power (unit: W or MW) of the electrolyzer in the hourly cycle refers to the electrolyzer's operating power in the hourly cycle. The actual electrical power consumed in the process of converting electrical energy into hydrogen energy per hour is dynamically adjusted according to the output of renewable energy and energy storage dispatch strategies. Rated power (unit: W or MW) refers to the maximum stable operating power of the electrolyzer, which is the core parameter for measuring the hydrogen production capacity of the electrolyzer and determines the maximum hourly hydrogen production of the electrolyzer. For the first The actual energy storage capacity of the hourly energy storage system (unit: Wh or MWh), that is, the energy capacity of the energy storage device (lithium battery energy storage in this solution) corresponding to the real-time state of charge (SOC) in hour t; Rated capacity (unit: Wh or MWh) refers to the maximum electrical energy storage capacity designed for energy storage equipment. It is a core specification parameter of the energy storage unit and determines the energy storage system's ability to mitigate renewable energy fluctuations and ensure the stable operation of the electrolyzer.

[0041] S3.2.3, Operational characteristic constraints: Electrolytic cell (Load rate ≥ 30%), number of start-ups and shutdowns per year Energy storage (charge / discharge rate ≤ 0.5C) .

[0042] in, The charging efficiency of an energy storage system refers to the ratio of the actual electrical energy stored by the energy storage device (in this solution, lithium battery energy storage) to the total electrical energy absorbed from the system during the charging process. It is a core parameter for measuring energy loss in the energy storage charging process. The discharge efficiency of an energy storage system refers to the ratio of the actual electrical energy released into the system by the energy storage device during the discharge process to the total electrical energy stored in the device itself. It is used to measure the energy loss in the energy storage discharge process.

[0043] S3.2.4 Carbon emission reduction accounting constraints: , Green hydrogen purity ≥ 99.97%, oxygen purity ≥ 99.5% (environmental protection requirements).

[0044] S4. Optimize the solution steps: S4.1 Data Input: As the foundation for optimization solutions, data acquisition must adhere to the principles of high precision, long time series, and multi-dimensionality, specifically including four core data categories. First, renewable energy resource data: this requires collecting photovoltaic irradiance data (time resolution ≤ 15 minutes, measured on-site using a total radiation meter, calibrated using satellite remote sensing data) and wind speed time series data (height aligned with the wind turbine hub center, sampling frequency ≥ 1Hz, excluding extreme weather anomalies). The data should then be used to predict resource changes over the next 20 years (system lifecycle) through trend extrapolation. Second, equipment parameters: for electrolyzers, the efficiency curve as a function of load (e.g., the efficiency range corresponding to a load rate of 30%-100%), start-up and shutdown energy consumption, and lifespan degradation coefficient must be clearly defined. For energy storage equipment, the parameters must be... The system provides parameters such as charge / discharge efficiency, rated power, capacity decay rate, and charge / discharge depth limits. All equipment investment costs must include the equipment itself, installation, commissioning, and transportation. Thirdly, market parameters are required: green hydrogen pricing must differentiate between industrial and transportation applications; carbon pricing data must be collected from both regional and international carbon markets; and electricity pricing must specify peak, flat, and off-peak periods and their implementation times. Fourthly, policy parameters are necessary, including carbon emission reduction subsidy standards, planned capacity limits, renewable energy electricity price subsidies, and green certificate trading policies, ensuring comprehensive policy coverage. All data must be encrypted and transmitted to the optimization control module's database. A data quality verification mechanism must be established, using interpolation to supplement missing values ​​and the 3σ criterion to remove outliers.

[0045] S4.2, Carbon Reduction Benefit Modeling: Based on the input carbon accounting parameters and market parameters, a dynamic carbon emission reduction benefit calculation model is constructed. First, the characteristics of the project's location are determined. The weighting coefficients are combined with the latest carbon accounting standards to calibrate the benchmark values; secondly, based on renewable energy power generation data and electrolyzer operation data, the life cycle assessment (LCA) method is used to calculate the results in stages. The implied emissions from photovoltaic modules are calculated at 0.8 kg CO2 per watt, and from wind turbines at 800 kg CO2 per kilowatt, and are amortized based on the equipment's service life. Subsequently, ΔE is calculated using hydrogen production data (corrected for purity) to generate a carbon emission reduction ledger. Finally, carbon price data is obtained in real-time through the carbon market API interface and determined in conjunction with policy subsidy standards. The final value is determined by the model's automatic smoothing mechanism when carbon prices fluctuate drastically, using the 30-day moving average price as the calculation basis to ensure... The stability of the calculation. During the modeling process, a sensitivity analysis curve for carbon emission reduction benefits needs to be generated simultaneously to clarify the impact of carbon price and hydrogen production on the benefits.

[0046] S4.3 Optimize model construction: The model is constructed using a hierarchical architecture of main objective and sub-constraints, the core of which is the organic integration of the main objective of maximizing overall benefit with multi-dimensional constraints. In the main objective function, It is necessary to differentiate the price differences between different sales channels (such as long-term contract customers and spot customers). The time value of money needs to be introduced, and a dynamic capital recovery factor (taking into account loan interest rates and inflation rates) should be adopted. The maintenance costs for each component need to be detailed (approximately RMB 0.02 / W·year for photovoltaic modules, RMB 0.03 / W·year for wind power, and RMB 0.05 / W·year for electrolytic cells), and included in the equipment failure repair cost budget. The costs must include depreciation of the hydrogen storage tank (calculated over a 20-year straight-line depreciation period), hydrogen leakage loss costs (calculated at 0.5% of annual production), and transportation energy costs (calculated at 0.01 kWh / kgH2 per kilometer for pipeline transportation). In constructing the constraints, in addition to basic constraints, new constraints include grid interaction (if connected to the grid, the purchased / sold power must meet grid dispatch requirements to avoid impacting grid frequency), equipment lifespan constraints (electrolyzer start-up / shutdown times ≤ 3000 times / year, energy storage charge-discharge cycles ≤ 1500 times / year), and environmental constraints (oxygen emissions must meet relevant industrial gas emission standards). For multi-objective scenarios, the carbon emission reduction maximization sub-objective is integrated into the main objective using a weighted summation method. The weighting coefficients can be dynamically adjusted according to the project investor's benefit preferences (economic priority or environmental priority), achieving a transformation from multiple objectives to a single objective. Alternatively, the Pareto optimal solution method can be used to generate multiple sets of optimization schemes for selection.

[0047] S4.4, Algorithm Solution: Based on the nonlinear and integer variable characteristics of the model, suitable optimization algorithms are selected and improved to ensure both accuracy and efficiency. The improved Particle Swarm Optimization (PSO) algorithm is suitable for optimization scenarios with continuous variables. By introducing adaptive inertia weights (0.9 in the initial iterations, linearly decreasing to 0.4 later) and chaotic mutation operators, it addresses the problem of traditional PSO easily getting trapped in local optima. The algorithm parameters are set as follows: number of particles 50-100, maximum number of iterations 50-100, and learning factors c1=c2=2. Mixed Integer Linear Programming (MILP) is suitable for scenarios with discrete variables (such as the number of devices). The CPLEX solver is called through the YALMIP toolkit, and the branch and bound method is used to improve the solution speed. For large-scale problems, variable reduction techniques are introduced to eliminate redundant variables. The solution process adopts a two-stage strategy of coarse-fine calculation: the first stage aims to quickly obtain a feasible solution, simplifying some constraints and determining the approximate range of component capacity; the second stage refines the constraints based on the coarse calculation results to improve solution accuracy. The final optimized output includes two parts: first, a static capacity configuration scheme, which clarifies the specific installed capacity of photovoltaic / wind power, the number of electrolyzers and their power per unit, and the capacity and type ratio of energy storage; second, a dynamic operation strategy, which generates hourly electrolyzer load adjustment curves (accurate to 10 minutes), energy storage charge and discharge timetables, grid power purchase / sale timing and power plans, and marks the operating parameters of key nodes (such as electrolyzer temperature and energy storage SOC value).

[0048] S4.5, Solution Verification: A three-dimensional verification system, including simulation testing, field verification, and risk assessment, was constructed to ensure the feasibility and reliability of the optimized solution. In the simulation testing phase, a green hydrogen system simulation model was built using MATLAB / Simulink. Nearly one year of measured resource data and market data were imported for offline simulation to verify the operational performance of the solution under different scenarios (such as extreme sunlight, strong winds, and soaring carbon prices). The focus was on evaluating power balance accuracy (error ≤ 5%), overall benefit achievement rate, and constraint compliance. Simultaneously, a grid simulation model was built using DIgSILENT to verify the safety and stability of the system after grid connection, ensuring that the harmonic distortion rate is ≤ 5%. In the field verification phase, a pilot system of 10% scale was selected for small-scale trial operation. Real-time monitoring equipment was installed to collect operational data, which was compared with the simulation results to correct model parameters (such as the electrolyzer efficiency curve and energy storage charge / discharge efficiency). Finally, a risk assessment was conducted to identify potential resource risks (irradiance lower than predicted), market risks (carbon price crash), and technical risks (equipment failure) facing the optimized solution, and to develop countermeasures (such as increasing backup energy storage capacity and signing carbon price hedging agreements). If the verification process finds that the scheme does not meet the constraints (such as the renewable energy consumption rate being less than 85%), return to step 3 to readjust the objective function weights or constraint parameters until the scheme passes the verification.

[0049] Example 2: This embodiment illustrates and supplements the method in Embodiment 1 through a specific project, including: S1. Determine the implementation scenario: A centralized green hydrogen project, planned to cover 1,000 mu (approximately 67 hectares), aims to produce 10,000 tons of green hydrogen annually and connect to the regional carbon market (carbon price). =50 yuan / ton CO2), renewable energy resources are a combination of photovoltaic and wind power.

[0050] S2, Parameter Input: S2.1 Renewable Energy Data: Annual photovoltaic irradiance 1500 kWh / m², annual average wind speed of wind farm 6.5 m / s.

[0051] S2.2 Equipment parameters: Alkaline electrolytic cell efficiency 75%, investment cost 1200 yuan / kW; photovoltaic module investment cost 3.5 yuan / W, wind power investment cost 5.2 yuan / W; lithium battery energy storage charge and discharge efficiency 90%, investment cost 1.8 yuan / Wh.

[0052] S2.3 Market parameters: Green hydrogen is priced at 35 yuan / kg, and the peak and off-peak electricity prices are 0.8 yuan / kWh and 0.3 yuan / kWh, respectively.

[0053] S2.4 Carbon accounting parameters: =13.5kgCO2 / kgH2, =0.2kgCO2 / kgH2.

[0054] S3, Optimization Process: S3.1 Calculation of carbon emission reduction benefits: Based on the carbon accounting parameters specified above ( =13.5kgCO2 / kgH2, =0.2kgCO2 / kgH2) and the project's annual production target of 10,000 tons of green hydrogen (H=10 6 kgH2), according to the baseline method formula ΔE= ( The calculation is performed using -E_act) × H. Where, The values ​​were derived by weighting the proportion of fossil fuel hydrogen production capacity in the northern region (coal-based hydrogen production weighted at 65%, natural gas-based hydrogen production weighted at 35%). The emissions are determined through full life cycle accounting (the implicit emissions from photovoltaic modules are approximately 0.12 kg CO2 / kg H2, and the emissions from the electrolytic cell auxiliary system are approximately 0.08 kg CO2 / kg H2).

[0055] The specific calculation process is as follows: ΔE = (13.5 - 0.2) × 10 6 =1.33×107 kgCO2; Converted to commonly used units in the carbon market, this is 1.33 × 10⁻⁶. 4 The carbon price is based on the regional carbon market's quarterly average price of 50 yuan / ton of CO2, therefore the carbon emission reduction benefit is... =1.33×10 4 ×50=6.65×10 5 The revenue of RMB [amount] has been included in the project's annual carbon asset revenue ledger and can be directly used for carbon market trading settlement.

[0056] S3.2 Model Construction: Construct a single-objective optimization model that maximizes overall benefits, with the objective function clearly defined as: ; in, (Yuan / Year); (RMB / year, electricity purchase fee from the power grid: RMB 1.2 million); (RMB / year).

[0057] Substituting core constraint boundaries: , Electrolytic cell (Load rate 40%-100%), annual start-stop ≤2500 times; energy storage Charging and discharging power ≤ 40MW (0.5C); Power purchased from the grid By constraining boundaries, we ensure that the model output meets the requirements of both the equipment and the power grid.

[0058] S3.3, Algorithm Solution: An improved particle swarm optimization (PSO) algorithm was used to solve the problem. The algorithm parameters were set as follows: 80 particles (covering the component capacity combination space), a maximum number of iterations of 50, adaptive inertia weight linearly decreasing from the initial 0.9 to 0.4, learning factors c1=c2=2, and a chaotic mutation operator (mutation probability 0.05) was introduced to avoid local optima. The solution is implemented in two stages: the first stage (iterations 1-20) simplifies equipment loss constraints and quickly identifies feasible ranges of 170-190MW for photovoltaic installations and 110-130MW for wind power; the second stage (iterations 21-50) refines the constraints and finally converges to obtain the optimal configuration scheme: 180MW photovoltaic installations (using 540W PERC modules, a total of 333,000 modules), 120MW wind power installations (using 4MW permanent magnet direct drive units, a total of 30 units), a total electrolytic cell power of 50MW (using 5 10MW alkaline electrolytic cells), and a lithium battery energy storage capacity of 80MWh (lithium iron phosphate batteries, charge / discharge rate 1C). At the same time, a dynamic operation strategy is output: the electrolytic cell load rate is maintained at 40%-90%, the lithium battery is charged during the peak output of photovoltaic power at noon (11:00-14:00), and the wind power is discharged during the off-peak hours at night (20:00-6:00 the next day), and the power purchase from the grid is concentrated in the off-peak period (0:00-8:00).

[0059] S3.4, Solution Verification: A dual verification approach of simulation and pilot testing was adopted. The simulation test used MATLAB / Simulink to build a system model, importing nearly one year of measured data from the project site (15-minute resolution for photovoltaic irradiance, 1Hz sampling for wind speed), simulating three scenarios: extreme high temperature (40℃), strong wind (12m / s), and a surge in carbon prices (70 yuan / ton CO2). Results showed: power balance error ≤3%, electrolyzer start-up and shutdown frequency 1800 times / year (below the upper limit of the constraint), renewable energy integration rate 92% (95% for photovoltaic and 88% for wind power); annual comprehensive benefit 2.8 × 10⁻⁶. 8 Yuan, compared to the traditional optimization scheme that only considers cost (profit 2.37 × 10). 7 The 18% increase in revenue (RMB) is mainly due to carbon reduction benefits and lower electricity purchase costs resulting from increased renewable energy consumption. A pilot verification project was conducted using a 10% scale (18MW photovoltaic, 12MW wind power, etc.) to build a test system. The measured data and simulation results after three months of trial operation showed a deviation of ≤5%, verifying the feasibility of the solution. Risk assessment indicates that even if carbon prices fall by 20%, the project revenue can still maintain 2.6 × 10⁻⁶. 7 Yuan, possessing strong risk resistance capabilities.

[0060] S4. Implementation Results: This optimization scheme, through a collaborative design across the entire resource-equipment-market-revenue chain, truly achieves a dynamic balance and optimal synergy between renewable energy output, electrolyzer operation, energy storage matching, and carbon emission reduction benefits. Its core collaborative mechanism is reflected in the following: during peak photovoltaic output at midday, the lithium battery energy storage system prioritizes absorbing excess energy, avoiding curtailment while reserving energy for evening electrolyzer operation; during periods of stable wind power output, the electrolyzer maintains high-load operation (80%-90%), maximizing the conversion of clean electricity; when carbon prices fluctuate, the optimized control module automatically adjusts the electrolyzer's operating rhythm, increasing hydrogen production to enhance carbon emission reduction benefits when carbon prices are high, forming a virtuous cycle of multi-linked linkages. Regarding return on investment, based on the previously defined equipment investment cost (total initial investment of approximately RMB 1.56 billion) and annual comprehensive return (2.8 × 10⁻⁶), the scheme achieves a return on investment of approximately RMB 1.56 billion. 7 The initial investment payback period for the project is estimated at approximately 12 years, 2.5 years shorter than traditional schemes that do not account for carbon emission reduction benefits (14.5 years). If local government subsidies for green hydrogen are added (up to 0.5 yuan / kgH2 in some regions), the payback period can be further reduced to less than 10 years, significantly enhancing the project's investment attractiveness. Regarding the implementation of carbon emission reduction benefits, the carbon emission reduction ledger generated by the scheme (13,300 tons of CO2 per year) fully complies with the relevant management regulations for voluntary greenhouse gas emission reduction trading. It has been certified by a third-party carbon verification agency and can be directly registered for trading in the regional carbon market, simplifying the trading process and ensuring stable returns. Simultaneously, this carbon emission reduction can supplement corporate carbon emission rights compliance, providing additional carbon asset value for project partners. Overall, the scheme reduces production costs through optimized component configuration and operational strategy adjustments, while activating the economic value of environmental benefits through standardized carbon emission reduction accounting, truly achieving a dual improvement in economic efficiency and environmental benefits. This provides a replicable practical path for the large-scale commercialization of green hydrogen projects.

[0061] Example 3: This embodiment provides a green hydrogen system configuration optimization system that takes into account carbon emission reduction benefits, including: The system architecture creation module is configured to: build the green hydrogen system architecture; The optimization model building module is configured to: construct a multi-objective optimization model based on the green hydrogen system architecture, with power balance, component capacity, operating characteristics, and carbon emission reduction accounting as constraints, and maximizing the comprehensive benefits of the green hydrogen system throughout its entire life cycle as the objective function; wherein, the indicators of the objective function include green hydrogen sales revenue, carbon emission reduction revenue, total life cycle investment cost, annual operation and maintenance cost, and hydrogen energy storage and transportation cost; The configuration scheme optimization module is configured to use the particle swarm optimization algorithm to solve the multi-objective optimization model and determine the green hydrogen system configuration scheme.

[0062] The working method of the system is the same as the green hydrogen system configuration optimization method taking into account carbon emission reduction benefits in Example 1, and will not be repeated here.

[0063] Example 4: This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the green hydrogen system configuration optimization method considering carbon emission reduction benefits described in Embodiment 1.

[0064] Example 5: This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor executes the program, it implements the steps of the green hydrogen system configuration optimization method that takes into account carbon emission reduction benefits as described in Embodiment 1.

[0065] Example 6: This embodiment provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the steps of the green hydrogen system configuration optimization method that takes into account carbon emission reduction benefits as described in Embodiment 1.

[0066] The above description is merely a preferred embodiment of this practice and is not intended to limit the scope of this practice. Various modifications and variations can be made to this practice by those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of this practice should be included within the protection scope of this practice.

Claims

1. A method for optimizing the configuration of a green hydrogen system taking into account carbon emission reduction benefits, characterized in that, include: Build a green hydrogen system architecture; Based on the green hydrogen system architecture, a multi-objective optimization model is constructed with power balance, component capacity, operating characteristics, and carbon emission reduction accounting as constraints, and maximizing the comprehensive benefits of the green hydrogen system throughout its entire life cycle as the objective function. The indicators of the objective function include green hydrogen sales revenue, carbon emission reduction revenue, total life cycle investment cost, annual operation and maintenance cost, and hydrogen energy storage and transportation cost. The particle swarm optimization algorithm is used to solve the multi-objective optimization model and determine the configuration scheme of the green hydrogen system.

2. The green hydrogen system configuration optimization method considering carbon emission reduction benefits as described in claim 1, characterized in that, The carbon emission reduction benefit is the product of the carbon emission reduction amount and the carbon price.

3. The green hydrogen system configuration optimization method considering carbon emission reduction benefits as described in claim 2, characterized in that, The carbon emission reduction for: ； in, For the annual production of green hydrogen; As a benchmark carbon emission level; This represents the actual carbon emissions of green hydrogen.

4. The green hydrogen system configuration optimization method considering carbon emission reduction benefits as described in claim 1, characterized in that, The objective function is: ； in, For the comprehensive benefits of the green hydrogen system throughout its entire life cycle; For green hydrogen sales revenue; For carbon emission reduction benefits; Cost of investment over the entire life cycle; Annual operating and maintenance costs; Costs associated with hydrogen storage and transportation.

5. The green hydrogen system configuration optimization method considering carbon emission reduction benefits as described in claim 4, characterized in that, The green hydrogen sales revenue, the total life cycle investment cost, the annual operation and maintenance cost, and the hydrogen energy storage and transportation cost are respectively: ； ； ； ； in, For the first Hourly hydrogen production; For the first The hourly price of green hydrogen; The unit investment cost of photovoltaic modules; This refers to the unit investment cost of wind turbine units; This refers to the unit investment cost of the electrolytic cell; Unit investment cost for energy storage equipment; For the installed capacity of photovoltaic power plants; For wind farm installed capacity; This refers to the rated power of the electrolytic cell; Rated capacity of the energy storage device; This is the capital recovery coefficient; Annual operating and maintenance rate per unit for photovoltaic modules; The unit annual operation and maintenance cost rate for wind turbine units; The annual operating and maintenance cost per unit of electrolytic cell; The power purchased by the grid in hour t; Time-of-use pricing; Unit cost of hydrogen storage tanks; Leakage rate; For conveying distance; This refers to the energy consumption cost of transmission.

6. The green hydrogen system configuration optimization method considering carbon emission reduction benefits as described in claim 5, characterized in that, The power balance constraint, the component capacity constraint, the operating characteristic constraint, and the carbon emission reduction accounting constraint are respectively: ； , ; ; Electrolytic cell Number of start-stop cycles per year Energy storage , ; ， ； in, For the first The actual output power of the hourly photovoltaic power station; For the first The actual output power of the wind farm in one hour; For the first Discharge power of the hourly energy storage system; The charging power of the energy storage system in hour t; For the first The actual operating power of the hourly electrolytic cell; This refers to the rated power of the electrolytic cell; For the first The actual energy storage capacity of the hourly energy storage system; This refers to the rated capacity of the energy storage system. The charging efficiency of the energy storage system; This refers to the discharge efficiency of the energy storage system.

7. The green hydrogen system configuration optimization method considering carbon emission reduction benefits as described in claim 1, characterized in that, The particle swarm optimization algorithm introduces adaptive inertia weights and chaotic mutation operators. The solution process adopts two stages: coarse calculation and fine calculation. The coarse calculation stage aims to quickly obtain a feasible solution, simplify some constraints, and determine the range of component capacity. The fine calculation stage refines the constraints based on the coarse calculation results. The optimization results include the specific installed capacity of photovoltaic / wind power, the number of electrolyzers and the power of a single electrolyzer, the capacity and type ratio of energy storage, as well as the generation of hourly electrolyzer load adjustment curves, energy storage charging and discharging time series tables, and grid power purchase / sale timing and power plans.

8. A green hydrogen system configuration optimization system that takes into account carbon emission reduction benefits, characterized in that, include: The system architecture creation module is configured to: build the green hydrogen system architecture; The optimization model building module is configured to: construct a multi-objective optimization model based on the green hydrogen system architecture, with power balance, component capacity, operating characteristics, and carbon emission reduction accounting as constraints, and maximizing the comprehensive benefits of the green hydrogen system throughout its entire life cycle as the objective function; wherein, the indicators of the objective function include green hydrogen sales revenue, carbon emission reduction revenue, total life cycle investment cost, annual operation and maintenance cost, and hydrogen energy storage and transportation cost; The configuration scheme optimization module is configured to use the particle swarm optimization algorithm to solve the multi-objective optimization model and determine the green hydrogen system configuration scheme.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, When the processor executes the program, it implements the steps of the green hydrogen system configuration optimization method that takes into account carbon emission reduction benefits as described in any one of claims 1-7.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the green hydrogen system configuration optimization method taking into account carbon emission reduction benefits as described in any one of claims 1-7.

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