Traffic hydrogen chain energy carbon benefit dynamic evaluation method and system

Abstract

The invention discloses a traffic hydrogen chain energy carbon benefit dynamic evaluation method and system. The method comprises the following steps: configuring one or more hydrogen energy supply chain paths; configuring scene parameters in one-to-one correspondence with the hydrogen energy supply chain paths, wherein the scene parameters are used for indicating corresponding geographic parameters and time parameters; based on scene parameters configured by a user, obtaining dynamic input parameters corresponding to each node of the hydrogen energy supply chain path from a preset dynamic parameter library; on the basis of the dynamic input parameters corresponding to the nodes of the hydrogen energy supply chain path, energy consumption and carbon emission corresponding to the nodes are calculated, corresponding carbon benefit data are generated, and the carbon benefit data comprise the carbon emission intensity corresponding to the hydrogen energy supply chain path and the total benefit. According to the method, the core defects of parameter staticizing and averaging in an existing hydrogen energy evaluation method can be overcome, and dynamic and high-precision analog simulation is achieved.

Description

Technical Field

[0001] This invention relates to the field of energy and environmental systems engineering, and in particular to a method and system for dynamic assessment of the carbon benefits of transportation hydrogen chain energy. Background Technology

[0002] Against the backdrop of the world actively addressing climate change and promoting deep decarbonization of the energy system, hydrogen energy, with its zero-carbon combustion, wide availability, and long energy storage cycle, is regarded as a key energy carrier for achieving deep decarbonization in the transportation sector, especially in sectors that are difficult to reduce emissions, such as aviation, shipping, and heavy freight.

[0003] However, the cleanliness of hydrogen energy is not absolute. Its energy efficiency and carbon footprint throughout its entire life cycle are highly dependent on the energy structure of upstream hydrogen production (such as renewable energy electrolysis, fossil energy reforming plus carbon capture), production process efficiency, and the energy consumption and losses of midstream storage and transportation methods (high-pressure gaseous, low-temperature liquid, organic liquid, etc.). Summary of the Invention

[0004] This invention addresses the shortcomings of existing technologies that lack a scheme for accurately assessing carbon emissions across the entire hydrogen energy industry chain in the transportation sector, by providing a method and system for dynamically assessing the carbon benefits of the transportation hydrogen energy chain.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0006] Firstly, this invention proposes a method for dynamically evaluating the carbon benefits of the hydrogen chain energy in transportation, comprising the following steps:

[0007] Configure one or more hydrogen energy supply chain paths; the hydrogen energy supply chain paths are used to indicate the assessment start point, assessment end point, hydrogen production method and storage and transportation method, as well as the direction of hydrogen energy supply;

[0008] Configure scenario parameters that correspond one-to-one with hydrogen energy supply chain paths, wherein the scenario parameters are used to indicate the corresponding geographical parameters and time parameters;

[0009] Based on user-configured scenario parameters, dynamic input parameters corresponding to each node of the hydrogen energy supply chain path are obtained from a preset dynamic parameter library.

[0010] Based on the dynamic input parameters corresponding to each node in the hydrogen energy supply chain path, the energy consumption and carbon emissions corresponding to each node are calculated, and corresponding carbon benefit data is generated. The carbon benefit data includes the carbon emission intensity and total benefit corresponding to the hydrogen energy supply chain path.

[0011] As one possible implementation method, the hydrogen energy supply chain path can be configured as follows:

[0012] Configure system boundaries, configure one or more hydrogen production modules, configure one or more storage and transportation modules, and connect the configured hydrogen production modules and storage and transportation modules in series to generate corresponding hydrogen energy supply chain paths; the system boundaries are used to determine the evaluation start point and evaluation end point; the hydrogen production modules correspond one-to-one with the hydrogen production methods, and the storage and transportation modules correspond one-to-one with the storage and transportation methods.

[0013] The system boundary types include a first boundary type and a second boundary type, wherein the first boundary type takes primary energy extraction or acquisition as the evaluation starting point, and the second boundary type takes the entrance of a hydrogen production plant as the evaluation starting point.

[0014] As one possible implementation method:

[0015] The hydrogen production module includes steam methane reforming, coal gasification, alkaline water electrolysis, proton exchange membrane water electrolysis, and biomass hydrogen production.

[0016] The storage and transportation module includes high-pressure gaseous storage and transportation, cryogenic liquid storage and transportation, and material-based storage and transportation.

[0017] As one possible implementation method, the specific steps for calculating the energy consumption and carbon emissions of each node in the hydrogen energy supply chain, based on the dynamic input parameters corresponding to each node, are as follows:

[0018] Based on each node of the hydrogen energy supply chain path, corresponding sub-models are extracted from a pre-set sub-model library and connected in series according to the hydrogen energy supply chain path to obtain a corresponding sub-model chain; the sub-models are used to indicate the calculation rules for material conversion and energy consumption in the corresponding links; the links correspond to the nodes of the hydrogen energy supply chain path.

[0019] By injecting corresponding dynamic input parameters into each sub-model in the sub-model chain, the corresponding simulation model is obtained. According to the sub-model chain, each simulation model calculates energy consumption and carbon emissions based on the injected dynamic data parameters and the corresponding calculation rules.

[0020] The energy consumption includes primary energy consumption and secondary energy consumption, wherein primary energy consumption refers to the consumption of primary energy as a material; and secondary energy consumption refers to the energy consumption that occurs when various types of primary energy are used for energy consumption.

[0021] The carbon emissions include direct emissions and indirect emissions; the direct emissions are those corresponding to the first energy consumption; and the indirect emissions are those corresponding to the second energy consumption.

[0022] As one possible implementation method:

[0023] The carbon emissions corresponding to each simulation model are calculated, and the total carbon emissions are generated based on the statistical results.

[0024] Determine the total mass of hydrogen delivered;

[0025] The corresponding carbon emission intensity is obtained based on the total carbon emissions and the carbon emission intensity per unit mass of hydrogen produced from the total mass of hydrogen delivered.

[0026] As one possible implementation method:

[0027] The total calorific value is obtained by converting all primary and secondary energy consumption along the hydrogen energy supply chain path into calorific values.

[0028] The total benefit is calculated based on the total mass of hydrogen delivered, the lower calorific value of hydrogen, and the total calorific value.

[0029] As one possible implementation method, the preset dynamic parameter library contains several uncertain parameters and the probability distribution of each uncertain parameter;

[0030] When extracting dynamic input parameters corresponding to the hydrogen energy supply chain path from the dynamic parameter library, the uncertain parameters that are used as dynamic input parameters are recorded as target parameters;

[0031] Based on the probability distribution of each target parameter, several sets of target parameter combinations are generated, and dynamic input parameter combinations corresponding to each target parameter combination are created. Simulation is performed based on each dynamic input parameter combination.

[0032] Construct sub-model chains corresponding to the hydrogen energy supply chain path;

[0033] For the aforementioned sub-model chain, simulations were performed based on each combination of dynamic input parameters, and the carbon emission intensity and total energy efficiency obtained from each simulation were recorded.

[0034] Calculate the average carbon emission intensity and output the average value as the final carbon emission intensity estimate;

[0035] Calculate the average value of the total energy efficiency and output the average value as the final total energy efficiency estimate.

[0036] As one possible implementation, creating or updating a dynamic parameter library also includes a step of reverse calibration of the probability distribution of uncertain parameters, specifically:

[0037] Collect data from several benchmark cases;

[0038] For each baseline case, the corresponding uncertainty parameter is used as the decision variable to optimize (P C -R C ) 2 To achieve the objective, an optimization solution is performed to obtain the optimal parameter combination corresponding to the benchmark case data; where P CTo simulate the carbon emission intensity obtained from the simulation, R C The carbon emission intensity is obtained by actual measurement or verification of the corresponding benchmark case data;

[0039] Statistical analysis is performed on the optimal parameter combinations corresponding to all benchmark case data to tighten the probability distribution range of each uncertainty parameter, and the tightened probability distribution is written into the dynamic parameter library.

[0040] Secondly, a dynamic assessment system for the carbon benefits of the hydrogen chain energy in transportation is provided, including:

[0041] The path configuration module is used to configure one or more hydrogen energy supply chain paths; the hydrogen energy supply chain path is used to indicate the evaluation start point, evaluation end point, hydrogen production method and storage and transportation method, as well as the direction of hydrogen energy supply;

[0042] The spatiotemporal parameter configuration module is used to configure scenario parameters that correspond one-to-one with the hydrogen energy supply chain path. The scenario parameters are used to indicate the corresponding geographical parameters and time parameters.

[0043] The dynamic parameter retrieval module is used to retrieve the dynamic input parameters corresponding to each node of the hydrogen energy supply chain path from a preset dynamic parameter library based on the user-configured scenario parameters.

[0044] The simulation module is used to calculate the energy consumption and carbon emissions of each node in the hydrogen energy supply chain based on the dynamic input parameters corresponding to each node, and to generate corresponding carbon benefit data, which includes the carbon emission intensity and total benefit of the hydrogen energy supply chain.

[0045] Thirdly, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the method described in any of the preceding claims.

[0046] This invention, by adopting the above technical solutions, has significant technical effects:

[0047] This invention, through the design of scenario parameters and a dynamic parameter library with spatiotemporal labels, can retrieve dynamic input parameters that are spatiotemporally matched with the hydrogen energy supply chain path from the dynamic parameter library based on scenario parameters. Thus, during the simulation process, the results can accurately reflect the carbon benefits under specific time and specific region. This invention can solve the core defects of static and averaged parameters in existing hydrogen energy assessment methods, and achieve dynamic and high-precision simulation. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 This is a flowchart illustrating a dynamic assessment method for the carbon benefits of a hydrogen chain energy system in transportation, as proposed in this invention. Detailed Implementation

[0050] The present invention will be further described in detail below with reference to the embodiments. The following embodiments are explanations of the present invention, but the present invention is not limited to the following embodiments.

[0051] Existing mature carbon emission management technologies focus on real-time collection, monitoring, accounting, and short-term trend prediction of energy consumption data from existing energy facilities (such as factories and buildings), as shown in the patent publications CN114814093A and CN117078190A. Their core technologies are based on statistical analysis of historical data (such as the ARIMA-SVM combined prediction model) and IoT data visualization. Such solutions are suitable for scenarios with relatively fixed operating conditions and clear emission sources, and belong to the "monitoring-accounting-management" model.

[0052] During the planning and construction phase, the carbon emissions of the transportation hydrogen energy chain are unknown and need to be predicted, and the system composition is complex and variable. General systems cannot conduct forward-looking, engineering-mechanism-based, and refined simulation and carbon emission modeling of a hydrogen energy supply chain that does not yet exist and is composed of a combination of various cutting-edge technologies.

[0053] Example 1: Addressing the issue that existing general carbon emission simulation schemes are not applicable to the transportation hydrogen energy chain, this example proposes a dynamic assessment method for the energy and carbon benefits of the transportation hydrogen energy chain, such as... Figure 1 As shown, it includes the following steps:

[0054] S100, Data Configuration:

[0055] S110, configured with one or more hydrogen energy supply chain paths;

[0056] The hydrogen energy supply chain path is used to indicate the assessment start point, assessment end point, hydrogen production method and storage and transportation method, as well as the direction of hydrogen energy supply;

[0057] Taking the configuration of a hydrogen energy supply chain path as an example, the specific configuration method is as follows:

[0058] S111, Predefine boundary types and build a technology route library;

[0059] The boundary type is used to indicate the system boundary, which is used to determine the evaluation start point and evaluation end point, i.e., where to start and where to end. In this embodiment, the evaluation end point for all boundary types is the hydrogen refueling station, i.e., it ends at the point where the hydrogen fuel cell vehicle is driving and doing work.

[0060] In this embodiment, the boundary types include a first boundary type and a second boundary type;

[0061] The first boundary type is WTW (Well-to-Wheel), which takes primary energy extraction or acquisition as the starting point of the assessment and covers the entire process from primary energy extraction or acquisition.

[0062] For example:

[0063] For hydrogen production from natural gas reforming: the boundary begins with the extraction, purification, and transportation of natural gas fields.

[0064] For renewable energy electrolysis hydrogen production: the boundary begins with the construction and operation of power generation facilities such as photovoltaic power plants and wind farms (i.e., including the production emissions of equipment such as wind turbines and photovoltaic panels).

[0065] The second boundary type is PtW (Pump-to-Wheel), which takes the hydrogen production plant’s inlet (such as a natural gas pipeline valve, grid connection point, or water electrolyzer inlet) as its assessment starting point. It refers to the energy consumption process from refueling to the vehicle (such as a car or truck), covering energy use and direct emissions during vehicle operation.

[0066] The boundary focuses on the production and use of hydrogen fuel, and is usually used to compare the relative advantages and disadvantages of different production technologies and storage and transportation routes, excluding the impact of differences in upstream primary energy infrastructure.

[0067] Those skilled in the art can set several first evaluation starting points for the first boundary type and several second evaluation starting points for the second boundary type according to the actual situation. This specification does not limit them in detail.

[0068] The technology route library includes a hydrogen production route library and a storage and transportation route library. The hydrogen production route library includes several hydrogen production modules, and the storage and transportation route library includes several storage and transportation modules.

[0069] In this embodiment, the hydrogen production route library includes the following five hydrogen production modules classified according to the type of primary energy source:

[0070] Hydrogen production from steam methane reforming and coal gasification based on fossil energy sources;

[0071] Hydrogen production by alkaline water electrolysis and hydrogen production by proton exchange membrane (PEM) electrolysis based on renewable energy power;

[0072] Hydrogen production based on biomass gasification or fermentation.

[0073] In this embodiment, the storage and transportation route warehouse includes the following three types of storage modules:

[0074] High-pressure gaseous storage and transportation: compressed hydrogen is transported via long-tube trailers (suitable for short to medium distances) or pipelines (suitable for large-scale, long-distance transport).

[0075] Cryogenic liquid storage and transportation involves liquefying hydrogen and then transporting it via liquid hydrogen tank trucks or ships.

[0076] Material-based storage and transportation, namely solid / organic liquid storage and transportation, is carried out through media such as metal hydrides and liquid organic hydrogen carriers.

[0077] Those skilled in the art can set up hydrogen production and storage and transportation modules according to actual conditions, and this specification does not limit them in detail.

[0078] This embodiment enables users to flexibly configure hydrogen energy supply chain paths by pre-constructing a systematic and modular technology path library.

[0079] S112. Configure system boundaries, configure one or more hydrogen production modules, configure one or more storage and transportation modules, connect the configured hydrogen production modules and storage and transportation modules in series to generate the corresponding hydrogen energy supply chain path.

[0080] Specifically:

[0081] Determine the assessment endpoint;

[0082] The evaluation starting point is determined based on the hydrogen production module and the boundary type. For example, several evaluation starting points are pre-configured, and each evaluation starting point is associated with the boundary type and the hydrogen production method. In actual use, the associated evaluation starting point can be found based on the boundary type and hydrogen production module configured by the user, and the hydrogen energy supply chain path can be generated automatically or by the user selecting the corresponding evaluation starting point.

[0083] By logically connecting the evaluation starting point, the corresponding hydrogen production module, the corresponding storage and transportation module, and the evaluation endpoint, the corresponding hydrogen energy supply chain path can be obtained:

[0084] As an example, the hydrogen energy supply chain path is shown below:

[0085] Route A: Photovoltaic power generation → PEM electrolysis for hydrogen production → Liquid hydrogen storage and transportation → Hydrogen refueling station.

[0086] Route B: Power grid supply (specific area) → Alkaline electrolysis hydrogen production → High-pressure gaseous pipeline transportation → Hydrogen refueling station.

[0087] Route C: Natural gas → Steam methane reforming to produce hydrogen → High-pressure gaseous trailer transportation → Hydrogen refueling station.

[0088] For example, if a user selects PEM electrolysis hydrogen production module, and the user selects the first boundary type WTW, the first evaluation starting point corresponding to renewable energy power will be selected based on the category of the hydrogen production module for the user to choose from, such as photovoltaic power generation or grid power supply. If the customer selects the second boundary type PtW, the second evaluation starting point corresponding to renewable energy power will be selected based on the category of the hydrogen production module for the user to choose from, such as grid access point.

[0089] Users can configure one or more paths based on actual planning or research scenarios. For example, they can establish a corresponding hydrogen energy supply chain path based on actual conditions for simulation to monitor and predict carbon emissions. Alternatively, they can establish several corresponding hydrogen energy supply chain paths based on planning for simulation to compare the carbon emissions and energy consumption of each path and assist designers in designing the optimal hydrogen energy supply chain path.

[0090] This embodiment, through the design of system boundaries and hydrogen production and storage modules, can construct and simulate any hydrogen energy supply chain path. This method overcomes the limitations of existing assessment paths being isolated and cases being static, achieving flexible configuration and unified quantitative comparison of complex technical solutions.

[0091] S120, configuration of scenario parameters that correspond one-to-one with the hydrogen energy supply chain path;

[0092] The assessment starting point, the corresponding hydrogen production module, the corresponding storage and transportation module, and the assessment endpoint are taken as nodes in the corresponding hydrogen energy supply chain path; the scenario parameters are used to indicate the geographical and time parameters corresponding to each node in the corresponding hydrogen energy supply chain path. For the hydrogen production module and the storage and transportation module, the scenario parameters also include the corresponding scale parameters.

[0093] For example, for a hydrogen production module, the scale parameter is the target hydrogen production capacity; for a storage and transportation module, the scale parameter is the corresponding transportation distance.

[0094] Note: Transportation distance is optional.

[0095] S200, Obtain dynamic input parameters:

[0096] Based on user-configured scenario parameters, dynamic input parameters corresponding to each node of the hydrogen energy supply chain path are obtained from a preset dynamic parameter library.

[0097] For the assessment starting point, based on its corresponding geographical parameters and time parameters, the corresponding energy emission factors and technical and economic parameters are obtained as corresponding dynamic input parameters to indicate the inherent carbon emission intensity and time-series power generation characteristics of the energy used in the production, conversion and use process, as well as the corresponding market price.

[0098] For the hydrogen production module, based on its corresponding hydrogen production method, the key equipment used, the technical and economic parameters of the hydrogen production equipment, and the geographical and project parameters are used as corresponding dynamic input parameters to indicate the equipment technical performance parameters that determine the system efficiency, the efficiency and cost learning curves of carbon capture and storage technology, and when the hydrogen production method is water electrolysis, the energy consumption and cost of local water resource acquisition are also included.

[0099] For the storage and transportation module, the corresponding geographical parameters are used as dynamic input parameters to obtain the corresponding geographical and project parameters of the storage and transportation module, so as to indicate the corresponding transportation distance.

[0100] In this embodiment, a dynamic parameter library is pre-built. After the user configures the path and scene parameters, the dynamic parameters that match the time and space of each node are selected from the dynamic parameter library based on the geographical and time parameters of the scene parameters and then injected.

[0101] This embodiment addresses the shortcomings of static and averaged parameters in existing hydrogen energy assessment methods by acquiring dynamic input parameters corresponding to each node in the hydrogen energy supply chain. By establishing and maintaining a dynamic parameter library that is continuously synchronized with the external real world, it ensures that the assessment results can reflect the technical and economic conditions and policy environment under specific time and region, thereby supporting accurate decision-making.

[0102] This embodiment's dynamic parameter library includes and is dynamically updated with various energy and emission factors, technical and economic parameters, and geographical and project parameters.

[0103] Energy and emission factors are used to characterize the inherent carbon emission intensity and time-series power generation characteristics of different energy sources during production, conversion and use;

[0104] The energy emission factor is used to indicate the inherent carbon emission intensity and time-series power generation characteristics of the corresponding energy during production, conversion and use; technical and economic parameters: mainly including equipment technical performance parameters that determine system efficiency and economic parameters that affect project revenue and cost;

[0105] Geographic and project parameters: These are specific boundary data that characterize the local resource conditions, infrastructure conditions, and spatial logistics constraints faced by a project when it is implemented at a specific location. However, if the user has not configured the transportation distance, the corresponding transportation distance can be automatically generated based on the user-configured geographic parameters.

[0106] The details are shown in Table 1:

[0107] Table 1

[0108]

[0109]

[0110] In this embodiment, dynamic parameters are automatically retrieved from an external database by a dynamic update engine, verified, and managed according to timestamps and geographic tags. Specifically:

[0111] (1) Data crawling and verification: The engine crawls the latest data from a preset trusted source based on a preset period according to the preset API interface or data subscription; the crawled data is checked for range and logical consistency, and the data is stored in the database after the verification is passed (e.g., electricity price and carbon price usually have a positive correlation trend).

[0112] (2) Spatiotemporal tag association: Label the data entering the database each day with a timestamp and a geographic region tag;

[0113] When the user-configured path indicates that the East China region adopts grid electrolysis for hydrogen production in 2025, it will search and call the dynamic parameter set for the corresponding time period in the region based on the user-configured environmental parameters (East China region in 2025), and inject the corresponding dynamic parameters into each node in the path.

[0114] (3) Uncertainty parameters: Uncertainty parameters are parameters with large fluctuations (such as future electricity prices). Those skilled in the art can set the configuration method of uncertainty parameters themselves (manual configuration or automatic screening of parameters with fluctuations greater than a preset ratio). This specification does not limit them in detail. For uncertainty parameters, this embodiment obtains their point estimates for subsequent sub-model simulation.

[0115] (4) Version management and traceability: When data is updated, the corresponding historical data version is backed up and saved to ensure that any assessment can be reproduced and to conduct comparative analysis under different policy or market conditions (such as comparing the impact of carbon prices in 2025 and 2030).

[0116] S300 calculates the energy consumption and carbon emissions of each node based on the dynamic input parameters corresponding to each node in the hydrogen energy supply chain, and generates corresponding carbon benefit data.

[0117] The carbon benefit data includes carbon emission intensity (carbon emission intensity per unit mass of hydrogen over its entire life cycle), total energy efficiency, fossil energy consumption, and carbon reduction costs.

[0118] The specific steps are as follows:

[0119] S310, Construct several sub-models based on pre-defined boundary types and technical route libraries;

[0120] The sub-model is used to indicate the calculation rules for material conversion and energy consumption in the corresponding stage; the stage corresponds to the node of the path;

[0121] The sub-models in this embodiment include a liquefaction model based on thermodynamic equations, an electrolysis model based on electrochemical equations, a pipeline transportation model based on fluid dynamics equations, and a steam methane reforming model based on chemical reaction kinetics and thermodynamic equations.

[0122] The following Table 2 lists some sub-models as examples. Those skilled in the art can construct one or more corresponding sub-models based on the pre-configured first assessment starting point (the assessment starting point corresponding to primary energy extraction or acquisition), each hydrogen production module, each storage and transportation module, and the assessment endpoint. Each path node with energy consumption and carbon emissions should have at least one corresponding sub-model. The sub-model should be able to calculate the material conversion result and / or energy consumption result of the node. This specification does not impose detailed limitations on it.

[0123] Table 2

[0124]

[0125]

[0126] For calculation rules, those skilled in the art can set sequential calculation rules or iterative calculation rules according to actual needs;

[0127] When there is no loop in the link corresponding to the sub-model, configure the corresponding sequential calculation rules so that the sub-model can directly calculate the corresponding material conversion data and / or energy consumption data based on the corresponding dynamic input parameters, and calculate the corresponding energy consumption (converted to primary energy) and carbon emissions (including direct emissions and indirect emissions caused by electricity consumption) based on the energy consumption data.

[0128] When a loop exists in the corresponding link of the sub-model, an accurate solution cannot be obtained by sequential calculation in one go. Those skilled in the art can configure the corresponding iterative calculation rules based on the actual situation, iterate until the corresponding key parameters converge, output the corresponding material conversion data and / or energy consumption data, and calculate the corresponding energy consumption and carbon emissions based on the energy consumption data.

[0129] The key parameters refer to the core variables that are coupled and mutually determined in the physical loop. Those skilled in the art can set the key parameters of the corresponding sub-model based on the actual situation, such as the recovered cold energy, the outlet temperature of the key heat exchanger, the material reflux rate in the hydrogen production process with unreacted gas circulation, the efficiency or load distribution of the energy recovery system, and the key pressure or concentration when the system reaches equilibrium. Without explicit notification, those skilled in the art can determine the key parameters and configure the corresponding iterative calculation rules according to the characteristics of the corresponding link in the transportation hydrogen chain energy of the corresponding sub-model.

[0130] As an example, taking a hydrogen liquefaction plant as an example, its typical cold energy recovery loop aims to utilize the cold energy of the liquefied cryogenic fluid to pre-cool the intake gas, thereby reducing total energy consumption. The simulation calculation process is as follows (mathematically, numerical methods such as the fixed-point iteration method or the Newton-Raphson method are often used for efficient solutions):

[0131] The corresponding iterative calculation rules include:

[0132] Set corresponding initial estimates for key parameters in the loop (such as cold energy recovery rate);

[0133] Iterative calculations are performed based on the initial estimate until convergence is determined, at which point the corresponding simulation results are output.

[0134] The iterative calculation process in the current iteration step is as follows:

[0135] Based on the current estimate, calculations begin from the corresponding node inlet (such as the intake compressor). The current estimate is either the initial estimate or the calculated cooling capacity output from the previous iteration.

[0136] When calculating the cold energy recovery heat exchanger, the current estimate is used to calculate its outlet temperature.

[0137] The state of the liquefied fluid is determined based on the outlet temperature.

[0138] Based on the state of the liquefied fluid and the set recovery process, the actual recoverable cold energy value is recalculated to obtain the corresponding cold energy calculation value.

[0139] Calculate the difference between the current estimated value and the calculated cooling capacity. If the difference is greater than the convergence criterion, it is determined that the system has not converged. Then, the calculated cooling capacity is used as the current estimated value for the next iteration, and the iterative calculation continues. If the difference is less than or equal to the convergence criterion, it is determined that the system has converged, the iteration terminates, and all parameters (temperature, pressure, flow rate, energy consumption) in the system reach self-consistency. Simulation results are generated based on the parameters involved in the current iteration.

[0140] S320. Construct a sub-model chain corresponding to the hydrogen energy supply chain path;

[0141] That is, based on each node of the hydrogen energy supply chain path, the corresponding sub-models are extracted from the sub-model library and connected in series according to the hydrogen energy supply chain path to obtain the corresponding sub-model chain.

[0142] S330. Inject the corresponding dynamic input parameters into each sub-model in the sub-model chain to obtain the corresponding simulation model. According to the sub-model chain, let each simulation model calculate energy consumption and carbon emissions according to the injected dynamic data parameters and the corresponding calculation rules.

[0143] That is, the dynamic input parameters corresponding to each node of the hydrogen energy supply chain path are injected into the corresponding sub-model. The sub-model calculates material conversion and / or energy consumption according to the injected dynamic input parameters and the corresponding calculation rules. It records the energy consumption and carbon emissions corresponding to each link of the sub-model chain, providing data support for subsequent data analysis. It also generates the material flow and energy flow corresponding to the hydrogen energy supply chain path based on the material conversion and energy consumption corresponding to each link.

[0144] The energy consumption includes primary energy consumption and secondary energy consumption. Primary energy consumption refers to the consumption of primary energy as a material, such as the consumption of natural gas in the steam methane reforming process. Secondary energy consumption refers to the energy consumption that occurs when various primary energy sources are used for energy consumption, such as electricity consumption in hydrogen production and oil consumption in transportation.

[0145] The carbon emissions include both direct and indirect emissions;

[0146] The direct emissions are the emissions corresponding to the first energy consumption, i.e., the direct emissions in the process (such as the flue gas emissions of SMR).

[0147] The indirect emissions refer to emissions corresponding to the second energy consumption, such as grid emissions corresponding to electricity consumption.

[0148] This embodiment transforms the hydrogen energy supply chain path defined in step S100 and the dynamic input parameters provided in step S200 into quantified energy consumption and carbon emission results. Compared with existing macroscopic estimation methods based on simple emission factor multiplication, this embodiment, through the design of sub-models and refined process simulation based on material balance, energy balance, and equipment-level performance curves, ensures the accuracy and physical reliability of the calculation results at the mechanistic level, reducing the key execution links of carbon emission simulation errors.

[0149] S340. Based on the energy consumption and carbon emissions calculated from each simulation model, generate corresponding carbon benefit data;

[0150] Specifically:

[0151] S341. Calculate the life-cycle carbon emission intensity of a unit mass of hydrogen based on the aforementioned carbon emissions;

[0152] The carbon emissions (including direct and indirect emissions) corresponding to each simulation model are statistically analyzed, and the total carbon emissions are generated based on the statistical results.

[0153] Determine the total mass of hydrogen delivered (the total mass of hydrogen delivered within the system boundary);

[0154] Based on the total carbon emissions and the total mass of hydrogen delivered, the carbon emission intensity (kg CO2-eq / kg H2) generated per unit mass of hydrogen over the entire life cycle is obtained, which is the carbon emission intensity corresponding to the hydrogen energy supply chain path.

[0155] As one possible implementation method, the direct and indirect emissions corresponding to each simulation model are summed, and the sum is taken as the total carbon emissions.

[0156] As another possible implementation method, the total carbon emissions are calculated based on the first carbon emissions and the second carbon emissions;

[0157] The first carbon emission is the total carbon emission (including direct and indirect emissions) corresponding to each simulation model;

[0158] The second carbon emission is the total amount of implicit carbon emissions corresponding to the relevant infrastructure, which includes electrolyzers, storage tanks, and pipelines. Users can configure the type and quantity of infrastructure corresponding to the hydrogen energy supply chain path according to their actual situation.

[0159] In this embodiment, the life-cycle carbon emission intensity (CI) H2 The calculation formula is:

[0160]

[0161] in:

[0162] E direct,i For the direct emissions of the i-th stage, that is, the direct emissions calculated by the i-th simulation model;

[0163] E indirect,i For the indirect emissions of the i-th stage, that is, the indirect emissions calculated by the i-th simulation model;

[0164] E cap,allocated To assess the embodied carbon emissions of all infrastructure within the system (such as electrolyzers, storage tanks, and pipelines), the total emissions have been allocated over the entire assessment period based on their design life and capacity.

[0165] This represents the total mass of hydrogen delivered within the system boundary.

[0166] Note:

[0167] The method for obtaining the implicit carbon emissions corresponding to a single infrastructure as the target infrastructure is as follows:

[0168] Obtain the total embodied carbon of the target infrastructure;

[0169] Obtain the hydrogen production volume throughout the entire lifecycle of the target infrastructure;

[0170] The total implicit carbon emissions over the entire life cycle are allocated based on the hydrogen production over the entire life cycle to obtain the unit implicit carbon emissions of the target infrastructure.

[0171] The total embodied carbon is distributed over all hydrogen produced throughout the infrastructure's entire service life, i.e., the embodied carbon E of a given infrastructure. cap,facility During its lifespan of L years, the average annual hydrogen production M is distributed as follows: annual Above can be represented as

[0172]

[0173] Those skilled in the art can determine the total embodied carbon of the equipment by pre-determining the bill of materials corresponding to various types of infrastructure. For example, they can pre-disassemble the equipment (such as a hydrogen storage tank) to determine the composition (steel and carbon fiber usage), query the carbon emission factors of the production and processing of these raw materials, and then sum them up to obtain the total embodied carbon of the equipment.

[0174] Those skilled in the art can obtain equipment parameters corresponding to various infrastructures, such as power per unit (kW), capacity per unit (kg / H2), and service life, through professional LCA databases, industry technical reports, and Environmental Product Declarations (EPDs), and quantify their hydrogen production throughout their entire life cycle.

[0175] This embodiment avoids carbon leakage and assessment bias caused by ignoring the implicit carbon in infrastructure by making it explicit.

[0176] S342. Calculate the total energy efficiency based on the energy consumption;

[0177] The formula for calculating the total energy efficiency is as follows:

[0178]

[0179] η sys Energy efficiency (%) across the entire chain from primary energy to hydrogen end-product delivery.

[0180] LHV H2 It has the low calorific value of hydrogen (~120 MJ / kg).

[0181] E primary This refers to the total calorific value (MJ) obtained by converting all primary energy sources consumed along the hydrogen energy supply chain path, including electricity, fossil fuels, etc., i.e., the total calorific value obtained by converting primary energy consumption and secondary energy consumption.

[0182] S343. Determine fossil energy consumption based on the energy consumption;

[0183] That is, the net consumption (MJ / kg H2) of fossil energy is selected from the obtained energy consumption. Fossil energy includes coal, oil and natural gas.

[0184] S344, Cost of Carbon Emission Reduction;

[0185] Calculate the incremental cost per unit of CO2 emission reduction compared to a baseline scenario (such as diesel-powered trucks);

[0186] Based on the S200 dynamic parameter library, the cost of various resources in a specified region and time can be known, such as the market price time series data of natural gas, electricity, carbon quotas, etc., the cost of water resources, etc.

[0187] Using the S300 simulation results, the consumption of various resources can be calculated, such as energy consumption and water consumption in the scenario of hydrogen production by water electrolysis.

[0188] After accurately simulating and obtaining the energy consumption and water consumption of each stage based on the simulation model, and obtaining the corresponding unit cost parameters of each resource and the cost corresponding to the emission reduction process based on the dynamic parameter library based on region and time, those skilled in the art can easily set the calculation rules for calculating the corresponding costs for the simulation model, thereby calculating the cost corresponding to each stage; and calculating the carbon emission reduction cost (CRC) corresponding to the entire path.

[0189] In this embodiment, the formula for calculating carbon emission cost (CRC) is as follows:

[0190]

[0191] in, To correspond to the levelized cost of the hydrogen energy supply chain path, This represents the total carbon emissions along the corresponding hydrogen energy supply chain path; C 基准 E is the corresponding baseline cost determined based on the baseline scenario. 基准 The corresponding baseline carbon emissions are determined based on the baseline scenario;

[0192] Total path cost The resource consumption (such as electricity, natural gas, and water) of each stage obtained from the S300 simulation is multiplied by the corresponding spatiotemporal specific unit price in the S200 and then summed. It also includes equipment depreciation and carbon quota costs.

[0193] In this embodiment, the corresponding first emission reduction cost can be calculated in the corresponding simulation model based on the energy consumption and technical and economic parameters indicating the corresponding market price.

[0194] In the corresponding simulation model, the second emission reduction cost can be calculated based on the technical and economic parameters corresponding to the carbon capture and storage process.

[0195] In the corresponding simulation model, the third emission reduction cost can be calculated based on the water resources consumed and geographical and project parameters indicating the cost of water resources.

[0196] The total cost is obtained by calculating the first, second, and third emission reduction costs based on the simulation models corresponding to the hydrogen energy supply chain path;

[0197] The incremental cost per unit of CO2 emission reduction is calculated based on the total cost obtained.

[0198] Example 2: The values ​​of the uncertainty parameters in Example 1 are changed from point estimates to probability distributions;

[0199] That is, in this embodiment, when creating or updating the dynamic parameter library for uncertain parameters, not only are point estimates of each uncertain parameter obtained, but also their probability distribution characteristics (such as mean and standard deviation) are generated based on historical volatility, providing input for subsequent Monte Carlo simulations;

[0200] For example:

[0201] The power grid carbon factor can be defined as a normal distribution with the official forecast value as the mean and the historical standard deviation of the fluctuation as the variance.

[0202] Equipment efficiency can be defined as a triangular distribution with the nominal value as the median and the technical tolerance range as the upper and lower bounds.

[0203] Binary events (such as whether a CCS device is available): are defined as Bernoulli distributions.

[0204] In this embodiment, when extracting dynamic input parameters corresponding to the hydrogen energy supply chain path from the dynamic parameter library, the uncertain parameters that are used as dynamic input parameters are recorded as target parameters.

[0205] Construct sub-model chains corresponding to the hydrogen energy supply chain path;

[0206] Based on the Monte Carlo simulation algorithm, for the sub-model chain, several simulations are performed according to the probability distribution of each target parameter, and the life-cycle carbon emission intensity and total energy efficiency obtained from each simulation are recorded; that is, according to the probability distribution of each target parameter, several sets of target parameter combinations are generated, and dynamic input parameter combinations corresponding to each target parameter combination are created, and simulations are performed based on each dynamic input parameter combination.

[0207] Calculate the life cycle carbon intensity (CI) H2 The average value is used as the final carbon emission intensity estimate.

[0208] Calculate the average value of the total energy efficiency and output the average value as the final total energy efficiency estimate.

[0209] Those skilled in the art can also analyze the results of each simulation, calculate the standard deviation to characterize the dispersion of the results, and calculate the life-cycle carbon emission intensity (CI). H2 The corresponding confidence interval, such as the 95% confidence interval, means that there is a 95% certainty that the true value falls within this interval; it can also generate probability distribution histograms and cumulative distribution function graphs to intuitively show the uncertainty of the results.

[0210] This embodiment upgrades a single point estimation result into an interval estimation that includes probability information, greatly improving the scientific rigor and decision-making reference value of the evaluation results.

[0211] In this embodiment, the steps of each simulation, as well as the calculation methods for the carbon emission intensity and total energy efficiency throughout the life cycle, are the same as in Embodiment 1, so they will not be described again.

[0212] Furthermore, when creating or updating the dynamic parameter library, a step of reverse calibration of the probability distribution of the uncertain parameters is also included;

[0213] Create a set containing various uncertainty parameters, each with its own prior uncertainty range;

[0214] Collect data from several benchmark cases (such as full lifecycle verification reports of operational hydrogen energy projects);

[0215] For each baseline case, the uncertainty parameter is used as the decision variable, and the optimization (P) is performed. C -R C ) 2 To achieve the objective, an optimization solution is performed to obtain a set of uncertainty parameter combinations that minimize the error of the benchmark case data, thus obtaining the optimal parameter combination; where P C The simulation prediction value obtained based on the above simulation method, such as the calculated life-cycle carbon emission intensity CI. H2 R C For the actual measured values ​​(verified values) of the corresponding case data, such as the actual measured life-cycle carbon emission intensity;

[0216] Statistical analysis is performed on the optimal parameter combinations corresponding to all benchmark case data to narrow down the probability distribution range of each uncertainty parameter. For example, if a certain uncertainty parameter stabilizes within a narrow range after calibration in all cases, this range is recorded as its new, more accurate probability distribution in the dynamic parameter library for subsequent use in carbon benefit assessment of the corresponding hydrogen energy supply chain path based on Monte Carlo simulation algorithms.

[0217] This embodiment, through the design of a reverse calibration algorithm, combined with the working of the aforementioned dynamic input parameters and sub-models, systematically controls the simulation error of the carbon emission intensity throughout the entire life cycle to an industry-leading level of ≤7%.

[0218] Furthermore, after assessing the carbon benefits of the corresponding hydrogen energy supply chain path based on the Monte Carlo simulation algorithm, a visualization output step is also included.

[0219] This embodiment presents carbon emission intensity assessment data, total energy efficiency assessment data, cost assessment data, and a summary of conclusions in the form of a dashboard, including:

[0220] Carbon emission intensity assessment data: Displays the estimated carbon emission intensity, 95% confidence interval (e.g., 3.5 ± 0.2 kg CO2-eq / kg H2), and visually indicates its level relative to industry benchmarks or policy targets using colors (green / yellow / red).

[0221] Total energy efficiency assessment data: Total energy efficiency estimates and corresponding confidence intervals.

[0222] Cost assessment data: Based on the simulation process that is closest to the carbon emission intensity estimate, the percentage of each stage of hydrogen production, storage, transportation and refueling costs in the total cost is shown in a stacked bar chart.

[0223] Conclusion Summary: When comparing and analyzing multiple hydrogen energy supply chain paths, the text describes the corresponding path comparison results, such as "In the given scenario, path A has 85% lower carbon emission intensity than path B, but its cost is 40% higher."

[0224] This embodiment also compares the results of multiple paths evaluated by the user in the same coordinate system, generating a corresponding comparison chart, which includes:

[0225] Radar chart comparison: The radar chart normalizes 5-6 key indicators such as carbon emission intensity estimate, total energy efficiency estimate, fossil energy consumption, total cost, and water consumption and plots them on the same radar chart, clearly showing the comprehensive advantages and disadvantages of each path. Users can configure the indicators for comparative analysis themselves.

[0226] Bar chart comparison: The estimated carbon emission intensity and total energy efficiency of each path are displayed side by side in a bar chart, and the confidence interval error bar is marked at the top of the bar.

[0227] In this embodiment, carbon intensity will be used as the X-axis, hydrogen levelized cost as the Y-axis, and bubble size will represent system scale or fossil energy consumption.

[0228] This diagram can intuitively divide the various paths into quadrants such as "low cost and low carbon", "high cost and low carbon", and "low cost and high carbon", which helps with strategic positioning.

[0229] This embodiment also performs carbon footprint source tracing and link contribution analysis based on the simulation process that is closest to the carbon emission intensity and the carbon emission intensity estimate, and outputs the corresponding visualization results, specifically as follows:

[0230] Sankey diagram: A visual representation of the direction, magnitude, and losses of carbon and energy flows at each stage of the entire process from primary energy input to hydrogen delivery.

[0231] Contribution stacking chart: Clearly displays the percentage contribution of each stage of hydrogen production, purification and compression, storage and transportation, and refueling to total carbon emissions and total costs, quickly identifying "carbon hotspots" and "cost bottlenecks".

[0232] This embodiment also performs sensitivity analysis and identifies key driving factors, outputting corresponding visualization results, as follows:

[0233] Tornado diagram: Based on several simulations using the Monte Carlo simulation algorithm, this study analyzes the impact of various uncertain input parameters (such as electricity price, grid carbon factor, electrolyzer efficiency, and transportation distance) on target indicators (such as CI). H2 The magnitude and direction of the impact; the length of the bars represents the degree of sensitivity, visually indicating the risk factors that need to be focused on control or investigation.

[0234] Single-factor change curve: Shows the trajectory of the target indicator when a key parameter (such as carbon price) changes within a certain range.

[0235] Users can customize the output format according to their actual needs, for example:

[0236] Interactive and configurable reports: Users can customize the comparison path combinations, select the indicators to focus on, and adjust the chart types on the generated base report, and the report is updated in real time.

[0237] Digital twin supply chain diagram: Dynamically links the corresponding hydrogen energy supply chain path with key indicators. Clicking on any node in the path will bring up detailed energy consumption, carbon emissions, cost data and real-time operating status (simulation) of that node.

[0238] Automatically generate decision briefings: Based on the analysis results, the system automatically extracts key findings, major risks, and recommendations to generate a one-page decision briefing in Word or PPT format.

[0239] In summary, the method proposed in this embodiment first defines the system boundary and configures a supply chain path that includes multiple hydrogen production and storage routes; second, it accesses and updates dynamic input parameters that match the spatiotemporal attributes of the paths; next, it calls the corresponding sub-models to perform full-chain simulation calculations; then, it calculates the energy and carbon efficiency indicators and uses Monte Carlo simulation to quantify uncertainties, ensuring that the carbon emission simulation error is ≤7%; finally, it generates a visual report of multi-path comparison and sensitivity analysis. This invention solves the problems of isolated paths, static parameters, and insufficient accuracy in existing evaluation methods, and achieves quantitative, dynamic, and high-confidence evaluation of complex hydrogen energy supply chains.

[0240] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0241] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0242] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0243] This invention is described with reference to flowchart illustrations and / or block diagrams of the method, terminal device (system), and computer program product according to the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0244] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0245] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0246] It should be noted that:

[0247] The phrase "an embodiment" or "an embodiment" used in this specification means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. Therefore, the phrase "an embodiment" or "an embodiment" appearing in various places throughout the specification does not necessarily refer to the same embodiment.

[0248] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0249] Furthermore, it should be noted that the shapes and names of the parts and components described in the specific embodiments described in this specification may differ. All equivalent or simple variations made to the structure, features, and principles described in this patent concept are included within the protection scope of this patent. Those skilled in the art to which this invention pertains may make various modifications or additions to the described specific embodiments or use similar methods to replace them, as long as they do not depart from the structure of this invention or exceed the scope defined in these claims, they should all fall within the protection scope of this invention.

Claims

1. A method for dynamic evaluation of the carbon benefits of the hydrogen chain energy in transportation, characterized in that, Includes the following steps: Configure one or more hydrogen energy supply chain paths; the hydrogen energy supply chain paths are used to indicate the assessment start point, assessment end point, hydrogen production method and storage and transportation method, as well as the direction of hydrogen energy supply; Configure scenario parameters that correspond one-to-one with hydrogen energy supply chain paths, wherein the scenario parameters are used to indicate the corresponding geographical parameters and time parameters; Based on user-configured scenario parameters, dynamic input parameters corresponding to each node of the hydrogen energy supply chain path are obtained from a preset dynamic parameter library. Based on the dynamic input parameters corresponding to each node in the hydrogen energy supply chain path, the energy consumption and carbon emissions corresponding to each node are calculated, and corresponding carbon benefit data is generated. The carbon benefit data includes the carbon emission intensity and total benefit corresponding to the hydrogen energy supply chain path.

2. The dynamic assessment method for the carbon benefits of the transportation hydrogen chain energy according to claim 1, characterized in that, The configuration method for the hydrogen energy supply chain path is as follows: Configure system boundaries, configure one or more hydrogen production modules, configure one or more storage and transportation modules, and connect the configured hydrogen production modules and storage and transportation modules in series to generate corresponding hydrogen energy supply chain paths; the system boundaries are used to determine the evaluation start point and evaluation end point; the hydrogen production modules correspond one-to-one with the hydrogen production methods, and the storage and transportation modules correspond one-to-one with the storage and transportation methods. The system boundary types include a first boundary type and a second boundary type, wherein the first boundary type takes primary energy extraction or acquisition as the evaluation starting point, and the second boundary type takes the entrance of a hydrogen production plant as the evaluation starting point.

3. The dynamic assessment method for the carbon benefits of the transportation hydrogen chain according to claim 2, characterized in that: The hydrogen production module includes steam methane reforming, coal gasification, alkaline water electrolysis, proton exchange membrane water electrolysis, and biomass hydrogen production. The storage and transportation module includes high-pressure gaseous storage and transportation, cryogenic liquid storage and transportation, and material-based storage and transportation.

4. The dynamic assessment method for the carbon benefits of the transportation hydrogen chain energy according to claim 1, characterized in that, Based on the dynamic input parameters corresponding to each node in the hydrogen energy supply chain, the specific steps for calculating the energy consumption and carbon emissions corresponding to each node are as follows: Based on each node of the hydrogen energy supply chain path, corresponding sub-models are extracted from a pre-set sub-model library and connected in series according to the hydrogen energy supply chain path to obtain a corresponding sub-model chain; the sub-models are used to indicate the calculation rules for material conversion and energy consumption in the corresponding links; the links correspond to the nodes of the hydrogen energy supply chain path. By injecting corresponding dynamic input parameters into each sub-model in the sub-model chain, the corresponding simulation model is obtained. According to the sub-model chain, each simulation model calculates energy consumption and carbon emissions based on the injected dynamic data parameters and the corresponding calculation rules. The energy consumption includes primary energy consumption and secondary energy consumption, wherein primary energy consumption refers to the consumption of primary energy as a material; and secondary energy consumption refers to the energy consumption that occurs when various types of primary energy are used for energy consumption. The carbon emissions include direct emissions and indirect emissions; the direct emissions are those corresponding to the first energy consumption; and the indirect emissions are those corresponding to the second energy consumption.

5. The dynamic assessment method for the carbon benefits of the transportation hydrogen chain according to claim 4, characterized in that: The carbon emissions corresponding to each simulation model are calculated, and the total carbon emissions are generated based on the statistical results; Determine the total mass of hydrogen delivered; The corresponding carbon emission intensity is obtained based on the total carbon emissions and the carbon emission intensity per unit mass of hydrogen produced from the total mass of hydrogen delivered.

6. The method for dynamic evaluation of carbon benefits in the hydrogen chain energy of transportation according to claim 4, characterized in that: The total calorific value is obtained by converting all primary and secondary energy consumption along the hydrogen energy supply chain path into calorific values. The total benefit is calculated based on the total mass of hydrogen delivered, the lower calorific value of hydrogen, and the total calorific value.

7. The dynamic assessment method for the carbon benefits of the transportation hydrogen chain energy according to any one of claims 1 to 6, characterized in that, The preset dynamic parameter library contains several uncertain parameters and the probability distribution of each uncertain parameter; When extracting dynamic input parameters corresponding to the hydrogen energy supply chain path from the dynamic parameter library, the uncertain parameters that are used as dynamic input parameters are recorded as target parameters; Based on the probability distribution of each target parameter, several sets of target parameter combinations are generated, and dynamic input parameter combinations corresponding to each target parameter combination are created. Simulation is performed based on each dynamic input parameter combination. Construct sub-model chains corresponding to the hydrogen energy supply chain path; For the aforementioned sub-model chain, simulations were performed based on each combination of dynamic input parameters, and the carbon emission intensity and total energy efficiency obtained from each simulation were recorded. Calculate the average carbon emission intensity and output the average value as the final carbon emission intensity estimate; Calculate the average value of the total energy efficiency and output the average value as the final total energy efficiency estimate.

8. The dynamic assessment method for the carbon benefits of the transportation hydrogen chain energy according to claim 7, characterized in that, When creating or updating a dynamic parameter library, a step of inverse calibration of the probability distribution of uncertain parameters is also included, specifically: Collect data from several benchmark cases; For each baseline case, the corresponding uncertainty parameter is used as the decision variable to optimize (P C -R C ) 2 To achieve the objective, an optimization solution is performed to obtain the optimal parameter combination corresponding to the benchmark case data; where P C To simulate the carbon emission intensity obtained from the simulation, R C The carbon emission intensity is obtained by actual measurement or verification of the corresponding benchmark case data; Statistical analysis is performed on the optimal parameter combinations corresponding to all benchmark case data to tighten the probability distribution range of each uncertainty parameter, and the tightened probability distribution is written into the dynamic parameter library.

9. A dynamic assessment system for the carbon benefits of the hydrogen chain energy in transportation, characterized in that, include: The path configuration module is used to configure one or more hydrogen energy supply chain paths; the hydrogen energy supply chain path is used to indicate the evaluation start point, evaluation end point, hydrogen production method and storage and transportation method, as well as the direction of hydrogen energy supply; The spatiotemporal parameter configuration module is used to configure scenario parameters that correspond one-to-one with the hydrogen energy supply chain path. The scenario parameters are used to indicate the corresponding geographical parameters and time parameters. The dynamic parameter retrieval module is used to retrieve the dynamic input parameters corresponding to each node of the hydrogen energy supply chain path from a preset dynamic parameter library based on the user-configured scenario parameters. The simulation module is used to calculate the energy consumption and carbon emissions of each node in the hydrogen energy supply chain based on the dynamic input parameters corresponding to each node, and generate corresponding carbon benefit data, which includes the carbon emission intensity and total benefit of the hydrogen energy supply chain.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-8.

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