Carbon flow conversion and carbon metering method and system of electrical hydrogen-coupled micro energy network

Abstract

A method and system for carbon flow and carbon metering in an electric-hydrogen coupled micro-energy network utilizes direct emission source monitoring data and a multi-energy flow monitoring model based on the total system carbon emissions, total system energy input, and operating boundaries to determine the real-time carbon intensity of the power grid. When the real-time carbon intensity of the power grid exceeds the real-time emission threshold, the system uses carbon emission data from the production process of electrolyzers and gas-fired energy equipment, monitoring data from the carbon transfer carriers of gas storage devices and hydrogen storage devices in fuel cells, carbon emission data from the coupling interaction layer, and negative carbon unit evaluation data to correct the direct emission source monitoring data, production process carbon emission data, carbon transfer carrier monitoring data, coupling interaction layer carbon emission data, and negative carbon unit evaluation data, with the goal of optimizing the total system carbon emissions. This results in carbon flow and carbon metering, enabling precise quantification and efficient management of carbon emissions across all stages of the micro-energy network.

Description

Technical Field

[0001] This invention belongs to the field of energy internet technology, specifically relating to a carbon flow and carbon metering method for micro energy networks that couple electricity, gas and hydrogen energy, thereby realizing integrated energy system optimization, carbon emission measurement and control, and multi-energy flow network management. Background Technology

[0002] Renewable energy sources such as wind and solar power are characterized by intermittency and volatility. Their large-scale centralized grid connection poses severe challenges to the power system's regulation capacity, supply-demand balance mechanism, and carbon management system. Electrogenation technology can convert surplus electricity into hydrogen for storage, and then use hydrogen fuel cells and other equipment to supply electricity in reverse, forming a flexible regulation mechanism of "electricity-hydrogen" bidirectional coupling. Meanwhile, natural gas, as an important transitional energy source in the current energy structure, can further enhance the reliability and flexibility of micro-energy networks through synergistic coupling with electricity and hydrogen. Natural gas can achieve combined heat and power (CHP) through gas turbines and gas boilers to meet diverse energy demands, can be blended with green hydrogen to form low-carbon fuels to reduce combustion emissions, and can also achieve spatiotemporal energy transfer through gas storage facilities, compensating for the uncertainty of renewable energy output. Against this backdrop, multi-energy coupled micro-energy networks integrating electricity, hydrogen, and natural gas have become an important vehicle for achieving efficient energy utilization and a low-carbon transition.

[0003] Existing technologies mostly focus on bidirectional electro-hydrogen conversion, treating the energy flow and carbon transfer of electricity and hydrogen energy systems in isolation. They lack systematic consideration of the coupled interaction scenarios of gas with electricity and hydrogen. In actual operation, after gas is input, it undergoes multiple paths, including direct combustion for energy supply, mixing with green hydrogen to optimize combustion efficiency, and emission treatment through carbon capture equipment. The dynamic interaction with electro-hydrogen production and energy storage / hydrogen storage systems results in a multi-dimensional coupling characteristic of energy flow: "electricity-hydrogen-gas". A dynamic coupling model between gas-fired energy consumption equipment and carbon capture equipment and electro-hydrogen production devices has not been established, making it impossible to capture the carbon emission coefficients and carbon retention characteristics (such as carbon capture efficiency and carbon sequestration of energy storage systems) at each stage. The carbon retention capacity changes dynamically with the energy conversion path, resulting in a lack of a synergistic optimization mechanism for carbon retention in gas combustion and electric hydrogen systems, leading to a higher carbon emission rate during multi-energy flow interactions. Existing carbon measurement methods are mostly based on single energy system design and rely on energy balance equations to approximate carbon flow paths. They lack minute-level monitoring capabilities for key aspects such as the dynamic response of carbon capture equipment, resulting in coarse carbon footprint granularity that is difficult to adapt to complex carbon emission scenarios in multi-energy flow coupling environments. They cannot accurately identify carbon sources in cross-linking links such as gas leakage, gas-to-electricity conversion, and hydrogen-gas mixed fuels, and it is difficult to quantify carbon emission fluctuations during multi-energy flow coordinated operation at different times. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method and system for carbon flow and carbon metering in an electrical-hydrogen coupled micro-energy network. Based on the multi-energy flow coupling characteristics, it constructs a triple carbon emission model encompassing electricity, hydrogen, and natural gas, and establishes a carbon flow tracking mechanism covering multiple energy conversion chains. This provides a carbon metering system that combines dynamic modeling, high-precision tracking, and real-time collaborative optimization, meeting the needs of real-time carbon flow monitoring and low-carbon scheduling in micro-energy networks. By integrating key technologies such as carbon source identification, real-time monitoring, time-series analysis, and optimized control, it achieves accurate quantification and efficient management of carbon emissions across all stages of the micro-energy network, providing crucial technical support for the low-carbon development of new energy systems.

[0005] The present invention adopts the following technical solution.

[0006] This invention proposes a method for carbon transfer and carbon metering in an electrically-hydrogen-coupled micro-energy network. The micro-energy network includes an electrolyzer, a fuel cell, and gas-fired energy-consuming equipment; including:

[0007] A multi-energy flow monitoring model is constructed based on the total carbon emissions of the system, the total energy input of the system, and the operating boundary. Using direct emission source monitoring data, the real-time carbon intensity of the power grid is determined based on the multi-energy flow monitoring model. When the real-time carbon intensity of the power grid exceeds the real-time emission threshold, the system switches to a shared power generation mode involving electrolyzers, fuel cells, and gas-fired energy equipment. Under this shared power generation mode, based on the carbon emission data from the production process of the electrolyzers and gas-fired energy equipment during operation, the monitoring data of the carbon transfer carriers in the gas storage devices of the gas-fired energy equipment and the hydrogen storage devices of the fuel cells, the carbon emission data from the coupled interaction layer, and the negative carbon unit evaluation data, with the goal of optimizing the total carbon emissions of the system, the carbon flow and carbon metering results are obtained after correcting the direct emission source monitoring data, production process carbon emission data, carbon transfer carrier monitoring data, coupled interaction layer carbon emission data, and negative carbon unit evaluation data.

[0008] In the electrically coupled hydrogen micro-energy network, a monitoring terminal is deployed to acquire real-time data of electrolyzers, fuel cells, and gas-fired energy-consuming equipment at different time periods, including carbon emissions, carbon capture, and energy input.

[0009] The total carbon emissions of the system are calculated by using the difference between the total carbon emissions of the electrolyzer, fuel cell, and gas-fired energy-consuming equipment and the total carbon capture of the electrolyzer and fuel cell. The total energy input of the system is calculated by using the total energy input of the electrolyzer, fuel cell, and gas-fired energy-consuming equipment. Operating boundaries are set for the carbon emission equipment of the electrolyzer, fuel cell, and gas-fired energy-consuming equipment, and operating boundaries are set for the carbon capture equipment of the electrolyzer and gas-fired energy-consuming equipment.

[0010] The weighted sum of the real-time carbon intensity of the power grid and the carbon emission factor of natural gas was used as the real-time emission threshold.

[0011] In the shared power generation mode, after constraining the efficiency of the electrolyzer by using the spatiotemporal distribution coefficient of the real-time carbon intensity of the power grid, the implicit carbon emissions of hydrogen production by the electrolyzer are determined based on the carbon emissions of the electrolyzer, which serves as the carbon emission data of the electrolyzer's production process; and the carbon emission data of the production process of gas-fired energy-consuming equipment are calculated.

[0012] Dilution sensitivity coefficient This indicates the reduction in carbon intensity resulting from a 1% increase in renewable energy output in the power grid. ,use Fix runtime segment Renewable energy output as a percentage of total power generation in the domestic power grid The difference between 1 and the corrected proportion of renewable energy output is used as the spatiotemporal distribution coefficient of the real-time carbon intensity of the power grid. ;

[0013] Using the spatiotemporal distribution coefficient to evaluate the efficiency of electrolyzers Apply the following constraints:

[0014]

[0015] In the formula, For the basic efficiency of the electrolytic cell, The efficiency response coefficient represents the increase in electrolyzer efficiency for every 10% decrease in the real-time carbon intensity of the power grid.

[0016] In the runtime segment Internally, utilizing the real-time carbon intensity of the power grid Spatiotemporal distribution coefficient The impact of the constrained electrolytic cell efficiency, the capture ratio of the electrolytic cell carbon capture device, and the capture response time correction term on the electrolytic cell operating power. After making corrections, the implicit carbon emissions from hydrogen production by the electrolyzer were obtained. As shown below:

[0017]

[0018] In the formula, runtime segment The capture ratio of carbon capture equipment in internal electrolytic cells. runtime segment Correction item for the capture response time of the carbon capture equipment in the internal electrolytic cell;

[0019] in, runtime segment Carbon emissions from internal electrolyzers The implicit carbon emissions from hydrogen production in an electrolyzer are the carbon emission data from the electrolyzer's production process.

[0020] In the runtime segment Internally, the input power of the pretreatment device of the gas-fired energy-consuming equipment is corrected by using the real-time carbon intensity of the power grid and the green hydrogen blending ratio coefficient, so as to obtain the auxiliary energy consumption carbon emission of the pretreatment device of the gas-fired energy-consuming equipment, which is used as the carbon emission data of the production process of the gas-fired energy-consuming equipment.

[0021] The carbon migration rate of the gas storage device of the gas-using energy equipment and the proportion of impurity carbon in the hydrogen storage device of the fuel cell are calculated as monitoring data for carbon transfer carriers during the operation period.

[0022] runtime segment Total carbon migration from gas storage devices of internal combustion energy-consuming equipment and operating time The ratio of these ratios is used as the migration rate of the carbon component. Set runtime segment The carbon migration rate constraint for the gas storage device of internal combustion energy equipment is: ;

[0023] The ratio of the actual amount of carbon-containing gas retained in the hydrogen storage device to the total amount of carbon-containing gas adsorbed within the device is obtained. The difference between 1 and this ratio is taken as the impurity carbon ratio of the hydrogen storage device in the fuel cell. Set the impurity carbon percentage constraint as follows: .

[0024] Compute runtime segment The indirect carbon emissions from the energy consumption of the internal electrolytic cell cooling system and the cross-equipment collaborative capture of mixed exhaust gas are used as carbon emission data for the coupling interaction layer.

[0025] Among them, the sum of the direct carbon emissions from gas combustion in gas-fired energy-consuming equipment and the indirect carbon emissions from fuel cell power generation is corrected by using cross-equipment capture efficiency and cross-equipment capture synergy factor to obtain the cross-equipment synergistic capture amount of mixed exhaust gas.

[0026] Compute runtime segment The actual carbon emissions captured by the internal carbon capture device and the net negative carbon benefit of the carbon storage process are used as assessment data for the negative carbon unit, including:

[0027] Utilizing cross-device capture efficiency and runtime segment Acquisition response time correction term for cross-device acquisition The total carbon emissions from the combustion of gas in gas-fired energy equipment and the indirect carbon emissions from fuel cell power generation are corrected to obtain the actual carbon emissions captured by the carbon capture equipment.

[0028] By using the non-leakage rate of geological sequestration to correct the actual carbon emissions captured by carbon capture equipment, the net negative carbon benefit of the carbon storage process can be obtained.

[0029] Based on carbon emission data from the production process, monitoring data from carbon transfer carriers, carbon emission data from the coupling interaction layer, and assessment data from negative carbon units, the optimal value for the total carbon emission of the system is the minimum difference between the optimized value of the sum of carbon emissions from electrolyzers, fuel cells, and gas-fired energy-consuming equipment and the optimized value of the total carbon capture.

[0030] Based on carbon emission data from the electrolyzer production process, calculate the runtime. Optimized carbon emission values ​​for internal electrolyzers ,as follows:

[0031]

[0032] In the formula, For real-time carbon intensity of the power grid, runtime segment Operating power of internal electrolytic cell This refers to the efficiency of the electrolytic cell.

[0033] The percentage of impurity carbon in hydrogen storage devices based on fuel cells, and the calculation of the operating period. Optimized carbon emissions of internal fuel cells ,as follows:

[0034]

[0035] In the formula, Carbon intensity for hydrogen power generation runtime segment Internal fuel cell output power, The percentage of impurity carbon in the hydrogen storage device for fuel cells.

[0036] The operating time is calculated based on the actual carbon emissions captured by the carbon capture equipment. Optimized carbon emission values ​​for internal gas-fired energy equipment ,as follows:

[0037]

[0038] In the formula, Carbon emission factors from fuel gas runtime segment The amount of gas burned by internal gas-powered energy-consuming equipment. To improve cross-device capture efficiency. To capture collaborative factors across devices.

[0039] Based on the net negative carbon benefit of carbon storage, the calculation runtime is... Total internal carbon capture ,as follows:

[0040]

[0041] In the formula, This refers to the capture ratio of carbon capture equipment in an electrolytic cell. runtime segment Operating power of internal electrolytic cell The capture ratio of carbon capture equipment in gas-fired energy consumption equipment. runtime segment The amount of gas burned by internal gas-powered energy-consuming equipment. This represents the rate of non-leakage during geological sealing.

[0042] The optimal value of the total carbon emissions of the system is mapped to control commands. Based on a three-level control architecture of data acquisition, hierarchical diagnosis and model predictive control, the system corrects the carbon flow and carbon metering results by analyzing the monitoring data of direct emission sources, carbon emission data of production processes, monitoring data of carbon transfer carriers, carbon emission data of coupled interaction layers, and assessment data of negative carbon units.

[0043] This invention also proposes a carbon transfer and carbon metering system for an electrically hydrogen-coupled micro-energy network, comprising:

[0044] The operation mode control module is used to construct a multi-energy flow monitoring model based on the total carbon emissions of the system, the total energy input of the system, and the operating boundary; to determine the real-time carbon intensity of the power grid based on the multi-energy flow monitoring model using monitoring data from direct emission sources; and to switch to a mode in which the electrolyzer, fuel cell, and gas-fired energy-consuming equipment generate electricity when the real-time carbon intensity of the power grid is greater than the real-time emission threshold.

[0045] The carbon flow and carbon metering module is used in co-generation mode. Based on the carbon emission data of the production process of electrolyzers and gas-fired energy equipment during operation, the monitoring data of carbon transfer carriers of gas storage devices and hydrogen storage devices of fuel cells, the carbon emission data of the coupling interaction layer, and the evaluation data of negative carbon units, the module aims to optimize the total carbon emissions of the system. After correcting the monitoring data of direct emission sources, carbon emission data of production process, monitoring data of carbon transfer carriers, carbon emission data of coupling interaction layer, and evaluation data of negative carbon units, the module obtains the carbon flow and carbon metering results.

[0046] The present invention is also a terminal, including a processor and a storage medium; the storage medium is used to store instructions; the processor is used to perform operations according to the instructions to execute the steps of the method.

[0047] The present invention is also a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method.

[0048] The beneficial effects of this invention are as follows: Compared with existing technologies, by deploying monitoring terminals at each stage of the electric-hydrogen coupled micro-energy network, real-time data of the three energy flows (electricity, hydrogen, and gas) are acquired. A total carbon emission model incorporating parameters such as grid carbon intensity and gas emission factors is constructed, and equipment operating boundaries and total energy constraints are set, forming a monitoring system covering multiple energy flows. This solves the problem of the one-sidedness of traditional models in calculating multi-energy interaction scenarios. Furthermore, a closed-loop dynamic control architecture of "monitoring-diagnosis-adjustment-feedback" is designed. Based on real-time sensor data and model predictive control algorithms, the ratio of the three energy flows (electricity, hydrogen, and gas) and equipment operating parameters are dynamically optimized. The system can automatically adjust strategies according to changes in grid power supply structure and load fluctuations, prioritizing the consumption of low-carbon energy and matching carbon capture efficiency in real time, forming a multi-energy collaborative intelligent control mechanism. At the metering level, three-dimensional monitoring of carbon emissions across the entire micro-energy network is achieved, providing precise data support for low-carbon strategy formulation. At the control level, the system's responsiveness to changes in energy structure and load fluctuations is improved, ensuring energy demand while continuously reducing carbon emissions, and promoting efficient multi-energy flow collaboration and low-carbon scheduling. Compared with existing technologies, this invention constructs a carbon management system that combines dynamic modeling and real-time optimization, providing an engineeringable technical path for the low-carbon development of new micro energy networks. Attached Figure Description

[0049] Figure 1 This is a flowchart of a carbon transfer and carbon metering method for an electrically hydrogen-coupled micro-energy network proposed in this invention. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.

[0051] The electro-hydrogen coupled micro-energy network includes an electrolyzer, a fuel cell, and gas-fired energy consumption equipment. This invention provides a method for carbon transfer and carbon metering in the electro-hydrogen coupled micro-energy network, including energy identification and preliminary analysis, energy flow monitoring, carbon source calculation and quantification, optimization control and dynamic adjustment, etc. Figure 1 As shown, the method includes:

[0052] Step 1: Obtain the carbon emissions and carbon capture amounts from the electricity, hydrogen, and gas energy links to determine the total carbon emissions of the system; use the energy inputs from the electricity, hydrogen, and gas energy links to determine the total energy input of the system; set operating boundaries for the equipment in the electricity, hydrogen, and gas energy links; and construct a multi-energy flow monitoring model based on the total carbon emissions, total energy input, and operating boundaries of the system.

[0053] Specifically, step 1 includes:

[0054] Step 1.1: Deploy monitoring terminals in the electro-hydrogen coupled micro-energy network to acquire real-time data of electrolyzers, fuel cells, and gas-fired energy-consuming equipment at different time periods, including: carbon emissions, carbon capture, and energy input.

[0055] Specifically, in the electricity sector, current and voltage sensors are deployed to collect power exchange with the power grid, and power transmitters are used to monitor the input power of the electrolyzer in real time; in the hydrogen energy sector, mass flow meters are used to measure the hydrogen production rate, and gas analyzers are used to detect the hydrogen within the hydrogen storage equipment. Adsorption capacity; gas flow sensors are installed in the gas pipeline to monitor the input and output flow rates, pressure transmitters provide real-time feedback on the gas storage capacity, and infrared gas sensors detect the composition of exhaust gas after combustion in gas-powered equipment.

[0056] Step 1.2: Use the difference between the total carbon emissions of the electrolyzer, fuel cell, and gas-fired energy equipment and the total carbon capture of the electrolyzer and fuel cell as the total carbon emissions of the system.

[0057] Existing carbon emission models typically only consider single or dual-energy flow systems, neglecting direct carbon emissions from gas combustion and their dynamic capture, and failing to distinguish between carbon capture efficiencies in the electricity and gas stages. This leads to underestimation of carbon emissions from gas systems and distortion of capture efficiency. Furthermore, existing models often overlook the amplification effect of electrolyzer efficiency on grid power consumption, resulting in significant implicit biases in carbon emission calculations. To address these issues, this invention introduces a gas carbon emission factor and capture parameters from carbon capture devices at each stage. , and electrolytic cell efficiency correction item The total carbon emissions of the system are calculated by summing the carbon emissions corresponding to the operating power of the electrolyzer (corrected for electrolyzer efficiency), the carbon emissions from hydrogen power generation (determined based on fuel cell output power), and the carbon emissions from gas-fired power generation equipment, and then subtracting the carbon capture amounts from the electrolyzer carbon capture equipment and the gas-fired power generation equipment carbon capture equipment. The result is shown below:

[0058]

[0059] In the formula, Total carbon emissions of the system (unit: ), Real-time carbon intensity of the power grid (unit: This represents the carbon emissions corresponding to each kilowatt-hour of electricity generated by the power grid. Carbon emission factor of fuel gas (unit: (), which represents the amount of carbon dioxide emitted per cubic meter of combusted gas, and is related to the composition of the gas; The electrolyzer's operating power (unit: kW) represents the input power of electrical energy used for water electrolysis to produce hydrogen. The output power of the fuel cell (unit: kW) represents the output power of hydrogen gas converted into electrical energy through the fuel cell. The amount of gas burned by gas-powered equipment (unit: (), indicates the amount of gas consumed by gas-powered equipment per unit time; Electrolyzer efficiency represents the efficiency with which electrical energy is converted into hydrogen energy. This refers to the capture ratio of carbon capture equipment in an electrolytic cell. The capture ratio of carbon capture equipment in gas-fired energy consumption equipment. Carbon intensity of hydrogen power generation (unit: This represents the carbon emissions produced per kilowatt-hour of hydrogen power generation. Carbon dioxide capture capacity per kilowatt of power for the electrolytic cell carbon capture equipment (unit: ), Carbon capture capacity per kilowatt of power for gas-fired energy equipment (unit: );

[0060] Determining the Adjustable Equipment Variable Vector Based on the Total Carbon Emission Model of Micro-Energy Networks for:

[0061]

[0062] Step 1.3: Use the sum of the energy inputs from the electrolyzer, fuel cell, and gas-powered equipment as the total energy input of the system;

[0063] The total system energy input is obtained by summing the electrical energy input corresponding to the electrolyzer's operating power after efficiency correction, the hydrogen energy input corresponding to the fuel cell's output power after energy conversion efficiency correction, and the gas energy input corresponding to the gas energy consumption equipment after efficiency correction, as shown below:

[0064]

[0065] In the formula, For fuel cell energy conversion efficiency, For the efficiency of gas-powered energy-consuming equipment. The total input energy of the system (kW).

[0066] Step 1.4 sets operating boundaries for carbon emission equipment such as electrolyzers, fuel cells, and gas-fired energy-consuming equipment, including:

[0067] Electrolyzer operating power boundary:

[0068] Fuel cell output power boundary:

[0069] Gas combustion capacity boundary for gas-powered energy equipment:

[0070] Setting operating boundaries for carbon capture equipment in electrolyzers and gas-fired energy-consuming equipment, including:

[0071] , .

[0072] A multi-energy flow monitoring model for micro energy networks is constructed using the total carbon emissions of the system, the total energy input of the system, the operating boundaries of carbon emission equipment, and the operating boundaries of carbon capture equipment.

[0073] Step 2: Using direct emission source monitoring data, determine the real-time carbon intensity of the power grid based on a multi-energy flow monitoring model; when the real-time carbon intensity of the power grid is greater than the real-time emission threshold, switch to a mode in which electrolyzers, fuel cells, and gas-fired energy-consuming equipment generate electricity together.

[0074] Direct emission source monitoring data; Direct emission source monitoring focuses on explicit carbon emissions from energy consumption, reflecting explicit carbon emissions from the use of primary energy sources such as electricity and gas; Monitoring data of direct emission sources includes: carbon emissions from purchased electricity, direct emissions from gas-fired energy-consuming equipment, and direct emissions from gas turbine power generation;

[0075] In this embodiment, the real-time monitoring data of grid carbon intensity of purchased electricity, the combustion emissions of gas-fired power generation equipment, and the trans-energy flow explicit emission measurement of the electro-gas coupling equipment (gas turbine power generation) are used as direct emission source monitoring data. The real-time monitoring data of grid carbon intensity of purchased electricity is the product of grid carbon intensity and purchased electricity, wherein the purchased electricity is obtained by calculating grid interaction power and time integration. The combustion emissions of gas-fired power generation equipment are the product of gas carbon emission factor and gas combustion volume, wherein the gas combustion volume is obtained by real-time monitoring through gas flow sensor. The carbon emission correction factor of gas-to-electricity conversion is used to correct the product of gas carbon emission factor and actual gas combustion volume of gas turbine to obtain the carbon dioxide emissions generated during gas turbine power generation, which is used as the trans-energy flow explicit emission measurement of the electro-gas coupling equipment (gas turbine power generation), wherein the actual gas combustion volume of gas turbine is obtained by real-time monitoring through flow sensor, and the carbon emission correction factor of gas-to-electricity conversion is used to correct the impact of gas turbine power generation efficiency on actual emissions.

[0076] The weighted sum of the real-time carbon intensity of the power grid and the carbon emission factor of natural gas is used as the real-time emission threshold, representing the upper limit of the system's acceptable comprehensive carbon emissions per unit of energy during the current period (unit: );

[0077]

[0078] In the formula, The lower heating value of the fuel gas, in kJ / m³, satisfies... The weighting coefficients are set according to the system control strategy. When priority is given to controlling the carbon source of electricity... This threshold serves as a reference upper limit for the joint emissions of electricity and gas energy flow, used to determine whether to trigger the control mechanism. When the real-time carbon intensity of the power grid exceeds the real-time emission threshold, it automatically switches to a mode in which the electrolyzer, fuel cell, and gas-fired energy-consuming equipment generate electricity, reducing the power of the electrolyzer and increasing the power of the fuel cell. By adjusting the proportion of fuel cell power generation, the real-time carbon intensity of the power grid is reduced to below the real-time emission threshold, effectively solving the problem of lack of real-time response to fluctuations in the carbon intensity of the power grid and excessive combustion of gas. When the real-time carbon intensity of the power grid is not greater than the real-time emission threshold, the existing power generation mode is maintained.

[0079] Step 3: In the co-generation mode, after constraining the efficiency of the electrolyzer by using the spatiotemporal distribution coefficient of the real-time carbon intensity of the power grid, the implicit carbon emissions of hydrogen production by the electrolyzer are determined based on the carbon emissions of the electrolyzer, which are used as the carbon emission data of the electrolyzer's production process; and the carbon emission data of the production process of the gas-fired energy-consuming equipment are calculated.

[0080] Specifically, step 3 includes:

[0081] Step 3.1: Correct the runtime segment using the dilution sensitivity coefficient. Renewable energy output as a percentage of total power generation in the domestic power grid The difference between 1 and the correction value is used as the spatiotemporal distribution coefficient, and the calculation formula is as follows:

[0082]

[0083] In the formula, runtime segment The proportion of renewable energy output in the internal power grid For the dilution sensitivity coefficient, This indicates the reduction in carbon intensity resulting from a 1% increase in renewable energy output in the power grid. hour, This indicates that the entire power grid is supplied by thermal power units. Therefore, during peak electricity demand, if inter-regional power transfer is involved, ;

[0084] Spatiotemporal distribution coefficient of real-time carbon intensity of power grid It is used to reflect the degree of shift in carbon emission factors at different time periods, and is used to dynamically correct the real-time carbon intensity of the power grid. Based on regional power grid emission factor statistics, the value range is as follows: The figure is higher during peak hours and slightly lower during off-peak hours to reflect the dilution effect of renewable energy on the carbon intensity of the power grid. The higher the proportion of renewable energy output, the lower the carbon emissions per unit of electricity.

[0085] Step 3.2: Constrain the electrolyzer efficiency using the spatiotemporal distribution coefficient of the real-time carbon intensity of the power grid, as follows:

[0086]

[0087] In the formula, The basic efficiency of the electrolyzer is given, with a value range of [value missing]. , The efficiency response coefficient represents the increase in electrolyzer efficiency for every 10% decrease in the real-time carbon intensity of the power grid, with a value range of [value missing]. The efficiency response coefficient is obtained by fitting historical operating data based on the specific type of electrolyzer (alkaline / PEM / SOE) and the characteristics of the scheduling system.

[0088] By constraining the efficiency of the electrolyzer through a spatiotemporal distribution coefficient, the electrolyzer's operating efficiency can be adjusted in real time according to the proportion of renewable energy output in the power grid. This further increases the proportion of renewable energy consumption, improves the network's economics, and reduces the system's total carbon emissions. When the proportion of renewable energy output in the power grid increases, the electrolyzer efficiency is increased to a high-efficiency operating range to reduce hydrogen production power consumption. The high-efficiency operating range of the electrolyzer efficiency is... When the proportion of renewable energy output in the power grid decreases, the auxiliary energy consumption of the electrolyzer (accounting for about 10%–15% of the total energy consumption), such as water circulation pumps, heat exchangers, and gas compressors, can be reduced or operated off-peak during the high carbon intensity phase of the power grid through predictive scheduling, which can effectively reduce the overall carbon emissions of the system.

[0089] Step 3.3, during runtime Internally, utilizing the real-time carbon intensity and spatiotemporal distribution coefficient of the power grid The impact of the constrained electrolytic cell efficiency, the capture ratio of the electrolytic cell carbon capture device, and the capture response time correction term on the electrolytic cell operating power. After making corrections, the implicit carbon emissions from hydrogen production by the electrolyzer were obtained. As shown below:

[0090]

[0091] In the formula, runtime segment The capture ratio of carbon capture equipment in internal electrolytic cells. runtime segment Correction item for the capture response time of the carbon capture equipment in the internal electrolytic cell;

[0092] Because the carbon transfer and carbon metering process proposed in this invention involves dynamic evaluation of negative carbon units, a capture response time correction term and a capture ratio are introduced to address the actual carbon absorption capacity of the carbon capture, storage, and reuse stages. This establishes the implicit carbon emissions from hydrogen production in electrolyzers under dynamic correction of the capture rate. The capture response time correction term of the electrolyzer carbon capture equipment is based on the operating time. The start-up and shutdown status of the internal electrolytic cell is dynamically adjusted. In the embodiment, during the running period... When the internal electrolytic cell is running stably A value of 1.0 is used as the standard setting. At this point, the carbon capture equipment has completed preheating, vacuum adjustment, and adsorption medium activation, and is operating at its rated condition. The capture ratio can be directly considered effective; therefore, a value of 1.0 is taken, representing the rated performance calibrated by the equipment, and no correction is needed. Operating period This refers to the start-up and shutdown periods of the electrolytic cell. In the initial stage (<30% load), the capture ratio is <50%; in the middle stage (30–80% load), the capture ratio is 70–90%. Therefore, the operating boundary value is taken as 0.4~0.9.

[0093] runtime segment Carbon emissions from internal electrolyzers ;

[0094] Step 3.4: Using the carbon emissions of the electrolyzer, its spatiotemporal distribution coefficient, the capture ratio of the electrolyzer's carbon capture equipment, and the capture response time correction term, determine the implicit carbon emissions from hydrogen production by the electrolyzer, as follows:

[0095]

[0096] The implicit carbon emissions from hydrogen production by electrolyzers are carbon emission data from the electrolyzer's production process. They reflect the indirect carbon emissions generated by the power consumption of the power grid during the conversion of electricity into hydrogen energy. By quantifying the implicit carbon emissions in the energy conversion process, a link between carbon flow and carbon measurement is realized, from monitoring data of direct emission sources to carbon emission data of the production process.

[0097] Step 3.5, the carbon emission data of the production process of gas-fired energy equipment, is the auxiliary energy consumption carbon emission of the pretreatment device of the gas-fired energy equipment, as follows:

[0098]

[0099] In the formula, Auxiliary energy consumption and carbon emissions of pretreatment devices for gas-fired energy-consuming equipment (unit: ), Input power (unit: kW) for the pretreatment device of gas-powered energy equipment. The green hydrogen blending ratio coefficient reflects the effect of green hydrogen blending on reducing the auxiliary energy consumption of the pretreatment unit.

[0100] The carbon emission data of the production process of electrolyzers and gas-fired energy equipment quantifies the hidden carbon consumption in processes such as electrolytic hydrogen production and gas mixing, and realizes carbon flow and carbon measurement in the carbon emission data of the production process.

[0101] Step 4, calculate the runtime segment The carbon migration rate of gas storage devices in internal combustion energy equipment and the proportion of impurity carbon in hydrogen storage devices of fuel cells are used as monitoring data for carbon transfer carriers.

[0102] Specifically, step 4 includes:

[0103] Step 4.1, using the runtime segment Total carbon migration from gas storage devices of internal combustion energy-consuming equipment and operating time The ratio of these ratios is used as the migration rate of the carbon component. Set runtime segment The carbon migration rate constraint for the gas storage device of internal combustion energy equipment is: ;

[0104] The carbon component migration rate in the gas storage tank is then defined as the mass difference between the input and output of gas carbon components per unit time. This is measured using an online gas component analyzer. The concentration of carbon components is used to calculate the carbon migration rate by combining the gas storage volume and residence time. This rate reflects the total amount and speed of carbon migration in the gas along the energy storage path.

[0105] Step 4.2: Obtain the ratio of the actual amount of carbon-containing gas retained in the hydrogen storage device to the total amount of carbon-containing gas adsorbed in the hydrogen storage device. The difference between 1 and this ratio is taken as the impurity carbon ratio of the hydrogen storage device in the fuel cell. Set the impurity carbon percentage constraint as follows: ;

[0106] The proportion of impurity carbon can also be determined through hydrocarbon isotope ratio analysis, reflecting the proportion of non-target carbon released from impurities in the feedstock water or equipment materials for electro-hydrogen production, and demonstrating the precise removal of impurity carbon and the quantification of target carbon retention during hydrogen storage.

[0107] To address the issue of distorted calculations of carbon retention and migration in energy storage processes due to the failure to consider the proportion of impurities in hydrogen storage tanks and the pressure-driven carbon migration in gas storage tanks, this paper first defines constraints and then dynamically adjusts the operating parameters of the energy storage equipment based on these constraints. The goal is to reduce carbon accumulation and latent release in the energy storage path and improve the controllability of carbon flow. Adjustments can be made by regulating the gas storage pressure and inlet / outlet rates in the gas storage tanks, controlling the compressor start / stop frequency, and setting a start-up delay for the pigment cell. This allows for control of gas residence time, reducing CO2 enrichment, and preventing equipment operation when carbon is not completely eliminated in the initial stage.

[0108] Step 4.3, Runtime Segment The carbon migration rate of gas storage devices in internal combustion energy equipment and the proportion of impurity carbon in hydrogen storage devices of fuel cells are used as monitoring data for carbon transfer carriers.

[0109] Under the constraints of impurity carbon ratio in hydrogen storage tanks and carbon component migration rate in gas storage tanks, the operating parameters of energy storage equipment are adjusted, and carbon flow and carbon measurement are carried out in the monitoring data of carbon transfer carriers to achieve carbon transfer carrier monitoring and focus on the physical migration characteristics of carbon in energy storage and transportation.

[0110] Step 5, calculate the runtime segment The indirect carbon emissions from the energy consumption of the internal electrolyzer cooling system and the cross-equipment collaborative capture of mixed exhaust gas are used as carbon emission data for the coupling interaction layer.

[0111] Specifically, step 5 includes:

[0112] Step 5.1, based on the runtime segment The operating power of the internal electrolytic cell is calculated, along with the indirect carbon emissions generated by the energy consumption of the electrolytic cell's cooling system.

[0113] In the identification of carbon emissions in the coupling interaction layer, considering the rheological changes of implicit carbon sources and cross-equipment synergistic effects in the electro-hydrogen-gas multi-energy flow coupling process, a quantitative mechanism for implicit carbon emissions in the coupling process is established to identify cross-carbon sources in multi-energy flow interaction scenarios by capturing the cross-influence of emissions from the electrolyzer auxiliary system and carbon capture equipment. The indirect carbon emissions from the electrolyzer cooling system during the electro-hydrogen coupling process are related to the electrolyzer power, as follows:

[0114]

[0115] In the formula, Indirect carbon emissions from energy consumption of electrolytic cell cooling system (unit: ), The value represents the operating power of the electrolyzer, and 0.05 represents the carbon emission coefficient per unit power cooling energy consumption, reflecting the quantitative relationship between the power consumption of the electrolyzer's heat dissipation system and carbon emissions. The indirect carbon emissions generated by the energy consumption of the electrolyzer's cooling system reflect the implicit carbon emissions of the auxiliary systems during the operation of the electro-hydrogen coupling equipment.

[0116] Step 5.2: Using cross-device capture efficiency and cross-device capture synergy factor, the sum of the direct carbon emissions from gas combustion in gas-fired energy-consuming equipment and the indirect carbon emissions from fuel cell power generation is corrected to obtain the cross-device synergistic capture amount of mixed exhaust gas.

[0117] Cross-device coordinated capture of mixed exhaust gases ,as follows:

[0118]

[0119] In the formula, Direct carbon emissions from gas combustion (unit: ), Indirect carbon emissions from fuel cell power generation (unit: ), To improve cross-device capture efficiency. The cross-device capture synergy factor reflects the effect of mixing gas exhaust and fuel cell exhaust on the capture efficiency, which may improve or inhibit the capture efficiency. The synergistic effect capture amount reflects the cross-influence quantification of the collaborative operation of capture devices in multi-energy flow coupling scenarios.

[0120] Step 5.3: The indirect carbon emissions generated by the energy consumption of the electrolytic cell cooling system and the cross-equipment synergistic capture of the mixed exhaust gas constitute the carbon emission data of the coupled interaction layer, so as to characterize the synergistic carbon effect in the cross-link of energy conversion.

[0121] Step 6, Calculate the runtime segment The actual carbon emissions captured by the internal carbon capture device and the net negative carbon benefit of the carbon storage process are used as assessment data for the negative carbon unit.

[0122] Specifically, step 6 includes:

[0123] Step 6.1, Utilize cross-device acquisition efficiency and runtime segment Acquisition response time correction term for cross-device acquisition The total carbon emissions from the combustion of gas in gas-fired energy equipment and the indirect carbon emissions from fuel cell power generation are corrected to obtain the actual carbon emissions captured by the carbon capture equipment. ,as follows:

[0124]

[0125] Step 6.2: Correct the actual carbon emissions captured by the carbon capture equipment using the geological sequestration non-leakage rate to obtain the net negative carbon benefit of the carbon storage process, as follows:

[0126]

[0127] In the formula, Net carbon negative benefit of carbon storage (unit: ), Carbon emissions actually captured by carbon capture equipment (unit: ), The leakage rate of geological storage is updated in real time through data monitoring the permeability of the storage medium. Reflecting the effects of geological structure on carbon sequestration. The leakage ratio is a dynamic assessment of the actual carbon absorption capacity of the carbon storage process.

[0128] Step 6.3 uses the actual carbon emissions captured by the carbon capture equipment and the net negative carbon benefit of the carbon storage process as the assessment data for the negative carbon unit; the dynamic assessment of the negative carbon unit introduces the response time correction of the capture equipment and the assessment of the leakage rate of geological storage, and establishes a dynamic correction mechanism to take into account the deviation in the calculation of negative carbon benefits caused by equipment operation delays and carbon storage leakage, based on the actual carbon absorption capacity of the carbon capture, storage and reuse process.

[0129] The method proposed in this invention obtains various carbon source identification data, including: direct emission source monitoring data, production process carbon emission data, carbon transfer carrier monitoring data, coupled interaction layer carbon emission data, and negative carbon unit evaluation data. When micro-energy networks face complex scenarios such as gas type switching, changes in grid power supply structure, and multi-energy flow coupled operation, the five-layer carbon source identification system achieves accurate calculation of carbon source rheology across all stages through the following methods: The first layer targets explicit emissions such as purchased electricity and gas turbine power generation, dynamically updating the carbon emission calculation of purchased electricity based on the real-time carbon intensity of the grid, and using a gas-to-electricity conversion carbon emission correction factor to perform efficiency correction on the cross-energy flow emissions of the gas turbine; The second layer targets energy conversion stages such as hydrogen production and gas pretreatment, quantifying the electrolyzer using the spatiotemporal distribution coefficient of grid carbon intensity. The implementation of this solution addresses several key aspects of carbon capture and storage. First, it considers the implicit carbon emissions from hydrogen production and dynamically adjusts the auxiliary energy consumption and carbon emissions of the gas pretreatment stage based on the green hydrogen blending ratio. Second, it addresses hydrogen and gas storage equipment by using hydrocarbon isotope analysis to distinguish between impurity carbon and target carbon retention in the hydrogen storage tank, and tracks carbon migration caused by pressure fluctuations based on the coupling relationship between gas tank pressure and methane volume fraction. Third, it addresses the electro-hydrogen-gas coupling stage by identifying implicit emissions from the electrolyzer cooling system through a unit power cooling energy consumption coefficient, and quantifies the cross-industry capture synergy factor to assess the cross-influence of hydrogen-gas mixture tail gas on carbon capture efficiency. Fourth, it addresses the carbon capture and storage stage by dynamically correcting the capture response time on the real-time capture volume based on the start-stop status of the capture equipment, and assessing the net negative carbon benefit of the carbon storage stage through geological storage leakage rate. This embodiment effectively solves problems such as underestimation of explicit emissions, implicit emission deviations, missing carbon sources in the coupling stage, and inaccurate assessment of negative carbon benefits in multi-energy flow coupling scenarios through the dynamic correlation and synergistic operation of core variables at each layer, providing a systematic carbon metering solution for the low-carbon operation of micro-energy networks.

[0130] Five types of carbon source identification data form a logically ordered and model-coupled data chain, constructing a link structure for carbon flow and carbon metering. This link is centered on the structured correlation between the five layers of carbon source identification data. By establishing carbon source classification and data exchange mechanisms at different stages of the power, hydrogen, and gas systems, it achieves complete tracking of carbon flow paths and accurate metering of multi-level carbon emissions. The proposed five-layer carbon source identification system covers direct emissions, latent emissions, and carbon transfer paths, integrating multi-energy coupling interactions and negative carbon device operating parameters. This constructs a main carbon metering link that runs through the entire energy flow process of the micro-energy network, supporting hierarchical mapping, source tracing, and closed-loop control of carbon emissions across the entire system. The link enables multi-scale metering and feedback regulation of carbon at the source, flow, and storage stages in the micro-energy network, solving the problem of missed calculations of carbon source rheology in coupling stages caused by static hierarchical layering in existing technologies.

[0131] Step 7: Based on carbon emission data from the production process, monitoring data from the carbon transfer carrier, carbon emission data from the coupling interaction layer, and assessment data from the negative carbon unit, the optimal value of the total carbon emission of the system is determined by minimizing the difference between the optimized value of the sum of carbon emissions from the electrolyzer, fuel cell, and gas-fired energy equipment and the optimized value of the total carbon capture.

[0132] The optimal total carbon emissions for the system are as follows:

[0133]

[0134] In the formula, This represents the optimal value for the system's total carbon emissions. runtime segment Carbon emissions from internal electrolyzers runtime segment Carbon emissions from internal fuel cells, runtime segment Carbon emissions from internal combustion engine equipment runtime segment Total internal carbon capture;

[0135] Among them, the operating time is calculated based on the carbon emission data of the electrolyzer production process. Optimized carbon emission values ​​for internal electrolyzers. ,express and , , Related, Spatiotemporal distribution coefficient of real-time carbon intensity of power grid Related;

[0136] The percentage of impurity carbon in hydrogen storage devices based on fuel cells, and the calculation of the operating period. Optimized carbon emission values ​​for internal fuel cells. ,express Carbon intensity of hydrogen power generation Fuel cell output power The proportion of impurity carbon in hydrogen storage devices for fuel cells Related;

[0137] The operating time is calculated based on the actual carbon emissions captured by the carbon capture equipment. Optimized carbon emission values ​​for internal combustion engine power equipment. ,express With gas carbon emission factors The amount of gas burned by gas-powered energy equipment Cross-device capture efficiency Cross-device capture of synergistic factors Related;

[0138] Based on the net negative carbon benefit of carbon storage, the calculation runtime is... Total internal carbon capture

[0139] ,express and , , , , Related;

[0140] Therefore, based on the link structure consisting of carbon emission data from the production process, monitoring data from carbon transfer carriers, carbon emission data from the coupled interaction layer, and assessment data from negative carbon units, the carbon emissions and carbon retention at each stage are quantified in the time series dimension. Utilizing the carbon flow and carbon metering results and key characteristic parameters at each layer, a dynamic and hierarchical expansion of the system's total carbon emissions is achieved; during operation... Internally, carbon emissions are decomposed into time-series sub-items from electricity, hydrogen, and gas processes. Each sub-item is integrated into the key parameters of a five-layer carbon source identification system, enabling tiered quantification of explicit emissions, implicit emissions, carbon transfer, implicit emissions from coupled processes, and negative carbon benefits. Compared to the static model in step 1, its refinement lies in: distinguishing multi-level carbon sources such as indirect emissions from hydrogen production and implicit emissions from the electrolyzer cooling system; characterizing the synergistic impact of gas-hydrogen tail gas mixing on capture efficiency; and providing real-time data support for the three-tiered architecture of "data acquisition - tiered diagnosis - model predictive control".

[0141] Step 8: Map the optimal value of the total carbon emissions of the system to control commands. Based on the three-level control architecture of data acquisition-hierarchical diagnosis-model predictive control, correct the data of direct emission source monitoring, carbon emission data of production process, monitoring data of carbon transfer carrier, carbon emission data of coupled interaction layer, and negative carbon unit evaluation data to obtain carbon flow and carbon measurement results.

[0142] The design incorporates a carbon regulation execution mechanism and a closed-loop feedback system, mapping the optimal total carbon emissions of the system into executable equipment control commands. The feedback mechanism enables closed-loop verification of the regulation effect, establishing a three-tiered control architecture of "data acquisition - hierarchical diagnosis - model predictive control (MPC)." This architecture represents a closed-loop implementation of the "carbon flow and carbon measurement" objective. It serves as both an additional intelligent control module and a crucial link in the engineering of the carbon chain, truly transforming the multi-layered carbon emission paths identified in the carbon flow chain and the constructed target system into "executable and dynamically optimizable" control strategies.

[0143] Specifically, the data acquisition layer acquires real-time status data of the five-layer carbon source identification system through a deployed sensor network, including explicit emissions (purchased electricity and gas combustion emissions), implicit emissions (implicit carbon emissions from hydrogen production and energy consumption emissions from electrolyzer cooling systems), carbon transfer (migration rate of carbon components in gas storage devices and impurity carbon ratio in hydrogen storage equipment), coupling effects (cross-device collaborative capture volume), and negative carbon units (capture volume of carbon capture devices and net negative carbon benefits of carbon storage).

[0144] The stratified diagnostic layer compares the collected data from each layer with preset stratified carbon emission targets to diagnose whether there are deviations such as excessive emissions or insufficient retention. Specifically:

[0145] 1) Visible emission layer: Compare real-time monitored electricity and gas emissions with the set visible emission target values.

[0146] 2) Implicit emission layer: Compare the implicit carbon emissions of hydrogen production with the expected carbon intensity target per unit of hydrogen production.

[0147] 3) Carbon transfer layer: Monitor the migration rate of carbon components in gas storage and the proportion of carbon impurities in hydrogen storage to ensure compliance with carbon retention constraints in the energy storage process.

[0148] 4) Coupling effect layer: Evaluate the capture synergy effect in the electro-hydrogen coupling process to prevent uncounted carbon emissions from increasing.

[0149] 5) Carbon Negative Unit Layer: Verify the operating efficiency of carbon capture equipment and the net carbon negative benefit of geological storage to prevent a decline in carbon negative capacity due to equipment response delays or increased leakage rates.

[0150] The diagnostics achieve comprehensive identification and accurate diagnosis of carbon emission behavior in multi-energy flow coupling scenarios by comparing target values ​​of five carbon source pathways.

[0151] Based on the hierarchical diagnostic results, the model predictive control layer combines equipment operation constraints and the optimization objective of minimizing total system carbon emissions to form a future-oriented time-domain control strategy. By dynamically adjusting control variables such as electrolyzer power, fuel cell power, gas combustion rate, and carbon capture efficiency, it ensures that the system maintains optimal low-carbon operation under scenarios such as load changes and grid carbon intensity fluctuations.

[0152] Through the above closed-loop process, this invention constructs a control system of "data-driven - model-supported - dynamic optimization" to ensure that the system always maintains the optimal low-carbon operating state in the scenario of carbon source rheology and multi-energy flow coupling, and realizes the adaptive optimization of the control strategy for the scenario of carbon source rheology and multi-energy flow coupling.

[0153] It should be noted that the carbon flow and carbon metering method described in this invention focuses on the full-process monitoring, hierarchical diagnosis, and accurate metering of carbon source paths in multi-energy flow networks, providing fundamental data support and decision-making basis for subsequent energy system optimization scheduling and control strategies. Those skilled in the art can, within the framework of this invention and in conjunction with actual operating scenarios, introduce methods such as energy load balancing, electricity price optimization, negative carbon unit sensitivity analysis, and model predictive control to achieve multi-objective comprehensive optimization of energy system operation; these are reasonable extensions of the technical solution of this invention.

[0154] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0155] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0156] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0157] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0158] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A method for carbon transfer and carbon metering in an electrically hydrogen-coupled micro-energy network, wherein the micro-energy network includes an electrolyzer, a fuel cell, and gas-fired energy-consuming equipment; characterized in that, include: A multi-energy flow monitoring model is constructed based on the system's total carbon emissions, total system energy input, and operating boundaries; Using direct emission source monitoring data, the real-time carbon intensity of the power grid is determined based on a multi-energy flow monitoring model. When the real-time carbon intensity of the power grid exceeds the real-time emission threshold, the system switches to a shared power generation mode involving electrolyzers, fuel cells, and gas-fired energy devices. Under this shared power generation mode, based on the carbon emission data from the production process of electrolyzers and gas-fired energy devices during operation, the monitoring data of the carbon transfer carriers of the gas storage devices and hydrogen storage devices of fuel cells, the carbon emission data of the coupled interaction layer, and the evaluation data of the negative carbon unit, with the goal of optimizing the total carbon emissions of the system, a closed-loop feedback control is performed on the deviations between the direct emission source monitoring data, the carbon emission data from the production process, the monitoring data of the carbon transfer carriers, the carbon emission data of the coupled interaction layer, and the evaluation data of the negative carbon unit and the corresponding target values ​​to obtain the carbon flow and carbon metering results. Among them, the carbon component migration rate of the gas storage device of the gas-using energy equipment and the impurity carbon ratio of the hydrogen storage device of the fuel cell are calculated as monitoring data of carbon transfer carriers during the operation period; the indirect carbon emissions generated by the energy consumption of the electrolyzer cooling system and the cross-equipment collaborative capture of mixed tail gas are calculated as carbon emission data of the coupling interaction layer. The actual carbon emissions captured by the carbon capture equipment during the operating period and the net negative carbon benefit of the carbon storage process are used as evaluation data for the negative carbon unit.

2. The method for carbon transfer and carbon metering in an electrically hydrogen-coupled micro-energy network according to claim 1, characterized in that, In the electrically coupled hydrogen micro-energy network, a monitoring terminal is deployed to acquire real-time data of electrolyzers, fuel cells, and gas-fired energy-consuming equipment at different time periods, including carbon emissions, carbon capture, and energy input. The total carbon emissions of the system are calculated by using the difference between the total carbon emissions of the electrolyzer, fuel cell, and gas-fired energy-consuming equipment and the total carbon capture of the electrolyzer and fuel cell. The total energy input of the system is calculated by using the total energy input of the electrolyzer, fuel cell, and gas-fired energy-consuming equipment. Operating boundaries are set for the carbon emission equipment of the electrolyzer, fuel cell, and gas-fired energy-consuming equipment, and operating boundaries are set for the carbon capture equipment of the electrolyzer and gas-fired energy-consuming equipment.

3. The carbon transfer and carbon metering method for an electrically hydrogen-coupled micro-energy network according to claim 1, characterized in that, In the co-generation mode, after constraining the efficiency of the electrolyzer by using the spatiotemporal distribution coefficient of the real-time carbon intensity of the power grid, the implicit carbon emissions of hydrogen production by the electrolyzer are determined based on the carbon emissions of the electrolyzer, which serves as the carbon emission data of the electrolyzer's production process. Calculate carbon emission data during the production process of gas-fired energy-consuming equipment; Dilution sensitivity coefficient This indicates the reduction in carbon intensity resulting from a 1% increase in renewable energy output in the power grid. ,use Fix runtime segment Renewable energy output as a percentage of total power generation in the domestic power grid The difference between 1 and the corrected proportion of renewable energy output is used as the spatiotemporal distribution coefficient of the real-time carbon intensity of the power grid. ; Using the spatiotemporal distribution coefficient to evaluate the efficiency of electrolyzers Apply the following constraints: In the formula, For the basic efficiency of the electrolytic cell, The efficiency response coefficient represents the increase in electrolyzer efficiency for every 10% decrease in the real-time carbon intensity of the power grid.

4. The carbon transfer and carbon metering method for an electrically hydrogen-coupled micro-energy network according to claim 3, characterized in that, In the runtime segment Internally, utilizing the real-time carbon intensity of the power grid Spatiotemporal distribution coefficient The impact of the constrained electrolytic cell efficiency, the capture ratio of the electrolytic cell carbon capture device, and the capture response time correction term on the electrolytic cell operating power. After making corrections, the implicit carbon emissions from hydrogen production by the electrolyzer were obtained. As shown below: In the formula, runtime segment The capture ratio of carbon capture equipment in internal electrolytic cells. runtime segment Correction item for the capture response time of the carbon capture equipment in the internal electrolytic cell; in, runtime segment Carbon emissions from internal electrolyzers The implicit carbon emissions from hydrogen production in an electrolyzer are the carbon emission data from the electrolyzer's production process.

5. The method for carbon transfer and carbon metering in an electrically hydrogen-coupled micro-energy network according to claim 1, characterized in that, In the runtime segment Internally, the input power of the pretreatment device of the gas-fired energy-consuming equipment is corrected by using the real-time carbon intensity of the power grid and the green hydrogen blending ratio coefficient, so as to obtain the auxiliary energy consumption carbon emission of the pretreatment device of the gas-fired energy-consuming equipment, which is used as the carbon emission data of the production process of the gas-fired energy-consuming equipment.

6. The carbon transfer and carbon metering method for an electrically hydrogen-coupled micro-energy network according to claim 1, characterized in that, by runtime segment Total carbon migration from gas storage devices of internal combustion energy-consuming equipment and operating time The ratio of these ratios is used as the migration rate of the carbon component. Set runtime segment The carbon migration rate constraint for the gas storage device of internal combustion energy equipment is: ; The ratio of the actual amount of carbon-containing gas retained in the hydrogen storage device to the total amount of carbon-containing gas adsorbed within the device is obtained. The difference between 1 and this ratio is taken as the impurity carbon ratio of the hydrogen storage device in the fuel cell. Set the impurity carbon percentage constraint as follows: .

7. The carbon transfer and carbon metering method for an electrically hydrogen-coupled micro-energy network according to claim 1, characterized in that, By utilizing cross-device capture efficiency and cross-device capture synergy factor, the sum of direct carbon emissions from gas combustion in gas-fired energy-consuming equipment and indirect carbon emissions from fuel cell power generation is corrected to obtain the cross-device synergistic capture amount of mixed exhaust gas.

8. The method for carbon transfer and carbon metering in an electrically hydrogen-coupled micro-energy network according to claim 1, characterized in that, Utilizing cross-device capture efficiency and runtime segment Acquisition response time correction term for cross-device acquisition The total carbon emissions from the combustion of gas in gas-fired energy equipment and the indirect carbon emissions from fuel cell power generation are corrected to obtain the actual carbon emissions captured by the carbon capture equipment. By using the non-leakage rate of geological storage to correct the actual carbon emissions captured by carbon capture equipment, the net negative carbon benefit of the carbon storage process can be obtained.

9. The method for carbon transfer and carbon metering in an electrically hydrogen-coupled micro-energy network according to claim 2, characterized in that, Based on carbon emission data from the production process, monitoring data from carbon transfer carriers, carbon emission data from the coupling interaction layer, and assessment data from negative carbon units, the optimal value for the total carbon emission of the system is the minimum difference between the optimized value of the sum of carbon emissions from electrolyzers, fuel cells, and gas-fired energy-consuming equipment and the optimized value of the total carbon capture.

10. A carbon transfer and carbon metering system for an electrically hydrogen-coupled micro-energy network, used to implement the carbon transfer and carbon metering method for an electrically hydrogen-coupled micro-energy network as described in any one of claims 1 to 9; wherein, Micro-energy networks include electrolyzers, fuel cells, and gas-fired power generation equipment; characterized in that they include: The operation mode control module is used to construct a multi-energy flow monitoring model based on the total carbon emissions of the system, the total energy input of the system, and the operating boundary; to determine the real-time carbon intensity of the power grid based on the multi-energy flow monitoring model using monitoring data from direct emission sources; and to switch to a mode in which the electrolyzer, fuel cell, and gas-fired energy-consuming equipment generate electricity when the real-time carbon intensity of the power grid is greater than the real-time emission threshold. The carbon flow and carbon metering module, used in co-generation mode, calculates carbon flow and carbon metering results based on carbon emission data from the production process of electrolyzers and gas-fired power generation equipment during operation, monitoring data of carbon transfer carriers in the gas storage devices of gas-fired power generation equipment and hydrogen storage devices of fuel cells, carbon emission data from the coupling interaction layer, and negative carbon unit evaluation data. The goal is to optimize the total carbon emissions of the system. After correcting the direct emission source monitoring data, production process carbon emission data, carbon transfer carrier monitoring data, coupling interaction layer carbon emission data, and negative carbon unit evaluation data, the module calculates the carbon component migration rate of the gas storage devices of gas-fired power generation equipment and the impurity carbon ratio of the hydrogen storage devices of fuel cells during operation, serving as carbon transfer carrier monitoring data. It also calculates the indirect carbon emissions generated by the energy consumption of the electrolyzer cooling system and the cross-equipment collaborative capture of mixed exhaust gas during operation, serving as coupling interaction layer carbon emission data. Finally, it calculates the actual carbon emissions captured by the carbon capture equipment and the net negative carbon benefit of the carbon storage stage during operation, serving as negative carbon unit evaluation data.

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