A method for calculating the extended carbon emission flow of an optimal energy flow under electricity hydrogen heat energy system

Abstract

The application provides a kind of optimal energy flow under electricity hydrogen heat energy system extended carbon emission flow calculation method, it is related to energy system carbon metering technical field, establishes the carbon emission flow calculation submodel of power network, the node carbon potential characteristics in branch carbon flow density of power network injection node is constrained distribution by carbon emission flow calculation submodel, obtains the steady-state node carbon potential of power network;Establish the carbon emission flow calculation submodel of hydrogen network, the linear conversion of each pipeline carbon flow density of injection hydrogen network node is carried out by carbon emission flow calculation submodel, obtains the tracing carbon potential field of hydrogen network;The steady-state node carbon potential and tracing carbon potential field are converted into heat flow, and the load carbon potential of each output port of energy concentrator electricity, hydrogen and heat is determined.The application can quantitatively distribute the load carbon potential to the carbon emission of multi-network coupling, to improve the integrity of carbon metering result of multi-energy coupling system.

Description

Technical Field

[0001] This application relates to the field of carbon metering technology for energy systems, and more specifically, to a method for calculating the extended carbon emission flow of an electrohydrogen-thermal energy system under optimal energy flow. Background Technology

[0002] Carbon metering for energy systems is a standardized measurement technology that uses integrated energy systems combining electricity, hydrogen, and heat as the measurement object and quantifies the carbon emission intensity of various energy production, transmission, and conversion processes based on energy flow trajectories. This technology is widely applied in engineering scenarios such as park-level integrated energy stations combining electricity, hydrogen, and heat, regional multi-energy interconnected networks, and renewable energy coupled with hydrogen production scheduling. This technology can quantify the carbon emission levels corresponding to terminal electricity, hydrogen, and heat loads, providing quantitative data support for low-carbon energy scheduling, carbon emission ownership accounting, and renewable energy consumption benefit assessment. It is a fundamental technical means for the low-carbon operation and management of integrated energy systems.

[0003] However, existing conventional technologies mostly employ a separate carbon accounting architecture for each network, lacking a unified carbon potential conversion and balance constraint logic for multi-energy coupling (electricity, hydrogen, and heat). This results in a lack of unified matching of carbon potential dimensions for multiple energy types, fragmented carbon emission transfer processes across networks, and deviations in the allocation of carbon emission intensity across various end-user loads. Furthermore, carbon traceability results struggle to cover the cross-carbon transfer characteristics brought about by the bidirectional circulation of hydrogen energy, thus failing to accurately and synchronously characterize the spatiotemporal coupling distribution patterns of energy flow and carbon emission flow across the entire region. Therefore, how to quantitatively allocate load carbon potential for multi-network coupled carbon emissions to improve the completeness of carbon metering results for multi-energy coupled systems is a key challenge facing the industry. Summary of the Invention

[0004] This application provides a method for calculating the extended carbon emission flow of an electrohydrogen-thermal energy system under optimal energy flow, which can quantitatively allocate the load carbon potential of carbon emissions from multi-network coupled systems to improve the completeness of carbon metering results for multi-energy coupled systems.

[0005] Firstly, this application provides a method for calculating the extended carbon emission flow of an electrohydrogen-thermal energy system under optimal energy flow, wherein the method is as follows: An energy flow calculation model for an integrated electric-hydrogen-thermal energy system is constructed, and the energy flow calculation model is solved to obtain energy flow distribution data. A carbon emission flow calculation sub-model for the power network is established based on the energy flow distribution data. The carbon emission flow calculation sub-model is used to constrain and allocate the nodal carbon potential of each node in the power network to obtain the steady-state nodal carbon potential of the power network. A carbon emission flow calculation sub-model for the hydrogen network is established based on the energy flow distribution data. The carbon emission flow calculation sub-model is used to linearly transform the carbon flow density of each pipe injected into the hydrogen network node according to the node airflow flow matrix and the pipe flow distribution matrix in the sub-model, so as to obtain the source carbon potential field of the hydrogen network. Heat flow calculations are performed on the steady-state node carbon potential and the source carbon potential field to obtain the carbon potential balance constraint of the energy hub. Based on the carbon potential balance constraint, the load carbon potential of the three output ports of the energy hub (electric, hydrogen, and heat) is determined.

[0006] In this embodiment, solving the energy flow calculation model to obtain energy flow distribution data specifically includes: The nonlinear power flow equations of the power network in the energy flow calculation model are linearized and decoupled, and the nonlinear energy flow equations of the hydrogen network are piecewise linearized to obtain a linearized energy flow calculation model. The linear programming solver is invoked to solve the linearized energy flow calculation model, thereby obtaining the global energy flow distribution data of the integrated electric-hydrogen-thermal energy system.

[0007] In this embodiment, establishing a sub-model for calculating the carbon emission flow of the power network based on the energy flow distribution data specifically includes: The active power data of each branch of the power network and the injected power data of each node are extracted from the energy flow distribution data to form the node injected power set and the branch power flow set. A node active power flux matrix is ​​constructed using the diagonal elements of the node injected power set, and a branch power flow distribution matrix is ​​constructed using the off-diagonal elements of the branch power flow set. A sub-model for calculating carbon emission flow of the power network is established by combining the inverse matrix of the branch power flow distribution matrix and the active power flux matrix of the nodes.

[0008] In this embodiment, the carbon potential of each node in the power network is constrained and allocated using the carbon emission flow calculation sub-model to obtain the steady-state node carbon potential of the power network, specifically including: Using each node of the power network as the distribution unit, the branch carbon flow density and the corresponding active power of all injection nodes are collected to determine the power proportion of each branch. The carbon flux density of each branch is accumulated by multiplying the product of the corresponding power ratio, and the accumulated result is used as a weighted constraint condition for the nodal carbon potential. The weighted constraints are embedded into the carbon potential balance equations of each node, and the carbon potential balance equations are solved simultaneously to output the steady-state node carbon potential of each node.

[0009] In this embodiment, the carbon flux density of each branch is accumulated by multiplying the product of the corresponding power percentage, and the accumulated result is used as a weighted constraint condition for the nodal carbon potential. Specifically, this includes: The accumulated result is correlated with the carbon potential variable of the node by establishing an equation, forming an algebraic constraint equation with the carbon potential variable as the unknown quantity. The algebraic constraint equations are used as the weighted constraint conditions and combined with the carbon potential balance relationship in the nodal carbon potential matrix equations to update the constraint system of the nodal carbon potential matrix equations.

[0010] In this embodiment, establishing a carbon emission flow calculation sub-model for the hydrogen network based on the energy flow distribution data specifically includes: The hydrogen flow rate data of each pipeline in the hydrogen network and the injection flow rate data of each node are extracted from the energy flow distribution data to form the node injection gas volume set and the pipeline tidal gas volume set. A node airflow matrix is ​​constructed using the diagonal elements of the node injection airflow set, and a pipeline power flow distribution matrix is ​​constructed using the off-diagonal elements of the pipeline power flow airflow set. The carbon emission flow calculation sub-model of the hydrogen network is established by multiplying the pipeline flow distribution matrix with the inverse of the node gas flow matrix and introducing the carbon potential vector injected from the upper-level hydrogen network.

[0011] In this embodiment, the carbon emission flow calculation sub-model linearly transforms the pipe carbon flow density of each pipe in the hydrogen network node based on the node airflow flux matrix and pipe flow distribution matrix in the sub-model, to obtain the source carbon potential field of the hydrogen network. Specifically, this includes: The hydrogen flow rate ratio of each pipeline is extracted from the concentrated flow gas volume of the pipeline. The algebraic transformation equation between the nodal carbon potential and the pipeline carbon flow density is established by using the product of the carbon flow density of each pipeline and the corresponding ratio as the definite solution condition. Using the nodal carbon potential as the variable to be determined, the inverse of the gas flow matrix is ​​multiplied on the left by the product of the pipeline flow distribution matrix and the nodal carbon potential vector, and the carbon potential contribution term of the upper-level hydrogen network is separated to obtain the linear transformation relationship. The carbon potentials of each node in the hydrogen network are arranged globally according to their spatial distribution order to form a spatial distribution sequence of carbon potentials covering the entire network, and the source carbon potential field is output.

[0012] In this embodiment, the carbon potential of each node in the hydrogen network is globally arranged according to its spatial distribution order to form a spatial distribution sequence of carbon potential covering the entire network. This specifically includes: The spatial topological location identifiers of each node in the hydrogen network are extracted from the energy flow distribution data, and the global arrangement order of carbon potential is determined based on the upstream and downstream spatial distribution relationship of each node in the hydrogen network. The carbon potential values ​​of each node are sequentially encoded into a carbon potential sequence according to the global arrangement order, and the carbon potential sequence is used to output a spatial distribution sequence of carbon potential covering all nodes of the hydrogen network.

[0013] In this embodiment, the heat flux calculation of the steady-state node carbon potential and the source carbon potential field to obtain the carbon potential balance constraint of the energy hub specifically includes: Extract the carbon potential value of the energy hub input port from the steady-state node carbon potential and the source carbon potential field, identify the port type of each internal conversion device, and establish the port carbon potential correlation relationship. Based on the aforementioned correlation, the carbon potential transfer path of each conversion device is determined. The input carbon potential and output carbon potential of each carbon potential transfer path are balanced and verified according to the principle of carbon emission flow conservation, and the output carbon potential balance constraint is determined.

[0014] In this embodiment, determining the load carbon potential of the three output ports of the energy hub (electric, hydrogen, and heat) based on the carbon potential balance constraint specifically includes: Based on the carbon potential transfer path and conversion efficiency coefficient of each device in the carbon potential balance constraint, determine the carbon potential balance equation set between the carbon potential of each input port and the load carbon potential of each output port. The carbon potential balance equations are solved according to the energy flow distribution ratio to obtain the load carbon potential value of each output port. The load carbon potential of the corresponding port is then extracted from the load carbon potential value according to the three port types: electric, hydrogen, and thermal.

[0015] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: An energy flow calculation model for an integrated electric-hydrogen-thermal energy system is constructed, and the energy flow calculation model is solved to obtain energy flow distribution data. Based on the energy flow distribution data, a carbon emission flow calculation sub-model for the power network is established. The carbon emission flow calculation sub-model is used to constrain and allocate the nodal carbon potential of each node in the power network to obtain the steady-state nodal carbon potential of the power network. Based on the energy flow distribution data, a carbon emission flow calculation sub-model for the hydrogen network is established. Using the nodal airflow matrix and pipeline power flow distribution matrix in the sub-model, the pipeline carbon flow density of each pipeline injecting into the hydrogen network nodes is linearly transformed to obtain the source carbon potential field of the hydrogen network. Heat flow conversion is performed on the steady-state nodal carbon potential and the source carbon potential field to obtain the carbon potential balance constraint of the energy hub. Based on the carbon potential balance constraint, the load carbon potential of the three output ports of the energy hub (electricity, hydrogen, and heat) is determined.

[0016] Therefore, this application demonstrates that the load carbon potential of the three output ports of the energy hub (electricity, hydrogen, and heat) can be determined according to the carbon potential balance constraint. Specifically, determining the steady-state node carbon potential of the power network yields a unified and stable quantitative result of the unit energy carbon emission intensity of each node in the power grid, thus overcoming the shortcomings of existing technologies that suffer from time-sharing discrete accounting, resulting in fluctuations in grid carbon distribution and the lack of a fixed benchmark for node carbon levels. This feature relies on branch power weighted allocation rules to achieve layer-by-layer balanced carbon emission distribution, eliminating the metering distortion problem caused by direct assignment of carbon flow values ​​from a single line, and fully matching the real-time active power flow transmission path of the power grid. By relying on stable and unchanging node carbon potential values, unified conversion of multi-energy coupling links is achieved, establishing a data link for carbon emission transmission between the power grid and the energy hub, effectively improving the continuity and integrity of electricity-side carbon emission metering within the integrated electricity-hydrogen-heat energy system. Determining the traceability carbon potential field of the hydrogen network generates a continuous spatial carbon potential distribution sequence covering all hydrogen network nodes and pipelines, thus overcoming the shortcomings of existing technologies that neglect bidirectional hydrogen circulation and only calculate hydrogen pipeline carbon content in a single direction, causing traceability failure. This feature relies on the spatial topology of the pipeline network to fully reconstruct the cross-carbon emission transmission patterns brought about by the reciprocating transport of hydrogen, enabling the reverse location of various carbon emission sources along the hydrogen transport path. After the standardized conversion of carbon potential in the hydrogen dimension is completed, it can be used in conjunction with the carbon potential of steady-state nodes on the power side to participate in the solution of carbon balance constraints of energy hubs, thus making up for the shortcoming of full-domain quantification of carbon emissions in hydrogen energy networks.

[0017] In summary, the technical solution adopted in this application can quantitatively allocate the carbon potential of multi-network coupled carbon emissions to improve the integrity of carbon metering results of multi-energy coupled systems. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application 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 for this embodiment of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is an exemplary flowchart of a method for calculating the extended carbon emission flow of an electrohydrogen-thermal energy system under optimal energy flow, provided in this application. Figure 2 This is a flowchart illustrating the process for determining the carbon potential field for tracing sources, provided in this application. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] This application provides a method for calculating the extended carbon emission flow of an electro-hydrogen-thermal energy system under optimal energy flow. The core of this method is to construct an energy flow calculation model for the integrated electro-hydrogen-thermal energy system, solve the energy flow calculation model to obtain energy flow distribution data, establish a carbon emission flow calculation sub-model for the power network based on the energy flow distribution data, and use this sub-model to constrain and allocate the nodal carbon potential of each node in the power network to obtain the steady-state nodal carbon potential of the power network. Based on the energy flow distribution data, a carbon emission flow calculation sub-model for the hydrogen network is established. Using this sub-model, based on the nodal airflow matrix and pipeline power flow distribution matrix in the sub-model, the pipeline carbon flow density of each pipeline injecting into the hydrogen network nodes is linearly transformed to obtain the source carbon potential field of the hydrogen network. Heat flow conversion is performed on the steady-state nodal carbon potential and the source carbon potential field to obtain the carbon potential balance constraint of the energy hub. Based on the carbon potential balance constraint, the load carbon potential of the three output ports of the energy hub (electric, hydrogen, and heat) is determined.

[0022] To better understand the above technical solutions, a detailed description of the technical solutions will be provided below in conjunction with the accompanying drawings and specific embodiments. (Refer to...) Figure 1 As shown in the figure, this is an exemplary flowchart of a method for calculating the extended carbon emission flow of an electrohydrogen-thermal energy system under optimal energy flow, according to an embodiment of this application. The method includes the following steps: In step S1, an energy flow calculation model of the integrated electric-hydrogen-thermal energy system is constructed, and the energy flow calculation model is solved to obtain energy flow distribution data.

[0023] In practical implementation, the energy flow calculation model for the integrated electric-hydrogen-thermal energy system can be constructed in the following way: First, analyze all equipment and pipeline parameters of the three-layer architecture of the system—power network, hydrogen network, and energy hub—and construct an objective function with the goal of minimizing the total system operating cost, integrating three categories of sub-items: upstream energy procurement cost, coupled equipment operation and maintenance cost, and renewable energy abandonment penalty cost. Then, establish linearly decoupled AC power flow constraints for the power network and piecewise linearized airflow constraints for the hydrogen network, limiting branch transmission power, pipeline hydrogen flow, and node voltage and pressure safety boundaries, while simultaneously establishing balance equations for electric-hydrogen node power and hydrogen flow. Supplement with operating characteristic equations for energy hub conversion equipment such as renewable power generation, photocatalytic hydrogen production, electrolyzers, fuel cells, and electric heating. Combine all objective functions with equations and inequalities to form a unified energy flow calculation model. Use a mature linear optimization algorithm for iterative solution, and after convergence, output the optimal flow for each node and branch in the entire network, generating energy flow distribution data.

[0024] It should be noted that the integrated energy system of electricity, hydrogen, and heat refers to an integrated energy operation system consisting of an electric network, a hydrogen network, and an energy hub, which can synergistically supply three types of loads: electricity, hydrogen, and heat, and integrate multi-energy production, transmission, coupling, and conversion. The energy flow calculation model is a unified mathematical optimization model that aims to minimize the system operating cost by combining the steady-state equations of the electricity and hydrogen networks with the constraints of the energy hub equipment to solve for the optimal energy flow across the entire domain.

[0025] In this embodiment, solving the energy flow calculation model to obtain energy flow distribution data can be achieved through the following steps: The nonlinear power flow equations of the power network in the energy flow calculation model are linearized and decoupled, and the nonlinear energy flow equations of the hydrogen network are piecewise linearized to obtain a linearized energy flow calculation model. The linear programming solver is invoked to solve the linearized energy flow calculation model, thereby obtaining the global energy flow distribution data of the integrated electric-hydrogen-thermal energy system.

[0026] In practical implementation, firstly, the original AC power flow equations of the built-in power network in the energy flow calculation model are extracted. Based on the linear decoupling AC power flow processing rules, active and reactive power coupling terms are decomposed, and the nonlinear coupling relationship between voltage amplitude and phase angle is eliminated to generate a linearized power flow constraint equation set. Then, the original nonlinear equations of the hydrogen network airflow are retrieved. Two sets of auxiliary variables are introduced for the node pressure square coupling terms. The pressure value intervals are decomposed according to fixed intervals to complete piecewise linear approximation, generating a linear hydrogen flow constraint equation set. Finally, the linearized power constraints, piecewise linear hydrogen network constraints, steady-state hydraulic and thermal constraints of the thermal network, linear operation constraints of the energy hub, and the system cost objective function are simultaneously integrated, unifying variable dimensions and constraint boundaries to construct a complete linearized energy flow calculation model. Then, all objective functions, equality constraints, and safety boundary inequality constraints of the linearized energy flow calculation model are fully imported into the linear programming solver. The convergence thresholds are set as follows: power residual less than 0.001 MW, hydrogen flow residual less than 0.001 Nm³ / h, and thermal power residual less than 0.001 MW. These threshold values ​​are determined based on the metering accuracy standards for power, hydrogen, and thermal pipeline networks. The solver iteratively optimizes the feasible region using the simplex algorithm, continuously correcting the branch flow and node supply and demand variables until the convergence thresholds are met. The converged energy flow distribution data is extracted. This dataset covers the active and reactive power of all nodes in the power network, branch transmission power, and node voltage amplitude and phase angle; the injection flow and pressure of all nodes in the hydrogen network, and the hydrogen flow in the pipelines; the thermal power and temperature of all nodes in the thermal network, and the heat transfer medium flow in the pipelines; and the power data of all input and output ports of the energy hub, forming complete multi-energy system energy flow distribution data.

[0027] It should be noted that in this application, the nonlinear power flow equation of the power network refers to the nonlinear mathematical constraint equation set of the steady-state power transmission law of the power grid; linear decoupling refers to the processing method of splitting the active and reactive power coupling correlation terms of the power flow, eliminating the cross nonlinear terms of variables, and converting the nonlinear power flow constraint into an independent linear equation set; the nonlinear energy flow equation of the hydrogen network is the nonlinear pipeline flow constraint equation describing the coupling relationship between the pipeline flow and the square of the node pressure in the hydrogen network; piecewise linearization refers to the processing method of dividing the pressure value interval and introducing auxiliary variables to approximately fit the nonlinear pressure-flow relationship of the hydrogen network, and converting the nonlinear airflow constraint into a linear constraint; the linearized energy flow calculation model refers to a unified linear optimization mathematical model that integrates the linear power flow of the power grid, the piecewise linear hydrogen network airflow, and the energy hub constraints, with the goal of minimizing the system operating cost; calling the linear programming solver refers to the standardized operation of loading all constraints and objective functions of the linear optimization model and iteratively calculating the globally optimal energy flow solution based on the simplex algorithm; the global energy flow distribution data refers to the output after the model converges and solves, which includes the global energy operation parameters such as the flow, voltage, pressure, and equipment output of the power and hydrogen network nodes and branches.

[0028] In step S2, a carbon emission flow calculation sub-model of the power network is established based on the energy flow distribution data. The carbon emission flow calculation sub-model is used to constrain and allocate the node carbon potential of each node in the power network to obtain the steady-state node carbon potential of the power network.

[0029] In this embodiment, the carbon emission flow calculation sub-model of the power network based on the energy flow distribution data can be established in the following manner: The active power data of each branch of the power network and the injected power data of each node are extracted from the energy flow distribution data to form the node injected power set and the branch power flow set. A node active power flux matrix is ​​constructed using the diagonal elements of the node injected power set, and a branch power flow distribution matrix is ​​constructed using the off-diagonal elements of the branch power flow set. A sub-model for calculating carbon emission flow of the power network is established by combining the inverse matrix of the branch power flow distribution matrix and the active power flux matrix of the nodes.

[0030] In practical implementation, firstly, the global energy flow distribution data output after convergence of the linear programming solution is retrieved. All operating parameters belonging to the power network dimension are then filtered. Active power data from bidirectional transmission of each power transmission line is extracted by branch number, and net injected power data from the interaction of generation, load, and energy hubs at each node is extracted by node number. The extracted node injected power is then arranged and summarized by node number to form a node injected power set. Similarly, the extracted branch active power flow is organized and summarized according to the branch topology connection order to form a branch power flow set. Next, the net injected active power of each node in the node injected power set is sequentially filled into the corresponding positions on the main diagonal of the matrix, with all other matrix positions set to zero, generating a square node active power flux matrix of the same order, with the matrix order consistent with the total number of power network nodes. Based on the grid topology connection relationship, node pairs with power interaction within the branch power flow set are selected, and the corresponding branch active power flow values ​​are filled into the corresponding non-diagonal row and column positions of the matrix. Pairs of nodes without direct electrical connection are set to zero, generating an equal-dimensional branch power flow distribution matrix. Finally, the active power flux matrix at each node is solved using standard matrix inversion to obtain the corresponding inverse matrix. The determinant of the verification matrix is ​​not zero to ensure the inverse matrix is ​​uniquely solvable. The branch power flow distribution matrix is ​​then multiplied with the obtained inverse matrix to obtain the power transmission coupling coefficient matrix. This coupling coefficient matrix is ​​combined with the carbon emission intensity benchmark vector of the power nodes to construct a power network carbon emission flow calculation sub-model that maps the migration pattern of carbon emissions with active power.

[0031] It should be noted that in this application, active power data refers to the value of active electrical energy transmitted per unit time period by each transmission line of the power network; injected power data refers to the net active power input and output value after superimposing various power sources, loads, and coupling equipment at a single node of the power grid; node injected power set refers to the net injected active power of all nodes in an orderly manner according to the node number of the power grid; branch power flow set refers to the active power flow value of all transmission lines in the order of the branch topology of the power grid; the node active power flux matrix is ​​a basic matrix that quantifies the scale of active power carrying capacity within each node by constructing a square matrix with the injected power of each node as the diagonal element; the branch power flow distribution matrix is ​​a matrix that maps the topological relationship of active power transmission between power grid nodes by constructing a square matrix with the active power of branch interaction filled with off-diagonal elements; and the carbon emission flow calculation sub-model of the power network refers to a special mathematical model that quantifies the migration and distribution law of carbon emission carried by active power in the power grid.

[0032] In this embodiment, the steady-state node carbon potential of the power network is obtained by constraining the allocation of nodal carbon potentials at each node through the carbon emission flow calculation sub-model, which can be achieved through the following steps: Using each node of the power network as the distribution unit, the branch carbon flow density and the corresponding active power of all injection nodes are collected to determine the power proportion of each branch. The carbon flux density of each branch is accumulated by multiplying the product of the corresponding power ratio, and the accumulated result is used as a weighted constraint condition for the nodal carbon potential. The weighted constraints are embedded into the carbon potential balance equations of each node, and the carbon potential balance equations are solved simultaneously to output the steady-state node carbon potential of each node.

[0033] In practical implementation, firstly, all nodes of the power network are used as carbon potential allocation units, and the carbon potential of each node is set as the variable to be solved. First, the carbon flow density values ​​of all incoming branches output by the carbon emission flow calculation sub-model are retrieved (as known inputs), and the active power of the corresponding transmission branches within the energy flow distribution data is matched synchronously. For each power network injection node, the active power of all injection branches of that node is summed to obtain the total input active power of the node. The active power of a single branch is divided by the total input active power of the node to obtain the power proportion corresponding to that branch. Then, the branch carbon flow density and branch power proportion of each injection branch are read one by one, and the two are multiplied to obtain the weighted carbon contribution of a single branch. All injection branches of the node are traversed, and all weighted carbon contributions are accumulated. The accumulated total weighted carbon contribution is used as the weighted constraint condition for the carbon potential of that node. Next, an independent carbon potential balance equation is constructed for each power network node. The weighted constraint conditions are written as fixed constants on the right side of the equation. The left side of the balance equation includes the node output carbon potential variable and the local power source's native carbon emission intensity variable (if any). The carbon potential balance equations of all nodes in the entire network are integrated to form a system of linear equations about the node carbon potential. The Gaussian elimination method is used to solve the system of equations simultaneously. The iteration continues until the carbon potential values ​​of all nodes converge and stabilize. The carbon potential values ​​of all nodes after convergence are extracted, and the steady-state node carbon potential of the entire power network is output.

[0034] It should be noted that, in this application, a power grid injection node refers to a grid node that receives power and carbon emission inputs from multiple external transmission branches; branch carbon flux density refers to the carbon emission flux carried per unit of active power of a single transmission branch; node carbon potential characteristic refers to the carbon emission level per unit power formed after the node aggregates all input branch carbon emissions; constraint allocation refers to the carbon allocation calculation mechanism that limits the boundary of the node carbon potential solution; the power proportion of each branch refers to the ratio of the active power of a single injection branch to the total input active power of the node; weighted constraint condition refers to a fixed value obtained by multiplying and accumulating the branch carbon flux density and the power proportion; carbon potential balance relationship refers to the equivalent correspondence between the node's input carbon emissions and output carbon potential; steady-state node carbon potential refers to the carbon emission intensity corresponding to a unit of active power of the grid node.

[0035] In addition, in this embodiment, the product of the carbon flux density of each branch and the corresponding power percentage is accumulated, and the accumulated result is used as a weighted constraint condition for the nodal carbon potential. This can be achieved by the following steps: The accumulated result is correlated with the carbon potential variable of the node by establishing an equation, forming an algebraic constraint equation with the carbon potential variable as the unknown quantity. The algebraic constraint equations are used as the weighted constraint conditions and combined with the carbon potential balance relationship in the nodal carbon potential matrix equations to update the constraint system of the nodal carbon potential matrix equations.

[0036] In practice, firstly, a fixed value obtained by multiplying and accumulating the carbon flow density of the branch and the power ratio is extracted. This value is a constant term with no unknowns. The carbon potential variable of the corresponding power network node is selected as the only parameter to be solved in the equation. Based on the carbon emission conservation rule, an equation is constructed, making the weighted cumulative constant term directly equal to the node carbon potential variable, thus constructing a univariate linear algebraic equation. The units on both sides of the equation are verified to be consistent, ensuring that the units of the carbon flow density and the node carbon potential are completely matched. Irrelevant interference parameters in the equation are eliminated, retaining only the fixed cumulative constant and the unknown carbon potential, generating an algebraic constraint equation. Then, the pre-constructed global node carbon potential matrix equation is retrieved, where each row of the matrix corresponds to the original carbon potential balance relationship of a single node, and the matching matrix row vector of the current node is located. Retain all coefficients and constants of the original carbon potential balance equation for that row, and combine the algebraic constraint equation generated in the previous step as a weighted constraint condition with the original carbon potential balance equation (i.e., add constraint terms of the algebraic constraint equation to the corresponding row of the matrix). Together, they constitute the updated constraint system for that node, maintaining the matrix row and column dimensions and the node index order to adapt to the adjustment. Traverse all grid nodes to complete row-by-row replacement, and update the entire matrix equation constraint system. The updated set of matrix equations is the weighted constraint condition for the node carbon potential adapted to the power weighted allocation rule.

[0037] It should be noted that, in this application, the carbon potential variable refers to the unknown parameter of the carbon emission intensity per unit of active power to be solved at a single node; the algebraic constraint equation refers to the univariate linear equation formed by the association of the weighted cumulative constant and the carbon potential variable equation.

[0038] In step S3, a carbon emission flow calculation sub-model for the hydrogen network is established based on the energy flow distribution data. The carbon emission flow calculation sub-model is used to linearly transform the carbon flow density of each pipe injected into the hydrogen network node according to the node airflow flow matrix and the pipe flow distribution matrix in the sub-model, so as to obtain the source carbon potential field of the hydrogen network.

[0039] In this embodiment, the carbon emission flow calculation sub-model of the hydrogen network based on the energy flow distribution data can be achieved by the following steps: The hydrogen flow rate data of each pipeline in the hydrogen network and the injection flow rate data of each node are extracted from the energy flow distribution data to form the node injection gas volume set and the pipeline tidal gas volume set. A node airflow matrix is ​​constructed using the diagonal elements of the node injection airflow set, and a pipeline power flow distribution matrix is ​​constructed using the off-diagonal elements of the pipeline power flow airflow set. The carbon emission flow calculation sub-model of the hydrogen network is established by multiplying the pipeline flow distribution matrix with the inverse of the node gas flow matrix and introducing the carbon potential vector injected from the upper-level hydrogen network.

[0040] In practical implementation, firstly, the global energy flow distribution data output after solving the linearized energy flow calculation model is retrieved. All operating parameters belonging to the hydrogen network are then selected. Bidirectional hydrogen flow data for each pipeline is extracted according to its pipeline number. Net injection flow data, combining hydrogen production, storage, and purchased hydrogen, is extracted according to the node number. The net injection gas volume of all nodes is then systematically aggregated by node number to form a node injection gas volume set. The pipeline hydrogen flow is then regularized into a pipeline tidal gas volume set based on the pipeline topology connection order. The net injection hydrogen flow of each node within the node injection gas volume set is then filled sequentially to the corresponding positions on the main diagonal of the square matrix. All other matrix positions are set to zero, generating a square node gas flow matrix of the same order as the total number of hydrogen network nodes. Based on the pipeline topology connection relationship, node pairs with hydrogen interaction within the pipeline tidal gas volume set are selected. The corresponding pipeline hydrogen flow is filled to the non-diagonal rows and columns of the matrix. Node pairs without direct pipeline connections are set to zero, generating a pipeline tidal distribution matrix with the same dimension as the former. Finally, the standard matrix inversion operation is performed on the nodal gas flow matrix. The non-zero determinant of the verification matrix ensures the unique solvability of the inverse matrix. Matrix multiplication is then performed between the pipeline flow distribution matrix and the solved inverse matrix to obtain the hydrogen transport coupling coefficient matrix. The carbon emission intensity value corresponding to the purchased hydrogen from the upstream network is retrieved and regularized according to the hydrogen network node order to form the upstream hydrogen network injection carbon potential vector. The coupling coefficient matrix and this carbon potential vector are then combined to construct a hydrogen network carbon emission flow calculation sub-model that can quantify the carbon emission migration and distribution patterns during hydrogen transport.

[0041] It should be noted that in this application, hydrogen flow data refers to the volume of hydrogen transported per unit time period by each pipeline in the hydrogen network; injection flow data refers to the net hydrogen input and output values ​​after the superposition of hydrogen production, storage, and purchased hydrogen at a single node in the hydrogen network; node injection gas volume set refers to the net injected hydrogen flow of all nodes in an orderly manner according to the node number of the hydrogen network; pipeline tidal gas volume set refers to the hydrogen flow values ​​of all pipelines in the order of the hydrogen network pipeline topology; node gas flow matrix refers to a square matrix constructed with the net injected hydrogen flow of each node as the diagonal elements; pipeline tidal distribution matrix refers to a square matrix constructed by filling off-diagonal elements with the pipeline interactive hydrogen flow; upper-level hydrogen network injection carbon potential vector refers to a one-dimensional vector composed of the carbon emission intensity of purchased hydrogen at each node; and carbon emission flow calculation sub-model refers to a dedicated mathematical model of the carbon emission migration and distribution law in the hydrogen transportation process.

[0042] Preferably, in this embodiment, the carbon emission flow calculation sub-model linearly transforms the pipe carbon flow density of each pipe in the hydrogen network node based on the node airflow flux matrix and pipe flow distribution matrix in the sub-model to obtain the source carbon potential field of the hydrogen network, referencing... Figure 2 As shown in the figure, this is a schematic flowchart of determining the source carbon potential field in some embodiments of this application. In this embodiment, determining the source carbon potential field can be achieved by the following steps: In step S31, the hydrogen flow rate ratio of each pipeline is extracted from the pipeline tidal gas flow rate concentration. The cumulative sum of the carbon flow density of each pipeline and the corresponding ratio is used as the definite solution condition to establish the algebraic transformation equation between the nodal carbon potential and the pipeline carbon flow density. In step S32, with the nodal carbon potential as the variable to be determined, the inverse matrix of the gas flow matrix is ​​multiplied on the left by the product of the pipeline flow distribution matrix and the nodal carbon potential vector, and the carbon potential contribution term of the upper-level hydrogen network is separated to obtain the linear transformation relationship. In step S33, the carbon potential of each node in the hydrogen network is arranged globally according to the spatial distribution order to form a carbon potential spatial distribution sequence covering the entire network, and the source carbon potential field is output.

[0043] In specific implementation, firstly, the pipeline flow distribution matrix and node airflow matrix generated during the establishment of the aforementioned hydrogen network carbon emission flow calculation sub-model are retrieved. The off-diagonal elements of the pipeline flow distribution matrix are filled with the hydrogen flow rate of each pipeline in the hydrogen network from the energy flow distribution data. Its row and column numbers correspond to the first and last node numbers of the pipeline, respectively, used to map the topological coupling relationship of hydrogen transport between hydrogen network nodes. The diagonal elements of the node airflow matrix are filled with the injected hydrogen flow rate of each node in the hydrogen network from the energy flow distribution data. Its matrix order is consistent with the total number of hydrogen network nodes, used to quantify the injection scale of local gas sources or purchased hydrogen for each node. The standardized pipeline flow gas flow set is retrieved, and the hydrogen flow rate of all connected pipelines is collected for a single hydrogen network injection node. The single pipeline flow rate is divided by the total inflow hydrogen flow rate of the node to obtain the hydrogen flow rate percentage. The carbon flow density of each pipeline is multiplied by the corresponding hydrogen flow rate percentage. The results of the calculations for all pipelines are summed to obtain a fixed constant, which is set as the boundary condition for the system of equations. Based on the hydrogen carbon emission conservation rule, the cumulative constant is set equal to the unknown nodal carbon potential, and a univariate algebraic transformation equation is constructed that includes only pipeline carbon flow density, flow rate ratio, and nodal carbon potential variables. Then, all nodal carbon potential variables in the entire network are arranged in order of node number to generate nodal carbon potential vectors. A matrix multiplication operation is performed between the solved pipeline flow distribution matrix and the nodal carbon potential vectors. The inverse matrix of the pre-calculated nodal gas flow matrix is ​​retrieved, and the inverse matrix is ​​multiplied on the left by the aforementioned product. The external carbon emission component corresponding to purchased hydrogen in the calculation formula is split as the carbon potential contribution term of the upper-level hydrogen network, and the remaining part is the carbon potential transfer component corresponding to the internal hydrogen circulation in the pipeline network. The two types of components after integration and splitting are arranged into a standard linear equation to form a linear transformation relationship; the linear transformation relationship of the hydrogen network carbon potential can also be determined by the following formula: , Represents the carbon potential vector of a node in a hydrogen network; It is the inverse matrix of the nodal airflow matrix; It is the pipeline power flow distribution matrix; This is the carbon potential contribution vector of the upper-level hydrogen network. Finally, after solving the linear transformation relationship simultaneously, the carbon potential values ​​of all nodes in the entire hydrogen network that converge and stabilize are read. The node carbon potential values ​​are then sorted sequentially according to the geographical topology of the hydrogen network. Carbon potential gradient interpolation data between adjacent nodes is supplemented according to pipeline connectivity, constructing a continuous and complete spatial distribution sequence of carbon potential. This sequence fully records the quantified carbon potential values ​​corresponding to each pipeline node and pipeline segment, completely covering the entire spatial range of the hydrogen network. This ordered spatial sequence is then integrated and output to obtain a hydrogen network source-tracing carbon potential field with carbon emission source tracing capabilities.

[0044] It should be noted that, in this application, the injected hydrogen network node refers to a hydrogen network node that receives hydrogen from multiple pipelines and corresponding carbon emission inputs; the pipeline carbon flow density refers to the carbon emission flux carried by hydrogen per unit volume in the hydrogen pipeline; the matrix in the carbon emission flow calculation sub-model refers to the matrix of the bidirectional hydrogen interaction flow coupling relationship between the hydrogen network pipelines; linear transformation refers to the standardized calculation process of establishing an equal mapping between pipeline carbon flow and node carbon potential by performing linear transformations on the matrix in the carbon emission flow calculation sub-model based on matrix algebra operations; hydrogen flow ratio refers to the ratio of the hydrogen flow of a single input pipeline to the total inflow hydrogen flow to the node; the boundary condition refers to the fixed constant obtained by multiplying and accumulating the pipeline carbon flow density and the hydrogen flow ratio, which limits the unique solution of the algebraic transformation equation; the algebraic transformation equation refers to the basic formula for directly converting the pipeline carbon flow density and node carbon potential values ​​based on the equation constructed by the conservation of hydrogen carbon emissions; pipeline flow distribution... The layout matrix refers to a square matrix constructed by filling off-diagonal elements with hydrogen flow rates from pipeline interactions, mapping the topological coupling relationship of hydrogen transport between nodes in the hydrogen network; the node carbon potential vector refers to a one-dimensional column vector formed by aggregating all unsolved carbon potential variables of all nodes in the hydrogen network, serving as the set of unknowns for linear transformation operations; the carbon potential contribution term of the upper-level hydrogen network refers to the separated external carbon emission component of purchased hydrogen, separately representing the basic carbon potential value brought by the input of the upper-level hydrogen network; the linear transformation relationship refers to the standard linear equation obtained by left multiplication of the matrix and component splitting and rearranging, characterizing the linear correlation between hydrogen circulation and node carbon potential; the carbon potential of each node in the hydrogen network refers to the carbon emission intensity corresponding to a unit volume of hydrogen in a single node of the hydrogen network; the carbon potential spatial distribution sequence refers to the set of carbon potential values ​​of all nodes arranged in an ordered manner according to the spatial topological order of the hydrogen network; the source-tracing carbon potential field refers to the global carbon potential distribution system constructed based on the carbon potential spatial distribution sequence, which can trace the carbon emission source backward along the hydrogen transport path.

[0045] In addition, in this embodiment, the carbon potential of each node in the hydrogen network is globally arranged according to its spatial distribution order to form a spatial distribution sequence of carbon potential covering the entire network. This can be achieved by the following steps: The spatial topological location identifiers of each node in the hydrogen network are extracted from the energy flow distribution data, and the global arrangement order of carbon potential is determined based on the upstream and downstream spatial distribution relationship of each node in the hydrogen network. The carbon potential values ​​of each node are sequentially encoded into a carbon potential sequence according to the global arrangement order, and the carbon potential sequence is used to output a spatial distribution sequence of carbon potential covering all nodes of the hydrogen network.

[0046] In practical implementation, firstly, the solved energy flow distribution data is retrieved, and the spatial topology location identifiers of all nodes in the hydrogen network embedded in the dataset are selected. These identifiers synchronously record the node's planar coordinates and pipeline connectivity information. Based on the steady-state hydrogen transport direction, each node is divided into upstream and downstream levels. Upstream nodes are hydrogen source input nodes, and downstream nodes are load and energy storage consumption nodes. Adjacent nodes connected by pipelines are grouped into the same level. The nodes are arranged according to the hierarchical order of transmission from source to load, distinguishing the arrangement order of parallel branch nodes to form a global carbon potential arrangement order adapted to the spatial orientation of the pipeline network. Then, the carbon potential values ​​of all nodes obtained after the linear transformation relationship is solved and converged are read. A mapping data table is established that corresponds one-to-one with node numbers and carbon potential values. The node numbers corresponding to each position in the global arrangement order are traversed, and the matching node carbon potential values ​​are retrieved from the mapping data table and sequentially filled into a one-dimensional ordered array to form a carbon potential sequence.

[0047] It should be noted that in this application, spatial topology location identifier refers to the identifier parameter that records the coordinates of hydrogen network nodes and their connection with pipelines; upstream and downstream spatial distribution relationship refers to the pipeline spatial logic that distinguishes gas source input nodes from end-consumer nodes; global arrangement order refers to the node sorting rules determined according to the upstream and downstream levels of the hydrogen network and their connection with pipelines; node carbon potential value refers to the carbon emission intensity corresponding to a unit volume of hydrogen at a single hydrogen network node.

[0048] In step S4, heat flow is calculated on the steady-state node carbon potential and the source carbon potential field to obtain the carbon potential balance constraint of the energy hub. Based on the carbon potential balance constraint, the load carbon potential of the three output ports of the energy hub (electric, hydrogen, and heat) is determined.

[0049] In this embodiment, the carbon potential balance constraint of the energy hub can be obtained by performing heat flux calculation on the steady-state node carbon potential and the source carbon potential field using the following steps: Extract the carbon potential value of the energy hub input port from the steady-state node carbon potential and the source carbon potential field, identify the port type of each internal conversion device, and establish the port carbon potential correlation relationship. Based on the aforementioned correlation, the carbon potential transfer path of each conversion device is determined. The input carbon potential and output carbon potential of each carbon potential transfer path are balanced and verified according to the principle of carbon emission flow conservation, and the output carbon potential balance constraint is applied.

[0050] In practical implementation, the operating parameters of the thermal network are first extracted from the energy flow distribution data, including: the thermal power of each thermal node, the supply and return water temperatures, the flow rate and thermal power of each pipeline heat medium, and the input thermal power of the energy hub's thermal input ports. Simultaneously, the input power data of each electrical input port and hydrogen input port of the energy hub are extracted from the energy flow distribution data as the energy flow boundary conditions for heat flow conversion. Then, all numerical data of the carbon potential of the steady-state nodes of the power network and the traceable carbon potential field of the hydrogen network are read to locate the two types of input ports of the energy hub connecting to the power grid and the hydrogen network, and to extract the matching electrical and hydrogen dimension carbon potential values ​​for the ports. Furthermore, based on the fuel type and consumption of the heat source equipment in the thermal network, the baseline carbon emission intensity of the thermal network injection nodes is determined as the heat source boundary conditions for the carbon potential conversion of the heat ports. Finally, all conversion equipment within the energy hub, including hydrogen fuel cells, electro-hydrogen production, and electro-heat production, is traversed, distinguishing between three types of ports: electrical input ports, hydrogen input ports, and thermal output ports. A mapping and pairing system is established between the carbon potential at the input port and the equivalent carbon potential at the output port of the equipment, based on the fixed energy conversion efficiency of the equipment. The thermal power is corrected by incorporating the heat network transmission loss coefficient, and the dimensions of electricity, hydrogen, and heat are unified to form the port carbon potential correlation. Then, the carbon potential transfer path from the input port to the output port of each conversion device is systematically analyzed according to the port carbon potential correlation, distinguishing between single-energy direct supply paths and multi-energy coupled conversion paths. Using the conservation of total system carbon emissions as the core criterion, the input port carbon potential is multiplied by the corresponding energy input power to obtain the total input carbon amount, and the equivalent carbon potential of each output port of the equipment is multiplied by the output power to obtain the total output carbon amount. The total output carbon amount of the heat energy output port needs to be superimposed with the basic carbon emissions injected by the heat source at each node of the thermal network. The two sets of carbon amounts are compared to complete the balance check. An equation is constructed by setting the total input carbon amount equal to the total output carbon amount, and all equations of the conversion devices are integrated to form a complete set of constraints.

[0051] It should be noted that in this application, heat flux conversion refers to a standardized processing method to eliminate the dimensional differences in carbon potential among multiple energy sources; an energy hub refers to a core device that integrates electro-hydrogen-thermal conversion equipment, completes multi-energy input coupling, internal allocation, and supplies loads to external sources through its ports; the carbon potential value of the input port refers to the unit energy carbon emission intensity corresponding to the input port of the energy hub connected to the power grid and hydrogen grid; the port carbon potential correlation refers to the mapping rule that establishes a one-to-one correspondence between the input port carbon potential and the equivalent carbon potential on the output side of the equipment based on the energy efficiency of the conversion equipment; the carbon potential transfer path refers to the directional transmission link in which carbon potential flows with electricity and hydrogen between conversion equipment within the energy hub; the balance verification refers to the verification process that compares the total input carbon amount and the total output carbon amount of the conversion equipment based on the carbon emission conservation rule to verify the self-consistency of the carbon transfer logic; and the carbon potential balance constraint refers to the set of equation constraints that ensure the multi-energy conversion links within the energy hub meet the total carbon emission conservation.

[0052] In this embodiment, determining the load carbon potential of the three output ports of the energy hub (electric, hydrogen, and heat) based on the carbon potential balance constraint can be achieved through the following steps: Based on the carbon potential transfer path and conversion efficiency coefficient of each device in the carbon potential balance constraint, determine the carbon potential balance equation set between the carbon potential of each input port and the load carbon potential of each output port. The carbon potential balance equations are solved according to the energy flow distribution ratio to obtain the load carbon potential value of each output port. The load carbon potential of the corresponding port is then extracted from the load carbon potential value according to the three port types: electric, hydrogen, and thermal.

[0053] In practice, firstly, the carbon potential transfer paths of all devices recorded in the carbon potential balance constraint are traversed, and the fixed conversion efficiency coefficients corresponding to hydrogen fuel cells, electric hydrogen production, and electric heat production equipment are extracted. These coefficients are taken from the steady-state operating parameters of the energy hub, and their value range is determined by the rated operating conditions of the equipment. The equivalent carbon potential after conversion of the electricity and hydrogen input ports is used as a known constant term in the equations, and the load carbon potential of the electricity output port, hydrogen output port, and heat output port is set as an unknown variable to be solved. Based on the carbon emission conservation rule, an equation is established to equalize the input carbon quantity and the output carbon quantity along each carbon potential transfer path. The independent equations corresponding to all conversion devices are integrated, the variable dimensions and units are unified, and a complete set of carbon potential balance equations is formed. Then, the energy flow distribution ratio of each output port inside the energy hub is retrieved from the energy flow distribution data record. This ratio represents the ratio of the energy power supplied by each port to the total output power, and is used as a weight constraint condition for solving the equations. The Gaussian elimination method was used to solve the carbon potential balance equations linearly. Iterative calculations were performed until the residuals of the equations converged to the allowable threshold for engineering measurement. After the solution was completed, the load carbon potential values ​​corresponding to all output ports were obtained. A mapping relationship between port type and carbon potential value was established. Ports were classified and screened according to three categories: electrical energy output, hydrogen output, and thermal energy output. The load carbon potential of each category was extracted as an independent output.

[0054] It should be noted that, in this application, the carbon potential transfer path of the equipment refers to the directional transmission link through which the carbon potential flows with the electric and hydrogen media through the energy hub conversion equipment to complete the energy form switching; the conversion efficiency coefficient refers to the fixed value of the energy conversion efficiency of various coupling devices inside the energy hub; the carbon potential balance equation system refers to the linear equation system formed by the simultaneous solution of multiple carbon potential transfer path conservation equations; the energy flow distribution ratio refers to the ratio of the output energy power of each output port of the energy hub to the total output power of the system; the load carbon potential value refers to the quantitative value obtained after the convergence solution of the carbon potential balance equation system; and the load carbon potential refers to the unit energy carbon emission intensity index specific to the electric, hydrogen, and heat output ports of the energy hub.

[0055] Therefore, this application demonstrates that the load carbon potential of the three output ports of the energy hub (electricity, hydrogen, and heat) can be determined according to the carbon potential balance constraint. Specifically, determining the steady-state node carbon potential of the power network yields a unified and stable quantitative result of the unit energy carbon emission intensity of each node in the power grid, thus overcoming the shortcomings of existing technologies that suffer from time-sharing discrete accounting, resulting in fluctuations in grid carbon distribution and the lack of a fixed benchmark for node carbon levels. This feature relies on branch power weighted allocation rules to achieve layer-by-layer balanced carbon emission distribution, eliminating the metering distortion problem caused by direct assignment of carbon flow values ​​from a single line, and fully matching the real-time active power flow transmission path of the power grid. By relying on stable and unchanging node carbon potential values, unified conversion of multi-energy coupling links is achieved, establishing a data link for carbon emission transmission between the power grid and the energy hub, effectively improving the continuity and integrity of electricity-side carbon emission metering within the integrated electricity-hydrogen-heat energy system. Determining the traceability carbon potential field of the hydrogen network generates a continuous spatial carbon potential distribution sequence covering all hydrogen network nodes and pipelines, thus overcoming the shortcomings of existing technologies that neglect bidirectional hydrogen circulation and only calculate hydrogen pipeline carbon content in a single direction, causing traceability failure. This feature relies on the spatial topology of the pipeline network to fully reconstruct the cross-carbon emission transmission patterns brought about by the reciprocating transport of hydrogen, enabling the reverse location of various carbon emission sources along the hydrogen transport path. After the standardized conversion of carbon potential in the hydrogen dimension is completed, it can be used in conjunction with the carbon potential of steady-state nodes on the power side to participate in the solution of carbon balance constraints of energy hubs, thus making up for the shortcoming of full-domain quantification of carbon emissions in hydrogen energy networks.

[0056] In summary, the technical solution adopted in this application can quantitatively allocate the carbon potential of multi-network coupled carbon emissions to improve the integrity of carbon metering results of multi-energy coupled systems.

[0057] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. 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 apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0058] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compactdisc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0059] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

Claims

1. A method for calculating the extended carbon emission flow of an electrohydrogen-thermal energy system under optimal energy flow, characterized in that, The method includes: An energy flow calculation model for an integrated electric-hydrogen-thermal energy system is constructed, and the energy flow calculation model is solved to obtain energy flow distribution data. A carbon emission flow calculation sub-model for the power network is established based on the energy flow distribution data. The carbon emission flow calculation sub-model is used to constrain and allocate the nodal carbon potential of each node in the power network to obtain the steady-state nodal carbon potential of the power network. A carbon emission flow calculation sub-model for the hydrogen network is established based on the energy flow distribution data. The carbon emission flow calculation sub-model is used to linearly transform the carbon flow density of each pipe injected into the hydrogen network node according to the node airflow flow matrix and the pipe flow distribution matrix in the sub-model, so as to obtain the source carbon potential field of the hydrogen network. Heat flow calculations are performed on the steady-state node carbon potential and the source carbon potential field to obtain the carbon potential balance constraint of the energy hub. Based on the carbon potential balance constraint, the load carbon potential of the three output ports of the energy hub (electric, hydrogen, and heat) is determined.

2. The method for calculating the extended carbon emission flow of an electro-hydrogen-thermal energy system under optimal energy flow as described in claim 1, characterized in that, Solving the energy flow calculation model yields energy flow distribution data, specifically including: The nonlinear power flow equations of the power network in the energy flow calculation model are linearized and decoupled, and the nonlinear energy flow equations of the hydrogen network are piecewise linearized to obtain a linearized energy flow calculation model. The linear programming solver is invoked to solve the linearized energy flow calculation model, thereby obtaining the global energy flow distribution data of the integrated electric-hydrogen-thermal energy system.

3. The method for calculating the extended carbon emission flow of an electro-hydrogen-thermal energy system under optimal energy flow as described in claim 1, characterized in that, The specific steps for establishing a carbon emission flow calculation sub-model for the power network based on the energy flow distribution data include: The active power data of each branch of the power network and the injected power data of each node are extracted from the energy flow distribution data to form the node injected power set and the branch power flow set. A node active power flux matrix is ​​constructed using the diagonal elements of the node injected power set, and a branch power flow distribution matrix is ​​constructed using the off-diagonal elements of the branch power flow set. A sub-model for calculating carbon emission flow of the power network is established by combining the inverse matrix of the branch power flow distribution matrix and the active power flux matrix of the nodes.

4. The method for calculating the extended carbon emission flow of an electro-hydrogen-thermal energy system under optimal energy flow as described in claim 1, characterized in that, The carbon emission flow calculation sub-model is used to constrain the nodal carbon potential of each node in the power network, resulting in the steady-state nodal carbon potential of the power network, specifically including: Using each node of the power network as the distribution unit, the branch carbon flow density and the corresponding active power of all injection nodes are collected to determine the power proportion of each branch. The carbon flux density of each branch is accumulated by multiplying the product of the corresponding power ratio, and the accumulated result is used as a weighted constraint condition for the nodal carbon potential. The weighted constraints are embedded into the carbon potential balance equations of each node, and the carbon potential balance equations are solved simultaneously to output the steady-state node carbon potential of each node.

5. The method for calculating the extended carbon emission flow of an electro-hydrogen-thermal energy system under optimal energy flow as described in claim 4, characterized in that, The carbon flux density of each branch is multiplied by its corresponding power percentage, and the accumulated result is used as a weighted constraint condition for the nodal carbon potential. Specifically, this includes: The accumulated result is correlated with the carbon potential variable of the node by establishing an equation, forming an algebraic constraint equation with the carbon potential variable as the unknown quantity. The algebraic constraint equations are used as the weighted constraint conditions and combined with the carbon potential balance relationship in the nodal carbon potential matrix equations to update the constraint system of the nodal carbon potential matrix equations.

6. The method for calculating the extended carbon emission flow of an electro-hydrogen-thermal energy system under optimal energy flow as described in claim 1, characterized in that, The specific sub-model for calculating carbon emission flow in the hydrogen network based on the energy flow distribution data includes: The hydrogen flow rate data of each pipeline in the hydrogen network and the injection flow rate data of each node are extracted from the energy flow distribution data to form the node injection gas volume set and the pipeline tidal gas volume set. A node airflow matrix is ​​constructed using the diagonal elements of the node injection airflow set, and a pipeline power flow distribution matrix is ​​constructed using the off-diagonal elements of the pipeline power flow airflow set. The carbon emission flow calculation sub-model of the hydrogen network is established by multiplying the pipeline flow distribution matrix with the inverse of the node gas flow matrix and introducing the carbon potential vector injected from the upper-level hydrogen network.

7. The method for calculating the extended carbon emission flow of an electro-hydrogen-thermal energy system under optimal energy flow as described in claim 1, characterized in that, Based on the nodal airflow matrix and pipeline flow distribution matrix in the carbon emission flow calculation sub-model, the pipeline carbon flow density of each pipeline in the hydrogen injection network node is linearly transformed to obtain the source carbon potential field of the hydrogen network, specifically including: The hydrogen flow rate ratio of each pipeline is extracted from the concentrated flow gas volume of the pipeline. The algebraic transformation equation between the nodal carbon potential and the pipeline carbon flow density is established by using the product of the carbon flow density of each pipeline and the corresponding ratio as the definite solution condition. Using the nodal carbon potential as the variable to be determined, the inverse of the gas flow matrix is ​​multiplied on the left by the product of the pipeline flow distribution matrix and the nodal carbon potential vector, and the carbon potential contribution term of the upper-level hydrogen network is separated to obtain the linear transformation relationship. The carbon potentials of each node in the hydrogen network are arranged globally according to their spatial distribution order to form a spatial distribution sequence of carbon potentials covering the entire network, and the source carbon potential field is output.

8. The method for calculating the extended carbon emission flow of an electro-hydrogen-thermal energy system under optimal energy flow as described in claim 7, characterized in that, The carbon potential of each node in the hydrogen network is globally arranged according to its spatial distribution order, forming a spatial distribution sequence of carbon potential covering the entire network. Specifically, this includes: The spatial topological location identifiers of each node in the hydrogen network are extracted from the energy flow distribution data, and the global arrangement order of carbon potential is determined based on the upstream and downstream spatial distribution relationship of each node in the hydrogen network. The carbon potential values ​​of each node are sequentially encoded into a carbon potential sequence according to the global arrangement order, and the carbon potential sequence is used to output a spatial distribution sequence of carbon potential covering all nodes of the hydrogen network.

9. The method for calculating the extended carbon emission flow of an electrohydrogen-thermal energy system under optimal energy flow as described in claim 1, characterized in that, The heat flux calculation of the steady-state node carbon potential and the source carbon potential field yields the carbon potential balance constraints of the energy hub, specifically including: Extract the carbon potential value of the energy hub input port from the steady-state node carbon potential and the source carbon potential field, identify the port type of each internal conversion device, and establish the port carbon potential correlation relationship. Based on the aforementioned correlation, the carbon potential transfer path of each conversion device is determined. The input carbon potential and output carbon potential of each carbon potential transfer path are balanced and verified according to the principle of carbon emission flow conservation, and the output carbon potential balance constraint is determined.

10. The method for calculating the extended carbon emission flow of an electro-hydrogen-thermal energy system under optimal energy flow as described in claim 1, characterized in that, The specific load carbon potentials of the three output ports of the energy hub (electric, hydrogen, and heat) determined according to the aforementioned carbon potential balance constraints include: Based on the carbon potential transfer path and conversion efficiency coefficient of each device in the carbon potential balance constraint, determine the carbon potential balance equation set between the carbon potential of each input port and the load carbon potential of each output port. The carbon potential balance equations are solved according to the energy flow distribution ratio to obtain the load carbon potential value of each output port. The load carbon potential of the corresponding port is then extracted from the load carbon potential value according to the three port types: electric, hydrogen, and thermal.

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