A method and device for drawing a spatial carbon entropy distribution map of a global multi-energy flow network

Abstract

The application discloses a global multi-energy flow network spatial carbon entropy distribution map drawing method and device, relates to the technical field of low-carbon power, and utilizes a target function in combination with equation constraints and inequality constraints to construct a multi-energy collaborative optimization scheduling model; global optimization is performed on system parameters by using the multi-energy collaborative optimization scheduling model, based on a solving result, effective inequality constraints are extracted to construct an effective constraint vector function; a multi-energy coupling Jacobian matrix is constructed based on the effective constraint vector function, and the multi-energy coupling Jacobian matrix is used to determine a power response sensitivity vector; based on a carbon emission coefficient vector and the power response sensitivity vector, node marginal carbon intensity is determined to draw a global multi-energy flow network spatial carbon entropy distribution map, in an ideal operating condition without taking into account pipe network transmission physical loss and random fluctuations on both sides of the source and load, the node marginal carbon intensity of multi-energy cross-medium conversion coupling effect is accurately quantified, and global carbon footprint visualization tracing is realized.

Description

Technical Field

[0001] This invention relates to the field of low-carbon power technology, and in particular to a method and apparatus for drawing spatial carbon entropy distribution maps of a global multi-energy flow network. Background Technology

[0002] In multi-energy coupled integrated energy systems, existing carbon emission measurement and allocation methods are often based on the principles of steady-state power flow tracking and proportional sharing, focusing on the calculation of regional average carbon emissions after the fact. It is difficult to accurately reveal and quantify the dynamic marginal impact of small increases in spatial node loads on the carbon emissions of the entire system under the framework of optimized scheduling.

[0003] Existing solutions for carbon emission measurement and marginal intensity calculation in integrated energy systems mainly fall into two categories, neither of which can meet the requirements of the aforementioned underlying benchmark measurement: The first category is based on the traditional carbon emission flow theory based on the principles of steady-state power flow tracking and proportional sharing. This approach cannot reveal the marginal mutation effect caused by the micro-increase in node load under the global joint optimization scheduling framework, and it is difficult to generate a real-time marginal carbon price signal to guide demand-side response. The second category is based on the traditional marginal emission factor assessment method based on isolated single network measurement or physical boundary decoupling. This method ignores the coupling compensation effect generated by energy conversion hubs in cross-media interaction and cannot quantify the carbon flow transfer caused by cross-network energy substitution.

[0004] As can be seen from the above, how to overcome the computational problems caused by the coupling of multiple parameters under nonlinear operating conditions, and accurately quantify the nodal marginal carbon intensity of the multi-energy cross-medium conversion coupling effect under ideal operating conditions that do not take into account the physical loss of pipeline transmission and the random fluctuations on both sides of the source and load, so as to realize the full-domain carbon footprint visualization and traceability, is a problem to be solved in this field. Summary of the Invention

[0005] In view of this, the purpose of this invention is to provide a method and apparatus for drawing spatial carbon entropy distribution maps of a global multi-energy flow network. This method overcomes the computational problems caused by multi-parameter coupling under nonlinear operating conditions. Under ideal operating conditions that do not consider physical losses in pipeline transmission and random fluctuations on both sides of the source and load, it accurately quantifies the nodal marginal carbon intensity of the multi-energy cross-medium conversion coupling effect, achieving full-domain carbon footprint visualization and source tracing. The specific solution is as follows: Firstly, this application discloses a method for drawing a spatial carbon entropy distribution map of a global multi-energy flow network, including: Obtain the system parameters of the integrated energy system. Based on the system parameters, construct equality constraints and inequality constraints that characterize energy conservation and the operational boundaries of equipment and networks. Using a preset objective function and combining the equality constraints and inequality constraints, construct a multi-energy collaborative optimization scheduling model. The system parameters are globally optimized using the multi-energy collaborative optimization scheduling model to obtain the solution results. Based on the solution results, effective inequality constraints that satisfy preset conditions are extracted from the inequality constraints. An effective constraint vector function is constructed using the equality constraints and the effective inequality constraints. Based on the effective constraint vector function, a multi-energy coupling Jacobian matrix is ​​constructed, and the power response sensitivity vector of the integrated energy system is determined using the multi-energy coupling Jacobian matrix. Obtain the carbon emission coefficient vector of the integrated energy system, determine the node marginal carbon intensity of the integrated energy system based on the carbon emission coefficient vector and the power response sensitivity vector, and use the node marginal carbon intensity to draw the spatial carbon entropy distribution map of the global multi-energy flow network.

[0006] Optionally, the step of obtaining system parameters of the integrated energy system, and constructing equality and inequality constraints characterizing energy conservation and the operational boundaries of equipment and networks based on the system parameters, includes: Obtain the system parameters of the integrated energy system; the system parameters include the physical network topology of the integrated energy system, equipment parameters, and multi-energy load reference vectors of each node. Based on the physical network topology and the device parameters, equality constraints and inequality constraints characterizing energy conservation and the operational boundaries of devices and the network are constructed; the equality constraints are energy conservation equality constraints; the inequality constraints include device output extremum inequality constraints and network effective transmission capacity inequality constraints.

[0007] Optionally, the step of constructing a multi-energy collaborative optimization scheduling model using a preset objective function, combined with the equality constraints and inequality constraints, includes: Construct an objective function with the goal of minimizing the total overall operating cost of the system; Using the objective function and combining equality and inequality constraints under operating conditions that do not consider pipeline losses and random risks, a multi-energy collaborative optimization scheduling model is constructed.

[0008] Optionally, the step of using the multi-energy collaborative optimization scheduling model to globally optimize the system parameters and obtain the solution result, and based on the solution result, extracting effective inequality constraints that satisfy preset conditions from the inequality constraints, includes: The system parameters are substituted into the multi-energy collaborative optimization scheduling model to perform global optimization and obtain the solution results; Based on the KKT conditions and complementary relaxation theorem in the solution results, effective inequality constraints that satisfy the preset conditions are extracted from the inequality constraints.

[0009] Optionally, constructing an effective constraint vector function using the equality constraints and the effective inequality constraints includes: Combine equality constraints with effective inequality constraints to construct an effective constraint vector function; The effective constraint vector function is: ; in, For the set of system equality constraints, A subset of valid inequality constraints that satisfy preset conditions in the system. The system output vector, This represents the demand vector for multi-energy loads.

[0010] Optionally, constructing the multi-energy coupled Jacobian matrix based on the effective constraint vector function includes: Perform total differential operation on the effective constraint vector function; Extract the partial derivative relationship between the force change and the load change from the function after total differential operation; A multi-energy coupled Jacobian matrix is ​​constructed based on the aforementioned partial derivative relationship.

[0011] Optionally, the power response sensitivity vector of the integrated energy system is determined using the multi-energy coupling Jacobian matrix, including: The inverse matrix is ​​obtained by inverting the multi-energy coupled Jacobian matrix. From the partial derivative relationship, extract the negative gradient vector of the effective constraint set relative to the nodal load variable; The negative gradient vector is used as a perturbation component; The inverse matrix and the disturbance component are multiplied and mapped to determine the power response sensitivity vector of the integrated energy system.

[0012] Optionally, obtaining the carbon emission coefficient vector of the integrated energy system includes: Based on the preset standard information unit type parameters, the carbon emission coefficient vector of the unit is obtained; the carbon emission coefficient vector includes the carbon emission factor.

[0013] Optionally, determining the nodal marginal carbon intensity of the integrated energy system based on the carbon emission coefficient vector and the power response sensitivity vector includes: The nodal marginal carbon intensity of the integrated energy system is obtained by performing an inner product and superimposing the carbon emission factor and power response sensitivity vector in the carbon emission coefficient vector; The formula for calculating the marginal carbon intensity of the node is: ; in, For the first integrated energy system i The nodal margin carbon intensity of each node, For the first jCarbon emission coefficient vector of each functional device The system output vector, This represents the demand vector for multi-energy loads.

[0014] Secondly, this application discloses a device for plotting the spatial carbon entropy distribution of a global multi-energy flow network, comprising: The scheduling model construction module is used to obtain the system parameters of the integrated energy system. Based on the system parameters, it constructs equality constraints and inequality constraints that characterize energy conservation and the operational boundaries of equipment and network. Using a preset objective function and combining the equality constraints and inequality constraints, a multi-energy collaborative optimization scheduling model is constructed. The function construction module is used to perform global optimization of the system parameters using the multi-energy collaborative optimization scheduling model to obtain the solution results. Based on the solution results, it extracts effective inequality constraints that satisfy preset conditions from the inequality constraints and constructs an effective constraint vector function using the equality constraints and the effective inequality constraints. The vector determination module is used to construct a multi-energy coupling Jacobian matrix based on the effective constraint vector function, and to determine the power response sensitivity vector of the integrated energy system using the multi-energy coupling Jacobian matrix. The operational status assessment module is used to obtain the carbon emission coefficient vector of the integrated energy system, determine the node marginal carbon intensity of the integrated energy system based on the carbon emission coefficient vector and the power response sensitivity vector, and use the node marginal carbon intensity to draw a spatial carbon entropy distribution map of the global multi-energy flow network.

[0015] As can be seen, this application provides a method for drawing a spatial carbon entropy distribution map of a global multi-energy flow network. This method includes obtaining system parameters of the integrated energy system; constructing equality and inequality constraints representing energy conservation and the operational boundaries of equipment and the network based on these system parameters; using a preset objective function and combining equality and inequality constraints to construct a multi-energy collaborative optimization scheduling model. This overcomes the shortcomings of traditional static carbon metering in failing to reflect dynamic marginal effects, fully restoring the cross-medium coupling and interaction mechanism of heterogeneous energy flows (electricity, gas, and heat), and avoiding calculation result distortion and operating condition overruns caused by missing model constraints. The multi-energy collaborative optimization scheduling model is used to globally optimize and solve for the system parameters, obtaining the solution results. Based on the solution results, effective inequality constraints satisfying preset conditions are extracted from the inequality constraints. Using equality constraints and effective inequality constraints, an effective constraint vector function is constructed, eliminating redundant constraints that have no impact on the system's marginal state. Co-constraints reduce the computational cost of matrix operations, ensure numerical stability, and accurately pinpoint the core constraints limiting system operation under the current optimal conditions. A multi-energy coupling Jacobian matrix is ​​constructed based on effective constraint vector functions. This matrix is ​​used to determine the power response sensitivity vector of the integrated energy system, addressing the inability of traditional methods to quantify cross-network energy substitution effects. It accurately characterizes the coupling correlation characteristics between multiple energy flows, improving the computational accuracy of power response sensitivity. The carbon emission coefficient vector of the integrated energy system is obtained. Based on the carbon emission coefficient vector and the power response sensitivity vector, the node marginal carbon intensity of the integrated energy system is determined. The node marginal carbon intensity is used to plot the spatial carbon entropy distribution map of the entire multi-energy flow network, accurately quantifying the total system carbon emission increment corresponding to a small increase in load at a single node. This visually presents the spatial distribution characteristics of carbon emission intensity across the entire multi-energy flow network, enabling full-domain carbon footprint visualization and traceability. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0017] Figure 1 This is a flowchart of a method for drawing a spatial carbon entropy distribution map of a global multi-energy flow network disclosed in this application; Figure 2 A flowchart illustrating the spatial carbon entropy distribution map of a global multi-energy flow network disclosed in this application; Figure 3 This is a schematic diagram of the structure of a device for drawing spatial carbon entropy distribution maps of a global multi-energy flow network disclosed in this application. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] In multi-energy coupled integrated energy systems, existing carbon emission measurement and allocation methods are often based on steady-state power flow tracking and proportional sharing principles, focusing on ex-post regional average carbon emission calculations. These methods struggle to accurately reveal and quantify the dynamic marginal impact of small increases in spatial node loads on the overall system's carbon emissions within an optimized scheduling framework. Existing schemes for carbon emission measurement and marginal intensity calculation in integrated energy systems mainly fall into two categories, neither of which meets the aforementioned requirements for underlying benchmark measurement: The first category is based on traditional carbon emission flow theory using steady-state power flow tracking and proportional sharing principles. This approach fails to reveal the marginal abrupt changes caused by small increases in node loads within a globally joint optimized scheduling framework, making it difficult to generate real-time marginal carbon price signals to guide demand-side response. The second category is based on traditional marginal emission factor assessment methods using isolated single-network calculations or physical boundary decoupling. This method ignores the coupling compensation effect generated by energy conversion hubs in cross-media interactions, failing to quantify the carbon flow transfer resulting from cross-network energy substitution. As can be seen from the above, how to overcome the computational problems caused by the coupling of multiple parameters under nonlinear operating conditions, and accurately quantify the nodal marginal carbon intensity of the multi-energy cross-medium conversion coupling effect under ideal operating conditions that do not take into account the physical loss of pipeline transmission and the random fluctuations on both sides of the source and load, so as to realize the full-domain carbon footprint visualization and traceability, is a problem to be solved in this field.

[0020] See Figure 1 As shown in the figure, this invention discloses a method for drawing a spatial carbon entropy distribution map of a global multi-energy flow network, which may specifically include: Step S11: Obtain the system parameters of the integrated energy system. Based on the system parameters, construct equality constraints and inequality constraints that characterize energy conservation and the operational boundaries of equipment and network. Using a preset objective function and in combination with the equality constraints and inequality constraints, construct a multi-energy collaborative optimization scheduling model.

[0021] In this embodiment, system parameters of the integrated energy system are obtained. These parameters include the physical network topology, equipment parameters, and multi-energy load baseline vectors for each node. Based on the physical network topology and equipment parameters, equality and inequality constraints characterizing energy conservation and the operational boundaries of equipment and the network are constructed. An objective function is constructed with the goal of minimizing the overall system operating cost. Using this objective function, and combining the equality and inequality constraints under operating conditions that do not consider pipeline losses and random risks, a multi-energy collaborative optimization scheduling model is constructed. The equality constraints are energy conservation equality constraints. The inequality constraints include equipment output extremum inequality constraints and network effective transmission capacity inequality constraints.

[0022] In this step, the physical network topology, equipment parameters, and reference vectors of electricity, gas, and heat multi-energy loads of each node of the integrated energy system are obtained. With the minimization of the total operating cost of the system as the objective function, and combined with the energy conservation equation constraint, equipment output extreme value inequality constraint, and network effective transmission capacity inequality constraint under ideal operating conditions without considering pipeline losses and random risks, a lossless deterministic multi-energy collaborative optimization scheduling model is constructed.

[0023] Based on the underlying mathematical structure of economic dispatch, this application formulates the economic dispatch problem of the power grid as an optimization problem with node output as a parameter, aiming to minimize the system operating cost. The objective function is as follows: ; ; in, This indicates finding the minimum value of the function. For the first i The output of the generator set, , , For operating cost parameters related to the unit, , For the first i The upper and lower limits of the output of the Taiwanese generator set. For natural gas prices, This refers to the amount of gas supplied to the gas source.

[0024] After constructing the objective function, ideal physical constraints on the energy conversion hub need to be established. For extraction-condensing CHP (Combined Heat and Power) units, their electrothermal coupling characteristics manifest as a polygonal feasible region. Under ideal conditions, without considering nonlinear losses such as internal mechanical friction, their operation is constrained by energy conservation and operational boundaries: ; ; in, For back pressure operation, the electrothermal ratio is... To achieve the minimum exhaust steam heat-to-power ratio, For the power generation efficiency of combined heat and power units, For the gas consumption of combined heat and power units, The electrical power output of a combined heat and power (CHP) unit. This refers to the thermal power output of a combined heat and power (CHP) unit.

[0025] For EB (Electric Boiler) and GB (Gas Boiler), unidirectional energy coupling is achieved, with thermal power proportional to the power consumed at the source, and is limited by the installed capacity of the equipment. ; ; ; ; in, To improve the operating efficiency of electric boilers, To improve the operating efficiency of gas-fired electric boilers, , For the first i Upper and lower limits of the output of Taipower boilers; , For the first i The upper and lower limits of the output of the gas boiler.

[0026] In the process of cross-network energy interaction and transmission, although heterogeneous energy flows possess different physical properties, they all follow a generalized energy balance under ideal deterministic boundaries. Let the source-end injection vectors of heterogeneous energy sources such as electricity, gas, and heat in the system be denoted as . The corresponding spatial node load vector is In a lossless ideal scenario, the operational characteristics of the electrical, gas, and thermal subnetworks are uniformly abstracted into generalized linear equations based on energy conservation and topological mapping: ; Among them, the correlation matrix It characterizes the ideal transfer characteristics of heterogeneous energy flow under lossless conditions, and its internal elements are jointly determined by the ideal topology of each network and the physical correlation characteristics of the energy conversion hub.

[0027] Step S12: Use the multi-energy collaborative optimization scheduling model to perform global optimization on the system parameters and obtain the solution results. Based on the solution results, extract the effective inequality constraints that satisfy the preset conditions from the inequality constraints. Use the equality constraints and the effective inequality constraints to construct an effective constraint vector function.

[0028] In this embodiment, the system parameters are substituted into the multi-energy collaborative optimization scheduling model for global optimization and solution to obtain the solution results; based on the KKT conditions and complementary relaxation theorem in the solution results, effective inequality constraints that satisfy the preset conditions are extracted from the inequality constraints; the equality constraints and effective inequality constraints are combined to construct the effective constraint vector function. The effective constraint vector function is: ; in, For the set of system equality constraints, A subset of valid inequality constraints that satisfy preset conditions in the system. The system output vector, This represents the demand vector for multi-energy loads.

[0029] In this step, the multi-energy load baseline vector in the system parameters is substituted into the multi-energy collaborative optimization scheduling model for global optimization and solution. The solution results for each device are obtained. Based on the KKT (Karush-Kuhn-Tucker) conditions and complementary relaxation theorem of the solution results, effective inequality constraints that reach the physical limit and are activated are extracted from the inequality constraints. The equality constraints and effective inequality constraints are combined to construct a dimension-reduced effective constraint vector function. .

[0030] Assume the system faces a given multi-energy load demand vector. The output vector of each generator set and multi-energy conversion equipment is determined through optimization. When the solver converges to the optimal solution At this point, the system must satisfy strict KKT conditions. According to the KKT conditions and the complementary relaxation theorem, within the domain of the optimal operating point, only constraints that reach the physical boundary limit will have a substantial and effective impact on the marginal state of the system. Some inequality constraints that do not reach the physical boundary have zero dual multipliers and have no effect on marginal changes.

[0031] This invention identifies and extracts a set of key constraints that actually limit the system, and constructs an effective constraint vector function that dominates the current operating state. This function is composed of all the energy conservation equality constraints and effective inequality constraints of the system.

[0032] Step S13: Construct a multi-energy coupling Jacobian matrix based on the effective constraint vector function, and use the multi-energy coupling Jacobian matrix to determine the power response sensitivity vector of the integrated energy system.

[0033] In this embodiment, the effective constraint vector function is subjected to total differential operation; from the function after total differential operation, the partial derivative relationship between the output force change and the load change is extracted; a multi-energy coupling Jacobian matrix is ​​constructed based on the partial derivative relationship; the multi-energy coupling Jacobian matrix is ​​inverted to obtain the inverse matrix; from the partial derivative relationship, the negative gradient vector of the effective constraint set relative to the node load variable is extracted; the negative gradient vector is used as a disturbance component; the inverse matrix and the disturbance component are multiplied and mapped to determine the power response sensitivity vector of the integrated energy system.

[0034] In a multi-energy system, when any spatial node i The load experiences a very small disturbance. In order to keep the system in an optimal state and prevent it from going out of bounds, the control variables must generate small adjustment vectors. Based on the implicit function theorem, taking the total differential of both sides of the effective constraint equation system: ; By using algebraic transformations, the partial derivative relationship between the unknown output change and the known load change is separated, yielding the core incremental equation of the dominant system's marginal response: ; Define the left side of the equation as It represents the Jacobian sensitivity matrix of a lossless, effectively constrained system to multi-energy output; the right side is defined as the perturbation component. , which represents the negative gradient vector of the effective constrained system to external node load fluctuations. Since in the lossless case, H It possesses full rank. Therefore, the analysis of the system state variables can be concisely expressed as: .

[0035] Step S14: Obtain the carbon emission coefficient vector of the integrated energy system, determine the node marginal carbon intensity of the integrated energy system based on the carbon emission coefficient vector and the power response sensitivity vector, and use the node marginal carbon intensity to draw the spatial carbon entropy distribution map of the global multi-energy flow network.

[0036] In this embodiment, the carbon emission coefficient vector of the unit is obtained according to the preset standard information unit type parameters; the carbon emission factor and the power response sensitivity vector in the carbon emission coefficient vector are inner productted and superimposed to obtain the node marginal carbon intensity of the integrated energy system; the carbon emission coefficient vector includes the carbon emission factor; The formula for calculating the marginal carbon intensity of the node is: ; in, For the first integrated energy system iThe nodal margin carbon intensity of each node, For the first j Carbon emission coefficient vector of each functional device The system output vector, This represents the demand vector for multi-energy loads.

[0037] In this step, the carbon emission coefficient vector of the unit is obtained based on publicly available market information or unit type parameters. Then, the carbon emission factor in the carbon emission coefficient vector and the power response sensitivity vector are inner productted and superimposed to obtain the marginal carbon emission intensity of all nodes, which is the characterization of the node carbon entropy.

[0038] In integrated energy systems, the cross-medium conversion and network transmission of multi-mass energy flows such as electricity, gas, and heat are accompanied by complex coupling effects. This invention introduces the core theoretical concept of "carbon entropy," using it from the intersection of thermodynamics and physical economics to characterize the disorder in carbon emission distribution caused by multi-energy synergy and energy utilization, as well as the environmental cost incurred by the system to maintain a multidimensional energy supply and demand balance. Since carbon entropy is a macroscopic physical quantity that characterizes the overall environmental externalities of a multi-energy flow system, this invention utilizes the LMCE (Locational Marginal Carbon Emission) index in integrated energy systems to characterize the carbon entropy of multi-energy flow systems in order to perform microscopic analysis and extraction at spatial topological nodes. This index reflects the carbon emission reduction potential of the entire system.

[0039] LMCE measures the increase in total carbon emissions of a system caused by a unit increase in load at a specific node. It is a key indicator for quantifying the carbon footprint of the power grid and guiding low-carbon response on the demand side. In both mathematical and physical senses, it represents the marginal change rate of pure coupled carbon entropy without considering network losses and stochastic risks. Through the transmission of the multi-energy coupled Jacobian matrix H, the multi-energy network reconfiguration and marginal equipment switching phenomena caused by a small increase in load at a single spatial node are quantified into a benchmark quantity with physical interpretability.

[0040] This application proposes a comprehensive energy system metering method that, under ideal operating conditions without considering physical losses in pipeline transmission and random fluctuations on both the source and load sides, strictly adheres to the multi-energy flow coupling mechanism, accurately traces the source, and quantifies the impact of load increments at each node on the optimal carbon emissions of the entire system. This method enables the mapping of the spatial carbon entropy distribution of the entire multi-energy flow network. The specific process is as follows: Figure 2 As shown, the steps are as follows: 1. Data Collection and Input: Based on the physical network topology, equipment parameters, and multi-energy load baseline vectors of each node input from the data layer, a multi-energy collaborative optimization scheduling model is constructed to obtain the baseline output of each unit and the power flow distribution data of the lines. This step establishes the optimal baseline operating point of the system under the condition of satisfying multi-dimensional energy supply and demand balance, and determines the physical operating ground state for subsequent lower-level deconstruction.

[0041] 2. Construction of Effective Constraint Set: Based on the solution obtained after global optimization, an effective constraint vector function is constructed using the KKT conditions and the complementary relaxation theorem. This process focuses subsequent calculations on a few key constraints, reducing the computational load of the system.

[0042] 3. Mathematical Model Analysis: Based on the implicit function theorem, the negative gradient vector of the effective constraint set relative to the nodal load variables is separated, the core incremental equation is established, and the partial derivatives of the effective constraint set with respect to the control variables and nodal load variables are obtained to obtain the disturbance components. Thus, the power response sensitivity vector of the integrated energy system is solved, and the energy substitution effect caused by the slight increase in the load of a single node is quantified.

[0043] 4. Carbon Entropy Synthesis and Output: After obtaining the power response sensitivity vector, it is superimposed with the unit carbon emission coefficient vector of the data layer through inner product to finally calculate the node marginal carbon intensity, which is output as the carbon entropy signal of the integrated energy system. The spatial carbon entropy distribution map of the global multi-energy flow network is drawn, and the underlying physical response is transformed into a carbon signal with economic significance, providing a tool with strong physical interpretability for objectively assessing the carbon emission of multi-energy systems.

[0044] The main objective of this invention is to address the problems of outdated existing research and accounting mechanisms, and the ambiguity in the decoupling and identification of multi-energy flow tracing. Specifically, it includes the following two points: (1) By establishing an ideal deterministic operating condition boundary that does not take into account the physical transmission loss of the pipeline network and the risk of random fluctuations in the source load, this invention reflects the underlying compensation mechanism of multi-energy flow collaborative optimization scheduling, realizes the independent extraction of the carbon effect of pure energy, and establishes an operating benchmark for evaluating the low-carbon conversion efficiency of energy conversion hubs in integrated energy systems.

[0045] (2) The present invention establishes a benchmark carbon entropy measurement model with strong physical interpretability, providing a marginal carbon intensity signal that reflects the compensation of pure multi-energy coupling. The nodal marginal carbon intensity quantified by this method has clear thermodynamic and physical economic significance, providing a reliable technical and theoretical basis for low-carbon economic dispatch and carbon source tracing of integrated energy systems.

[0046] The advantages of this invention are as follows: To address the problem of overlapping physical meanings in analytical matrices in existing full-element calculation methods, this invention constructs a dimension-reduced, purely coupled Jacobian matrix based on the ideal characteristics of multi-energy conversion and the limit of effective network transmission capacity. This analytical framework replaces the complex full-dimensional derivative process of traditional coupling, achieving solution separation on an algebraic topological level. This invention ensures the full rank and numerical stability of the underlying sensitivity matrix at the mechanistic level, effectively overcoming the computational problems caused by multi-parameter coupling under nonlinear conditions. To solve the problems caused by traditional static carbon emission allocation and isolated accounting of single networks, this invention establishes a dynamic mapping relationship between spatial node load increments and global joint optimal scheduling of multi-energy flows based on implicit function differential theory. By reverse-analyzing the purely coupled Jacobian matrix, this invention quantifies the system-level marginal energy supply device switching effect driven by cross-network energy substitution. This method successfully decouples the pure baseline component of node marginal carbon intensity, scientifically characterizes the system carbon entropy change rate, and helps guide flexible resources to achieve system-level carbon reduction and efficiency improvement by responding to marginal carbon signals, providing a scientific decision-making basis for the coordinated evolution of the green electricity market and the carbon market.

[0047] This application provides a method for drawing a spatial carbon entropy distribution map of a global multi-energy flow network. The method includes obtaining system parameters of a comprehensive energy system; constructing equality and inequality constraints characterizing energy conservation and the operational boundaries of equipment and the network based on these parameters; using a preset objective function and combining the equality and inequality constraints to construct a multi-energy collaborative optimization scheduling model. This overcomes the shortcomings of traditional static carbon metering, which cannot reflect dynamic marginal effects, and fully restores the cross-medium coupling and interaction mechanism of heterogeneous energy flows (electricity, gas, and heat), avoiding calculation result distortion and operating condition out-of-bounds problems caused by missing model constraints. The method uses the multi-energy collaborative optimization scheduling model to globally optimize and solve for the system parameters, obtaining the solution results. Based on the solution results, effective inequality constraints satisfying preset conditions are extracted from the inequality constraints. Using the equality constraints and effective inequality constraints, an effective constraint vector function is constructed, eliminating redundant constraints that have no impact on the system's marginal state. This approach reduces the computational burden of matrix operations, ensures numerical stability, and accurately identifies the core constraints limiting system operation under optimal conditions. Based on effective constraint vector functions, a multi-energy coupling Jacobian matrix is ​​constructed. This matrix is ​​then used to determine the power response sensitivity vector of the integrated energy system, addressing the inability of traditional methods to quantify cross-network energy substitution effects. This accurately characterizes the coupling correlation characteristics between multiple energy flows, improving the computational accuracy of power response sensitivity. Furthermore, the carbon emission coefficient vector of the integrated energy system is obtained. Based on this vector and the power response sensitivity vector, the node marginal carbon intensity of the integrated energy system is determined. The node marginal carbon intensity is then used to plot the spatial carbon entropy distribution map of the entire multi-energy flow network, accurately quantifying the total system carbon emission increment corresponding to a small increase in load at a single node. This visually presents the spatial distribution characteristics of carbon emission intensity across the entire multi-energy flow network, enabling full-domain carbon footprint visualization and traceability.

[0048] See Figure 3 As shown, this embodiment of the invention discloses a device for plotting the spatial carbon entropy distribution of a global multi-energy flow network, which may specifically include: The scheduling model construction module 11 is used to obtain the system parameters of the integrated energy system, and based on the system parameters, construct equality constraints and inequality constraints that characterize energy conservation and the operational boundaries of equipment and network. Using a preset objective function and in combination with the equality constraints and inequality constraints, a multi-energy collaborative optimization scheduling model is constructed. The function construction module 12 is used to perform global optimization of the system parameters using the multi-energy collaborative optimization scheduling model to obtain the solution result. Based on the solution result, it extracts effective inequality constraints that meet the preset conditions from the inequality constraints and constructs an effective constraint vector function using the equality constraints and the effective inequality constraints. Vector determination module 13 is used to construct a multi-energy coupling Jacobian matrix based on the effective constraint vector function, and use the multi-energy coupling Jacobian matrix to determine the power response sensitivity vector of the integrated energy system. The operational status assessment module 14 is used to obtain the carbon emission coefficient vector of the integrated energy system, determine the node marginal carbon intensity of the integrated energy system based on the carbon emission coefficient vector and the power response sensitivity vector, and use the node marginal carbon intensity to draw a spatial carbon entropy distribution map of the global multi-energy flow network.

[0049] In some specific embodiments, the scheduling model construction module 11 may specifically include: The system parameter acquisition module is used to acquire the system parameters of the integrated energy system; the system parameters include the physical network topology of the integrated energy system, equipment parameters, and multi-energy load reference vectors of each node. An equation and inequality constraint construction module is used to construct equation and inequality constraints characterizing energy conservation and the operational boundaries of devices and networks based on the physical network topology and the device parameters; the equation constraints are energy conservation equation constraints; the inequality constraints include device output extreme value inequality constraints and network effective transmission capacity inequality constraints.

[0050] In some specific embodiments, the scheduling model construction module 11 may specifically include: The objective function building module is used to construct an objective function with the goal of minimizing the overall total operating cost of the system. The multi-energy collaborative optimization scheduling model construction module is used to construct a multi-energy collaborative optimization scheduling model by utilizing the objective function and combining equality and inequality constraints under the operating conditions that do not consider pipeline losses and random risks.

[0051] In some specific embodiments, the function construction module 12 may specifically include: The global optimization solution module is used to substitute system parameters into the multi-energy collaborative optimization scheduling model to perform global optimization and obtain the solution results. The effective inequality constraint extraction module is used to extract effective inequality constraints that satisfy preset conditions from the inequality constraints based on the KKT conditions and complementary relaxation theorem in the solution results.

[0052] In some specific embodiments, the function construction module 12 may specifically include: The simultaneous constraint module is used to combine equality constraints with effective inequality constraints to construct an effective constraint vector function; The effective constraint vector function is: ; in, For the set of system equality constraints, A subset of valid inequality constraints that satisfy preset conditions in the system. The system output vector, This represents the demand vector for multi-energy loads.

[0053] In some specific embodiments, the vector determination module 13 may specifically include: The total differential operation module is used to perform total differential operations on the effective constraint vector function; The partial derivative relationship extraction module is used to extract the partial derivative relationship between the force change and the load change from the function after total differential operation; A multi-energy coupled Jacobian matrix construction module is used to construct a multi-energy coupled Jacobian matrix based on the partial derivative relationship.

[0054] In some specific embodiments, the vector determination module 13 may specifically include: The inversion operation module is used to invert the multi-energy coupled Jacobian matrix to obtain the inverse matrix; The negative gradient vector extraction module is used to extract the negative gradient vector of the effective constraint set relative to the nodal load variable from the partial derivative relationship; The perturbation component determination module is used to take the negative gradient vector as a perturbation component; The product mapping module is used to perform product mapping on the inverse matrix and the disturbance component to determine the power response sensitivity vector of the integrated energy system.

[0055] In some specific embodiments, the operating status evaluation module 14 may specifically include: The carbon emission coefficient vector acquisition module is used to acquire the carbon emission coefficient vector of the unit based on the preset standard information unit type parameters; the carbon emission coefficient vector includes the carbon emission factor.

[0056] In some specific embodiments, the operating status evaluation module 14 may specifically include: The inner product and superposition module is used to perform inner product and superposition of the carbon emission factor and power response sensitivity vector in the carbon emission coefficient vector to obtain the nodal marginal carbon intensity of the integrated energy system. The formula for calculating the marginal carbon intensity of the node is: ; in, For the first integrated energy system i The nodal margin carbon intensity of each node, For the first j Carbon emission coefficient vector of each functional device The system output vector, This represents the demand vector for multi-energy loads.

[0057] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, 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 a process, method, article, or apparatus. Without further limitations, 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 said element.

[0058] The above provides a detailed description of the method and apparatus for drawing spatial carbon entropy distribution maps of a global multi-energy flow network provided by the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for drawing a spatial carbon entropy distribution map of a global multi-energy flow network, characterized in that, include: Obtain the system parameters of the integrated energy system. Based on the system parameters, construct equality constraints and inequality constraints that characterize energy conservation and the operational boundaries of equipment and networks. Using a preset objective function and combining the equality constraints and inequality constraints, construct a multi-energy collaborative optimization scheduling model. The system parameters are globally optimized using the multi-energy collaborative optimization scheduling model to obtain the solution results. Based on the solution results, effective inequality constraints that satisfy preset conditions are extracted from the inequality constraints. An effective constraint vector function is constructed using the equality constraints and the effective inequality constraints. Based on the effective constraint vector function, a multi-energy coupling Jacobian matrix is ​​constructed, and the power response sensitivity vector of the integrated energy system is determined using the multi-energy coupling Jacobian matrix. Obtain the carbon emission coefficient vector of the integrated energy system, determine the node marginal carbon intensity of the integrated energy system based on the carbon emission coefficient vector and the power response sensitivity vector, and use the node marginal carbon intensity to draw the spatial carbon entropy distribution map of the global multi-energy flow network.

2. The method for drawing a spatial carbon entropy distribution map of a global multi-energy flow network according to claim 1, characterized in that, The process involves obtaining system parameters of the integrated energy system, and based on these parameters, constructing equality and inequality constraints characterizing energy conservation and the operational boundaries of equipment and the network, including: Obtain the system parameters of the integrated energy system; the system parameters include the physical network topology of the integrated energy system, equipment parameters, and multi-energy load reference vectors of each node. Based on the physical network topology and the device parameters, equality constraints and inequality constraints characterizing energy conservation and the operational boundaries of devices and the network are constructed; the equality constraints are energy conservation equality constraints; the inequality constraints include device output extremum inequality constraints and network effective transmission capacity inequality constraints.

3. The method for drawing a spatial carbon entropy distribution map of a global multi-energy flow network according to claim 1, characterized in that, The process of constructing a multi-energy collaborative optimization scheduling model by utilizing a preset objective function and combining the equality and inequality constraints includes: Construct an objective function with the goal of minimizing the total overall operating cost of the system; Using the objective function and combining equality and inequality constraints under operating conditions that do not consider pipeline losses and random risks, a multi-energy collaborative optimization scheduling model is constructed.

4. The method for drawing a spatial carbon entropy distribution map of a global multi-energy flow network according to claim 1, characterized in that, The process involves using the multi-energy collaborative optimization scheduling model to globally optimize the system parameters, obtaining the solution results, and then extracting valid inequality constraints that satisfy preset conditions from the inequality constraints based on these results. This includes: The system parameters are substituted into the multi-energy collaborative optimization scheduling model to perform global optimization and obtain the solution results; Based on the KKT conditions and complementary relaxation theorem in the solution results, effective inequality constraints that satisfy the preset conditions are extracted from the inequality constraints.

5. The method for drawing a spatial carbon entropy distribution map of a global multi-energy flow network according to claim 1, characterized in that, The construction of an effective constraint vector function using the equality constraints and the effective inequality constraints includes: Combine equality constraints with effective inequality constraints to construct an effective constraint vector function; The effective constraint vector function is: ； in, For the set of system equality constraints, A subset of valid inequality constraints that satisfy preset conditions in the system. The system output vector, This represents the demand vector for multi-energy loads.

6. The method for drawing a spatial carbon entropy distribution map of a global multi-energy flow network according to claim 1, characterized in that, The construction of the multi-energy coupled Jacobian matrix based on the effective constraint vector function includes: Perform total differential operation on the effective constraint vector function; Extract the partial derivative relationship between the force change and the load change from the function after total differential operation; A multi-energy coupled Jacobian matrix is ​​constructed based on the aforementioned partial derivative relationship.

7. The method for drawing a spatial carbon entropy distribution map of a global multi-energy flow network according to claim 6, characterized in that, Determining the power response sensitivity vector of the integrated energy system using the multi-energy coupled Jacobian matrix includes: The inverse matrix is ​​obtained by inverting the multi-energy coupled Jacobian matrix. From the partial derivative relationship, extract the negative gradient vector of the effective constraint set relative to the nodal load variable; The negative gradient vector is used as a perturbation component; The inverse matrix and the disturbance component are multiplied and mapped to determine the power response sensitivity vector of the integrated energy system.

8. The method for drawing a spatial carbon entropy distribution map of a global multi-energy flow network according to any one of claims 1 to 7, characterized in that, The process of obtaining the carbon emission coefficient vector of the integrated energy system includes: Based on the preset standard information unit type parameters, the carbon emission coefficient vector of the unit is obtained; the carbon emission coefficient vector includes the carbon emission factor.

9. The method for drawing a spatial carbon entropy distribution map of a global multi-energy flow network according to claim 8, characterized in that, Determining the nodal marginal carbon intensity of the integrated energy system based on the carbon emission coefficient vector and the power response sensitivity vector includes: The nodal marginal carbon intensity of the integrated energy system is obtained by performing an inner product and superimposing the carbon emission factor and power response sensitivity vector in the carbon emission coefficient vector; The formula for calculating the marginal carbon intensity of the node is: ； in, For the first integrated energy system i The nodal margin carbon intensity of each node, For the first j Carbon emission coefficient vector of each functional device The system output vector, This represents the demand vector for multi-energy loads.

10. A device for plotting the spatial carbon entropy distribution of a global multi-energy flow network, characterized in that, include: The scheduling model construction module is used to obtain the system parameters of the integrated energy system. Based on the system parameters, it constructs equality constraints and inequality constraints that characterize energy conservation and the operational boundaries of equipment and network. Using a preset objective function and combining the equality constraints and inequality constraints, a multi-energy collaborative optimization scheduling model is constructed. The function construction module is used to perform global optimization of the system parameters using the multi-energy collaborative optimization scheduling model to obtain the solution results. Based on the solution results, it extracts effective inequality constraints that satisfy preset conditions from the inequality constraints and constructs an effective constraint vector function using the equality constraints and the effective inequality constraints. The vector determination module is used to construct a multi-energy coupling Jacobian matrix based on the effective constraint vector function, and to determine the power response sensitivity vector of the integrated energy system using the multi-energy coupling Jacobian matrix. The operational status assessment module is used to obtain the carbon emission coefficient vector of the integrated energy system, determine the node marginal carbon intensity of the integrated energy system based on the carbon emission coefficient vector and the power response sensitivity vector, and use the node marginal carbon intensity to draw a spatial carbon entropy distribution map of the global multi-energy flow network.

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